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THE HEURISTIC ANALYSIS OF TEMPORAL AND SPATIAL VARIATION WITHIN THE ZOOPLANKTON COMMUNITY STRUCTURE AT THE CRYSTAL RIVER ESTUARY: A MULTIVARIATE APPROACH BY William Ingram III A DISSERTATION PRESENTED TO THE GRADUATE COUNCIL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY UNIVERSITY OF FLORIDA 1980 ACKNOWLEDGEMENTS I can not possibly acknowledge all the faculty, staff, fellow students and friends who have contributed materially to the fruition of this effort. However, I would like to specially acknowledge a few individuals who were instru mental in the realization of this dissertation. First, without my wife Janice's steady encouragement, support, aid in preparation of the manuscript, and unending patience, it would have been impossible to complete the dissertation. To Dr. Frank J. S. Maturo, Jr. goes my sincere appreci ation for his support in this project. His suggestions dur ing the planning of the project and the critical review he provided during the writing, improved the clarity and organization of the dissertation. Through a long association with Dr. James T. McClave, on this project and others, I have gained an appreciation for the art and science of data analysis. This skill was central to the accomplishment of this research project. This study was supported by a contract with the Florida Power Corporation, through the University of Florida Marine Laboratory. The Computing facilities were provided by the North East Regional Data Center (NERDC) and the Center for Instructional and Research Computing Activities. TABLE OF CONTENTS SECTION PAGE ACKNOWLEDGEMENTS ....................... ............ ii LIST OF TABLES .................................... vi LIST OF FIGURES .................................. vii ABSTRACT ........................................ ix I. INTRODUCTION ................................... 1 II. MATERIALS AND METHODS ........................ 4 Study Site .................................. 4 Physical features ...................... 4 Hydrography ............. ............... 11 Hydrographic areas .................... 11 Circulation patterns .................. 13 Field Sampling ............................. 15 Station placement ...................... 15 Sample collection technique ........... 19 Sample size determination ............. 21 Biweekly Sampling Program .............. 23 Diurnal Sampling Program ............... 24 Laboratory Methods ........................... 26 Sample Processing ....................... 26 Zooplankton Categories ................. 27 Biomass Determination .................... 28 Data Analysis ................................. 29 General Considerations ................... 29 Approaches to community structure analysis ............................ 31 Heuristic analysis ................. 35 Analysis models ..................... 38 Underlying assumptions .............. 39 Selecting the analysis model ........ 40 Displaying the results .............. 42 Biweekly Analysis ........................ 43 Determination of seasonality ........ 43 Community structure analysis ........ 47 Structure plot analysis ............. 48 Canonical discriminant analysis ..... 48 Diurnal Analysis ......................... 54 Diurnal response surface analysis ... 56 Dependent Variable Transformations ....... 60 III. Results ...................................... 62 Biweekly Analyses ............................. 62 Determination of Seasonal Boundaries ..... 62 Biweekly Canonical Discriminant Analysis 70 Canonical discriminant function I ... 78 Canonical discriminant function II .. 80 Canonical discriminant function III 82 'ooplankton Community Structure Analysis 84 Factor 1 ............................ 93 Factor 2 ........................... 96 Factor 3 ........................... 97 Structure Plot (SPLOTS) Analysis.......... 97 Diurnal Series ............................... 100 Factor Analysis ......................... 100 Stepwise Regression ..................... 102 Factor Loadings ......................... 116 Factor 1............................ 118 Factor 2 ............................ 119 Factor 4............................ 119 Factor 5............................. 121 Factor 7 ............................ 121 IV. Discussion..................................... 122 Biweekly Data ................................. 122 Canonical Discriminant Analysis.......... 122 Community Structure Factor Analysis....... 129 Diurnal Data................................... 135 Associations of Community Structure with the Environment..................... 135 V. Summary and Conclusion ......................... 145 References.......................................... 150 Appendices.......................................... 157 Appendix 1: Environmental Parameters......... 158 Appendix 2 : Biweekly Statistics.............. 173 Appendix 3 : Seasonal Distribution Maps....... 374 Appendix 4 : Diurnal Stepwise Regression...... 529 Appendix 5 : Diurnal Response Surfaces......... 556 LIST OF TABLES TABLE PAGE 1. Correlation coefficients between each canonical variable and the dependent variables .......................... 65 2. Determination of the number of CDFA axes to retain for biweekly data ........... 71 3. Correlation coefficients between each canonical variate and the dependent variables for the biweekly data analysis ........ 85 4. Portion of zooplankton community structure variation explained by the factor analysis ...... 86 5. ANOVA for biweekly community structure factor analysis ................................. 94 6. Rotated factor pattern loadings of the dependent variables for the biweekly data ............................................ 95 7. Rotated factor pattern loadings of the dependent variables for the diurnal data analysis ........................... 101 8. Portion of the zooplankton diurnal variation explained by the factor analysis ............................. 103 9. Parameters selected by stepwise regression for each factor ...................... 104 10. Response surface parameter coefficients for diurnal analysis ............................ 105 vi LIST OF FIGURES FIGURE PAGE 1. Hydrographic zones and location of the study region ............................... 6 2. Physiographic features and station placement ..... 18 3. Cluster dendrogram of biweekly environmental variable mean vectors ( a= 1st biweekly in month, b= 2nd biweekly in month) ............. 63 4. Season means for CDF axis I and CDF axis II ...... 67 5. Plot of the geographic distribution of canonical variate I. (a) Fall, (b) Winter, (c) Spring, (d) Summer ............................ 73 6. Plot of the geographic distribution of canonical variate II. (a) Fall, (b) Winter, (c) Spring, (d) Summer ............................ 75 7. Plot of the geographic distribution of canonical variate III. (a) Fall, (b) Winter, (c) Spring, (d) Summer ............................ 77 8. Plot of the geographic distribution of factor scores for factor 1. (a) Fall, (b) Winter, (c) Spring, d) Summer ................ 88 9. Plot of the geographic distribution of factor scores for factor 2. (a) Fall, (b) Winter, (c) Spring, d) Summer ................ 90 10. Plot of the geographic distribution of factor scores for factor 3. (a) Fall, (b) Winter, (c) Spring, d) Summer ................ 92 11. Structure plots (SPLOTS) for the factor scores for the three factors. (a) Factor 1 (b) Factor 2, (c) Factor 3 ....................... 99 FIGURE 12. Response surface plot for diurnal factor scores for Factor 1 ............................ .. 107 13. Response surface plot for diurnal factor scores for Factor 2 .............................. 109 14. Response surface plot for diurnal factor scores for Factor 4 .............................. 111 15. Response surface plot for diurnal factor scores for Factor 5 .............................. 113 16. Response surface plot for diurnal factor scores for Factor 7 .............................. 115 viii PAGE Abstract of Dissertation Presented to the Graduate Council of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy HEURISTIC ANALYSIS OF TEMPORAL AND SPATIAL VARIATION WITHIN THE ZOOPLANKTON COMMUNITY STRUCTURE AT THE CRYSTAL RIVER ESTUARY: A MULTIVARIATE APPROACH William Ingram III August, 1980 Chairman: Frank J. S. Maturo, Jr. Major Department: Zoology A study was made of the spatial and temporal variations of the zooplankton community at the Crystal River estuary. Included in the study were considerations of the effects of power plant operations on the structure of the zooplankton community. The power plant was seen to have a discernible effect on the community structure and its patterns of variation. The estuary was divided into 6 regions based on its physical features. The environmental data collected during the study were used to determine the seasons. The interactive effects of season and region on the zooplankton community structure were investigated through nultivariate pattern recognition techniques. These effects were most apparent during the warm seasons of the study. The zooplankton community structure differences separated the estuary into 2 regions: an inshore region consisting of the inner estuary and the discharge side portion of the mixing zone, and an outer region. The zooplankton community differences were related to shifts in the standing crop of thermally sensitive or thermally tolerant organisms. The thermally sensitive organisms included: Paracalanus quasimoto, Lucifer sp., and Labidocera sp. These are neritic organisms that are not adapted to the high ambient water temperatures found in a subtropical estuary during the spring and summer months. The thermally tolerant organisms, which had higher standing crops in the inshore areas, included: bivalve larvae, Temora turbinata, barnacle larvae, and Tortanus setacaudatus. These represent coastal zooplankters adapted to higher ambient water temperatures usually found in the inshore regions of subtropical estuary systems. The differences between the seasonregion combinations represent a differential response of the zooplankters to the selection pressure of thermal stress and the influence of the power plant operation. These differences are subtle and do not include the dominant members of the zooplankton community. The zooplankton community structure was decomposed by factor analysis into 3 basic components: the seasonally abundant meroplankters, the neritic holoplankters and the The zooplankton community structure was decomposed by factor analysis into 3 basic components: the seasonally abundant meroplankters, the neritic holoplankters and the coastal or estuarine holoplankters. The response surface analysis of the diurnal data estimates from field data the fitness surface for a group of zooplankton categories. The fitness set mappings showed temperature and salinity as the most important environmental factors affecting the distribution of the zooplankton. This indicates that the summertime temperaturesalinity combinations observed in the discharge canal and thermal plume areas represent suboptimal witnesses for all components of the zooplankton community. The results of these analyses validate earlier laboratory and field studies. This validation is necessary, especially for laboratory studies, to assure that the conclusions drawn by the earlier studies are supported by data collected in a field survey. The analysis procedures followed provided an orderly process for the successful investigation of a set of "noisy," ecological field survey data. The approach employed in this study can serve as a model for analysis procedures in similar studies. SECTION I INTRODUCTION The importance of the zooplankton community to the ma rine, and particularly to the estuarine, environments has long been recognized. In the estuarine regions, the zoo plankton serve as the primary food sources for many larger consumers. As a result of their short generation times and high metabolic rates, they are important in energy and nu trient cycling within the estuarine environment (Gunter, 1967; de Sylva, 1973; Williams et al., 1968). The fact that many larger, often commercially impor tant, marine species spend an appreciable portion of their immature life as temporary members of the planktonic commu nity adds to the importance of the zooplankton. Coastal regions and