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GALVESTON BA,Y PROJECT 


DBRARY 
TH~ Ol'flVERSITY OF TEXAS AT AUSTI!'( 
MARINE SCIENCE INSTITUTE 
PORT ARANSAS, TEXAS 78',/'Y'f,7 

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BOOK 
DONATED BY 
Dr. and Mrs. Chase Van Baalen 
to 
LIBRARY 
MARINE SCIENCE INSTITUTE 
University of Texas at Austin 
Port Aransas, Texas 

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This Project and Report 
Was Coordinated and Edited By: Carl H. Oppenheimer William B. Brogden Kennith G. Gordon 
LIBRARY 
THE UNIVERSITY OF TEXAS AT AUSTltil 
MARINE SCI ENCE INSTITUTE 
PORT ARANSAS, TEXAS 78373-1267 

TOXICITY STUDIES OF GALVESTON BAY PROJECT 
TABLE OF CONTENTS 
Page 
Preface Х . . . . . . . . . . i 
Purpose Х . . . . . . . . . . . . . . . . . . ii 
Chapter I. Introduction . . . . . . I-1 
Bibliography . . . . . . . . . . I-7 
Chapter II. Galveston Bay Ecosystem Freshwater 
Requirements and Phytoplankton Productivity. II-1 
Armstrong, N.E. and M.O. Hinson, JrХХ II-1 
Introduction II-1 
Review of Previous Work II-3 
Environmental requirements-freshwater flows II-3 
Productivity Х II-5 
Methodology . . . II-12 
Environmental requirements II-12 
Productivity . . . . . . . . . II-14 
THE LIBRARY 


O.t'T:' Results II-27 THE l)NIVERSITY 
Environmental requirements II-27
.--╖ .
OF TEXAS 
AT Productivity Х II-39 
AUSTil T 
Discussion Х Х II-74 
Environmental requirements . . . II-74 
Productivity Х . . . . . . . . II-79 
Conclusions . . . . 

II-84 Environmental requirements . . . . . . . II-84 Productivity Х Х Х Х Х Х Х Х II-86 AR 
MAR1, IE :orNcr r <~, ': TE ORT ARAN;As r . s ' 373-26Z 

2 

Page Acknowledgements Х Х Х Х Х 
II-90 Bibliography Х Х Х Х Х Х Х Х Х . . . . . . . II-91 List of Tables Х Х . . . . . . . . . II-95 List of Figures II-97 Appendix Figures Х Х Х Х Х Х Х Х II-98 Chapter III. Shrimp Bioassay of Galveston Bay Waters. Gordon, K.G., J. Gillespie, W.B. Brogden and C.H. Oppenheimer III-1 Results . . . . . . . . III-6 Discussion . . . . . . III-6 Conclusion Х . . . . . . . . . . . . III-15 Bibliography Х . . . III-15 Chapter IV. Respiratory Metabolism of the Striped Mullet as an Assay of Low Level Stresses in Galveston Bay. Wohlschlag, D.E. IV-1 Introduction . . . . . . . . . . . . . . Х Х IV-1 Methods and Materials IV-4 Collection of Galveston Bay waters Х IV-4 Collection and acclimatization of fish Х Х IV-7 Measurement of oxygen consumption rates IV-8 Multiple regression data analysis . . IV-11 Results Х Х Х Х Х Х Х Х Х 
IV-12 Discussion of Results 
IV-16 Equations " . . . . . . . . . . . IV-23 Variations in seasons and localities . . . IV-26 
3 

Page 
Theoretical consideration of biological 
production rates Х Х Х Х . . . . . . . . IV-28 Components of metabolism Х . . . . . . IV-30 Non-random distribution of fishes . . . . IV-33 Acknowledgements Х . . . . . . . IV-34 Conclusions . . . . . . . . . . . . . . . . IV-35 References . . . . . . . . . IV-37 Appendix A Metabolism Data for Galveston Bay Striped Mullet Х Х Х Х Х Х Х Х Х IV-42 Appendix B Operating Sequence and Data Recording for Continuous-Flow Respiration Chambers Х Х Х Х Х Х Х Х Х Х ╖ Х Х Х Х Х Х Х IV-75 Chapter V. A Blue-Green Algal Assay. of Water Quality. Van Baalen, c., W. Pulich and R. O'Donnell V-1 Introduction . . . . . . . . . V-1 Sample Collection, Preparation and Assay V-1 Results Х Х Х Х Х Х Х Х Х Х Х Х Х Х Х Х Х Х V-2 Discussion Х . . . . . . . . . . V-10 References Х Х Х Х Х Х Х Х Х Х Х Х V-11 Chapter VI. Galveston Bay Benthic Comrra.inity Structures as an Indicator of Water Quality. Holland, J.S., N.J. Maciole~, and C.H. Oppenheimer VI-1 Introduction Х Х Х Х Х Х Х Х Х . . . . . VI-1 Area, Methods and Materials . . VI-2 Description of study area Х Х Х Х VI-2 Field and laboratory rrethods Х VI-4 Data interpretation methods . . . . . . . VI-4 
4 

Results ХХ . . . . . 
Discussion . . . . . . . . . . . . . . . . . 
Conelusions Х Х . . . . . . . . Х 

Х 
Acknowledgements . . . . . . . . . . . . . . References . . . . . . . . . . . . . . Chapter VII. A Definition of BOD in Galveston Bay 
Oppenheimer, D.H., N. Gordon and 
B. Brogden ХХ . . . 
Introduction Objectives Background Procedures Results ХХ Discussion Summary ХХ References 
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 
Appendix. Description of Method of Fitting BOD Data to a First Order Equation . . . Chapter VIII. The Nursery Environment of Galveston Bay Oppenheimer, C.H. References Х Х Х Х Х Х Х Х Х Х Х Х Х Х Х Х Chapter IX. Chemical and Physical Separation Procedures for Toxic Materials . . . . . . . . . . . 
W.B. Brogden 
Introduction Х Х 

Methodology Х Х Х . . . . . . . . . . . . 
Page 
VI-6 
VI-18 
VI-25 
VI-25 
VI-26 
VII-1 VII-1 VII-1 VII-1 VII-2 VII-4 VII-4 VII-22 VII-23 
VII-24 VIII-1 
VIII-10 
IX-1 
IX-1 
IX-1 
IX-2
Results . . . . . . . . . . . . . 
5 

Page Discussion Х . . . . . . . . . IX-3 Summary 
. . Х Х Х Х Х Х Х Х Х Х Х Х Х Х Х IX-4 Chapter X. Discussion and Summary Oppenheimer, C.H. X-1 Discussion Х . . . . . . . . X-1 References Х . . . . . . . . . . . . . . . X-5 Summary Х Х Х Х Х Х Х Х Х X-7 Appendix A Hydrographic Data Collected by the Marine Science Institute Field Party During the Four Sampling Periods. 

Preface 
The Galveston Bay Project is a comprehensive program to study specific features of the Galveston Bay system, its water sources and industrial and urban impacts. The Marine Science Institute contracted through the Texas Water Quality Board to conduct one portion of the project relating to toxicity studies on living communities in the Bay and its primary productivity. 
The toxicity portion of the Galveston Bay project was conducted by a team of scientists from the Marine Science Institute and the Department of Civil Engineering and their associates and assistants. Dr. Carl H. Oppenheimer of the Marine Science Institute directed the project. Dr. Kennith Gordon acted as assistant project director for the planning field organization and the shrimp toxicity program during the first half of the project. After that time Dr. William Brogden assisted the project director in the final preparation of the report and managed the computer programming of all data in ENVIR. The report was compiled of chapters contributed by the individual principal investigators and the Introduction and Discussion and Summary were written by Drs. Oppenheimer, Gordon and Brogden. 
Individual Program Directors 
Carl H. Oppenheimer MSP~ Coordinator and Project Director Dr. Kennith Gordon MSI Assistant project director Dr. William Brogden MSI Assistant project coordinator Dr. Donald Wohlschlag MSI Principal investigator on Fish toxicity Dr. Chase Van Baalen MSI Principal investigator on Algal toxicity Dr. John Holland MSI Programmer on Benthic toxicity Dr. Neal E. Armstrong Dept. Civil Eng. Principal investigator on 
freshwater control and primary productivity *Marine Science Institute Port Aransas 
Editing of the completed manuscript was by Dorothy Oppenheimer and comments were contributed by many associates at the Marine Science Institute. 
Although the toxicity studies were conducted by several teams, each responsible for a portion of the work, coordination and selection of sample stations, collection of water and organisms and field work was maintained throughout the study. Chapter I provides a description of such coordination, a list of the stations maintained by all projects and coordinated with stations maintained and data collected by other studies of the B~y such as the U.S. Geological Survey, the State Health Department, Texas A&M, etc. Much of the data collected was submitted to either the Computer Center of the Texas Water Development Board included in the Environmental Data System (ENVIR) at the University of Texas Computer Center. ENVIR data are available to all interested persons. ENVIR is an environmental Data Management Computer System developed in conjunction with a team of 5 programs in 5 Universities through coordination with the Gulf Universities Research Consortium (GURC EDMPAS 1972). . 
i 
TOXICITY STUDIES OF GALVESTON BAY PROJECT 

Purpose: 
As a part of a composite study of Galveston Bay, a bay-estuarine environment, an interdisciplinary research program was conducted by several principal investigators of the University of Texas College of Engineering and the Marine Science Institute at Port Aransas. The scope of this effort was to determine the water quality of relatively stable salinity/temperature areas representing five general locations in the Galveston Bay system. This endpoint was to be determined by analysis of the water during an annual series of samples by bioassay with several species of indigenous living organisms, through the analysis of BOD toxicity indicated during the previous Galveston Bay Study, an evaluation of the nursery ground, nutrient, nitrogen and primary productivity values, a study of bottom fauna and comparison with other data parameters of the total bay study that may be pertinent to the evaluation of water quality to the biological regime. 
The specific tasks of this study were: 
(1) 	
to provide a description of the waters of Galveston Bay by toxicity bioassays in the more stable areas of temperature, salinity and dissolved oxygen 

(2) 	
to provide information for fresh water control methodology 

(3) 	
to determine the nutrient effects upon primary productivity 

(4) 	
to provide a definition of B.O.D. toxicity 

(S) 	
to provide a definition of the nursery areas from the existing literature 

(6) 	
to help develop a predictive bay model formulated around circulation, nutrients, B.O.D., salinity, temperature, current flows, species inventory, light penetration and mixing and exchange rates. 


ii 
CHAPrER I 
Introduction 
A very extensive description of Galveston Bay is contained in the report by Copeland and Fruh (1970), Ecological Studies of Galveston Bay 1969, and by several TRACOR Reports, Phase l Technical Report 1968 and Phase 2 Technical Progress Report 1971 submitted to the Texas Water Quality Board. The following report is the result of an interнdisciplinary treatment of the water quality of the Bay from the fall of 1971 to the surruner of 1972. The previous reports offer considerable evidence of a diverse body of water subject to a wide range of natural and man-induced forces that have produced the transitional water body of today's Galveston Bay. 
Texas Bay systems, because of the geographical urban and industrial features and temperature regime, are subject to a wide variation of tide, fresh water, and inputs by man's activities. Development of the Bay has changed the circulation of the system through the dredging of channels, spoil bank placement, construction of jetties, marinas, docks, and causeways. The shallow water and continuous wind characteristic of the Texas coast continually stir the waters, creating a well-mixed water mass with a variable degree of turbidity and light penetration. The natural runoff from the productive natural environment, grazing areas, farms, urban and industrial complexes, and the river drainage basin continuously have an input to the system. Tidal changes by lunar and wind forces continually flush the system and periodic high-intensity storms create periods of rapid change due to Gulf water input and massive rainfall runoff. 
The present biota have adjusted to natural and man-made large scale fluctuations, as evidenced by the continuous large corrunercial fish catch for the area as shown by statistics in Texas Landings as compiled by the National Marine Fisheries Service of NOAA. 
The question then arises as to man's effect on the bay systems as related to the biota and esthetic considerations, assuming that natural fluctuations are "normal". Sea water at best is an ill-defined water mass varying in chemical content, especially in estuarine environments where land has an impact. The relative proportions of minor eiements in relation to sodium chloride will change due to the effects of evaporation and dilution by both Gulf water and rainfall runoff with its leaching effect on the surrounding land mass. At best, "normal" should be defined by ranges as compared to averages. 
In this dynamic body of water are living and chemical forces that actively metabolize, produce organic matter through carbon fixation and photosynthesis, chelate or flocculate organic and inorganic molecules involved with sedirrentary processes or sediment diagenesis. 
I -l 
Such is the complex system of Galveston Bay. The following series of reports are included as the results of the various investiнgations and their individual projects. The discussion of the projects will be restricted to summarize the individual projects and is offered to the Galveston Bay Project for incorporation of the final overall report. 
Selected Sites for Biological Toxicity Assay 
Natural distribution of living organisms in a complex Bay system such as the Galveston Bay is normally a function of temperature and salinity with other parameters playing a lesser role. Therefore, we identified five areas in Galveston Bay and two in the Gulf of Mexico that are TTAreas of Relative Stability11 (ARS) within described limits of temperature and salinity to more accurately measure toxic effects. A report by Copeland and Bechtel (1971) describes temperature and salinity boundaries for certain commercially important indicator species of organisms as identified from the literature and from data from the previous study of Galveston Bay. 
The rationale of selecting stations in relatively stable temperatureнsalinity zones in the bays was one to eliminate the effects of temperature and salinity as variables. Of course, such selections of stations are difficult in such a transitional environment and certain average limitations were set. The impossibility of selecting stations within Copeland's biological limits are evident in inspecting the data for Trinity Bay during the January collection period. At that time a heavy rain in the adjacent area caused the Trinity Bay to decrease its salinity to almost zero. This is a normal transition and it is unfortunate that it happened during the experimental program. During normal conditions it would be expected that the organisms that could not tolerate the rapid salinity change would either move to a higher salinity, burrow into the more stable bottom sediments or would be killed. 
The number of variables that can cause a change in the distribuнtion of living organisms is so large that it is not possible to include all variables within the scope of our portion of the present Galveston Bay Study. By using the ARS and water quality measured as a part of the routine sampling procedures, we reduced the number of variables to a point where toxicity studies as outlined in the following section can be meaningful. 
When applicable, any toxicity within the ARS can be compared to known toxic effects of the various industrial and other effluents to determine dilution significance. These evaluations can be made through model experiments using the data to be obtained with past data on a predictive numerical basis. 
Figure 1 indicates the sample areas. Each point represents a discrete area of the Bay complex and reference points were established 
I -2 
Houston Ship 

Figure 1. Location of Galveston Bay Sampling Sites I -3 
so that sample points could be repeated during each sampling period. Sample periods were October 26, l97l; January 25, 1972; April 25, l972; and July 25, l972. Each intensive sampling period was approxнimately one week in duration, at which time members from each of the investigation teams participated and samples were taken for metal determinations by the U.S. Geological Survey. The stations were also maintained at monthly intervals for hydrographic sampling by the U.S. Army Corps of Engineers, the State Health Department and the U.S. Geological Survey. The data from these investigations were made available for comparative analytical purposes. 
Field Data 
Field data were collected in situ aboard either the R/V LONGHORN or two auxillary small craft during---:erie four intensive sampling periods. The data obtained included dissolved oxygen, temperature, salinity and meteorological conditions and are presented in Appendix I-A. In water greater than lO ft. in depth, at least three measurements were attempted --l ft. below the surface, mid-depth and 1 ft. above the sediment. In water less than lO ft. in depth, two readings were generally taken one l ft. below the surface and at two-thirds depth. 
YSI Model 54 Dissolved Oxygen Meters with pressure compensated Clark-type sensors and temperature probes were used to determine dissolved oxygen, air and water temperature. Calibration was with water saturated air; oxygen measurements were recorded as percent saturation. Conversion to mg 02/l were carried out upon returning to the Institute. Salinity was obtained by withdrawing water from depth and reading with a temperature-compensated, direct reading American Optical refractometer. 
Waters for the bioassays, to be conducted at the Institute, were collected from mid-depth on the last day of sampling. Water for the shrimp and algal bioassays were collected with a peristaltic pump equipped with plastic impellors placed in rinsed plastic containers, and held in cool storage until returned to the Institute. Water for the fish bioassay was collected with the aid of a sea water pump on board the R/V LONGHORN and placed in specially designed wood coated with epoxy holding tanks to await unloading in Port Aransas. 
Analyses of heavy metals by the u.s. Geological Survey from samples collected during the intensive sampling program are given in Appendix I-B. 
ENVIR Data Management Program 
The treatment of the large amount of data from the hydrographic stations required special consideration. Therefore we selected a data management program called ENVIR (GURC, l972) for estuarine evaluation purposes. The total inclusion of all hydrographic data from the Galveston Bay program is far from being complete because of 
I -4 

the magnitude of the data available. However, data input was concentrated on those points of interest for this report and thus plots of ENVIR are found throughout the report. The data system will be continued as part of another project on Bay and Estuarine Management criteria (GI 348 70X) as supported by the National Science Foundation/RANN and the Coastal Resources Management Program of the Office of the Governor. 
During the period of the Galveston Bay Study, the Marine Science Institute was also participating in a research program in Environmental Data Management sponsored by NASA through the Gulf Universities Research Consortium (NASA-26897). This project involved facilities and personnel at the Mississippi Test Facility of NASA, and four other universities around the Gulf Coast. One of the early products of this project was an information retrieval system called ENVIR, for Environнmental Information Retrieval. Data from the Galveston Bay Project was used as a test case for the development of additional capabilities for the ENVIR system, thus benefiting both projects. 
The data retrieval section of ENVIR has been thoroughly described (Graham, 1972). In brief, it is possible to ask for data which fit a very complex logical statement, such as, all oxygen measurements taken at depths greater than l meter in the months of July or August which were greater than 7.0 mg/liter. However, a single "item" of information in this bank consists of a single measurement located in time and space; therefore, it is not possible to request points where both salinity and temperature are specified within a certain range. This difficulty has been overcome by a second stage of data reduction which can associate measurements stored as separate items and retrieved by means of the logical statement type of inquiry. The descriptors used to file and retrieve the chemical, physical, and biological measurements are given in the following table. 
Table 1. Descriptors used for Galveston Bay Data Bank. 
# Name Type of Descriptor 
l) . Station Alphanumeric Name of station 2) Latitude Numeric -station position -degrees, minutes, and 
tenths of minutes 3) Longitude Numeric -degrees, minutes and tenths of minutes 4) Yr Numeric -year S) Mo Numeric -month 6) Dy Numeric -day 7) Time Numeric -military time, i.e. ll PM= 2300 8) Depth Numeric -depth in tenths of meters 9) Ship Alphanumeric name of ship 10) Cruise Alphanumeric name of cruise ll) Param Alphanumeric name of parameter 12) Units Alphanumeric, units in which value is expressed 13) Value Numeric -the measured value of the parameter 14) Phase Alphanumeric name of the physical phase measured, 
such as dissolved or particulate15) Corrunents Alphanumeric corrunents such as "less thanTT 16) Method Alphanumeric name of method used for analysis 
I -S 
Furth~r treatment of the selected data points is at present restricted to determining the maximum, minimum, mean and standard deviation of each parameter and plotting the data under the operator's control. Plots produced through this system appear throughout this report headed TTENVIRn. Other data treatments which are being developed by the Environmental Data Management Project but which were not operational in time for this report include regression analysis and two, three and four dimensional contouring. 
At the time of this report, the Galveston Bay data bank consisted of about 36,000 measurements derived from a number of sources as follows: 
1) The GBP Data Management System (Espey, et al., 1971) as maintained by TRACOR, Inc. was used as the main source of data since it contained almost all of the routine measurements from 1968 to May, 1972. A copy of this data on magnetic tape was kindly provided by TRACOR and converted into the ENVIR format by specially written programs. 
2) Secchi disc readings collected by the Army Corps of Engineers field teams since ea1'ly 1971 were transcribed from the field data sheets or from the data summarized by the Corps of Engineers data system. 
3) USGS analyses of stream samples from selected areas and some analyses of bay waters were also transcribed into the ENVIR format. Only a small fraction of the total available USGS data had been placed in the data bank at the time of this report. 
4) The field measurements taken by UT MSI researchers during the four intensive sampling periods. 
5) The Algal Bioassay measurements made by Dr. Van Baalen's group on samples taken during both the routine and intensive sampling trips. 
6) Trace metal analyses conducted by the Houston City Health Department Laboratory at several times during the years 1971 and 72. 
7) The routine sample analyses done by the Houston City Health Department and the routine field measurements done during June, July, and August, 1972, which were not present on the TRACOR data 
system tape when the copy was made. 
This data bank will be maintained and updated after the termination of the present contract. Requests for further information should be directed to Dr. William B. Brogden at the Marine Science Institute. 
I -6 References 
Copeland, B. J. and T. J. Bechtel. 1971. Some Environmental Limits of Six Important Galveston Bay Species. Cont. 20, Pamlico Mar. Lab. N.C. State Univ. 
Copeland, B. J. and E. G. Fruh. 1970. Ecological Studies of Galveston Bay 1969. Report to Texas Water Quality Board, Galveston Bay Study Program. 
Espey, W. H., Jr. et al. 1968. Galveston Bay Study Phase I Technical Progress Report. TRACOR, Inc., Austin, Texas. 
Espey, W. H., Jr., A. J. Hays, et al., 1971. Galveston Bay Project Water Quality Modeling and Data Management, Phase II Technical Progress Report, TRACOR, Inc., Austin, Texas. 
Graham, E. D. 1972. Program Documentation for Environmental Information and Retrieval, Preliminary Report TUOOOl, General Electric, Mississippi Test Facility, 221 pp. 
GURC, 	1972. "EDMPAS" A Data/Information System for EnvironmentalнDependent Management Process Automation and Sirra.llation. Gulf Universities Research Consortium, Galveston, Texas. 
I -7 


CHAPTER II 

GALVESTON BAY ECOSYSTEM FRESHWATER REQUIREMENTS 
AND 
PHYTOPLANKTON PRODUCTIVITY 
by 
Neal E. Armstrong 
and 
Melvin O. Hinson, Jr. 

INTRODUCTION 
The only programs within the Galveston Bay Project Toxicity Studies which did not deal with toxicity directly were the studies dealing with the freshwater flows needed to maintain environmental limits for important Galveston Bay organisms and the determination of nutrient concentrations affecting phytoplankton productivity in Galveston Bay. Though these proнgrams were not analyzing for toxicity per~' an integral part of these programs was the consideration of the effects of toxicity in assessing freshwater flow requirements and the study of the effects of nutrient concentration on phytoplankton populations. 
That portion of this study concerned with freshwater requirements was somewhat broader than the name implied. Not only were salinity requirements, the environmental factor most directly affected by freshнwater inflows, to be considered, but also temperature, and seasonal trends of these two factors were to be included in this analysis. These two environmental factors of course have direct influence on the activity rates of organisms and are most influential in determining the location where organisms are found and the survival of these organisms in these locations. However, a more subtle portion of this study deals with the impact of organic material into Galveston Bay via river inflows. These natural inflows or organic material provide food material for a wide variety of 
II-1 

fish and shellfish in the Bay and thus may constitute as important a role in determining organism survival and activity as salinity and temperature. Thus, the specific objectives of determining the freshwater flow requireнments included the definition of the natural environmental limits for the environmental factors mentioned above, a comparison of the predicted distributions of organisms as controlled by these limits to the observed distribution of these organisms in Galveston Bay, and the establishment of the freshwater flows required to either maintain the present (or some desired) distribution of organisms in the Bay. 
The second portion of this study, the determination of nutrient concentrations affecting phytoplankton productivity was an extension of the work done in the previous phase of the Galveston Bay Project. In that phase, some definition of the chemical nutrients required for phytoplankton growth in Galveston Bay was made using laboratory bioassays; however, in this phase enrichment studies were to be conducted in situ in Galveston Bay using radioactive carbon to detect the changes in phytoplankton activн
ity in the presence of increased or modified chemical nutrient environment. This objective was modified to the extent that light was included as one of the "nutrients," and the radioactive carbon technique was dropped in favor of other techniques which will be described later. A second objecнtive was to construct mass balances for those nutrients found to be limiting in Galveston Bay and to suggest control measures needed for those nutrients. It was anticipated that nitrogen would be the limiting nutrient (based on the previous laboratory bioassays) and that nitrogen fixation tests would also be necessary to determine the magnitude of this source in the Bay. Again, these objectives were modified slightly to provide a more direct and applicable methodology to achieve the objecнtives. For example, the study of the light regime in Galveston Bay was given great importance. Also, a further study of the phytoplankton populations in Galveston Bay was considered since it is presumably 
11-2 
these populations which need to be controlled by limiting the input of the critical nutrient to Galveston Bay. The question of proper phytoplankton populations and proper species composition should also be considered in a study such as this; however, data limitations did not permit such attenнtion. 
REVIEW OF PREVIOUS WORK 
Environmental Reguirements -Freshwater Flows 
The major document used to define the environmental requirements of nine important organisms in Galveston Bay was the report by Copeland and Bechtel (1971) which was completed as part of the first phase of the Galveston Bay Project. In that study, data regarding the catch of these organisms were collected from the scientific literature and from files of various state and federal agencies. A catch ratio was defined as the number of catch successes divided by the number of attempts, and then the catch ratios for each study or collection referenced were related to salinity, temperature, location, and season in single and multiple interнaction correlations. Data from Galveston Bay and other Texas bays were included in this analysis and thus the results in the Copeland and Bechtel report represent at least in part the situation in Galveston Bay. Suppleнmentary papers by Parker (1970), Truesdale (1970), and others have helped to define the interrelationships between salinity, temperature, season, and location. Also, the subtle environmental factor mentioned in the Introduction has been included, that being the import of organic material carried with river inflow. 
The Copeland and Bechtel report (1971) directly and indirectly points out the behavioral patterns of the six important Galveston Bay biota types (represented by nine species) and the very strong interdependence of the environmental factors themselves. For example, the brown shrimp, Penaeus aztecu?, moves into Galveston Bay in the spring as temperatures 
II-3 

are rising in the water and as river inflows are increasing because of 
incr~asing rainfall in tributary areas. At the same time, marshes on the Bay periphery are growing following the winter death period. Thus, the increase in temperature, the decrease in salinity, the suitability of the marsh habitat, and the seasonal increase in catch of the brown shrimp are all very interdependent factors. The catch ratios of the brown shrimp reflect the preference for a moderate temperature, for marshes and bayous and edges of the Bay, for the spring months, and for wide varieties of salinities resulting from variations in stream flows into the Bay. 
The seasonality of the brown shrimp, the environment it moves into, and the material on which it feeds are extremely important and must be maintained if the brown shrimp are to be maintained. The freshwater flows which are required to maintain such a seasonality are those which will be defined to some extent in this study. Despite the great amount of attention given to shrimp populations on the Texas Gulf Coast, and to crabs, oysters, and finfish, there are a number of aspects about the environmental requirements of the nine species of organisms considered here which are still unknown or unclear. What is clear is that a great deal of effort remains to define the environmental requirements of these organisms and from those requirements to define the freshwater flows required to maintain the Galveston Bay system in a state suitable for these organisms. 
One approach used in two previous studies (Bureau of Commercial Fisheries, 1964 and Copeland, 1966) to establish a gross relationship between freshwater inflow and "yield" of an estuary was the correlation of annual inflows of freshwater to the estuaries and the commerical catch obtained from those estuaries the same or the following year. The studies showed that a relationship did exist and could be used for predicting the decrease in commercial catch in the estuary due to reduced freshwater inflows. This approach was refined and used in this study for the same 
purpose. Il-4 
Productivity 
Phytoplankton Distribution 
There is surprisingly little information about the distribution or species composition of phytoplankton in Galveston Bay. The only readily 
available source of such information comes from the first phase of the Galveston Bay Project in work done by Copeland and Fruh (1970). Their work covers the four seasons of 1969, but only four visits were made to 
the Bay during that year. Nevertheless, some seasonal patterns do show 
up in their data, and a good definition of the species composition was 
obtained for thaj year. These data are presented later. 
Controlling Factors 
Inorganic Nutrients: The enrichment tests performed by Copeland and Fruh (1970) using water from various Galveston Bay stations was the work prerequisite to this study. They defined several relationships between the chemical nutrients in Galveston Bay and the growth of phytoнplankton. The conclusions of that study were that sufficient nutrients were present in Galveston Bay to support larger phytoplankton crops, but some physical factor (such as light penetration) or chemical factor (such as toxicity) was limiting the phytoplankton's capability to use nutrients. Nitrogen appeared to be the only nutrient to significantly stimulate phytoнplankton growth in the laboratory bioassays conducted. Iron availability was also of importance at different stations for various periods of the year. The rationale for their methodology was derived from a great deal of previous work done on enrichment tests by many workers to elucidate limiting nutrients and limiting nutrient concentrations. It is appropriate to mention a few of these workers, namely Ryther and Riley, who worked in the estuarine and marine environments, and Goldman who has utilized the radioactive carbon tests in freshwaters in many parts of the world. 
Data on the chemical composition of the waters from which samples were taken have been extracted from the routine monitoring program 
11-5 

results carried out by the Corps of Engineers for the Galveston Bay 
Project. 
Light: One of the recommendations in the report by Copeland and Fruh (1970) on the Phase I biological work was that routine monthly sampling should include light penetration measurements. Such measureнments were initiated by the Corps of Engineers, but they were taken as Secchi disk measurements instead of perhaps more meaningful light transнmission measurements. Light transmission in any body of water is always a limiting "nutrient." The fact that light is absorbed by water and at some depth the amount of light available is inadequate to support phytoнplankton growth may be interpreted as a mandatory light limitation on phytoplankton production in any body of water (Ryther, 1956). Ryther and Yeutsch (1957) have used the availability of light radiation over the surface of the earth, the average absorption of light in marine waters, the amount of chlorophyl "a" in various parts of the ocean, and the assimilation number for chlorophyl (the amount of organic material produced per unit mass of chlorophyl "a") to determine the potential productivity of the world's oceans. They found that for conditions of average radiation (200 to 400 gram cal/cm 2I day) that the theoretical gross organic production varied between 23 and 32 grams dry weight/m2I dq.y with net organic production 
2
ranging between 8 and 19 grams dry weight/m/ day. Under maximum radiнation conditions (750 gram cal/cm 2I day) the values for gross and net organic production were 38 and 27 grams dry weight produced/m 2I day, respectively. The data reported by Odum, et al. (1963a) for studies 
during 1961and1962 in Galveston Bay are near these theoretical limits if the equivalency of grams of oxygen produced/m 2I day and grams dry 
2
weight organic matter produced/m / day is valid. 
With regard to the limitation of light in estuarine waters, Ragotzski (1959) found in the marshes of Georgia that for some measurement periods, light was found to be limiting. He used a relationship defined by Sverdrup 
11-6 

(1953) for oceanic waters to determine if the depth of the water over which algae are mixed is so deep that light penetration or the frequency of turnover in that mixing layer (or the frequency of exposure of the algae to adequate light in that mixing layer) were adequate to support algal populations in that situation. The relationship is as follows: 

where D = compensation depth, meters, 
c 
D = critical depth, meters, 

er 
k = extinction coefficient, meter-l. 

Ragotzski found that when the critical depth was less than the mixing depth, there was no net production of phytoplankton, but when the critical depth exceeded the mixing depth, there was always a net production of plant material. Thus, this method appears to be a legitimate guideline for assessing the availability of light in an estuary as well as the ocean, and the method has been used herein to test light limitation using the field light and production measurements made. 
The materials in water which absorb light are very important if light is always limiting in the water. Pure water absorbs light selectively at various wavelengths, particles in the water will absorb and reflect light, and dissolved materiai in the water will tend to absorb light such that with high concentrations of either particles and/or dissolved material, high light absorption will occur in relatively shallow depths. Kalle (1966) has found that light absorbing dissolved materials may be found in the 
Baltic Sea and especially in near-shore waters. He concluded that these materials originate from two sources: from the washing of decaying organic material into the coastal waters by river flow; and by the decomнposition of organic plant material in the ocean. Since estuaries are 
Il-7 
concentrators of many of the materials brought in from river inflow, the presence of this decaying organic material in the estuary is expected. Also, decaying organic material from waste discharges may also make up a portion of the light absorbing material. Rickert and Hunter (1972) in a study of the colloids and fine particles in waste discharges showed that a large portion of the material in waste discharges is in a colloid form which would tend to contribute to the light absorbing decaying material in estuaries. Obviously, if this material in waste discharges does contribute to the absorption of light in estuarine water, the decrease of waste material may contribute to the increase of light transparency. Thus, for a bay with an existing light deficiency and a controlled phytoplankton population because of that deficiency, an increase in transparency would be expected to increase the overall production in that estuary. The presence of light absorbing material in Galveston Bay water was therefore investigated. 
Toxicity: Although this study was not directly oriented to detecting or defining toxic materials in the waters of Galveston Bay, some of these results may be correlated with other portions of the Toxicity Studies, and toxic effects may be elucidated. The Phase I report by Copeland and Fruh (1970) and the paper of Odum, et al. (1963a) have indicated that toxicity must play a role in the limitation of species distribution and production in Galveston Bay. 
Microcosm Studies 
Due to the vast biological and hydrological complexity of an estuarine ecosystem such as Galveston Bay, ecosystem interactions with wastes or other inputs must be simplified to a workable level before any predictive inferences can be drawn concerning potential changes caused by these inputs. The use of microcosms, or model ecosystems, offers a practical method for illustrating basic system responses under specified conditions. Comнmunity metabolism, the sum total of the system's biological functions, can be quantified most easily by investigating an isolated unit (either 
II-8 
natural or simulated) that possesses characteristics similar to the natural 
prototype system. The ability to verify simple community changes in 
response to variable environmental conditions must be achieved before 
actual alterations in the Galveston Bay system, such as inhibition or 
stimulation of phytoplankton production due to wastewater toxicity and/ 
or enrichment, can be predicted. 
The microcosm concept for determining the principal components of community metabolism -phytosynthetic production and community respiration -is well established in aquatic system investigations (Abbott, 1967; Beyers, 1963; Cooper, 1970; McConnell, 1962; Odum and Hoskin, 1957). Traditional field light-dark bottle experiments for phytoplankton producнtivity analysis utilize an isolated community within the glass bottles to provide simultaneous estimates of net production and community respiraнtion (Odum and Hoskin, 1958). Laboratory model ecosystems have been developed to simulate a whole spectrum of aquatic systems and conditions. Actual experimental designs have included flowing artificial streams (Mcintire, Garrison, Phinney, and Warren, 1964; Odum and Hoskin, 1957), static carboy and aquaria systems (Abbott, 1967; McConnell, 1962; Whitaker, 1961), outdoor pools (Whitworth and Lane, 1969; Odum, et al., 1963b), and continuous series estuarine units that maintained a distinct salinity gradient (Cooper, 1970). Regardless of the idiosyncrasies of the experimental design, microcosms are, by definition, miniature isolated representatives of the ecosystems being simulated and are assumed to function and respond similarly like the prototype system under experimental conditions. 
The need to derive quantitative measures of a system's innate biological activity (community metabolism) led to the early investigations by Odum and Hoskin (1957; 1958). Their approach to defining the systems and its interactions allowed community metabolism to serve as a comparaнtive parameter between different ecosystems and the same ecosystem under different conditions. Beyers (1963) tested this concept by estabнlishing and manipulating twelve freshwater "microecosystems." His 
II-9 

extensive research evaluated the diurnal metabolism pattern of microcosms using both carbon dioxide and oxygen methods to examine production and respiration relationships. Each method, which measures the gaseous substrates being evolved and/or assimilated during phytosynthesis and respiration, indicated that microcosms achieve a characteristic steady-state balance. Beyers (1963) and Abbott (1966) both noted that this equiнlibrium, developed within microcosm communities, contributes to the replicability essential for laboratory analysis. 
As Odum and Hoskin (1957) and Beyers (1963) developed the theoretical basis and methodology for evaluating microcosm responses, Whittaker (1961) reported that microcosms could be used to illustrate the fate of inorganic nutrients in aquatic systems. His tracer studies with radiophosphorus 
32p movement in aquarium microcosms demonstrated the nutrient interнactions between the water, sediments, and biota under oligotrophic and eutrophic conditions. Previously, Hayes and Phillips (1958) had also used the radiotechnique to trace labelled phosphorus equilibrium between abiotic and biotic components under reduced and oxidized conditions. These invesнtigations, as well as Abbott's (1967) batch nutrient additions to microcosms, further defined the representative energy and material pathways existing in microcosms and emphasized their potential for bioassay study. 
McConnell (1962) developed a methodology for analyzing gross productivity and respiration in carboy microcosms that proved suitable for long-term experimentation. Construction of a simple diurnal oxygen curve was based on only three oxygen determinations taken at specified times daily -sunrise, sunset, and the following sunrise. The diurnal oxygen fluctuations within a water mass supporting an autotrophic comнmunity exhibit distinct peaks and depressions at these times, and experiнmental systems using artificial lighting with a prescribed light-dark cycle can easily standardize the sampling times. Under McConnell's scheme, the observed increase in dissolved oxygen between sunrise and sunset yields 
II-10 
the value for net production (PN) during the daylight period. The decrease in dissolved oxygen during the nighttime period (sunset to sunrise) reflects the community's respiratory requirements, and this value for respiration 
(R) is also assumed to remain constant throughout the daylight period. Thus, the discrete values for daylight dissolved oxygen increase and nightнtime dissolved oxygen decrease (Figure II-1) represent usable estimates of net production and respiration after correction for gaseous diffusion. 
The three point dial oxygen method was adopted by Whitworth and Lane (1969) for detecting the toxic effects of various hazardous materials in the productivity of outdoor pools. Their investigation used large outdoor pools to evaluate long-term community metabolism response to pesticides, inorganic chemicals, and other toxicants frequently used in nuisance fish and vegetation control. Community metabolism did become depressed by addition of toxicants, with the rate of recovery dependent on the magnitude of the initial toxicant concentrations. Considering these efforts, it appears valid that residual toxicity in Galveston Bay water can be detected by depression of community production and respiration with established microнcosms. 
Cooper's functional Trinity Bay model, presented in the previous Galveston Bay Study (Copeland and Fruh, 1970), utilized a series of interнconnected microcosms to relate salinity regime, benthic and zooplankton diversity, and production to variable freshwater inflows. Since the scope of this research limits the model ecosystems to simulation of four widely spaced stations in the Galveston Bay complex (Upper Galveston Bay -Station 22; Trinity Bay -Station 26; West Bay -Station 14; East Bay -Station 29), microcosms of this hydrological complexity were deemed infeasible. In order to maintain similar hydrological characteristics, microcosms were established using actual Galveston Bay water collected at the specified field stations and exhibiting the nutrient and salinity regimes prevalent at the season of collection. Constant inflow and outflow rates assured a 
II-ll 
residence time of 30 days, which is representative of many areas of the Bay. under normal flow conditions. Productivity measurements, using 
the diel three-point oxygen method were calculated daily over an entire residence period to reveal the inhibitory or stimulatory effects associated with the water mass surrounding that station. A control unit received only artificial seawater (20 ░/oo salinity and devoid of nutrients) and was assumed to represent a senescent microcosm community free from enrichment or toxicity responses. 
METHODOLOGY 
Environmental Reguirements 
The general procedure for determining the environmental requireнments of six important organisms in Galveston Bay was to use the inforнmation in the Copeland and Bechtel (1971) report and other papers to define the salinity, temperature, location, and seasonal requirements of those organisms. The next step was to determine if the environmental requireнments applied to Galveston Bay and if so, to predict the distribution of organisms by salinity, temperature, and other environmental factors as they might be expected to be distributed according to their environmental tolerances. Once these predictions had been made, comparison to observed distributions of organisms could then be made to ascertain whether there were areas in the Bay which should have organisms but do not because of the presence of toxic materials or other waste products. Correlations between the presence or absence of specific organisms and the presence of toxic materials as detected in other parts of this program or by preнdiction from modeling efforts of Tracor would then be developed and be used to determine the required freshwater inputs needed to maintain the proper levels of the environmental factors. Only the first was completed, that is, the Copeland and Bechtel (1971) report was used to define the limits 
II-12 
for various environmental factors, and that report with other information from the scientific literature has been used to describe in a cursory fashion the seasonal requirements for freshwater inflows for these imporнtant organisms in Galveston Bay. 
Another approach used was the relation between freshwater flows to the Texas bays and the commercial catch in these bays assuming a dependency of the organisms in the bays on the salinity changes caused by the freshwaters and the organic material brought to the bays with the freshwater. This approach was used by Copeland (1966) and by the Bureau of Commercial Fisheries (1964) to estimate the impact of reduced flows to the bays, and it has been extended herein in a "normalized" fashion for comparison of the bays on an "equal" basis. 
Finally, to obtain estimates of the mass loading of organic material to Galveston Bay, a mass balance was constructed for organic material inflows using the equivalency between S-day biochemical oxygen demand (BODS} in mg/I and volatile suspended solids (VSS} in mg/I dry weight of organic material in suspension. Values for BOD in incoming stream flows were obtained from United States Geological Survey data collected as part of the Galveston Bay Project. Average seasonal flows for the major inflowing streams were obtained from Tracor, Inc. Waste flows from the Houston Ship Channel (422, 700 lbs BODlday) were estimated as the difference between the total BODS discharge into the Channel given in the Tracor Phase II Report (1971) (470, 000 lbs BOD/day) and the estimated rural runoff contribution (47, 300 lbs BOD/day). Mass discharges from Texas City and Galveston are rough estimates only. A population was assumed and the mass discharge coefficient 0. 2 lbs BOD/cap/day was applied. Waste treatment equal to SO per cent BOD removal was also assumed. 
This mass balance is for inflowing streams only; it does not include organic material produced at the Bay periphery by marshes and submerged macrophytes or in the Bay waters by the phytoplankton. These contributions 
II-13 

are considered, however, in a second mass balance constructed to consider 
the daily flux of organic material in the Bay. This is an annual average only and thus does not reflect seasonal changes. Nor does it reflect geoнgraphical patterns in the Bay. 
Productivity 
To achieve the objectives of the productivity studies, laboratory and field studies were initiated. The intent of the laboratory studies was to define the changes in algal growth rate that occurred with increases in nutrient concentrations such that the impact of increased nutrient disнcharges, and hence algal growth and total system production, could be ascertained. Growth rate-substrate relationships were derived through batch culture tests using controlled substrate concentrations in artificial sea water. These results were to be used for comparison to growth rates in Galveston Bay waters to determine if the Bay waters supported greater or lesser amounts of growth than the laboratory controls with and withoqt nutrient additions. Microcosm studies were also conducted in the laboraнtory in an attempt to simulate portions of the prototype Galveston Bay. Production measurements were made in these microcosms for comparison to those made in the field using water from the Bay and Bay water enriched with nutrients. 
In the field, production and respiration measurements were made for the planktonic system. The measurements were made during the quarterly sampling periods in the Bay with other Marine Science Institute personnel. 
A more detailed description of these studies and methods used is given below. 
Laboratory Enrichment Tests 
Enrichment tests were conducted initially to verify to some degree the results found by Copeland and Fruh (1970) in the first phase of the 
II-14 
Galveston Bay Project. These results indicated in general that nitrogen was a limiting factor during part of the year and that light and iron may be limiting factors also. They used the blue-green algae, Coccochloris elebans, which Van Baalen is using in concurrent studies of blue algae bioнassays. Attempts to culture .f..=. elebans for reuse in these laboratory enrichment tests were consistently futile. Thus, the blue-green algae was abandoned and a green flagellate, Dunaliella, was used for the bioнassays. However, this change of organism did not take place until April, 1972, thus, only the April samples from Galveston Bay were assayed in this fashion. 
The procedure for conducting this laboratory assay was to take a 100 ml aliquot of Bay water from each of five stations (Stations 14, 17, 22, 26, and 29), place it in a 300 ml Erlenmeyer flask, add various nutriнents to the sample (0. 5 mg PO -P, 2. 0 mg N0-N, or none), and finally
43inoculate with 1 ml of Dunaliella from a stock culture. These flasks were then placed near a 30 watt fluorescent lamp (about 300 foot-candles) in a constant temperature room in which the temperature was about 25░C. After inoculation of the samples, the concentration of Dunaliella was measured in each culture each day thereafter for a period of about ten days using a Bausch and Lomb Spectronic 20 Spectrometer using the 420 millimicron wavelength (bandwidth of 20 m╡ ). Readings were measured as optical density and converted to mass of cells using a calibration curve derived earlier for Dunaliella. The growth rates were computed for each sample and for each addition of nutrients. In the second series of laboratory batch tests, attempts were made to develop growth rate vs. substrate concentration curves by growing the algae in ASP-2 media containing various concentrations of nitrate nitrogen and phosphate. Constituents in the ASP-2 media are given in Copeland and Fruh (1970). Stock Dunaliella was cultured in a large flask in ASP-2 media with nitrogen and phosphorus present in full concentration. Into 300 ml 
II-15 

flasks containing ASP-2 media with various combinations of nitrate nitrogen and phosphate were placed 1 ml innocula from the stock culture. After 
inoculation, concentrations of Dunaliella were measured each day as desнcribed above for five days. During this measurement period, Dunaliella would normally reach the maximum cell concentration to be attained under the substrate conditions imposed. Nutrient concentrations were measured at the end of the experiment on filtered samples, and growth rates, maxiнmum cell concentrations, lag time, and cell yield were calculated. 
To be compared to these growth rates under controlled nutrient conditions were growth rates obtained from similar inoculations in Bay water with measured nitrogen and phosphorus concentrations. Galveston Bay water was collected from Stations 14, 17, 22, 26, and 29 and stored at 4░C in the dark until use. Nitrogen and phosphorus concentrations were measured in these samples before and after inoculation with Dunaliella and at the end of the experiment using the Technicon Autoanalyzer methods for nitrate and the methods of Strickland and Parsons (1968) for phosphate. The growth rates obtained from the batch cultures could then be compared to the growth rates in the controlled tests for corresponding nitrogen or phosphorus concentrations. Deviations of growth rates in Galveston Bay waters from those of the controls were then correlated with geographical location in the Bay or season of the year to discover possible explanations 
for the deviations. 
Unfortunately in these experiments, carryover of nutrients in the inoculum from the stock culture obscured the results of these experiнments. However, because the amounts of nitrogen and phosphorus added through this carryover are enough to remove nutrient limitations on these tests, the net result was a bioassay test similar to Van Baalen's blueнgreen algae bioassay with comparable results. 
II-16 

Laboratory Microcosm Studies 
One of the methods used by Copeland and Fruh (1970) for assessing the impact of organic material brought in with freshwater flow to Galveston Bay was the use of microcosms. These microcosms were established to represent various segments of Trinity Bay. Such methods have been used previously by Odum et al. (1963b), and it has been shown that these microнcosms or small pieces of estuaries actually reflect the processes which occur in the prototype estuary. 
Microcosm Experimental Design: Five fifteen-gallon aquaria (all glass construction) were initially inoculated with water and sediments collected in the Galveston Bay system (See Figure II-2 for complete experнimental design). Forty-five liters of estuarine water (salinity -17 ░/oo), collected along the shoreline of West Bay near Galveston on March 18, 1972, was added to each aquarium. The microcosm substrate consisted of well-washed sediments (wave-segregated sand with little organic mud) obtained from shallow lagoons at Bolivar Roads at the same time. Special care was exercised to prevent the transported sediments from becoming anaerobic, so that benthic organisms would survive to develop a substrate community. Sediment depth was 2. 5 cm in the aquaria, while the estuarine water was 25 cm deep for phytoplankton activity. 
Physical Characteristics: Complete mixing of the microcosm waters was accomplished with one-inch magnetic stirring bars spinning on top of an inverted petri dish cover at substrate level. Air-driven magnetic stirнring units located underneath the aquaria provided adequate rotation of the in situ stirring bars, and were free from thermal problems associated with heat generation by electrically-powered units. The mixing regime in a model system was initially tested and tentatively verified by the observed dispersion of a dye tracer (methylene blue) throughout the aquarium. 
Average light intensities of 630 to 780 foot-candles were present at the microcosm water surface. A Weston sunlight illumination meter 
II-17 

was used to determine the radiant output from twin 40-watt General Electric cool-white fluorescent tubes suspended 4 to 5 inches above the water surface and a single fluorescent tube located 16 inches away. Average illumination values are based on three surface readings (right side, center, left side) taken at the prevailing water level, and observed differences between microcosms (Table :rr -1) resulted when slight gaps between the light fixtures (longer than aquarium length) occurred over an aquarium unit. This degree of illumination closely approximates or even exceeds the average daily light intensities observed at Galveston Bay field stations during the January 26-27 and April 25-27 sampling sessions (overнcast skies), but falls far short of the high summer light levels (over 3000 foot-candles) obtained during the clear July weather. All of this light is not "photosynthetically available" (i.e. 400-700 m ╡wavelength) to the community autotrophs, but Cooper (1970) reported that approximately 60 per cent of the radiant output from fluorescent fixtures falls within the usable wavelengths. 
The established microcosms were allowed over two months to develop a steady-state community structure under a 12 hours light, 12 hours dark lighting cycle. During this time period, no additional nutrients were added or water inflow permitted, except for distilled water added regularly to compensate for the 30 ml/day evaporation occurring in the 26░c culture room where the model systems were located. Alterations in the magnetic stirrer setup and lowering of the light fixtures to increase surface illumination during the initial development period resulted in some short-term phytoplankton "blooms" that suggested continuous autoнtrophic succession in response to the new environmental conditions (improved mixing regimes and light intensities). 
Influent Water and Flow Rates: Feed water for this constant inflow system was collected onsite at Galveston Bay stations 14, 22, 26, and 29 during the spring and summer sampling cruises. All microcosm water was 
II-18 
TABLE II -1 

LIGHT INTENSITY AND SALINITY REGIMES 
FOR GALVESTON BAY MICRbCOSMS 

Spring Influent Summer Influent 
Microcosm  Light  Salinity  Influent  Salinity  Salinity  Influent  Salinity  
Intensity  at Start  Salinity  at End  at Start  Salinity  at End*  
(ft-candles)  (o/oo)  (o/oo) ╖  (o/oo)  (o/oo)  (o/oo)  (o/oo)  

A (Station 26)  781  16.8  11  20.5  21. 6  8  
B (Station 14)  638  17  21  27  23.5  26  
l"""'4 l"""'4 I t--l '░  c (Artificial SW)  780  15.5  19.8  21  22.0  20  
D (Station 22)  680  15  15  18.5  20.2  16  
E (Station 29)  760  16. 2  18.5  23  22.8  26  

*Experimentation Incomplete placed in a 4░C coldstorage room and vacuum-filtered as needed through 
0. 45 ╡Millipore filters to remove suspended sediments, organic debris, viable algal cells, and bacteria. This procedure insured that water quality characteristics of the influent would not be changed by continuing microнbial degradation or algal growth during the prefilte;red state or within the influent reservoirs and tubing system. 
A Durram variable-flow pump provided 10 individually-controlled channels that maintained a constant flow rate into each model system. Five gallon carboys served as influent reservoirs and were located on a shelf above the microcosms for better hydraulic head pressure. Influent water travelled along individual glass tubing lines with flexible Tygon plastic "joints", then through rocker-arm type pump and into the specified microcosm. An independent effluent system for each unit reversed the process, moving an identical volume of water out of the aquaria via the Durram pump and into a large effluent receptacle. Flow rates were set at 1. 5 liters/day, so that one displacement volume would be furnished within a thirty day period. A retention time of only thirty days generally overestimates actual Galveston Bay displacement rates but was selected as sufficiently long-term for representative system modeling . Cooper's 
(1970) continuous series microecosystems exhibited average retention 
times of 60 days for his normal Trinity Bay inflow series. 
The preliminary experimental influent consisted of Galveston Bay water (unaltered other than filtering) collected during the spring cruise. One displacement volume of spring water was introduced over a 30-day period, the representative retention period. Then, the flow rate was doubled, so that one displacement volume of artificial seawater (salinity 20 ░/oo and no added nutrients) was delivered over 15 days. This condition was assumed to test the model system response to nutrient-depleted waters and also served to flush out toxic or growth-inhibiting substances generated by algal growth and microbial decomposition of senescent cells 
II-20 

and other organic debris. Low nitrogen levels were present in the spring water used in the tests. Nitrogen concentrations in the summer waters were also low and were enriched to a level of one mg/I nitrate (nitrogen) 
to differentiate between nutrient and toxicity effects. Due to time restrictions on this project, data for only half of the normal 30-day cycle are presented for summer influent analysis. No phosphate was added since 
this nutrient appears to maintain a constant equilibrium between the water and sediments (Hayes and Smith, 1958; Whittaker, 1961; Pomeroy, 
Smith, and Grant, 1965). Nitrate addition is based on Copeland and Fruh's 
(1970) estimation that Galveston Bay nitrogen may be limiting during the 
summer months. 
Supplemental Physic-Chemical Sampling: Physic-chemical sampling of the microcosms, in addition to the dissolved oxygen measurements, included optical density of the water, temperature, salinity, phosphate, and nitrite-nitrate nitrogen concentrations. Temperatures (░C) were measured daily at the same time as dissolved oxygen determinations (each 12 hours at the lights on and lights off times). Salinity (0/oo) was meaнsured with an American Optical refractometer at the beginning and termiнnation of influent flows. Optical density of the microcosm water, an indirect measure of turbidity and/or phytoplankton growth, was taken at three day intervals. Actual readings were taken with a Bausch and Lomb Spectronic 20 colorimeter, using a 1. 9 cm light pathway at 420 mu. waveнlength. Nutrient analysis c~:..:1.sisted of weekly measurements for reactive phosphorus and total nitrite-nitrate nitrogen. A manual colorimetric test for reactive phosphorus (total dissolved phosphorus) and an automated procedure utilizing the Technicon AutoAnalyzer for total nitrite-nitrate are described in the previous section on Laboratory Enrichment Studies. 
Chlorophyll "a" content of a microcosm water sample was determined by acetone-extraction of the photosynthetic pigment (Vollenweider, 1971). 
II-21 

Spectrophotometer readings of the extracted pigments were taken at 665, 645, and 630 m╡ wavelengths to separate chlorophyll "a" from other chlorophyll pigments and degradation productions. Absorbance peaks at those wavelengths were weighted for chlorophyll "a" by the following formula (Richards and Thomas, 1952): 
chlorophyll a in mg/l = 16. 6 d-2. 0 d-0. 8 d
665 645 630 
where d = optical density at x wavelength 
Community Metabolism Analysis: Dissolved oxygen production in aquatic systems series as a direct indicator for in situ photosynthetic activity. This gaseous endproduct of the photosynthetic reaction: 
radiant energy > 


is evolved in direct proportion to the carbon dioxide assimilated or the amount of organic substrate formed. The community autotrophs (phytoнplankton, benthic algae, diatoms, etc. ) are able to generate oxygen in greater amounts than their individual respiratory needs, and the excess is released into the surrounding aquatic medium. The relatively slow diffusivity of oxygen between the air and saline waters allows dissolved oxygen levels to increase or decrease primarily as a function of biological activity. By measuring the in situ dissolved oxygen concentrations over the light-dark cycle, the respiratory oxygen utilization and net production (excess oxygen produced during the day) can be estimated. 
The three point diurnal method of McConnell (1962), Abbott (1967), or Whitworth and Lane (1969) provides adequate production and respiration estimates with a minimum of actual dissolved oxygen measurements per day. Three samples are required by this procedure based on expected conнcentration fluctuations along a normal diurnal curve. Sampling times are 
II-22 
coordinated with changes in the light regime, i.e., lights on (sunrise), lights off (sunset), and lights on (sunrise) for the next day. Oxygen 
concentrations at sunrise represent depressed dissolved oxygen levels 
due to nighttime community respiration (R), whereas the sunset concenн
tration should reflect the maximum excess oxygen production (PN) resultн
ing from photosynthetic activity. A value for gross production (total 
daylight photosynthesis) can be calculated from the sum of net production 
(daylight excess) and the nighttime respiration (nocturnal decrease), when 
the assumption is made that observed nighttime respiration equals dayнlight respiration (occurring simultaneously with gross production). These 
basic relationships are illustrated by the equations used to calculate field 
productivity with the light-dark bottle method. However, a diffusion 
correction factor must be added to these observed values to compensate 
for the oxygen that does diffuse into or out of the microcosms in response 
to saturation deficits between the water and air. 
Microwinkler Oxygen Determinations: Oxygen determinations were performed using the Microwinkler technique originated by Fox and Wingfield (1938). This procedure, which only requires a five ml water sample for analysis1 minimizes disruptions that might occur with the excessive removal of microcosm water for oxygen analysis. The azide modification of the Winkler technique (Standard Methods, 1965) reduces the interference from dissolved and suspended organics during the thiosulfate titrations. The entire series of Winkler reactions can be performed within the same reaction syringe fitted with a special glass tip (one syringe for each microнcosm). Standardized thiosulfate is dispensed from a Micrometric syringe microburet unit and titrated to a clear endpoint using starch indicator. 
Oxygen Diffusion: The observed values for oxygen flux during the diel cycle were corrected for the diffusion across the microcosm airнwater interface in response to differences in oxygen saturation. The 
II-23 

following method for diffusion correction was utilized by Whitworth and Lane (1969): Diffusion correction (D) =reaeration coefficient x average saturation deficit x time period where: reaeration coefficient= 0. 42 mg/l/hr/100% deficit 
(S . -S . )+ (S -S . ) 
t. (S) d f. "t (M) sunrise air sunset air
average sat ura ion e ici 10 = 
2 
time period= 12 hours 

