SEPA
United States
Environmental Protection
Agency
Office of Research and
Development
Washington, DC 20460
EPA/600/R-92/012
September 1991
Analysis and
Interpretation of
Zooplankton Samples
Collected During
Phase II of the
Eastern Lake Survey
Alkalinity
Dystrophy \
i
Stratification
'ft*.
Salinity
/v
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EPA/600/R-92/012
September 1991
ANALYSIS AND INTERPRETATION OF ZOOPLANKTON SAMPLES
COLLECTED DURING PHASE II OF THE
EASTERN LAKE SURVEY
Report No. 88-18
By
Alan J. Tessier and Richard J. Horwitz
The Academy of Natural Sciences of Philadelphia
19th and the Parkway
Philadelphia, PA 19103
The research described in this report has been funded by the U.S. Environmental Protection
Agency through cooperative agreement CR813666 with the Academy of Natural Sciences of
Philadelphia. This report has been submitted to the Agency's peer and administrative review
and approved for publication. Mention of trade names or commercial products does not con-
stitute endorsement or recommendation for use.
U.S. Environmental Protection Agency
Region 5, Library (P/-??:)
77 West Jackson Bou^rd, 12[h
Chicago, IL 60604-3590
A report submitted to the U.S. Environmental Protection Agency Environmental Research Laboratory, 200
SW 35th Street, Corvallis, OR 97333.
rS> Printed on Recycled Paper
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TABLE OF CONTENTS
Section Page
Summary 1
1. INTRODUCTION 2
1.1 Background 3
1.2 Zooplankton Methodology 4
1.3 Data Files 6
2. OBJECTIVES OF ZOOPLANKTON ANALYSIS 8
2.1 Primary Objectives 8
2.2 Secondary Objectives 8
2.3 Statistical Techniques 8
2.3.1 Principal Components Analysis (PCA) 8
2.3.2 Other Ordination Procedures 9
2.3.3 Tests for Differences between Zooplankton Communities
and between Discrete Classes of Lakes 9
2.3.4 Tests for Relationships among Continuous Variables 10
3. RESULTS 11
3.1 Quality Assurance 11
3.2 Distribution of Abundance 12
3.3 Zooplankton Patterns 13
3.3.1 Abundance of Major Taxonomic Groups 13
3.3.2 Species Richness 15
3.3.3 Species Diversity 16
3.3.4 Size Structure 19
3.3.5 Abundance of Major Genera 20
3.3.6 Canonical Discriminant Analysis 23
3.4 Physical/Chemical Gradients 25
3.4.1 Description of Chemical Data and Statistical Methods 25
3.4.2 Comparison of Fall and Summer Data Sets 27
3.4.3 Correlation between Physico-Chemical Parameters: Calculation
of Chemistry Gradients and Relationships of Gradients
to Original Parameters 29
3.5 Relationships between Physico-Chemical Gradients and Zooplankton 32
3.5.1 Relationship between Zooplankton Community Factors
and Environmental Gradients 33
3.5.2 Relationships between Genera and Environmental Gradients 35
3.5.3 Relationships between Species Richness and Species Diversity
and Environmental Gradients 37
3.6 Individual Species Patterns 37
3.7 Canonical Correspondence Analysis (CCA) 39
4. INTERPRETATIONS 42
5. GENERAL RECOMMENDATIONS 44
6. LITERATURE CITED 45
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FIGURES FOR ALL SECTIONS 47
TABLES FOR ALL SECTIONS 179
APPENDICES 253
Appendix A. List of Names, Numbers, Lake Identification Codes, Geographic
Locations, Region Associations, and Chemistry Cluster Numbers
for All 147 Lakes Sampled in the ELS-II 255
Appendix B. Codes for Genera Used in Generic Analyses 258
Appendix C. Formats of SAS Files 260
Appendix D. List of All Species and Their Summary Statistics for Abundance
(Untransformed) Found in All 147 ELS-II Lakes and a Separate Listing
for Each Chemistry Cluster 284
Appendix E. List of, and Summary Statistics for, 38 Genera Found
in All 147 ELS-II Survey Lakes, Plus a Separate Listing
for Each Chemistry Cluster 296
Appendix F. List of Common Species by Genera and Their Code Numbers 300
HI
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SUMMARY
Samples from 146 lakes in the northeastern United States, collected during Phase II of the Eastern
Lake Survey (ELS-II) were analyzed for abundance of each species and each size class of zooplankton.
An estimate of sampling and counting error was provided by replication of lake sampling and sample
subsampling. Among-lake variance in zooplankton assemblages was large compared to sampling and
counting errors.
The composition of zooplankton assemblages (not abundance) exhibited clear relationships with
physical and chemical features of the lakes. Assemblage structure was examined at various levels:
diversity, major genera, and individual species. Genera and species level identifications revealed the
clearest relationships to water chemistry, although significant relational patterns were discerned using
only major taxonomic groups or size structure information.
Significant regional influences (biogeographic patterns) were also observed at various levels of
organization (major taxonomic groups, genera, species, and diversity). Only rarely did these biogeo-
graphic patterns interact with the general chemistry relationships. In a few cases, however, relationships
between chemistry and zooplankton assemblages did depend on geographic region.
These results illustrate the utility of studying zooplankton assemblages as sensitive indicators of
water chemistry. This report makes recommendations for improving methods of relating zooplankton
species to environmental factors.
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1. INTRODUCTION
The primary goal of this study was to provide a broad-scale objective representation of
zooplankton assemblages in lakes in the northeastern United States. Secondly, we looked for
relationships between zooplankton communities and the physical and chemical components of these
lakes.
Ultimately, a mechanistic (causal) understanding of lake plankton communities will require detailed,
process-oriented, and generally experimental approaches. By their very nature, however, such studies
are limited in scope. This report provides a context within which the generality of results from more
selective, process-oriented studies can be evaluated.
From an ecological perspective, lakes are islands, yet they are characterized by strong coupling
with the surrounding land and atmosphere. Research directed toward understanding the regulation of
biological communities of lakes must acknowledge the interplay of physical, chemical, and biological
variables operating both within a lake and in relationships among lake, land, and atmosphere. Five
broad classes of influence can be recognized.
1. Morphometry: physical structure of the lake basin
2. Watershed factors: land use and development, topography, hydrology, geology, and
vegetation
3. Atmospheric factors: turbulence and chemical loading
4. Biogeography: species ranges and immigration rates
5. Species interactions: direct and indirect effects of competition, predation, etc.
Since geographic gradients to many of these factors exist, regional patterns might be expected to
emerge. However, these factors interact in highly complex ways to create tremendous local variation
among lakes in physical, chemical, and especially biological structure.
The National Surface Water Survey (NSWS) conducted by the U.S. Environmental Protection
Agency (EPA) has provided an opportunity to evaluate local variation in biological communities against a
framework of geographic region and water chemistry. Phase I of the Eastern Lake Survey (ELS-I) of the
NSWS sampled 768 lakes in the northeastern United States during fall turnover 1984. These lakes were
selected from a map population of 10,758 (identified from 1:250,000-scale maps), according to a strat-
ified design with equal allocation of lakes randomly chosen from each stratum. These strata comprised
five geographic regions (Figure 1-1) and three alkalinity classes (acid neutralizing capacity [ANC] < 100
neq/L; ANC 100-200 neq/L; ANC > 200 neq/L), as identified by Omernik and Powers (1982) and
Omernik and Kinney (1985). Each alkalinity stratum was nested within each region. Samples were col-
lected at a 1.5-m depth at a central point in each lake and analyzed for a variety of chemical parameters.
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A complete discussion of sampling design, chemical and physical parameters measured, and results are
presented in Linthurst et al. (1986) and Landers et al. (1988). Field and laboratory analytical methods
are detailed in Hillman et al. (1986).
Results from the ELS-I formed the basis of the Eastern Lake Survey - Phase II (ELS-II) sampling,
which is the focus of this report. The goals of ELS-II were to examine seasonal variability in water
chemistry for a representative subset of ELS-I lakes in the northeastern United States and to assess the
status of biological communities in these lakes. The only biological component of ELS-II was a sampling
of zooplankton communities during the summer. This report examines the zooplankton data in detail
and considers the physical and chemical data only to the extent that it relates to the biology. A
complete discussion of the ELS-II design is provided in Thornton et al. (1986); only a brief summary is
provided here.
1.1 BACKGROUND
ELS-II lakes were randomly chosen from the 768 ELS-I lakes after exclusion of lakes shown in
ELS-I to possess characteristics of low interest (i.e., cultural enrichment, surface area > 2,000 ha,
maximum depth < 1.5 m, ANC > 400 neq/L). Using a diversive cluster algorithm (CLUSB), the ELS-I
lakes were classified according to their physical and chemical parameters. Three distinct clusters of
lakes were identified along a major gradient of ANC. For lake selection, these three clusters of lakes
were further defined as (1) cluster 1 = ANC < 25 neq/L, (2) cluster 2 = 25 < ANC <
100 neq/L, and (3) cluster 3 = 100 < ANC < 400 neq/L
ELS-II sampled 147 lakes during mid-summer and again during fall turnover 1986. These lakes
represented a stratified random sample of the ELS-I lakes. The three chemistry clusters defined by level
of ANC constituted the strata. Lakes were chosen as a systemic variable probability sample, with fixed
sample sizes (Thornton et al., 1986). Before lake selection, the lakes in each chemistry cluster were
sorted by region (major) and site depth (minor). The goal was for each chemistry cluster to contain 50
lakes and for all clusters to have similar yet broad distributions with respect to geography and lake
depth.
Table 1-1 presents a summary of the number of lakes in each region and chemistry cluster, and
the summary statistics of lake depth for each chemistry cluster. As is apparent from this table, and from
Figure 1-2, a broad geographic distribution of lakes was achieved. Furthermore, the mean, range, and
distribution of lake depths represented in the chemistry clusters are quite similar (a consequence of the
stratified sampling design). A complete listing of lake names, identification numbers, geographic loca-
tions, and chemistry clusters is provided in Appendix A for all 147 lakes.
Of the 147 lakes, only 146 were actually sampled for zooplankton. In addition, complete chemistry
data were available for only 146 of the lakes. Because of the discrepancy caused by the missing data,
analyses of zooplankton patterns were done for 146 lakes, analyses of chemistry patterns were done for
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147 lakes, correlations of zooplankton to most chemical parameters were done for 146 lakes, and com-
parisons of zooplankton to complete suites of chemical parameters (e.g., principal components analysis
[PCA]) were done for 145 lakes.
1.2 ZOOPLANKTON METHODOLOGY
The objective of the zooplankton sampling was to achieve comparable estimates of community
structure among lakes. The sampling design of this or any study imposes overall limitations on inter-
pretation of results. The major limitations of this study were the use of a single mesh size, a single
sampling location, and a single sampling date for each lake. Use of an 80-/im mesh net is a justifiable
compromise for sampling both large and small species with moderate efficiency. Small, soft-bodied roti-
fers are underestimated with this net, but if abundant in a lake, they will still be caught. If a smaller
mesh size is used, to improve capture efficiency of small rotifers, capture efficiency of larger species is
reduced. Since large species of zooplankton can be important to aquatic food webs, even in low num-
bers, it was important for this study that a mesh no finer than 80 jum be used to sample the lakes.
The major focus of this study was to derive robust conclusions regarding variance among lakes
and the relationship of zooplankton to water chemistry, not to evaluate within-lake (horizontal or
seasonal) variability in zooplankton composition. Among-lake variance, however, can be judged signifi-
cant only when contrasted with some measure of within-lake variance. This is particularly evident for
abundance data (whether by species or body size) that involves a sample splitting and counting error in
addition to lake sampling error. Therefore, each lake was sampled by three separate, vertical hauls of a
net. These three samples were treated individually throughout analysis and therefore provide a measure
of within-lake (within-site) variance. We further analyzed replicate subsamples for evaluation of sample
splitting and counting error.
Zooplankton were sampled from each of 146 lakes with three vertical hauls of a Wisconsin bucket
net (80-/zm mesh). The net was lowered to 1 m from the lake bottom and pulled toward the surface at a
constant rate of approximately 10 m/min. The three tows were made a few meters apart from each
other. Zooplankton from each net tow were rinsed into separate 250-mL glass jars using deionized
water (3 jars per lake). The contents of each jar was preserved with a buffered sugar formalin solution
(final concentration in the jar was 10% formalin, 4% sucrose, and enough sodium acetate to give a pH of
approximately 7.5-8.0. Each jar was labeled with the lake identification number, depth of tow, tow
number (1, 2, or 3), date, and time of day.
The three replicate tows from each lake were analyzed separately. Each jar was first examined for
taxonomic composition. Individuals were pulled out from the samples and examined using both a Zeiss
stereoscope and a Zeiss Universal compound microscope. Examiners attempted to identify all indi-
viduals of Copepoda, Cladocera, Rotifera, and Chaoborus to species level. Major keys used included
Stemberger (1979), Pennak (1978), Deevy and Deevy (1971), Edmondson (1959), and Cook (1956). In
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addition, several minor keys were consulted, including Berner (1982), Megard (1967), and Frey (1980).
For consistency, all taxonomic identification was done by the same person (A. Tessier), although C.
Goulden was consulted regarding several problem taxa.
In addition to generating a comprehensive species list for each lake, an ocular micrometer was
used to measure the sizes (body length) of each species in each sample. Generally, several size classes
were designated for each species in each lake. This sizing step was done separately for each lake; we
did not assume that the size classes for a given species were constant across lakes. Size classes were
designated at 0.1-mm intervals for animals less than 1.0 mm total length, and in 0.2-mm intervals for
larger animals. For all species, body length was designated as the maximum length minus caudal seta
in Copepoda and minus tail spines in Cladocera.
Using the taxonomic and size class listing as a guide, each jar was then quantified for total abun-
dance of organisms in each species and size class. Generally, if overall densities in the jar were < 300
individuals (excluding nauplii), the entire sample was counted. Otherwise, the jar was split, using a
funnel splitter (George et al., 1984). For those jars that were split, approximately 15% were replicated
(i.e., replicate splits were counted). This replication allowed estimation of precision for the combined
splitting and counting methodology. Furthermore, since there were three jars for each lake, we were
able to separate the counting error from the lake sampling error.
Samples (or split samples) were dispensed to a partitioned chamber and counted, using a Zeiss
stereoscope equipped for magnification up to 75X. All splitting and counting was performed by one
person (N. Roberts). Count data from a given lake were entered onto count sheets that had the species
and size class listing for that lake. These sheets were examined for consistency and completeness
before computer entry. We used REFLEX (1986) software operating on MS-DOS microcomputers for
data entry and as a preliminary database manager system. The following information was entered into
REFLEX.
• EPA-NSL lake identification code
• Jar code (3-digit code identifying sample bottle and replicate)
• Species identification code (4-digit code, including order, family, genus, and species
information)
• Body size code (2-digit code for body length in mm)
• Subsample size (fraction of sample actually counted)
• Depth of net tow
• Lake number (assigned number 1-147) for tracking convenience
• Count (number of individuals of each species and size class)
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A separate file contained header information about each lake: lake identification code, lake name,
lake number, latitude, longitude, state in which lake was located, and chemistry cluster (see Appendix
A). Raw counts were divided by fraction of sample counted and then divided by depth of tow to give
values of abundance comparable among lakes of different depth. Units for abundance are thus the
number of individuals per meter of net tow. Data files are currently available on REFLEX for use in rapid
searching and for summarizing and exploring the data. Data files were exported to SAS (1985) on a
VAX mainframe computer for final data management and statistical analysis. Some statistical analysis
was also performed using SYSTAT (Wilkinson, 1986).
1.3 DATA FILES
Because some rare species were found in the initial taxonomic examination of samples, but were
not abundant enough to be counted after sample splitting, we created a new data set in which we arbi-
trarily set the abundance of such rare species at 0.01/m/net tow. The original data set was left
untouched. Next, two broadly different data sets were created from this new file. One file was based on
taxonomy (species) and the other was manipulated to generate more reduced data sets. In the most
reduced form, all species were lumped into major taxa groups (Rotifera, Cladocera, Copepoda, Crus-
tacea, etc.). In a less aggregated form, species were lumped into genera and subgenera categories.
This was largely a functional or ecological grouping.
Replicate counts from the same tow and replicate tows from the same lake were kept separate for
analyses of counting and sampling precision and for calculation of species diversity and the proportion
of each species in each tow. The averages of abundances and proportions of each species over the
three replicate tows per lake were used for between-lake comparisons of abundance and community
structure. Replicate counts were not used in forming these averages. A new file was formed containing
these averages. The average abundance over three replicates of species noted in the original taxonomic
scan but not in the quantitative count of the split samples (i.e., those with abundance arbitrarily set to
0.01 for individual samples) was typically not 0.01; however, the average abundances of these species
was still less than the abundances of species recorded in the quantitative counts. The averages of
diversity indices over the three replicate tows were used in between-lake comparisons of diversity.
These files were used as a basis for further analytical work. Various additional files were created
in the course of the analysis, for example, adding factor scores from PCA, or adding selected chemical
variables. The basic files used in the study were as follows. Species and genus codes used in coding
data and naming variables are documented in Appendix B. Descriptions of the formats of these are
contained in Appendix C.
• ASCII files: One record for each species occurrence in each sample:
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EPATSPE1.DAT and EPATSPE2.DAT: Finalized raw data files, each containing taxo-
nomic data from half the lakes.
SIZE.DAT: Finalized raw data file containing size data.
• SAS files: One record for each species occurrence in each sample (i.e., record structure is
lake number, jar number combining information on replicate tows and replicate counts,
species code, and density of that species in the tow, standardized for the volume of the tow,
and the fraction of the total sample counted):
SIZE.SSD: Size data
EPATSPE.SSD: Taxonomic data with species recorded only in initial scan set to 0.01.
The data from split samples can be recovered by setting all abundances of 0.01 to 0.
• SAS files: Matrix format, with one record for each tow or replicate count (i.e., record
structure is lake number, jar number, and abundance of each of the 142 taxa in the tow, with
0 for species not recorded). Abundance of each species formatted as a separate variable:
EPASP.SSD: Taxonomic data with species recorded only in initial scan set to 0.01. The
data from split sample counts can be recovered by setting all abundances of 0.01 to 0.
• SAS files: Matrix format with the abundances of species and proportions of species stored
as elements of two arrays:
EPASPAR.SSD: Taxonomic data with species recorded only in initial scan set to 0.01.
The data from split sample counts can be recovered by setting any array values of 0.01
too.
EPAMSPAR.SSD: Average of three replicate tows (replicate counts of single tows not
included), including species recorded only in initial scan. The data from split sample
counts can be recovered by setting any average abundances < 0.01 to 0.
• SAS files: Special files used for data analysis:
EPACGPCA.SSD: Abundance of each of 38 major, nonlittoral genera and species
groups (average over tows for each lake; variables R1 through R4 for rotifers, CL1
through CL12 for cladocerans, and COP1 through COP12 for copepods), first 10 factor
scores from PCA of generic abundances, first 10 factor scores from PCA of chemical
parameters, and selected chemical variables.
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2. OBJECTIVES OF ZOOPLANKTON ANALYSIS
2.1 PRIMARY OBJECTIVES
This study had two broad objectives. In addressing both of these objectives, we considered
organism abundance, species diversity, taxonomic composition (at various levels), and body size
structure.
1. Zooplankton patterns: We attempted to characterize summer zooplankton communities for
all lakes in the northeastern United States. Since the lakes had already been categorized by
geographic region and water chemistry (as determined from samples taken in fall turnover),
we examined the ability of these groupings to explain variance in community structure.
2. Relationships with chemistry and morphometry: We examined general relationships
between summer zooplankton communities and summer water chemistry and morphometry,
focusing particular attention on indices of acidification.
2.2 SECONDARY OBJECTIVES
Secondary objectives included the following.
• Assess functional shifts in zooplankton communities that are consistent with the observed
structural shifts concordant with acidification.
• Evaluate zooplankton sampling and counting precision.
• Make recommendations concerning lake selection criteria for future, long-term monitoring of
lakes.
2.3 STATISTICAL TECHNIQUES
2.3.1 Principal Components Analysis (PCA)
We used principal components analysis (PCA) to define gradients of community structure and
environmental factors. The number of taxa and environmental parameters defines a huge number of
correlations. Analysis using each of the taxa and parameters would have been unwieldy and would have
created problems of separating real patterns from spurious correlations. PCA defines a set of principal
components, each a linear combination of the raw variables. The principal components are orthogonal
and ordered by their contribution to the total variance among all variables. There are as many principal
components as raw variables, but the ordering allows the use of a small subset to encompass much of
the variation in the original variables. Unless otherwise noted, PCA was performed on the correlation
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matrix of the original variables (i.e., the original variables are standardized to unit mean and variance, so
that the analyses do not give disproportionate weight to highly changeable variables.
In the remainder of the report, principal components derived from a specific PCA are referred to
as factors. The value of any factor for a given lake is the factor score for that factor and lake. Different
sets of factors are defined by different analyses; these are differentiated by context or by the use of con-
sistent names for each set of factors.
PCA was used to describe community patterns in several ways. The original variables in each
PCA were:
• Abundances (typically log transformed) of the major taxonomic groups (rotifers, cladocerans,
calanoid copepods, cyclopoid copepods), of genera, or of species.
• Proportions (typically arc sine transformed) of major taxonomic groups, genera, or species.
• Indices of species richness and species diversity.
Some analyses were done over all zooplankton in a single PCA; separate analyses were also done over
taxa within each of the four major taxonomic groups. The components derived in these analyses have
been used descriptively, to summarize patterns of covariance among taxa, and analytically, to serve as
dependent variables in analyses of community differences among the three ANC clusters and along
environmental gradients.
PCA was also used to define environmental gradients, using the water chemistry and morphometry
data collected in conjunction with the study. The resultant factors were used to analyze environmental
variation within and between the three ANC clusters, and functioned as independent variables for
analysis of zooplankton community/environmental relationships.
2.3.2 Other Ordination Procedures
Other algorithms, such as detrended correspondence analysis (DCA) and canonical correspon-
dence analysis (CCA) were used to define community gradients. These procedures are useful for
describing gradients involving unimodal patterns of taxonomic abundance along environmental gradi-
ents. These techniques are described in the sections of the report relating to their specific applications.
2.3.3 Tests for Differences between Zooplankton Communities and between Discrete Classes of
Lakes
Differences in the abundances, proportions, and diversities of zooplankton taxa among the ANC
clusters and the geographic subregions were determined using multiple analysis of variance (MANOVA).
This statistical test analyzes differences among classes for a number of variables, assuming some
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correlation in residual variance among the different variables. Analogous tests were performed using
factors defined by various PCAs.
2.3.4 Tests for Relationships among Continuous Variables
The relationships between zooplankton abundance and environmental conditions were tested
using multiple regression, with the abundance of zooplankton taxa or factor scores from community
analyses as dependent variables, and chemistry factors as independent variables. Individual chemical
parameters were not used in multiple regressions because of the extensive covariation among groups of
chemical parameters.
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3. RESULTS
3.1 QUALITY ASSURANCE
Of the original set of 150 lakes, only 147 were actually sampled in summer 1986. Furthermore,
one of these lakes (1A1-015) was sampled for chemistry but not for zooplankton, leading to a final count
of 146 lakes for this report. In 2 of the 146 lakes, only 2 net tows were made. Jar 2 is missing for Lake
97 (1C3-032) and jar 3 is missing for Lake 39 (1D1-056). One lake was sampled twice, but on different
dates. Samples 128 and 129 are both from Lake 1B3-052. This second sampling has been treated as a
different lake in the analyses of zooplankton community structure. The 147 different lakes are used in
analyses of environmental gradients. The second sampling of Lake 1B3-052 (Lake 129) is deleted from
most analyses of zooplankton/environmental relationships.
All available jars ([147x 3] - 2 = 439) were completely analyzed without loss. Further, 52 jars
were counted twice, using different split subsamples, for an estimate of splitting and counting precision.
This process involved an overall counting replication of 35% of all lakes and 12% of all jars.
Precision was calculated separately for each species in each of the 52 replicates, according to the
following formula:
where: P-t- is the precision estimate for species i in jar j,
A- and By are the counts of species i in jar j for the two replicate splits, and
Xjj is the mean count for species i in jar j of the two replicates.
This precision statistic was averaged across all species in a jar to obtain a single estimate of
precision for each of the 52 replicated jars. Summary statistics on the 52 estimates of precision are
provided in Figure 3-1 and Table 3-1. In both presentations, precision was calculated including all
species and also excluding species below some variable abundance. In this way, we could explore how
the precision estimate was affected by the rarity of a species. When all species were included, the
grand mean precision was 0.569, whereas if only the more common species were considered (abun-
dance > 8 individuals per meter net tow), a mean precision of 0.336 was achieved. This range of pre-
cision was somewhat high, but not alarmingly so, considering that both splitting and counting errors
were combined in this estimate.
To better quantify the variance in abundance attributable to splitting and counting and compare
that to the within-lake (sampling) and among-lake variance, we performed nested analyses of variance
using Proc Nested in SAS. Tables 3-2 through 3-6 present the results of this analysis for total
abundance and for abundance of major taxa. In these tables, CHEMGRP indicates the chemistry cluster
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(ELS-I designation). In general, the splitting and counting error (subsample) was about 1% of the total
variance in the samples, and the within-lake variance (samples) was about 5% on average. It is interest-
ing to observe the low variance attributable to chemistry cluster (zero for Cladocera) compared to the
high among-lake variance. Generally, these estimates suggest that sampling and subsampling errors
were of negligible consequence in comparisons of zooplankton abundance among lakes. They further
suggest that the major groups of zooplankton were sampled and subsampled with comparable
precision.
To examine sampling error in more detail, we performed nested analysis of variance on the
species richness data. We calculated species richness in two ways for this analysis. First, we
considered the number of species with abundance > 1 individual per meter net tow (Table 3-7). Table
3-8 presents results for only the common species (abundance > 10). These results suggest that
subsampling and sample errors are nearly comparable. Even when combined, they are not much more
than 10% of the total variance. As with abundance, most variance in species richness was due to
among-lake contrasts; however, large variation was attributable to chemistry cluster when less common
species were considered (Table 3-7).
Tables 3-9 and 3-10 explore the partitioning of variance among lakes, samples, and subsamples
separately for each water chemistry cluster. Tables 3-11 and 3-12 do the same for species richness. In
general, these results suggest that sampling precision was similar across all water chemistry clusters.
The only appreciable contrast was that the precision of abundance estimation was apparently less in
acidic lakes due to greater variance in the among-samples (within-lake) component. Species that
dominate in acidic lakes may exhibit greater horizontal variation.
3.2 DISTRIBUTION OF ABUNDANCE
As was apparent in Section 3.1, abundance data were log(e) transformed before analysis. The
justification for this transformation, as opposed to some other or no transformation, is illustrated by
power function plots of variance and mean abundance estimates. The slope of the relationship obtained
by plotting log(e) mean versus log(e) variance for each species or sample provides information on the
underlying distribution. If the slope is 1, then abundance is Poisson distributed (variance increases
proportionate with the mean) and a square root transformation is appropriate. If the slope is 2, then a
logarithmic transformation is suggested. When the slope is greater than 2, a negative power function is
the appropriate transformation. Figure 3-2 presents the power function plot of variance to mean for all
species calculated separately for each lake (among-sample, within-lake estimates). Figures 3-3 through
3-5 present the same information but for each water chemistry cluster separately. There is consistency
in that a slope somewhat less than 2 but greater than 1.5 is evident in all figures. These plots strongly
suggest that a log(e) transformation would normalize the data.
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We also examined the distribution of species abundance among lakes. Figures 3-6 through 3-9
present the power function plots for among-lake variance versus mean abundance, where each point
indicates a different species. Species that are very rare in the entire data set show a slope approaching
4. However, for the most common species (log mean abundance > 1), a slope of 2 is typical.
We concluded from this analysis that a log(e) transformation of the abundance data would be
most appropriate in achieving normality. The success of this transformation is illustrated in frequency
histograms of total abundance after log(e) transformation (Figure 3-10). An even more sensitive tech-
nique was to examine probability plots of the same data. We presented this information separately for
each chemistry cluster (Figure 3-11). The distribution of abundance was similar in all chemistry clusters.
With the exception of some skew associated with very low abundance, a normal distribution was evident.
3.3 ZOOPLANKTON PATTERNS
3.3.1 Abundance of Major Taxonomic Groups
Tables 3-13 and 3-14 present summary statistics for total abundance of zooplankton and abun-
dance of major taxonomic groups for all lakes sampled. Both transformed and untransformed values
are given. On a numeric basis, Rotifera were dominant, followed by Cladocera and Copepoda (exclud-
ing nauplii), which were of similar abundance. Tables 3-15 and 3-16 present the summary statistics of
the four major groups of zooplankton (rotifers, cladocerans, cyclopoids, and calanoids) calculated separ-
ately for each water chemistry cluster. Box plots illustrate these results in Figures 3-12 through 3-15.
There was a weak effect of water chemistry cluster on total zooplankton abundance (Table 3-17; p =
0.055), which was due to a low total abundance in chemistry Cluster 3 (100 < ANC < 400
neq/L). However, there was no evidence of strong declines or increases in zooplankton abundance
with increased lake acidity (Figure 3-12).
Univariate and multivariate analyses of variance did suggest evidence of changes in the composi-
tion of zooplankton communities attributable to water chemistry cluster (Table 3-18). Examination of this
table and the figures reveals that, with the exception of the Cladocera and nauplii, all major groups show
shifts in abundance across water chemistry clusters. Large decreases in cyclopoid abundance and large
increases in calanoid abundance characterized Cluster 1 lakes.
Since only weak changes in overall abundance were detected across chemistry cluster, we con-
verted the absolute abundance data to relative (proportional) abundances by dividing through by the
total abundance in each lake. Proportions were arc sine square root transformed to help stabilize the
variance among lakes. Figures 3-16 and 3-17 illustrate the proportional dominance of each major group
for each chemistry cluster. Table 3-19 presents the results of univariate and multivariate analyses of
variance of these major zooplankton groups partitioned by chemistry cluster. As was observed for the
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absolute abundance data, all major groups except cladocerans showed radical shifts in dominance asso-
ciated with water chemistry.
In order to better summarize variance in composition of zooplankton communities among the 147
lake samples, we performed PCA using the four major zooplankton groups. PCA was performed both
on absolute abundance (log transformed) and on the proportional abundance data. For each approach,
we standardized the variables (zooplankton groups) to zero mean and unit variance (using a correlation
matrix). In this way, each group was treated as being of potentially equal importance in explaining
overall variation.
Table 3-20 presents the results of PCA using the log transformed absolute abundance data. As
shown in Table 3-21, water chemistry was important in explaining variance among lakes in each of the
first three PCA factors. The first factor describes variance in overall abundance of cladocerans, rotifers,
and cyclopoids contrasted to abundance of calanoids. The second factor was essentially abundance of
calanoids, and the third factor largely describes variance in rotifers contrasted to abundance of
cyclopoids. All three factors were apparently influenced by the large shifts in absolute abundance of
calanoids and cyclopoids that occurred across clusters.
The PCA of the proportional data allowed us to focus on compositional variance in zooplankton
communities, independent of variance in total abundance. Table 3-22 presents the PCA results for the
major groups of zooplankton. A somewhat different pattern emerged from this analysis than from the
previous PCA. The first PCA factor (explaining 50% of the compositional variance) describes variance in
the dominance of rotifers versus crustaceans. The second factor describes the contrast between cala-
noid dominance and cyclopoid dominance. This second factor is analogous to the first two factors in
the previous PCA. Not surprisingly, this second factor is strongly dependent on water chemistry cluster
(Table 3-23). Calanoids predominate in the Cluster 1 lakes (Factor 2, Figure 3-18). Of great interest is
the observation that the first PCA factor, clearly the largest gradient in composition of these commu-
nities, is essentially independent of water chemistry. There is only a slight and nonsignificant tendency
for crustaceans to dominate in Cluster 1 lakes (Figure 3-18).
An important question to ask is, "To what extent are the apparent shifts in zooplankton community
(as described for major taxa) robust with respect to geographic region?" The ideal would be for conclu-
sions about the dependence of zooplankton communities on water chemistry to be independent of geo-
graphic region. This is comparable to a lack of statistical interaction between chemistry and region with
regard to overall shifts in community. To answer this question, we performed cross-factored (3 chemis-
try clusters x 5 geographic regions), multivariate (4 major taxa) analyses of variance. Strong chemistry
effects were expected (previous analyses), but with this analysis, we could determine whether the chem-
istry patterns we described were independent of region.
Tables 3-24 and 3-25 present the results of the cross-factored, multivariate analyses of variance for
absolute abundance and proportional abundance, respectively. Regardless of analysis, no significant
multivariate or univariate interactions were detected. Hence, regional and chemical effects on zoo-
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plankton communities were independent at the level of major taxa. Strong chemical effects were again
noted (see detailed analysis given previously), as well as a single strong regional influence. Calanoid
copepods were distinctly less abundant in Region 2 (Poconos/Catskills). This contrast between Region
2 and the other geographic regions is illustrated in Figure 3-19. In terms of both absolute abundance
and proportional abundance, there is a relative scarcity of calanoids in Region 2. It is interesting that the
basic influence of water chemistry on calanoid abundance is not affected by this geographic influence.
Table 3-26 shows how in all regions calanoids become more abundant in acidic (Cluster 1) lakes, but
the overall mean is lower in each cluster of Region 2.
3.3.2 Species Richness
The number of species in a given lake was not generally predictable from information on abun-
dance. We considered all species definitely observed in a lake, regardless of abundance, in our calcu-
lations of species richness (total number of species in sample). Over all 147 lakes, there was a tendency
for species richness to decrease with species abundance (r = -0.157). However, on further examination,
it was found that species richness was negatively correlated with abundance only in the Cluster 1 lakes
(r = -0.225), and even in Cluster 1 lakes this relationship was nonsignificant. In lakes with ANC > 25
jzeq/L (Clusters 2 and 3), there was no relationship between species richness and zooplankton density.
In acidic lakes, large variance in abundance was somewhat attributable to a few species.
Summary statistics of species richnesses are presented in Tables 3-27 (all lakes) and 3-28 (separ-
ately by chemistry cluster). As is apparent from these tables, and even more clearly from Figure 3-20,
species diversity of all major groups decreases dramatically in Cluster 1 lakes. There are, however,
secondary patterns. Observe in Figure 3-20 that rotifer richness displays a tendency to increase in
Cluster 2 lakes compared to Cluster 3 lakes. In contrast, crustacean richness declines monotonically
with chemistry cluster.
To better synthesize the species richness data, we performed PCA on the richness of the four
major taxonomic groups (Table 3-29). Factor 1 of this analysis describes variation in richness for all
groups and explains 50% of the total variation. Factor 2, which describes variation in richness of
calanoids, is the only other factor of major significance. Hence, variation in richness of calanoids
appears to be somewhat independent of variation in richness of the other three major groups.
Having reduced most of the variation in species richness down to orthogonal factors, we per-
formed a cross-factored, multivariate analysis of variance to test for the significance of chemistry cluster
and geographic region in explaining this variation (Table 3-30). As expected from the univariate plots,
there was evidence for strong influences of water chemistry. The univariate analyses suggest that much
of the chemistry effect is acting on PCA Factor 1 (richness of all groups). Interpretation of this chemistry
effect was confounded, however, by a strong regional effect and a marginal interaction effect. Interest-
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ingly, the regional and interaction effects appeared to be driven largely by variation in Factor 2 (calanoid
richness).
Figures 3-21 and 3-22 illustrate the relationship between PCA Factor 1 and geographic region and
chemistry cluster, respectively. The marginal significance of region in explaining variation in Factor 1
appeared to be due to slightly greater richness in central New England (Region 3) and perhaps Maine
(Region 5). The very strong decrease in richness associated with chemistry Cluster 1 (most acidic) lakes
was much more apparent. Univariate analysis of variance of Factor 1 revealed no significant interaction
between region and chemistry. The pattern of decreased species richness in Cluster 1 lakes was inde-
pendent of geographic region.
The significant regional effect on PCA Factor 2 is illustrated in Figure 3-23. Region 2 scores
lowest on this factor, which was interpreted as richness of calanoids. It was interesting that Region 2
was also significantly lowest in abundance of calanoids (see previous analysis of abundance patterns).
Apparently, both richness and abundance of calanoids are reduced in the Poconos/Catskills region.
Factor 2 also showed a weak interaction between region and chemistry cluster. Graphical examin-
ation of Factor 2 plotted against chemistry cluster for each region revealed two distinct patterns. Figure
3-24 summarizes these findings. In Maine and the Adirondacks (Regions 1 and 5), Factor 2 (calanoid
richness) declined in the less buffered lakes. In Regions 2, 3, and 4, there was no significant change in
richness across chemistry cluster.
Summary statistics for species richness of major taxonomic groups calculated separately for each
region are provided in Table 3-31. Consistent with the interpretations of the PCA Factor 2 patterns,
caianoid richness in the Poconos/Catskills region was half that of the other regions.
To summarize the species richness analysis, Cluster 1 lakes are characterized by a much reduced
richness of species compared with Cluster 2 or Cluster 3 lakes. This effect is independent of geographic
region. A strong regional influence is observed, but is confined largely to calanoid richness.
3.3.3 Species Diversity
Species richness ignores what can be interpreted as large contrasts in the abundance of various
species among samples. Species richness examines the data from a presence-absence view, whereas
other measures of diversity take into account the relative abundance of each species. Indices of species
diversity measure the evenness of species abundance within samples, as well as the total number of
species; they may be more sensitive to changes in dominance and evenness among species and less
sensitive to the occurrence of a few individuals of rare species than species richness. The following
diversity indices were calculated for the average zooplankton abundances for each lake:
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Shannon-Wiener H = -S p. *
Simpson's S = S p2
Ln(Simpson's) L = ln(S)
where: PJ is the proportion of the ith species in the sample.
Indices were calculated for each tow; the average of the diversities for each of the three tows per
lake (excluding replicate counts) were used in subsequent analyses. Indices were calculated for the
entire zooplankton community and for each of the four major taxonomic groups (rotifers, cladocera,
calanoid copepods, and cyclopoid copepods) using proportion of each species over all taxa or propor-
tion over each group. The rare species with abundances arbitrarily set were included in the calculation
of the indices. The contribution of these species to the estimated diversity of a sample is twofold: (1)
the inclusion of sums associated with these species increases the estimated diversity, whereas (2) the
decrease in proportions of other species by inclusion of the rare species decreases the estimated
diversity. Thus for each index, the sum associated with these species is an overestimate of the
contribution of these species to the estimated diversity for each sample. These sums were calculated
for each sample and index and were found to form a very small part of the total sum. The arbitrary
abundances used for these rare species do not affect the comparisons between lakes.