estuarine environments often function as spawning and nursery grounds for nonresident species, many of which are commercially important (Gunter, 1967). Thus, the estuarine areas of the world are well noted for their contributions to the productivity and stability of marine ecosystems. The productivity of these regions is important to man not only as a food source, but also for recreational functions (Reeve, 1973). In addition, the estuaries and other coastal regions are important to man as a sink for his waste products (Cronin, 1967; Biglove and LaFleur, 1967; DeFalco, 1967). Thermal waste is one of these products that has recently be come of interest to researchers (Odum and Kroodsma, 1976). The consideration of the impact of thermal wastes on the en vironment is of increased importance and concern in a tropi cal or subtropical environment, such as is found in Flori da. Although estuarine organisms have typically evolved be havioral and physiological mechanisms that enable them to a dapt to large fluctuations in temperature and salinity (Hutchinson, 1976; Kinne, 1964; Kinne, 1967), maninduced perturbations to the magnitude and timing of these changes may not be tolerated by the estuarine species. With the recognition of the importance of the zooplank ton community to the estuarine environment and the potential impact of thermal pollution in a subtropical environment, this study was designed to investigate variation in the na tural and thermally stressed environments of a subtropical estuary. The Crystal River estuary located in the vicinity of a Florida Power Corporation steam generating plant provided the ideal study site. This area represented the typical shallow water, middle salinity estuary found in the sub tropical Gulf coast region of Florida. The study consisted of two general components for data collection: a diurnal series and a biweekly series. Each component was designed to investigate a different portion of the community variation problem. The diurnal series collected data on the short term diel variation of the zooplankton community at differ ent localities and seasons. These data were used to analyze the effect of environmental parameter variation on the community. The parameters considered fell into five general areas: localized microenvironmental conditions, spatial positioning within the estuary (and to some extent within the water column), thermal addition, temporal and tidal cycles. The biweekly series collected data on the longterm, or macro, variations experienced by the zooplankton community. Environmental conditions were generally integrated over time, thermal addition operating characteristics, seasonal factors and spatial factors were examined for their effect on the zooplankton community. SECTION II MATERIALS AND METHODS Study Site Physical Features The plant site is in Citrus County, Florida, approxi mately 12 km north of the town of Crystal River (fig. 1). For the duration of this study the power plant consisted of two oil fired steam generating units with a combined capaci ty of 897 megawatts (Mw). The cooling system was a "once through" type with a flow of 2410 m3/min. The maximum tem perature rise from intake to discharge was designed to be 6.1 C. Unit 1 began operation in July 1966; Unit 2 began operation in November 1969. Unit 3, under construction dur ing the study period, began operation in July 1977. Unit 3 requires an additional 2580 m3/min. of cooling water flow. The combined system has a net temperature rise of approx imately 8.1 C. The power plant is on the landward edge r_ 0 B' U4 r, 4J 0 X: 0 4J 0 *r. 0 u 04 0 U 0 a 'Ir r ,.? O :'? \ 'r r~ m of a tidal salt marsh. This marsh, dominated by Juncus roemarianus, and others similar to it cover large areas along the Gulf coast of Florida, often extending one or two kilometers landward. Narrow bands of Spartina occur along the marsh Gulf interface. The coastal shelf adjacent to the plant site is part of the drowned limestone karst topography characteristic of this portion of west central Florida. It has a shallow sloping bottom (45 km to the 9 m contour) and extends far into the Gulf of Mexico (230 km to the 100 m contour) (Jones et al., 1973). This area is part of what Tanner(1960) and Walton(1973) have classified as the low waveenergy section of the Florida Gulf Coast. The immediate coastal area contiguous to the plant site is comprised of a series of shallow basins separated by oys ter reefs. These oyster reefs have developed parallel to the coastline and extend seaward 3 to 4 km. Among these reefs are the estuarine bays characteristic of this area. These bays recieve freshwater input from terrestrial runoff and from two rivers. The salinities range from 17 to 30 ppt and the normal water temperatures range from 14 to 30 C. (McNulty et al., 1972; Maturo et al., 1974). Two major community types occur within these estuarine bays. On the landward edge of the reef systems are the inner bays, characterized by shallow flats which are often exposed at low tides or by strong winds. The average depth of these bays is approximately one to two m. The average tidal range in this region is approximately one m. This system tends to be benthic dominated with dense summer growths of sea grasses (Halodule, Thalassia, etc.). Seaward of the shallow inner bays, the depth increases to an average of two to three meters. These outer bays are characterized by deeper flats where species of shade adapted red and green algae (Gracilaria, Spyridia, and Caulerpa) dominate the benthic flora (Van Tine, 1974; and McKellar, 1976). In these deeper bays the phytoplankton gain an im portant role in the daily production of organic matter. Patches of attached Sargassum are also common. Bay ecosys tems similar to these at Crystal River have been described from the Cedar Key region (Reid, 1954). The two major freshwater sources to the area are the Crystal River 4.8 km to the south, with an average flow of 1500 m3/minute, and the WithlacoocheeCross Florida Barge Canal complex, with an average flow of 2150 m3/minute. The average combined flow of these freshwater sources is about 1.5 times the circulating water flow of the power plant. The Withlacoochee River flows into the Gulf of Mexico end of the unfinished CrossFlorida Barge Canal. Spoil islands from the Barge Canal continue approximately 6.5 km into the Gulf. These as well as the cooling water canal dikes for the power plant, influence the local hydrographic circu lation patterns described below. Pritchard (1967) defined an estuary as .a semien closed coastal body of water which has a free connection with the open sea and within which sea water is measurably diluted with fresh water derived from land drainage. He further identified four classes of estuaries: (1) drowned river valleys, (2) fjordtype estuaries, (3) barbuilt estuaries, and (4) estuaries produced by tectonic processes. The Crystal River area fits the definition of a barbuilt estuary (Pritchard, 1967) as modified by Caspers (1967). Caspers points out that many definitions of estuar ies are remarkably similar to and often include lagoons. Estuaries are distinguished from lagoons by examining the stability of their salinity. When the inflow of fresh water into a separated basin develops a stable body of brackish water, which is relatively uniform throughout the area, this is termed a lagoon. However, if the mixing of fresh and marine waters is not stable, but shows periodic changes, the basin may be considered an estuary. This latter definition applies to the oyster reef bay system at Crystal River. Thus, although the region does not possess the physical features of the classical estuary, as demonstrated by the Chesapeake Bay estuary, it does exhibit the salinity gradi ents and instabilities observed in classical estuaries. In addition, the oyster bar estuary system found at Crystal River is characteristic of near shore systems all along the Gulf coast (Odum et al., 1974). The intake and discharge canals have been cut through the salt marsh and coastal bay systems west of the plant site, displacing approximately 1.1 km2 of marsh and 1.9km2 of bay (fig. 1). The intake channel extends from the plant approximately 12.5 km into the Gulf of Mexico. It is laterally confined with double bulkheading for the initial 5 km. The mean depth of the intake canal is approximately 6.5m, which is in contrast to the 1 to 3 m depth of the adjacent bays. The width at mean low water (MLW) ranges from 90 to 110 m. The intake canal was designed to accommodate the movement of fuel barges to and from the plant. The intake current velocity averages 9 cm/sec (Carder, et al., 1974). The discharge canal is significantly shorter than the intake canal. The double bulkhead portion of the discharge canal is approximately 2 km in length. The entire length of the canal is 3.8 km. The discharge canal was designed with a smaller crosssectional area (4.5 m deep, 60 m wide) in order to maintain a higher velocity (20 cm/sec.) and in sure adequate lateral flow entrainment and mixing upon dis charge to the shallower bay receiving waters (Carder et al., 1974). Hydrography Detailed descriptions of the hydrography have been pre sented in technical reports to an environmental impact study group (Florida Power Corporation, 1974). The methods em ployed during the hydrographic studies included: salinity temperaturetime series, dye and drogue movement time series, and circulation modelling and simulation. The pertinent features of the reports will be discussed here (Carder et al., 1974; Klausewics, 1974; Kemp, 1977). Hydrographic areas From a physiographic and circulation point of view the Crystal River estuary may be divided into three parallel areas that run northsouth (see fig. 2): All water within the area bounded on the west by the last Gulfward string of oyster bars and on the east by the shore. All water within the area bounded on the east by the first area and on the west by an imaginary line running northsouth through the end of the intake canal spoil banks. All water west of the second area. These general areas exist in bands along the Gulf coast wherever the bar built estuary system is found. Area 1 corresponds to the area considered to be the estuary. Area 2 represents a mixing zone between the estuarine bay region and the open Gulf water. In area 2 there are some oyster bars and other physiographic relief features that present a restriction to complete mixing with either the open Gulf or the bay waters. In area 3 we have the open Gulf waters. These three areas are further subdivided by the follow ing manmade or natural features found in the area: The Withlacoochee RiverFlorida Barge Canal Complex, as discussed above, provides several important features. First by virtue of its fresh water input, it is an important sustaining force of the estuarine environment. Secondly, the spoil islands located off shore from the mouth of the Withlacoochee River serve to provide a northern boundary to the study area. These spoil islands, like the discharge and intake canal dikes, form an effective barrier to long shore currents. These barriers tend to drive the long shore currents out offshore into the Gulf of Mexico. This has the effect of limiting the source of Gulf water to that brought in by the tidal cycle, thus, in this region of low wave energy beaches, increasing the residency time of the water mass. The cooling water circulation system of intake and discharge canals serves to draw primarily from region 3. Thus providing a source for introducing Gulf origin water into the estuarine system. However, because of the barrier affect of the intake dike, this mixing only has an effect on the discharge side of the system. The Crystal River provides another freshwater input to the estuary. This input enters the system from the south. The southern, or intake, portion of the study area is open to influence from long shore currents. Thus, physical features also play an important role in the makeup of the environment to which the zooplankton community is exposed. Circulation patterns Timed collection of tidal data, including current speed and direction, salinity and temperature data, over three seasons and all tidal regimes were reported by Carder (1973,1974) to the Crystal River Environmental Impact Study Group. Those data are summarized below. On the average the source