Saturation percentages for the oxygen values were determined from Standard Methods (1965) for the prevailing microcosm salinity and temperнature. The reaeration coefficient (0. 42 mg/l/hr/100% deficit) was experнimentally determined in an abiotic aquarium setup with similar physical characteristics (i.e. equal water depth, mixing by magnetic stirring bar, 20 o;oo salinity, and 25░C temperature) to the actual microcosms. Comнplete deoxygenation of the aquarium was accomplished by adding sodium sulfite and cobalt chloride as catalyst (Maney and Westgarth, 1962). Oxygen measurements to calculate this reaeration value were continued over a six-hour period after the first dissolved oxygen was detected in the aquarium by Winkler titrations. Abbott (1967) found that reaeration varied from O. 54 to 0. 26 mg/l/hr/100% saturation deficit for his estuarine microcosms, while Whitworth and Lane (1969) reported that an assumed value of only 0. 1 mg/l/hr was used in his calculations. 
Field Tests 
Four cruises were completed in which light transmission data were acquired; on three of these cruises production data were taken. The dates of these cruises, the stations visited, and remarks about these cruises 
are given in Table II-2. On the January, April, and July cruises, phytoplankton production was measured by use of the light and dark bottle technique. The standard 
II-24 
TABLE II-2 SUMMARY OF PRODUCTIVITY SAMPLING EFFORT IN GALVESTON BAY 












Activity 
Laboratory 
Chlorophy11 "a"
Light
Light-Dark 
Light 
Bioassay 
Samples
Samples
Absorbance
Station 
Bottle Tests 
Transmission
Date 
14 Oct. 26-28, 1971 
x 
x 
x 
x 
x
Jan. 25-27, 1972 
x 
x
Apr. 25-27, 1972 
x 
x 
x 
x
July 25-27, 1972 
x 
x 
x 
x 
17 Oct. 26-28, 1971 
x 
x 
Jan. 25-27, 1972 
x 
x
x 
Apr. 25-27, 1972 
x
x 
x 
x
x 
x
July 25-27, 1972 
x
x 
x 
x 
Oct. 26-28, 1971 
x
22 
x 
Jan. 25-27, 1972 
x 
x
x 
Apr. 25-27, 1972 
x
x 
x
x 
x 
x
July 25-27, 1972 
x 
x 
x 
x 
x
26 
Oct. 26-28, 1971 
x 
x
x
Jan. 25-27, 1972 
x 
x
x 
x
Apr. 25-27, 1972 
x 
x 
x
x 
x
x 
x
July 25-27, 1972 
x
29 
Oct. 26-28, 1971 
x 
x
x
x
Jan. 25-27, 1972 
x 
x
x
Apr. 25-27, 1972 
x 
x 
x 
x
x
x
July 25-27, 1972 
x 
biochemical oxygen demand bottle with a 300 ml volume was used in all the tests. Combinations of light and dark bottles were suspended at various depths at each of the five stations in Galveston Bay. These bottles were set out usually in early morning and retrieved in late afternoon such that a 6 to 8 hour period of exposure was available. Production using the light and dark bottle technique can be calculated in the following way: 
Light Bottle = Gross Production -Respiration =Net Production 
Dark Bottle = Respiration 
Gross Production = Net Production + Respiration 
= Light Bottle + Respiration 

where: = final dissolved oxygen content of light bottle (mg/l)
LF = initial dissolved oxygen content of light bottle
LI 
(mg/l) = final dissolved oxygen content of dark bottle
DF 
(mg/l) = initial disso~ved oxygen content of dark bottle
DI 
(mg/I) 
Chlorophyl "a" concentrations were also measured on the April and July cruises. One liter of sample collected at each station was Millipore filtered, the filter with the algae was then ground up in a mortar and pestle with sand, chlorophyll was extracted with 100 ml of 90% acetone, and then the optical density of this mixture was then read at 665 milliнmicron wavelength using the Bausch and Lomb Spectronic 20 spectrometer. These optical density measurements were then converted to chlorophyll "a" concentrations by the equations given earlier. 
Light transmission in the water column was measured at each station several times during the cruise period using a submarine photomнeter purchased from Kahl Scientific Company. Transmission of light m the red, green, and blue wavelength bands as well as total light was 
II-26 
measured at various depths down to and just past the depth at which one percent light remaining was achieved. These percent transmission data were then plotted and extinction coefficients calculated by the following equation: 
k =ln {Ic/I)/d where k = extinction coefficent, 1=light intensity at water surface
0 
and I = light intensity a depth d. 
Samples of Bay water were also returned to the laboratory for further optical analysis. Using a Cary spectrophotometer, a continuous tracing of light transmission in the sample was made from wavelengths of 200 mu up to 800 mu. The Bay samples were then subjected to filtration through 0. 45 micron pore size Millipore filters or to centrifugation (2000 rounds per minute for 15 minutes in an Internation Centrifuge) and were then retested on the spectrophotometer. Results from the spectrophotoнmeter are in absorbance units, and can be converted to percent transmission (%T) by the equation: 
-A
%T = 10 x 100 where: 
A = absorbance. The patterns of these curves were then used to reveal the materials which absorb light in Galveston Bay and how their patterns of light absorbance change throughout the Bay. 
RESULTS 
Results of this study will be considered in two major groups, Environmental Requirements and Productivity. Environmental Reguirements 
The Copeland and Bechtel report (1971) specifies environmental requirements based on the catch success of certain species in waters of various salinities, temperature, location and season. As stated above, 
II-27 
these environmental factors are very interdependent, and the species caught, the age or life stage of the species, and the success of the catch are time and behavior dependent. Thus, in discussing environmental requirements using the Copeland and Bechtel report, it is necessary to utilize the multi-factor interaction data to delineate these requirements. However, the single factor correlations are useful for determining bounds of average catch ratios and the effect of that environmental factor on those catch ratios. 
Single Factor vs Catch Ratio 
A summary of single environmental factor effects on catch ratios compiled in the Copeland and Bechtel report is given in Table II-3 for the following organisms: trout(Cynoscion nebulosus-speckled trout, and Cynoscion arenarius-sand trout); redfish (Sciaenops ocellata); menhaden {Brevoortia patronus); shrimp (Penaeus setiferus-white shrimp, P. 
aztecus-brown shrimp, ..R_. duorarum-pink shrimp); crab (Callinectes sapidus-blue crab); and the oyster {Crassostrea virginica-American oyster). 
For each environmental factor, a range of the factor values in which catches were reported is given as well as the range in which optimum catches (greater than a specified catch ratio) were found. The location notation is self-explanatory except for the designations primary, secondнary, etc. Primary refers to a bay or river unit nearest the ocean or bay, respectively. Secondary refers to near primary, and so forth (See Figure II-3 for location of bay provinces). 
Examination of Table II-3 and the ranges of temperature and salinity in which the organisms are found reveals that a large tolerance of temperнature and salinity levels by the organisms listed does exist. Without 
exception the organisms listed are found in waters with salinity and temнperature ranges normally found in estuaries and indeed in Galveston Bay. With exception of the redfish, these organisms are found in estuaries 
II-28 
TABLE II-3 ENVIRONMENTAL LIMITS FOR NINE IMPORTANT SPECIES IN GALVESTON BAY*** (Copeland and Bechtel, 1971) 

for all or most of the year. These organisms are also found in most parts of the estuary while they are in the estuary. It would be misleading to use 
thes.e range data above to delineate environmental requirements for these organisms, for seasonal migrations into and out of the estuaries and changing requirements of larval forms as they age tend to be evident as narrower ranges of temperature and salinity due primarily to the season in which these organisms move into the bay. Thus, seasonal preferences need to be examined. 
Seasonal Considerations 
The environmental factor interdependence mentioned earlier is most easily comprehended in a seasonal pattern context, for some of the organisms listed in Table 11-3 are strongly tied to seasonal patterns in their life cycles and behavior. The organisms for which these patterns have been investigated are given in Table 11-4. The brown and white shrimp both have a strong seasonal life cycle utilizing the ocean during adult, spawning and early larval stages and the bays during later larval and juveнnile stages. The bayward migrations of these organisms is apparently keyed by temperature changes and in the bays salinity and the presence of organic material influence their distribution. 
The blue crab responds to temperature and salinity and to the preнsence of organic material. It exhibits a strong seasonal pattern with gravid females migrating to the Gulf waters to spawn and the young returning to the estuary to age where they are typically found at the head of estuaries in the marshes and bayous encountering a wide range of salinities. 
An important consideration for the shrimp and the crabs is that they both inhabit the marshes, bayous and bay edges in the larval and juvenile stages and feed on the detritus produced by the marshes and the organic material imported to the bays by the rivers. Thus, both marsh production and the influx of river-transported organic material are important seasonal factors for these organisms, and these factors also show seasonal pulses which are in phase with those of the organisms. 
11-30 
TABLE Il-4 

SEASONAL ENVIRONMENTAL REQUIREMENTS 
OF FOUR SPECIES IN GALVESTON BAY 

Organism 
Penaeus aztecus (brown shrimp) 
P. 	setiferus (white shrimp) 
Callinectes sapidus 
Crassostrea virginica 
Temperature (0 C) 
Major population moveнment in nursery areas in early Spring as water temperature increases 
Major population moveнment to nursery areas in summer as water temperature nears maxн
imum 
Major population moveнment to Gulf for spawnнing as temperature increases in spring 
No seasonal preference low salinities; adult females found in high salinities while most crabs found in salinнities 26 ppt 
Salinity (ppt)  Location  
Seasonal  Marshes, bayous  
preferences  and bay edges  
obscurred by  used as nursery  
need to be in  
food-rich  
marsh areas  
Seasonal  Same as P.  
preferences  aztecus  
obscurred by  
need to be in  
food-rich  
marsh areas  
Young prefer  Marshes and bayous  

Not clear, Head of estuary periodic low to mouth salinity needed for predator removal 
11-31 

The oyster apparently has little salinity preference although Copeland and Bechtel (1971) describe four types of oyster groups in Galveston Bay depending on their location in the Bay. The "health" of these groups is related to the salinity changes that occur in their habiнtats and the predation activity of the oyster drill and other parasites. A periodic low salinity is toxic to the drill and other parasites, and those oyster beds experiencing low salinities occasionally are less plagued by the drill and other predators than beds in areas of frequent low salinities and near constant high salinities. 
Flow vs Salinity 
Because some of the organisms considered here apparently do have salinity preferences, maintaining the salinities at the proper time of the year is necessary with sufficient freshwater inflows. In an attempt to derive simple but meaningful relationships between river inflow to Galveston Bay and the salinities observed in the Bay, simple correlations were performed between flows in the Trinity River and Houston Ship Channel and salinities at stations ''downstream" from these points of discharge. Because there is some time lag between river influx and staнtion salinity response, a 30-day average of flow was computed for correнlation with the salinity values. This 30-day average was for the 30 days prior to a given salinity value. These 30-day flow averages correlated with station salinity all have a form similar to that shown in Figure 11-4. The correlation is non-linear and a reasonably good fit of data to a line drawn through the data is evident. The correlation shown is for Station 38 which is affected most strongly by flows from the Trinity River and which should be representative of stations near to the marsh periphery on the north side of the bay. Thus, the river flow required to maintain certain salinity levels at Station 38 may also be required to maintain the same salinities in the marsh area. Under this assumption and using 
11-32 
Figure 11-4 as a guide, the freshwater flow from the Trinity River required to maintain a chlorosity (a measure of salinity) of 4 ppt (for crab larvae) is 4, 000 cfs. Based on historical flows in the Trinity River, the only seasons when such flows are available are the winter and spring. 
Organic Material Inflows 
An environmental requirement of several of the organisms considнered here is the influx of decaying organic material with river flow and from marshes, phytoplankton, and dead and decaying plants and animals. This natural organic material serves as a primary food source for the larval stages and adults. Waste discharges also provide organic material to the Bay, but these are "unnatural" sources of organic material. Howнever, because most of ▒his material is utilized by the Bay biota, it is desirable to examine the sources of it. Presented in Table 11-5 are the calculated contributions of organic material from river inflow and waste discharges to Galveston Bay. For the "external" sources, river inflow and waste discharges, the waste discharges contribute by far the larger amount, some 77. 2 per cent of their sum. 
However, even these large inflows are small compared to the proнduction of organic material in the Bay by the phytoplankton and on the Bay periphery by the marshes. The contributions of these two sources are given in Table II -6 along with the "external" sources. The phytoнplankton produce about 94 per cent of the total organic material reaching Galveston Bay each day, and marshes are next in importance with 3. 6 per cent. Waste and river inflows contribute very small amounts. 
More pertinent to the environmental requirements discussion is the food chain and hence the organisms in_the food chain supported by the organic material produced. The two basic food chains present are phytoplankton based and detritus based. The latter includes the river and waste inflows and marsh production. Although there is some overlap in the twofood chains, it is apparent that phytoplankton produce the vast 
11-33 
TABLE 11-5 
MASS DISCHARGE OF ORGANIC CARBON TO GALVESTON BAY, TEXAS (Measured as BOD} 













Mass Discharge of BOD
Average 
Average 
Percent
5
Discharge* 
of Total
BOD5 
(lbs/day) 
(104Ibs/season)
Season
Source 
(104Ibs/yr)
(cfs} 
(mg/I) 
(%) 
115.7 
4.46 
2,770 
24.95
1. Chocolate Bayou 
Sp 
2.98 
1, 600 
14.40
Su 
122.4 
58.0 
1. 96 
612
F 
s.ss 
0.3
89.5 
626
w 
1. 3 
5.64 
50.54 
3.9
77.1 
1, 620 
14.58
Sp
2. Dickinson Bayou 
81. 6 
2.33 
1,022
Su 
9.22 
450 
4.05
2.16
F 
38.7 
1. 5 
482
59.7 
4.32 
32.19 
0.2
w 
6,350
16.2 
57.20
Sp 
72.8
3. Double Bayou 
905 
8.15
77.0 
2.18
Su 
536 
4.83
F 
36.5 
2.73 
0.4
79.61
56.3 
3.45 
1,048 
9.43
w 
53.8
4. Clear Creek 
Sp 
7.17 
2,080 
18.72 
Su 
3.96
24.1 
514 
4.63 
F 
13.5 
3.54 
257 
2.32 
w 
33.2 
661
3.7 
5.95 
31. 62 
0.2 
5. Trinity River 
Sp 
13,377.5 
3.05 
219, 500 
1,972.0 
Su 
2,455.1 
2.50 
33,000 
297.0 
F 
3,070.8 
2.15 
35,500 
320.0 
w 
5,917.3 
1. 40 
44, 600 
402.0 
2, 991. 0 
14. 9 
TABLE II -5(CONT.) 

I 
Average 
Discharge* 
Source 
Season 
(cfs) 
6. Cedar Bayou 
Sp 
42.2 
Su 
18.9 
F 
10.6 
w 
26. 0 
7. Brays Bayou 
140.4
Sp 
Su 
58.3 
F 
47.6 
w 
111. 4 
Sp 
173.6
8. Sims & Vance 
Bayous 
I Su 
72.1 
F 
58.9
I 
w 
137.8 
187.9
9. Greens & Hunting 
Sp 
Bayous 
Su 
78.0 
F 
63.7 
149.0
w 
10. San Jacinto River 
Sp 
2,223.4 
& Carpenter Bay 
Su 
922.9 
754.5
F 
1, 765.3
w 
6.0
11. Goose Creek 
Sp 
Su 
2.7 
1. 5
F 
3.7
w
I I 
I 
Average BOD5 {mg/1) 
3.68 
1. 48 
1. 97 
2.55 
9.15 
5.75 
22.0 
10.9 
8.5 
5.26 
9.84 
10. 9 
12.28 
7.35 
7.84 
10.5 
2.74 
1. 58 
1. 7 
2.05 
2.78 
2.85 
3.64 
3.3 
(lbs/day) 
835 374 113 357 
6, 910 1, 810 5, 650 6, 550 
7,940 2,210 3,120 8,070 
12,410 3,090 2,685 8,420 
32,850 7,850 6, 900 19,500 
90 41 29 66 
Mass Discharge of BOD5 
I
(104lbs/season) 
7.52 
3.36 
1. 02 
3.21 
62. 20 
16.30 
50.90 
59.00 
71. 40 
19.90 
28.10 
72.60 
111. 90 
27.80 
23.90 
75.80 
296.0 
70.60 
62.10 
175.50 
0.81 
0.37 
0.26 
0.60 
{104lbs/yr) 
15.11 

188.40 

192.0 

239.4 

604.20 

2.04 

Percent of Total (%) 
1. 0 

1. 2 

TABLE II-S(CONT.) 

Average Average Discharge* BODs Source Season (cfs} (mg/l)  Mass Discharge of BOD5 I(1041.bs/season}(lbs/day} I (1041.bs/yr)  Percent of Total (%)  
12. Buffalo Bayou Sp 242.S 6. 72 Su I 100.6 3.14 F 82.3 5.8 w I 192.S 4.53 113. Houston Ship Channel Wastes**  8,780 79.0 1,700 15.16 2,570 23.15 4, 690 42.20 422,700 3, 800.0 159.51 15,200.0  0.7 75.4  
14. Texas City(38, 900x 0. 2 lbs BOD5/cap/day} x . SO***  3, 890 3S.O 140.0  0.7  
lS. Galveston(61, 800 x 0. 2 lbs BODs/cap/day) x . SO***  6,180 SS.6 222.4  1.1  

-442, 700 -
I 
TOTAL 20,148.0 100.0
w 
0\ 
*Communication from Tracor, Inc. ** Total loading (470, 000 lbs BODs/day} -Winter runoff load (47, 300 lbs BODs/day) *** SO per cent treatment assumed 
TABLE Il-6 
DAILY FLUX OF ORGANIC MATERIAL IN GALVESTON BAY, TEXAS 

Detritus Based 
Phytoplankton Based 
Total Organic 
Food Chains
Sources & Sinks 
Material Influx
Food Chains 
{lOS lbs/day) 
{lOS lbs/day) 
{lOS lbs/day)
{%Total) 
{%Total) 
{%Total) 
Sources 
1

River Inflow 
1
V\Taste Discharges Phytoplankton Production2 Marsh Production3 
Totals
-
I 
-
w 
9.0 
?
1. 2 
?
4.4 
33.1 
? 
200.0 
7.7 
S7.9 
? 
100.0 
200.0
13.3 
0.4
1. 2 
4.4 
2.1 
I
100 
200. 
93.9 

3.6
7.7
-
100.0
100 
213.3 
'-l 
Sinks 
4 

240.0 
?
Plankton Respiration 
Net Inflow 
-27.7 
1
From mass balance of organic material inflows. 
2

Average gross production in Galveston Bay, 6. 7 gms/m2I day x Bay area. 
32

Estimated marsh net production {Keefe, 1972), 2000 gms/m2I day x SS. 2 mimarsh area on Bay periphery {Bureau of Economic Geology, 1972) x 0. 4S fraction exported to estuary. 4 2
Average plankton respiration, 8. 0 gms/m ; day x Bay area. 
majority of the organic material for the phytoplankton based food chain and that marshes produce most of the material for the detritus based food chain although waste :li.scharges are next in importance. The contriнbution of the marshes is of course more predominant in the Bay periphery where the marshes are located and the waste inputs are absent. Neverнtheless, the overwhelming contributions of in situ biologically produced organic material are clearly evident. 
Impact of Freshwater Flows 
Another approach for assessing the impact of freshwater flows on estuaries (besides organic material input) is a correlation between annual freshwater inflows to Texas bays and commercial catch in the bays. Copeland (1966) used this approach to assess the impact of return flows to Texas bays. Although this type of correlation is of a very gross nature because of the inherent nature of the catch data, it does provide an interesting insight into the response of Texas bays to river inflow. 
Two response patterns are observed in these bays in curves relating displacement rate (annual river flow I bay volume) to total commercial catch (Figure II-5 ). Curves for Galveston and Aransas Bays both show that peak commercial catch is achieved at a displacement rate of once per two years or a residence time of two years. Matagorda and San Antonio Bays both produce peak catch at twice per year displacement rates (resiнdence time of 0. 5 year). Corpus Christi Bay catch increases with inflow but never peaks; its curve probably would follow the same pattern as for Matagorda and San Antonio Bays. 
It is important to note here that displacement rates exceeding twice per year apparently cause a decrease in total commercial catch. Displaceнment rates less than this produce variable results dependent somewhat on the composition of the catch; however, more data need to be examined to clarify this point. 
II-38 

Productivity 
Phytoplankton Populations 
Distribution: The only readily available data on phytoplankton populations in Galveston Bay may be found in the Copeland and Fruh report (1970). Although the data for each station sampled were originally grouped such that data were presented in that report for sections of the Bay, this report shows the seasonal abundance and species composition of phytoplankнton in all parts of the Bay for 1969. Only February, April, July, and October sample times were available, but seasonal trends were still eviнdent. 
The phytoplankton data for each sampling period are plotted in Figures II -6 through II -9. Two facts are of particular interest, these being the concentrations of the phytoplankton and the patterns of distriнbution. In February the highest populations of phytoplankton were found in East Bay, but substantial numbers were found in Upper Bay and Lower Bay. The magnitude of these numbers is not unusual for estuaries receiving large nutrient discharges, or for the season of the year. Most estuaries and oceanic waters show an early spring bloom of algae followed by a marked decline in the late spring with lowest populations in the sumнmer. A fall bloom is frequently seen but is not as great as the spring bloom~ This pattern is followed in Galveston Bay. The April populations are very low probably due to normal population decline and being washed out of the Bay by the large spring river flows. The July and October popнulations (Figures II-8 and II-9) are perhaps more "normal" for the. Bay. A surprising fact was that the populations in Bolivar Roads were very high throughout the spring and summer. One would expect this station to reflect oceanic conditions more than Bay conditions, and hence lower phytoplankton populations. 
Species Composition: The 1969 phytoplankton population in Galveston Bay was dominated by relatively few types of algae at all stations. Little 
II-39 
geographical zonation is evident, and the algae identified are common components of estuary phytoplankton. Table II -7 gives the dominant genera of algae found at various locations in the Bay for the sampling periods. Except in those areas near freshwater inflows, the dominant genera are diatoms, typical components of estuaries. 
Laboratory Bioassays 
The initial laboratory bioassays, once Dunaliella was chosen as the test organism, were simple enrichment tests in which nitrate-nitrogen or phosphorus were added to water obtained from stations in Galveston Bay. A control was run with these tests, and the growth rates obtained in the assay flasks with nutrient additions were compared to growth rates in the controls. Results of these tests are given in Table II -8. Nitrogen obviously stimulated the growth of Dunaliella much more than phosphorus, and both nutrients stimulated growth over the control. The growth rates of Dunaliella do not seem to correlate with the existing nutrient levels in the Bay (lower part of Table II-8). For example, the growth rates with phosphorus addition might reflect the prevailing nitrogen concentration since phosphorus would theoretically be in excess and nitrogen could be limiting. 
The second and third sets of bioassays were intended to delineate limiting concentrations of nutrients in controls and in Galveston Bay water. As mentioned earlier, nutrient carryover in the inoculum obscured those results. However, growth rate depression may be observed in a manner similar to Van Baalen's bioassay. In Figure II-10 are the results of these bioassays; each point represents the average of triplicate tests. Growth rates of Dunaliella in the control media with enough nutrients to 
1
be nonlimiting averaged about 1.1 day-. The growth rates of Dunaliella 
in Galveston Bay waters were all less than this rate, even with high nutriн
ent levels, thus growth rate depressants that appeared in Van Baalen's assays with blue-green algae also appear here. Station 29 water contained 
the least of these inhibitors since growth rates were 0. 96 and 0. 90 day-l 
II-40 

TABLE II -7 

DOMINANT GENERA OF PHYTOPLANKTON IN GALVESTON BAY 
DURING 1969 

Study Area  February  April  July  October  
Trinity Bay  Leptocylindricus Nitzschia Skeletonema  Nostocaceae Oscillatorнiaceae  Nostocaceae Oscillatorнiaceae  Thalassionema Filamentous Chlorophyta  
Upper Galv.  Euglenoid  Thalassio- Cyanophyta  Cyanophyta  
Bay  Chaetoceros  thrix  C hlorophyta  
Nitzschia  Nitzschia  Skeletonema  
Skeletonema  Skeletonema  
Lower Galv.  Skeletonema  Chaetoceros  Skeletonema  Thalassionema  
Bay  Chaetoceros Nitzschia  Nitzschia Skeletonema  Chaetoceros  
Ditylum  
Thala s sionema  
East Bay  Skeletonema Asterionella  Skeletonema  Cyanophyta Coscinodis- Thalassionema  
Chaetoceros  
cus  Ditylum  
Nitzschia  Rhizosolenia  
West Bay  Chaetoceros  Nitzschia  C haetoceros  Chaetoceros  
Nitzschia  Skeletonema  Nitzschia  
Rhizosolenia  Thalassionema  

 

11-41 

TABLE II-8 

GROWTH OF DUNALIELLA IN GALVESTON BAY 
WATER COLLECTED APRIL 25-27, 1972 

Station 14 17 22 26 29  Control 0.010 0.007 0.014 0.014 0.007  Growth Rates (60D/day) With 2. 0 mg/1 With 0. 5 mg/1 N03-N P04-P 0.19 0.014 0.11 0.017 0.10 0.03 0.19 0.014 0.07 0.023  N/C Ratio 19 16 7 14 10  P/C Ratio 1 2 2 1 3  

Nutrient Data April 25-May 2, 1972 
Total  
Nitrogen  Phosphorus  
Station  NH -N3  N02+N03-N  P04-P  
{mg/I)  (mg/I)  (mg/I)  
14  0  0.31  0.01  
17  0  0.02  0.05  
22  0  0.21  0.28  
26  0  0.02  0.25  
29  0  0.1  0.45  

Il-42 
for April and July respectively. Station 17 water also contained small levels of inhibitors. Water at Stations 14, 22, and 26 apparently contained growth inhibitors, as April and July samples from Station 14 produced 1ittle growth of Dunaliella and the July samples from Station 22 and 26 
did likewise. The geographical and seasonal patterns of growth depression generally coincide with Van Baalen's results. 
Field Production Studies 
Light-Dark Bottle Results: Field production measurements were made during January, April, and July using the light-dark bottle technique. Typical results of the measurement are shown in Figure II -11 for bottles suspended at five depths. Gross production and respiration values plotted were determined from the changes in dissolved oxygen content as desнcribed earlier. Net production is the difference between gross production and respiration. Note that the compensation depth, the depth at which respiration equals gross production and net growth is zero, is estimated to be four feet (1. 22 m) at Station 17 for these.measurements made in January. Daily areal production and respiration rates were computed from period of sunlight and water depth measurements. 
Results from other stations for the January, April, and July sampling periods are given in Tables II -9 through II -13. The compensation depth (defined above), mixing depth (water depth), extinction coefficient, and critical depth (defined above) are also given. Production and respiration values were highest at Station 22 near the Houston Ship Channel and lowest overall at Station 14 in West Bay. However, only at Station 17 was net production significantly greater than zero indicating autotrophic condiнtions. At Stations 22 and 26 in the upper portion of Galveston Bay net production was usually much less than zero indicating heterotrophic conнditions or large consumption of organic imports from river inflow, marshes, and waste discharges. 
11-43 

y TABLE Il-9 
PHYTOPLANKTON PRODUCTION AT STATION 14 ! IN GALVESTON BAY2 TEXAS 
Х 1\ 
" 
~ 
Oct. 26, Jan. 27, April 25, July 25, Item 1971 1972 1972 1972 
Gross Pro~ction 3.9 1. 51 5.12 (gms O/m /day) 
Net Production 2.2 -1. 89 3.73 
2
(gms O/m/day) 
Total Respiration 1. 7 3.40 1. 39 
(gms o/m2/day) 
Compensation Depth >L 83 0.4 1. 52 
(m) 
Mixing Depth (m) 1. 83 1. 85 1. 83 
Extinction Coef. 1. 96 1. 06 6.52 3.75 

(m-1) 
Critical Depth (m) 6.5 1. 67 79 
Net Prod. Possible? Yes No Yes 
Ammonia-N (mg/I) 0 0 0 ? 
Nitrite+ Nitrate-N 0.11 0.2 0.31 0.01* 
(mg/I) 
Total Phosphate-P 0.45 0.2 0.01 0.088* 
(mg/I) 
Chlorophyll "a" (mg/I) 

0.038 
*In-House analyses. 
11-44 
TABLE 11-10 PHYTOPLANKTON PRODUCTION AT STATION 17 
IN GALVESTON BAY, TEXAS . 
Oct. 26, Jan. 27, April 25, Item 1971 1972 1972 
Gross Production 4.9 11. 92 (gms O/m2/day) 
Net Production -0.5 9.83 (gms o2/m2/ day) 
Total Res~iration 5.4 2.09 (gms O/m I day) 
Compensation Depth 1. 22 2.2 
(m) 
Mixing Depth (m) 13.7 13.7 13.7 
Extinction Coeг. 1. 92 1. 68 2.30 (m-1) 
Critical Depth (m) 4.6 70 Net Prod. Possible? No Yes Ammonia-N (mg/I) 0 0 0 
Nitrite+ Nitrate-N 0.12 0.2 0.02 (mg/1) Total Phosphate-P 0.59 0.13 0.05 
(mg/l) 
Chlorophy11 "a" (mg/l) 

* In-house analyses. 
Il-45 
July 25, 1972 
8.69 
2.33 
6. 36 
1. 8 (1. 3) 
13.7 
2.01 
18. 4(6. 8) 
Yes 
? 
0.01* 

0.19* 
0.055 

/ 

TABLE II--11 PHYTOPLANKTON PRODUCTION AT STATION 22 
IN GALVESTON BAY, TEXAS 
Oct. 27, Jan. 27, April 25, Item 1971 1972 1972 
Gross Production 10.8 (gm O/m2/ day) 
Net Production -5.5 
2
(gms Oim /day) 
Total Respiration 16. 3 (gms o/m2/ day) 
2Compensation Depth (m) 0.70 Mixing Depth (m) 3.0 3.0 
Extinction Coeг. 2.35 3.01 (m-1) 
Critical Depth (m) 2.7 
Net Prod. Possible? No Ammonia-N (mg/I) 0 0 Nitrite+ Nitrate-N 0.32 0.03 
{mg/I) 
Total Phosphate-P 1.12 0.38 
(mg/I) 
Chlorophy11 "a" (mg/I) 

* In-house analyses. 
Il-46 
1. 65 -7.11 
8.76 
<0.3 
3.0 
7.74 
<l. 3 
No 0 
0.21 
0.28 
July 25, 1972 
19.7 
9.5 
10.2 
1. 40 
3.0 
3.50 
Yes 0 0.18* 
0.49* 
TABLE II-12 PHYTOPLANKTON PRODUCTION AT STATION 26 
IN GALVESTON BAY, TEXAS 

Item  Oct. 27, 1971  Jan. 27, 1972  
Gross Production (gms o2/m2/ day)  0.76  
Net Production (gms o2/m2/ day)  -4.4  
Total Respiration (gms O/m2/day)  5.2  
Compensation Depth (m)  <<0.3  
Mixing Depth (m)  2.7  2.7  
Extinction Coef. (m-1)  4. 61  8.44  
Critical Depth (m)  <<L 5  
Net Prod.  Possible?  No  
Ammonia-N (mg/I)  0  0  
Nitrite+ Nitrate-N (mg/I)  0.12  0.08  
Total Phosphate-P (mg/I)  1. 74  0.3  
Chlorophyll "a" (mg/I)  
* In-house analyses.  

April 27, 1972 
2.25 -3.92 
6.17 
<<0.3 
2.4 
15.72 
<<7.2 No (?) 0 
0.02 
0.25 
July 25, 1972 
9.00 -0.87 
9.87 
0.5 
2.7 
4.02 
7.85 No 
? 
. 01 * 
0. 43 * 
0.0166 
II-47 

TABLE II-13 
PHYTOPLANKTON PRODUCTION AT STATION 29 IN GALVESTON BAY, TEXAS 
Oct. 26, Jan. 27, April 25, July 25, Item 1971 1972 1972 1972 
Gross Production 9.1 2.43 7.24 (gms o2/m2/ day) 
Net Production 
2
(gms O/m/day) 0.6 -8.49 1. 27 
Total Res~iration 8.5 10.92 5.97 (gms O/m /day) Compensation Depth(m) 0.98 <<<l. 3 1. 4 Mixing Depth (m) 1. 83 1. 83 1. 83 2.0 Ex!inction Coef. 3.39 1. 53 3. 77 3. 71 
(m ) 
Critical Depth (m) 2.9 <<0.8 42 
Net Prod. Possible? Yes No Yes 
Ammonia (mg/I) 0 0 0 ? 
Nitrite+ Nitrate-N 0.11 0.3 0.105 . 01* 

(mg/1) 
Total Phosphate-P 0. 63 0.12 0.45 0.22* 
(mg/1) 
Chlorophy11 "a" 0.052 

*In-house analyses. 
Il-48 
There is little correlation of production values with nutrient concentrations; however, this is not surprising since high production would tie up nutrients and result in low ambient concentrations. The generally higher production values in Upper Galveston Bay do correlate with the consistently higher nutrient levels there. Likewise, the higher respiration values in the Upper Bay appear to be due to the availability of large amounts of organic material. On a more detailed basis though, production values appear to vary more with light availability than any other factor. The measure of critical depth used by Ragotzkie (1959), which indicates whether net production is possible given the mixing depth and the compensation depth, was applied here, and without exception when net production was predicted to be less than zero, the measured value was indeed negative. Thus, there is strong evidence that light can be a limitнing factor to growth of algae in Galveston Bay. 
Light Transmission: Examples of light transmission measurements taken concurrently with production measurements are shown in Figure II -12 for Station 17 during January, 1972. Light transmission is given as percent transmission for the submarine photometer with no filters (none) and with red, green, and blue filters. The selectivity of light absorption shown in this figure is interesting because in pure water wavelengths in the red region are usually absorbed more rapidly than those in the green, and those in the green region are absorbed more rapidly than those in the blue region. However, the results here indicate that wavelengths in the blue region are absorbed more rapidly than those in the red or green regions. Thus, there is material in suspension or solution absorbing light very strongly in the blue region. The computed extinction coefficients for 
each spectral region,  each station, and each sampling period are given in  
Tables II-14, II-15, -1 are meter .  Il-16,  Il-17, and  II-18.  Units of these coefficients  

II-49 

TABLE II-14 

LIGHT ABSORPTION COEFFICIENTS FROM 
SUBMARINE PHOTOMETER AT STATION 14 
IN GALVESTON BAY 

{m-l) 

II-50 

TABLE II-15 
LIGHT ABSORPTION COEFFICIENTS FROM 
SUBMARINE PHOTOMETER AT STATION 17 
IN GALVESTON BAY 

(m-1) 
Date  Time  Total  Blue  Green  Red  Remarks  
October 26, 1971  1530  2. 74 1.102  4.19 1. 875  2.09 1. 212  1.99  0.00-0.58m 0.58 -0.98 m 0.98 -1.01 m > 1. 01 m  
October 28, 1971  1600  1. 71 1.24  3.35 1. 73  1. 51  1.137 2.20 1. 33  0.00 -0.98 m 0.98-l.22m 1.22 -1.83 m 1.83 -1.92 m > 1. 92 m  
January 25, 1972  142 0  Х324 2.36 1.22  2.51 0.905  1.49 2.25 1.20  2.16 1. 69  0. 00 -0. 3 05 m 0 . 3 0 5 -0 Х 42 7 m 0 . 42 7 -1. 31 m 1.31 -1.75 m 1.75 -1.98 m 1. 98 -2. 04 m > 2. 04 m  
January 27,1972  1300  1. 68  2.95 1. 56  1.198 0.505  3.16 .695 1.23 0.714  0.00-0.67m . 67 -1. 4 m 1. 4 -2. 44 m 2. 44 -4. 2 4 m 4.24 -4.95 m > 4. 95  
April 2 5 , 19 7 2  12 00  2.09  3.59  1. 99  2.04  
April 2 5 , 19 7 2  1800  2.52  
July 25, 1972  0939  1. 92  2.9  1. 36  >0 m  
July 25, 1972  1530  2. 71  >0 m  


11-51 
THE UNIV -~IT. Of TF~, A~ 'r STI~ 
M< RIN-~(tE~~~t ;N.~ .;..--╖u 
~kt ~ f(/ N~S.. t~s rs3i.-12'1 

TABLE II-16 

LIGHT ABSORPTION COEFFICIENTS FROM 
SUBMARINE PHOTOMETER AT STATION 22 
IN GALVESTON BAY 

{m-1) 

II-52 

TABLE II-17 

LIGHT ABSORPTION COEFFICIENTS FROM 
SUBMARINE PHOTOMETER AT STATION 26 
IN GALVESTON BAY 

(m-1) 

II-53 

TABLE Il-18 
LIGHT ABSORPTION COEFFICIENTS FROM 
SUBMARINE PHOTOMETER AT STATION 29 
IN GALVESTON BAY 

(m-1) 

Il-54 
The geographical distribution of extinction coefficients reflects the turbid conditions in Upper Galveston Bay and Trinity Bay and relatively clear conditions in the lower areas of the Bay. A typical distribution of light extinction coefficients is shown in Figure II -13 for the October 1971 sampling period. This distribution is also apparent in the Secchi disc readings made as part of the routine monthly sampling. 
Light Absorbance: Results from the Cary spectrophotometer express the absorbance of light by materials either in suspension or in solution since the absorbance by water alone is corrected by continuous comparison of light transmission through pure water with that through the sample. Typical results from this analysis are shown in Figure 14. Note the strong absorption of light in the blue wavelengths, the less intense absorption in the greens, and the milder absorbance in the reds. Note also the improvement in light transmission with sample filtering and with centrifugation. To compar.e curves quantitatively, absorption coefficients (with units of decimeter-l) have been computed for each curve at wavelengths 420 m ╡. and 650 m╡; the results of these computaнtions are given in Tables 19, 20, 21, 22, and 23. 
The difference in the curves from a given station and date are due to the sample processing (filtering or centrifuging). It is assumed that filtering removes particles at least to the 0. 45 ╡. diameter and slightly smaller, and the increase in light transmission is due to lack of absorpнtion by suspended particles. Centrifugation removes other light absorbнing material which is apparently in suspension and small enough to pass through the filter. The difference in these curves from station to station for a given date indicate the variable composition in materials in the water column at these stations. Inspection of the tables containing the coeffiнcients of absorption show the magnitudes and patterns of these differences. In the winter and spring at stations in the upper parts of the Bay, light seems to be absorbed more by material in suspension than by material in 
II-55 

TABLE 11-19 

LIGHT ABSORPTION OF 
STATION 14 WATER 

(dm-1) 

II-56 

TABLE II-20 

LIGHT ABSORPTION OF 
STATION 17 WATER 

(dm-l) 

Il-57 
TABLE 11-21 
LIGHT ABSORPTION OF 
STATION 22 WATER 

(dm-1) 
Date  Treatнment  %T 4200X  Absorbance 4200X  %T 65ooX  Absorbance 65ooX  K4200  F/C 4200X 
K6500  
Oct. 26/28, 1971  N  9.0  1. 05  20.0  . 692  1. 52  
F  36. 5  . 439  63  .207  2.13  
c  38.0  . 421  71. 0  .147  2.84  1. 04  
Jan. 25, 1972  N  13  .894  30  . 519  1. 7  
F  35  . 453  64  .193  2.35  
c  43  .367  70  .156  2.35  1. 24  
April 25, 1972  N  10.0  . 994  21  . 686  1. 45  
F  87.0  . 06  98  . 009  6. 66  
c  95.0  0.022  97.0  . 013  1. 69  2.73  
July 26, 1972  N  20.5  . 688  36.0  . 422  1. 56  
F  78.0  . 018  94  . 027  4 I  
c  47  . 328  58.0  .237  1. 38  0.33  

11-58 

TABLE Il-22 
LIGHT ABSORPTION OF 
STATION 26 WATER 

(dm-1) 

Il-59 
TABLE Il-23 
LIGHT ABSORPTION OF STATION 29 WATER (dm-l) 

11-60 

solution while at stations in the lower part of the Bay the reverse is true. A trend is thus evident which implies that organic material and soil parнticles are present in the upper portions of the Bay and this material is degraded or settled out by the time it reaches the lower portions of the Bay. Light absorption is much the same throughout the Bay, but the absorbing material is different. 
The differences between curves for the same station but different dates reflect the changes in the composition of suspended and dissolved materials at a station throughout the year. For example, the dissolved material which absorbed so strongly in October is "diluted" by suspended material in April because of very high freshwater flows. This pattern is maintained through July as indicated by the ratios of absorbance of filtered 
(F) and centrifuged (C) water. For F/C values less than 1. 0, the filtration 
process removes more light absorbing material then centrifugation. 
Microcosm Studies 
Physical Characteristics: Proper functioning of the aquarium microcosms allowed the five model systems to effectively simulate the Galveston Bay prototype throughout a wide series of environmental conн
ditions. The experimental model systems maintained relatively constant physical characteristics (mixing regimes, light intensities, temperatures, and salinities), so that internal conditions did not exceed those considered normal for the actual bay prototype. Measurements of these physioн
chemical parameters continued during each experimental inflow to insure 
that community response resulted from the nutrient inputs or residual water toxicity, rather than excessive temperatures or salinities within the model systems themselves. 
The temperature pattern within each microcosm was monitored twice daily at the change between light-dark periods. These values were required in the calculation of oxygen saturation for diffusion corrections in productivity analysis, as well as determination of model system thermal variation. The culture room in which the model systems were located 
II-61 

0
maintained a 26 !_ 1. s c temperature in the aquaria throughout the experimentation. The water temperatures in the microcosms (see Table 
II -24 for mean and range of temperatures) only exhibited daily fluctuaнtions of 0. 98░ to 1. 76░C between average sunrise and sunset temperatures. This slight variation resulted from heat transfer into the water during daylight hours due to the closeness (4-5 inches) of the twin-bulb fluoresнcent fixtures and re-radiation of absorbed heat during the dark period. Microcosm diurnal fluctuations were much smaller than the daily thermal changes recorded in Galveston Bay during quarterly field sampling, and less than the multi-degree daylight-nighttime difference maintained by Cooper (1970) on his continuous-series microecosystems. However, this lack of environmental variability means that the fairly constant temperнature regime bolsters the basic assumption of equal nighttime and daylight community respiration. The lack of Qio effects (biochemical reaction rates double with 10░C temperature increases) from this thermal conнsistancy helps to validate the essential assumption needed for adequate productivity estimates by the diurnal curve method, since both nighttime and daylight respiration occur under near identical temperature values. 
Salinity variation in the model systems appears to be directly related to the salinity of the influent. Additions of distilled water to offset the 30 ml/day evaporation rate helped to maintain the salinity at the initial level (17 ░/oo), but the slow influx of new estuarine water of different salinity allows the microcosms to slowly evolve a new salinity regime. Salinities tended to increase during the later experimental phases, partially due to the addition of artificial seawater (20 ░ I oo) to all aquaria over several weeks between the spring and summer influent phases (to flush out toxic metabolites or degradation products and to test community response to rapidly decreasing nutrient inputs). The highest salinities (over 25 ░/oo) detected in the microcosms were well within the range of normal estuarine waters, especially lower Galveston Bay station;; during 
II-62 

TABLE II-24 MEAN MICROCOSM TEMPERATURES DURING PRODUCTIVITY ANALYSIS 

II-63 

low or normal freshwater inflows. Salinity differences do not appear to 
significantly affect the community metabolism of these microcosms, since euryhaline estuarine organisms composing the microcosm community could have adapted easily due to slow salinity fluctuations. 
Breakdowns in the air-powered magnetic stirrer units occurred occasionally during the spring influent phase. Inadequate mixing during these periods resulted in the formation of an oxygen gradient with depth, due to the benthic oxygen demand and oxygen diffusion across the surface interface. Sampling at the surface for the Microwinkler analysis under this condition yielded abnormally low estimates of nighttime respiration, which in turn depressed subsequent gross productivity values. Steep depressions in the daily gross production estimates (as observed in the Figures in the Appendix) correlated often with temporary stoppages in 
m.icrocosrn mixing. Problems with the faulty stirrers were rectified 
before pumpage of the summer influent began, and the resulting figures exhibited much smoother curves through the 15-day experimental period. 
Evolution of Microcosm Biological Community: The biological association of benthic algae, phytoplankton, benthos, and zooplankton that develops in estuarine microcosms is constantly interacting to evolve a steady-state community related to the prevailing environmental condiнtions. Cooke (1967) found that "a mature aquatic ecosystem is characterнized by a low stable metabolic rate, large structure, less than maximum diversity, and high efficiency of system chlorophyll for photosynthesis." His studies on autotrophic succession in laboratory microcosms indicated that early developmental stages of communities, natural or modeled, are metabolically unstable, and that changes in metabolism during sucнcession may be related to nutrient levels or secretion of metabolic reguнlators. These basic observations on different levels of community succession appear to be confirmed by the preliminary patterns observed within the microecosystems used here. 
II-64 After inoculation with estuarine sediments and untreated water, 
small populations of larval and juvenile invertebrates (ctenophores, blue crabs, barnacles, sipunchulid worms, etc. ) grew in the aquaria and began cropping the developing phytoplankton and diatom assemblages. However, by the end of the first month, most larger invertebrate herbivores had 
disappeared due to low algal prey levels. Resultant phytoplankton popuнlations, free from predation, varied mainly with physical system changes, such as realignments of the stirrer mechanisms or lowering of light fixн
tures to increase light intensity. Small-scale phytoplankton "blooms" seemed to end after 10 weeks, when Aufwuchs (attached and benthic algae, mostly Cyanophyta) growth began to coat the glass aquarium walls heavily. 
Zooplankton densities decreased drastically as succession favored the 
developing wall communities over their phytoplankton prey. Benthos, in particular polychaete worms, continued to be present in aquaria throughout all developmental and experimental phases, and remained active as sediнment-burrowers whose excreta is important in nutrient regeneration. After an elapsed period of three months and the establishment of a relaн
tive steady-state autotrophic community, experimental influent conditions were begun on the previously isolated models. This long initial development period circumvented the metabolic instability (increasing production and respiration rates) associated with "young" communities and allowed sucнcession to run its course toward larger biomass structure. Cooper's (1970) continuous-series microecosystems required approximately three months before the production/respiration ratios stabilized, indicating a community in relative equilibrium. To be effective as a bioassay tool, the community must exhibit this steady-state equilibrium that can be altered by nutrient additions or toxicity, as implied by Cooke (1967). 
Two indirect measures of microcosm phytoplankton concentration (optical density of the water and chlorophyll''a''determination) were perнformed on the model systems under study. Optical density, which measures all light-absorbing or light-scattering material in the water samples, 
II-65 
differed only slightly between microcosms (see data in Appendix:). Since the relationship between turbidity and phytoplankton concentration cannot be elucidated properly, fairly constant readings obtained during the experнimentation revealed little about community changes. Only Station 26 (low salinity Trinity Bay water) and Station 22 (upper Galveston Bay) microнcosms exhibited steady increases in light-absorbing materials during both spring and summer influent regimes. Turbidity from suspended sediments did not appear to be significant, since a heavy Aufwuchs mat covered most exposed substrate and prevented resuspension of fine sediments. Even the constant introduction of clean Millipore-filtered influent did not appreciably clear the water, so the presence of low phytoplankton levels or products of bacterial decomposition are the logical sources of lightнabsorbing material. This was evident in aquarium A (Station 26) during the spring influent phase when cloudiness developed, but low chlorophyll "a" determinations (0. 034 mg/I) did not substantiate the presence of a phytoplankton bloom as the causative agent. However, the role of phytoнplankton in decreasing light transmission could not be completely examined, since only during the summer influent period were the chlorophyll "a" conнcentrations gathered regularly (Table II -25). Generally, these data show downward trends for each individual microcosm, except the control aquaria which increased somewhat during the experimental phase. 
Analysis of Community Metabolism: The three-point diel oxygen curve originated by McConnell (1962) yielded comparable estimates of gross production and respiration for the microcosms. Oxygen concentraнtions in the aquaria followed the hypothetical diurnal curve, increasing during the daylight hours due to phytosynthesis and decreasing at night as respiration needs prevail. Figure 11-15 illustrates typical diurnal changes for each microcosm and measures oxygen fluctuations at two-hour intervals, rather than the 12 hours sampling periodocity of the threeнpoint method. A significant deviation from the hypothetical curve that 
II-66 TABLE II-25 
CHLOROPHYLL "a" CONCENTRATIONS IN MICROCOSM WATER SUMMER !NFLUENT 













Chlorophyll "a" Concentrations (mг/l) Microcosm 
Prototype 
Station 
Day 6 
Day 12 Day 18 Day 24Day 1 


Day~ 
Station 26 
0.0897 
0.0584 
0.0759 
0.0484 (Trinity Bay) 
B 
0.0420 
0.0425
A 
0.0585 
0.0308 (West Bay) 
Artificial 
0.1091 
0.0726 
0.0669
Station 14 
0.0435 
0.0278 Sea Water 
D 
0.0413
0.0333 
o. 0272 
0.0411 
0.0428 
0.0435 
0.0555 
0.0413 
0.0403 (Upper Galveston Bay) 
E 
0.0579 
0.0559
Station 22 
0.0894 (East Bay) 
0.0406 
0.0316 
0.0439 
0.0497
0.0731
Station 29 
I 
occurred in the sample diurnal fluctuation for Aquarium E was the presence 
of an oxygen peak several hours before the normal sunset maximum. This 
pulse may result from some form of photo-oxidation (phytosynthetic 
inhibition) or rapid nutrient depletion by photysynthetic organisms during 
the daylight period, as well as resulting from temporarily improper mixн
ing regimes in the model systems (i.e. thermal stratification). Whatever 
the reason for the midday oxygen pulse, it means that community respiraн
tion and net production values for that day are both underestimated by the 
three-point method. 
The magnitude of the overall underestimation from midday oxygen pulses cannot be readily determined due to the nature of the sampling proнgram. However, another potential underestimation of metabolic rates may have resulted from the presence of surface films (low density degraнdation products) that slowed oxygen diffusion across the water interface. By using a smaller reaeration rate than the 0. 45 mg/l/hr/100% saturation deficit experimentally determined for an abiotic aquarium (no film), difнfusion corrections are reduced and metabolic parameters increase slightly. Again, the magnitude of this underestimation cannot be properly ascerнtained at this time. 
With normal response verified for the model systems, experimental 
inflow conditions were begun. Galveston Bay water collected during the 
spring cruise was Millipore-filtered and pumped into the microcosms at 
the thirty-day retention rate. The control microcosm received artificial 
seawater without nutrients to evaluate the contributions of system nutriн
ent cycling. The nutrient levels within prototype spring influent water 
(Table .[-26) were normal for phosphate, but below normal for nitrateн
nitrate nitrogen (0. 001-0. 009 5 mg/I) in the bay prototype. Copeland and 
Fruh (1970) determined that nitrogen was the potential "limiting" nutrient 
for the Galveston Bay system, especially during the summer months. 
Under these low nitrogen inflow conditions, this nutrient was the most probн
able "limiting" compound for the system autotrophs. 
II-68 
TABLE II-26 

NUTRIENT REGIMES IN MICROCOSMS 
RECEIVING GALVESTON BAY INFLOW 

SPRING !NFLUENT 







Nutrient Concentration (mg/l)
Microcosm 
Nutrient 
Type 
Influent 
Aquaria
Aquaria 
Aquaria 
Aquaria 
Day 7 
Dayl5
DaylO 
Day 21 
. 004 
. 008 
. 0095 
Nitrate-N 
. 001 
A 
. 0001 
. 0001 
. 038 
.180 
. 025 
Phosphate-P 
. 004 
B 
. 001 
. 001 
.004 
. 001 
Nitrate-N 
. 00017 
. 030 
. 0014 
. 080 
Phosphate-P 
.180 
. 001 
. 001 
. 007 
. 004 
c 
Nitrate-N 
0 
Phosphate-P 
0 
. 00017 
. 079 
. 009 
. 015 
. 001 
. 004 
. 007 
. 001 
. 0095 
Nitrate-N
D 
. 00001 
. 00002 
Phosphate-P 
.046 
. 025 
.i85 
. 004 
. 001 
. 001 
Nitrate-N 
. 002 
. 002 
E 
. 00001 
Phosphate-P 
. 017 
. 030 
. 042 
. 023 
SUMMER !NFLUENT  
Microcosm Nutrient Type  Nutrient Concentration (mg/l)  
Influent  Aquaria Aquaria Aquaria Day 1 Day 5 Day 10 Aquaria Day 15  
A Nitrate-N  1. 045  . 005 . 015 . 0015 . 0015  
Phosphate-P  0.5  <.00001 <.00001 . 057 . 048  
B Nitrate-N  1.16  <.001 <.001 . 001 <.001  
I Phosphate-P  0.15  . 0006 . 004 . 0015 . 021  
c Nitrate-N  0  . 001 . 001 . 0015 . 0015  
Phosphate-P  0  <.00001 . 004 <.00001 . 01  
D Nitrate-N  1. 035  . 001 . 0015 . 001 . 001  
Phosphate-P  0.52  . 05 . 057 .110 . 080  
E Nitrate-N  1. 53  . 002 . 0015 I . 001 . 001  
Phosphate-P  0.39  . 015 . 002 . 01 .006  
I  I  