Summary statistics for the diversity indices (and species richness for comparison) for all species
and for the four groups (Table 3-32) show that lakes in Cluster 1 (low ANC) tend to have lower diversity
than lakes in Cluster 2 or 3. The differences between the clusters were generally significant (MANOVA, p
< 0.0001). With one exception, In(Simpson's) for rotifers, every comparison for all species, rotifers,
cladocera, and cyclopoid copepods was highly significant (univariate p < 0.0005 unadjusted for the
number of comparisons). In each of these significant differences, multiple comparison tests (Tukey's
honest significant difference, and the Ryan-Einot-Gabriel-Welsch Multiple F test; SAS, 1985) showed
Cluster 1 lakes to have significantly lower diversity than lakes in Clusters 2 and 3; diversity in lakes in
Clusters 2 and 3 were not significantly different in any of these comparisons. There was a weakly signi-
ficant difference (p < 0.013) in Shannon-Wiener diversity of calanoid copepods. The REGWF compari-
son indicated significantly higher diversity in Cluster 3 lakes than in Cluster 1 or 2 lakes. There also
appear to be some regional differences in species diversity; the extent of variation in Shannon-Wiener
diversity among regions and chemistry clusters is compared in Table 3-33.
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These results closely parallel the differences in species richness and indicate that the diversity and
richness of zooplankton communities of Cluster 1 lakes are significantly lower than those of Cluster 2
and 3 lakes, with the difference occurring through lower diversities of rotifers, cladocera, and cyclopoid
copepods.
PCA was performed on the Shannon-Wiener diversity of each of the four taxonomic groups in
each of the lakes to determine the extent of correlation in group diversity within each lake. Diversities of
groups that are high or low in the same lakes should correlate together and with the same sign on the
same principal components. The results indicate that the diversity of the four main taxonomic groups
did not tend to be strongly associated, each with the others, although there is a general trend within
each group for lower diversity in Cluster 1 lakes. This weak correlation is demonstrated in the plot of
Shannon-Wiener diversity of cladocera versus that of rotifers (Figure 3-25), which shows no strong
correlation.
To better interpret this variance in diversity, we used principal components to extract simpler
trends in diversity of the four major zooplankton groups. Table 3-34 presents the results of the PCA for
Shannon-Wiener diversity. In contrast with the results of the species richness data, the first two PCA
factors were equally important in explaining variation in diversity. The first factor describes a contrast
between diversity of rotifers and diversity of crustaceans. The second factor describes a contrast
between the diversity of calanoids and diversity of other zooplankton. Together these two axes account
for nearly 80% of the diversity variation. The change in calanoid diversity relative to other zooplankton
diversity was noted in the species richness analysis. The equally strong gradient contrasting rotifer and
crustacean diversity was not observed in the species richness analysis. This contrast suggests that
many rotifer and crustacean species coexist in a large number of lakes, but that there is large variance
among these lakes in the relative diversity of the two groups.
As shown in Table 3-35, we employed these first two PCA factors in a multivariate analysis of vari-
ance to examine the influences of chemistry cluster and geographic region and their interaction. Chem-
istry cluster was very significant in explaining variation in overall diversity (multivariate test statistic),
whereas region and interaction effects were weaker. Examination of the univariate statistics suggested
that PCA Factor 1 was influenced only by chemistry cluster, and even that is weak compared to the
influence of chemistry on Factor 2. Hence, a strong gradient exists among lakes from rotifer diversity to
crustacean diversity; this gradient is largely unaffected by chemistry or region. The one weak chemistry
effect on this gradient is that in Cluster 2 lakes, rotifer diversity is generally higher than crustacean
diversity (Figure 3-26).
Diversity Factor 2, contrasting diversity of calanoids to other zooplankton, showed strong chem-
istry and regional influences. Figure 3-26 illustrates how the chemistry cluster effect is due to large
decreases in Factor 2 in Cluster 1 lakes (calanoid diversity remains high despite large decreases in
diversity of other zooplankton). Figure 3-27 summarizes the location of all lakes in PCA Factor 1 and 2
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space. We have labeled each lake according to cluster type in this figure. Notice that Factor 2 is
important in separating out many of the acidic, Cluster 1 lakes.
Regional influence was interpreted according to Figure 3-28. The Adirondacks and Southern New
England are distinctly lower in Factor 2 scores than other regions. Part of the explanation for the low
mean score in the Adirondacks is due to the over-representation of Cluster 1 lakes in this region. The
interaction effect of chemistry and region was examined by plotting scores for each cluster in each
region separately. Our conclusion from this examination is that there are differences in the magnitude of
cluster effects depending on region, but the general direction of change is consistent among regions.
3.3.4 Size Structure
In addition to taxonomic identifications, abundance data were also categorized by body size.
Tables 3-36 through 3-38 summarize zooplankton abundance for all 147 lakes according to 0.2-mm size
classes; for example, Size Class 1 includes animals < 0.2 mm and Size Class 2 includes animals
between 0.2 and 0.4 mm. Logarithmic transformation of the data helped stabilize the variance among
size classes, but apparent in Table 3-37 is the large decrease in variance (and mean) for size classes
greater than Size Class 9 (> 1.8 mm). Also, since the proportional abundance of zooplankton in these
larger size classes was negligible (Table 3-38), we excluded Size Classes 10 and larger from analysis.
Tables 3-39 through 3-41 present size class abundance as a function of chemistry cluster. On the
basis of abundance, we recognized three groups of size classes. Animals < 0.4 mm were uniformly
very abundant (rotifers, nauplii, and small cladocerans), followed next by animals between 0.4 and
1.0 mm, which were approximately one-tenth as abundant. Finally, animals > 1.2 mm were approx-
imately one-one-hundredth as abundant as the small animals. A Kolmogorov-Smirnov test of distribu-
tional heterogeneity among chemistry clusters was nonsignificant. However, distributional tests are
relatively insensitive in detecting heterogeneity in subsections of the overall distribution when these
subsections comprise a small number of the overall counts. For example, Table 3-41 suggests increases
in the relative abundance of animals > 1.0 mm with chemistry cluster. However, since the numbers of
individuals in these size classes were so minor compared to the small organisms, they had little influ-
ence on the overall size distribution.
We treated each size class as a separate variable in a multivariate analysis of variance to examine
for heterogeneity among chemistry clusters (Table 3-42). A highly significant effect of water chemistry
was detected. Univariate tests suggest that this heterogeneity was caused largely by variance in
medium- to large-size crustaceans.
We used PCA to reduce this variance into simpler factors (Table 3-43). The first four PCA factors
successfully describe > 70% of the overall variance in size class distributions. The first factor is a simple
abundance factor; all size classes loaded positively. The second factor (accounting for 20% of the total
variance) describes a contrast between dominance of small (< 0.6 mm) and large (> 1.2 mm) size
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classes. The third factor is a contrast of very small (< 0.2 mm) and medium size classes; the fourth
factor represents a contrast between the two largest size classes.
Summary statistics for the first four size factors were calculated separately for each water
chemistry cluster in Table 3-44. Multivariate and univariate analyses of variance of these four factors are
given in Table 3-45 to test for both chemistry cluster and geographic region effects. Strong effects of
chemistry cluster but only weak regional and interaction effects were evident. PCA Factor 1 was not
influenced by either region or chemistry. Variance in Factor 2 was, however, strongly affected by
chemistry cluster. As illustrated in Figure 3-29, there is a shift in the size structure between Cluster 3
and Cluster 2 lakes. Cluster 3 lakes are characterized by the presence of large size classes, compared
to Cluster 2 or Cluster 1 lakes. Factor 3 was the only component in which both regional and chemical
influences were seen. In general, lake scores on Factor 3 increased with chemistry cluster. The more
acidic the lake, the greater the likelihood of dominance by medium size classes. The interaction effect of
region and chemistry on Factor 3 was caused by lakes in Regions 2 and 4 (the most southern regions)
each having a distinctly different score for Factor 3 than lakes in Region 1, 3, and 5. This difference is
illustrated in Figure 3-30. In all regions, Factor 3 scores were lowest in Cluster 1 lakes. The contrast
between Cluster 2 and 3 lakes depends on region.
To summarize the size structure analyses: There was no evidence of systematic effects of
chemistry cluster or region on total abundance of zooplankton. The major component of variance in
size structure among all lakes was a contrast between small and large size classes. This contrast was
largely independent of geographic region, but was strongly influenced by water chemistry. Cluster 3
lakes were more likely to contain large zooplankton species than were Cluster 1 or Cluster 2 lakes.
Finally, there was significant variance among lakes in the relative abundance of medium versus very
small sized animals. This gradient was influenced by region, but regardless of region, Cluster 1 lakes
were characterized by a relatively greater abundance of animals in the medium size classes.
3.3.5 Abundance of Major Genera
A complete listing of all species found in the samples (147 lakes) along with summary statistics on
individual species abundance is provided in Appendix D. Also presented is a separate listing for each
chemistry cluster. For several reasons, we decided to lump species into genera or subgenera, depend-
ing on ecological similarity, before analysis. First, the taxonomic certainty of several species was
unclear (e.g., Eubosmina hagmanni and E. tubicen were not clearly distinguished). Secondly some taxa
were not identified to species due to lack of adequate adult specimens (e.g., some Ceriodaphnia and
cyclopoid specimens). Lastly, attempts to ordinate on the basis of species met with little success due to
the complexity of the species-specific patterns.
We reduced the data set down to 38 genera by lumping species into genera and subgenera and
eliminating taxa known to be benthic or littoral in habitat. The elimination of inappropriate taxa removed
20
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only very rare species of Cladocera and rotifers. Taxa such as Chydorus, which were occasionally
abundant in the plankton samples, were not eliminated. A listing of the 38 genera and summary
statistics are provided in Appendix E.
To ordinate the 38 genera, we performed PCA using both absolute abundance (log transformed)
and relative (proportional) abundance. We also explored results from using both the variance-
covariance matrix and the correlation matrix in PCA. However, failure to standardize for the variance of
each species (use of covariance rather than correlation matrix) focused most attention on the rotifers,
due to their large variance in abundance. Since absolute and proportional data produced similar
interpretations, we presented only the PCA results based on the absolute (log transformed) abundance
standardized for mean and variance of each species.
Table 3-46 presents the results of PCA on the 38 major genera. Only 30% of the variance in all 38
genera is accounted for by the first four generic community factors. These first four factors all show a
significant influence of chemistry cluster in univariate analyses of variance (results not presented).
Factor 1 was correlated strongly with four rotifer genera (Keratella, Trichocerca, Asplanchna, Polyarthra),
three cladocera genera, all of which have small body size forms (Bosmina, Diaphanosoma, Daphnia par-
vula), and the smallest size copepod (Tropocyclops). In addition, one calanoid genus (Leptodiaptomus)
loads strongly, but negatively. Hence, Factor 1 can be interpreted as a gradient of lakes ranging from a
diversity of small zooplankton on one end to a dominance of Leptodiaptomus at the other end. The
effect of water chemistry on this factor is illustrated in Figure 3-31. Of interest is the observation that this
factor shows a tendency to increase from Cluster 3 to Cluster 2 lakes before decreasing dramatically in
Cluster 1 lakes.
Factor 2 was defined by the rotifers Kellicottia and Conochilus, the cladocerans Leptodora and
Sida, two groupings of large-size Daphnia, and the cyclopoids Mesocyclops and Eucyclops. We inter-
preted this gradient as representing an ecologically different group of rotifers and relatively large-size
crustaceans, compared to Factor 1. In general, Factor 2 scores decreased linearly across the chemistry
clusters (Figure 3-31).
Factor 3 was defined by four crustacean groups that appear to change dramatically between
Cluster 3 and Cluster 2 lakes (Figure 3-32). These groups were composed of the cladocerans
Diaphanosoma, Eubosmina, and small Daphnia, and the cyclopoid Mesocyclops.
Factor 4 was positively correlated with several groups (mainly Leptodora and Sida) that also corre-
lated positively with the second and third factors. However, the fourth factor was negatively correlated
with several groups (Gastropus, Conochilus, the Daphnia galeata group, and Skistodiaptomus) that were
positively correlated with the second or third factors. It is likely that the fourth factor represents residual
positive or negative correlation among taxa remaining after their primary correlations were removed.
Remaining factors individually account for a relatively small among of variance in the data set and
appear to express single genus trends or pairwise contrasts. We made no intensive effort to interpret
these factors.
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Interpretation of the first three generic factors was aided by examining the occurrence of individual
genera across the three chemistry clusters. We offer the following interpretation. Cluster 3 lakes are
inhabited by a diversity of rotifers and crustaceans, but are characterized by large-size species of
Daphnia. In Cluster 2 lakes, there is a notable reduction of these large Daphnia and a comparable
reduction in some rotifers (e.g., Kellicottia). However, other rotifers and several small-size cladocerans
actually increased in importance in Cluster 2 lakes. We observe here that previous results illustrate an
increase in diversity of rotifers in Cluster 2 lakes and a trend toward greater abundance. One genus of
rotifer (Keratella) showed a dramatic increase in abundance in Cluster 2 compared to Cluster 3 lakes.
Also, the major result of the size structure analysis was a loss of large-size zooplankton in Cluster 2
lakes compared to Cluster 3 lakes.
In Cluster 1 lakes, there was a reduction in all genera except the calanoid Leptodiaptomus (almost
entirely the species Leptodiaptomus minutus). It is interesting to observe that the second major gradient
identified in the size structure analysis was an increased dominance of medium-size (0.6-1.0 mm) ani-
mals in Cluster 1 lakes. L. minutus was apparently responsible for this size pattern.
We examined for interaction effects by region and by chemistry cluster by region on the first three
generic community factors. As might be expected from the previous regional analyses, there were
strong region influences on the first two PCA factors. However, there was no significant interaction
effect of region and chemistry. We explore some of these regional effects in Section 3.3.6.
To better interpret some of the genera patterns in the data, we analyzed each of the three major
taxonomic groups separately (rotifers, cladocerans, and copepods). Since genera within each group are
more comparable in range of abundance, we used the covariance matrix in PCA. In this way, the results
are affected by differences (variance) in abundance characteristic of each genus; rarer species are less
important to the results. Tables 3-47 through 3-49 present the PCA results for copepods, rotifers, and
cladocerans, respectively.
Approximately 75% of the variance in copepod composition is explained by the first three PCA
factors. The first factor describes the contrast between Leptodiaptomus (mostly due to L. minutus) and
the other major copepod genera. This factor was strongly influenced by region and chemistry cluster,
but interpretation was clouded by the strong interaction effect (Table 3-50). Examination of each region
separately reveals that the effect of chemistry cluster on Factor 1 was similar in all regions except
Region 3 (central New England). Figure 3-33 shows this contrast. In all regions except Region 3, L.
minutus became dominant in the Cluster 1 lakes to the exclusion of most other copepods. In Region 3,
other copepods (especially Skistodiaptomus and Mesocyclops) remain important in Cluster 1 lakes.
Copepod PCA Factor 2 describes a gradient of abundance in Mesocyclops, Tropocyclops, and
Leptodiaptomus. However, this gradient was independent of water chemistry and geographic region
(Table 3-50). Factor 3 represents a contrast between Mesocyclops and Tropocyclops abundance.
These two cyclopoid forms differ greatly in size but they also show a weak tendency to sort out along
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chemistry cluster. Tropocyclops was less likely to be present in the more acidic lakes (results not
presented), but this effect was weak and was also clouded by regional effects.
Nearly 50% of the variance in abundance of major rotifer genera were accounted for in the first
two PCA factors (Table 3-48). Furthermore, both factors describe gradients in the rotifer data that were
strongly affected by chemistry cluster, with only weak regional influences and no significant interaction
effects (Table 3-51). Factor 1 describes overall abundance of the major genera of rotifers. Figure 3-34
illustrates how this factor changes with chemistry cluster. Observe that rotifer abundance increases in
Cluster 2 lakes before a sharp decline in the more acidic Cluster 1 lakes.
Rotifer PCA Factor 2 contrasts relative dominance of two groups of genera: Kellicottia and
Conochilus versus Keratella, Trichocerca, and Asplanchna. Figure 3-34 illustrates how Cluster 3 and
Cluster 2 lakes are similar in relative composition of these genera. However, in Cluster 1 lakes, the latter
three genera become relatively more important. Factor 2 was also dependent on geographic region
(Table 3-51; Figure 3-35). The most northern regions (central New England and Maine) displayed higher
mean scores for Factor 2, indicating a greater predominance of Kellicottia and Conochilus relative to
Keratella, Trichocerca, and Asplanchna.
PCA of the Cladocera accounted for 47% of the variation in the first two factors. Factor 1 is a
contrast of large Daphnia forms versus small forms (Bosmina, Diaphanosoma, and Daphnia parvula). As
Table 3-52 shows, a weak regional influence on Factor 1 was due entirely to southern New England.
Despite this influence, a strong chemistry cluster effect was obvious and is illustrated for the remaining
regions in Figure 3-36. The loss of large Daphnia in Cluster 2 lakes compared to Cluster 3 lakes is seen
in this figure.
Factor 2 from the Cladocera PCA describes a contrast of Bosmina versus Eubosmina and Diapha-
nosoma. It is interesting that chemistry cluster was not important in explaining variation in this factor
(Table 3-53). However, geographic region was significant. Figure 3-37 illustrates how the two most
western regions (Adirondacks and Poconos/Catskills) displayed lower Factor 2 scores, indicating
increased importance of Bosmina compared to Eubosmina or Diaphanosoma in these regions.
3.3.6 Canonical Discriminant Analysis
In view of apparent taxonomic differences attributable to chemistry cluster, we decided to use dis-
criminant analysis as a means of evaluating the uniqueness of each cluster. We performed discriminant
analysis on the size structure, major taxonomic groups, species richness, and genera/subgenera data
sets. In addition, we examined the use of both absolute abundance and relative (proportional) abun-
dance data sets.
Discriminant analysis of the species richness, major taxa groups, and size structure was only
partially successful. Only 52% of the lakes could be successfully assigned to the correct chemistry
cluster on the basis of discriminant analysis of the major taxa groups. Analysis of size structure data
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increased the percentage of successful classifications to 63%. A similar classification success was
achieved using only the species richness data (61%). However, species richness data allowed correct
assignment of Cluster 1 lakes in 88% of the cases. The dramatic declines in noncalanoid species
richness in the Cluster 1 lakes clearly identify these lakes.
Although Cluster 1 lakes were easily recognized using only species richness data, Cluster 2 and 3
lakes were not readily distinguished without including more taxonomically explicit data. By using the 38
genera/subgenera data set, we were much more capable of defining all three chemistry clusters. Best
results were obtained using the proportional abundance data, although qualitatively similar patterns and
classification success were achieved with the absolute abundance. Table 3-54 presents the overall can-
onical correlations and taxonomic loadings for the two canonical factors. Table 3-55 summarizes the
separation among the three clusters, achieved from this analysis, by presenting average Mahalanobis
distances from each cluster to other clusters. Observe that Cluster 1 lakes are a little better defined
(average distance of 1.077 compared to 1.304 and 1.33 for Cluster 2 and 3 lakes, respectively. How-
ever, the degree of separation of all three clusters from each other suggests a linear ordering to the
clusters, with the distance between Clusters 1 and 2 similar to that between Clusters 2 and 3.
The degree of separation of the clusters on the basis of these two canonical factors is illustrated in
Figure 3-38. Observe that canonical Factor 1 was mainly responsible for separating lakes in Cluster 1
from those in Cluster 3. Cluster 2 lakes, however, overlap with both Clusters 1 and 3 along this first
factor. Factors 1 and 2 together help define Cluster 2 lakes.
An 82% successful classification of all lakes was achieved from this analysis. Table 3-56 summa-
rizes the classification of lakes based on the two canonical factors. Notice that 90% of the Cluster 1
lakes can be correctly identified as Cluster 1 on the basis of this analysis. Only 77% of the Cluster 2
lakes and 78% of the Cluster 3 lakes were successfully classified.
We attempted to use discriminant analysis to distinguish among the five geographic regions.
However, as shown in previous analyses, regional influences were relatively weak. Table 3-57 presents
the canonical correlations and loadings on each of the canonical factors. Consistent with our previous
findings is the importance of contrasts between Eubosmina and Bosmina and between Leptodiaptomus
and other copepods in defining the first three canonical factors. Classification success is summarized in
Table 3-58. Overall, 77% of the lakes are correctly classified as to region in this analysis. The
Adirondacks (Region 1), with its higher percentage of Cluster 1 lakes, has the highest classification
success (83%).
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3.4 PHYSICAL/CHEMICAL GRADIENTS
3.4.1 Description of Chemical Data and Statistical Methods
The chemistry cluster groups provide a discrete classification of continuous variation in ANC.
Additional information on the relationship between zooplankton and water chemistry was gained through
use of the measurements of chemical parameters. These allow analysis of gradients in ANC, as well as
investigation of contributions of other chemical factors.
The validated data set on summer water chemistry analyses was used to develop measures of
water chemistry for correlation with zooplankton community structure. The chemistry data set, named
SUSFIM01, was provided in SAS format by statisticians at ManTech Environmental Technology, Inc.
(formerly Northrop). We performed PCA to derive six statistically independent components that encom-
pass the major sources of variation in water chemistry across the lakes. Most of the analysis focuses on
the first four of these factors, which encompass most of the total variance and which are interpretable in
terms of the original variables.
The chemistry data consisted of measurements taken at a single site, near the deepest part of
each lake. Measurements were taken at one or more depths at each site as follows.
• The full set of chemistry measurements was taken at one or two depths at a single site within
each lake. In shallow water (< 3 m), only one depth, 1.5 m below the surface, was used.
Measurements were taken at 0.5 m below the surface in one lake of 1.1 m maximum depth.
In stratified lakes, the full set was measured at 1.5 m below the surface and at the middle of
the hypolimnion. In unstratified lakes, measurements were taken at 1.5 m below the surface
and at 1.5 m above the bottom.
• Profiles of temperature and conductivity were taken at a series of depths 1 m apart, from
0.5 m below the surface to 1.1-1.7 m from the bottom.
• Dissolved oxygen (DO) was measured at 0.5 m below the surface, 1.5 m below the surface
(where this depth occurred), and at 1.5 m above the bottom (in lakes > 3 m in depth). In
stratified lakes, additional measurements of DO were taken at the top of the hypolimnion and,
where present, at the middle of the metalimnion.
• For PCA, variables from the epilimnion sample (1.5 or 0.5 m below the surface) were used.
In addition, several variables were calculated as measures of vertical heterogeneity; these
used the conductivity, temperature, and DO profiles. Lake size and site depth (near
maximum lake depth) were also used as variables.
• The following variables were used in PCA, with the code name in the SAS data set given in
parentheses. PCA was done on the correlation matrix (i.e., variables were standardized in
mean and variance), using the SAS algorithm. Unless indicated otherwise, the logarithmic
transform (Y = natural logarithm [X+1]) of the raw data was used in the analysis because of
the nonlinear relationships among many of the variables.
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Labile monomeric aluminum (ALDI98). This was calculated by subtraction from other
measurements. A few negative values were present; these were set to 0. The log trans-
form was used.
Alkalinity (ALKA11) in units of jueq/L The log transform ln(ALKAH +50) was used.
Calcium (CA98), chloride (CL98), fluoride (FTL98), carbonate (HCO398), potassium
(K98), magnesium (MG98), sodium (NA98), ammonium (NH498), nitrate (NO398), and
sulfate (S0498), all in units of peq/L, log transformed.
Ratio of cations to anions (ANCAT98), log transformed.
Dissolved organic carbon (DOC02), dissolved inorganic carbon (DIC02), dissolved
oxygen at 1.5 m (D0_0151d), iron (FE11), manganese (MN11), total phosphorus
(PTL11), and silica (SIO211), all in units of mg/L, log transformed.
Conductivity at 1.5 m (C0151D), Secchi depth (mean of depth at disappearance and
depth at reappearance (SECME98), temperature at 1.5 m (T0151D), hydrogen depos
in g/m2/yr (HDEP99), and turbidity (TUR02), all log transformed.
Lake size (surface area in ha; LKSIZ99) and depth at measurement site (DPSIT1D), both
log transformed. The value for depth (4.2 m) for Grass Lake was inconsistent with the
depths listed for the sample depths (e.g., 4.5 m listed for depth at 1.5 m above bottom);
the depth was considered to be 6.0 m.
Field pH (PH02) and pH at 1.5 m (PH0151D), not log transformed.
Depth of the aerobic layer (named DPAER) and proportion of the anaerobic layer
(named RANAER). A DO concentration of 1.0 mg/L was considered to mark low oxygen
conditions limiting most zooplanktors. The depth of the water column with DO > 1.0
mg/L was estimated from the oxygen profiles. Where no DO values were recorded
< 1.0, DPAER was set equal to the site depth. In shallow lakes with only one oxygen
measurement, DPAER was also set equal to the site depth. In lakes with some DO
measurement of 1.0 or less, the depth at which 1.0 was reached was estimated by linear
interpolation between the two sample points with DO nearest (above and below) 1.0.
The proportion of the depth of the anaerobic layer to the site depth was defined as
RANAER; it is 0 for lakes with no anaerobic layer.
Difference in DO between top and bottom of aerobic layer. The difference between
surface DO (i.e., DO 0.5 m below the surface) and bottom DO was defined as DIFDO.
The bottom DO was set at 1.0 for lakes with low bottom DO. In lakes with no DO < 1.0,
the DO at the sample nearest the bottom (usually the DO 1.5 m above the bottom) was
used. For shallow lakes in which only one DO measurement was taken, DIFDO was set
too.
Difference in temperature between the top and bottom of the aerobic layer (DIFTMP).
This is calculated analogously to DIFDO, with two differences. In some lakes, the
deepest point in the temperature profile was deeper than the 1.5 m above-bottom
sample, and the temperature from the deepest profile was used as bottom temperature.
In lakes with low DO, the temperature at the bottom of the aerobic layer was estimated
from the nearest temperature profile point within the aerobic layer.
Difference in conductivity between the top and bottom of the aerobic layer (DIFCON).
This was calculated identically to the difference in temperature, using the conductivity
profile in place of the temperature profile.
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3.4.2 Comparison of Fall and Summer Data Sets
Correlations of zooplankton abundance with environmental parameters were based on the summer
data set, that is, from one sampling time per lake. Since the responses of biological communities inte-
grate conditions over periods of time, it is important to determine the robustness of the analysis of
relationships between zooplankton and water chemistry. We analyzed the robustness by comparing the
summer and fall data sets. We studied two related questions: (1) How similar are the patterns of
covariation between chemical parameters within each of the two data sets? and (2) How similar are
parameter values or gradient positions from the two periods for each of the study lakes? We performed
analogous PCA on each data set, using the same variables, and compared the similarity of the results
(correlations of raw variables with principal components and relative importance of the principal com-
ponents) for the two data sets. Secondly, we calculated the correlations between factor scores for each
lake for the two analyses; we also measured correlations between original parameters. These analyses
indicated very strong correspondences between the two periods in the pattern of covariation of parame-
ters and scores for the various lakes. The few differences between water chemistry measurements from
the two periods could be accounted for by natural seasonal changes or by measurement variance.
The sampling design for the fall study differed somewhat from the summer design. Since fall
sampling was done after lake turnover, less information was gathered on vertical variation. Temperature,
dissolved oxygen, and conductivity were measured at points 1.5 m below the surface and at 1.5 m
above the bottom, but detailed vertical profiles were not measured as in the summer. Other chemical
parameters were measured at 1.5 m below the surface. Forty-nine lakes were chosen to be sampled
three times during the fall; the second sampling corresponded to the survey time of the other lakes.
Three of these could be sampled only once. For the comparison of summer and fall data, only one
sampling period was used for each lake: the single, routine sampling for most lakes, the second
sampling for the multiple-sampled lakes, and the early sampling for the three remaining lakes.
Two separate PCAs were performed on the summer and fall data sets, using the same variables
described in Section 3.4.1, except that variables relating to vertical stratification (DIFTMP, DIFDO,
DIFCON, RANAER, DPAER) could not be calculated for the fall data because of the different sampling
procedure. The fall data set contains information on the difference between surface and bottom
temperature, on DO, and on conductivity, but these are not identical to the summer variables that
estimate differences between the surface and the bottom of the aerobic region. The stratification
variables were not used in the comparison PCAs for either data set. Thus, the comparison PCA for the
summer data set differs from that described in Section 3.4.1 in the exclusion of these variables.
Because of missing values for some parameters for a few lakes, the two PCAs were done over slightly
different sets of lakes.
The correlation structure of the two chemical parameters was similar for the two periods (Table
3-59). The first four principal components are correlated with the same parameters; in most cases, the
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magnitude and direction of the correlations are very similar. In both periods, the first component is a
hardness gradient, correlating positively with alkalinity, conductivity, calcium, DIG, carbonate, potassium,
magnesium, pH, and total phosphorus, and negatively with aluminum. The directions of the gradients
defined by the second component are different (i.e., parameters negatively correlated with the second
component in summer are positively correlated in fall, and vice versa), but the basic correlation structure
of the original variables is the same. For both periods, the second component reflects positive corre-
lations between lake size, Secchi depth, and site depth, and between color, DOC, iron, turbidity, and
total phosphorus, and negative correlations between parameters in these two groups. The third compo-
nent in both periods is positively correlated with conductivity, chloride, sodium, sulfate, temperature,
manganese, and potassium. In both periods, the fourth component is positively correlated with calcium,
lake surface area, nitrate, sulfate, and depth. When stratification variables are included in PCA of
summer data, they tend to correlate with the fourth component; see Section 3.4.1).
Some differences in the correlation structure for the two periods are noted (Table 3-59). Several
parameters (e.g., magnesium, manganese, sodium, nitrate, turbidity, DOC, iron) correlate moderately
with two of the first four components in one of the data sets, but with only one component in the other.
A few parameters (i.e., ammonia, total iron) load strongly on one of the first four components only in one
period. Dissolved oxygen is correlated differently between the two periods. It was strongly negatively
correlated with the fourth component in summer, whereas in fall, it was moderately positively correlated
with the second component and weakly negatively correlated with the fourth component. Silicate is also
correlated differently in the two periods.
The relative importance of the principal components is also similar between the two periods (Table
3-60), with the first component accounting for 28% of the total variation in the summer and 31% in the
fall, and the first four components accounting for 65% of the variation in the summer and 67% in the fall.
The factor scores for each lake are also similar between the two periods (Table 3-60) for the first
five components. The correlation coefficient (R) for the first component is .96. The magnitude of the
correlation for the second through fourth varies from .53 to .81. The correlation is negative for the
second, reflecting the switch in polarity of the gradient in the two periods. These correlations were
calculated over the 144 lakes for which all values were present in both data sets. Correlations between
original parameters were done over all lakes for which that parameter was measured for both periods
(usually 144 to 147 lakes). The correlations are also high between the values of the original parameters
for each of the lakes (Table 3-60). The median correlation for the 26 variables used in the PCA is .82.
The correlations are particularly high (> .95) for many of the parameters correlating with the first and
third components (hardness and salinity parameters). A correlation was also calculated between
DIFTMP (estimated difference in temperature between the surface and bottom of the aerobic layer) in
summer and the surface-bottom temperature difference in fall; this was moderate (R = .59), even though
the fall measurements were taken after turnover, which would disrupt temperature stratification.
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A few parameters were poorly correlated between the two periods. Differences between the two
periods could arise from a number of sources of variation. These differences may indicate seasonal
changes in parameter values and patterns of covariation. However, since there is only one value per
season, other sources of variation (horizontal spatial heterogeneity, short-term temporal variability,
measurement error) would also contribute to the observed variation. The multiple sampling of some
lakes in the fall study could be used to determine the relative importance of these sources. However,
we did not conduct such an analysis, since it would have been peripheral to the major issue of
robustness of the environmental gradients used in zooplankton analysis.
Dissolved oxygen was not highly correlated between the two periods. This finding probably repre-
sents true seasonal change, since summer values should be strongly affected by biological processes
(production, etc.) and summer temperatures, whereas fall values would be affected by fall temperatures.
Ammonia is poorly correlated and nitrate is only moderately correlated; these results may reflect low
measurement precision or changes in nutrient relationships between summer and fall. As might be
expected, most of the variables that were not highly correlated between the two seasons also showed
different correlations with other parameters within each season.
The correlations between summer and fall scores for the first four principal components were high,
as were correlations for individual parameters. For each of the first four components, correlations were
higher for some of the parameters loading on the component than for the component itself. For
example, correlations for alkalinity (.97) and calcium (.99) were higher than correlations for the first
principal component (.96), which is correlated with these parameters within each of the seasons. The
absolute value of the correlation for the second component is .71, lower than that for color (.81), DOC
(.89), or site depth (.97). The correlation for the third component (.77) is lower than that for sodium
(.95), chloride (.98), or sulfate (.97). Thus, some of the variation in principal component scores probably
reflects temporal or other sources of variation in some of the constituent parameters. Because of their
consistency, some of the original parameters may be more useful in defining gradients between lakes
than the principal components.
The correlation analysis indicates thai for most parameters, the position of each lake on environ-
mental gradients defined by principal components or by single parameters is consistent over seasons.
In particular, the main hardness gradient is well defined and stable, as defined either by the first principal
component or by some of the original parameters.
3.4.3 Correlation between Physico-Chemical Parameters: Calculation of Chemistry Gradients
and Relationships of Gradients to Original Parameters
The lakes were chosen and placed in the three clusters on the basis of measurements of ANC
taken in ELS-I studies. The measurements of alkalinity, calcium, and pH taken during the ELS-II studies
(Figures 3-39 and 3-40) indicate that the clusters generally separate lakes well on the basis of these
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parameters, although there was some overlap between Cluster 1 and Cluster 2 lakes at alkalinities
around 0 to 30 peq/L. Within clusters, lakes were chosen randomly; for example, there was no relation-
ship (Figure 3-41) between chemistry cluster (and therefore alkalinity) and site depth (which is close to
the maximum depth of each lake). As expected, there was significant variation in parameters within
clusters. Gradients were defined by PCA to identify correlated groups of parameters (e.g., alkalinity and
pH) that are independent from each other (e.g., alkalinity and lake depth), in order to relate zooplankton
community structure and abundance to the continuous variation in chemical parameters.
PCA was performed on the 35 variables described in Section 3.4.1. The first four principal compo-
nents contained 60% of the total variation, and the first eight components contained 77%. The first four
components are interpretable in terms of the original variables and were used as the primary measures
of water chemistry (Tables 3-61 and 3-62). In the following discussion, only relatively large correlations
(absolute value > 0.2, unless otherwise noted) between the original variables (or their log transforms)
and the principal components are mentioned.
The first component (containing 24% of total variance) represents a water hardness gradient, with
positive correlations with alkalinity, conductivity, DIG, carbonate, potassium, magnesium, and the two pH
measures. Aluminum is negatively correlated (-.19) with this component, reflecting the decrease in
aluminum with increasing pH (Figure 3-42). The plots of the factor scores versus measured alkalinity
and pH (Figures 3-43 and 3-44) show the relationship between these parameters and the first factor.
The dominance of this component reflects the selection of lakes to span a gradient in alkalinity. As
expected, this component correlates strongly with the initial clustering (groups 1, 2, and 3) based on
ANC. Cluster 1 lakes generally had PRIM values < 0; Cluster 2 lakes generally had PRIN1 values
between -2 and 2; Cluster 3 lakes had PRIN1 values > 0.
The second component (containing 16% of total variance) can be considered a measure of dys-
trophy. It is positively correlated with lake size (Figure 3-45), Secchi depth, top-bottom temperature
difference, site depth (Figure 3-46), and depth of the aerobic layer. It is negatively correlated with color
(Figure 3-47), DOC, iron, phosphorus, and turbidity. Thus, high values represent deep, clear lakes with
low iron and phosphorus concentrations and a deep aerobic layer.
The third component (containing 12.5% of total variance) can be considered a salinity gradient. It
is positively correlated with chloride (Figure 3-48), sodium, and sulfate, as well as conductivity,
temperature, and hydrogen deposition. It is negatively correlated with the anion/cation ratio. Most of
the lakes with high values for this component are near the coast (coastal Maine, Massachusetts,
Connecticut, and Rhode Island), whereas most of the interior lakes (e.g., Adirondack lakes) have low
values, indicating that the salinity represents oceanic deposition. Figure 3-49 shows the relationship
between chloride and distance from the coast (DISM99 from the chemistry data set); since most of the
missing values represent interior lakes for which no distance was calculated, the figure underestimates
the strength of the relationship. Road salting could contribute to the gradient (e.g., in interior lakes).
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The fourth component (containing 7.7% of total variance) can be considered a measure of stratifi-
cation and vertical heterogeneity. It was positively correlated with the difference in temperature and DO
(see Figure 3-50) and negatively correlated with the difference in conductivity (reflecting greater con-
ductivity in deeper water). It was positively correlated with site depth and proportion of the anaerobic
layer to total depth. This component reflects the greater opportunity for heterogeneity in deeper lakes.
This pattern is reinforced by the way DIFTMP, DIFDO, DIFCON, and RANAER are calculated: in shallow
lakes with only one sample point, DIFTMP, DIFDO, DIFCON, and RANAER are automatically set to 0.
PCA was also performed using different sets of chemical parameters (e.g., without the four
parameters defining vertical heterogeneity and the aerobic/anaerobic layer) and with different transfor-
mations of some variables. These had some effect on the results, but the basic patterns were similar:
the parameter loadings on the first three factors were similar; the first component was always a hardness
gradient. In some analyses, the relative importance of the dystrophy and salinity gradients varied; that
is, in some versions, the salinity gradient was the second factor. The higher factors of the final PCA
(e.g., the fourth) were strongly correlated with the vertical heterogeneity parameters; obviously, higher
factors in models without these parameters differ. In general, these various analyses indicate that the
gradients defined by the environmental parameters are robust with respect to the precise details of the
PCA.
For ease in graphical display, indices were defined based on the values of the first four principal
components for each lake:
Index Value of Principal Component
0 < -4.0
1 -4.0 to -2.0
2 -2.0to-1.0
3 -1.0 to 1.0
4 1.0 to 2.0
5 2.0 to 4.0
6 > 4.0
The values of these indices are plotted on schematic maps of the study lakes to indicate possible
regional differences in water chemistry (Figures 3-51 through 3-54). For comparison, the same maps are
shown labelled by cluster (Figure 3-55) and subregion (Figure 3-56). The first component (hardness)
shows some regional differentiation, although a range of values is found within each region (Figure 3-51).
Most of the Adirondacks lakes (Subregion A) have low PRIN1 values, whereas the Subregion B lakes
(southern New York, Pennsylvania, and New Jersey) have mainly high values. The other regions have a
range of PRIN1 values.
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There is only weak regional differentiation (Figure 3-54) in the second component (dystrophy).
Low values (more dystrophic) are more prevalent in Subregion B (Figures 3-52 and 3-56), whereas high
values are more prevalent in Subregion E (Maine lakes).
As expected, the third component (identified as a salinity gradient) shows strong regional
differences (Figures 3-53 and 3-56). High values are noted for Subregion D (mainly coastal lakes in
Massachusetts, Connecticut, and Rhode Island). High and moderately high values are seen in Sub-
regions B (Southern New York, Pennsylvania, and New Jersey) and C (southern New Hampshire and
southern Maine), and in coastal parts of Subregion E (Maine). Values are mostly low or moderate in the
Adirondacks and very low in the interior lakes in Maine.
There is only weak regional differentiation (Figure 3-54) in PRIN4 (depth and vertical hetero-
geneity). Values tend to be low in the interior parts of Subregion E and the eastern part of Subregion D
(mainly coastal Massachusetts lakes), with a range of values in the remainder of the area.
