• GALVESTON BAY TOXICITY STUDIES October 1971 -July 1972 '· University of Texas Marine Science Institute • T HE UNIVE-RSH Of TiXAll AT AVST!N Submitted • December 1, l972 c. H. Oppenheimer &W. B. Brogden • TOXICITY STUDIES OF GALVESTON BAY PROJECI' Chapter 1 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 School of Engineering and the Marine Science Inst·itute at Port Aransas. The purpose 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 p'roductivity values, a study of bottom fauna and comparison with other data parameters of the total bay study that may be pertlnent to the evaluation of water quality to the biological regime. The purpose of this study was six-fold: • (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 (5) to develop a predictive bay model formulated around circulation, nutrients, B.O.D., salinity, temperature, current flows, species inventory, light ~enetration and mixing and exchange rates (6) to provide a definition of the nursery· areas from the existing literature• • • ;,i \:.. INTRODUCTION j I.I ,, ,11 A very extensive description of Galveston Bay is contained in I'. ' 'l I 1(J the report by Copeland and Fruh, Ecological Studies of Galveston Bay ,. 1969, and by several TRACOR Reports, Phase l Technical Report 1968 ·. 1· ' ~ \/j( '. •I and Phase 2 Technical Progress Report 1971 submitted to the Texas Water Quality Board. The following report is the result of an rt• t interdisciplinary treatment of the wat~r quality of the Bay from the fall of 1971 to the summer of 1972. The previous reports offer wide considerable evidence of a diverse body of water subject to a range of natural and man-induced forces that have produced the transitional water body of todays 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 syst em and periodic high-intensity storms create periods of rapid change due to Gulf water input and massive rainfall. The biota have adjusted to such large scale fluctuations, excluding for the present man-induced toxic factors, as evidenced by the continuous large commercial 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 and esp~cially in estuarine environments where land has an impacta The relative proportions of minor elements in relation to sodium chloride will change due to the effects of evaporation and dilution by both Gulf water and rainfall with its leaching effect on the surrounding land mass. At best, "normal" can only be defined by extremes. 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 sedimentary processes or sediment · diagenesis. I -1 • 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 "Areas of Relative Stability" (ARS) within described limits of temperatu're 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 This is a normal transition and it issalinity to almost zero.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 By using the ARS and water quality measured asGalveston Bay Study. 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. • Each point represents aFigure 1 indicates the sample areas. discrete area of the Bay complex and reference points were established I -2 • TRINITY (Ja) BAY® (39) • 29°10' GULF OF MEXICO • 95°00' Figure 1. Galveston Bay System showing collection sites. I -3 • • • l i The total inclusion of all hydrographic data from the Galveston Bay program is far from being complete because of 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 (NAS8-26897). 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 1 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 it 1) 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 5) Mo Numeric -month 6) Dy Numeric -day 7) Time Numeric -military time, i.e. 11 PM = 2300 8) Depth Numeric -depth in tenths of meters 9) Ship Alphanumeric name of ship 10) Cruise Alphanumeric name of cruise 11) 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 particulate 15) Corrunents Alphanumeric comments such as "less than" 16) Method Alphanumeric name of method used for analysis . I -4 • so that sample points could be repeated during each sampling period. Sample periods were October 26, 1971; January 25, 1972; April 25, 1972; and July 25, 1972. Each intensive sampling period was approx­ imately 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 the 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 10 ft. in depth, at least three measurements were attempted; _1 ft. below the surface, mid-depth and l ft. above the sediment. In water less than 10 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 AO 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 ~ pump on board the R/V LONGHORN and placed in specially designed 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 gata management program called ENVIR for estuarine evaluation purposes • • I -5 '· • • • 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 "ENVIR". 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 early 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 -.G • Acknowledgements We should like to express our thanks to Dorothy Paul, Dinah Bowman and Tom Isensee for their assistance in the preparation of this report. Note must be made that this report is the effort of a team of scientists and their staff and, therefore, each of the enclosed reports stands alone as a contribution from the specific principal investigator. 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 • • I -7 • CHAPTER II • GALVESTON BAY ECOSYSTEM FRESHWATER REQUIREMENTS AND PHYTOPLANKTON PRODUCTIVITY by Neal E. Armstrong and Melvin 0. Hinson5l 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 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 require­ ments 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, l.n 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 9'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 Il -2 \ I I. ll I I • these populations which need to be controlled by limiting the input of ~he 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 atten­ tion~ REVIEW OF PREVIOUS WORK Environmental Requirements -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 Iiterature 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 inter­ action 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. Supple­ mentary papers by Parker (1970), Truesdale (1970), and others have helped to define the interrelationships between salinity, temperaturej season, and locationo Also, the subtle environmental factor mentioned in the Introduction has been included, that being the import of organic material carried with river in£1ow. • The Copeland and Bechtel report (1971) directly and indirectly points out the behavioral patterns of the six important Galveston Bay organisms (represented by nine species) and the very strong.interdependence of the environmental factors themselves" For example, the brown shrimp, Penaeus aztecus, moves into Galveston Bay in the spring as temperatures :n -3 • • • are rising in the water and as river inflows are increasing because of increasing rainfall in tributary areas. At the same time, marshes on the Bay periphery are growing following the winter death per.iod. 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 seaso:nality 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 es~aries 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 purp~se. U:-4 I '; I, 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 that 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 pr'ogram n:-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 tli.at 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 na" in various parts of the ocean, and the assimilation number for chlorophyl (the amount of organic material produces] per unit mass of chlorophyl "a0 ) 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 2 between 23 and 32 grams dry weight/m/ day with net organfo production 2 ranging between 8 and 19 grams dry weight/m/ day. Under maximum radi­2 ation conditions (750 gram cal/cm / day) the values for gross and net 2 organic production were 38 and 27 grams dry weight produced/m/day, respectively. The data reported by Odum, et al. (1963a) for studies during 1961 and 1962 in Galveston Bay are near these theoretical limits if the equivalency of grams of oxygen produced/m2I day and grams dry weight org~nic matter produced/m2/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 lI-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: D er where D = compensation depth, meters, c D = critical depth, meters, er . . ff" . t -l k = extinction coe 1c1ent, me er .. 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 material 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 absorping dissolved materials ~ybe 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 JI-7 • concentrators of many of the materials brought in from river ~nflow, 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. Com­ • munity metabolism, the·sum total of the systemw s biological functions» can be quantified most easily by investigating an isolated unit (either rr-a • • • 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 tox1city 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,~ al., 1963b), and continuous series estuadne units that maintained a distinc"t salinity gradient (Cooper, 1970). Regardless of the idiosyncrasies of the experimental design, ~crocosms are, by definition, miniature isolated representatives of the ecosystems being simulated an_d 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 rr-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 equi­ librium, 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 inter­ • actions between the water, sediments, and biota under oligatrophic 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 Abbott9s (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 Ca.rboy 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 lr-10 • • • '\I I j : I '. l : . ~ : ' . \ . 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 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 concentrationso 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. Cooperws functional Trinity Bay model, presented in the previous Galveston Bay Study (Copeland and Fruh, 1970), utilized a series of inter­connected microcosms to relate salinityregime, 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 lI-11 • 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 artifi~ial 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 require­ ments 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 impor-· tant 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/l and volatile suspended solids (VSS) in mg/l 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 BOD/day) were estimated as the difference between the :otal 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 dis.charge coefficient O. 2 lbs BOD/cap/~ywas applied. Waste treatment equal to 50 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 geo­ graphical 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 in.creases 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 without 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 compari_son 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 lI -14 • • • I ,. ; I I' I' I I' 1 ' I l J ~ 1 • I J, '.; I ' • ; 1 ~ ' I • ' • c I ; ; i _,·.L G.filveston ~ay Projec~. Th~se resultfs.i!ldiG~ted in general that nitr~gen . •' ,• ' ' ,I ' II/ II ' •'•!I ' • ' was a limiting factor during part of the year and that light and iron may be limiting factors also. They used 1the blue-gre'en algae, Coccochloris elebans, which Van Baalen is using in concurrent studies of blue algae bio­assays. Attempts to culture C. 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 bio­ assays. 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 nutri­ ents 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 roo~ in which the temperature was about 25°C. After in~culation 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 1I -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 des­ cribed 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, maxi­ mum 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 qse. 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• • n:-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 micro.... cosms 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 ll'-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 magn~tic 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 obs~rved 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 ll-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 p~evailing water level, and observed differences between microcosms (Table TI-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 lI -18 Microcosm Light Intensity (ft-candles) A (Station 26) 781 t::i I t-l '° B (Station 14) c (Artificial SW) 638 780 D (Station 22) 680 E (Station 29) 760 · *Experimentation Incompl~te TABLE Il-1 LIGHT INTENSITY AND SALINITY REGIMES FOR GALVESTON BAY MICROCOSMS Spring Influent Salinity Influent Salinity Salinity . at Start Salinity at End at Start (o/oo) (o/oo) (o/oo) (o/oo) 16.8 11 20.5 21. 6 17 21 27 23.5 15.5 19.8 21 22.0 15 15 .18.5 20.2 16. 2 18.5 23 22.8 . ---·--.. Summer Influent Influent Salinity Salinity at End* (o/oo) (o/oo) 8 26 20 16 26 • • • placed in a 4°C coldstorage room and vacuum-filtered as needed through O. 45 µMillipore filters to remove suspended sediments, orga~ic 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 prefiltered 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 o~ 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 displaceinent 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 n: -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/l 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 Physio-Chemical Sampling: Physio-ch.emical sampling of the microcosms, in addition to the dissolved oxygen measurements, included optical density of the water, temperature, salinity, phosphate, and nitrite-ttltrate 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 mea­ . sured 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 colorime~er, using a 1. 9 cm light pathway at 420 mµ wave­ length. Nutrient analysis consisted 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 Auto.Analyzer 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)• JI-21 I' 11 Ii ' I i I ' / • Spectrophotometer readings of the extracted pigments were taken at 665, 645, and 630 m µ 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-O. 8 d 665 645 630 where d =optical density at x w~velength 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 con­ • centration fluctuations along a normal diurnal curve. Sampling times are 1I -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 concen­tration 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 ~y­light 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 analysis, 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 froin . dissolved and suspended organics during the thiosulfate titrations. The entire series of Winkler reactions can be performed within the same reaction syringe £itted 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. Q?.cygen Diffusion: The observed values for oxy~en flux during the diel cycle were corrected for the diffusion across the microcosm air-. water interface in response to differences in oxygen saturation. The rr-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. . . . . (Ssunrise-Sair) +(Ssunset-Sair) average saturation (S) deficit (%) = 2 time period = 12 hours • Saturation percentages for the oxygen values were determined from Standard Methods (1965) for the prevailing microcosm salinity and temper­ ature. The reaeration coefficient (0. 42 mg/l/hr/100% deficit) was exper­ imentally determined in an abiotic aquarium setup with similar physical characteristics (i.e. equal water depth, mixing by magnetic stirring bar, 20 °/oo salinity, and 25°C temperature) to the actual microcosms. Com­ plete 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 O. 26 mg/1/hr/100% saturation deficit for his estuarine microcosms, while Whit:worth and Lane (1969) reported that an assumed value of only 0.1 mg/1/hr was used in his calculations. Field Tests Four cruises were completed in which light transmission data were acquired; on three of these cruises produ~ tion data were taken. The dates of these cruises, the stations visited, clld remarks about these cruises are given in Table !L-2. • On the January, April, and July cruises, phytoplankton production was measu~ed by use of the light and dark bottle technique. The standard Ir -24 TABLE I(-2 SUMMARY OF PRODUCTIVITY SAMPLING EFFORT IN GALVESTON BAY Station 14 Ft I N Ul 17 22 26 29 Date Oct. 26-28, 1971 Jan. 25-27, 1972 Apr. 25-27, 1972 July 25-27, 1972 Oct. 26-28, 1971 Jan. 25-27, 1972 Apr. 25-27, 1972 July25-27, 1972 Oct. 26-28, 1971 Jan. 25-27, 1972 Apr. 25-27, 1972 . July 25-27, 1972 Oct. 26-28, 1971 . Jan. 25-27, 1972 Apr. 25-27, 1972 July 25-27, 1972 Oct. 26-28, 1971 Jan. 25-27, 1972 Apr. 25-27, 1972 July 25-27, 1972 Light-Dark Bottle Tests x x x x x x x x x x x x x x x Light Transmission x x x x x x x x x x x x x x x x x x X. x - Activity Light Absorbance x x x x x x x x x x x x x x x x x x x x Laboratory Bioassay Samples X· x x x x x x x x x • .Chlorophyll "a" Samples x x .• x x - x x x x x 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 + Re~piration = Light Bottle + Respiration = (LF-LI) + (DI-DF) where: = final dissolved oxygen content of light bottle (mg/I) LF = initial dissolved oxygen content of light bottleLI (mg/l) • final dissolved oxygen content of dark bottleDF = (mg/l) = initial dissolved oxygen content of dark bottleDI (mg/1) 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 t11e optical density of this mixture was then read at 665 milli­ micron 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 time~ during the cruise period using a submarine photom­eter purchased from Kahl Scientific Company. Transmission of light in • the red, green, and blue wavelength bands as well as total light was ]! -26 \, • measured at various depths down to and just past the depth at which one percent light remaini~g was achieved. These percent transmission data • were then plotted and extinction coefficients calculated by the following equation: k = ln (I /I)I d 0 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 mµ up to 800 mµ. 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 spectrophotom~ eter are in absorbance units, and can be converted to percent transmission (% T) by the equation: %T = 10-A 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 Requirements 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, lr-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 ] -3 for the following organisms: trout(Cynoscion nebulosus-speckled trout, and Cynoscion arenarius-sand trout); reafish ( SciaenoEs 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 r~ver unit nearest the ocean or bay, respectively. Secondary refers to near primary, and so forth (See Figure n -3 for location of bay provinces). Examination of Table n:-3 and the ranges of temperature and salinity in which the organisms are found reveals that a large tolerap.e::e 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 Ir-3 ENVIRONMENTAL LIMITS FOR NINE IMPORTANT SPECIES IN GALVESTON BAY (Copeland and Bechtel, 1971) Environmental Factor Effect (Range/Optimum) Organism S2ecific Name Cynoscion nebulosus Cynoscion arenarius Sciaenops ocellata Brevoortia patronus ~ I Penaeus setiferus t'-' '° P. aztecus P. duorarum Callinectes sapidus Crassostrea virginica Common Name Speckled Trout* Sand Trout Redfish* Menhaden White Shrimp Brown Shrimp Pink Shrimp Blue Crab American Oyster Temperature Salinity (OC) (ppt) 5-40/ 1-40/ 5-30/ 0-40/ 20-30 Same 0-301 0-31/ 0-40/ 0-26/ 25-30 0-12 10-40/ 0-38/ 20-35 Same 15-35/ 0-40/ 20-35 Same 5-38/ 8-36 20-38 20-35 0-40/ 0-40/ 10-35 0-27 0-39/ 0-45/ Season . . July-Sept. , Dec. -Feb. All months/ Mav-Nov. Dec .-Feb./ All months/ Aor. -Aua. All mon_ths/ Tulv-Dec. Mar.-Dec./ Mar.-Sept. All months/ Summer to Winter All months/ · Sprina & Fall * Insufficient data to establish firm limits. ** Location legend: P = primary, S = secondary, T = tertiary, R =river, Str = stream, E = shelf, M =marsh, N = nearshore, TP = tidal pass, B =bay Location** ... S.Str.;P,S,T-B;TP/ All E (except M)/ Same P. S. R: P.S-B: NI P-R. to-P-B/ P-R to T-B All Loe./ P , S . T-B: S-Str. : M : S-Str. to CS/ None establ. S-Str. to CS/ S-B to CS All E locations/ P-R; S-Str: M & T-B estuary, CS = continental • • 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 these 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 TI-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 TI -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 as salinity in the bays does not appear to be an important limiting factor. The blue crab on the other hand responds to salinity in its larval stages more than the shrimp and is apparently not keyed to temperature. The larval stages prefer low salinities, but as the larval forms age this preference declines as adults are found in 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• rr -3o 1.--­ • TABLE I!-4 SEASONAL ENVIRONMENTAL REQUIREMENTS OF FOUR SPECIES IN GALVESTON BAY Location Marshes, bayous, and bay edges used as nursery Same as' P • aztecus Marshes and bayous Head of estuary to mouth \, Organism Penaeus aztecus (brown shrimp) .f..:. setiferus (white shrimp) Callinectes sapidus • Crassostrea virginica Temperature(°C) Ent er nursery areas in Spring when temp. > 2O; Leave when temp.> 28 Enter nursery in summer when temp. > 2 8 for a month; Leave when temp. < 28 No seasonal preferences No seasonal preference Salinity(ppt) No seasonal preferences No seasonal preferences Juveniles prefer Sal. < 8; Adults have no prefer­ence Not clear, periodic low salinity needed for predator removal • IT-31 • The. oyster apparently has 1ittle salinity preference although Copeland and Bechtel (19?1) 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 habi­ tats 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 IT-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 rr -32 • • • Figure JI-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 th~ 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 this material is utilized by the Bay biota, it is desirable to examine the sources of it. Presented in Table JI-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 rr -6 afong 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 lI-33 TABLE li -5 MASS DISCHARGE OF ORGANIC CARBON TO GALVESTON BAY, TEXAS Source Season Average Discharge* (cfs) Average BOD5 (mg/1) Mass Discharge of BOD5 (lbs/day) (104lbs/season) --· (1o4Ibs/yr) Percent of Tota] (%) 1. Chocolate Bayou Sp Su F w 115.7 122.4 58.0 .89. 5 4.46 2.98 1. 96 1. 3 2,770 1, 600 612 626 24.95 14.40 s.ss 5.64 50.54 0;3 ~ I w ~ 2. Dickinson Bayou 3. Double Bayou Sp Su F w Sp Su F w '<: 77.1 81. 6 38.7 59.7 ~ 72.8 .77. 0 36.S 56. 3· 3.9 2.33 2.16 1. 5 16.2 2.18 2.73 3.45 1, 620 1,022 450 482 . 6, 350 905 536 1,048 14.58 9.22 4.05 4.32 57.20 8.15 4.83 9.43 32.19 79.61 0.2 0.4 4. Clear Creek Sp Su F w 53.8 . 24.1 13.5 33.2 7.17 3.96 3.54 3. 7 . 2,080 514 257 661 18.72 4. 63 2.32 5.95 31. 62 0~2 5. Trinity River Sp Su F w 13,377.5 2,455.1 3,070.8 5,917.3 3.05 2.50 2.15 1. 40 219,500 33,000 35,500 44,600 1,972.0 297.0 320.0 402.0 2, 991. 0 14. 9 - TABLE rr-loNT.) Average Average Mass Discharge of BOD5 Percent Discharge* Source Season (cfs) 6. Cedar Bayou Sp 42.2 Su 18.9 F 10.6 w 26.0 7. Brays Bayou Sp 140.4 Su 58.3 F 47.6 w 111. 4 8. Sims & Vance Sp 173.6 Bayous Su 72.1 ~ F 58.9 w I w 137.8 U1 9. Greens & Hunting -l--- Sp 187.9 Bayous Su 78.0 F 63.7 w 149.0 10. San Jacinto River Sp 2,223.4 & Carpenter Bay Su · 922.9 F 754.5 w 1,765.3 11. Goose Creek Sp 6.0 Su 2.7 F 1. 5 w 3.7 BOD5 (mg/l) 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 of Total (1o4lbs/season) (104lbs/yr) (%) 7.52 3.36 1. 02 3.21 15.11 0.1 62.20 16. 30 50.90 59.00 188.40 0.9 71. 40 19.90 . 28.10 72.60 192.0 1. 0 111. 90 27.80 23.90 75. 80. 239.4 1. 2 296.0 70.60 62.10 175. so . 604.20 3.0 0.81 0.37 0.26 0.60 2.04 0.0 TABLE If-5(CONT.) • Average Average Mass Discharge of BOD5 Percent Discharge* BOD5 of Total Source Season (cfs) (mg/1) (lbs/day) (1041.bs/season) (1o4ibs/yr) (%) 12. Buffalo · Bayou Sp 242.5 6.72 8, 78.0 79.0 Su 100.6 3.14 1,700 15.16 F 82.3 5.8 2,570 23.15 w 192.5 4.53 4,690 42.20 159.51 0.7 13. Houston Ship Channel Wastes ** 422,700 3,800.0 15,200.0 75.4 14. Texas City(38, 900x 0. 2 lbs BODsfcap/day) x . 50 *** 3, 890 . 35. 0 140.0 0.7 15. Galveston(61, 800 x O. 2 lbs BOD5/ cap/day) x . SO*** 6,180 55.6 222.4 1.1 ~ 442,700 TOTAL w I 20,148.0 100.0 0\ *Communication from Tracor, Inc. ** Total loading (470, 000 lbs BOD5/ day) -Winter runoff load (47, 300 lbs BOD5/ day) *** 50 per cent treatment assumed • TABLE 1!-6 DAILY FLUX OF ORGANIC MATERIAL IN GALVESTON BAY, TEXAS Detritus Based Phytoplankton Based Total Organic Sources & Sinks Food Chains Food Chains Material Influx 5 5 5 (10lbs/day) (%Total) (10lbs/day) (%Total) (10lbs/day) (%Total) Sources . 1 River Inflow 1 Waste Discharges Phytoplankton Production2 Marsh Production3 ~ I Totals w 'J Sinks 4 Plankton Respiration Net Inflow 1 1. 2 9.0 ? 1. 2 0.4 4.4 33.1 ? 4.4 2.1 ? 200.0 100 200. 93.9 7.7 57.9 ? 7.7 3.6 - 13.3 100.0 200.0 100 213.3 100.0 240.0 ? -27.7 From mass balance of organic material inflows. 2 Average gross production in Galveston Bay, 6. 7 gms/m 2I day x Bay area . 2 3Esti~ated marsh net production (Keefe, 1972), 2000 gms/m2I day x 55. 2 mimarsh area on Bay periphery (Bureau of Economic Geology, 1972) x 0. 45 fraction exported to estuary. 4 Average plankton respiration, 8. 0 gms/m2I 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 discharges 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 contribu~ions 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 b~y volume) to total com~ercial catch (Figure lI-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 rate (resi­dence time of O. 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. • Productivity Phytoplankton Populations Distribution: The only rea~y 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 1I -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 th~ year. Most estuaries a~d oceanic waters show an early spring bloom of algae followed by a marked .decline in the late spring with lowest populations in the sum-' mer. 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 riv.er flows. The July and October pop­ulations (Figures rr-8 and 11-9) 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 n:~39 • • • geographical zonation is evident, and the algae identified are common components of estuary phytoplankton. Table Ir-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 rr-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 -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 be nonlimiting averaged about 1.1 day-1 . 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 O. 90 day-l lI-40 • TABLE JI -7 DOMINANT GENERA OF PHYTOPLANKTON IN GALVESTON BAY Study Area Trinity Bay Upper Galv. Bay Lower Galv. Bay •East Bay West Bay February Leptocylindricus Nitzschia Skeletonema Euglenoid Chaetoceros Nitzschia Skeletonema Skeletonema Chaetoceros Nitzschia Skeletonema Asterionella Chaetoceros Nitzschia DURING 1969 ,April Nostocaceae Oscillator­iaceae -' Thalassio­ thri:x Nitzschia Skeletonema Chaetoceros Nitzschia Skeletonema Thala s sionema Skeletonema Nitzschia July October Nostocaceae Thalassionema Oscillator-Filamentous iaceae C hlorophyta Cyanophyta Cyanophyta . C hiorophyta Skeletonema Skeletonema Thalas sionema Chaetoceros Ditylum Cyanophyta Thalassionema Coscinodis-Chaetoceros cus Ditylum Nitzschia Rhizosolenia Chaetoceros Cha etoceros Skeletonema Nitzschia Rhizosolenia Thalassionema I • II-41 • Station 14 17 22 26 29 • Station 14 17 22 26 29 TABLE rr: -8 GROWTH OF DUNALIELLA IN GALVESTON BAY WATER COLLECTED APRIL 25-27, 1972 Growth Rates (~OD/day) With 2. 0 mg/I With 0. 5 mg/l N/C P/C Control N03-N P04-P Ratio Ratio 0.010 0.19 0.014 19 1 0.007 0.11 0.017 16 2 0.014 0.10 0.03 7 2 0.014 0.19 0.014 14 1 0.007 0.07 0.023 10 3 Nutrient Data April 25-May 2, 1972 Total Nitrogen Phosphorus NH3-N N0+N03-N P04-P 2 (mg/1) (mg/l) (mg/l) 0 0.31 0.01 0 0.02 0.05 0 0.21 0.28 . 0 0.02 0.25 0 0.1 0.45 • JI>-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 little 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 l!-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 [-9 through [-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 fro~ river·inflow, marshes, • and waste discharges• Jr-43 • TABLE '[-9 PHYTOPLANKTON PRODUCTION AT STATION 14 IN GALVESTON BAY, TEXAS Oct. 26, Jan. 27, April 25, July 25, Item 1971 1972 1972 1972 1. 51 5.12 Gross Procl_rction 3.9 (gms o2/ m I day) 2.2 -1. 89 3.73 Net Production (gms o2/m2/day) Total Respiration 1. 7 3.40 1. 39 (gms oifm2/ day) 0.4 1. 52 • Compensation Depth >1.83 (m) 1. 83 1.85 1. 83Mixing Depth (m) 1. 06 6.52 3.75 Extinction Coe£. 1. 96 (m-1) Critical Depth (m) 6.5 1. 67 79 Net Prod. Possible? Yes No Yes Ammonia-N (mg/1) · 0 0 0 ? 0.11 0.2 0.31 0.01* Nitrite+ Nitrate-N (mg/1) Total Phosphate-P 0.45 0.2 0.01 0.088* (mg/I) 0.038 Chlorophy11 "a" (mg/I) • *In-House analyses. [-44 . • TABLE JI-10 PHYTOPLANKTON PRODUCTION AT STATION 17 IN GALVESTON BAY, TEXAS Oct. 26, Jan. 27, Item 1971 1972 Gross Production 4.9 (gms o2/m2/ day) Net Production -0.5 {gms o2/ m 2/ day) Total Res~iration 5.4 {gms o2/ m I day) Compensation Depth 1. 