II-69 

Analysis of the microcosm nutrient levels showed nitrogen levels varied only slightly from the influent concentration, whereas phosphate tended to disappear within each model system. Uptake of phosphate may have occurred due to the equilibrium previously reported between sediнments (adsorption), algae (excess nutrient storage), and the water. Nitrogen, the nutrient in lowest concentrations, probably dictated the rate of photosynthetic production during the spring influent phase. The low but relatively constant nitrogen values indicated a sensitive equilibrium between algal uptake and regeneration by microbial degradation that determined production rates, rather than reliance on incoming nutrients. 
The initial inflow of nutrient-containing spring water resulted in a large productivity pulse for all microcosms, but daily gross production dropped rapidly and pulsed erratically (sometimes on a 6-7 day cycle) throughout the total displacement volume. Mean production values ranged between 1. 31 and 2. 04 gms O/m2I day, comparable to rates reported by Abbott (1967), Beyers (1963) and McConnell (1962). Figure II-16 shows that the model systems under spring inflow maintained ratios of gross production to total respiration that approximated one, indicating the maintenance of system self-sufficiency at the low nutrient levels preнsent. Other than microcosm A (Station 26 -Trinity Bay) and microcosm D (Station 22 -Upper Galveston Bay) which exhibited higher mean respiraнtion than mean production, the microcosms responded autotrophically during adaptation to low nitrogen levels and possible phosphate storage for later use. Odum,~ al. (1963) reported P/R ratios less than one for Upper Galveston and Trinity Bays, an observation corroborated by microнcosm metabolism under simulated spring inflow from these areas. Since filtering removed any obvious organic input from the influent water, this metabolic pattern might be related to potential toxicity effects from influent water or, less likely, the introduction of small organic molecules 
(less than the 0. 45 ╡Millipore pore size) as a supplemental food source. 
II-70 
The proximity of these prototype locations (Stations 22 and 26) to large-scale domestic and industrial wastewater outflows lends tenuous support to toxicity as the cause of the observed model system behavior. 
An influent regime in which artificial seawater devoid of added nutrients was utilized proceeded over a 15-day period only, since inflow and outflow rates were doubled. Production decreased, as expected, in all microcosm units, except for the control (Aquarium C) that regularly received this nutrient-free input and microcosm D. The higher inflow rate may have been successful in flushing out toxic metabolites or influent toxicity from these two systems. Even with depressed influent nutrient levels, the P/R ratio generally exceeds one in the model systems (except for microcosm B -Station 14). Nutrient storage by the photosynthetic community and accelerated nutrient recycling may have allowed this autoнtrophic metabolic pattern to continue. 
The low nitrogen concentrations in the spring influent dictated the addition of 1 mg/I nitrate-nitrogen to each prototype station influent. This enrichment exceeded normal nitrogen levels for the Galveston Bay system and insured that this influent was stimulatory to the nitrogen starved system autotrophs. Mean gross production values increased during the summer influent phase over the artificial seawater regime, but except for Station 26, production actually decreased with summer influent in comparison to spring rates. Station 14 (microcosm B) only produced 
1.1 gms o/m2/day, whereas other stations produced greater amounts
2up to 1. 81 gms 0/m2I day (See Table II-27 illustrating community metabolism through all influent regimes). Microcosms B (Station 14), C (Control), and D (Station 22) exhibited steadily decreasing production rates, indicating that toxicity and/or advanced senescence affected the viable biomass of the biological community. The nutrient levels in aquarium water (Table II-26) showed that even normal phosphate and enriched nitrogen inputs were immediately 
II-71 
TABLE II-27 METABOLISM RELATIONSHIPS DURING EXPERIMENTAL INFLUENT PERIODS 



















2
Average Gross Production (gms/m/ day) 
Influent 

B 
c 
DA 
E Type 
(Station 2 6) 
(Station 14) (Control) (Station 22) (Station 29) 
1. 31 1. 46 1. 42Spring Bay Water 1. 52 
2.04 
(Normal-Filtered Only) 

Artificial Sea Water 
1. Q5
1.19 
1. 51 1. 86 
1. 71 
(Nutrient Free) 

Summer Bay Water 
1.10
1. 37 
1.13 
1. 49 
1. 81 
(N0Enriched)

3 
Average Total Respiration (gms/m 2I day) I Influent Type 
B 
c 
D EA 
I 
1. 35 1. 44 
1. 41Spring Bay Water 1. 62 
2.05 
(Normal-Filtered) 

Artificial Sea Water 
1.17 
1.14 
1. 49 1. 77 
1. 65 
(Nutrient Free) 

I 
1. 03 0.93
Summer Bay Water 
1. 36 
1. 48 
(N03 Enriched) 




_:_j 
decreased by rapid algal uptake. However, this nutrient supply was not significantly large enough to spur additional photosynthetic production. Station 29 and 26 water resulted in stabilized community production and respiration whereas the other three model systems decreased in community metabolism (see data in Appendix). Stations 22 and 29 showed greater respiration than production during the later phases of the experimental period. The slow infusion of normalized nutrient levels did not result in appreciably larger production increases (Figures 17 and 18), indicating a significant role for nutrient recycling within the model systems. A toxicity factor may have been involved at Stations 14 and 22 that offset any production gains from the enriched influent and resulted in the declinнing values during the summer water introduction. The lack of production increase in any of the microcosms with nitrogen addition may indicate that "normal" concentrations introduced long retention times may have minimal effects on established autotrophic communities. The other possibility is that light may have been limiting throughout the study as it is in the proнtotype Bay during parts of the year. 
Nutrient Inflows 
The mass flow of nutrients (organic and inorganic nitrogen and total phosphorus) to Galveston Bay were computed in the same manner as organic material flows. U. S. Geological Survey data for flow and concentration were used to compute runoff from areas draining to the Houston Ship Channel and to the Bay directly. These amounts (for organic, ammonia, nitrite, and nitrate nitrogen, and total phosphorus) were then added to and categorized by general nutrient type, that is, the amounts for ammonia, nitrite and nitrate were combined for the category inorganic nitrogen to go with organic nitrogen and total phosphorus. Mass flows from domestic wastes were computed using mass discharge coefficients of 0. 035 lbs organic-NIcap/day, 0. 025 lbs ammonia-NIcap/day, and 0. 012 lbs total phosphorus/cap/day for the Houston Metropolitan Area (Houston, Deer 
II-73 

Park, and Pasadena; total population 1, 334, 852), Texas City (population 32, 000), and Galveston (population 68, 000) and assuming 30 per cent removal of the nutrients in primary or secondary treatment processes. Industrial waste discharges to the Houston Ship Channel were computed by difference using total mass flows to the Houston Ship Channel as presented in Table 4. 2 of the Tracor (1972) report on nitrogen modeling and subtracting the amount computed above for domestic wastes and runoff (to the Channel only). 
The results of these computations are presented in Table II-28. The influx of organic and inorganic nitrogen is distributed unevenly among the three principal sources shown. For organic and inorganic nitrogen the main source, as would be expected, is domestic wastes which contriнbutes about 80 per cent of the total inflow. For total phosphorus the runoff source becomes more important although waste discharges still contribute the major portion of the phosphorus. 
DISCUSSION It is important to point out here that this discussion is necessarily 
I 
incomplete because of the inability to analyze the biological data with all 
data from other portions of the Galveston Bay Study. This was due in part to the initial scheduling and data processing lags in associated proнgrams and in part to the rescheduling of the modeling and engineering tasks with which interaction would have been highly desirable. However, to the extent possible the original objectives of this work have been accomplished and are discussed below. 
Environmental Requirements 
Salinity and Temperature Requirements 
The environmental requirements as defined in Copeland and Bechtel (1971) and summarized in Tables II-3 and II-4 herein and are considered to represent the limits of environmental factors for those organisms considered in their report. 
II-74 
TABLE II-28 
SUMMARY OF NUTRIENT MASS INPUTS TO GALVESTON BAY, TEXAS 

Source  Organic Nitrogen  Inorganic Nitrogen  Total Phosphorus  
4(10 lbs/yr)  (%of Tot.)  4(10 lbs/yr)  (%of Tot.)  4(10 lbs/yr)  (%of Tot.)  
Runoff1  
Excluding HSC  372.0  16.1  387.9  15.8  180.0  16.3  
Into HSC  105.2  4.6  645.2  26. 3  295.9  26.8  
D .omestic2  
Houston Metro. Area  1,705.3  73.8  1,218.0  49.8  584.7  52.9  
Texas City  40.9  1. 8  29.2  1. 2  14.0  1. 3  
Galveston  86.9  3.7  62.0  2.5  29.8  2.7  
Industry3 To HSC  ?  107.0  4.4  ?  
Total  2,310.2  100.0  2,449.3  100.0  1,104.4  100.0  

1
obtained from average concentrations at U.S. G. S. stations x long term flow averages. 2
obtained by lb/cap/day x 365 x 0. 7 (assumed 30% removal). 3
Net of total discharge to Houston Ship Channel (HSC) minus calculated runoff minus Houston Metro. Area domestic input. 
It is assumed that these limits will apply to the corresponding organisms in Galveston Bay, namely for the trout, redfish, menhaden, shrimp, oysters, and blue crab. Salinity requirements for these organнisms, with the exception of the menhaden, do not appear to be very stringent based on the catch data. Whether this finding can be substanнtiated by laboratory or other data is not known, but they do contrast with the historical literature. The need for pulses of freshwater in the spring to kill oyster predators is well substantiated {salinities of 10 to 15 ppt in the early spring for several days being adequate to kill the oyster drill, Thais haemastoma, the fungus Dermocystidium marinum, and the oyster disease MSX ) and the apparent preference of juvenile crabs for low salinнities has also been observed. The amounts of freshwater needed for the oyster could only be determined by use of the Tracor, Inc. salinity model for the Bay. This model was not available for this purpose through 1972, and thus these required freshwater flows could not be determined. Based on a simple salinity at Station 38 vs. Trinity River inflow correlation, an inflow of at least 4, 000 cfs was necessary to maintain the preferred salinities (< 8, 000 mg/l) for the juvenile crabs. This flow is available throughout most of the year except during dry periods. 
Cursory comparisons of distributions of organisms in Galveston Bay to those expected or anticipated from the environmental requirements has revealed two important points: (1) it is not possible at the present time to differentiate present distributions from those expected because the populations being considered are very mobile, inadequate long term data exist to define present distributions, and some of the data used by Copeland and Bechtel (1971) came from Galveston Bay, reflecting condiнtions in the Bay and thus biasing environmental requirements for the existing water quality in the Bay; and (2) if differences in distribution are so difficult to detect, effects or presence of toxicity may not be delineated. A third point which was also considered by Copeland and 
II-76 
Bechtel {1971) was the scarcity of data in general and the lack of geoнgraphically specific data in Galveston Bay for some species. Toxicity effects would have to be quite pronounced for significant differences in organisms distribution to be evident; thus, that particular method for defining toxicity-affected areas could not be accomplished. 
Organic Material Inflow 
Consideration of salinity and temperature requirements are not truly adequate by themselves, for other requirements, namely food sources are very important. For example, Copeland and Fruh (1970) showed the importance of organic material imports to community respiration in their "micro-ecosystem bay model" of Trinity Bay, and they stressed the importance of the Trinity River inflow and the organic load carried by this inflow as a source of organic material. To determine the relative magnitude of this source of organic material compared to other sources, a mass balance of organic material was computed using BOD concentration as an indicator (on a one to one basis) of organic material concentration. The results, shown earlier in Table II-6, indicated that river inflow con_. tributed a small amount compared to other sources such as the marshes and waste discharges. In fact, for the detritus based food chain, the marshes contributed the major portion of organic matter. If the conнtribution to Trinity Bay alone were considered (waste discharges essenнtially omitted), the marshes would contribute six times as much organic material as the river inflow based on the estimates used. Thus, the results of Copeland and Fruh (1970) could be reinterpreted as the necessity for maintaining present levels of organic material input in Trinity Bay from whatever source. The marshes, being the major contributor of organic material, must be preserved to support the detritus based food chain (or the shellfish and some of the fish) in Trinity Bay. The river inflow then is a secondary source of organic matter. 
II-77 

Freshwater Inflows 
The freshwater inflow requirements are extremely difficult to analyze except on a gross basis because of the serious lack of relationнships between salinity levels and organism preference and survival. The substantial amount of information gathered by Copeland and Bechtel (1971) was used to show relationships between catch and salinity levels, but this provides only partial information about survival in or preference of variнous salinity levels. The gross correlation of freshwater inflow and comнmercial catch was developed as a means of circumventing this problem, at least until adequate data may be accumulated to solve the problem. This approach, used by others in a less refined form,was pursued in the hope of developing an overall understanding of the relationship between freshwater inflows and secondary production (or somewhat analogously fish and shellfish catch). The analysis of the data showed that some of the Texas bays produce a large catch at an optimum displacement rate of 2. 0 per year and others (including Galveston Bay) produce the largest catch at displacement rates less than 2. 0 per year and estimated to be near 0. 5 per year. For Galveston Bay in particular, displacement rates are normally in excess of 2. 0 per year, and thus the largest catch is attained during years of low inflow of freshwater. One may only specuнlate about the cause(s) for decreased catches during periods of high flows. A rather simple explanation would be that high flows in the spring flush organic detritus from the marshes out of the system leaving little food for young fish and shellfish in the summer when they are feeding most heavily. This explanation and any others, however, need verification which cannot be provided at this time. 
Considering freshwater inflows and their importance to Galveston Bay now from the above discussion, there is evidence that freshwater inflows (excluding wastes) contribute only a small amount of organic material to Galveston Bay relative to other natural sources. There is 
II-78 

incomplete evidence that freshwater inflows could be reduced without 
decreasing secondary production as long as seasonal pulses of certain flows are maintained and the marsh areas are preserved. These concluн
sions should be substantiated further before being used as design criteria in any water resource or water quality management plan. 
Productivity 
The productivity studies were oriented somewhat differently than originally proposed. This is due in part to the change in the principal investigator, the difficulty of the originally proposed radioactive carbon technique and data interpretation, but most importantly to two facts evident from previous productivity studies in Galveston Bay. First, the phytoplankton populations do not now appear to exist at concentrations which could be considered detrimental to the Bay system. Indeed, the question of what concentrations of phytoplankton should be maintained to insure the high productivity of the Bay and the high nursery value it now has must be answered before attempts should be made to limit nutrient inputs to the Bay. Second, the delineation of a single limiting factor for subsequent control through waste management schemes has questionable value, for control of phytoplankton populations may shift from one factor to another seasonally. Copeland and Fruh (1970) found that nitrogen was limiting during some parts of 1969, but iron was also limiting at times and light and toxicity were also possible limiting factors. Thus, control of one of these factors may be adequate for only a short period of time, and the cost to achieve that short control thus becomes questionable. Similar conclusions have been reached in studies of other estuaries, namely in San Francisco Bay, California, in Jamaica Bay, New York, and in Delaware Bay, Delaware. In actuality, light was found to be as important a conнsideration in these bays as was any chemical nutrient; the same results appear to be true for Galveston Bay. 
II-79 

Laboratory Bioassays 
Laboratory bioassays have limited usefulness for explaining processes in the prototype system because the environmental conditions in the bioassay are much different than those in the prototype. The bioassay's usefulness is showing the response of an organism to some factor with all other factors not influential or stressing. Bioassays such as those reported herein have indeed shown nitrogen to be limiting (the test organism was stimulated by the addition of nitrogen to the sample from Galveston Bay), but one of the environmental factors which was limiting in the prototype but not in the bioassay was light. With the light limitation removed, other limiting factors such as nitrogen became evident. 
Because of the nature of the laboratory bioassays used herein, the results of these bioassays were additional validation of Van Baalen's blue-green algae bioassays for toxicity but using the green flagellate Dunaliella. Similar depressions of algal growth rates occurred at corнresponding seasons and stations. Substrate growth rate relationships could not be derived from these studies. 
Field Studies 
Conducting the bioassay in the prototype or modeling the prototype in a microcosm is more realistic than strictly laboratory bioassays. These two approaches were used in this study. 
The in situ bioassays, the light and dark bottle tests, in conjuncнtion with the light transmission measurements revealed that light may have been limiting on one or more sampling periods at each of the five stations. Using the critical depth concept, it was found that light limitation was predicted in every case at those stations where net proнduction was less than zero (see Tables II-9 through II-13). Also found were several overall patterns. High production and respiration rates were found in Upper Galveston Bay and Trinity Bay compared to those 
Il-80 
values in lower Galveston Bay. However, respiration exceeded production in the upper portions of the Bay and equaled or was less than production in the lower portions. These results generally agreed with historical 
data from Odum,~ al. (1963) and reflect the consequences of a large influx of organic material from wastes and natural sources in the upper 
portions of the Bay. Large community metabolism is supported by the influx, and the large production is spurred by the large nutrient inputs which accompany the organic material. Excess production, however, is apparently controlled by light availability to some extent. 
Microcosm Studies 
The model aquarium systems effectively simulate the Galveston Bay prototype in most physical parameters. Light intensity within the microcosms closely resembles the levels found in the upper waters at prototype field stations, except during periods of intense summer radiaнtion or high wind-generated turbidity. Average light intensities are comнparable between the model and prototype systems; however, the constant illumination of microcosms contrasts significantly to the normal daylight curve with peak intensity at midday. Complete mixing occurs in the aquarium units with the aid of magnetic stirrers, but is not sufficiently strong to cause resuspension of microcosm sediments. Thus, the model systems lack the light-limiting turbidity that occurs in Galveston Bay waters as the result of vigorous wind and tidal action. This limitation of light penetration by suspended material greatly affects community metabolism in the prototype system, but the maintenance of these phyнsical conditions for model system productivity analysis is not practicable. 
Daily water temperatures are much more constant in the microнcosms than for the prototype, but this lack of thermal variability does not appear to have any significant effect on productivity analysis. Artiнficial fluorescent lighting introduces far less radiant heat into the water than solar radiation, so diurnal temperature fluctuations are greatly 
11-81 

reduced in the model systems. Decreased microcosm heating results in low evaporation rates and relatively constant salinity regimes with the aquaria. Salinity patterns within the microcosms tended to reflect the influent salinity during experimental conditions, and resultant levels are entirely consistent with the regions of the Bay being simulated. Although the model systems lack the actual freshwater and seawater inputs that characterize estuarine systems, water collected on-site at: the field stations simulates the salinity behavior of that water mass over longнterm (30-day) retention periods. Changes in a conservative substance, such as salinity, result from evaporation and water exchange, two processes controllable by the microcosm design. 
Because of its reliability for detecting small changes in dissolved oxygen, the microwinkler method was used to detect diurnal oxygen change in the microcosms. The three-point diel curve provided reliable estimates of gross production and total respiration with minimal samples necessary. Although problems with midday oxygen peaks and surface films that inhibit reaeration may have allowed some underestimation of the metabolic paramнeters, the values appear comparable to previously-reported microecosystem 
2
data. Gross production and total respiration, calculated as gms!O/m/day, are generally less for the microcosms than the prototype estuary; but, when considered as gms!O/m3I day (not integrated over depth), the metabolic rates compare favorably to volume rates for the uppermost meter of proнtotype surface waters. 
Indirect community monitors, the optical density and chlorophyll "a" concentrations in the microcosm water, apparently lacked the sensiнtivity need to demonstrate specific system responses. Even so, chlorophyll "a" decreases in microcosms during the summer influent phase did appear to correlate with overall productivity declines. The nature of the dominant Aufwuchs community limits the majority of biomass to the exposed subнstrate area, where this assemblage cannot quite respond like phytoplankton 
II-82 

to nutrient inputs (i.e. population growth increases measurable chlorophyll). The high nutrient uptake and storage capacity of the established biomass denies these essential nutrients (nitrogen and phosphorus) for growth and maintenance of the low-density phytoplankton populations. The equality of gross production and total respiration (P/R ratio near one) exhibited by the microcosms result from the successional maturity of the autoнtrophic communities. 
There appears to be little correlation between gross production and the amount of nutrients added to each microcosm. Even enrichment with higher than normal prototype nitrogen levels does not yield significantly larger production values. Response to phosphate appears to be uptake for storage, rather than uptake for immediate production increases. The successional maturity of the microecosystem autotrophs may contribute to the dampened system responses observed, whereas younger successional stages (phytoplankton dominant) can generate greater unit productivity in response to nutrient additions. Mechanisms for phosphate exchange between the water and sediments in both model and prototype systems should insure a sufficient reservoir of this nutrient for maximized proнduction, since stripping of adsorbed phosphate from suspended material and bacterial regeneration from organic detritus can occur easily in either system. 
Mean estimates of productivity for spring inflows would probably be higher than those calculated, if depressions resulting from stirrer stoppages were eliminated. Considering the minimal production response to enriched summer water (only the Station 26 microcosm exhibited increases during the summer influent over spring levels), the experimental nutrient regimes appear to have only slight impact on the stimulation of system autotrophs. Alternate pathways (i.e. sediment absorption or excessive uptake by nutrient-starved algal cells) may effectively circumнvent the expected production response to nutrient inputs. 
II-83 
The model system simulating Station 22 exhibited some suppression of community metabolism during both spring and summer phases (mean values compared to other model systems), but actual production rates are still respectable. Steady declines in community metabolism during the summer phase appear significant for both Stations 14 and 22. These depressions in both respiration and production are not related to lowered nutrient concentrations, so influent toxicity may be a factor here. Residнual toxicity that exists even in Millipore-filtered Galveston Bay water does appear to be capable of reducing production in a successionally-advanced autotrophic community. This evidence may imply that a phytoplankton community limited by light and/or nutrients in Galveston Bay could also be significantly affected by wastewater toxicity. Short-term sampling, such as light-dark bottle productivity estimates, would be unable to separate these subtle toxicity effects from simultaneous physical (light), chemical (nutrient availability), and biological (population species compoнsition) parameters in the actual estuary. 
CONCLUSIONS 
The following conclusions have been derived from the results of this study. Environmental Requirements 
1. 
The information on environmental requirements presented in the Copeland and Bechtel (1971) report is very useful for defining limits for salinity and temperature, preference for geographical areas of estuнaries, seasonal abundance, etc. , but the interdependency of the environнmental factors strongly influence these results and must be considered in their interpretation. 

2. 
Salinity and temperature are interrelated in controlling popuнlation distributions of the six common organisms of Galveston Bay. 


II-84 
It is very difficult, however, to separate salinity and temperature requirements and preference from other environmental requirements. 
a. 
Shrimp populations apparently move in response to temнperature, salinity, and food sources, although this response may vary among species. 

b. 
Oyster populations are found in a wide range of salinities, but periodic low salinities are required only to kill oyster predators. 

c. 
Fish populations are found in a wide range of salinities, but highest populations are found in specific ranges dependent on the fish species (these specific ranges may simply reflect geographical prefнerence). 

d. 
Young blue crabs apparently prefer low salinity water while the adults may tolerate a wider salinity range. 


3. An examination of food sources (organic material) for the Bay revealed that: 
a. 
Phytoplankton contribute almost 94 per cent of this material per year, marshes are next in magnitude at 3. 6 per cent, waste discharges follow at 2.1 per cent, and runoff is last at 0. 4 per cent. 

b. 
For sources external to the Bay (waste discharges and runoff), over 70 per cent is derived from wastes; runoff constitutes a minor source. 

c. 
For detritus based food chains which include the shrimp, oysters, crabs, and some fish, marshes are the principal source of organic material in Galveston Bay (contributing 58 per cent to the Bay as a whole and a larger proportion in the Bay periphery near marshes) and a necessary habitat for young forms of these organisms. 

d. 
For phytoplankton based food chains, the phytoplankton constitute the major source of organic material. 

e. 
Organic material from waste discharges does not provide a significant food source to the Bay as a whole but may be important in localized areas. 


II-85 

4. Based on the data available, the following requirements for freshwater inflows were established: 
a. 
A winter and spring seasonal inflow of at least 4, 000 cfs from the Trinity River may be required to maintain low salinities in the Trinity Bay marsh areas for young crab and shrimp. 

b. 
A pulse of freshwater of some as yet undetermined magнnitude and duration is required in the spring to control oyster predators. The Tracor, Inc. salinity model for Galvestion Bay should be used to estiнmate the flows and duration required to lower salinities in the vicinity of the oyster reefs to less than 15 ppt and preferably to 10 ppt for some period longer than two days. Such flows should be available at least once and preferably several times during the spring to insure kill of the oyster predators at desired reef sites. 

c. 
Definite freshwater requirements for other organisms or for marshes could not be determined from the literature data available. 

d. 
Based on the incomplete evidence presented for commercial catch vs. freshwater inflow, an average annual inflow of 3, 300 cfs would be required for one displacement per year to achieve the largest total catches for the period investigated; a flow of one-half that amount would correspond to the displacement rate at which maximum catches are estimated. Productivity 


1. The laboratory bioassay data (first set) show that growth rates of the test organism, Dunaliella {a green flagellate), in water taken from Galveston Bay in April, 1972 could be increased by the addition of large amounts of nitrate or phosphate. 
a. 
The largest increase in growth rate was produced by nitrate additions although phosphorus additions were stimulatory also. 

b. 
Nitrogen and phosphorus could be considered limiting in the Bay water at that time for waters with similar light intensities. 


2. The laboratory bioassay data (second set) provided additional 
II-86 
support, usmg a different algal form, for the blue-green bioassay conducted in a concurrent study by Van Baalen. 
a. 
Depressions (below growth rates in controls) in the growth rate of Dunaliella were observed for Stations 14, 22, and 26 using unfiltered samples taken from the Bay in April and July, 1972; the greater depression occurred with the July water samples at Station 22 and 26 and with the April sample from Station 14. 

b. 
These depressions in growth rate were also accompanied by lags in initial growths and low maximum concentrations of cells. 


3. Plankton productivity measurements in Galveston Bay revealed the following conclusions: 
a. 
Production in the upper portions of the Bay is very high and is apparently stimulated by the larg-e influx of nutrients from wastes and runoff as well as those nutrients recycled from organic matter by bacteria. 

b. 
Production in the lower portions of the Bay is higher than in bays not influenced by wastes but lower than in the upper portions of the Bay. 

c. 
Respiration is also high in the upper Bay, usually greatly exceeding production and about equal to production in the lower Bay. 

d. 
The imbalance of production and respiration in the upper Bay is probably caused by the larg-e influx of organic material from marshes, runoff, and waste discharges; however, this imbalance reflects a large secondary production. 

e. 
Light was apparently limiting to production at all stations sampled for at least one sample period; light was always limiting at Station 26 in Trinity Bay, usually limiting at Station 22, and sometimes limiting at Stations 14 and 17. 

f. 
Production levels could not be correlated with nutrient concentrations at the time of sampling but only with the proximity of 


II-87 the station to nutrient sources or to favorable light conditions. 
g. Compensation depths were 1. 0 meter or less (one-half the water depth or less) at stations in the Upper Bay and 1. 5 to 2. 0 meters (slightly less than or equal to the water depth) at stations in the lower Bay. 
4. 
Light measurements in the Bay showed that wavelengths of light between 200 m╡ and 800 m╡ are selectively absorbed by material in solution and in suspension; these materials are present in different amounts according to locality in the Bay and season. The nature and source of these materials was not determined; however, these materials are worthy of further consideration because of their influence on water appearance and its aesthetic attractiveness and on light transmission and subsequently production. 

5. 
From the constant inflow microcosm (microecosystem) experнiments, the following conclusions were drawn: 


a. 
Production and respiration levels in these systems were similar to those in the prototype Bay. 

b. 
Additions of nutrients in excess of normal concentrations in the Bay failed to stimulate additional production in these systems; this may have been due to light limitations but just as likely to inhibition of production by toxic materials. 


(1) 
Decreasing levels of production were found using filtered influent water taken from Stations 14 and 22 during the summer; the algal bioassays showed growth depression at both of these stations. 

(2) 
The algal bioassay also revealed growth depression at Station 26 during the summer, but when filtered Station 26 water was used in the microcosm, no decline in production was observed; this disнparity may result from the variable absorption of toxic material onto suspended material with the toxic material being removed from Station 26 water by filtering and removed in lesser amounts from water taken at Stations 14 and 22. 


II-88 

(3) The variable toxicity of the waters tested appears 
to be a function of proximity to toxic waste sources, proximity to sources of suspended materials (e.g. Trinity River), ,_the season of the year, the nature of the suspended material at a station (i.e. its state of degradaнtion if organic), the nature of material in solution and certainly the properties of the toxic material. 
6. 
A survey of nitrogen and phosphorous influx to Galveston Bay showed that the major contributors of such materials are waste discharges; any attempts to decrease nutrient discharges to the Bay would have to be applied to the waste discharges to have any significant impact. 

7. 
It is apparent that primary production in Galveston Bay has been stimнulated by the large nutrient discharges to the Bay and that secondary production has been stimulated by the 1arge influx of organic material from wastes or is supported largely by phytoplankton and marsh production. 


a . Nevertheless, phytoplankton populations do not appear (based on 1969 data) to be detrimental to uses of the Bay. 

b. 
A decrease in food sources will probably be accompanied by a reduction in secondary production. 

(1) 
Reduction of phytoplankton populations and marsh areas will cause the most marked decline of secondary production. 

(2) 
Reduction of organic material in waste discharges may also cause a reduction in secondary production, although it is not clear whether this material is consumed directly by fish and shellfish or indiнrectly through mineral recycling and subsequent primary production. 

c. 
Waste management plans for the Bay should consider the consequences of the apparent toxic effects of wastes on phytoplankton in the Bay and the probable use of organic material in waste discharges by fish and shellfish. 


II-89 

d. Plans for management of freshwater flows to the Bay 
should include the necessity for those flows o:E seasonal occurrence and 
undetermined duration needed for activity control in the Bay and for 
maintenance of marshes (freshwater requirements undetermined at the 
present time). 
ACKNOWLEDGMENTS 
We gratefully acknowledge the generous support of the Texas Water Quality Board and the staff support provided by them. Especially, we appreciate the help of Colonel Frank P. Bender and Mr. Mant Nations of the Galveston Bay Project Office, but we also recognize the help of those from other agencies participating in the project. Also, we wish to thank Mr. Charles R. Olling, Miss Nora G. Galindo, Mrs. Linda A. Broyles, and Mr. Kenneth R. Immenhauser for their hard work in the laboratory. Their untiring effort made this work possible. Finally, Mrs. Sharon R. Thornhill and Mrs. Frances E. Tisdale deserve special recognition for their clerical and secretarial support of this project. Mrs. Tisdale shouldered most of the work, and her effort is most appreciated. 
II-90 

BIBLIOGRAPHY 
1. 	
Abbott, Walter. 1966. ''Microcosm Studies on Estuarine Waters: I. 
The Replicability of Microcosms. 11 Journal WPCF 38(2): 258-270. 


2. 
Abbott, Walter. 1967. "Microcosm Studies on Estuarine Waters: 

II. The Effects of Single Doses of Nitrate and Phosphate." Journal WPCF]J(l): 113-122. 

3. 	
Allen, Stephan D. and Thomas D. Brock. 1968. "The Adaptation of 
Heterotrophic Microcosms to Different Temperatures." Ecology 
49(2): 343-346. 


4. 	
American Public Health Association. 1965. Standard Methods for the 
Examination of Water and Wastewater. Twelfth Edition: 405-410. 


5. 	
Beyers, R. J. 1962. "Relationship between Temperature and the 
Metabolism of Experimental Ecosystems." Science 136:980. 


6. 	
Beyers, Robert J. 1963. "A Characteristic Diurnal Metabolic Pattern in Balanced Microcosms." Pub. Inst. Mar. Sci. Univ. of Texas. 

9: 19-27. 

7. 	
Beyers, Robert J. 1963. "The Metabolism of Twelve Aquatic Laboratory Microcosms." Ecol. Monog. 33(4): 281-305. 

8. 	
Cooke, Dennis. 1967. "The Pattern of Autotrophic Succession in 
Laboratory Microcosms. 11 Bioscience 17(10): 717-722. 


9. 	
Cooper, David. 1970. "River Input Studies Using Continuous-Series 
Microecosystems. " Final Report, Galveston Bay Study Program, 
Texas Water Quality Board. 


10. 	
Copeland, B. J. and T. J. Bechtel. 1971. "Some Environmental Limits of Six Important Galveston Bay Species," Report to the Galveston Bay Study Program, Texas Water Quality Board, Contract IAC (48-70). 

11. 	
Copeland, B. J. and E. G. Fruh. 1970. "Ecological Studies of Galveston Bay-1969." Final Report, Galveston Bay Study Program, Texas Water Quality Board. 

12. 	
Dunstan, W. M. and David W. Menzal. 1971. "Continuous Cultures of Natural Populations of Phytoplankton in Dilute, Treated Sewage Effluent." Limnology and Oceanography 16 (4) : 623-632. 

13. 	
Fox, H. M. and C. A. Wingfield. 1938. "A Portable Apparatus for the Determination of Oxygen Dissolved in a Small Volume of Water." J. Exp. Biol. 15:437. 

14. 	
Hayes, F. R. and J. E. Phillips. 1958. "Radiophosphorus Equilibrium with Mud, Plants, and Bacteria under Oxidized and Reduced Conditions." Limno. and Oceanog. J.: 459-475. 

15. 	
Hulbert, Edward M. 1970. "Competition for nutrients by Marine Phytoplankton in Oceanic, Coastal, and Estuarine Regions. " Ecology 51 (2) : 47 5-484. 

16. 	
Kalle, K. 1966. "The Problem of Gelbstoff in the Sea." Oceanogr. Mar. Biol. Ann. Rev. 1, 91-104. 

17. 	
Kevern, N. R. and R. C. Ball. 1965. "Primary Productivity and Energy Relationships in Artificial Streams." Limno. & Oceanog. 


II-91 
10 : 74-87. 
18. 	
Maney, C. H. and W. C. Westgarth. 1962. "A Galvanic Cell Oxygen Analyzer." Journal WPCF 34: 1037-1051. 

19. 	
McConnell, William J. 1962. "Productivity Relations in Carboy Microcosms." Limno. & Oceanog. ]: 335-343. 

20. 	
McConnell, William J. 1965. "Relationship of Herbivore Growth to Rate of Gross Photosynthesis in Microcosms. " Limno. & Oceanog. 10 (4) : 539-543. 

21. 
Mcintire, C. D., Robert L. Garrison, Harry K. Phinney, and Charles 

E. Warren. 1964. "Primary Production in Laboratory Streams." Limno. & Oceanog. 9 (1) : 92-102. 

22. 	
Mitchell, Dee. 1971. "Eutrophication of Lake Water Microcosms: Phosphate vs. Nonphosphate Detergents." Science 174 (4011): 827-829. 

23. 	
Odum, H. T., R. P. Cuzon du Rest, R. J. Beyers, and C. Allbaugh. 1963a. "Diurnal Metabolism, Total Phosphorus, Ohle Anomaly, and Zooplankton Diversity of Abnormal Marine Ecosystems of Texas." Puhl. Inst. Mar. Sci. t 404-453. 

24. 	
Odum, H. T. and C. M. Hoskin. 1957. "Metabolism of a Laboratory Stream Microcosm." Pub. Inst. Mar. Sci. Univ. of Tex. VI 

(2) : 115-133. 

25. 	
Odum, H. T. and C. M. Hoskin. 1958. "Comparative Studies on the Metabolism of Marine Waters." Pub. Inst. Mar. Sci. Univ. of Texas з.: 16-46. 

26. 	
Odum, H. T., W. L. Siler, R. J. Beyers, and N. Armstrong. 1963b. "Experiments with Engineering of Marine Ecosystems." Puhl. Inst. Mar. Sci. 2_, 373-403. 

27. 	
Parker, J. C. 1963. "Distribution of Juvenile Brown Shrimp (Penaeus aztecus Ives) in Galveston Bay, Texas, as Related to Certain Hydrographic Features and Salinity." Contr. in Mar. Sci. ~ 1-12. 

28. 	
Pomeroy, Lawrence R., E. E. Smith, and C. M. Grant. 1965. "The Exchange of Phosphate between Estuarine Water and Sediments." Limno. & Oceanog. 10 (2): 167-172. 

29. 	
Ragotzkie, R. A. 1959. "Plankton Productivity in Estuarine Waters of Georgia." Puhl. Inst. Mar. Sci. ~ 146-158. 

30. 	
Richards, F. A. and T. G. Thompson. 1952. "The Estimation and Characterization of Plankton Populations by Pigment Analysis. 

II. A Spectrophotometric Method for the Estimation of Plankton Populations." J. Mar. Res. 11: 156-172. 

31. 	
Rickert, D. A. and J. V. Hunter. 1972. "Colloidal Matter in Wastewaters and Secondary Effluents." J. Wat. Poll. Contr. Fed. 44 (1), 134-139. 

32. 	
Ryther, J. H. 1956. "Photosynthesis in the Ocean as a Function of Light Intensity." Limnol. Oceanogr. 1 61-70. 

33. 	
Ryther, J. H. and C. S. Yentsch. 1957. "The Estimation of PhotoнPlankton Production in the Ocean from Chlorophyll and Light Data." Limnol. Oceanogr. ~' 281-286. 

34. 	
Strickland, J. D. H. and T. R. Parsons. 1968. A Practical Handbook of Seawater Analysis, Fisheries Research Board of Canada, Bulletin 167, Queens Printer, Ottawa, Canada. 

35. 	
Sverdrup, H. U. 1953. "On Conditions for the Vernal Blooming of Phytoplankton." J. Cons. Int. Explor. Mer. 18, 287. 

36. 	
Tracor, Inc. 1971. "Phase II Technical Progress Report." Prepared by W. H. Espey, Jr. et al., July, 1971, Tracor Document No. T70-AU-7636-U. 

37. 	
Tracor, Inc. 1972. "Report on Nitrogen Model Verification for the Galveston Bay System." Prepared by N. J. Cullender and A. J. Hays, Jr., March 30, 1972. Tracor Document No. T72-A.U-9522-U. 

38. 	
Truesdale, F. M. 1970. "Some Ecological Aspects of Commercially Important Decapod Crustaceans in Low Salinity Marshes." Ph.D. Dissertation, Texas A&M University, College Station, Texas, 164 pp. 

39. 	
Vollenweider, Richard A. 1971. A Manual on Methods for Measuring Primary Production in Aquatic Environments. International Biological Program Handbook No. 12 . 

40. 	
Whittaker, R. H. 1961. "Experiments with Radiophosphorus Tracer in Aquatic Microcosms." Ecol. Monog. 31: 157-188. 

41. 	
Whitworth, Walter R. and Thomas H. Lane. 1969. Effects of Toxicants on Community Metabolism in Pools." Limno. & Oceanog. 14 (1): 53-58. 

42. 	
Bureau of Commercial Fisheries 1964. Fishery Research, Biological Laboratory, Galveston. Fiscal Year 1964, Circular 230. 


II-92 II-93 
II-94 

LIST OF TABLES No. Title 
II-1 Light Intensity and Salinity Regimes for Galveston Bay Microcosms II-2 Summary of Productivity Sampling Effort in Galveston Bay II-3 Environmental Limits for Nine Important Species in Galveston Bay II-4 Seasonal Environmental Requirements of Four Species in Galveston Bay II-5 Mass Discharge of Organic Carbon to Galveston Bay, Texas II-6 Daily Flux of Organic Material in Galveston Bay, Texas JI... 7 Dominant Genera of Phytoplankton in Galveston Bay during 1969 II-8 Growth of Dunaliella in Galveston Bay Water Collected April 25-27, 1972 II-9 Phytoplankton Production at Station 14 in Galveston Bay, Texas II-10 Phytoplankton Production at Station 17 in Galveston Bay, Texas II-11 Phytoplankton Production at Station 22 in Galveston Bay, Texas II-12 Phytoplankton Production at Station 26 in Galveston Bay, Texas II-13 Phytoplankton Production at Station 29 in Galveston Bay, Texas II-14 Light Absorption Coefficients from Submarine Photometer at Station 14 in Galveston Bay (m-1) II-15 Light Absorption Coefficients from Submarine Photometer at Station 17 in Galveston Bay (m-1) II-16 Light Absorption Coefficients from Submarine Photometer at Station 22 in Galveston Bay (m-l) II-17 Light Absorption Coefficients from Submarine Photometer at Station 26 in Galveston Bay (m-1) 
II-95 
LIST OF TABLES (Cont. ) No. Title 
II-18  Light Absorption Coefficients fr~T: Submarine Photometer  
at Station 29 in Galveston Bay (m  )  
II-19  Light Absorption of Station 14 Water  
II-20  Light Absorption of Station 17 Water  
II-21  Light Absorption of Station 22 Water  
II-22  Light Absorption of Station 26 Water  
II-23  Light Absorption of Station 29 Water  
II-24  Mean Microcosm Temperatures During Productivity Analysis  
II-25  Chlorophyll 11 a11 Concentrations in Microcosm Water Summer  
Influent  
II-26  Nutrient Regimes in Microcosms Receiving Galveston Bay  
Inflow  
II-27  Metabolism Relationships During Experimental Influent  
Periods  
Il-28  Summary of Nutrient Mass Inputs to Galveston Bay,  Texas  

II-96 

LIST OF FIGURES 
No. Title 
II-1  Hypothetical Diurnal Oxygen Curve Used to Illustrate Three  
Point Microcosm Productivity Method  
Il-2  Experimental Design for Constant Flow Microcosms  
II-3  Explanation of Terms for Organism Location  
II-4  Chlorosity at Station 38 as Influenced by Flow From  
Trinity River  
II-5  Total Commercial Catch Annual Variations with Freshwater  
Inflow (1959-1964)  
II-6  Phytoplankton Concentrations (#/ml) in Galveston Bay  
February 18-27, 1969  
II-7  Phytoplankton Concentrations (#/ml) in Galveston Bay  
April 15-24, 1969  
Il-8  Phytoplankton Concentrations (#/ml) in Galveston Bay  
July 14-18, 1969  
II-9  Phytoplankton Concentrations (#/ml) in Galveston Bay  
October 14-17, 1969  
II-10  Algal Assay of Galveston Bay Waters  
II-11  Phytoplankton Production in Galveston Bay  
II-12  Light Absorption at Station 17 in Galveston Bay  
II-13  Distribution of Light Absorbance in Galveston Bay  
October, 1971  
II-14  Percent Transmission in Galveston Bay Water, Station  
14, October 26-28, 1971  
II-15  Diurnal Oxygen Fluctuation in Galveston Bay Microcosms  
(August 17-18, 1972)  
II-16  Mean Gross Production нMean Total Respiration Relationн 
ships in Aquarium Microcosms  
II-17  Relationship between Gross Production and Influent Nitrate  
II-18  Relationship between Gross Production and Influent  
Phosphate  
II-97  





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DIRECTION 
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PRODUCTION 
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I 
20 24 CYCLE 
GLASS-TYGON TUBING NETWORK INFLUENT SUPPLY (20 liters) о STATION 14 VARIABLE-FLOW PUMP LIGHTING FIXTUREUr-------JTWIN 40-WATT FLUORESCENT BULBS AQUARIUM CONTAINING 45HtersWATER SEDIMENT MICROCOSM о PETRI DISH STIRRING BAR AIR-POWERED MAGNETIC STIRRER TO WASTE CONTAINER  
GALVESTON BAY PROJECT TOXICITY STUDIES  EXPERIMENTAL DESIGN FOR CONSTANT FLOW MICROCOSMS  FIGURE 2  

SECONDARY BAY PRIMARY BAY TIDAL PASS CONTINENTAL SHELF\ OCEAN 200 METERS DEPTH  
GALVESTON BAY PROJECT TOXICITY STUDIES  EXPLANATION OF TERMS FOR ORGANISM LOCATION  FIGURE 3  

10 ~ ' C' 5 E 0 0 """" >н2 ~ CJ) 0a:: 0 _J ::c: (.) .5 .2  FLOW ( 2,000 cfs)  
 
GALVESTON BAY PROJECT TOXICITY STUDIES  CHLOROSITY AT STATION 38 AS INFLUENCED BY FLOW FROM TRINITY RIVER  FIGURE 4  

30 J'\ Х GALVESTON BAY A MATAGORDA BAY,' ' 6 SAN ANTONIO BAY I \ o ARANSAS BAY I \ o CORPUS CHRISTI BAY ..,...... \25Q) 0 \._ 0 c ' fl) o'~ \ '-' \20 :c (.) ..... <t AB(.) ...J <t 15 (.) a= LLJ ::E ::E 0 10(.) ...J <t ..... 0 ..... 5 \ \ \ Х ' ' ' ' Х Х DISPLACEMENT RATE (year-I)  
GALVESTON BAY PROJECT TOXICITY STUDIES  TOTAL COMMERCIAL CATCH ANNUAL VARIATIONS WITH FRESHWATER INFLOW ( 1959-1964)  FIGURE 5  


PHYTOPLANKTON CONCENTRATION
GALVESTON BAY 
FIGURE
IN GALVESTON BAY ( no ./ml.)
PROJECT 
FEBRUARY 18-27, 1969 
TOXICITY STUDIES 

PHYLOPLANKTON CONCENTRATION
GALVESTON BAY 
FIGURE 
IN GALVESTON BAY {nojml.}
PROJECT 
APRIL 15-24, 1969 
TOXICITY STUDIES 

PHYTOPLANKTON CONCENTRATIONS
GALVESTON BAY 
FIGURE
IN GALVESTON BAY {no./ml.)
PROJECT 
JULY 14-18, 1969 
TOXICITY STUDIES 

GALVESTON BAY 
PROJECT 
TOXICITY STUDIES 

PHYTOPLANKTON CONCENTRATION 
FIGURE 
IN GALVESTON BAY (no. /ml.) OCTOBER 14-17, 1969 
'  . .  I  I  I  .  '  I  I  I  I  '  r--: 0  
- >н5Х+╖~╖...,  Х  <D-0  
-Х "" Х  _J g: 0 0 <l<> <( z 0 J-v I'-C\J <D (1) <( --NC\JC\J Jн(/)  0 _J ~ J-z 0 u  Х 0  +  <l  Х  0  ~  lO-0 v. 0 rt>-0 C\J-0  ,,...._ -'C' E __, (.) z 0 () a.. I LU Iн<t :I: a.. en 0 :I: a..  
~  0  01╗ 0  0 0 00 0  0  <> 0  0  -. 0  
~ - I rt) - I C\J - I -- I q- I (1)  I CD.  I I'н I ~  I lO  I v.  I ~  I C\J  I  0 0  
( 1_p ) 3.1.'1~  H.1.M0~А)  

GALVESTON BAY PROJECT TOXICITY STUDIES  ALGAL ASSAY OF GALVESTON BAY WATERS  FIGURE 10  

STATION 17 JAN. 27, 1972 ( 1245-1805) GROSS PRODUCTION (g/m3 /d) RESPIRATION ( g/m3 Id) 0o,_.-------,2r---------..4------_;.6______~8 :I: ..... a. LLJ c 8 9 10---------.________L--------L--------I -4 -2 0 2 NET PRODUCTION (g/m3/d) PG= 4.9 g 0 2 /m2 /d PN =-0.5 g 02 I m2 Id R= 5.42 g 0 2 /m 2 /d 4  
GALVESTON BAY PROJECT TOXICITY STUDIES  PHYTOPLANKTON PRODUCTION IN GALVESTON BAY  FIGURE II  

100-----------------------------~--------------------н........ ~ 0 """' (!) z z <t :i UJ a:: tн:I: (!) _J t-z lJ.J (.) a:: UJ a. 50 20 10 5 2 .5 .2 JANUARY 27, 1972 1300 hrs. .1--_...._.__._~.............__._._---L.._.__._.1.......JL.-L--L-'--'-~IM-JL-.L~--'-~ 0 5 10 15 20 DEPTH (ft) 25  
GALVESTON BAY PROJECT TOXICITY STUDIES  LIGHT ABSORPTION AT STATION 17 IN GALVESTON BAY  FIGURE 12  


DISTRIBUTION OF LIGHT ABSORBANCE
GALVESTON BAY 
FIGURE
IN GALVESTON BAY
PROJECT 
OCTOBER, 1971 
TOXICITY STUDIES 
0 w (!) :::> LL 0:: ~ z w (.) 0 0 0 O'> 0 a:>  0,.._ 0 <D 0 lO 0 v 0 LtJ 0:: LtJ ~ ...J 0 rt) 0 C\J 0 LtJ 0:: w ~ LL z :::> 0 ( 0/o) NOISS I~SN\f~l. l.N3~~3d 0 0 0 CD 0 0 0,.._ 0 0 0 <D 0 0 0 lO 0 0 0 v 0 0 0 rt) 0 0 0 C\J 0 ...-.. Х<t-::c ..... (!) z LI.I ....I LI.I ~ ~  
GALVESTON BAY PROJECT TOXICITY STUDIES  PERCENT TRANSMISSION IN GALVESTON BAY WATER, STA. 14 OCTOBER 2s-2╖a, 1971  FIGURE 14  

10  
5  
0  
10  
....... 'ca E """"' z LLJ (!) >нx 0 c LLJ >-' 0 CJ) CJ) Ci  5 0 10 5 0 10  
5  
0  
10  
5  
0 0  2 4 6 8 LIGHTS ON  10  12  14  16 18 LIGHTS  20 OFF  22  24  

0 
о 
й 

о 
о 

DIURNAL OXYGEN FLUCTUATION IN
GALVESTON BAY 
FIGURE
GALVESTON BAY MICROCOSM
PROJECT 
AUGUST 17-18, 1972 
TOXICITY STUDIES 
2.0 z 0 tн1.5 u ::> 0 0 -~a:: 0a.. "C 1.0' CJ) NCJ) E0 a:: '(!) O'-0.5z <( w ~ 00 2.0 z 0 t-u ::> 1.50 0 a:: ╖a.. -~ CJ) 0 1.0CJ) "C 0' a::N <!> E 'z O' <( -0.5w ~ 0o  SPRING INFLUENT 0.5 1.0 1.5 2.0 MEAN TOTAL RESPIRATION ( g/m2/day) SUMMER INFLUENT MICROCOSMS 0 A 0 B 6 cХ DХ E 0.5 1.0 1.5 2.0 MEAN TOTAL RESPIRATION ( g/m2/day)  
GALVESTON BAY PROJECT TOXICITY STUDIES  MEAN GROSS PRODUCTION-MEAN TOTAL RESPIRATION RELATIONSHIPS IN AQUARIUM MICROCOSMS  FIGURE 16  

z 0 2.0Х tн1.5 s g ~ c Х0 ......... a: ~ a. .g ~ CJ)~ 1.0 Х en Eo, a: C\I (!) 0 z C) ~ ._, 0.5Х ~ I 0.5  I 1.0 I ХХ 1.5 INFLUENT NITRATE-N (mg/I) e SPRING & SUMMER I 2.0  
GALVESTON BAY PROJECT TOXICITY STUDIES  RELATIONSHIP BETWEEN GROSS PRODUCTION AND INFLUENT NITRATE  FIGURE 17  

z 0 ..... (.) :::> 2.0"'" Х 0 _...... 1.5~0 ~ ~ c ~ -0 a.. ' N CJ) E ~ CJ) ' ~ N 1.0 Х (.!) 0 z~ <t w ~ 0.5~ I 0.2  ХХ Х I Х I I 0.4 0.6 0.8 1.0 INFLUENT PHOSPHATE (mg/ I)  
GALVESTON BAY PROJECT TOXICITY STUDIES  RELATIONSHIP BETWEEN GROSS PRODUCTION AND INFLUENT PHOSPHATE  FIGURE 18  

APPENDIX Il-1 
MICROCOSM EXPERIMENTAL DATA 

II-98 

\. \ 0 w 0. 0. 0 IнCJ) a:: w a:: a:: IнCJ) --+ \. \ I I I 0 0 '░ 0 '░ 0 '░ 0 '░ 0 '░ '░ 0 ~ '░ (\J q ~ '░ (\J 0 "': ~ ,,, (\J (\J (\J (\J -0 (,(DP/zW/GO D ) NOll.~nao~d SSO~El \. \ '\  0,,, '░ (\J 0 (\J -"' ~ c "C ""' '░ LaJ ~ Iн0  
GALVESTON BAY PROJECT TOXICITY STUDIES  DAI LY GROSS PRODUCTION DURING SPRING GALVESTON BAY INFLUENT AQUARIUM E-STATION 29  FIGURE APPENDIX  

0 q rt) q C\J  IO ~. 0 IO IO C\J 0 0 Cl w ~ ~ 0 tн(/) a:: w a:: a:: tн(/) 0 IO d IO C\J 0  IO C\J 0 C\J ........_. (/) IO ~ c "'C-LLJ ~ Iн0  
GALVESTON BAY PROJECT TOXICITY STUDIES  DAILY GROSS PRODUCTION DURING SPRING GALVESTON BAY INFLUENT AQUARIUM C -ARTIFICIAL SEA WATER  FIGURE APPENDIX  

-+ --+ -+ -+ 0 I() C\I &O C\I C\I 0 0 C\I  0 I() 0 0 I(),_... 0 0 w a.. a.. 0 I-en a:: wa:: a:: I-en -+ 0 I() 0 I() C\I 0  0 "' I() C\I 0 C\I ~ U) ~ c "'O I() -LLJ ~ Iн0  
GALVESTON BAY PROJECT TOXICITY STUDIES  DAILY GROSS PRODUCTION DURING SPRING GALVESTON BAY INFLUENT AQUARIUM A -STATION 26  FIGURE APPENDIX  

0 ~ (\J I() (\J (\J 0 q (\J 0 I() I() (\J 0 q 0 w CL CL 0 ~ CJ) a:: w a:: a:: ~ CJ) -+ 0 I() 0 I() (\J 0 0 rt) I() (\J 0 (\J """ (/) >. c I() -0 '-' LLJ :E tн0  
GALVESTON BAY PROJECT TOXICITY STUDIES  DAILY GROSS PRODUCTION DURING SPRING GALVESTON BAY INFLUENT AQUARIUM B нSTATION 14  FIGURE APPENDIX  


 0 w a.. a.. 0 .... en a:: w a:: a:: .... en ..... 0 lO 0 0 rt> lO (\I 0 (\I lO 0 ,....... fl) ~ c -c ._,_,,, w ~ Iн 
GALVESTON BAY PROJECT TOXICITY STUDIES  DAILY GROSS PRODUCTION DURING SPRING GALVESTON INFLUENT AQUARIUM D-STATION 22  FIGURE APPENDIX  

r--------........--------r--------..-ff,,______--r-________,,_________ ~ 


C\J

g c: +: .2 
0 +н
~ c 
"O ╖= 
0 ~ 
... (/) 
a.. Q) 
a: 
(/) н
0
(/) c 
0 +н
... 0 
<!> ~ 
0 0 

C\J 
0 0 rr> C\J 
(~DP/ zW/D) NOll'1CildS3~ 1'1101. No11onaoe1d sso~~ 

TIME SERIES AVERAGED COMMUNITY
GALVESTON BAY 
FIGURE
METABOLISM FOR MICROCOSM A
PROJECT 
APPENDIX
(STATION 26) SUMMER INFLUENT 
TOXICITY STUDIES 

C\J 
0 

C\J 
0 
TIME SERJES AVERAGED COMMUNITY
GALVESTON BAY 
FIGURE
METABOLISM FOR MICROCOSM C
PROJECT 
(ARTIFICIAL SEA WATER) 
APPENDIX 
SUMMER INFLUENT
TOXICITY STUDIES 
t-zww3н:ELL enzN(/)_ o a::z U WO 0 :Ei=5 :E~ -:E :::>en(/)....;,, ( ADP/ zW/f>) NOIJ.'t~ldS3~ 1'1101 No11onao~d sso~~ 0 c c 0 0 ;+: (,) 0 ::J'tJ ╖= Q. o en ... Q) a.. a:: en -en o o~ CiJ ~ 0 0 LO 0  LO v f') C\J 0 en ,,.... co (f) ~ c "'C ~ ~ w :E U) ..... LO C\J 0  
GALVESTON BAY PROJECT TOXICITY STUDIES  TIME SERIES AVERAGED COMMUNITY METABOLISM FOR MICROCOSM E {STATION 29) SUMMER INFLUENT  FIGURE APPENDIX  