3.5 RELATIONSHIPS BETWEEN PHYSICO-CHEMICAL GRADIENTS AND ZOOPLANKTON
The large number of taxa recorded, the number of chemical parameters measured, and the variety
of types of relationships (linear, unimodal, threshold, etc.) allows a variety of measures of
interrelationship. The number of possible correlations can promote spurious correlations. Six basic
approaches to analyzing zooplankton and water chemistry were used to examine a variety of plausible
relationships:
1. We examined the relationship between the major, independently defined gradients of commu-
nity structure and water chemistry (representing covarying taxa and parameters). The
principal components (called the community factors) of the generic analysis and the water
chemistry analysis were used as the major gradients. These have the advantage of indepen-
dence, providing robust multiple regressions between components. Detrended correspon-
dence analysis of the zooplankton communities was also used to define community gradi-
ents; this may be more effective where species patterns are not monotonic over the
environmental gradient.
2. We examined the relationships between major gradients of community structure and selected
water chemistry parameters. This may be advantageous if some of the chemical parameters
are irrelevant to community structure or if the one-time measurements do not provide reliable
estimates. The variation in these parameters affects the overall gradients in chemistry, but
weakens any relationships between the community and chemistry gradients. Only a few
selected parameters (pH, alkalinity, calcium) were used, to avoid the spurious correlations
likely with large numbers of comparisons.
3. We examined the relationships between individual taxa and major water chemistry gradients.
These are more easily interpreted in terms of the biology of individual taxa and do not
depend on the validity of the algorithm for calculating community gradients.
4. We examined the relationships between individual taxa and selected water quality parameters.
These are the most direct type of analyses and the most easily interpreted, but they also
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provide the greatest opportunity for spurious correlation. They are used mainly to
demonstrate the importance of unimodal responses to environmental gradients and to aid in
the interpretation of the gradient-based approaches.
5. We defined gradients of zooplankton community structure and water chemistry relative to
each other to maximize correlation between the two types of data. Although potentially the
most concise and powerful approach to relating the two data sets, this approach is the most
procedure sensitive. We used two statistical approaches. One, canonical correlation,
appeared to be ineffective because of prominent unimodal relationships between taxa abun-
dance and water chemistry gradients. Preliminary analysis has been conducted using
canonical correspondence analysis, which is designed to detect unimodal responses.
6. We analyzed relationships of species richness and diversity to chemistry gradients and
parameters.
3.5.1 Relationship between Zooplankton Community Factors and Environmental Gradients
The first 4 zooplankton community factors (based on PCA of the 38 genera and species groups)
were regressed against the first 6 chemical factors, using the maximum r2 improvement procedure (SAS,
1985), which picks the best set of independent variables for models with any given number of variables.
For all four community factors, there was a significant (p < .0001) regression with the first chemistry
factor (hardness). This chemistry factor was the best variable in all one-variable models and appeared in
all models with more than one independent variable. The second chemistry factor was significant in
regressions with the first (p < .0001) and third (p < .01) community factors. The third, fifth, and sixth
chemistry factors were significant only in regressions with the third chemistry factor. These regression
results are summarized in Table 3-63. These results support the earlier finding that zooplankton
community structure is correlated with hardness parameters, and suggest the influence of other
chemical factors as well. However, despite the significant relationships, model fit was generally poor (<
.13 for single variable models and only .27 for the one significant five-variable model). The plots of the
generic factors versus the chemistry factors also demonstrate the relatively weak relationship between
these factors (Figures 3-57 through 3-63).
Analysis of the relationships of community factors and individual genera with individual chemical
parameters suggests that the poor model fit results from at least three causes. First, the chemistry
factors include variation of some parameters of little apparent relevance to community structure, so the
relationship of community data with chemistry factors is weaker than the relationship of the community
data with individual parameters. Second, the relationships between the abundance of most genera and
water chemistry are non-monotonic, so the community factor-chemistry factor regressions ineffectively
model the complexity of the relationships. Third, in many cases, genera may be abundant only in lakes
with a restricted range of water chemistry conditions; however, these genera are often absent from lakes
within the suitable range of chemical conditions. The first two complications relate to statistical
methodology; the third may be caused by sampling error, but it probably reflects the importance of
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other factors in determining the occurrence and abundance of the genera. These complications can be
illustrated by comparing the relationships between community factors, generic abundance, chemical
parameters, and chemical factors.
The relationships between the community factors and the hardness gradient are shown by the
significant regressions with the first chemistry factor (Figures 3-57 through 3-60). On the other hand, the
community factors may be related directly to individual parameters, such as alkalinity (Figures 3-64
through 3-67). For these plots, positive slopes signify higher abundances at higher parameter levels for
genera positively loading on the community factors (Table 3-46) and lower abundances at higher
parameter values for genera negatively loading on the community factors. These relationships are
tighter than those with the first chemistry factor. The first community factor is seen to consist of genera
with a modal response (Figure 3-64); the first factor scores are highest in some of the mid-alkalinity
lakes, lowest in the low alkalinity lakes, and low in many of the higher alkalinity lakes as well.
In contrast, the second community factor shows a more monotonic relationship with alkalinity
(Figure 3-65), with factor scores increasing from low alkalinity to high alkalinity lakes. The third
community factor (Figure 3-66) also shows a pattern of increasing factor scores in higher alkalinity lakes,
but there is little trend in factor scores over a broad range of alkalinity. Most of the low alkalinity lakes
have low third factor scores, whereas most of the high alkalinity lakes have high scores. Lakes of
moderate alkalinity are highly variable in the abundance of these genera. Scores on the fourth
community factor (Figure 3-67) are low in some of the lakes with highest alkalinity, whereas there is little
difference over the range from low to moderate alkalinity.
For three of the four factors, community factor scores are low in low alkalinity lakes. All four
community factors differ in relative abundance at various regions of higher alkalinity. Given the corre-
lations between alkalinity and other parameters, similar relationships may be expected between commu-
nity factors and these parameters. For example, the relationship between the first community factor and
pH (Figure 3-68) shows the occurrence of high factor scores at lakes with intermediate pH; considering
the range of pH among low alkalinity lakes and the range of alkalinity among high pH lakes, the pattern
is similar to that between the first community factor and alkalinity. As noted above, the covariation of
many of the individual parameters prevents inference about the relative importance of each parameter in
producing the community factor-parameter correlation, unless there are strong differences in the
strength of the correlations.
The points in Figure 3-57 through 3-68 are labelled by chemistry cluster for each of the lakes.
These figures demonstrate the clear differences in community structure among the clusters (see Section
3.3). They also show extensive heterogeneity within the clusters. Lakes in Cluster 1 are especially
heterogeneous in the second and third community factor scores. The heterogeneity in the second factor
scores appears to be correlated with alkalinity differences within these lakes. Cluster 3 lakes are
especially heterogeneous in first, second, and fourth factor scores. The heterogeneity in fourth factor
scores is clearly correlated with alkalinity differences within these lakes. Cluster 2 lakes are very
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heterogeneous in community factor scores for all four factors. Third factor scores appear weakly corre-
lated with alkalinity differences within Cluster 2 lakes, but the heterogeneity is otherwise unrelated to
alkalinity.
We observed significant correlations between the first and third community factors and the second
chemical factor. However, these relationships are not more easily interpreted in terms of the parameters
that form the second chemical factor (e.g., color, site depth). Whereas factors other than the alkalinity
gradient appear to affect zooplankton community structure, these factors are not identifiable as single
chemical parameters.
3.5.2 Relationships between Genera and Environmental Gradients
Since the first four community factors were significantly correlated with the first chemical gradient
(interpreted as a hardness gradient), correlations were also expected between species loading strongly
on these community factors and alkalinity. The relationships between zooplankton abundance and alka-
linity for most common genera show (1) a unimodal response, with each group occurring mainly in a
specific range of alkalinity, and (2) a variability of occurrence within these suitable ranges. The rotifer
genera Trichocerca, Keratella, Asplanchna, and Polyarthra are moderately correlated with the first com-
munity factor. The abundances of these genera along the alkalinity gradient (Figures 3-69 through 3-72)
show most occurrences and peak abundances occurring in lakes with low to moderate alkalinity (e.g.,
0-100 peq/L). For comparison, the relationships between the abundance of Trichocerca, the first
chemical factor, and the first community factor are shown in Figures 3-73 and 3-74. The occurrence of
these species in lakes with low to moderate alkalinity is consistent with the relationship between the first
factor scores and alkalinity (Figure 3-64), with high scores found mainly in Cluster 2 lakes.
The rotifer Kellicottia shows a similar pattern along the alkalinity and first chemical gradients
(Figures 3-75 and 3-76). However, it is correlated primarily with the second community factor (Figure
3-77), indicating that, although found in similar alkalinities as the other rotifer genera, it was usually found
in different lakes within that alkalinity range.
The cladocera Eubosmina, Diaphanosoma, and Daphnia parvula and the cyclopoid copepod
Mesocyclops are correlated (negatively) with the third community factor (Figures 3-78 and 3-79);
Mesocyclops is also correlated with the second community factor. These are also most common in low
to moderate alkalinities (Figures 4-80 through 4-87), but within a somewhat different range than the other
genera.
The Daphnia galeata group correlates negatively on the fourth community factor. Unlike most of
the other groups discussed, it showed highest abundances at high alkalinities (Figure 3-88).
The most frequent and abundant calanoid copepod was Leptodiaptomus. It tended to show a
monotonic relationship with alkalinity and the first chemical factor (Figures 3-89 and 3-90), being most
35
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common at very low alkalinities (i.e., mainly in Cluster 1 lakes). It was not strongly correlated with any
single community factor, but was weakly correlated with the first and fourth factors.
The abundances of several genera were also correlated with site depth, which is not highly corre-
lated with alkalinity (Figure 3-41). Keratella, Diaphanosoma, the Daphnia parvula group, and
Leptodiaptomus were rare or absent in deep lakes (Figures 3-91 through 3-94). A similar pattern is seen
for all rotifers (Figure 3-95). This relationship explains some of the variability of occurrence of these
groups within ranges of suitable alkalinity.
Plots of both the community factors and many of the individual taxa versus alkalinity formed
clearer patterns than the corresponding plots based on the first chemical factor. This suggests that
some of the variation on parameters forming the hardness gradient are not as relevant to zooplankton
community structure as alkalinity. This may result from lack of causal relationship between some
parameters and zooplankton, from measurement errors in these parameters, or from the inadequacy of
the one-time measurement of these parameters for correlation with zooplankton abundance. This does
not imply that alkalinity is the best parameter for correlation or that it is the causal basis of the observed
relationships. For example, the concentration of calcium is strongly correlated with alkalinity.
Relationships between abundance of genera and calcium are similar to those described for alkalinity;
however, the correlation between alkalinity and calcium is weakest for high values of both. Many genera
are rare or absent in lakes with high values, so that the differences between the two parameters do not
greatly affect correlations with genera.
The unimodal nature of the abundance-alkalinity relationships for many species affects the
calculation and interpretation of the community gradients. For example, the correlation of abundance
between two taxa with overlapping but distinct modes would be complex: they would be positively
correlated over the regions in which both occur and in which neither occurs, but would be negatively
correlated over the regions in which only one occurs. As a result, each taxon may load on several
principal components. Thus, the major community gradients show the general partitioning of taxa
across the environmental gradients and do not indicate the taxon-specific responses, which are shown
by the single-taxon analyses.
We also performed multiple regressions using the ordinations of each of the three major
zooplankton groups separately. Principal component Factors 1 through 5 from the analyses of genera
of rotifers, cladocerans, and copepods, each performed separately, were the dependent variables. The
first four physical/chemical PCA factors were the predictor variables. We also included ANC, lake depth,
lake size, and depth of aerobic zone as predictor variables. These latter variables were included in an
effort to maximize the chances of achieving a significant relationship between the genera PCA factors
and the environment.
All significant regressions from these analyses are summarized in Table 3-64. A significant amount
of variance in the first two rotifer PCA factors and the first copepod PCA factor was explained by the
predictor variables. Although also significant, less overall variance in cladoceran assemblages was
36
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explained by this approach. The first four environmental factors, along with ANC, appeared to be impor-
tant predictor variables. The relative significance of each varied greatly with the dependent variable
being examined. For rotifer Factor 1, environmental Factors 1 and 3 were of major importance (Figure
3-96). For rotifer Factor 2, all four of the environmental factors were important. For the copepods and
cladoceran Factor 1, environmental Factors 1 and 2 were of major significance. Figure 3-97 shows the
relationship between copepod Factor 1 and environmental Factor 1. For cladoceran Factor 2, environ-
mental Factor 3 was the most important, and for cladoceran Factor 3, environmental Factor 4 explained
the most variance. Lake depth and aerobic zone size and depth did not make a negligible contribution
to any of the regressions.
3.5.3 Relationship between Species Richness and Species Diversity and Environmental Gradients
Species richness and diversity change over the chemistry gradients, paralleling the absence or
rarity of many species at the low and high ends of the hardness gradients. Overall species richness
(Figure 3-98) increases steeply from low to moderate alkalinities, and then declines slightly from
moderate to high alkalinities. A similar trend can be seen from the relationship with the first chemical
factor (Figure 3-99). In addition, there is a weak negative relationship with the third chemistry factor
(Figure 3-100). Similar patterns were observed for Shannon-Wiener diversity (Figures 3-101 through
3-103). Many of these trends are caused by changes in rotifer richness and diversity (Figures 3-104
through 3-109). Similar, though weaker, trends are also seen for the diversity of calanoid copepods
(Figures 3-110 and 3-111) and cyclopoid copepods (Figures 3-112 and 3-113). No pattern was seen for
cladoceran richness or diversity.
We compared the Shannon-Wiener diversity of each of the four major zooplankton groups to the
physical/chemical PCA factors using Spearman rank order correlation. PCA Factor 1 was significantly
positively correlated with cyclopoid (0.365) and rotifer (0.406) diversity. Calanoid diversity was negatively
correlated with PCA Factor 1 (-0.40). The only other significant trend was a negative correlation between
rotifer diversity and PCA Factor 3 (salinity, -0.225). PCA Factors 2 and 4 had no significant effect on the
diversity of any group. There was no significant relationship between cladoceran diversity and PCA
Factor 1.
3.6 INDIVIDUAL SPECIES PATTERNS
Although most of the patterns of zooplankton community structure were apparent at the genera/
subgenera level (presented in Section 3.5), some species distributions enhanced or modified these con-
clusions. We examined most of the major species on an individual basis, but for brevity, we present
only some of the most robust patterns here. First, however, it is important to recognize that there is a
fundamental difference between common and rare species. We can define the commonness of species
37
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either in terms of abundance among habitats, abundance within habitats, or number of occupied habi-
tats. Figure 3-114 depicts a plot of various definitions of commonness against each other. Each
species (rotifers, cladocerans, and copepods) is represented as a single point in each plot. There is a
significant correlation between the number of habitats (lakes) occupied by a species and its mean
abundance among all lakes (r = 0.885), or its mean abundance within those lakes it inhabits (r = 0.652).
In short, common species not only are found in many lakes, but also are generally abundant in those
lakes. It is useful to observe that despite these general correlations, there is still tremendous variation in
the abundance of species inhabiting a certain number of lakes.
It is easy to identify the common species by scanning the first column of Appendix F. We
examined patterns of distribution of each of these common species as a function of region, chemistry
cluster, and physical/chemical gradients (PCA analysis). Many of the patterns resembled those
observed for the genera/subgenera groupings. A few species, however, exhibited divergence from their
mean, genus pattern. We present some of those exceptions here.
Figure 3-115 contrasts the abundance of the two most common species of Keratella, K. crassa
and K. taurocephala, as a function of the first PCA factor from the physical/chemical ordination (ANC/
pH/calcium gradient). Keratella crassa, typical of most rotifers, becomes most abundant in the Cluster
2 lakes characterized by intermediate scores on physical/chemical PCA Factor 1. In contrast, K. tauro-
cephala reaches peak abundance in the most acidic Cluster 1 lakes, represented by negative scores for
PCA Factor 1.
Figures 3-116 and 3-117 contrast the abundance of Kellicottia longispina and K. bostonensis
along physical/chemical Factors 2 and 4. There is little difference in distribution of these two species
along PCA Factor 1 (ANC); both prefer the Cluster 2 lakes. However, as shown in these figures, K.
bostonensis achieves greater abundance in the more dystrophic and/or the more strongly stratified
lakes compared to K. longispina.
A final contrast occurs between Trichocerca multicrenis and T. cylindrica (Figure 3-118). Tricho-
cerca multicrenis appears capable of inhabiting more saline lakes (generally coastal) than T. cylindrica.
As with the rotifers, few Cladocera species displayed simple monotonic distributions along the
PCA gradients. However, simple plots of abundance versus PCA factors show evidence of species
having distinctly different ranges of peak abundance, especially with respect to physical/chemical PCA
Factors 1 and 4. Figure 3-119 contrasts two species of Daphnia: D. galeata mendotae and D. parvula.
Daphnia parvula is clearly more tolerant of less buffered, softer waters than is the larger species, D.
galeata mendotae. Daphnia catawba broadly overlaps both these species, but in general it is more
intermediate in distribution along PCA Factor 1. This replacement of large Daphnia by smaller Daphnia
and other species contributes to the observed size structure shift. Another example, shown in Figure
3-120, is the replacement of large D. galeata mendotae by Bosmina longirostris along the ANC gradient.
For the cladocerans, distributions observed at the species level were not compromised greatly by
being lumped into the higher taxonomic groups that we created for the previous analyses. We did need
38
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to divide the genus Daphnia into three subgroups in order to maintain the integrity of the responses
displayed by the different species. Distribution patterns of the cladoceran genera along physical/
chemical PCA factors have already been presented.
In contrast to the rotifers and cladocerans, several species of copepods exhibited monotonic dis-
tributions across the major environmental ANC gradient. Figure 3-121 illustrates the distribution of the
most common copepod, Leptodiaptomus minutus, along chemistry PCA Factor 1. Observe that this
species, like L leptopus, also shown in this figure, prefers acidic lakes. In contrast to these species are
two cyclopoids, Tropocyclops prasinus-mexicanus and Cyclops scutifer, that display a clear preference
for lakes of higher ANC (Figure 3-122). In general, cyclopoids were rare in acidic waters.
There was little indication that physical/chemical PCA Factors 2 and 4 (depth, color, stratification,
etc.) explained much of the distribution of copepod species. This is in contrast to the rotifers and clado-
cerans, where Factors 2 and/or 4 were important influences on species and genus distributions. How-
ever, Factor 3 (salinity) was important for several copepod species, with perhaps the greatest segre-
gation observed between the two species of Epischura (Figure 3-123). Epischura lacustris was
restricted to the less saline lakes, whereas E. nordenskioldi appeared to prefer the more saline lakes.
Similarly, other species showed salinity preferences, though these were weaker than those specifically
noted.
3.7 CANONICAL CORRESPONDENCE ANALYSIS (CCA)
PCA proved quite useful in distilling variation within each of the major groups of zooplankton into
simpler factors. In several places in this report, however, we indicate that the patterns of abundance of
many species and genera were better described as bell-shaped (unimodal) rather than linear or mono-
tonic with respect to the major physical/chemical gradients. PCA is most successful with linear gradi-
ents. Other ordination techniques are available that assume unimodal patterns and attempt to maximize
the spread of species optima in ordination space. We used CANOCO, a FORTRAN program for corre-
spondence and detrended correspondence analysis, to ordinate the major species and genera of roti-
fers, cladocerans, and copepods separately (ter Braak, 1987). Since our major goal was to describe
variation in zooplankton communities in terms of the major physical/chemical factors, we employed
canonical correspondence analysis (CCA) as a direct means of relating species abundance patterns to
environmental factors.
We compared CCA, detrended CCA, and redundancy analysis results for each of the major groups
of zooplankton. The first four PCA factors of the physical/chemical data were treated as the environ-
mental factors of interest. Species level data were used, but occasionally, congeners were lumped into
higher groupings, especially for the Cladocera. In all, 30 rotifer, 16 copepod, and 12 cladoceran
groupings were used. Table 3-65 compares the eigenvalues and species-environment correlation
coefficients for each analysis for each major zooplankton group. In all cases, CCA and detrended CCA
39
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provided a better fit of species to the environmental factors (larger eigenvalues and larger correlations
between species axes and environmental axes) than did redundancy analysis (constrained PCA).
Hence, a unimodal model is better than a linear model in relating the zooplankton species abundance
patterns to environmental gradients.
Figure 3-124 presents a plot of the major rotifer species and the four major physical/chemical PCA
factors (environment factors). The major axes of this plot correspond to the first two species axes of the
CCA. Table 3-66 presents the correlation coefficients of these first two species axes to the four environ-
ment factors. These four environmental factors are displayed on the plot as four arrows. The direction
and length of each arrow represents the direction of change across the plot, and the strength of gradient
of each of the four environmental factors. Hence, the plot summarizes the distribution of species with
respect to each other and the major environmental gradients.
Figure 3-124 illustrates some of the major conclusions previously mentioned for the rotifers.
Observe that the first species axis is strongly correlated with the first environmental factor (PCA factor
for ANC, pH, calcium, etc.) The second species axis is strongly correlated with Factors 2 and 4 (color,
depth, stratification intensity, etc.). Most species occur at intermediate levels on the ANC gradient, but
acid loving species like Keratella taurocephala occur toward the 4:00 position. In summary, the major
species axis is correlated most strongly with the first environmental factor (PCA Factor 1 = ANC). The
second major species axis explains only half as much variation and is correlated most strongly with
environmental Factors 2 and 4.
Figure 3-125 presents a plot of the major copepod species and the four environmental factors.
The major species axis is correlated most strongly with the first environmental factor (Table 3-66).
Observe that species such as Leptodiaptomus minutus, whose optima are in acidic waters, occur to the
extreme left side of the plot. In contrast to the rotifers, environmental Factors 2 and 4 are not as
important in explaining variance in copepod assemblages. Rather, the second major copepod axis is
correlated strongly to environmental Factor 3 (salinity). Observe the extreme separation of the two
Epischura species along this secondary axis.
Figure 3-126 presents a plot of the major cladoceran genera and subgrouping of Daphnia species.
Species axis 1 of this plot is strongly correlated to the first three environmental factors (Table 3-66),
whereas axis 2 is correlated with environmental Factor 4. In general, attempts to relate cladocerans to
the environmental factors met with greater success than did the other zooplankton groups (Table 3-65).
Unfortunately, this success depended largely on two genera, Ceriodaphnia and Eubosmina, the former
of which was not very common in the samples. Ceriodaphnia occurred in lakes having a unique com-
bination of environmental Factor 1 and Factor 3 scores. Similarly, Eubosmina occurred in a unique
combination of the ordination space. The analyses presented in Figures 3-127 and 3-128 ignore Cerio-
daphnia and both Ceriodaphnia and Eubosmina, respectively. Tables 3-67 and 3-68 present the eigen-
values and environmental correlations for these analyses. Observe that all four environmental factors are
still important in explaining variation in these remaining cladoceran groupings (species axis 1).
40
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Although the significant correlations presented in Table 3-66 argue for the statistical significance of
the relationships between environmental factors and zooplankton assemblages, we further tested the
robustness of these relationships by performing Monte Carlo permutation tests for the first species axis
(first eigenvalue) of each analysis (ter Braak, 1987). In all cases, the first eigenvalue was significant at
the p < 0.02 level.
In summary, CCA has revealed significant relationships among all four environmental factors and
the pattern of zooplankton species abundance. It clearly demonstrates that optima for zooplankton
species exist at different positions along the various environmental gradients; gradient of acidity (ANC,
pH, calcium, etc.) is of major importance.
41
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4. INTERPRETATIONS
In the analyses described in Section 3, we employed various approaches to examine patterns of
zooplankton assemblages among lakes in the northeastern United States. We used two fundamentally
different approaches: (1) analysis of variance, by chemistry cluster and geographic region, and (2)
ordination, by principal components analysis (PCA) and canonical correspondence analysis (CCA).
Because lakes were preclassified as to chemistry and region, and because sampling was random
(stratified random) within these groupings, results and interpretations from analyses of variance are valid
in a general sense. The single most general conclusion of these analyses is that water chemistry
(chemistry cluster) has a large and predictable influence on the composition of zooplankton assem-
blages. We use the word "predictable" because although some dramatic regional effects were observed,
interactions of chemistry and region were generally minor, or at worst, were easily categorized. We
emphasize the word "composition," since no significant effects of chemistry on abundance were
observed.
Cluster 1 lakes (the least buffered) are essentially unique in zooplankton composition even when
very crude measures of composition are used. They are characterized by increased calanoid abun-
dance and diversity, decreased diversity of rotifers and cyclopoids, and dominance of a few acid-loving
species. Discriminant analyses on the basis of size classes or major genera are equally successful in
distinguishing Cluster 1 lakes as unique.
In composition, Cluster 2 and 3 lakes overlap much more than either group does with Cluster 1
lakes. On the basis of general taxonomic groups, it is difficult to separate these lakes. However, large
shifts in the composition of major genera are apparent, and even significant separation in size structure
is seen. Cluster 3 lakes are more frequently inhabited by large Daphnia species than are Cluster 2 lakes.
Associated with the decreased importance of large Daphnia in Cluster 2 lakes is an increased diversity
and abundance of rotifer species. Several crustaceans also have their peak abundances in the Cluster 2
lakes, rather than in the most buffered Cluster 3 lakes. However, many of these species are smaller in
size than the Daphnia forms, which are much scarcer in Cluster 2 lakes. As a consequence, the size
structure of Cluster 2 assemblages shifts to smaller forms in comparison with Cluster 3 assemblages.
Although discriminant analysis was able to achieve rather good separation of Cluster 2 and 3
lakes, it is apparent that information on the relative abundance of major genera (or species) is needed.
Consideration of size structure or species presence/absence data alone would not be enough to resolve
chemistry influences in this range of lakes. We emphasize that there is little change in overall diversity
or species richness among Cluster 2 and 3 lakes; however, the compositional shifts are quite significant.
Further, the size structure shifts are easily understood (causal understanding) as shifts in the abundance
of particularly common species or groups of species. Size structure of assemblages did prove sensitive
to chemistry, but its resolving power appears more limited than species data.
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Although regional influences rarely confounded interpretation of chemistry effects, we note that
some of these regional patterns were large and worthy of consideration in the design of future studies.
Of particular interest are differences in central New England copepod assemblages, and the difference of
the Poconos/Catskills calanoid assemblages from those of all other regions. The east-west segregation
of Bosmina and Eubosmina also is of interest, since these species are taxonomically similar, yet have
very different responses to environmental variables. There is some suggestion in the data that this
segregation may be related to salinity.
The second approach of this study was one of exploratory pattern searching, or more precisely,
attempts to relate zooplankton assemblage patterns to the measured physical/chemical (environmental
parameters. We used linear models of PCA, multiple regression analysis, and redundancy analysis, and
unimodal models of correspondence analysis and CCA and their detrended versions. A general conclu-
sion is that regardless of analytical approach, substantial variation in the zooplankton assemblages can
be significantly explained in terms of simple physical and chemical parameters. Furthermore, because
the optima for many species are within the range of measured environments, unimodal models are more
successful than linear models in describing assemblage variation. This is particularly true for the
Cladocera, which, in contrast to the copepods, were successfully related to environmental variables only
with a unimodal model.
Results of both linear and unimodal approaches emphasized the importance of chemistry gradi-
ents other than simply ANC (general pH, calcium, ANC gradient). For copepods, salinity was of
considerable significance in understanding species distributions. For rotifers, lake depth, dystrophy,
stratification intensity, and turbidity (summarized by environmental PCA Factors 2 and 4) were also
important. All four of the major physical/chemical gradients were significant in describing variance in
cladoceran assemblages.
A final conclusion from this study concerns the use of zooplankton in future monitoring of lakes
with regard to chemical impacts. Sampling and subsampling precision were such that counting error
appeared to have little influence on among-lake comparisons. In addition, within-lake variation (among
samples) was small relative to among-lake variation. In short, zooplankton assemblages are relatively
easy to sample and quantify with precision. Furthermore, the results of this and previous studies
illustrate that zooplankton assemblages are very sensitive to water chemistry, and this sensitivity can be
described with enough resolution to allow calibration of the zooplankton gradients to the chemical
parameters. We have not explored such calibration in this report, but emphasize that it is a logical next
step for use of these data.
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5. GENERAL RECOMMENDATIONS
The most limiting aspect of this report is that fisheries data are not available for these lakes.
Zooplankton assemblages are notoriously sensitive to the impacts of planktivorous fish. We strongly
recommend that future sampling include an effort to quantify fish populations from the standpoint of
intensity of planktivory. Even rank ordering data would be enormously useful. Along the same lines, it
was apparent that additional biological variables, especially measures of phytoplankton abundance and
type, would have aided in separating dystrophy from eutrophy gradients. Since the dystrophy gradient
proved important in explaining variation in both rotifer and cladoceran assemblages, we recommend that
future efforts attempt to refine this gradient.
Many of the chemistry influences on zooplankton assemblages were shown to be independent of
region. We suggest that these gradients be employed to choose a smaller subset of lakes for longer
term monitoring. Shifts in assemblage structure at the high end of the ANC gradient (Cluster 2 and 3
lakes) are particularly noteworthy. The loss of large Daphnia in Cluster 2 lakes needs more examination.
This appears to be related more to calcium changes than to pH, according to summary statistics of
Cluster 2 and Cluster 3 lakes.
We strongly recommend calibration as a statistical tool, using unimodal models. Even without
fisheries data, CCA was able to find strong associations between the zooplankton assemblages and the
environmental factors. If fisheries data were available, they could be treated as a covariate in these
analyses, which should allow substantial predictability of water chemistry from zooplankton samples (or
vice versa).
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6. LITERATURE CITED
Berner, D.B. 1982. Key to the Cladocera of Par Pond on the Savannah River. Savannah River Plant,
National Environmental Research Park Program, U.S. Department of Energy, Aiken, SC.
Cook, E.F. 1956. The Nearctic Chaoborinae (Diptera: Culicidae). Technical Bulletin 218. University of
Minnesota, Agricultural Experiment Station, Minneapolis St. Paul, MN.
Deevy, E.S., and G.B. Deevy. 1971. The American species of Eubosmlna Seligo (Crustacea,
Cladocera). Limnol. Oceanogr. 16:201-218.
Edmondson, W.T., ed. 1959. Ward and Whipple, Freshwater Biology, 2nd ed. John Wiley & Sons, New
York.
Frey, D.G. 1980. On the plurality of Chydorus sphaericus (O.F. Muller) (Cladocera, Chydoridae), and
designation of a neotype from Sjaelso, Denmark. Hydrobiologia 69:83-123.
George, D.M., M.A. Hurley, and B. Winstanley. 1984. A simple plankton splitter with a note on its
reduced subsampling variance. Limnol. Oceanogr. 29:429-432.
Hillman, D.C., J.F. Potter, and S.J. Simon. 1986. National Surface Water Survey, Eastern Lake Survey
(Phase I - Synoptic Chemistry), Analytical Methods Manual. EPA/600/4-86//009. Environmental
Monitoring Systems Laboratory, U.S. Environmental Protection Agency, Las Vegas, NV. 158 pp.
Landers, D.H., J.M. Eilers, D.F. Brakke, and P.E. Kellar. 1988. Characteristics of acidic lakes in the
eastern United States. Verh. Int. Verein. Limnol. 23:152-162.
Linthurst, R.A., D.H. Landers, J.M. Eilers, D.F. Brakke, W.S. Overton, E.P. Meier, and R.E. Crowe. 1986.
Characteristics of Lakes in the Eastern United States. Volume I. Population Descriptions and
Physico-Chemical Relationships. EPA/600/4-86/007a. U.S. Environmental Protection Agency,
Washington, D.C. 136 pp.
Megard, R.O. 1967. Three new species of Alona (Cladocera, Chydoridae) from the United States. Int.
Rev. Gesamten Hydrobiol. Syst. Beih. 52:37-50.
Omernik, J.M., and C.F. Powers. 1982. Total alkalinity of surface waters - a national map. Ann. Assoc.
Am. Geog. 73:133-136.
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Omernik, J.M., and A.J. Kinney. 1985. Regional alkalinity maps. U.S. EPA Environmental Research
Laboratory, Corvallis, OR.
Pennak, R.W. 1978. Freshwater Invertebrates of the United States, 2nd ed. John Wiley & Sons, New
York. 803 pp.
REFLEX. 1986. Borland/Analytica, Inc. San Rafael, CA.
SAS. 1985. Version 5 ed. SAS Institute, Inc., Gary, NC.
Stemberger, R.S. 1979. A Guide to Rotifers of the Laurentian Great Lakes. EPA/600/4-79/021. U.S.
Environmental Protection Agency, Washington, D.C.
ter Braak, D.J.F. 1987. Unimodal Models to Relate Species to Environment. Agricultural Mathematics
Group, Wageningen, The Netherlands.
Thornton, K.W., J.P. Baker, K.H. Reckhow, D.H. Landers, and P.J. Wigington, Jr. 1986. National
Surface Water Survey, Eastern Lake Survey - Phase II Research Plan. U.S. EPA Environmental
Research Laboratory, Corvallis, OR.
Wilkinson, L 1986. SYSTAT: The System for Statistics. SYSTAT, Inc., Evanston, IL
46
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FIGURES FOR ALL SECTIONS
47
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Maine (1 E)
Adirondacks (1A)
entral New England (1C)
Poconos/Catskills (1
Southern New England (1D)
Figure 1-1. Identification of five geographic regions used in the design of the sampling
program for ELS-I and ELS-II of the National Lake Survey. Code in Q identifies
region association as part of each lake identification code (see Appendix A).
49
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Figure 1-2. Location of initial 150 lakes picked for sampling in ELS-II. Final sampling of 147
lakes differs in some cases from this map due to substitution of alternate lakes.
50
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BOX PLOT OF VARIABLE: PRECIS
GROUPED BY VARIABLE: MIN_ABUN
0.02
MINIMUM
, N = 260
1.12
MAXIMUM
* *
.000
1.000
2.000
4.000
8.000
Figure 3-1. Box plot of precision estimate calculated for all species (0) and excluding species
with minimum abundance < 1, 2, 4, or 8 individuals per meter.
51
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LGVAR
16
15
14
13
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9.0
188 OBS HIDDEN
Figure 3-2. Plot of log variance versus log mean abundance for each species in each lake separately. Variance calculated as
wrthinHake (among-sample) variance.
-------
O1
LGVAR
16
12
I
CHEHGRP=1
A = 1 occurrence, B = 2 occurrences, etc.
ABA
A
A B
A A
AA AB A
A A A
AA A A A
A AA A A
AA A A A AA
B A AA B A
A
AB A
AA A
B A
A AA AA B A A A
AA A
C A AAAB A B
AA ABAAA A
AA A A AA AAB B AA
A A AAAABBAA AA BA A
AC BBB ABA A AA A
A A A A A BA
A C B B A BBAA BA A
B A A ACA A AAAA B A
AABBBA A A A A A
A B AA A BA A
B A B A B
A A
A
A A
A A
| B A AABA A BA AAB AA A
| AAB BAA ABAA B BAAA A A
+ BABBABABAA A
| A E A ABAAB A AA AAAA A A
| B AAA ABA A BAA A
+ DCG B BACA A A AA A
| F B AAA AA AD B A
| A AAACB AA A AC A AA A
+ CFAADABA BAA AAAA
| DMD AB ABAAA AA A BAA A A
| AGDDA A ABA AAB A
+ZEFDBB BABB AA A
A
AA
0.0
0.6
24 OBS HIDDEN
1.8
2.4
3.0
3.6
4.2 4.8
LCMEA
7.2
Figure 3-3. Plot of log variance versus log mean abundance for each species in each Cluster 1 lake. Mean and variance are within
lake.
-------
CHEMGRP=2
CJi
LGVAH
16
15
A = 1 occurrence, B = 2 occurrences, etc.
A
A A
A
AA A
A AA
A A
A A A AA A
A AA A A A
A A B A A A A
A A BA AAB ABA A
A AB B AABBAA BAA A
A A AD AAA A A A
B
AAA AAABBBAA
A BAA AAC BAB AA AAA B
A AAAAA AAA BB B A A
A A A AB CBBCBB B A BABA A
B A BBCBAABAAA AA
A A AB C A ABA B A BB
A AAD A B AABAACBAABBA AA A AB
AD BB A A CC B AAB A A A
AB A
+ C A BAAABABB AAB ACA
| CAA A BABD BD AB BB BA AA
| BOB BACAAAAB DB B BD A AAAA B
+ B B AAAAD C A ABBA BA B A
| DA AADBBACACBD CAB AAAD ABB AA
| BBA ABCABA A A B AB B A A
+ BH CA BBCB BAEDCB BB ABA A B AA B
| G BACBCA AA CADAAAA C BA A AA A
j B ADABBAACBB A AECB AA AAA A A
+ C AACG ACAAABAAB BB BAA AAA A
| CF A AAAA A CBC AB BA AA A A A
| I BBEFA ECCC BBA A
+ JHABEFACBBBC AAAAA AA A A AA
| JEBDDAAC BDCD ACB BBA A A A
| DNIFDGA AA C AA A A A
+ZFGFDAB A ABAB AA AAAAA A A
0.0
0.6
1.2
—I
1.8
A A
2.4
3.0
3.6
4.2 4.8
LGMEA
62 OBS HIDDEN
Figure 3-4. Plot of log variance versus log mean abundance for each species in each Cluster 2 lake. Mean and variance are within
lake.
-------
LGVAE |
16 +
I
I
15 +
I
I
A = 1 occurrence, B = 2 occurrences, etc.
I A BBA
+ B A
I B A
I A A
+ AAAA
I A A A A A A
I A A AA A A A A
+ A AAA AA AA A
I A A A AB A B
I A A BA A C C C
+ CAAABBACBA BA
| A A ABA AC A A A
I A BA A AAAAA AA B
+ A A B CAA AAAA AA A A
| AAA AAAAB AB B A
I AA A BA ABCB A C EAA AAA AA A A
*• B A B A AAAABAB B A CA A
| A ABAABBECAA AA BABC B
| BA AAA B BCAAC ADD AAAA B
f AAAA CB A BAAAD AA A A A A A A
1 A AC AAAABA CB D AA C BA BB A A A
A B AC ABA A DBAS CAB A AAAA A
ADA A C AAACBBBBB C AAA A AA
A A AFBBBD DDABDAA AAAA C A A
AA B G DA A DAB AA ABA A A
+ HGBAA CCAAB A BABAABA A A A
| EC BDDCDAABCBDCAEAAEAB B A AA A
I KF ACBDA ABGAACA A A AAA A
-I- CC BABBAACBBB BBAAA AB A A A A
| BOEGBCGAAB CAAB BAAA B A B AA
| AFLBGEED BBABACAAA AA AA A A
+ZBLCDBBCAABBACCB ABAAA AAA B
0.0 0.6 1.2 1.8 2.4 3.0 3.6 4.2 4.8 5.4 6.0 6.6
27 OBS HIDDEN
Figure 3-5. Plot of log variance versus log mean abundance for each species in each Cluster 3 lake. Mean and variance are within
lake.
-------
LGVAR |
16 +
I
I
15 +
I
I
14 t
I A = 1 occurrence, B = 2 occurrences, etc.