waters come primarily from area 3 (46.9%), and about equally from areas two (26.9%) and 1 (26.3%). Thus the majority of the water entrained by the intake canal is of Gulf origin. This water is then heated by traveling through the plant's condensers and re turned to the environment via the discharge canal. An exam ination of what occurs on each of the tidal cycles gives a more detailed picture of the dynamics of the interaction of the tidal cycle and the pumping action of the power plant. During ebb tide, a profile of the canal shows surface waters flowing Gulfward, while the deeper water, of Gulf origin, is flowing toward the plant. The current set up by the circulating pumps is strong enough to counter balance the tidal effect along most of the canal. This establishes a point of no return. Brackish, warm water enters the sur face water of the canal on the latter stages of the ebb tide, which has delivered nearshore water far enough west to be available for entrainment. During flood tide, the brackish water that has moved in front of the canal mouth is pushed shoreward and replaced by cooler, more saline water of Gulf origin. Some of the brackish water is entrained by the power plant. The entire cycle of entering the intake canal and being entrained by the power plant takes about twelve hours. Cross sectional analysis of the intake canal waters over a full regime of tidal cycles shows an interesting stratification of the water layers. The water column in the intake canal is divided into a deeper, cooler, more saline layer of Gulf origin and an upper layer of estuarine or mix ing zone origin. This is significant as most of the power plant's draw is from the deeper portion of the intake canal. Field Sampling Station Placement Based on the physical features described above, the study area was divided into six regions (see fig. 1). The selection of the boundaries for these regions was a result of considering the topography of the estuary floor, studies performed on the physical oceanography, water mass resident times, benthic organism distribution, sediment type distri bution patterns, and a degree of arbitrary boundary assign ment. The area, average depth, and volume varies greatly between the six regions depending, to a large extent on the physiography of the bottom. Region 1 represents the shallow, limited tidal exchange estuarine region on the intake side of the power plant. Re gion 2, beyond the major oyster bar barrier on the intake side of the power plant, represents the mixing zone of estu arine waters and the more saline Gulf of Mexico waters. Region 3, which is the largest and deepest region, covers the portion of the study region that is beyond the dikes of the intake/discharge canals and represents the open waters of the Gulf of Mexico. Region 4 represents the special environment of the intake canal. Region 5 is the area containing the plume of the thermal discharge. Region 5 bounded on the Gulfward side by oysterbars, as is Region 1, represents an estuarine region. Region 6, is basically comparable to Region 2, except it is subjected to the thermal discharge that comes from Region 5 across the oyster bar barrier. Region 6 represents a mixing zone of the Gulf waters and the thermally impacted estuarine waters. The stations to be sampled were arranged within the re gions as follows (fig. 2): Region 1 contains two stations. Station 1 is located adjacent to Negro Island, in shallow water (usually less than one meter). This station could be reached only during high tide periods.Station 2 is situated between the inner and outer parallel lines of oyster bars. Region 2 has one station, station 3, located centrally to the its boundaries. Region 3 contains two stations. Station 4 is placed south of the end of the solid intake dike at the beginning of the intake canal spoil islands. Station 5 was located slightly Gulfward to the end of the intake canal spoil is land chain. C (U 0 4) C, SJ 0 a) U (: (N 0r a 4;. ~' Fi.c 1 n' 40 0 Uo~~ 'S S.  Region 4, the intake canal, also has two stations. Station 6 was located at the mouth of the enclosed intake canal, and station 7 was situated at the intake screens. Region 5 had three stations. Station 8 was located at the point of thermal discharge from the power plant into the discharge canal. Station 9 was placed at the point of dis charge from the enclosed portion of the discharge canal into the receiving estuary. Station 10 was situated at the cen ter of the thermal plume area. Region 6 had one sampling point, station 11, which was located centrally to its boundaries. Samples were collected at each of the eleven stations biweekly from November 1973 until September 1974. All samp les up to and including July 1974 were processed. Those collected after July 1974 were not processed but were ar chived. Sample collection technique The samples consisted of a complement of net tows. This complement of subsamples was designed to sample as thoroughly and as completely as possible the water column environment found at the station. Minimally, the sample consisted of a surface tow of a halfmeter 202 micron standard plankton net equipped with a General Oceanics flow meter. Where tidal and physiograph ic features made it possible, a simultaneous depth 202 mi cron sample was also collected. Depth samples were taken by employing a subsurface depressor, which was designed to create a drag on the towing line. Each sample consisted of two separate tows, which were combined and fixed in the field in a jar of buffered 10% formalin. These sample jars were then transported to the laboratory for detailed examination. The length and speed of towing the sample collection nets, one minute at a speed of two knots, was designed to provide a 202 micron sample which filtered an average volume of fifteen cubic meters. For each set of samples obtained a complete complement of environmental parameters was measured at each station. The parameters measured included: salinity, water tempera ture, air temperature, wind speed, wind direction, surface current speed, current direction, dissolved oxygen, secchi disk, tidal condition, precipitation, and sea conditions. In addition a concurrent study, examining the phyto plankton at the same sampling stations, collected a comple ment of water chemistry parameters. The parameters measured levels of dissolved: organic carbon, phosphate, nitrate, nitrite, ammonia and silicate (Hopkins and Gibson, 1974). Sample size determination Prior to the beginning of this project, it was necessary to determine the desired sample size for the field collec tion effort. As a result of the industry funding for this project, the upper bound for the collection effort was not necessarily limited to that which a single investigator could perform. Rather the requirements of the study for accuracy and precision and the variablity of the data could shape the sampling effort. Thus an analysis of previously collected preliminary data was made to determine the sample size required. To do this an estimate of the variance of the zooplank ton community population vector is required. This problem is somewhat complex, since the ideal situation would be to predict the sample size required at all stations at all sea sons for all categories of the zooplankton community. As each category would have different estimates of required sample size, the variances associated with the observed abundances of calanoid copepods was selected. This category was selected because of its overwhelming importance to the zooplankton community, both in numbers and biomass. An iterative formula was used to determine the sample size (Sokal and Rohlf, 1969). The formula is: n hl r) 6% + Z(,ptjI Where n= number of replications, 0' = the true standard deviation (estimated by s), ( = the smallest true difference that it is desired to detect, ~ = degrees of freedom of the sample standard deviation, 0( = significance level for the type I error, p = significance level for the type II error, and t = values from a two tailed ttable with degrees of freedom. An alpha=.01 and an alpha=.05 with a p=.50 were selected for investigation. The error degrees of freedom were 420, thus Ztables were used in place of tstatistics. The coefficient of variation for the Calanoid Copepod data was 28%. The acceptable level of difference for detection was set at 25%. This resulted in a required n of 9 or 17 samples for testing at the .05 or .01 level, respectively. Therefore, the sampling program should gather at least 9 or 17 samples for each station/temporal period. The time period selected for examination was the season. An additional constraint placed upon the sampling design was the requirement for sampling at regular inter vals. Since it was impossible to predict the timing of the seasons, sampling across the transect of time was to occur at constant intervals. The solution to this problem was to sample biweekly, collecting two samples at each station. This would provide sampling on regular intervals. If the seasons averaged three months in length, the temporal period would have twelve samples, or if the seasons were four months long the temporal block would have sixteen samples. These data were utilized in the final design of the sampling protocol. Biweekly Sampling Program For logistical reasons, the stations sampled biweekly were divided into three groups and assigned to sampling teams: the discharge area (stations 8,9,10, and 11), the inner bay of the intake area (stations 1,2,6, and 7), and the outer bay of the intake area (stations 3,4, and 5). All stations within a sampling area were sampled twice within a tidal cycle, in a randomized order. Thus producing at an ideal station two surface 202 micron samples comprised of four separate tows) and two depth 202 micron samples. Depth samples were not collected at stations 1, 2 and 9 because the water was too shallow, nor at station 8 since local mixing of the water column was assured. Diurnal Sampling Program During the period of time that biweekly samples were being collected on a regular basis, a quarterly diurnal sam pling schedule was also being conducted. The purpose of this sampling protocol was to collect sufficient samples from the intake and discharge sides of the estuary and exam ine the samples for short term variations. The method of collecting the samples was basically the same as for the biweekly sampling, except that no tows were pooled during the diurnal collections. The same environmen tal parameters as were collected during the biweekly series were collected during the diurnal series. Each quarter the detailed protocol for accomplishing the diurnal sampling was slightly different. Therefore, each quarterly diurnal samp ling period will be described separately. The fall quarterly diurnal sampling program was per formed during November 1215, 1973. Stations 7,3,11, and 9 were sampled on successive days. Each station was sampled over a 24 hour period, beginning at 0700. Two surface tows with a 202 micron net were taken each hour, usually at the first and last quarters. The winter quarter diurnal series was collected during January 2125, 1974. The winter quarterly diurnal followed the same sampling regime as outlined for the fall quarterly diurnal sampling program. The spring quarterly sampling program was performed on April 29, 1974. The sampling schema for the spring diurnal changed somewhat from that followed in the fall and winter. Stations 3 and 11 were dropped and simultaneous surface and depth samples were added. Rather than sampling each station on successive days as was done in the fall and winter, sta tions 7 and 9 were sampled concurrently. The length of time to complete the diurnal was shortened from four days to one day. The total number of samples collected remained the same. The summer diurnal sampling program was performed on July 25, 1974. The sampling procedures were identical to the spring diurnal sampling program. Laboratory Methods Sample Processing Samples were returned to the laboratory at the Univer sity of Florida Marine Laboratory (UFML), Gainesville, Florida. Each sample was subjected to the following protocol: (1) The samples were split using a Folsom plankton splitter. The first split resulted in a sample re serve which was archived at the UFML, and the sub sample which was to be counted. (2) The subsample which was