The heuristic analysis of temporal and spatial variation within the zooplankton community structure at the Crystal River...

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Title:
The heuristic analysis of temporal and spatial variation within the zooplankton community structure at the Crystal River estuary a multivariate approach
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xi, 640 leaves : ill., maps ; 28 cm.
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Ingram, William, 1945-
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Zooplankton   ( lcsh )
Zooplankton -- Florida -- Crystal River   ( lcsh )
Zoology thesis Ph. D
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non-fiction   ( marcgt )

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Thesis--University of Florida.
Bibliography:
Bibliography: leaves 150-156.
Statement of Responsibility:
by William Ingram III.
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Typescript.
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Vita.

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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 Acti-vities.












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 season-region 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 temperature-salinity

combinations observed in the discharge canal and thermal

plume areas represent sub-optimal 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 non-resident 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 sub-tropical 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), man-induced

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 sub-tropical environment,

this study was designed to investigate variation in the na-

tural and thermally stressed environments of a sub-tropical

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 micro-environmental 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 long-term, 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




































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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 wave-energy 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 run-off

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 Withlacoochee-Cross 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 Cross-Florida 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 semi-en-

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) fjord-type estuaries, (3)

bar-built estuaries, and (4) estuaries produced by tectonic

processes. The Crystal River area fits the definition of a

bar-built 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 cross-sectional 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-

temperature-time 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 north-south (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 north-south 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 man-made or natural features found in the area:

The Withlacoochee River-Florida 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 near-shore 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.









































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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

half-meter 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 t-table 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 Z-tables were used in place of t-statistics.

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 12-15, 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 21-25, 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 category-season-sizeclass

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 one-at-a-time 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








salinity-temperature 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 Shannon-Weiner 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 hypothetico-deductive-observational 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 inter-populational

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 R-square 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 t-test 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-

by-side, 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(n-l)/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 sub-community. Following the factor analy-

sis, the factor scores were analyzed with an ANOVA to deter-

mine if the zooplankton sub-communities 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 chi-square 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 p2-1 CDFI's can be tested by the chi-square

statistic with (pl-l)(p2-1) 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 chi-square 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

variable-CDFA 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 survey-type 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 twenty-seven 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 n-variates, 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 one-variable model that produces the

highest R-square. For each of the remaining independent

variables F-statistics are then calculated. These F-statis-

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 F-statistics to the minimum entry

level. If none exceed the minimum entry level, the proce-

dure stops. Otherwise, the variable with the largest F-sta-

tistic is entered into the model. The F-statistic 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 F-statistic 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 F-statistics 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 log-normal 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 log-nor-

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
















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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 CDF-space (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

CDF-original variable correlations do display significant

F-values for the ANOVA. In addition, the variable S103 also

shows at least marginal significance (p=0.034). With this

information a re-examination of the CDF-original 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 (28-30 ppt.),








falls steadily to a low in the early spring (20-22 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 (8-10

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

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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. 5-7). 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.

5a-d). The Fall season displays little relief, in

comparison to the Spring and Summer. The major feature of

the Fall-CDFI 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 non-thermally 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. 6a-d). 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. 7a-d). 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








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