22 • (m) Mixing Depth (m) 13.7 13.7 Extinction Coef. 1. 92 1. 68 (m-1) Critical Depth (m) 4.6 Net Prod. Possible? No Ammonia-N (mg/1) 0 0 Nitrite+ Nitrate-N 0.12 0.2 (mg/1) Total Phosphate-P 0.59 0.13 (mg/1) Chlorophy11 "a" (mg/1) April 25, 1972 11. 92 9.83 2.09 2.2 13.7 2.30 70 Yes 0 0.02 0. 05 .· 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 • * In-house analyses. .ll-45 • TABLE U:-11 PHYTOPLANKTON PRODUCTION AT STATION 22 IN GAL VEST ON BAY, TEXAS Oct. 27, Jan. 27, Item 1971 1972 Gross Production 10.8 {gm Oifm2/ day) Net Production -5. 5: 2 (gms o2/m I day) • Total '.Respiration 16.3 (gms o2/m2/ day) Compensation 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/1) • * In-house analyses• !I-46 April 25, 1972 1.65 -7.11 8.76 <0.3 3.0 7.74 <1. 3 No 0 0.21 0.28 July 25, 1972 19.7 9.5 10.2 1. 40 3.0 3.50 . 39 Yes 0 0.18* 0.49* 0.082 TABLE rr-12 • PHYTOPLANKTON PRODUCTION AT STATION 26 IN GALVESTON BAY, TEXAS \ ,_ Oct. 27, Jan. 27, April 27, July 25, Item 1971 1972 1972 1972 ·O. 76 2.25 9.00 Gross Production (gms o2/m2/ day) -4.4 -3.92 -0.87 Net Production (gms 0 2/m2/ day) Total Respiration 5.2 6.17 9.87 (gms o2/m2/day) Compensation Depth <<0.3 <<0.3 o.s • (m) Mixing Depth (m) 2.7 2.7 2.4 2.7 15. 72 4.02 Extinction Coef. 4. 61 8.44 (m-1) Critical Depth (m) <<1. 5 <<7.2 7.85 ·No No (?) No Net Prod. Possible? 0 0 0 ?· Ammonia-N (mg/l) Nitrite+ Nitrate-N 0.12 0.08 0.02 . 01 * (mg/l) Total Phosphate-P 1. 74 0.3 0.25 o. 43 * (mg/1) 0.0166 Chlorophy11 "a" (mg/1) • * In-house analyses• 11-47 • TABLE lI-13 PHYTOPLANKTON PRODUCTION AT STATION 29 IN GALVESTON BAY, TEXAS Oct. 26, Jan. 27, April 25, July 25, Item 1971 1972 1972 1972 Gross Production 9.1 2.43 7.24 I, (gms o2/m2/ day) Net Production (gms o2/m2/ day) 0.6 ~8.49 1. 27 • Total Res~iration 8.5 10.92 5.97 (gms o2/ m I day) Compensation Depth(m) 0.98 <<:0. 3 1.4 Mixing Depth (m) 1. 83 1. 83 1. 83 2.0 Ex!inction Coe£. 3.39 1. 53 3.77 3.71 (m ) Critical Depth (m) 2.9 <<0.8 42 Net Prod. Possible? Yes No Yes Ammonia (mg/1) 0 0 0 ? Nitrite+ Nitrate-N 0.11 0.3 0.105 . 01* (mg/1) Total Phosphate-P 0.63 0.12 0.45 0.22* (mg/l) Chlorophy11 "a" 0.052 • *In-house analyses. TI-48 • There is little correlation of production values with nutrient concentrations; however, this is not surprising since high production would tie up nutrients and result in low ambient concentrations. The generally higher production values in Upper Galveston Bay do correlate with the consistently higher nutrient levels there. Likewise, the higher respiration values in the Upper Bay appear to be due to the availability of large amounts of organic material. On a more detailed basis though, I, • production values appear to vary more with light availability than any other factor. The measure of critical depth used by Ragotzkie (1959), which indicates whether net production is possible given the mixing depth and the compensation depth, was applied here, and without exception when net production was predicted to be less than zero, the measured value was indeed negative~ Thus, there is strong evidence that light can be a limit­ing factor to growth of algae in Galveston Bay. Light Transmission: Examples of light transmission measurements taken concurrently with production measurements are shown in Figure U -12 for Station 17 during January, 1972. Light transmission is given . as percent transmission for the submarine photometer with no filters, and with red, green, and blue filters. The selectivity of light absorption shown in this figure is interesting because in pure water wavelengths in the red region are usually absorbed more rapidly than those in the green~ and those in the green 1:egion are absorbed more rapidly than those in the blue region. However, the results here indicate that wavelengths in the blue region are absorbed more rapidly than those in the red or green regions. Thus, there is material in suspension or solution absorbing light very strongly in the blue region. The computed extinction coefficients for each spectral region, each station, and each sampling period are given in Tables JI-14, II -15, TI -16, Il -17, and JI -18. Units of these coefficients • -1 are meter . lI-49 • TABLE TI -14 LIGHT ABSORPTION COEFFICIENTS FROM SUBMARINE PHOTOMETER AT STATION 14 IN GALVESTON BAY (m-l) Date Time Total Blue Green Red Remarks October 26, 1971 0730 1.96 3.14 1. 797 1. 91 0 -0. 76 m 1.49 > 0. 76 m .. October 2 6 , 19 71 0800 1.98 4 .14 1.98 2.19 0 ~ .0. 53 m 2.21 > 0. 53 m October 26, 1971 1800 2.43 3.87 2.43 2.74 0.00 -0.53 m 1.19 0.53 -0.55 m 2.69 0.55 -0.82 m 1.15 > 0. 82 m • October 28, 1971 1725 2.17 3.08 1.99 1.84 0 -0 .488 m 2 .12 > 0.488 m January 27, 1972 0930 .446 ' 2.29 .669 1.225 > 0. 00 m January 2 7, 1972 1530 1.68 3.02 1.29 1.80 0.00 -0.84 m 1.14 0.84 -0.915 m .935 > 0.915 m April 25, 1972 0845 4.65 April 25, 1972 1615 8.39 July 25, 1972 0745 3.6 4.6 3.1 >Om July 25, 1972 1330 2.61 >0 m • 11-50 • TABLE Ir-15 LIGHT ABSORPTION COEFFICIENTS FROM . SUBMARINE PHOTOMETER AT STATION 17 IN GALVESTON BAY (m-1) Date Time Total Blue Green Red Remarks October 2 6, 1971 1530 2.74 4.19 2.09 1.99 0.00 -0.58 m 1.875 0. 58 -0. 98 m J 1.212 0.98 -1.01 m 1.102 > 1. 01 m October 28, 1971 1600 1. 71 3.35 1. 51 1.137 0.00 -0.98 m 1. 73 0.98 -1.22 m 2.20 1.22 -1.83 m 1. 33 · 1.83 -1.92 m 1.24 > 1.92 m •January 25, 1972 1420 •324 2.51 1.49 2.16 0. 00 -0. 305 m 2.25 0 . 305 -0 •42 7 m 2.36 0 . 42 7 -1. 31 m 1. 69 1.31 -1.75 m 1.20 1.75 -1.98 m 1.22 1. 98 -2. 04 m 0.905 > 2. 04 m January 2 7, 19 72 1300 1.68 2.95 1.198 3.16 . 0.00-0.67m .695 . 67 -1. 4 m 1.56 1. 4 -2. 44 m . 1.23 2 .44 -4 .24 m 0.714 4.24 -4.95 m 0.505 > 4 .95 April 25 , 19 72 1200 2.09 3.59 1. 99 2.04 April 25, 1972 1800 2.52 July 25, 1972 0939 1. 92 2.9 1. 36 >0 m • July 25, 1972 1530 2.71 >0 m lI-51 • TABLE ·TI-16 LIGHT ABSORPTION COEFFICIENTS FROM SUBMARINE PHOTOMETER AT STATION 22 IN GALVESTON BAY (m-1) Date Time Total Blue Green Red Remarks October 2 7 , 19 71 1045 2.80 3. 02 3.28 2.13 0.00-0.58m 2.37 0.58 -0.70 m 2.03 0.70 -1.22 m 1.90 > 1.22 m October 2 7 , 19 71 1430 1.09 2.80 2.155 1.94 0. 00 -656 m 2.27 0.656 -2.14 m 2.97 > 2 .14 October 28, 1971 1000 3. 82 6.04 2.96 2. 72 0.00 -.396 m • 2.22 0.396 -0.67 m 1. 77 0.67 -0.855 m 1.853 0.855 -0.96 m 1.48 > 0 .96 January 26,19 72 .1105 3.01 3 .42 4.01 3.71 April 26, 1972 0945 9 .2 6 10.8 7.90 April 26,1972 1500 6.22 9.86 7.61 April 27,1972 1430 9.49 July 26, 1972 0900 3.72 ·5.63 1. 65 >0 m July 26, 1972 1440 2.27 >0 m July 2 6, 1972 1715 3.77 >Om July 27, 1972 1900 3.77 >0 m • 1I -52 • TABLE ll-17 LIGHT ABSORPTION COEFFICIENTS FROM SUBMARINE PHOTOMETER AT STATION 26 IN GALVESTON BAY (m-1) Date Time Total Blue Green Red Remarks October 27,1971 0823 3.43 5.14 4.66 2.695 0.00 -0.915 m 1.645 0.915 -1.19 m 1.13 1.19 -1. 83 m 2.02 1.83 -2.16m 4 .15 > 2 .16 m October 27, 1971 1230 3.97 3.77 3.28 3.28 0.00-0.58m 2.21 0.58 -0.64 m • 1.68 0. 64 -1. 04 m 2. 68 1.04 -1.07 m 2.13 > 1.07 m October 28, 1971 0800 2.80 4.22 4.14 2.33 0.00 -0.305 m 2 .12 >0.305m January 26,19 72 1420 8.44 11.14 7.12 6.15 > 0. 00 m April 27,1972 0945 11.5 1700 19.94 July 26, 1972 1030 2.47 4.68 1. 73 >0 m July 26, 1972 1545 1. 64 >0 m • II-53 • TABLE 11 -18 LIGHT ABSORPTION COEFFICIENTS FROM SUBMARINE PHOTOMETER AT STATION 29 IN GALVESTON BAY (m-1) Date Time Total Blue Green Red Remarks October 28 , 19 71 1415 2 .12 4.14 2.515 2.25 0.00 -1.28 m 4.65 > 1.28 m January 25,1972 1645 5.04 10.2 6.34 5.39 0.00-0.32m 1.21 0. 32 -0. 61 m 1.03 0. 61 -0. 64 m.666 > 0. 64 m • January 27,1972 1130 1.54 1.80 2.53 2.70 0.00 -0.64 m 2. 62 0. 64 -0. 915 m 3. 62 > 0.915 m January 2 7, 19 72 1700 1.52 2.50 1. 76 2. 50 ' 0.00 -0.427 m 1.33 > 0 .42 7 m April 25, 1972 1430 3.77 . 9 .22 8.39 July 25, 1972 1055 3.1 4.16 2.05 >0 m July 25, 1972 1655 1. so 0-0. 6 m 2.96 >0.6 m July 27, 1972 1045 2.64 5.9 1. 36 >0 m • Ir-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 compare curves quantitatively, absorption coefficients (with units of decimeter-l) have been computed f~r each curve at wavelengt~s 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 coeffi­ • cients 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 JI -55 • TABLE 1I -19 LIGHT ABSORPTION OF STATION 14 WATER (dm-1) I 0 I K4200 Treat-%T Absorbance %T Absorbance F/C Date ment 4200X 42005{ 6sooX 6sooX · K6500 4200R Oct. 26/28, N 7 1.17 17 • 78 1. 5 1971 F 23 • 646 41. 0 . 392 1. 65 c 77 . • 119 87.0 . 06 19.8 5.43 Jan. 26, N 44 .355 63 .204 1. 74 • 1972 F 58 .237 78 .111 2.13 c 73 .138 88 • 057 2.42 1. 72 April 25, N ----1. 5 1. 83 -­1972 F 90 . 048 98 .008 6 c 84 . 077 96 . 020 3.85 0. 62 July 25, N 4.0 1. 4 13 . 922 1. 52 1972 ·00 F 94 • 028 100 .oo c 90.0 • 046 99.5 • 002 23.0 o. 61 • TI-56 • TABLE lI-20 LIGHT ABSORPTION OF STATION 17 WATER (dm-1) . K4200 Treat-%T ·Absorbance %T Absorbance F/C Date ment 4200.X 4200.X 65ooR 6500.X K6500 4200R Oct. 26/28, N 30.0 . 531 49.0 .309 1. 72 1971 F 59.0 . 233 79 .10 2.33 c 71 .151 89 . 049 3.81 1. 54 Jan. 25, N 45 .344 60 .220 1. 56 • 1972 F 74 .134 88 . 058 2.31 i, c 74 .130 90.0 .046 2.81 1. 03 April 25, N 24 . 612 39 •413 1. 48 1972 F 85 . 069 99 .005 .13. 8 c 87 . 061 99 . 005 12.2 1.13 July 25, N 49 .313 63.0 • 2 1. 56 1972 F 92 . 038 100 •00 00 c 87.0 • 063 98 •007 9.0 0.60 • ]I-57 • TABLE TI -21 LIGHT ABSORPTION OF STATION 22 WATER (dm-1} I K4200 Treat-%T :Absorbance %T :Absorbance F/C Date ment 4200R 4200R 6sooR 6sooR I K6500 4200R I Oct. 26/28, N 9.0 1. 05 20.0 . 692 1. 52 1971 F 36.5 . 439 63 . 207 2.13 c 38.0 .421 71. 0 .147 2.84 1. 04 Jan. 25, N 13 . 894 30 . 519 1. 7 • 1972 F 35 . 453 64 .193 2.35 c 43 . 367 70 .156 2.35 1. 24 April 25, N 10.0 .994 21 . 686 1. 45 1972 F 87.0· . 06 98 . 009 6.66 c 95.0 0.022 97.0 . 013 1. 69 2.73 July 26, N 20.5 . 688 36.0 . 422 1. 56 1972 F 78.0 . 018 94 . 027 4 c 47 .328 58.0 .237 1. 38 0.33 • . JI-58 • Treat-Date ment Oct. 28, N 1971 F c Jan. 25, N • 1972 F c April 25, N 1972 F c July 26, N 1972 F c • TABLE TI-22 LIGHT ABSORPTION OF STATION 26 WATER (am-1) %T Absorbance %T 4200.R 4200.R 6500.R 13 .886 26. 2 48.0 .316 73.3 59.0 . 231 83.0 ------20.4 ------41.l 52 . • 281 83.2 14.0 . 862 32.0 83.0 • 082 95.7 84.0 . 077 98.2 49.6 .306 63. 5 86.0 • 065 95.3 81. 0 .• 091 93.8 IT-59 Absorbance K4200 F/C 6500.R K6500 4200R . 575 1. 54 .135 2.34 . 08 2.88 1. 37 • 691 --­ .386 --­ . 080 3. 51 . so 1. 72 . 019 4.32 . 008 9. 63 1. 07 .197 1. 55 . 022 2.95 . 027 3.37 0.72 • TABLE ].[ -23 LIGHT ABSORPTION OF STATION 29 WATER {dm-1) K4200 Treat-%T Absorbance %T Absorbance F/C Date ment 42001{ 42001{ 6soo){ 6500.R K6500 4200.R Oct. 26/28, N 24 • 629 38 . 42 I 15.0 1971 F 67 .176 87 . 062 ·2. 84 I c 71. 0 .146 92.0 . 035 1. 42 1. 21 Jan. 25, N 11 •952 25 . 594 1. 6 • 1972 F 26 • 593 so .302 1. 92 c 44 .353 71 .149 2.36 1. 68 April 25/27, N 8 1.12 14 . 865 1.18 1972 F 90.0 .049 98 . 011 4.46 c 90.0 • 047 98 . 008 5.87 1. 04 I July 25, .N 21 • 67 37 . 437 1. 53 1972 F 88 . 058 96 . 020 2.9 I c 76 .118 91 • 040 2.95 0.49 • :n: -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 1I -61 • • • maintained a 26 ~ 1. s0 c temperature in the aquaria throughout the experimentation. The water temperatures in the microcosms (see Table TI -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 Qlo 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 °/op), but the slow influx of new estuarine water of different salinity allows the microcos~s 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 stations during J! -62 • TABLE l! -24 MEAN MICROCOSM TEMPERATURES DURING PRODUCTIVITY ANALYSIS I Sunrise Sunset I Microcosm Mean Temperature Mean Temperature Mean Daily I Temperature Range Temperature Range Fluctuation (OC) (°C} (°C} (°C} (oC) SPRING !NFLUENT A 24.45 24. 0 ""26. 9 25.43 24. 8 -26. 8 0.98 B I 25.54 24. 0 -26. 8 25.58 25.0 -27.0 1. 04 c 24.67 24.2 -26. 7 25.85 25.2 -27.0 1.18 I I D 24.67 24. 2 -27. 0 26.21 25.9 -27.4 1. 54 • ~­ E 24.59 24. 2 -26. 9 26.21 25. 9 -27. 3 1. 62 I SUMMER !NFLUENT A 24.45 24. 0 -25. 0 25.65 25.4 -26.0 1. 20 B 24.31 23. 9 -24. 8 25.81 25. 3 -26. 0 1. so I c 24.19 24. 0 -24. 7 25.76 25. 3 -26. 0 1. 57 D 24.09 24. 0 -24. 3 25.85 25. 5 -26. 1 1. 76 E 24.28 24.1 -24. 7 25.96 25.7 -26.1 -1. 68 I • Ir-63 • low or normal freshwater inflows. Salinity differences do not appear to significantly affect the ~ommunity 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 I, • depressions in the daily gross production estimates (as observed in the Figures in the Appendix) correlated often with temporary stoppages in microcosm 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 regu~ lators. These basic observations on different levels of community • succession appear to be confirmed by the preliminary patterns observed within the microecosystems used here. '[-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 fix­tures 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 h~avily. 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 rela­tive 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 per-1, formed on the model systems under study. Optical density, which measures all light-absorbing or light-scattering material in the water samples, '[ -65 • diff~red only slightly between microcosms (see data in Appendix). Since the relationship between turbidity and phytoplankton concentration cannot I I ~· I I I I I '• be elucidated properly, fairly constant readings obtained during the exper­ imentation 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 phyto­ plankton in decreasing light transmission could not be completely examined, since only during the summer influent period were the chlorophyll "a" con­centrations gathered regularly (Table li-25). Generally, these data show downward trends f~r 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 (~962) yielded comparable estima~es of gross production and respiration for the microcosms. Oxygen co.ncentra­tions 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 !I -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 1J: -66 TABLE 11 -25 CHLOROPHYLL "a" CONCENTRATIONS IN MICROCOSM WATER SUMMER INFLUENT Chlorophyll "a" Concentrations (m~l) Prototype Microcosm Station Day 1 Day 6 Day 12 Day 18 Day 24 Day 30 A Station 26 0.0897 0.0584 0.0759 0.0420 0.0425 0.0484 (Trinity Bay) 0.0585 0.0308 B Station 14 0.1091 0.0726 0.0669 0.0435 (West Bay) t=t 0.0272 0.0411 0.0428 0.0413 0.0278 c Artificial 0.0333 I 0\ 'J Sea Water D Station 22 0.0579 0.0435 0.0555 0.0559 0.0413 0.0403 (Upper Galveston Bay) 0.0406 0~0316 0.0439 0.0497 0.0894 E Station 29 0.0731 (East Bay) -···---­ • 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 mix­ing regimes in the model systems {i.e. thermal stratification). Whatever the reason for the midday oxygen pulse, it means that community respira­tion 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/1/hr/100% saturation deficit experimentcilly 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 retentfon rate. The control microcosm received artificial seawater without nutrients to evaluate the contributions of system nutri­ent cycling. The nutrient levels within prototype spring influent water (Table If-26) were normal for phosphate, but below normal for nitrate­nitrate nitrogen (0. 001-0. 0095 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. • I, Under these low nitrogen inflow conditions, this nutrient was the most prob­able "limiting" compound for the system autotrophs. • TABLE ]: -26 NUTRIENT REGIMES IN MICROCOSMS RECEIVING GALVESTON BAY INFLOW SPRING !NFLUENT Microcosm Nutrient Nutrient Concentration (mg/l) Type Influent Aquaria Aquaria Aquaria Aquaria Day 7 Day lO DaylS Day 21 ; A Nitrate-N . 0095 . 001 ---.004 . 008 I I Phosphate-P .180 . 025 . 0001 . 0001 . 038 B Nitrate-N . 001 . 001 .004 . 001 .004 Phosphate-P ·.180 . 030 .0014 . 080 . 00017 c Nitrate-N 0 • 001 . 007 . 004 . 001 Phosphate-P 0 . 00017 . 079 . 009 . 015 D Nitrate-N . 007 . 001 . 0095 . 001 .004 I Phosphate-P .285 . 046 . 025 . 00002 . 00001 • E Nitrate-N . 002 . 001 . 001 . 002 . 004 I Phosphate-P . 042 . 017 . 023 . 030 . 00001 I SUMMER !NFLUENT Microcosm Nutrient Nutrient Concentration (mg/l) Type Influent Aquaria Aquaria Aquaria Aquaria Day 1 Day 5 Day 10 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 Phosphate-P 0.15 .0006 . 004 . 0015 . 021 c Nitrate-N 0 . 001 . 001 I . 0015 I . 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 I E Nitrate-N 1. 53 • 002 • 0015 I . 001 I . 001 l I Phosphate-P 0.39 • 015 . 002 . 01 .006 I I I • ][-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 sedi­ ments (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 ll.-16 showed 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 pre­ sent. Other than microcosm A (Station 26 -Trinity Bay) and microcosm D (Station 22 -Upper Galveston Bay) which exhibited higher mean respira­ tion than mean production, the microcosms responded autotrophically during adaptation to low nitrogen levels and possible phosphate storage for later use. Odum, et al. (1963) reported P/R ratios less than one for Upper Galveston and Trinity Bays, an observation collaborated by micro­ cosm 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 O. 45 µ Millipore pore size) as a supplemental food source. "[ -70 • • • The proxii::nity of these prototype locations (Stations 2 2 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 auto­ trophic metabolic pattern to continue. The low nitrogen concentrations in the spring influent dictated the addition of 1 mg/l 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 2 . ' 1.1 gms .O/m /day, whereas other stations produced greater amounts up to 1. 81 gms O/m2I day (See Table TI-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 TI -26) showed that even normal phosphate and enriched nitrogen inputs were immediately 11-71 TABLE TI.-27 METABOLISM RELATIONSHIPS DURING EXPERIMENTAL !NFLDENT PERIODS I 2 Average Gross Production (gms/m/day) Influent A B c D E Type (Station 26) {Station 14) (Control) (Station 22) {Station 2 9) Spring Bay Water 1.31 1. 46 1. 42 1. 52 2.04 (Normal-Filtered Only) Artificial Sea Water 1.19 1. 05 1. 51 1. 86 1. 71 {Nutrient Free) \::l I Summer Bay Water 1. 37 1.10 1.13 1. 49 1. 81 ""'-l tv (N0Enriched) 3 ·­ 2 Average Total Respiration (gms/m /day) Influent Type· A B c D E Spring Bay Water 1. 35 1. 44 1. 41 1. 62 2.05 (Normal-Filtered) Artificial Sea Water 1.17 1.14 1. 49 1. 77 1. 65 (Nutrient Free) I Summer Bay Water · 1. 36 1. 03 0.93 1. 48 1. 82 (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 B~y 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 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 O. 035 lbs organic-NIcap/day, O. 025 lbs ammonia-NIcap/day, and 0. 012 lbs total phosphorus/cap/day for the Houston Metropolitan Area (Houston, Dear TI-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 1I. -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 contri­ butes 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 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 pro­ grams and in part to the rescheduling of the modeling and engineering tasks with which intera'ction would have been highly desirable. However, to the extent possible the original objectives of this work have been accom~lished and are discussed be~ow. Environmental Requirements Salinity and Temperature Requirements The environmentai requirements as defined in Copeland and Bechtel (1971) and summarized in Tables .Jr-3 and Jr -4 herein and are considered to represent the limits of environmental factors for those organisms· considered in their report. -74 • TABLE 1I -28 SUMMARY OF NUTRIENT MASS INPUTS TO GALVESTON BAY, TEXAS Organic Nitrogen Inorganic Nitrogen Total PhosphorusSource 4 4 4 (10 lbs/yr) (%of Tot.) (10 lbs/yr) (%of Tot.) (10 lbs/yr) (%of Tot.) 1 Runoff 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 Do . 2 ~ mestic I . '-.] Houston Metro. Area 1,705.3 73.8 1,218.0 49.8 584.7 52.9 01 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 3 I Industry 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 (RSC) 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 oased on the catch data. Whether this finding can be substan­tiated by laboratory or other data is not known, but they do cont rast 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 haeonastoma, 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 freshw~ter 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/1) 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 1I -76 • • • Bechtel (1971) was the scarcity of data in general and the lack of geo­ graphically 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 con­ tribution to Trinity-Bay alone were considered (waste discharges essen­ tially 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. 11-77 • • • 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 t he 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 lI-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 :-t'. · i:-,tained 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 w~re 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. n: -79 • Laboratory Bioassay~ • 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 cor­ responding 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 bioa~says, 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 pro­ • duction was less than zero (see Tables Il -9 through 1I -13). Also f ound were several overall patterns. High production and respiration rates were found in Upper Galveston Bay and Trinity Bay compared to those TI -80 • • • values in low 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 radia­ tion or high wind-generated turbidity. Av~rage light intensities are com­ parable 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 suffic·:ently 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 phy­ sical conditions for model system productivity analysis is not practicable. Daily water temperatures are much more constant in the micro­ cosms than for the prototype, but this lack of thermal variability does not appear to have any significant effect on productivity analysis. Arti­ ficial fluorescent lighting introduces far less radiant heat into the water than solar radiation, so diurnal temperature fluctuations are g.re"'~ tly ].": -81. • • • reduced in the model systems. Decreased microcosm heating r esults in low evaporation rates and relatively constant saEnity regimes wit h t he aquaria. Salinity patterns within the microcosms t enced 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 fiel d 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. Bec~use 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 data. Gross production and total respiration, calculated as gms!O/m2I 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 monitor s, 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 t he dominant Aufwuchs community limits the majority of biomass to the exposed sub­strate area, where this a.ssemblage cannot quite respond like phytoplankton TI-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 reserv~ir of this nutrient for maximized pro­ duction, 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. JI-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 lowerc:~ nutrient concentrations, so influent toxicity may be a factor here. Resid­ual toxicity that exists even i.n 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 rlignt), chemical (nutrient availability), and biological (population species compo­sition) parameters in the actual estuary. CONCLUSIONS The following conclusions have been derived from the re~ults 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. , qut the interdependency of the environ­mental factors strongly influence these results and must be considered in their interpretation. 2. Although conclusive evidence is lacking at this time, salinity • and temperature are interrelated in controlling population distributions . TI-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 salini~ies 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 reflec"'.: geographical pref­erence). d. Young blue crabs apparently prefer low salinity water while older blue crabs prefer higher salinities. .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 for·ms of these organisms. d~ For phytoplankton based food chains, the phytoplankton constitute the major source of organic mate~ial. • e. Organic material from waste discharges does not proviL:o a significant food source to the Bay as a whole but may be important in localized areas. JI -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 Galveston 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 correspo!i c"l to the displacement rate at which maximum catches are estimated. Productivity 1. The laboratory bioassay data 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 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 under similar light intensities. • 2. The laboratory bioassay data provided additional support using IC-86 • a different algal form for the blue-green algae bioassay conducted in a concurrent study by Van Baalen. • a. Depressions in the growth rate of Dunaliel1C!_, a green flagellate, were observed for Stations 14, 22, and 2 6 using samples taken from the Bay in April and July, 1972; the greater depression occurred with the July water samples at Stations 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 large 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 'Qy the large influx of organic material from marshes, runoff, and waste discharges; however, this imbalance reflects a large secondary production of animals such as fish and shellfish. e. Light was apparently Hmiting to production at all stations sampled for at least one sample period; light was always limiting at Station 2·6 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 proxi:i;nity of II -87 • • • the station to nutrient sources or to f avorable light conditions. g. Compensation dept hs were 1. 0 meter or less (one-half . the water depth or less) at stations in t he Upper Bay and 1. 5 to 2. 0 meters (slightly less than or equal to the water depth) at st at ions in the lower Bay. 4. Light measurements in the Bay showed that wavelengt hs 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 t hese materials was not determined; however, these materials are worthy of further f'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 wer e 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 f~om 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 St ation 26 during the summer, but when filtered Station 26 water was used in the microcosm, no decline in production was observed; this dis­ parity 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 t aken a t Stations 14 and 22. 1I -88 • (3) The variable toxicity of the waters tested appears to be a function of proximity to toxic waste sources, proximit y 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 t he 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 production in Galveston Bay h2s been stim­ulated by the large nutrient discharges to the Bay an x r ('") -u < ;o Pl LIGHTS LIGHTS -t 0 CJ) -< .ON OFF (I) ~d -t -IOz c 2 0 OJ 0 (Tl l> en -<. I­ <{ a:: ._ ..,. ~· z I.LI s: c :I: (.) ()CJ) -< ;o f1i \J z 000 0 0-; :i u ......... Oo ( !) f11 <.,. _. z -........ ~· r: n w en -or· l> ::o c r ~~ 0 (J") >< 0 --~ .9. 0 c )Jc :~ :.:; ~~ Cl -(fl ) .• w < r· > j:io a ';o >< ? fTj ·< (/) ~ rr1 (;') Cf) m rr• . :i -u :i 0 0 0 -~-0 o2 c t........--I · ·~ . ~ I --1 ;!J < 0 4 8 12 fil AIR SATURATION I {100 %) I ------1­ --~--­ · I NET I PF-~ODUCTION I ( PN) I -----! I I I I I I I L-­ ~ ~ ---­ NIGHTTIME RESP IRAT ION (R) - LIGHTS Of\J ,. DouT . · DIRECTION -~ ~·"'" OF. OXYG EN D 1 DIFFUSION IN . GROSS PRODU CTION P = P -R+D . G N TOTf-,L RESPIRATIO i\J R =2R ·'-r) T '" I .. ~-~-~·~----L--··--~~'--~--· t.~ 16 20 EXPERI M~·~ NT/\L LIGt-~ T-Dt\RK CYCL E .,, .( hou t~ ~-: } .~ c: :.AJ m G:;'·rn""Tll:! .. -~-~· ... • •=·-~""~···:o...'......e-i~· r.. :;:..:'~­ - (J) 0 cc. I­ 0 _J .:. ~· ::r: l (..) .J j I I I I I I I I I ! I I I f I ! ~ ·'o 5 10 15 = 20 25 FLOW ( 2,000 cfs) I I • ~ j ~ ~ ! CHi..OROS TY AT . STAT 0 1\J ~a AS G.~ LVESTON BAY 1r=LUE iCED BY FLO 'J FRO TR NITY .~: ~ ·:2 ·-4~ :E T '\J~v PR I,( RIVER 4 TOXICITY STUDIES • • • I 30 }­ I l ~ © 25 h­(.) c ........ (/') ..£;} ........ 20 :r: u n r aM'• .. .....,....1i¥C;-p;;;. -.,~ ~ r­ • I ( ~~ c1!~ ~ ~ ~ \_ l/ 0 I 0 I o_ ~ tr <.D 0 0 ~~ t ~ o_ 0 C~~ iO l 0 ~ • \ \ ; .. . • ·GALVESTON BAY PHYTO PLAN 'TQ,,J CO'\JCC:~ TR.::. -.-; QN . , l G . V=. TON ot\Y { no. I :·:i '.J 1 FE'"~HURY 18-27, 1969 TOX!C!TY ~TUDIES . ! . '-'...~~ I /1 J rL! I'· \_J~JJ rl -•. i . 0 / :· ; \ l -o 0 \ /~t\ o_ t g UI •· • • ~ ·GALVEs-i-oN BAY I PHYLOPLA WTON CONCENTRATION. . IN GALVE STOe BAY (nojm!.) PROJECT _ APRIL l~.-24, 1969 TOXICITY STUDiES 1 • ~1~ 0 , "-: • • ·GALVESTON uAY PHYTO? L l' '.' 0 CO 1CE, 'TRATIO~ S 1 GALVESTO BAY ( i . ./ •. ) ' 1 PROJECT JULY 14.-18, 1969 . •. • • ·GA. V -STON ?'. "y -"""'·· -=·"""'·n = . . ;::..;,,...., PH (TQ I /'. !\ .. ,, --~. ? ' !'\ F:'C"' I ' ~ '" 'v .,c ;;;CENTRATION .v -( . . ; G !-VES ;o. BAY { 10./ml.) . • · TOXIC T !OCTOc:-E R .c.-7 Iq;9 STUDIES j · . ' " -~~·· ·~.__,... . 'C'?"h~ ·~~~-~~~~~ • >­ ~e+~ 0 _ J --' ( ...,,,. 0 0 0 00 0 0 0 0 10 l q (j) • GALVESTO ' BAY ALGAL OF GALVESTON Fl . E BAY :·?vJECT TOXICITY STUD E' • STATIO l 17 ·~ r . r, '. 912 < 12~· s-k/'·;:) G 2 3 . 4· I w 61­ • 0 ! ~ . T :t ~ -2 0 2 4 .ET PRODUCT ON ( g/m3/d) PG=.4.9 g 0 /m 2 / ~ D =-Q.5 g 02/m2/d R= 5.42 g 0 2 /m 2/ ,,,.... - 'U-CT l.....