13 +
» i
I A A
11 + A AA A
I A
10 +
I
I A A AAA AA
9 + AA A AA A
I A A
I BAA
8 + A A AAA
I B A
01 ' AAA A
O) 7 + ABA
I A AAA B B
I AA A AAA A A A
6 + A C A A
I A A
I A A
5 + A A AAA A A
I A A AAB A A
| AAA B A
4 + A A BA AAA
| A BCA A
| AB CAA AAB B A
3 <• ABA
| BA
| AAAA B
2 1- BA B B A
| BC CD
| CAA
I + CGAA
| CBAB
j DMC
0 +ZZCA
0-0 0.5 1.0 1.5 2.0 2.5 3.0
3.5 4.0 4.5 5.0 5.5 6.0
LGMEA
Figure 3-6. Plot of log variance versus log mean abundance for each species. Mean and variance calculated among all lakes.
-------
CHEMGRP-1
01
-vl
13
A = 1 occurrence, B = 2 occurrences, etc.
A A
A A A
A A
A A A
A A
A A
BEA
0 +RIA
-H 1—
~t 1 1 1 1 I I 1-
0.0 0.5 1.0 1.5 2.0 2.5
3.0
LGHEA
3.5 4.0 4.5 5.0 5.5 6.0
Figure 3-7. Plot of log variance versus log mean abundance for each species. Mean and variance calculated among Cluster 1
lakes.
-------
CHEHGRP-2
Ol
00
tOVMt
12
11
2
A = 1 occurrence, B = 2 occurrences, etc.
A A
A A
AA A
A BA
ABA
A
B A
BB
BA A
BA
A
CA
CE
•WH
0.0 0.5 1.0 1.5 2.0 2.5
3.0 3.5
LGMEA
4.0 4.5 5.0 5.5 6.0
Figure 3-8. Plot of log variance versus log mean abundance for each species. Mean and variance calculated among Cluster 2
lakes.
-------
CHEMGRP-3
LOVAR
12
11
10
9
8
7
6
01 5
CO
4
3
2
1
0
C
A
h
A
A
A
A = 1 occurrence, B = 2 occurrences, etc.
A
A
A A A
A
A A
B A
A
A
A
A
A A
A
A A
B
A
AA
A A
A A
A A
A
A A
B B
AA B
A
B A
B B A A
AB
A
B
AB
CAB
AA
CB
PLAA
_l lilt ) ) | [ | | 1 |
.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0 5.5 6.0
LGMEA
Figure 3-9. Plot of log variance versus log mean abundance for each species. Mean and variance calculated among Cluster 3
lakes.
-------
PROPORTION PER STANDARD UNIT
1.0 4-
.9 •-
.8 •-
.7 •-
.6 •-
.5 •-
.4 ••
.3 --
.2 •-
.1 --
COUNT
23
17
15
11
9
7
5
3
2.90
9.07
LTOTAL
Figure 3-10. Frequency histogram of log(e) transformed total abundance among all 147 lakes.
-------
CHEMGRP
1.000
NORMAL PROBABILITY PLOT, N
EXPECTED
VALUE
3
2
1
0
-1
-2
-3
| "" - - ' | 1 1 1 1 1
^ • "
2 •
4 •
32
..3.
• 4 2
22«
32
1 "
• • "
1 , 1 -1 L
4 5 6 7 8 9 10
LNTOT
CHEMGRP = 2.000
NORMAL PROBABILITY PLOT, N - 53
EXPECTED
VALUE
3
2
1
0
-1
-2
-3
-) 1 1 1 f— 1 1
• •
23
• 2*2*
• S
22 • 2
24«
2>
•
I . -I— - L
3 4 5 6 7 8 9
LNTOT
CHEMGRP =• 3.000
NORMAL PROBABILITY PLOT, N = 47
EXPECTED
VALUE
3
2
1
0
-1
-2
-3
1 1 1 1 1 1
*
2 •
• 2 2
23
• •22
232
3 2
• »
-
3456789
LNTOT
Figure 3-11. Probability plot of total abundance among lakes plotted separately for Cluster 1, 2,
and 3 lakes (solid square = 1 data point; number = total of overlapping data
points).
61
-------
BOX PLOT OF VARIABLE: LTOTAL
GROUPED BY VARIABLE: CHEMGRP
2.90
MINIMUM
, N = 147
9.07
MAXIMUM
1.000
2.000
3.000
Figure 3-12. Box plot of total log(e) transformed abundance of zooplankton for each water
chemistry cluster plotted separately.
62
-------
BOX PLOT OF VARIABLE:
GROUPED BY VARIABLE:
0.00
MINIMUM
LROTOT , N = 147
CHEMGRP
9.02
MAXIMUM
1.000
2.000
3.000
BOX PLOT OF VARIABLE:
GROUPED BY VARIABLE:
•9950E-02
MINIMUM
LCLADTOT
CHEMGRP
N =
147
7.74
MAXIMUM
1.000
2.000
3.000
Figure 3-13. Box plots of total rotifer (LROTOT) and total cladoceran (LCLADTOT) log(e)
transformed abundance for each water chemistry cluster plotted separately.
63
-------
BOX PLOT OF VARIABLE:
GROUPED BY VARIABLE:
0.00
MINIMUM
LCALTOT
CHEMGRP
147
6.59
MAXIMUM
* *
1.000
2.000
3.000
BOX PLOT OF VARIABLE:
GROUPED BY VARIABLE:
0.00
MINIMUM
LCYTOT
CHEMGRP
N =
147
6.48
MAXIMUM
1.000
2.000
3.000
Figure 3-14. Box plots of total calanoid (LCALTOT) and total cyclopoid (LCYTOT) log(e)
transformed abundance for each water chemistry cluster plotted separately.
64
-------
BOX PLOT OF VARIABLE: LNAUP
GROUPED BY VARIABLE: CHEMGRP
1.67
MINIMUM
147
7.17
MAXIMUM
1.000
2.000
3.000
BOX PLOT OF VARIABLE: LNCOT
GROUPED BY VARIABLE: CHEMGRP
1.47
MINIMUM
147
6.59
MAXIMUM
1.000
2.000
3.000
BOX PLOT OF VARIABLE:
GROUPED BY VARIABLE:
2.41
MINIMUM
LNCRUST
CHEMGRP
147
8.21
MAXIMUM
1.000
2.000
3.000
Figure 3-15. Box plots of total nauplii (LNAUP), copepods (LNCOT), and crustaceans
(LNCRUST) log(e) transformed abundance for each water chemistry cluster plotted
separately.
65
-------
BOX PLOT OF VARIABLE:
GROUPED BY VARIABLE:
O.OO
MINIMUM
PROT
CHEMGRP
, N = 147
1.49
MAXIMUM
l.OOO
2.0OO
3.OOO
BOX PLOT OF VARIABLE:
GROUPED BY VARIABLE:
.2212E-O2
MINIMUM
PCLADT
CHEMGRP
, N = 147
O.95
MAXIMUM
l.OOO
2.OOO
3.OOO
Figure 3-16. Box plots of proportional abundance of rotifers (PROT) and cladocerans (PCLADT)
for each water chemistry cluster plotted separately.
66
-------
BOX PLOT OF VARIABLE:
GROUPED BY VARIABLE:
0.00
MINIMUM
PCALT
CHEMGRP
N = 147
1.46
MAXIMUM
l.OOO
* *
2.OOO
3.OOO
BOX PLOT OF VARIABLE:
GROUPED BY VARIABLE:
O.OO
MINIMUM
PCYT
CHEMGRP
, N = I'17
O.90
MAXIMUM
l.OOO
2.OOO
3.OOO
Figure 3-17. Box plots of proportional abundance of calanoids (PCALT) and cyclopoids (PCYT)
for each water chemistry cluster plotted separately.
67
-------
BOX PLOT OF VARIABLE: FACTOR(l) , N = 147
GROUPED BY VARIABLE: CHEMGRP
-2.12
MINIMUM
1.91
MAXIMUM
1.000
2.000
3.000
BOX PLOT OF VARIABLE: FACTOR(2) , N
GROUPED BY VARIABLE: CHEMGRP
-3.61
MINIMUM
147
2.23
MAXIMUM
1.000
2.000
3.000
BOX PLOT OF VARIABLE: FACTOR(3)
GROUPED BY VARIABLE: CHEMGRP
-3.54
MINIMUM
N = 147
2.60
MAXIMUM
1.000
2.000
3.000
Figure 3-18. Box plots of first three factors from principal components analysis (PCA) of major
zooplankton taxa. Factors plotted separately for each chemistry cluster.
68
-------
BOX PLOT OF VARIABLE: LCALTOT , N = 147
GROUPED BY VARIABLE: REGION
I MUM
*
(
*
/
)
/
/
/
6.59
MAXIMUM
' — i.ooo
) 3.000
1
) — 4.000
5.000
BOX PLOT OF VARIABLE: PCALT
GROUPED BY VARIABLE: REGION
0.00
MINIMUM
147
1.46
MAXIMUM
1.000
2.000
3.000
4.000
5.000
Figure 3-19. Box plots of log(e) transformed (LCALTOT) and proportional (PCALT) abundance of
calanoid copepods, plotted separately for each geographic region.
69
-------
BOX PLOT OF VARIABLE: TOTAL
GROUPED BY VARIABLE: CHEMGRP
5.00
MINIMUM
N = 147
30.00
MAXIMUM
1.000
2.000
3.000
BOX PLOT OF VARIABLE:
GROUPED BY VARIABLE:
0.00
MINIMUM
ROT
CHEMGRP
N = 147
15.00
MAXIMUM
1.000
2.000
3.000
BOX PLOT OF VARIABLE:
GROUPED BY VARIABLE:
3.00
MINIMUM
CRUS
CHEMGRP
N =
147
17.00
MAXIMUM
1.000
2.000
3.000
Figure 3-20. Box plots of species richness for total zooplankton (TOTAL), rotifers (ROT), and
total crustaceans (CRUS), plotted separately for each chemistry cluster.
70
-------
BOX PLOT OF VARIABLE:
GROUPED BY VARIABLE:
-2.39
MINIMUM
FACTOR(1) ,
REGION
N = 147
2.48
MAXIMUM
1.000
2.000
3.000
4.000
5.000
Figure 3-21. Box plot of Factor 1 from principal components analysis (PCA) of species richness
plotted separately for each geographic region.
71
-------
BOX PLOT OF VARIABLE:
GROUPED BY VARIABLE:
-2.39
MINIMUM
FACTOR(1) , N
CHEMGRP
147
2.48
MAXIMUM
( 4- )
I
1.000
2.000
3.000
Figure 3-22. Box plot of Factor 1 from principal components analysis (PCA) of species richness,
plotted separately for each water chemistry cluster.
72
-------
BOX PLOT OF VARIABLE:
GROUPED BY VARIABLE:
-2.43
MINIMUM
FACTOR(2) , N
REGION
147
2.60
MAXIMUM
1.000
2.000
3.000
4.000
5.000
Figure 3-23. Box plot of Factor 2 from principal components analysis (PCA) of species richness,
plotted separately for each geographic region.
73
-------
BOX PLOT OF VARIABLE:
GROUPED BY VARIABLE:
-1.25
MINIMUM
FACTOR(2) ,
CHEMGRP
N =
70
1.89
MAXIMUM
£
1.000
2.000
3.000
BOX PLOT OF VARIABLE:
GROUPED BY VARIABLE:
-2.43
MINIMUM
FACTOR(2)
CHEMGRP
/ N =
77
2.60
MAXIMUM
'-Li
i
1.000
2.000
3.000
Figure 3-24. Box plots of Factor 2 from principal components analysis (PCA) of species
richness, plotted separately for each water chemistry cluster and calculated
separately for Regions 1 and 5 (top) and for Regions 2, 3, and 4 (bottom).
74
-------
H
0
I
V
C
L
3 A
0
i
i
i
i
i
0
0
0
0
0
. D •
.6 -
.4 -
.2 -
.0 -
.8 •
.6 -
.4 -
.2 •
n y *
X D
\f X X rj D
o x x°D x x' x
DX X # D Jl X D
XD DX £ "X* D
' IP *D nD&D nD n
^ ^AnAXo X D
> ** */ -
' 00^ DXyQ QDX DxX)h
A A x D x
0 X D D °
> D
oA^ o
X<> no x xo ° * x X
0 AD . n
v
/v >n A A A X^ n n
0.0 0.2 0.4 0.6 0.9 1.0 1.2 1.4 1.6 1.8 2.0 2.2 2.4
HDIVROT
01 02 X 3
Rgure 3-25. Plot of Shannon-Wiener diversity of cladocerans and rotifers for 146 lakes from the summer sampling of the ELSHI.
-------
BOX PLOT OP VARIABLE:
GROUPED BY VARIABLE:
-2.33
MINIMUM
FACTOR(l) , N
CHEMGRP
146
2.23
MAXIMUM
1.000
2.000
3.000
BOX PLOT OF VARIABLE:
GROUPED BY VARIABLE:
-2.17
MINIMUM
FACTOR(2) ,
CHEMGRP
N = 146
1.76
MAXIMUM
±
1.000
2.000
3.000
Figure 3-26. Box plots of Shannon-Wiener Diversity for PCA Factors 1 and 2, determined
separately for each water chemistry cluster.
76
-------
FACTOR(1)
-1
-2
-3
H
"S A S
H SS S A A
A AS A H S H H
A SA AA "A A AH H H"H" S H S
A A A " "H H "" H " "
A AA AA" A S HS HH"SS H" SH
A A A 2 SS "S H HSHH
A A S A H HH S HSH H
A A H S " S
A A HSSH SA
SA S H SH "S SS
S H S S H S "
S
H
-3
-2
-1 0
FACTOR(2)
Figure 3-27. Plot of all lakes as a function of Shannon-Wiener Diversity for PCA Factors 1 and 2.
A = Cluster 1 lakes; S = Cluster 2 lakes; H = Cluster 3 lakes; 2 = two lakes with
same score;" = two or more lakes with same score.
77
-------
BOX PLOT OF VARIABLE:
GROUPED BY VARIABLE:
-2.17
MINIMUM
FACTOR(2) ,
REGION
146
1.76
MAXIMUM
1.000
2.000
3.000
4.000
5.000
Figure 3-28. Box plots of Shannon-Wiener Diversity for PCA Factor 2, determined separately for
each geographic region.
78
-------
BOX PLOT OF VARIABLE:
GROUPED BY VARIABLE:
-2.46
MINIMUM
FACTOR(2)
CHEMGRP
N =
147
2.63
MAXIMUM
**
1.000
2.000
3.OOO
BOX PLOT OF VARIABLE:
GROUPED BY VARIABLE:
-3.46
MINIMUM
FACTOR(3)
CHEMGRP
» N =
147
2.85
MAXIMUM
l.OOO
2.000
3.OOO
Figure 3-29. Box plots of Factors 2 (top) and 3 (bottom) of principal components analysis (PCA)
of size structure, plotted separately for each water chemistry cluster.
79
-------
BOX PLOT OF VARIABLE:
GROUPED BY VARIABLE:
-3.32
MINIMUM
FACTOR(3)
CHEMBRP
N =
102
1.52
MAXIMUM
I
l.OOO
2.OOO
3.OOO
BOX PLOT OF VARIABLE:
GROUPED BY VARIABLE:
-3.46
MINIMUM
FACTOR(3) , N
CHEMBRP
45
2.85
MAXIMUM
—t -- )
_( ..
l.OOO
2.OOO
3.OOO
Figure 3-30. Box plots of Factor 3 of principal components analysis (PCA) of size structure
plotted separately for each water chemistry cluster and calculated separately for
Regions 1, 3, and 5 (top) and Regions 2 and 4 (bottom).
80
-------
BOX PLOT OF VARIABLE:
GROUPED BY VARIABLE:
-1.86
MINIMUM
FACTOR(1) , N = 146
CHEMGRP
4.33
MAXIMUM
**
,* * * *
1.000
2.000
3.000
BOX PLOT OF VARIABLE:
GROUPED BY VARIABLE:
-1.90
MINIMUM
FACTOR(2)
CHEMGRP
N = 146
2.72
MAXIMUM
1.000
2.000
3.000
Figure 3-31. Box plots of Factors 1 and 2 of principal components analysis (PCA) of 38 taxa
groups, plotted separately for each water chemistry cluster.
81
-------
BOX PLOT OF VARIABLE:
GROUPED BY VARIABLE:
-2.71
MINIMUM
FACTOR(3)
CHEMGRP
N = 146
2.38
MAXIMUM
**
1.000
2.000
3.000
BOX PLOT OF VARIABLE:
GROUPED BY VARIABLE:
-3.02
MINIMUM
FACTOR(4)
CHEMGRP
N = 146
00*
2.97
MAXIMUM
1.000
2.000
3.000
Figure 3-32. Box plots of Factors 3 and 4 of principal components analysis (PCA) of 38 taxa
groups, plotted separately for each water chemistry cluster.
82
-------
BOX PLOT OF VARIABLE:
GROUPED BY VARIABLE:
-2.66
MINIMUM
FACTOR(l) , N
CHEMGRP
32
5.30
MAXIMUM
1.000
2.000
3.000
BOX PLOT OF VARIABLE:
GROUPED BY VARIABLE:
-4.66
MINIMUM
FACTOR(l) ,
CHEMGRP
115
4.49
MAXIMUM
* *
0 0
1.000
2.000
3.000
Figure 3-33. Box plots of Factor 1 of principal components analysis of copepod groups, plotted
separately for each chemistry cluster and calculated separately for Region 3 (top)
and all other regions (bottom).
83
-------
BOX PLOT OF VARIABLE:
GROUPED BY VARIABLE:
-5.07
MINIMUM
FACTOR*1) , N = 147
CHEMGRP
±
7.63
MAXIMUM
1.000
2.000
3.000
BOX PLOT OF VARIABLE:
GROUPED BY VARIABLE:
-4.97
MINIMUM
FACTOR(2)
CHEMGRP
N = 147
5.20
MAXIMUM
1.000
2.000
3.000
Figure 3-34. Box plots of Factors 1 and 2 of principal components analysis (PCA) of rotifer
groups, plotted separately for each chemistry cluster.
84
-------
BOX PLOT OF VARIABLE:
GROUPED BY VARIABLE:
-4.97
MINIMUM
FACTOR(2)
REGION
N =
147
5.20
MAXIMUM
i
I
1.000
2.000
3.000
4.000
5.000
Figure 3-35. Box plot of Factor 2 of principal components analysis (PCA) of rotifer groups,
plotted separately for each geographic region.
85
-------
BOX PLOT OF VARIABLE:
GROUPED BY VARIABLE:
-3.54
MINIMUM
FACTOR(l) ,
CHEMGRP
N =
122
7.63
MAXIMUM
i
1.000
2.000
3.000
Figure 3-36. Box plot of Factor 1 of principal components analysis (PCA) of cladoceran groups,
plotted separately for each chemistry cluster, excluding Region 4.
86
-------
BOX PLOT OF VARIABLE:
GROUPED BY VARIABLE:
-4.56
MINIMUM
•• )
FACTOR(2)
REGION
N = 147
I
( •• )
5.95
MAXIMUM
1.000
2.000
3.000
4.000
5.000
Figure 3-37. Box plot of Factor 2 of principal components analysis (PCA) of cladoceran groups,
plotted separately for each geographic region.
87
-------
FACTOR(2)
-2
-4
1 1 1
A
AHAH
A" AA A A SA A
A" "" A AS A H SH
AA »"A "AAH SA A
A AAS SS AS SH SS
A AA ASA A S HS
S S S
A S "S"H
" S SSS
S
III
1 1
HH "
H
H HHH " H H
H HHH H
HHH " " H H
SH
S
S"S" HHS S H HH
"H "" "SS
S S
S S
I 1
-4
-2
FACTOR(1)
Figure 3-38. Plot of first two canonical axes of discriminant analysis of proportional abundance
of 38 major taxa. A = Cluster 1 lakes; H = Cluster 3 lakes; S = Cluster 2 lakes;" =
two or more lakes with same scores.
88
-------
p
H
0
2
9.3 -
9.0 -
8.5-
8.0 -
7.5 •
7.0 •
6.5 -
6.0 -
5.5-
5.0 •
4.5 •
A 0 •
D
X
X X x *
D Jc xJ**xXx x ^ x X
l°*1l$&**
A*a x
£
$ *
<*»P
-100 -50
50 100 150 200 250 300 350 400 450 500
ALKA
01 02 X 3
Figure 3-39. Plot of pH and alkalinity for 147 lakes from the summer sampling of the ELS-II.
-------
c
A
500
450
400
350 •
300 -
250 •
200
150 •
100 -
50 -
0 -
-1C
x X
X
y w w y X
X X *v
JC *
X *
X *
x x x x
X X
x xx
o x x x * x
^ to ^ ow^jXxxXx
^Jgo^Ba, "
^OW^ ^T D
0 v
10 '50 •' 50 100 ISO 200 250 300 350 Jfl'o js'a ca
ALKA
01 02 X 3
Rgure 3-40. Plot of concentration of calcium and alkalinity for 147 lakes from the summer sampling of the ELS-II.
-------
CD
aa -
30
25-
20 •
D
R 15-
S
I
T 10-
5 -
0 -
-11
0 a
x
x
x x
a a
o x
% x
' D ° * °
^BB§- onx x X x x
^0 .Itf^ u ^P X< X x X
* W *%B a" Vx *XX x
10 -50 0 50 100 ISO 200 250 300 350 400 450 50
ALKA
01 D 2 X 3
Rgure 3-41. Plot of depth of sampling site, which is close to the maximum depth of the lake, and alkalinity for 146 lakes from the
summer sampling of the ELSHI.
-------
300
270 +•
O)
240 +
210 +
CHEMISTRY CLUSTER
NOTE: 30 OBS HIDDEN
r>
^> 180 -
52 150 -
DC
UJ
(D 2
N> O
-
1 1
-
1 1
— ^ 120 +
O
5
LU
:-J 90 -
CO
^j
1
1 11
1
1
1 11
1
60+1 1
2 2
1 11 22 2
1 12
30 + 1 1 11 22 3 3 3
1 1 222 222 33
1 1 11 3 2 3 2 33 2 3 233 333 33 33 3
1 11 11 2 2 12 23 32222 333 333 33 333 2
0 + 1 222 232 22 33
1 3
1 1 1 J 1 1 1 1 L J 1 1
4.40 4.80 5.20 5.60 6.00 6.40 6.80 7.20
STATION pH FORM 2
7.60
8.00
8.40
8.80
Figure 3-42. Plot of labile monomeric aluminum and pHfor 147 lakes from the summer sampling of the ELS-II.
-------
c
u
n
E
M
P
1
0 i
a •
7 -
6 •
5 •
4 -
3 •
2 -
i -
0.
-1 •
-2 •
-3 •
-4 -
-5 -
-6 •
-7 •
•
x X x
X
D X
x x
x x x
0 n° X X X X * x X
o °ox * x 'W x X
QmnO#*Xx *x
0 ^^^rt "J X
f£*v fl °
o • 9 *
O A
%«*
ir
0 0
Illlllllllll
-100 -50 0 50 100 150 200 250 300 350 400 450 50
ALKA
D 2 X 3
Figure 3-43. Plot of CHEMP1 and alkalinity for 146 lakes from the summer sampling of the ELS-II. CHEMP1 is the first chemical
factor calculated by PCA of the chemistry data.
-------
c
H
E
M
P
1
0 1
7 -
6 -
5 •
4 •
3 -
2 -
i •
0 -
-1 -
-2-
-3 •
-4 -
-5 •
-6 -
-7 -
X
X
Xx x »
X X X
X )U
On *XX*X X X
D°l v ^ X XX
X rfli H* 1m * X
D s$ °
»/\v» v
« *« *» **0 *
A A ^
^0
* ^ ^ °
^ *
1 1 1 I I 1 I 1 I 1 1
4.0 4.5 5.0 5.5 6.0 6.5 7.0 7.5 8.0 8.5 9.0 9.
PH02
02 X 3
Figure 3-44. Plot of CHEMP1 and field pH for 146 lakes from the summer sampling of the ELS-II. CHEMP1 is the first chemical
factor calculated by PCA of the chemistry data.
-------
CO
01
c
H
E
M
P
2
0 1
D •
5-
4 -
3 -
2 -
1 -
0 -
-1 -
-2 -
-3 -
-4 -
-5 -
-6 -
-7 •
D X R D
D X v
X/*
a
x Qn x Qn o D
_ V r*
A$ D n . QXx n 5 *
AJ jj^ W A y n ^ M
D dn A. v ID X
^ ^DVD ^ Dn ^
Q x xx 0 ox it Xx
X AD X^ r^ ^ X n °
wP ^F ** 1 Kj* *^ LJ
Y ox^ ^^ a
X ^ X D
v °D D *
D D
X0
012345678
LLKSIZ
D 2 X 3 1
Figure 3-45. Plot of CHEMP2 and log of lake surface area (ha) for 145 lakes from the summer sampling of the ELS-II. CHEMP2 is
the second chemical factor calculated by PCA of the chemistry data.
-------
c
H
E
H
P
2
0 1
o -
5 -
4 -
3 -
2 -
1 -
•
-1 -
-2 -
-3 -
w
-4 -
-5 -
-6 •
-7 -
n° D v* * D
x n x x v
X D x
D nn* 0
X X D AX 0<> A +
n xfU, xV n D
j^O^ B °D X *
D X\ AH D X D
^ <& f ^5 D
*0"Y X X
»*v n>Vp i 0 wA
rn»i ^vij &*
X r#*®&
^ DO ° o
D A
D°
X
i i i i i i i
0 5 10 15 20 25 30 35
DPSIT
D 2 X 3 1
Figure 3-46. Plot of CHEMP2 and the depth of the sampling site, which is close to the maximum depth of the lake, for 145 lakes from
the summer sampling of the ELS-II. CHEMP2 is the second chemical factor calculated by PCA of the chemistry data.
-------
c
H
E
H
P
2
D -
5-
4 •
3 •
2 •
1 •
1 -
2 -
3 -
t .
^
5 •
6 -
7 -
e
x
D 9
X
* I
A $
« i
$ «
x
1
J
«5
a
9
*
8
:i
a
$
D
O
X
5^ DX
*ag x a
0
SB
B n
X M Ji
* x *** A9 x
o ft $ * a D
«* * * » B
° 0 „ °D °
5 x
o a
B
x
1.5
2.0
2.5
3.0
3.5
4.0
4.5
5.0
5.5
0 1
D 2
LCOLOR
X 3
Figure 3-47. Plot of CHEMP2 and log of color for 146 lakes from the summer sampling of the ELS-II. CHEMP2 is the second
chemical factor calculated by PCA of the chemistry data.
-------
700
600
500 +
SUBREGION
NOTE: 1 OBS HAD MISSING VALUES 25 OBS HIDDEN
==. 400 H
CT
d>
3-
UJ
Q
g 300 H
o
_J
o
C D D
D
C D D C
h D
C 3 DD
DC
DC , D
200 + E CC B D
C ED
A A D
CA E E
100 + C E E E C
0
E E E A B B
E E CEB EEC
CE EBEC AC CC B A B B C
E EE CCC EE C AACA C A DB A C BA BCA B
E C EE A AAAAAAA A AAA A AA A
j . ..__!_..._._ J^ J_ .__._! 4_ - - U 4^ t 1 — i ...
n f-1 +• •— ' — — -f- - + . __ . _| _(_ - - r T | |- T
-6-5-4-3-2-1 0 1 23 4 5
PRIN3 (3rd chemical factor)
Figure 3-48. Plot of chloride and PRIN3 for 146 lakes from the summer sampling of the ELSHI. PRIN3 is the third chemical factor
calculated by PCA of the chemistry data.
-------
700 +
600 +
500 +
cr 400 +
0)
ID
Q
g
X
o
300 +
200 +
100 +
0 +
D
D D
D D
DD
DD
D
E D
D
E
E EE
E E E
E
D
D
DD
D
D
D
E B
SUBREGION
NOTE: 62 OBS HAD MISSING VALUES 7 OBS HIDDEN
B
C C
C E
C C
B
C E E EC E
E B C E CC E E CE
D E C E C CEC CC
E
—h-
73
—I—
82
—I—
91
10 19 28 37 46 55
DISTANCE FROM COAST (km)
64
Figure 3-49. Plot of chloride and distance from coast for 146 lakes from the summer sampling of the ELS-II. Most missing values
are for interior lakes for which distance from coast was not calculated.
-------
8
c
H
E
N
p
•
4
0 1
a •
4 •
3 •
2 •
1 •
0 •
-1 •
-2 •
-3 •
-4 -
•5 -
X
* 0
0 4
* So * D
o x \So
Or . w y 0 ft Y. Xtf o
^ x ^ nX x •* *Q x
^ fly 0 yO
D X4 0
X
• i i r i • i — • — i — « — i — « — i — i — i — i — i — i — r
-3 -2 -i « 1 2 3 4 5 6 7 8 9 11
OIFDO
D 2 X 3
Figure 3-50. Plot of CHEMP4 and the difference between surface and bottom dissolved oxygen for 145 lakes from the summer sam-
pling of the ELSHI. CHEMP4 is the fourth chemical factor calculated by PCA of the chemistry data.
-------
48
47 +
46
45 +
44
43 +
42 +
41
40 +
1ST CHEMICAL FACTOR
NOTE: 11 OBS HIDDEN
5 1
02 5
00 3 21
0 03 3 523 5
230 01 2
0 10 2 0
1101
4
6 4
65 1
43 4 6
334 2
6 4
4 23
5 45 3 23
53 314
5 6
4 3 31
6 5 3 4 5 54
44 3
4 33 1
3 4
34 3
3 3
33 5 1
215
0
33 11 5 3
5 535 6 50
0 51 22 3
1 5
4
5
-77
-76
-75
-74
-73 -72
LONGITUDE
-71
-70
-69
-68
-67
Figure 3-51. Schematic map of 146 lakes from the summer sampling of the ELSHI. Axes are in decimal degrees of west longitude
and north latitude. The scales of the two axes are not equivalentto true geographic distances. Labels are indices of
size of factor score of first chemistry factor, with 0 representingthe most negative factor scores and 6 the most
positive. See text for explanation.
-------
48 +
47 +
46 +
45 +
44 +
LU
Q
43 +
42 +
41 +
40 +
2ND CHEMICAL FACTOR
NOTE: 11 OBS HIDDEN
6 2
36 5
23 4 55
2 15 1 223
315 24 4
2 21 1 5
0114
1
0 5
22
03 1
0 0
3 4
0 5
2 33 0
3 6
3
4
5 4
16
3 25
5 5
36 4
4 5
43 2 3
341
3
43 30 4 2
2 122 4
3
3 0
33 33 2
4 5
6 4
543
6 45
5 1 13
-77
-76
-75
-74
-73 -72
LONGITUDE
-71
-70
-69
-68
-67
Rgure 3-52. Schematic map of 146 lakes from the summer sampling of the ELSHI. Axes are in decimal degrees of west longitude
and north latitude. The scales of the two axes are not equivalent to true geographic distances. Labels are indices of
size of factor score of second chemistry factor, with 0 representingthe most negative factor scores and 6 the most
positive. See text for explanation.
-------
o
co
48 +
47 +
46 +
45 +
LU
Q
44 +
§43
42 +
41
40 +
-77
3RD CHEMICAL FACTOR
NOTE: 11 OBS HIDDEN
3 2
32 3
43 2 21
3 33 2 222 3
333 32 2
3 33 2 3
2322
3
3 3
43 3
34 6 5
432 4
3 3
1 11
1 11 0 12
21 233
2 2
3 3 33
3 3 3 3 2 35
13 3
3 22 4
3 2
44 3
3 3
34 4 4
444
5
55 52 5 5
5 354 5 60
5 55 55 6
5 5
4
6
-76
-75
-74
-73
-72
-71
-70
-69
LONGITUDE
-68
-67
Figure 3-53. Schematic map of 146 lakes from the summer sampling of the ELSHI. Axes are in decimal degrees of west longitude
and north latitude. The scales of the two axes are not equivalent to true geographic distances. Labels are indices of
size of factor score of third chemistry factor, with 0 representingthe most negative factor scores and 6 the most
positive. See text for explanation.
-------
48 +
47 +
46 +
45 +
LU
Q
44
43 +
42 +
41 +
40
4TH CHEMICAL FACTOR
NOTE: 11 OBS HIDDEN
3 1
23 3
24 2 14
5 33 2 533
424 43 3
4 53 3 4
5433
6
55
34 1
3 3
2 3
33
3
3
1
3 4
22
55
2 3
3 3
1
33
431
4 3
35 34
2
4
552 4
3
2 0
13 22
2 1
3 2
3 12
2 44
4 3 53
—i
-76
-77
-75
-74
-73 -72 -71
LONGITUDE
-70
-69
-68
-67
Figure 3-54. Schematic map of 146 lakes from the summer sampling of the ELSHI. Axes are in decimal degrees of west longitude
and north latitude. The scales of the two axes are not equivalent to true geographic distances. Labels are indices of
size of factor score of fourth chemistry factor, with 0 representingthe most negative factor scores and 6 the most
positive. See text for explanation.
-------
Ill
Q
48
47 +
46 +
45 +
44 +
42 +
41 +
40
CHEMISTRY CLUSTER
NOTE: 11 DBS HIDDEN
3 1
12 3
11 2 21
1 13 2 322 3
131 11 2
1 11 2 1
1111
3
3 3
33 2
22 1 3
123 1
3 2
2 22
3 33 2 22
32 212
3 3
3 2 21
3 3 2 2 3 22
23 2
3 22 1
2 3
23 2
2 2
22 2 1
212
1
22 11 3 1
3 313 3 21
1 21 11 1
1 3
2
2
-77
-76
-75
-74
-73 -72
LONGITUDE
-71
-70
-69
-68
-67
Figure 3-55. Schematic map of 146 lakes from the summer sampling of the ELS-JI. Axes are in decimal degrees of west longitude
and north latitude, labelled by chemistry cluster. The scales of the two axes are not equivalent to true geographic
distances. See text for explanation.
-------
48 +
47 +
46 +
45 +
LU
Q
ID
44
42 +
41
40 +
SUBREGION
NOTE: 11 OBS HIDDEN
A
A
A A A
AA A AA
A AA A AAA
AAA AA A
A AA A A
AAAA
B
B B
BB B
BB B
B B B B
B B
E E
E
E
E
C E E
C CC E
C C
C E
E
E E
CC
C C E
C C
CC C
C C
CC C C
C C C
C
CC CC D D
D DDD D
D
D D
DD DD D
D D
E E
E E E
E EE
E E EE
-77
-76
-75
-74
-73 -72
LONGITUDE
-71
-70
-69
-68
-67
Figure 3-56. Schematic map of 146 lakes from the summer sampling of the ELSHI. Axes are in decimal degrees of west longitude
and north latitude, labelled by subregion. The scales of the two axes are not equivalentto true geographic distances.
See text for explanation.
-------
6
E
N
P
1
6
0 1
D 2
CHENP1
X 3
Figure 3-57. Plot of GENP1 and CHEMP1 for 145 lakes from the summer sampling of the ELSHI. GENP1 is the first communrtyfactor
calculated by PCA of the generic abundance data and CHEMP1 is the first chemical factor calculated by PCA of the
chemistry data.
-------
o
00
6
E
N
P
2
0 1
i 1 •
10 •
9 •
8 -
7 •
6 •
5 •
4 •
3 -
2 -
1 -
0 -
-1 -
-2 -
-3 •
-4 -
x
x
X
ox x x
D ^0 * * j( rf x x X
° *" !ffl *|^Xo**xo*« X **
0 0 £ * ™0 $1 D D x
A , V AD ''XwV
^ X D
» A JL V
^ o ^
* i • i • i • i • i 1 1 1 1 i 1 i r
-B -8 -4 -2 0 2 4 6 8
CHEMP1
D 2 X 3
Figure 3-58. Plot of GENP2 and CHEMP1 for 145 lakes from the summer sampling of the ELSHI. GENP2 is the second community
factor calculated by PCA of the generic abundance data and CHEMP1 is the first chemical factor calculated by PCA of
the chemistry data.
-------
6
E
N
P
3
0 1
4 -
3 -
2-
i -
-1 •
-2 -
-3 -
-4 -
_c .
° ° o * x"xxD
Dv Xx
x X X
0 ^ 0 *vU,X XX XX
*
-------
6
E
N
P
4
0 1
11 -
10 -
9 -
8 -
7 -
6 -
5-
4 -
3 -
2 -
1 -
0 -
-1 -
-2 -
-3 -
-4 -
-5 •
X
0
D
0
^Afc ^4 4fo O?^ $ 0^5n% D «. A 5 D X X D
teXCft V.^^o ^ B|n ''IT Jhra >HFy
o yr 0^ 'B^i * DOD? x * x x x
^ 0 n 0* ^ X X X x
A
D X Xx
X
• I'i'i'i'I'i'i*
-8-6-4-20 2 4 6 B
CHEMP1
D 2 X 3
Figure 3-60. Plot of GENP4 and CHEMP1 for 145 lakes from the summer sampling of the ELSHI. GENP4 is the fourth community
factor calculated by PCA of the generic abundance data and CHEMP1 is the first chemical factor calculated by PCA of
the chemistry data.
-------
6
E
N
P
0 1
IV -
9 •
a -
7 -
6 -
5 -
4 -
3 -
2-
1 -
v -
-1 -
-2 -
-3 -
-4 -
-5 •
D
X
D
a
D n D X
* X n D
D XDD X
o * v a
X D D n nn
X^^ ^^ A ^^ ^^fluD ^1J ^B
fm ^Hv )f ^I*^ U ^H JC
fTl ?f ft MIT^^^ _rJ l^i n i_r^
0 D £ *" ^>X DVD ""^^ ^ A ^ D ~ ^
lr ^9C jf ^^ ^/ - j^» -_ ^*
^1 T* ^^J^P »» ^^ Tir ^^ ^\
0 iA^AAO^D . DXD
A 0 w A A X
A
A
• 1 • 1 • 1 • 1 • 1 • 1 • 1 • 1 • 1 • 1 • 1 • 1 •
-7-6-5-4-3-2-10 1 2 3 4 5 6
CHEMP2
D 2 X 3
Rgure 3-61. Plot of GENP1 and CHEMP2 for 145 lakes from the summer sampling of the ELS-II. GENP1 is the first communityfactor
calculated by PCA of the generic abundance data and CHEMP2 is the second chemical factor calculated by PCA of the
chemistry data.
-------
N>
G
E
N
P
3
0 1
3-
2 •
1 •
-i -
i
-2 •
-3 •
-4 -
-5 •
DO x X
D
x" x
X y D X
v x x v x .
n A J & x° Xxx
x **x * * vrft X °A* x x ° ^D ° n°
-------
6
E
N
P
3
-5
0 1
02
CHENP6
X 3
Rgure 3-63. Plot of GENP3 and CHEMP6 for 145 lakes from the summer sampling of the ELS-HI. GENP3 is the third community
factor calculated by PCA off the generic abundance data and CHEMP6 is the sixth chemical factor calculated by PCA of
the chemistry data.
-------
6
E
N
P
0 i
i* •
9 •
B -
7 -
6.
5 •
4 •
a .
J
2 •
1 -
0 •
-i -
-2 •
-3 •
-4 -
-5 •
H
0
X
0
„ a
D Da X
4 0
u fi X
0 D ° X X
o a x g
o/1*^ |c x x x *
A ff ^n M ^v^n \* ^ JT*^ y _ _ x v
$ /^^A^ ip6r~n n x x x
^^^1^ o xxxxxx x xXXx
^r * a x x
v \T^ Ad D D X
$ X
*
iii iiii i i i i
-100 -50 0 50 100 150 200 250 300 350 400 450 50
ALKA
D 2 X 3
Figure 3-64. Plot of GENP1 and alkalinity for 146 lakes from the summer sampling of the ELSHI. GENP1 is the first communnyfactor
calculated by PCA of the generic abundance data.