used for counting was flush ed with water through a series of graduated sieves to separate the organisms into different size class es. The sieve sizes used were: 2000, 850, 600, 300, and 202 microns. The greater than 4000 micron and less than 202 micron portions were discarded. This would reduce some of the bias introduced through gear selectivity that might result from net clog ging. (3) Each size class was split using the folsom plankton splitter until a countable number was reached. In general, the plankton counter split the sample until a portion containing 250 to 2000 organisms/size class was obtained. (4) The samples were counted to determine the standing crop values for the zooplankton categories. (5) The biomass determination was made. (6) Upon the completion of the sample, the data were keypunched, verified and added to the data base. All standing crops were expressed in numbers of or ganisms/cubic meter. Zooplankton Categories The following zooplankton categories were identified in the processing of the samples: Calanoid Copepods Acartia tonsa Labidocera sp. Paracalanus crassirostris Temora turbinata Pseudodiaptomus coronatus Tortanus setacaudatus Centropages hamatus other calanoids Cyclopoid Copepods Oithona sp. other cyclopoids Harpacticoid copepods Euterpina acutifrons Longipedia helgolandia Metis sp. Gastropod Veligers Bivalve Veligers Barnacle larvae Penaeid Shrimp Larvae Lucifer sp. Other Shrimp Larvae Crab Larvae Other Crustaceans Polychaete larvae Chaetognaths Tunicates Medusae Fish Eggs Fish Larvae Biomass Determination Initially biomass was to be determined by drying the entire size class subsample and weighing it. However,the amount of non zooplankton debris in the samples precluded this approach. Also, the volume of samples processed made this approach unfeasible. Therefore, an alternative method of biomass determination was selected. The approach chosen was motivated by the assumption that the within sizeclass variation in organism biomass was not detectable given the precision and accuracy of the balances being used. This assumption was tested and supported by an analysis of variance. For each organism categoryseasonsizeclass combination, 5,000 to 10,000 organisms were selected from a random assortment of samples. These were dried in tared weighing containers and then their biomass was determined. These data were analyzed for significant effects due to season, size class and category. The results of this analysis were used to calculate the biomass values to be added to the data base. Data Analysis General Considerations As we are made aware of the need for effective resource management, the necessity to develop the tools to under stand the patterns of natural fluctuations in communities, and to examine the effect of man's activities on these patterns comes into focus (Cronin, 1967). However, in spite of its importance, there have been few studies of the zooplankton community dynamics that have applied modern statistical methods to the analysis of the problem (Holt, 1976). Until recently, the investigation of community spatial and temporal dynamics was restricted to the examination of a few variables in a oneatatime mode, i.e. a univariate ap proach (Crovello, 1970; Gould and Johnston, 1972). In order to draw conclusions about the community as a whole, the in vestigator had to combine the results of many single analy ses into a comprehensible and consistent picture. Several major problems are encountered in this approach to the analysis of data collected during field ecology stud ies. Except in the most carefully controlled experiments, the number of variables that may potentially enter into con sideration often reaches an unmanageable number. The inde pendent variables are often interrelated, complicating the structure of the analysis model, and precluding the use of conventional univariate statistical analysis methods. Employing the univariate approach in a multivariate situation may lead the researcher to ignore important rela tionships that exist between the variables. The interac tion, or synergistic effect, is the type of relationship most likely to be ignored (Harris, 1975; Cooley and Lohnes, 1971). It is this interaction effect that is often of most interest to the biologist (Alden et al., 1976). These problems in the analysis and interpretation of the data often cause confusing and contradictory results which will lead to the conclusion that the data collected by the field study are too variable for meaningful interpreta tion. This belief often frustrates the investigator and results in the selection of a simplistic or cursory analysis as the only approach for dealing with these "messy" data. However, the results of Cassie's investigations suggest that zooplankters are not randomly distributed throughout the water column (Cassie, 1959, 1960, 1961, 1962, 1963a, 1963b, 1967a, 1967b, 1969a, 1969b). Rather, they seem to show distributions that are understandable and highly cor related with the current conditions and historical aspects of their immediate environment. Cassie demonstrated that an estuary is characterized by a unique profile of physical and chemical parameters that affect reproduction, survival, and the movement of estuarine organisms. These physical factors interact to define physi ologically and ecologically distinct environments within the estuary which will shape the structure of its biological community. There are several approaches to the analysis of community variation data to discover the underlying struc ture of the biological community. Approaches to community structure analysis One approach is to remove the survey atmosphere from the study and perform a tightly controlled experiment. The salinitytemperature studies reviewed by Alden et al. (1976) present good examples of this approach. Alden (1979) has improved on this method by bringing the laboratory into the field. The reduction in the "noise" in the data is accom plished by reducing the number of environmental effects studied and usually limiting the range of these effects to a small number of discrete levels. This is usually achieved by designing a laboratory experiment to test the hypothesis and then extrapolating to the field environment. The selection of this approach insures that the data will be well structured and can be analyzed with classic univariate statistical procedures. The major disadvantage is that the number of environmental effects that can be con trolled in an experiment is usually three or less. In addition, the assumption that the performance of an organism in a laboratory environment is comparable to its action in the field is not always well founded. This is not to say that laboratory studies are not well advised for ecological research. However, there are definite limitations to their applicability. A second approach involves retaining the field survey method of obtaining the data and employing statistical anal ysis techniques for explaining the noise in the data. The most commonly employed procedure for analyzing field survey data is the "indicator" variable approach. Pielou (1969) provides an excellent review of these methods. The indicator variable approach may take two directions. The first is to select a subset of the potential dependent variables and only consider them in the analysis. For example, many zooplankton field studies have dealt almost exclusively with the most abundant copepods. The advantage of this method is that it reduces the number of dependent variables that must be considered to a manageable number. Another analysis technique employs derived variables that have the property of integrating into a single variable the information about the biological community that is stored in each of the separate original variables. A typical example is to employ biomass or species diversity as the variable for analysis. When such summary statistics are employed, valuable information concerning the patterns of interaction between the variables may be lost, although simplicity is gained through a reduction in the analytic dimensionality (Bary, 1964; Pielou, 1966; Holt, 1976). The key to the analysis of ecological data generated from field survey programs is in reducing the dimensionality of the problem to one that is manageable by a human. The dimensionality of a problem is defined by the number of sep arate variables that must be in active consideration simul taneously in order to accomplish the solution and interpre station. Psychological studies in computer program design have shown that man is capable of keeping track of a maximum of approximately seven separate factors simultaneously (Martin, 1973). This concept applies to the analysis of data as well. If the dimensionality of the problem exceeds seven variables, the ability of the ecological researcher to deal with the problem is extremely limited. Another problem in analyzing data serves to limit the acceptable dimensionality even further. The ability to for mulate meaningful interpretations of the biological or eco logical implications of the data analysis results is greatly enhanced by the researcher's ability to visualize the re sults. The connection between numbers and meaning is often achieved by a graphical presentation of the results. (Lindgren, 1968) Thus, the practical dimensionality of the analysis is in the range of three to five. The accomplishing of the reduction in dimensionality has been the goal, although often an unwritten or a subcon cious one, of the myriad of data analysis methodologies that have been proposed for analyzing ecological field data (Crovello, 1970). The approaches listed above all have the structure and dimensionality of the solution predetermined by the analysis technique. For example, many studies employ the ShannonWeiner species diversity index, or some compar able derivative, to examine community variation. The dimen sionality of the solution is one, at least from the depen dent variable point of view. Regardless of the magnitude or pattern of the observed variation, all information about that variation is constrained to be mapped into the single variable: species diversity. Heuristic analysis The approach selected in this study was quite differ ent. The philosophy was to select data analysis methods that were responsive to the underlying structure of the sys tem under investigation. In other words, these analysis procedures allowed the data to dictate the dimensionality of the problem, rather than to enforce one that is predetermined. Thus, these methods would investigate and describe the patterns of variation found in the data. This approach to data analysis is closely aligned to Tatsouka and Tiedeman's description of the structure of science (1954) as a hypotheticodeductiveobservational pro cedure. They recognize within the scientific method a step that leads from the initial observational data to the set of theoretical constructs, or hypotheses. This step is termed "creative invention" and includes assistance from statisti cal methods and analogies from principles in related scien ces. In the creative invention stage at which theory is in itiated, primarily by the use of analogies, statistics has a heuristic role in the discovery and refinement of cons tructs. This is a different role than is usually assigned to statistics, which comes toward the end of the scientific process in the testing of hypotheses deduced from theory against empirical results. Statistical procedures that fulfill this heuristic construct seeking role are termed statistical pattern recognition techniques (Lachenbruch, 1975; Cooley and Lohnes, 1971). They include discriminant analysis, factor analysis, cluster analysis, etc. and fall within the mul tivariate general linear hypothesis (Finn, 1974). Through pattern recognition techniques, one can reduce the dimen sionality of the data by uncovering the underlying patterns of variation, while simultaneously accounting for the maximum amount of the variation observed in the data. These methods depend heavily on linear functions fitted to complexes of variables by multivariate procedures as the method for specifying the details of the constructs and the relationship between constructs. In the beginning stages of a scientific endeavor, which is the position enjoyed by all survey studies, the heuristic uses of these