~ .,.,.. ... ., ~ PHYTOPLANKTON ?: v""""';.;,........... 0----'"""'~ ----=-· GAL JESTC>! ~l"'i I Ii' GALVESTO. BAY F Gt' , PROJC:~'"." • I I4 TOXICITY STUD!ES 'irKR • • g ; i i I I OO t\ 6 i w l i ~ w I i • I " '' \ • " -~ / J. ·uA 2?, 1972 1300 r -J I I I ......... 10 £ ~ -[' '~ 0 . i­ ...._, !.. \ I ~ ,. .....: ~ 2 ~::,;.... z l -' t- w () J -I ~ 0 w r 1 Sm a.. ; .2 .1 ........l..-.ol----"'"--'---'---'--'-_._..L..-...l-.L-L-l~--L--L.-'-~~-L-~..L-L-.1 0 5 10 15 20 25 DEPTH { 1t) ~--------------------~:i---------~----------------~----·.-----~~ LIGHT ABSORPTION AT STATION 17 G LVESTO BAY F GuR­ 1: ~ G/"LlE~TON BAY PROJEc·r 2 • l TO .(!ClTY STUu!ES '----------------------~--------------------------------L------....-..J . ,I I I I J • -· .... . ....... ~.....Pi-· ~ r "" • 1 ! I I .! :() ·1 ,;• ~·•._,,/ L h I ) lu.o :> ! \l ) J \ 0 L:J (9 ::J lJ.. .­ I • I. • ! ~ I I 0 0 0 (J') 0 w l I ~ 0 0 0 r-<.D lO ( 0/o) NOISS l~ 0 !_1J ~-1' ....__ w ~ \. ~ " LL. 0 z L!J ::::> fr: w .._ _J I I ~ I 0 0 0 0 tj" i'0 C\J ~N\1~.l l 3~tl3d I lg _1 ·o i !'­ 1 · I lo - - :r.: t­ (.V) z l 1 _i L > <( ;;;;p r .J c GLVES:ON AY PERCE· 1T TiAj 'S~ 'i 1'"'" 0:-w IN Gf..• _vEs 0 1 F!G1 RE PROJECT 3AY VJ · -~c. T? S..... A. 14 ',,/' ,._..., OCTOB.:. i-\ 2o-28~ l971 • I I TOXICITY C'TUD E'-1 I • I Or ~ I_,..._~ I ~ . o~---~---------------~-----__._~------_.._-­ I 0 · ~ 5 i. -® -oj~ z w 10~ I (:: I 0 5 _:_ --~ 0 • > w _I · 0 (J) (/) Cl 10 IOr 0'"----&. '~~~-'-~-'-~..._--i:~--:...~--J-~....i-~.i:...---i~_, 0 2 4 6 L!Gf-'TS or GALVESTOI BAY DIURNAL OXYGd1 F....UCT A 'ON IN F G ·.E pr-', ·.ECT G~~LV"--STON BAY. !'. lC?OC OS ?. • l F. ~,:,. t'\ ·1 di: '"!­ • • '--~ 2.T 0 :z: 1.sl i- u :J 0 0 1 '.>'\ a:: :. .01 0... -.w ......... Cf) 1 (f) E 0 ......... c-: •":..... O'l t 0 ... .. z -. .:Jt ...... g .. - ~ I I I I~ 1 0 .5 1.0 .5 . 2 .0 MEA, l TGT/\~ RE PlRAT ON ( g/m2 /day) s 2.0 ·-------------. z r-/ 0 ~ ~ ) ::; 0 / 0 0:: 0.. ­ ~ Cf) 0 (() -a 1.0 c OCO~~· ;S 0 ......... ,,.,... C\l 0 ~ (5 E 0 B ......... z Cl 6 c _....._ 0 1.5 0 >. ­ 0 ) a=: "t:J a.. (f) t\l' o' en E ~ (\J 1.0­ oc (!) 0 c> z .......­ <( • w ~ 0.5 ­ .--" u • i ~ { • -\ A .. • .. • • 1 l • ~ • • i I I I I I 0.2 0.4 .0.6 -o.a 1.0 INFLUENT PHOSPHATE (mg/ I) ,. ....... ~ • GALVESTON BAY RELATIONSHIP BETWEEN GROSS FIG . •. r-c.. PROJECT PRODUCTION AND INFLUENT PHOSPHATE I 18 ...:• TOXICITY STUDIES ~ : • BIBLIOGRAPHY 1. Abbott, Walter~ 1966. ''Microcosm Studies on Estuarine Waters: I. The Replicability of Microcosms." 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 WPCFJ.2(1): 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. s. 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 Succ.ession in Laboratory Microcosms." 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: Be~htel. 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. " l . : ' ' .; •11• ~ I I 1 • • 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. 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. .J.: 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. 2, 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. .. • 1 'I • • • 25. Odum, H. T~ and ·C. M. Hoskin. 1958. "Comparative Studies on the Metabolism of Marine Waters." Pub. Inst. Mar. Sci. Univ. of Texas E.: 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." Pu~l. 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 ~. S. Yentsch. 1957. "The Estimation of Photo­Plankton Production in the Oc.ean from Chlorophyll and Light Data." Limnol. Oceanogr. 1, 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-AU-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 Ha!ldbook 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• • \, • • APPENDIX II-1 MICROCOSM EXPERIMENTAL DAT A • • • \. \ '\ 0 ti') 0 w a.. a.. 0 .... CJ) lO N a:: w a:: a:: t- CJ) -+ 0 N ....... en ~ 0 "'C ......, • LO w ~ I­ 0 I I I \. -----.~,--~-----"'----'----L.----1----.L----L----L----1.----.1--\ \. .' \ lO 0 lO0 0 lO 0 lO 0 lO 0 N ·o ~ lO 0 ~ lO N q ~ lO I') ~ N N N N ---0 (,(op/zw1zo f> ) NOl.lOnao~d sso~s • DAILY GROSS PRODUCTION DURING · GALVESTON BAY FIGURE SPRING GALVESTON BAY INFLUENT PROJECT AQUARIUM E-STATION 29 APPENDIX TOXICITY STUDIES • 0 rt) " I() C\I 0 • w a. a. 0 I­ CJ) 0 C\I a:: w a:: a:: I­ ~ .en (/) I() ~ --+ c • -0 ........ w :E .... 0 0 O· lO 0 I() . 0 I() .o 0 IO . N 0 . "' I() N. I I rt) C\i -= d d 0 DAILY GROSS .PRODUCTION DURING • GALVESTON BAY FIGURE SPRING GALVESTON BAY INFLUENT PROJECT AQUARIUM C -ARTIFICIAL SEA WATER APPENDIX TOXICITY STUDIES • 0 ,..., 0 w a. -.. 0 a. ..... --+ en ~ a:: &() ., -+ w C\J a:: a:: r ..... en -+ -+ 0 -+ C\J ,..... ~ "' c '"C &() ......, w .... 0 -+ • ~ &() &() 0 0 0 0 0 &() q q . l'-&() C\J. ~· . - N N -0 0 0 • DAILY GROSS . PRODUCTION DURING GALVESTON BAY FIGURE SPRING GALVESTON BAY INFLUENT PROJECT AQUARIUM· A -STATION 26 APPENDIX TOXICITY STUDIES .. • rt) 0 w a.. a.. 0 t ~ CJ) ~ C\I '° 0::: w 0::: l 0::: ~ CJ) --+ 0 C\I ,....... (JJ c ~ '° "C ........ w ::::? 0 • I­ 0 0 0 0 0 &() ~· C\I r--&() ' . C\I .q r--C\I ":'° '° q '°. . '°. '° ~ . ~ - C\I C\I <\i' C\I --· 0 0 0 (.) NOIJ.Onao~d SSO~e> ·.. • DAILY GROSS PRODUCTION DURING GALVESTON BAY FIGURE SPRING GALVESTON BAY INFLUENT PROJECT AQUARIUM B -STATION 14 . APPENDIX TOXICITY STUDIES • 0 rt) 0 LLJ a. a. 0 t- CJ) LO a::: C\J LLJ . a::: . a::: .... CJ) -+ 0 C\J ,..... c ~ • "' LO "'C . '-"'" " w -~ ..... 0 LO . ~ . LO rt) 0 • DAILY GROSS PRODUCTION DURING GALVESTON BAY FIGURE · SPRING GALVESTON INFLUENT PROJECT AQUARIUM D-STATION 22 APPENDIX TOXICITY STUDIES .--------...,--------.---------,rf:B------.,.--------,--------,~ • g c C\J :;:. -~ o­ ::J 0 "'C -~ 0 0. ... U> a.. G> a: U> -0 U> 0 o­ ... 0. e> ·~ 0 0 • L() 0 (ADP/ zW/D) NOIJ.~~ldS3~ l'1J.OL No11onao~d sso~~ • GALVESTON BAY TIME SERIES AVERAGED COMMUNITY FIGURE METABOLISM FOR MICROCOSM A PROJECT (STATION 26) SUMMER INFLUENT APPENDIX TOXICITY STUDIES l{) • ¢ t(') - 0:: LLJ . .. t-(\J <( t- ;: ·c z c 0 LLJ <( ·-0 ·-4-~ :::> L&.J '.O c 0 _J CJ) :::J .: LL -c Q. 0 (/) ~ z _J ... Q) 0 CJ) -<( Q. 0:: 0 0:: (3 LLJ _ .. ­ 0 (/) (/) 0 ~ !:!:: 0 4-c . 0:: ... 0 <» :E t­ ·O (!) t­ ::::> 0:: :t - CJ)~ 0 0 ,-.. tn c ~ --0 • """" ~ w ~ (.0 i= '° .. ~ t(') (\J . &q LO . 0 N . '°-. 0 ( J:o p/ GwI f> ) NOl.L'1~1dS3~ l'iJ.OJ.. NOl.Lonao~d sso~e TIME SERIES.~ AVE.RAGEo.·coMMUNITY GALVESTON BAY FIGURE METABOLISM FOR MICROCOSM C -· PROJECT . (ARTIFICIAL SEA WATER) . APPENDIX SUMMER INFLUENT TOXICITY STUDIES l. ; . . q ·~ q . I(). I() 0 -J• ...~ rt). N N 0 (ADP/ GW/D) NOIJ.'t~ldS3~ lV'lOl NOIJ.Onao~d SSO~f> • GALVESTON BAY TIME SERIES AVERAGED COMMUNITY METABOLISM FOR MICROCOSM E FIGURE PROJECT . (STATION 29 ). SUMMER INFLUENT . APPENDIX TOXICITY STUDIES ... ' I • lO ~ r<> c: 'c: 0 ~ 0.-(\J ·-... ... 0 0 ... :::J ·­ '"O ~­ e Cl> ( a.. a:: en- en o o.­ 0 ..... ... 0 (!) t- z w :::::> o~ £D _J ­ LL Ol ~ ~Z­ (/) ,..... 0 a:: z .. .u wO·· (X) "' ~ 0 ~· ..... . 0 5~~ -c -........ -:::::> CJ) • :E CJ)-· r- w :E CD i= &() \, ~· ' . q q LO . ~ q' 0 -• JI, rt>. C\J 0 (ADP/ GW/D) NOll'1~1dS3~ l'tJ.O.L No11onao~d sso~e • TIME SERIES AVERAGED COMMUNITYGALVESTON BAY FIGURE METABOLISM FOR MICROCOSM B PROJECT (STATION: 14) .SUMMER INFLUENT. APPENDIX TOXICITY STUDIES • r--------.,.--------r--------ir--------e-.-~------.---------~ ,. (\J c c 0 O· +-+­0 J 0 ::I '­ "'C c. 0 '­ Q) a.. "' a:: 0 "' . 0 E +­ t­(!) {!. z en w· 3 0 0 ,....., Cl lJ.. ~· en ~ z. (\J" (X) ~ (/) -(\J. c '"O o a:: z· UwO o, :?! r . r-' ~ a:: :?! <:( w u :::> t-. :E ~CJ)~ CD i= &() ¢ ti) (\J • &() q &()C\i (\J -. ·:.. 0 0 ' .. (~OP/zW/D) NOll'1~1dS3~ l'iJ.Ol. NOl.Lbnao~d sso~e • TIME SERIES AVERAGED COMMUNITYGALVESTON BAY FIGURE METABOLISM FOR MICROCOSM D PROJECT APPENDIX (STATIO·N 22) SUMMER INFLUENT TOXICITY STUDIES ' -· .. • SPRING INFLUENT >­ I­ CJ) z w c ...J <( (.) I­ a.. 0 .01 I 5 10 15 20 25 30 DAY NUMBER SUMMER INFLUENT 1­ F·-o..... : CJ) ...... . z · . -·-<:>-·-·­ w • >­ c _J n 17 in Galveston Bay, Texas -11 Phytoplankton Production at Station 22 in Galveston Bay, Texas -12 Phytoplankton Production at Station 26 in Galveston Bay, Texas -13 Phytoplankton Production at Station 29 in Galveston Bay, · Texas -14 Light Absorption Coefficients from Submarine Photometer at Station 14 in Galveston Bay (m-l) -15 Light Absorption Coefficients from Submarine Photometer at Station 17 in Galveston Bay (m-1) .' . -16 Light Absorption Coefficients from Submarine Photometer at Station 22 in Galveston Bay (m-1) . • -17 Light Absorption Coefficients from Submarine Photometer at Station 26 in Galvesto.~ Bay (m-1) . . ... • No. -18 -19 -20 -21 -22 -23 -24 -25 • -26 -27 -28 LIST OF TABLES (Cont. ) Title Light Absorption Coefficients fr~T: Submarine Photometer at Station 29 in Galveston Bay (m ) · Light Absorption of Station 14 Water Light Absorption of Station 17 Water Light Absorption of Station 22 Water Light Absorption of Station 2 6 Water Light Absorption of Station 29 Water Mean Microcosm Temperatures During Productivity Analysis Chlorophyll "a" Concentrations in Microcosm Water Summer Influent Nutrient Regimes in Microcosms Receiving Galveston Bay Inflow Metabolism Relationships During Experimental Influent Periods Summary of Nutrient Mass Inputs to Galveston Bay, Texas . • . _, ,1 • No. -1 -2 -3 -4 -5 -6 • -7 -8 -9 -10 -11 -12 -13 -14 -15 -16 -17 • -18 LIST OF FIGURES Title Hypothetical Diurnal Oxygen Curve Used to Illustrate Three Point Microcosm Productivity Method Experimental Design for Constant Flow Microcosms Explanation of Terms for Organism Location Chlorosity at Station 38 as Influenced by Flow From Trinity River Total Commercial Catch Annual Variations with Freshwater Inflow (1959-1964) Phytoplankton Concentrations (#/ml) in Galveston Bay February 18-27, 1969 Phytoplankton Concentrations (#/ml) in Galveston Bay April 15-24, 1969 Phytoplankton Concentrations (#/ml) in Galveston Bay July 14-18, 1969 Phytoplankton Concentrations (#/ml) in Galveston Bay October 14-17, 1969 Algal Assay of Galveston Bay Waters Phytoplankton Production in Galveston Bay Light Absorption at Station 17 in Galveston Bay Distribution of Light Absorbance in Galveston Bay October'· 1971 ·, • Percent Transmission in Galveston Bay Water, s.t~tion 14, October 26-28, 1971 · Diurnal Oxygen Fluctuation in Galveston Bay Microcosms (August 17-18, 1972} Mean Gross Production -Mean Total Respiration Relation­ ships in Aquarium Microcosms . Relationship between Gross Production and Influent Nitrate Relationship between Gross Production and Influent Phosphate • • • CHAPTER III Shrimp .Bioassay by K. G. Gordon, J. Gillespie, W. B. Brogden and C. Ho Oppenheimer University of Texas Marine Science I~stitute Port Aransas, Texas The purpose of this investigation was to determine the gross toxic effects, if any, of the native waters obtained from the five areas of relative stability upon the respiratory metabolism of the penaeid shrimp, Penaeus setiferus. ~o date, most progress in toxicity evaluations has been largely centered around t he chemical and bioassay determinations of an acute tolerance med.ian (TLm) i.e. that concentration of a given material in which 50% of t he more experimental organisms survive a specified test period, usually than 72 hours in duration and in fresh water utilizing freshwater species. While the employment of the standard bioassay technique is acceptable for acute concentration levels, it is not capabl e of delineat ing the effects of sublethal toxic concentrations. Often times, it is desirable to ascertain t he i mmediate, delayed and/or long term effects of sublethal concentrations of individual toxins or groups of toxins. Moreover, it is likewi se desirable t? resolve the synergistic effects of sublethal toxins, especially as they occur in situ. It is with these concentration levels in mind t hat thi~ bioassay was developed, for these _are the concentrations which are potentially capable of altering pnysiological parameters, many of which may not become apparent until future generations. The multiple and varied sources of p6llution (~.go industrial and domestic wastes, herbicides and pesticides, heated efflue~ts , severe and prolonged temperature extremes, fresh water flushes are recognized as distinct stresses upon a community, although their environmental impact may sometimes go unnoticed by conventional monitoring techniques. If an estuarine system, such as t he Galveston Bay ar eQ, were subjected to an energy demanding stress, the energy required to sat isfy that expenditure must be removed from components of the energy budget, normally set aside f or species growth, reproduction, diversification and daily metabolic funct ioning. The .range and ultimat e survival of a species t hen is, in large part, determined by the species tolerance to energy stress and the subsequent adaptive response to the environmental change. The • individual as well as the species must adapt, move or die if it is subjected to a lethally stressing environment. The mobility and metabolic response to a 11toxic factorn then, is a key to the adaptive response of the individual. An increase in active metabolism (as oxygen consumption) is directly dependent upon the level of activity; the higher the activity level, the higher the oxygen consumption. Nonlocomotor activity, such as labored active respiration in the preserice of a respiratory stimulator, may also be reflected as a metabolic increase. It is with these thoughts {n mind, that this technique for measuring activity and respiration of an individual in suspected toxic waters was developed. Recent . publ ications (Waller and Cairns, 1972; Sparks, Cairns, and Waller, 1972) have i ndicated that fish movement can be used to detect sublethal stre~s due to pollutants. The recording of locomotor activity in small aquatic animals presents difficulties because the water movements produced are too weak to be detected by devices previously described (Heusner and Ruhland, 1959; Spoor, 1946). • Optical devices have been used (Gunzler, 1964) but t his procedure interferes with light conditions during the experiment. Heusner and Enright (1966) eliminated this difficulty by detecting variations in heat conductance associated with slight water movements using a hot wire thermister device • Respiration response to sublethal stress has been measured for a number of organisms. Examples of this type of work were conducted at the University of Texas Marine Science Institute at Port Aransas by Steed and Copeland (1967) and Cech (1970). Oxygen determinations in experiments of this sort are made by Winkler titration and/or amperometric sensors like the polarographic probe introduced by Clark (1956). The operating principle of the Clark probe has been described by Phillips and Johnson (1961) and by Kinsey and Bottomly (1963). Todt (1958) and Johnson et al. (1964) experimented with so­called 1'self-generating11 probes that might be used in aquatic systems. Gordon and Oppenheimer (1970) designed a s ystem utilizing an amperometric system capable of long-term monitoring of dissolved 02 and temperature in mar1ponds. After making certain design i mprovements, i.e., stable power supply and frequency response, o~ t~2 activity sensor of Heusner and Enright (1966), it was combined with an 02 monitoring system so that simultaneous me surements of activity, temperature, and 02 consumption could be mad e under controlled conditions of light, temperature, salinity, and time. The instrumentation was used in a controlled temperature room and test animals were accl imated for a minimum of 24 hours prior to each experiment. (Note: The sys~em electronics pescription is not included in this report. However, techni cal details can be provided upon request.) A block diagram of the • system is shown in Figure 1. III-2 PUMP ~·~ ~o f::t:z E-lH ANALOG ~ M..ANUAL H GH t ~RECORDERr ~ DATA H f-=&-·...,,..--­ 'H ~~ REDUCTION CJ vs 0 'T ANALOG _.L­ AQUARI UM RECORDER' SPECIMEN CHAMBEI~ TELETYPLJ (1) Temp &02 Probe 7 ALPHANU MERIC ( 2) Activity Probe ~~:lllfO CIC 6600 R X-Y ·1 DISPLAY(Located at UT Austin) PLOTTEJ.~ l!'r~~-···~..:~..,....~··" ~' • ?i> GRAPHICALDI SPLAY t! +::> Figure 1. Bl oc k diagram of respirometer syste m. located in controlled environment room.~Specimen chamb21" and recorder hardware _..:­ • • • Water management was controlled by a variable speed peristalt ic pump and manifold system t hat allows variation in f l ow rate ar_d direction. Oxygen consumption was determined by recording the inflowing and outflowing 02 tension ~nd temperature on a multi­channel recorder after signal conditioning and amplification. Activity signals were recorded in analog form after ampl i f ication, or digital form after passing t hrough a modified Heusner and Enright (1966) integrator and a base-twelve counter-splitter designed at t he Institute. Penaeid shrimp were periodically collected by trawling in estuaries in the area surrounding Port Aransas and pl aced in a holding pond covered to maintain salinity regimes and i nhibit algae blooms. This pond was continuously fed with clarified channel water, r ecircu­ lated, aerated and filtered through the sediments to prevent excessive anaerobic activity. During the winter months, a hot water tank supplied the pond with r~ecirculating, heated salt water~ Temperature and -salinity were adjusted and monitored so as to remain in the normal range of the surrounding estuaries throughout the year. While housed in this pond, the shrimp were observed daily and fed a high energy protein, vitamin enriched, pelletized food developed by Oppenheimer and Subrahmanyam (1971). Shrimp so maintained were then removed to the controlled environ-. mental room in which the experimental testing equipment and acclimation tanks were located. Ambient room temperature was maintained at 23.00 C (+ l.0°C); waterbath temperature was 23.o0c+Oo2s0c. Photoperiod was regulated as close to the naturally occurring diel cycle as possible. Salinity acclimation was a minimum of 24 hours in the salinity of the selected ARS station salinity. In those cases where there 1. was a 10% or greater difference in the holding tank salinity and the salinity of the test station, the salinity was adjusted 5% (with Instant Ocean or distilled water) for 24 hours until the ARS salinity was reached. The primary holding tank sal~nity was 2 °/oo, upper l imit for£. aztecus (Copeland &Bechtel, 1971). Experimental organisms were placed in acclimation tanks of this salinity at f irst room introduction into ~he controlled temperature prior to further testing. In one instance, i-~· the winter runs at Station 26, the organisms were not able to be repeatably maintained at the low salinity level present at that statio~. Seve~al runs were attempted with the waters as they were obtained from the field. However, all of the organisms died within a twelve hour period. Salinity of t his water was adjusted to 24 °;oo with the addition of a prepared sea salt, -~stant Ocean, to determine if the original water was too fresh for the organisms tested. Animals were weighed, measured (tip of carapace to the base of the tail ) and sex determined prior to each of the 24 hour r uns. ·salinity was measured with a direct reading refractometer and noted on the recorder charts. Behavioral activity was periodically noted III -4 • • • several times throughout the 24 hour period and any abnormal activity such as loss of equilibrium and disorientation was noted. Base (i.e. 11 zeroj') rate was determined by observing a period in which the test-animal was resting in t he chamber and exhibiting no visible sign of swimming or locomoter activit y. Whenever the animal began to swim, move around , or make a tail thr ust, the recorder pen displacement was annotated. The delta pen displacement between the base and active values was def ined as a Hstrokeu. During a given run, whenever the recorder pen was displaced by a value equal to or in excess of the delta, it was tabul ated as a 11 stroken. At tre end of the 24 hour test period, activity and oxygen recordings were manually reduced and transcribed. Oxygen measurements were taken 1, over a ten minute period and an average rate was recorded for that time interval. Total activity measurements (strokes above basal rate) were likewise tabulated and recorded for the same ten minute time period. These two measurements of activity and oxygen were then synchronized by shifting the activity up ten minutes to compensate for the lag time in oxygen measurements at t he outflowing oxygen probe. (A dye analysis of water movement within the experimental chamber demonstrated a lag time of 8 to 11 minutes, flow rate dependent.) Flow rate was determined by monitoring the volume at the outflow with respect to time. Optimum flow rate for the size class individuals was determined by observing that rate at which the animals appeared least disturbed , i-~· did not actively swim against either the inflow or outflow baffles in ncleann waters • Computer processing of shrimp respiration and activity data The measurement of shrimp respiration and activity for periods of 24 hours or more produced hundreds of data points. A FORTRAN IV program RESPAC was written by W. B. Brogden to read this data and convert it into usable information. This program was operated on the University of Texas timesharing computer network, TAURUS, i n a conversational mode from the Marine Science Institute 1s teletype terminal. The main body of the raw data consisted of the apparent concentration of oxygen in the inflowing water, the concentration in the outflowing water, and the total activity during the prev~ous 10 minute period. This raw data was punched on paper tape, trans­mitted to the central computer, edited, and stored on magnetic tape 77 permanent filesn. During data· reduction using-RESPAC, the operator ::=ed in the following information: the saturation concentration of oxygen (or the temperature and salinity so the computer could compute the saturation value) the weight of the animal,. the pumping flow rate, and the time interval per data line. The raw data was then r ecd from the ' file, checked for consistency and gross errors, and u ed to compute the respiration rate. Suspect data was p~ i.ted o -con the teletype and corrected by the operator. Additional computations were performed to compute the rr.ea.n maximur:., minimum and standard · III -5 • deviation of both respiration and act "vity, the product moment correlation coefficient and the lea~t squares fit linear regression of grcphi c equation. The operator could sel ect three different types output to s ummarize t he data; 1) a plot of activity and respiration versus time, 2) a histogram of t he observed occurrences of 10 classes of respiration and activi~y and 3) a point by point plot of respira­tion versus activity and the least squares fit line. Examples of these outputs are shown in Figures 2, 3, and 4. Results A total of 92 separate experimental r uns were made with control Galveston Bay. Although there was frequently seawater and samples from a significa~t correlation of respiration with activity, this correl a­tion was not always evidenced, in fact, there were several patterns in which the correlation was negative. The general patte~n of observations is summarized in Figure 5 which contains the data from all seasons. Figures 6 through 12 summarize the data graphically. Figure 12 shows all respiration runs, plotting respiration as a function of salinity; it appears that there is no overall relation between salinity and respiration. Discussion • Examination of the relation between activity and respirat ion indicated that, in general, the slope of the regression was small, in other words, t he aver~ge respiration was not greatly different from the predicted zero activity respirat ion (the intercept on the activity axis in Figure 4). In addition, t here was great difficulty in maintaining a satisfactory standard between runs, so although it is interesting to noLe that the average activity for control shrimp higher than any of the shrimp exposed to Galveston Bay water I, was (Figure 5), it is not felt t hat significance should be attached to this without further statistical analysis. An additional difficulty in interpretation is caused· by the fact that in the winter season, the available shrimp tended to be smaller and due to various instrllinental difficul ties no control runs v~itn sc,all shrimp·were conducted. Tabl e 1 summarizes t he respirat~on Cata, excluding the four shrimp smaller than 8.0 grams, by station ~nd season. The ~ossibility of doing statistical tests for sig~i£ic~nce of the diff~rences between stations and seasons was investigated, however, i t appears that standard t ests can not be applied because of the inhomogeneity of variance bet ween st tions and seasons. These results are therefore discussed on a qualitative basis beginning with a comparison between controls and experimental respiration rates • • III -6 • 50327 6 1 t ' • o.o TIME MIN Figure 2. Plot of activity and respiration vers s time • S0327 1 o .so~ ~ 11 I j I--£"'.-,t I ; 1 ; ! 0 • 0 0 ~L---.<1--.1---.i.--i.~__...:_--'----------'--~...J..--L--...L.--"--"----''--" 0.481 l • 327 o.o • RESP AC T Figure 3. Histogram of observed occurrences of respiration and activity. III -7 • so:3 2 7s1 c=: Lf.. ...-1, ' ' .I • . . ~ ~ ·. -: : . ~-.---~---: . . .. . ... . . CL Cf') • I LL-IH er:: H H CD L...---.-----·-.. o.o R C i:-.----r r ' t; -UJ Figure 4. Ee spj :ration· plotted versus activity with least s c'.uar <_:s -regress ion line. • Figure 5. This figure summarizes 92 individual shrimp respiration and activity measurement runs, the controls YTC;r and 5 stations fo, al l four seasons. T~e upper sectio represents the average activity by the short horizonta~ bar , the standard deviation of average activity (between :runs .J by the vertical line, and the aver0.ge va~iance of act~vi~y within runs ·y the cl osed circle. The activity ~-..;..;. _ ,_e represents TTstrokes 11 per 10 minutes. The ~ower sec~ion represents the average respiration in mg of oxygen per gram wet weight ?2r hour by t he s ort horizontal bar, the standard devi tion (between runs) by the vertical line, and the avE::r-.g·e variance of respiration (within runs) by t he closed circle. • • III -9 100 • •90 ~ 80 ­.ea > 50 r¢m y 0 0 30 20-= 10 • c i-., \ ~i( n'i r ­~,j 4 0 1 0 -1 22 0 ; , .~ --""' H _h 0 ·L. !:J i;..l \, 2 1.5 1 . 5'·•• r1 i .J r;. 1;1f': -.-~'~ 1..J <:~ •i v ,,-.­,1. 1 ·. '1 ;:1 H ,, ·i ~i I~ .! ,j,., ~i ·' ·:J '·,J ,., -q ~ i . iJ ;·.! t; r; ·' ~ 0 ~.-.. v 1 26 • • • Li) Q_ en l..LJ ; 0::: LLi > + a: (!) (!Jf­ [!)+J 0 0 0 o.oo F· gure 6. Fig: res 7, CONTR OL S ENV IR ST 14 I I l 0 Figure 6 Figure 7 0 Li) ~ + J Q_ Q_ en en w w 0::: 0::: L!J , w + > > + a: a: + (!) + (!) + + (!) (!) [!) (!) + + + ++ + (!) (!) +q; ~ ~ 0 iJ ' 0 &~ t'l'I 0 ...... + i'" 0 [!) + ~~ 0 C!f0 f 0 o.oo WT 75.00 o.oo WT 75.00 ST 17 Y2= SIGRESP ENVIR ST 22 Y2=S IGRESP ENVI R I 0 Figure 8 I 0 Figure 9 0 0 Li) r .cb Q_ (fl . !1 -I (!) O:::_J l!l ~~ + i cb + + + ++ + (!) (!) . + fJ [!) + :o 0 %+ -o 0 + (!) $r1~ rb~ 1~ ~ 0 WT 75.00 o.oo WT 75. 00 Average respiration in mg o2/gm-hr for t e control sl"..r ·mp is plotted against weight in grams as the s quare symbols, from 0. to 5. 0. The standard deviation of respiration with­in. each run is plotted as the + symbol from 0. 0 · o 1. 0. The scales are divided into ten r.:.q ·11 1 portions for all of these computer drawn plots. 8, and 9. Similar plots are drawn for all shrimp for stations ll , 1 7, and 22 respeeively. III -11 • i, ENV I. ST 26 Y2=SIGRES P EN V q ST 29 Y2= sIGR ESP a 0 Figure 10 r o Figure 11 '.f) ~ ~ ' ~ Q_ Q_ ()) en ~~ w w I~ 0:: 0:: r 1-C w w : (f) > > + '~ a: a: + + j + + [!] ;. ~ + + + 10 0 [!] + @ ~Efl • 0 a + El cp EB CJ + 01----..,.-~-..-~.----.-~......---,.----.-~-,---,.---; I o. oo WT 75.00 o.od 75:00 ~1 WT r~ RLL SHR7 P ENVIR 0 0 Figure 12 [!] ~ l!l [!]~ C!l L)£J C!l C!l >-l!JC!Jd[!J l!l C!l C!l l!l I-C!l [!J C!l ~ z [!I!Ja5l C!l 1-1 ~[!f] _J [!)(!) a: [:J (). 'r:-·~ Wil!l'!l C!l C!l C!l 0 0 l!I!J OL..----.-~-.-~,---.,...~-.--~,---,-~-r-~.---! o.o p 5. 0 Figures 10,, and 11. Average respiration in mg o2/gm-hr or 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 resp~ration within eac·~ 2~ i .1. ... is plotted a.:::; f-: e + symbol rom 1' 0. 0 t~,, 1. . T .1.C scales are div' ~ e, .. ito t ,n e }:....1 portio_ s for all of these computer drawn plots . Figure 12. The relationship of average respiration to the salinity of the water.. either natural or adjusted. ITT . l f") .,..._-~---· ____ -~----·-----­ -------------==.....,,..,,.~-­ ... .. • Table l. Surnrna1 y of shrimp re p:i.ro.t ion measurements excluclir g shrimp weighing less t ' LUll. t5 grams. Station Sec.son 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 1..353 0.. 29 0.647 s= Oo067 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 0.268 " s= 0.255 1.020 0.104 0.300 d= 4 3 0 0 n= 5 3 2 3 26 wt= 24.2 1207 51.. 6 29.3 r= 0~324 0.533 0.,243 0.183 s= 0.131 0.. 444 0.128 0.. 035 d= 0 2 0 0 n= 2 4 3 4 29 wt= 21.4 21.4 31 .. 7 24.l r= 0. 503 00489 ':_; ~JS7 0.294 s= 0 .. 035 o. 72. 0.066 0.133 d= 0 0 0 n v n= nu.rt1ber of experimental animals rt= t~.e average weight r= the avei-ag·e respiration rate in mg 02/;-':.-~·. :_· yi -.,__ ~ s= the standard deviation of respiration ....~1..