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GALVESTON BAY 
PROJECT 

TOXICITY STUDIES 

TIME SERIES AVERAGED COMMUNITY 
FIGURE
METABOLISM FOR MICROCOSM B (STATION 14) SUMMER INFLUENT APPENDIX 

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PROJECT 
APPENDIX
(STATION 22) SUMMER INFLUENT 
TOXICITY STUDIES 
>н.... CJ) z w c ...J <( (.) tнa. 0 >н.... CJ) z w c ...J <t (.) tнa. 0  SPRING INFLUENT ____f'll. --н15:s.-----=-.:-::__ -"9--..: ~:0 ╖-╖-╖-╖-╖..... .01'--~~--L~~~---'~~~~.&-~~~~~~~--~~----н1 5 10 15 20 25 DAY NUMBER SUMMER INFLUENT F╖-o...-. .. -╖'"()-╖-╖нo-н-_.t:)--н-н-------<r---н----н30 .01,_______._~~-----'-~~~---1~--~~...._~--~-----------...... I 5 10 15 DAY NUMBER 1 Optical Density Measured With Bal Spectronic 20 н420 m╡нWavelengt-h Through 1.9 cm Pathway 20 MICROCOSM A o--o B o---0 c 0-╖--0 D~ E l::r--6 25 30  
GALVESTON BAY PROJECT TOXICITY STUDIES  OPTICAL DENSITIESI OF MICROCOSM WATER-SPRING AND SUMMER INFLUENT  FIGURE APPENDIX  


CHAPTER III 
Shrimp Bioassay 
of 
Galveston Bay Waters 

by 
K. 	G. Gordon, J. Gillespie, W. B. Brogden and C. H. Oppenheimer 
University of Texas 

Marine 	Science Institute Port Aransas, Texas 
The purpose of this investigation was to determine the gross toxic effects, if any, of the native waters obtained from the five areas of relative stability (ARS) (See Chapter l for station locations) upon the respiratory metabolism of the penaeid shrimp, Penaeus setiferus and P. aztecus. To date, most progress in toxicity evaluations has been largely centered around the chemical and bioassay determinations of an acute tolerance median (T1m) i.~. that concentration of a given material in which 50% of the experiнmental organisms survive a specified test period, usually more than 72 hours in duration and in fresh water utilizing freshwater species. While the employment of the standard bioassay technique is acceptable for acute concentration levels, it is not capable of delineating the effects of sublethal toxic concentrations. 
Often times, it is desirable to ascertain the immediate, delayed and/or long term effects of sub.lethal concentrations of individual toxins or groups of toxins. Moreover, it is likewise desirable to resolve the synergistic effects of sublethal toxins, especially as they occur in situ. It is with these concentration levels in mind that this DToassay was developed, for these are the concentrations which are potentially capable of altering physiological parameters, many of which may not become apparent until future generations. 
The multiple and varied sources of pollution (e.g. industrial and domestic wastes, herbicides and pesticides, heated-effluents, severe and prolonged temperature extremes, fresh water flushes) are recognized as distinct stresses upon a community, although their environmental impact may sometimes go unnoticed by conventional monitoring techniques. If an estuarine system, such as the Galveston Bay area, were subjected to an energy demanding stress, the energy required to satisfy that expenditure must be removed from components of the energy budget normally set aside for species growth, reproduction, diversification and daily metabolic functioning. The range and ultimate survival of a species then is, in large part, determined by the species tolerance to energy stress and the subsequent adaptive response to the environmental change. The 
individual as well as the species must adapt, move or die if it is subjected to a lethally stressing environment. 
The mobility and metabolic response to a "toxic factor" then, is a key to the adaptive response of the individual. An increase in active metabolism (as oxygen consumption) is directly dependent upon the level of activity; the higher the activity level, the higher the oxygen consumption. Nonlocomotor activity, such as labored active respiration in the presence of a respiratory stimulator, may also be reflected as a metabolic increase. It is with these thoughts in mind, that this technique for measuring activity and respiration of an individual in suspected toxic waters was developed. 
Recent publications (Waller and Cairns, 1972; Sparks, Cairns, and Waller, 1972) have indicated that fish movement can be used to detect sublethal stress due to pollutants. The recording of locomotor activity in small aquatic animals presents difficulties because the water movements produced are too weak to be detected by devices previously described (Heusner and Ruhland, 1959; Spoor, 1946). Optical devices have been used (Gunzler, 1964) but this procedure interferes with light conditions during the experiment. Heusner and Enright (1966) eliminated this difficulty by detecting variations in heat conductance associated with slight water movements using a hot wire thermister device. 
Respiration response to sublethal stress has been measured for a number of organisms. Examples of this type of work were conducted at the University of Texas Marine Science Institute at Port Aransas by Steed and Copeland (1967) and Cech (1970). Oxygen determinations in experiments of this sort are made by Winkler titration and/or amperometric sensors like the polarographic probe introduced by Clark (1956). The operating principle of the Clark probe has been described by Phillips and Johnson (1961) and by Kinsey and Bottomly (1963). Todt (1958) and Johnson et al. (1964) experimented with soнcalled "self-generatingn probes that might be used in aquatic systems. 
Gordon and Oppenheimer (1970) designed a system utilizing an amperometric system capable of long-term monitoring of dissolved 02 and temperature in mariponds. 
After making certain design improvements, i.e., stable power supply and frequency response, on the activity sensor of Heusner and Enright (1966), it was combined with an 02 monitoring system so that simultaneous measurements of activity, temperature, and 02 consumption could be made under controlled conditions of light, temperature, salinity, and time. The instrumentation was used in a controlled temperature room and test animals were acclimated for a minimum of 24 hours prior to each experiment. (Note: The system electronics description is not included in this report. However, technical details can be provided upon request.) A block diagram of the system is shown in Figure 1. 
III-2 
H H H 

Water management was controlled by a variable speed peristaltic pump and manifold system that allows variation in flow rate and direction. Oxygen consumption was determined by recording the inflowing and outflowing 02 tension and temperature on a multiнchannel recorder after signal conditioning and amplification. Activity signals were recorded in analog form after amplification, or digital form after passing through a modified Heusner and Enright (1966) integrator and a base-twelve counter-splitter designed at the Institute. 
Penaeid shrimp were periodically collected by trawling in estuaries in the area surrounding Port Aransas and placed in a holding pond covered to maintain salinity regimes and inhibit algae blooms. This pond was continuously fed with clarified channel water, recircuнlated, aerated and filtered through the sediments to prevent excessive anaerobic activity. During the winter months, a hot water tank Stlpplied the pond with recirculating, heated salt water. Temperature and salinity were adjusted and monitored so as to remain in the normal range of the surrounding estuaries throughout the year. While housed in this pond, the shrimp were observed daily and fed a high energy protein, vitamin enriched, pelletized food developed by Oppenheimer and Subrahmanyam (1971). 
Shrimp so maintained were then removed to the controlled environнmental room in which the experimental testing equipment and acclimation tanks were located. Ambient room temperature was maintained at 23.0░ C (+ l.o0c); waterbath temperature was 23.o0c+o.2s0c. ~ Photoperiod was regulated as close to the naturally occurring diel cycle as possible. 
Salinity acclimation was a minimum of 24 hours in the salinity of the selected ARS station salinity. In those cases where there was a 10% or greater difference in the holding tank salinity and the salinity of the test station, the salinity was adjusted 5% (with Instant Ocean or distilled water) for 24 hours until the ARS salinity was reached. The primary holding tank salinity was 27░/oo below the upper limit for f. aztecus (Copeland & Bechtel, 1971). Experimental organisms were placed in acclimation tanks of this salinity at first introduction into the controlled temperature room prior to further testing. In one instance, i╖~╖ the winter runs at Station 26, the organisms were not able to be repeatably maintained at the low salinity level present at that station. Several runs were attempted with the waters as they were obtained from the field. However, all of the organisms died within a twelve hour period. Salinity of this water was adjusted to 24 ░/oo with the addition of a prepared sea salt, Instant Ocean, to determine if the original water was too fresh for the organisms tested. 
Animals were weighed, measured (tip of the rostrum to the end of the telson and sex determined prior to each of the 24 hour runs. Salinity was measured with a direct reading refractometer and noted on the recorder charts. Behavioral activity was periodically noted 
III -4 

several times throughout the 24 hour period and any abnormal activity such as loss of equilibrium and disorientation was noted. 
Base (i,.~. rrzero1') rate was determined by observing a period in which the test animal was resting in the chamber and exhibiting no visible sign of swimming or locomoter activity. Whenever the animal began to swim, move around, or make a tail thrust, the recorder pen displacement was annotated. The delta pen displacement between the base and active values was defined as a nstroke". During a given run, whenever the recorder pen was displaced by a value equal to or in excess of the delta, it was tabulated as a "stroke". At the end of the 24 hour test period, activity and oxygen recordings were manually reduced and transcribed. Oxygen measurements were taken over a ten minute period and an average rate was recorded for that time interval. Total activity measurements (strokes above basal rate) were likewise tabulated and recorded for the same ten minute time period. These two measurements of activity and oxygen were then synchronized by shifting the activity up ten minutes to compensate for the lag time in oxygen measurements at the outflowing oxygen probe. (A dye analysis of water movement within the experimental chamber demonstrated a lag time of 8 to 11 minutes, flow rate dependent.) Flow rate was determined by monitoring the volume at the outflow with respect to time. Optimum flow rate for the size class individuals was determined by observing that rate at which the animals appeared least disturbed, i,.~. did not actively swim against either the inflow or outflow baffles in nclean" waters. 
Computer processing of shrimp respiration and activity data 
The measurement of shrimp respiration and activity for periods of 24 hours or more produced hundreds of data points. A FORTRAN IV program RESPAC was written by W. B. Brogden to read this data and convert it into usable information. This program was operated on the University of Texas timesharing computer network, TAURUS, in a conversational mode from the Marine Science Institute's teletype terminal. The main body of the raw data consisted of the concentration of oxygen in the inflowing water, the concentration in the outflowing water, and the total activity during the previous 10 minute period. This raw data was punched on paper tape, transнmitted to the central computer, edited, and stored on magnetic tape "permanent files". 
During data reduction using RESPAC, the operator fed in the following information: the saturation concentration of oxygen (or the temperature and salinity so the computer could compute the saturation value) the weight of the animal, the pumping flow rate, and the time interval per data line. The raw data was then read from the file, checked for consistency and gross errors, and used to compute the respiration rate. Suspect data was printed out on the teletype and corrected by the operator. Additional computations were performed to compute the mean maximum, minimum and standard 
III -5 
deviation of both respiration and activity, the product moment correlation coefficient and the least squares fit linear regression equation. The operator could select three different types of graphic output to summarize the data; 1) a plot of activity and respiration versus time, 2) a histogram of the observed occurrences of 10 classes of respiration and activity and 3) a point by point plot of respiraнtion versus activity and the least squares fit line. Examples of these outputs are shown in Figures 2, 3, and 4. 
Results 
A total of 92 separate experimental runs were made with control seawater and samples from Galveston Bay. Although there was frequently a significant correlation of respiration with activity, this correlaнtion was not always evidenced, in fact, there were several patterns in which the correlation was negative. The general pattern of observations is summarized in Figure 5 which contains the data from all seasons. Figures 6 through 12 summarize the data graphically. Figure 12 shows all respiration runs, plotting respiration as a function of salinity; jt appears that there is no overall relation between salinity and respiration. 
Discussion 
Examination of the relation between activity and respiration indicated that, in general, the slope of the regression was small, in other words, the average respiration was not greatly different from the predicted zero activity respiration (the intercept on the activity axis in Figure 4). In addition, there was great difficulty in maintaining a satisfactory standard between runs, so although it is interesting to note that the average activity for control shrimp was higher than any of the shrimp exposed to Galveston Bay water (Figure 5), it is not felt that significance should be attached to this without further statistical analysis. 
An additional difficulty in interpretation is caused by the fact that in the winter season, the available shrimp tended to be smaller and due to various instrumental difficulties no control runs with small shrimp were conducted. Table l summarizes the respiration data, excluding the four shrimp smaller than 8.0 grams, by station and season. The possibility of doing statistical tests for significance of the differences between stations and seasons was investigated, however, it appears that standard tests can not be applied because of thenonheterogeneity of variance between stations and seasons. These results are therefore discussed on a qualitative basis beginning with a comparison between controls and experimental respiration rates. 
III -6 

5032761 


o.o Fig. 2.  TIME MIN 2s a. Plot of activity and respiration versus time. Respiration in mg of oxygen per wet gram of shrimp and activity in strokes per minute per shrimp.  
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Fig. 3. 	Histogram of observed occurrences of respiration and activity. Respiration in mg of oxygen per wet gram of shrimp and activity in strokes per minute per shrimp. 
III -7 

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Figure 4. 	Respiration plotted versus activity with least squares regression line. 
Figure S. 	This figure summarizes 92 individual shrimp respiration and activity measurement runs, the controls ncn and 5 stations for all four seasons. The upper section represents the average activity by the short horizontal bar, the standard deviation of average activity (between runs) by the vertical line, and the average variance of activity within runs .by the closed circle. The activity scale represents 11strokes11 per 10 minutes. 
The lower section represents the average respiration in 
mg of oxygen per gram wet weight per hour by the short 
horizontal 	bar, the standard deviation (between runs) 
by the vertical line, and the average variance of 
respiration (within runs) by the closed circle. 
III -9 

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III-10 
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Figure 6.  Average respiration in mg o2I gm-hr for the control shrimp  
is plotted against weight in grams as the square symbols,  

from 0. 0 to 5. 0. The standard deviation of respiration withнin each run is plotted as the + symbol from 0. 0 to 1. 0. The scales are divided into ten equal portions for all of these computer drawn plots. 
Figure_s 7, 8, and 9. Similar plots are drawn for all shrimp for stations 14, 1 7, and 22 respectively. 
III -11 
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Figures 10,  and 11.  Average respiration in mg o2/ gm-hr for stations 26,  
and 29 respectively is plotted against weight in grams as the  
square symbols,  from 0. 0 to 5. 0.  The standard deviation of  
respiration within each run is plotted as the + symbol from  
0. 0 to 1. Q.  The scales are divided into ten equal portions for  
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Figure 12.  The relationship of average respiration to the salinity of the  
water,  either natural or adjusted.  
III  -12  

Table 1.  Summary of shrimp respiration measurements shrimp weighing less than 8 grams.  excluding  
Station  Season  
Fall  Winter  Spring  Summer  

n= 5 l 	8 3 
Controls 	wt= 26.2 37.9 33.5 28.8 r= .330 1.21 0.463 0.447 s= 0.206 0.378 0.284 d= 0 0 0 0 
n= 4 3 l 	3 
14 	wt= 21. 5 11.5 30.6 22.3 r= 0.516 l.353 0.29 0. 64 7 s= 0.067 0.53 0.149 d= 2 l 0 0 
n= 2 4 2 	3 
17 	wt= 24.8 17.4 36.6 29.8 r= 0.309 0.787 0.146 0.268 s= 0.281 0.128 0.115 0.155 d= 0 0 0 0 
n= 8 8 	3 4 
22 	wt= 29.6 9.58 36.0 35.2 r= 0.320 1.694 0.287 o.268 s= 0.255 1.020 0.104 0.300 d= 4 3 0 0 
n= 5 3 2 	3 
26 	wt= 24.2 12.7 51.6 29.3 r= 0.324 o.533 o.243 0.183 s= 0.131 0.444 0.128 0.035 d= 0 2 0 0 
29  n= wt= r= s= d=  2 21.4 0. 503 0.035 0  4 21.4 0.489 0.072 0  3 31. 7 0.357 0.066 0  4 24.1 0.294 0.133 0  
n= wt= r= s= d=  nwnber of experimental animals the average weight the average respiration rate in mg 02/gm-hr the standard deviation of respiration rate the number of experimental animals which died within 48 hours of the end of the experiment  
III  -13  

Station 29 appears to be different from other bay stations in that the average respiration stays near the same value throughout the year and the standard deviation of respiration rate between runs is usually smaller than the other stations. This is interpreted as relative stability and apparent lack of toxicity. 
Station 26 shows reasonable similarity to station 29 and the controls during fall and spring, with relatively depressed respiration in the summer. The most interesting results were obtained with the winter water samples, which had a salinity of less than 2 ppt; the two experimental animals exposed to this water died within 12 hours. This water was made up to 24 ppt salinity with TTinstant Ocean11 , and two more shrimp were exposed; these survived for the duration of the experimental run. One of these shrimp weighed less than 8.0 grams and equipment malfunction prohibited further experimental runs, hence only three runs are averaged in the table. Because of the low salinity it was impossible to determine if other toxic factors were present at station 26 in the winter. 
Station 22 gave dramatically higher respiration rates in the winter samples, as well as three deaths within 48 hours. Some of these organisms were run at the naturally occurring salinity of 9 ppt and some were run with the salinity raised using ninstant Oceann. Those with raised salinity had approximately the same respiration as those in the normal water. These results are interpreted as indicating toxicity at station 22 during the winter sampling run. During the other three seasons, the respirations resemble the controls and other stations but the 4 deaths out of 8 organisms in the fall samples were apparently due to a toxic factor. 
Station 17 shows apparent depression of respiration during the spring, unfortunately, instrumental difficulties prevented running more than two shrimp with this sample. Respiration during the winter appears to be high, but the significance of this is uncertain. 
Station 14 during the fall resembles the controls and station 29 but shows high respiration during the winter and summer. Again, the significance of this is unclear. Two animals died during the fall and one during the winter. 
The control respirations averaged about the same for all seasons but the variability between runs was unexpectedly high. It is this high variability which makes statistical interpretation difficult and has reduced the potential value of these measurements for toxicity detection. 
III -14 

Conclusion 
Although the high variability between animals exposed to the same water has made it difficult to distinguish between toxic effects and variability of the animals, there appears to be qualitative evidence for toxicity at station 22 during fall and winter, and indications of stability and lack of toxicity at station 29. 
It appears that if this type of toxicity assay is to be of further use in pollution research, better control of the sources of variability will be required. 
References 
Cech, 	J. J. 1970. Respiratory responses of the striped mullet, Mugil cephalus, to three environmental stresses. Master's thesis, Univ. Tex. at Austin. 115 p. 
Clark, L. c., Jr. 1956. Trans. Am. Soc. Artificial Internal. Organs. 
2: 41. 
Copeland, B. J. &T. J. Bechtel. 1971. Some environmental limits of fix important Galveston Bay species. Cont. 20 Pamlico Mar. Lab. N.C. State Univ. 
Gordon, K. G. &C. H. Oppenheimer. 1970. A multi-channel, modular input, chart recording system for use in mariponds. Univ. Tex. Mar. Sci. Inst. (Mimeographed) 41 p. 
Gordon, K. G., W. B. Brogden &J. s. Holland. 1972. A preliminary toxicity analysis of the sediments of La Quinta Channel, Corpus Christi Bay, Texas: A report prepared for the U.S. Army Engineer District, Galveston, Texas. Univ. Tex. Mar. Sci. Inst. (Mimeographed) 25 p. 
Gordon, K. G. & C. H. Oppenheimer. 1972. An instrumentation system for measuring animal respiratory metabolism and activity in polluted estuarine waters. (Non-published). 
Gunzler, E. 1964. Biol. Zentralbl. 83: 677. 
Heusner, A. A. & M. L. Ruhland. 1966. J. Physiol. Paris. 51: 580. 
Heusner, A. A. &J. T. Enright. 1966. Long-term activity recording in small aquatic animals. Science. 154: 532-533. 
Johnson, M. J., J. Borkowski &c. Engblom. 1963. Biotechnology and Bioengineering 6: 456-468. 
III -15 
Kinsey, D. W. &R. A. Bottomley. 1963. J. Inst. Brewing 69: 164. 
Oppenheimer, C. H. &C. B. Subrahymanyam. 1970. Food preference and growth of grooved Penaeid shrimp. In Mar. Tech. Soc. Proc. Food and Drugs from the Sea. pp.~5-75. 
Phillips, D. H. &M. J. Johnson. 1961. J. Biochem. Microbiol. Technol. Eng. 3: 261. 
Sparks, R. E., J. Cairns, Jr., &w. T. Waller. 1972. Using fish as sensors in industrial plants to prevent pollution in streams. In Abstracts of papers submitted for the thirty-fifth annual meeting. Am. Soc. Limn. and Oceano., Inc. 
Spoor, W. A. 1946. Biol. Bull. 91: 312. 
Steed, D. L. &B. J. Copeland. 1967. Metabolic responses of some estuarine organisms to an industrial effluent. Contr. Mar. Sci. Univ. Tex. 12: 143-159. 
Subrahmanyam, c. B. &C. H. Oppenheimer. 1970. The influence of feed levels on the growth of grooved Penaeid shrimp in mariculture. In Proceedings of the First Annual Workshop World Mariculture Soc. pp. 91-95. 
Todt, 	F. (ed.) 1958. Electrochemische Sauerstoffmessungen. de Gruyter, Berlin. 
Waller, W. T. &J. Cairns, Jr. 1972. The use of fish movement patterns to monitor zinc in water. Water Research 6: 257-269. 
III -16 

CHAPrER IV 
Respiratory Metabolism of the Striped Mullet as an Assay of Low Level Stresses in Galveston Bay 
Donald E. Wohlschlag 
University of Texas 
Marine Science Institute 
Port Aransas, Texas 78373 
For a portion of a study under Contract IAC (72-73)-183 
Texas Water Quality Board, 11 Toxicity Studies Galveston 
Bay Project,n C. H. Oppenheimer, Principal Investigator 

INTRODUCTION 
The purpose of this study is to elucidate the extent of subнlethal, chronic effects of Galveston Bay stresses that affect the respiratory metabolism of the striped mullet, Mugil cephalus,over and above effects of nnaturaln environmental stresses. 
On the assumption that respiratory metabolism is directly related to both growth and maintenance requirements of a common fish like the striped mullet, it would seem reasonable that any kind of pollution plus any kinds of persistent natural environнmental extremes would tend to depress the general metabolism. Such depression is indicated by temperature extremes with a sublethal petrochemical pollutant (Wohlschlag and Cameron, 1967) or by salinity. extremes with a sublethal petrochemical pollutant (Kloth and Wohlschlag, 1972). 
At sublethal levels of pollution, there is usually no direct, and easily conducted, method for assessment of mortality as there is in conventional short-term toxicity bioassay studies. In most ordinary bioassay techniques for fishes, the levels of mortality are exorbitantly high with respect to survival requirements of the species. While the effects of low level stresses may be to decrease growth rates or survival rates, or both, slightly, these effects would not ordinarily be recognizable from ordinary statistical procedures, analytically applied to fisheries, until a time period of several years to a decade or more of analysis had transpired (Copeland and Wohlschlag, 1968; Wohlschlag and Copeland, 1970). By contrast, slight stress effects on growth of bacteria, algae or some animal species with short life cycles can be assessed by comparative measurements of growth rates (Copeland and Fruh, 1970). 
The striped mullet, Mugil cephalus, as a species suitable for the evaluation of the sublethal toxicity is particularly useful for study. It is one of the species that is common to almost all coastal 
environments of the Gulf of Mexico and the central and southern portion of the Atlantic coast of the United States. The general distribution of the species is worldwide in tropical to warm temperate waters. The species feeds largely on plant materials, or is iliophagous; hence it has a year-round food supply and can feed in most coastal environments along the Gulf coast throughout the year. Further, it is a hardy species that can tolerate wide ranges of salinity, temperature, levels of dissolved oxygen, turbidity and other natural variables. With these feeding habits and broad tolerances to natural environmental variations, it can be found at almost any given location throughout the year in contrast to many other less eurytopic species that undergo seasonal migrations or that occur only when natural environmental variables are within narrower limits. 
The striped mullet is fairly well known in terms of life history and distributional characteristics (Thomson, 1963, 1966). That the mullet respond to environmental differences to produce population differences has been demonstrated by Broadhead (1953, 1958), Peterson and Shehadeh (1971), de Sylva, Stearns and Tabb (1956), among others. Food habits are well known (W. E. Odum, 1966, 1968); the rrtelescopingrr of ecotrophic relationships of the mullet have also received intensive study by Odum (1966, 1970). Along the Texas coast the mullet occurs with moderate to high populations in a wide range of environments (Hellier and Hoese, 1962). The species also has commercial usage as food, usage as a bait species, and considerable promise as a pond-cultured species, largely because it is fast growing and herbivorous and because it is highly eurytopic and vigorous in such a wide range of environments. Further the mullet is fairly well known to both the scientific community and the public at large along warmer coastal areas of the world. 
The choice of fishes in general for environmental assay studies has several favorable aspects. In the first place, fish do respond both to environmental extremes --either natural or man-induced -нand to many kinds of pollutants in a manner that can be detected by changes in metabolism, as indicated by the references above. In addition changes in many behavioral responses are induced by low level stresses; many of these response aberrations are obvious to the most casual observations. 
In the second place, fishes are found in all marine environments, throughout the world, except for a few localized and highly stressed inshore environments. This ubiquitous distribution means that fishes can be easily compared from one type of environment to the next by any number of physiological or behavioral attributes. 
Third, fishes as a whole, especially bony fishes, have rather generalized physiological mechanisms for adaptional functions 
IV -2 
throughout the world, whereas marine invertebrate groups may have a 
broader range of physiological mechanisms and may be relatively 
more limited to restricted marine habitats. 
Fourth, fishes have both recreational and commercial attributes that are commonly recognized and valued both by lay public and by research oriented specialists. The fact that these attributes extend to many common fishes engenders fascination and concern that are extended to a very wide range of environments. Perhaps more than for any other common renewable natural resources, fishes and the environнmental effects on them attract a great public awareness. 
The first of these considerations contains the implication that concerns this study. When fishes are subjected to stresses of sufficiently low level that can be tolerated for extended periods, several physiological courses are open to the fish. In this instance, the fish could expend an additional amount of metabolic energy for regulation, and thus function at a generally higher level of metabolism, a type of regulation that might be expected for some species when exposed to natural changes in temperature, dissolved oxygen levels, turbidity, salinity, or other environmental variables. The extra energy requirements could be utilized for ventilatory, circulatory, and other types of regulation. Whether the total energy requirements remain the same or are capable of being increased determines whether growth rate and swimming propensities must corresнpondingly decrease to allow for the extra regulatory energy expenditures or, alternatively, whether the extra metabolic energy is available to maintain growth and swimming propensities. When the total energy requirements are unavailable for compensatory regulation, either swimming activity and/or growth must be depressed (Fry, 1971). 
Further, a fish exposed acutely to stresses quite often exhibits a tendency to have a great increase in metabolism to the extent that "metabolic loadingn is evident. This means that an oxygen debt builds up and must eventually be alleviated if survival is to ensue. During alleviation of an oxygen debt the metabolism of a fish tends to be suppressed and erratic. It is possible that similar "loadings" occur when a fish is exposed to any acute stresses to which its metabolic machinery cannot be quickly adjusted. If true adjustment is not possible for this type of stress condition, it is also likely that the overall metabolism would be depressed. 
Fry (1971) notes that sublethal stresses due to toxic agents probably damage the metabolic machinery, while noting that natural environmental stresses can be compensated for, providing these natural stresses remain within a TTzone of tolerance" and are not cumulatively harmful. Further discussions of environmental stresses in relation to metabolism of fishes are in Brett (1958), Kinne (1963, 1964a, 1964b), Doudoroff and Warren (1965), in addition to discussions that occur in many recent papers on toxic effects of specific substances. 
IV-3 

In most cases of more toxic pollutants, simple chemical analyses of waters and even a superficial knowledge of the toxicity to fishes or other aquatic organisms provide a rationale for chemical monitorнing, particularly if point sources of toxic materials can be identified. However, when there are suspected a multiplicity of toxic materials from a multiplicity of sources all functioning at sublethal levels for aquatic organisms, when there may be further residual effects of erstwhile deposited toxic materials, and when there may be biologically concentrating systems of toxic materials within organisms, there is obviously no simple chemical or biological analytical system available to isolate suspected or unknown pollutants with a quantitative measure of their toxicity. 
With these analytical limitations in mind, the following experiments are designed to evaluate any measurable effects that waters from selected locations in the open parts of Galveston Bay may have on respiration of the mullet. Over a wide range of seasonal environmental conditions in Galveston Bay, the experiments are organized to compare the respiratory levels of mullet in these waters with mullet in relatively nnaturaln waters from near Port Aransas, which are subjected to much less industrial and domestic effluents than waters within Galveston Bay. In making comparisons of responses that the mullet have to these two types of waters, the responses must be considered as being only comparative, inasmuch as there was neither direct evidence nor experimentation either to inculpate or exculpate possible pollutant effects in the Port Aransas waters. 
METHODS AND MATERIALS 
Collection of Galveston Bay Waters. 
Water for experimental work was collected by teams gathering physical, chemical and biological data on the Bay and operating off the Research Vessel LONGHORN. Four stations were selected to provide waters in areas of relative stability (ARS) in the Bay to obviate changes of obtaining highly polluted waters from spot sources nearer the edges or inflowing waters of the Bay. These locations, shown on Figure l are: 
Station 17. Texas City Dike. Lat. N. 29░ 22.4r Long. W. 94░ 50.8' Station 22. Kemah. Lat. N. 290 33.8r Long. W. 94░ 58.2' Station 26. Trinity Bay. Lat. N. 290 39.9' Long. W. 94░ 47.2r Station 29. Hanna Reef. Lat. N. 290 28.2' Long. W. 94░ 44.7' 
Each of these stations has rather specific characteristics in terms of sources of water influxes and general circulation dynamics within the Bay as a whole. The dates of water collections and measurements of temperature, dissolved oxygen and salinity averaged over 24-hour 
periods at one-foot depths, along with average log weights of mullet used in the experiments, are in Table 1. 
The water was transported in separate epoxy coated containers of about 500-gallon capacity, which were left sealed and stored at the Marine Science Institute until used for experimental study. 
IV -4 


Figure 1. Map of Galveston Bay system showing collection sites. IV-5 
Table l. 	Collection data for sampling stations in Galveston Bay and average log weights of mullet used in experiments. Temperature, dissolved oxygen (D.O.) in parts per million (ppm) and salinity 
in parts per thousand (ppt) refer to samples one foot '(30 cm.) below surface. 
Station and No. Collection Temp. 0 c D.O. ppm Salinity Average Dates ppt Log Wt. rams 
Texas  City  17  26-29 x 71  23.57  8.68  23.82  2. 3564  
Dike  
Kemah  22  26-28 x 71  23.50  8.36  20.23  2.0054  
Trinity Bay  26  26-28 x 71  23.97  9.35  20.06  2. 5152  
Hanna Reef  29  26-28 x 711  23.17  7.64  19.86  l.9458  
Texas  City  17  25-27 I  72  16.26  10.18  16.86  2. 3399  
Dike  
Kemah  22  25-27 I  72  16.22  11.23  10.45  2.1898  
Trinity Bay  26  25-27 I  72  16.00  9.22  3.18  2.1881  
Hanna Reef  29  25-27 I  72  16.84  9. 93  13. 60  2.2017  
Texas  City  17  25-27 IV 72  23.10  8.42  21.41  2.1674  
Dike  
Kemah  22  25-28 IV  72  22.41  7.49  16.04  2.0907  
Trinity Bay  26  25-27 IV  72  22.77  8.00  11.91  1.9923  
Hanna Reef  29  25-27 IV  72  22.36  8.55  20.45  2.1151  
Texas  City  17  25-27 VII  72  30.19  8.17  23.48  2.3244  
Dike  
Kemah  22  25-28 VII  72  29.81  9.53  14.83  2.2268  
Trinity Bay  26  25-27 VII  72  30.17  8.30  15. 03  2.1906  
Hanna Reef  29  25-28 VII 72  30. 56  7.90  17.77  2.2382  
1 Includes data from both 1-and 3-foot depths.  

IV-6 

A separate collection of water from the Trinity River near its discharge into Trinity Bay was obtained during the spring 1972 period for comparison with water from Station 26 in Trinity Bay. The fish could not readily be acclimated to the very low salinity water by any rap:id means. Accordingly the Trinity River water was "saltedn with TTinstant OceanTT sea salt to 13.2 --16.5 ppt salinity. While this salinity adjustment was arbitrary, it was within the springtime salinity range for Trinity Bay and with the n estimated percent composition of water11 from a special TRACOR report prepared by Chen, et al. (1972). The special report was based on earlier TRACOR models of Galveston Bay prepared by Espey, et al. (1971) and by Cullender, et al. (1972). 
Collection and Acclimation of Fish 
Collection of mullet was by means of seines or cast nets from beaches, bays and passes near the Marine Science Institute at Port Aransas, Texas. Transport of the fish to the laboratory involved the use of large insulated live-boxes equipped with ae,ration systems. Mortality of fishes en route to the laboratory was a problem only when the aeration system failed. 
Following capture, the mullet acclimation process began either in large, covered concrete holding tanks, which were equipped with circulating and aerating pumps, or directly in the 450-liter experiнmental aquaria. The concrete tanks served as a holding facility only for a day or so when they were utilized; otherwise the ready supply of mullet from the local area served the requirements for acclimation. It is important to note at this point that aeration and recirculation failures in the concrete tanks usually were followed by fish mortality or an indeterminate degree of morbidity, which would necessitate new collections. Even with these precautions in handling the fish, biases resulting in the acquisition of the healthier and more resistant fish are suspected. At the pre-experimental stages it may generally be assumed that deaths among newly captured mullet were attributable to poor physical condition, inasmuch as such fish usually appeared somewhat morbid or emaciated. 
Acclimation to experimental conditions in the 450-liter aquaria ensued for two days at conditions of temperature and salinity of the Galveston Bay or control waters before respiratory measurements began. In the summer, when fish were within 2░c of the experimental temperatures in their natural environments, only one day was allowed for acclimation before experimentation. The thermally regulated, insulated aquaria were preset at temperatures representative of low and high seasonal extremes for each of the stations. Salinities varied between stations depending on natural conditions in Galveston Bay at the time of the water collections. The TRACOR model for the Bay (Espey, 1971) served to establish reasonable limits for the experimental temperatures. Also, all the experimental and environнmental temperatures and salinities were within the known ranges for 
IV-7 

the eurythermal, euryhaline striped mullet (Thomson 1966). The acclimation temperatures and salinity ranges are given in Appendix Tables A.l to A.20. During acclimation the dissolved oxygen levels were kept near saturation levels by continuous aeration. Ordinarily from four to eight fish were acclimated at one time in each of the aquaria. During acclimation, mortality was usually low. However, there were several occasions when fish would consistently die. This mortality and its significance will be discussed below. 
Measurement of Oxygen Consumption Rates 
For these experiments, it was desirable to measure oxygen consumption rates for resting fish with as little spontaneous (nonнlocomotory) activity as possible. Respiratory chambers were of the flow-through type of two sizes. The clear acrylic plastic chambers were essentially tubes of 1/4-inch thick commercially available extruded tubing. The smaller chambers were 3~-inch inside diameter by 16 inches long; the larger chambers were 5~-inch inside diameter by 24 inches long. At the ends of each chamber was a ~-inch acrylic plate fitted with a soft neoprene gasket of appropriate diameter slightly inset into a groove machined into the plate at a width slightly exceeding the thickness of the tubing walls. A short length of inlet or outlet plastic tubing of ~-inch inside diameter was cemented into the enter of each end plate. Attached to each end plate was a parallel diffusion plate, either 3~-inch or 5~-inch diameter, centered so that it fitted rather snugly into the large tubing; several small 1/16-inch diameter holes were used to perforate this diffusion plate which was spaced 3/4-inches inside the large tubing by three small pieces of acrylic plastic cemented to the end plate. Each end plate was roughly triangular in shape with ~-inch holes near each apex. By utilizing three threaded ~-inch stainless steel rods just outside the larger tubes and through the inflow and outflow end plates, the entire chamber could be sealed by tightening wing nuts on each end of the rods. With the use of the diffusion plates there was little likelihood that the water would flow directly through without being miXed with the water already in the chamber. 
A set of four each of the smaller and another set of four each of the larger chambers were assembled in parallel on respective leadнweighted cradles and held in place with shock cords. The two sizes of chambers were adequate for the size ranges of mullet used in the study. 
To avoid unnecessary handling and transfer of fishes from acclimation aquaria to experimental aquaria, a set of the chambers was immersed parallel and slightly below the water level of the acclimation aquarium. Fish were gently coaxed into the chambers, after which the end plates were affixed and tightened by means of the three rods on each chamber. In this manner four respiration rate experiments could be performed simultaneously. 
IV-8 

To assure a reasonably constant rate of flow of the waters into the chambers, a standpipe of 4-inch PVC tubing was assembled at one side of the aquarium. Five outlets at the bottom of the standpipe were pTovided. A small submersible pump kept a constant overflow at the top of the open standpipe, which was about 16 inches above the aquarium water level. The overflow system also aided aeration. 
Four plastic tubing outlets from the standpipe served as flow inlets to the four respiration chambers. From each of the chambers a plastic tubing outlet was connected via a large three-way glass stopcock to a Yellow Springs Instrument (YSI) Co. oxygen-temperature probe, which, in turn, was connected to a YSI 54BP oxygen meter. A fifth outlet from the standpipe was directly connected via a large three-way stopcock to a fifth oxygen probe and meter; this setup was for measuring the inlet level of dissolved oxygen in the respiration chambers and the aquarium as a whole. 
Each of the electrodes was screwed vertically down against a neoprene washer and into a small acrylic plastic cell with the inlet from the respiration chamber coming up from the bottom. A lateral outlet was at the uppermargin of the cell just above the level of the electrode membrane. This arrangement assured a continual flow over the membrane and obviated the formation of bubbles in the cell. The outlets to each of the four cells connected to the respiration chambers and the outlet of the single cell connected directly to the standpipe were for return of water to the aquarium or for direct and timed measurements of flow rates. Flow rates were determined by measurement of time required for a one-liter graduated cylinder to fill. Depending on the size of the fish, temperature, and general level of metabolism, flow rates could be held quite constant at rates from about 100 ml per minute to about 1200 ml per minute. The electrodes and the stopcock setup was arranged in a console bank of five units mounted over the outside edge of the aquarium. 
A small manifold constructed of ~-inch PVC pipe with five inlets along its length and a single outlet at one end was mounted along the console. From each of the three-way stopcocks, there could thus be a bypass through the manifold and back through any one of the other electrodes, or back through the manifold outlet into the aquarium. This arrangement allowed both for the checking of any one electrode with any of the others or for the alternating use of any one electrode to measure the oxygen level when another was not functioning. The entire integrated setup was connected with plastic tubing with quick-connect plastic joints at various points in the system to allow for easy disassembly for cleaning or for transfer of the whole console to another aquarium. The electrodes could be removed easily for calibration in the air if necessary. Temperatures and barometric pressures were routinely recorded for the air calibraнtions. Water temperatures likewise were routinely recorded both for the two-day acclimations in the aquaria and during the respiratory metabolism experiments. 
IV-9 

After fish were in the respiration chambers, and after the end plates were sealed, water was allowed to flow through the enclosed chambers. Experience with preliminary runs indicated that about one hour was required for excitement from handling and confinement to subside so that oxygen consumption rates were at a low rate with minimum variability. Accordingly, the fish were run without oxygen rate measurements for l~ to 2 hours after being put into the chambers at a given flow rate, empirically determined by the size of the fish and the temperature. At this point the electrodes were shunted in and flow rates and oxygen consumption rates were measured each 20-minute period for one hour or longer if there was any additional excitement of the fish or if there were any technical difficulties, such as shed scales or other debris clogging any of the lines and affecting uniform flow rates. Following each run, the individual fish were weighed, measured and sexed; a scale sample was also taken for future reference. The flow rate and oxygen consumption rate data were recorded on self explanatory forms, an example of which is in Appendix B.l. Note that salinity is recorded as well as baroнmetric pressure and temperature since these variables determine the degree of oxygen saturation possible. 
For each of the 20-minute intervals (usually four) over which flow rates and dissolved oxygen levels were recorded, the respiration (oxygen consumption rates) Q were calculated on the basis of differences in dissolved oxygen concentrations AC between the inflow and outflow points of the respiration chamber times the flow rate F. Thus 
Q= ACxF. 
To compute Q in mg 02 consumed per hour, a simple computer program was organized to account for the theoretical saturation values (SV) of oxygen in sea water in mg 02 liter at given temperatures and salinities as derived from Green and Carritt (1967) times a vapor correction factor adjusted to standard conditions of pressure (760 mm Hg) on the basis of observed barometric pressure (BP) and vapor 
pressure (VP) of seawater at a given pressure and temperature. The flow rate (F) in ml per min multiplied by 60 and divided by 1000 gives the rate per hour per liter, i.e., in the same units as the theoretical saturation values. Thus 
Q = (% 02 in -%02 out) x SV x (BP -VP)/760 x (F x 60)/1000. 
The vapor pressure component of the equation was generally trivial. Also, usually trivial, is a correction for time lag of oxygen consumed in the chamber, which was not utilized but which can be appreciable at flow rates slower than those used and for fish that may be active and have highly variable oxygen consumption rates (Evans, 1972). The separate Q values for each fish were averaged, except in cases where there were discrepant values for each of the 20-minute intervals usually caused by obvious erratic behavioral activity. In such cases the Q values were discarded. Also, if fish died near the end of runs, all the earlier Q values were discarded. Some fish that appeared 
IV-10 

11normaln but tended to remain supine. These nbelly upn fish were 
excluded as well. 
With the exclusion of weakened and morbid fishes from the time of field collections, through the acclimation periods and through the experimentation procedures, the obvious bias in oxygen consumption rates is definitely upward. Especially during acclimaнtion to various samples of the Galveston Bay waters, were erratic behavior, morbidity and even death common. The only control waters that were unsatisfactory were those used during some of the spring experiments, when there was a distinct possibility of unidentified sublethal pollution. Unfortunately the seriousness of the problem of using these waters for the controls was not fully recognized until the experiments were completed. 
Another possible upward bias that could not be evaluated would be the tendency of the differentially selected mullet to be generally more vigorous and active after acclimation and after introduction into the chambers to the extent that confinement would tend to promote spontaneous (nonlocomotory)activity and thus higher respiration rates. Exclusion of the fish that nappeared" to be morbid and the selection of fish that might have higher irritability and greater spontaneous activity would bias the respiration rates above a good parametric average for the population. If this type of bias were evident, it should have appeared more frequently in the Galveston Bay waters than in control waters. Statistical evidence of greater variability in repiratory measurements that occur under stresses will follow in the discussion of results. 
Multiple Regression Data Analysis 
Analysis of the data for each station and control group was by means of the conventional multiple regression method relating the dependent variable of oxygen consumption rates as a measure of respiratory metabolism to the independent variables of the body weights, the temperatures, and, in some cases, salinities. The equations are in the form, 

where: 
..... 
Y 	is the expected log oxygen consumption rate; 
a 	is a constant depending on the averages of the independent variables and on the partial regression coefficients; 
bw 	is a partial regression coefficient, the increase in Y per unit log weight increase at a constant temperature and salinity; 
IV-11 
Xw  is the log weight  in grams;  
bt  ~ is a partial regression coefficient, the increase in Y per oc increase in temperature at a constant weight and salinity;  
Xt  is the temperature  in ░c;  
bs  ~ is a partial regression coefficient, the increase in Y per unit increase in salinity (parts per thousand, ppt) at a constant weight and temperature; and  
Xs  is the salinity in parts per thousand.  

These equations were calculated by a straightforward FORTRAN program, which also gave statistical estimates of the respective means, the standard errors of the entire equation and of the respective partial regression coefficients, and the multiple correlation coefficient, R. Most of the general principles of multiple regression analysis are found in general statistical textнbooks and manuals, e.g. Goulden (1952), Steele and Torrie (1960), among others. In cases where the slight changes in salinity failed to yield partial regression coefficients that were statistically not different from zero, the salinity variable was deleted by means of conventional computations. 
Because of delays in getting experiments underway during the autumn only a few measurements were made for each station and no controls were run. Also, during the spring the controls were run in water that may have been slightly toxic with corresponding metabolic depression. Fortunately parallel experiments on Muqil cephalus had been run for these waters as a part of the independent research by Mr. Richard H. Moore. His equations provide the estimates used for the autumn and the spring controls. These equations have an additional partial regression component for swimming velocity, which is simply set equal to zero so that the calculations give a measure of respiration rates at least near a routine rate. 
RESULTS 
Table l contains the averages of the temperatures, dissolved oxygen levels, and salinities of the near-surface waters at a depth of one foot, except where noted for the autumn Hanna Reef area where both one-foot and three-foot samples were averaged. In Table l are the average log weights of the mullet utilized for the separate experiments. 
IV-12 
In Table 2 are the statistical estimates of the constants and partial regression coefficients, along with pertinent multiple correlation coefficients and standard errors used for tests of significance and for comparisons of variability among the various stations and controls. By substitution of average log weights, temperatures, and, in some cases, salinities, into the equations using these estimates of constants and coefficients, it is possible to make straightforward comparisons of respiratory metabolism rates between the Bay stations and the appropriate control. The control equations utilized from the studies of R. H. Moore are indicated in the footnotes to Table 2. 
The winter respiration experiments with Trinity Bay water (see also equation B in Table 2) were highly unsatisfactory because of the high death rates and the high degree of morbidity of fishes in this low salinity water. At the outset of the experiments, fish were continually removed at the acclimation period as they became morbid; these fish were replaced with new and healthy fish until sufficient numbers were available for the respiration measurements. It was obvious that acclimation to the low salinity waters, which were likely to be highly polluted, was not easily accomplished. The coefficients for weight-respiration relationships would vary considerably upon the deletion of a single set of variables. For example, among the original 26 fish run, an equation was calculated for N = 26: 
" 
Y = l.294B + 0.3220 Xw -0.019B Xt -0.3760 Xs. (Equation Ba) 
The equation and the coefficients were not statistically significant; the temperature coefficient was negative; the salinity coefficient was not only negative, it was extremely large; and the weight coefficнient was very low. By deleting one set of measurements where the 
╖observed respiration rate was about three standard errors different from the expected rate calculated from Equation Ba, Equation B was calculated for N = 25 as shown in Table 2. However, the differences between these two equations for providing estimated respiration rates, Y, for a given set of weights, salinities, and temperatures are extreme. For example, at an average log weight of 2.lBBl, average temperature of 16.00░ and average salinity of 3.lB ppt, the expected log oxygen consumption rate from Equation B (with the deleted data) would be 1.2990 mgs 02 hr-1 while Equation Ba (with all the data) would give an expected oxygen consumption rate of only 0.4B69 mgs 02 hr-1, a greatly depressed, but perhaps realistic, value. 
Similarly the use of the winter control Equation 10 with salinity as a variable or Equation 11 with the salinity variable deleted was hardly justified for extrapolated comparisons with the Trinity Bay waters at salinities of 3.lB ppt, when equations 10 and 11 were based on an average salinity of 16.39 ppt. Unfortunately time did not permit holding mullet long enough for satisfactory acclimation to low salinities so that a separate set of low-salinity control experiments could be conducted. 
IV-13 

Table 2. Multiple regression constants and statistics characterizing mullet respiratory metabolism in Galveston Bay and control waters. (See text.) 
Station and Season  N  Standard Error of  R  a  Weight Coeffi-Standard  Temperature Coeffi-Standard  Equation No.  
Estimate  cient  Error  cient  Error  

17 Autumn  16  0.0819  0. 9 7~╖:~':  -0.9122  0. 6004~'d:  0.0685  0. 0504~'d:  0.0041  1  
22  Autumn  16  0.1200  0. 87~':-J:  0.5100  0. 2032~'d:  0.0672  o. 020 5~h':  0.0061  2  
26 Autumn  15  0.2296  0.44  0.5106  0. 30 35  0.2466  0.0225  0.0161  3  
H < I ~ H::i..  29 Autumn Autumn Control1 17 Winter2 22 Winter  16 27 28 30  0.3417 0.1489 0.2402 0.2414  0.51 0. 93-Jd: 0.49 o. 55~'d:  0.3319 -0.6959 -0.9560 -0.0931  0.2220 0. 7723~'d: 0.2916 0.2171  0.1721 0.0627 0.2658 0.2189  0.0208 0. 0336~'d: 0.0371 0. 0467~h':  0.0165 0.0045 0.0186 0.0141  4 5 6 7  
26 Winter  25  0.1581  0. 73~h':  -0.1974  0.2232  0.1878  0. 0630'/d:  0.0127  8  
29 Winter Winter Control3  32 32  0.1295 0 .1460  o. 55~t:1: o. 9o~'d:  -0.6585 -4.2017  0. 58 77~'d: 0. 6904~'d:  0.1759 0.1500  0. 02081: 0. 0859~h':  0.0080 0.0135  9 10  
Winter Control  32  0.1652  o. 87~h':  -L 5794  0. 9192'/d:  0.1465  0. 0504'/d:  0.0076  11  
11 Spring  27  0.0796  0. 87~'d:  -0.3584  0. 6538'/d:  0.0765  0. 0106~':  0.0042  12  
22  Spring  32  0.1715  0. 77'/d:  -0.3788  0. 4457-Jd:  0.1397  0. 0301'/d:  0.0050  13  
26 Spring Trinity River4  32 31  0 .159 5 0.1595  0. 76~'d: 0. 83~h':  -0.1045 -1.1123  0. 2944~': 0. 7619'/d:  0.1104 0.1499  0. 0329'/d: 0.0316~'d:  0.0059 0.0053  14 15  
29 Spring  30  0.1234  0. 91~h':  -1.0320  0. 7594-Jd:  0.0877  0. 0297~'d:  0.0041  16  

Table 2 (cont.)  
Station and Season  N  Standard Error of Estimate  R  a  Weight Coeffi-Standard cient Error  Temperature Coeffi-Standard cient Error  Equation No.  

Spring Control5 29 0.0877 o. so-.╖:-.': -0.4349 0. 5745-.'d: 0.0954 0. 0170-.'d: 0.0045 17 Spring Control6 24 0.1894 o.9 s~╖:~': -1. 8490 1. 0471-.'d: 0.0965 0. 0503-.'d: 0.0080 18 17 Summer 28 0.1171 0. g4-Jd: -1. 3454 0. 792 3-Jd: 0.1056 0. 0370-Jd: 0.0068 19 
22 Summer 29 0.1773 0. g4-.'d: -0.6994 0. 6688-.'d: 0.1284 0. 0213-.': 0.0100 20 26 Summer 30 0.0846 0. 95-.'d: -0.6936 0. 7508-.'d: 0.0553 0. 0155-.'d: 0.0046 21 1--1 29 Summer 34 0.1175 0. 93-.'d: -1. 5223 0. 9753-.'d: 0.0980 0. 0272-.'d: 0.0040 22 <: 
I 
~ Summer Control 28 0.1224 0. 86-.'d: -0.2751 0. 5187-.'d: 0.1158 0. 0298-.'d: 0.0053 23 
CJl 
-:: Statistically significant at 0.05>P~O.Ol 
-.'d: Statistically significant at P< 0.01 
Notes: 

1. 	
From autumn data supplied by R. H. Moore (Unpubl.); average salinity of 16.9 ppt. 

2. 	
Includes salinity partial regression coefficient of 0.0521, with standard error of 0. 039 3. 

3. 	
Includes salinity partial regression coefficient of 0 .1592-Jd:' with standard error of 0.0526. 

4. 	
Salinity of Trinity River water raised artificially with sea salts to 14.2 ppt. 

5. 	
Water for spring control experiments may have been polluted. 

6. 	
From spring data supplied by R. H. Moore (Unpubl.); average salinity of 31.3 ppt.; regression includes partial regression coefficient of 0.2471** for swimming speeds in standard lengths per second, with a standard error of 0.0774. 


