t \ j\ GALVESTON BA,Y PROJECT DBRARY TH~ Ol'flVERSITY OF TEXAS AT AUSTI!'( MARINE SCIENCE INSTITUTE PORT ARANSAS, TEXAS 78',/'Y'f,7 UNl~1l1i~iij1~~1i~~~1i1ij1~~J1]l111i111l11111t~i1111111111 11111111 §§§§§§§§§§§§§§§§§§§§§§§§§§§§§§§§§§§§§§§§§§§§§§§§§§§§§§§§§§§§§§§§§ BOOK DONATED BY Dr. and Mrs. Chase Van Baalen to LIBRARY MARINE SCIENCE INSTITUTE University of Texas at Austin Port Aransas, Texas §§§§§§§§§§§§§§§§§§§§§§§§§§§§§§§§§§§§§§§§§§§§§§§§§§§§§§§§§§§§§§§§ 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 • . . . . . . . . . . i Purpose • . . . . . . . . . . . . . . . . . . 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, Jr•• II-1 Introduction II-1 Review of Previous Work II-3 Environmental requirements-freshwater flows II-3 Productivity • 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 • II-39 AUSTil T Discussion • • II-74 Environmental requirements . . . II-74 Productivity • . . . . . . . . II-79 Conclusions . . . . II-84 Environmental requirements . . . . . . . II-84 Productivity • • • • • • • • II-86 AR MAR1, IE :orNcr r <~, ': TE ORT ARAN;As r . s ' 373-26Z 2 Page Acknowledgements • • • • • II-90 Bibliography • • • • • • • • • . . . . . . . II-91 List of Tables • • . . . . . . . . . II-95 List of Figures II-97 Appendix Figures • • • • • • • • 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 • . . . . . . . . . . . . III-15 Bibliography • . . . 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 . . . . . . . . . . . . . . • • IV-1 Methods and Materials IV-4 Collection of Galveston Bay waters • IV-4 Collection and acclimatization of fish • • IV-7 Measurement of oxygen consumption rates IV-8 Multiple regression data analysis . . IV-11 Results • • • • • • • • • 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 • • • • . . . . . . . . IV-28 Components of metabolism • . . . . . . IV-30 Non-random distribution of fishes . . . . IV-33 Acknowledgements • . . . . . . . IV-34 Conclusions . . . . . . . . . . . . . . . . IV-35 References . . . . . . . . . IV-37 Appendix A Metabolism Data for Galveston Bay Striped Mullet • • • • • • • • • IV-42 Appendix B Operating Sequence and Data Recording for Continuous-Flow Respiration Chambers • • • • • • • • • • · • • • • • • • 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 • • • • • • • • • • • • • • • • • • V-2 Discussion • . . . . . . . . . . V-10 References • • • • • • • • • • • • 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 • • • • • • • • • . . . . . VI-1 Area, Methods and Materials . . VI-2 Description of study area • • • • VI-2 Field and laboratory rrethods • VI-4 Data interpretation methods . . . . . . . VI-4 4 Results •• . . . . . Discussion . . . . . . . . . . . . . . . . . Conelusions • • . . . . . . . . • • Acknowledgements . . . . . . . . . . . . . . References . . . . . . . . . . . . . . Chapter VII. A Definition of BOD in Galveston Bay Oppenheimer, D.H., N. Gordon and B. Brogden •• . . . Introduction Objectives Background Procedures Results •• Discussion Summary •• 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 • • • • • • • • • • • • • • • • Chapter IX. Chemical and Physical Separation Procedures for Toxic Materials . . . . . . . . . . . W.B. Brogden Introduction • • Methodology • • • . . . . . . . . . . . . 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 • . . . . . . . . . IX-3 Summary . . • • • • • • • • • • • • • • • IX-4 Chapter X. Discussion and Summary Oppenheimer, C.H. X-1 Discussion • . . . . . . . . X-1 References • . . . . . . . . . . . . . . . X-5 Summary • • • • • • • • • 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 inter­disciplinary 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 investi­gations 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 temperature­salinity 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 distribu­tion 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 approx­imately 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 Environ­mental 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 Environmental­Dependent 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 pro­grams 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 fresh­water 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 require­ments 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 activ­ 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 objec­tive 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 objec­tives. 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 atten­tion. 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 inter­action 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. Supple­mentary 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 phyto­plankton. 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 phyto­plankton 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 measure­ments were initiated by the Corps of Engineers, but they were taken as Secchi disk measurements instead of perhaps more meaningful light trans­mission 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 phyto­plankton 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 radi­ation 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 decom­position 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. Com­munity 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 produc­tivity analysis utilize an isolated community within the glass bottles to provide simultaneous estimates of net production and community respira­tion (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 compara­tive parameter between different