-------
6
E
N
P
2
0 1
it -
10 -
9-
B -
7 •
6 •
5 •
•
3 •
2 -
1 •
0 -
-1 -
-2 -
-3 -
-4 -
-1(
x
x
X
0 X y X
D SnV X xx x x x x
* ^fn/fliAj^o x x x x X
A^t Hi " n X
vCnP Q D Hr
^ ^ H x xx
o ^ D ° o° xx
»0 -50 0 50 100 150 200 250 300 350 400 450 50
ALKA
D 2 X 3
Figure 3-65. Plot of GENP2 and alkalinity for 146 lakes from the summer sampling of the ELS-II. GENP2 is the second community
factor calculated by PCA off the generic abundance data.
-------
O)
6
E
N
P
3
0 i
3 -
2 -
1 -
0 -
-1 •
-2 -
-3 -
-4 -
-5 -
-1
0 0 X X
o
0
oo xvx
Dn X X X
o x x
V Y
A V V
A. rf^ ^ x X Y
^ v DO v v Y v y
*V ^ *I*™DIL x x
*$ * D ° °X
A000 D
o o* o o o
0 0
BO -50 0 50 100 150 200 250 300 350 400 450 50
ALKA
D 2 X 3
Figure 3-66. Plot of GENP3 and alkalinity for 146 lakes from the summer sampling of ELSHI. GENP3 is the third communityfactor
calculated by PCA of the generic abundance data.
-------
6
E
N
P
4
-5 -
XXX
XX
X
-100 -50 0
50 100 150 200 250 300 350 400 450 500
0 1
D 2
ALKA
X 3
Figure 3-67. Plot of GENP4 and alkalinity for 146 lakes from the summer sampling of the ELSHI. GENP4 is the fourth community
factor calculated by PCA of the generic abundance data.
-------
00
6
E
N
P
1
-5
4.0
0 1
D 2
PH02
X 3
Figure 3-68. Plot of GENP1 and pH for 146 lakes from the summer sampling of the ELSHI. GENP1 is the first communityfactor
calculated by PCA of the generic abundance data.
-------
CD
4900 •
4000 •
3500 •
3000 •
2500 •
R
1500 •
1000 •
500 •
0 •
-1
0
D
0
* °o » x
A 0
<>0 A D°
* _ D _ D w v
A Q X XxX X
f rrfMim Hraifljfef^- "^ry-^ x v^iLx^ot^ x
10 -50 0 50 100 150 200 250 300 350 400 450 50
ALKA
01 D 2 X 3
s 3-69. Relationship between abundance of the rotifer genus Keratella and alkalinity for the 1 46 lakes in the ELSHI.
-------
to
o
R
6
43V '
400 -
350 -
300 -
250 -
200 -
150 -
100 -
50 -
0 -
X
X
D
o rP CD D x
0
* Jf J^AlinO- .. .* ^v w „, fc v X JC „
-100 -50 0 50 100 150 200 250 300 350 400 450 50
ALKA
01 D 2 X 3
Rgure 3-70. Relationship between abundance off the rotifer genus Asplanchna and alkalinity for the 146 lakes in the ELS-II.
-------
N>
R
7
0 i
re 3-71. R
11VV -
1000 -
900 •
800 -
700 -
600 •
500 •
400 -
300 •
200 -
100 •
0 -
-1
0
0
D
X*
X
D X
D
0° D xy xx
_, D v X
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nvfiiLiF'ipiifi °1 ir i< T w vv*x y x i ^ v w v x
JO -50 0 50 100 150 200 250 300 350 400 450 50
ALKA
D 2 X 3
Blationship between abundance of the rotifer qenus Polyarthra and alkalinity for the 1 46 lakes in the ELS-II.
-------
IN3
ro
3HH -
BOO -
700 -
600 •
500 -
R
3
300 -
200 -
100 -
0 -
X
X
D
D
0
0
D rf1
»
, A 0 D „ B * * "
O V ff^H D n *4w D X
» i ftArtflni n»n rfh vX.^»v . Xv ^. w A/ X v* ., X
-100 -50 0 50 100 150 200 250 300 350 400 450 501
ALKA
01 D 2 X 3
Rgure 3-72. Relationship between abundance of the rotifer genus Trichgcerca and alkalinity for the 146 lakes in the ELS-II.
-------
ro
CO
auu •
BOO -
700 -
600 -
500 -
n 40° •
R
3
300 -
200 •
100 •
0 •
X
X
D
0
D
D
0° D
.. a
0 D
A D x D Bx
<& ^rwfl rid v rv x B
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• 1 " 1 '1 I11! 1 | 1 ' 1 • 1 •
-B -6 -4 -2 0 2 4 6 8
CHEMPi
01 D 2 X 3 T
3-73. Relationship between abundance of the rotifer genus Trichocerca and the first chemical factor (CHEMP1 ) calculated by
PCA of the chemistry data for 145 lakes in the ELS-II.
-------
3WW ~
800 •
700 •
600 •
500 •
R
3
300 -
200 -
100 -
0 -
X
X
0
0
a
a
* a
0 D °
X0 ° n X° DD
* ° JBft* jJn ~ # x n
- - ^ i •• ^^•K»fc,n vJX A
-6-4-20 2 4 6 8 10
GENP1
01 02 X 3
3-74. Relationship between abundance of the rotifer aenus Trichocercaand the first communityfactor (GENP1) calculated by
PCA of the generic abundance data for 146 lakes in the ELSHI.
-------
N>
tn
R
2
3UUU -
2800 -
2600 -
2400 -
2200 -
2000 -
1800 -
1600 -
1400 -
1200 -
1000 -
800 -
600 -
400 -
200 -
0.
0
D
D
D
»
0
D X
0 0
[fl X
D rj
. .A. AAl -AiSi BilL. AY «r^X Xy £ X vi|^JLvYwX X
i""""™™i'^^^^^^^^^^^^^^^^^^^^^i^^"^'"^i^r^^^^^ ii i i i i T
-100 -50 0 50 100 150 200 250 300 350 400 450 50
0 1
D 2
ALKA
X 3
Figure 3-75. Relationship between abundance of the rotifer genus Kellicottia and alkalinity for 146 lakes in the ELSHII.
-------
R
2
nv v v •
2800 •
2600 •
2400 -
2200 •
2000 •
1800 -
1600 •
1400 •
1200 -
1000 -
800 -
600 -
400 -
200 -
0 -
1
0
D
D
D
A
y
D D
-B
-6
-4
-2
0 i
D 2
CHEMP1
X 3
Rgure 3-76. Relationship between abundance of the rotifer genus Keilicottia and the first chemical factor (CHEMP1) calculated by
PCA of the chemistry data for 145 lakes in the ELS-II.
-------
ro
-j
3000
2BOO
2600
2400
2200
2000
1BOO -
1600
1400 -
1200 •
1000 •
800
600
400
200 H
0
10
12
01
D 2
6ENP2
X 3
Figure 3-77. Relationship between abundance of the rotifer genus Kellicottia and the second communityfactor (GENP2) calculated
by PCA of the generic abundance data for 146 lakes in the ELS-U.
-------
c
L
6
3Z» •
300 -
280 •
260 •
240 -
220 •
200 -
180 -
160 •
140 •
120 •
100 -
80 -
60 -
40 -
20 -
0 •
«
* °n *
« 0
A
A
D
0
D * X° *
* 0 * #A*°° x X X
DA. g^^flBA ft „ nii».flYn ^x. -x.^x n ° *
-5
-4
-3
-2
-1
0 1
D 2
6ENP3
X 3
Rgure 3-78. Relationship between abundance of the cladocera Eubosmina and the third communityfactor (GENP3) calculated by
PCA of the generic abundance data for 146 lakes in the ELSHI.
-------
ro
CO
n
u
0
3-79.
»•• '
451
411 -
351 •
399 -
251 -
299 •
151 •
100 -
51 -
1 •
0
0
"
$ 0
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^F
* X
GLD Q
U. |J y A V
** _ n ft ^o v^ ^f ]C %* ^^ ^^
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Q D N B Qiflk* X n_ 2 Dl ^^_y» 0
• i • i • i • i ^^T^^^^T^^^^^^^^^^^^^^^^^r^^^^^^^^T^^1^^^^^^
-5 -4 -3 -2 -1 0 1 2 3 4
GENP3
1 02 X 3
Relationship between abundance of the cyclopoid copepod Mesocvcloos and the third community factor (GENP3) calcu-
lated by PCA of the generic abundance data for 146 lakes in the ELSHI.
-------
CO
o
c
L
6
JCV -
300 -
280 •
260 -
240 •
220 •
200 •
180 •
160 -
140
120 •
100 •
80 -
60 -
40 -
20 -
0 -
0
*• D
0 0
0
a
*«D00
*?fcn ° X v
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-------
c
L
6
CO
320
300 -
280 -
260 •
240
220
200
180
160
140 •
120 •
100
80
60
40
20
0
1
D 2
CHEMP1
X 3
Rgure 3-81. Relationship between abundance of the cladocera Eubosmina and the first chemical factor (CHEMP1) calculated by PCA
of the chemistry data for 145 lakes in the ELS-II.
-------
CO
(O
c
L
L»
1
i
14V
130 -
120 -
110 -
100 •
90 •
80 -
70 -
60 -
50 -
40 -
30 -
20 -
10 -
0 •
0 0
D
X
0 D
0
D D
0
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0 ° X
o o o DD x x x
AD *
fi n n AaM^adfci Jfaft^Lf1^»f VnmnmatYiMnr tr w X w w wV or w w %•/ w
^•^•^••^^•••••^^^^^^^^•^^^^••^•^^^•^^•^•^••••^H J^ | j ™ ^| ^ |
-100 -50 0 50 100 ISO 200 250 300 350 400 450 50
D 2
ALKA
X 3
Rgure3-82. Relationship between abundance off the cladocera Daphnia parvula group and alkalinity for 146 lakes in the ELS-II.
-------
c
L
4
1
1
0
*3-8
14V *
130 -
120 •
110 •
100 •
90 •
80 -
70 •
60 •
SO •
40 -
30 -
20 •
10 •
0 -
0 A
0
X
0 °
v
D 0
0
D DQ X
0 ° X
$ 0 X X
. A D X
• i • i • i > i • I • 1 i i •
-8-6-4-20 2 4 6 B
CHEMPi
1 D 2 X 3
3. Relationship between abundance of the cladocera Daphnia parvula group and the first chemical factor (CHEMP1)
calculated by PCA of the chemistry data for 145 lakes in the ELS-II.
-------
co
c
0
6
0 1
3VH *
450 -
400 -
350 -
300 -
250 -
200 -
150 -
100 -
50 -
0 *
0
D
0 °
0 °
A
A
x
A 111 Of V
v JD u dpD * ^X )K x
* ^«lJ£.I^3^Xi^)0( V X X y X
A AJUB^MlLU BfeB rfl FHHjCwMMXA/ Vfcf ^ W W WWAW
-100 -50 0 50 100 150 200 250 300 350 400 450 50
ALKA
D 2 X 3
Rgure 3-84. Relationship between abundance of the cyciopoid copepod Mesocvclops and alkalinity for 146 lakes in the ELSHI.
-------
c
0
p
6
509
450 H
409
350
300 -
250 -
200
150
100
50
0 1
D 2
CHEMP1
X 3
Figure 3-85. Relationship between abundance of the cyclopoid copepod Mesocvclops and the first chemical factor (CHEMP1)
calculated by PCA of the chemistry data for 145 lakes in the ELSHI.
-------
U
O)
c
L
2
ouw •
550 •
500 •
450 •
400 •
350 -
300 •
250 -
200 •
150 •
100 •
50 •
0 •
-1
^%
^%
0
0° X
D
D
r*,-*j&M^0Haf2L JL J*wv v~* X „ *~ * - v -j -
10 -50 0 50 100 150 200 250 300 350 400 450 50
D 2
ALKA
X 3
Figure 3-86. Relationship between abundance of the cladocera Diaphanosoma and alkalinity for 146 lakes in the ELSHI.
-------
CO
-si
c
L
2
600 -
550 •
500 -
450 -
400 -
350 -
300 -
250 •
ZOO -
150 -
100 •
50 -
0
0 1
D 2
CHEMP1
X 3
Rgure 3-87. Relationship between abundance of the cladocera Diaphanosoma and the first chemical factor (CHEMP1) calculated by
PCA of the chemistry data for 145 lakes in the ELS-II.
-------
c
L
n
V
$
re 3-i
3H •
45 -
40 •
35 •
30 •
25 -
20 -
15 -
10 -
5.
0.
X
X
X
X
X
a a x x
X
° v X
x v y ^
0 * * y X
0_ * X
III Illlllll
-100 -50 0 50 100 150 200 250 300 350 400 450 501
ALKA
i D 2 X 3
88. Relationship between abundance of the cladoceran Daphnia aaleata aroup and alkalinity for 1 46 lakes in the ELS-II.
-------
CO
c
\f
0
p
3
ovu •
750 -
700 -
650 •
600 -
550 -
500 •
450 -
400 -
350 •
300 •
250 -
200 •
ISO -
100 -
50 •
0 •
0
A
^r
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V
^V A.
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.
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^^^^^^^^^^n^^^^l^^^^^^^^^^^^^^ 1 1 1 1 1 1
-100 -50 0 50 100 150 200 250 300 350 400 450 50
ALKA
01 D 2 X 3
Figure 3-89. Relationship between abundance of the calanoid copepod Leptodiaptomusand alkalinity for 146 lakes in the ELS-II.
-------
c
0
p
3
ouu -
750 •
700 •
650 •
600 •
550 •
500 •
450 •
400 •
350 •
300 •
250 •
200 -
ISO -
100 -
50 -
0 -
0
a
0
A
AA
A
O 0
A
A
0 A A o
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•1
-6
-4
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0 1
D 2
CHEMPi
X 3
Figure 3-90. Relationship between abundance of the calanoid copepod Leptodiaptomusand the first chemical factor (CHEMP1)
calculated by PCA of the chemistry data for 145 lakes in the ELS-II.
-------
R
1
0
e3-9
49VV •
4000 •
3500 •
3000 •
2500 •
2000 •
1500 •
1000 -
500 •
0 •
0
0
«
Tf- *
00
a ** o o
£a D° °*
A ^£k^^V^°ft ^ * - -v * „ n.
5 10 15 20 25 30 3!
DPSIT
1 D 2 X 3
1 . Relationship between abundance of the rotifer genus Keratella and depth of sampling site, which is close to the
maximum depth of the lake, for 146 lakes in the ELS-HI.
-------
c
L
2
0
e3-9
WWW -
550 -
500 •
450 •
400 •
350 •
300 -
250 -
200 -
150 -
100 -
50 -
0 -
0
0
D
X D D
0
0
a
*fc°)P 0 I
5 10 15 20 25 30 35
DPSIT
1 02 X 3
2. Relationship between abundance of the cladocera Diaphanosoma and depth of sampling site, which is close to the
maximum depth of the lake, for 146 lakes in the ELS-II.
-------
c
L
1
1
0
03-93
14V -
130 •
120 •
110 •
100 •
90 •
80 •
70 -
60 -
50 •
40 •
30 •
20 •
10 •
0 •
0
o a
D
X
0
a o
a
a .
*D 0
o a J* ° x
5 10 15 20 25 30 3!
DPSIT
1 D 2 X 3
Relationship between abundance of the cladoceran Daohnia oarvula aroup and depth of sampling site, which is close to
the maximum depth of the lake, for 146 lakes in the ELSHI.
-------
0
p
3
0
J3-9-
owu -
750 -
700 •
650 -
600 -
550 •
500 -
450 -
400 -
350 -
300 -
250 -
200 -
ISO -
100 -
50 -
•
0
°
°
« «
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0 0
ir
Q
^
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D 00 i£
° $ 0° * o 0
ffX X«J ^ ^ D 0 x
•PruJF^^l Jfr ^MO vnnn ^X X y X
0 5 10 15 20 25 30 35
DPSIT
1 D 2 X 3
t. Relationship between abundance of the calanoid copepod Leptodiaptomusand depth of sampling site, which is close to
the maximum depth of the lake, for 146 lakes in the ELSHI.
-------
01
p
0
t
0 i
3WVV •
8000 •
7000 •
6000 •
5000 -
4000 •
3000 •
2000 •
1000 -
0 -
0
•
D
X
°o '
x*x x
a o
a m a
/Mf^fe x n,* x
5 10 11 it 25 31 39
DPSIT
D 2 X 3
Figure 3-95. Relationship between abundance of rotifers and depth of sampling site, which is close to the maximum depth of the
lake, for 146 lakes in the ELS-II.
-------
FACTOR ( 1 )
10
5
0
-5
-10
1 1 1 1 l 1 1
• 2 • • •
• 2 ••• • 2 • "2 •
• • ml m m mm m
2 »34 222««22«2"2 ••
2m m 13222 "2«« m mm 2 •
• • B2 2322 m2 • 2m mm m
mm m 23 2 2" •• ••2« 2
'
-
-6-4-2 0 2 4 6
CHEM(3)
Figure 3-96. Plot of rotifer genera PCA Factor 1 as a function of environmental PCA Factor 3.
Solid square = 1 data point; number = total of overlapping points.
146
-------
FACTOR(1)
4
2
-6
-10
• 2 2
•
••2
2 22 ••• •
• 2 •
• 2 • •
•• 2 ••
3«4«" 2 ••
22 •••3 ••
• 2 5 «
• 2 i
22 2 •
-5
0
CHEM(l)
10
Figure 3-97. Plot of copepod genera PCA Factor 1 as a function of environmental PCA Factor 1.
Solid square = 1 data point; number = total of overlapping points.
147
-------
N
S
P
E
c
T
Q
%^
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0 i
ac •
30 •
2B •
26 -
24 •
22 •
20 •
18 •
16 •
14 •
12 •
10 •
B •
6 •
4 •
2 •
X
X
cm
D X
D X
o m m x
DO X
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0 0 X X X X
a a xx
0 D D Q 0 X X
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0
-------
s
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c
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0
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0 1
30 •
28 -
26 -
24 •
22 -
20 -
IB •
16 -
14 -
12 -
10 -
6 -
6 -
4 -
2 -
X
X
D 0
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0 OXX XX X
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-B -6 -4-20 2 4 6 8
CHEHP1
D 2 X 3
Figure 3-99. Plot of total species richness and CHEMP1 for 146 lakes from the summer sampling of the ELS-II. CHEMP1 is the first
chemical factor calculated by PCA of the chemistry data.
-------
tn
o
N
S
E
C
T
T
0 1
JC -
30 -
28 •
26 •
24 •
22 •
20 -
IB •
16 -
14 •
12 -
10 -
8 -
6 -
4 -
2 -
X
X
0 D
D X
CK
0 DX D D
0 X 0
xx D nDnxnnxn D
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0 D X 0 D X XD
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0 0 000 0
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-7-6-5-4-3-2-10 1 2 3 4 5 6
CHEMP3
D 2 X 3
Rgure 3-100. Plot of total species richness and CHEMP3 for 145 lakes from the summer sampling of the ELS-II. CHEMP3 is the third
chemical factor calculated by PCA of the chemistry data.
-------
H
D
I
V
e.o •
2.6 •
2.4 •
2.2 •
2.0 •
1.8 •
1.6 •
1.4 •
1.2 •
1.0 •
0.8 •
0.6-
0.4 •
0.2 •
0.0 •
X v x
n X
A^1 D^jOSji D X X X
$0 D tfD X XX X X
v |JP rT £ \r fjy x XX XX
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-100 -50 0 50 100 ISO 200 250 300 350 400 450 50
ALKA
01 D 2 X 3
Rgure 3-101. Plot of Shannon-Wiener diversity and alkalinity for 146 lakes from the summer sampling of the ELSHI.
-------
en
H
D
I
V
0 1
c
2
2
2
2
1
1
1
1
1
0
0
0
0
0
. o •
.6 -
.4 -
.2 -
.0 -
.8 •
.6 -
.4 -
.2 -
.0 •
.8 -
.6 -
.4 •
.2 •
.0 •
x x X
A „ ^| ° v D * D Xy
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A A AA A & rTvXr* x v* x
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0 A
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• 1 • 1 • 1 • 1 • 1 • 1 • 1 •
-8-6-4-20 2 4 6 8
CHENP1
02 X 3
Figure 3-102. Plot of Shannon-Wiener diversity and CHEMP1 for 146 lakes from the summer sampling of the ELS-II. CHEMP1 is the
first chemical factor calculated by PCA of the chemistry data.
-------
H
D
I
V
0 1
C . D '
2.6 -
2.4 -
2.2-
2.0 -
l.B -
1.6 -
1.4 -
1.2 -
1.0 -
O.B -
0.6 -
0.4 -
0.2 -
00 -
x X x
II ^f *• jf Dv^ •^A n
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D
-7-6-5-4-3-2-10 1 2 3 4 5 6
CHEMP3
02 X 3
Rgure 3-103. Plot of Shannon-Wiener diversity and CHEMP3 for 145 lakes from the summer sampling of the ELSHI. CHEMP3 is the
third chemical factor calculated by PCA of the chemistry data.
-------
N
S
p
t
E
C
R
n
u
10 -
15-
14 -
13-
12 -
11 -
10 -
9 -
8 -
7 -
6 -
5 -
4 -
3 -
2 -
1 -
0 -
D
m o on xx
DODO
$ 0 0 0 OX X X X
0 00 X* XX XX
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00 WOO U X XX X X
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0 +
otooomv
046M* 0
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1 1 1 1 1 1 1 1 1 1 1
•100 -50 0 50 100 150 200 250 300 350 400 450 50
ALKA
01 D 2 X 3
Rgure 3-104. Plot of rotifer species richness and alkalinity for 146 lakes from the summer sampling of the ELS-II.
-------
01
en
N
S
p
r
E
C
R
n
u
T
0 i
16 -
15-
14-
13 -
12 -
11 •
10 •
9-
I •
7
6 -
5
4
3
2
1 •
D
D a a x x a
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DO X *J XXX X X
0 0 DOBDDXX1XXDXXXX
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o 0000009 xaxa
00 0
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-8-6-4-20 2 4 6 8
CHENP1
D 2 X 3
Rgure 3-105. Plot of rotifer species richness and CHEMP1 for 146 lakes from the summer sampling of the ELS-II. CHEMP1 is the
first chemical factor calculated by PCA of the chemistry data.
-------
N
S
P
i
E
C
R
0
T
0 1
ID -
15 -
14 -
13 -
12 -
11 -
10 -
9 -
8 -
7 -
6 -
5 -
-
3 -
2 -
1 -
D
X XHD D D
am D
x x o DK x> a o a
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xxx] x x mx mo xa x a a
XX QXDXQ XXX
QXXHDX X OB D
DO on mo *D xo o no
D X 0000 X XO 00 *
00 X OGQO 0 D X 000
00 0
» 00 00 0*
00 0 0 000 4$ 0
A
-7-8-5-4-3-2-10 1 2 3 4 5 6
CHEMP3
D 2 X 3
Figure 3-106. Plot of rotifer species richness and CHEMP3 for 145 lakes from the summer sampling of the ELS-IL CHEMP3 is the
third chemical factor calculated by PCA of the chemistry data.
-------
Cn
H
D
I
V
R
0
T
0 1
c . « -
2.2 -
2.0 -
1.8 -
1.6 -
1.4 -
1.2 -
1.0 -
0.8 *
0.6 •
0.4 -
0.2 -
On -
. H
D x
D 0
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u ^yy X*
n Q D ° A * X X
r£h X X x X
AQn B" pflt X X
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A 0 0 J* X
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. A V
vv A^> A ^ n ^
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v ^^ A^ ^&
A A.^ ^^r ^D
^ A
1 1 1 I 1 1 1 1 1 1 1
-100 -SO 0 SO 100 ISO 200 250 300 350 400 450 50
ALKA
D 2 X 3
Rgure 3-107. Plot of Shannon-Wiener diversity of rotifers and alkalinityfor 146 lakes from the summer sampling of the ELS-II.
-------
en
00
H
0
I
V
R
0
T
0 1
C.4 -
2.2 -
2.0 -
1.8 -
1.6 -
1.4 -
1.2 •
1.0 -
0.8 •
0.6 •
0.4 -
0.2 -
0.0 •
D D
D ./ " « *vanX "
TJD X »/X X D x
00 n r$ $1 x XX* x<
0 D n rj " n 0 AU x X
« « « DD ° n x ? x x x
0 * D D X X
0 0 0 n D X rj
° S* \ x" ^°x x
0 0 D 0 X *
0 6 H 0
n D
V V ^
0000 x
0 ^ 0 D
°0 0 $
A A A
• I i 1 • 1 • 1 • 1 • I • | •
-8-6-4-20 2 4 6 8
CHEMPi
D 2 X 3
Rgure 3-108. Plot of Shannon-Wiener diversity of rotifers and CHEMP1 for 145 lakes from the summer sampling of the ELS-II.
CHEMP1 is the first chemical factor calculated by PCA of the chemistry data.
-------
Ol
CO
H
D
I
V
R
0
T
0 1
e . * •
2.2 -
2.0 -
1.8 -
1.6 -
1.4 -
1.2 -
1.0 -
0.8 -
0.6 -
0.4 -
0.2 -
0.0 •
° X
D D
V *>» V ^i
X v ^M uQk v D n f\
° D ° * * ° °« x
D * * * * * * D
_V1 D ^y ^r D
D rJP Q x o
v Xjl Q
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nX AC^^ D^A
D ° «? A A ^
^ A ^
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^ D ^
A * $ A
A AY
• I • I • I < ( • | • | • | • | ' I 1 J • I • I '
-7-6-5-4-3-2-10 1 2 3 4 5 6
CHEMP3
D 2 X 3
Figure 3-109. Plot of Shannon-Wiener diversity of rotifers and CHEMP3 for 145 lakes from the summer sampling of the ELSHI.
CHEMP3 is the third chemical factor calculated by PCA of the chemistry data.
-------
H
D
I
V
c
A
L
A • A
1.0 •
0.9 -
O.B -
0.7 -
0.6 -
0.5 -
0.4 -
0.3 -
0.2 -
0.1 -
0.0 -
X
X
° X X X
^ D X
$ X
' D X X
D X
* ° 5
D D v
D X X X
Q y
$ D D X
0 J3
^^kflftdBfltaaWl&HmrAvnfBMr tr «*/* \A/ yw \/ \AA/ w y y \/ \/
-100 -50
50
100 150 200 250 300 350 400 450
500
D 2
ALKA
X 3
Rgure 3-110. Plot of Shannon-Wiener diversity of calanoid copepods and alkalinity for 146 lakes from the summer sampling of the
ELSHI.
-------
O)
H
D
I
V
C
A
L
i.i-
i.O -
0.9 -
0.8 -
0.7 -
0.6 •
0.5 -
9.4 -
0.3 -
0.2 -
0.1 -
n n -
X
X
£ X X X
00 ° x "
« a x x
fl X X
A D X X
X
o x x°x
Q \f
o D a x
o
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Aj^Ak. AAjkA 4^A A^uukj>k^riMli}"frii*Kl^AnnBrn«nv\jinMrwiw«L«B «/ VAf %/ n \/ \f\/ \t
-6
-4
-2
1
D 2
CHEMP1
X 3
Figure 3-111. Plot of Shannon-Wiener diversity of calanoid copepods and CHEMP1 for 145 lakes from the summer sampling of the
ELSHI. CHEMP1 is the first chemical factor calculated by PCA of the chemistry data.
-------
ro
H
D
I
V
C
Y
C
0 1
1.4 -
1.3 -
1.2 -
1.1 -
1.0 -
0.9 -
0.8 -
0.7 -
0.6 -
0.5 -
0.4 •
0.3 -
0.2 -
0.1 -
0.0 -
D
D X
X X
X X x
O LJ On Q X
$ a xx
,x>DDan X xx XX
A 1 D X X X x
^ A rfi n y
A v CH v *
0 ^A g, x x
0 0 D ° * X
jA 0 "n 9 y X
ta| >L A n i i n i
ill i i i i i i i i
-100 -50 0 50 100 150 200 250 300 350 400 450 50
ALKA
D ? X 3
Rgure 3-112. Plot of Shannon-Wiener diversity of cyclopoid copepods and alkalinity for 146 lakes from the summer sampling of the
ELSHI.
-------
H
D
I
V
C
Y
C
0 1
1.4 -
1.3 -
1.2 -
1.1 •
1.0 -
0.9 -
0.8 -
0.7 •
0.6 •
0.5 •
0.4 •
0.3 •
0.2 -
0.1
On
0
n x
X X
D XX X X
Jr"^ $ X
° ° 6° D D ° A* QC XX
o o° BD *DX ilx * *
0 0 * D D ° XD
o O A AV y n "
>^ » V ._ Q fj^ w Ll/ V
' * « « " "D X " Xx D
* A° * DXXXX
9. S « D * " x
* *
n 0 00
-B -6 -4 -2 0 2 4 6 B
CHEMPi
D 2 X 3
Figure 3-113. Plot of Shannon-Wiener diversity of cyclopoid copepods and CHEMP1 for 145 lakes from the summer sampling of the
ELS-HI. CHEMP1 is the first chemical factor calculated by PCA of the chemistry data.
-------
LHAB
•m
i 2
• 2
i 2
m2mmm mmmm m ;
2 •• 4 • •• •
•9i2«2 "6" ••• 2 •
2 •• •
4 6
LABUND
10
LHAB
• • 2 •
2 •• • «2
«2
•3«" 2" •
2 • • 22
•9 «2 • 2 "42
i • ••
•3" •••
• ••
• • 2 3
i 2
• • •
• • • 2
••
3 4
LSABUND
Figure 3-114. Plot of the number of lakes occupied by a species (LHAB = log transformed
number) versus the abundance of that species in all lakes combined (LABUND
log transformed abundance, top), or the average abundance of that species in
those lakes it does occupy (LSABUND = log transformed abundance, bottom).
Solid square = 1 data point; number = total of overlapping points.
164
-------
R(3)
1
10
1 — I.I 1.
• • 2 2 •
• 2 •
•43 • • •
• • 33 22 2 • • •
• • 2 • 2 ••
•• • • 23 ••
• "2 2 • •
• •• • • •
• • 2 •
324 "4 3««3««22 • 2" 2" •
-5 0 5 10
CHEM(1)
R(4)
10
8
6
4
2
-10
• 2 •
i* •
• 2 «2
• 2 •!
"22" 2
• ••
»2 2 2 • •
• ••••3 •• 2
2 ••• ••••
• 2" ««22 • • •
• 222 "2 • 2
2 • 3252 4"2« i
• 2 •
-5
10
CHEM(1)
Figure 3-115. Abundance of Keratelia crassa [R(3)] and K. taurocephala [R(4)J, log(e) trans-
formed, as a function of environmental PCA Factor 1. Solid square = 1 data point;
number = total of overlapping points.
165
-------
• • 3
• • 22 ••
• •• •
23 •
• 2 •••
• •
• • "22
• • • 2" •
i«2 23 35i543523433434»3««
-i 1
-10
-5
CHEM(2)
10
R(12)
8
-10
2 • 2
• • • ••
• •• 2 •••
• • »2 • «2"
25M43533433923«4«2
-5
0
CHEM(2)
10
Figure 3-116. Abundance of Kellicottia lonaisoina [R(11)] and K. bostonensis [R(12)], log(e)
transformed, as a function of environmental PCA Factor 2. Solid square = 1 data
point; number = total of overlapping points.
166
-------
R(12)
-4
i 2" • •
• 2 ••• ••
332523«5745233
-2
0
CHEM(4)
R(ll)
0 •
-4
•• •
••
3 • •
2i334322*33432
-2
CHEM(4)
Figure 3-117. Abundance of Kellicottia lonoispina [R(11>] and K. bostonensis [R(12)], log(e)
transformed, as a function of environmental PCA Factor 4. Solid square = 1 data
point; number = total of overlapping points.
167
-------
R(27)
8
-6
R(28)
-6
2«
2 2
3«4
I ••• •!
• 2« 3 • •
•••3426«3««23
2 •
2 22
25
-4
-2
CHEM(3)
• «22"«2«
44253462553""2«4«22 42«2«25«2"
-4
-2
CHEM(3)
Figure 3-118. Abundance of Trichocerca multicrenis [R(27)] and T. cylindrica [R(28)J, log(e)
transformed, as a function of environmental PCA Factor 3. Solid square = 1 data
point; number = total of overlapping points.
168
-------
CL(25)
344 25242343533796485838536322
-10
-5
0
CHEM(1)
10
CL(29)
5
4
3
2
-10
345 25232333343583454749527223 3« • 2
-5
10
CHEM(l)
Figure 3-119. Abundance of Daphnia qaleata grouping [CL(25)] and D. parvula grouping [CL(29)],
log(e) transformed, as a function of environmental PCA Factor 1. Solid square = 1
data point; number = total of overlapping points.
169
-------
CL(5)
8 -
-100
• 2 • •
i
i" 223
••• 2 ••
•22 7653«««32«2 3 •• ••••
100
200
ALK
300
400
500
CL(25)
-100
•235999883867836422422«2«" 2 ••
100
200
ALK
300
400
500
Figure 3-120. Abundance of Bosmina lonqirostris [CL(5)] and Daphnia galeata grouping [CL(25)],
log(e) transformed, as a function of ANC. Solid square = 1 data point; number -
total of overlapping points.
170
-------
C0(6)
-10
2«
2«
• •2 •
2 •
• 2
i
2«2 2i
• •
«2 •
• 2 2i
22
3 •
• 2«2 «25««3 4*3423 23 •• • 2 •
-5
CHEM(l)
L
10
C0(4)
3.0
2.5
2.0
1.5
1.0
0.5
0.0
-10
324 24«42343543796485949538423 3*
1 1 i_
-5
0
CHEMd!
10
Figure 3-121. Abundance of Diaptomus minutus [CO(6)J and D. leptopus [CO(4)J, log(e)
transformed, as a function of environmental PCA Factor 1. Solid square = 1 data
point; number = total of overlapping points.
171
-------
co(17;
-10
• 2 • •
2 •• «2 • «3
•• • • 3 •
» • 2
• • 2"
344 23«42«23i22232«52« 3« «2 2
1 — 1
-5
0
CHEM(l)
10
C0(19)
4
-10
343 22«423425437944759395383»3 3»
-5
0
CHEM(1i
10
Figure 3-122. Abundance of Tropocyclops prasinus-mexicanus [CO(17)J and Cyclops scutifer
[CO(19)], log(e) transformed, as a function of environmental PCA Factor 1. Solid
square = 1 data point; number = total of overlapping points.
172
-------
co(i:
3.0
2.5
2.0
1.5
1.0
0.5
0.0
-6
2«2
•• • • •
794586623743«2«53642«42«2«25«2i
-4
-2
CHEM(3)
C0(2)
3.0
2.5
2.0
1.5
1.0
0.5
0.0
-6
324«2 •3«2795788725743»»»53542«2«i2« 2i
-4
-2
0 2
CHEM(3)
Figure 3-123. Abundance of Epischura lacustris [CO(1)J and E. nordenskioldi [CO(2)], log(e)
transformed, as a function of environmental PCA Factor 3. Solid square = 1 data
point; number = total of overlapping points.
173
-------
C < . >PLO.TK
/I
ASC.OV OKEL.BO
POL. RE | OSYN.PE
Figure 3-124. Plot of CCA for 30 major rotifer species and first 4 environmental PCA factors. See
text for explanation of arrows.
174
-------
> EUC.PRI
I
>OHT.«OD >CYC.BIC
I 4
>CYC.SCU|
| >DIA.SPA
Figure 3-125. Plot of CCA for 16 major copepod species and first 4 environmental PCA factors.
See text for explanation of arrows.
175
-------
>DAP.GAL
>EUBOSM
Figure 3-126. Plot of CCA for 12 major cladoceran species, and groupings of species, and first 4
environmental PCA factors. See text for explanation of arrows.
176
-------
I > DAP. GAL
Figure 3-127. Plot of CCA for 11 major groupings of cladocera (excludes Ceriodaphnia) and first
4 environmental PCA factors. See text for explanation of arrows.
177
-------
Figure 3-128. Plot of CCA for 10 major groupings of cladocera (excludes both Ceriodaphnia and
Eubosmina) and first 4 environmental PCA factors. See text for explanation of
arrows.
178
-------
TABLES FOR ALL SECTIONS
179
-------
Table 1-1. Number of Lakes Sampled in Phase II of the Eastern Lake Survey Organized by
Geographic Region and Water Chemistry Cluster and Summary Statistics of Maximum
Lake Depth (m) for Each Water Chemistry Cluster
Region
1
2
3
4
5
Adirondacks
Poconos/Catskills
Central New England
Southern New England
Maine
Chemistry Cluster
1
20
6
5
12
4
2
9
5
17
5
18
3
7
8
10
8
12
All
36
19
32
25
34
All 47 54 45 146
Mean of depths 7.65 7.95 8.44
Range of depths 1.1-30.5 2-30 2-27.3
Variance in depths 27.95 28.42 38.21
181
-------
Table 3-1. Summary Statistics for Precision Estimates among Subsamples*
Precision Statistic
Minimum Abundance Threshold
Mean
Minimum
Maximum
Standard error
> 0
0.569
0.054
0.926
0.025
> 2
0.491
0.075
1.055
0.029
> 8
0.336
0.023
0.747
0.024
N = 52 independent split samples. See text for definition of precision and minimum abundance.
Table 3-2. Nested Analysis of Variance of Total Zooplankton Abundance (Log[e] Transformed)
Variance Source
D.F.
Sum of Squares
Percent
Total
Chemgrp.
Lake
Sample
Subsample
479
2
143
285
49
507.36251
9.1108703
475.82659
22.087124
0.33793343
100
0.662191
92.5886
6.10254
0.64666
182
-------
Table 3-3. Nested Analysis of Variance of Total Copepoda Abundance (Log[e] Transformed)
Variance Source
Total
Chemgrp.
Lake
Sample
Subsample
Table 3-4. Nested
Variance Source
Total
Chemgrp.
Lake
Sample
Subsample
Table 3-5. Nested
Variance Source
Total
Chemgrp.
Lake
Sample
Subsample
D.F.
479
2
143
285
49
Analysis of Variance
D.F.
479
2
143
285
49
Analysis of Variance
D.F.
479
2
143
285
49
Sum of Squares
596.01891
56.656737
499.4831
38.598155
1.2809258
of Nauplii Abundance
Sum of Squares
545.64655
1.8075923
511.38949
31.754623
0.69484469
Percent
100
11.8984
78.3478
7.74428
2.00943
(Log[e] Transformed)
Percent
100
0
91.0192
7.75414
1.22668
of Total Rotifera Abundance (Log[e] Transformed)
Sum of Squares
1247.1051
42.950498
1163.953
38.38451 1
1.8171371
Percent
100
3.10055
92.0922
3.4041
1.40314
183
-------
Table 3-6. Nested Analysis of Variance of Total Cladocera Abundance (Log[e] Transformed)
Variance Source
Total
Chemgrp.