~ ~ d= ·che number of experir. ental animals which cied within 48 hours of the end of the experir:Lent • III -13 • • • i, Station 29 appears to be ~ifferent from otrer bay stations in that the average respiration stays near the same value t hroughout 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 reason .ble similarity to station 29 and the controls during fall and sp~·ir-Lg , with rela·cively 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 dour s .. This water was made up to 24 ppt salinity with TTinstant Ocean11 , and two more shrimp were exposed ; these survived for the duratio~ of the experimental r un. One of t hese shrimp weighed less t han 8.,0 grams and equipment malfunction prohibited further experimental runs, hence only three rur~s are averag·ed in the table.. Because of t he low salinity it was impossible to determine if other toxic factors \11ere present at station 26 in the winter. Station 22 gave dramatica~-Y higher respiration rates in the winter samples, as well as three deaths within 48 hours. Some of these organisms were run 2.-::: the naturally occurring salinity of 9 ppt and some were run ·ic:-~ the salin~ty raised using· ninstant Oceann. Those with raised salinity had approximately the sarne 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 de ths out of 8 organisms in the fall samples were apparently due to a tox~c f ctor. Station 17 shows apparent depression of respiration during the spring, unfortunate:;, instrumental difficulties prevented runn.:..ng more than t ·io sf-.rimp with -cnis sampl ., Respiration during t h2 winter appears to be high, but the significance of this is uncertain. Station 14 cu~ing the fall resembles t he controls and station 29 bt:-:: snows high respiration during the winter and sum...er. Ag·ain, the significance of this is unclear. Two animals died during the fall ar.d one during the \vinter. The control res;drations ave:" ged abo:..:t the sar.e for all seasons but the v.:.riability b t \·;e2· runs was unexpectedly high. It =-~:; this high variability which makes statistical interpretation d:..== :..c~:~ and · has reduced the potential value of these measurements for "coxic:.."~y detection • III -14 • Conclusion Although t he high variability between animals exposed to the same water has made it difficult to distinguish between LOXic effects and variability of the animals, there appears to be quc:.~~itative evidence for toxicity at station 22 during fa_l a~d winter, and indications of stability and lack of toxicity at station 290 It appears that if this type of toxicity assay is to be of ~urther use in pollution research , better control of the sources of variability will be required. Re:ferences 1 Cech, J. J~ 970. Respiratory responses of the str~ped mullet , Auqil cephalus , to three environmental stresses. ~aster'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. So~e environmenta_ limits of fix important Galveston Bay species o Cont. 20. Pamlico Mar. Lab. N.C. State Univ• • Gordon, K. G. & C. H. Oppenheimer. 1970. A mu~ti-channel, modu_ar input, chart recording system for use in mariponds. Univ. Tex. Mar. Sci. Inst. (Mimeographed) 41 p. Gordon, K. G.. , W. B. Brog-den & J. S. Holland .. 1972 . _ pre_ir inary toxicity analysis of the sediments of La Quinta Channel, Corpus Christi Bay, Texas: A report prepared for the · s. Army :S.-:]:-~ _eer District, Galveston, Texas. Univ. Tex. Mar. Sci. -ns~. (Mimeographed) 25 p. Gordon, K. G~ & C. H. Oppenheimer. 1972. An instrumentaLion system I"O::'.:' -, .•. :.'"'ing· an..:.mal respirc..-'-ory metabolism and activity in ~o~::..~ -: c e:-.:tua:. i:-L2 "J·aters. (Non-published). Gunzler, E. 1964. Biol. Zentralbl. 83: 677. Heusner, A. P. & M. L. Ruhland. 1966v .J e Physiol. Paris. 51: 580. Heusner, h. Ae &J. T. Enright. 1966. Long-term activity recording in small aquatic a~i~als. Science. 154° 532-533. Johnson, y,; . J., J • .borkowski & c. Engblom. 1963. Biotechnology and Bioengineering 6: 456-~SS• • 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.-S-5-75. Phillips, D. H. &M. J. Johnson. 1961. J. Biochem. Microbial. Technol. Eng. 3: 261 . Sparks, R. Ev, J. Cairns, Jro, &Wo T. Waller~ 1972. Using fish as sensors in industrial plants to prevent pollution in str~a. s . In Abstracts of papers submitted for the t hirty-fifth annual meeting. k : .... Soc .. Limn .. and Oceano .. , Inc. Spoor, W. A. 1946 .. Biol. Bull. 91: 312. Steed, D. Lo &B. J .. Copeland. 1967 .. Metabolic responses __ some estuarine organisms to an industrial effluent. Cont~. ~~r . Sci. Univ. Tex. 12: 143-159. Subrahmanyam, C. B. & Co H. Oppenheimer. 1970. 7he influence of feed leve~ s on t he growth of grooved Penaeid shrimp in mariculture . In Proceedings of the First Annual Workshop World Mariculture Soc. pp. 91-95. • Todt, F. (edo) 1958. Electrochemische Sauersto~~messungen• I, 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 • CHAPTER 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)-183Texas Water Quality Board, "Toxicity Studies Galveston Bay Project," 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 natural environmental stresses. • On the assumption .that respiratory metabolism is directlyrelated to both growth and maintenance requirements of a common fish like the striped mullet, it would seem reasonable that anykind 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 sublethal toxicity is particularly useful for study. It is one of the species that is conman 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 generaldistribution of the species is worldwide in .tropical to warm temperate waters. The species feeds largely on phytoplankton, or is iliophagous;hence it has a year-round food supply and can feed in most coastal environments along the Gulf coast throughoutthe year. Further, it is a harx:iy 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 l.imits. • The striped mullet is fairly well known in terms of life history and distributional characteristics (Thompson, 1963, 1966) .­That the mullet respond to environmental differences to producepopulation 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 "telescoping" 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 highpopulations in a wide range of environments (Hellier and Hoese,1962). The species also has conunercial 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 bec~use 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 respondboth to environmental extremes --either natural or man-induced -­and to many kinds of pollutants in a manner that can be detected bychanges in metabolism, as indica~ed 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. • Thirx:i, fishes as a whole, especially bony fishes, have rather generalized physiological mechanisms for adaptational 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 loading" 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 occursuppressed and erratic. It is possible that similar "loadings" 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 "zone of tolerance" and are not cwnulatively harmful. Further discussions of environmental stresses in relation to metabolism of fishes are in Brett (1958), Kinne (1963, • f964a, 1964b), Doudoroff and Warren (1965), in addition to discussions that occur in many recent papers on toxic effects of specific substances •. IV-3 In cases of more toxic pollutants, simple chemical analysesof 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 biologicalanalytical 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 "natural" waters from near Port Aransas. MEI'HODS 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 providewaters in areas of relative stability (ARS) in the Bay to obviate chances of obtaining highly polluted waters from spot sources nearer the edges or inflowing waters of the Bay. These locations, shown on Figure 1 are: Station 17. Texas City Dike. Lat. N. 29° 22.4' Long. w. 94° 50.8' Station 22. Kemah. Lat. N. 29° 33.8' Long. W. 940 58.2' Station 26. Trinity Bay. Lat. N. 29° 39.9' Long. w. 94° 47.2' 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 TRINITY (Ja) · BAY® 29~~0· (39) • 29°10' GULF OF MEXICO • 95°00' Figure 1. Map of Galveston Bay system snowing collection sites. IV-5 • Table 1. 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. (grams) Texas City · 17 26-29 x 71 23.57 8.68 23.82 2.3564 Dike Kemah 22 26.-28 x 71 23. so 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 1.9458 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 rapid means. Accordingly the Trinity River water was "salted" with "Instant Ocean" 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 "estimated percent composition of water" from a special TRAOOR report prepared by Chen, et al. (1972). The special report was based on earlier TRAOOR 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 aeration 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 eitherin 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 onlyfor 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 resistantfish are suspected. At the pre-experimental stages it may generallybe assumed that deaths among newly captured mullet were attributable to poor physical condition, inasmuch as such fish usually appearedsomewhat morbid or emaciated. Acclimation to experimental conditions in the 450-liter aquariaensued 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 TRAOOR model for the Bay (Espey, 1971) served to establish reasonable limits for the E!xperimental temperatures. Also, all the experimental and environ­mental temperatures and salinities were within the known ranges for IV-7 • • • the eurythennal, euryhaline striped mullet (Thompson, 1966). The acclimation temperatures and salinity ranges are given in AppendixTables 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-incnes 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 alreadyin 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 ahambers 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 irrunersed 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 provided. 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 respirationchambers 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· oth~r electrodes, or back through the manifold outlet into the aquarium. This arrangement allowed both for the checking of any oneelectrode 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 plastictubing 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 fhe two-day acclimations in the aquaria and during the respiratorymetabolism 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 affect~ng 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 detennine 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= A.CXF. To compute Q in mg 02 consumed per hour, a simple computer program was oryanized 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 \ \ • • • "normal" but tended to remain supine. These "belly up" 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 th~ fish that "appeared'' 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 ~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 tDe 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 salinity; constant weight and 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 FORI'RAN 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 multiplecorrelation 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 correspondingmetabolic depression. Fortunately parallel experiments on Mugil cephalus had been run for these waters as a part of the independentresearch 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 1 contains the averages of the temperatures, dissolved oxygen levels, and salinities of the near-surface waters at a depthof one foot, except where noted for the autumn Hanna Reef area where both one-foot and three-foot samples were averaged. In Table 1 are the average log weights of the mullet utilized for the separateexperiments • 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 f rorn 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 Taple 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 coeffic~ ient 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 2.1881, 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 0.4869 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 pennit holding mullet long enough for. satisfactory ~cclimation 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 N Standard R a Weight Temperature Equation Season Error of Coeffi-Standard Coeffi-Standard No. ~timate cient Error cient Error 17 Autumn 16 0.0819 0.97** -0.9122 0.6004** 0.0685 0. 0504*-!: 0.0041 1 22 Autumn 16 0.1200 0.87** 0.5100 0.2032** 0.0672 0. 0205*-l: 0.0061 2 26 Autumn 15 D.2296 0.44 o. 510.6 0. 3035 0.2466 0.0225 0.0161 3 29 Autumn 16 0.3417 o.51 0.3319 .0.2220 0.1721 0.0208 0.0165 4 Autumn Control1 27 0.1489 0. 93*-.'" -0.6959 0. 7723*-:.': 0.0627 0. 0336** 0.0045 5 ..... < I 17 Winter2 28 o.2402 0.49 -0.9560 0.2916 0.2658 0.0371 0.0186 6 t-A ~ 22 Winter 30 0.2414 o.55** -0.0931 0.2171 0.2189 0. 0467*-a': 0.0141 7 26 Winter 25 0.1581 0.73** -0.1974 0.2232 0.1878 0.0630** 0.0127 8 29 Winter 32 0.1295 0. 55*"l': -0.6585 0.5877** 0.1759 0.0208* o. 0090· 9 3 Winter Contro132 0.1460 0.90** -4.2017 0.6904** 0.1500 0. 0959-.':* 0.0135 10 Winter Control 32 0.1652 0.87** -1.5794 0.9192** 0.1465 0. 0504*-a': 0.0076 11 11 Spring 27 0. 0796 0.87** -0.3584 0.6538** 0.0765 o·. 0106* 0.0042 12 22 Spring 32 0.1715 0.77** -0.3788 0. 4467~': 0.1397 0.0301** o.ooso 13 . 26 Spring 32 0.1595 0.76** -0.1045 0.2944* 0...1104 0. 0329*"4': 0.0059 14 Trinity River4 31 0.1595 0.83** -1.1123 0.7619** 0.1499 0. 0316** 0.0053 15 0.0877 0. 0297*-a~ 0.0041 16 29 Spring 30 0.1234 0.91** -1.0320 0.7594** Table 2 (cont.) Station and N Standard R a Weight Temperature Equation Season Error of Coeffi-Standard Coeffi-Standard No. Estimate cient Error cient Error Spring Contro15 29 0.0877 a.so** -0;4349 0.5746** 0.0954 0.0170** 0.0045 17 Spring Control6 24 0.1894 0.95...:* -1.8490 1.0471*-.': 0.0965 0. 0503*"4': 0.0080 18 17 Summer 28 0.1171 . 0.84** -1. 3454 0. 7923*"'': 0.1056 0.0370** 0.0068 19 22 Summer 29 0.1773 0. 84*-.': . -0. 6994 0. 6688*-.': 0.1284 0. 0213* 0.0100 20 26 Summer 30 0.0846 0.96** -0.6936 0. 7508"'':* 0.0553 0. 0165-.':-.': 0.0046 21 1-1 29 Summer 34 0.1175 0. 93*"4': -1.5223 0. 9763"J'd: 0.0980 0. 0272*"'': 0.0040 22 < I Summer Control 28 0.1224 0.86** -0.2751 0. 5197-.t:-.': 0.1158 0. 0298*"'': 0.0053 23 t-4 CJ1 *Statistically significant at o.05>P:>O.Ol ** 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 0.0393. 3. Includes salinity partial regression coefficient of 0.1592**, with standard error of 0.0526. 4. Salinity of Trinity River water raised artificially with sea salts to 14.2 ppt. S. Water for spring control experiments may have been polluted. 6. From spring data supplied by R.H. Moore {Unpubl.)l average salinity of 31.3 ppt.; regression includes partial regression coefficient of 0.2471*0 for swimming speeds in standard lengths per se~ond, 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 un­ recognized 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 in Table 3. For each comparison stations for each of the seasons are the expected log oxygen consumption rates, ~' 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 Yvalue 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, ~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 6r 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 Yi~ mg 02 hr-1 and Y in mg 02 kg-1 hr-1. Per cent decrease in oxygen consumption rate per kilogr~m from control level. ControlsGalveston Bay Stations ' Res.pirator~ Rates Res2irator~ Rates Sta. 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'·-..... ·.... ... ... ·: ····· :} ~::;:. a.'"•·.·:·..• .... -·.··: ·.... ····· ':. .... .·.·. ··{:.·. ··=·· • t •• :. :~ ·.... :-.:· ... .. •.. .: ... .... ·.· ~. ... ;:~ :: ~t;:: ~}: :r .. ·~.· ~ =:·:: -:.:·. ':;:: I::::: ·.·.· ~ .:.::.. .·.•· ., ·:';. . -~.} ··.·:· ,, .·~~:: .... .... i~t t.': # :_;,:: .::· ·:·:·; ... ,:::,•:-·: ... :.;·. ~·:::: ... :·:··. ....... .. ... -.... .... ::~:~ ::·· .,:-: ··~.. .·,.: .. ·:·.· 1-;~:: • .... ,· ... ' •... : ..-..... t.~ ... ,..•. D.... i:;·: ::~= .... ~ .... ::. ;~:~~; ..···· ... ~· ~·· :·:~. ·:~~. .. ··.. . '.·""· 0.0 ·.. ~ t7 22 26 29 17 22 26 29 17 22 26 29 17 22 26 29 I • ~AUTUMN· WINTER I' SPRING SUMMER ~ ~t -1 . Comparisons of log. mgs oxygen consumed kg hr for mullet 1n waters from four Galveston Bay Figur~-2. stations (stippled bars) and fr.orb control waters (open bars). .. .. , • • • Technigues. From the introductory statements, it would appear that the choice of striped mullet as a test fish has several naturalistic, real advantages over most Gulf coastal fishes. The fact that it is a eury­ topic fish and an iliophagous or plankton feeder means that it is naturally ttavailable" to a wide range of temporal, geographical, chemical, biological and physical situations. Further, the striped mullet 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 mullet, 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 "positive'' 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 ''positive" bias appears by the selection of areas of relatively stable open waters for the samples. Inasmuch 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 ~alveston 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. 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 c~ptured, through all holding and transportation processes, and through the acclimation regimes. During all these procedures fish that appeared to be dying or that were otherwise moribund were removed. These fish would have 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 17) 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 les·s 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 mo.l!bidit,y 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 nselected" 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 foreignsubstances 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 "standard" 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 possiblethat the acelimation water would contain some particulate nutrients,but the effects of "feeding" 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 "shock" effects. However, the majoreffect 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 respiratorymetabolism 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 standa:ro. conditions; such would appearto be the case of the mullet in the present study due chiefly to spontaneous activity. There were no cases observed during the yearwhen the level of spontaneous activity observed was different betweenthe control and the experimental groups, however. For this reason the comparisons between .an experimental ,group with the respectivecontrols 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 ., I \ ! l I, • • • 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 ~s the departures from the means increase. It is reasonable to assume rectilinearity for the oxygen con­ sumption 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 o~ a log-log basis would .Yield a bw coefficient of about a.a 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 tendency, a 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, popula­ tion 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 helpexplain 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 isunder 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) environments. 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 pinfish 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 nonnally adapted. The constant tenn of the regressions needs little discussion in ordinary biological applications of multiple regression equations. The constant tenn 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 cµld 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 p:ruductivity of Trinity Bay mullet d~ring 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 "salted" 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 f :rum 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 ~n 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 expected, 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 production 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 r~tes 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 ac·cordingly. 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 accQmpanied 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 (Q;noscion 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 perform~d 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 impl{es the utilization of food, it is reasonable to partition all the ·energy components of metabolism sensu fil.Q.. 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 Qr 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 "active" functions. Kerr (197la,b,c) partitions the total metabolism, TT, into: TT = Ts + TF + Tc + TR ' where Ts is cost of standard metabolism, TF is cost of foraging 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 HT-~n • Energy of food materials ,.c;--­ --~ Energy of faeces Energy of assimilated ma ter-ial s Qu . ~-----~7f Energy of nitrogenous Energy of metabolizable matePials lost through materials excretion (physiologic fuel value) Qv 'V Net energy • Nonutilized energy freed (physiologically useful through deamination and energy) other processes Standard Activity Growth metabolism Q Qg a Qs __ ...;.. Qr= energy of metabolism • From--Warren and Davis (1967) Figure 3. Categories. 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 "scope for activity" (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 "standard 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 "specific dynamic action." Thus ,growth, as well as activity, would be suppressed if stfesses 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 iliophagousfeeding. Should stresses lower either the scope or restrict the range over which activity can take place in Fry'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 a.a (Table 2). The second aspect results in a patchy qistribution 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 "patchyn 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 is: (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 "surges" 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 • ~hrough 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 summa~ized 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 • • • ! I I iiI I 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 incipientlethal 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 "pulse" 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 • s. 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 fisherystatistics, which are presently unavailable. 7. The possibility tha't 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 P.hysical-chemical models of Galveston Bay waters to "adjust" 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 sulfideto 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. Ins. 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 exploitedfish populations. (Fishery investigations Series II), London,Her Majesty's Stationery Office, 14: 1-533. Brett, J. R. 1958. p. 69-83. Implications and assessments ofenvironmental stress. In P. A. Larkin (ed.), The Investigationof Fish-Power Problems-.-The H. R. MacMillan lectures infisheries. 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, Wiley­ Interscience, Ltd • . Brett, J. R. 1971. Energetic responses of salmon to temperature.A study of some thermal relations in the physiology andfreshwater ecology of sockeye salmon (Oncorhynchus nerka). Am. Zoologist 11: 99-113. Broadhead, G. c. 1953. Investigations of the black mullt, Mugilcephalus L. in northwest Florida. Fla. St. Bd. Cons. Tech.Ser. 7: 1-33. Broadhead, G. c. 1958. Growth of black mullet-(Mugil cephalusLinnaeus) in west and northwe~t Florida. Fla. St. Bd.Cohserv. Tech. Ser. 25: 1-29. Cech, J. J., Jr., and D. E. Wohlschlag. In Press. Respiratoryresponses 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 onrefinement and operation for the Galveston Bay system (GBPTasks 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 • .~loyna 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 IX> 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. Populationsof the black mullet (Muqil 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 oxygenrequirement of fishes. Inc. M. Tarzwell (ed.), BiologicalProblems 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-AU­ 7636-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 (Muqil 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. 197lco 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.Helgeland. Wiss. Meeresunters. 9: 433-458• Kinne, O. 1964b. The effects of temperature and salinity on marine and brackish water animals. II. Salinity and temperaturesalinity 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. 19720 Size-related metabolic responses of the pinfish, Lagodon rhomboides, to salinityvariations and sublethal petrochemical pollution. Contr. Mar. Sci. Univ. Tex. 16: 125-137. Odum, w. E. 1966. Food and feeding of the striped mullet, Muqilcephalus, in relation to the environment. M.S. Thesis, Univ. of Miami. 118 pp. Odum, W. E. 1968. The ecological significance of fine particleselection 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 Muqil 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 electropherograms and nuclear-eye­ lens weights. Mar. Biol. 11: 52-60~ ' J \ • \ l 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: 22-31• IV-40 • Wohlschlag, D. E., and B. J. Copeland. 1970. Fragile estuarine systems--ecological considerations. Water Resources Bull. 6: 94-105. J \ • • IV-41 APPENDIX IV-A. Metabolism Data for Galve.ston Bay Striped Mullet • Table A.l. Autumn Metabolism Data, Texas City Dike Station 17 • Experimental Salinity 26.1 ppt. Log mg 02 Log Weight Temp. consumed hr.-1 (g.) oc l. 3277 l. 2529 1.4220 1.1731 1.0488 0.7172 l.0065 0.9301 1.7248 • l. 5980 1.6010 1.9236 1.3163 l. 6851 1.4413 1.4502 2. 7536' 2.7226 2.7093 2.5866 2.0645 1.8451 2.1303 2.2041 2.4249 2.5328 2.3909 2.7731 1.9445 2.4233 1.9868 2.2095 12.0 12.0 12.0 12.0 11.6 11.6 11.6 11.6 21.8 21.8 21.8 21.8 21.9 21.9 21.9 21.9 • IV-42 • Table A.2 • Autumn Metabolism Data, Kemah Station 22. Experimental salinity 20.9 ppt. Log mg o2 Log Weight Temp. oc consumed hr-1 (g.) 1.0794 0.9747 1.2341 1.2209 1.2209 1.2528 1.3603 1. 5910 1.5186 1.4149 • 1.4657 1. 6760 1.4568 1. 3492 1. 3179 1.4457 1.9445 14.7 2. 0969 14.7 2.1239 14.7 2.0719 14.7 2.1461 14.8 2.1847 14.8 2.3181 14.8 2.6253 14.8 2.2227 24.0 2.0334 24.0 2.0086 24.0 2.1703 23.5 2.4314 23.5 2.0645 23.5 1.9912 23.5 1.6532 23.5 IV-43 • • Table A.3 • Autumn Metabolism Data,, Trinity Bay Station 26. Experimental salinity 20.S ppt. Log mg 02 Log Weight Temp. consumed hr-1 (g.) . oc • 1.9268 1.9324 1. 6796 1.8197 1. 5425 1.4402 1.8483 1. 5161 1.1070 1.2947 1.4307 1.7424 1.5867 1.7672 1.6663 2.8413 14.S 2.7497 14.S 2.4969 14.5 2.7075 14.5 2.7760 12.0 2.4440 12.0 2.5502 12.0 2.2625 12.0 2. 3464 13.0 2.5866 13.0 2.4249 13.0 2.5011 21.5 2.7396 21.5 1.8175 21.5 2.4843 21.5 • IV-44 • Table A.4. Autumn Metabolism Data, ~anna Reef Station 29. Experimental salinity 22.0 ppt. Log mg 02 Log 'Wel.ght Temp. consumed hr-1 (g.) oc 0.8520 2.4885 13.8 13.8 0.9689 2.4248 0.9689 2.1430 13.8 13.8 0.1119 2.0791 14.1 1. 3001 2.3579 *1.4775 2.5502 14.1 14.1 1.0390 1.6989 1.2107 2.0934 14.1 21.7 1.1605 1.8195 21. 7 *0.7730 1. 3979 • 1.4383 2.1335 21. 7 21. 71.4230 2.1461 1. 6125 2.5820 21.9 0.8754 1. 3010 21.9 21.9 1.2619 1.7558 1.4424 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 02 consumed hr-1 1.2356 0.5778 0.6860 1.1051 1.4532 1. 3695 1.0265 1.2769 1.0642 1. 6405 1.4397 • 1.5777 1.6010 1.1608 1.1742 1.1894 1.1731 1. 3231 L.6045 1.4583 1. 3543 1.2498 1.1300 Log Weight (g.) 2.2455 2.1931 2.1271 2.6831 2. 8376 . 2. 7474 2.4742 2.2672 2.2601 2.2577 2.0170 2.2175 2.0294 2.2175 2.4314 2.2504 2.2305 2.5635 2.4346 2. 3979 2.2553 2.3617 2.3692 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 . 1. 5305 2.5403 17.5 1.4193 2.2504 17.S 1.5003 2.3424 17~5 1.4365 2.2095 1 SRR4 2.3054 17"·. 5 • Table A.6. Winter Metabolism Data, Kemah Station 22. Experimental salinity 11.0-12.1 ppt. Log mg 02 Log Weight Temp. oc consumed hr-1 (g.) 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 2.187.S 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.S 0.7230 2.2923 10.S TV-47 • Table A.6 • (cont.) Log mg o2 consumed hr-1 0.8512 0.6121 0.9514 • Log Weight Temp. (g.) oc 2.2355 10.5 : \ 2.4216 10.5 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 Log !Weight Temp. consumed hr-1 (g.) oc 1.0665 1.2676 0.9995 0. 9369 0.9355 0.7401 0.8196 0.7369 1.1045 1.1314 • 1.1733 1.1583 1. 3417 1. 3794 1.2639 1.1086 1.3256 1.4155 1. 3167 l.3016 1. 5530 1.4488 2. 6454 2.5888 2.3838 2.1903 2.0294 2. 0934 2.2253 2.1038 2.2253 2.2718 2.2601 2.2041 2. 3284 2.0531 2.0720 2.0863 2.1761 2.2201 2.4099 2.1673 2.0828 2.0334 11.0 '· 11.0 11.0 11.0 11.0 11.0 11.0 11.0 10.5 10.5 16.0 16.0 16.0 16.0 17.5 17.5 17.5 17.5 15.5 15.5 15.5 15.5 • . 1.2959 2. 0969 15.5 1.1894 1.8451 15.5 1.4277 i.9085 15.S IV-49 • Table A. 8. Winter Metabolism Data, Hanna Reef Station 29 .• Experimental salinity 13.75-15.4 ppt • Log mg 02 Log Weight · Temp. consumed hr-1 (g.) oc • 1.0434 1.1497 0.8036 0.9373 1.0781 0.8865 0.9117 0.7445 1.2432 1.0641 1.0067 0.9492 0.7904 1.0416 0. 8397 o. 6031 0.8312 0. 8926 . 0.7902 0.8211 0.8246 0.9587 . 1.0531 2.3304 16.5 2.3748 16.5 2.2878 16.5 2.0000 16.5 2.1931 16.75 1. 