ecosystems and the same ecosystem under different conditions. Beyers (1963) tested this concept by estab­lishing 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 equi­librium, 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 inter­actions 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 inves­tigations, 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 com­munity exhibit distinct peaks and depressions at these times, and experi­mental 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 night­time 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 micro­cosms. Cooper's functional Trinity Bay model, presented in the previous Galveston Bay Study (Copeland and Fruh, 1970), utilized a series of inter­connected 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 require­ments of six important organisms in Galveston Bay was to use the infor­mation 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 require­ments 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 pre­diction 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 impor­tant 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 geo­graphical 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 dis­charges, 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 labora­tory 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 bio­assays. 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 bio­assays. 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 nutri­ents 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 mµ ). 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 des­cribed 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, maxi­mum 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 experi­ments. 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 blue­green 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 micro­cosms 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 exper­imental 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 stir­ring 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 (over­cast 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 µwavelength) 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 auto­trophic 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 µMillipore 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 micro­bial 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 mea­sured with an American Optical refractometer at the beginning and termi­nation 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. wave­length. 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 mµ 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 (phyto­plankton, 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 con­centration 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 concen­ tration should reflect the maximum excess oxygen production (PN) result­ 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 day­light 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 micro­cosm). 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 air­water 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 temper­ature. The reaeration coefficient (0. 42 mg/l/hr/100% deficit) was exper­imentally 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. Com­plete 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 milli­micron 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 photom­eter 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 spectrophoto­meter 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, second­ary, 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 temper­ature and salinity levels by the organisms listed does exist. Without exception the organisms listed are found in waters with salinity and tem­perature 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 juve­nile 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 pre­sence 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 move­ment in nursery areas in early Spring as water temperature increases Major population move­ment to nursery areas in summer as water temperature nears max­ imum Major population move­ment to Gulf for spawn­ing as temperature increases in spring No seasonal preference low salinities; adult females found in high salinities while most crabs found in salin­ities 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 habi­tats 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 sta­tion salinity response, a 30-day average of flow was computed for corre­lation 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 consid­ered 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. How­ever, 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 pro­duction 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 phyto­plankton 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 contri­bution of the marshes is of course more predominant in the Bay periphery where the marshes are located and the waste inputs are absent. Never­theless, 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 (resi­dence 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. Displace­ment 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 phytoplank­ton in all parts of the Bay for 1969. Only February, April, July, and October sample times were available, but seasonal trends were still evi­dent. 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 distri­bution. 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 sum­mer. 