For the winter comparisons the highly selected experimental fish in the Trinity Bay waters yield no straightforward conclusion available from the various equations, because the relatively high toxicity observed for the Trinity Bay waters at low salinities killed fish too directly. For waters at this level of toxicity, direct measurements of mortality rates by conventional bioassay techniques would be more appropriate. 
The results of the spring control experiments presented essentially the same problems: fish in the control waters were highly selected for survival in what was probably a sample of unнrecognized polluted waters; such highly selected fish would have essentially higher than average metabolism, while fish with depressed metabolism would have tended toward morbidity or death and thus be excluded from the measurements. 
Comparisons between the controls and respective Galveston Bay stations for each of the seasons are in Table 3. For each comparison the expected log oxygen consumption rates, Y, were calculated on the basis of average log weights in grams, temperatures, and salinity (where appropriate) listed .in Table 1 and from the equations referring ~o Table 2. For each Galveston Bay station the appropriate control, Y, was calculated using the same averages. In order to compare all stations and controls on a weight-equivalent basis, the resultant -Y calculations in log mgs oxygen consumed hr-1 were expressed on the basis of log mgs oxygen consumed hr-1 kg-1, which is~obtained by subtracting the appropriate log weight from a given Y value and adding 3 (the log of 1,000 grams). These weight-adjusted values are compared for each station and corresponding control as a per cent decrease from the control (Table 3). In Figure 2 the same comparisons are plotted as histograms, which also indicate seasonal trends. 
DISCUSSION OF RESULTS 
While the results as tabulated or as illustrated in Figure 2 are rather direct evidence that Galveston Bay waters suppress respiratory metabolism of the mullet, there are several related subjects worth additional discussion. The techniques of sampling and experimentation have some interesting implications both for the interpretation of this study and for the design of future stress evaluations. Several of the multiple regression equations have some interesting peculiarities that reflect, pari passu, heuristically useful peculiarities of the fish and the environment. There are aspects of seasonal and spacial differences in Galveston Bay of interest for better interpretation of sublethal stress effects. Finally, of general interest to both theorists and pragmatists, is the rationale by which general metabolism and growth theory of fish or other aquatic organisms may be related to the detection and evaluation of natural and man-induced stresses. 
IV-16 
Table 3. 	Seasonal comparisons of respiratory metabolism of striped mullet in water from Galveston Bay stations~and in unpolluted control water. Respiratory rates expressed as log oxygen consumption Y in mg 02 hr-1 and Y in mg 02 kg-1 hr-1. Per cent decrease in oxygen consumption rate per kilogram from control level. 
Galveston Bay Stations  Controls  
Sta.  No.  Season  Equation  Res2irator~ Rates y Yper  Equation  Res2irator~ Rates Y ? per  Per Cent  
unit  wt.  unit  wt.  Decrease  

17  Autumn  1  1. 6905  2.3341  5  1. 9159  2.5595  8.81  
22  Autumn  2  1. 3992  2.3938  5  1. 6425  2.6371  9.23  
26  Autumn  3  1. 8183  2. 3031  5  2. 0 520  2.5368  9.21  
H  
<: I  29  Autumn  4  1. 2458  2.3000  5  1. 5854  2. 639 6  12.87  
1-l  
-l  
17  Winter  6  1.2080  1.8681  10  1.4946  2.1547  13.30  
22  Winter  7  1.1398  1.9500  11  1.2510  2.0612  5.39  
26  Winter  81  1. 2990  2.1109  101  1.2383  2.0502  
29  Winter  9  0.9857  l.7840  11  1. 2931  2.0914  14.70  
17  Spring  12  1. 3035  2.1361  218  1. 5824  2.4150  11. 55  
22  Spring  13  1.2297  2.1390  182  1.4674  2.3767  10.00  
26  Spring  14  1.2312  2.2389  182  1. 3825  2.3902  6.33  
29  Spring  16  1.2383  2.1232  182  1.4904  2.3753  10.61  
17  Summer  19  1. 6133  2.2889  23  1. 8302  2.5058  8.66  

Table 3 (cont.) 
Galveston Bay Stations Controls Res12irator~ Rates Res12irator~ Rates Sta. No. Season Equation > y V per Equation Y Y per Per Cent unit wt. unit wt. Decrease 
22 Summer 20 1.4248 2.1980 23 1. 7683 2.5415 13.52 26 Summer 21 1.4489 2.2583 23 1. 7602 2. 5696 12.11 29 Summer 22 1.4941 2.2559 23 1. 7965 2.5583 11. 82 
Notes: 
1. Neither equation may be realistic. See text. 
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2. Control equation 17 may have been based on polluted waters. 
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-1 -1
Figure 2. Comparisons of log mgs oxygen consumed kg hr for mullet in waters from four Galveston Bay stations (stippled bars) and from control waters (open bars). 
Techniques. 
From the intrqductory statements, it would appear that the choice of striped IIUlllet as a test fish has several naturalistic, real advantages over most Gulf coastal fishes. The fact that it is a euryнtopic fish and an iliophagous and phytophagous feeder means that it is naturally navailablen to a wide range of temporal, geographical, chemical, biological and physical situations. Further, the striped JIUlllet is a biologically durable species that can tolerate both naturally wide ranging environmental fluctuations and fairly broad ranges of man-induced stresses. Other less eurytopic species would not only have smaller tolerance ranges, they also would not be expected to occur in all parts of such a large system as Galveston Bay at all times of the year. However, more stenotopic species often include valuable commercial or recreational resources which could occur only under favorable conditions of environmental quality and only at favorable times and places. Thus, for the interpretation of this study with the striped JIUlllet, it is essential that interpretations are biased toward survival of a durable species and not to the demise of more delicate species. For the purpose of this discussion, a bias toward better survival and a bias of respiration rates that would be upward toward the rates for control organisms would both be considered as TTpositive" biases. 
Another source of a bias in this positive sense would be the result of the timing of the collections at predetermined quarterly periods. For a rigorous emphasis on pollution effects, collections made at times when pollutants were suspected of being released would have made results of the respiratory metabolism measurements seem even more adverse. 
A similar "positiveTT bias appears by the selection of areas of relatively stable open waters for the samples. InasIIUlch as most sources of polluted influent waters would be nearer the shorelines of Galveston Bay or in the tributary rivers, streams or effluentнcharged bayous, collections made from such locations could also be expected to yield more adverse results. From observations by the captain of the LONGHORN and the field crews, no visible releases of pollutants from the oil and gas fields or from ships and barges appeared during any of the four sampling and study periods in any of the open bay areas. The open bay stations, except for the winter influx of fresh waters into Trinity Bay, would also be less influenced by "pulse" or "slug" releases of pollutants than areas within or near inlets. Following heavy localized rainfall, it is quite common for spot pollution to result when effluents normally held in ponds or other devices are suddenly flushed into rather specific localities. At such times, it is common knowledge that both fish and invertebrate kills are high enough to receive attention by the local news media. Thus, by sampling in the more open areas of Galveston Bay, the biases are in favor of less pollution effects. 
IV -20 
The technical problems of holding water samples from Galveston Bay for several weeks before concluding experiments with them are several. The freshly collected and unfiltered water was undoubtedly anaerobic during storage in the sealed containers. Under these anaerobic conditions, followed by intense aeration before experimentaнtion, some considerable changes in toxicity, if any, could accrue. It is likely that both biological and chemical reactions would tend to break down some of the toxic substances both in the anaerobic and in the aerobic stages. These processes would be essentially the same as the processes prevalent in modern sewage treatment. During these degradational processes, the more volatile substances would be displaced inasmuch as all experiments were conducted in highly aerated waters. The aeration would tend to continue the breakdown of any of the more chemically labile substances. Thus it may be reasonably concluded that the level of any pollutants might be lowered, rather than raised, in the stored samples. Accordingly, the metabolic depression observed in the experiments could be lessened to produce a positive bias in the oxygen consumption measurements. Epoxy lined systems for the aquaria and holding tanks in both experimental and control setups have been utilized for several years with no adverse effects on the fish. 
One of the more important sources of an upward bias in the measurement of the oxygen consumption rates of fish in Galveston Bay waters arises from the selection of fishes. This topic is covered in the outline of methodology, but several additional features of this sort of bias are pertinent to the interpretation of the experimental results. In the first place, the selection was always in favor of more healthy fish from the time fish were initially captured, through ~11 holding and transportation processes, and through the acclimation regimes. During all these procedures fish that appeared to be dying had very low oxygen consumption rates. Unfortunately, there were inadequate records for these removals. Some data were noted for deaths, however; these data provide a clue to the differential toxicity of the Galveston Bay waters. In the winter, 20 mullet died initially in the Trinity Bay (Station 26) water at the low temperature range, while in other waters they survived reasonably well with a minimum of morbidity. In the spring samples both the Texas City Dike and the Kemah waters appeared to be rather toxic, while the Trinity Bay and Hanna Reef waters were not; at Texas City Dike (Station l7) there were 10 deaths at the low temperature range and 7 at higher temperatures; at Kemah (Station 22) there were 9 deaths at the low temperatures and 5 at the higher temperatures; from Table 3 and Fig. 2, it may be noted that the Trinity Bay waters had a slight depressive effect, while water from the Hanna Reef had about the same respiratory depressive effect as water from Texas City Dike and Kemah, but without causing deaths. Because of the higher temperatures, difficulties in handling fish in the summer were increased even with great precautions and good aeration. For the summer experiments there were pre-acclimation deaths recorded for all but the Trinity Bay Station as follows: 16 deaths for Texas City Dike at low temperatures; 17 deaths at Kemah low temperatures and 16 at high temperatures; and at Hanna Reef there 
IV-21 

were 8 deaths at the low temperatures and 11 at the high temperatures. From these data on observed deaths during early acclimation stages, there is little to be said of any particular pattern with regard to the station locations or the acclimation temperature and the number of deaths. Even when no deaths occurred (or when deaths may not have been tabulated), there is still about the same pattern of depression of oxygen consumptions rates as noted in Table 3 and Fig. 2. In any case there was selection of experimental fish for ability to survive. 
One other source of evidence for evaluating the degree of resistance that mullet have to the various waters was carefully recorded for the summer data. This evidence came from observations of fish that had a tendency to lose their equilibrium while in the respiration chambers. By recording the numbers of fish which were respiring more or less normally, but were supine (belly up) at least briefly during the time that they were in the chambers, some measure of their well being could be ascertained. The fractions of supine to total fish were: Texas City Dike, 8/28; Kemah, 10/29; Trinity Bay, 8/30; Hanna Reef, 9/34; and control, 3/28. These fractions tend to correspond with the severity of metabolic depression as indicated in Table 3 or Fig. 2, but the fractions do not take into account the time required for first occurrence of supination after introduction into the chamber or the total time that these fish remained supine during the separate runs. Quite obviously the fact that loss of equilibrium and supination is an initial and primary symptom of stress and the onset of morbidity suggests immediately that such behavior could well be used as an assay technique for toxicity at or slightly below lethal levels. 
Because the mullet were selected for ability to survive, there would be a positive or upward bias in the level of respiration. Whether this bias would have occurred had the source of experimental fish been from Galveston Bay, where they would have had a longer and natural acclimation to the Bay conditions, is not known. It seems reasonable to suppose that the striped mullet from Galveston Bay would already have been 11selected11 for survival. The use of fish from waters that would ordinarily not be polluted, such as the waters near Port Aransas has the advantage of detecting more readily any comparative differences between the two populations of fishes. There is no evidence that mullet from relatively unpolluted waters could not in time be acclimated, through selective mortality, to waters of the type found in Galveston Bay. Likewise there is no evidence to suggest that mullet from any source would have respiratory levels different from those used in these experiments. Furthermore, mullet chosen from relatively unpolluted waters and subjected to low, sublethal stress levels should show a true range of respiratory responses to given stresses, while mullet already selected for survival under sublethal stresses should have a narrower range of responses, due to differential selection. Fry (1971) has an excellent summary of the manner by which natural stresses as limiting factors 
IV-22 
may reduce scope and shift metabolic optima as a consequence of increased physiological costs of regulation. Wohlschlag (1963, 1964) has examples of reduced variation in respiratory rates with increasing temperature stress above the optimum temperature of antarctic fish. Presumably stresses that are man-induced either by alteration of natural ranges of natural stresses or by introduction of foreign substances into the environment would affect respiratory variability similarly. Whatever may be the biases that result from reduction or expansion of general respiratory variability, the selection of resistant fishes for experimental subjects would nevertheless tend to yield upward biases in the measured respiratory rates. 
One other source of upward biases in measurements of respiratory rates is the generally increased rates over and above a sustained minimum rrstandard11 rate due to spontaneous activity. Standard metabolism for fishes in practice is difficult to define and measure as compared to, say, the corresponding basal metabolic rates for mammals. In fishes an unnaturally long acclimation time often is required for a specific set of environmental situations. Often the time required for laboratory acclimation to constant conditions is unreasonable with respect to the short-term variability of these same conditions in the natural environment. Fry (1971) has a good review of recent concepts of standard metabolism. In the present study the fish were not in a completely (or even minimum) quiescent state of activity, in natural schools, or at a diel minimum of metabolism. While the mullet were fasted for a reasonable time, it is possible that the acclimation water would contain some particulate nutrients, but the effects of TTf eeding" during thermal acclimation would be minimal. Probably, there is a minimum effect of handling the fish, inasmuch as they were acclimated in the same water in which the experiments were conducted, a procedure that the author has found extremely useful in reducing "shockn effects. However, the major effect of confining the mullet in the tubular respiratory chambers, was to maintain, or possibly increase, their level of spontaneous, 
i.e. non-locomotory, activity. From the many types of respiratory metabolism experiments reviewed by Fry (1957, 1971) or other authors, the oxygen consumption rates of fish not completely at rest may be two or more times as high as at standard conditions; such would appear to be the case of the mullet in the present study due chiefly to spontaneous activity. There were no cases observed during the year when the level of spontaneous activity observed was different between the control and the experimental groups, however. For this reason the comparisons between an experimental group with the respective controls would be valid inasmuch as all variables except the source of water affected all fish in the same manner. Thus both the control fish and the experimental fish would be biased upward to the same degree. 
Equations 
The separate equations relating oxygen consumption rates to 
IV-23 

weight, temperature, and, in some cases, to salinity have some special characteristics that should be discussed in order to explain some of the values found in Table 2. It is especially important to note that the computed constants and coefficients for each equation will yield a minimally biased estimate of the expected oxygen consumption rate at the mean weight, temperature, and salinity of the experiment. Extrapolation beyond these mean values becomes increasingly less useful as the departures from the means increase. 
It is reasonable to assume rectilinearity for the oxygen conнsumption and weight relationships over a broad range for similar relationships derived from a large number of investigations on many kinds of fishes. Winberg (1956) noted that the oxygen consumption and weight relationship on a log-log basis would yield a bw coefficient of about 0.8 as a good average for many species. Values for bw both larger and smaller are common in the large literature, and considerable discussion may be found in Wohlschlag and Juliano (1959), Wohlschlag (1964),Wohlschlag and Cameron (1967) and Wohlschlag and Cech (1970) for equations of the type reported here. In this study there is a tendency, except in the summer data, for the bw to be very low for the fish in the Galveston Bay waters compared to the respective bw for the control waters. There are several reasons for the low values. The first is that through statistical vagaries either a few smaller fish had higher than usual oxygen consumption rates or a few larger fish had depressed rates, or both. The second is that there may be two, or more, populaнtion components combined in such a way that the bw from the combined components are less than the bw could have been for each component separately. For this second alternative explanation Wohlschlag (1964) found that sex differences were sufficiently great for males and females to have separate but equivalent regressions, but with sufficient disнplacement of the constant value, a, to make the combination regression unrealistic with respect to bwХ It should be noted that a combination of population components might still yield regression statistics that could realistically represent the entire population. For the mullet in this study no particular breakdown of the data by sex, maturity, size, or geographic source of the experimental fish yielded any clear insight into possible component subpopulation bw values that would help explain the low bw from the several equations of Table 2. Accordingly, the third alternative, that the smaller fish had relatively higher oxygen consumption rates while the larger had depressed rates, would seem the most likely explanation for the low bw for the fish in Galveston Bay waters, except in the summer. Size-related differences in respiratory metabolism of the Gulf pinfish appear to bear out this explanation: the larger the fish the more depressed the metabolism is under the conditions of low level stresses. Wohlschlag and Cameron (1967), Wohlschlag and Cech (1970) and Kloth and Wohlschlag (1972) discuss this type of metabolic depression differential based on the size of fish. In the experiments with the mullet the size-related depression in oxygen consumption rates associated with the Galveston Bay waters apparently holds true even though the selection of fish for survival in these waters was fairly rigorous. For the smaller mullet 
IV-24 

used in the experiments there is also the distinct possibility that 
they could have had more freedom of movement in the chambers, and 
under the influence of the Galveston Bay waters could have had a 
greater amount of spontaneous activity than the larger fish; however, this type of increase in spontaneous activity for the smaller fish 
would not explain why the control bw values or why the summer bw 
values for both control and Galveston Bay waters were of the order 
usually expected. 
Of all the variables affecting metabolism of organisms, temperaнture has been studied intensively for many years and is currently still actively investigated. The earlier reviews by Fry (1957), Winberg (1956), among others working with fishes, are particularly pertinent. Later reviews by Fry (1971), Brett (1970a, 1970b, 1971) are especially useful for interpreting fish metabolism. In general the coefficients bt for metabolism-temperature tend to be higher for the lower temperaнtures, say of the order of 0.05 to 0.06, and tend to be lower at the higher temperatures, say of the order of 0.03 to 0.02, or even less. From the above reviews, and from work by Wohlschlag and Juliano (1959), Wohlschlag (1964), Wohlschlag and Cameron (1967), Wohlschlag, et al. (1968) it would appear that linear expression of the bt would be valid over temperature ranges of about 100 for eurythermal species, and over a much smaller range for stenothermal species such as those from polar (and possibly tropical) environmP.nts. The mullet data of Table 2 are generally within the ranges expected, although the winter control value of 0.0859 (Equation 10) is perhaps high. From Table 3, it appears that the degree of depression over each of the seasons is not particularly temperature dependent as was the case with pinf ish subjected to a low level of petrochemical pollutant and a temperature range from 10░ to 300 in the study of Wohlschlag and Cameron (1967), which indicated that there was a tendency for great depression at the lowest temperatures, slight depression at intermediate temperatures, and moderate depression at upper temperatures. All the temperatures in the mullet experiments were adjusted to cover or slightly exceed the natural range from Galveston Bay at each of the seasons. 
The salinity coefficients bs should not be considered definitive, inasmuch as the experiments were not designed to cover sufficient ranges. Rather, the salinities were allowed to increase during the several weeks of a few experiments following normal evaporation in the laboratory. Consequently, as the temperature levels were increased for the evaluation of temperature effects, the salinities in these few cases were also increased slightly. The fish used at these different temperature levels were not the same fish, so that any measurements of oxygen consumption rates would reflect a situation confounded by concomitant temperature and small salinity increases with different fish having different thermal acclimation histories. In fact, the partial correlation coefficients for the .experiments in which the salinities were allowed to increase with temperature were very high. However, when the experiments that had salinity increases were analyzed by the multiple regression technique, the Xs values 
IV-25 

were utilized to calculate abs; if the bs were not statistically significant, the data were reanalyzed by omitting this variable. When the bs values were significant they were utilized to bring all the data to the same average level of salinity that is tabulated in Table 1. There appeared to be no easy way to extrapolate to low salinities in the several winter control equations that were derived from studies at higher salinities. It would appear quite obvious that the bs values in Table 2 footnotes, even though statistically significant, are unrealistically high. Much further work will be required to evaluate the respiratory metabolic effect of the wide ranges of salinity to which mullet can be normally adapted. 
The constant tenn of the regressions needs little discussion in ordinary biological applications of multiple regression equations. The constant term usually has little pertinence biologically when all the independent variables equal zero--at least not in the same sense as in physical systems. Furthermore, the magnitude of the constant reflects both the mean values for each of the variables with the statistical dispersion about these means and the several partial regression coefficients with the dispersion about them. 
Variations in Seasons and Localities 
Several sets of variables and interactions among them are important for the interpretation of the various results in Tables 1, 2, and 3. These may be listed as follows for any one station at any one time: 
1. 	
Pollution sources may be point sources or generalized; 

2. 	
The degree of pollution or amount of pollutants may be chronic or acute in terms of distribution; and 

3. 	
The distribution of mobile biota like fishes may be random with a reasonably uniform distribution over wide areas that include the sampling stations, or the distribution of a mobile fauna may be naturally concentrated in areas of minimal stresses, whether these stresses are man-induced or natural. 


To understand how a fish like the mullet may provide an assessment of stress conditions in Galveston Bay in strictly ecological terms, the requirement for complete knowledge of seasonal and spacial distribution is obvious. Equally obvious would be the sampling problems with the use of conventional fishing techniques. For example, Copeland and Fruh (1970) with extensive trawl samples in Galveston Bay rather rarely took mullet from most of the stations for the simple reason that trawl sampling is inefficient for pelagic and actively swimming species like the mullet. Yet the mullet schools can be observed more or less continuously throughout Galveston Bay throughout the year. One exception was that mullet were not observed 
IV-26 

by the field parties at Trinity Bay Station 26 during the January 
sampling period. These recently freshened waters were highly toxic 
as indicated above due either to low salinities or to unidentified 
pollutants, or both. Yet mullet can be commonly found at these 
lower salinities elsewhere along the Texas coast. To interpret the 
low respiration rates from the experiments and the high rates of 
mortality for mullet introduced into the recently freshened Trinity 
Bay waters, the first consideration is that Copeland, Fruh (1970) 
noted that the Trinity River waters were highly depressive to algal 
growth tests. With a heavy influx from Trinity River, the absence 
of observed mullet in the adjacent Trinity Bay, and the evidence 
that mullet die or have severely depressed metabolism when introduced 
into these waters, it is evident that the productivity of Trinity 
Bay mullet during the winter influx period would be near zero. 
In contrast to the toxicity studies of Copeland and Fruh (1970), there is evidence that the Trinity River waters are not continuously toxic. To isolate these waters as a source of pollutants during the spring period, essentially fresh Trinity River waters were secured and nsalted" with Instant Ocean sea salts to a salinity of 13.2-16.5 ppt. A set of metabolism experiments performed with these waters yielded the data of Appendix Table 13, and regression data of Equation 15 in Table 2. Calculations of oxygen consumption rates from this equation were about 2-4% higher than the calculations of control Equation 18 in Table 3. This slight difference of the salted Trinity River waters and the control waters from Port Aransas is not significant and would indicate that the Trinity River water at this time was in good condition. The good condition of the Trinity River waters in the spring season does not exclude the possibility that pollutants from this source could have been flushed out during the high winter flow period, however. An additional inflow source of pollutants into Trinity Bay would be the high flow through the Cedar Bayou from which a large portion could come from the rather heavily polluted intake areas nearer the Houston ship channel. 
In the Galveston Bay environment generally, there are undoubtedly many sublethal stress factors that contribute to the reduction of respiratory metabolism that is exhibited at all stations throughout the year. For fishes subjected to a multiplicity of low level stresses, research has indicated in a general way that increasing only a single stress slightly can have acute, lethal effects in a very short period of time. This indication would seem to be borne out by the sudden lethal characteristics of the Trinity Bay Station 26 waters in winter. At this time the Trinity River flow had increased and the salinity at Station 26 had dropped considerably as indicated in Table 1. In addition to the salinity drop, there could have been a great release of accumulated toxic substances susceptible to release into the Trinity River drainage during periods of high flow such as occurred in January, 1972. The release of toxic materials into the Bay from Trinity River and the Houston Ship Channel, and possibly from other drainage areas, following heavy rainfall, would seem quite apparent 
IV-27 

judging from the publicly reported fish kills at such times. The diversion of these sporadic toxic releases through Cedar Bayou could also have been a possible explanation of the toxic water quality at Trinity Bay Station 26 in January. Even if point source control of these releases were feasible, there would still be the problem of evaluation of the exacerbating effects of sudden salinity drops on fishes subjected to low levels of chronic pollution. Thus, the combination of several stresses, even if each by itself is unimportant, could have been considerable. 
It is a reasonably well known phenomenon that fish exposed to a first environmental condition at stress levels experience less adverse effects when a second environmental condition is near optimum levels than when the second condition is at stress levels. Two or more environmental conditions therefore at less than optimum conditions could be expected to interact negatively and to have a depressive effect on the fish or other organisms. Adelman and Smith (1972) have a good discussion and review of this phenomenon as does, Kinne in numerous publications (see Kinne 1958, 1960, 1963, 1964a, 1964b, 1966). 
As far as metabolic depression is concerned, the waters from areas near Texas City Dike (Sta. 17), Kemah (Sta. 22), Trinity Bay (Sta. 26) except in winter, and Hanna Reef (Sta. 29) show no particular areal pattern and no seasonal pattern except the expected fluctuation with seasonal temperature changes. As expect~d, the fluctuations for the respiratory metabolic rates for the mullet in the control waters also follow closely the seasonal temperatures, although the summer rates may be somewhat depressed. Other studies currently being processed indicate that at least some summer depression of these rates may be expected. 
Theoretical Consideration of Biological Production Rates 
Without available data on the dynamics of mullet or other populations in Galveston Bay, no direct calculations can be made of effects of metabolic depression on biological production rates. Biological prod uction rates are simply defined for a given population as the time rate of elaboration of biological materials, usually expressed in weight units, for gross rates. Net rates include the effects of both growth rates and death rates due to all causes. Thus, for a workable and pragmatic use of modern fishery theory, the population size, recruitment and growth rates, emigration/immigration rates, death rates due to natural causes, death rates due to exploitation, and death rates due to any specific cause like pollution stresses all need to be known. The general principles of fishery management are in Ricker (1958) or in Beverton and Holt (1957). Beamish and Dickie (1967) have a good account of the effects of various types of environmental variables that affect metabolism and biological production of fishes; they have a good summary of data requirements for the evaluation of the level of biological production. Until such data are available, some useful approximations can be made 
IV-28 

for simulations that can be verified or rejected as more fishery research data accumulate. 
One of the most useful approaches to estimate gross production over unit time, whether the fish survive or not, is summarized in Ricker (1968) as 
P = G B , where 
P is the production rate, 
G is the instantaneous growth rate, and 
B is the average population biomass over the time period. 
When metabolism rates are suppressed, it might be supposed that one of the first effects would be to suppress the growth rate for a given species. If this were the case, for a given average population biomass, the production rate would be decreased accordingly. In case of this study, there would be a decrease of about 10% in the level of metabolism; hence the production of mullet under otherwise favorable conditions would decline about 10%. If the metabolic depression observed for the mullet in Galveston Bay was accgmpanied by even slightly increased mortality rates the value of B would be reduced substantially to have the effect of further reducing the production rate. It would thus seem conservative to estimate that the production of mullet in Galveston Bay would be reduced by at least the order of 10%. 
For other species, it might be presumed that metabolic depression would be considerably more severe than for mullet. This would appear to be true at least for the spotted seatrout (C'ynoscion nebulosus), on which experiments were initiated to accompany those on the mullet. Unfortunately, the spotted seatrout were much more highly sensitive to the Galveston Bay waters, and so many deaths occurred during acclimation that re-supply of this species became exceedingly difficult. For this reason none of these data are reported here; but it might be noted that metabolic depression, in the few cases measured, appeared to be severe. For species like several of the croakers, hardhead catfishes, etc., there could be anticipated a greater resistance to stresses than exhibited by the mullet; for these "resistant" species, the expected metabolic depression would be therefore less than for the mullet. However, this sort of speculation for species other than mullet should be regarded with caution until other experiments are performed and comparative mortality and growth rate data are available for different environments that have known differences in natural or culturally-induced stresses. 
IV-29 

Components of Metabolism 
Beamish and Dickie (1967) conclude that measurements of metabolic parameters can provide information on the means by which fishes of different species and different environmental situations adapt. It is usually recognized by physiologists, at least, that respiratory metabolism is a measure of what a fish is and does, with the clear implication that metabolism is an integrative function. Because metabolism in oxygen consumption, or caloric, units implies the utilization of food, it is reasonable to partition all the energy components of metabolism sensu lato. 
Warren and Davis (1967) have summarized the energy categories of consumed food in a reasonably complete form developed from extensive studies of earlier workers. Their scheme of energy partion is illustrated in Figure 3 and is taken directly from their publication (Warren and Davis, 1967). 
In this scheme the total metabolism of a fish would be Or as outlined by the dashed line in Figure 3, which contains no component for spontaneous (non-locomotory) activity that is separated from locomotory swimming activity required for foraging, migrating and other similar nactiven functions. Kerr (197la,b,c) partitions the total metabolism, TT, into: 

where  
Ts  is  cost of standard metabolism,  
TF  is cost of f ora.ging activity,  
Tc  is cost specific to utilization of food,  and  
TR  is cost of spontaneous activity.  

These terms are comparable with those of Figure 3, except for the cost for spontaneous activity. 
Routine metabolism in the sense used by many workers is what is measured in this study. Routine metabolism is a combination of energy costs for standard metabolism plus spontaneous activity (Warren and Davis, 1967; Fry, 1971). For fish in the natural environнment, Kerr (197lc) eliminates the cost for spontaneous activity. However, for laboratory experiments not conducted under conditions that can yield the lowest possible metabolic rates--standard rates-нconsistent with survival, it is necessary to consider routine metabolism. 
Quite likely both energy partitioning systems above have deficiencies of several sorts. For example, under low level stress conditions such as from lowered dissolved oxygen or from a petroнchemical industrial effluent, Cech and Wohlschlag (In press) note that the routine metabolism changes very little, but that the energy 
IV-30 

Energy of food materials 

-------QC----н
~------н
---
-----
-.:::::.... Energy of faeces Energy of assimilated materials
Qu 
,-?r 
--╖----~--╖-------------н
-......::.::::..------------нEnergy of nitrogenous Energy of metabolizable materials lost through materials excretion (physiologic fuel value) Qv 
,.,,-╖ 
I 
_/ 
_.,/'/ 
I
// 
i 
// 
,./ 	w 
--------╖---,,...,.....~ ---
Net energy
/ ,,,/ 
Nonutilized energy freed (nhysiologically useful through deamination and 1 energy ) other processes I _.>_:?l
-?╖ i 
~I '/ I 
(sPe ciri c : 	---------
I,,.. / _/ 
t 
I 	~--╖ / .╖' I
dynamic ' / 1 / 
a c tion ) j Pro ces 3 es of d i g; es t i on , / ./╖ 1 i1 

1
!movement 	and deposition_.,.// / // I 
I of food materials / _ 
/ ,., // 	I 
l // I /' 
~ ~ 	}
Standard Activity Growth metabolism 
Qa 	Qg 
Qs 
Qr= energy of metabolism 
From--Warren and Davis (1967) 
Figure 3. 	Categorie9. into which food-derived energy is partitioned as losses and uses. 
IV-31 
costs for ventilation (gill movements) rise several times above normal; the question immediately arises as to which of the terms in the above energy partitioning schemes these extra costs should be assigned. For another example, it is quite clear that for many fish species the costs of ventilation are met in part by ram ventilation made possible by swimming activity; for tunas it is known that gill ventilation alone could not possibly supply enough oxygen to sustain a standard metabolism cost. Thus, it might be suggested that schemes like those above should break energy costs down with appropriate designations for interactions among the various components, i.e., to allow for "feedback loops" among the components. 
The question of variations and limits of the separate energy cost components is understood for only a few species. However, total metabolism at sustained maximum activity and standard metabolism are reasonably well understood, the difference between them being designated nscope for activityn (Fry, 1957). Beamish and Dickie (1967) discuss situations whereby the level of standard metabolism could change with reference to efficiencies of performance. Fry (1971) summarizes the relations involving the scope whereby: (1) lethal factors restrict the range for activity to the zone of tolerance but do not affect the scope; (2) the cost of physiological regulation may reduce the scope and shift the optimum; or (3) limiting factors reduce the scope, shift the optimum, and may be lethal. Fry (1971) also points out the fact that nstandard metabolism probably represents the regulatory energy required by the quiescent animal and is related to the level of controlling factors which impinge on the organism. Beyond that the cost of regulation is some function of activity." It is quite likely that standard metabolism, therefore, cannot be reduced very much under stresses without disrupting the regulatory machinery that ameliorates effects of controlling factors on the energy cost components specific to the utilization of food or for 11specific dynamic action." Thus ,growth, as well as activity, would be suppressed if stresses tended to reduce the energy component for standard metabolism. In the case of the mullet, more or less continuous swimming activity is a specific requirement for plankton or iliophagous feeding. Should stresses lower either the scope or restrict the range over which activity can take place in Fry1 s (1971) sense, feeding-нand hence growth--would be reduced as well. 
The two aspects of this type of metabolic suppression that show up most clearly in this study are first that the larger mullet tend to be suppressed metabolically under stress more than the smaller, and second that the movements of the mullet may be such that avoidance of unusual stress conditions may be possible. The first aspect, as pointed out in an earlier section, results in lower log metabolismнlog weight coefficients--in some cases considerably below the expected values of about 0.8 (Table 2). The second aspect results in a patchy distribution of fishes with some portions of the environment being essentially devoid of a given species at a given time. Large-scale quantitative sampling studies of mullet in Texas coastal waters that 
IV-32 

would yield information pertinent to Galveston Bay mullet distribution are unfortunately unavailable. 
Non-random Distribution of Fishes 
From trawl catch data, Copeland and Fruh (1970) indicate that the catches of the striped mullet in various Galveston Bay areas are extremely npatchyn although trawl sampling is rather selective against the capture of the rapidly swimming and schooling mullet. The importance of understanding a patchy, or harlequin, distribution with respect to environmental stresses with characteristic geographical sources, timing, duration, and environmental distribution {s: (1) the fishes might be naturally absent from a stressed area either continually or sporadically so as not to have any requirements for stress adaptaнtion; (2) vagile fishes approaching a stressed area might have appropriate sensory and motor systems to provide for migrations away from a stressed area; (3) regardless of vagility and taxic abilities, some fishes might well remain in a stressed area and suffer adverse metabolic effects or die. Quite obviously these three considerations are extremely important in assessing quantitatively any mobile living resource in any environment. Horn and MacArthur (1972) discuss the nature of some of the mathematical formulations that may be useful to describe competition among fugitive species in a harlequin environнment. Just how many 'species and individuals could shuttle among the many localized habitat conditions and what localized extinction and dispersion mechanisms are involved become major tasks in assessing a maximum capability for any fish population. Thus, the proportion of the total available habitat that remains unavailable to a species through stresses must be understood. Natural stresses, such as temperature, salinity, dissolved oxygen level, turbidity, food supply and other natural controlling factors, need to be separated from culturally-induced stresses on an area-wide basis in order to assess both the reasons for any patchiness of the environment and the degree of patchiness of individual species. 
These needs can be met to a large degree by application of conventional fisheries techniques. The degree of stresses beyond those experienced naturally by a population of fishes could probably best be ascertained on an area-wide basis by application of techniques, such as those used in this study. The application of metabolic studies to waters derived from point sources, diluted and adjusted to simulate open estuary, bay or coastal situations by means of appropнriate models certainly has much pertinence. However, models based principally on physical and chemical data must be continually revised to take into account any biological information on stress sources. Additionally, models must take into account nsurgesTT of stresses from all the input sources so that any physical, chemical or biological causes of stresses can eventually be identified before the surge declines and the stresses become partially dissipated through entire coastal aquatic systems and persist at low, sublethal or mildly chronic levels that are much more difficult to detect and evaluate. 
IV-33 
ACKNOWLEDGEMENTS 

It is a pleasure to acknowledge the assistance of many individuals who made this study possible. Particularly helpful was the great efforts of coordinating the experimental and the computer analysis by R. H. Moore, who with the help of F. R. Parker, Jr., W. L. Longley, Jr., J. H. Collins, and Nancy Maciolek conducted the field and laboratory work. The many individuals of the Galveston Bay sampling groups and the crew of the R/V LONGHORN all contributed to the collection of the water samples and the data summarized in Table l; the environнmental observations of these individuals were invaluable. Several of the members of TRACOR, Inc. helped immeasurably with the construction and the interpretation of the component water mass models; special thanks are due W. H. Espey, Jr. and A. J. Hays, Jr. of that organization. Finally the encouragement and valuable advice of Col. F. P. Bender, Project Director, Galveston Bay Project proved most useful. 
IV-34 

CONCLUSIONS 

1. 
The open waters of four Galveston Bay stations had a tendency to suppress the respiratory metabolism of the striped mullet. 

2. 
Except for the winter Trinity Bay water, there were no particular areal or seasonal patterns of metabolic depression. 

3. 
The levels of metabolic depression, compared to controls over all the seasons and stations, except for Trinity Bay station in winter, had a conservative average about 10 per cent below control data. 

4. 
The winter Trinity Bay water was toxic near the incipient lethal level. The toxicity followed a freshening of the Trinity Bay waters that were directly influenced by heavy rainfall over the Trinity River influent system. There seemed to be no direct evidence for any particular wintertime pollutants from either the Trinity River or the Cedar Bayou, which are the major sources of Trinity Bay water, although the possibility of a "pulseTT source from either of these influents cannot be ruled out. Experiments in the spring on waters from the Trinity River revealed that no metabolically depressive effects occurred at that time. 

5. 
There is good evidence that the log respiration rate-log weight multiple regression coefficients are depressed during much of the year for the mullet. This evidence is interpreted to mean that larger mullet are metabolically more adversely affected than the smaller mullet under slight stress conditions. 

6. 
The conservatively estimated average metabolic reduction of about 10 per cent is discussed in terms of lowering the gross production rate of mullet by about the same amount on the assumption that no additional mortality is induced. More elegant interpretive possibilities are discussed in terms of conventional fishery statistics, which are presently unavailable. 

7. 
The possibility that parts of the Galveston Bay environment might be avoided by the highly vagile mullet at certain times as a result of environmental stress is discussed. 

8. 
The future utility of the metabolic types of studies is evaluated from two standpoints. First, from the results of this study, the overall quality of the open Galveston Bay system can be assessed. Second, this study suggests immediately that the quality of influx waters most likely to be the source of stress substances could be assayed by similar techniques by utilizing physical-chemical models of Galveston Bay waters to nadj ust1' the 


IV-35 

influx waters to pertinent Bay conditions, such as salinity and temperature, before testing fishes or other organisms. From either standpoint, economical biological monitoring systems that take advantage of common, useful organisms could be set up for either continuous or sporadic assays of water quality. 
IV-36 

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IV-37 
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IV-38 

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IV-39 

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Steel, R. G. D., and J. H. Torrie. 1960. Principles and Procedures of Statistics. McGraw-Hill, New York. xvi + 481 p. 
Thomson, J. M. 1963. Synopsis of biological data on the grey mullet Mugil cephalus Linneaus 1758. CSIRO Fish Oceanogr. Fish. Synopsis, sections 1: 1-8:14. 
Thomson, J. M. 1966. The grey mullets. Oceanogr. &Mar. Biol. Ann. Rev. 4: 301-335. 
Warren, C. E., and G. E. Davis. 1967. p. 175-214. Laboratory studies on the feeding, bioenergetics, and growth of fish. In s. D. Gerking (ed.), The biological basis of freshwater fish production. Blackwell, Oxford. 
Winberg, G. G. 1956. Rate of metabolism and food requirements of fishes. Fish. Res. Bd. Canada, Transl. Ser. 194: 1-253. 
Wohlschlag, D. E., and R. o. Juliano. 1959. Seasonal changes in bluegill metabolism. Limnol. Oceanogr. 4: 195-209. 
Wohlschlag, D. E. 1963. An Antarctic fish with unusually low metabolism. Ecology, 44(3): 557-564. 
Wohlschlag, D. E. 1964. p. 33-62. Respiratory metabolism and ecological characteristics of some fishes in McMurdo Sound, Antarctica. In H. W. Wells (ed.) Antarctic Res. Ser., Vol. 1. Am. Geophys.-Union, Wash. D.C. 
Wohlschlag, D. E., and J. N. Cameron. 1967. Assessment of a low level stress on the respiratory metabolism of the pinfish (Lagodon rhomboides). Contr. Mar. Sci. Univ. Tex. 12: 160-171. 
Wohlschlag, D. E., J. N. Cameron, and J. J. Cech, Jr. 1968. Seasonal changes in the respiratory metabolism of the pinfish (Lagodon rhomboides). Contr. Mar. Sci. Univ. Tex. 13: 89-104. 
Wohlschlag, D. E. and J. J Х. Cech, Jr. 1970. Size of pinfish in relation to thermal stress response. Contr. Mar. Sci. Univ. Tex. 15: 22-3l. 
IV-40 
:╖:_╖ -.' 
Wohlschlag, D. E., and B. J. Copeland. 1970. Fragile estuarine systems--ecological considerations. Water Resources Bull. 
6: 94-105. 
IV-41 LIBRARY THE UNIVERSITY OF TEXAS AT AUSTI~ MARlNE SCIENCE INSTITUTE 
PORT ARANSAS, TEXAS 78373-1267 
APPENDIX IV-A. Metabolism Data for Galveston Bay Striped Mullet 
Table A.l. 	Autumn Metabolism Data, Texas City Dike Station 17. Experimental Salinity 26.l ppt. 
Log mg 02  Log Weight  Temp.  
consumed hr.-1  (g.)  oc  
l.3277  2.7536  12.0  
l.2 529  2. 7226  12.0  
1.4220  2.7093  12.0  
1.1731  2.5866  12.0  
l.0488  2.0645  11. 6  
0.7172  l.8451  11.6  
l.0065  2.1303  11.6  
0.9301  2.2041  11. 6  
l.7248  2.4249  21. 8  
l.5980  2.5328  21.8  
l.6010  2.3909  21. 8  
1.9236  2.7731  21.8  
l.3163  l.9445  21. 9  
l.6851  2.4233  21. 9  
1.4413  l.9868  21. 9  
1.4502  2.2095  21.9  

IV-42 

Experimental salinity 20.9 ppt. 
Table A.2. Autumn Metabolism Data, Kemah Station 22. 

Log mg o2consumed hr-1  Log Weight (g.)  Temp. oc  
l. 0794  l.9445  14.7  
0.9747  2. 09 69  14.7  
l. 2341  2.1239  14.7  
l. 2209  2.0719  14.7  
1.2209  2.1461  14.8  
l. 2528  2.1847  14.8  
l.3603  2.3181  14.8  
l.5910  2.6253  14.8  
1.5186  2.2227  24.0  
1.4149  2.0334  24.0  
1.4657  2.0086  24.0  
l.6760  2.1703  23.5  
1.4568  2.4314  23.5  
1.3492  2.0645  23.5  
l.3179  l.9912  23.5  
1.4457  l.6532  23.5  

IV-43 

Table A.3. Autumn Metabolism Data, Trinity Bay Station 26. 
Experimental salinity 20.5 ppt. 
Log mg 02 consumed hr-1  Log Weight (g.)  Temp. oc  
1.9268  2.8413  14.5  
l.9324  2.7497  14.5  
l.6796  2.4969  14.5  
l.8197  2.7075  14.5  
l.5425  2.7760  12.0  
l.4402  2.4440  12.0  
l.8483  2. 5502  12.0  
l.5161  2.2625  12.0  
1.1070  2.3464  13. 0  
l.294 7  2.5866  13.0  
1.4307  2.4249  13.0  
l.7424  2.5011  21. 5  
l.5867  2.7396  21. 5  
l.7672  l.8175  21. 5  
l.6663  2.4843  21. 5  

IV-44 

Table A.4. Autumn Metabolism Data, Hanna Reef Station 29. 
Experimental salinity 22.0 ppt. 
Log mg 02 Log Weight Temp. consumed hr-1 (g.) oc 
0.8520 2.4885 
13.8 0.9689 2.4248 
13.8 0.9689 2.1430 13.8 0.1119 2.0791 13.8 
1. 3001 2.3579 14.1 
-.Х:i. 4 775 2.5502 14.1 1.0390 1.6989 14.l 
l.2107 2. 0934 14.l 
1.1605 1.8195 21. 7 
-.t:o. 7730 1. 3979 21. 7 1.4383 2.1335 21. 7 1.4230 2.1461 21. 7 l.6125 2.5820 21.9 0.8754 1.3010 21.9 1.2619 l.7558 21.9 
l.4424 2.1614 21.9 
*1st Reading discarded -high. 
IV-45 

Table A. 5. Winter Metabolism Data, Texas City Dike Station 17. 
Experimental salinity 19.25-22.50. 
Log mg o2 Log Weight consumed hr-1 (g.) 
1. 2356 2.2455 0.5778 2.1931 0.6860 2.1271 1.1051 2.6831 1.4532 2.8376 1. 3695 2.7474 1.0265 2.4742 1. 2769 2.2672 1.0642 2.2601 1. 6405 2.2577 1.4397 2.0170 1. 5777 2.2175 1.6010 2.0294 1.1608 2.2175 1.1742 2.4314 1.1894 2.2504 1.1731 2.2305 1. 3231 2.5635 1. 6045 2.4346 1.4583 2. 39 79 1. 3543 2.2553 1. 2498 2.3617 1.1300 2.3692 1. 530 5 2.5403 1. 5003 2.3424 1.419 3 2.2504 1.4365 2.2095 1.5884 2.3054 
T"l T A C! 
Temp. oc 
11.0 
11.0 
11.0 
11.0 
11.0 
11.0 
11.0 
11.2 
11.2 
11.75 
11.75 
11.75 
11.75 
12.8 
12.8 
12.8 
12.8 
16.0 
16.0 
16.0 
16.0 
16.0 
16.0 
16.0 
17.5 
17.5 
17.5 
17.5 
Table A.6. Winter Metabolism Data, Kemah Station 22. Experimental salinity 11.0-12.1 ppt. 

Log mg 02 consumed hr-1  Log Weight (g.)  Temp. oc  
1.2312  2.0253  15.75  
0.8319  2.0253  15.75  
1.2358  2.0000  15.75  
1. 3380  2.0043  15.75  
1. 0158  2.0414  16.0  
1. 3027  2.1271  16.0  
0.7519  1. 5563  16.0  
1. 2874  1. 7993  16.0  
1.1587  2.2305  18.0  
1. 2001  2.1875  18.0  
. 1.1182  2.1461  18.0  
1. 6752  2.4233  16.5  
1. 5253  2.4654  16.5  
1.0120  2.3424  18.5  
1.0972  2.4150  18.5  
1.1254  2.4914  18.5  
0.8301  2.0864  10.5  
0.8398  2.0531  10.5  
1. 2829  2.0719  10.5  
0.8221  2. 3096  10.5  
1.1109  2.3222  11. 5  
0.6296  2.2553  11. 5  
1. 2759  2.3096  11. 5  
0.4594  2.1303  11.5  
0.7393  2.2695  10.5  
1.0284  2.4133  10.5  
0.7230  2.2923  10.5  

IV-47 

Table A. 6.  (cont.)  
Log mg o2consumed hr-1  Log Weight (g.)  Temp. oc  
0.8512  2.2355  10.5  
0.6721  2.4216  10.5  
0.9514  2.2430  10.5  

IV-48 

Table A. 7. Winter Metabolism Data, Trinity Bay Station 26. 
Experimental salinity l.0-2.2 ppt. 
Log mg 02 consumed hr-1  Log Weight (g.)  Temp. oc  
1.0665  2.6454  11.0  
1.2676  2.5888  11.0  
0.9995  2.3838  11.0  
0. 9 369  2.1903  11.0  
0.9355  2.0294  11.0  
0.7401  2. 0934  11.0  
0.8196  2.2253  11.0  
0.7369  2.1038  11.0  
1.1045  2.2253  10.5  
1.1314  2.2718  10.5  
1.1733  2.2601  16.0  
1.1583  2.2041  16.0  
l.3417  2. 3284  16.0  
l.3794  2.0531  16.0  
1.2639  2.0720  17.5  
1.1086  2.0863  17.5  
1. 3256  2.1761  l7.S  
1.4155  2.2201  17.5  
1.3167  2.4099  15.5  
l.3016  2.1673  15.5  
l.5530  2.0828  15.5  
1.4488  2.0334  15.5  
1.2959  2. 0969  15.5  
1.1894  l.8451  15.5  
1.4277  l.9085  15.5  

IV-49 

Table A.8. Winter Metabolism Data, Hanna Reef Station 29. 
Experimental salinity 13.75-15.4 ppt. 
Log mg 02 consumed hr-1  Log Weight (g.)  Temp. oc  
1.0434  2.3304  16.5  
1.1497  2.3748  16.5  
0.8036  2.2878  16.5  
0.9373  2.0000  16.5  
1.0781  2.1931  16.75  
0.8865  1. 8751  16.75  
0.9117  2.2095  16.75  
0.7445  2.0212  16.75  
1.2432  2.3766  16.5  
1.0641  2.0531  16.5  
1.0067  2.2227  16.5  
0.9492  1.9638  16.5  
0.7904  1.9345  16.5  
1.0416  2.2989  16.5  
0. 839 7  2.1614  16.5  
0. 6031  1.9191  16.5  
0.8312  2.3181  10.5  
0.8926  2.2742  10.5  
0.7902  2.2742  10.5  
0.8211  2.1987  10.5  
0.8246  2.4082  10.5  
0.9587  2.4116  10.5  
1.0531  2.3385  10.5  
1.0524  2. 3010  10.5  
0.7282  2.2788  10.0  
0.6254  2.1673  10.0  

IV-50 

Table A.8.  (cont.)  
Log mg  02  Log Weight  Temp.  
consumed  hr-1  (g.)  oc  
0.8006  2.1430  10.0  
0.8346  2.2201  10.0  
1.1639  2.2175  10.0  
0.9888  2.2041  10.0  
0.9465  2.2788  10.0  
0.8116  2.1987  10.0  

IV-51 

Table A.9. Winter Metabolism Control Data. Experimental salinity 14.85-17.6 ppt. 

Log mg o2consumed hr-1  Log Weight (g.)  Temp. oc  
1.4219  2.2253  19.l  
1.1605  2.0970  19.l  
1.2956  2. 3010  19.1  
1.4471  2.0294  19.l  
l. 3617  2.0828  19.7  
1.2306  l.9 590  19.7  
l.3309  2.4166  19.7  
1.3400  2.3263  19.7  
l.5929  2. 50 52  18.6  
1.6522  2.4914  18.6  
l. 5123  2.4314  18.6  
l.6127  2.4346  18.5  
l.8218  2.5185  18.5  
l.7270  2.3927  18.5  
l.8576  2.5119  18.5  
1.6730  2.4742  20.0  
l.7348  2.3503  20.0  
l.9348  2.4328  20.0  
1.6491  2.4014  20.0  
l.6086  2.3820  20.0  
l.6886  2.4609  20.0  
l.34 71  2.1732  20.0  
l.7030  2. 540 3  20.0  
l.0082  2.4014  10.5  
1.4759  2. 5159  10.5  
0.9876  l.9685  10.5  

IV-52 

Table A.9.  (cont.)  
Log mg 02 consumed hr-1  Log Weight (g.)  Temp. oc  
0.6108  2.0334  10.5  
0.6164  2. 0930  10.5  
1.3180  2.6703  11.0  
1.3571  2.7193  11.0  
1.4699  2.4200  11.5  
1.1002  2.1139  11.5  

IV-53 

Table A.10. Spring Metabolism Data, Texas City Dike Station 17. 
Log mg 
o2
consumed hr-1 1.4406 1.4050 1.1599 
1.0872 l.52 38 1.4452 1. 5253 1.1964 1.2790 1.2419 l.3268 l.2043 1.2002 l.2828 1.2083 1.4719 1.4228 1.1489 
l.2901 
1.4239 
l.5356 
1.4203 
1.4277 
0.9499 1.04859 1.2792 
1.1547 
Experimental salinity 22.0-25.3 ppt. Log Weight 
(g.) 2.5211 2.3874 2.0934 2.0414 2.6335 2.4829 
2. 6064 2.0719 2.0756 2.0756 2.2068 2.0170 2.0492 2.2095 l.9638 2.4330 2.3874 2.1038 2.0899 2.0756 2.2480 2.2601 2.1139 
l.7160 
l.8261 
1.8573 
l.9731 
Temp. oc 
18.0 
18.0 
18.0 
18.0 
18.0 
18.0 
18.0 
18.0 
20.3 
20.3 
20.3 
20.3 
20.6 
20.6 
20.6 
26.2 
26.2 
26.2 
26.2 
26.2 
26.2 
26. 2 
26.2 
29.0 
29.0 
29.0 
29.0 
IV-54 

Table A.11. 
Log mg 02 consumed hr-1 
l.3075 l.2451 1.2786 1.3042 1.4774 1.4390 l.3287 l.3224 1.4517 1.3909 1.2625 1. 3933 l.3754 l.5598 1. 5076 1.4741 l.3754 1.1834 0.6856 l.3569 0.9546 l.3606 1.0232 0.8755 
Spring Metabolism Data, Kemah Station 22. Experimental salinity 17.6-18.7 ppt. 
Log Weight  Temp.  
(g.)  oc  
2.0414  28.3  
l.9685  28.3  
2.0682  28.3  
2. 003~  28.3  
2.0170  28.3  
l.9590  28.3  
2.0453  28.3  
2.0043  28.3  
2.0864  27.4  
2.0719  27.4  
2.0719  27.4  
l.9590  27.4  
2.1399  27.4  
2.1523  27.4  
2.1761  27.4  
2.1761  27.4  
2.5011  15.S  
2.3909  15.5  
l.8633  15.5  
l.9138  15.5  
2.3181  15.5  
2.4216  15.5  
l.8808  15.5  
l.8325  15.5  

IV-55 

Table A.11. (cont.) 
Log mg 02 consumed hr-1 1.1149 0.8466 0.6718 1.2251 1.0238 0.6667 0.8240 
1. 3607 
Log Weight (g.) 
2.2718 1.9912 1.6812 1. 7782 2.6454 1. 7782 2.3010 2.3636 
Temp. 
oc 
15.5 
15.5 
15.5 
15.5 
15.5 
15.5 
15.5 
15.5 
IV-56 

Table A.12. Spring Metabolism Data, Trinity Bay Station 26. 
Log mg 02 consumed hr-1 1.0181 1.4204 1.1206 1.4054 1.5104 1.4053 1.3620 1.4884 1.2109 
l.3544 1.1923 l.0697 1.4018 1.1174 l.3439 1.7267 1.4680 1.0513 0. 9 539 0.8748 0.9850 0.9659 1.1575 
l.394 7 
1.1022 
Experimental salinity 14.8-17.6 ppt. Log Weight 
(g.) 2.0414 2.2504 2.0414 2.2648 2.2648 2.0792 2.0864 2.3692 
l.9191 1.1644 l.9345 l.8129 2.2253 l.8195 2.4654 l.6233 2.2833 l.7404 1.9345 l.8751 2.0086 2.0170 2.1206 2.3655 2.0000 
Temp. oc 
22.0 
22.0 
22.0 
22.0 
26.0 
26.0 
26. 0 
26.0 
26. 0 
26.0 
26.0 
26.0 
26.0 
26. 0 
26.0 
26.0 
26.0 
26.0 
15.0 
15.0 
15.0 
15.0 
15. 9 
15.9 
15.9 
IV-57 

Table A.12  (cont.)  
Log mg 02  Log Weight  Temp.  
consumed hr-1  (g.)  oc  
0.7375  l.6628  15. 9  
0. 9627  l.8451  16.2  
0.9135  l.9085  16.2  
1.0265  l.9912  16.2  
0.8653  1. 6990  16.2  
1.0470  2.0086  16.2  
0.8819  l.9395  16.2  

IV-58 

Table A.13. Spring Metabolism Data, Trinity River Waters with Added Sea Salts. Experimental salinity 13.2-16.5 ppt. 
Log mg 02 consumed hr-1 1.2306 0.9328 1.1388 
l.2460 1.4175 1.1205 0.8745 1.1657 0.7048 0.9988 0.7649 0.6975 0.6415 1.1415 0.9514 0.6624 1.2006 1.5001 1.4869 1.2110 l.3197 1.5919 1.4290 
l.0916 
Log Weight (g.) 
2.1931 1.9912 2.0792 2.3927 2. 39 79 2.1271 1.7782 2.0719 2.0864 1.9777 1.9912 2.0792 2.0000 2.0212 2.0645 2.2504 2.0000 2. 3979 2.3096 2.0170 2.0864 2.3729 2.3483 
1.9191 
Temp. oc 
15.8 
15.8 
15.8 
15.8 
15.8 
15.8 
15.8 
15.8 
15.0 
15.0 
15.0 
15.0 
16.2 
16.2 
16.2 
16.2 
26.5 
26.5 
26.5 
26.5 
26.5 
26.5 
26.5 
26.5 
IV-59 

Table A.13.  (cont.)  
Log mg 02  Log Weight  Temp.  
consumed hr-1  (g.)  oc  
1. 3207  1. 9868  26.6  
1.0982  1.9085  26.6  
1.0210  1. 7243  26.6  
1.4552  2.2095  26.6  
1. 3081  1.9823  26.6  
1. 6887  2.4843  26.6  
1. 2115  1. 8130  26.6  

IV-60 

Table A.14. 
Log mg 02 consum~d hr-1 
0.9778 0. 89 30 1. 2993 1.3224 1.1271 0.7712 1.2010 0.9491 0.9008 0.6576 0.7119 1.4052 1.1615 1. 3039 1. 0946 1. 0446 1.2293 1. 2415 1.2801 1.2850 l.0998 1.2078 1.6662 
1. 3736 
Spring Metabolism Data, Hanna Reef Station 29. Experimental salinity 19.25-24.3 ppt. 
Log Weight  Temp.  
(g.)  oc  
1. 8129  15.5  
1.9031  15.5  
2.3118  15.5  
2. 4362  16.0  
2.3365  16.0  
1. 7559  16.0  
2.4942  16.0  
2.0000  15.5  
1. 6532  15.5  
1. 7782  15.5  
1.9243  15.5  
2.4393  16.0  
2.4281  16.0  
2.4564  16.0  
1. 8692  25.9  
2.0170  25.9  
2.0000  25.9  
1.8195  25.9  
1. 9345  27.7  
1. 9731  27.7  
1.8513  27.7  
1. 8976  27.7  
2.3284  26.8  
2.2227  26.8  

IV-61 

Table A.14. (cont.) 
Log mg o2 Log Weight Temp. consumed hr-l (g.) oc 
1. 7312 2.4624 
26.8 
1. 7158 2.3424 
26.8 
1. 7053 2.2945 
26.6 
1. 3466 2.0864 26.6 
1.2947 2.2553 26.6 
1.5101 2.3692 26.6 
IV-62 

Table A.15. Spring Metabolism Control Data. Experimental salinity 19.25-26.4 ppt. 
Log mg 02 
consumed hr-1 
1.1306 
1.4134 
1.2365 
1.1227 
1.1594 
1.1913 
1.0424 
1.0751 
1.5410 
1. 3895 1.4581 1.0744 0.9547 1.0825 1.1593 1.2365 1.1647 0.9984 1.0136 1.1319 1.2792 1.3445 1.1547 1.1033 1.2591 
Log Weight (g.) 2.1584 2.4150 2.4564 2.1139 2.1139 
2. 3160 2.2553 2.0719 2.5315 2.4503 2.3010 2.0682 1. 7243 2.1399 2.5453 2.4116 2.2625 2.0086 2.3674 2.1367 2.2878 2. 230 5 2.1644 2.1818 2.1987 
Temp. oc 
18.0 
18.0 
18.0 
18.0 
18.0 
18.0 
18.0 
18.0 
25.0 
25.0 
25.0 
25.0 
25.0 
16.0 
16.0 
16.0 
16.0 
16.0 
16.0 
16.0 
16.0 
23.5 
23.5 
23.5 
23.5 
IV-63 

Table A.15.  (cont.)  
Log mg 02 consumed hr-1  Log Weight (g.)  Temp. oc  
1.1993  2.2833  23.5  
1.2943  2.3579  23.5  
1.1643  2.1072  23.5  
1.1559  2.0792  23.5  

IV-64 

Table A.16. Summer Metabolism Data, Texas City Dike Station 17. 
Experimental salinity 26.95-31.90 ppt. (Asterisks indicate fish that were belly-up at end of run.) 
Log mg 02 Log Weight Temp. 
cons urned hr-1 (g.) oc 