8751 16.75 2.2095 16.75 2.0212 16.75 2.3766 16.5 2.0531 16.5 2.2227 16.5 1.9638 16.5 1.9345 16.5 2.2989 16.5 2.1614 16.5 1.9191 16.5 2.3181 10.S 2.2742 10.5 2.2742 10.5 2.1987 10.5 2.4082 10.5 2.4116 10.5 2.3385 10.5 • 1.0524 2. 3010 10.S 0.7282 2.2788 10.0 0.6254 2.1673 10.0 IV-50 Table A.8. (cont.) • Log mg 02 consumed hr-1 0.8006 0.8346 1.1639 0.9888 0.9465 0.8116 • • Log Weight (g.) 2.1430 2.2201. 2.2175 2.2041 2.2788 2.1987 Temp. '· oc 10.0 10.0 10.0 10.0 10.0 10.0 IV-51 • Table A.9. Winter Metabolism Control Data. Experimental salinity 14.85-17.6 ppt • ' I Log mg 02 Log Weight Temp.consumed hr-1 (g.) oc 1.4219 ~. 2.25,3 19.1 1.1605 2.0970 19.1 . ,, 1.2956 2.3010 • 19.1 1.4471 2.0294 19.1 1. 3617 2.0828 19.7 1.2306 1.9590 19.7 1.3309 2.4166 19.7 1.3400 2.3263 19.7 1.5929 2. 5052 18.6 1.6522 2.4914 18.6 1.5123 2.4314 18.6 1.6127 2.4346 18.5 1.8218 2.5185 18.5 1. 7270 2. 3927 18.5 1.8576 2.5119 18.5 1.6730 2.4742 20.0 1.7348 2.3503 20.0 1.9348 2.4328 20.0 1.6491 2.4014 20.0 1.6086 2.3820 20.0 1.6886 2.4609 20.0 1.3471 2.1732 20.0 • 1.7030 2.5403 20.0 1.0082 2.4014 10.S 1.4759 2. 5159 10.S 0.9876 1.9685 ·10. 5 IV-52 Table A.9. (cont.) • Log mg 02 consumed hr-1 0.6108 o.6164 1. 3180 1.3571 1.4699 1.1002 • • IV-53 Log Weight ( 9".) l \.1 2.0334 2.0930 2.6703 2.7193 2.4200 2.1139 Temp. oc 10.5 io.s 11.0 11.0 11.S 11.5 lJ • Table A.10. Spring Metabolism Data, Texas City Dike Station 17. Experimental salinity 22.0-25.3 ppt. Log mg_02 Lbg Weight Temp • consumed ·hr-1 (g•. ) oc 1.4406 2.5211 18.0 1.4050 2.3874 18.0 1.1599 2.0934 18.0 1.0872 2.0414 18.0 1.5238 2.6335 18.0 1.4452 2.4829 18.0 1.5253 2. 6064 18.0 1.1964 2.0719 18.0 1.2790 2.0756 20.3 1.2419 2.0756 20.3 • 1. 3268 2.2068 20.3 1.2043 2.0170 20.3 1.2002 2.0492 20.6 ·1.2828 2.2095 20.6 1.2083 1.9638 20.6 1.4719 2.4330 26.2 1.4228 2.3874 26.2 1.1489 2.1038 26.2 1.2901 2.0899 26.2 ·'t 1.4239 2.0756 26.2 I I '·I 1.5356 2.2480 26.2 ,:·1 I'· 1.4203 2.2601 26.2 I/ 1' 1, 1.4277 I 2.1139 111,, 26.2 \'1I '1 0.9499 1.7160 29.0 II 1;~! 1.04859 1.8261 29.0 '' • I ti·' 1.2792 1.8573 ·1 29.0 1.1547 1.9731 I ' (, 29.0 I nT-~A r. • Table A.11. Spring Metabolis~ Data, Kemah Station 22. Experimental salinity 17.6-18.7 ppt • Log mg 02 Log we'ight Temp. consumed hr-1 (g.) oc 1.3075 2.0414 28.3 1.2451 1.9685 28.3 1.2786 2.0682 28.3 1.3042 2.0034 28.3 I 1.4774 2.0170 28.3 '. ,,.,. j• 1.4390 · l.9590 28.3 I I ;t '·I I 1. 3287 2.0453 28.3 '.I I ~ '' I 1. 3224 2.0043 28.3 l; 1.4517 2.0864 27.4 .i: ,d. i.1 1.3909 2.0719 27.4 1\•/.'• • Ii 11 1.2625 2.0719 27.4 /' 1'1 t'r ._ 1, 1.3933 1.9590 27.4 '" ' ~I,· 1.3754 2.1399 27.4 l' (,'." t\I, 1.5598 2.1523 27.4 1'1 ~I ! ;i. I. I· I 1.4741 2.1761 27.4 1. 5076 2.1761 27.4 ' 1. 3754 2. 5011 15.5 f:... '1 1.1834 2.3909 15.5 1l'.I i1 ,. 0.6856 1.8633 15.5 , ~I .. \\ 1.3569 1.9138 15.5 11 I 0.9546 2.3181 15.5 ti \ 1. 3606 2.4216 15.5 I I • ,., 1,'1 . 1.0232 1.8808 15.5 . ') 0.8755 1.8325 15.S '' I' IJ· IV-55 ,, \ ,, ~· I \.; I , • Table A.11. (cont.) Log mg 02 Log Weight Temp. consumed hr-1 (g. )'l oc 15.5 0.8466 1.9912 1.1149 2.2718 15.5 0.6718 1.6812 15.5 1.2251 1. 7782 15.5 1.0238 2.6454 15.5 0.6667 1.7782 15.5 0.8240 2.3010 15.5 1.3607 2.3636 15.S • • IV-56 • Table A.12. Spring Metabolism Data, Trinity Bay Station 26. Experimental salinity 14.8-17.6 ppt • Log mg o2 Log Weight Temp. oc consumed hr-1 (g.) I! , i t : ." i 1.0181 2.0414 22.0 .-. ! \ 22.0 .,. 1.4204 2.2504 1.1206 2.0414 22.0 1.4054 2.2648 22.0 26.0 1.5104 2.2648 1.4053 2.0792 26.0 26.0 1.3620 2.0864 1.4884 2.3692 26.0 26.0 1.2109 1.9191 1.3544 1.1644 26.0 • 26.0 1.1923 1.9345 1.0697 1.8129 26.0 1.4018 2.2253 26.0 1.1174 1.8195 '26.0 1.3439 2.4654 26.0 26.0 1.7267 1.6233 1.4680 2.2833 26.0 26.0 • 1.0513 1.7404 0.9539 1.9345 15.0 0.8748 1.8751 15.0 0.9850 2.0086 15.0 \,, 0.9659 2.0170 15.0 1.1575 "' 2.1206 15.9 1.3947 2.3655 15.9 1.1022 2.0000 15.9 IV-57 • Table A.12 (cont.) . Log mg 02 consumed hr-1 0.7375 0.9627 0.9135 1.0265 0.8653 1.0470 0.8819 • • IV-58 Log Weight (g.) I \ 1. 6628 1.8451 1.9085 1.9912 1.6990 . 2.0086 1.9395 Temp. oc 15.9 16.2 16.2 16.2 16.2 16.2 16.2 .. 1 • ' Table ,.A.13. Spring Metabolism Data, Trinity River Waters with Added Sea Salts. Experimental salinity 13-. 2-16. 5 ppt. Log mg 02 Log Weight Temp. consumed hr-1 (g.) oc 1.2306 2.i931' . 15.8 0.9328 1.9912 15.8 1.1388 2.0792 15.8 1.2460 2. 3927 15.8 15.8 \ ,, 1.4175 2. 3979 1.1205 2.1271 15.8 0.8745 1.7782 15.8 1.1657 2.0719 15.8 0.7048 . 2.0864 15.0 0.9988 1.9777 15.0 0.7649 1.9912 15.0 0.6975. 2.0792 15.0 o.6415 2.0000 16.2 1.1415 2.0212 16.2 0.9514 2.0645 16.2 0.6624 2.2504 16.2 1.2006 2.0000 26.S 1.5001 2.3979 26.5 1.4869 2.3096 26.5 1.2110 2.0170 26.5 1.3197 2.0864 26.5 1. 5919 2.3729 26.5 .1.4290 2.3483 26.5 ­1.0916 . 1.9191 26.5 IV-59 • Table A.13 • (cont.) Log mg 02 Log Weight Temp. consumed hr-1 (g.) oc 1.3207 l.~H368, . 26.6 1.0982 1.9085 26.6 . 1. 7243 26.6 1.0210 1.4552 2.2095 26.6 1.3081 1.9823 26.6 1.6887 2.4843 26.6 26.6 1.2115 1.8130 • • IV-60 • Table A.14. Log mg ·o2 cons urned hr-1 • 0.9778 0. 8930 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 1.0998 1.2078 . 1.6662 • 1.3736 Spring Metabolism Data, Hanna Reef Station 29. Experimental salinity' l9.25-24.3 ppt • Log Weight (g.) 1.8l29 : 1.9031 2.3118 2.4362 2.3365 1. 7559 2.4942 2.0000 1.6532 1.7782 1.9243 2.4393 2.4281 2.4564 1.8692 2.0170 2.0000 1.8195 1.9345 1.9731 1.8513 1.8976 2.3284 2.2227 IV-61 Temp. oc 15.5 15.5 15.5 16.0 16.0 16.0 16.0 15.5 15.5 15.5 15.5 16.0 .16.0 16.0 25.9 25.9 25.9 25.9 27.7 27.7 27.7 27.7 26.8 26.8 • Table A.14 • Log mg o2consumed hr-1 1.7312 1.7158 1.7053 1.3466 1.2947 ·1.s101 • • I,; I (cont.) Log Weight Temp. (g.) oc I ,, '·' 2.4624 26.8 2. 3424 26.H 2.2945 26.6 2.0864 26.6 2.2553 26.6 2.3692 26.6 IV-62 •' I · • Table A.15. Spring Metabolism Control Data. Experimental salinity 19.25-26.4 ppt • Log mg 02 Log Weight Temp. consumed hr-1 (g.) oc • 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 ' 2.1584 18.0 2.4150 18.0 2.4564 18.0 2.1139 18.0 2.1139 18.0 2.3160 18.0 2.2553 18.0 2.0719 18.0 2.5315 25.0 2.4503 25.0 2.3010 25.0 2.0682 25.0 1.7243 25.0 2.1399 16.0 2.5453 16.0 2.4116 16.0 2.2625 16.0 2.0086 16.0 2.3674 16.0 2.1367 16.0 2.2878 16.0 2.2305 23.5 2.1644 23.5 • 1.1033 2.1818 23.5 1.2591 2.1987 23.5 IV-63 Table A.15 • (cont.) • Log mg 02 Log Weight Temp. consumed hr-1 (g.) oc 1.1993 2 e 26-33. I 23.5 23.5 1.2943 2.3579 23.5 1.1643 2.1072 23.5 1.1559 2.0792 • • 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 Te.mp. oc consumed hr-1 (~.) *l.9407 2.6767 29.5 *1.8553 2.5391 29.5 : I.' '. 1.5207 2.1206 29.S 1.6621 2.2553 30.0 1.6468 2.4232 30.0 *1.0742 1.4472 30.0 1.5238 2.1614 30.b 1.3456 2.3424 27.6 1.4030 2.3385 27.6 1.3411 2.2175 27.6 *1.2685 2.1761 28.5 *1.3217 2.1553 28.5 1.3166 2.1399 28.5 1.3397 1.9638 28.5 1.5035 2.4684 22.3 1.3561 2.2856 22.3 1.0439 2.2577 22.3 1.3917 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 *1.6917 2.6656 21.0 1.1672 2.3617 21.0 1.7655 2.4150 21.0 HT-R!=\ Table A.16 • (cont.) • Log mg 02 Log Weight Temp. consumed hr-1 (g.) oc 1.2775 2.2900 21.0 21.0 1. 5506 2. ?~6~, " • • IV-66 :I 1 Table· A.17. Summer Metabolism bata, Kemah Station 22. • Log mg 02 cons urned hr-1 1.7347 *1.7150 1.6649 *1.8829 *2.0249 *1.3173 *1.6088 *1.6618 1.1508 *1.4317 .1. 3884 1.3396 ·­ 1.4073 1.2319 *1.5601 *1.4012 1.0010 0.9035 ' *1.0242 1.1468 1.6901 0.9261 • . 0.9847 Experimental salinity 17.6-23.1 ppt. (Asterisks indicate fish that ·were belly-up at end of run.) Log Weight Temp. (g.) oc ' ' I'.· I,' 2.4031 30.05 2.5563 30.05 2.4684 30.05 2.4911 30.05 2.7597 30.05 2.2253 30.05 2.3711 30.05 2.4183 30.05 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 Log Weight Temp. consumed hr-1 (g.) oc 0.9353 1.6532 20.0 1. 7482 (, . 20.0 0.9375 20.0 1.1035 1.9191 1.4323 2.4216 21.0 21.0 1.3519 2.1703 21.0 1.2231 2.1461 • • IV-68 l. I 1 '· Table A. 18. Swruner 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 LOg 1 'Weight Temp. consumed hr-1 (g.) oc : 'v i • 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 *0.8571 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 *1.7098 2.5514 28.3 *1.3088 1.9868 28.3 *1.7496 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 o2 Log Weight Temp• consumed hr-1 (g.) oc *1.3457 2. 3160 .. 27.8 ~ \ { l, I 1.3487 2.3766 27.8 *1.2891 2.1553 27.8 *l. 5430 2.3856 29.1 1.4799 2.1931 29.1 1.5343 2.4914 29.1 • • IV-70 ~ : I ' • • ' Table' A.19. Summer Metabolism Da.t'a, Hanna Reef Station 29. Experimental salinity 18.15-22.55 ppt. (Asterisks • indicate fish that we~ belly up at end of run.) Log mg 02 Log Weight Temp. consumed hr-1 oc <.g.) 1.4158 2.3541 20 .-a I ·. 0.9884 2 .'0934 ' 20.8 1.0645 2.1072 • 20.8 1.6196 2.5911 20.8 1.5860 2.4151 21.0 1.2544 2.2041 21.0 1.2857 2.4065 21.0 1.5140 2.4233 21.0 *1.1012 2.1173 18.3 1.0320 1.9823 18.3 0.8766 2.1584 18.3 1.1895 2.4265 18.3 1.1422 2.2553 18.0 *0.6964 1.9345 18.0 1.1842 2.2148 18.0 0.9311 1.9542 .18.0 1.0949 2.0000 18.0 1.1993 2. 3010 18.0 0.8866 2.0170 18.0 *1.8270 2.5944 29.S 1.2384 2.0000 29.5 *1.2407 2.0414 29.5 1. 7479 2.4771 ' 29.5 • *1.4716 2.3483 29.5 *1.2841 2.1553 29.5 IV-71 Table A.19. (cont.~ • Log mg 02 consumed hil-1 1.5593 1.5686 *1.4322 1.5878 *1.0116 2.0542 1.3894 1.5937 *1.5159 • Log Weight (g.) 2. 3ao2 · 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.S 29.S 30.0 30.0 30.0 \,, • IV-72 , • Table A.20. Surruner ·Metabolism ·eontrol Data. Experimental salinity 19.8-20.9 ppt. (Asterisks indicate fish that were belly-up at end of run.) 1 Log,, mg 02 Log Weight Temp. consumed hr-1 *1.9633 1.5470 *1.8940 1.6889 1.5664 1.4969 1.4024 1. 5959 1.7109 1.7904 • 1.8168 1.9829 1.9577 1.9746 2.1102 2.1111 1.4402 1.7606 *1.6034 1.8413 1.6444 1.2821 (g.) l : 2.5682 1.9623 2.4914 2.3324 2.4639 2.4346 2.3838 2.5900 .2. 3541 2.5211 2.4216 2.6170 2.3617 2. 5289 2.6212 2.7559 2.1987 2. 5490 2.5315 2. 5682 2. 5238 2.0864 oc 29.5 29.5 29.5 29.5 20.0 20.0 20.0 20.0 29.5 29.5 29.5 29.5 .29. 5 29.5 29.5 29.5 20.2 20.2 20.2 ·20.1 20.5 20.5 • . 1. 5058 2. 3979 20.5 20.5 1.7069 2.5465 20.s 1.6092 1.9868 '·· IV-73 Table 20.A• (cont.) • Log mg 02 Log Weight Temp. consumed hr-1 (g.) oc 20.5 1.6356 1.9590 1.3581 2.2480· ' 20.5 1.3851 2.1847 20.5 • I I j • IV-74 APPENDIX IV-B. Operating Seiuence and Data Recording for Continuous­ -· low Respiration Chambers. ; Galveston Bal~~ . ·. Dute ,2 7 #a z:. /f72 _ Sourco A:(e-rn.a..A (:ii_2 --.. --.. -·­ f .. • Temperature ~~.3 0 c Aooli.wation Cond.,-2.d+; ' Salin:l.ty //,If. o /Y-L. Barometric Preas ,,..10.().1-q ~ 7?~/~. ., 4 I \i '.}hambcr 1¥ :l 2 l. Species ~ C'~e«....f GDS~ o "'-?2-GBS=> ~2'l3 GBSa, 0..29,L GBS-.., tP,2. _J _J a: a: ..:•... • 0. 0 l/] U) 0 0 o.oo YR 1 • 0 o.o YR 1 • 0 ST 1 2/3 DEPTH ST 1 SURFACE FIGURE 3 0 FIGURE 4 0. . (Y)(Y) ::::s:::::::s::: _J _J a: a: C>C> _J _J a: a: .. •e • .. ! ~: ~ 0 • J 0 U) I • I I I u:> I I I I • I I I J~; . I . I o~---~..--.-~........--.-~.....---,.~-.-~~~ o.od YR I 1 .oo 0 o.o YR 1 • 0 • Fig. 1. Summary of growth rates (k) of organism 17a found with filtered water samples (dots). Summary of control growth rates of orga­nism 17a (solid line). See text. Fig. 2. Summary of growth rates of organism 17a found with unfiltered water samples. Figs. 3 & 4. See legend for Fig. 5. v -6 • ST 17 SURFACE ST 14 . . FIGURE 5· 0 FIGURE 6 0 . enen ~ ~ • _J_J a: a: • C>C> _J_J a:a: • .. • • : ~ tO !. 0 0 • •J I I I I I IiIf~! lJ) Al~1--__.1.__~l·--~1--•i..---..•~~•.__.1__-. ~ LI? I • OL.--.-~~---r~....,-...,....,~--r-~,..----r-~-:r-=--; OL--.-~~---r~-r---r~-r-~r---r-:---r=---t o.o YR 1. o.o y R 1 • o ST 17 2/ DEPTH STATION 18 FIGURE 7 FIGURE 8 0 en ~ _J _J a: a: C) . C) _J • _J a: a: .. .. .. . tt I ··i 0 0 lJ) •• lJ) . .. . . I 1••f_·J o..._-r-~~---.-~.....----.~--~..-----.-~-.---1 o.o 1 • 0 YR Fig. 5. Growth rates of organism 17a on water samples from stations (ST) • indicated. Abscissae indicate sampling times beginning in October, 1971 (0. 00). 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. 6-16. See legend for Fig. 5. u ­ . ,.. • ST ST TION 26 0 0 FIGURE 10 . (I") _J _J a: a: 0 0 _J _J a: a: .. .. .. .. .. .. ~· •J ----·_____.._____, ____,_£...•! l/) l/) .. -. . . 11 I Ii.~ IS~ Ol-.-__.--,.--__.__,___________.___ o.__--.-__,.-------.-----..---------.-~ ~ o.o 1 • 0 o.o YR 1 • 0 ST ST 33 SURFACE • 0 0 0 FIGURE 11 0 FIGURE 12 ~ ~. _J _J a: a: • 0 0 _J _J a: a: • .. .. .. ... • ~i 0 I • J 0 l/) l/) I I . I I I II I 1r.: I I I 0 0 o.o YR 1 • 0 o.o YR 1 • 0 • v -8 / • 0 ~ _J a: C) __J a: 0 lJ) ST 33 2/3 DEPTH FIGURE 13 0 _J • a: C) _J a: • -~ ~ _,__, ......I • • ..__I___f____I IL.....--....&&.II. :~ ..__I__ o~--..-----r~---...........~----__,.~--------1 o.o YR 1 • 0 ST 39 ST 0 FIGURE 14 ~ . _J a: C) _J a: 0 lJ) • I ; I I o.__--r-----r~------~---------~---------1 o.o YR 1 • 0 STATION 41 0 . ('t') ~ _J a: C) _J a: . . ~ • I 0 ti.) I I • I I I I I I~ I J o._______..-----------------~------~ o.o YR 1 • 0 FIGURE 15 OL-----~----r~.._--,-.--~-,-.---,r--~ o.o 1 • 0 YR • v -9 • RL STATIONS ENVIR RLL STATIONS EN'IIR FIGURE 17 0 0. FIGURE 18 .·. :.. -.. . ... . "' . . .... .. ~ .. . . _J :• . ...... ~ _J 1'• cr .. ·. ....:·. ·­a: .• ... . .. I C> .. .. . C> i . t _J _J t~"cr ... a: .. ,~·, • Q • ,,I 1' I," l/) 0 l/) .::> 0 . 0 .oo EMP 40.00 o.o NIT I E o. 5 ALL STATIONS ENVIR I ALL STATIONS ENVIR ·1. • 0 0. FIGURE 19 CJ FIGURE 20 I, CJ. "' .~ ...... ... rn ~ .. .. . . .: . . .... .: . ... : . .. . .. ... . . ..... :. ... _J cr . . .. . . . ... .... . ~ ~. C> . . .. cr .•••• I i •:.•. i (.!) _J _J . . . ~ _J ! cr . ~ .. a: I .... . ~ •Ii ~ l/) Cl 0. c. l/) f). 0 TY 4Q.OO o.oo ECCH 7. 0 • Figs. 17-20. Summary plots of the growth rates of organism 17a found withunfiltered water samples versus selected environmental variables• v -10 • • • CHAPI'ER VI Galveston Bay Benthic Community 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 trans­ portation 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) 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 community 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 I I • to investigate the structure of macrobenthic communities in marine \, and estuarine "soft mud" 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 "soft bottom" 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 broade~t 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 conflu~nce 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 on 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 • . t\ t ; I, t.'· r ,, l, ~ .' ~. 1; ·' ~ {'. TRINITY (Ja) ~ BAY® ,~, ~t: ,, .f f~ (39) r ' ~ •.• 29°10' 95°<>0' · • .Figure 1. Location of Galveston ·Bay Sampling . Site~ VI :... .~· . • 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. 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. 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) " seived in a graduated seive 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 • bf 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 bels/individual i = 1 The computational method used followed the use by Bechtel and Copeland (1970). Wilhm (1968) explains the use of H" as an estimator of H'. S Ni Ni H" = -E N loge N i = 1 Calculations for H" were programmed for a Monroe 1766 desk computer. Our collections correspond to the Type B collections of Pielou (1966) 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 d is then useful in comparing corrununity 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 (~)! Dmin = log2 N! log2 ( N -(S-1) ]! the calculation of D, Dmax and Dmin allows the calculation of redundancy, R, of a community. Redundancy is a dimensionless expression of the • dominance of one or more species in a community. VI -5 • 'i • 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. 2 6 7 = ( N ) ( s Ni ) N-T 1 -i~I (N) where Ni = number of individuals in the ith species and N = total number of individuals. 61 (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, 61 is the probability that they will be of different species. P.I0 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 communities 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 communities. 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 Tabl• I. List of SINIC1H and Abundances tn SMplH Taken at 5 Galv.ston Bay Stattons (Coll•ct1on dates ar.: 1 -October, 1971; 2 -.January, 1972; 3 -Apr11, 1972; 4 -.July, 1972; 41 -August, 1972) Statton 14 Station 17 Statton 22 Statton 26 Statton 29 Si!!c1es 1 ..,-,---41 ) z j 4• 1 ..,-,---4 ) ..,-,---4 1 ..,-,---4 • ~"· °'*' Co9l•ter1ta: CftrJ.MlthH •l'.f11 Rl\ynCOC:09ll: #e,,.,,rto•n11 .....toU: 110...todft• 11 AnMltda: PolychMta: Lepidonotua •ubl•vl• z.e,,1duthen1• ~nsal.1.• 14 l'ol!lno1d A, Sthenel•1• ­"'Sc•lewor."' l'suedoeurythoe alllll.l.gua A>aph1nolll.d A. Jrullfdea 8Mlgll1nfta 57 Nere.l.ph!Jll• rr•vJ.l.I.• G!ll't1• v1ttat• Anc1•t.roe!lll1• jonu.1. l'aranclaHa r•uveH 2'11-11ll.I.• oorall1oolo.I..,.. Syll1• er. corall.l.aolo.l.dM 4 /lend• •ucc.l.ne• 13 74 11 60 93 42 14 Ceratonen1• .l.rr.l.tA111l1• Laonere.I.• culver1 Gl!lcera ._rJ.cana 7 2 Glyc.l.nde aol1 tu1a 3 2~ .D1o,,.tra CUl'lt'ft• 13 lfarph!JH •anqulnea ' LulllllrJ.nen.I.• ,,.lt'ftl*lata £wobr1nen.l.d A. DrJ.lonen.I.• ..,... lbrv1•ll• rvclolllh1 lbrv1ell.td A. Ar1c.l.dea jerreZ'!ld Ar1c1de• •I'· Stnbloaplo l>ene41ct1 328 1 l'rJ.onosplo ,,.1.nnata fi 14 .Polydora cJ.l1ata 5 Polydora aoc.l.aH• ftlar!I• ••t199ra ..,__ lleatp>dua lled.l..,...tua oal1rom.l.eMU 3 4 57 ReteZOIMfttua rJ.lUora1• 14 ••tero.utua elongata Adothella torquata oal.l.da 15 Adothella -~· •rllllChJ.oaal}chJ.• ...r.1.­lfaldan.l.d A. 1'9ct1nu.l.a gou.141 llel.l.nna ..cui.ta 47 10 Allphl.ct1• !JUlllWri rlorJ.du ·~t.1.claeor~­ P.1.ata ,,.i..ta 26 57 • P.1.ata •I'• 116 70 .1Upo.l1J..U• crudconda 253 Chone duner.I. 3 llegal-b1oculat... 3 Sabella •lanoat.1.va. z Sabella alc.rof)tlullam 5 Sal>ell1d A, 1 Sabell1d or Sarpil.l.d 1 •!ld.ro.1..r-.,,. 21 31 54 •ypan.tola """"9r1 rlod.dm alllen1l•Mlura 1ni:Ua l.bident.1.r.1.ecr ....r11 •r lbl.l.ctent.1.r1..r wo.ra •r 01190CMta: MltlllllQ: 8utropodl: .r.tttodcUna •phjnotw~ 16 Crepidula J>J.ana Crepidula rom.1.cata ' Cnpidula •P· (11 rtla.1• ,,..__ AnachJ.• obes• 42 13 28 AnadU• avua rurf>on1lla (.l.ntarrupta P} l'yraatdftlla cnnulata 1tnlwt11 #uculana ooncentr.l.oa Anadara transwna Allygdalua • p. llod.l.olllft cleat..... •rac:h1donte• e.lrllfttm Qrtzwa 9loth1Jra •ll!lth.I. •l.mft• trunoata l.!/OM1• byal.l.na nor1clmm 28 203 Artllropoda: Peynopfdl: 1 z CNltaClllr •uanua abu.mellft 1 40 121 13 4711 1470 31 cua.1.c1.1.n.1.c1ea iun.1.r-13 Amp.I. tlloe • ,,. Ait,pel1•ca ab41 ta Q:>roJlh1 WI louJ.aJ.an• 3 lfeHta •P· 26 51 20 8 Allphl.J)Od •A• 4 Allphl.J)Od ••• 1 Panoptua herbst.1..1. 24 25 •2' • l'lllOptua .,,. P.l.nn1:da nt.l.nena • Cl.l.banar.l.ua Yittatua Bepttua pud.l.bunollft RhJ. th.ro,,anopeu• harr.l.•11 8 7 1 ht.roliatllea armat.. 1 4 12 5 ••H,,.nopellft augwrt.1.r-1 . .IUZ'!/,,.UOptUft de~ •eo,,.nope t. aay.I. Da1dent.l.r1ed •A• lln1dent.l.t'1ed ••• UDJcfftnt.1.r.1...r .1rant1a.1.• #eopenope te.lrana te•- Stpuncultdl: rue.I.ala atl'Ol61 .., Phoronf4a: Phoroa.1.• udl.l.taota (J} Echt..-,..tl: Ophfurotdla: 111.crophoU• atra hal..phol.I.• elaapta ' ltolotllurotdla: a-r.1.• Qlordltl: Alcf4f1CM: ,.,,Ua --~.I.• (Pl PflCllll llWl'OJll&U ,..atat• t7T • • • 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 numbers 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 numbers of polychaetes. Analysis of variance of H" and P. I.E. values (Tables 4 and 5) did not indicate significant differences in benthic populations through time. Analysis of H'1 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 maximum 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 five species, Nereis succinea, Streblospio benedicti, Mediomastus californiensis, Balanus eburneus and Panopeus herbstii, were found at all five stations. Four other species, Diopatra cuprea, Anachis obesa, 'Mulinia lateralis 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 -8 • • • ..,...... .. . ··:..__;._-:--.:.:__---:._ ----· -,-­ H" values for the samples from various stations were computed (Table 2) • October values f.or Station 14 for both H" 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 H" 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 Stati,on 17 was confirmed. Values of D, d, Drnax, Drnin and R for all collections are given in Table 6. D and d values calculated with log2 and loge are given. ·The d values based on loge are the same as H" values (Table 2). Note that the a log2 values are approximately 1.44 times as large as the d 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 H" 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 H" values. The lack of organisms at Station 17 in July is again reflected. Very close correspondence between the H" and P.I.E. values was evident. The correlation coefficient r, where n E ln X ln Y -(E ln X) (E ln Y) r = was calculated using a Monroe 1766 program for power curves. The r value (0.9890) was significant at the 99% level. Variation of community structure through time and space was analyzed by a randomized blocks analysis of variance (Tables 4 and 5)of both H" and P.I.E. values. Both tests showed significant variation between stations but no significant differences\ between seasonal collections. Duncan's new multiple range test (Li, 1966) was performed on both H" and P.I.E. data (Fig. 2 A &B). A sharp distinction was obtained with the P.I.E. values indicating that Stations 17 and 26 were significantly different from Stations 14, 22 and 29 (Fig. 2A). H" values resulted in a similar but not as distinct division between stations (Fig. 2B). Rarefaction methodology (Sanders, 1968) was used to generate "species diversity" 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 are abruptly truncated due to the small numbers of organisms collected at the various stations• VI -9 • Table 2 H" Values 14 17 22 26 29 October 0 0.7298 1.0397 0.7277 1.7700 January 2. 9698 0.9443 1.7158 1. 5296 2. 3919 April 2.0043 1.8033 1.2778 0.0970 1.9033 July 2. 5406 0 2.1336 0.3914 2.0032 - x 2.3347 0. 8693 1. 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. 6505 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 o.3633 0.7662 0. 3232 0.7997 • VI -10 • Table 4 Analysis of Variance of H" Values Source· of Degree of Sum of Squares Mean Squares F variation Freedom Total 19 12.63146 Blocks 3 1.30902 0.43634 1. 62322n.s • Treatments 4 8009664 2.02416 7.53004** Error 12 3.22573 0.26881 Table 5 Analysis of Variance of P.I.E. Values • Source of Degree of Sum of Squares Mean Squares F variation Freedom Total 19 1.69072 0.90174n.s. Blocks 3 0.10227 0003409 Treatments 4 1.13475 0.28368 7. 50 358~'(* Error 12 .45368 0.3780 n.s. = not significant ** = significant at 1% level • VI -11 . Table 6 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 a 14 1 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 0.460 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 ~ 0 0 0 0 0 0 0 I\) 17 4 22 1 6.001 J:. 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 1 154. 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 0 068 114.637 . '0 .464 267.279 1.770 2. 392 29 2 2208.692 3.451 3090.955 250.912 0.311 1530.808 29 3 466.861 2.746 648.981 117.510 o. 343 323.574 1.903 2.003 29 . 4 289.031 2.890 352.608 85.203 o.238 200.322 • A. P. r.E. 26 17 22 29 14 Hn B. 26 17 22 29 14 • Figure 2. Duncan's New Multiple Range test of P. I.E. and H" values. Groups of stations underlined by the same line were not sig­ nificantly different• • VI -13 • \4·3 100 200 300 . 500 600 2 ----------17·2 100 200 300 '(/) 400 500 600 H~ 20 u~ Poi • 0 '(/) 22-3 ~ ~ ~ ~ 100 200 p~ 20 z 10 '2.&·3 26·1 100 200 20 29-2 10 100 200 300 400 500 600 NUMBER OF INDIVIDUALS • Figure 3. Rarefaction -curves of Polychaete-Bivalve fractionof Benthos collections from Galveston Bay. Numberson lines indicate station and collection number. VI -14 i I'I • Examinati'drt 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 decided"iy 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. 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. Sanders (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 communities 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 sedimentsagrees with that of Bechtel (1970) who used the same stations. Stations 14 and 29 had much firmer mud with large quanti.ties 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 lower 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 H" values (Tables 4 and 5) 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-W~aver H' index was computed using loge while the • Margalef f~nctions D, d, and R used log2. It was interesting to note that when d was calculated using loge, the results were ~dentical to H". VI -15 • • • 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 be used for estuarine fish studies. Their 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 Dorris (1968) were working with stream . macrobenthic communities and using a 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 flaw in Bechtel and Copeland's comparison of their data to that of Wilhm and Dorris is in the comparison of H' to d values. The H' statistic used by Bechtel and Copeland uses loge in it~ calculation while l·og2 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 d 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 appears to be the fortuitous result of mistakenly using H' to compare with d. As both H' and d were calculated for the present study, it 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 dealing with estuarine communities. Quite possibly, we should define n1ower estuarine stress" 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 "lower estuarine stress". 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 "higher diversity" 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 mud communities. The soft mud communities should show lower · diversities (Gage, 1972). We believe the similarity of the curves to VI -16 • • • be due to ' the difference in latitude between his tropical estuaries and Galves'ton Bay. The similarity of these curves points out the "naturalnessn of the high diversity stations we observed, assuming that the tropical estuaries observed by Sanders were ·unpolluted. The third method for 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 "probability of interspecific encountern (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 community as having high P.I.E. values. He says that this. type of community .can tolerate little randomness in its search for mates, food or hosts, noting that the most random method of plant nmate­ seekingn, wind dispersal of pollen, is essentially absent from high P.I.E. communities such as rain forests. The converse should also be true: communities which are highly stressed, showing great randomness 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. The very high correlation 11 (>99%) between P.I.E. and Hvalues was of much interest as it tends to corroborate the findings of each. All of our data analysis methods appear to give similar results. The rarefaction curves agree very closely with the H", d 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 H", d and P.I.E. values are high. Stations 17 and 26 are definitely areas of greater stress. Analys·is of variance of both P. I.E. and Hn values indicated differences between the ·stations. DuncanTs new multiple range test (Fig. 2) shows the differences clearly as do the rare­ faction 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 dim:inution of diversity. At Station 26, the January collection showed H1T, d and P.I.E. values similar to the "high 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, ~. eburneus. The redundancy, R, clearly demonstrates the dominance of the barnacle in both of these collections. Rare­ faction 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 "higher diversity" stations. Species richness during October and VI -17 • • • April was severely diminished. Salinity at Station 26 in January was very low (circa 3%, Fig. 4). At first glance, one tends to interpret t 'l\E:( abundance of species at this time and lack at all other times as an ill effect of high salinity upon this community. This does not seem to be the case as a close inspection of Table l 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 Bayo This fluctuation in polychaete species numbers and individuals for most stations is apparent in Table l but was not shown to be significant by analysis of variance of either P.I.E. or H" values. Variance among seasonal samples was analyzed by Bartlett's test and was found to be homogenouso The reason for the statistical non­significance of the apparent fluctuation is ·believed to be a function of the mathematical treatment of numbers of species and individuals to give index numbers but the exact reason is not known. The fluctuation in numbers of polychaete individuals collected seasonally is believed to be real. It is felt that the existence of greater than normal stress at Stations 17 and 26 is indicated by the macrobenthic communities at these stations. Examination of all physical parameters existing at these sites indicates that the stress at Station 26 is probably primarily natural stress. The station is only about five miles from the mouth of the Trinity River and is probably subjected to the maximum "natural" stress of temperature and salinity to be found in the Galveston Bay complex. The analysis of heavy metals in the sediment at Station 26 (Table 7) shows no unusually high concentrations. Arsenic (2.6 ppm.) was the only heavy metal at this station that was high relative to the other sampling sites. Hann and Slowey (1972) state that coastal sediments have an average of 3 ppm. arsenic. Arsenic is found in concentrations of 3-15 ppm. in deep sea sediments (I.D.O.E., l972), so that the observed arsenic levels in Galveston Bay are not thought to. be limiting. Pesticide analysis (Table 8) indicates only traces of two pesticides at Station 26. There does not appear to be any man-made cause for the low diversity structure of the macrobenthos at Station 260 The stress indicated at Station 17 is probably due to pollution. This station is low enough in the bay to have a very stable temperature­salinity regime. Its salinity shows the least variation of the sites sampled. It is a relatively deep station so that temperature changes should be moderated. All physical parameters available to us were investigated. None showed evidence of causing the low values ~ncountered. Concentration of heavy metals in the sediment of this station are consistently either the highest or second highest of all stations sampled. It is not known that the observed concentrations VI -18 -~-­ -----------------~---~---· ----­ ~4 32 30 28 26 24 ~ 22 20 ~,-·. ·-..