Lake
Sample
Subsample
Table 3-7. Nested
than a
Variance Source
Total
Chemgrp.
Lake
Sample
Subsample
Table 3-8. Nested
than a
Variance Source
Total
Chemgrp.
Lake
Sample
Subsample
D.F.
479
2
143
285
49
Analysis of Variance
Mean Abundance of
D.F.
490
2
144
292
52
Analysis of Variance
Mean Abundance of
D.F.
490
2
144
292
52
Sum of Squares
905.83574
0.443936
869.06462
35.31706
1.0101297
of Total Species Richness
1 (see text)
Sum of Squares
93.893063
17.189175
69.487651
6.5067985
0.70943823
of Total Species Richness
10 (see text)
Sum of Squares
132.17289
7.9422076
112.44387
11.071268
0.71554215
Percent
100
0
93.9831
4.94614
1.07072
Excluding Species Rarer
Percent
100
23.7328
65.9282
3.80425
6.53475
Excluding Species Rarer
Percent
100
6.99987
80.0307
8.00705
4.96236
184
-------
Table 3-9. Nested Analysis of Variance of Total Zooplankton Abundance (Log[e] Transformed)
Analyzed by Water Chemistry Cluster
Variance Source
Total
Lake
Sample
Subsample
Chemgrp.
100
88.41
10.73
0.86
Percent Variance
1 Chemgrp. 2
100
96.11
3.22
0.67
Table 3-10. Nested Analysis of Variance of Abundance for Rotifera
Transformed) Analyzed by Water Chemistry Cluster
Chemgrp.3
100
93.95
5.65
0.40
and Crustacea (Log[e]
Percent Variance in Rotifera Abundance
Variance Source
Total
Lake
Sample
Subsample
Variance Source
Total
Lake
Sample
Subsample
Chemgrp.
100
93.2
5.45
1.35
Chemgrp.
100
86.88
12.45
0.67
1 Chemgrp. 2
100
96.75
1.57
1.68
Percent Variance in Crustacea
1 Chemgrp. 2
100
94.29
5.13
0.58
Chemgrp.3
100
95.9
2.96
1.13
Abundance
Chemgrp.3
100
92.95
6.77
0.28
185
-------
Table 3-11. Nested Analysis of Variance of Total Species Richness Analyzed Separately by
Water Chemistry Cluster and Excluding Species Rarer than a Mean Abundance of 1
(see text)
Variance Source
Total
Lake
Sample
Subsample
Chemgrp. 1
100
85.2
2.8
12.0
Percent Variance
Chemgrp. 2
100
89.2
5.5
5.3
Chemgrp.3
100
83.2
6.6
10.2
Table 3-12. Nested Analysis of Variance of Total Species Richness Analyzed Separately by
Water Chemistry Cluster and Excluding Species Rarer than a Mean Abundance of
10 (see text)
Variance Source
Total
Lake
Sample
Subsample
Chemgrp. 1
100
81.9
4.7
13.4
Percent Variance
Chemgrp. 2
100
89.6
9.2
1.2
Chemgrp.3
100
84.7
11.1
4.2
186
-------
Table 3-13. Summary Statistics for Major Zooplankton Groups among All Phase II Survey
Lakes3
Total Observations: 147
Log(e) Transformed Values
LTOTAL
LROTOT LCLADTOT LCALTOT LCYTOT
Number of cases
Minimum
Maximum
Mean
Variance
Standard deviation
Standard error
Skewness
Kurtosis
147
2.898
9.069
6.209
1.243
1.115
0.092
-0.075
0.299
147
0.000
9.025
5.353
2.619
1.618
0.133
-0.288
0.156
147
0.010
7.744
3.990
2.065
1.437
0.119
-0.41 1
0.089
147
0.000
6.586
3.479
2.731
1.652
0.136
-0.494
-0.410
147
0.000
6.480
2.896
3.176
1.782
0.147
-0.256
-0.891
Untransformed Values
Total Observations: 147
TOTAL
ROTOT
CLADTOT
CALTOT
CYTOT
Number of cases
Minimum
Maximum
Mean
Variance
Standard deviation
Standard error
Skewness
Kurtosis
147
17.140
8676.626
909.420
1542641.317
1242.031
102.441
3.233
12.951
147
0.000
8304.773
634.733
1246026.919
1116.256
92.067
3.674
17.392
147
0.010
2307.567
127.599
52319.834
228.735
18.866
6.379
55.007
147
0.000
723.563
87.357
16471.239
128.340
10.585
2.783
8.761
147
0.000
650.667
59.731
10080.560
100.402
8.281
3.063
11.003
Total = all zooplankton; ROTOT = Rotifera; CLADTOT = Cladocera; CALTOT = Calanoid Copepoda; and CYTOT =
Cyclopoid Copepoda. L indicates transformed values. Note: one duplicate sample, thus actual lake total = 146.
187
-------
Table 3-14. Summary Statistics for Various Zooplankton Groups among All Phase II Survey
Lakes8
Untransformed Abundance
Total Observations: 147
RDCO
NAUP
RDCRUS
RDCH
RDMISC
Number of cases
Minimum
Maximum
Mean
Variance
Standard deviation
Standard error
Skewness
Kurtosis
147
3.357
723.573
151.997
23084.085
151.934
12.531
1.718
2.919
147
4.317
1301.333
21 1 .856
49959.449
223.516
18.435
2.401
7.198
147
10.087
3676.473
491.551
232567.324
482.252
39.776
2.877
13.012
147
0.000
65.377
2.644
46.370
6.810
0.562
6.107
48.987
147
0.000
14.093
0.242
1.518
1.232
0.102
9.907
107.457
Total Observations: 147
Log(e) Transformed Abundance
LNCOT
LNAUP
LNCRUST
LNCHT
LNMISC
Number of cases
Minimum
Maximum
Mean
Variance
Standard deviation
Standard error
Skewness
Kurtosis
147
1.472
6.586
4.532
1.176
1.085
0.089
-0.372
-0.294
147
1.671
7.172
4.889
1.071
1.035
0.085
-0.376
0.241
147
2.406
8.210
5.798
0.937
0.968
0.080
-0.605
0.929
147
0.000
4.195
0.661
0.915
0.957
0.079
1.281
0.690
147
0.000
2.714
0.112
0.113
0.335
0.028
4.448
25.554
Total = all zooplankton; RDCO = total Copepoda; NAUP = nauplii; RDCRUS = total Crustacea; RDCH = total
Chaoborus; and RDMISC = total miscellaneous (see text). L indicates transformed values. Note: one duplicate sample,
thus actual lake total = 146.
188
-------
Table 3-15. Summary Statistics for Untransformed Zooplankton Abundance Determined
Separately for Each Water Chemistry Cluster*
Chemgrp. = 1.000
Number of cases
Minimum
Maximum
Mean
Variance
Standard deviation
Standard error
Skewness
Kurtosis
TOTAL
48
42.143
8676.626
1064.094
2562509.102
1600.784
231.053
3.074
10.047
ROTOT
48
0.000
8304.773
686.018
2074628.390
1440.357
207.898
3.658
15.052
CLADTOT
48
0.010
2307.567
163.834
122060.429
349.371
50.427
5.024
27.918
CALTOT
48
0.000
723.563
166.830
26263.062
162.059
23.391
1.660
2.482
CYTOT
48
0.000
476.460
47.411
11587.759
107.646
15.537
2.970
8.125
Chemgrp. = 2.000
Number of cases
Minimum
Maximum
Mean
Variance
Standard deviation
Standard error
Skewness
Kurtosis
TOTAL
53
17.140
6284.487
980.501
1251307.652
1118.619
153.654
2.615
8.450
ROTOT
53
7.440
5601.790
730.032
1031296.171
1015.528
139.493
2.743
8.954
CLADTOT
53
2.393
465.127
110.893
14629.949
120.954
16.614
1.290
0.672
CALTOT
53
0.000
685.820
65.479
12349.779
111.130
15.265
3.780
17.027
CYTOT
53
0.000
650.667
74.098
13094.529
114.431
15.718
3.057
11.055
Chemgrp. = 3.000
Number of cases
Minimum
Maximum
Mean
Variance
Standard deviation
Standard error
Skewness
Kurtosis
TOTAL
46
26.363
4482.237
666.123
790681.708
889.203
131.106
2.651
7.146
ROTOT
46
15.190
4285.570
471.419
643344.817
802.088
118.261
3.104
10.362
CLADTOT
46
2.637
848.890
109.036
23276.235
152.566
22.495
3.144
11.148
CALTOT
46
0.000
111.110
29.635
1032.186
32.128
4.737
1.107
0.054
CYTOT
46
0.000
310.667
56.034
5052.623
71.082
10.480
2.257
4.783
Total = all zooplankton; ROTOT = Rotifera; CLADTOT = Cladocera; CALTOT = Calanoid Copepoda; CYTOT =
Cyclopoid Copepoda.
189
-------
Table 3-16. Summary Statistics for Log(e) Transformed Zooplankton Abundance Determined
Separately for Each Water Chemistry Cluster*
Number of cases
Minimum
Maximum
Mean
Variance
Standard deviation
Standard error
Skewness
Kurtosis
Number of cases
Minimum
Maximum
Mean
Variance
Standard deviation
Standard error
Skewness
Kurtosis
Number of cases
Minimum
Maximum
Mean
Variance
Standard deviation
Standard error
Skewness
Kurtosis
LTOTAL
48
3.765
9.069
6.348
1.121
1.059
0.153
0.425
0.413
LTOTAL
53
2.898
8.746
6.358
1.248
1.117
0.153
-0.477
0.634
LTOTAL
46
3.309
8.408
5.892
1.270
1.127
0.166
0.002
-0.034
LROTOT
48
0.000
9.025
5.047
3.657
1.912
0.276
-0.276
0.132
LROTOT
53
2.133
8.631
5.762
2.079
1.442
0.198
-0.386
-0.180
LROTOT
46
2.784
8.363
5.200
1.955
1.398
0.206
0.287
-0.744
Chemgrp. = 1.000
LCLADTOT
48
0.010
7.744
3.906
3.024
1.739
0.251
-0.410
-0.077
Chemgrp. = 2.000
LCLADTOT
53
1.222
6.144
4.018
1.792
1.339
0.184
-0.358
-0.681
Chemgrp. = 3.000
LCLADTOT
46
1.291
6.745
4.045
1.459
1.208
0.178
-0.199
-0.152
LCALTOT
48
0.000
6.586
4.527
2.023
1.422
0.205
-1.526
2.618
LCALTOT
53
0.000
6.532
3.223
2.429
1.558
0.214
-0.376
-0.256
LCALTOT
46
0.000
4.719
2.680
2.040
1.428
0.211
-0.460
-0.798
LCYTOT
48
0.000
6.168
2.041
3.837
1.959
0.283
0.508
-0.865
LCYTOT
53
0.000
6.480
3.345
2.558
1.599
0.220
-0.424
-0.440
LCYTOT
46
0.000
5.742
3.271
2.180
1.477
0.218
-0.724
0.075
a Total = all zooplankton; ROTOT = Rotifera; CLADTOT = Cladocera; CALTOT
Cyclopoid Copepoda. L indicates transformed values.
Calanoid Copepoda; CYTOT
190
-------
Table 3-17. Analysis of Variance of Log(e) Transformed Total Zooplankton Abundance
Partitioned among Three Water Chemistry Clusters and Five Geographic Regions
Dependent Variable: LTOTAL
Number: 147
Multiple R: .385
Squared Multiple R: .148
Source
Sum of Squares
Analysis of Variance
D.F. Mean Square
F-ratio
Chemgrp.
Region
Chemgrp.*
Region
6.965
3.718
16.918
2
4
3.482
0.930
2.115
2.973
0.794
1.806
0.055
0.531
0.081
Error
154.604
132
1.171
191
-------
Table 3-18. Multivariate Analysis of Variance of Four Major Zooplankton Taxa as Determined by
Water Chemistry Cluster
Number of lakes: 147
Dependent variable means: LROTOT
5.353
Squared multiple correlations: LROTOT
0.038
Test for effect called: CHEMGRP
Univariate
Variable SS
LROTOT 14.436
Error 367.989
LCLADTOT 0.519
Error 301.000
LCALTOT 85.517
Error 313.173
LCYTOT 52.191
Error 411.466
Multivariate Te
Wilks' Lambda = 0.679
F-statistic = 7.516
Pillai trace = 0.334
F-statistic = 7.125
Hotelling-Lawley trace = 0.452
F-statistic = 7.906
Theta = 0.286 S = 2, M = .5, N = 69.5
LCLADTOT
3.990
LCLADTOT
0.002
F-tests
DF MS
2 7.218
144 2.555
2 0.260
144 2.090
2 42.758
144 2.175
2 26.096
144 2.857
st Statistics
DF = 8, 282
DF = 8, 284
DF = 8, 280
LCALTOT LCYTOT
3.479 2.896
LCALTOT LCYTOT
0.214 0.113
F P
2.824 0.063
0.124 0.883
19.661 0.000
9.133 0.000
PROB = 0.000
PROS = 0.000
PROB = 0.000
PROB = 0.000
192
-------
Table 3-19. Multivariate Analysis of Variance of Four Major Zooplankton Taxa as Determined by
Water Chemistry Cluster3
Number of lakes: 147
Dependent variable means:
Squared multiple correlations:
Test for effect called: CHEMGRP
Variable
PROT
Error
PCLADT
Error
PCALT
Error
PCYT
Error
Wilks' Lambda = 0.681
F-statistic = 7.468
Pillai trace = 0.327
F-statistic = 6.934
Hotelling-Lawley trace = 0.457
F-statistic = 8.001
Theta = 0.301 S = 2, M = .5, N
PROT PCLADT
0.836 0.402
PROT PCLADT
0.082 0.026
Univariate F-tests
SS DF MS
1.356 2 0.678
15.228 144 0.106
0.162 2 0.081
5.997 144 0.042
2.973 2 1 .487
10.975 144 0.076
0.643 2 0.322
5.106 144 0.035
Multivariate Test Statistics
DF = 8, 282
DF = 8, 284
DF = 8, 280
= 69.5
PCALT PCYT
0.379 0.253
PCALT PCYT
0.213 0.112
F P
6.412 0.002
1.948 0.146
19.505 0.000
9.068 0.000
PROB = 0.000
PROB = 0.000
PROB = 0.000
PROB = 0.000
Analysis performed on arc sine square root transformed relative abundance. PROT = Rotifera; PCLADT = Cladocera;
PCALT = Calanoid Copepoda; PCYT = Cyclopoid Copepoda. P indicates proportional abundance.
193
-------
Table 3-20. Principal Components Analysis (PCA) of Four Major Zooplankton Groups in All
Phase II Survey Lakes Using Log(e) Transformed Abundance*
Correlation Matrix
LROTOT LCLADTOT LCALTOT LCYTOT
LROTOT
LCLADTOT
LCALTOT
LCYTOT
Component Loadings
LROTOT
LCLADTOT
LCALTOT
LCYTOT
Percent of Total Variance Explained
1.000
0.324
-0.152
0.232
1
0.621
0.821
-0.261
0.818
1
44.927
1.000
0.034
0.564
2
-0.186
0.365
0.926
0.072
2
25.760
1.000
-0.173
3
0.752
-0.089
0.218
-0.412
3
19.762
1.000
4
0.118
-0.431
0.163
0.395
4
9.551
a ROTOT = Rotifera; CLADTOT = Cladocera; CALTOT = Calanoid Copepoda; CYTOT = Cyclopoid Copepoda. L
indicates transformed values.
194
-------
Table 3-21. Multivariate Analysis of Variance of First Three Principal Component Analysis (PCA)
Factors of the Four Major Zooplankton Groups, Partitioned among the Water
Chemistry Clusters
Number of lakes: 147
Dependent variable means: Factor 1 Factor 2
-0.000 -0.000
Squared multiple correlations: Factor 1 Factor 2
0.080 0.157
Test for effect called: CHEMGRP
Univariate F-tests
Variable SS DF MS
Factorl 11.716 2 5.858
Error 134.284 144 0.933
Factor 2 22.857 2 11.428
Error 123.143 144 0.855
Factors 8.488 2 4.244
Error 137.512 144 0.955
Multivariate Test Statistics
Wilks' Lambda = 0.712
F-statistic = 8.780 DF = 6, 284
Filial trace = 0.295
F-statistic = 8.245 DF = 6, 286
Hotelling-Lawley trace = 0.396
F-statistic = 9.312 DF = 6, 282
Theta = 0.271 S = 2, M = .0, N = 70.0
Factor 3
-0.000
Factor 3
0.058
F P
6.282 0.002
13.364 0.000
4.444 0.013
PROB = 0.000
PROB = 0.000
PROB = 0.000
PROB = 0.000
195
-------
Table 3-22. Principal Components Analysis (PCA) of Four Major Zooplankton Groups in All
Phase II Survey Lakes Using Arc Sine Square Root Transformed Relative
Abundance*
Correlation Matrix
PROT PCLADT PCALT PCYT
PROT
PCLADT
PCALT
PCYT
Component Loadings
PROT
PCLADT
PCALT
PCYT
Percent of Total Variance Explained
1.000
-0.571
-0.694
-0.382
1
-0.957
0.745
0.496
0.559
1
50.695
1.000
-0.041
0.429
2
0.263
0.413
-0.855
0.659
2
35.116
1.000
-0.222
3
0.051
0.522
-0.119
-0.502
3
13.516
1.000
4
0.115
0.052
0.095
0.044
4
0.673
a PROT = Rotifera; PCLADT = Cladocera; PCALT = Calanoid Copepoda; PCYT = Cyclopoid Copepoda. P indicates
proportional abundance.
196
-------
Table 3-23. Multivariate Analysis of Variance of First Three Principal Component Analysis (PCA)
Factors of the Four Major Zooplankton Groups, Partitioned among the Water
Chemistry Clusters*
Test for effect called:
Variable
Factor 1
Error
Factor 2
Error
Factor 3
Error
Wilks' Lambda =
F-statistic =
Pillai trace =
F-statistic =
Hotelling-Lawley trace
F-statistic =
Theta = 0.279 S = :
CHEMGRP
Univariate F-tests
SS DF MS
4.873 2 2.436
141.127 144 0.980
37.648 2 18.824
108.352 144 0.752
1.793 2 0.896
144.207 144 1.001
Multivariate Test Statistics
0.703
9.102 DF = 6, 284
0.304
8.528 DF = 6, 286
0.412
9.675 DF = 6, 282
2, M = .0, N = 70.0
F P
2.486 0.087
25.017 0.000
0.895 0.41 1
PROB = 0.00
PROB = 0.00
PROB = 0.00
PROB = 0.00
PCA used relative abundance data.
197
-------
Table 3-24. Multivariate Analysis of Variance of the Four Major Zooplankton Groups (Log(e)
Transformed), Partitioned among Three Water Chemistry Clusters and Five Geo-
graphic Regions8
Test for effect called: CHEMGRP
Variable
LROTOT
Error
LCUVDTOT
Error
LCALTOT
Error
LCYTOT
Error
Wilks' Lambda =
F-statistic =
Filial trace =
F-statistic =
Hotelling-Lawley trace =
F-statistic =
Theta = 0.243 S = 2,
Univariate F-tests
SS DF MS
12.039 2 6.020
332.096 132 2.516
0.054 2 0.027
271.283 132 2.055
58.298 2 29.149
257.664 132 1.952
31.038 2 15.519
364.216 132 2.759
Multivariate Test Statistics
0.721
5.742 DF = 8, 258
0.291
5.535 DF = 8, 260
0.372
5.947 DF = 8, 256
M = .5, N = 63.5
F P
2.393 0.095
0.013 0.987
14.933 0.000
5.624 0.005
PROB = 0.000
PROB = 0.000
PROB = 0.000
PROB = 0.000
3 ROTOT = Rotifera; CLADTOT = Cladocera; CALTOT = Calanoid Copepoda; CYTOT = Cyclopoid Copepoda. L
indicates transformed values.
(Page 1 of 3)
198
-------
Table 3-24. Multivariate Analysis of Variance of the Four Major Zooplankton Groups (Log(e)
Transformed), Partitioned among Three Water Chemistry Clusters and Five Geo-
graphic Regions (Continued)*
Test for effect called: REGION
Variable
LROTOT
Error
LCLADTOT
Error
LCALTOT
Error
LCYTOT
Error
Wilks1 Lambda =
F-statistic =
Filial trace =
F-statistic =
Hotelling-Lawley trace =
F-statistic =
Theta = 0.146 S = 4,
Univariate F-tests
SS DF MS
8.689 4 2.172
332.096 132 2.516
6.468 4 1.617
271.283 132 2.055
41.889 4 10.472
257.664 132 1.952
13.780 4 3.445
364.216 132 2.759
Multivariate Test Statistics
0.811
1.755 DF = 16, 394
0.197
1.712 DF = 16, 528
0.224
1.785 DF = 16, 510
M = -.5, N = 63.5
F P
0.863 0.488
0.787 0.536
5.365 0.000
1.249 0.294
PROB = 0.035
PROB = 0.041
PROB = 0.030
PROB = 0.016
ROTOT = Rotifera; CLADTOT = Cladocera; CALTOT = Calanoid Copepoda; CYTOT = Cyclopoid Copepoda. L
indicates transformed values.
(Page 2 of 3)
199
-------
Table 3-24. Multivariate Analysis of Variance of the Four Major Zooplankton Groups (Log(e)
Transformed), Partitioned among Three Water Chemistry Clusters and Five Geo-
graphic Regions (Continued)8
Test for effect called: CHEMGRP BY REGION
Univariate F-tests
Variable
LROTOT
Error
LCLADTOT
Error
LCALTOT
Error
LCYTOT
Error
Wilks' Lambda =
F-statistic =
Filial trace =
F-statistic =
Hotelling-Lawley trace =
F-statistic =
Theta = 0.099 S = 4,
SS
25.305
332.096
21.133
271.283
14.120
257.664
27.072
364.216
Multivariate
0.792
0.975
0.225
0.982
0.243
0.967
M = 1.5, N = 63.5
DF MS
8 3.163
132 2.516
8 2.642
132 2.055
8 1.765
132 1.952
8 3.384
132 2.759
Test Statistics
DF = 32, 477
DF = 32, 528
DF = 32, 510
F P
1.257 0.271
1.285 0.256
0.904 0.515
1 .226 0.288
PROB = 0.509
PROB = 0.498
PROB = 0.521
PROB = 0.620
3 ROTOT = Rotifera; CLADTOT = Cladocera; CALTOT = Calanoid Copepoda; CYTOT = Cyclopoid Copepoda. L
indicates transformed values.
(Page 3 of 3)
200
-------
Table 3-25. Multivariate Analysis of Variance of the Four Major Zooplankton Groups (Arc Sine
Square Root Transformed Relative Abundance), Partitioned among Three Water
Chemistry Clusters and Five Geographic Regions8
Test for effect called: CHEMGRP
Univariate F-tests
Variable
PROT
Error
PCLADT
Error
PCALT
Error
PCYT
Error
Wilks1 Lambda =
F-statistic =
Pillai trace =
F-statistic =
Hotelling-Lawley trace =
F-statistic =
Theta = 0.247 S = 2,
SS
0.793
14.015
0.136
5.653
2.069
9.363
0.472
4.720
Multivariate
0.738
5.296
0.267
5.013
0.349
5.576
M = .5, N = 63.5
DF MS
2 0.396
132 0.106
2 0.068
132 0.043
2 1.035
132 0.071
2 0.236
132 0.036
Test Statistics
DF = 8, 258
DF = 8, 260
DF = 8, 256
F P
3.734 0.026
1.588 0.208
14.585 0.000
6.604 0.002
PROB = 0.000
PROB = 0.000
PROB = 0.000
PROB = 0.000
PROT = Rotifera; PCLADT = Cladocera; PCALT = Calanoid Copepoda; PCYT = Cyclopoid Copepoda. P indicates
proportional abundance.
(Page 1 of 3)
201
-------
Table 3-25. Multivariate Analysis of Variance of the Four Major Zooplankton Groups (Arc Sine
Square Root Transformed Relative Abundance), Partitioned among Three Water
Chemistry Clusters and Five Geographic Regions (Continued)8
Test for effect called: REGION
Univariate F-tests
Variable
PROT
Error
PCLADT
Error
PCALT
Error
PCYT
Error
Wilks' Lambda =
F-statistic =
Pillai trace =
F-statistic =
Hotelling-Lawley trace =
F-statistic =
Theta = 0.172 S = 4,
SS
0.540
14.015
0.069
5.653
0.682
9.363
0.084
4.720
Multivariate
0.763
2.289
0.252
2.222
0.292
2.329
M = -.5, N = 63.5
DF MS
4 0.135
132 0.106
4 0.017
132 0.043
4 0.171
132 0.071
4 0.021
132 0.036
Test Statistics
DF = 16, 394
DF = 16, 528
DF = 16, 510
F P
1.271 0.285
0.403 0.807
2.404 0.053
0.590 0.670
PROB = 0.003
PROB = 0.004
PROB = 0.003
PROB = 0.003
PROT = Rotifera; PCLADT = Cladocera; PCALT = Calanoid Copepoda; PCYT = Cyclopoid Copepoda. P indicates
proportional abundance.
(Page 2 of 3)
202
-------
Table 3-25. Multivariate Analysis of Variance of the Four Major Zooplankton Groups (Arc Sine
Square Root Transformed Relative Abundance), Partitioned among Three Water
Chemistry Clusters and Five Geographic Regions (Continued)8
Test for effect called: CHEMGRP BY REGION
Univariate F-tests
Variable
PROT
Error
PCLADT
Error
PCALT
Error
PCYT
Error
Wilks1 Lambda =
F-statistic =
Pillai trace =
F-statistic =
Hotelling-Lawley trace =
F-statistic =
Theta = 0.117 S = 4,
SS
0.617
14.015
0.233
5.653
0.991
9.363
0.271
4.720
Multivariate
0.746
1.235
0.280
1.242
0.308
1.227
M = 1.5, N = 63.5
DF
8
132
8
132
8
132
8
132
Test Statistics
DF = 32,
DF = 32,
DF = 32,
MS
0.077
0.106
0.029
0.043
0.124
0.071
0.034
0.036
477
528
510
F P
0.726 0.668
0.679 0.709
1.747 0.093
0.948 0.480
PROB = 0.179
PROB = 0.172
PROB = 0.187
PROB = 0.430
PROT = Rotifera; PCLADT = Cladocera; PCALT = Calanoid Copepoda; PCYT = Cyclopoid Copepoda. P indicates
proportional abundance.
(Page 3 of 3)
203
-------
Table 3-26. Mean Abundance (Log(e) Transformed) of Calanoid Copepods as a Function of
Water Chemistry Cluster
Region
Water Chemistry Cluster
1,3, 4, and 5
3.458
(0.974)
4.709
0.167)
1.61
(0.991)
3.391
(0.202)
1.41
(0.401)
2.948
(0.219)
Region 2 compared with mean of all other regions; value in Q = standard error.
Table 3-27. Summary Statistics for Zooplankton Species Richness of All Phase II Survey Lakes8
Total Lakes: 147
TOTAL
ROT
CLAD
COP
CAL
Number of cases
Minimum
Maximum
Mean
Variance
Standard error
147
5.000
30.000
17.374
29.112
0.445
147
0.000
15.000
7.789
11.798
0.283
147
1.000
11.000
4.456
3.907
0.163
147
1.000
6.000
3.374
1.359
0.096
147
0.000
3.000
1.435
0.508
0.059
CYC
CRUS
Number of cases
Minimum
Maximum
Mean
Variance
Standard error
147
0.000
4.000
1.939
0.811
0.074
147
3.000
17.000
8.830
7.498
0.226
TOTAL = all species; ROT = rotifera; CLAD = cladocera; COP = copepoda; CAL = calanoida; CYC = cyclopoida; CRUS
= all Crustacea.
204
-------
Table 3-28. Summary Statistics for Zooplankton Species Richness Calculated Separately for
Each Water Chemistry Cluster*
Number of cases
Mean
Variance
Number of cases
Mean
Variance
TOTAL
48
12.146
14.595
CYC
48
1.396
0.840
CHEMGRP = 1.000
ROT CLAD
48
4.438
5.783
CRUS
48
7.000
4.723
48
3.250
2.149
COP
48
2.750
1.043
CAL
48
1.354
0.446
CHEMGRP = 2.000
TOTAL ROT CLAD
COP
CAL
Number of cases
Mean
Variance
53
19.868
19.078
CYC
53
9.585
8.055
CRUS
53
4.943
3.362
53
3.566
1.289
53
1.396
0.475
Number of cases
Mean
Variance
53
2.170
0.682
53
9.509
5.909
Number of cases
Mean
Variance
Number of cases
Mean
Variance
CHEMGRP = 3.000
TOTAL ROT CLAD
46
19.957
13.865
CYC
46
2.239
0.497
46
9.217
5.063
CRUS
46
9.957
7.154
46
5.152
4.221
COP
46
3.804
1.183
CAL
46
1.565
0.607
TOTAL = all species; ROT = rotifera; CLAD = cladocera; COP = copepoda; CAL = calanoida; CYC = cyclopoida; CRUS
= all Crustacea.
205
-------
Table 3-29. Principal Components Analysis (PCA) of Spepies Richness for Four Major
Zooplankton Groups among All Phase II Survey Lakes*
Correlation Matrix
ROT
CLAD
CAL
CYC
Component Loadings
ROT
CLAD
CAL
CYC
Percent of Total Variance Explained
ROT
1.000
0.412
-0.083
0.425
1
0.752
0.809
0.150
0.797
1
46.914
CLAD
1.000
0.208
0.462
2
-0.333
0.227
0.951
-0.095
2
26.890
CAL
1.000
0.031
3
0.504
0.043
0.112
-0.541
3
14.010
CYC
1.000
4
0.264
-0.541
0.247
0.254
4
12.186
a ROT = rotifera; CLAD = cladocerans; CAL = calanoid copepods; CYC = cyclopoid copepods.
206
-------
Table 3-30. Multivariate Analysis of Variance of Principal Components Analysis (PCA) Factors
of Species Richness for the Four Major Zooplankton Groups, Partitioned among
Three Water Chemistry Clusters and Five Geographic Regions
Test for effect called: CHEMGRP
Variable
Factor 1
Error
Factor 2
Error
Factor 3
Error
Factor 4
Error
Wilks' Lambda =
F-statistic =
Filial trace =
F-statistic =
Hotelling-Lawley trace =
F-statistic =
Theta = 0.426 S = 2,
Univariate F-tests
SS DF MS
36.166 2 18.083
75.848 132 0.575
2.660 2 1 .330
102.634 132 0.778
4.494 2 2.247
125.416 132 0.950
4.133 2 2.066
124.935 132 0.946
Multivariate Test Statistics
0.556
11.007 DF = 8, 258
0.458
9.640 DF = 8, 260
0.775
12.400 DF = 8, 256
M = .5, N = 63.5
F P
31.470 0.000
1.710 0.185
2.365 0.098
2.183 0.117
PROB = 0.000
PROB = 0.000
PROB = 0.000
PROB = 0.000
(Page 1 of 3)
207
-------
Table 3-30. Multivariate Analysis of Variance of Principal Components Analysis (PCA) Factors
of Species Richness for the Four Major Zooplankton Groups, Partitioned among
Three Water Chemistry Clusters and Five Geographic Regions (Continued)
Test for effect called: REGION
Univariate F-tests
Variable
Factor 1
Error
Factor 2
Error
Factor 3
Error
Factor 4
Error
Wilks' Lambda =
F-statistic =
Pillai trace =
F-statistic =
Hotelling-Lawley trace =
F-statistic =
Theta = 0.208 S = 4,
SS
5.807
75.848
26.450
102.634
2.869
125.416
2.388
124.935
Multivariate
0,695
3.116
0,332
2.984
0.400
3.187
M = -.5, N = 63.5
DF MS
4 1.452
132 0.575
4 6.613
132 0.778
4 0.717
132 0.950
4 0.597
132 0.946
Test Statistics
DF = 16, 394
DF = 16, 528
DF = 16, 510
F P
2.526 0.044
8.505 0.000
0.755 0.556
0.631 0.641
PROB = 0.000
PROB = 0.000
PROB = 0.000
PROB = 0.000
(Page 2 of 3)
208
-------
Table 3-30. Multivariate Analysis of Variance of Principal Components Analysis (PCA) Factors
of Species Richness for the Four Major Zooplankton Groups, Partitioned among
Three Water Chemistry Clusters and Five Geographic Regions (Continued)
Test for effect called: CHEMGRP BY REGION
Univariate F-tests
Variable
Factor 1
Error
Factor 2
Error
Factor 3
Error
Factor 4
Error
Wilks' Lambda =
F-statistic =
Filial trace =
F-statistic =
Hotelling-Lawley trace =
F-statistic =
Theta = 0.161 S = 4,
SS
2.403
75.848
15.543
102.634
8.085
125.416
13.884
124.935
Multivariate
0.693
1.562
0.344
1.555
0.392
1.563
M = 1.5, N = 63.5
DF
8
132
8
132
8
132
8
132
Test Statistics
DF = 32,
DF = 32,
DF = 32,
MS
0.300
0.575
1.943
0.778
1.011
0.950
1.735
0.946
477
528
510
F P
0.523 0.838
2.499 0.015
1.064 0.392
1.834 0.076
PROB = 0.028
PROB = 0.028
PROB = 0.027
PROB = 0.089
(Page 3 of 3)
209
-------
Table 3-31. Summary Statistics of Species Richness Calculated Separately for Each Geographic
Region*
Total Lakes: 36
Number of cases
Mean
Variance
Standard error
Total Lakes: 20
Number of cases
Mean
Variance
Standard error
Total Lakes: 32
Number of cases
Mean
Variance
Standard error
Total Lakes: 25
Number of cases
Mean
Variance
Standard error
Total Lakes: 34
Number of cases
Mean
Variance
Standard error
ROT
36
6.389
10.702
0.545
ROT
20
8.200
7.642
0.618
ROT
32
9.594
9.797
0.553
ROT
25
5.560
15.340
0.783
ROT
34
8.971
5.484
0.402
Region = 1.000
CLAD
36
4.194
4.161
0.340
Region = 2.000
CLAD
20
3.650
1.608
0.284
Region = 3.000
CLAD
32
5.156
3.814
0.345
Region = 4.000
CLAD
25
3.640
2.573
0.321
Region = 5.000
CLAD
34
5.147
4.553
0.366
CAL
36
1.472
0.313
0.093
CAL
20
0.750
0.303
0.123
CAL
32
1.594
0.572
0.134
CAL
25
1.680
0.643
0.160
CAL
34
1.471
0.378
0.105
CYC
36
1.778
0.921
0.160
CYC
20
1.900
0.726
0.191
CYC
32
2.313
0.802
0.158
CYC
25
1.680
1.143
0.214
CYC
34
1.971
0.393
0.108
TOTAL
36
15.639
32.237
0.946
TOTAL
20
16.200
18.063
0.950
TOTAL
32
20.656
24.749
0.879
TOTAL
25
14.160
26.307
1.026
TOTAL
34
19.176
16.089
0.688
Region 1 = Adirondacks; 2 = Pocono Mountains and New Jersey; 3 = Central New England; 4 = Southern New England; 5
= Maine. ROT = rotifers; CLAD = cladocerans; CAL = calanoid copepods; CYC = cyclopoid copepods.
210
-------
Table 3-32. Species Richness and Diversity Indices for Zooplankton in Three ANC Clusters for
146 Lakes from ELS-II*
Cluster Rotifers
Species Richness
1 4.44 (2.38)
2 9.54 (2.81)
3 9.27 (2.23)
All 7.79 (3.42)
Species Richness Mode
1 2(4)
2 10 (10)
3 10 (9)
All 10 (8)
Shannon-Wiener Diversity
1 0.61 (0.40)
2 1.37 (0.42)
3 1.35 (0.40)
All 1.11 (0.54)
Simpson's Diversity
1 0.65 (0.23)
2 0.36 (0.17)
3 0.37 (0.17)
All 0.46 (0.23)
Ln(Simpson's) Diversity
1 0.47 (0.36)
2 1.13 (0.43)
3 1.10(0.43)
All 0.91 (0.51)
Cladocera
3.25 (1.45)
4.96(1.81)
5.13 (2.05)
4.46 (1.97)
(Median)
2(3)
5(5)
4(4)
3=4 (4)
0.54 (0.40)
0.90 (0.39)
0.95 (0.40)
0.80 (0.44)
0.69 (0.23)
0.50 (0.20)
0.50 (0.20)
0.56 (0.22)
0.44 (0.36)
0.78 (0.37)
0.78 (0.38)
0.67 (0.40)
Calanoid
Copepods
1.35 (0.66)
1.41 (0.68)
1.56 (0.78)
1.44 (0.71)
1 0)
1 0)
1 0)
1 0)
0.06 (0.15)
0.09 (0.17)
0.18 (0.25)
0.11 (0.20)
0.92 (0.21)
0.89 (0.24)
0.84 (0.24)
0.89 (0.24)
0.52 (2.29)
0.71 (2.67)
0.66 (2.35)
0.63 (2.43)
Cyclopoid
Copepods
3.10 (1.21)
3.96 (1.17)
4.00 (1.07)
3.69 (1.23)
3(3)
4=5 (4)
5(4)
5(4)
0.40 (0.31)
0.61 (0.27)
0.65 (0.27)
0.56 (0.30)
0.77 (0.18)
0.66 (0.16)
0.64 (0.16)
0.69 (0.18)
0.29 (0.25)
0.45 (0.26)
0.49 (0.27)
0.41 (0.28)
All Species
12.15 (3.78)
19.87 (4.29)
19.96 (3.72)
17.37 (5.38)
10 (12)
23 (20)
19 (19)
18 = 19 (18)
1.37(0.45)
1.87 (0.14)
1.91 (0.11)
1.73 (0.47)
0.37(0.17)
0.25 (0.14)
0.24 (0.11)
0.29 (0.15)
1.10 (0.46)
1.52 (0.44)
1.52 (0.43)
1.38 (0.48)
Unless otherwise noted, the value in each entry is the mean value over lakes in the cluster, with 1 standard deviation given in
parentheses.
211
-------
Table 3-33. Means for Shannon-Wiener Diversity for the Three Chemistry Clusters and the Five
Geographic Regions, Calculated Separately for Each of the Four Major Zooplankton
Groups*
Rotifers
Cladocerans
Cyclopoids
Calanoids
Rotifers
Cladocerans
Cyclopoids
Calanoids
1
0.43056
0.28975
0.39392
0.26032
1
0.580
0.353
0.427
0.236
Chemistry Cluster
2
0.92280
0.32233
0.49824
0.15035
Region
234
0.728 0.881 0.547
0.352 0.338 0.284
0.557 0.513 0.479
0.123 0.167 0.219
3
0.82198
0.37615
0.55425
0.14967
5
0.889
0.328
0.471
0.162
Region 1 = Adirondacks; 2 = Poconos/Catskills; 3 = Central New England; 4 = Southern New England; 5 = Maine.