1:1. 9407 2.6767 29.5 
~╖:1. 8553 2.5391 29.5 1.5207 2.1206 29.5 1.6621 2.2553 30.0 
l.6468 2. 42 32 30.0 ~╖:1.0742 1.4472 30.0 
1. 52 38 2.1614 30.0 
l.3456 2.3424 27.6 
1.4030 2.3385 27.6 
l.3411 2.2175 27.6 ~╖:1.2685 2.1761 28.5 ~╖:1. 3217 2.1553 28.5 l.3166 2.1399 28.5 1.3397 l.9638 28.5 l.5035 2.4684 22.3 l.3561 2.2856 22.3 1.0439 2.2577 22.3 l.3917 2.4771 22.3 1.4521 2.4472 22.0 1.4747 2. 3483 22.0 1.2503 2.4314 22.0 
1. 3092 2.4031 22.0 1.4968 2.5315 21.0 ~╖:i. 6917 2.6656 21.0 
1.1672 2.3617 21.0 
l.7655 2.4150 21.0 
IV-65 

Table A.16.  (cont.)  
Log mg o2consumed hr-1  Log Weight ( g.)  Temp. oc  
1.2775  2.2900  21.0  
1.5506  2.5563  21.0  

IV-66 

Table A.17. 
Log mg o2 cons urned hr-1 
1. 734 7 -.':1. 7150 
1. 6649 -.':i. 8829 -.':2. 0249 -.':1. 3173 -.':i. 6088 -.':i. 6618 
1.1508 -.':l. 4317 
1.3884 
1. 3396 1.4073 1.2319 -::i. 5601 -.':1.4012 1.0010 0.9035 -.':i. 0242 1.1468 
l.6901 
0. 9261 
0.9847 
Summer Metabolism Data, Kemah Station 22. Experimental salinity 17.6-23.1 ppt. (Asterisks 
indicate fish that were belly-up at  end of run.)  
Log Weight  Temp.  
(g.)  oc  
2.4031  30.05  
2.5563  30.05  
2.4684  30. 05  
2.4911  30 .OS  
2.7597  30.05  
2.2253  30.05  
2.3711  30. 05  
2.4183  30. 0 5  
2.0792  29.5  
2. 3010  29.5  
2.3202  29.5  
2.2041  29.5  
2.1761  29.5  
2.1644  29.5  
2.5315  29.5  
2.3222  25.4  
1. 8062  25.4  
1. 8386  25.4  
2.7634  25.4  
1.9191  24.5  
2.6675  24.5  

1. 7324 24.5 
2.0000 20.0 
IV-67 
Table A.17.  (cont.)  
Log mg 02 consumed hr-1  Log Weight (g.)  Temp. oc  
0.9353  1. 6532  20.0  
0.9375  1. 7482  20.0  
1.1035  1.9191  20.0  
1.4323  2.4216  21.0  
1.3519  2.1703  21.0  
1.2231  2.1461  21.0  

IV-68 

Table A. 18. Summer Metabolism Data, Trinity Bay Station 26. Experimental salinity 16.5-20.35 ppt. (Asterisks 

indicate fish that  were  belly up  at end of  run.)  
Log mg 02 consumed hr-1  Log Weight (g.)  Temp. oc  
1. 3611  2.2672  21.0  
1.4569  2.3324  21.0  
1.1457  2.0000  21.0  
1.0088  1. 7782  21.0  
1. 6774  2.5563  23.0  
1.6694  2.5378  23. 0  
1.3816  2.1461  23. 0  
1. 6577  2. 5798  23.0  
1. 0396  1.7924  20.2  
~╖:o.8571  1. 8062  20.2  
1.1219  1. 9542  20.2  
1.0279  1. 9345  20.2  
1.4234  2. 5132  21.0  
0.9249  1. 6990  21.0  
0.9674  1. 7404  21.0  
~':1. 8890  2.6749  28.3  
~╖:i. 7098  2.5514  28.3  
~╖:1. 3088  1.9868  28.3  
'/:l. 7496  2.5502  28.3  
1.4186  2.0792  28.3  
1.1927  1. 7482  28.3  
1. 3624  2.0934  28.3  
1. 2179  1. 9542  28.3  
1.6823  2. 5238  27.8  

IV-69 

Table A.18. (cont.) Log mg o2consumed hr-1 ~╖:1. 3457 
1.3487 
~╖:1.2891 
~╖:i. 5430 
1. 4 799 
1. 5343 
Log Weight  Temp.  
( g.)  oc  
2. 3160  27.8  
2.3766  27.8  
2.1553  27.8  
2.3856  29.1  
2.1931  29.1  
2.4914  29.1  

IV-70 

Table A.19. 	Summer Metabolism Data, Hanna Reef Station 29. Experimental salinity 18.15-22.55 ppt. (Asterisks indicate fish that were belly up at end of run.) 
Log mg 02 consumed hr-1 1.4158 0.9884 1.0645 
l.6196 l.5860 1.2544 1.2857 1.5140 1:1.1012 1.0320 0.8766 1.1895 1.1422 ~╖:o. 6964 1.1842 0.9311 1.0949 1.1993 0.8866 1:1.8270 1.2384 
~╖:1.2407 
l.7479 
~╖:i.4716 
1:i.2841 
Log Weight  Temp.  
(g.)  oc  
2.3541  20.8  
2.0934  20.8  
2.1072  20.8  
2.5911  20.8  
2.4151  21.0  
2.2041  21.0  
2.4065  21.0  
2.4233  21.0  
2.1173  18.3  
1.9823  18.3  
2.1584  18.3  
2.4265  18.3  
2.2553  18.0  
l.9345  18.0  
2.2148  18.0  
1.9542  18.0  
2.0000  18.0  
2.3010  18.0  
2.0170  18.0  
2.5944  29.5  
2.0000  29.5  
2.0414  29.5  
2.4771  29.5  
2.3483  29.5  
2.1553  29.5  

IV-71 

Table A.19. (cont.) 
Log mg 02 
consumed hr-1 

1. 5593 
1.5686 
~:1.4322 
1.5878 
~╖:1.0116 
2. 0542 
1.3894 
1. 5937 
~╖:i. 5159 
Log Weight (g.) 2.3802 2.4713 2.1461 2.2122 
1.9542 2.7497 2.0090 2.1400 2.4624 
Temp. oc 
29.5 
29. 5 
29.5 
29.5 
29.5 
29.5 
30.0 
30.0 
30.0 
IV-72 

Table A.20. 
Log mg 02 consumed hr-1 -.Х:1. 9633 1.5470 -.':1. 8940 1.6889 1.5664 1.4969 1.4024 
l.5959 
1.7109 
l.7904 
1.8168 
1.9829 
1.9577 
l.9746 
2.1102 
2.1111 
1.4402 
1.7606 

-.':1.6034 1.8413 1.6444 1.2821 
l.5058 
l.7069 
l.6092 
Summer Metabolism Control Data. Experimental salinity 
19.8-20.9 ppt. (Asterisks  indicate fish that  were  
belly-up at end of run.)  
Log Weight  Temp.  
(g.)  oc  
2.5682  29.5  
l.9823  29.5  
2.4914  29.5  
2.3324  29.5  
2.4639  20.0  
2.4346  20.0  
2.3838  20.0  
2.5900  20.0  
2.3541  29.5  
2.5211  29.5  
2.4216  29.5  
2.6170  29.5  
2.3617  29.5  
2. 5289  29.5  
2.6212  29.5  
2.7559  29. 5  
2.1987  20.2  
2.5490  20.2  
2.5315  20.2  
2. 5682  20.7  
2.5238  20.5  
2.0864  20.5  
2. 3979  20.5  
2.5465  20.5  
l.9868  20.5  

IV-73 

Table 20.A.  (cont.)  
Log mg 02 cons urned hr-1  Log Weight (g.)  Temp. oc  
l.6356  l.9590  20.5  
l.3581  2.2480  20.5  
l.3851  2.1847  20.5  

IV-74 

APPENDIX IV-B. Operating Seguence and Data Recording for ContinuousнFlow Respiration Chambers. Galveston E.a~ ~tu9Jг 
Dute__.2_7_~.e1 ___ Sourco k(e-n-2a-/j (i2-)
__,._y_/_1.....,72 
1 
Temperature ~f..3 ░ C J\.ocliiwition Conde_~.d~ 
Salinity / 7 t:. o P/'L . Barometric Presa -i.3cJ.t).J-/f -77/,/?-v~ 


I  
Input check Air oheok Aquo oheok  9'!  Х  93  Jg  I I.I I  f 2  I Rt/I /DD r i  
\/eight ( g") SL (miuo) FL (mmo)  // 0 /&' o ;2._ 0 _) -н 93 /i'o  /I J 17Z  f /&J' I /so 1~3  


Remarks h.s'4 #'4 /6~A-..s ~~~╖; ~cc~~La/~ ~~..5 ~l ~d..e,"' 

IV-75 

SEQUENCE OF OPERATIONS FOR USE OF CONTINUOUS-FLOW RESPIROMETERS 

1. 	
Turn on electrodes (to 0-100% or 0-200%) as soon as possible before experiments begin; the circuitry needs to stabilize for at least an hour or two or else there will be drift problems. 

2. 	
Set up chambers, pump, constant head reservoir, and water lines from reservoir to chambers. 

3. 	
Capture fish and put them in chambers; close chamb~rs, and try to remove as many bubbles as possible. 

4. 	
As a preliminary measure, set the input electrode in air to 100%. Acceptable results for this process can be obtained by holding the electrode over the aquarium water at a place where there is an air-stone bubbling; there seems to be enough air movement in such an area to keep the membrane surface area from becoming 02 depleted. If you are unable to adjust the meter to 100%, or if the needle is erratic, check (1) the 0-ring on the membrane or (2) replace the membrane. Then take 2 of the rubber washers, and put the electrode in the plexiglas electrode holder. Hook-up the input line and let water flow past the input electrode. Allow several minutes to let the input line be flushed and the temperature compensation circuit come to equilibrium, and then read the input saturation. Refer to manufacturer's instructions for operation of electrodes and meters if malfunctions seem evident. 

5. 	
Check electrodes 1,2,3,4 for erratic readings or inability to adjust meter, and if necessary replace membranes. Insert all four electrodes plus washers into their appropriate electrode holders. Make certain they are not in too tight or else the water flow past them can be restricted. 

6. 	
Turn stopcock on input so the black dot points to the right, that is so the water flow is into the grey PVC pipe manifold behind the stopcocks. Be certain of the present input saturation reading. Be certain the cork is in the Tygon (outlet) tube at the left end of the manifold (it is a good idea after turning the input stopcock to take the cork out of this tube for a minute to flush out the manifold). Then, one by one, turn the stopcocks for electrodes 1,2,3,4 to the left (so that water comes from the manifold to the electrode), and set the meter for that electrode to the reading the input meter had just before you turned its stopcock. Allow enough time for the reading to become steady before adjusting the meter. After the meter is set, turn the stopcock back so the dot is up and flow is coming through the chambers to the electrode. Then go to the next electrode until the 4 are set. 

7. 	
Adjust flow rates with pinch clamps on hose leading from chamber to stopcock. The flow rate should be such that there is at least a 10% difference between input saturation and chamber saturation. 

8. 	
Let the animals acclimate to the chambers for 1.5-2 hrs. 

9. 	
When nearly ready to begin the run, remove electrode (Input only) and recheck the air calibration (as previously described in #4). Reset if necessary, but give the electrode time to come to thermal equilibrium with the air before resetting). Then reinsert input electrode into holder and measure the input saturation. Recheck the settings for electrodes 1,2,3,4 in the manner described in #6 above, being certain the manifold is flushed before the check is made. After the check is made recheck the flow rates to be certain the minimum 10% difference is maintained. 

10. 	
Certain data necessary for the data sheet is to be found in the log book, such as fish numbers, etc. Be certain to record the barometer reading. Official methodology follows that of Nathaniel Bowditch.; tap the face lightly with finger once or twice, then read. 

11. 	
Take readings of % saturations and flow rates at least once every ZO minutes for l hour; continue with further 20-minute readings if the first readings appear erratic. 

12. 	
When the last reading is taken, get data for drift measurement. Read input saturation and enter on sheet on input check line. In the same manner as #г, shunt input water into manifold, flush manifold, and see what the reading is for input water flowing past electrodes 1,2,3,4. When this is done, shut off input water, and remove input electrode; then measure air saturation as in manner of #4 above. Record this saturation figure on data sheet line that for air check in column which reads input. The check of the #1,2,3,4 electrodes with input water should be recorded on line "input check" and the appropriate columns. 

13. 	
Weigh, measure fish, record sex and maturity stage, and take scale samples. 


IV-76 

IV-77 


CHAPI'ER V 
A Blue-green 	Algal Assay of Water Quality 
by 
C. 	Van Baalen, Warren Pulich, and R. ornonnell University of Texas 
Marine 	Science Institute Port Aransas, Texas 
INTRODUCTION 
The prior use of the blue-green algal bioassay as an index of water quality is described in detail in IAC (68-69)-408, Ecological Studies of Galveston Bay, Copeland, Ward, and Fruh, p. 179. Any modifications in procedure, introduced here as a result of increased experience with the assay, are noted where appropriate. 
It was, as stated in the proposal IAC (72-73)-183, the purpose of this section to re-examine the thesis that an algal growth assay for determination of water quality has merit. The degree to which this may be true will be considered in the discussion of the results obtained. 
Sample Collection, Preparation and Assay 
All samples (Note a) were collected in new, carefully rinsed (distilled water) polyethylene bottles. Immediately upon receipt of the samples a portion was filtered through a 0.45 um Millipore Filter, then the filtered and unfiltered samples were either immediately assayed or stored frozen until assays could be done, see following protocol. 

Filter lOml through Mix, remove lOml and sterile 0.45 um Millipore heat at 95░C for 10 mins. filter. In early sample periods 
Oct., Nov., Dec. and Jan. a sample was also autoclaved. 
2. 
Add 9 ml 	of autoclaved ASP-2 Medium (Note b) to each sample. 

3. 	
Inoculate sample plus ASP-2 Medium in growth tubes with 1 ml (approximately 6x107 cells) = 3x106 cells/ml of organism 17a at start. 

4. 	
Immediately incubate under growth conditions, 39░C, 4 (2 on each side) fluorescent lamps, F20Tl2D, 7.5 cm from lamp front edge to level of growth tubes, approximately 350 ft. c. Tubes continuously bubbled with 1% C02-in-air. 

5. 	
Read growth tubes turbidimetrically on a 1.umetron Model 402-E Colorimeter, every 3 to 4 hours. 


Note a. 	Samples received on a monthly basis from the U.S. Corps of Engineers through the kind cooperation of Mr. Ernest H. Whittig were from stations l (l foot and 2/3 depth), 14 (mid-depth) 17 (l foot and 2/3 depth), 18 (mid-depth), 22 (mid-depth) 33 (l foot and 2/3 depth), 38 (mid-depth), 39 (mid-depth), and 41 (mid-depth). Samples received on a quarterly basis through UTMSI were from stations 14, 17, 22, 26, and 29. Sample locations are indicated on the map by circled numbers or numbers in parentheses (see page V-3). 
Note b. 	ASP-2 is the same medium as described on page 181, Table 32 of IAC (68-69)-408 Report, Ecological Studies of Galveston Bay, except that the Vitamin B12 was raised to 8 pg/liter and the NaN03 was lowered to 1 gm/liter. 
Results 
On Figures l through 16, the specific growth rate constant k, in log10 units/day is plotted on the ordinate. The larger the value of k the faster the growth rate, when k = 0.3 the generation time is one day, for example if k = 2.4 there are 8 generations/day or one generation time every 3 hours. The abscissa (Figs. 1-16) represents the date of sample collection, beginning in October, 1971. Arrows on graphs 3-16 indicate samples collected by UTMSI during their four intensive sampling periods. All other points represent water samples collected for us by the U.S. Corps of Engineers. 
The╖ solid circles on Figures 3-16 represent the growth rate of organism 17a on filtered water samples; solid squares the growth rate on unfiltered water samples. The half-shaded circles indicate the following: (1) For the period Oct., Nov., Dec., and Jan., the halfнshaded points (one only) are unfiltered water samples, autoclaved at l2l░C for 15 minutes. Since these autoclaved samples came out the same as the unfiltered heated samples this sample treatment procedure was dropped after January. (2) All other half-shaded points shown on Figures 3-16 represent reassays (heated) of a given sample at a later date. For example, on Figure 4, January sample time there are 4 half-shaded points, one represents an autoclaved sample, the other three represent reassays at three later times. 
In all the growth work estimates of lag time, that is initiation of growth in the heated samples as compared to the control tubes, was 
V-2 

v -3 

Description of Figures 
Fig.  1  Summary of growth rates  (k) of organism 17a found with filtered  
wate~ samples (dots) and control growth rates of organism 17a  
(solid line).  See  text for method of measurement.  The time  
scale in this and subsequent figures shows  a  single year  
starting with October 1, 1972,  the divisions  are  at 0.1 year  
intervals,  or 1.20 months.  The growth rate scale divisions  
are  at intervals of 0.25/day (base 10).  
Fig.  2  Summary of growth rates of organism 17a found with unfiltered  
water samples.  Same units  as  Figure 1.  

Figs. 3-16 Growth rates of organism 17a on water samples from stations (ST) indicated. Abscissae indicate sampling times from October, 1971 (0.00) to September, 1973. Solid circles represent filtered water samples, solid squares represent unfiltered water samples, half-shaded circles represent either autoclaved or reassays of same sample (see text), bars represent lag time in hours as compared to controls. 
Figs. 17-20 Summary plots of the growth rates of organism 17a found with unfiltered water samples versus selected environmental variables. The environmental variables are expressed in the following units; Temperature is in degrees c, Nitrite in ppm Nitrogen, Salinity in parts per thousand, and Secchi disc reading in feet. 
v -4 

RLL STRTIONS 
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v -5 



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v -6 
STATION 22 STRTION 26 

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v -7 

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v -8 

RLL STRTIONS ENVIR 

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v -9 

made. This information is shown as the bars under the sample symbols. The scale is shown on the lower right-hand side of the figures 
The solid line in Figure 1 represents the control growth rates of organism 17a in medium ASP-2, medium ASP-2 diluted in half with distilled water, or medium ASP-2 diluted in half with offshore sea water. The data simply indicate the amount of spread commonly found in such repetitive growth assays. They also provide a degree of reliability estimate against which the assays of the natural samples can be judged. It should be noted that the maximum values of k in this work are 20-25% higher than in the original work IAC (68-69)-408, an advantageous situation since it allows small differences in growth rate to be more easily seen. 
The dots in Figure 1 are a summary of the growth rates found using the filtered water samples. The growth rate of these samples is the same, within limits of experimental error, as the controls in Figure 1. This can be taken as a firm indication of the thesis that there is no combination or concentration of common ions expected to be found in filtered natural waters which when added to ~ strength Medium ASP-2 has any deleterious effect on growth of organism 17a. 
Discussion 
The controls, the filtered natural water samples, the replicaнtion of patterns from the same station at different water depths and replication of selected samples at different times, all serve to validate the blue-green algal growth assay in terms of reproducibility. 
The major difficulty in interpreting the data is the meaning of the depression of growth rates seen in the unfiltered, heated samples. In my opinion (CVB), without experiments designed to solubilize material off the particulate load, which will show similar depressions of the growth rate of 17a, we cannot at this time speak with certainty upon the nature of toxicity in the samples. One approach to this problem is potassium persulfate oxidation of the unfiltered water samples. This should enable us, if material X is inorganic, to release it from the particulate material and hopefully then to demonstrate that a real toxic material does exist largely bound to the particulate load. ~~ If the material affecting growth is organic, then persulfate oxidation should destroy it. 
Keeping in mind the above reservation the following general comments can be made on the results. 
1. 	
Depressions of k in the range 2.0 are not significant. 

2. 	
Depressions of k to values below 2.0 are significant, and may indicate sample toxicity. 

3. 	
Depressions of k in the range of 1.0 are highly significant and are at the present time interpreted as indicating that materials deleterious to the growth of 17a are present in the particulate load only. 

4. 	
Lag times, longer than 4-5 hours, are also interpreted as in point 3 above. There is also some correlation between lag time and growth rate, the longer the lag, the slower the growth rate. 

5. 	
Although some of the unfiltered, heated samples induced long lags or slow growth rates, none was completely inhibitory to growth. 

6. 	
Figure 2 summarizes the growth rates found with unfiltered samples (Figures 3-16). Clearly there is considerably more spread in the data than in Figure 1. In addition there is a greater frequency of toxic samples in the period January through April. Figures 3-16 show in more detail patterns of growth depression with time of year and with station location. Positive correlation of these patterns with other types of data would reinforce the indications from the algal assay that certain water masses in Galveston Bay are toxic! 

7. 	
Preliminary attempts at correlation of the algal assay results with temperature, nitrite, salinity, and Secchi disk readings are shown in Figures 17-20. The only obvious correlation is, as might be anticipated, with the Secchi disk readings. 


v -	10 
Without further careful probing of the nature of the response of the organism 17a to the particulate load in natural water samples there is little argument that can be made for its (or any other type of microbial assay) routine use as a monitor of the water quality for an area such as Galveston Bay. However, this whole area of the use of or observations on the biology of a given area as an aid in defining the environmental "quality" is fraught with difficulty (1). For this reason alone, it seems useful to continue study of a reproducible growth assay such as performed herein with organism 17a, for whatever information it may yield. The fact that toxic samples were found needs to be followed up, hopefully the toxic material(s) could be isolated and identified. 
References 
1Fjerdingstad, E. 1971. Microbial criteria of environment qualities. Ann. Rev. Microbiol. 25, 563. 
v -11 


CHAPI'ER VI 
Galveston Bay Benthic Comrm.lnity Structure* As An Indicator of Water Quality 
by 
J. s. Holland, Nancy J. Maciolek and C. H. Oppenheimer University of Texas Marine Science Institute Port Aransas, Texas 
INTRODUCTION 
Galveston Bay is one of the most economically important and ecologically endangered estuaries on the Texas Gulf coast. Its economic importance stems from its proximity to one of the major population centers in Texas which depends on the bay for transнportation of goods, waste disposal, cooling water, recreation and aesthetic appeal. Galveston Bay is also economically important as a major nursery and fishery area for commercially important estuarine and marine organisms. Unfortunately, these two uses of the bay tend to be mutually exclusive and the predominance of the former has brought about the question of the ecological status of Galveston Bay. 
A great deal of significant estuarine research has been done in the Galveston Bay complex in the past decade. Several state universities (University of Texas, Texas A&M University), state agencies (Texas Water Quality Board) and federal agencies (NOAA, NMF Laboratory and the E.P.A.) have had major research programs which dealt directly with different ecological problems in Galveston Bay. The present study to determine seasonal and spatial variations in benthic communities was undertaken to augment water toxicity studies. Funding was provided through an interagency contract between the Texas Water Quality Board and the University of Texas. 
It has been proposed (Wilhm and Dorris, 1968) that comrm.lnity structure of aquatic organisms may provide a better interpretation of water quality than standard physical and chemical analysis or toxicity studies. Hohn (1959) used diatom populations as a measure of water quality in selected areas of Galveston Bay. Bechtel and Copeland (1970) claimed that diversity indices calculated from numbers and weights of fish collected in Galveston Bay provided useful indicators of "environmental and pollution" stress. 
Benthic fauna have been used in many studies of the relation between community structure and environmental stress, both natural and man-made. Boesch (1972) used Shannon's species diversity formula 
*Published in Cont. Mar. Sci. 17:169-188, 1973 
to investigate the structure of macrobenthic communities in marine and estuarine nsoft mudn habitats. He found that diversity increased with depth offshore and decreased with naturally stressed and polluted estuarine areas. Sanders (1968) made a classic study of marine nsoft bottomn fauna of different depths, latitudes, temperatures and salinities. He defined a diversity index known as the rarefaction method and proposed a stability-time hypothesis to explain community differences. Wilhm (1967, 1968, 1970) stresses the particular suitability of benthic macroinvertebrates for the study of water quality as their habitat preferences and low motility cause them to be directly affected by substances that enter their environment. He champions the use of diversity indices as associations or populations of benthic organisms are more reliable than singular ''indicator" species and diversity indices simplify the presentation of much data concerning numbers and kinds of organisms in a community. 
These papers and many others (Wilhm and Dorris, 1966; Coull, 1972; Gage, 1972; Spence and Hynes, 1971 and Barnard, 1970 among the most recent) have indicated that benthic community structure may indeed be a useful indicator of water quality. The present study is an attempt to determine water quality at selected Galveston Bay sites through investigation of macrobenthic community structure. 
AREA, METHODS AND MATERIALS 
Description of Study Area 
The Galveston Bay complex is the largest estuary system on the Texas coast. It is comprised of several drowned river mouths and several barrier island bays. Five sampling stations representing the broadest possible coverage of the bay were selected for this study (Fig. 1). 
Three stations were located in the lower (seaward) portion of the bay. Station 14 in West Bay was selected on the hypothesis that its position, far from the major population areas and with openings to the Gulf at each end of the bay, would provide a "clean" site with the relatively stable conditions normally attributed to lower estuarine areas. Station 17, in the Texas City ship channel, was of interest for several reasons. It is at the confluence of several man-made channels, in an area most influenced by inflowing Gulf water and is possibly affected by the huge industrial complex in Texas City. Station 29 in East Bay was selected for the relative stability of its physical parameters and because water from the upper bay may have an effect o╖n macrobenthos of this area. 
Two stations were chosen from the upper bay regions. Station 22, at the mouth of the Kemah Channel, is close to the Houston ship channel and this area might show effects from outflow of the Clear Lake region. 
VI-2 
Houston Ship 