>-18 ~-29 c ·- 16- a ' ~' c4 14 ·.·.... ' ~ ........................26 ·.. ' I ...r •12 .... ', , ..··· \. ~I .· ~ 10 .. .. . . 8 ••.... ..... •• . . •.. ..·• 6 . . . . •. . • 4 .. . .· 2 Oct Jan Ap_r Jul \. Collection Months Figure 4. Mean Bottom Salinities at Galveston Bay Sampling Sites • VI -19 • Table 7 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 *Used with permission from Hann & Slowey, 1972. • • VI -20 Table 8 Pesticide Analysis of Galveston Bay Stations* Station PPB CJ ::c: i:Q I CJ ::c: i:Q I i:Q Q) s::: res '2 •rl H H 0 rl ..c CJ rtS .µ Pl Q)::r: H 0 rl Q) ~'d CJ ·rl res x .µ 0 ~~ Q) i:L1 ::c: s::: ·rl H 'd rl Q) ·rl Q s::: •rl H 'd rl ~ s::: ·rl H '2 i:L1 i:L1 QQ ..... ~ ~ ~ Q Q Q -~ ~ 0 Q § -~ ~ ~ E-l § -~ ~ 0 E-l Q Q -~ ~ ~ Q) s::: res ~ .µ rl Q) >.< Q) s::: res 'd H 0 rl ~ CJ <: H I\) ~ 14 17 22 26 29 p 0.42 0.22 p 0.49 -p -0.66 . -0.69 0.16 -p -2.63 2.63 --- -p - --- -p p 0.45 p - p p -p -p -p p P = trace *Used with permission from Hann & Slowey, 1972 • of metals are toxic but the consistently high concentrations are believed to be indicative of a large amount of pollution, probably from the Texas City industrial complexo The sediment heavy metals analysis at this site was made by Hann and Slowey (used with their permission), in July 1972. This was just prior to the final benthos collection in which no living organisms were found. The apparent seasonality of the low diversity values may be due to greater or different pollution during the warmer months or to a temperature interaction affecting the toxicity of the pollutants. Further investigation of the benthic communities in the Texas City channel and the pollution abatement control of the Texas City industrial complex is indicated. CONCLUSIONS I. Benthic macroinvertebrate communities are valid indicators of estuarine water quality. II. Water quality in Galveston Bay is good in most areas sampled. III. Station 26, in Trinity Bay, is highly stressed due to natural causes. • IV. Station 17, in the Texas City ship channel, is the only station sampled that indicates probable effects of man-made stress (pollution). Possible water temperature-toxicity interaction or more damaging pollution during summer months may be occurring at this station. ACKNOWLEDGEMENTS This study was made on an Interagency contract with the Texas Water Quality Board. We particularly wish to acknowledge the expertise of Dr. Don Harper of Texas A&M University in the identification of many of our specimens.· Mr. Larry McKinney of Texas A&M University and Miss Julie Gillespie of U.T.M.S.I. also assisted in identifications. Drs. Roy Hann and J. Frank Slowey of Texas A&M University graciously made the results of their sediment analyses of the Galveston Bay Stations available to us. Mr. Don Rauschuber of the Texas Water Development Board in Austin kindly calculated several diversity indices. Special thanks go to the crew of the LONGHORN, the research vessel used in this study•. The expert assistance of Captains Don Gibson and Elgie Wingfield and Mates Don Gutsch and Ron Musial greatly expedited field work• VI -22 • • • • References 1972. Baseline Studies of Pollutants in the Marine Environment ~~~~ and Research Recommendations: The IIX)E Baseline Conference, May 24-26, 1972, New York. 54 pp. Barnard, J. L. 19700 Benthic ecology of Bahia de San Quintin Baja California. Smithsonian Contr. to Zool. 44: 1-60. Bechtel, T. J. 1970. Fish species diversity indices as pollution indicators in Galveston Bay, Texas. M.Ao Thesis, Univo Tex. Austin• .Bechtel, T. J. & B. J. Copeland. 1970. Fish species diversity indices as indicators of pollution in Galveston Bay, Texas. Contr. Mar. Sci~ Univ. Texo 15: 103-132. Boesch, D. F. 1972. Species diversity of marine macrobenthos in the Virginia area. Ches. Sci. 13(3): 206-211. Coull, B. C. 1972. Species diversity and faunal affinities of meio­ benthic Copepoda in the deep sea. Mar. Biol. 14: 48-51. Gage, J. 1972. Community structure of the benthos in Scottish sea-lochs • I. Introduction and species diversity. Mar. Biol. 14: 281-297. Hann, R. W., Jr. & J. F. Slowey. 1972. Sediment analyses -Galveston Bay. Estuarine Systems Project, Technical Report No. 24, Texas A&M Research Foundation. 57 pp. Hohn, M. H. 1959. The use of diatom populations as a measure of water quality in selected areas of Galveston and Chocolate Bay, Texas. Puhl. Inst. Mar. Sci. Univ. Tex. 6: 206-212. Hurlbert, S. T. 1971. The nonconcept of species diversity: a critique and alternative parameters. Ecol. 52(4): 577-586. Jackson, H. W. 1970. A ·controlled-depth, volumetric bottom sampler. Prog. Fish. Cult. 32(2): 113-115. Johnson, R. G. 1970. Variations in diversity within benthic marine communities. Amer. Natur. 104(937): 285-300. Li, J. C. R. 1966. Statistical Interference, Vol. I, 658 pp. Edwards Brothers, Inc. Ann Arbor, Mich. Margalef, R. 1956. Informacion y diversidad especi~ica en las communidades de o~ganismos. Inv. Pesq. 3: 99-106. Menhinick, E. F. 1964. A comparison of some species-individuals diversity indices applied to samples of field insects. Ecol. 45(4): 859-861. VI -23 • Pielou, E. C. 1966. Species-diversity and pattern-diversity in the study of ecological succession. J. Theoret. Biol. 10~ 370-383. Pielou, E. C. 1967. Use of information theory in the study of diversity of biological populations. In Biology and Problems of Heulth. Proceedings of the 5th Gerkeley Symposium on Mat hematical Statistics and Probability. 4: 163-177, Univ. Calif. Press, Berkeley. Sanders, H. L. 1968. Marine benthic diversity: a comparative study. Amer. Nat. 102(925): 243-282. Snedecor, G. W. and W. C. Cochran. 1967. Statistical Methods. Iowa State Univ. Press, Ames. 593 pp. Spence, J. A. & H. B. N. Hynes. 1971 Differences in benthci~ upstream and downstream of an impoundment. J. Fish. Res. Bd. Can. 28: 35-43. Wilhm, J. L. 1967. Comparison of some diversity indices applied to populations of benthic macroinvertebrates in a stream receiving organic wastes. Jrl. WPCF 39(10: 1673-1683. • Wilhm, J. L. 1968. Use of biomass units in Shannon's formula. Ecol. 49(1): 153-156• Wilhm, J. L. 1970. Range of diversity index in benthic macroinvertebrate populations. Jrl. WPCF Vol. 42 5(2): R221-R224. Wilhm, J. L. &T. C. Dorris. 1966. Species diversity of benthic macro­invertebrates in a stream receiving domestic and oil refinery effluents. Amer. Midland Natur. 76: 427-449. Wilhm, J. L. &T. C. Dorris. 1968. Biological parameters for water quality criteria. Bioscience, Vol. 18(6): 477-481 • • VI -24 • • • CHAPTER VII A Definition of BOD Toxicity by c. H. Oppenheimer and Nadine GoJXlon University of Texas Marine Science. Institute Port Aransas, Texas INTRODUCTION Objectives The purpose of this research was to measure the Biological Oxidation Demand (BOD) of Galveston Bay waters at five points in order to compare the relationships between time and organic content of the water. This information will be correlated with previous studies that indicated a so-called toxicity effect on BOD. Background Previous studies of the Biological Oxygen Demand (BOD) of waters relating to Galveston Bay indicated that 5 and 20 day tests showed marked increases in oxygen demand when diluted. This effect was suggested to be due to the reduction of some toxic factor by dilution. We initiated research on this subject by a reevaluation of the concept and development of the BOD test. Unfortunately, it has become common practice to use the BOD test as an indication of organic matter in water even though the technique was developed as a yard stick for the assessment of the pollution potential of waste material and has been a common procedure used by regulating agencies for waste disposal in 'natural waters. In reality, the application of the BOD test to natural waters, especially marine, is the subject of considerable controversy because of the interrelationships of so many parameters of oxygen uptake. In the original concept, BOD was used as a laboratory test under defined conditions in monitoring sewage and other organic wastes. The procedure was of such a nature as to give valid results for defined conditions. Subsequent treatment to eliminate nitrite and other inhibitors and the use of seed microorganisms was added .to provide uniformity of the procedure. Thus the five day BOD evolved as a good indicator of the quantity of oxygen consumed by • • • microorganisms during the degradation of sewage and other waste materials tnat were monitored daily for long periods of time. The application ot the BOD test to natural water, especially sea water, is more difficult to make arid some believe that the BOD test is still in the research stage rather than a reliable parameter for use in research (Gaudy, 1972). Oppenheimer, et al. (1953) in a study of the filterable organic matter in sea water pointed out that difficulties encountered with the BOD may be due to the presence of variable organic· materials and especially the humic or larger molecular weight materials that are more refractive to microbial activity. The variability of natural waters is well kno\.Jn (ZoBell, 1946). Thus the BOD in estuarine waters will be a function of the balance of specific·molecu1es at any one time. At the same time there is competition for oxygen by higher organisms during photo­ synthesis-respiration cycles, and by anaerobic products from the sediments in shallow, well-mixed bays such as Galveston. To complicate the application of the procedure to the natural environ­ ment, one must consider_the tjiversity of microorganisms and the complexity of the organic matter to be oxidized. Elmore (1954) presented a review of the dilution technique vs. reaeration and stated that in long-term BOD, dilution caused inversions in the curve that could be attributed to several factors, including problems involving the low oxygen measurements of the blank and application of the blank correction due to scatter of replicates • The inverted curve shown by Reynolds and Eckertfelder (1970) does not fit the concept of dilution of some toxic factors, where the greatest effect would be expected with the largest dilution. The inverted curve does fit the concept of ZoBell (1946) regarding the effect of solid surface area on .microbial activity. Thus dilution of natural waters will increase proportionately the effective surface area and could account for the inverse curve fit o Our approach to determine the significance of BOD in relation to toxicity and organic matter was to select five areas in the bay that would represent points of integration of various water sources to the bay as shown by Figure 1, Chapter I. A comparison was made between regular BOD5, Dilution BOD30, Reaeration BOD30, size of container and organic carbon in the water. All collected data taken during the sampling period were also entered in ENVIR and comparisons of BOD5 were correlated between salinity, nutrients, turbidity and oxygen. Procedures Water for BOD measurements was treated in the field to eliminate changes due to time factoro The samples were collected during regular field operations at which time routine hydrographic data were taken by the U.S. Geological Survey and at stations where monthly data were . taken during the Galveston Bay Project.. The standard procedures for VII -2 • • • BOD were those generally used in Standard Methods for Water Analysis • However the method of treating the samples varied. All BOD samples were initially bottled at the time of collection in the field and incubated under uniform conditions. This eliminated any change due to storage of the samples or transport of the samples. Routine BOD for periods up to 45 days were conducted in the usual 300 ml BOD bottles. Five gallon containers and the large 500 gallon water storage tanks used by Dr. Wohlschlag were used to determine the effect of container size on the BOD. At each station, water was collected with a peristaltic pump from mid-depth into two, five gal. glass bottles. Water was pumped from the 5 gal. bottles into each of 28 BOD bottles. Twelve of these were used for regular 28 to 45 Day BOD's and 16 were used for the reoxygenated 28 to 45 Day BOD'so Into 12 BOD bottles we put water diluted 1:5 with sterile artificial sea water. Millipore filtered water was dispensed into 12 BOD bottleso Two of the bottles from each of the 4 sets were titrated immediately by the Winkler method to determine the amount of oxygen initially present. All were then stored in the darko On the 5th, 10th, 20th, 30th and 45th day, 2 each per day of the regular, diluted and millipore filtered BOD's were titrated. The BOD was the difference between the initial titration and the corresponding day the titration was done • On the reoxygenated bottles, beginning with the 5th day, 2 bottles were titrated and the remaining 12 were reoxygenated, using cylinder oxygen bubbled through an air stone suspended in the bottle for 20 seconds each. One of these was then titrated to determine the amount of 02 that had been bubbled into each of the bottles. This acted as a control for the 10th Day. On the 10th Day, two bottles . were removed and titrated. This amount was subtracted from the 5th Day control. The BOD for the 10th Day was then the amount for the 5th Day plus the difference between the 10th Day and the 5th Day control. On each test day the same procedure was used --remove 2, titrate, .reoxygenate the remaining, remove one and titrate for a control for the next test day. The reoxygenated BOD's were the cummulative totals of the oxygen used from day "0". For large container BOD, water was pumped at each station from mid-depth by peristaltic pump into a five gallon bottle, which was capped, put into a wooden box and covered with aluminum foil. Into another bottle, a 1:5 dilution was made with sterile artificial seawater. This was also capped, boxed and covered with foil. At 2-4 day intervals readings were taken with a YSI oxygen meter which had been air calibrated. Water and air-temperature was 33°c and atmospheric pressure was 760 mm Hg • VII -3 • • • Results of the BOD tests The results of the BOD tests for the various stations and times are given in Figures l through 12. All repl icas were within limits of error and are repo·rted as one point on the graphs. All bottles were maintained in uniform conditions of dark and temperature. Thus the differences are due to the type of BOD test employed. Hydrographic data for all field stations are given in Appendix A of Chapter X. Table 1 lists the total organic carbon in water sample's taken at the time the BOD tests were set up. These analyses wer·e made on frozen samples with a Beckman Carbon Analyzer. The Jft.lY>samples were run by DuPont, Victoria, Texas, as our carbon analyze_r<' was not functioning properly. .... Table 1. Total Organic Carbon in mg/l Station January 1972 April 1'972 July 1972 22 22 42 6.6 . 29 16 34 87 17 14 36 38 26 13 42 31 14 43 39 Clear Lake 38 ENVIR printout was used to plot several parameters of turbidity, salinity, oxygen, nitrogen and phosphorous against BOD5. These . data are presented in Figurre 13 through 20 for the years 1971 and 1972 as. indicated. Discussion The first impression provided by the data is that there is little ~al correlation between the various BOD treatments and the amount of dissolved or total organic carbon. The highest oxygen values are for the samples diluted l:~ 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 0f NH3 per liter would utilize 4 mg of 02 per lite-r. 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 also little real correlation between the regular BOD, diluted or reaerated or size of BOD container for each station• VII -4 ---------• ----------22 -------·29 6-· 17 5 4 '­ . 4> . I I · / / __.... 26 +­ < ·­ ::t ....... ·3 .......... lJ1 0N . 2 E I I 11 I .. ­ I /Pl 1 30 35 40. .45 5 10 .15 20 25 DAYS Figure 1. 28 Day BOD -January~ 1972 --.. • 6 -------------------~----26 -~------------------------.22 5 .._____: ________14 ·4 ________..:_;.__..__cl 17. '­ Cl) 29 +­ < . __. H ·­ H 3 ........... O"l N 0 -~ 2 E 1 5 10 .15 20 25 .30 35 40 45 DAYS · Figure 2. Normal 30 Day BOD -April, 1972 .. 6 5 4 '­ G) .... < . ·­ H -I 3 H --.......... N "' 0 - 2 E 1 5 10 .15 20 .. 25 30· 35 40 45 DAYS Figure 3. Diluted 1:5, 30 Day BOD -April, 1972 .. 12 11 I I 22 10i · · .~26 9 J .·~·· /14 8 L. fD : I ~/ / .J'cl ... -I ·-7· I ~/ /~17 ............ < N 6 ~ ·o 5 a:> ­ E i/ / / ____.... 29 Al 3 2 1 5 .10 15 20 25 30 35 AO 45 DA.. YS Figure 4. Re°"l'ygenated 30 Day BOD -April,, _1972 6 4 '­ ..G> ~-----22 <.: -· _, 3 l=t ......__ U) 0N .... c I . . 2 ,. .... ­ \ -----14 E \ ,,... \ ,,, / \ -~ ,,, 26 .·\ •' ······ ······· /' // ..... --------------­ ...,_,_ ---·­ 1 •"-. . , , .>""····... ­ ...· \------:...,.... . /,,/ ..••... ------17 .·· .. , , , / . ··•· . -­ . ' , / ... ..· ' ,....., ,.......... ...... •• . •••• 29 --' ~ •.. ______,... .... 5 10 .15 20 . 25 30 35 40 -45 DAYS Figure 5. Millipore Filtered 30 Day BOD -April, 1972 .. -. ==· -,__ -­ -----:=----~~~.;...._-- -.. --... ---·-.---· .!::::::::::::: =*-·. =-SC::: ----. ··----===-­ 22 L---===~~-26 6 :::::;:...:::=:_ ~ 5 4~ . 11 17 '-4> I II -~-29 +­ < .·-_, H 3 H ......_.,_ ~­ N 0 0 - 2 E 1 - 20 .. 25 30 35 40 .45 5 10 .15 DAYS Figure 6. 500 Gal. Box 30 Day BOD -April~ 1972 6-· 22 5 4 G> .. '­ I ~ . 14 ~ ·:3 3J . r-i ""' r-' N 0 29 ~ 21I 11 26 I/// v 1 5 10 .15 20 25 DAv·s 30 35 40 . 45 Figure 7. Normal 45 Day BOD -July~ 1972 .. 12 11 10 9 '-8 G) .... 7· _, '---. < 6 H 0NH ri . ~ -E 5 4 3 2 1 - 5 .10 . 15 20 25 30 35DAYS Figure 8. Reoxygenated 45 Day BOD -July,, 1972 ,/ • 6 - · • 5 4 '­ Q) .+­ ·­ < ...J H 3 H '--... I f-' ON VI • 2-e 1 5 .10 .15 20 25 DAYS 30 35 40 45 Figure 9. Diluted 1:5, 45 Day BOD -July, 1972 .. 6 5 5 10 15 20 25 30 35 DAYS Figure 10. Millipore Filtered 45 Day BOD -July, 1972 6 5 • -, .. 22 ~· · ..t I' r-17 . 4 . . I 29 . '-. I I / / ~ CD +-·1 .11 14 <: / /. H .. ·­ . H ...I 3 ........... ....... Vl ON - 2 E 1 • 5 10 .15 20 25 30 35 40 .45 DAYS - Figure 11. 5 Gal. Undiluted BOD -July,, 1972 .. 6 5 . +­·­...... --....... N 0 E 4. . 31 . 2­ ,, -- - . // - I - •·22 .. 17 14 .,....-26 1 30 35 40 .45 5 10 .15 20 25 DAYS - ' Figure 12. 5 Gal. Diluted 1 :5 BOD -July~ 1972 .. • • • There appears to be a slight correlation between Station 22 and the highest BOD activity; however, there is a discrepancy in the diluted April BOD test. As this station is closest to the Houston ship channel there is a possibility that this reflects the organic matter coming from the channel. However, there wa$ little evidence from the organic carbon values that Station 22 was significantly higher 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. 20). At this station the higher .BOD was associated with higher salinity. This seems to correlate with the data of Armstrong 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. Another minor correlation between BOD and oxygen seems to be indicated by the higher amount of oxygen in the water as related to a higher BOD (Figures 4 and 8). 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. We have plotted the BOD's obtained during our diurnal sampling period with time, oxygen content, and depth of water. 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 duri~g a period of little wind. This indicates that when wind mixing decreases the large amount of organic matter irrunediately affects the oxygen content in the water due to metabolism. When the wind increased the low oxygen disappeared. 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. 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 13-20). It is apparent that there is no correlation between the BODs analyzed at all stations during ~he period and salinity, organic nitrogen, total phosphorous, turbidity or oxygen. The lack of correlation between BODS and these important biological parameters clearly indicates that the BOD test VII -17 • AL BAY STATIONS ENVIR ALL BAY ST ENVIR 0 0 ... 0. Figure 13 0 Figure 14 0 0 - .. . . . L/) lJ) I . . .. I ... . . .. .. . .. 0 0 ... .... .... 0 . ... . ..'. ... .. .8 . 0 . -. .. -·. .. ... ....: I . • ... u . . . ' .. , .. ...• I .: .-... ... .... .. .•: . . . .. ... )• . ... . . . ... ... .. ... • .. ... .• .. ... . .. . ... . .. . ..,.. . • ... • 'lo ........ 0 . 0 0. •• .. 0. 0 a.a ECCH 7. a 0 a.a OTAL P 2. a AL BAY STATION ENVIR ENVIR 0 0 l!J l!J Figure 15 Figure 16 0 l!J 0. 0 0 l!J .. .. .. ... L/) I L/) . . I . 0 0 .. .. .. . c::J . . . 0 . . ... .. . .. . .. .. .... . .. ... ... ... ·-··-­ .:: ·. ..., . . . .. ···---­ ..-..•.... 0 . 0 0 ·~ . 0. 01--___~...........--~...----r-~~.-.,.~-,---,r-~ o...._-.-~------~-P----~-..----...~-.-----.~~ o.o 2a.aa a.a RG N Figure 13. Five day BOD (mg/l) by Houston Health Dept. for 1971-72 routine sampling stations. Secchi disc (ft) reading by Army Corps of Engr. • Figure 1~. BOP (mg/l) and total phosphorus (ppm P) for 1971-72 routine sampling stations• Figure 15. · BOD (mg/l) and organic nitrogen (ppm N) for 1971-72 routine sampling stations. Figure 16. BOD (mg/l) and oxygen (mg/l) for 1971-72 routine sampling stations. · VII -l8 I ' ·, , ' ' ,,, I l I ., • ALL BAY ST ENVIR STATION 26 ENVIR 0 0 0 Figure 17 0 Figure 18 0. 0 0 - -• 0 N + + + _J + a: lJ) lJ) l!J l­I I o + + .,__ 0 . . . .. . 0 0 .: ., 0 + • + + .. . . . .. ... . l!Jffi ... . ... z .. . . l!J . . . .. .. . l!J .. . ... .. .. . . . .. . + • • ~ .. . . . l!J. a: l!J •• • • I ••• • l!J • 8 • • • • • •• • • • . .. • ••o.• • • •• 0 0 ..• 0 • • 0. 0. 0. . 0 I 0 o.od · SAL'I NI TY; '4o.!oo 0 o.o 40.00 ST ENVIR ST N 26 ENVIR • . 0 0 0. 0 Figure 19 0 Figure 20 0 0 Ul 0 N 0 a.... lJ)_J a: I .,__ 0 0 0 . + z C!> a:: 0 0 0 0 0. 0 0. a 0 OL---..-~.----..-~..--~---,r--"-.----ir----r-----1 o.o YR 2. o . o.oo YR 2 .. o Figure 17. Five day BOD (mg/l) by Houston Health Dept. versus salinity (ppt) for 1971-72 routine sampling stations. • Figure 18. Five day BOD (mg/1)--m, organic nitrogen (ppm)--+, and total phosphorus (ppm)--• versus salinity (ppt) as an indicator of fresh water inflow for station 26 for 1971-72. Figure 19. Organic nitrogen (ppm)--+, and total phosphorus (ppm)--• versus time in years starting 1I1I71 for station 26. Figure 20. Five day BOD (mgI1)--•, and salinity (ppt)--+ versus time in years starting 1/1/71 for station 26. ........ """ • • • as applied in this research program has no significance and cannot be used for 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 infonnation 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. In this study it was evident that the oxygen requirement by organic matter in the water was not reflected by the BOD 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 the man's input of waste materials. Summary The variability of all tests and the lack of correlation clearly indicates the invalidity of using the BOD test for toxicity estimates. One would assume that the so-called BOD toxicity from previous Galveston Bay projects is an artifact of the procedure. References Custer, s. w. &R. G. Krutchkoff. 1969. Stochastic models for bio­chemical oxygen demand and dissolved oxygen in estuaries. Virginia Polytechnic Inst., Water Resources Research Center, Bulletin 22. Elmore, H. L. 1954. Determination of B.O.D. by a reaeration technique. Sewage Works Journal, p. 993-1001. Gaudy, A. F., Jr. 1972·. Biochemical oxygen demand. P• 305-332. In Ralph Mitchell (ed.) Water Pollution Microbiology. Wiley­Interscience, New. York. Hays, A. J. et al. 1970. Anaerobic Modeling for the Houston Ship Channel. Technical paper presented to the 9th Texas Water Pollution Control Association Conference Houston, July 9-10, 1970.. VII -20 • Oppenheimer, C. H., D. L. Fox & J. s. Kittredge. 1953. 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, 12(2): 233-243. Reynolds, T. D. &Wo 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, 1970. Stack, V. T., Jr. 1972. 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 -21 \, • CHAPI'ER VIII The Nursery Environment of Galveston Bay by Carl H. Oppenheimer University of Texas Marine Science Institute Port Aransas, Texas The Nursery Environment of Galveston Bay Texas Bays have been identified as nursery areas because of the seasonal behavioral characteristics of commercial and sport fish and • shellfish, and usually only indirectly relates to primary productivity of attached grasses and algae or phytoplankton. Such a definition takes for granted that the supporting population of the necessary food chain is present and that the physical chemical characteristics of the system are sympathetic to the ultimate growth of the commercial fish and shellfish. The nursery ground is defined as the shallow water area w·ith grass and algae coverage. It extends to the depth of photo­synthesis of the organisms in the bay environment • Most of the commercial fish and shellfish of the Texas coastal area have definite seasonal migratory and spawning behavioral patterns. During some part of the year they will move out of the bays to the open Gulf where they spawn. The larvae then move through tidal forces back into the bay where they develop into adults to complete the cycle. Whether the migratory cycle is dependent on water quality or stability o'f the Gulf water is not known at this time. Also, there is a certain proportion of the fish and shellfish that may stay within the bays for one or more years. A certain proportion of the blue crabs may spawn within the bays, and oysters have their complete life cycle within the bays. In general, the nursery areas are assumed to be the grass flats and marsh areas, as shown by Figures 1, 2, and 3. These areas are highly productive and offer protection and attachment surface as the larval forms go through their various development stages. The grass areas are quiet with soft, high-organic, fine sediments. The blades of grass act as surface for various food chain organisms required by the developing larvae and when dead add a relatively large amount of organic matter to fertilize the adjacent bay or estuarine waters • • ~I~ Vb/ f} / / fl 5 ' ~ c:::::? ~7 ~6 ~c3? 11 ~}} )j)/ to ~I~~ ~1~~3 ~ . 15 ~ 17 2F 26 • • • - • • \, • VIII -2 • THALASSIA GRASSFLAT 1. Laqodon rhomboides -Pinfish 2. Penaeus aztecus -Brown shrimp 3. Cynoscion nebulosus -Spotted sea trout 4. Hydrozoan 5. Spirorbus sp. -Serpulid worm 6. Spirorbus sp. -Serpulid worm 7. Paleomonetes vulgaris -Grass shrimp 8. Cerithidea turrita -Horn shell 9. Neritina reclivata -Olive nerite 10. Gracilaria sp. -Red alga 11. Minidia beryllina -Tidewater silverside 12. Sciaenops ocellatus -Juvenile redfish 13. Thyone sp. -Sea cucumber · 14. Ophiothrix sp. -Br.ittle star 15. Odostomia gibbosa -Small gastropod 16. Clibinarius vittatus -Hermit crab 17. Neopanope lexana -Mud crab 18. Callinectes sapidus -Blue crab 19. Halophila engelmanni -Sea grass 20. Diplanthera. wrightii -Shoal grass 21. Phacoides pectinatus -Lucina clam • 22. Thalassia testudinum Turtle grass 23. Ensis minor -Razor clam 24. Rhitropanopeus harrissi -Burrowing crab 25. Chione cancellata -Venus clam 26. Phacoides pectinatus -Lucina clam 27. Penaeus duorarum -Pink shrimp 28. Phascolosoma qouldii -Mud worm 29. Ceratium sp. -Dinoflagellate 30. Nitzchia sp. -Diatom \ 31. Cymbella sp. -Diatom 32. Oscillatoria sp. -Blue green alga 33. Dunaliella paupera~-Saline euglenoid 34. Microcystis sp. (colony) -Green alga 35. Microcystis sp. (individual) -Green algae • VIII -3 • 0 CJ a • .. . • • • • VIII 4 • .. • 1. 2. 3. 4. s. 6. 7. 8. 9. 10. 11. 12. 13. 14• 15. 16. 17. 18. 19. 20. 21. 22 • SPARTINA SALT MARSH Ardea herodias -Great blue heron Butorides virescens. -Green heron Anas discors -Blue winged teal Ajaia ajaja -Roseate spoonbill Casmerodius albus -Common egret Avicennia germinans -Black mangrove Eudocimus albus -White ibis Salicornia bigelovii -Glasswort Procyon lotor -Racoon Distichlis spicata -Saltgrass Spartina alterniflora -Smooth cordgrass Rallus longirostris -Clapper rail Pagurus sp. -Hermit crab Telmatodytes palustris -Longbilled marsh Croton punctatus -Beach tea Sesuvium portulacastrum -Sea purselane Batis maritima -Salt wort Uca pugnax -Fiddler crab Avicennia germinans -Black mangrove Littorina irrorata -Periwinkle Avicennia germinans -Black mangrove Distichlis spicata· -. Saltgrass wren \, • VIII -5 • 0 ~ ~_. • .Li ~ .