212
-------
Table 3-34. Principal Components Analysis (PCA) of Diversity of Major Groups of Zooplanktorf
Correlation Matrix
CALDIV
CYDIV
CLADIV
ROTDIV
Component Loadings
CALDIV
CYDIV
CLADIV
ROTDIV
Percent of Total Abundance
CALDIV
1.000
-0.042
0.034
-0.496
1
0.574
0.584
0.695
-0.61 1
Explained
1
38.165
CYDIV
1.000
0.507
0.034
2
-0.644
0.647
0.526
0.613
2
37.148
CLADIV
1.000
-0.082
3
0.457
0.278
-0.255
0.405
3
12.897
ROTDIV
1.000
4
0.218
-0.403
0.418
0.295
4
11.790
a CALDIV = calanoid oopepods; CYDIV = cyclopoid copepods; CLADIV = cladocerans; ROTDIV = rotifers.
213
-------
Table 3-35. Multivariate Analysis of Variance of First Two Factors from Principal Components
Analysis (PCA) of Species Diversity of Major Taxa
Test for effect called: CHEMGRP
Variable
Factor 1
Error
Factor 2
Error
Wilks' Lambda =
F-statistic =
Filial trace =
F-statistic =
Hotelling-Lawley trace =
F-statistic =
Theta = 0.341 S = 2,
Univariate F-tests
SS DF MS
7.402 2 3.701
127.474 131 0.973
31.591 2 15.796
74.794 131 0.571
Multivariate Test Statistics
0.642
16.130 DF = 4, 260
0.367
14.728 DF = 4, 262
0.544
17.538 DF = 4, 258
M = -.5, N = 64.0
F P
3.803 0.025
27.666 0.000
PROB = 0.000
PROB = 0.000
PROB = 0.000
PROB = 0.000
(Page 1 of 3)
214
-------
Table 3-35. Multivariate Analysis of Variance of First Two Factors from Principal Components
Analysis (PCA) of Species Diversity of Major Taxa (Continued)
Test for effect called: REGION
Variable
Univariate F-tests
SS DF
MS
Factor 1
Error
Factor 2
Error
Wilks' Lambda =
F-statistic =
Pillai trace =
F-statistic =
Hotelling-Lawley trace =
F-statistic =
Theta = 0.113 S = 2,
3.632
127.474
6.184
74.794
Multivariate
0.882
2.101
0.118
2.060
0.133
2.141
M = .5, N = 64.0
4 0.908 0.933 0.447
131 0.973
4 1.546 2.708 0.033
131 0.571
Test Statistics
DF = 8, 260 PROB = 0.036
DF = 8, 262 PROB = 0.040
DF = 8, 258 PROB = 0.033
PROB = 0.015
(Page 2 of 3)
215
-------
Table 3-35. Multivariate Analysis of Variance of First Two Factors from Principal Components
Analysis (PCA) of Species Diversity of Major Taxa (Continued)
Test for effect called: CHEMGRP BY REGION
Variable
Univariate F-tests
SS DF
MS
Factor 1
Error
Factor 2
Error
Wilks1 Lambda =
F-statistic =
Pillai trace =
F-statistic =
Hotelling-Lawley trace =
F-statistic =
Theta = 0.151 S = 2,
5.861
127.474
11.842
74.794
Multivariate
0.815
1.747
0.191
1.726
0.219
1.768
M = 2.5, N = 64.0
8
131
8
131
Test Statistics
DF = 16,
DF = 16,
DF = 16,
0.733 0.753 0.645
0.973
1.480 2.593 0.012
0.571
260 PROB = 0.039
262 PROB = 0.042
258 PROB = 0.036
PROB = 0.023
(Page 3 of 3)
216
-------
Table 3-36. Summary Statistics of Zooplankton Abundance for All Phase II Survey Lakes
Determined Separately for Each 0.2-mm Size Class8
Total lakes: 147
Number of cases
Mean
Variance
Number of cases
Mean
Variance
Number of cases
Mean
Variance
SIZ(1)
147
512.454
1060036.852
SIZ(6)
147
11.048
408.769
SIZ(11)
147
0.208
1.179
SIZ(2)
147
360.521
209758.667
SIZ(7)
147
8.196
260.452
SIZ(12)
147
0.144
1.183
SIZ(3)
147
71.878
13998.461
SIZ(8)
147
2.970
48.308
SIZ(13)
147
0.042
0.260
SIZ(4)
147
53.647
4737.793
SIZ(9)
147
1.189
48.832
SIZ(5)
147
64.438
6116.811
SIZ(IO)
147
0.208
2.644
Values are untransformed abundances and exclude organisms > 2.6 mm.
217
-------
Table 3-37. Summary Statistics of Zooplankton Abundance for All Phase II Survey Lakes
Determined Separately for Each 0.2-mm Size Class8
Total lakes: 147
Number of cases
Mean
Variance
Number of cases
Mean
Variance
Number of cases
Mean
Variance
Number of cases
Mean
Variance
Number of cases
Mean
Variance
SIZ(1)
147
5.131
2.689
SIZ(6)
147
1.516
1.918
SIZ(11)
147
0.077
0.121
SIZ(16)
147
0.013
0.026
SIZ(21)
147
0.029
0.076
SIZ(2)
147
5.209
1.601
SIZfT)
147
1.293
1.675
SIZ(12)
147
0.042
0.083
SIZ(17)
147
0.236
0.363
SIZ(22)
147
0.038
0.065
SIZ(3)
147
3.295
2.277
SIZ(8)
147
0.627
1.053
SIZ(13)
147
0.013
0.026
SIZ(18)
147
0.016
0.038
SIZ(23)
147
0.029
0.062
SIZ(4)
147
3.275
1.795
SIZ(9)
147
0.275
0.444
SIZ(14)
147
0.190
0.270
SIZ(19)
147
0.068
0.179
SIZ(5)
147
3.399
2.159
SIZ(10)
147
0.065
0.099
SIZ(15)
147
0.000
0.000
SIZ(20)
147
0.002
0.000
Values are log(e) transformed abundances and include all organisms.
218
-------
Table 3-38. Summary Statistics of Zooplankton Abundance for All Phase II Survey Lakes
Determined Separately for Each 0.2-mm Size Class8
Total lakes: 147
Number of cases
Mean
Variance
Number of cases
Mean
Variance
Number of cases
Mean
Variance
PSIZ1
147
0.374
0.058
PSIZ6
147
0.018
0.001
PSIZ11
147
0.001
0.000
PSIZ2
147
0.348
0.046
PSIZ7
147
0.014
0.001
PSIZ12
147
0.000
0.000
PSIZ3
147
0.070
0.007
PSIZ8
147
0.007
0.000
PSIZ13
147
0.000
0.000
PSIZ4
147
0.070
0.007
PSIZ9
147
0.002
0.000
PSIZ5
147
0.095
0.014
PSIZ10
147
0.000
0.000
Values are expressed as relative abundance in each size class and exclude organisms > 2.6 mm.
219
-------
Table 3-39. Summary Statistics of Zooplankton Size Classes Determined Separately for Each
Water Chemistry Cluster*
Number of cases
Mean
Variance
Number of cases
Mean
Variance
Number of cases
Mean
Variance
Number of cases
Mean
Variance
Number of cases
Mean
Variance
Number of cases
Mean
Variance
SI2(1)
48
632.189
1945832.647
SIZ(6)
48
6.340
203.896
SIZ(1)
53
530.274
802138.457
SIZ(6)
53
10.596
380.607
SIZ(1)
46
366.982
442702.233
SIZ(6)
46
16.480
619.413
CHEMGRP =
SIZ(2)
48
296.018
130669.640
SIZ(7)
48
7.147
312.053
CHEMGRP =
SIZ(2)
53
465.385
293778.217
SIZC7)
53
7.530
216.127
CHEMGRP =
SIZ(2)
46
307.007
184279.382
SIZ(7)
46
10.057
264.114
1.000
SIZ(3)
48
91.330
24922.531
SIZ(8)
48
3.115
69.369
2.000
SIZ(3)
53
67.028
6846.934
SIZ(8)
53
0.560
3.008
3.000
SIZ(3)
46
57.168
10822.536
SIZ(8)
46
5.596
66.894
SIZ(4)
48
81 .865
9272.229
SIZ(9)
48
0.224
0.839
SIZ(4)
53
46.377
2665.460
SIZ(9)
53
1.876
129.474
SIZ(4)
46
32.579
1241.783
SIZ(9)
46
1.406
6.344
SIZ(5)
48
98.106
6131.714
SIZ(5)
53
57.917
8751.113
SIZ(5)
46
36.818
1290.007
Values are untransformed abundances and exclude organisms > 1.8 mm.
220
-------
Table 3-40. Summary Statistics of Zooplankton Size Classes Determined Separately for Each
Water Chemistry Cluster3
Number of cases
Mean
Variance
Number of cases
Mean
Variance
48
4.864
4.551
SIZ(6)
48
1.015
1.577
CHEMGRP = 1.000
SIZ(2) SIZ(3)
48
5.046
1.449
SIZ(7)
48
1.030
1.636
48
3.333
3.062
SIZ(8)
48
0.496
1.119
SIZ(4)
48
3.788
1.423
SIZ(9)
48
0.097
0.131
SIZ(5)
48
4.107
1.545
Number of cases
Mean
Variance
Number of cases
Mean
Variance
53
5.470
1.710
SIZ(6)
53
1.521
1.758
CHEMGRP = 2.000
SIZ(2) SIZ(3) SIZ(4)
53 53 53
5.463 3.436 3.073
1.783 1.967 2.267
SIZ(7) SIZ(8) SIZ(9)
53 53 53
1.249 0.220 0.227
1.561 0.284 0.524
SIZ(5)
53
3.112
2.333
CHEMGRP = 3.000
SIZ(2)
SIZ(3)
Values are log(e) transformed abundances and exclude organisms > 1.8 mm.
SIZ(4)
SIZ(5)
Number of cases
Mean
Variance
Number of cases
Mean
Variance
46
5.018
1.772
SIZ(6)
46
2.032
2.006
46
5.086
1.501
SIZ(7)
46
1.617
1.740
46
3.091
1.850
SIZ(8)
46
1.231
1.332
46
2.973
1.297
SIZ(9)
46
0.515
0.602
46
2.992
1.893
221
-------
Table 3-41. Summary Statistics of Zooplankton Size Classes Determined Separately for Each
Water Chemistry Cluster*
Number of cases
Mean
Variance
Number of cases
Mean
Variance
Number of cases
Mean
Variance
Number of cases
Mean
Variance
Number of cases
Mean
Variance
Number of cases
Mean
Variance
PSIZ1
48
0.360
0.264
PSIZ6
48
0.011
0.022
PSIZ1
53
0.390
0.230
PSIZ6
53
0.017
0.029
PSIZ1
46
0.371
0.230
PSIZ6
46
0.027
0.032
CHEMGRP = 1.000
PSIZ2
48
0.292
0.227
PSIZ7
48
0.009
0.019
CHEMGRP = 2.000
PSIZ2
53
0.381
0.210
PSIZ7
53
0.011
0.025
CHEMGRP = 3.000
PSIZ2
46
0.369
0.197
PSIZ7
46
0.022
0.040
PSIZ3
48
0.074
0.091
PSIZ8
48
0.006
0.020
PSIZ3
53
0.066
0.070
PSIZ8
53
0.001
0.004
PSIZ3
46
0.071
0.083
PSIZ8
46
0.013
0.020
PSIZ4
48
0.099
0.120
PSIZ9
48
0.001
0.003
PSIZ4
53
0.061
0.072
PSIZ9
53
0.002
0.013
PSIZ4
46
0.052
0.036
PSIZ9
46
0.004
0.007
PSIZ5
48
0.147
0.148
PSIZ5
53
0.070
0.104
PSIZ5
46
0.069
0.070
Values are expressed as relative abundances of each size class and exclude organisms > 1.8 mm.
222
-------
Table 3-42. Multivariate Analysis of Variance of Zooplankton Size Classes Partitioned among
Water Chemistry Clusters'
Number of lakes: 147
Dependent variable means: SIZ(1) SIZ(2) SIZ(3)
5.131 5.209 3.295
SIZ(6) SIZ(7) SIZ(8)
1.516 1.293 0.627
SIZ(4)
3.275
SIZ(9)
0.275
SIZ(5)
3.399
Test for effect called: CHEMGRP
Univariate F-tests
Variable
SIZ(1)
Error
SIZ(2)
Error
SIZ(3)
Error
SIZ(4)
Error
SIZ(5)
Error
SIZ(6)
Error
SIZ(7)
Error
SIZ(8)
Error
SIZ(9)
Error
Wilks' Lambda =
F-statistic =
Pillai trace =
F-statistic =
Hotelling-Lawley trace =
F-statistic =
Theta = 0.356 S = 2,
SS
10.103
382.515
5.392
228.379
3.033
329.461
19.010
243.111
36.041
279.109
24.274
255.771
8.241
236.333
26.395
127.320
4.279
60.505
Multivariate
0.542
5.423
0.515
5.279
0.742
5.567
M = 3.0, N = 67.0
DF
2
144
2
144
2
144
2
144
2
144
2
144
2
144
2
144
2
144
Test Statistics
DF = 18,
DF = 18,
DF = 18,
MS
5.052
2.656
2.696
1.586
1.517
2.288
9.505
1.688
18.021
1.938
12.137
1.776
4.121
1.641
13.198
0.884
2.139
0.420
272
274
270
F
1.902
1.700
0.663
5.630
9.297
6.833
2.511
14.927
5.091
PROB = 0.000
PROB = 0.000
PROB = 0.000
PROB = 0.000
P
0.153
0.186
0.517
0.004
0.000
0.001
0.085
0.000
0.007
Analysis used log(e) transformed abundances and excluded sizes > 1.8 mm.
223
-------
Table 3-43. Principal Components Analysis (PCA) of Zooplankton Size Classes Using Log(e)
Transformed Abundance"
Correlation Matrix
SIZ(1)
SIZ(2)
SIZ(3)
SIZ(4)
SIZ(5)
SIZ(6)
SIZ(7)
SIZ(8)
SIZ(9)
SIZ(6)
SIZ(7)
SIZ(8)
SIZ(9)
SIZ(1)
1.000
0.310
0.354
0.050
-0.024
0.024
0.014
-0.140
-0.079
SIZ(6)
1.000
0.396
0.294
0.125
SIZ(2)
1.000
0.487
0.384
0.198
0.133
0.110
-0.107
0.039
SIZ(7)
1.000
0.314
0.270
SIZ(3)
1.000
0.393
0.221
0.123
0.035
-0.025
-0.145
SIZ(8)
1.000
0.047
SIZ(4)
1.000
0.588
0.216
0.201
0.089
0.024
1.000
SIZ(5)
1.000
0.179
0.332
0.057
0.073
Component Loadings
SIZ(1)
SIZ(2)
SIZ(3)
SIZ(4)
SIZ(5)
SIZ(6)
SIZ(7)
SIZ(8)
SIZ(9)
0.282
0.626
0.618
0.765
0.671
0.499
0.523
0.205
0.131
-0.536
-0.430
-0.508
-0.054
0.142
0.423
0.557
0.587
0.436
0.560
0.152
0.115
-0.431
-0.540
0.369
0.225
0.225
0.148
0.076
0.197
-0.179
-0.050
0.058
-0.183
0.115
-0.519
0.787
-0.436
0.351
0.193
0.086
-0.320
0.079
-0.343
0.207
0.232
Percent of Total Variance Explained
12345
27.470 19.755 12.072 11.313 7.570
a Excludes organisms larger than 1.8 mm; includes all Phase II survey lakes.
224
-------
Table 3-44. Summary Statistics for First Four Principal Components of Zooplankton Size
Classes, Determined Separately for Each Water Chemistry Cluster*
Number of cases
Mean
Variance
Standard deviation
Standard error
Number of cases
Mean
Variance
Standard deviation
Standard error
Number of cases
Mean
Variance
Standard deviation
Standard error
CHEMGRP
Factor 1
48
0.064
0.812
0.901
0.130
CHEMGRP
Factor 1
53
-0.045
1.138
1.067
0.147
CHEMGRP
Factor 1
46
-0.015
1.075
1.037
0.153
= 1.000
Factor 2
48
-0.157
0.817
0.904
0.130
= 2.000
Factor 2
53
-0.307
0.750
0.866
0.119
= 3.000
Factor 2
46
0.517
1.114
1.055
0.156
Factor 3
48
-0.718
1.009
1.005
0.145
Factor 3
53
0.204
0.798
0.893
0.123
Factor 3
46
0.513
0.400
0.632
0.093
Factor 4
48
-0.132
0.661
0.813
0.117
Factor 4
53
0.177
0.941
0.970
0.133
Factor 4
46
-0.066
1.407
1.186
0.175
Principal components analysis (PCA) used log(e) transformed abundances.
225
-------
Table 3-45. Multivariate Analysis of Variance of Zooplankton Size Class Principal Components
(1-4) Partitioned among Three Water Chemistry Clusters and Five Geographic
Regions8
Test for effect called: CHEMGRP
Variable
Factor 1
Error
Factor 2
Error
Factor 3
Error
Factor 4
Error
Wilks' Lambda =
F-statistic =
Filial trace =
F-statistic =
Hotelling-Lawley trace =
F-statistic =
Theta = 0.302 S = 2,
Univariate
SS
0.539
128.615
16.198
118.336
27.973
87.798
4.830
123.570
F-tests
DF MS
2 0.270
132 0.974
2 8.099
132 0.896
2 13.986
132 0.665
2 2.415
132 0.936
F P
0.277 0.759
9.034 0.000
21.028 0.000
2.580 0.080
Multivariate Test Statistics
0.628
8.449
0.403
8.190
0.544
8.706
M = .5, N = 63.5
DF = 8, 258
DF = 8, 260
DF = 8, 256
PROB = 0.000
PROB = 0.000
PROB = 0.000
PROB = 0.000
Principal components are based on log(e) transformed abundances.
(Page 1 of 3)
226
-------
Table 3-45. Multivariate Analysis of Variance of Zooplankton Size Class Principal Components
(1-4) Partitioned among Three Water Chemistry Clusters and Five Geographic
Regions (Continued)8
Test for effect called: REGION
Variable
Factor 1
Error
Factor 2
Error
Factor 3
Error
Factor 4
Error
Wilks' Lambda =
F-statistic =
Pillai trace =
F-statistic =
Hotelling-Lawley trace =
F-statistic =
Theta = 0.126 S = 4,
Univariate F-tests
SS DF MS
6.541
128.615
5.268
118.336
8.357
87.798
7.843
123.570
Multivariate
0.799
1.885
0.214
1.868
0.236
1.885
M = -.5, N = 63.5
4 1.635
132 0.974
4 1.317
132 0.896
4 2.089
132 0.665
4 1 .961
132 0.936
Test Statistics
DF = 16, 394
DF = 16, 528
DF = 16, 510
F P
1.678 0.159
1.469 0.215
3.141 0.017
2.095 0.085
PROB = 0.020
PROB = 0.021
PROB = 0.020
PROB = 0.048
Principal components are based on log(e) transformed abundances.
(Page 2 of 3)
227
-------
Table 3-45. Multivariate Analysis of Variance of Zooplankton Size Class Principal Components
(1-4) Partitioned among Three Water Chemistry Clusters and Five Geographic
Regions (Continued)8
Test for effect called: CHEMGRP BY REGION
Univariate F-tests
Variable
Factor 1
Error
Factor 2
Error
Factor 3
Error
Factor 4
Error
Wilks' Lambda =
F-statistic =
Pillai trace =
F-statistic =
Hotelling-Lawley trace =
F-statistic =
Theta = 0.173 S = 4,
SS
9.519
128.615
2.387
118.336
15.049
87.798
13.589
123.570
Multivariate
0.694
1.553
0.341
1.540
0.392
1.561
M = 1.5, N = 63.5
DF MS
8 1.190
132 0.974
8 0.298
132 0.896
8 1.881
132 0.665
8 1.699
132 0.936
Test Statistics
DF = 32, 477
DF = 32, 528
DF = 32, 510
F P
1.221 0.292
0.333 0.952
2.828 0.006
1.815 0.080
PROB = 0.029
PROB = 0.031
PROB = 0.027
PROB = 0.052
Principal components are based on log(e) transformed abundances.
(Page 3 of 3)
228
-------
Table 3-46. Principal Components Analysis (PCA) of 38 Major Genera of Zooplankton*
Component Loadings
Keratella
Kellicottia
Trichocerca
Gastropus
Ascomorpha
Asplanchna
Polyarthra
Synchaeta
Ploesoma
Filinia
Hexarthra
Conochilus
Conochiloides
Collotheca
Leptodora
Diaphanosoma
Sida
Holopedium
Bosmina
Eubosmina
Chydorus
Polyphemus
Daphnla (pulex)
Daphnia (galeata)
Daphnia (parvula)
Ceriodaphnia
Epischura
Aglaodiaptomus
Leptodiaptomus
Skistodiaptomus
Onychodiaptomus
Mesocyclops
Tropocyclops
Cyclops
Orthocyclops
Eucyclops
Ectocyclops
Macrocyclops
1
0.551
0.284
0.738
0.221
0.360
0.606
0.641
0.173
0.325
0.006
0.257
-0.103
0.180
0.187
-0.008
0.428
-0.056
0.116
0.515
-0.092
0.058
-0.125
-0.206
-0.182
0.337
0.530
-0.264
-0.176
-0.446
0.185
0.321
0.332
0.563
0.040
-0.013
0.075
0.236
-0.157
2
-0.302
0.551
-0.096
0.226
-0.010
-0.164
0.103
-0.318
-0.177
-0.024
-0.274
0.380
0.081
-0.081
0.466
0.375
0.592
0.221
-0.270
0.175
0.102
-0.147
0.591
0.368
0.244
0.084
0.360
0.198
-0.114
0.198
-0.017
0.445
0.311
0.325
-0.050
0.392
0.063
-0.008
3
-0.142
0.049
0.093
-0.125
-0.157
0.252
0.348
0.253
0.112
0.281
0.266
0.173
0.241
0.317
0.302
-0.408
0.223
-0.077
-0.049
-0.496
0.089
-0.016
0.171
0.290
-0.552
0.057
0.232
0.049
-0.315
-0.152
-0.089
-0.476
0.038
0.176
0.041
0.053
-0.060
-0.056
4
0.057
-0.124
0.034
-0.359
-0.144
0.080
0.012
0.113
0.034
-0.129
0.071
-0.331
0.238
0.149
0.529
0.030
0.370
0.254
0.090
0.253
-0.315
-0.071
-0.314
-0.374
0.145
-0.010
0.046
-0.056
0.326
-0.272
-0.167
-0.098
-0.127
0.079
-0.067
0.637
0.006
0.033
5
0.279
-0.095
0.273
0.013
-0.249
0.171
-0.010
-0.076
0.186
-0.040
0.002
0.419
-0.363
0.028
-0.032
0.341
0.029
0.152
0.183
0.056
0.213
0.360
-0.106
0.303
-0.012
-0.199
0.186
0.076
0.396
-0.434
-0.142
-0.147
0.009
0.252
-0.337
-0.158
0.290
0.125
Percent of Total Variance Explained
1
10.731
2
8.118
3
5.575
4
5.200
5
4.808
PCA used log(e) transformed abundance standardized for mean and variance (correlation matrix).
229
-------
Table 3-47. Principal Components Analysis (PCA) of Log(e) Transformed Abundance of Major
Copepod Genera
Component Loadings
Epischura
Aglaodiaptomus
Leptodiaptomus
Skistodiaptomus
Onychodiaptomus
Mesocyclops
Tropocyclops
Cyclops
Orthocyclops
Eucyclops
Ectocyclops
Macrocyclops
1
-0.054
-0.032
-1.950
0.882
0.050
0.737
1.050
0.037
0.021
-0.000
0.010
-0.020
2
-0.089
0.043
0.821
-0.330
0.015
1.355
0.849
-0.057
-0.026
0.035
0.014
-0.006
3
-0.091
0.034
-0.103
0.421
-0.018
0.871
-1.152
-0.248
0.057
0.012
-0.005
0.001
4
-0.129
0.148
-0.518
-1.057
0.069
0.177
-0.208
0.076
0.034
0.003
0.008
-0.005
5
0.152
0.025
0.032
0.078
-0.037
0.119
-0.111
0.990
-0.057
0.001
-0.005
0.003
Percent of Total Variance Explained
12345
39.645 21.336 14.927 9.603 6.621
230
-------
Table 3-48. Principal Components Analysis (PCA) of Log(e) Transformed Abundance of Major
Rotifer Genera
Component Loadings
Keratella
Kellicottla
Trichocerca
Gastropus
Ascomorpha
Asplanchna
Polyarthra
Synchaeta
Ploesoma
Filinia
Hexarthra
Conochilus
Conochiloides
Collotheca
1
1.102
0.917
1.360
0.093
0.213
0.883
1.578
0.262
0.254
0.017
0.232
-0.038
0.285
0.089
2
-0.587
1.780
-0.384
0.078
0.024
-0.377
0.018
-0.156
-0.262
0.112
-0.263
0.933
0.125
-0.068
3
0.114
-0.584
0.458
0.097
-0.120
0.244
-0.155
-0.061
0.057
-0.003
0.120
1.576
-0.476
0.025
4
-0.948
-0.372
-0.335
0.022
-0.097
0.305
1.043
0.002
-0.241
0.010
0.100
0.053
-0.073
0.037
5
-0.793
0.081
0.879
-0.020
0.022
0.193
-0.446
-0.093
0.058
0.072
0.231
-0.180
0.275
0.049
Percent of Total Variance Explained
12345
28.040 18.211 12.715 9.032 6.820
231
-------
Table 3-49. Principal Components Analysis (PCA) of Log(e) Transformed Abundance of Major
Cladocera Genera
Component Loadings
Leptodora
Diaphanosoma
Sida
Holopedium
Bosmlna
Eubosmina
Chydorus
Polyphemus
Daphnia (pulex)
Daphnia (galeata)
Daphnia (parvula)
Ceriodaphnia
1
-0.005
1.102
-0.026
0.179
1.451
0.212
-0.002
-0.003
-0.819
-0.196
0.678
0.313
2
0.016
0.979
0.050
0.235
-0.927
1.067
0.019
-0.022
0.482
0.144
0.594
0.065
3
0.013
0.708
0.053
0.061
0.341
-0.824
0.142
0.020
1.175
0.233
-0.213
0.134
4
0.001
-0.340
0.013
1.270
0.196
0.191
-0.016
-0.024
0.182
-0.045
-0.137
0.174
5
0.004
0.244
0.030
-0.009
0.269
0.425
-0.016
0.012
0.019
0.084
-0.694
-0.779
Percent of Total Variance Explained
12345
26.503 20.584 16.029 10.728 8.010
232
-------
Table 3-50. Analysis of Variance of Principal Components 1 and 2 for Copepoda Major Genera
Partitioned among Three Water Chemistry Clusters and Five Geographic Regions
Dependent variable: Factor 1
N: 147
Multiple R: .710
Squared Multiple R: .504
Analysis of Variance
Source
Region
Chemgrp
Region*
Chemgrp
Error
Dependent variable:
N: 147
Multiple R: .258
Squared Multiple R:
Source
Region
Chemgrp
Region*
Chemgrp
Sum of Squares
113.060
108.400
119.457
451.113
Factor 2
.066
Sum of Squares
7.761
5.576
11.783
DF
4
2
8
132
Analysis
DF
4
2
8
Mean Square F-Ratio
28.265 8.271
54.200 15.859
14.932 4.369
3.418
of Variance
Mean Square F-Ratio
1 .940 0.560
2.788 0.805
1.473 0.425
P
0.000
0.000
0.000
P
0.692
0.449
0.904
Error 457.284 132 3.464
233
-------
Table 3-51. Analysis of Variance of Principal Components 1 and 2 for Rotifera Major Genera
Partitioned among Three Water Chemistry Clusters and Five Geographic Regions
Dependent variable: Factor 1
N: 147
Multiple R: .601
Squared Multiple R: .362
Analysis of Variance
Source
Region
Chemgrp
Region*
Chemgrp
Error
Dependent variable:
N: 147
Multiple R: .494
Squared Multiple R:
Source
Region
Chemgrp
Region*
Chemgrp
Sum of Squares
180.496
53.366
82.202
699.346
Factor 2
.244
Sum of Squares
52.464
46.219
33.293
DF
2
4
8
132
Analysis
DF
2
4
8
Mean Square F-Ratio
90.248 17.034
13.341 2.518
10.275 1.939
5.298
of Variance
Mean Square F-Ratio
26.232 6.438
11.555 2.836
4.162 1.021
P
0.000
0.044
0.059
P
0.002
0.027
0.423
Error 537.820 132 4.074
234
-------
Table 3-52. Analysis of Variance of Principal Component 1 for Cladocera Major Genera,
Partitioned among Three Water Chemistry Clusters and Five Geographic Regions"
Dependent variable: Factor 1
N: 147
Multiple R: .439
Squared Multiple R: .193
Analysis of Variance
Source
Region
Chemgrp
Region*
Chemgrp
Error
Dependent variable:
N: 122
Multiple R: .416
Squared Multiple R:
Source
Region
Chemgrp
Region*
Chemgrp
Error
Sum of Squares
43.315
32.432
39.669
549.801
Factor 1
.173
Sum of Squares
16.183
57.168
18.350
486.334
DF
4
2
8
132
Analysis
DF
3
2
6
110
Mean Square F-Ratio
10.829 2.600
16.216 3.893
4.959 1.191
4.165
of Variance
Mean Square F-Ratio
5.394 1 .220
28.584 6.465
3.058 0.692
4.421
P
0.039
0.023
0.309
P
0.306
0.002
0.657
First analysis (N = 147) includes all Phase II survey lakes; second analysis excludes lakes from Region 4 (southern New
England).
235
-------
Table 3-53. Analysis of Variance of Principal Component 2 for Cladocera Major Genera,
Partitioned among Three Water Chemistry Clusters and Five Geographic Regions
Dependent variable: Factor 2
N: 147
Multiple R: .460
Squared Multiple R: .211
Analysis of Variance
Source
Region
Chemgrp
Region*
Chemgrp
Sum of Squares
60.733
1.948
27.168
DF
4
2
8
Mean Square
15.183
0.974
3.396
F-Ratio
4.803
0.308
1.074
P
0.001
0.735
0.385
Error 417.255 132 3.161
236
-------
Table 3-54. Discriminant Analysis of Major Genera of Rotifera and Crustacea Zooplankton in All
Phase II Survey Lakes8
Canonical Correlations
1 2
0.804 0.644
Canonical Loadings
(Correlations between conditional dependent variables and dependent canonical factors)
Keratella -0.167 -0.155
Kellicottia 0.301 -0.273
Trichocerca 0.223 -0.343
Gastropus 0.008 -0.155
Ascomorpha 0.119 -0.132
Asplanchna 0.154 -0.180
Polyarthra 0.304 -0.135
Synchaeta 0.066 0.039
Ploesoma 0.060 -0.140
Flllnla 0.061 0.038
Hexarthra 0.030 0.053
Conochilus 0.079 0.151
Conochiloides 0.014 0.053
Collotheca 0.071 -0.127
Leptodora 0.142 0.104
Diaphanosoma 0.014 -0.206
Sida 0.154 -0.010
Holopedium 0.033 -0.144
Bosmina -0.088 -0.036
Eubosmina -0.159 -0.062
Chydorus 0.106 0.200
Polyphemus -0.069 0.093
Daphnia (pulex) 0.240 0.313
Daphnia (galeata) 0.260 0.205
Daphnia (parvula) 0.051 -0.099
Ceriodaphnia 0.245 0.164
Epischura 0.137 0.023
Aglaodiaptomus 0.069 -0.223
Leptodiaptomus 0.183 -0.036
Skistodiaptomus 0.082 -0.143
Onychodiaptomus -0.019 -0.088
Mesocyclops -0.125 -0.103
Tropocyclops 0.088 0.199
Cyclops -0.037 0.102
Orthocyclops 0.249 0.274
Eucyclops 0.290 0.198
Ectocyclops 0.031 -0.144
Macrocyclops 0.281 0.138
a
Arc sine square root transformed relative abundance; canonical correlations and loadings on discriminant axes are shown.
237
-------
Table 3-55. Summary Statistics of Mahalanobis Distances for Each Water Chemistry Cluster3
Total lakes: 48 CHEMGRP = 1.000
Distance 1 Distance 2 Distance 3
Number of cases 48 48 48
Mean 1.077 2.809 3.426
Standard error 0.085 0.109 0.124
Total lakes: 53 CHEMGRP = 2.000
Distance 1 Distance 2 Distance 3
Number of cases 53 53 53
Mean 2.806 1.304 2.512
Standard error 0.149 0.087 0.114
Total lakes: 46 CHEMGRP = 3.000
Distance 1 Distance 2 Distance 3
Number of cases 46 46 46
Mean 3.504 2.481 1.330
Standard error 0.147 0.151 0.107
a Distances based on discriminant analysis of arc sine square root transformed relative abundance of major genera of Rotifera
and Crustacea.
238
-------
Table 3-56. Table of Classification of Lakes on the Basis of Discriminant Analysis of Chemistry
Clusters*
Table of
Frequencies Group
1
1 43
2 9
3 2
Total 54
(Rows)
2
4
41
8
53
by Predict (Columns)
3 Total
1 48
3 53
36 46
40 147
Group = actual chemistry cluster; predict = classification results.
239
-------
Table 3-57. Discriminant Analysis of Geographic Regions Using Major Genera (Logfe] Trans-
formed Abundance) of Rotifers and Crustacea Zooplankton in All Phase II Survey
Lakes8
Canonical Correlations
Canonical Loadings
(Correlations between
Keratella
Kellicottia
Trichocerca
Gastropus
Ascomorpha
Asplanchna
Polyarthra
Synchaeta
Ploesoma
Filinia
Hexarthra
Conochilus
Conochiloides
Collotheca
Leptodora
Diaphanosoma
Sida
Holopedium
Bosmina
Eubosmina
Chydorus
Polyphemus
Daphnia (pulex)
Daphnia (galeata)
Daphnia (parvula)
Ceriodaphnia
Epischura
Aglaodiaptomus
Leptodiaptomus
Skistodiaptomus
Onychodiaptomus
Mesocyclops
Tropocyclops
Cyclops
Orthocyclops
Eucyclops
Ectocyclops
Macrocyclops
1
0.729
conditional dependent variables and
0.039
0.435
0.124
0.115
0.090
0.119
0.138
0.018
0.097
-0.035
-0.172
0.110
-0.027
0.125
0.062
0.160
0.164
0.048
-0.217
0.214
0.066
-0.095
0.029
0.055
0.179
-0.068
-0.116
0.082
-0.110
0.253
-0.143
0.167
0.057
0.025
0.033
0.187
0.107
-0.028
2
0.693
dependent
0.076
0.060
-0.017
0.013
-0.008
-0.033
0.098
0.168
0.026
-0.031
-0.237
-0.106
-0.198
-0.123
0.047
0.000
0.033
0.133
0.207
-0.255
-0.005
0.092
0.048
0.160
-0.048
-0.241
0.012
0.197
0.324
-0.251
-0.163
-0.009
-0.259
0.202
0.024
0.032
-0.010
-0.071
3
0.648
canonical factors)
-0.109
-0.183
-0.142
-0.092
-0.147
-0.198
-0.355
-0.037
-0.001
-0.144
-0.267
0.210
-0.055
0.169
0.070
-0.098
-0.009
0.103
-0.226
0.266
0.021
-0.097
0.063
-0.025
-0.133
-0.142
0.388
-0.045
0.371
0.044
-0.244
-0.142
-0.173
0.100
0.003
-0.048
0.001
0.148
4
0.457
0.148
0.096
0.180
0.020
-0.136
0.168
-0.076
0.055
0.379
-0.180
0.022
-0.287
0.036
0.045
0.262
0.185
0.018
0.068
0.368
0.018
-0.139
-0.069
-0.080
-0.155
0.204
0.185
-0.014
-0.103
-0.048
0.111
0.104
0.337
0.240
0.008
-0.075
0.089
0.215
-0.132
Canonical correlations and loadings on axes are shown.
240
-------
Table 3-58. Table of Classification of Lakes on the Basis of Discriminant Analysis of
Geographic Regions"
Table of
Frequencies Group
1
1
2
3
4
5
30
1
2
2
2
Total 37
2
1
15
1
1
0
18
(Rows) by Predict
3
1
0
23
1
5
30
4
1
2
2
20
2
27
(Columns)
5 Total
3
2
4
1
25
36
20
32
25
34
35 147
Group = actual region; predict = classification results.
241
-------
Table 3-59. Loadings of Original Parameters with Summer and Fall Principal Components9
Summer PCA Fall PCA
Parameter PRIN1 PRIN2 PRIN3 PRIN4 PRIN1 PRIN2 PRIN3 PRIN4
Aluminum
Alkalinity
Anions/Cations
Conductivity
Calcium
Chloride
Color
DIG
DOC
DO
Fluoride
Carbonate
Potassium
Lake size
Magnesium
Manganese
Sodium
Ammonia
Nitrate
pH (laboratory)
pH (field)
Phosphorus
Secchi
Silicate
Sulfate
Temperature
Turbidity
Site depth
-.21
.30
.23
.29
-.35
.26
-.31
.23
.31
.21
.21
.28
-.22
.19
.29
.30
-.30
.37
-.25
.27
.22
-.23 -.19
.30
.21
.33
-.17
.32
.25
.24
.22
.30
.50
.44
.26 .22
.35
.22
-.25 .21
.30
.20
.28
.28
.21
.30
.19
-.20
.25
.16
.23
.29
.29
.21 .26
-.28
.21 .23
-.21
-.24
.31
.40
.28
-.25
.31
.22
.30
.34
.30
-.30
.24
.28
-.26
.22
.24
-.45
.22
.23
-.19
.22
.23
.22
.19
.41
PRIN = principal component; loadings > .20 in absolute value are shown; some loadings < .20 are indicated to facilitate
comparison between seasons. All parameters except pH were log transformed.
242
-------
Table 3-60. Correlations of Principal Components and Chemical Parameters between Summer
and Fall Chemistry Data Sets8
Principal Proportion of Variance Correlation (R) between
Component Summer Fall Summer and Fall Scores
PRIN1 .28 .31 .96
PRIN2 .16 .16 -.71
PRIN3 .14 .13 .77
PRIN4 .062 .069 .53
PRIN5 .049 .056 .81
PRIN6 .037 .045 -.08
Correlation (R) between
Parameter Summer and Fall Values
Aluminum (log) .76
Alkalinity (log) .97
Calcium (log) .99
Anion/Cation ratio (log) .80
Chloride (log) .98
Color (log) .81
DIG (log) .91
DOC (log) .89
Dissolved oxygen (log) .30
Iron (log) .72
Fluoride (log) .83
Carbonate (log) .96
Potassium (log) .94
Magnesium (log) .99
Manganese (log) .54
Sodium (log) .95
Ammonia (log) .15
Nitrate (log) .61
pH (laboratory measured) .88
pH (field measured) .91
Total phosphorus (log) .76
Secchi depth (log of mean of disappear, reappear) .76
Silicate (log) .80
Sulfate (log) .97
Temperature (log) .30
Turbidity (log) .71
Site depth (log) .97
DIFTMP (temperature at 1.5 m - temperature at bottom of
aerobic layer, summer data only) and TMPD (temperature
1.5 m above bottom, fall data only); not used in PCA .59
Principal components were calculated for the same 26 parameters from the two data sets; the proportion of variation
accounted for by the first six principal components from each analysis are also given.