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 pop­ulations (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 nutri­ 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 Oscillator­iaceae Nostocaceae Oscillator­iaceae 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 des­cribed 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 condi­tions. At Stations 22 and 26 in the upper portion of Galveston Bay net production was usually much less than zero indicating heterotrophic con­ditions 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 • 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 Coe£. 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 Coe£. 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 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 •324 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 • 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 µ. and 650 mµ; the results of these computa­tions 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 µ. diameter and slightly smaller, and the increase in light transmission is due to lack of absorp­tion by suspended particles. Centrifugation removes other light absorb­ing 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 coeffi­cients 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 Treat­ment %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 par­ticles 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 con­ 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 physio­ 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 fluctua­tions 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 fluores­cent 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 temper­ature 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 con­sistancy 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 condi­tions. Cooke (1967) found that "a mature aquatic ecosystem is character­ized 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 suc­cession may be related to nutrient levels or secretion of metabolic regu­lators. 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 popu­lations, free from predation, varied mainly with physical system changes, such as realignments of the stirrer mechanisms or lowering of light fix­ 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 sedi­ment-burrowers whose excreta is important in nutrient regeneration. After an elapsed period of three months and the establishment of a rela­ 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 suc­cession 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 per­formed 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 exper­imentation revealed little about community changes. Only Station 26 (low salinity Trinity Bay water) and Station 22 (upper Galveston Bay) micro­cosms 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 light­absorbing 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 phyto­plankton in decreasing light transmission could not be completely examined, since only during the summer influent period were the chlorophyll "a" con­centrations 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 concentra­tions 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 three­point 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 (m£/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 mix­ ing regimes in the model systems (i.e. thermal stratification). Whatever the reason for the midday oxygen pulse, it means that community respira­ 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 pro­gram. However, another potential underestimation of metabolic rates may have resulted from the presence of surface films (low density degra­dation 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), dif­fusion corrections are reduced and metabolic parameters increase slightly. Again, the magnitude of this underestimation cannot be properly ascer­tained 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 nutri­ ent cycling. The nutrient levels within prototype spring influent water (Table .[-26) were normal for phosphate, but below normal for nitrate­ 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 prob­ 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 sedi­ments (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 pre­sent. Other than microcosm A (Station 26 -Trinity Bay) and microcosm D (Station 22 -Upper Galveston Bay) which exhibited higher mean respira­tion 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 micro­cosm 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 µMillipore 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 auto­trophic 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 declin­ing 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 pro­totype 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 contri­butes 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 pro­grams 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 organ­isms, with the exception of the menhaden, do not appear to be very stringent based on the catch data. Whether this finding can be substan­tiated 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 salin­ities 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 condi­tions 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 geo­graphically 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 con­tribution to Trinity Bay alone were considered (waste discharges essen­tially 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 relation­ships 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 vari­ous salinity levels. The gross correlation of freshwater inflow and com­mercial 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 