procedures are far more important than the hypothesis testing. As Cattell (1966) argued in his support of the observational aspects of the survey approach over the manipulative experimental ap proach, the potency of this approach is high because it takes life's own manipulations and by statistical finesse teases out the causal connections among data that could not be manipulated. These heuristic procedures do not allow for testing of inferential hypotheses in the traditional statistical sense. Instead, the statistical methods are viewed as quantitative tools for exploring patterns to be revealed in data. Thus, the "test statistics" produced by the statistical methods, and their concomitant significance levels can not be inter preted as tests of significance. However, the test sta tistics may be used as a quantitative measure of the strength with which the observed data support the proposed pattern. In order to avoid lengthy, repetitive explanations at each point in the analysis, the terms significant and nonsignificant will often be employed when referring to the use of these quantitative measures as aids in decision making. The question besetting the ecological researcher in deciding to employ multivariate analysis procedures is which from among all the many possible methods should be used? The answer to this question lies in how many sets of vari ables and how many populations are included in the design of the study. Analysis models Those heuristic studies of the data which involve a single population and a single set of variables are classed as interdependence models by Kendall (1957). The methods employed to analyze those data are principal components, factor and cluster analysis (Morrison, 1967; Sneath and Sokal, 1974). Principal components and factor analysis methods are also employed to reduce a complex, large set of dependent variables to a smaller set of independent factors or components prior to further analyses (Atchley, 1974). When one is dealing with more than one set of vari ables, or more than one population, Kendall (1957) terms these interdependence models. In the case of more than one set of variables, the pro cedure usually described is canonical correlation. However, based on the experience obtained in this study, the results obtained by combining factor analysis, stepwise multiple re gression, and response surface fitting, as described below, are more satisfactory. If the data call for the analysis of interpopulational variation, one of two analysis approaches is recommended. If the intent of the analysis is to discover the differences between known groups, then the techniques of discriminant analysis are required. On the other hand, if the goal is to discover what, if any groups there may be, the methods of principal components and factor analysis are suggested. Underlying assumptions In these methods several underlying assumptions are in herent: first, the data are assumed, in some manner, to have the properties of the multivariate normal distribution. This may not always be the situation. In some instances a transformation of the data is the solution. In other cases, the admonition of Tukey (1962, 1969) may be the only answer. Tukey suggested that data analysts must be "willing to seek for scope and usefulness rather than security", and to be "willing to err modestly often in order that inadequate evi dence shall more often suggest the right answer". Further, one must use scientific judgement more than mathematical, but not one to the exclusion of the other. Second, the structures uncovered are linear in nature. This might raise criticisms that structures in nature are seldom linear. This is answered in two ways. If an under lying structure is hypothesized to have a particular form, as was the case in this study, then appropriate transforma tion may be employed (Cassie, 1960, 1962, 1963a). Also, the purpose of these methods is exploratory analysis; thus searching for linear functions is probably appropriate, as a first approximation. Third, the multivariate techniques that are subsumed by the general linear hypothesis attack the problem of reducing the original variable space to the minimum number of dimen sions needed to describe as much of the relevant information contained in the original observations as is possible, i.e., the dimensionality problem. Different multivariate tech niques, i.e., different models within the general linear model, differ in the types of information that they preserve. Selecting the analysis model Factor analysis and principal components analysis "dis cover" the underlying pattern of variability such that the number of variables may be reduced to a smaller set of fac tors or components which account for the observed variabil ity in the data. Canonical correlation is a procedure for factoring two variable sets simultaneously, with the goal of extracting factors which are uncorrelated within their respective vari able sets but provide maximum correlation of factor pairs across variable sets. Canonical correlation discovers the factors which express the maximum redundancy, or overlap, between the two set of variables. The approach to the variable set interdependency prob lem which was used in this study has a different orientation than canonical correlation. It finds the set of factors that best describe the observed variation in one set of var iables, the dependent variables. It then finds the set of variables, from amongst the predictor variables, that have the greatest Rsquare with the dependent variable factors. This approach finds the important patterns of variation of the dependent variables and then finds the patterns of vari ation in the predictor variables that best explain the vari ation patterns of the dependent variables. Canonical discriminant analysis addresses the problem of discriminating between known groups. It is a procedure for forming a set of variables into a group of factors, the canonical discriminant functions, that are viewed as axes spanning the variable space. These axes are constructed in such a manner that they maximally separate the groups (Lachenbruch, 1975). The factors are formed so that they describe the observed variation between groups. The multivariate analysis of variance, MANOVA, is a generalization of the univariate ANOVA. The MANOVA extends the researcher's ability to investigate the differences be tween groups. Just as the ttest and linear contrasts are employed in the univariate ANOVA to examine intergroup dif ferences, canonical discriminant analysis can be used in conjunction with the MANOVA to partiton a specific component of the variation for discrimination analyses. Each of these methods is best used for a particular type of problem. The first step in selecting the appropri ate multivariate pattern recognition procedure is to identi fy what pattern of variability needs to be explored. As was stated above, probably the single most important feature of this analysis approach is that the data are used to define the dimensionality of the analytic procedure. Each of these procedures provide quantitative, objective mea sures for selecting the cutoff point in the dimension reduc ing process. Displaying the results The final aspect of the heuristic analysis process is a consideration of the data presentation. The best technique for presenting and assimilating the results of pattern rec ognition analysis is visual. The use of graphics is essen tial to the analysis process. Of course, the use of graph ics places some limitation on the dimensionality that is ar bitrary. It is not yet possible to represent easily more than three dimensions. However, that can be circumvented by plotting three dimensions and controlling the values of the higher dimensions to a low, intermediate, and high value. This produces a series of "frames" that can be viewed side byside, or in sequence. The detailed techniques of the multivariate analyses will not be described here. The interested reader is referred to the texts referenced in this work. Biweekly Analysis The analysis of the biweekly data had two main objec tives: first, the discovery of the structure of the major zooplankton communities and the patterns of spatial and temporal variation they exhibit; second, what components of the zooplankton communities could be used to discriminate between the areas defined for the estuary. This second ob jective may be likened to a search for indicator organisms. The area of particular interest is the thermally affected regions of the discharge canal and thermal plume. The ques tion is: can one distinguish the thermally affected regions from the other regions within the study area? Both of these objectives call for multivariate analysis techniques. However, as each asks a slightly different question of the data, two different sets of analyses are required. Determination of seasonality As discussed above, the factors of primary interest were the seasonal and spatial patterns of variation. The regions were determined, as described above, by examinations of the hydrographic features of the estuary. The determination of the season boundaries was not as easy. The seasons along the west coast of central Florida may be di vided into wet or dry and cool or hot. The influences of offshore Gulf of Mexico water ameliorate the changes obser ved in the atmospheric conditions. The interaction of these factors indicate that the traditional number and kind of seasons and their boundary points may have little meaning within the subtropical estuary found at Crystal River. Thus, the data collected on environmental parameters were used to define the number, kind and boundary points for the seasons during the study period. The determination of the seasonality for the study period involved two main steps. First, perform a cluster analysis to use the observed data to define the seasonal breaks and then employ MANOVA and CDFA as tools to investi gate the strength of the separation of the cluster analysis defined seasonal groups and to identify the environmental variables responsible for the group separations. The following constraints were imposed on the analysis of the weather data: 1) The variables were selected to characterize the en vironment of the zooplankton community. They can be divided into three groups: Those variables that directly measured a facet of the estuarine environment to which the zooplankton community was exposed (water temperature, salinity, water chemistry values, water clarity measures, etc). Those variables that directly measured an aspect of the water transport mechanisms which affect the zooplankton community ( tidal variables and wind speed). Those variables that measured an external effect which was thought to exert or respond to a force shaping the environment and its seasonality (rainfall, daily sunshine, and organic carbon). 2) Biweekly averages of environmental conditions for the intake and open Gulf areas would be used as data points. This would attempt to remove from the seasonal factor any effect of the power plant's thermal addition (see Appendix 1). 3) The seasons would have to be contiguous temporally. Thus, the seasonal analysis would be employed to indicate the general pattern of the seasons, but not to dictate the exact nature. The cluster analysis method employed was based on the techniques discussed by Johnson (1967). It is an algorithm for partitioning objects into optimally homogeneous groups using a single linkage joining technique. It forms nonoverlapping hierarchical clusters using the euclidean distance as the metric. The alogorithm was structured such that it would have the following features: 1. The input should consist of the n(nl)/2 similiarity measures among the n objects. 2. There should be a clear, explicit, and intuitive description of the clustering; i.e., the clusters should mean something. 3. The clustering procedure should be essentially in variant under monotone transformations of the similarity data. The next step was to examine the data in order to as certain the reasons for the clusters forming as they did. If the distribution of environmental data that caused the observed clustering pattern could be used to form a reason able explanation of the seasonality, then the cluster defin ed seasons would be used for the remainder of the biweekly data analysis. Two techniques were used to examine the data for sea sonality discriminators: 1. A MANOVA was performed on the vector of environ mental variables. 