~ $ 0 • • • VIII 6 • JUNCUS FRESH·WATER MARSH 1. Branta canadensis -Canadian geese 2. Butorides virescens -Green heron 3. Spartina alterniflora -Smooth cordgrass 4. Juncus sp. -Reed s. Scirpus sp. -Bullrush 6. Rattus ·norvegicus -Norway rat 7. Procambarus burrow · 8. Fulica americana -Coot 9. Mycteria americana -Wood ibis 10. Smilax sp. -Bamboo briar 11. Typha domingensis -Cattails 12. Scirpus sp. -Bullrush 13. Didelphis mesamericana -Opossum and young 14. Spartina patens -Marsh hay cordgrass 15. Crotalus atrox -Western diamondback rattlesnake 16. Uca pugnax -Fiddler crab 17. Procambarus clarki -Crayfish 18. Cyprinodon variegatus -Sheepshead minnow 19. Agkistrodon piscavoris -Cottonmouth snake 20. Scirpus sp. -Bullrush 21. Typha dominqensis -cattail • 22. Sporobolus virqinicus -Seashore dropseed • VIII -7 , ,I ! I . '· • The shallow grass flats are always highly oxygenated because of the wind mixing action on the water and oxygen diffusion. Such areas offer well-oxygenated protection and highly nutritive living areas for the young organisms, and later provide other types of food for the adult organisms. Most of the commercial species of fish and shellfish have defined migratory and maturation times. For example, the Penaeid shrimp and blue crabs (Callinectes) mature in one season, whereas, the redfish (Sciaenops) mature in five years. Most other fish such as the mullet (Mugil), spotted trout,(Cynoscion), drum (Pogonias), and croaker (Micropogon) mature in two years. Whereas the life cycles of the various commercial organisms have been developed and identified, the distribution of the larvae and young of the species in the Texas bays is not well identified. There is a general agreement that the larvae and small young live in the grass flats (Herke, 1971). As adults, each species identifies itself with some specific bay environment. The mullet and blue crab are ubiquitous, as they are detritis feeders. The adult trout prefer sand bottoms, the red fish prefer open waters, and the shrimp are found in both mud and sand bottoms. While the literature is not well defined, a general conclusion can be assumed that the grass flats provide the living habitat for the growing larval forms and as the organisms grow they assume discrete environmental niches. It is quite unrealistic that after 50 plus years of biological research that so little has been documented about the life history distribution of the commercial fishes of the Texas coastal environment. Most of the literature is purely descriptive with little attempt at quantification. For example, Copeland (1965) postulated, from Aransas Pass tide trap information indicated, that the Corpus Christi Redfish Bay regime had a net productivity of 233 kg/acre per year and that 3.9X106 kg/yr of penaeid shrimp passed Aransas Pass during the experimental period. Juveniles of all my major species seemed to show a response to minor variations in some environmental factors. Most organisms probably have optimum ranges for all environmental factors that vary with the species and individual. When an environmental factor deviates from the optimum such as a heavy rainfall it probably creates a stimulus tending to cause the organism to migrate to more optimum · conditions. Optimum ranges of all environmental factors for a species will almost never occur at the same point in time and space; therefore, the reaction to the numerous stimuli is necessarily a compromise. • Other studies have indicated that detritus from emergent marsh or grassflat plants forms the base of many estuarine food webs, which suggests the marsh receives ·more usage by my major species than the VIII -8 • other segments of the estuarine zone. Thus, the land poTtion of the marsh or grassflat is apparently the most important segment for production of plant material for the base of the food webs, and the water portion is probably the site where the greatest proportion of this material is converted into animal biomass. Therefore, although all sections of the estuarine zone are needed, and some use is undoubtedly made of all sections by all sizes of juveniles, where it exists, the marsh and grassflat is the most important section. Since most estuarine-dependent species spawned in the Gulf have similar life cycles, the majority of the immigrating young of many species probably proceed into the estuary as far as they can swim in a particular section, or as far as competition and their minimum salinity tolerance allows. Contrary to the often expressed opinion that juveniles leave the nursery area as a more or less mass movement outward, emigration usually occurs over an extended period. It is apparently a 11bleeding offn process which involves primarily the larger individuals in the nursery at any particular time. • Our estuaries have existed for centuries. Throughout this period, organisms have been evolving life patterns and responses to environmental factors that are of maximum advantage to the species. This has been an extremely slow process, but it has brought forth a marvelously resilient system--one that has maintained productivity indefinitely with no need of assistance from man. The system has withstood and bounced back from every possible natural calamity so long as the estuarine zone was not itself destroyed. For manipulation of the estuarine environment to our maximum benefit we must learn a great deal more about its ecosystem. The money presently being expended in attempts to 11 improven on the natural system would benefit mankind more if it were spent to achieve a better understanding of the natural system. The workings ­of this system are every bit as intricate as a nervous system, and species in the system can respond to stimuli just as delicate. Their responses are too deeply ingrained to allow the organisms to be suddenly forced into unnatural life patterns without causing significant stress. The results of the literature survey are as follows:· 1. The literature does not contain specific information to quantify the relationship between estuarine environments and the distribution and quantity of annual biomass of the commercial fish and shellfish. Quantitative information on commercial fish catch merely indicates minimum populations and has no bearing on the .total capacity of the bays on a surface relationship. • VIII -9 • 2. There is a general agreement that the grass flats are important to the survival of the larval forms and offer feeding areas for the adults. While this has not been clearly documented in Texas, the general agreement by scientists indicates that the grass flats should be preserved until quantitative data are available. • 3. Most commercial fish and shellfish species spawn in the open Gulf and, therefore, the channels and passes between the Gulf and the bays are needed for proper biological balance. Clear regulations should be initiated as they were in the latter part of the 1800's on restriction of fishing in shallow passes between the Gulf and the bays. 4. Research should be initiated immediately to quantify the primary productivity of the ·bay environments, the distribution and growth rates of various cor&nercial species in the different bayenvironments, and the rate of biomass migration out of and into the bays through the various passes .connecting the bays with the Gulf of Mexico. Until these data are made available, it will be impossible to provide any quantitate values on the use and management of the grassflats or the open bay waters with respect to the commercial and sport activities • References Aldrich, D. V. &L. M. Wiesepape. 1970. Effects of temperature and salinity on thermal death in postlarval brown shrimp Penaeus aztecus. U.S. Gov't. Res. Develop. Rep. 70(24), p. 44. PB 195 175. Aldrich, D. V., C. E. Wood &K. Baxter. 1968. An ecological inter­pretation of low temperature responses in Penaeus aztecus and f. setiferus post larvae. Bull. Mar. Sci. 18(1): 61-71. Allea, D. M. and T. J. Costello. 19660 Releases and recoveries of marked pink shrimp, Penaeus duorarum Burkenroad, in South Florida Waters, 1958-64. U.S. Fish Wildlife Serv. Data Rept.No. 11: 1;_77. Anderson, W. W. 1966. The shrimp and the shrimp fishery of the southern U.S. U.S. Dept. of the Interior -Fish &Wildlife Service Fisheries Leaflet 589. Barr, L. 1969. Methods of estimating the abundance of juvenile spotshrimp in a shallow nursery area. Abso Pandalus-Platyceros.Proco Nat. Shellfish Ass. 60: 12. • I, VIII -10 • Baxter, K. N. 1963. Abundance of postlarval shrimp -one index of future shrimpi ng success. Proc. Gulf & ca.rib. Fish. Inst., 15th Ann. Session, 1962: 79-87. Baxter, K. N. &W. Co Renfro. 1967. Seasonal occurrence and size distribution of postlarval brown and white shrimp near Galveston, Texas and notes on species identification. U.S. Fish Wildl. Serv. Fish. Bull. 66: 149-158. Beardsley, A. J. 1969. Some behavioral aspects of shrimp in relation to commercial trawl gear. Abs. Pandalus-Jordani Vertical Distribution. Proco Nat. Shellfish Ass. 60: 13. Bechtel, T. J. 1970. Fish species diversity indices as pollution indicators in Galveston Bay, Texas. M.A. Thesis, Univ. of Texas at Austin. Carthy, J. D. & D. R. Arthur (Ed.) 1968. The biological effects of oil pollution on littoral communities. Proceedings of a symposium. 17-19 Feb. 1968. Onelton Field Centre, Pembroke, Wales. Field Studies Council, Londono Chambers, G. V. &A. K. Sparks. 19590 An ecological survey of the Houston ship channel and adjacent bays. Inst. Mar. Sci. Univ. Tex. 6: 213-250. • Chapman~ V. J. 1960. Salt marshes and salt deserts of the world. Plant Science Memographs. Interscience Publ., Inc., N.Yo 392 p. Chin, E. 1960. The bait shrimp fishery of Galveston Bay, Texas. Trans. Amer. Fish. Soc. 89(2): 135-141. Chin, E. 1961. A trawl study of an estuarine nursery area in Galveston Bay with particular reference to penaeid shrimp. Diss. Abst. 22( 5): 1751. Chin, E. & D. M. Allen. 1957. Toxicity of an insecticide to two species of shrimp, Penaeus aztecus and Penaeus setiferus. Tex. J. Sci . 9(3): 270-278. Christmas, H., G. Gunter & P. Musgrave. 1966. Studies of annual abundance of postlarval penaeid shrimp in the estuarine waters of Mississippi, as related to subsequent commercial catches. Gulf Res. Rep. 2(2): 177-212. Conte, F. s., H. G. Applegate &D. c. McNeill. 1971. Mortality of penaeid shrimp caused by malathion. Abstract Penaeus-setif erus­aztecus. GL chromatography. Tex. J. Sci. 22(2-3): 281-282. le -11 VIII • Cook, H. L. 1965. A generic key to the protozoan, mysis, and post­larval stages of the littoral penaeidae of the northwestern Gulf of Mexico. Fish. Bull. 65(2): 437-448. Cook, H. L. & M. J. Lindner. 1970. Synopsis of biological data on the brown shrimp Penaeus aztecus -Fishery Taxonomy distribution. FAO(Food Agr. Organ. UN) Fish Rep. 57(4): 1471-1497. Cooley, N. R. 1970. Estuarine faunal inventory. U.S. Dept. Fish Wildlo Serv. Circular 335: 12-16. Copeland, B. J. 1965. Fauna of the Aransas Pass Inlet, Texas. I. Emigration as shown by tide trap collections. Publ. Inst. Maro Sci. Univ. Tex. 10: 1-8. Copeland, B. J. 1966. Effects of decreased river flow on estuarine ecology. J. Water Poll. Contr. Fed. 38(11): 1831-1839. Copeland, B. Jo 1969. Oligohaline Regime. In Odum, H. T., B. J. Copeland & E. McMahan (eds.) Coastal Ecological Systems of the U.S. -A Source Book for Estuarine Planning. Rpt. to FWPCA, Vol. II pp. 789. • Copeland, B. J. & T. C. Dorris. 1964. Community metabolism in ecosystems receiving oil refinery effluents. Limnol. Oceanog. 9: 431. Copeland, B. J. &M. V. Truitt. 1966. Fauna of the Aransas Pass Inlet, Texas. II. Penaeid shrimp postlarvae. Tex. J. Sci. 28(1): 65-74. Copeland, B. J. & E. Go Fruh. 1970. Ecological studies of Galveston Bay, 1969. Final Report to the Texas Water Quality Board, Austin, Texas. Costello, T. J. & D. M. Alleno 19650 Migrations and geographic distribution of pink shrimp, Penaeus duorarum of the Tortugas and Sanibel Grounds, Florida. U.S. Fish & Wildl. Serv. Fish. Bull. 65(2): 449-460. Costello, T. J. & D. M. Allen. 1970. Synopsis of biological data on the pink shrimp, Penaeus duorarum. Fishery taxonomy distribution. FAO(Food Agr. Organ. UN) Fish Rep. 57(4): 1499-1537. Conte, F. S. & J. c. Parker. 1971. Ecological aspect of selected crustacea of a marsh embayment of the Texas coast. Texas A&M Univ. Unpubl. Darnell, R. M. 1956. A note on the occurrence of the pink shrimp Penaeus duorarum in Louisiana waters. Ecology 37(4): 844-846• • VIII -12 I, • Dobkin, S. 1961. Early developmental stages of pink shrimp, Penaeus duorarum, from Florida waters. Fish &Wildl. Serv. Fish. Bull. 61(190): 321-349. Eldred, B. 1960. On the grading and identification of domestic commercial shrimps (Family Penaeidae) with a tentative world list of commercial Penaeids. Quart. J. Fla. Acad. Sci. 23(1): 89-118. Eldred, B. 1962. Biological shri mp studies (Penaeidae) conducted by the Florida State ·Board of Conservation Marine Laboratory, In Proceedings 1st National Coastal and Shallow Water Research Conference, October 1961, pp. 411-140 Ewald, J. J. 1965. The laboratory rearing of pink shrimp, Penaeus duorarum Burkenroad. Bull. Mar. Sci. 15(2): 436-4490 Fontenot, B. J. &H. E. Rogillioo 1970. A study of estuarine sportfishes in the Biloxi March Complex, Louisiana. Louisiana Wildlife and Fisheries Commission, La. 1-172. Frolander, H. F. 1964. Biological and chemical features of tidal estuaries. J. Water Poll. Contr. Fed. 36(8): 1037-1048. • Fuss, C. M. 1964. Observations on burrowing behavior of the pink i, shrimp, Penaeus duorarum Burkenroad. Bull. Mar. Sci. Gulf & Carib. 14(1): 62-73. Fuss, Co M. 1966. Factors affecting activity and burrowing habits of the pink shrimp, Penaeus duorarum Burkenroad. Biol. Bull. 130(2): 170-191. Gallaway, B. J. 1970. Seasonal abundance, distribution and growth of commercially important marine crustaceans at a hot water discharge in Galveston Bay, Texas. M.S. Thesis, Texas A&M Univ. Gunter, G. 1950. Seasonal population changes and distributions as related to salinity, of certain invertebrates of the Texas coast, including the commercial shrimp. Publ. Inst. Mar. Sci. Univ. Tex. 1(2): 7-51. Gunter, G. 1961. Habitat of juvenile shrimp (Family Penaeidae). Ecology 42(3): 598-600. Gunter, G. 1962. Shrimp landings and production of the state of Texas for the period 1956-1959, with a comparison with other Gulf states. Publ. Inst. Maro Sci. Univ. Tex. 8: 216-226. • VIII -13 • Gunter, G. , J. Y. Christmas & R. Killebrew. 19 64. Some relations of salinity to population distributions of mobile estuarine organisms, with special reference to penaeid shrimp. Ecology 45: 181-185. Herke, W. H. 1971. Use of natural, and semi-impounded, Louisiana tidal marshes as nurseries for fishes and crustaceans. Dissertation, Louisiana State Univ. 242 pp. Hildebrand, H. H. 1954. A study of the fauna of the brown shrimp (P. aztecus Ives) grounds in the western Gulf of Mexico. Publ. Inst. Mar. Sci. Univ. Tex. 3(2): 231-266. Hildebrand, H. H. 1955. A study of the fauna of the pink shrimp (Penaeus duorarum Burkenroad) grounds in the Gulf of Campeche. Publ. Inst. Mar. Sci. Univ. Tex. 4(1): 168-232. Hoese, H. D. 1960a. Juvenile penaeid shrimp in the shallow Gulf of Mexico. Ecology 41(3): 592-593. Hoese, H. Do 1960b. Biotic changes in a bay associated with the end of a drought. Limnol. &Oceanog. 5(3): 326-336. • Hoese, H. D. &R. S. Jones. 1963. Seasonality of large animals in a Texas turtle grass community. Publ. Mar. Sci. Univ. Tex. 9: 37-47. . Hughes, D. A. 1969. Responses to salinity change as a tidal transport mechanism of pink shrimp, Penaeus duorarum. Biol. Bull. 136: 43-53. Inglis, A. 1960. Brown shrimp movements. In Fishery Research, Galveston Biol. Lab. Circ. 92 Washington, D. C. pp. 66-69. Iversen, E. S. 1962. Estimating a populat~on of shrimp by the use of catch per effort and tagging data. Bull. Mar. Sci. Gulf & Carib. 12(3): 350-398. Johnson, M. ~. 1956. Propagation of the white shrimp, Penaeus setiferus (Linn.), in captivity. Tulane Stud. Zool. 4(6): 175-190. Kurz, H. &K. Wagner. 1957. Tidal marshes of the Gulf and Aransas coasts of northern Florida and Charleston, South Carolina. Fla. State Univ. Studies No. 24, 168 p. Kutkuhn, J. H. 1962. Gulf of Mexico commercial shrimp populations ­trends and characteristics, 1956-59. Fish. Bull. 62(212): 343-402 • • VIII -14 I, • Kutkuhn, J. H. 1962. Dynamics of a Penaeid shrimp population and management implications. U.S. Fish Wildl. Serv. Fish. Bull. 65(2): 313-338. Kutkuhn, J. H. 1966. The role of estuaries in the development and perpetuation ·of commercial shrimp resources. Amer. Fish. Soc. Spec. Publ. No. 3: 16-36. Kutkuhn, J. H., H. L. Cook & K. N. Baxter. 1969. Distribution and density of prejuvenile Penaeus shrimp in Galveston entrance and the nearby Gulf of Mexico, Texas. FAO(UN) Fish Rep. 3(57): 1075-10990 Ladd, H. S. 1951. Brackish water and marine assemblages of the Texas coast, with special reference to molluscs. Publ. Inst. Mar. Sci. Univ. Tex. 2(1): 125-164. Lindner, M. J. &W. W. Anderson. 1956. Growth, migrations, spawning and size distribution of shrimp Penaeus setiferus. U.S. Fish. Wildl. Serv. -Fish. Bull. 56: 555-645. Lindner, M. J. & H. L. Cook. 1967. Synopsis of biological data on the white shrimp Penaeus setiferus. Reprinted from FAQ Fish Rep. 57(4): 1970. Lindner, M. J. & H. L. Cook. 1970. Synopsis of biological data on the white shrimp Penaeus setiferus/Fishery taxonomy distribution. FAO(Food Agr. Organ. UN) Fish Rep. 57(4): 1439-1469. • Loesch, H. C. 1962. Ecological observations on penaeid shrimp in Mobile Bay, Alabama. Ph.D. Diss., Texas A&M Univo Louisiana Wildlife and Fisheries Commission. 1971. Cooperative Gulf of Mexico Estuarine Inventory and Study, Louisiana. Phase I Area Description and Phase 4 Biology. New Orleans, La. 1-175. McCoy, E. G. & J. T. Brown. 1968. Preliminary investigations of migration and movement of North Carolina commercial penaeid shrimP.s. Proc. 21st S.E. Ass. Game Fish Comm. 277-295. Menzel, R. W. 1971. CheQklist of the marine fauna and flora of the Apalachee Bay and the St. Georgers Sound area. Contro Dept. of Oceanog., The Fla. State Univ~ 126 p. Mock, C. R. 1966. Natural and altered estuarine habitats of penaeid shrimp. Gulf & Caribo Fisheries Inst., 19th Annual Session, pp. 86-98. Mock, C. R. 1967. Natural and altered estuarine habitats of penaeid • shrim?. Proc. Gulf Carib. Fish. Inst., 19th Ann. Session, pp. 86-98 • VIII -15 • Moore, D. R. 1963. Distribution of the sea grass, Thalassia in the United States. Bull. Mar. Sci. Gulf Caribb., 13(2): 329-342. Munro, J. L. et al. 1967. Counts of larval penaeid shrimp and • oceanographic data from the Tortugas Shelf, Florida, 1962-64. U.S. Fish & Wildl. Ser. Data Rpt. 16. Contr. #218 BCF Galveston, Texas. Nash, C. B. 1947. Environmental characteristics of a river estuary. J. Mar. Res. 6(3): 147-174. Parker, J. C. 1966. A study of the distribution and condition of brown shrimp in the primary nursery areas of the Galveston Bay system, Texas. M.S. Thesis, Texas A&M Univ. 55 p. Parker, J. C. 1970. Distribution of brown shrimp in the Galveston Bay system, Texas, as related to certain hydrographic features and salinity. Contr. Mar. Sci. Univ. Tex. 15. · Pearson, J. Co 19280 Natural history and conservation of Redfish and other commercial Sciaenids on the Texas coast. Bull. Bur. Fish Texas 44:1929. Penfound, W. and E. S. Hathaway. 1938. Plant communities in the marshlands of southeastern Louisiana. Ecological Monographs 8: 1-56• Phillips, R. C. 1969. Temperate grass flats. In H. T. Odum, B. J. Copeland & E. McMahan (eds.). Coastal Ecological Systems of the U.S. -A Source Book for Estuarine Planning. Rpt. to FWPCA, 3 Vol. pp. 737-773. Pullen, E. J. and N. L. Trent. 1969. White shrimp emigration in relation to size, sex, temperature and salinity. FAO Fish. Rep. 3(57): 1001-1014. Reid, G. K. 1955. A summer study of the biology and ecology of East Bay, Texas. Part II. The fish fauna of East Bay, the Gulf beach and summary. Tex. J. Sci. 7: 430-453. Reid, G. K. 1956. Observations on the eulittoral ichthyofauna of the Texas Gulf coast. The Southwestern Naturalist 1(4): 157-1 5. Reid, G. K. 19570 Biologic and hydrographic adjustment in a disturbed Gulf coast estuary. Limnol. and Oceanogr. 2(3): 198-212. Renfro~ W. C. 1964. Life history stages of Gulf of Mexico brown shrimp. Fishery Research Biological Laboratory, Galveston. Circ. Fish. Wildl. Ser., Wash. (183): 94-98 • • VIII -16 • Reynolds, T. D. and W. W. Eckenfelder, Jr. 1970. Reaction rates of Houston ship channel waters, a report to the Texas Water Quality Board and the Galveston Bay Project, Austin, Texas. St. Amant, L. s., K. C. Corkum &J. G. Brown. 1963. Studies on growth dynamics of brown shrimp, Penaeus aztecus, in Louisiana waters. Proc. Gulf Carib. Fish Inst., 15th Ann. Session, 14-26. Saloman, C. H. 1968. Diel and seasonal occurrence of pink shrir.p, Penaeus duorarum Burkenroad, in two divergent habitats of Tampa Bay, Florida. U.S. Fish. Wildl. Serv. Spec. Sci. Rept. Fish. No. 561. Saloman, C. H. 1965. Bait shrimp (Penaeus duorarum) in Tampa Bay, Florida -Biology, Fishery Economics, and changing habitat. U.S. Fish. Wildl. Serv. Spec~ Sci. Rept. Fish. No. 520: 1-16. Shidler, J. K. 1960. Preliminary survey of invertebrate species (Galveston Bay). Tex. Parks & Wildl. Dept. Ann. Rept. 1959-60. Project No. MO. l-R-2. Snow, G. W. 1969. Detailed shrimp statistical program in the Gulf states. FAO(UN) Fish Rep~ 3(57): 947-956. • Subrahmanyam, C. B. 1962. Oxygen consumption in relation to body weight and oxygen tension in the prawn, Penaeus indicus (Milne Edwards) • Proc. Indian Acad. Sci. (B), 55: 152-161. Tabb, D. C., D. L. ~ubrow &R. B. Manning. 1962. The ecology of northern Florida Bay and adjacent estuaries. Tech. Ser. Fla. St. Bd. Conserv. (39): 81. Temple, R. F. 1965. Vertical distribution of the pl anktonic stages of penaeid shrimp. Publ. Inst. Mar. Sci. Univ. Tex. 10: 59-67. Thayer, G. 1968. Nutrient factors controlling estuarine phytoplankton production. U.S. Fish Wildl. Serv. Bur. Commer. Fish. Circ. 309: 9-10. Thomas, T. M. & c. L. Fishback. 1969. A computer program f or handling data on the abundance of shrimp and associated animals. FAO(UN) Fish. Rep. 3(57): 1041-1053. Train, R. E. 1968. The challenge of the estuary. Proc. Nat. Shellfish Ass. 59: 14-17. Trent, L. 1967. Size of brown shrimp and time of emigration from the Galveston Bay system, Texas. Proc. Gulf Carib. Fish. Inst. 19th Ann. Session 7-16• • VIII -17 • Truesdale, F. M. 1969. Some ecological aspects of commercially important decapod crustaceans in low salinity waters. Ph.D. Diss., Texas A&M Univo 164 PPo Weymouth, F. W., M. s. Lindner &W. w. Anderson. 1933. Preliminary report on the life history of the common brown shrimp Penaeus setiferus (Linn.) U.S. Bur. Fish. Bull. 48: 1-26. Wiesepape, L. M. & D. V. Aldrich. 1970. Effects of temperature and salinity on thermal death in postlarval brown shrimp, Penaeus aztecus. Sea Grant Publ. No. TAMU-SG-71-201, 70 p. Williams, A. B. 1969. Penaeid shrimp catch and heat summation -an apparent relationship. FAO(UN) Fish Rep. 3(57): 643-656. Williams, A. B. 1960. The influence of temperature on osmotic regulation in two species of estuarine shrimps (Penaeus). Biological Bulletin 119(3): 560-5710 Williams, A. B. 1958. Substrates as a factor in shrimp distribution. Limnol. &Oceanogr. 3(3): 283-290. • Wood, E. J. F. 1963. A study of the diatom flora of fresh sediments of the south Texas bays and adjacent waters. Publ. Inst. Mar. Sci. Univ. Tex. 9: 237-310 • Zein-Eldin, Z. P. 1963. Effect of salinity on growth of postlarval penaeid shrimp. Biol. Bull. 125(1): 188-196. Zein-Eldin, z. P. & D. V. Aldrich. 1965. Growth and survival of postlarval Penaeus aztecus under controlled conditions of temperature and salinity. Biol. Bull. 129(1): 199-216. Zein-Eldin, z. P. & Go W. Griffith. 1966. The eff ect of temperature upon the growth of laboratory-held postlarval Penaeus azt ecus. Biol. Bull. 131(1) : 186-196. Zein-Eldin, Z. Po &Go W. Griffith. 1969. An appraisal of the effects I, of salinity and temperature on growth and survival of postlarval penaeids. FAQ Fish. Repto 57: 1015-1026. Zein-Eldin, Z. P. & E. Fo Klima. 1965. Effects of injected biologic al stains on oxygen uptake by shrimp. Trans. Amer. Fish. Soc. 94 ( 3) : 277-278 • • VIII -18 • CHAPTER IX Chemical and Physical Separation Procedure for Toxic Materials by W. B. Brogden University of Texas Marine Science Institute Port Aransas, Texas INTRODUCTION Earlier work of Copeland and Fruh (1970) indicated that the toxic or inhibitory factors found in seawater could sometimes be removed by such mild procedures as filtration or autoclaving. Separation techniques designed to separate chemical classes of compounds were intended to be applied to waters already shown to be toxic. The fractions resulting· from such procedures were then • to be analyzed by the bioassay method. Of the procedures planned, only cation exchange and particulate matter,< resuspension were attempted • Methodology The cation exchange experiments involved the use of Chemex 100 resin which has a strong affinity for the divalent transition metals known to be toxic. The resin was washed successively w~th approximately O.SN solutions of HCl and NaOH, with a final wash of HCl. After several distilled water wa$hes, both distilled and clean seawater (collected offshore from ·Port .Aransas) were eluted. These washings were subjected to an al~~l bioassay which indicated that there was no toxic factor due to the ion exchange procedure. At the time of these experiments, no extremely inhibitory or toxic samples had been encountered. Frozen samples from the January 1972 sampling period, known to be moderately inhibitory, were selected for preliminary runs. (Van Baalen, in Chapter V of this report, demonstrated that freezing does not appear to alter the effect of toxic factors.) The problem of exposing the resin to the • water samples without having the resin act as a filter was partially solved by mixing 1.0 ml of wet resin (equivalent to 0.24g dry resin) with 25 to 30 ml of a well mixed suspension of the sample in graduated glass 50 ml Erlenmeyer flasks. Samples, distilled water blanks and suspensions without resin were al lowed to stand for 24 hours with occasional shaking. As the r esin settled much more rapidly than the particulate matter, the sample solution could • • • be removed without decanting the resin. These samples were then subjected to routine algal bioassays. In the second set of experiments, an attempt was made to collect an adequate supply of particulate matter from the five ARS stations during the April sampling trip. A 10 cm. diameter pressure millipore filtration apparatus was used to filter the particulate found that if the samples matter from five gallons of waLer. It was were immediately filtered after they were collected, both the glass fiber prefilter and the membrane filter clogged after passage of a few liters. However, if the samples were allowed to stand for several hours, t he particulate matter coagulated and settled out. If the water sample was then decanted carefully, the settled particulate matter could be collected as a dense suspension in less than 250 ml of water. was The particulate matter collected at stations 22 and 26 removed from the glass fiber prefilters by gentle washing with filtered water and gentle rubbing with a spatula. This recovery was not quantitative, but a roug-h estimat e that the original sample contained more than 100 mg/liter of suspended matter could be made. Samples for algal bioassay were prepared by diluting the concentrated suspension of particulate matter with clean sea water to give final concentrations from 0.1 to 10 times the particulate concentration in the original water samples. The bioassay results were difficult to interpret because of the tendency of the particulate matter to promote clumping of the algal cells. It was possible to determine growth rates for some samples, and score the final algal concentration visually, even on those samples in which growth rates were not measured. Results The results of the cation exchange experiment are shown in Table 1: Table 1. Algal Growth .Rate for Various Stations and Treatments Station Depth Original Exposed to Not Exposed Rate.,,'• Resin.,,'• to Resin.,,'• 1 l ft 1.42 2.12 2.48 33 1 ft 2.00 2.28 2.04 38 Mid-1.25 1.80 2.04 depth Dist. 2.40 2.36 water .,,••1og10 IX -2 I, • The results of particulate matter resuspension are given in Table 2: Table 2o Algal Bioassay of Resuspended Particulate Matter Sampl es St ation . Relative Growth Visual Est . of Remarks Cone. Ratea Final Growthb 22 1 1.92 Unf i l tered Control 10 +3 5 +3 2.5 +3 1 0.9 +2 0.25 1.2 +3 0.1 2.36 +5 • 26 1 1.41 Unfiltered Control 10 +3 5 0.6 +205 2.5 LO +3 1 1. 