specu­late 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 conclu­ 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 con­sideration 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 cor­responding 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 conjunc­tion 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 pro­duction 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 radia­tion or high wind-generated turbidity. Average light intensities are com­parable 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 phy­sical conditions for model system productivity analysis is not practicable. Daily water temperatures are much more constant in the micro­cosms than for the prototype, but this lack of thermal variability does not appear to have any significant effect on productivity analysis. Arti­ficial 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 long­term (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 param­eters, 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 pro­totype surface waters. Indirect community monitors, the optical density and chlorophyll "a" concentrations in the microcosm water, apparently lacked the sensi­tivity 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 sub­strate 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 auto­trophic 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 pro­duction, 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 circum­vent 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. Resid­ual 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 compo­sition) 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 estu­aries, seasonal abundance, etc. , but the interdependency of the environ­mental factors strongly influence these results and must be considered in their interpretation. 2. Salinity and temperature are interrelated in controlling popu­lation 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 tem­perature, 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 pref­erence). 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 mag­nitude and duration is required in the spring to control oyster predators. The Tracor, Inc. salinity model for Galvestion Bay should be used to esti­mate 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 mµ and 800 mµ 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) exper­iments, 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 dis­parity 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 degrada­tion 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 stim­ulated 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 indi­rectly 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 §.: 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 Photo­Plankton 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 ­Mean Total Respiration Relation­ ships in Aquarium Microcosms II-17 Relationship between Gross Production and Influent Nitrate II-18 Relationship between Gross Production and Influent Phosphate II-97 -t G> 0 J> x r­ 0 ""O < LIGHTS LIGHTS ::0 fTI =i Oen ON OFF -< c.. -I CJ) 11'1 0 I Oz -t I -I c z I2 m 0 fTI J> I (/) -< ..... I <( I z ~ a::: I I IJ.I ~ c :I: () -en~ Om-o AIR SATURATION I I :::0 0 0 ~ 0 -I I (100 °/o) () 0 -I :I: ~--------­ -------1------- Oom Cl> -I NIGHTTIME z ::::: ~r=o l&J C' NET : RESPIRATION -or l> ~E PRODUCTION 1 (R), :::tJC r x_, ocno ( PN) i o-t ­ 0 c: :::0 c ' .I GROSS 0 )> :::tJ ' c " -i-1 z I PG= PN -R +D -rTI )> l&J < r I TOTAL :::j-1 0 I ~:I: x d RT=2R+D :::0 ~ I en ~ m G> en mmm I -1-o z Cl I ~Oo I oZC I I I I -I :::0 0 4 8 12 16 < m EXPERIMENTAL LIGHT-DARK ,, ( hours ) - G> -c :::0 ,,, LIGHTS ON · DouT DIRECTION OF . OXYGEN DIFFUSION -· -----' ---­ DIN ,) PRODUCTION RESPIRATION I 20 24 CYCLE GLASS-TYGON TUBING NETWORK INFLUENT SUPPLY (20 liters) ® STATION 14 VARIABLE-FLOW PUMP LIGHTING FIXTUREUr-------JTWIN 40-WATT FLUORESCENT BULBS AQUARIUM CONTAINING 45HtersWATER SEDIMENT MICROCOSM ® 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 """" >­2 ~ 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'\ • 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 (.) ..... ­5•+·~·..., • <( z 0 J-v I'-C\J -0 C\J-0 ,,...._ -'C' E __, (.) z 0 () a.. I LU I­ 0 0 -. 0 ~ - I rt) - I C\J - I -- I q- I (1) I CD. I I'­ I ~ I lO I v. I ~ I C\J I 0 0 ( 1_p ) 3.1.'1~ H.1.M0~€) 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-----------------------------~--------------------­........ ~ 0 """' (!) z z LL 0:: ~ z w (.) 0 0 0 O'> 0 a:> 0,.._ 0 0 ( 0/o) NOISS I~SN\f~l. l.N3~~3d 0 0 0 CD 0 0 0,.._ 0 0 0 ­x 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 ® © ® ® DIURNAL OXYGEN FLUCTUATION IN GALVESTON BAY FIGURE GALVESTON BAY MICROCOSM PROJECT AUGUST 17-18, 1972 TOXICITY STUDIES 2.0 z 0 t­1.5 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 c• D• 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 2.0• t­1.5 s g ~ c •0 ......... a: ~ a. .g ~ CJ)~ 1.0 • en Eo, a: C\I (!) 0 z C) ~ ._, 0.5• ~ I 0.5 I 1.0 I •• 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"'" • 0 _...... 1.5~0 ~ ~ c ~ -0 a.. ' N CJ) E ~ CJ) ' ~ N 1.0 • (.!) 0 z~ . c I() -0 '-' LLJ :E t­0 GALVESTON BAY PROJECT TOXICITY STUDIES DAILY GROSS PRODUCTION DURING SPRING GALVESTON BAY INFLUENT AQUARIUM B ­STATION 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 ~ I­ 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 +­ ~ c "O ·= 0 ~ ... (/) a.. Q) a: (/) ­ 0 (/) c 0 +­ ... 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-zww3­: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 lO ~ rt') c:: c:: o.2 C\J ·­ ~ ~c 0 ~ :J ·­'O ~ e Cl> a.. a:: en ­ en c o~ ~ 0 z ~ 0 CH- w 00 :::> m _J­ 0) LL ~ ~Z­ CJ) ,_ 0 a::: z tn uwO CX> ~ o~~ c "'C 5~~ ........