2. The MANOVA was supported by a graphic display which plotted each of the variables against time (see appendix 1). The graphic presentation was used primarily to support and aid in the interpretation of the MANOVA results. The model used for the MANOVA was a oneway analysis with season as the main effect. The seasons used were the ones determined by reconciling the clusters formed during the cluster analysis and the need for temporally contiguous seasons. Thus the observed separation between seasons might not be expected to be as distinct as that between the orig inal clusters. Following the seasonal analysis all of the samples were assigned to their correct season, in preparation of the bi weekly analyses. Community structure analysis The discovery of the zooplankton community structure involved a factor analysis of the vector of zooplankton cat egories. This resulted in series of factors that represent those zooplankton categories that demonstrated the same pat tern of variation, or in other words, each factor represent ed a zooplankton subcommunity. Following the factor analy sis, the factor scores were analyzed with an ANOVA to deter mine if the zooplankton subcommunities displayed patterns of variation that could be explained by spatial or temporal effects. Structure plot (SPLOTS) analysis of biweekly data The ANOVA of the community structure factor scores pro vides an analysis of the temporal progression of the spatial patterns of the zooplankton community structure by considering the study region as a unit. An equally impor tant question involves the temporal variation of the commun ity structure at a particular region. Consideration of this aspect allows the investigation of which geographic areas are more variable with regards to their community structure. While the data to be investigated are the same as for the previous biweekly analyses, the vantage point from which it will be viewed is somewhat different. The analytical tools to be employed are also different. In this situation, the view of the data will be limited to a single area con sidered for all seasons. Thus, three dimensional geographic plots will not provide the insight necessary. Instead, the structure, or box and whisker, plots described by Tukey (1975) will be used. Canonical discriminant analysis The second analysis problem calls for the class of mul tivariate analysis known as canonical discriminant function analysis (CDFA) coupled with the multivariate analysis of variance (MANOVA). The rationale employed was to propose a conceptual model for the observed pattern of community vari ation that could be explored using these statistical tech niques. This was accomplished using a two step procedure: 1) Propose a model that grouped the myriad of in dependent variables into broad classes of independent fac tors. Form a MANOVA and subject the main effect or inter action Sum of Squares and Cross Products (SSCP) matrices to a CDFA. 2) The CDF variates and their correlations with the original dependent variates along with the mapping of the factor means in the CDF space would provide information to be used in interpretation or in formulating further des criptive analyses. The model employed for the biweekly sampling program for step one for the MANOVA and the ANOVA was: Yijk = Region + Stat + Seas + Biwk + Region*Seas + error. where: Y = The dependent response variables, the zooplankton category counts (see Appendix 2); Region = The six regions of the study area; Stat = The eleven stations situated throughout the study area; Seas = The seasons of the sampling period; Biwk = The biweekly period in which the sample was collected. If the test statistic resulting from the MANOVA or ANOVA for the interaction term surpasses the cutoff point, the main effects will not be interpreted. In this case, the presence of the main effects in the model will serve to par titon, and thus reduce, the error sum of squares. However, if the test statistic for the interaction term does not sur pass the cutoff point, then the main effect terms will be assumed to affect the response independently and analyzed accordingly. A CDFA constructed to discriminate between the twenty four possibly distinct season/area means will result in fif teen separate orthogonal axes. It is definitely possible that not all of these new variates, linear combinations of the original variables, should be considered as valid. The CDF's, or axes, considered to be invalid, or not represent ing valid pattern discriminators, may be discarded. Bartlett (1941, 1947) has proposed a test of the signi ficance of canonical correlations which may be applied to the test of significance of CDF's. The test statistic lambda is defined as The null hypothesis that the groups can not be discriminated by the CDFI's is tested by a function that is distributed approximately as a chisquare with pl*P2 degrees of freedom: 9( I ) . nd = (p where J=+1 If the null hypothesis can be rejected, the contribution of the first CDFI to A' can be removed and the significance of the remaining p21 CDFI's can be tested by the chisquare statistic with (pll)(p21) degrees of freedom. In general, with r CDF's removed from / the signif icance of the remainder can be tested in the same fashion by the chisquare statistic. It should be noted that initial calculation of A yields a test statistic that is equivalent to the Wilks' Criterion (Cooley and Lohnes, 1971). The calculation of this test statistic was performed for the season/region interaction as an aid in deciding which of the CDF's, if in fact any, could be discarded in further analyses and interpretations. Another quantitative aid used in deciding how many of the CDF's to consider for further analyses was the percent of the total between groups variation for which a particular CDF accounted. The next step in the analysis of the biweekly data in volves the interpretation of the CDF's. The basis for this interpretation is founded in the following points: The Canonical Discriminant Analysis method forms new variates that are linear combinations of the original vari ates. This is accomplished by solving for the eigenstruc ture of a determinental equation. The determinental equa tion that is solved to generate the CDF's is formed so as to emphasize, or discriminate between, the differences of the treatment mean vectors, in this case the season/area combi nations. The CDF's are orthogonal to each other. Each function, given the linearity and orthogonality constraints expressed above, "explains" the greatest amount of between groups var iation possible, consistent with the functions that have been extracted before it. Thus, the CDF's represent independent axes that span a space of reduced dimensionality. The orientation of the axes within this space is such that the projection of group separation along any single axis is maximized. The axes, which are linear functions of the original variates, can be analyzed to determine which of the many original dependent variates are responsible for the observed separation of the treatment mean vectors. This is accom plished by computing the correlation coefficient of the variate. The correlation coefficients are used in a subjective interpretation to determine which of the original variates contribute significantly to the variation of the axis. The larger the absolute value of the coefficient the greater the effect a variation in the single dependent variate will have on the CDF score. The sign of the coefficient tells us whether an increase in the value of the original variate will tend to raise or lower the CDF score. The greatest operational problem encountered in per forming a CDFA involves the interpretation of the original variableCDFA variate correlation coefficients. There has not been an objective quantitative method proposed for determining which of the coefficients are the important ones. In this area the CDFA is subjective. The selection criterion employed in this study is analogous to determining the descending runs of the coefficients and partitioning the group of coefficients into homogeneous subsets. The list of coefficients is sorted into descending order with regard to coefficient magnitude but not sign. This sorted list is then examined for breaks in the descending run of coeffi cients. The first "significant" break is selected as the cutoff point. In most instances this procedure provides a clearcut selection point, but occasionally the dividing line is not clear and an arbitrary decision must be made. When analyzing an interaction effect, it is useful to look at the area to area variation within a single season and of course, the reverse, the season to season variation within a single area. The overall impact of the area to area viewpoint is best seen through using geographically oriented plots, or maps, of the CDF variate scores. The primary technique employed here is to examine the relief of the geographic plot (or map it is a map of the geographic distribution of the CDF score). The greater the relief, the greater the discrimination of the CDF for the particular season being investigated. Any of the areas that are at the same level of relief would appear to be a plateau and can not be distinguished by the CDF. In addition, to support the validation of the CDF coefficient analysis, the same mapping treatment is applied to the original variates singly (see appendix 3). The analysis and interpretation then involves the integration and synthesis of all these quantitative indices and data display techniques to relate these data into a bio logical framework. The Diurnal Analysis The diurnal analysis procedure, while conceptually the same as the biweekly analysis, was operationally quite different. Although the analysis of both sampling programs employed multivariate pattern recognition techniques, there the similiarity ends. The biweekly program was designed to examine macrovariation in the zooplankton community. It collected data to look at large scale variation in both time and space at the study region. As a result of this, the region and season variables, were used in the independent portion of the multivariate linear model. These factors were used instead of the continuous environmental variables that actually drive the seasonal and spatial variation. The effect these continuous variables have on the zooplankton community is an integrative one and probably not measurable in a meaningful fashion by single point sampling on a biweekly schedule. In the light of this situation, the approach was to model with the large scale "known" factors and then to work backwards utilizing external knowledge to synthesize an interpretation. This interpretation is then presented both as an explanation of the functioning of the zooplankton community and also as a hypothesis to be tested by future workers. In the case of the diurnal data, the samples were col lected frequently enough to warrant using the continuous en vironmental variables on the independent side of the linear model. Therefore, a regression approach was selected. How ever, in a regression approach several problems can arise, and probably will arise in most surveytype data collection projects. The first problem centers around performing a multivar iate multiple regression analysis. Although, the techniques are well developed for the multivariate extension of multi ple regression, generally termed Canonical Correlation, it is the author's opinion is that Canonical Correlation poses severe problems in the interpretation of the results. An alternative to the Canonical Correlation approach involves the use of Factor Analysis, or Principle Components Analysis, and multiple regression. A form of the approach used in this study was employed by Atchley(1974). This ap proach provides that the maximum amount of the dependent variable set variation will be used in the regression. The analysis approach is outlined below. The discussion below will identify each step in the four step procedure. Diurnal Response Surface Analysis The first step is to identify the dependent variable set. In this study, it was the twentyseven element zoo plankton community standing crop vector. Next is the iden tification of the independent variables: water temperature, salinity, dissolved oxygen, pyroheliometer measure, tidal height and tide direction. The initial step in analyzing the data involves performing a Factor Analysis on the dependent variable set. A discussion of the details of Factor Analysis and which op tions to specify is beyond the scope of this section (the interested reader is referred to a multivariate analysis text). The purpose for performing the factor analysis is to orthogonalize and reduce the dimensionality of the dependent variate set. Thus, by starting with nvariates, the Factor Analysis will derive a new set of variates, usually much less than n in number. These new variates are formed under the constraints that they: are linear combinations of the original variates, are orthogonal, or independent, to each other, and explain the maximum amount of the original set's variance, subject to the first two constraints. Once the dependent variate set has been made tractable, albeit, at the expense of some loss of information, the next step is to begin work on the independent side of the model. One of the major problems with multiple regression, espe cially in field survey data, is the multicolinearity of the independent variables. This means that the independent var iates are in fact not independent from one another. This can cause problems and result in misleading results from the linear model. The approach selected for this study was a screening of the independent variables employing stepwise regression. By utilizing stepwise regression, one is reasonably confident that the variables that passed the screening procedure have a low level of multicolinearity. The stepwise method was a combination of the forward and backward model building techniques. The technique be gins by finding the onevariable model that produces the highest Rsquare. For each of the remaining independent variables Fstatistics are then calculated. These Fstatis tics reflect the variable's contribution to the model if it were to be included. The second variable to be entered into the model is selected by comparing the Fstatistics to the minimum entry level. If none exceed the minimum entry level, the proce dure stops. Otherwise, the variable with the largest Fsta tistic is entered into the model. The Fstatistic for the remaining independent variables is then recalculated, and the evaluation process is repeated. After a variable is added, the procedure examines all the variables already included in the model, and deletes any variable that does not produce an Fstatistic greater than the minimum stay criterion. Only after this check can another variable be added to the model. Variables are thus added singly to the model until no remaining variables produce Fstatistics greater then the minimum entry, or the variable to be added was just deleted. The next step involves the statistical modelling procedures of response surface techniques, which is a special set of the ANOVA/Multiple Regression class of the General Linear Model. This process entails selecting each factor, or component, as a dependent variable then selecting each of the independent variates that passed the stepwise regression screening. Next, a linear model is formed with terms for all linear effects, quadratic effects, and cross product effects. The resulting model is manipulated using the full model/reduced model approach so that the appropri ate tests may be performed for the significance of the class of linear, quadratic, or crossproduct effects. Then the model is reformulated including only those effects that proved to be significant. The only exception to this invol ves the linear terms. If any of the higher order terms are significant, then include the linear terms also. With the new model use the partial regression coefficients to test for the significance of a specific term. Now the model is formulated once more and the coefficients for the included terms are determined. The final step creates a plot of the predicted values for the response surface. This plot is used to interpret the relationships of the dependent and independent varia bles. The dependent variables are factors, linear combina tions of the original variables, which represent a portion of the zooplankton community that displays a common pattern of variation. The factor loadings are original variable factor score correlation coefficients and are used in a manner similar to the CDF analysis. Dependent Variable Transformations Prior to the analysis of the data by any of the pattern seeking models, it was thought necessary to determine if the data needed to be transformed in any manner. This is desirable since the analysis procedures assume that the error terms follow a normal distribution, either multivari ate or univariate, and that effects specified in the various models are additive. The transformation that seemed to be appropriate was the log transformation, X = logl0(X+l). From a theoretical point of view, Cassie (1957) found that zooplankton in oceanic situations displayed an over dispersed pattern of the lognormal type (see Pielou, 1969). The data from an earlier study were tested and found to conform to the log normal assumptions (Maturo et al., 1974). Another justification for using the log transformation is discussed by Cassie (1960). He suggests that a lognor mal distribution of plankton may result from the relative abundances of plankton species being geometrically related 61 to the variations in the physical properties of their envir onment. SECTION III RESULTS Biweekly Analyses Determination of Seasonal Boundaries The results of the cluster analysis of environmental variables displayed two well separated groups (see fig. 3). The dividing point occurred between March and April. A less well defined separation occurred within each of the two major groups. This indicated that the study could be divided into four seasons. The individual sample points were not temporally contiguous. However, the general grouping was apparent. The fall and summer seasons (seasons were named based on their temporal proximity to the "true" seasons) were cohesive, well separated from the other samples and reasonably temporally contiguous. For the purpose of the MANOVA/CDF analysis the biweekly sampling periods were divided into seasons such that: the fall season consisted of the November and December biweekly samples; winter included the January and February biweekly samples; spring contained the March and April biweekly samples and the first biweekly sample in May; BIWEEKLY SAMPLING PERIODS N N D J F F D J M O O E A E E E A A V V C N B B C N R 7 7 7 7 7 7 7 7 7 3 3 3 4 4 4 3 4 4 M A M J A A PA U P R R Y N R 7 7 7 7 7 4 4 4 4 4 77777 44444 Fig. 3. Cluster dendrogram of biweekly environmental variable mean vectors ( a= 1st biweekly in month b=2nd biweekly in month). J J J U U U N L L 7 7 7 4 4 4 777 444 and summer was assigned as the season for the remaining samples. The MANOVA test statistics showed conflicting results ranging from significant (Pillai's Trace, p=.0305) to nonsignificant (Wilk's Criterion, p=.2527). The data do not present a clearly detectable seasonal difference. However, there is an indication that the difference does exist. This result may be a consequence of the requirement for temporal contiguity. An examination of the characteristic roots showed that approximately 95% of the between seasons variation could be explained by the first two Canonical Descriminant Functions (see table 1). The correlations between the original variables and the two CDF's provided a mechanism for interpreting the possible meaning of the CDF's (see table 1). The first CDF, explaining approximately 78% of the observed between season variation,had its highest correlations occurring with water temperature (0.121) and pyroheliometer (0.115). Correlations of secondary importance were found with organic carbon (0.072), dissolved oxygen (0.068), and salinity (0.063). The second CDF, explaining approximately 17% of the observed between seasons variation, had its highest correlation with salinity (0.197), with correlations of slightly lesser importance with wind speed (0.162) and organic carbon (0.157). 65 C) 4, Cl) c W I H m ofCmn r rN 4 a) > C D 0 C D00 D0 0 0 COD0 Q0 m a 00000000000000 NN C C C) I II I NNNN 0 IIII t oN VD CO D m to i UC U Z 0 0 M C0nZ '7 Z >) 0. r o 4 E 4J 000< < E " W U 1 1 u > J U 1 1 1 1W1 w0 0 lm Oc 0M EQ 2 2 WZW 4 Z a.ZWO w O E. 0 E C C Z(AC .OO O< m U U) AZ E C) 0 uEmuz0 D '4'3 IO O ,4 C .~3t C)iif f k r' ~ ''t ^ ^' i o s r L. < o o o o oo o o^w a A plot of the means of the CDF variates was examined to obtain a visual image of the relationship of the four seasons to each other when mapped onto the CDFspace (see fig. 4). Considering each axis separately the seasons fall into groups. The first CDF served to place the seasons into three groups: 1) fall; 2) winter and spring; and 3) summer. The second CDF formed two groups: 1) fall, summer and winter; and 2) spring. However, when the two axes are considered together, the four seasons separate. An examination of the univariate ANOVA's shows that most of the variables included for analysis display no detectable differences when grouped into the proposed seasons. However, the variables emphasized by the CDForiginal variable correlations do display significant Fvalues for the ANOVA. In addition, the variable S103 also shows at least marginal significance (p=0.034). With this information a reexamination of the CDForiginal variable correlations showed that SI03 could be considered of tertiary importance. The next phase in the interpretation of the seasonal analysis employed the plots of the original variables. Having used the MANOVA and CDFA to provide an understandable pattern from the data, the next step is to compare the CRYSTAL RIVER ENVIRONMENTAL DATA SEASONAL MEANS FOR CDFI AND CDFII 22.5 23.0 23.5 24.0 24.5 25.0 SUMMER FALL WINTER SPRING 45.0 47.5 50.0 52.5 CDF I AXIS Fig. 4. Season means for CDF axis I and CDF axis II. proposed pattern to the original data and evaluate its goodness of fit. This evaluation will be done qualitatively through visual inspection. The comparisons of the original data plots for the variables emphasized by the first CDF show the following patterns: 1. The variables with negative correlations, water temperature, pyroheliometer, and organic carbon, have basically the same pattern. The values remain constant, or decrease slightly, from fall through winter. At the beginning of spring an increase is seen which continues through the summer. In the late spring, there is a decrease in temperature and pyroheliometer values, but this is more likely an anomalous condition, such as a short cold snap. The pattern shown by the organic carbon variable follows that observed for the water temperature and pyroheliometer variables except that the summer values level off and then decrease. Also, all of the variables, except possibly water temperature, display one or more rather dramatic outliers. These outliers tend to cloud the picture and probably have a strong effect on the significance levels of the test statistics from the original MANOVA. 2. The variables with positive correlations, salinity and dissolved oxygen, each have their own pattern. The salinity starts in the fall with high values (2830 ppt.), falls steadily to a low in the early spring (2022 ppt.), and then begins to climb again in the summer. The dissolved oxygen begins in the fall with low values (7.0), climbs in to a high in the winter (8.5) and then drops steadily through the spring and the summer (6.0). The comparisons of the original data plots for the variables emphasized by the second CDF involved many of the same variables as examined for the first CDF. This suggests that the environmental variables are not responding to the seasons in a strictly linear fashion. Thus the CDFA was able to decompose these original variables into two linear components that each serve to separate the seasons in a different manner. The original variable that was emphasized by the second CDF that was not emphasized by the first CDF was wind speed. The pattern for wind speed across the seasons shows relatively low values for fall and summer (6 mph) and higher average values for winter and spring (810 mph). Spring shows the highest average values. The combination of these various steps in the analysis allows a characterization of each of the seasons determined: 1. Fall: The highest salinity, with the rest of the parameters that are considered important measuring as considerably lower than those experienced in the spring or summer. 