Figure 1. Location of Galveston Bay Sampling Sites VI -3 
Station 26, in Trinity Bay, is in an oil field area, very close to the mouth of the Trinity River and subject to great salinity variations relative to other bays in the complex but less variations than many other parts in Trinity Bay. 
The study areas had basically two sediment types. Stations 17, 22 and 26 had predominantly soft mud bottoms with small amounts of shell fragments while Stations 14 and 29 had harder mud-shell sediments. 
The difference in sediment types and position in the bay divided the stations into three types: lower bay, hard bottom; lower bay, soft bottom; and upper bay, soft bottom. In any investigation of the benthos, these differences, particularly the differences in sediment types, must be considered. 
Field and Laboratory Methods. 
Benthic collections were made at the five stations in Galveston Bay (Fig. 1) in October, 1971, January, April and July, 1972. Each sample was comprised of four grabs made with a modified Jackson controlled-depth, volumetric bottom sampler (Jackson, 1970). The sampler was chosen because with it depth of maximum penetration can be preset and the sample can be taken from a uniform depth (approximately 7 cm. in this study). The sampler covered an area of 25 x 18 cm. so that in four grabs approximately 12,600cc. or 1/2 cubic foot of sediment was collected. In the harder sediment and shell of Stations 14 and 29, maximum penetration was not achieved on all grabs consequently, less sediment was obtained. Handles on the grab limited use to water of less than 14 ft. This affected only Station 17 when benthos collections were made just out of the channel. Field preservation of the samples varied slightly as better techniques were developed. The basic technique was to anesthetize the organisms in the sample with a 5% mixture of menthol crystals in 40% ethanol by adding the mixture directly to the sediment sample. The samples were later (4-6 hours) sieved in a graduated sieve box whose smallest mesh was 1.5 mm. All organisms retained by the screen were preserved in 70% alcohol (either ethanol or isopropanol). The preserved collections were sorted to species and identified. Counts of number of species and numbers of individuals per species were made. 
Salinity, water temperature and dissolved oxygen were the major physical parameters recorded at each collection. 
Data Interpretation Methods. 
Three methods of interpreting community structure were used. 
I. Margalef (1956) proposed possibilities of analyzing natural communities through use of the information theory. Species diversity in natural communities may be equated with the uncertainity concerning the species of an individual drawn at random from the community. The more species in the community and the more evenly the individuals are distributed, the greater the uncertainty of selecting one individual of a certain species, hence the greater the diversity. Information theory is a means of quantifying the amount of uncertainty which is then interpreted as diversity. Pielou (1967) gives an excellent explanation of the fundamentals of information theory as applied to biological communities. 
VI -4 
A. 	The Shannon-Weaver diversity index -provided one interpretation. s 
H' = -E Pi loge Pi natural bels/individual 
i = 1 
The computational method used followed the use by Bechtel and Copeland (1970). WiThm (1968) explains the use of Hn as an estimator of H'. s Ni Ni 
ff' = -EN loge N natural bels/individual 
The term natural bels/individual is used as natural logrithms were used in this formula (Pielou, 1966b). Calculations for Hn were programmed for a Monroe 1766 desk computer. 
Our collections correspond to the Type B collections of Pielou (1966b) as they were considered to be representative of a larger parent population about which inferences were to be made. The basic assumptions involved in our use of Shannon-Weaver H' are that all species found in the population will be represented in the sample and that parent population does indeed exist as a homogenous entity. The former assumption may present a weakness in this methodology. 
B. Margalef's (1956) index for community diversity, D, was calculated: 
s Ni
D = E 	Ni log2 N 
i = 1 
where D = community diversity, s = number of species, Ni = individuals in the species and N = total number of individuals. 
The community diversity index D is closely related to numbers of individuals (sample size) and therefore is not useful in comparing the structures of unequally abundant communities. By dividing D by the number of individuals, an individual diversity index a, which is not related to numbers of individuals, is obtained. The index a is then useful in comparing community structures using samples of differing sizes. 
The theoretical maximum diversity (Dmax) and minimum diversity (Dmin) were calculated for each collection: 
Dmax 	= log2 N ! S log2 (N/S) ! 
Dmin 	= log2 N! log2 [N (S-1)]! 
The calculation of D, Dmax and Dmin allows the calculation of redundancy, 
R, of a community. Redundancy may be approximated by several formulas using Dmax and DminХ With small samples, such as our benthic samples, 
the following equation is most accurate. 
[
~ 1og2 Ni! X Log (N/S) !] 
R = i = 1 
~~~~~~~~~~~~~~~~~~~~~н
(Log2 	(N -S + l)! -S Log (N/S)!] 
VI -5 

D, d, Dmax, Dmin and R were calculated on a 1106 UNIVAC computer by personnel of the Texas Water Development Board. 
II. The Sander's rarefaction method is a method of allowing direct comparison of the numbers of species in samples by reducing (rarefying) them to a common size (Sanders, 1968). This methodology is limited to comparison of communities in similar habitats and to use only on similar groups of organisms. The normal application is to specified fractions of a.community (the polychaete-bivalve fraction was used in the present study). This method has been used by Gage (1972) for macrobenthos studies and Coull (1972) for analyses of species diversity of meiobenthic copepods of the deep sea. 
III. The "probability of interspecific encounter" method, according to its author (Hurlbert, 1971), is not a species diversity index. 
Ni 
2)
-N ) ( 1 -sE
Lil = 
(N~ 1 i =1 N 
where Ni = ournber of individuals in the ith species and N = total number of individuals. Lil (P.I.E.) is the probability that an encounter between two members of a community will be interspecific. Or, if an organism from outside the community (biologist, etc.) encounters at random two organisms in the community, Li 1 is the probability that they will be of different species. P.I.E. values may be valid indicators of water quality. Hurlbert (1971) states that communities with high P.I.E. values are comprised of organisms which can tolerate few random components in their interaction with their environment. Sanders (1968) describes the same sort of community as predominantly biologically controlled or lacking in physical stress. It follows then that stressed areas should be characterized by communities with low P.I.E. values. Hurlbert states that corrununities having different species composition are not intrinsiнcally arrangeable in linear order on a diversity scale and that P.I.E. values were not intended as a new diversity index. Use of P.I.E. in this study is based on the hypothesis that probability of interspecific encounter may be a measure of stress in estuarine macrobenthic comrru.mities. 
RESULTS 
Sampling of the macrobenthos at five stations in Galveston Bay resulted in the collection of 9381 individuals comprising 127 species (Table 1). Almost two-thirds of the individuals were of one species of barnacle, Balanus eburneus. The dominant taxocenes were polychaetes, bivalves and crustaceans. Individuals from ten other taxocenes were collected. 
Fifty-nine species of polychaetes were collected. Polychaetes were found at all stations but were more common in lower bay stations and noticeably scarce at Station 26 in Trinity Bay. Polychaetes were 
VI -6 
Table 1. List of Species and Abundances in Samples Taken at 5 Galveston Bay Stations (Collection dates are: 1 -October, 1971; 2 -January, 1972; 3 -April, 1972; 4 -July, 1972; 4a -August, 1972) 
Station 14 Station 17 Station 22 Station 26 Station 29 Phylum Order Species 1 2 3 4a 1 2 3 4a 1 2 3 4 1 2 3 4 1 2 3 4 
Coelenterata: Cerianthes sp. (?) 1 Rhyncocoela: Nemerteans 1 4 4 Nematoda: Nematodes 1 1 11 Annelida: Polychaeta: Lepidonotus sublevis 2 
Lepidasthenia commensalis 5 1 3 14 7 Polynoid A. 1 Sthenelais boa 3 "scaleworm"--1 Psueoourythoe ambigua 1 Ampfonomid A. 1 Eumida sanguinea 57 Nereiphylla fragilis 4 2 Gyptis vittata 1 1 5 Anc1strosyII1s jonesi 1 Parandaiia f auveii 2 2 Typosyllis corallicoloides 3 5 Syllis cf. corallicoloides 4 Nereis succinea 9 13 1 7 2 74 11 3 60 6 7 93 42 14 Ceratonereis irritabilis 1 Laonereis culveri 1 Glycera americana 2 9 1 4 7 2 2 Glycinde solitaria 3 2 2 Diopatra cuprea 1 1 13 9 2 1 Marphysa sanquinea 1 4 I.umbrinereis parvapedata 3 liiffibrrnereid A Х 1 Drilonereis magna 5 1 2 3 1 Stauronereis rudolphi 1 6 Dorvilleid A. 1 Aricidea jefferysi 1 Aricidea sp. 1 Streblospio benedicti Х 1 328 1 1 1 5 Prionospio pinnata 6 1 14 5 Polydora ciliata 5 Polydora socialis 2 
VI -7 

Table 1 (continued) 
Station 14 Station 17 Station 22 Station 26 Station 29 
Phylum Order Species 	1 2 3 4a 1 ~ 3 4a 1 2 3 4 1 2 3 4 1 2 3 4 
Tharyx setitera 3 3 1 Notomastusemipodus 1 Mediomastus californiensis 7 3 .. 4 1 57 
Heteromastus rilirormis .. 0. .. 14 3 . 2 Heteromastus elon~ata 1 Q!ymenella torqua a calida 4 15 1 Clymenella mucosa 5 Branchioas~chis americana 1 Maldanid A. 1 .. Pectinaria gouldi 2 2 Melinna maculata 1 47 5 2 9 .. 10 Amphictis gunneri f loridus .. 1 
Ampharetidae or Terebellidae .. .. 1 Pista palmata 26 57 l51"S'ta s p Х 196 70 Eupolymnia crassicornis 253 .. Chone duneri 3 Megalomma bioculatum 3 2 Sabella melanostigma 2 .. Sabella micropthaima 5 Sabelhd A. 1 Sabellid or Serpulid 1 
Eupomatus dianthus 1 21 2 .. 	31 54 7 
Hypaniola gunneri f loridus 1 Ehlersileanura incisa ;3 UnidentHied worm "X" l 
Unidentified worm "Y" .. 1 Oligochaeta: 1 Mollusca: Gastropoda: Littoridina sphinctostoma '. 16 
Crepidula plana .. 2 1 .. 1 	9 
Crepiduia rornicata 1 l Crepidula sp. (?) 1 1 Thais haemostoma 3 Anachis obesa 1 1 1 42 13 28 Anachis avara 5 TUrbonilla (interrupta ?) 3 pYramidella crenurata 1 
Bivalvia: 	Nuculana concentrica 2 1 .. Anadara transversa 2 1 1 
VI -8 
Table 1 (continued) 
Station 14 Station 17 Station 22 Station 26 Station 29 Phylum Order Species 1 2 3 4a 1 2 3 2fa 1 2 3 4 1 2 3 4 1 2 3 4 
Am~gdalllm sp. 1 1 7 1 1 
Mo io1iis demissus 1 
Brachidontes exustus 3 
Ostrea eguestris 1 .. 
Crassostrea virginica 2 
Aligena texasiana 35 
Laevicardium mortoni 1 
Mlllinia lateralis 21 28 1 1 9 1 95 1 
Rangia f lexuosa 49 1 1 
Rangia cuneata 3 
Ensis minor 1 1 
Macoma mitchelli 1 
Macoma brevifrons 1 5 
Tellina aequistriata 12 
Tagelus divisus 1 
Abra aequalis 1 
t:Umrngia tellinoides 1 8 4 1 
Cyclinella tenuis 1 1 
Congeria leucophoeta 1 1 4 1 
Mercenaria c. texana 1 2 1 
Diplothyra smythi 1 4 1 
Barnea truncata 1
.' 
Iijonsia hyalina f loridana 28 203 Arthropoda: Pcynogonida: 1 2 1 
Crustacea: Balanus eburneus 7 40 12l 13 ХХ 4711 1470 39 7 
Cassidinidea lunifrons 13 
Ampithoe sp. 5 
Ampelisca abdita .. 3 
Corothium louisianum 1 4 3 
Meh a sp. 3 1 .. 6 26 51 2 20 8 
Amphipod "A" 4 
Amphipod "B" 1 1 
Panopeus herbstii 1 l 2 4 24 25 8 26 
l?anopeus sp. .. .. 2 
Pinnixia retinens .. .. 2 .. 
Clibanarius vittatus 1 
He~atus pudibundus 1 
Rhithropanopeus harrisii .. .. 8 7 1
.' 
Petrolisthes armatus 1 4 1 12 5 
VI -9 
Table l (continued) 
Station 14 Station 17 Station 22 Station 26 Station 29 Phylum Order Species l 2 3 4a l 2 3 4a l 2 3 4 l 2 3 4 l 2 3 4 
Hexapanopeus augustifrons .. .. .. 1 
Eurypanopeus CleEressus .. .. . . 1 2
Х 41 
Neopanope t. ." .. 1 8 4 1 
Unidentified ~"A" .. .. 2 
Unidentified Crab "B" .. l 
Unidentified xanthids .. 0. 4
0 Х 
Neopanope texana texana 1 Sipunculida: 'Phascolion-strombi 2 
Phoronida: Phoronis architecta (?) .. l .. 2 7 Echinodermata: Ophiuroidea: Micropholis atra 3 6 6 5 Hemipholis elon~ata .. l .1 
Х 9 
Х !I Х 9 
Holothuroidea: CUcumerid .. .. 1 Chordata: Ascidiacea: Mol~.:S.manhattensis (?) 1 
Pisces: Myrophis punctatus 1 
VI -10 
not collected at this station in October and were represented by only 
one species, Nereis succinea, in April and July. The January collection 
at Station 26 had small nuffibers of seven polychaete species. It seems 
apparent (Table 1) that the polychaete fraction of the samples were 
variable through the year. The first and last collections at all 
stations appear to have both fewer species and number of polychaetes. 
Analysis of variance of H" and P.I.E. values (Table 4 for P.I.E.) did 
not indicate significant differences in benthic populations through 
time. Analysis of H" values computed for polychaetes only indicated 
no significant variation in that fraction of the community through time. 
Twenty-five species of bivalves were collected. No pattern in the general distribution of bivalves through time was apparent. Bivalves were least diverse at Stations 17 and 26 although large numbers of several species were collected at Station 26 in October. Station 14 showed the greatest diversity of bivalves. 
Twenty-two species of crustaceans were collected. The population explosion of B. eburneus at Station 26 in April and July made crustaceans far outnumber-all other taxocenes. As with bivalves, no particular distribution pattern through time was observed. Some peculiarities in crustacean spacial distribution were noted. Station 14. almost lacked decapod crustaceans, while the other mud-shell sediment station, 29, had more crabs than any other station. All stations except 29 show maXilTQ.lm numbers of the barnacle, B. eburneus, in April. Isopods were collected only at Station 26. AmPhipods appeared in the upper bay stations only in the last two collections. 
Only four species, Nereis succinea, Streblospio benedicti, Mediomastus californiensis, and Balanus eburneus, were found at all five stations. Five other species, Diopatra cuprea, Anachis obesa, Mulinia lateralis, Panopeus herbstii and Melita sp. were each found at four stations. All other species were collected at three stations or less. 
The ephemeral nature of the contribution to community structure of some species was noted at many stations. Isopods were found at Station 26 only during the July collection. No barnacles were found at Station 26 in the first two collections but a total of over 6000 were taken in the last two collections. A few Lyonsia hyalina floridana were collected at Station 14 in January, while over 200 were collected at Station 14 in April and none in July. It is apparent that many species either fluctuate widely in abundance or change their habitat preference so as to become less available to the sampling procedures used. 
The complexity of community structure when observed in terms of numbers of species, numbers of individuals and varying patterns of distribution invites some quantitative methods of comparison among communities. Analysis of the present data by three different methods gave similar results. 
VI -11 
Hn values for the samples from various stations were computed (Table 2). October values for Station 14 for both Hn and P.I.E. computations are replacement values following the method of Snedecor and Cochran (1967) for replacement of a single missing value in randomized blocks design analysis of variance. This was necessary because this collection dried out in a defective container. The Hn value of zero at Station 17 in July reflects the lack of organisms found in that collection. This sample was recollected in mid-August and the lack of organisms at ~tation 17 was confirmed. 
Values of D, 9, Dmax, Dmin and R for all collections were given in T9ble 5. D and d values calculated with log2 and loge are given. The d val~es based on loge are the same as Hn values (Table 2). Note that the d log2 values are approximately 1.44 times as large as the a loge values. The zero values found in both tables for Station 14, collection 1 indicate that this data was lost and not replaced as previously noted for the Hn values. 
Probability of interspecific encounter (P.I.E.) values were calculated (Table 3). Low values in April and July at Station 26 are evident in both these and the Hn values. The lack of organisms at Sta╖tion 17 in July is again reflected. 
Variation of community structure through time and space was analyzed by an analysis of variance of both H" and P.I.E. values. (Table 4 for P.I.E. analysis). Both tests showed significant variations between stations but no significant differences between seasonal collections. Duncan's new multiple range test (Li, 1966) was performed on P.I.E. data (Fig. 2). A sharp distinction was obtained indicating that Stations 17 and 26 were different from Stations 14, 22 and 29. 
Rarefaction methodology (Sanders, 1968) was used to generate nspecies diversityn curves (species richness curves following Hurlbert's terminology) for each collection (Fig. 3A through E). These curves were based on the polychaete-bivalve fraction of each collection. Many of the curves were abruptly truncated due to the small numbers of organisms collected at the various stations. 
Examination of the rarefaction species richness curves indicates that species richness was not decidedly different between Stations 14, 22 and 29. Stations 14 and 29 had the greatest numbers of individuals and thus the most complete curves. The initial slopes of these curves are very similar to those found at Station 22. Species richness curves for Stations 17 and 26 indicate some differences. At Station 17, collections 2 and 3 follow the general curve indicated at other stations. The curve for the first collection at this station is decidedly lower than the general pattern at other stations and no curve could be generated for the fourth collection as only one organism was collected. At Station 26, collections 2 and 4 apparently follow the generalized pattern of Stations 14, 22 and 29 but collections 1 and 3, though containing more individuals, show a different species richness. 
VI -12 
Table 2 H" Values 
14 17 22 26 29 
October 0 0.7298 1.0397 0.7277 . l. 7700 January 2. 9698 0.9443 l. 7158 l. 5296 2. 3919 April 2.0043 l. 8033 l. 2778 0.0970 l. 90 33 July 2.5406 0 2.1336 0.3914 2.0032 
-
2.3347 0. 8693 l. 5417 0.6864 2.0171 
Table 3 
P.I.E. Values 
14 17 22 26 29 

October  0  0 .4509  0.8333  0.4740  0.7557  
January  0.9203  0.3253  0. 779 5  0. 650 5  0.8552  
April  0.7686  0.6772  0. 6060  0.0321  0.7617  
July  0.9338  0  0.8460  0.1364  0.8262  
- 
x  0.8967  0.3633  0.7662  0.3232  0.7997  

VI -13 

Table 4  
Analysis of Variance  of P.I.E. Values  
Source of variation  Degree of Freedom  Sum of Squares  Mean Squares  F  

Total  19  1.69072  
Blocks  3  0.10227  0.03409  0.90174n.s.  
Treatments  4  1.13475  0.28368  7. 50358-Jd:  
Error  12  .45368  0.3780  

n.s. = not significant ** = significant at 1% level 
VI -14 

Table 5 Diversity indices D, d, Dmax' Dmin and Redundancy. Values for each station by collection. D and a values for both Log2 and Loge. Log2 Loge 
Station Coll. Diversity d Dmax Dmin Redundancy Diversity d 
14  l  0  0  0  0  0  0  0  
14  2  698.457  4.285  788.943  272.513  0.175  484.089  2.970  
14  3  2001.197  2.900  3441. 268  310.111  o.460  1386.996  2.010  
14  4  102.641  3.666  97.953  65.414  -0.144  71.139  2.541  
<  17  1  18.955  1.053  24. 034  8.258  0.322  13.137  0.730  
H  17  2  545.041  1.363  1694.400  172.196  0.755  377.759  0.944  
17  3  187.336  2.602  262.268  101.937  0.467  129.839  1.803  
1---l  17  4  0  0  0  0  0  0  0  
Ul  
22  l  6.001  1. 500  4.585  3.585  -1.415  4.159  1.040  
22  2  94.073  2.476  107.297  40.846  0.199  65.201  1. 716  
22  3  416.678  1.844  842.872  108.900  0.581  288.792  1.278  
22  4  255.520  3.079  310.324  87.583  0.246  177.097  2.134  
26  l  154. 354  1.050  225.530  14. 391  0.337  106.980  0.728  
26  2  119.179  2.207  186.646  56.274  0.518  82.601  1.530  
26  3  670.847  0.140  13419. 641  73.354  0.955  464. 953  .097  
26  4  894.089  0.565  6175.239  148.728  0.876  619.675  0. 391  
29  1  385.637  2.554  620.068  114.637  0.464  267.279  1. 770  
29  2  2208.692  3.451  3090.955  250.912  0.311  1530.808  2.392  
29  3  466.861  2.746  648.981  117.510  0.343  323.574  1.903  
29  4  289.031  2.890  352. 608  85.203  0.238  200.322  2.003  

P.I.E. 26 17 22 29 14 
Figure 2. 	Duncan's New Multiple Range test of P.I.E. values. Groups of stations underlined by the same line were not significantly different. 
VI -16 


100 200 300 AOO 500 600 
20 
~---------17-2 
10 

600100 200 300 400 500 
20 
Cl) ╡:i H 
u 
╡:i 10 P-l 
Cl) 
W-i 
0 
~ 
╡:i 	100 200 


100  200  
20  
29-2  

10 

100 200 300 400 500 600 
NUMBER OF INDIVIDUALS 
Figure 3. 	Rarefaction curves of polychaete-bivalve fraction of Benthos collections from Galveston Bay. Numbers on lines indicate station and collection number. 
VI -17 
DISCUSSION 

It has been observed (Johnson, 1970; Sanders, 1968; Boesch, 1972) that heterogeneity of physical environment is a major factor influencing differences in benthic community structure. Boesch (1972) documented benthic diversity differences through an estuarine system to the continental shelf. Diversity was highest on the shelf and decreased with distance up the estuary. Saiders (1968) explains this variation with his stability-time hypothesis. With the exception of Station 17, a similar gradient occurred in Galveston Bay. Heterogenous sediment types also influence variance in benthic species diversity. Gage (1972) found more diverse corrununities in muddy sand than in soft mud. No real analysis of the sediment types at the different stations was attempted but our description of the sediments agrees with that of Bechtel (1970) who used the same stations. Stations 14 and 29 had much firmer mud with large quantities of shell and the most diverse communities. The other stations had soft mud bottoms and lower diversity values, except Station 22, which was more similar in diversity to the lov..er bay stations (14 and 29). Duncan's new multiple range test (Fig. 2) and Sanders' rarefaction curves (Fig. 3) indicate this similarity. Mean P.I.E. and Hn values (Tables 2 and 3) for Station 22 are only slightly lower than those of Stations 14 and 29. 
The three basic methods of data analysis appeared to be compatible. Several authors have applied different indices to their data to compare effects (Menhinick, 1964; Wilhm, 1967). Coull (1972) applied several indices in his study but reported only the rarefaction analysis. The present debate (Hurlbert, 1971) concerning the validity of species diversity indices prompted the use of several methodologies of investigating community structure. The Shannon-Weaver and Margalef indices are similar species diversity indices based upon information theory. The Shannon-Weaver H' index was computed using loge while the Margalef functions D, d, and R used log2Х 
No reports were found which contained either H' or d values for estuarine macrobenthic communities. Wilhm (1967, 1968, 1970) has done a great deal of work on fresh water macrobenthos using several indices. Bechtel and Copeland (1970) indicate that the guidelines proposed by Wilhm and Dorris (1968) for biological interpretation of water quality may Qe used for estuarine fish studies. The'ir guideline, based on d, states that index values less than one indicate heavy pollution, values between one and three indicate moderate pollution, and values above three indicate no pollution. These values are probably too high for estuarine community studies. Wilhm and Dorri~ (1968) were working with stream macrobenthic communities and using d as their species diversity index. Bechtel and Copeland (1970) worked with estuarine communities and used the diversity index H'. The estuarine communities, under non-polluted conditions, would be subject to greater natural stresses than those in an unpolluted stream and consequently should show lower diversity index values. The second objection to Bechtel and Copeland's comparison of their data to that of Wilhm and Dorris is in the 
VI -18 
-
comparison of H' to d values. The H' statistic used by Bechtel and Copeland uses loge in itз calculation while log2 is used by Wilhm and Dorris in computing d. An H' value as calculated by Bechtel and Copeland has to be multiplied by approximately 1.44 to convert it into a a value. Thus it appears that the index values used by Bechtel and Copeland were only approximately two-thirds of the value of those used by Wilhm and Dorris. If their data did follow the guidelines as proposed by Wilhm and Dorris, it appearз to be the fortuit9us result of mistakenly using H' to compare with d. As both H' and d were calculated for the present study, ~t is felt that H' values of macroнbenthic communities above 2.0 and d values above 2.5 indicate areas of normal estuarine stress (clean estuarine water). It is difficult to define a clear cut range of values in d~aling with estuarine comrrumities. Quite possibly, we should define n1ower estuarine stressn as the set of normal conditions found in the most stable, seaward portion of the estuary and simply place a single value on clean lower estuary water. We can then acknowledge that lower values indicate areas of greater than n1ower estuarine stress11 Х The cause of the stress, be it pollution or natural stress, cannot be determined from the index value. A value indicating clean, lower estuarine water would vary slightly between estuaries but should remain fairly similar. 
The second of the three basic methods for analyzing community structure was Sanders' rarefaction methodology. It is not a diversity index that attaches a specific number to a given community. It allows the rarefaction (making smaller) of a larger community (sample) to compare the number of species taken to that which would have been taken if a smaller number of individuals had been collected. It is, then, a direct method of comparing species richness between communities. Curves generated for our nhigher diversity11 stations agree closely with those of Sanders (1968) for a tropical estuary. Our curves showed almost identical diversity to those of Sanders although his were made on soft JIUld communities. The soft JIUld communities should show lower diversities (Gage, 1972). We believe the similarity of the curves to be due to the difference in latitude between his tropical estuaries and Galveston Bay. The similarity of these curves points out the nnaturalnessn of the high diversity stations we observed, assuming that the tropical estuaries observed by Sanders were unpolluted. 
The third method of analyzing community structure was proposed by Hurlbert (1971). Hurlbert is highly critical of species diversity as a concept and provides several biologically oriented alternatives. His 1'probability of interspecific encounter11 (P.I.E.) is one used in this study. In his description of the biological significance of P.I.E., Hurlbert describes the low stress, highly biologically adapted coffiJIUlnity as having high P.I.E. values. He says that this type of coffiJIUlnity can tolerate little randonmess in its search for mates, food or hosts, noting that the most random method of plant TTmateнseeking1', wind dispersal of pollen, is essentially absent from high 
P.I.E. communities such as rain forests. The converse should also 
VI -19 
be true: corrununities which are highly stressed, showing great randorrmess in their search for food, mates or hosts, should show low P.I.E. values. In an estuarine situation, with the great variation in natural stress, it was hypothesized that macrobenthic community P.I.E. values might indicate water quality. 
All of our data analysis methods appear to give similar results. The rarefaction curves agree very closely with the HTT, a and P.I.E. values. 
Our data indicate that Stations 14, 22 and 29 are generally areas of normal estuarine stress. The rarefaction curves generally agree with those of Sanders, as previously mentioned and the HTT, d and P. I.E. values are high. Stations 17 and 26 are definitely areas of greater stress. Analysis of variance of both P.I.E. and HTT values indicated differences between the stations. Duncan's new multiple range test (Fig. 2) shows the differences clearly as do the rarefaction curves. 
The greater stress at Stations 17 and 26 apparently is periodical. At Station 17, collections taken in the cooler months (January and April) appear to be close to the average species diversity of the other stations. Those collections made in months when the water temperature was high (July and October) show definite diminution of diversity. At Station 26, the January collection showed HTT, a and P.I.E. values similar to the rrhigh diversityn stations. October values for this station were similar to those at Station 17 while the indices for April and July were very low due to the dominance of the barnacle, B. eburneus. The redundancy, R, clearly demonstrates the dominance of the barnacle in both of these collections. Rarefaction curves for Station 26, which do not take the explosion of the barnacle population into account, indicate that the species richness during January and possibly July was similar to that of the nhigher diversityTT stations. Species richness during October and April was severely diminished. Salinity at Station 26 in January was very low (circa 3░/Jo, Fig. 4). At first glance, one tends to interpret the abundance of species at this time and lack at all other times as an ill effect of high salinity upon this coJilJIUlnity. This does not seem to be the case as a close inspection of Table 1 shows that most of the organisms collected at Station 26 in January were polychaetes which were found at higher salinities at other stations. This was the only collection at Station 26 in which polychaetes were prevalent. Several of the polychaete species found at this time were also found in large numbers at other stations. The high diversity at Station 26 in January is probably due to the annual reproduction cycle of certain euryhaline polychaete species in Galveston Bay. 
This fluctuation in polychaete species numbers and individuals for most stations is apparent in Table 1 but was not shown to be significant by analysis of variance of either P. I.E. or Hrr values. Variance among seasonal samples was analyzed by Bartlett's test and was found to be homogenous. The reason for the statistical nonнsignificance of the apparent fluctuation is believed to be a function 
VI -20 

24 
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.. 
╖н
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. Х ХХ . 
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Oct Jan Apr Jul Collection Months 
Figure 4. Mean Bottom Salinities at Galveston Bay Sampling Sites VI -21 
Table 6 
Concentration of Selected Heavy Metals in Galveston Bay Sediment1: Station Depth As Ba Cr Cu Fe Pb Mn Hg Ni Zn (in.) ppm ppm ppm ppm ppt ppm ppm ppb ppm ppm 
14 0-6 1.4 16 14 8.9 8.4 19 212 56 11 52 17 0-6 3.4 61 89 36 12.7 48 727 111 33 122 22 0-6 1.9 93 50 80 8.0 39 630 114 57 83 26 0-6 2.6 36 25 10 6.8 19 234 53 21 42 29 0-6 1.4 15 24 5.1 5.2 9 260 28 17 32 
1:used with permission from Hann & Slowey, 1972. 
VI -22 

Table 7  
Pesticide Analysis of Galveston Bay Stations*  
Station  PPB  
() ::r:: ~ I lj  () ::r:: ~ I ~  Q) i:: res 'O i:: ╖rl ...:I  H 0 r-1 ..c: CJ res .╡ 0.. Q) ::r::  H 0 r-1 Q)..c: 'O CJ ╖rl res x .╡ 0 0.. 0.. Q) ╡,:i ::r::  i:: ╖rl H 'O r-1 Q) ╖rl q  i:: ╖rl H 'O rl i:::i:  i:: ╖rl H 'O i:: ╡.:i  ╡.:i q q -P4 " P4  q q q -P4 " 0  q з -P4 " P4  E-l з -A.I " 0  E-l q q -P4 " P4  Q) i:: res ..c: .╡ r-1 Q) ~  Q) i:: res 'O H 0 rl ..c: ()  
<H  14  p  p  0.45  
tv VJ  17 22  0.42 0.22  0.66  0.69 0.16  p  p  p  p  p  
26  p  p  
29  0.49  2.63 2.63  p  p  p  p  p  
P  = trace  
~-:used with permission from Hann &Slowey,  1972  

of the mathematical treatment of numbers of species and individuals to give index numbers but the exact reason is not known. The fluctuation in numbers of polychaete individuals collected seasonally is believed to be real. 
It is felt that the existence of greater than normal stress at Stations 17 and 26 is indicated by the macrobenthic corrununities at these stations. Examination of all physical parameters existing at these sites indicates that the stress at Station 26 is probably primarily natural stress. The station is only about five miles from the mouth of the Trinity River and is probably subjected to the maxirrrum TTnaturaln stress of temperature and salinity to be found in the Galveston Bay complex. The analysis of heavy metals in the sediment at Station 26 (Table 6) shows no unusually high concentrations. Arsenic (2.6 ppm.) was the only heavy metal at this station that was high relative to the other sampling sites. Hann and Slowey (1972) state that coastal sediments have an average of 3 ppm. arsenic. Arsenic is found in concentrations of 3-15 ppm. in deep sea sediments (I.D.O.E., 1972), so that the observed arsenic levels in Galveston Bay are not thought to be limiting. Pesticide analysis (Table 7) indicates only traces of two pesticides at Station 26. There does not appear to be any man-made cause for the low diversity structure of the macrobenthos at Station 26. 
The stress indicated at Station 17 is possibly due to pollution. This station is low enough in the bay to have a very stable temperatureнsalinity regime. Its salinity shows the least variation of the sites sampled. It is a relatively deep station so that temperature changes should be moderated. Our collections were made out of the main channel due to depth restriction on our sampler. Channel dredging should have had little effect on the benthic corrununity at our collection site. All physical parameters available to us were investigated. None showed evidence of causing the low values encountered. Concentration of heavy metals in the sediment of this station are consistently either the highest or second highest of all stations sampled. It is not known that the observed concentrations of metals are toxic but the consistently high concentrations are believed to be indicative of a large amount of pollution, probably from the Texas City industrial complex. The sediment heavy metals analysis at this site was made by Hann and Slowey (used with their permission), in July 1972. This was just prior to the final benthos collection in which no living organisms were found. The apparent seasonality of the low diversity values may be due to greater or different pollution during the warmer months or to a temperature interaction affecting the toxicity of the pollutants. Periodic occurrence of low DO values have been suggested as a possible cause of the observed low diversities at this station. However, no low 02 values were observed during the sampling periods. Further investigation of the benthic communities in the Texas City channel and the pollution abatement control of the Texas City industrial complex is indicated. 
VI -24 

CONCLUSIONS 

I. 	Water quality in Galveston Bay based on benthic macroinvertebrate communities is good in most areas sampled. 
II. 	Benthic macroinvertebrate comrra.inity structure indicates that Station 26, in Trinity Bay, is highly stressed. This is thought to be due to natural causes. 
III. 	Station l7, in the Texas City ship channel, is the only station sampled that indicates probable effects of man-made stress (pollution). Possible water temperature-toxicity interaction or more damaging pollution during summer months may be occurring at this station. 
ACKNOWLEDGEMENTS 
This study was made on an Interagency contract with the Texas Water Quality Board. We particularly wish to acknowledge the expertise of Dr. Don Harper of Texas A&M University in the identification of many of our specimens. Mr. Larry McKinney of Texas A&M University and Miss Julie Gillespie of U.T.M.S.I. also assisted in identifications. Drs. Roy Hann and J. Frank Slowey of Texas A&M University graciously made the results of their sediment analyses of the Galveston Bay Stations available to us. Mr. Don Rauschuber of the Texas Water Developrrent Board in Austin kindly calculated several diversity indices. Special thanks go to the crew of the LONGHORN, the research vessel used in this study. The expert assistance of Captains Don Gibson and Elgie Wingfield and Mates Don Gutsch and Ron Musial greatly expedited field work. 
VI -25 

References 
1972. Baseline Studies of Pollutants in the Marine Environment and Research Recornnmdations: The IDOE Baseline Conference, May 24-26, 1972, New York. 54 pp. 
Barnard, J. L. 1970. Benthic ecology of Bahia de San Quintin Baja California. Smithson. Contr. Zool. 44: 1-60. 
Bechtel, T. J. 1970. Fish species diversity indices as pollution indicators in Galveston Bay, Texas. M.A. Thesis, Univ. of Tex. Austin. 
Bechtel, T. J. & B. J. Copeland. 1970. Fish species diversity indices as indicators of pollution in Galveston Bay, Texas. Contr. mar. Sci. Univ. Tex. 15: 103-132. 
Boesch, D. F. 1972. Species diversity of marine macrobenthos in the Virginia area. Chesapeake Sci. 13(3): 206-211. 
Coull, B. C. 1972. Species diversity and faunal affinities of meioнbenthic Copepoda in the deep sea. Mar. Biol. 14: 48-51. 
Gage, 	J. 1972. Community structure of the benthos in Scottish sea-lochs. 
I. Introduction and species diversity. Mar. Biol. 14: 281-297. 
Hann, 	R. w., Jr. & J. F. Slowey. 1972. Sediment analyses -Galveston Bay. Estuarine Systems Project, Technical Report No. 24, Texas A&M Research Foundation. 57 pp. 
Hohn, 	M. H. 1959. The use of diatom populations as a measure of water quality in selected areas of Galveston and Chocolate Bay, Texas. Publs. Inst. mar. Sci. Univ. Tex. 6: 206-212. 
Hurlbert, s. T. 1971. The nonconcept of species diversity: a critique and alternative parameters. Ecology 52(4): 577-586. 
Jackson, H. W. 1970. A controlled-depth, volumetric bottom sampler. Prog. Fish. Culturist, U.S. Dept. Interior 32(2): 113-115. 
Johnson, R. G. 1970. Variations in diversity within benthic marine communities. Am. Nat. 104(937): 285-300. 
Li, J. C. R. 1966. Statistical Inference, Vol. I, Edwards Brothers, Inc. Ann Arbor, Mich. 658 pp. 
Margalef, R. 1956. Informacion y diversidad especifica en las corrununidades de organismos. Inv. Pesq. 3: 99-106. 
Menhinick, E. F. 1964. A comparison of some species-individuals diversity indices applied to samples of field insects. Ecology 45(4): 859-861. 
VI -26 
Pielou, E. c. 1966a. Species-diversity and pattern-diversity in the study of ecological succession. J. theor. Biol. 10: 370-383. 
Pielou, E. c. 1966b. The measurement of diversity in different types of biological collections. J. theor. Biol. 13: 131-144. 
Pielou, E. C. 1967. Use of information theory in the study of diversity of biological populations. In Biology and Problems of Health. Proceedings of the 5th Berkeley Symposium on Mathematical Statistics and Probability. 4: 163-177, Univ. Calif. Press, Berkeley. 
Sanders, H. L. 1968. Marine benthic diversity: a comparative study. Am. Nat. 102(925): 243-282. 
Snedecor, G. W. and W. c. Cochran. 1967. Statistical Methods. Iowa State Univ. Press, Ames. 593 pp. 
Spence, J. A. &H. B. N. Hynes. 1971. Differences in benthos upstream and downstream of an impoundment. J. Fish. Res. Bd. Can. 28: 35-43. 
Willun, J. L. 1967. Comparison of some diversity indices applied to populations of benthic macroinvertebrates in a stream receiving organic wastes. J. Wat. Pollut. Control Fed. 39(10): 1673-1683. 
Willun, J. L. 1968. Use of biomass units in Shannon's forJIU.lla. Ecology 49(1): 153-156. 
Willun, J. L. 1970. Range of diversity index in benthic macroinvertebrate populations. J. Wat. Pollut. Control Fed. Vol. 42 5(2): R221-R224. 
Willun, J. L. &T. c. Dorris. 1966. Species diversity of benthic macroнinvertebrates in a stream receiving domestic and oil refinery effluents. Am. Midl. Nat. 76: 427-449. 
Willun, J. L. &T. c. Dorris. 1968. Biological parameters for water quality criteria. BioScience, Vol. 18(6): 477-481. 
VI -27 


CHAPI'ER VII 
A Definition of BOD in Galveston Bay 
by 
C. 	H. Oppenheimer, Nadine Gordon and Bill Brogden University of Texas 
Marine 	Science Institute Port Aransas, Texas 
INTRODUCTION 
Objectives 
The purpose of this research was to conduct BOD tests on Galveston Bay waters to determine if the BOD toxicity noted in previous Houston Ship Channel investigations could be detected in the Bay (Reynolds and Eckenfelder, l970). 
Background 
Previous studies of the Biological Oxygen Demand (BOD) of waters relating to the Houston Ship Channel indicated that 5 and 20 day tests showed marked increases in oxygen demand when diluted. This effect was suggested to be due to the reduction of some toxic factor by dilution. 
We initiated research in Galveston Bay on this subject by a reevaluation of the concept and development of the BOD5 test. Unfortunately, it has become common practice to use the BOD5 test as an indication of available organic matter in water even though the technique was developed as a yard stick for the assessment of the pollution potential of waste material and has been a common procedure used by regulatory agencies for waste disposal in natural waters. In reality, the application of the BOD5 test to natural waters, especially marine, is the subject of considerable controversy because of the interrelationships of so many parameters of oxygen uptake by microнorganisms as compared to the state and amount of organic matter present. 
In the original concept, BOD5 was used as a laboratory test under defined conditions in monitoring sewage and othe~ organic wastes. The procedure was of such a nature as to give valid results for defined conditions. Subsequent treatment to eliminate nitrite and other inhibitors and the use of seed microorganisms was added to provide uniformity of the procedure. Thus the five day BOD evolved as a good indicator of the quantity of oxygen consumed by microorganisms during the degradation of sewage and other waste materials that were monitored daily for long periods of time. 
The application of the BOD5 test to natural waters, especially sea water, is more difficult to make and some believe that the BOD5 test is still in the research stage rather than a reliable parameter for use in estuarine research (Gaudy, l972). Oppenheimer, et al. (l953) in a study of the filterable organic matter in sea water pointed out that difficulties encountered with the BOD5 may be due to the natural variation of organic materials and especially the humic or larger molecular weight materials that are more refractive to microbial activity within a five day period. The molecular variability of natural waters is well known (ZoBell, l946). Thus the BOD5 in estuarine waters will be a function of the balance of specific molecules at any one time. At the same time there is competition for oxygen by higher organisms during photosynthesis-respiration cycles, and by anaerobic products from the sediments in shallow, well-mixed bays such as Galveston. To complicate the application of the procedure to the natural environment, one must consider the diversity of microorganisms vs. the complexity of the organic matter to be oxidized. 
Elmore (l954) presented a review of the dilution technique vs. reaeration and stated that in long-term BOD, dilution caused inversions in the curve that could be attributed to several factors, including problems involving the low oxygen measurements of the blank and application of the blank correction due to scatter of replicates. The inverted curve shown by Reynolds and Eckenfelder (1970) does not fit the concept of dilution of some toxic factors, where the greatest effect would be expected with the largest dilution. The inverted curve does fit the concept of ZoBell (1946) regarding the effect of solid surface area on microbial activity. Thus dilution of natural waters will increase proportionately the effective surface area and could account for the inverse curve fit. 
Our approach to determine the significance of BODs in relation to toxicity and organic matter was to select five areas in the bay that would represent points of integration of various water sources to the bay as shown by Figure 1, Chapter I. A comparison was made between regular BOD5, Dilution BOD30, Reaeratiort BOD30, size of container and organic carbon in the water. All collected data taken during the sampling period were also entered in ENVIR and comparisons of BOD5 were correlated between salinity, nutrients, turbidity and oxygen. 
Procedures 
The BOD procedures used here were based on the technique in Standard Methods of Water Analysis with variation of time, container size and dilution. Water for BOD measurements was treated in the field to eliminate changes due to time factor. The samples were collected during regular field operations at which time routine hydrographic data were taken by the U.S. Geological Survey and at stations where monthly data were taken during the Galveston Bay Project. 
VII -2 
All BOD samples were initially bottled at the time of collection in 
the field and incubated under uniform conditions. This eliminated any change due to storage of the samples or transport of the samples. Routine BOD for periods up to 45 days were conducted in the usual 
300 ml BOD bottles. Five gallon containers and the large 500 gallon water storage tanks used by Dr. Wohlschlag were used to determine the effect of container size on the BOD. All series of samples were 
incubated at room ambient temperature approximately 24░C and thus 
calculations were not corrected for temperature. 
At each station, water was collected with a pump from mid-depth into two, five gal. glass bottles. Water was pumped from the 5 gal. bottles into each of 28 BOD bottles. Twelve of these were used for regular 28 to 45 day BOD's and 16 were used for the reoxygenated 28 to 45 day BOD's. Into 12 BOD bottles we put water diluted 1:5 with aerated sterile artificial sea water. Millipore filtered aerated water was dispensed into 12 BOD bottles to remove particulate organic matter for control purposes. 
Two of the bottles from each of the 4 sets were titrated immediately by the Winkler method to determine the amount of oxygen initially present. All were then stored in the dark. On the 5th, 10th, 20th, 30th and 45th day, 2 each per day of the regular, diluted and millipore filtered BOD's were titrated. The BOD reading was the difference between the initial titration and the corresponding day the titration was done. 
On the reoxygenated bottles, beginning with the 5th aay, 2 bottles were titrated and the remaining 12 were reoxygenated using cylinder oxygen bubbled through an air stone suspended in the bottle for 20 seconds each. One of these was then titrated to determine the amount of 02 that had been bubbled into each of the bottles. This acted as a control for the 10th day. On the 10th day, two bottles were removed and titrated. This amount was subtracted from the 5th day control. The BOD for the 10th day was then the amount for the 5th day plus the difference between the 10th day and the 5th day control. On each test day the same procedure was used --remove 2, titrate, reoxygenate the remaining, remove one and titrate for a control for the next test day. The reoxygenated BOD's were the cumulative totals of the oxygen used from day "O". 
For large container BOD, water was pumped at each station from mid-depth into a five gallon bottle which was capped, put into a wooden box and covered with aluminum foil. Into another bottle, a 
1:5 dilution was made with sterile artificial seawater. This was also capped, boxed and covered with foil. At 2-4 day intervals readings were taken with a YSI oxygen meter which had been air calibrated. Water and air-temperature were 33░C and atmospheric pressure was 760 mm Hg. 
As we were interested in natural oxygen demand the indigenous microorganisms were considered the "seed" in the standard test. The various procedures were designed for self control by intercomparison so glucose-glutanic acid checks were not employed. 
VII -3 
Results of the BOD tests 
The results of the BOD tests for the various stations and times are given in Figures 1 through 13. All values are given in or corrected to mg/liter, except where noted in Appendix data for dilution tests. All replicas were within limits of error and are reported as one point on the graphs. All bottles were maintained in uniform conditions of dark and temperature. Thus the differences are due to the type of BOD test employed. Hydrographic data for all field stations are given in Appendix A of Chapter X. Data for diluted tests are given in terms of diluted water and JIUlSt be JIUlltiplied by 5 to correct for procedure. 
Table 1 lists the total organic carbon in water samples taken at the time the BOD tests were set up. These analyses were made on thawed frozen samples with a Beckman Carbon Analyzer. The July samples were run by DuPont, Victoria, Texas, as our carbon analyzer was not functioning properly. 
Table 1. Total Organic Carbon in mg/l wet basis. 
Station  January 1972  April 1972  July 1972  
No.  
22  22  42  66  
29  16  34  87  
17  14  36  38  
26  13  42  31  
14  43  39  
Clear Lake  38  

There appears to be a correlation between BOD5 and total organic carbon as shown in Figure13where a linear correlation is present at individual sample periods. The figure suggests that in the winter the BOD5 available organic matter as a function of total carbon was greater than the summer months and represents a rate relationship based on amount of carbon present. This indicates that the season, the type of organic matter, and the amount of carbon will affect the BOD5 results. One lTa.lSt also consider that the total organic carbon procedure used in the study was determined by a Beckman Analyzer which excluded larger particulate material that may also affect the BOD5. 
ENVIR printout was used to plot several parameters of turbidity, salinity, oxygen, nitrogen and phosphorous against BOD5. These data are presented in Figures 14 through 21 for the years 1971 and 1972 as indicated. 
Discussion 
The first impression provided by the data is that there is little real correlation between the various BOD treatments and the amount of dissolved or total organic carbon. The highest oxygen values are for 
VII -4 


Q) 
26 

5  10  15  20  25  30  35  40  45  
DAYS  
Figure 1.  28 Day BOD -January,  1972  

6 

------------------------26 -------------------------22
5 
------------14
4 

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DAYS  
Figure 2.  Normal 30 Day BOD -April,  1972  

6 


26 
H 
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17
0 
22 


5  10  15  20  25  30  35  40  45  
DAYS  
Figure 3.  Diluted 1:5, 30 Day BOD -April,  1972  

12  
11  
22  
10  26  


5  10  15  20  25  30  35  40  45  
DAYS  
Figure 4.  Reoxygenated 30 Day BOD - April,  1972  

6 
5 
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----26 
~-----22 
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5 10 15 20 25 30 35 40 45 DAYS 
Figure 5. Millipore Filtered 30 Day BOD -April, 1972 

17
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Figure 6. 500 Gal. Box 30 Day BOD -April, 1972 
22
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2 
E 
1 
5 10 15 20 

DAYS 
Figure 7. Normal 45 Day BOD -July,, 1972 
12 
11 
10 

9 
8 
~ 
C1' 
+-7 -" 
......_ < 6
H N 
H 0 
f-1 
I') 5 E 
A 
3 
2 
1 
5  10  15  20  25  30  35  40  45  
DA.VS  
Figure 8.  Reoxygenated 45 Day BOD - July,  1972  

6 
5 
.. '-Q) 
<: _. 3
H H 
........... 

N
j-J 0VJ 
2 
E 
1 

5  10  15  20  25  30  35  40  45  
DAYS  
Figure 9.  Diluted 1:5, 45 Day BOD  -July,  1972  

6 
5 
4 
L.. 
Q) 
~ < ~ 3
H 
H 
............ 

C".I 
1---1 
_p,. 
0 
17
2 
E 

26 29
1 

5 10 15 20 25 30 35 40 45 DAYS 
Figure 10. Millipore Filtered 45 Day BOD -July, 1972 
6 

5 
22 
17 

4 

29 
'н
Q) 
.... 14

5  10  15  20  25  30  35  40  45  
DAYS  
Figure 11.  5 Gal.  Undiluted BOD  -July,  1972  


5  10  15  20  25  30  35  40  45  
DAYS  
Figure 12.  5 Gal.  Diluted 1:5  BOD -July,  1972  

7 
ХJan 1972 
ХJuI 1972 6 
stations 22 e Apr 1972
Х 
22Х 
5 
22

...I 
e 
N 26 
""
0 
e
4 
E 
<: 
~ ~ 
an 
""""' 
17Х29 -l c 3 17 
0 
al 
26Х e 
14Х
17
2 
14 

e 
2929 
e Х 
1 
26Х 
25  50  75  95  
Total  Organic  C a r b o n  m g ╖/L .  
Figure 13  

ALL BAY STATIONS ENVIR 

0 0 
0
-

LO 
I 0 
. . 
0 
. .. : ... 
... 

Х ХХ Х Х I Х 
. . 

. .: ..
. . .. ..
. ..

.. ..

. .. . . . 

.. . . 
Х Х I Х Х
.. ... . . Х. 
.... . 
0 0 
.. . 

O----~-T-"----.~-.-~..----,...~.....---.r---..--н
o.o  ECCH  7. o  
ALL  BAY  STATIONS  ENVIR  
0 0  [!]  [!]  [!]  Figure 16  
0- [!]  
[!]  [!]  
[!] i::I [!] [!] [!] t!:I [!]  [!]  [!]  

LO 
I 

0 
0 
0 0 

o.o Figure 14. Figure 15. 
Figure 16. Figure 17. 
Figure 14 
ALL BAY ST ENVIR 

0 
0 
0
-
LO I 
. . . 
. . .
0 
...
0 
... . ..╖. 
... . -╖.
. ' .. .
-......: .. 
Х Х Х Х Х I
... . . . . .
. .. ..
.-: . . 
.1╖.╖-.╖:╖: .
... . 
.╖, . ... 
0 
0 

Figure 15 
. . .
.. . 
o.__~╖~╖~-r-----.~-.-~~---.-~.....---.r--..-~ 
0 0 
0 
LO 
I 

0 0 
0 0 
o.o OTRL P 2. 0 
ENVIR 
Figure 17 
.
. 
.. 
.. . .. 
.
.. .
.. . .
. . . . . Х 
.....: . 
╖-╖╖╖╖ 
.., . 
. . . 
. . ..Х.... 
. ...
-
0-------~-----..~--~~--~-.---.~-.----н
o.o 
20.00 
Five day BOD (mg/l) by Houston Health Dept. for 1971-72 routine 
sampling stations. Secchi disc (ft) reading by Army Corps of Engr. 
BOD (mgI 1) and total phosphorus (ppm P) for 1971-72 routine 
sampling stations. 
BOD (mg/l) and organic nitrogen (ppm N) for 1971-72 routine 
sampling stations. 
BOD (mg/l) and oxygen (mg/l) for 1971-72 routine sampling 
stations. 

VII -18 
ALL BAY ST ENVIR 

D 
D 
Figure 18 
D
-. 
LJ) I 
0 D 
.: . , 
....... . 
. .. . .: .~ . 
D 
D 

D 

o.od ╖ SA L'I NIT Y; ' 40 !oo STATION 26 ENVIR 
D 
N 
0 
CL 
_J 
a: 
fн
a 
fн
+~ 
z 
cb 
a:: 
0 
D 
D 
D 
o.o Figure 18. 
Figure 19. 
Figure 20. Figure 21. 
. .. ..
-. ...
.. . ..╖-.. .
.... . . ..
.. .. I ХХХ .
. ..
. . .╖
..
. 
. .
.. 
Figure 20 

YR 2. o 
STATION 26 ENVIR 

D D  
D ......  
+  +  +  
LJ) I 0 0 D D  + [!] .t.  + .t.  [!] .t.  + C!lm .t. [!] .t. .t.  [!] + + .t. [!] [!]  
D o.o  ALIN  
STATION  26  

Figure 19  0 0.  
[!]  N  
_J  
a:  
1-н 
0  
fн 
+  +  
z  
CJ  
a::  
0  
0  
0.  
0  
TY  40.00  

ENVIR 

0 
0.
D 
Figure 21
D 
0 
l/)
D 
...... 
+ >н
1н
...... 
z 
......
LJ) 
_J 0 
I 
a: 
en
0 
0 
0D 
D 
0 
D L-------r--~--r---r-...............--.---,--,--r:---, 

o.oo YR 2. o 

Five day BOD (mg/l) by Houston Health Dept. versus salinity (ppt) for 1971-72 routine sampling stations. Five day BOD (mg/1)--Х, organic nitrogen (ppm)--+, and total phosphorus (ppm)--&. versus salinity (ppt) as an indicator of fresh water inflow for station 26 for 1971-72. Organic nitrogen (ppm)--+, and total phosphorus (ppm)--Х versus time in years starting 1I1/ 71 for station 26. Five day BOD (mg/1)--Х, and salinity (ppt)--+ versus time in years starting 1I1I71 for station 2 6. 
VII -19 
the samples diluted 1:5 but even these are not equivalent to the organic carbon if one assumes that the endpoint of biological oxidation is C02. These values would be even lower if one accounted for the effect of ammonia oxidation during protein diagenesis, as one mg of NH3 per liter would utilize 4 mg of 02 per liter. Values for organic nitrogen at the stations varied from 0 to 2.3 mg/liter. Because it is impossible to determine the molecular state of the organic nitrogen one cannot interpret the rate of ammonia released or oxidized during the BOD periods. 
There is little real correlation between the regular BOD, dil~ted or reaerated, or size of BOD container for each station. 
There appears to be a correlation between Station 22 and possibly 26 and the highest BOD activity; however, there is a discrepancy in the diluted April BOD test. As these stations are close to the Clear Lake and the Houston Ship Channel there is a possibility that this reflects the organic matter coming from these areas. However, there was little evidence from the organic carbon values that Station 22 had significantly higher organic matter than the other stations analyzed. 
We made other correlations between BOD and hydrographic features. The only correlation is shown for BOD and salinity for Station 26 (Fig. 21). At this station the higher BOD was associated with higher salinity. This seems to correlate with the data of Armstrong (see Chapter II) that indicates the fresh water input from the Trinity River had a low organic carbon value. However, our organic carbon values for Station 26 were quite similar to those of Station 22. Table 2 lists other comparisons. 
Table 2 
Observations on the ENVIR processed files of BOD, Org. N, total P, Salinity, and Oxygen for selected stations in Galveston Bay for two years, 1971, 72. 
Area: 	Upper bay near mouth of ship channel -Stations 22 and 23 (19 data points each) 
Plot 	Corrunents 
BOD-5 vrs Oxygen mg/l oxygens above 10 ppm have high BOD BOD-5 vrs Salinity ppt no trend, salinity range 6.8 to 25 BOD-5 vrs Org N ppm no real trend BOD-5 vrs total P ppm some trend to low total P at high BOD 
Area: 	Trinity Bay -Stations 26 and 38 (20 data points each) 
BOD-5 vrs Oxygen high BOD have medium to high oxygen BOD-5 vrs Total P ppm P possible trend to low P at high BOD &Org N, ppm N probably no trend BOD-5 vrs Salinity no obvious trend 
VII -20 
Table 2 (cont.) 
Area: East Bay -Station 29 (20 data points each) 
Salinity vrs Org N no trend & Total P possible correlation of high P with high sal. 
Area: Station 14 -West Bay (10 points each) 
all BOD5 plotted vrs time in years from 1971/1/1 to end of 72, 0.0-2.0 years note a big peak of BOD5 for Feb. 72 
Stations 29 -East Bay plotted the same way (10 points each) 
nothing obvious noted. 
Intercomparison of stations -BOD-5, Salinity 
BOD mg/l Salinity mg/l min max mean std min max mean std dev dev 
22&23 1.25 9.4 4.27 2.18 51% 6.8 25. 
26&38 0.1 8.4 3.47 2.1 60% 2.5 29.3 13.1 6.7 51% 

14 0.55 9.4 2.8 2.5 87% 19.5 32.5 24.4 3.8 16% 

29 0.7 6.8 3.3 1.7 52% 15.8 29.7 20.4 4.0 20% 


A minor correlation between BOD and oxygen seems to be indicated; the higher amount of oxygen in the water related to a higher BOD (Figures 5 and 9). This may be an effect of primary productivity where the fixed carbon enters into the BOD reaction. The correlation of higher total carbon (Table 1) in the water during more productive months tends to support such a conclusion. 
The BOD5 were those reported by the Houston Health Department and represent samples taken during the day when photosynthesis was actively producing both oxygen and fixed carbon. This fixed carbon would be replicated in the BOD. 
The ENVIR system was used to plot various hydrographic parameters vs. BOD (data obtained by u.s. Army Corps of Engineers and Houston Health Department teams (Figures 14-21). The BOD's obtained during our diurnal sampling period were compared with time, oxygen content, depth of water, etc. The diurnal oxygen plot has no correlation with time of day or BOD and is probably related more to wind and tidal mixing than biological or chemical factors. In one station the bottom oxygen decreased during a period of little wind. This indicates that when wind-mixing decreases, the large amount of organic matter irrunediately affects the oxygen content 
VII -21 in the water due to metabolism. When the wind increases the low oxygen disappears. Such a correlation occurred only once during our intensive sampling period and, therefore, should be mentioned but cannot be used as an absolute criteria. This wind anomaly suggests that routine sampling procedures may provide misleading results unless wind and tidal parameters are intensively investigated for their influence on the hydrographical and biological features of the bay system. 
It is apparent that there is no correlation between the BOD5analyzed at all stations during the period and salinity, organic nitrogen, total ph0sphorous, turbidity or oxygen. The lack of correlation between BOD5 and these important biological parameters clearly indicates that the BOD test as used in this research program may have no significance as applied to model experiments, oxygen balance or organic carbon balance in the bay system. During the organization of the program, we pointed out that organic carbon should be a part of the routine analytical procedures and that organic nitrogen, because of its wide variability in estuarine systems, does not provide good information about the total organic load. We also pointed out the need for total particulate matter instead of the Secchi disc information. Particulate matter or volatile materials are pertinent in any attempt to derive a balance between BOD, nitrogen and organic matter in the estuarine system. 
A computer program was written to fit the observed BOD values to the usual first order BOD curve by calculating the ultimate BOD values and decay constants. The data are presented in the Appendix to this chapter. The relatively low fit error for most stations indicates a fairly good fit to the standard equation, this is taken to indicate the general self-consistency of the BOD measurement process. 
In this study it was evident that the oxygen requirement by organic matter in the water was not reflected by the BOD5 data. This is undoubtedly due to the integration of the state of molecular distribution of the organic matter, and its state relative to whether the organic matter is produced the day before by primary productivity, whether it is the product of nighttime and daytime metabolism of indigenous organisms, or whether it is the result of man's input of waste materials. 
Surrunary 
No significant effect was noted that might be caused by a toxicity factor because of the non-correlation of the five day BOD, dilution or reaeration and larger container size. The high oxygen uptake in reaerated samples in non-dilution tests were used to relate to the toxicity in previous studies where dilution was used. 
The variability of BOD data as correlated with data on nutrients, turbidity, oxygen, etc. carried out during the one year study indicate that the BOD may not be a reliable test for organic matter content including toxicants in the Galveston Bay environment. The data will be kept on file for future references and research in Galveston Bay. 
VII -22 

References 
Custer, s. w. &R. G. Krutchkoff. l969. Stochastic models for bioнchemical oxygen demand and dissolved oxygen in estuaries. Virginia Polytechnic Inst., Water Resources Research Center, Bulletin 22. 
Elmore, H. L. l954. Determination of B.O.D. by a reaeration technique. Sewage Works Journal, p. 993-lOOl. 
Gaudy, A. F., Jr. l972. Biochemical oxygen demand. p. 305-332. In Ralph Mitchell (ed.) Water Pollution Microbiology. WileyнInterscience, New York. 
Hays, 	A. J. et al. l970. Anaerobic Modeling for the Houston Ship Channel. Technical paper presented to the 9th Texas Water Pollution Control Association Conference Houston, July 9-10, l970. 
Oppenheimer, c. H., D. L. Fox & J. s. Kittredge. l953. Microfiltration in oceanographic research. II. Retention of colloidal micelles by adsorptive filters and by filter-feeding invertebrates; proportions of dispersed organic to dispersed inorganic matter and to organic solutes. Sears Foundation: Journal of Marine Research, l2(2): 233-243. 
Reynolds, T. D. & W. w. Eckenfelder. 1970. Reaction rates of Houston ship channel waters. Joint Report of research performed on IAC (68-69)-237 (Univ. of Texas) and IAC (68-69)-244 (Texas A&M Univ.). March, l970. 
Stack, V. T., Jr. l972. Biochemical oxygen demand measurement, p. 801н
829. In Ciaccio, Leonard L. (ed.) Water and Water Pollution Handbook, Marcel Dekker, Inc., N.Y. 
ZoBell, C. E. 1946. Marine Microbiology: A Monograph on Hydroнbacteriology. Chronica Botanica Co., Waltham, Mass. 240 pp. 
VII -23 

Appendix to Chapter VII 
Description of method of fitting BOD data to a first order equation 
Biological Oxygen Demand is usually represented with a first order equation in which the rate of oxygen utilization is proportional to remaining "BOD": dO/dt = -k x(BOD) where 0 represents the remaining oxygen concentration. The BOD test yields the total oxygen utilized in a certain time, the above equation becomes dU/dt = k x(BOD) where U is oxygen utilization and BOD is remaining "BOD". Integrating this equation, we find that the total utilization at time t is given by: U ( t ) = Bo (1.O -e-kt) where Bo is the "BOD" at 5 = O and is the same as the utilization after infinite time. Our experimental data consists of 6 and U(t), we need to determine Bo and k values which give the best fit to the observations. 
The most corrunonly used criterion for fitting equations of this sort is that the sum of squares of errors be minimized, this can be stated G(k,B0 ) = U(t) -B0 (1.0 -e-kt) 2 where G(k,B0 ) is our error function to be minimized. We need to find k and Bo so that 
G/ k = 0.0 and G/ B0 = O.O, this can not be done exactly but can be done by an iterative procedure. 
The iterative procedure used is based on the fact that the equation obtained by setting the partial of G with respect to k equal to o.o, can be solved for B0 Х In other words, given a value of k, a value for B0 can be obtained which minimizes G. By taking various values of k, the one which gives the loVJest G value to any desired degree of accuracy can be determined. 
The iterative procedure has been programmed in FORTRAN for the University of Texas timesharing system, TAURUS. The program operates roughly as fallows: 
l) read the data set consisting of t,Y(t) observations 2) take a first guess at k based on the time at which Y(t) 
exceeds ~ Y max. 3) calculate the Bo optimum for this k 4) calculate the error of this equation in fitting the data points 5) if this does not result in an improvement in error, try a new 
guess for k based on the best previous k, with a smaller step size and in the opposite direction, go to 3. 
6) 	if 4 does result in an improvement in error, use this k as a base for subsequent guesses, step in the direction which resulted in this improvement. 
7) if the step size is now very small, further improvement is not needed, go to 8, otherwise go to 3. 8) print out the fit values for k and B0 , the original data, and the fit points. Go to 1 for next data set. 
VII -24 
station treatment ultimate BOD K-per day RMS Error as data base e fit error %of ultimate points m/liter m/liter BOD 
Jan  72  
17  std BOD  4  5.39  0.1473  .258  4.9  
22  2  6.90  .362  
26  4  3.31  0.196  .183  5.5  
29  3  6.27  0.152  .072  1.14  
April 72  
14  std BOD  3  5.92  0.0585  
17  4  3.98  0.0970  .364  9.1  
22  2  5.09  0.469  
26  2  5.85  0.252  
29  4  4.23  0.0503  .136  3.2  
clear lake  3  4.92  0.0820  .271  5.5  
April 72  
14  1:5 dilution  4  2.82  0.159  .244  8.6  
17  4  2.47  0.0882  .263  10.6  
22  suspicious point  
left out  3  3.10  0.193  . 068  2.2  
left in  4  2.71  0.273  .269  9.9  
26  4  3.97  0.135  .070  1.8  
29  4  5.67  0.0856  .160  2.8  
clear lake  4  5.36  0.186  .130  2.4  

VII -25 

station treatment ultimate BOD K-per day RMS Error as data base e fit error %of ultimate points m/liter m/liter BOD 
April 72  
14  reoxygenated  4  23.44  0.0142  .173  .7  
17  4  8.87  0.050  .244  2.8  
22  4  11. 78  0. 0687  .394  3.3  
26  4  15.07  0.0528  1. 044  6.9  
29  4  13.16  0.0132  .142  1.1  
clear lake  4  out of range?  close to  straight line?  
leave out last point  3  8.69  0.336  .089  1.0  
July 72  
14  5 gal  -diluted  8  3.07  0.199  0.085  2.8  
29  6  5.36  0.0633  0.245  4.6  
17  7  4.37  0.2626  0.0573  l. 3  
22  8  4.88  0.3725  0.346  7.1  
26  5  5.31  0.232  0.297  5.6  
July 72  
14  5 gal  -diluted 1:5  7  3.73  0.129  0.196  5.3  
17  -first points  5  2.72  0.458  0.082  3.0  
17 all points  8  3.673  0.198  0.334  9.1  
22  7  2.293  0.154  0.069  3.0  
26  6  2.79  0.1077  0.179  6.4  

VII -26 

Station & Treatment data ultimate BOD K per day RMS %of ultimate points m/liter fit error BOD m/liter 
April 72  
29  500 gal box  4  4.13  0.0684  0.145  3.5  
17  4  4.05  0.1387  0.052  l. 3  
26  4  6.68  0.1936  0.052  0.77  
22  4  6.66  0.2125  0.216  3.24  
July 72  
26  45 day BOD  -std  4  1.49  0.1405  0.076  5.1  
14  4  2.91  0.2308  0.113  3.9  
17  5  4.40  0.1578  0.577  13.1  
22  3  5.90  0.4753  0. 0207  0.35  
29  4  1. 96  0.0995  0.179  9.1  
July 72  
29  45 day  -std  5  2.26  0.1752  0.043  1.9  
diluted 1:5  
26  5  -impossible to fit curveн 
17  5  4.40  0.232  0.253  5.8  
22  5  4.57  0.159  0.827  18.1  
14  5  3.81  2.83  0.199  5.2  
July 72  
29  45 day  -std  5  7.25  0.0319  0.349  4.8  
reoxygenated  
26  5  6.90  0.0507  0.404  5.8  
14  5  2.97  0.190  0.0926  3.1  
17  5  4.50  0.101  0.207  4.6  
22  5  11. 79  0.0703  0.922  7.8  
July 72  5 gal  VII -27  

Intercomparison of April samples -parameters fit to BOD equation 
Stations BOD -ultimate m/l Decay constant k (base e) per day 
std method  std diluted 1:5  reoxy  500 gal box  std method  diluted 1:5  reoxy  500 gal box  
14  5.92  2.82  23.44  0.0585  0.159  0.0142  
17  3.98  2.47  8.87  4.05  0.0970  0.0882  0.050  0.1387  
22  5.09  3.10  11.78  6.66  0.469  0.193  0.0687 ~  0.2125  
26  5.85  3.97  15.07  6.68  0.252  0.135  0.0528  0.1936  
29  4.23  5.67  13.16  4.13  0.0503  0.0856  0.0132  0.0684  
<H H  clear lake  4.92  5.36  0.0820  0.186  

!:\:) 00  July stations std method  diluted 1:5  reoxy  5 gal.  5 galнdil_1:5  std method  diluted 1:5  reoxy  5 gal.  5 gal-diluted 1:5  
14  2.91  3.81  2.97  3.07  3.73  0.231  2.83  0.190  0.199  0.129  
17  4.40  4.40  4.50  4.37  2. 72-;':  0.158  0.232  0.101  0.263  0. 459-;':  
22  5.90  4.57  11.79  4.88  2.29  0.475  0.159  0.0703 0.373  0.154  
26  1.49  6.90  5.31  2.79  0.140  0.0507 0.233  0.108  
29  1.96  2.26  7.25  5.36  0.100  0.175  0.0319 0.0633  

*points left out where there was evidence of a second stage 
CHAPTER VIII 
The Nursery Environment of Galveston Bay 
by 
Carl ~. Oppenheimer 
University of Texas 
Marine Science Institute 
Port Aransas, Texas 
The Nursery Environment of Galveston Bay 
Texas Bays have been identified as nursery areas because of the seasonal behavioral characteristics of commercial and sport fish and shellfish, and usually only indirectly relates to primary productivity of attached grasses and algae or phytoplankton. Such a definition takes for granted that the supporting population of the necessary food chain is present and that the physical chemical characteristics of the system are sympathetic to the ultimate growth of the comm~rcial fish and shellfish. The nursery ground is defined as the shallow water area with grass and algae coverage. It extends to the depth of photoнsynthesis of the organisms in the bay environment. 
Most of the commercial fish and shellfish of the Texas coastal area have definite seasonal migratory and spawning behavioral patterns. During some part of the year they will move out of the bays to the open Gulf where they spawn. The larvae then move through tidal forces back into the bay where they develop into adults to complete the cycle. Whether the migratory cycle is dependent on water quality or stability of the Gulf water is not known at this time. Also, there is a certain proportion of the fish and shellfish that may stay within the bays for one or more years. A certain proportion of the blue crabs may spawn within the bays, and oysters have their complete life cycle within the bays. 
In general, the nursery areas are assumed to be the grass flats and marsh areas, as shown by Figures 1, 2, and 3. These areas are highly productive and offer protection and attachment surface as the larval forms go through their various development stages. The grass areas are quiet with soft, high-organic, fine sediments. The blades of grass act as surface for various food chain organisms required by the developing larvae and when dead add a relatively large amount of organic matter to fertilize the adjacent bay or estuarine waters. 

THALASSIA GRASSFLAT 
1. 
Lagodon rhomboides -Pinfish 

2. 
Penaeus aztecus -Brown shrimp 

3. 
Cynoscion nebulosus -Spotted sea trout 

4. 
Hydrozoan 

5. 
Spirorbus sp. -Serpulid worm 

6. 
Spirorbus sp. -Serpulid worm 

7. 
Palaemonetes vulgaris -Grass shrimp 

8. 
Cerithidea pliculosa -Horn shell 

9. 
Neritina reclivata -Olive nerite 

10. 
Gracilaria sp. -Red alga 

11. 
Menidia beryllina -Tidewater silverside 

12. 
Sciaenops ocellata -Juvenile redfish 

13. 
Thyone sp. -Sea cucumber 

14. 
Ophiothrix sp. -Brittle star 

15. 
Odostomia gibbosa -Small gastropod 

16. 
Clibanarius vittatus -Hermit crab 

17. 
Neopanope texana -Mud crab 

18. 
Callinectes sapidus -Blue crab 

19. 
Halophila engelmanni -Sea grass 

20. 
Halodule .wrightii -Shoal grass 

21. 
Phacoides pectinatus -Lucina clam 

22. 
Thalassia testudinum -Turtle grass 

23. 
Ensis minor -Razor clam 

24. 
Rhithropanopeus harrisii -Burrowing crab 

25. 
Chione cancellata -Venus clam 

26. 
Phacoides pectinatus -Lucina clam 

27. Penaeus du orarum -Pink shrimp 

28. 
Phascolosoma gouldii -Mud worm 

29. 
Ceratium sp. -Dinoflagellate 

30. 
Nitzschia sp. -Diatom 

31. 
Cymbella sp. -Diatom 

32. 
Oscillatoria sp. -Blue green alga 

33. 
Dunaliella paupera -Saline euglenoid 

34. 
Microcystis sp. (colony) -Green alga 

35. 
Microcystis sp. (individual) -Green algae 


VIII -3 


VIII -4 

SPARTINA (SALT WATER MARSH) 
1. 
Ardea herodias -Great blue heron 

2. 
Butorides virescens -Green heron 

3. 
Anas discors -Blue winged teal 

4. 
A']aia ajaja -Roseate spoonbill 

5. 
Casmerodius albus -Common egret 

6. 
Avicennia germinans -Black mangrove 

7. 
Eudocimus albus -White ibis 

8. 
Salicornia bigelovii -Glasswort 

9. 
Procyon lotor -Ra.coon 

10. 
Distichlis spicata -Saltgrass 

11. 
Spartina alterniflora -Smooth cordgrass 

12. 
Rallus longirostris -Clapper rail 

13. 
Pagurus sp. -Hermit crab 

14. 
Telmatodytes pulustris -Longbilled marsh wren 

15. 
Croton punctatus -Beach tea 

16. 
Sesuvium portulacastrum -Sea purslane 

17. 
Batis maritima -Salt wort 

18. 
Uca pugnax -Fiddler crab 

19. 
Avicennia germinans -Black mangrove 

20. 
Littorina irrorata -Periwinkle 

21. 
Avicennia germinans -Black mangrove 

22. 
Distichlis spicata -Saltgrass 


VIII -5 

. 
!"') 
VIII -6 
JUNCUS (FRESH WATER MARSH) 
1. 
Branta canadensis -Canadian geese 

2. 
Butorides virescens -Green heron 

3. 
Spartina alterniflora -Smooth cordgrass 

4. 
Juncus sp. -Reed 


s. Scirpus sp. -Bulrush 
6. 
Rattus norvegicus -Norway rat 

7. 
Procambarus burrow 

8. 
Fulica americana -Coot 

9. 
Mycteria americana -Wood stork 

10. 
Smilax sp. -Bamboo briar 

11. 
Typha domingensis -Cattails 

12. 
Scirpus sp. -Bulrush 

13. 
Didelphis marsupialis -Opossum and young 

14. 
Spartina patens -Marsh hay cordgrass 

15. 
Crotalus atrox -Western diamondback rattlesnake 

16. 
Uca pugnax -Fiddler crab 

17. 
Procambarus clarki -Crayfish 

18. 
Cyprinodon variegatus -Sheepshead minnow 

19. 
Agkistrodon piscivorus -Cottonmouth snake 

20. 
Scirpus sp. -Bulrush 

21. 
Typha domingensis -Cattail 

22. 
Sporobolus virginicus -Seashore dropseed 


VIII -7 

The shallow grass flats are always highly oxygenated because of the wind mixing action on the water and oxygen diffusion. Such areas offer wellнoxygenated protection and highly nutritive living areas for the young organisms, and later provide other types of food for the adult organisms. 
Most of the commercial species of fish and shellfish have defined migratory and maturation times. For example, the Penaeid shrimp and blue crabs (Callinectes) mature in one season, whereas, the redfish (Sciaenops) mature in five years. Most other fish such as the mullet (Mugil), spotted trout (Cynoscion), drum (Pogonias), and croaker (Micropogon) mature in two years. 
Whereas the life cycles of the various commercial organisms have been developed and identified, the distribution of the larvae and young of the species in the Texas bays is not well identified. There is a general agreement with the larvae and small young live in the grass flats (Herke, 1971); although Gordon Gunter in personal communication says he is not convinced that grass flats are required. As adults, each species identifies itself with some specific bay environment. The llUlllet and blue crab are ubiquitous, as they ar~ detritis feeders. The adult trout prefer sand bottoms, the red fish prefer open waters, and the shrimp are found in both mud and sand bottoms. 
While the literature is not well defined, a general conclusion can be assumed that the grass flats provide the living habitat for the growing larval forms and as the organisms grow they assume discrete environmental niches. It is quite unrealistic that after 50 plus years of biological research that so little has been documented about the life history distribution of the commercial fishes of the Texas coastal environment. Most of the literature is purely descriptive with little attempt at quantification. However some data exist,for example, Copeland (1965) postulated, from Aransas Pass tide trap information, that the Corpus Christi Redfish Bay regime had a net productivity of 233 kg/acre per year and that 3.9Xl06 kg/yr of penaeid shrimp passed Aransas Pass during the experimental period. 
Juveniles of all major species seemed to show a response to minor variations in some environmental factors. Most organisms probably have optimum ranges for all environmental factors that vary with the species and individual. When an environmental factor deviates from the optimum such as a heavy rainfall it probably creates a stimulus tending to cause the organism to migrate to more optimum conditions. 
Optimum ranges of all environmental factors for a species will almost never occur at the same point in time and space; therefore community reaction to the numerous stimuli is necessarily a compromise. 
Other studies have indicated that detritus from emergent marsh or grassflat plants forms the base of many estuarine food webs, which suggests the marsh receives more usage by major species than the 
VIII -8 
other segments of the estuarine zone. Thus, the land portion of the 
marsh or grassflat is apparently the most important segment for 
production of plant material for the base of the food webs, and the 
water portion is probably the site where the greatest proportion of 
this material is converted into animal biomass. Therefore, although 
all sections of the estuarine zone are needed, and some use is 
undoubtedly made of all sections by all sizes of juveniles, where 
it exists, the marsh and grassflat is the most important section. 
Since most estuarine-dependent species spawned in the Gulf have 
similar life cycles, the majority of the immigrating young of many 
species probably proceed into the estuary as far as they can swim 
in a particular section, or as far as competition and their minimum 
salinity tolerance allows. 
Contrary to the often expressed opinion that juveniles leave the nursery area as a more or less mass movement outward, emigration usually occurs over an extended period. It is apparently a "bleeding off" process which involves primarily the larger individuals in the nursery at any particular time. 
Our estuaries have existed for centuries. Throughout this period, organisms have been evolving life patterns and responses to environmental factors that are of maximum advantage to the species. This has been an extremely slow process, but it has brought forth a marvelously resilient system--one that has maintained productivity indefinitely with no need of assistance from man. The system has withstood and bounced back from every possible natural calamity so long as the estuarine zone was not itself destroyed. 
For manipulation of the estuarine environment to our maximum benefit we must learn a great deal more about its ecosystem. The money presently being expended in attempts to "improve" on the natural system would benefit mankind more if it were spent to achieve a better understanding of the natural system. The workings of this system are every bit as intricate as a nervous system, and species in the system can respond to stimuli just as delicate. Their responses are too deeply ingrained to allow the organisms to be suddenly forced into unnatural life patterns without causing significant stress. 
The results of the literature survey are as follows: 
1. 
The literature does not contain specific information to quantify the relationship between estuarine environments and the distribution and quantity of annual biomass of the commercial fish and shellfish. Quantitative information on commercial fish catch merely indicates minimum populations and has no bearing on the total capacity of the bays on a surface relationship. 

2. 
There is a general agreement that the grass flats are important to the survival of the larval forms and offer feeding areas for the adults. While this has not been clearly documented in Texas, the general agreement by scientists indicates that the grass flats should be preserved until quantitative data are available. 

3. 
Most commercial fish and shellfish species spawn in the open Gulf and, therefore, the channels and passes between the Gulf and the bays are needed for proper biological balance. Clear regulations should be initiated as they were in the latter part of the 1800's on restriction of fishing in shallow passes between the Gulf and the bays. 

4. 
Research should be initiated immediately to quantify the primary productivity of the bay environments, the distribution and growth rates of various commercial species in the different bay environments, and the rate of biomass migration out of and into the bays through the various passes connecting the bays with the Gulf of Mexico. 


VIII -9 
Until these data are made available, it will be impossible to provide any quantitate values on the use and management of the grass flats or the open bay waters with respect to the commercial and sport activities. 
References 
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Aldrich, D. V., C. E. Wood & K. Baxter. 1968. An ecological interнpretation of low temperature responses in Penaeus aztecus and ~. setiferus post larvae. Bull. Mar. Sci. 18(1): 61-71. 
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Anderson, W. W. 1966. The shrimp and the shrimp fishery of the southern U.S. U.S. Dept. of the Interior -Fish &Wildlife Service Fisheries Leaflet 589. 
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VIII -10 
Baxter, K. N. 1963. Abundance of postlarval shrimp -one index of future shrimping success. Proc. Gulf & Carib. Fish. Inst., 15th Ann. Session, 1962: 79-87. 
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VIII -11 
Cook, 	H. L. 1965. A generic key to the protozoan, mysis, and postнlarval stages of the littoral penaeidae of the northwestern Gulf of Mexico. Fish. Bull. 65(2): 437-448. 
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Copeland, B. J. 1966. Effects of decreased river flow on estuarine ecology. J. Water Poll. Contr. Fed. 38(11): 1831-1839. 
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Copeland, B. J. & T. C. Dorris. 1964. Community metabolism in ecosystems receiving oil refinery effluents. Limnol. Oceanog. 
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Copeland, B. J. & M. V. Truitt. 1966. Fauna of the Aransas Pass Inlet, Texas. II. Penaeid shrimp postlarvae. Tex. J. Sci. 28(1): 65-74. 
Copeland, B. J. & E. G. Fruh. 1970. Ecological studies of Galveston Bay, 1969. Final Report to the Texas Water Quality Board, Austin, Texas. 
Costello, T. J. & D. M. Allen. 1965. Migrations and geographic distribution of pink shrimp, Penaeus duorarum of the Tortugas and Sanibel Grounds, Florida. U.S. Fish & Wildl. Serv. Fish. Bull. 65(2): 449-460. 
Costello, T. J. & D. M. Allen. 1970. Synopsis of biological data on the pink shrimp, Penaeus duorarum. Fishery taxonomy distribution. FAO(Food Agr. Organ. UN) Fish Rep. 57(4): 1499-1537. 
Conte, F. s. & J. c. Parker. 1971. Ecological aspect of selected crustacea of a marsh embayment of the Texas coast. Texas A&M Univ. Unpubl. 
Darnell, R. M. 1956. A note on the occurrence of the pink shrimp Penaeus duorarum in Louisiana waters. Ecology 37(4): 844-846. 
VIII -12 
Dobkin, S. 1961. Early developmental stages of pink shrimp, Penaeus duorarum, from Florida waters. Fish & Wildl. Serv. Fish. Bull. 61(190): 321-349. 
Eldred, B. 1960. On the grading and identification of domestic commercial shrimps (Family Penaeidae) with a tentative world list of commercial Penaeids. Quart. J. Fla. Acad. Sci. 23(1): 89-118. 
Eldred, B. 1962. Biological shrimp studies (Penaeidae) conducted by the Florida State Board of Conservation Marine Laboratory, In Proceedings 1st National Coastal and Shallow Water Research Conference, October 1961, pp. 411-14. 
Ewald, J. J. 1965. The laboratory rearing of pink shrimp, Penaeus duorarum Burkenroad. Bull. Mar. Sci. 15(2): 436-449. 
Fontenot, B. J. & H. E. Rogillio. 1970. A study of estuarine sportfishes in the Biloxi March Complex, Louisiana. Louisiana Wildlife and Fisheries Commission, La. 1-172. 
Frolander, H. F. 1964. Biological and chemical features of tidal estuaries. J. Water Poll. Contr. Fed. 36(8): 1037-1048. 
Fuss, 	C. M. 1964. Observations on burrowing behavior of the pink shrimp, Penaeus duorarum Burkenroad. Bull. Mar. Sci. Gulf & Carib. 14(1): 62-73. 
Fuss, 	C. M. 1966. Factors affecting activity and burrowing habits of the pink shrimp, Penaeus duorarum Burkenroad. Biol. Bull. 130 ( 2) : 170-191. 
Gallaway, B. J. 1970. Seasonal abundance, distribution and growth of commercially important marine crustaceans at a hot water discharge in Galveston Bay, Texas. M.S. Thesis, Texas A&M Univ. 
Gunter, G. 1950. Seasonal population changes and distributions as related to salinity, of certain invertebrates of the Texas coast, including the commercial shrimp. Publ. Inst. Mar. Sci. Univ. Tex. 1(2): 7-51. 
Gunter, G. 1961. Habitat of juvenile shrimp (Family Penaeidae). Ecology 42(3): 598-600. 
Gunter, G. 1962. Shrimp landings and production of the state of Texas for the period 1956-1959, with a comparison with other Gulf states. Publ. Inst. Mar. Sci. Univ. Tex. 8: 216-226. 
VIII -13 
Gunter, G., J. Y. Christmas & R. Killebrew. 1964. Some relations of salinity to population distributions of mobile estuarine organisms, with special reference to penaeid shrimp. Ecology 
45: 181-185. 
Herke, W. H. 1971. Use of natural, and semi-impounded, Louisiana tidal marshes as nurseries for fishes and crustaceans. Dissertation, Louisiana State Univ. 242 pp. 
Hildebrand, H. H. 1954. A study of the fauna of the brown shrimp
(f. aztecus Ives) grounds in the western Gulf of Mexico. Publ. Inst. Mar. Sci. Univ. Tex. 3(2): 231-266. 
Hildebrand, H. H. 1955. A study of the fauna of the pink shrimp (Penaeus duorarum Burkenroad) grounds in the Gulf of Campeche. Publ. Inst. Mar. Sci. Univ. Tex. 4(1): 168-232. 
Hoese, H. D. 1960a. Juvenile penaeid shrimp in the shallow Gulf of Mexico. Ecology 41(3): 592-593. 
Hoese, H. D. 1960b. Biotic changes in a bay associated with the end of a drought. Limnol. &Oceanog. 5(3): 326-336. 
Hoese, H. D. &R. S. Jones. 1963. Seasonality of large animals in a Texas turtle grass corrununity. Publ. Mar. Sci. Univ. Tex. 
9: 37-47. 
Hughes, D. A. 1969. Responses to salinity change as a tidal transport mechanism of pink shrimp, Penaeus duorarum. Biol. Bull. 136: 43-53. 
Inglis, A. 1960. Brown shrimp movements. In Fishery Research, Galveston Biol. Lab. Circ. 92 Washington, D. C:-pp. 66-69. 
Iversen, E. S. 1962. Estimating a population of shrimp by the use of catch per effort and tagging data. Bull. Mar. Sci. Gulf & Carib. 12(3): 350-398. 
Johnson, M. C. 1956. Propagation of the white shrimp, Penaeus setiferus (Linn.), in captivity. Tulane Stud. Zool. 4(6): 175-190. 
Kurz, 	H. &K. Wagner. 1957. Tidal marshes of the Gulf and Aransas coasts of northern Florida and Charleston, South Carolina. Fla. State Univ. Studies No. 24, 168 p. 
Kutkuhn, J. H. 1962. Gulf of Mexico commercial shrimp populations нtrends and characteristics, 1956-59. Fish. Bull. 62(212): 343-402. 
VIII -14 
Kutkuhn, J. H. 1962. Dynamics of a Penaeid shrimp population and management implications. U.S. Fish Wildl. Serv. Fish. Bull. 65(2): 313-338. 
Kutkuhn, J. H. 1966. The role of estuaries in the development and perpetuation of commercial shrimp resources. Amer. Fish. Soc. Spec. Publ. No. 3: 16-36. 
Kutkuhn, J. H., H. L. Cook & K. N. Baxter. 1969. Distribution and density of prejuvenile Penaeus shrimp in Galveston entrance and the nearby Gulf of Mexico, Texas. FAO(UN) Fish Rep. 3(57): 1075-1099. 
Ladd, 	H. S. 1951. Brackish water and marine assemblages of the Texas coast, with special reference to molluscs. Publ. Inst. Mar. Sci. Univ. Tex. 2(1): 125-164. 
Lindner, M. J. & W. w. Anderson. 1956. Growth, migrations, spawning and size distribution of shrimp Penaeus setiferus. U.S. Fish. Wildl. Serv. Fish. Bull. 56: 555-645. 
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Mock, 	C. R. 1966. Natural and altered estuarine habitats of penaeid shrimp. Gulf & Carib. Fisheries Inst., 19th Annual Session, pp. 86-98. 
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VIII -15 
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Parker, J. C. 1970. Distribution of brown shrimp in the Galveston Bay system, Texas, as related to certain hydrographic features and salinity. Contr. Mar. Sci. Univ. Tex. 15. 
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VIII -16 
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Tabb, 	D. C., D. L. Dubrow & R. B. Manning. 1962. The ecology of northern Florida Bay and adjacent estuaries. Tech. Ser. Fla. St. Bd. Conserv. (39): 81. 
Temple, R. F. 1965. Vertical distribution of the planktonic stages of penaeid shrimp. Publ. Inst. Mar. Sci. Univ. Tex. 10: 59-67. 
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Train, R. E. 1968. The challenge of the estuary. Proc. Nat. Shellfish Ass. 59: 14-17. 
Trent, L. 1967. Size of brown shrimp and time of emigration from the Galveston Bay system, Texas. Proc. Gulf Carib. Fish. Inst. 19th Ann. Session 7-16. 
VIII -17 
Truesdale, F. M. 1969. Some ecological aspects of commercially important decapod crustaceans in low salinity waters. Ph.D. Diss., Texas A&M Univ. 164 pp. 
Weymouth, F. w., M. S. Lindner & W. w. Anderson. 1933. Preliminary report on the life history of the common brown shrimp Penaeus setiferus (Linn.) U.S. Bur. Fish. Bull. 48: 1-26. 
Wiesepape, L. M. & D. V. Aldrich. 1970. Effects of temperature and salinity on thermal death in postlarval brown shrimp, Penaeus aztecus. Sea Grant Publ. No. TAMU-SG-71-201, 70 p. 
Williams, A. B. 1969. Penaeid shrimp c&tch and heat summation -an apparent relationship. FAO(UN) Fish Rep. 3(57): 643-656. 
Williams, A. B. 1960. The influence of temperature on osmotic regulation in two species of estuarine shrimps (Penaeus). Biological Bulletin 119(3): 560-571. 
Williams, A. B. 1958. Substrates as a factor in shrimp distribution. Limnol. & Oceanogr. 3(3): 283-290. 
Wood, 	E. J. F. 1963. A study of the diatom flora of fresh sediments of the south Texas bays and adjacent waters. Publ. Inst. Mar. Sci. Univ. Tex. 9: 237-310. 
Zein-Eldin, z. P. 1963. Effect of salinity on growth of postlarval penaeid shrimp. Biol. Bull. 125(1): 188-196. 
Zein-Eldin, z. P. & D. V. Aldrich. 1965. Growth and survival of postlarval Penaeus aztecus under controlled conditions of temperature and salinity. Biol. Bull. 129 ( 1) : 199-216. 
Zein-Eldin, z. P. & G. W. Griffith. 1966. The effect of temperature upon the growth of laboratory-held postlarval Penaeus aztecus. Biol. Bull. 131(1) : 186-196. 
Zein-Eldin, z. P. & G. W. Griffith. 1969. An appraisal of the effects of salinity and temperature on growth and survival of postlarval penaeids. FAQ Fish. Rept. 57: 1015-1026. 
Zein-Eldin, Z. P. & E. F. Klima. 1965. Effects of injected biologk'. al stains on oxygen uptake by shrimp. Trans. Amer. Fish. Soc. 94(3): 277-278. 
VIII -18 

CHAPTER IX 
Chemical and Physical Separation Procedure for Toxic Materials 
by 
w. B. Brogden 
University of Texas 
Marine Science Institute 
Port Aransas, Texas 

INTRODUCTION 
Earlier work of Copeland and Fruh (1970) indicated that the toxic or inhibitory factors found in seawater could sometimes be removed by such mild procedures as filtration or autoclaving. Separation techniques designed to separate chemical classes of compounds were intended to be applied to waters already shown to be toxic. The fractions resulting from such procedures were then to be analyzed by the bioassay method. Of the procedures planned, only cation exchange and particulate matter resuspension were attempted. 
Methodology 
The cation exchange experiments involved the use of Chelex 100 resin which has a strong affinity for the divalent transition metals known to be toxic. The resin was washed successively with approximately O.SN solutions of HCl and NaOH, with a final wash of HCl. After several distilled water washes, both distilled and clean seawater (collected offshore from Port Aransas) were eluted. These washings were subjected to an algal bioassay which indicated that there was no toxic factor due to the ion exchange procedure. 
At the time of these experiments, no extremely inhibitory or toxic samples had been encountered. Frozen samples from the January 1972 sampling period, known to be moderately inhibitory, were selected for preliminary runs. (Van Baalen, in Chapter V of this report, demonstrated that freezing does not appear to alter the effect of toxic factors.) The problem of exposing the resin to the water samples without having the resin act as a filter was partially solved by mixing 1.0 ml of wet resin (equivalent to 0.24g dry resin) with 25 to 30 ml of a well mixed suspension of the sample in graduated glass 50 ml Erlenmeyer flasks. Samples, distilled water blanks and suspensions without resin were allowed to stand for 24 hours with occasional shaking. As the resin settled much more rapidly than the particulate matter, the sample solution could 
be removed without decanting the resin. These samples were then subjected to routine algal bioassays. 
In the second set of experiments, an attempt was made to collect an adequate supply of particulate matter from the five ARS stations during the April sampling trip. A 10 cm. diameter pressure millipore filtration apparatus was used to filter the particulate matter from five gallons of water. It was found that if the samples were immediately filtered after they were collected, both the glass fiber prefilter and the membrane filter clogged after passage of a few liters. However, if the samples were allowed to stand for several hours, the particulate matter coagulated and settled out. If the water sample was then decanted carefully, the settled particulate matter could be collected as a dense suspension in less than 250 ml of water. 
The particulate matter collected at stations 22 and 26 was removed from the glass fiber prefilters by gentle washing with filtered water and gentle rubbing with a spatula. This recovery was not quantitative, but a rough estimate that the original sample contained more than 100 mg/liter of suspended matter could be made. 
Samples for algal bioassay were prepared by diluting the concentrated suspension of particulate matter with clean sea water to give final concentrations from 0.1 to 10 times the particulate concentration in the original water samples. The bioassay results were difficult to interpret because of the tendency of the particulate matter to promote clumping of the algal cells. It was possible to determine growth rates for some samples, and score the final algal concentration visually, even on those samples in which growth rates were not measured. 
Results 
The results of the cation exchange experiment are shown in Table 1: 
Table 1. Algal Growth Rate for Various Stations and Treatments 
Station Depth Original Exposed to Not Exposed Rate* Resin* to Resin* 
l  l  ft  1.42  2.12  2.48  
33  1 ft  2.00  2.28  2.04  
38  Midн 1.25  1.80  2.04  
depth  
Dist.  2.40  2.36  
water  

*LoglO 
IX -2 
The results of particulate matter resuspension are given in Table 2: 
Table 2. Algal Bioassay of Resuspended Particulate Matter Samples 
Station Relative Growth Visual Est. of Remarks 
Cone. Ratea Final Growthb 

22 
26 
Clean Sea Water Dist. Water ASP2 Control 
l 
10 5 
2.5 
l 
0.25 
0.1 
l 
10 5 
2.5 
l 
0.1 
1.92 
0.9 
1.2 
2.36 
1.41 
0.6 
1.0 
l.7 
2.7 
2.7 
2.32 
2.36 
+3 +3 +3 +2 +3 +5 
+3 +2.5 +3 +2 +5 
+5 
Unfiltered Control 
Unfiltered Control 
aLog10 Growth Rate 
bvisual estimation on range from 0 to +S where +S is roughly equivalent to 0.5 mg dry weight of algal cells per millimeter of suspension 
Discussion 
It appears that exposure to a cation exchange resin does not have a consistent effect on the toxic or inhibitory nature of the samples. Difficulties encountered in this approach due to the inability to completely resuspend the particulate matter may contribute to this variability. 
IX -3 
High concentrations of particulate matter did not depress growth rates proportionately, although increases in concentration near that in natural waters behaved in a proportionate manner. Bioassay of unfiltered samples from the same stations does not indicate the same amount of growth inhibition as does the assay of a reconstituted suspension of filtered particulate at the original concentration. However, this may be due to the fact that samples for filtration and samples for routine bioassay were obtained at different times for each station. Another difficulty noted was that the particulate matter recovered frJm filtration clumped more readily than the particulates present in the unfiltered water. 
Summary 
The ion exchange experiment indicates that the growth inhibitor or toxic factor in selected samples is not a readily exchanged toxic metal cation. 
The experiments with various concentrations of suspended matter indicate that this material, suspended in clean sea water can inhibit growth similar to a natural unfiltered sea water of equal particulate concentration. Concentrations of particulate material much greater than those observed can slow growth but not inhibit it completely. It should be possible to determine a quantitative relationship between growth rate and suspended matter which could be used in modeling efforts. 
IX -4 

CHAPTER X 
Discussion and Summary of Galveston Bay Toxicity Studies 
by 
Carl H. Oppenheimer 
University of Texas 
Marine Science Institute 
Port Aransas, Texas 


Discussion 
The sample stations for this study were purposely established in representative areas which would integrate water from Gulf sources, natural rainfall, and man's effluents as a function of general circulation. While many past studies have indicated the toxicity or biological inhibition of effluents, few have followed such parameters as a function of dilution. In fact, the processes of sedimentation, chelation, ligand formation and biological removal of materials during water mixing has not been understood because of the complexity of the reactions involved. By taking samples and determining tests for areas that represent both natural effect and man's activities one should be able to integrate the differences between simple dilution and other natural chemical and mechanical processes. 
The overlapping toxicity and water quality measurements obtained by our team research is quite revealing. The total amount of toxicity for all bay stations is not outstanding and may indeed indicate the limits of the various procedures. While each of the investigators indicated some toxicity for some stations the amount of toxicity for combined stations may not be significant. In the studies of algal toxicity, turbidity is a factor. Filtered samples had little toxicity while nonнfiltered samples were somewhat depressing. Respiration tests for mullet indicated a general metabolic depression at most stations. Shrimp studies indicated an activity depression at station 22. A carbon balance clearly indicated that productivity of the bays was a major factor in organic carbon distribution, whereas, effluent input was an insignificant part of the carbon balance. Benthic organisms revealed a significant change due to unnatural causes at station 17. 
The benthic macroinvertebrate study, which should indicate integrated toxicity effects suggests that station 17 had the greatest toxicity; the algal toxicity experiments showed depression of the algal growth rate below 2.0 from February onward. However, station 22 shows consistent depression of the algal growth rate below 2.0 for the same period without an obvious effect on the benthic population. 
The shrimp bioassay appeared to indicate toxicity by an increase in respiration in contrast to the mullet which showed a general depression of respiration. The factors causing the toxic response by the shrimp do not appear to be the same as those causing the depression of fish respiration or the increase in shrimp respiration. The station 22 winter sample which gave the greatest respiration increase in the shrimp respiration test was not significantly toxic to the algae. 
Figure l was plotted to see if there was any relation between the algal growth rate in filtered water, ALGAL K-F, and the growth rate in unfiltered water, ALGAL K. From this figure, it appears that if there is a soluble toxic factor, it is unrelated to the particulate factor. In an attempt to assess the significance of the algal toxicity measurements to the ecology of the bay, we have searched for relationships between ALGAL K and a number of parameters measured in the routine sampling program. Figure 2 is an attempt to correlate the oxygen concentrations observed in the field with the growth rate in the assay. Low or high oxygens do not seem to be correlated with highly toxic stations. 
An attempt was made to correlate ALGAL K with the shrimp respiration measurements for the TTARSTT stations, however, no pattern could be found. 
ALL STATIONS 	ENVIR 

0 
LL I :::s:::: 
_J 
er: 
D 
_J 
er: 
0 
~ 
FIGURE 	1
. . 
.. . -..
Х. I-:.. Х Х Х ХХХ
.. Х 	I Х ХХ Х Х Х Х Х Х 
. .:...-:; ~:: . :.... 
:╖: :╖: :.~:.:╖ .: 
o~--.-~-r---..,.~-.---~..-----r-~-r----,.~-r----1 
0 
0 
0 N 
z 
w 
D 
>н
x 
0 
a 
o.s LGAL K 3. o BAY STATIONS ENVIR 
FIGURE 	3 
╖.. 
.. 
. ╖╖i 
: . . ╖:i:y . . . .. 
.╖ :::╖ ╖'1'╖╖╖ 	: .
0 
0
~ :v!Х.. . ; 
0 : I Х 
:.. . I.. . . : .. 
.Х. .-~ .... 
01----.-~-r---..,.~-.---~..-----r-~-r----,.~---r----1 
o.o 	7. 0
ECCH 
RLL STRTIONS 	ENVIR 

0 
0 (Y') 
_j 
er: 
(.'.) _J 
er: 
0 
~ 
FIGURE 	2 
.. 
. . 
, 
.. .. .
. . . . 
.,,
':....
....... 

. .. 
a...._-.--~-r---..,.~-.-~..---.-~-------..-----.----1 
o.oo XYGEN 20.00 
ALL BAY ST 	ENVIR 
-r---'-~......._~.___._~_.____.~_._~.L..-~~-r 

a  FIGURE 4  
N  
. Х.  
z  ..  
D  Х....  
0::  Х Х Х  
. ........  
ХХ Х  Х  ХХХ -& ХХ  
. .....  
. .  
. . . . .Х -. . . . . . .... -. ... .  
0  
0  

o.__-.-~-.---..~-.-~..--~~~--.~-.-~ 
o.o 	2. 0
OTAL P 
Fig. 1. 	The algal growth rate constant for unfiltered samples, ALGAL K plotted against the constant for the same sample filtered, for all measurements made during 1971-72. 
Fig. 2. The algal growth rate constant for unfiltered samples plotted against oxygen concentration at the time of collection in mg/liter. Fig. 3. Secchi disc readings in feet plotted against oxygen in mg/liter for 1971-72. Data from the Army Corps of Engineers field work. 
Fig. 4. 	Organic nitrogen (ppm) versus total phosphorus (ppm) for all Bay stations for 1971-72. Data from the City of Houston Health Dept. analyses. 
x -2 
Figures 3 and 4 are examples of our attempts to learn more about interrelations between chemical and physical factors in the bay. All of these data points are from years 1971 and 1972 and exclude the ship channel. 
In the natural state, the biological system of the Galveston estuary may be presumed to be at either short term or long term equilibrium, and the biological, chemical, geological and physical systems interact in complex, but self-balancing fashion. The superнimposition of rapidly expanding man's input to the Galveston Bay system can and will alter the biological attributes of the system if indiscriminately applied. However, some alteration may not necessarily be undesirable. It may be possible by systematic investigation, monitoring and prediction to maintain a productive bioeconomy that will benefit the greater natural system which includes man and the remaining biota. 
Other activities of man, such as sport and commercial fishing, mariculture, agriculture and activities associated with preserves and parks can cause the Galveston Bay system to react as a chain of events of varying magnitude and duration. Solid, liquid and gaseous wastes arriving in the estuarine area by accident or design will affect the biologic system --in some cases beneficially, in others, harmfully. The same is true for other activities such as the construction of piers, hurricane attenuators, jetties, channels and ship moorings. Changes in the estuarine biosystem can be expected as these activities occur. However, proper management decisions can produce a net benefit. 
In one way or another, biological and technological activity in the Galveston Bay system is not only an integral and indispensable component of the system itself, but also the human community around the system perimeter. Small (and often subtle) changes resulting from management decisions related to a given aspect of the biological system are often magnified and the result effect is sometimes farнremoved in space and time and of greater magnitude than the system can tolerate productively. 
It is recognized that the Bay system which once was pristine is being changed. Scientific knowledge and the supporting technology exists to recreate near natural conditions in those areas where major changes have taken place because of technology, but renewal will not be without its costs; decision-makers must collaborate with scientists and technologists in making renewal decisions. 
The body of literature treating toxicity and the bioassay for toxicity is almost exclusively limited to limnology. Of 198 scientific reports examined on the effects of specific toxic substances, only 6 (or 3%) were directly applicable to estuarine organisms. Pringle (unpublished data) in studies on the effects of lead on the American oyster (Crassostrea virginica), found a 12 week TLm value of 
0.5 mg/l and an 18 week TLm value of 0.3 mg/l. Concentrations of 0.1 
x -3 
to 0.2 mg/l induced noticeable changes in mantle and gonadal tissue under 12 weeks of exposure (Water Quality Criteria, p. 88, 1968). 
Apparent toxicity of low dissolved oxygen concentrations in marine waters were reported (J. WPCF, 36(7): 795) for marine flatнfish. These organisms evade when oxygen concentrations fall below 
3.7 ppm. 
Zinc was reported as TTdangerous" to oysters (Water Quality Criteria, p. 88). Pringle and Shuster (1967) reported that the maximum acceptable limit of zinc before becoming toxic to shellfish was 1500.0 ppm. 
Gordon et al. (1972) reported that suspended sediments in estuarine waters-Were toxic to penaeid shrimp and that respiration values increased three to five-fold over control rates during the bioassay. Mullet (Mugilidae) exhibited two-fold respiration increases. Both indicator species demonstrated increased activity. 
Some work is being done by Jackson (Doctoral dissertation, in progress) with selected toxic substanc~s and their effects on indicator organisms indigenous to the estuarine system. These experiments involve both TLm and stress thresholds. 
Miget (personal communication) is working with a number of estuarine organisms and the TLm of oil emulsions created by microbial degradation at the Marine Science Institute at Port Aransas. 
Brodgen (personal communication, University of Texas Marine Science Institute at Port Aransas) is currently investigating the toxic effects of carbon tetrachloride and a freon on estuarine organisms. 
It is likely that toxicity experiments related to estuarine organisms are being conducted currently by others, however, few reports have appeared in the literature. 
In summary, practically all of the previous toxicity work has been limited to TLm investigations of freshwater organisms, generally limited to the families Centrarchidae (sunfishes, basses, and crappies), Salmonidae (trouts, charrs and salmons), Cyprinidae (true minnows, exclusive of carp and goldfish) and Carostomidae (suckers). Sewage and Industrial Wastes, 23(11): 1383-1401, 1951. 
The absence of definitive TLm studies on estuarine-dependent organisms bears the strong implication that current water quality standards for bays and estuaries are artificial and have a poor basis of scientific fact. A major investigative thrust in this direction is imperative if true quantification of estuarine water quality criteria is to be achieved. 
x -4 
The heavy metal data for the waters of the selected samples as measured by the U.S. Geological Survey were analyzed. Unfortunately all chemical data were not completely placed in our ENVIR data system nor were they available in Storet between the last sampling period and the time of this report. A complete analysis of the many thousands of data points for the chemicals in the water in effluent inputs in the bay can only be treated after extensive computer compilation in some system that will allow for the data management such as EN\lIR. 
It is quite obvious that although the present study was in part organized to provide a significant amount of information for decision making purposes for the 1971-72 Galveston Bay Study there are obvious deficiencies. It is possible that the deficiences are artificial, due to the fact that the many parts of information derived from the past and present study cannot be effectively analyzed in the relatively short period of time available for this present report. Intensive study of the available data could produce information that would be valuable for decision making purposes. 
The complexity of Galveston Bay stands out as one attempts to relate existing data. While it is obvious that some species are changed in the bay the total yield for commercial purposes has increased over the years. Our bays have been subject to natural changes of catastrophic magnitude in the past and have survived. The populations that are currently present represent a balance between the natural imposed changes and those imposed by man. 
One of our problems is to separate the effects of man and those of the natural environment. When one looks at the Eastern seaboard and the effects of urban and industrial complex of New Jersey and New York City it is quite obvious that man has overcome his environнment because of the injudicious use of his byproducts. In the Texas bay systems we are still in a relatively natural state, as compared to New York and other highly populated environments. Can we use the experience of cleaning up the Hudson, the Thames and other rivers and apply them to our present problem? 
References 
Copeland, B. J. &T. J. Bechtel. 1971. Some environmental limits of six important Galveston Bay species. Cont. 20, Pamlico Mar. Lab. N.C. State Univ. 
Doudoroff, P., B. G. Anderson, G. E. Berdick, P. s. Galtsoff, W. B. Hart, R. Patrick, E. R. Strong, E. W. Surber &W. W. Van Horn. 1951. Bioassay Methods for the Evaluation of Acute Toxicity of Industrial Wastes to Fish. Sewage & Industrial Wastes. 23(11): 1380-1401. 
x -5 
Gordon, K. G., W. B. Brogden &J. S. Holland. 1972. A preliminary toxicity analysis of the seciments of La Quinta Channel, Corpus Christi Bay, Texas: A report prepared for the U.S. Army Engineer District, Galveston, Tex. Univ. Tex. Mar. Sci. Inst. (Mimeograph) 
Pringle, B. H. & C. N. Shuster, Jr. 1967. A Guide to Trace Metals in Shellfish. Northeast Marine Health Sciences Laboratory, Narragansett, R.I. 
x -6 

Summary 
It is quite appropriate that each of the projects have drawn their own conclusions based on individual results. As the interнdisciplinary project is only one of several, designed to study the bay system and whose information will be used for management purposes, it is presumptious for us to draw conclusions. The final conclusions of the 1971 and 72 study will come from an intensive study of all the parameters and models simulated during the study period. 
However, there are some rather important generalizations that can be separated from the reports of our portion of the grant project. 
1. 
Some level of inhibition appears to be present in various parts of the bay for specific test organisms at certain times. In one test the particulate matter was found to be the inhibiting factor. 

2. 
The marsh and grass flats of the bays are indispensible as nursery grounds and should be preserved at all costs. 

3. 
Primary productivity provides most of the carbon balance of the bay and imported carbon may be insignificant unless some specific molecules are at an inhibitory concentration. Nitrogen may not be as significant as thought. 

4. 
The bay is still in some reproductive equilibrium with nature and manTs activities. Dilution, sedimentation and other chemical reations must play a very important part in reducing the effects of the highly polluted Houston ship channel. 

5. 
An intensive years research is needed to evaluate all existing data for management decisions. 


x -7 


Date 
28/10/71 

?q/10/7] 
?5/10/71 

~ 
l 
co 
27/10/71 

Time 
0845 

1730 

2040 

0245 

0945 

1300 

16J i:; 
0100 

0330 

0800 

1045 

Sta. 
17 

17 

17 

17 

22 

22 

22 

22 

22 

22 

22 

Air Tempo (OC) 
Water Temp. (OC) 
24 ra l' 
23.5 ra 4.s' 
24 ra l' 
25 ra 20' 
24.5 ra 39' 

24 ra l' 
25 @20' 

25 ra 42' 
25 ra l' 
23 @ 20' 

?O ra -i;qt 
23 ra 1' 
23 ra 6' 
23 ra 11' 


23.5 ra 1' 

23.5 @ 6' 

23.5 ra 11' 
24 fa 1 T 
24 ra 6' 
?7i.5 ra 1?' 
23 ra 7' 

23 ra 7' 

23 ra 13' 

24 ra l' 
23 ra 7' 
23 ra 13' 
23 ra l' 
23 ra 5' 

23 ra 11' 
23 ra l' 

23.5 ra 6' 


23.5 @12' 

Dissolved Oxygen (mg/l) 
7.9 ra l' 
7.9 
ra 4.5' 


8.4 
ra 1' 


6.9 ra 20' 
2. 6 ra 39' 

7.8 @ l' 
7.2 @ 20' 

3.1 ra 42' 
7.1 
ra 1' 

8.4 
@ 20' 



-i; o ra ~q' 
5. 68 @ l' 
5. 68 ra 6' 

5.36 @ 11' 
8.2@ l' 8.2@ 6' 
8. 3 fa 11T 
7.4 
ra l' 

8.2 
ra 6' 


8.2 ra 12' 
8.8 ra 7' 

8.7 ra 7' 

8. 7 ra 13' 

8.4 ra 1' 
8.o ra 7' 
8.1 ra 13' 
8.o ra l' 
7.9 ra 5' 

7.8 ra 11' 
9.5 @ l' 
8.8 ra 6' 

8.9 @ 12' 
Salinity (o/oo) 
23.l 
23.1 

24.2 

24.8 

26.4 

24.2 

24.2 

25.3 

24.2 

26.4 

24 ? 

19 

19 

20 

21.4 

22.0 

21. 4 

21.4 

21. 4 

21. 4 

20.9 

20. 9 

20. 9 

20. 3 

20. 3 

20. 3 

19.8 

19.8 

19.8 

20.9 

20. 9 

20.9 

J &G, hzv 
GL'.'J ortv cldv, 6-8 knts 
Jackson & Gordon 
J & H 

J & G; 1gt hz, 3-5 mi 
J &G; lgt hz, 3-5 mi 
J &G: ortlv cldv 
GL, clr, 8-10 knts 
G 

G. clr'I 1-3 knts 
Gordon 
-


Air 
Water 
Dissolved 
Sta.
Date Time 
Temp. 
Temp. (OC) 
Salinity
Oxygen 
(OC) 
(mg/l) 
(o/oo) 
0600
28/1/71 
26 
23.5 ra i' 
9.6 ra l' 
18.65 
Gi .. elm.. 5rr swells 
23.5 ra 4.5' 
9.6 ra 4.5' 
18.65 
23.5 ra B.5' 
io.o ra 8.5' 
18.65 
0745 
26 
24 [a l' 
9.3 [al' 
18.75 
G & H.. cldv, 2-4 k.nts 
1 q R
24 fa 4.S' 
9.3 fa 4.S' 
lR. 7S
24 ra B' 
9.3 ra s' 
26/1/71 
1200 
29 
23 ra 3' 
7.7 ra 3' 
24.2 
Gi & H.. clr 
27/1/71 
0800 
29 
23 ra 3' 
1.0 ra 3' 
18.1 
Gi & H. cold front 1ust arrived 
1420 
29 
23 ra 3' 
8.o ra 3' 
19.8 
Gi & H.. cldv. ovct 
28/1/71 
0245 
29 
23 ra l' 
18.65
7.8 ra i' 
G & J. laht rain. 10 knt 
7.5 @. 3'
23 @3' 
18.65 
24 fa 3T
1430 
29 
19.7S 
Gi 6 H. 0lr. t-o nrtlv 0 l riv. +J' waves
7.85 ra 3' 
~ 
-
I 
..... 
-1-L 



Date  Time  Sta.  Air Temp. (OC)  Water Temp. (OC)  Dissolved Oxygen (mg/l)  Salinity (o/oo)  
I  
1/25/72  2030  26  13░  15 ra l'  9.3?1a l'  ? la ] f  (Hn F-,. ~;it)  
15 ra 7'  9.32fa 7'  2 ra 7'  
1/26/72  1330  26  16.5  15.5ra l'  9.5lfa l'  2. 7fa l T  E.. l T" 5-8 knt.. cldv .. f.=iir .. (WB f-,. MH)  
15.5fa 7'  9.5lfa 7'  --ra 7'  
1905  26  18  16 ra i'  8.93ra l'  4 la l T  0lr. SF._ JO knt. 0-J' (r, F-,. lT)  
16 @ 6'  8.93@ 6'  4 ra 6'  
16 ra s'  8.65ra 8'  4 ra R'  
1/27/72  1930  26  20  17.c:;ra J'  9.nra J'  4 fG 1 T  (H F-.. r,)  
17.SIG 6'  9.nra 6'  4 ra r;'  
17.5@11'  9.llfall'  4 fall'  
1/25/72  1630  29  12.2  16. C)IQ J T  Jo. oora J '  l? fG l T  Vis rrnnrl. rlPnsP l'VT'. NF._ lt;-?0 knrs 1 ~н?'  
16.5@ 2'  9.79@ 2'  12 @ 2'  (Gi & Hi)  
1/26/72  1310  29  18  16.2fa l'  9.3lla l'  1c:; la l'  4-5 mi Х h '7.V. E. J 0 mnh. l T ( r,i . Hn. ~i)  
16 fa 4T  9.78ra 4'  15 la 4'  - 
1/27/72  1100  29  19.9  17 ra l'  8.57fa l'  12 ra l'  10 mi.. hzv.. ---0' (r,i .. Hi .. Sj)  
~  17 fa 4T  8.57ra 4'  12 ra 4'  
I .........  1400  29  26  17 fa l T  11. 48fa 1 T  15 ra l'  25o/o cvr .. cld.. (Ho & r,n╖)  
UI  17 ra 3.5'  10.0lfa3.5'  15 fa3~5'  
17 ra 6'  11. 82fa 6T  15 ra 6'  
1642  29  20.l  17.5@ l'  10.27@ l'  14 ra l'  5mL hzv'l w'l 1-3 knt'l l(cao)~ (Gi'l HL Si)  
17. 8@ 4'  9.36@ 4'  14.8fa 4'  
1/25/72  1140  33  15.0  17 @l'  7.85@ l'  11. 2fa l T  (KG & WB) I  
17 fa 36 T  7.B5ra 36'  1L81a ~r;r  
1400  33  14.5  17 ra i'  s.55ra l'  lL8ra l'  Clr .. 0.. E.. 20 knt.. 0 . 2 T (WB. Kr,)  
16 ra2s'  8.03fa28'  ll.4fa28'  
1/27/72  1120  33  18.8  17 ra l'  8.88fa l'  9 Х 6fa l T  St. Hzv'l 40%'1 SE.. 5-8 knts'l 0.4' (WB .. NG)  
17 @25'  7.25@25'  17.3@25'  
16.5[a50'  6.79@50'  18.4fa501  
1315  33  20.2  17 fa l T  10.45[a l'  9 Х 6ra l T  Clr'l 50%'l SE'l 12 knts, (B'l J, G)  
17 @25'  7.34@25'  11. 2@25 T  
16.5@50'  6.50@50'  18.4@50'  
I  

Date  Time  Sta.  Air Tempo (OC)  Water Temp. (OC)  Dissolved Oxygen (mg/l)  Salinity (o/oo)  
4/25/72  0735 1404  29 29  17.3 22  22.5 25 ra 25 fa  ra l' l' 4T  7.18 8. 98 8.64  ra ra ra  1 T 1 T 4T  16 fa lT 18.6 @ lT 18.6 @ 4T  
lq4c;  ?q  ?O нc;  23 23  ra ra  l' 5'  FL74fa1T 8. 38 fa 5T  21. 4 21. 4  fa ra  1 T 5T  
4/26/72  2300 0100 n~nn  29 29 ?q  22.0 23 ?n  23 ra 9' 24.5 ra l' 22.5 [a 6' 24 @ 1 T -нfa h T ?0 . C) fa 1 T 22.0 ra 6'  8.92 ra 9' 8.36 fa lT 8.54 ra 6' 8 Х 18 @ 1 T R _44 ra f1 r R 74 ra 1' 8.88 ra 5T  2i.4 ra 9' 20 Х 8 @ 1 T 20.8 @ 6' 20 Х 8 @ 1 T 20. 8 fa 6T ?O.B ra l' 20.8 ra 6'  
0500  29  20  21. 0 fa 21. 5 fa  1 T 6T  8.38 ra l' 8.47 @ 6'  22.4 ra l' 22.4 @ 6'  
~  1515  29  19.5  20. 5 @ 1 T  9.05  @ l'  21. 9 @ l'  
~ m  21.0 @ 3'  8.97@ 3'  21. 9 @ 3'  
4/27/72  1100  ?q  21  22 22  ra ra  1 1 4'  8. 37 8.37  fa ra  1T 4'  23.0 23.0  ra ra  1 T 4'  
22  ra  8'  8.37  ra  8'  23  .o  ra  8'  
1300  29  21  22.5  ra  l'  8.10  ra  l'  23.o  ra  l'  
'  22.5  ra  4'  8.38  ra  4'  23.0 @ 4'  
.1800  29  20  ?? . C) fa RT 20.5@ l'  R -;;;R ra R' 10.02@ l'  23.0 20 Х 8  fa 8T @ 1 T  
20. -5  @ 5 T  9.37@ S'  19.2 @ 5'  
2000  29  19.15  ra  1 1  
19.1s  ra  2'  
2200  29  19.15  ra  l'  
4/28/72  ?LLnn 0200 0400  ')Q 29 29  lg le; ra l' i9.15 @ l' 20.8 @ l'  
nr:;no  ?g  20. 8  @ 1 T  

JH2 NS 2 NMi unrest, OllO, NE 2 15 knts, 1. 51. 2' JG & MH: unres, hzy, NW, 10, 1. 5-2f 
NG: 10 mi. 0/10. NE. 3-5 kntS" 0-1 T 
K & NG: clr'l NE, 6 knts, 0-""l T 
KG; 10 mi, 5/10, NE, 6-8 knts, 0-1 T I 
I 

K~: 10 mi. 5/10 .. NE. 6 knts .. l' 
KG: 10 mi.. 5/10, NE. 10 knts .. 2' 
H & G; clr, ES-E, 15 knts, 2-3' 
G & B: lt rain. cldv.. SE. 12-15 knts .. 18TT 
I 
I 

Gordon: 10/10, SE, 12 knts, 1. 5T 
JH; NM; unres, 10/10, ESE, 15-20' 2-3 T 
Х' 
JH. NM: rain 
1:. 
1,; 

~----.-╖ ╖ .. ╖~----~ 
l;Jater
Air 
Temp. (O C) c0c) 
Ti me 
Sta . 
Temp o
~ate 
----.-------~----+н
--╖ ---╖ -------4---╖~н___4j25i_7_2__ 

0800 14 _0._~ 26 ------------~----!-----╖╖--------26 @ 
1605 . 14 21 25 @ 1' ;__?_.._ 
?3 @ 1' ╖_.1-h_~_@__1=_'__ 
----------+--~ -----
--25@-
4, I 7.74@ 4' 25.2@ 4' 
---╖-------н
Di ssol ved 
Oxygen 

( ~-╖""/ l \
╖ ╖'::J -I 
7_. 66 @__J_~-2~~5 @ 1 T 6_. 94 @ fl:._'_j 2з_!_~~ 

-------╖╖╖ ----╖-----------╖-╖╖-нJG & MH; unl::!:_m, hzy~2.. 12 _knt_s_,_ 

_ 4/-~~~~~l~9~~]:_i-~-F~1fr-l :~~~]-~~-: -;-_--~: fRJ& LF; -~L~o, s,
6
:~~-=_:_=~~---+-12l5~╖-l 4_ , _is.'i i---:i~ ~-~~~{~4_@@)-~, -, ;;: : -_U~=--~-J~,NG; -~R, ~'~sE, lQ:-J2" _1.5 _____~l~Q_? , __ _
_ _1-----╖ _ ╖╖---__ 3 _i: T_; ---"-_________ -------~-------~--_
_ _
_____ -14 -'---_____ --╖ -╖--26._@--╖╖------╖-_ 
---------2 _29_, 1.4 ---t --
---
-╖-...,..╖2.8_..._9___@____
_ 
---╖--╖--╖ ╖╖ -╖-----╖
-╖--╖-╖╖-╖
--╖
---╖╖-----╖╖╖-н
--
--_-~~oo_t_l-_4__ _ _ 
_____ ╖-----+--╖ ╖
-╖-╖-╖-╖ ---
---╖ 26_._3__@._l' 1 __ _ _ ____ __ _____ ___, ___ ,____________ 
--.~-----╖-t :_ ~: _ 
-'-----==~~~~Sg@s-╖ --~----=~~--------~-----------
-------н
~~~6j-~~ ;-_----н;;; ~--~~~I ~! :=▒-----~----i ~:~~: ~:_1----------н
_4/27/]_1_ _ 12001. _14 . i.╖~---~~Q__ @__ 1_~ 8. 2 _26.85 _.@_l' NG & EW: unlim, cldv, SE~ 18 knts, 1-2 