­ -:::> CJ)~CJ) -r-­lJ.J :E w ..... l() C\J 0 q lO rt> C\J 0 ( ~DP/zW/0) NOll.'1~1dS3~ 1'1.LO.L NOl.Lonao~d sso~s GALVESTON BAY PROJECT TOXICITY STUDIES TIME SERIES AVERAGED COMMUNITY FIGURE METABOLISM FOR MICROCOSM B (STATION 14) SUMMER INFLUENT APPENDIX c 0 () - :l 'O 0 a.. '­ U) U) 0 .._ (!) 0 LO 0 c 0 - 0 '­ a. U) cu a:: {!. -0 0 l() ~ r0 C\J 0 m ,..... "' ~ c -0 ~ ~ w :E w I­ 0 TIME SERIES AVERAGED COMMUNITY GALVESTON BAY FIGURE METABOLISM FOR MICROCOSM D PROJECT APPENDIX (STATION 22) SUMMER INFLUENT TOXICITY STUDIES >­.... CJ) z w c ...J <( (.) t­a. 0 >­.... CJ) z w c ...J ­ .. > 50 .. " ~ 40 • 30 20 • • .---­ • 10 ·+ 0 c 14 17 22 26 29 2 1.5 0 .. c a ~ 1 a. "'Q) ~ .5 + • • • • 0 c 14 17 22 26 29 Figure 5 III-10 CONTROLS ENVIR ST 1 4 ENVIR Figure 6 0 0 0 0 lf) lf) CL CL (J1• (f1 w w o::_ 0:: w w + > > + a:­ a:­ + + [!] [!] [!] + + + [!] ~ + o- a· Ila+ wrJ 0 0 ~ qi WT I 0 o.oo 75 :oo I I 0 o.oo WT 75.00 ST ,1 7 Y2= SI G,RE ~p ENVIR ST 22 Y2=SIGRESP ENVIR 0 0 Figure 7 + U1 + [!] + [!] [!] ++ +ttl [!] + 0 i...0i m:1E + [!] 0 0 0 lf) Figure 8 0 lf) CL a: + .... .... .... CL (f1 w 0:: w > a: 0 0 0 [!] c!t [!]+J ++ + ~ (lj 0 0 0 Figure 9 ttJ [!] C!J t±l + + ~ + + [!] [!] \+ [!] + o.oo WT 75.00 o.oo WT 75.00 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 with­in 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 0 0 CL U1 w a:: CJ U1 0 0 0 ST 26 Y2,== Sl GR E S P ENVIR ST 29 Y2= SIGRESP ENVIR I I I Figure 11 + + + + f lfEI ffi!!I qJ [!) + I 0 0 0 0 [!) ~ o­ 0­ Figure 10 "..() 1.1) CL Cf) t'.L t'.L w u; c.n a:: w w C) o::_ 0:: .......... w w (/) > > a: a:­ + [!) + 0 + * + 0 + ,_ D 0 [!) l:Jlttl D D 0 [!) ~ [!) EB D o.oo [!) WT 75.00 0 o.od WT 75.00 RLL SHRIMP ENVIR 0 0 Figure 12 0 ...,... ~ ~~l!J [!) l!J >­I­ l!Jdl!Jl!J [!) l!J l!Jl!J l!J [!) [!) l!J [!Ill z C!I!l!I l!J 1--1 _J CIID!IB!IlC!l!J CI: 1!11!1 (!l!J l!J cm [!) l!J l!J l!J l!J 0 0 1!11!1 DL----.-~-..-~~-.-~-.-----,.-----r-~....---.~-i p 5. 0 o.o 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 all of these com puter drawn plots. 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 sub­lethal, 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 environ­mental 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 -­and 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 environ­mental 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 corres­pondingly 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 monitor­ing, 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 experi­mental 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 environ­mental 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 (non­locomotory) 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 lead­weighted 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 calibra­tions. 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 baro­metric 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 acclima­tion 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 text­books 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 coeffic­ient 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 un­recognized 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. 1-1 <: I 2. Control equation 17 may have been based on polluted waters. ~ co - - - - - ·­ - - - - ~ ... - ·.. :· - 8 ~ ..... :.·.. ~ ! •i I ... ·.··· Ir;-:-:-: ... :· ....:· 7: ·.··. - ... ::-: :·:·.·.•.. ·... , .:. ..... . ·.· :~~ ~: ...... .....· :-:: ·:···: :::~~ ,.·:. :··. ~ ... , ..... ... ·. ..·.· - .... ..:.· :~·:: ::.·: ..... ..·.· ~..... .. ·:.· It • '.• ···:. :...:·: ····· ,..:. ····· 2.0 ­.... ·.,. :.::· - ··.:: .:· ::::; ···' :···· .... - ·: .· ··:·· :.:.;; :/: :~:: .... •• !• ..:;.~. ... ·:·· ry .·.· .... ... ··:·:· - ·:··· ::·.. -I .:::·:::' ...•I .. . .... :.:·· ···: '!!;.· .. :·· .... ... .. 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I•.·. ·.··· :} .... :\~: ·.. ..·.::. "·.::;::· ····. ::·:·.· (~ ·~I f :·..· .;··: ·;...· -::.: ... :-.: :·: ..... .:::·. ... ·.. · ~: :::: :.: :-: ..... - .·.·. ~: ::· ·.:.·· ... .:·.·· 1~}\ .·.·. ... ,. :.~· :\: :~::: ····· ·.::: ..... ~:::· .... ·:·:·: ::::: - .:, .. ..... :·; ':: .·:·.. ··: .. It•·:•. ... ::: .·• .; ... :·: ~ ::··. . ·. ;:.. :· : " .:·:: .. .·. ··i··. ::::.:: .. . .....·. ····· . . ..... ·.··. :: ~ :: ... - ~::: :: .... ····· .... ... ··! •• ·:: ~·. '•~ I ~:·:.: .... :-::: :.:·. ·.·.· .... .:!:: ... .... :::: - ~. :> , ... .-:..:,. ·:·::·.: ... ;:::· .. .. ~ ...· ''·· :~ ··. \:-~· : .~I: ··.· ..... ~·..:.: ...; :::·>. ..... ·... 0.0 17 22 26 29 17 22 26 29 17 22 26 29 17 22 26 29 ~AUTUMN WINTER SPRING SUMMER -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 eury­topic 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 effluent­charged 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 experimenta­tion, 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 con­sumption 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, popula­tion 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 dis­placement of the constant value, a, to make the combination regression unrealistic with respect to bw• 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, tempera­ture 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 tempera­tures, 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 environ­ment, 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-­consistent 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 petro­chemical 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----­ ~------­ --- ----- -.:::::.... Energy of faeces Energy of assimilated materials Qu ,-?r --·----~--·-------------­ -......::.::::..