7 +2 0.1 2.7 +5 Clean Sea Water 2.7 +5 Dist. Water 2.32 ASP2 Control 2.36 aLog10 Growt h Rate bvisual estimation on range from 0 to +5 where +5. is roughl y equivalent to 0.5 mg dry weight of algal cells per millimeter of suspension Discussion It appears that exposure to a cation exchange resin does not have a consistent effect on the toxic or inhibitory nature of the samples. Difficulties encountered in this approach due to the inability to completely resuspend the particulate matter may contribute to this variability• • IX -3 • High concentrations of particulate matter did not depress growth rates proportionately, although increases in conce~tration near that in natural waters behaved in a proportionate manner. Bioassay of unfiltered samples from the same stations does not indicate the same amount of growth inhibition as does t he assay of a reconstituted suspension of filtered particulate at the original concentration. However, this may be due to the fact that samples for filtration and samples for routine bioassay were obtained at different times for each station. Another difficulty noted was that the particulate matter recovered from filtration clumped more readily than the particulates present in the unfiltered water. Summary The ion exchange experi~ ent indicates that the growth inhibitor or toxic factor in selected samples is not a readily exchanged toxic metal cation. The experiments with various concentrations of suspended matter • indicate that this material, suspended in clean sea water can inhibit growth similar to a natural unfiltered sea water of equal particulate concentration. Concentrations of particulate material much greater t han those observed can slow growth but not inhibit it completely. It should be possible to determine a quantitative relationship between growth rate and suspended matter which could be used in modeling efforts • • IX -4 • CHAPTER X Discussion and Summary of Galveston Bay Toxicity Studies by Carl Ho Oppenheimer University of Texas Marine Science Institute Port Aransas, Texas • • • • • Discuss i on T: e sample s~ations for this study were purposely establisne~ in representative areas which wou.ld integrate wate:-from Gulf sources , natural rainfall, and rnanrs e,ffluents as function of general cir culation. W~ile m ny past studies have indicated the toxicity or biological inhibition of effluents, few have followed such para~eters as a function of dilution. In fact , the processes of seaiQentation, chelation, ligand formatio nd biological removal of mater~als dJring water ;,i.xing· has not been understood because of the corr.p=_ex:..ty of the reactioL i~--.. 102-ved .. By takir.g sal.'.ples and deterrnin~ng t ests for areas that represent bo·c>l natural effect and man rs activities one should be able to integr ate the differences between simple dilution and other natural chemical and mechan:..cal processes. The overlapping toxicity and water quality measurements obtained by our team research is quite revealing.. Tr_: ·:c-::.:.. ~. c..~:10unt of t oxicity for all bay stations is not outstanding and may i ndeed indicate the limits of t he various procedures. Wile eacn of the ~nvest~~a~ors indicated some toxicity for some ta.tion t he amount of toxicity for combined stations may not be significant. In the studies of alg-a.l toxicity, turbidity is a factor. Filtered 2amples had little LOXicity while non-filtered sampl~s were somewhat depressing~ _ esp~ratio~ tests for mullet indicated a general metabolic depressio~ t most stations. Shrimp studies indicated an activity depression at station 22. A carbon balance clearly indicated that productivity of t~e Dciys was a major factor in organic carbon distribution, whereas, effluent input was an insignificant part of the carbon balancee Benthic organisms revealeo a significQnt change due to unnatural causes at station 17. Figure l was plotted to see if there was any re2.at~o· _ ~etween the alg-al growth rate in filtered water, -,_LGine Science Institute at Port Aransas) is currently investigating ~he toxic effects of carbon tetrachloride and a freon on estuarine organisms. i, 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 s1..:..m:--:. --:::~1 , practically all of the previous oxicity : .:".<-has been l..:..:·.~::..-ce,::. ·co 'l'Lm investig·ations ·of freshwa-ct:r organisms ; ~--::. rerally limite ' ~o the farnilies Centrarc:.idae (sur.fishes; -::.a'.::2 s, ~-~­ c~appies) , S~lm~nidae (trouts, char;~ and salmolP) , cs:I?rinice:.2 ·'~rue minr ows, exc~usive of car? and goldrish; and Carostomicae (s· ckers). Sewa.g·e and Indust:rial Wastes, 23(11) : 1383-1401, 1951. The absence of definitive TLm studies on estua~:i.ne-depe:-...:-..:. ::-Lt organisms bears the strong implication that current water qual ity standards for bays and estuaries are artificial and have a poor basis of scientific fact.. A major investigai:ive t hrust in this direction is imperative if true quantification of est'...1-.:-.. ine water • ~uality criteria is to be achieved • x -4 • The heavy metal data f or t he waters of the select ed saoples as measured by the U.S~ Geological Survey were analyzeG. ~ Unfort~nately all chemical data we-re not comp~etely placed in our :s~;VIR G.a.·::z:. .:., ~'SJ::. .. nor were they available i n Storet between the last sampli ng period and the time of this report. A complete analysis of ·c :-.e rr,a~ y t~ ousands of data points f or the chemicals in the watei in effluent inpuLs ~n t he bay can only be treated a rter extensive computer co~p~lation ~~ some system that will allow fo~ Lhe data management such as EN"JIR~ It is quite obvious ~-~t although the present study was i~ part organized to provide a si~nificant amounL of information for decision making purposes for the 1971-72 Galveston Bay Study t~ere are obviousuu deficiencies. It is possible that the deficiences are art~ficia_ , to the ::-ct tha-~ the many parts of information derived from t he past and present study carnet be effectively analyzed in the -relati;ely short J;)er:i.-..,d of tirne available for this pr·esent report . Iff~ nsive study o~ ~he available data could produce information L1~t would be valuable for decisio, ~aking purposes. • The co Jp~exity of Ga-veston Bay stands o~~ as one ~ttempts to relate existing data.. While it is obvious tha"'-.: some species a~e changed in the bay the total yie2-for commercial purposes ·has increased over the years. 0 111" ba.ys have been s:..:tject to natural changes of catastro~ ic magnitude in the past and nave survived. The populations that are currently present r epresent a balance between t he natural i mposed chang·es and those imposed by man • One of our problems is to sepo.-a·L:e the cffcc·-s of man and those of the natural environment. When .1." J.ooks at the ·:a~~t:crn scLlbcJar l and th~ effects of urban and j_ndustr·iu.1 complex o·. New Jc.;rs ~y c:t.n l New Yor·' City it is quite obvious there r:tan has OV C"'"'COd1C: ~ is c vi:r'on­ment because of t he injudicious use of his byproducts. In t e Texas bay sys-cerns we are still in a relat ively _atural state, as cor pa red to New Yor:<. d other highly populated environments. Can we use the experience l of cleaning up the Hudson, the Thames and other rivers and apply them to our present problem? References Cope:and, B. J. &T. Jo Bechtel. 1971. Some environme~tal l~c~~3 o~ six i ynportant Galveston Bay species. Cont. 20, Pamlico ~,=c:~ ::::.. . Laj. ~.C. s~ate Univ. Doudoroff, P., B. G. Anderson, G. E. Berdick, P. s. Galt soff, W. B. Hart, R. ?atrick, E. R. Strong, E. W. Surber & W. W. Van Horn. 1951. :ioassay Methods for the Evaluation of Acute Toxicity of Industrial ~astes to Fish. Sewage & Industrial Wastes~ 23(11): 13 0-~4Ci• • x -5 • Gore ;:,_., 'C G., VJ. B. Brogden f,. ~-. s. Hollc:.=--.c ~ j_972. A pr(:: =:_ ~_rninary toxicity anal ysis of t he seciments of La Oui~1ta Cha:_~-::::· , Cor~us Christi Bay, Texas : A r eport pr epar ed f or the u.~~ Army Engineer Distri ct, Galveston, Tex .. ~~iv. Te:x. Ear. Sci. I .st. (Mi meogr ph) Pringle , B. I-i. & C. N. Shuster, Jr. 1967. A Guide to Trace -~~ta:-...s in Shellfish ., l':ortheast Marine Health Sciences L.<.:Cc;.ra.~ory, Narragansett, R.I• • • x -6 • Summary It is quite appr8priate that each of the pr ojects h~v2 drawn their own concl usions ~ased on indiviCua l res~~~s . Js the in~er­disciplinary project i s only one of several , designed to study the bay syste,rl and whose. information wi..... l be used =·~ ;: ::lanage:..ent purposes, it is presumptious for us to draw conclusions . Tr_e final conclusions of the 1971 and 72 study will come f rom a~ -~~ensive study of all the parameters and models simulated during the s~udy periodo However, ther e are some rather important genera.l :.zations that can be se?arated from t he reports of our portion of the grant project. 1. Some level of inhibit ion appears to be present in various parts of t he bay for specific test organisms at certain t:.~es. In one test t he particulate matter was found to be t he inhibiting factor. 2. The marsh and grass flats of t he bays are indispensible as nursery grounds and should be preserved ~t all costs. • 3. Primary pro~~ctivity provides rrost of t he carbon balance of the bay and i mported ~~rjon may be insignificant u~:ess some spec~fic molecules are at an ~~hibitory concentration. Nit_ogen may not be as s ignifi c&:1.t -::_:; -..-::~ oLJ.ght . 4. The bay is st ill in some reproductive equilibrium with nature and man rs a.c·civities. Dilutio~, seci.. entc.t~on and otr.er chemic .1 r-~~:::.~~2 must play a very ~D?Ortant part in reducing the effects of the h~gh:y pol~uted Hocston ship ch~nnel. 5. ;..:;_ i~.".:ens:'..ve years · r esearch is needed to evaluate a 1 : existing r: _ -::3. for management decisions • • x -7 • APPENDIX I-A. Hydrographic data collec. by tb e Marine Science InstitlJt·~' field party during Jour s:1mpling period. • Air Water Dissolved Date _____ _ ; -~m:__ ' Sta.: ~g~~·-·--~e~p. (OC) -~~~J-~Ll-~~~-.-· ----·-----·------­ --··---.. ---· ------... ------. . . - -----·--,,_ --i ---------.---- -----------­?_(./____ 10/ '.L.1_(~00__ 14 _ L___ _.1_? 1 .2__£1 ) _: . 6 J _J(_l_ ~-'-· J_ __?4. 2 _c.laybreak; . r \9udv _______ ___ _ __ ------------­---- ---_----· -+J~300 14·. I ·--... l-'_-~-._~. __.__ .@_;r__ ____ -------~----+-2.].8 Iil:Lf Gi.L. l, . dus.. _}<~cloug:= . __________________ -·~------. 2:1/'ril·;,!·-10415-j 1:1-T --· --23,::J 21 ---~1-G . 5 7 -f,; :,;1· ·-12~ _;/··---i "<".::kson & ·\:·~i-.;~~(~i-~J-c~----=-:-:--~,~~l=-·:_.' ~'tc'l.i':ion --.-:1 ?00 Ir!: _.J . 23 .'; ra 3 I h-:2 r 3 ;· --1-2~~ '! ------·--I ('; -& Hi' ( -, ;i /~ ---0~-;-~t ~--;if-.. . . -----· -----­--·-··- --· 1 2~1-0 ·1;~ ---1 ---23.·(_;----r-··-r8~·r)-·Jj·-1 --- ,--?->. r-.,·----------i \·1·:c:1~~~-c 11. -~:,I' ~ ·--;:J.;.[,.,--rj... 2-k;i ··--------.. ~:~ -~---=-· -~-]~=---T-~~r-~ --_. rj_,~:~--;I:ho.-'.~-~;-.. -T ? '. .(-=-= [.~~--_-----~---~~ --=--·_-_ ==-~----==-~----~~-=---=·: __ _ -· ?" ·, Q[!~· j-07;;11 j•u__J _____ ~; ~ ~.:s;-J{-~~kfa,~ ~ -~->-_ js;.i-L' ·__ \· ]£____ _ -·--j 1-; 3rJ·1·14i ···----23-@ 3~--·Ta~0~--0 24.-2 ··----·-. -Gi (~ H. ,·. ·1-~r----~ ··-· -~-1-···-r·-------r --··-· . _--·--·-----+--------~_______:i___;_ --_ ••• --_---­ --·-····-----· 20~~i~}-[ ;'.~'-=F7 1 -·---~~~ -~~ i~,--=r~-Ft;~ I-}~-'.~~'-(;~~~~'-~ !,'.~~----~--~·-_ ----­ 1 1 24_ j.' _ _ I c:l_&_lh_c:1dy~ J.1 . __________....._ -~----l_ ___ _j___J___ . __ 1_§_~18 1 24.2 _I_ __ _ _______ _ ----·------­---------· 1_2 000 I_,}__ I 23 1'.:l_J_..:______ l._10.0 _@ L._____26.8 L_c~j_&_~--_____ _ _ -------·---------·----·----·-..-­-----I I ~-3?1JSLJ.Jl_'_ . _l.....'l,_1 ,, 18' J 26, CJ ·1.-------..---·---· ------· -----·-··----­ _?' __ ____ __ l_J n.9@ J8__ __l.~1r40 IJ_7 _ L_?,.1_~-:_J~_ ____ __ 1 C.h'~! ~ !-O?~~-~L-!= ·~t~~ni 1U-Y1{i-;=i-~~: ~~--:J ,~~:-~~-----·:: ----==-~: ·---------·-~==-­_ ___ --j Os~s_L1_:/T~:--I~; -\;--r~:~s-ii/;--:=c__~~:-6;,:~-~n-~-clctY: -t1_ =-----------=~-----­-·-· :=1-::-J--= ·r-~23 -~ 'Jj==f·s,o-{ 9' =-'=?3_.n~. --~1=--~~-=-:-:= -----·-.--~=­ 1 1 & H, cl dy I D95·J ! ·17 r23.5 8. 0 ['1 ir -1 .~;--IGi .. ·­ ------~ c. ·-i------• -. ···--------.. .-T--.1!-- --··-~~~. -----·-·-------------------· ---_ , _ _J -------23,~ _[<) 18 1 . 6. 5_1'._ _Jn' -,_?_'! ._:_I:_?____ ----- ·---· .. _______ -·----­ -·--· +1 :?2_c_1 j~L.. / ___ J 24(1' _ __ __ Jl_,Q_J.a _'~---I _2ti:l2_ . __ __ -----·---· -------·----·-----------·-­---. _________ ! _ I_ I 24 [c i 'i . 1I_ _5~ H ·: :-~-r_ 1 __ 25_._Q_ __ . __ _______ _ -··-----··-_____ ____ _ --11~01_-: l.L . 24 ra~,. ---~7. 4\_ .'l.J-~-!-~-'},2..__ ____ F-l_c_i & H ---·---. ·-· ------·-··---------·-----­. -. ----------­ ----I ---2~l~'... -·-'c-·-n ' 1 8__ . L 25. '.25 --------·---------­ --~ . _ 2z_I _ ~-_____. -~_ ·-----. -··-·-­--------~ ____ ._ J.---L_ _____ 2LJ @ 4 T 8. 2 f /j f J />2, ~~S / dj l:QLLL.711 00_._c-_l."'1..________24~ =PB. / :'.-~..J. 22· !~L C __§-_1__ _____ -- _ _____J r~_cr.:si. I_~·J___ j j 24 @ l' ____ _ s.o> i ' ~-I-~/<~-_______ ,.f _G... & J; -~·lr~ s~10 kn-,_~·-._____,, ____ ---_____! . ____L__ 2Li @ 4 r -; • 9' d !] T ,-? :~. G ·---r··-----· ---~---·~----·--------·----· --~----------~-----------------... ------------­ • Ai;f Water Dissolved Date T-j , -,r_; I St a . I Tempo Temp. 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C' i \ ...::_~:1..L___ _,:1. ,__ waY£s;_ ~_:_~-:__J_1i~2~. 2L I_?J)_j + --~~ ~;7--r ---~:,~-;f,-1---ii~-;f-=._(i,':=0~101-~~-~lnn~v~~---,:: .. =-.. ···~ · · ·-··~'--<-·---~----"-~· ,, .. ,_~-~-----'---"~·--· ~-··-=-·-. ~------2~---..-·-··~ l. --!-. l~___l___ t l ~~?H~-1~I _17_841 ' ]~-.--~~--~-----~------~-~-~ JJ rcv!J:~-.. +-' ___ 0 ·-··~·~ !·--~==J"~f .to~~---~j; :-~-~1'J-:t)~~~_t;·-t=__ ii}'f. T;~~~-~:~~~~j ._ -: ~~~~-'::;:: ~~faL'-'"L __ --·--~-­ IFrn~cj:-J·-~---.-~-~-;-JL.-·-·" ..·... ,:·-_'.'""·~ ~-· ·1'·. ·----~ ::;-1 .·I-~------.~-----::--;--·.rt .-:-----:, --.... -­ -.-~:. ~-f . ·--· ]-./ '?Li .___ · ...:!-99_0 .. ~22.. 1. .J-.C -~ ._.:l?_~ @__1__ _ _9, , :.1_l:,~-:! · ... _____ ~i"=-<_~~ -~"-·· S -~ -'-~--cJ 1" ._ny2___l:_~ · ·1y,, !'-E ~ _,-'>. ~::__ ~ : ~-c ,_)____ -----~~--..--­ --·--· ;--...-~.. 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NG ._l -. _J I . ,.._ '-l . -1 " '· t~ -· ,--·--·-· I --lC ··" -~~--] .. ·;:·411-@ 7 r I ---S_·. 1 T i6 '-~t •i •. ·-··8:9~ra13 T 1· ·-· (~--· ·;: f ---·· _,_ 1 1 ----·T-•·--1 --~ _,, -----·--·-­ I • /' • .. \ ., r Air Water Dissolved Date Time Sta. Temp. Temp. (OC) Oxygen Salinity (OC) (mg/l) (o/oo) L/26/72 0300 22 12 15 ra ir 10.44fa l' 9.4ra l' Sat~ & Ho. 15 ra10' 10.44@10' 9.4@10' 0820 22 14.5 16 @ l' 10.09@ l' --ovct, unline, ·5 knt, 0-1' (G) 16 ra 6' 8.552 61 -­16 ICUO' 9.17ralO' -­1030 22 17.5 l6 ra 1' 10.38ra l' 1. 9ra l' (WB.. MH) 16 @10' 10.28@10' 8.5@10' dr, 7/10 cover, 10 mph, E, l ' 1450 22 19 16 ra l' 11. 54@ 1' -­16 @ 6' 11. 45@ 6' --Hzy, ovct, 5 knts, E~ 0-1' (G) 16 ra10' 10.98@10' -­1630 22 20 16 ra l' 11. 96fa l' lo ra l' (G & J) 16 ra 6' 11. 78ra 6' 10 @ 6' 16 ra12' 11. 42@12' 10 @12' 1/27/72 1030 22 17.2 16 @ l'-11. 33@ l' 13 @ l' Hzy, 7/10. 2 knt, SE, 0-1' (B,G, & J) ~ 16 ra 6' 9.59@ 6' 13 @ 6' I 16 @10' 9.10@10' 13 @10' ...... 1230 22 19 i6.5@ l' 11. 54@ l' 9 @ l' Cl, 60%, cvr, SE, 12 knts, 1.5' (RJ & NS) 16. 5ra 6' 11. 13ra 6' 9 ra 6' ­16.2ra 9' l0.61ra 9' --ra 9T . 21 1400 22 19.5 16.5ra l' 11. 28ra l' 12 ra 1 T Clr, 50% cvr~-;-SE't 12 knts, (J & G) 16.5ra 6' 11. 38@ 6' 12 @ 6' 16.5@10' 10.83@ 9' --@ 9' 1645 22 20 17 @ l' 13.07@ l' 10.4@ l' Clr, pt, cld, SE, (J & G) 17 ra 6' 13.25fa 6' --ra 6' 17 ra10' 13.25ra10' l0.4ralO' 1900 22 17.8 16.9@ l' 13.82@ l' 12 @ l' .25 mi, fog, 1/2 cvr, SE, 1-2 mph, 0.5' (RJ & NS) 16.9@ 51 14.10@ 51 12 @ 5' 16.9@10' 13.82@10' 12 @10' 2100 22 17.3 16.9@ l' 13.87@ l' 16 @ l' . 25 mi, 1/2 cvr, SE, 1-2mph, 0. 5' ·(RJ & NS) 16.9@ S' 14.01@ 51 14 @ S' 16.7@ 8' 13.97@ 8' 15 @ 8' I!_._ _ __ C' - .. ---­• --· . ···­· Air ,,,.. Water ) I Dissolved • "'·,· · : "'·.-· ,,. - Date Time Sta. Tempo Temp . (OC) Oxygen Salinity . (OC) (mg/l) (o/oo) . v 2s1 12 2030 26 13° 15 ra 1' 9, 32ra 1' 2 ra J_T (Hn F.,. ~;:it) - .. ··- 15 ra 7' 9. 3~ 7 1 2 ra 71 . -~ ./ 26/72 ~ 1330 26 16.5 15. sra l' 15.sra 71 9.Sl fa 1 1 9.Sl["a 7' 2. 1ra ir --ra 1 1 E.. l' .. 5-8 knt.. cldv .. fair , (WB & MB) -­· - 1905 26 18 16 16 ra 1r @ 5r 8.93ra l' 8.93@ 6' 4 4 ra. ra l' 5r Clr, .SE, lO k.nt , 0-1' CG & ~n 16 ra 8r 8.65ra 8' 4 ra B' . 1/27/72 1930 26 20 17.sra l' 9. J Jra. 1' 41a.J' (H F-.. ~) 17.5ra 6' 9.11ra 6' 4 ra. 6' 17.5@ll' 9.ll!all' 4 ra11r 1/25/72 1630 29 12.2 16.sra l' 16.5@ 2' Jo.oora l' 9.79@ 2' l? ra 1 r 12 @ 2' Vis aood. dense (Gi & Hi) C"Vr. NP. . JS-?0 l< 1515 29 . ·19.5 20.5 @l' 9.05 @ l' 21. 9 @1' H &G; clr; ESE, 15 knts, 2-3' ~. - ~ en 21. 0 @ 3' 8.97@ 3' . 21. 9 @ 3' 4/27/72 1100 ?g 21 22 ra l' 8 _37 ra 1' 23.o ra ir G & B: lt rain.. cldv'I SE .. 12-15 knts .. 18" 22 ra 4' 8.37 ra 4' 23.o ra 4' 22 ra 8' 8.37 ra 8' 23 .o ra 8' 1300 29 21 22.5 ra l' 8.10 ra l' 23.0 @ l' Gordon; 10/10, SE, 12 knts, 1.5' .... 22.5 [a 4' 8.38 ra 4' 23.0 @ 4' ?? _c; ra A' A_ <:R la 8' ?3.o ra 8' 1800 29 20 20.5@ l' 10.02 @1' 20. 8 @ l' JH, NM; unres, 10/10, ESE, 15-20, 2-3' 20. 5 @ 5 T 9.37 @ 5' 19.2 @5' 2000 29 19.15 ra 1' JH.. NM: rain 19.1s ra 2' 2200 29 19.15 [a l' ?4.nn ')q 1g_1s ra 1 1 4/28/72 0200 29 i9.15 @.1 1 0400 29 20. 8 @ 1 T 0600 ?9 20. 8 @1 f -~... ! ·---.,._ .,.... _ .. - ---n• -..--... --,,__ ..,.,... • -. . ?Y~_.._'"!_~ - Air . Water Dissolved Date Time Sta. Tempo Temp. (OC) Oxygen Salinity , c0c) (mg/l) (o/oo) - 1---­ jl.25/72 1435 CL 21.1 230 @l' 7.49 @l' 13 .1 @1 T JSH, NPS, NJM; unres, clr, ESE, 10 knts, 4-6" 23u IQ 4' ->--7 • O_L_@_4T 13.1 IQ ~T .·-­··-230 @]~---6.88 @ 7' --@ 7' 4L21;12 1200 CL -~1.0 21. 5~ @ 1 T 7.21 IQ l' 14.0 IQ_ l' Jackson & Hinson: clr. 100%. SE 5 knts. 61' __ __ 220 @ 4' 6.86 [a 4' 15.3 @4' . -220 __@ 7' 7.04 @ 7' 15.3 [a 7' 1545 CL 22.0 23° [a l' 8.52 ia l' 15.3 ia l' MH, NM: unrs, part ESE, 15 knts, 6" -· -23° @ 4' 8.57 @ 4' 15.3@ 4' . 1 · 230 @8' 8.18 [a 8' --[a 8T 2000 CL ~-0 22. 5 IQ -1.!__~-1.....5_2_@_1' 12.6 IQ l' ~m F-.. R2; ~-4 m;. nPnSP:-NF., 5-J o· knts, capi]. I 22.6 @ 6' 7_.48 @ 6' -­ -· 22.7@ 12' "?_J2.?___@_~~i--­2200 CL 19.5 22.0 @. l' 8. 38 ia l' -1 9.8 @ l' JG & Ji.L3~·4 mi., 10/10, ENE, --~::-J2 knts,c~p:i_~ rair ~ ~-·-. 22.5 @_6' 7. 54 @ 6' l -­ 1---& I ! 22.8 ia 12' 7. 36 @ 12' I -­ - -j--­~ 2350 i CL 20.2 22 .1 @ _l' 7.27 @ l' 10.9 @ l' JG & B2; 3-4 mi, 10/10, ENE, 8-12 knts,caoil, L'd.L1 l I 22.1 @ 6' 7.00 @. 6' t -­ 22.3@. 12' 7. 00 @. 12' -­4/28/72 0210 CL 20.0 2i. o ra l' 7.09 ra l' 10.4 ra l' JG & B2: 3-4 mi. 10/10' ENE~ 10-15 knts ,_c_a:p-0 I 5' ra:rn 21.8 @ 6' 7 ._00 @. 6' -­0404 CL 21.0 21. 5 IQ l' 6.75 IQ l' 10.9 IQ l' JG & B2:3-4 mi, lOLlO, ENE, 8-10 knts, caQil1 21.8 @ 6' 6!57 @. 6' -­0510 CL 21. 0 2i. 5 ra l' 6.69 ra l' 10.9 ra l' JG & B2:3-4 mi, 10/JO, ENE, 8-10 knts. caoil. - 22.0 IQ 6' 6.34 IQ 6' -­ 22.2 @. 12' 6.26' @. 12' -­ . .. -' ,­ - Date f 25/72 ~/26/72 Time 0800 1605 0800 Sta. . 14 14 14 Air . Tempo (OC) 21. 5 21 19 Water Temp . (OC) 26 ra l' 26 @ 4t 25 @l' 25@ 4' 21 ra l' 21 @ 4' i i Dissolved Oxygen (mg/l) 7.66 @ l' 6.94 @4' 7.83 @l' 7.74@4' 7.74 la l' _-z. 92 @ 4' Salinity (o/oo) -26.85 ra 1' 26.85 ra 4' 25.2 ra l' 25.2@ 4' ?R_q la l' 25. ~ @ 4' JG & MH.. ~~ & MH; R, T f... T. 'P: .r , .unlim, O_i.10, B2 ? 2 1. 5-2 ·­--· .. unlim2 hz~2 E2 12 knts 2 -­GD_. SI l 0 .. S.. 20-2S . 3 ' 1.5-2 1215 14 ' -1 -­18.5 21 20 . 20 ra 7' ta l' @ 3' 8. 20 [a 7' 8.Q_@ l' 8.?4 @ 3' 23.o ra 7' 27.4 ra 1' 27.4 @ 3' JH, NG; , .. GR,_ 5/10, ESE, 10::J2, 1.. 5 --­- 1905 2020. 2200 2400 14 14 14 14 ! ·~· - - - -­ 26.3 !al' ·28. 9 la ] ' · 2_6.~laJ' ?6.3 la l' -- - --­ · ~ I ..,.a-- 01].Q_.1_4_--­0350 14 -- -··-· 25.75 -25. 75 la l' la 1' 0740 14 '25.75@ l' ~/27/72 ' 1200 14 21 20 @ l ·' 8.2 26.85 ra l' NG & EW: unlim, cldy, SE.. 18 knts, 1-2 : - 20 ra 3' 8.2 ?h ~c; la ~' .. - ... . .:~~.. .. -· • ---~-·----­ . ' .•. • Air Water Dissolved . )ate Time Sta. Temp. Temp. (OC) Oxygen Salinity . (OC) (mg/l) (o/oo) 10 knt, 0-1 -/25/72 0800 17 23 24 @l' 8.05 @ l' 21. 4 @ 1' NG; 10 mi, clr, NW, 24 @ 24T 9.45 [9 24T -­24 ra 44' 9.64 ra 44' -­1130 17 22 26 ra 1' 8 .10 ra i'. 20. 8 @ 1' JG & MH; good, hzy, N$, 20, 2' 26.5 ra 20' 7. 92 @ 20 T 26.3@ 20' Plume evident ?7 fa 71qT c;. Al ra 71q' 20. 8 fa 39 T 8. 38 @ l' 20. 8 @ 1' RJ & LF; good, 0/10, SE, 15-20, 3' 2000 17 19 23 @ l' 23 @ 3' 9. 48 @ 3' 20. 8 @ 3' 23 ra S' g. 12 ra c;' 20. 8 ra 5' 20 •8 @ 1 T RJ & LF; gd, 0/10, SE, 15-20, 3' 2?1S 17 16 22. 5 ra i-' 9 • 30 @ 1 T ??.S fa V q _s1 ra ~' 20. 8 fa 3T 22.5 @ 5' 9.60 @ 5' 20. 8 @ 5T RJ & LF~ qd, lt, SE, 20-25, 3' L/?b./7? nm i; 17 17 c; ?1 ra l' 8.36 ra 1' 20. 8 ra i' ~.... ' ?2 ra 3' 8.74 ra 3' 20. 8 [a 3' l ~· ... I . ­ 22 ra 5' 8.94 ra 5' 20. 8 [a 5' co ~ 8.56 ra 1' 20. 8 @ l' RJ & LF, qd, 0/10, SE, 15-20, 3' i/?Fi/7? 0?00 17 18 23 ra i' 23 ra 3' 8.94 ra 3' 21. 5 @ 3' 23.5 ra 5' 8.84 ra 5' 20. 8 @ 5' RJ & LF; qd, lt, SE, 20-25, 3.' OLLnO 17 17 21.5 ra i' 8. 35 ra i' 20. 8 @ 1' ?? ra ~' A_q~ ra ~' ?O. 8 ra 3' ' ?? ra c:;r 8.S3 fa c;r 20. 8 ra 5' nhnn 17 17 ?? fa l T "' 8.06 ra 1' 21. 9 fa 1 T RJ & LF: qd, 5/10, ESE, 20-25, 3' 22 ra 3' 9.02 ra 3' 21. 9 [a 3' 21. 9 @ 5 T 22.5 ra 5' 8.64 ra 5' " 1nLJ..c::. 17 '?l ?1 i; ra i ' R R? ra 1' 23 ra l' RJ & LF: ad. hzv, SE, 15, 3' 21. 5 [a 3' 7.93 ra 3' 23 @ 3' ?1 . s ra s' 8.82 ra 5' 24.1 ra 5' 23 @ l' 1320 17 26.5 23 @ l' 8.22 @ l' 23 ra 19' 7.02fCll9' 23 ra 19' 23 ra 38' 5.54 ra 38' . •• • • . -Air Water Dissolved . Date Time Sta. Temp. Temp. (OC) Oxygen Salinity . (OC) (mg/l) (o/oo) G2 8.03 ra lr 15. 3. ra l·r .. 10 mi.. clr.. NNE .. 10 knts .. 2-4r i/25/72 1030 22 24.5 24.5 ra 1r 24. 0 !Ci 5T· 8.96 !a 5' 15.3 !a 5r 24_0 ra 9' 8.96 ra 9' 15.3 ra 9' ·1255 22 21 24.0 ra l' 7.47 ra l' 15.3 !Cl lr JH'INS'lNM; unrest, zilitch, SE., 15-20 k.nts 24.0 !Cl 6' 7. 20 !Ci 6T 15.3 !Cl 6' -. ?4 _n ra 11 r 7_11 ra ,,, lC\ 3 la J]T 1530 22 26 25.0 !Cl l' 8.45 !Cl l' 17.0 @. l' NG; 10 mi, none, NNE, 15 knts, 1-2' ?c; _n ra c;r R_4c:; ra c; r 17.0 ra 5r ?4c;raqr R_hl ra qr 17.0 ra gr ~/?E/7? OROO ?? ?l ?? _c; ra 1.r 7_c;E ra l' 16.4 ra l' ,JC,: cldv.. SSE.. 10-12.. 1-2' ??.S ra E' 7.EE ra h' lh.4 ra 6' 22.5 !Cl 12' 7.92 !Cl 12' 17.0@. 12r NM & MH.. unres .. oart.. SSE'I 15 knts., l-2r 1455 22 22 23 ra l' 8.10 ra l' 15.0 !Cl l' 5r !'.><; 2400 22 20.5 22 ra 4.5' 6.92 ra 4.5' --@ 4.5' I ,;-. @ 9' --.I ~ " 22 @ 9' 6.83 @ 9' -­ ra i' 1.10 ra l' 16.4 @ l' Smith; unrstrd, ovcst, NE, 15-20 knts, 1.5-2' ~/28/72 0200 22 20.5 22 22@ 4.5' 6.92@ 4.5' --:I 22 @ 9' 6.92 @ 9' --.I Smith; unrest, ovcst, NE, 15-20, 1. 5-2' 0410 22 21.5 22 @l' 6.86 @ l' 16.4 @ l' 22 @ 4. 5f 6.86 @ 4.5' -­.... 22 ra 8' 6.86 ra 8' -­15 knts, 1.5-1' 0600 22 20.5 . 22 ra l' 1.11 ra l' 17.0 @l' Smith; hz, 70% StCu, Erly, ?? ra 4_c;r 7.02 ra 4.S' -­22 @ 8' 7.02 @8' -­ ·. . - ' •• • ·c) ~ • I Air Water Dissolved . Date Time Sta. Temp. Temp. (OC) Oxygen SalinitY . (OC) (mg/l) (o/oo) 4/25/72 1012 26 22 23.5 @l' 23.5 @ 6'. 8.19 7.93 ra ra l' 6' 9... 8 ra 9.8 ra l' 6' JH.. NS.. NM: unres .. clr.. E.. 15 k.nts. 23.5 [a 9' 7.93 ra 9' -­ 1245 26 23.5 24.0 [a l' 9.01 ra 1' 13.1 ra l' NG ~ RJ: 10 mi .. clr.. N.. 12-15 knts, 1-2' 24.0 @ 2' 9.09 W'2' 12.0 [a 2' 24. 0 @ 4' 9.86 ra 4' 12.6 ra 4' 1845 26 19 23.3 ra l' 8. 65 ra 1 ' 10_4 ra l' WR F-.. F.t4.7 ~ ~ m; . 0I1 0. F.~F.. 1?-1c; . 1 ' 24.o ra 8' 8.65 ra 8' -­ ra 8 T 2100 26 19 23.5 [al' 7.85 ra l' l0.4 ra l' WB & EW: ----­ 23.5 [a 8' - 7.82 [a 8' -­ 2300 26 . 19 23.o ra l' 7.76 ra l' l0.4 ra 1' WB & EW: clr.. 0/10.. ESE .. 8-10 .. l' · 23.5 ICl 8' 7. 68 ra 8' -­ 4/26/72 0100 26 17.5 23.0 @l' 7.79 ra l' 9.8 ra l' WB & EW: -­.. 3/10, ENE, 12-18.. l' 23.0 [a 8' 1.22 ra 8' 9.8 ra 8!' - :>< 0300 26 . . 16. 2 22.5@ l' · 1.82 @l' 9.8 @l' WB & EW: --~ 3)10, ENE, 12-18 k.nts, 1.5' . t\:) t\:) 22.5 ra 8' 7. 33 ra ·8' -­ 0500 26 15.5 2i. 5 ra l' 8.08 ra 1' 9.8 ra l' WB & EW: clr. ?/10, F.NP. .. ?0-?E. l.S-?' 22 fa 8T 8.08 ra 8' -­ 2230 26 22.5 23 ra l' 70 70 ra l T 14. 8 ra l T Jackson: clr.. 10/10.. SE .. 20-25 knt.. 2' ?? s ra - 4' R sc; ra 4' 14 R ra 41 --·~ ... ??.S ra 71 8.8? ra 7' 14.R ra 7' 4/27/72 0200 ?6 ?1 . 5 ?? . o ra 1 ' 22.0 @ 4' 7.7S 8. 28 ra 11 @ 4' 14 R ra 1' 14.8 @4' ,T;:i('lk~rm·- ('11-r.. ~/10. ~F.. . 1c;-?O.. 1 S' 22.5 ra 7' 8~72 ra 7' 14.8 ra 7' 0500 26 22.5 23 ra l' ( J 7.8o ra l' 14.8 ra l' Jackson: clr.. 10/10.. SE.. 15-20.. 1.5' 23 [a 4' 7.88 ra 4' 14.8 ra 4' 23 [a 7' 8. 50 ra 7' 14.8 [a 7' 0915 26 21. 5 22 @l' 7.66 [al' 12.0 @ l' Jackson & Hinson: Fe, 95/100, SE, 15, 1. 5 22 ra· 4' 7.75 [a 4' 13.1 ra 4' 22.5 ICl 6' 7.57 ra 6' 13.1 ra 6' 4/26/72 2400 26 21. 5 22.5 @ l' 7.84 @1' i4. 8 @l' Jackson; clr, 50/100, SE, 15-20, 1.5' 22.5 @ 4' 8.55 @4' 14. 8 @ 4' 22.5 @ 7' 8.82 @ 7' 14. 8 @ 7' • • . • Air Water Dissolved . Date Time Sta. Temp. Temp. (OC) Oxygen sa1initY . (OC) (mg/l) (o/oo) /27/72 1155 26 22 22 ra l' s.10 ra 1' 12. 0 @ 1' MH &NM; unres, part, ESE, 15, 2-3' 22 ra 6' 1. 75 ra 6' 12.0 ra 6' 22 @9' 7.75@ 9' -­ v' ' - - ----· ­ 1120 CleaJ 28 - ·-­ Lake . ----­1315 CL 28.5 --------.. ·-­1245 CL 31. 0 ' 31.0 1830 CL 28. 3 ---. 0730 CL --27.8 . 0755 CL ·-27.0 0800 CL-3 28.l OR c;1 ("T ?R_l 1150 CL-1•~ 28.8 1350 CLl-4 27.8 17Qc; rT.14 ?7_? - 2000 CL13 26.0 2200 CL 24.9 0001 CL 27.0 . .. 0?00 rT. ?7_Q 0400 (ll I 24.5 ----. 29 @ i' 30 ra --E)T -­ 30.S ra l' 30. 5 fa 6' 31. 0 @ l' 31.0 @ 6' 31.7@ l' 31. 8 ra 6' 31. 3 ra l' 33 ra l'---. 32 ra 7' 33.o ra l' 32.2 ra 7' -· 7;n _o ra 1' 30.0 @ 61 31. 3 @ l' 31.0 @ 61 3LO ra 13' . 31.l ra 1 r 31.1 ra 5' -i;1 _ c; ra 1 ' · 31. 9 ra 6' 7;1 _? ra i~' i 3L 9 ra l' ! 3L8 ra 6' 1 31.. 5 ra l' : 31.9 ra 6' . 33 @ l' '21? ra h' ~? ra l' 32.1 ra 6' 1 1 3L 2 ra 31.1 @ 6' 8.i4 ra l' ----1 •8s ra 6 ' 9. 40-ra l' 9.35 @ 6' -7. 69 @1' - 7 .4i @ 6' a:18-@ l' i:·79 ra -6' :f. 19 ra l' ~ ·5-~ 67 ra 1 ' -·" 4.37 ra 7' 4. 10 --ra 1' 4.05 ra 7' 4_R la l' ---· ­ 4.8 @ 61 7.17 @ -l' 5.07 @6' 4.99 la 131 i1 5.40 ra 5 1 5.96 ra . -· q_7 ra 1' 8.03 @ -6, c;_ 75 ra 13' l0.75 ra l' lo.oo ra 6' ---·­ 7.46 ra l' 7.5o ra 6' 6.15 @ 1 1, f.) _ qf) ra 6' 6.12 ra l' 6.04 @ 6' 6. 41 ra l '­5.J4 @ 6' 17 ra l' 16.5 @ 6' 17 @·--1' 16 @ 6' ·-. 16" @-i'"" 16-@- -6-, 18 gf l' -. . --··--­ 18 ra 6' 12 @ l' 11 @ l' --·. ·---­ 12 ra 1' 10 @ l' --·---­ 1~.s ra 1' 13.5 @ 6' ----· --· -----­ 9 @ l' --. .. -· --­ 11 @ 6' ---·-. 11 ra 13' 11 ra l' -­12 ra 5' --,. ­ -11_c; ra J' 13.0 @6' ---------·---­ --.. -·--. 11. 5 ra l' ------· -­ 11. o ra 6' 11. o ra l' 12.5 @6' 10 @ l' ---- --. 14..s ra 6' .. 14.0 ra l' 14.S @ 6' 12. 0--@ i""" .. ­14.-6"" @-5-r--. ---- --·­ Vis qood, O wind _vel, 2-6" waves; JSH,NJM,DB - Good, East wind, ~-1 knt, 2-6rr: JSH.. NJM.,DB . --·--­40/100, East,_ ?_~8 knt, 6rr; JG,RC,MOH 20/100, East, _5-10_ knt, O.OS' .. S, 1-2 knt_s, O: NS.. WB .. Buoy 14 , -, 0-1 kn.t, _9api1. NS, WB, Buoy 17 NS, WB .. Buov 3 ---­NS. WB .. Kemah l _andincr NS, WB; , W, 2-:-~ 1<.nts, -; Buoy 14 NS .. WB: unres_'l 4Ll0, S, 8-10, 3-4rr = Ruov i4 -NS. WR~ . S. _5=._]_ _knts.. 0. 2rr. Buov 14 ··-... NM.. LF.. MH: clr~. SS_E.._ 1-2 cts.. 2-6" = Buov 13 ·-· ··· · ­ - ---.. ---· -. - --------- . - --· ---·-· . --·------­- -· ­ --·-­ ·----·----· -­ . -Date 28/7/72 - Time ---­--. 0515 --­-- Sta. . . ·-­('TI Air Temp. (OC) -25.0 Water Temp. (OC) -. -· .. 32.0 ra l' 32. o··-·ra 6' ,_. . Dissolved Oxygen (rrq/l) 1.45 ra l' 5·:35 ra 6' Salinity (o/oo) i? ra l' 13 ra 6' -· - . -· -·­-· --·­ -­-- - -. -. - - - . - -­ -. - 25/7/72 0700 14 . -. . ­28 28. 5 ra 1' - . . S.84 ra l' 32 ra 1' - JG F... MOH: -·--­---unres ... NF.. -­J ··­knt-. . 3-S" 26/7/72 1330 1215 14 . ­-14 - 28 30.5 28. 5 ra 4' 31. 5 ra i' 31.0 @ 4' 31. o ra l' 29.5 ra 4' - 6.16 ra 4' 7.9 ra l'­7~9 @ 4' 7.3 ra l' 6.6 ra 4' 32 ra 4' · -. 30.5 ra i' 29.0 ­@ 4' 32.0. @ l' 32.0 ·@.4' --... -­------­-JG & MH: unres ... . E.. _5 knts.. 2" ----­KF .. JH & LF: unre_s_.. 3-5 knts .. 5-10 ---. cm. - I 1345 14 29.2 29. o 30 ra ra 6' l' 6.6 ra 6' -­-11. 7 ra -1' ·32.5 ra . -l' - Unres .. JH.. KG .. -. LE - 30. s ra 6' 11.7 ra 6' 32.5 ra 6' . --­ - v I N> VI ' 27/7/72 2000 2200 2400 0200 0400 14 14 14 . 14 . ·­-14 28.5 28.0 28.0 26.5 28.0 32.0 ra 32. o ra 30.0 ra 31. 0 ia 31.0 ra ~l • 0 fa 30. o ra 29.5 ra 30.0 ra i' 6' i' 6' l' 3T l' 3' l' 8.78 ra i' l0.4 ra 6' 8. 68 @1' -·-8.22 ra 6' -­. . -8.S2faJ' --­· 7.32 ra 3' 7.08ra l' 1.1s ra 3' 7.3s ra l' 33 ra i' -. .. ----­34 ra 6' ---- . 34 @ l' -·· · ··­--, 33.5 ra 6' -·-­·-. . --. 34_0 ra l' ~4.S la 6' . . ----­---­35. o ra1' . --34.o ra 3' . -34.5 ra l' Remalev.. EW: qoodl)__ SSE, 3-5 knts .. 6" ---Remaley & EW, .qoQd_,__ SE, 5-7 knts, 5n -· · .. RPmalev & F.W: aeon . .J0-12 knts .. l' -· -­--Remalev & EW: q_o_on . ..SSE.. 10 knts.. l' --TR & EW; qood, _SSE,_ 8-10 knts, l' 0600 14 27.0 31. 5 ra 30. o ra 3' i' 7.58 7. 68 ra 3' @ l' --· 34.5 ra 3' --·­-­-. . -34.5 ra l' - ­- TR & EW; qood, _ s.'l.. s_-8 knts, l' knt, 29. 0 @ 3' 8.82 @ 3' 33.5 @ 3' ·- 1010 14 30.5 30. 0 @ l' 6.38 @ l' 32.5 @ l' -·-­· ­·­- RC & KG; unlim, _h 2knts, 5"-1' 30. o ra 3' 6. oo ra 3' 32.5 ra 3' --- -­ ... ~ . . . -­ ---­·­ . . 25/7/72 0800 : 17 28. 0 29.o ra l' 1.03 ra l' _ 18 ra i' 29.o ra 12' 6.56@ 12' 18.5 @12' ---·-·· -. ­ -­ -- -­-­ --­ : 29.o ra 25' 6.61 ra ·· ·­ 25' 18.0 ra ·­-­- 25' . . ---­- -----­ 4 -.--­--­---·-· - --- ---------­-­- .. ·· -­ • - Air Water Dissolved late Time Sta. Temp. Temp. (OC) Oxygen . (OC) (mg/l) --· -. --.. -.. - - ----·--.. - --. - ;/7/72 1400 . .. 17 32.0 31 ra 1r 8.S61alr -~ ·---31. S ra 1 R r -i. 63 ia HP --··---­ 31. 5 @ 28 ,-. --7.i6 @.. 28' -. ­ 2010 17 32.0 30.5 ra lr 9.45 ra lr 30.5 fa 9r 9.6o ra 9' 30. 0 @18-,-7.40 @19r ·­ ??~n 17 7;0 , ra , r ra ~? n R ?R 1 ' --. ---,. .. ,.._. -· -----­ 30.1 ra 9r 1.11 ra 9' -----·· . 30. 0 [a' 18' ~7. 41 fa 18 T -·--­ ;/7/72 0000 17 32.0 30.1 @ir 8~13 @lr - ~n.2 ra gr 7.63 ra gr ... 30.0 ra 18' 7.03 [Cl 18' 0?00 J7 3?.0 7;n. o ra _, ' FL ?S ·---la 1 r 30.0 @9' 8.-57 @9' I 30.0 @18' 8.34@ 18r i......1 l'\.'l 05 04?0 17 32.0 30.o ra lr 7.4 ra lr 30.0 @ 9r 7.25 @ 9' 30. o ra 18' 7.4 ra 18r 0605 17 30.0 29.2 ra l' 6.66 ra 1' 29. 5 ra 9r 6.89 @9r 7.17 @18r 30.0 @18 1 ... --~ 0820 17 --·-30.0 30. o ra 1' -6. 96 ra ] r 30.0 @ 9 1 1.26 ra 9' -·· 30.o ra 18' 7.96 @18' 1000 17 30.0 29.9@ lr 7.54 @l' 29.8 @ 91 8.20 @ 9' 29.7 ra 18' 7.43 ra 18' 1340 17 30.7 30.9 ra l' 9.o ra l' ­ 29.S ICl 10' 7. 88 ra ·1o' 29.2 ra 20' 3. 30 ra 20' 17l5 17 29.8 29 .. 9 ra 1' 9.08 (Ci l' L~.9 ts 1U 1 8.33 @101 29. 5 ts 20 J 4. 32 -g20_' . --­ Salinity (o/oo) -. -­ --·· ·-­?c; ra i r .Tr~ F-M()H. <"'1T' • .. F.. c; l