Date 
-
4/27/72 
2000 2045 2200  22 22 22  21 21 20.5  22 22 22 23 24 -н20 22  ra l' ra 4.5' ra gt @ l' ra 5 t fa llT fa 1 T ra 4.5'  7.25 ra l' 1.11 ra 4.5t 7.2l@ gr 8.19@ it 8.38 ra 5t -нra 11' 7.10 ra l' 1.01 ra 4. 5T  16.4 @ l' -н@ 4. 5 t -н@ gt 16.4 @ l' -нra 5 t -нra 11T 16.4 @ l' -н Smith; unlimited, ovcst, NE, 10-15 knts, 1.5.L2' Isothermal (0? min layer @ 4 t) Smith; unrstd'I ovcst, NE, 15-20 knts, 2' (Isothermal)  
22  ra 9'  1.01 ra 9'  -н 
x I [:\j 1-4 4/28/72  2400 0200  22 22  20.5 20.5  22 22 22 22  ra l' ra 4.5' ra 9' ra l'  1.01 ra 1' 6.92 ra 4.5' 9'6.83 @ 1 .10 ra 1'  16.4 ra 1' -н@ 4.5' 9'-н@ 16.4 @ l'  I Smith; unrstrtd, ovcst, NE, 15-20 knts, 1. 5-2' Smith; unrstrd, ovcst, NE, 15-20 knts, 1. 5-2'  
-н 

Time 
l945 
0410 

0600 

Sta. 
22 

22 

22 

Air 
Tempo (OC) 
21 

21. 5 

20.5 

Water Temp. (OC) 
22 ra l' 
22 ra 5' 
22 ra 9' 
22 @ 4.5' 

22 @ 9' 22 @ l' 
22 @ 4.5' 

22 ra 8' 
22 ra l' 
?? ra 4_C)T 
22 @8' 

Dissolved Oxygen (mg/l) 
7.31 ra l' 
7.22 ra 5' 
1.22 ra 9' 
6.92 @ 4.5' 

6.92 @ 9' 

6.86 @ 1' 

6.86 @ 4.5' 

6.86 ra 8' 
1.11 ra l' 
7_02 ra 4 -C) T 
7.02 @ 8' 

Salinity (o/oo) 
18.6 @l' 
18.6 @5' 
18.6 ra 9' 

16.4 
@ l' 

17.0 
@1' 



Gordon; rain, cldv, NE, 8 knts, 2' 
Sm'ith; unrest, ovcst, NE, 15-20, 1. 5-2f 
Smith; hz, 70% StCu, Erly, 15 knts, 1. 5-2' 

Time
Date 
1155
4/27/72 
iX 
I 
L" 
w 
Sta. 26 
Air 
Temp. (OC) 
22 
, 
Water 
Temp. (OC) 

22 ra 1' 22 ra 6' 22 @ 9' 
Dissolved Oxygen (mg/l) 
s.10 ra 1' 7.75 ra 6' 7.75@ 9' 
Salinity 
(o/oo) 

MH & NM; unres, part, ESE, 15, 2-3'
l.2.0 @ 1' 
12.0 ra 6' 
I 
I 

I 
I 



Water
Air 
Sta.
Time 
Temp. (OC)
Temp.
Date 
(OC) 
-. 

-.. н
---. . ╖-╖ 
0930 
17 
28.0 
29.2 ra l'
27/7/72 
-╖ -╖╖ н
-. ╖-╖ --н
-
-29 .1 ra -6T 
╖-╖ ----н
17 
30.0
1230 
30. 5 ra l' 
Dissolved Oxygen (mg/l) 
6.4 ra l' --o-.23 @ 6' 
8.45 ra l' 
-
-
25/7/72 
~ 
I 
........ 

-J 
-
26/7/72 
?6/7/72 
7. r70 @ 9'
30.0 @ 9' 
-
3o.o ra 18' 7.55 ra --18' 
~-н
---╖ 
~') ') 
~1 R ra i r1 ~4.n 
17 
R r::..7 ra 1' 
╖╖ ╖н
6. 65 ╖ra. 9'
30.o ra 9' 
-
30.0 ra 18' 6.96 ra 18' 
ii.o -ra --1,
.1615 17 --
28. 3 31. o ra 1' н
31. 0 ra 4r 
9.63 ra 4' 
10.1@ -1,
1030 
30.0 
29. 5 @ l'
22 -н
7.63 @ 6'
28. 5 @ 6' 
28.5 ra 12' 
3.62 ra 12' 1240 
27.0 
30 .. o ra l'22 
13.4 ra l' 
28. 5 ra 6r 
1.0 ra 6' 
28.5 ra 12' 
3.97 ra 12' 1345 
22 
29.5 
29.8 ra l' 11. 9 ra l' 
28.8 ra 11' 
4.18 ra 11' 1600 
31.0 @ l' 12.╖
3 ~f 1 f 29 .. 8 ra lO' 
22 
29.0 
3.52 ra lO' 2400 
26.5 
29. 5 ra l' 
8.7 ra 1'
22 
╖-╖-╖ 
-
╖29. 5 ra 6' 
8.7 ra --6' 
29.5 ra 12' 
8.7 --ra 12' 0800 
29.o ra l'
22 
27.0 
1.16 ra l' 71
2R.R ra 
7.4? ra 7' 
---... 
8.S ra l'
J 0?>0 
?2 
?>O . c:; ra J '
27.1 
-

30. 5 ra 6r 
8.S la 6' 
30.5 ra 12' 
8.5 ra 12' 
11 ~o 
28.0
22 
30.0 ra l' 
11.7 ra i' 
6.4 @ 6'
28.5 ra 6' 
28.0 @ 8' 
5.1 @ 8' 
Salinity (o/oo) 
21. 5 ra l╖' 
21. 5╖ ia 6' 
24 ra l'  
@ 9'26  
26 ra 18'  
-- 
')l[. fa 1 T  
-- 
-н 

26 ra 18' 24 ra l' 
24 ra 4' 
╖-н
--╖ . 
17 @ l' 
17 @ 6' 

.  - 
17.5  ra- 12'  
..  
17.5 17.o  ra ra  l' 6'  

17.o ra 12' 
1i.o  ra- 1'  
...  .  
11. 0  ra  11T  

1,
11 @н
11 ra lO' 
13.5 ra 1' 
13.5 ra 12' 
13.5  ra  l'  
-... . .  
13.5 13.5  ra ra  7' l'  

17 ra l' 
17 ra 6' 17 @ 8' 
Flawn & Dan: qood" SW" 8-10 knts" 0.5' NG & RC: aood" SW.. 2 knts .. o.5' 
Rr' ~ N~╖ nT't-lv 0lrlv. ')' tA7.::l'i.J{:l~ 
. --н
LF: unres" SW, l0-12 knts, 1-1. 5' 
. .. --╖н
.JSHoiNJMoiDB; OK., _Q_wind, 2" 
-
-╖ . ╖╖--
н
---╖-----н
haze:  2"-4"  -н--н╖╖ --н 
Carr Carr  & &  Smith; Flawn;  -╖ ╖╖╖ -╖-╖ -н-н--╖ h_?,_.?;Y-, ----h_az;y,_  estrly; 5-8 knts, E, 5-8 k.nts, o.5'  o.5'  

JSH: dark.. SSE" _l"."'2 knts'l 6-12TT 
--╖-╖-. 
-- 
---------н-JSH & N'-JM: 0/10... --8E .._ 5-8 knts .. l' -. ----- 
--н--JG .. RC.. MOH; clr, н-3/lO'l -н'l O, capil.  



Date 
26/7/72 

><: 

w 
('""') 
27/7/72  0150  26  29.0  2g.5 @ l'  6. 95 @ l'  13.0 @ l'  VG;  Clr, E,  0-5 knts,  l'  
2g.5 @ 6'  6. 95 @ 6'  14.0 @ 6'  
2g.5 @ 11'  7.10@ 11'  14.0 @ 11'  
0300  26  28.0  31.0 @ l'  6.7 @ l'  14.0 @ l'  VG,  Clr, E,  0-5 knts,  l'  
I  

Time 
0250 

0500 

0615 

1010 

1545 

2230 

2358 

1015 

1520 

Sta. 
26 

26 

26 

26 

26 

26 

26 

26 

26 

Air Temp. (OC) 
26 

25.3 

25.3 

27.0 

30.5 

28.0 

29.0 

26.8 

27.2 

Water Temp. (OC) 
29.0 @l' 
29.2 @6' 
28.8 
@ l' 

29.0 
@ 6' 



28.8 
@ l' 

29.0 
@ 6' 


30.0 
@l' 


29.5 
@6' 


30.0 
@ 7' 



33.5 ra l' 
32.0 ia 6' 
31. 
5 @ 8' 


32.0 
@l' 


32.0 @ 6' 
32.0 @ 11' 

31. 5 @ l' 
31. 
5 @ 6' 


32.0 
@ 11' 



31.0 @ 6' 

28.0 @ l' 
2g.7@ l' 
2g.7@ 7' 
30. 8 @ l' 
30.5 @ 4' 

2g.5 @ 9' 
Dissolved Oxygen (mg/l) 
8.04 @ l' 
7.63@ 6' 

7.65 @l' 7.65@ 6' 
7.56@ l' 
7.30 @ 6' 

7.7 @ l' 
7.05 @ 6' 

6.9o ra 7' 
9.55 ra l' 
9.55 ra 6' 

6.46 @8' 

8.07 @ l' 
8.07 @ 6' 
7.75 @ 11' 

7.5 @ l' 
7.5 @ 6' 

7.65@ 11' 

6.7 ~ 6' 
6.64 
@ 11' 


7.25 
@ l' 


6.26 @ 7' 

8.83 @ l' 
8.10 @ 4' 

6.80 @ gt 
Salinity (o/oo) 
16.5 @ l' 
16.5 @ 6' 
16.5 @l' 
16.5 ra 6' 
16.5 ra l' 
15.5 
@ l' 

16.0 
@6' 



16.o ra 7' 
15.5 
ra l' 

16.0 
la 6' 


16.0 @8 T 
14.o ra l' 
13.0 @ 6' 

15.0 @ 11' 

13.0 @ l' 
13.0 @ 6' 

13.o ra 11' 
13.0 [a. 6' 
13.0 @11' 
11.0 @ l' 
11.5 @ 7' 

11.0 @ l' 
--@ 4' 

gt
11.0 @ 
MH & WB; 0.3 cover, SSW, 12-15 knts, l. 5' 
lunar eclipse, tide l' lower than on 7/25/72 MH & WB; unlim, 1/10, SW, 10-12 knts, l' waves 
MH & WB, unlim, 1/10, SW, 5-8 knts, 0.7' waves 
VG,RC & MOH; Clr, Hz, E, 5-8 knts, l' 
VG .. Rr .. MOH: Clr.. J/10 .. SE'l 8-10 .. l'-2' 
I 
VG .. RC.. MOH, Clr , E , 5 -l 0 , l' 
VG; Clr, E, 5-8 knts, l' 
NS & WB; 2/10, ESE, 5-8 knts, 0.5' 
NS & WB; unlim, SE, 10-12 knts, l' 
Time 25/7/72 
Date 
0700 
1033 1630 2000 
2200 26/7/72 
0001 0200 
><: 
I 
w 
0400 0530 1045 1200 
1300 1400 
1600 27/7/72 
0800 
Sta. 
29  27.5  28.5@. l'  7.2 @. l'  19.0 @ l'  JSH, NJM, DR, unlim, SSW, o, 2-6"  
28.5@ 4'  7.04@ 4'  18. 5 @ 4 T  
28.5 @7'  6.95 @. 7'  @. 7T-н ,,  
29  28.0  29.0 @. l'  7.74@ l'  21.5 @ l'  VG, MOH, hzy, N, capil.  
29.0 @ 4'  7.74@ 4'  32.0 @ 4'  
29  30.5  31. 0 @1 T  10.3 @ l'  16.0 @ l'  VG, MOH, Clr, E, 5, l'  
35.0 @ 4'  10.0 @ 4'  23.0 [a 4'  
29  31.0  31.1 @ l'  8.1 @ l'  17.0@ l'  TR, qood, SSW, 5 knts, 8 cm  

29 

29 

29 

29 

29 

29 

29 

29 

29 

29 
29 

Air 
Temp. (OC) 
27.0 

26.0 

26.0 

26.0 

24.0 

27.2 

32.0 

30.0 

28.0 

29.0 
27.0 
Water Temp. (OC) 
31. 0 @. 4 T 
28.8 
@. l' 

29.0 
@ 8' 


29.0 @ l' 
29.0 @ 6' 
29.0 @ l' 
29.0 @. 6' 

29.0 @ l' 
28.8 @ 6' 

27.5@ l' 
2708@ 6' 

29╖. 2 @ l' 
29.0 @ 6' 

31. 5 @l' 
29.6 ra 4' 
29.2@ 9' 

30.6@. l' 
30 Х 5 @. 4 T 
31. 0 @ 1 t 
30 Х 5 @ 4 T 
30.0 @. 6' 3400 @ l' 
32.2 @ 6' 

29.0 @ l' 
32. 5 @ 6' 

Dissolved Oxygen (mg/l) 
7.57@ 4' 

7.44@ l' 
7.44@ 8' 

7.95 @ l' 
7. 55 @ 6' 

7.54 @l' 
7. 29 @. 6' 

7.21 @. l' 
7.00 @ 6' 

7.03 @. l' 
6.93 @6' 

6.87 @ l' 
6.73@ 6' 

7.7 @ l' 
7.25 ia 4' 
6.96 @. 9' 

8.01 @l' 
7.84@ 4' 

7.81 @. l' 
6.87 @ 4' 

6.60 
@ 6' 


7.8 
@. l' 


7.58 @ 6' 

6.67 @ l' 
7.5~ @ 6' 
Salinity (o/oo) 
17.0@ 4' 

16.0 @l' 
20.0 @ 8' 

17.5@ l' 
17.5 @ 6' 

17.0@ l' 
16.0 
@6' 


17. 
5 @ l' 

18.0 
@ 6' 


17.0 @ l' 
16.5 @ 6' 
18.0 @. l' 
17.5 @ 6' 
15.0 @ l' 
16.o ra 4' 
16.0 
@9' 


17.5@ l' 

17. 
5 @ 4' 



13.5 @l' 
13.5@ 4' 

13.5 [a 6' 
13.5@. l' 
13.5 @6' 

20.5 @. l' 
20. 5 @6' 

TR, qood, SSW, 7-8 knts, 0.5' -strong current; probe may not have reached l' above sediment 
TR  & LE; qood,  S, 8-10 knts,  25  cm  
TR,  _unlim, s,  5 knts, 6"  
TR;  unlim, N.  5 knts, 6TT  

TR, dark, SSW, 2 knts, 2-6" 
LF & DB; Cly & Haze; SW, 5-10 knts, 0.5-1' 
Haze., capil. waves 
Unlim, SW, 5 knts, 0.5' waves 
NJM & EW; good, SSE, 8 mi, 6"-12TT 
SSE, 10 mi, 6"-12" 
R & W; good, SE, 3 mph, bTT 
APPENDIX I-B. 	Data for heavy metals in samples taken during the last three sampling periods as determined by the U. S. Geological Survey. (Samples were taken at mid-depth unless otherwise indicated. ) 
Analyses of total selected minor elements July 27, 1972 in micrograms per liter. 
Station 26 29 14 1 12 17 	22 
Aluminum (Al) 110 300 170 90 30 120 120 Arsenic (As) 0 0 0 0 0 0 0 Boron (B) Cadmium (Cd) 0 0 0 0 0 0 0 Chromium ( Cr ) : 
hexavalent total 0 0 0 0 0 0 0 
Cobalt (Co) 0 0 0 0 0 0 0 Copper (Cu) 3 2 43 2 2 38 4 Iron (Fe) 280 590 380 210 40 270 260 Lead (Pb) 0 0 0 0 0 0 0 Lithium (Li) 80 100 130 100 100 110 90 Manganese (Mn) 70 110 70 60 30 50 80
~ 
I 
w 
w 
Mercury (Hg) Х 2 .2 . 2 . 2 Х 2 . 2 .2 Nickel (Ni) 0 11 0 0 0 5 5 Silver (Ag) 0 Strontium (Sr) 4200 5100 7400 5600 5800 5700 4800 Zinc (Zn) 110 130 160 140 140 160 120 
Station 1 -at entrance Galveston Channel. 
12 -15 miles in Gulf outside Galveston Channel. 

Analyses of selected minor elements dissolved in water, April 25, 1972 in micrograms per liter. 
Station 26  29  14  l  12  17  22  
Aluminum  (Al)  
Arsenic  (As)  0  0  0  0  0  0  10  
Boron  (B)  
Cadmium  (Cd)  0  0  0  3  0  0  0  
Chromium  (Cr):  
hexavalent  0  0  0  0  0  0  0  
total  
Cobalt  (Co)  l  1  0  0  2  0  0  
Copper  (Cu)  5  2  4  6  3  3  3  
Iron  (Fe)  30  0  20  20  0  0  0  
Lead  (Pb)  0  0  0  5  0  0  0  
Lithium  (Li)  
~  Manganese  (Mn)  0  0  0  50  80  0  0  
I  
VJ ~  Mercury (Hg)  0.2  0.2  0.2  0.2  0.3  Х 2  Х 2  
Nickel  (Ni)  10  7  7  7  8  10  7  
Silver  (Ag)  
Strontium  (Sr)  
Zinc  (Zn)  180  190  220  210  230  210  190  

Analyses of se.lected minor elements dissolved in water, January 28, 1972 in micrograms per .liter. 
Station 26  29  14  12  17  22  
Surface  6). T  
Aluminum (Al) Arsenic (As) Boron (B) Cadmium (Cd) Chromium (Cr) : hexava.lent  0 0  0 0  0 0  0  0  0  
total  0  0  0  
~ I w CJl  Cobalt (Co) Copper (Cu) Iron (Fe) Lead (Pb) Lithium (Li) Manganese (Mn)  0 7 0 0 20  0 8 0 0 30  0 8 0 0 40  4 0 70  6 0 60  7 0  0  5 40  
Mercury (Hg) Nickel (Ni) Si.lver (Ag) Strontium (Sr) Zinc (Zn)  . 2 0 70  . 2 0 180  Х 2 0 190  0 350  0 190  140  0  190