------------­Energy 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-­and 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 metabolism­log 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 adapta­tion; (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 environ­ment. 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 approp­riate 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 environ­mental 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 REFERENCES CITED Adelman:, I. R. and L. L. Smith, Jr. 1972. Toxicity of hydrogen sulfide to goldfish (Carassius auratus) as influenced by temperature, oxygen, and bioassay techniques. J. Fish. Res. Bd. Canada 29: 1309-1317. Beamish, F. W. H., and L. M. Dickie. 1967. p. 215-242. Metabolism and biological production in fish. In S. D. Gerking (ed.), The Biological Basis of Freshwater Fish Production. Blackwell, Oxford. Beverton, R. J. H. and s. J. Holt. 1957. On the dynamics of exploited fish populations. (Fishery investigations Series II), London, Her Majestyrs Stationery Office, 14: 1-533. Brett, J. R. 1958. p. 69-83. Implications and assessments of environmental stress. In P.A. Larkin (ed.), The Investigation of Fish-Power Problems-.-The H. R. MacMillan lectures in fisheries. Univ. British Columbia, Vancouver. Brett, J. R. 1970a. p. 37-52. Fish--The Energy Cost of Living. In W. J. McNeil (ed.), Marine Aquiculture. Oregon State Univ. Press. Brett, J. R. 1970b. p. 515-616. Temperature. Animals. Fishes. In O. Kinne (ed.), Marine Ecology, Vol. 1, Part 1, Wiley­Interscience, Ltd. Brett, J. R. 1971. Energetic responses of salmon to temperature. A study of some thermal relations in the physiology and freshwater ecology of sockeye salmon (Oncorhynchus nerka). Am. Zoologist 11: 99-113. Broadhead, G. c. 1953. Investigations of the black mullt, Muqil cephalus L. in northwest Florida. Fla. St. Bd. Cons. Tech. Ser. 7: 1-33. Broadhead, G. C. 1958. Growth of black mullet (Mugil cephalus Linnaeus) in west and northwest Florida. Fla. St. Bd. Coriserv. Tech. Ser. 25: 1-29. Cech, J. J., Jr., and D. E. Wohlschlag. In Press. Respiratory responses of the striped mullet, Mugil cephalus, to hypoxic conditions. J. Fish Biol. Chen, R. M., D. E. Winslow, and A. J. Hays, Jr. 1972. Report on refinement and operation for the Galveston Bay system (GBP Tasks IV.C) TRACOR Project 077-005-08, Doc. No. T72-AU­9526-U. Processed. IV-37 Copeland, B. J. and D. E. Wohlschlag. 1968. p. 65-82. Biological responses to nutrients-eutrophication: saline water considerations. In E. F. Gloyna and w. w. Eckenfelder, Jr. (eds.), Advances in Water Quality Improvement. Water Resources Symposium, No. 1. xviii + 513 pp. Univ. Texas Press, Austin. Copeland, B. J. and E. G. Fruh. 1970. Ecological studies of Galveston Bay. 1969. Rept. Texas Water Qual. Bd. Contract IAC (68-69)-408. xxiv + 482 pp. processed. Cullender, M. J., D. E. Winslow, and A. J. Hays, Jr. 1972. Report on BOD and DJ model verification for the Galveston Bay system. TRACOR Doc. No. T71-AU-9612-U. processed. de Sylva, D. P., H.B. Stearns, and D. C. Tabb. 1956. Populations of the black mullet (Mugil cephalus L.) in Florida. Fla. St. Bd. Conserv. Tech. Ser. No. 19: 1-45. Doudoroff, P. and C. E. Warren. 1965. p. 145-155. Dissolved oxygen requirement of fishes. In C. M. Tarzwell (ed.), Biological Problems in Water Pollution, third seminar, 1962. U.S. Public Health Service Publ. No. 999-WP-25, Cincinnati. Espey, W. H., Jr., A. J. Hays, Jr., W. D. Bergman, J.P. Buckner, R. J. Huston, and G. H. Ward, Jr. 1971. Galveston Bay Project. Water quality modeling and data management. Phase II Technical progress report. TRACOR Project 002-070, Doc. No. T70-AU­7636-U. processed. Evans, D. O. 1972. Correction for lag in continuous-flow respirometry. J. Fish. Res. Bd. Canada 29: 1214-1216. Fry, F. E. J. 1957. p. 1-65. The aquatic respiration of fish. In M. E. Brown (ed.), The Physiology of Fishes. Academic Press, N.Y. Fry, F. E. J. 1971. p. 1-98. The effect of environmental factors on the physiology of fish. In W. S. Hoar and D. J. Randall (eds.), Fish Physiology, Vol. 6. Academic Press, N.Y. and London. Goulden, H. 1952. Methods of statistical analysis. John Wiley, New York, 7: 1-467. Green, E. J. and D. E. Carritt. 1967. New tables for oxygen saturation of seawater. Jour. Mar. Res. 25(2): 140-147. Hellier, T. R. and H. D. Hoese. 1962. A note on the schooling behavior of the striped mullet (Mugil cephalus) in Texas. Copeia 1962 (2): 453. IV-38 Horn, H. S. and R. H. MacArthur. 1972. Competition among fugitive species in a harlequin environment. Ecology 53: 749-752. Kerr, s. R. 197la. Analysis of laboratory experiments on growth efficiency of fishes. J. Fish. Res. Bd. Canada 28: 801-808. Kerr, S. R. 197lb. Prediction of fish growth efficiency in nature. J. Fish. Res. Bd. Canada 28: 809-814. Kerr, S. R. 197lc. A simulation model of lake trout growth. J. Fish. Res. Bd. Canada 28: 815-819. Kinne, O. 1958. Adaptation to salinity variations. p. 92-106. In C. L. Prosser (ed.) Physiological adaptation. Amer. Physiol. Soc., Washington, D. C. Kinne, O. 1960. Growth, food intake, and food conversion in a euryplastic fish exposed to different temperatures and salinities. Physiol. Zool. 33: 288-317. Kinne, O. 1963. The effects of temperature and salinity on marine and brackish water animals. I. Temperature. Oceanogr. Mar. Biol. Ann. Rev. 1: 301-340. Kinne, o. 1964a. Non-genetic adaptation to temperature and salinity. Helgoland. Wiss. Meeresunters. 