2. Winter: Similar to the Fall, except that it exhibits one of the lowest average salinities. 3. Spring: Has the highest Organic Carbon, wind speed, and dissolved oxygen. The rest of the selected variables are markedly higher than those displayed by the Winter or Fall. 4. Summer: Shows an increase from the Spring except for Organic Carbon, Dissolved Oxygen, and wind speed. The Organic Carbon and Dissolved Oxygen mean values are the lowest observed for the study. The water temperature and pyroheliometer values are the highest mean values observed during the study. Biweekly Canonical Discriminant Function Analysis The results of the MANOVA showed that the test statistic for the interaction effect surpassed the cutoff point (Pillai's Trace, p=0.0001; Wilks' Criterion, p=0.0001). This indicates that the regions respond differently for different seasons. Thus, the next step was to proceed to the CDFA. The application of Bartlett's test to the Eigenvalue data for the season/region interaction showed that three CDF's, or axes, are to be examined (see table 2). These three axes explain a total of 61.28% of the variation that was the result of the season/region interaction (see table 3). Thus, by applying the technique of Canonical Discriminant Analysis, the dimensionality of the discriminant space has been reduced from twentyseven to 4H t1000.030000000000 U 0 00 0 0 '10000000000 0 Q 0 OOO0VMMHNNCHHHA 0 0 O w 0 0 o a O0 0 r, V H 4L 0m CbUU, 4 1NNHHH 0 M M o a. V4 C M CDr(Nn0 mmUmomnIT(nN S 0 4 1 C M r a m m c o emma vuort rCOONcOMO E0 cC ^ m (N0 Na0mrc & M oH mrm o0 33 ) .0 Z I H 3 H w M c 3 U a0 0 0 0 ~ *r ooo0oooooooooo 40 Cas 4  01 0 0t C: IB *H 0  .0 0 .C O4 04 On o lj .4 '0 rc 41  *4t Q) 0 4J bL~ .i *1 d f // 3 .~* . /= r C Qe S o 04  cr 0 C, O4 0 C4I c c o o \) cri V. i ' / C 4 . t 'i y 7. (7,, / rt A oa 04 0 0 U4 O a, IJ (n .0 0~ 0 < ri 4J 0  >I O , t 7 r. " / p~ : / ~~; three, with a reduction in "explained" variation from 100% to 61%. The examination of the geographic plots gives a striking visual explanation of why the interaction was significant (see fig. 57). The pattern observed is different across the areas for each season. It is important to perform the MANOVA before proceeding to the graphic pattern analysis. The MANOVA provides a quantitative level on which to judge whether the observed pattern is significant or random error. Canonical discriminant function I The first CDF explains 34.36% of the between season/area variation. The zooplankton categories emphasized by this axis provide the greatest discrimination between season/region combinations. Examination of the seasonal geographic plots shows that the major relief is found upon mapping the Spring and Summer seasons (fig. 5ad). The Fall season displays little relief, in comparison to the Spring and Summer. The major feature of the FallCDFI map is the depression in the vicinity of region 6 and a lesser depression in Region 2. The Winter season has even less relief than does the Fall season. The only discernible relief feature in the Winter season is a depression located in Region 1. The Spring and Summer seasons show very similar relief patterns for their CDF1 maps. The highest portion of the map is located in the open Gulf of Mexico waters of Region 3. From there the raised portion of the map descends gradually to Region 2 forming what appears as a plateau covering the nonthermally stressed portion of the study area. The plateau continues as a lower ridge along the intake canal and into the barge turning basin. The plateau descends sharply to Region 1, the next highest region. The discharge canal and thermal plume area, Region 5, and Region 6 have the lowest relief observed on the map. The first Canonical Discriminant Axis separates the regions during the hot portion of the year, the Spring and Summer seasons. Further the CDF axis divides the study region into two basic portions: the first consisting of Regions 2 and 3, and the second containing Regions 1, 5 and 6. The differences in the community structure for the high offshore plateau and the nearshore valley can be investigated by an examination of the individual zooplankton category/CDF correlation coefficients. The nearshore areas possess lower standing crops for those categories which have large positive coefficients and higher standing crops for those categories that have large negative coefficients. These coefficients point out those categories that provide discernible differences between the nearshore and offshore areas. They are not necessarily, and probably will not be, the most numerous members of the community. For example, Acartia tonsa is not emphasized by any of the first three Canonical Discriminant Functions. Rather, the emphasized categories are those which display the differences in the community structure. The cutoff point for the first CDF was selected at 0.25. This resulted in three positive categories and four negative categories. The negative categories include bivalve larvae, Temora turbinata, barnacle larvae, and Tortanus setacauda. The positive categories include Paracalanus quasimoto, Lucifer sp., and Labidocera sp. (table 3). Canonical discriminant function II The CDFII explains 15.85% of the between season/region variation. Examination of the seasonal geographic plots show that, with the exception of Fall, each seasonal map displays a major relief feature (see fig. 6ad). As in the first Canonical Discriminant Function the major difference is between the near shore and off shore areas. However, the maps generated by this Canonical Discriminant Function show temporally progressing patterns when viewed chronologically. The Winter season shows a plateau centered around Region 2 and extending partially into Region 3. This plateau drops off sharply, resulting in a broad plain covering Regions 1, 5, 6, and the discharge portion of 3. In the Spring season, the relief of the map has changed. The plateau extends shoreward encompassing Regions 2, 3 and 6. Only the immediate nearshore areas remain in the plain, and the elevation of the plain has risen dramatically. The trend observed in the transition from the Winter season to the Spring season is continued as the Summer season CDF map is considered. The map for the Summer season shows the plateau has continued shoreward on the intake side to include Region 1. This leaves only Region 5, the area containing the discharge canal and the central portion of the thermal plume, at a lower elevation. Although the chronological sequence is disrupted, the Fall season, which actually precedes Winter temporally, shows the plateau encompassing the entire study region. Thus the CDFII is useful in tracking season/region differences that occur in a component of the zooplankton community across time. It also serves to separate the offshore areas from the nearshore areas. The separation of community types exposed by this axis is most prominent in the Winter season. The cutoff point for the CDFII was more difficult to select. Two possible breaks were present for selection. The cutoff could be placed at 0.29 or at 0.40. In either situation, only positive coefficients possess values with sufficient magnitude to be selected (table 3). If the first cutoff point is selected, there are five categories identified as important to the determination of the CDF score. Those categories are: other shrimp larvae, chaetognaths, gastropod veligers, Euterpina acutifrons, and Temora turbinata. If the cutoff point is extended to include the lower value, then the following zooplankton categories are added to the list: polychaete larvae, crab larvae, Paracalanus crassirostris, Oithona sp., and bivalve larvae. One distinctive difference between CDFI and II concerns the signs of the important coefficients. The second Canonical Discriminant Function contains only positive correlations that have magnitudes greater than either cutoff point. This indicates that the differences seen in the map are the result of presence or absence of the important categories. Canonical Discriminant Function III The CDFIII explains 11.07% of the between season/area variation. Examination of the CDF seasonal maps present a different analytical situation than experienced with the first two CDF's. They presented a reasonably simple, consistent series of patterns across the seasons. Both axes appeared to be discriminating between the nearshore, thermally affected regions and the more offshore regions. This does not seem to be the case with the third CDF (see fig. 7ad). In this situation the patterns displayed show differences between the intake and discharge portions of the study region. However, these patterns also seem to influenced by the nearshore/offshore effect on the community structure. The best approach to take in this situation is to describe the observed variation pattern on a season by season basis. The Fall season map shows a high plateau in Region 6. This plateau drops to a lower one in Regions 1 and 2. Region 5 is a low plain as is Region 3. The lowest elevation is in Region 3. The Winter season map shows its highest plateau connecting Regions 2 and 3. Regions 1 and 5 are lower than the previously mentioned plateau. The lowest elevation on the map is found in Region 6. Visually, the Winter season map appears to be opposite of the Fall map. The Spring map shows one high plateau covering Regions 1, 5 and 6. This is opposed by a low plain in Region 3, which descends into a valley covering Region 2. This seasonal map displays a nearshore/offshore contrast similar to ones seen earlier. The Summer map shows almost no relief and thus may be ignored. This CDF has two potential cutoff points: either 0.19 or 0.17. Selecting the first cutoff point results in ten coefficients being selected, three positive and seven negative. Extending the cutoff to the second point results in an addition of one more positive coefficient (table 3). The positive categories include: Tortanus setacauda, medusae, other shrimp, and Euterpina acutifrons. The negative categories include: Metis holothuriae, Temora turbinata, tunicates, Oithona sp., penaeid shrimp, polychaete larvae, and Longipaedia helogolandica. Zooplankton Community Structure Factor Analysis The vectors of log transformed zooplankton standing crop scores from the biweekly data were factor analyzed. The principal axis method of initial solution coupled with a varimax rotation was employed. A minimum eigenvalue of 1.0 was used to provide the cutoff for the number of factors to be selected for rotation. Three factors were retained (see table 4). They accounted for 86.0% of the original biweekly zooplankton 85 4 14 H VOOW'4Nmm aN m0 cNmo mrrmm v a, c 0 ew i mm m Or'O N 0%or [ a0 OO N N 44N 0(O ,T O4 MO Cr ol OO N, O 1 a) m CCfl i .0 CUD 0 0 0 00000 00 094 J 02 fI I1 I0 II u c Um om c ZU0ooooooo 000o0 ooooooo o00oo0 oooon 0 0 I I < 0 w0 0 V4 v m w rv D mrmkwv 0) S rIL 0 r NNNCNqNr C n 00 C4mCO O L C < O rCOaN rCnrOO O ONmvm NO 4(OOOiOMr 0 (( U 0 0 000 o ooooooooo00 0*4*' I 1 111 1 1I l I I I 4 02 4B 0 >1 0 U3 0 4O U. 4l 02 3 a (a0 0 0 4 (a (m 0 u > Q # m CD O l Mr O U * ( 0 0 1 D 0 :3 0 > 0) 30 M s 0 o1('0 a ) O O 0 2a  C < m 0 00 0 U 4. 4() 0 2 i > 20 Z0) 00 0 00 m02 u w M 0 4000 0 00 >O. 00 .ci0 0.10 a u4 40U > a 0> ae 4 1 0, 3 w (a :3 li :3 ao B o1 q $ 40 4 a) d) 2i E> 10 S 44 ~02 00 O> 02 0 40 02 01 4 42l U rO O0 *)U14. 00 >14 04 .0 (V > M > ( 44 Z 0 4J 1 >0 0020 0 .0141 w w 0i 0 wlO CO C U > m q 0 Vc B MI 0 > l r M 4l W W C UMm 1 o 0wC 02 0 w400 C020000 0 a, f 0> = 04 w (00) 201 0 00 > 0 0202 mji)0t 4J M m r_ w w w c 0 r 0 (a U2 '0 a C4 4m 'aSn4J0 20 > ( 0C 44 a4B) C0 . 04 U0 ( 000 0 > ul a 00 U to (a Z C uM ),a W (aa a ).0 )4( :3 CL I.'jc =cUCmA 0 0 02 C TABLE 4 Portion of Zooplankton Community Structure Variation Explained by the Factor Analysis FACTOR PORTION EXPLAINED CUMULATIVE PORTION 1 0.454 0.454 2 0.275 0.729 3 0.131 0.860 s 44 0 o 0 4.0 .0  O4 a 0 '0 0* U 44 1 IS 4L 0 ^ u & CTI~ *^o~ Ct 88 itP  .....~ ,, m+ ......~~~~ +;,)Im6+' P i : 0 41 U CI 4J O 4J1 .0 U ON 34 01 400 ar1ul j=Pj, 4J o ' YO'l U o ITIr &u 
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