9: 433-458. Kinne, o. 1964b. The effects of temperature and salinity on marine and brackish water animals. II. Salinity and temperature salinity combinations. Oceanogr. Mar. Biol. Ann. Rev. 2: 281-339. Kinne, O. 1966. Physiological aspects of animal life in estuaries with specific reference to salinity. Neth. J. Sea Res. 3: 222-244. Kloth, T. C., and D. E. Wohlschlag. 1972. Size-related metabolic responses of the pinfish, Lagodon rhomboides, to salinity variations and sublethal petrochemical pollution. Contr. Mar. Sci. Univ. Tex. 16: 125-137. Odum, W. E. 1966. Food and feeding of the striped mullet, Mugil cephalus, in relation to the environment. M.S. Thesis, Univ. of Miami. 118 pp. Odum, W. E. 1968. The ecological significance of fine particle selection by the striped mullet Muqil cephalus. Limnol. & Oceanogr. 13: 92-98. Odum, W. E. 1970. p. 222-240. Utilization of the direct grazing and plant detritus food chains by the striped mullet Mugil cephalus. In J. H. Steele (ed.) Marine Food Chains. Univ. Calif. Press, Berkeley and Los Angeles. IV-39 Peterson, G. L. and Z. H. Shehadeh. 1971. Subpopulations of the Hawaiian striped mullet Mugil cephalus: analysis of variations of nuclear eye-lens protein electr?pherograms and nuclear-eye­lens weights. Mar. Biol. 11: 52-60. Ricker, W. E. 1958. Handbook of computations for biological statistics of fish populations. Bull. 119, Fish. Res. Bd. Canada, 300 p. Ricker, W. E. (ed.) 1968. Methods For Assessment of Fish Production In Fresh Waters. Blackwell, Oxford and Edinburgh. xiii + 313. 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 •. 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 -.•: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 -.•: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 Continuous­Flow Respiration Chambers. Galveston E.a~ ~tu9J£ 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'! • 93 Jg I I.I I f 2 I Rt/I /DD r i \/eight ( g") SL (miuo) FL (mmo) // 0 /&' o ;2._ 0 _) -­ 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 #£, 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 half­shaded 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 0 FIGURE 1 0 .. (Y") LL I x::: _J a: D _J cc 0 l/) o.__-.-~-.----.,--.-~...,___,,--,-~~--.,.----4 o. oo YR l . 00 ST 1 2/3 DEPTH FIGURE 3 0 (Y') _J a: 0 _J a: 0 LO I I I • I I I I 0L_~~--.-~..---.-----.-----,,--,-~.,.--::---:i~~ o.od YR ' 1.00 ALL STATIONS FIGURE 2 0 (Y') .. .. . ~ : . . : ... _J a: 0 .. ' : . ~ : . ! . _J a: :•. . 0 LJJ o.____~~-----.,.------~~--~~~~---.~­ O.O YR 1 • 0 ST SURFACE FIGURE 4 0 _J a: 0 _J a: e e .. ~: ~ F' ! LO 0 ~~·L--~·L-~'~~~·L,..--•--..•1--~•---~·~~·! 01--~~--.-~..----~-,------.~-.-~-r-----.~~ o.o YR 1 • 0 v -5 ST 14 ST 17 SURFACE FIGURE 6 0 ~ ~ • _J _J a: • 0 a: 0 _J _J a: a: 20 c 15 ~ 0 _______________________L-&.___________._·: ! 0 lJ) l/) I I I •• • I • I I I ~­ o~-.--..-----.------.----.-----.---.-----.---.----1 o.o 1 • 0 o.o YR 1 • .0 YR ST 1 7 2/ DEPTH STATION 18 0 FIGURE 7 0 _J a: 0 _J • a: :ID 15 • 10 5 ~ ~·~·---•__,,......__...___.,____~-·-~'__.l.__1.......~ O'---.-~..-----.-~..---.-~,..--r-~-..--.--------i o.o YR 1 . 0 v -6 STATION 22 STRTION 26 a a a _J _J a: a: 0 0 _J _J a: a: a LJ) 0. oo YR 1 • 0 STRTION 29 ST 33 SURFACE a FIGURE 12 ~ _J _J a: a: • 0 D _J _J a: a: • a I a LJ) 5 LJ) ·r ... 1r!I I I I I I I II I I a a o.oo YR 1 . 0 o.o YR 1 • 0 1 • 0 v -7 ST 33 2/3 DEPTH ('f) ::::s::::: _J a: o _J a: 20 15~ ~ _____,__ j I l J __._..I :~ , I...__._I___________ r o=-::-::r----...,~-.--.~-.---r~-r-----r-~-.-----1 o.o YR 1 • 0 STATION 39 FIGURE 15 0 ('f) _J a: o __J a: 0 LI) DL---r-----r~-.--.~.---r~.,--,.-~r=--1 o.o YR i. o STATION 38 FIGURE 14 0 0 ('f) e ::::s::::: _J a: o _J a: 0 IJ) • I . . I I I ,ff :: '! o.__--.----...,~-.--.~-.---r~-r-----r-~-.-----1 o.o YR 1 • 0 0 ('f) _J a: o _J a: 0 LI) STATION 41 6 I I I I I I I f :1111 IS ~ IO f I 5! o.__~----,,---.-----,-~-.---r~-r-----r-~~_, o.o YR 1. 0 v -8 RLL STRTIONS ENVIR 0 _J er 0 _J er 0 IJ) .:l FIGURE 17 . : . .. . .. . . . . .. ....::· .... . . , .. o.oo 40.00 EMP RLL ST RT IONS ENVIR 0 FIGURE 19 (Y) .... ... .. ~ _J er 0 .. . . - ·. : .. . . . : .. . ..... .. ... .. _J er 0 l.(J 0 o.o RLIN TY 40.00 RLL 0 0­ ~ _J er 0 _J er o- IJ) 0 o.od RLL D D (Y') ~ ~~ _j I a:i ~ i . . ~i I o.oo STATIONS ENVIR . . .. .. : . FIGURE 18 I I 0 .2s NIT'RifE STATIONS ENV __; IR FIGURE 20 . . . . .. : . i . . .:· . ..... .. ~ .· . .. I o I I • ~ ... . I r .. . . . . ~ . . t I SE c'CH I I 1.so 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 replica­tion 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 trans­portation 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 Dmin• With small samples, such as our benthic samples, the following equation is most accurate. [ ~ 1og2 Ni! X Log (N/S) !] R = i = 1 ~~~~~~~~~~~~~~~~~~~~~­ (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 intrinsi­cally 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 • 1 Drilonereis magna 5 1 2 3 1 Stauronereis rudolphi 1 6 Dorvilleid A. 1 Aricidea jefferysi 1 Aricidea sp. 1 Streblospio benedicti • 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 • 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 •• 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 • 41 Neopanope t. ." .. 1 8 4 1 Unidentified ~"A" .. .. 2 Unidentified Crab "B" .. l Unidentified xanthids .. 0. 4 0 • 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 • 9 • !I • 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) µ:i H u µ:i 10 P-l Cl) W-i 0 ~ µ: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 log2• 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 it§ 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 appear§ 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 macro­benthic 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 • 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 TTmate­seeking1', 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 non­significance of the apparent fluctuation is believed to be a function VI -20 24 ~ >­ .. ·­ c ·- 16 .,,a 14 ~ •...................... 26 ... , .· 12 ·. ' , .·· ·. ' , .· ··•. ~ ..··· 10 . . . . . . •. .• . . . . 8 . . . . . . . . . .. 6 . . . . .. .. . • •• . 4 ..... 2 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 .µ 0.. Q) ::r:: H 0 r-1 Q)..c: 'O CJ ·rl res x .µ 0 0.. 0.. Q) µ,:i ::r:: i:: ·rl H 'O r-1 Q) ·rl q i:: ·rl H 'O rl i:::i: i:: ·rl H 'O i:: µ.:i µ.:i q q -P4 " P4 q q q -P4 " 0 q § -P4 " P4 E-l § -A.I " 0 E-l q q -P4 " P4 Q) i:: res ..c: .µ r-1 Q) ~ Q) i:: res 'O H 0 rl ..c: () ­ 1­ ...... 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)--•, 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)--• versus time in years starting 1I1/ 71 for station 26. Five day BOD (mg/1)--•, 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 bio­chemical 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. Wiley­Interscience, 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. 801­ 829. In Ciaccio, Leonard L. (ed.) Water and Water Pollution Handbook, Marcel Dekker, Inc., N.Y. ZoBell, C. E. 1946. Marine Microbiology: A Monograph on Hydro­bacteriology. 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 • 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 curve­ 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 ­ x 0 a o.s LGAL K 3. o BAY STATIONS ENVIR FIGURE 3 ·.. .. . ··i : . . ·:i:y . . . .. .· :::· ·'1'··· : . 0 0 ~ :v!•.. . ; 0 : I • :.. . I.. . . : .. .•. .-~ .... 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 . •. z .. D •.... 0:: • • • . ........ •• • • ••• -& •• . ..... . . . . . . .• -. . . . . . .... -. ... . 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 super­imposition 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 far­removed 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 flat­fish. 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 environ­ment 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 inter­disciplinary 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 ~­?' 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 • 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 • 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 • 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 ­c; 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 -­fa h T ?0 . C) fa 1 T 22.0 ra 6' 8.92 ra 9' 8.36 fa lT 8.54 ra 6' 8 • 18 @ 1 T R _44 ra f1 r R 74 ra 1' 8.88 ra 5T 2i.4 ra 9' 20 • 8 @ 1 T 20.8 @ 6' 20 • 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 • 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 •' JH. NM: rain 1:. 1,; ~----.-· · .. ·~----~ l;Jater Air Temp. (O C) c0c) Ti me Sta . Temp o ~ate ----.-------~----+­ --· ---· -------4---·~­___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' ---·-------­ Di ssol ved Oxygen ( ~-·""/ l \ · ·'::J -I 7_. 66 @__J_~-2~~5 @ 1 T 6_. 94 @ fl:._'_j 2§_!_~~ -------··· ----·-----------·-··-­JG & 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___@____ _ ---·--·--· ·· -·-----· -·--·-··-· --· ---··-----···-­ -- --_-~~oo_t_l-_4__ _ _ _____ ·-----+--· · -·-·-·-· --- ---· 26_._3__@._l' 1 __ _ _ ____ __ _____ ___, ___ ,____________ --.~-----·-t :_ ~: _ -'-----==~~~~Sg@s-· --~----=~~--------~----------- -------­ ~~~6j-~~ ;-_----­;;; ~--~~~I ~! :=±-----~----i ~:~~: ~:_1----------­ _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 -­20 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 -­ra 11' 7.10 ra l' 1.01 ra 4. 5T 16.4 @ l' -­@ 4. 5 t -­@ gt 16.4 @ l' -­ra 5 t -­ra 11T 16.4 @ l' -­ 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' -­ 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' -­@ 4.5' 9'-­@ 16.4 @ l' I Smith; unrstrtd, ovcst, NE, 15-20 knts, 1. 5-2' Smith; unrstrd, ovcst, NE, 15-20 knts, 1. 5-2' -­ 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) -. -.. ­ ---. . ·-· 0930 17 28.0 29.2 ra l' 27/7/72 -· -·· ­ -. ·-· --­ - -29 .1 ra -6T ·-· ----­ 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' ~-­ ---· ~') ') ~1 R ra i r1 ~4.n 17 R r::..7 ra 1' ·· ·­ 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' ­ 31. 0 ra 4r 9.63 ra 4' 10.1@ -1, 1030 30.0 29. 5 @ l' 22 -­ 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 -- -­ 26 ra 18' 24 ra l' 24 ra 4' ·-­ --· . 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 @­ 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~ . --­ LF: unres" SW, l0-12 knts, 1-1. 5' . .. --·­ .JSHoiNJMoiDB; OK., _Q_wind, 2" - -· . ··-- ­ ---·-----­ haze: 2"-4" -­--­·· --­ Carr Carr & & Smith; Flawn; -· ··· -·-· -­-­--· 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 --·-·-. -- ---------­-JSH & N'-JM: 0/10... --8E .._ 5-8 knts .. l' -. ----- --­--JG .. RC.. MOH; clr, ­-3/lO'l -­'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-­ ,, 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 • 5 @. 4 T 31. 0 @ 1 t 30 • 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) • 2 .2 . 2 . 2 • 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 • 2 • 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 • 2 0 190 0 350 0 190 140 0 190