243
-------
Table 3-61. Eigenvalues and Proportions of Variance Contained by Principal Components of 35
Chemistry Parameters in the Summer Chemistry Data Set from the EPA's Eastern
Lake Survey - Phase II (ELS-II) of 147 Lakes'
Component
PRIN1
PRIN2
PRIN3
PRIN4
PRIN5
PRIN6
PRINT
PRIN8
PRIN9
PRIN10
PRIN11
PRIN12
PRIN13
PRIN14
PRIN15
PRIN16
PRIN17
PRIN18
PRIN19
PRIN20
PRIN21
PRIN22
PRIN23
PRIN24
PRIN25
PRIN26
PRIN27
PRIN28
PRIN29
PRIN30
PRIN31
PR1N32
PRIN33
PRIN34
PRIN35
Eigenvalue
8.37061
5.68810
4.37620
2.68467
1.95926
1.47395
1.20593
1.14234
0.83790
0.81847
0.69566
0.64769
0.61806
0.58113
0.53299
0.47785
0.41649
0.39059
0.30903
0.27059
0.22859
0.18899
0.18178
0.15688
0.14576
0.12994
0.11809
0.10261
0.07033
0.06750
0.03333
0.03163
0.02667
0.01943
0.00095
Difference
2.68251
1.31190
1.69153
0.72540
0.48532
0.26801
0.06359
0.30445
0.01942
0.12281
0.04797
0.02963
0.03692
0.04814
0.05514
0.06136
0.02590
0.08156
0.03843
0.04201
0.03960
0.00720
0.02490
0.01113
0.01582
0.01184
0.01548
0.03228
0.00283
0.03417
0.00170
0.00496
0.00723
0.01848
Proportion
0.239160
0.162517
0.125034
0.076705
0.055979
0.042113
0.034455
0.032638
0.023940
0.023385
0.019876
0.018505
0.017659
0.016604
0.015228
0.013653
0.011900
0.011160
0.008829
0.007731
0.006531
0.005400
0.005194
0.004482
0.004164
0.003712
0.003374
0.002932
0.002009
0.001929
0.000952
0.000904
0.000762
0.000555
0.000027
Cumulative
0.23916
0.40168
0.52671
0.60342
0.65940
0.70151
0.73596
0.76860
0.79254
0.81593
0.83580
0.85431
0.87197
0.88857
0.90380
0.91745
0.92935
0.94051
0.94934
0.95707
0.96360
0.96900
0.97420
0.97868
0.98284
0.98656
0.98993
0.99286
0.99487
0.99680
0.99775
0.99866
0.99942
0.99997
1.00000
See text for explanation of parameters and transformations of variables used in the analysis.
244
-------
Table 3-62. Eigenvectors of First Six Principal Components from PCA of 35 Chemical Parame-
ters from the Summer Chemistry Data Set of the EPA's Eastern Lake Survey -
Phase II (ELS-II) of 147 Lakes8
Eigenvectors
LALDI98
LALKA11
LANCAT98
LC0151D
LCA98
LCL98
LCOLOR02
LDIC02
LDOC1 1
LDO 151D
LFE11
LFTL98
LHCO398
LHDEP99
LK98
LLKSIZ99
LMG98
LMN11
LNA98
LNH498
LNO398
PH0151D
PH02
LPTL1 1
LSECME98
LSI0211
LSO498
LT0151D
LTUR02
DIFDO
DIFCON
DIFTMP
LDPSIT1D
LDPAER
RANAER
PRIN1
-.194935
0.289521
0.122706
0.220683
0.283595
0.162311
0.118771
0.256824
0.181011
-.046392
0.021581
0.048709
0.304947
-.018775
0.212897
0.080624
0.273215
-.214861
0.188329
-.036060
-.106499
0.283680
0.288758
0.170141
-.135303
0.091955
0,021686
0.021672
0.174112
0.095971
-.028748
-.039046
-.024670
-.059827
0.095742
PRIN2
-.111644
0.109665
-.095970
0.033832
0.051607
0.059757
-.276256
-.044508
-.209357
0.199744
-.286977
0.009693
0.073750
-.022548
0.045516
0.223668
0.026337
-.136372
0.057231
-.109148
-.007168
0.160066
0.142837
-.280802
0.336704
0.017745
0.040794
0.052886
-.231913
0.077441
-.129300
0.248858
0.328558
0.354916
-.080631
PRIN3
-.016180
-.107705
-.268066
0.304508
-.070066
0.351153
-.080890
-.162322
-.178735
0.017791
0.151586
0.072991
-.145667
0.238902
0.258584
-.042445
0.146573
0.168330
0.314762
-.115955
0.014690
-.065482
-.097530
0.044169
0.035968
-.193464
0.275338
0.382629
0.047436
-.039200
0.001240
-.072831
-.054857
-.058383
0.012661
PRIN4
0.163654
-.056983
-.029896
0.029191
0.001492
-.006962
0.143313
0.076722
0.117890
-.196955
0.139892
0.134945
-.065301
0.132054
0.041786
-.097408
-.039166
0.096160
-.018933
0.006601
0.072835
-.116178
-.119062
0.089284
-.001465
0.101032
0.006894
-.010449
0.066618
0.481721
-.308559
0.362321
0.313279
0.139655
0.418591
PRIN5
0.186183
0.059472
-.123537
0.028368
0.217190
-.041026
0.143361
0.100311
0.058948
-.070530
0.205694
0.201205
0.044648
0.123857
-.062253
0.245388
0.055778
0.153277
-.021095
0.055529
0.420781
-.030935
-.010178
-.068720
-.029378
0.413753
0.269234
-.019618
-.023931
-.177812
0.292590
0.129379
0.045759
0.151349
-.275969
PRIN6
-.129032
-.115747
0.304933
0.066030
-.229966
0.220749
0.251671
-.154638
0.125926
0.093377
0.155655
0.411676
-.019087
0.022866
0.060365
0.186322
-.144832
-.073642
0.297056
0.039195
0.005639
-.067507
-.046778
-.068957
0.059700
0.149325
-.415552
-.039271
-.171017
-.110325
-.185209
-.026600
-.017514
0.039121
-.141953
Each principal component is a linear combination of the original variables; the eigenvector for each parameter and
component is the coefficient associated with that parameter to the principal component.
245
-------
Table 3-63. Results of Regressions of First Four Community Factors (Principal Components of Abundances of Zooplankton Genera
and Species Groups) and First Six Water Chemistry Factors (Principal Components of Major Water Quality Parameters)8
GENP1
Model
R2
GENP2
Model
R2
GENP3
Model
R2
GENP4
Model
R2
Best 1 Variable Best 2 Variable Best 3 Variable Best 4 Variable Best 5 Variable
Model Model Model Model Model
Ind. Var. p Ind. Var. p Ind. Var. p Ind. Var. p Ind. Var. p
CHEMP1 .0001 CHEMP1 .0001
CHEMP2 .0002
.098 .19
CHEMP1 .0001
.13
CHEMP1 .0001 CHEMP1 .0001 CHEMP1 .0001 CHEMP1 .0001 CHEMP1 .0001
CHEMP6 .0086 CHEMP6 .0074 CHEMP6 .0063 CHEMP6 .0054
CHEMP2 .0098 CHEMP2 .0084 CHEMP2 .0074
CHEMP3 .0093 CHEMP3 .0082
CHEMP5 .015
.12 .16 .20 .23 .27
CHEMP1 .0035
.06
Each regression is the best model for each number of independent variables. Only models with significant effects for all independent variables are shown.
-------
Table 3-64. Summary of Multiple Regressions8
Adjusted Squared
Dependent Factor Multiple R Value P Value
Rotifer PCA 1
Rotifer PCA 2
Cladoceran PCA 1
Cladoceran PCA 3
Cladoceran PCA 5
Copepod PCA 1
0.214
0.223
0.111
0.114
0.176
0.348
< 0.001
< 0.001
0.002
0.002
< 0.001
< 0.001
Dependent variables are principal component factors calculated separately for each major group of zooplankton. Predictor
variables are the first four physical/chemical PCA factors: ANC, lake depth, depth of aerobic zone, and lake size.
247
-------
Table 3-65. Comparison of Three Direct Analyses of Species-Environment Correspondence8
Analysis
RDA
DCCA
CCA
RDA
DCCA
CCA
RDA
DCCA
CCA
Axisl
0.1041
0.1850
0.1850
0.1573
0.2108
0.2108
0.0241
0.9817
0.9817
Eigenvalue
Axis 2
0.0323
0.0730
0.0916
Copepoda
0.0157
0.0937
0.0955
Cladocera
0.0007
0.2183
0.3488
Trace
0.1716
0.3928
0.3928
0.1881
0.3920
0.3920
0.0248
1.4250
1.4250
Correlation
Axis 1
0.7885
0.8135
0.8135
0.6502
0.6990
0.6990
0.1634
0.9924
0.9924
Coefficient
Axis 2
0.4692
0.6037
0.5808
0.4333
0.5289
0.5330
0.0842
0.5966
0.6803
RDA = redundancy analysis; DCCA = detrended canonical correspondence analysis; CCA = canonical correspondence
analysis. Eigenvalues for first two species axes and trace and species-environment correlation coefficients presented for each
analysis.
248
-------
Table 3-66. Results of CCA for Each of the Three Major Zooplankton Groups"
PCA Factor 1 PCA Factor 2 PCA Factor 3 PCA Factor 4
Rotifera
Axis 1 -0.7037 0.2873
Axis 2 -0.3648 -0.3400
Copepoda
Axis 1 0.6694
Axis 2 0.5021
Cladocera
Axis 1 -0.9701 0.9465 -0.9843 0.3952
Axis 2 0.5425
a Significant correlation coefficients between first two species axes and physical/chemical PCA factors.
249
-------
Table 3-67. Results of CCA for Cladocera Genera, Excluding Ceriodaphnia*
Species Axis 1 Species Axis 2 Trace
Eigenvalue 0.39768 0.09003 0.51560
Correlation
Environment axis 1 0.7024
Environment axis 2 0.5082
PCA Factor 1 0.3809
PCA Factor 2 -0.4797
PCA Factor 3 0.3512
PCA Factor 4 0.5800
a Eigenvalues for first two species axes and correlation coefficients of species axes with environmental axes and with original
PCA factors are shown.
250
-------
Table 3-68. Results of CCA for Cladocera Genera, Excluding Ceriodaphnia and Eubosmina"
Species Axis 1 Species Axis 2 Trace
Eigenvalue 0.13719 0.04747 0.20723
Correlation
Environment axis 1 0.5875
Environment axis 2 0.4304
PCA Factor 1 0.3885
PCA Factor 2 0.4179
PCA Factor 3
PCA Factor 4 0.3318
Eigenvalues for first two species axes and correlation coefficients of species axes with environmental axes and with original
PCA factors are shown.
251
-------
APPENDICES
253
-------
APPENDIX A
List of Names, Numbers, Lake Identification Codes, Geographic Locations,
Region Associations, and Chemistry Cluster Numbers
for All 147 Lakes Sampled in the ELS-II
Lake
Code
1A1-003
1A1-008
1A1-012
1A1-017
1A1-028
1A1-029
1A1-033
1A1-039
1A1-044
1A1-049
1A1-057
1A1-060
1A1-061
1A1-064
1A1-066
1A1-070
1A1-073
1A2-002
1A2-004
1A2-006
1A2-041
1A2-042
1A2-045
1A2-048
1A2-052
1A2-054
1A2-058
1A3-001
1A3-028
1A3-040
1A3-042
1A3-043
1A3-046
1A3-048
1A3-063
1A3-065
1B1-010
1B1-023
1B1-029
1B1-043
1B1-055
1B1-064
1B2-028
1B3-012
1B3-019
1B3-025
1B3-032
1B3-041
1B3-043
1B3-052
1B3-053
1B3-056
1B3-059
Chemistry
Cluster
1
2
1
1
1
3
3
1
2
1
1
3
1
2
1
3
1
1
1
2
2
1
1
1
1
1
3
2
1
2
2
3
2
1
3
1
1
2
3
1
2
2
1
3
3
2
3
2
3
1
3
1
1
Region
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
Lake Name
Hawk Pond
Cedar River Row
Whitney Lake
Constable Pond
Dry Channel Pond
Middle Pond
Kiwassa Lake
John Pond
Long Lake
Middle South Pond
Hitchcock Lake
Seventh Lake
Wolf Lake
Mt. Arab Lake
Woodhull Lake
Paradox Lake
Gull Lakes
St. John Lake
Duck Lake
Lake Frances
Mud Lake
North Branch Lake
Woods Lake
(No Name) Lily Lake
Chub Lake
Trout Lake
Trout Lake
Nate Pond
Curtis Lake
Zack Pond
Cheney Pond
Unknown
Long Pond
Grass Pond
No Name Windfall
South Lake
Ganoga Lake
Twin Ponds (Lower)
Skyview Lake
Penn Lake
Rock Hill Pond
Millpond No. 1
Mill Creek Reservoir
Little Butler Pond
Hartley Pond
Trout Lake
Wixon Pond
East Stroudsberg
Trout Lake
No Name
No Name Lake
Riga Lake
Island Pond
Latitude
43-57'25"N
43-42'30"N
43-35'15"N
43-50'00"N
44-21 '10"N
44-20'20"N
44-17'45"N
44-06'45"N
44-02'30"N
43-59'22"N
43-51 '00"N
43-44'45"N
43-37'45"N
44-11'18"N
43-35'30"N
43-53'00"N
43-51 '22"N
43-26'30"N
43-14'08"N
44-41 '45"N
43-20'26"N
43-18'45"N
43-15'10"N
43-07'39"N
43-15'30"N
43-20'48"N
44-21 '47"N
43-51 '30"N
43-20' 10"N
43-56'00"N
43-52'40"N
43-49' 10"N
43-38' 15"N
43-41 '35"N
43-41 '44"N
43-30'54"N
41-21'30"N
41-23'00"N
41-17'30"N
41-06'45"N
41-18'49"N
41-10'47"N
41-15'45"N
41-51'45"N
41-39'30"N
41-35'10"N
41-23'45"N
41-04 '00"N
41-00'15"N
41-29'23"N
41-54'18"N
42-01 '18"N
41-15'26"N
Longitude
74-57'30"W
74-28'30"N
74-33'45"W
74-47'45"W
74-26' 15"W
74-22'45"W
74-09'30"W
74-45'50"W
74-22'00"W
75-01 '06"W
75-02'30"W
74-45'00"W
74-39' 15"W
74-36'03"W
74-59' 13"W
73-42'30"W
74-49' 15"W
74-03'40"W
74-27'09"W
74-19'30"W
74-27' 14"W
74-47'40"W
74-19'00"W
74-35'20"W
74-31 '50"W
74-42'50"W
75-16'08"W
74-05'30"W
74-57'40"W
74-11'00"W
74-09'45"W
74-17'00"W
74-17'20"W
75-03'40"W
75-00' 18"W
74-53'32"W
76-19'15"W
74-54' 15"W
75-14'20"W
75-46' 10"W
75-00'58"W
75-24'39"W
75-45 '00"W
75-37'40"W
75-42'30"W
74-40'50"W
73-44'05"W
75-10'00"W
75-20'30"W
74-32'20"W
75-24'37"W
73-29'00"W
74-08'25"W
State
NY
NY
NY
NY
NY
NY
NY
NY
NY
NY
NY
NY
NY
NY
NY
NY
NY
NY
NY
NY
NY
NY
NY
NY
NY
NY
NY
NY
NY
NY
NY
NY
NY
NY
NY
NY
PA
PA
PA
PA
PA
PA
PA
PA
PA
NY
NY
PA
PA
NY
PA
CT
NY
Lake
Number
28
16
14
119
112
17
1
18
3
5
4
106
81
85
105
86
84
44
43
20
54
56
55
51
53
61
82
2
111
19
21
13
15
9
113
109
125
27
34
7
126
12
30
11
10
117
6
29
120
128
26
22
24
255
-------
Lake
Code
1B3-060
1B3-062
1C1-009
1C1-017
1C1-018
1C1-031
1C1-039
1C1-050
1C1-066
1C1-068
1C1-070
1C1-084
1C1-086
1C2-002
1C2-012
1C2-016
1C2-024
1C2-028
1C2-035
1C2-037
1C2-041
1C2-048
1C2-050
1C2-054
1C2-056
1C2-057
1C2-062
1C2-064
1C2-066
1C2-068
1C3-032
1C3-055
1C3-063
1D1-014
1D1-027
1D1-031
1D1-034
1D1-037
1D1-046
1D1-054
1D1-056
1D1-067
1D1-068
1D2-025
1D2-027
1D2-036
1D2-049
1D2-074
1D2-084
1D2-093
1D2-094
1D3-002
1D3-003
103-025
1D3-026
1D3-029
1D3-033
1D3-044
1E1-009
1E1-010
Chemistry
Cluster
3
3
3
3
3
2
2
2
2
1
2
2
2
2
2
3
3
2
2
1
1
2
2
1
3
2
2
2
2
3
3
1
3
3
2
1
1
1
1
2
1
1
1
2
1
1
3
2
3
3
1
1
3
3
3
1
3
2
1
3
Region
2
2
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
5
5
Lake Name
Sly Lake
Bassett Pond
Upper Baker Pond
Welhern Lake
Decker Pond
Hunt's Pond
Ossippee Lake
Billings Pond
Haunted Lake
Lincoln
Packard Pond
Upper Beach Pond
Star Lake
Iron Pond
Black Pond
Trafton Pond
Lake Waukewan
Sunset Lake
Smith Pond
Mendums Pond
Juggernaut Pond
Cranberry Pond
Moores Pond
Lake Wampanoag
Drury Pond
Babbidge Reservoir
Lake Pemigewasset
Hancock Pond
Turtle Pond
Quimby Pond
Bear Pond
Oarrah Pond
Martin Meadow Pond
Hamilton Reservoir
School House Pond
Kings Pond
Rocky Pond
Ezekiel Pond
Robbins Pond
Upper Mill Pond
Little West Pond
Round Pond
Little Sandy Pond
Little Quittacas
Sandy Pond
Micah Pond
Spring Grove Pond
Stetson Pond
Goose Pond
Ashland Reservoir
Snows Pond
Dykes Pond
Sandy Pond
Long Pond
Arnold Mill Reservoir
Killingly Pond
No Name
Middle Farm Pond
Peep Lake
Six Ponds
Latitude
41-49'25"N
41-35'33"N
43-54'30"N
45-12'45"N
45-11'45"N
44-05'00"N
43-47'30"N
43-17'00"N
42-59'00"N
42-40'10"N
42-38'00"N
43-38'54"N
43-27'43"N
45-27'30"N
44-08'45"N
43-50'45"N
43-39'30"N
43-28' 15"N
43-09'15"N
43-10'30"N
42-57'35"N
42-44'40"N
42-39'20"N
42-37'02"N
44-42' 15"N
42-56'05"N
43-36'55"N
44-57'20"N
43-15'15"N
44-59'27"N
44-09'15"N
42-49'52"N
44-26'30"N
42-02'19"N
41-24'00"N
41-54'40"N
41-53'10"N
41-48'15"N
41-42'20"N
41-43'51"N
41-55'17"N
41-58'17"N
41-47'47"N
41-47'30"N
41-46'20"N
41-38'20"N
41-54'35"N
42-01 '40"N
41-41 '38"N
42-14'22"N
41-45'30"N
42-36' 15"N
42-33'45"N
42-01'15"N
41-59'00"N
41-51'45"N
41-39'30"N
41-16'30"N
44-54'30"W
46-00'30"N
Longitude
75-20' 14"W
75-42'40"W
71-59'30"W
70-29'40"W
69-56'15"W
71-00'00"W
71-08'00'W
71-56'30"W
71-46'00"W
71-54'45"W
72-14'00"W
71-12'15"W
72-03'20"W
70-22'30'W
70-48WW
70-53'30"W
71-31'30"W
71-18'00'W
72-01 '45"W
71-04'00"W
72-00'45"W
73-26'00"W
72-20'50"W
71-57'45"W
70-14'30"W
72-13'00"W
71-35'45"W
69-59'10"W
71-31'00"W
70-44'31"W
70-43'00"W
71-26'40"W
71-36'30"W
72-09' 16"W
71-40'00"W
70-42' 15"W
70-41 '45"W
70-36'45"W
70-06'40"W
70-07'00"W
70-42'24"W
71-46'20"W
70-36' 13"W
70-55WW
70-39'15"W
70-22'45"W
71-39'00"W
70-49'39"W
70-00'28"W
71-27'52"W
70-51'10"W
70-43'46"W
71-33'15"W
71-49'00"W
71-23'45"W
71-47'45"W
73-11'30"W
71-58'40"W
67-53'30"W
68-55'30"W
State
PA
PA
NH
ME
ME
ME
NH
NH
NH
MA
MA
NH
NH
ME
ME
ME
NH
NH
NH
NH
NH
NY
MA
MA
ME
NH
NH
ME
NH
ME
ME
NH
NH
MA
Rl
MA
MA
MA
MA
MA
MA
Rl
MA
MA
MA
MA
Rl
MA
MA
MA
MA
MA
MA
CT
Rl
CT
CT
NY
ME
ME
Lake
Number
108
8
49
25
47
48
77
144
89
118
94
142
140
90
57
100
95
76
146
132
67
93
91
88
104
114
147
110
143
121
97
107
79
37
33
40
31
133
122
141
39
92
42
41
32
134
52
38
36
46
131
69
103
127
35
83
87
45
123
70
256
-------
Lake
Code
1E1-011
1E1-025
1E1-040
1E1-050
1E1-054
1E1-061
1E1-062
1E1-073
1E1-074
1E1-077
1E1-082
1E1-092
1E1-096
1E1-106
1E1-111
1E1-120
1E1-123
1E2-007
1E2-016
1E2-018
1E2-030
1E2-038
1E2-049
1E2-054
1E2-056
1E2-063
1E3-022
1E3-041
1E3-042
1E3-045
1E3-055
1E3-060
Chemistry
Cluster
2
3
2
2
2
2
2
2
2
2
2
2
2
2
1
2
2
2
3
3
3
1
1
3
2
2
3
3
3
3
3
3
Region
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
Lake Name
Fourth Davis
Bean Pond (Middle)
Little Greenwood
Lower Oxbrook Lake •
Duck Lake
Little Seavey Lake
Long Pond
Georges Lake
Craig Pond
Parker Pond
Stephens Pond
Great Pond
Middle Chain Lake
Greenwood Pond
Long Pond
No Name
Rrst Pond
Fairbanks Pond
Round Pond
Webster Lake
Round Pond
Nelson Pond
Gross Pond
Brettuns Pond
Peabody Pond
Kalers Pond
Number Nine Lake
Round Pond
Sand Pond
McClure Pond
Togue Pond
Millinocket Lake
Latitude
45-15'30"N
45-48'45"N
45-22'00"N
45-17'00"N
45-09'00"N
44-56'15"N
44-55'00"N
44-37WN
44-35'00"N
44-22'20"N
44-22'00"N
44-36'03"N
45-13'11"N
45-32WN
44-32'02"N
45-27'26"N
44-22'10"N
44-23'21"N
46-14'30"N
46-09'30"N
45-01 '00"N
44-24'55"N
44-03'30"N
44-23'30"N
43-56'32"N
44-06'29"N
46-25'00"N
44-44'20"N
44-34' 10"N
44-29'00"N
46-56'02"N
46-18'10"N
Longitude
69-23'40'W
69-11'30"W
69-24'30"W
67-50'30"W
68-06'00'W
67-38'00"W
68-16'11"W
68-14'30"W
68-40'00"W
68-42'30"W
69-18'00"W
68-17'00"W
68-04'45"W
69-13'58"W
68-10'13"W
68-50'46"W
68-36'00"W
69-49'52"W
69-33'45"W
69-05'00"W
67-16'00"W
70-15'45"W
69-23'35"W
70-15'00"W
70-41 '13"W
69-25'22"W
68-03'00"W
69-13'30"W
70-07' 10"W
68-57'50"W
68-53'31"W
68-52'30"W
State
ME
ME
ME
ME
ME
ME
ME
ME
ME
ME
ME
ME
ME
ME
ME
ME
ME
ME
ME
ME
ME
ME
ME
ME
ME
ME
ME
ME
ME
ME
ME
ME
Lake
Number
96
116
99
130
72
135
138
101
137
139
78
64
63
62
136
71
65
23
74
98
60
75
58
145
102
68
124
59
66
73
50
115
257
-------
APPENDIX B
Codes for Genera Used in Generic Analyses
Rotifers:
R1
R2
R3
R4
R5
R6
R7
R8
R9
R10
R11
R12
R13
R14
Keratella
Kellicottia
Trichocerca
Gastropus
Ascomorpha
Asplanchna
Polyarthra
Synchaeta
Ploesoma
Filinia
Hexarthra
Conochilus
Conochiloides
Collothea
Cladocera:
CL1
CL2
CL3
CL4
CL5
CL6
CL7
CL8
CL9
CL10
CL11
CL12
Leptodora
Diaphanosoma
Sida
Holopedium
Bosmina
Eubosmina
Chydorus
Polyphemus
Daphnia pulex, Daphnia catawba, and related species
Daphnia galeata and related species
Daphnia parvula, Daphnia retrocurva, and related species
Ceriodaphnia
Calanoid Copepods:
COP1 Epischura
COP2 Aglaodiaptomus
COP3 Leptodiaptomus
COP4 Skistodiaptomus
COPS Onychodiaptomus
Cyclopoid Copepods:
COP6
COP7
COPS
COP9
COP10
COP11
COP12
Mesocyclops
Tropocyclops
Cyclops
Orthocyclops
Eucyclops
Ectocyclops
Macrocyclops
258
-------
Size Codes
Code Size (mm)
m „ „ <- n 1
\j i •"•——«———«— ^ ^jm |
02 0.1 - 0.2
03 0.2 - 0.3
04 0.3 - 0.4
05 0.4 - 0.5
06 0.5 - 0.6 0.1 mm intervals
07 0.6 - 0.7
08 0.7 - 0.8
09 0.8 - 0.9
10 0.9-1.0
11 1.0-1.2
12 1.2-1.4
13 1.4-1.6
14 1.6-1.8
15 1.8-2.0
16 2.0-2.2
17 2.2-2.4
18 2.4 - 2.6
19 2.6 - 2.8
20 2.8 - 3.0 0.2 mm intervals
25 3.8 - 4.0
30 4.8 - 5.0
35 5.8 - 6.0
40 6.8 - 7.0
45 7.8 - 8.0
50 8.8 - 9.0
51 9.0 - 10.0
52 10.0-11.0
53 11.0-12.0
54 12.0 - 13.0
55 13.0 - 14.0
60 18.0-19.0 1.0 mm intervals
70 28 - 29
80 38 - 39
90 48 - 49
qq cy CD
C7O \J I ~ \S\J ———————————--—..——————™
259
-------
APPENDIX C
Formats of SAS Files
Contents of SAS Member EPAZOOP.EPATSPE
Number of Observations: 13,652 Number of Variables: 4
MEMTYPE: Data
Alphabetical List of Variables and Attributes:
# Variable Type Length Position
2 JARID NUM 8 8
1 LAKEID NUM 8 0
4 RDENSITY NUM 8 24
3 SPECCD NUM 8 16
260
-------
Contents of SAS Member EPAZOOP.EPASP
Number of Observations: 491
MEMTYPE: Data
Alphabetical List of Variables and Attributes:
Number of Variables: 145
#
2
3
1
4
5
6
7
8
g
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
Variable
CHEMGRP
JARID
LAKEID
RD1000
RD1001
RD1002
RD1003
RD1004
RD1005
RD1006
RD1007
RD1008
RD1009
RD1010
RD1011
RD1030
RD1031
RD1040
RD1041
RD1050
RD1051
RD1060
RD1070
RD1101
RD1102
RD1103
RD1104
RD1105
RD1110
RD1400
RD1401
RD1402
RD1403
RD1404
RD1405
RD1406
RD1407
RD1500
RD1501
RD1510
RD1511
RD1512
RD1800
Type
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
Length
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
Position
8
16
0
24
32
40
48
56
64
72
80
88
96
104
112
120
128
136
144
152
160
168
176
184
192
200
208
216
224
232
240
248
256
264
272
280
288
296
304
312
320
328
336
261
-------
Number of Observations: 491
MEMTYPE: Data
Alphabetical List of Variables and Attributes:
Contents of SAS Member EPAZOOP.EPASP (continued)
Number of Variables: 145
#
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
Variable
RD1809
RD1900
RD1901
RD1902
RD1903
RD1904
RD1910
RD1911
RD1912
RD1921
RD1922
RD1923
RD1924
RD2100
RD2101
RD2102
RD2200
RD2300
RD2301
RD2310
RD2311
RD3100
RD3101
RD3300
RD4100
RD5101
RD5102
RD5110
RD5201
RD5301
RD5310
RD5311
RD5312
RD5501
RD5502
RD5509
RD5510
RD5511
RD5512
RD5513
RD5519
RD5520
RD5530
Type
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
Length
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
Position
344
352
360
368
376
384
392
400
408
416
424
432
440
448
456
464
472
480
488
496
504
512
520
528
536
544
552
560
568
576
584
592
600
608
616
624
632
640
648
656
664
672
680
262
-------
Number of Observations: 491
MEMTYPE: Data
Alphabetical List of Variables and Attributes:
Contents of SAS Member EPAZOOP.EPASP (continued)
Number of Variables: 145
#
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
Variable
RD5540
RD5550
RD5560
RD5600
RD5701
RD5702
RD5703
RD5704
RD5705
RD5706
RD5707
RD5708
RD5709
RD5710
RD5801
RD5802
RD5803
RD5804
RD5805
RD5809
RD6300
RD6301
RD6309
RD6401
RD6402
RD6411
RD6412
RD6421
RD6422
RD6423
RD6429
RD6431
RD6500
RD7100
RD7101
RD7110
RD7111
RD7112
RD7121
RD7122
RD7123
RD7124
RD7129
Type
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
Length
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
Position
688
696
704
712
720
728
736
744
752
760
768
776
784
792
800
808
816
824
832
840
848
856
864
872
880
888
896
904
912
920
928
936
944
952
960
968
976
984
992
1000
1008
1016
1024
263
-------
Number of Observations: 491
MEMTYPE: Data
Alphabetical List of Variables and Attributes:
Contents of SAS Member EPAZOOP.EPASP (continued)
Number of Variables: 145
#
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
Variable
RD7131
RD7141
RD7142
RD7143
RD7144
RD7160
RD7200
RD7500
RD8000
RD9100
RD9101
RD9102
RD9199
RD9200
RD9201
RD9300
Type
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
Length
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
Position
1032
1040
1048
1056
1064
1072
1080
1088
1096
1104
1112
1120
1128
1136
1144
1152
264
-------
Contents of SAS Member EPAZOOP.EPASPAR
Number of Observations: 491
MEMTYPE: Data
Alphabetical List of Variables and Attributes:
Number of Variables: 289
#
2
289
3
1
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
Variable
CHEMGRP
I
JARID
LAKEID
PRAR1
PRAR2
PRAR3
PRAR4
PRAR5
PRAR6
PRAR7
PRAR8
PRAR9
PRAR10
PRAR11
PRAR12
PRAR13
PRAR14
PRAR15
PRAR16
PRAR17
PRAR18
PRAR19
PRAR20
PRAR21
PRAR22
PRAR23
PRAR24
PRAR25
PRAR26
PRAR27
PRAR28
PRAR29
PRAR30
PRAR31
PRAR32
PRARR33
PRAR34
PRAR35
PRAR36
PRAR37
PRAR38
PRAR39
Type
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
Length
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
Position
8
2304
16
0
1160
1168
1176
1184
1192
1200
1208
1216
1224
1232
1240
1248
1256
1264
1272
1280
1288
1296
1304
1312
1320
1328
1336
1344
1352
1360
1368
1376
1384
1392
1400
1408
1416
1424
1432
1440
1448
1456
1464
265
-------
Contents of SAS Member EPAZOOP.EPASPAR (continued)
Number of Observations: 491
MEMTYPE: Data
Alphabetical List of Variables and Attributes:
Number of Variables: 289
#
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
Variable
PRAR40
PRAR41
PRAR42
PRAR43
PRAR44
PRAR45
PRAR46
PRAR47
PRAR48
PRAR49
PRAR50
PRAR51
PRAR52
PRAR53
PRAR54
PRAR55
PRAR56
PRAR57
PRAR58
PRAR59
PRAR60
PRAR61
PRAR62
PRAR63
PRAR64
PRAR65
PRAR66
PRAR67
PRAR68
PRAR69
PRAR70
PRAR71
PRAR72
PRAR73
PRAR74
PRAR75
PRAR76
PRAR77
PRAR78
PRAR79
PRAR80
PRAR81
PRAR82
Type
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
Length
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
Position
1472
1480
1488
1496
1504
1512
1520
1528
1536
1544
1552
1560
1568
1576
1584
1592
1600
1608
1616
1624
1632
1640
1648
1656
1664
1672
1680
1688
1696
1704
1712
1720
1728
1736
1744
1752
1760
1768
1776
1784
1792
1800
1808
266
-------
Contents of SAS Member EPAZOOP.EPASPAR (continued)
Number of Observations: 491
MEMTYPE: Data
Alphabetical List of Variables and Attributes:
Number of Variables: 289
#
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
Variable
PRAR83
PRAR84
PRAR85
PRAR86
PRAR87
PRAR88
PRAR89
PRAR90
PRAR91
PRAR92
PRAR93
PRAR94
PRAR95
PRAR96
PRAR97
PRAR98
PRAR99
PRAR100
PRAR101
PRAR102
PRAR103
PRAR104
PRAR105
PRAR106
PRAR107
PRAR108
PRAR109
PRAR110
PRAR111
PRAR112
PRAR113
PRRAR114
PRAR115
PRAR116
PRAR117
PRAR118
PRAR119
PRAR120
PRAR121
PRAR122
PRAR123
PRAR124
PRAR125
Type
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
Length
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
a
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
Position
1816
1824
1832
1840
1848
1856
1864
1872
1880
1888
1896
1904
1912
1920
1928
1936
1944
1952
1960
1968
1976
1984
1992
2000
2008
2016
2024
2032
2040
2048
2056
2064
2072
2080
2088
2096
2104
2112
2120
2128
2136
2144
2152
267
-------
Number of Observations: 491
MEMTYPE: Data
Alphabetical List of Variables and Attributes:
Contents of SAS Member EPAZOOP.EPASPAR (continued)
Number of Variables: 289
#
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
Variable
PRAR126
PRAR127
PRAR128
PRAR129
PRAR130
PRAR131
PRAR132
PRAR133
PRAR134
PRAR135
PRAR136
PRAR137
PRAR138
PRAR139
PRAR140
PRAR141
PRAR142
RDAR1
RDAR2
RDAR3
RDAR4
RDAR5
RDAR6
RDAR7
RDAR8
RDAR9
RDAR10
RDAR11
RDAR12
RDAR13
RDAR14
RDAR15
RDAR16
RDAR17
RDAR18
RDAR19
RDAR20
RDAR21
RDAR22
RDAR23
RDAR24
RDAR25
RDAR26
Type
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
Length
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
Position
2160
2168
2176
2184
2192
2200
2208
2216
2224
2232
2240
2248
2256
2264
2272
2280
2288
24
32
40
48
56
64
72
80
88
96
104
112
120
128
136
144
152
160
168
176
184
192
200
208
216
224
268
-------
Contents of SAS Member EPAZOOP.EPASPAR (continued)
Number of Observations: 491
MEMTYPE: Data
Alphabetical List of Variables and Attributes:
Number of Variables: 289
#
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
Variable
RDAR27
RDAR28
RDAR29
RDAR30
RDAR31
RDAR32
RDAR33
RDAR34
RDAR35
RDAR36
RDAR37
RDAR38
RDAR39
RDAR40
RDAR41
RDAR42
RDAR43
RDAR44
RDAR45
RDAR46
RDAR47
RDAR48
RDAR49
RDAR50
RDAR51
RDAR52
RDAR53
RDAR54
RDAR55
RDAR56
RDAR57
RDAR58
RDAR59
RDAR60
RDAR61
RDAR62
RDAR63
RDAR64
RDAR65
RDAR66
RDAR67
RDAR68
RDAR69
Type
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
Length
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
Position
232
240
248
256
264
272
280
288
296
304
312
320
328
336
344
352
360
368
376
384
392
400
408
416
424
432
440
448
456
464
472
480
488
496
504
512
520
528
536
544
552
560
568
269
-------
Contents of SAS Member EPAZOOP.EPASPAR (continued)
Number of Observations: 491
MEMTYPE: Data
Alphabetical List of Variables and Attributes:
Number of Variables: 289
#
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
Variable
RDAR70
RDAR71
RDAR72
RDAR73
RDAR74
RDAR75
RDAR76
RDAR77
RDAR78
RDAR79
RDAR80
RDAR81
RDAR82
RDAR83
RDAR84
RDAR85
RDAR86
RDAR87
RDAR88
RDAR89
RDAR90
RDAR91
RDAR92
RDAR93
RDAR94
RDAR95
RDAR96
RDAR97
RDAR98
RDAR99
RDAR100
RDAR101
RDAR102
RDAR103
RDAR104
RDAR105
RDAR106
RDAR107
RDAR108
RDAR109
RDAR110
RDAR1 1 1
RDAR112
Type
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
Length
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
Position
576
584
592
600
608
616
624
632
640
648
656
664
672
680
688
696
704
712
720
728
736
744
752
760
768
776
784
792
800
808
816
824
832
840
848
856
864
872
880
888
896
904
912
270
-------
Number of Observations: 491
MEMTYPE: Data
Alphabetical List of Variables and Attributes:
Contents of SAS Member EPAZOOP.EPASPAR (continued)
Number of Variables: 289
#
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
288
Variable
RDAR113
RDAR114
RDAR115
RDAR116
RDAR117
RDAR118
RDAR119
RDAR120
RDAR121
RDAR122
RDAR123
RDAR124
RDAR125
RDAR126
RDAR127
RDAR128
RDAR129
RDAR130
RDAR131
RDAR132
RDAR133
RDAR134
RDAR135
RDAR136
RDAR137
RDAR138
RDAR139
RDAR140
RDAR141
RDAR142
RDTOTAL
Type
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
Length
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
Position
920
928
936
944
952
960
968
976
984
992
1000
1008
1016
1024
1032
1040
1048
1056
1064
1072
1080
1088
1096
1104
1112
1120
1128
1136
1144
1152
2296
271
-------
Contents of SAS Member EPAZOOP.EPAMSPAR
Number of Observations: 147
MEMTYPE: Data
Alphabetical List of Variables and Attributes:
Number of Variables: 286
#
2
1
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
Variable
CHEMGRP
LAKEID
PRAR1
PRAR2
PRAR3
PRAR4
PRAR5
PRAR6
PRAR7
PRAR8
PRAR9
PRAR10
PRAR11
PRAR12
PRAR13
PRAR14
PRAR15
PRAR16
PRAR17
PRAR18
PRAR19
PRAR20
PRAR21
PRAR22
PRAR23
PRAR24
PRAR25
PRAR26
PRAR27
PRAR28
PRAR29
PRAR30
PRAR31
PRAR32
PRAR33
PRAR34
PRAR35
PRAR36
PRAR37
PRAR38
PRAR39
PRAR40
PRAR41
Type
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
Length
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
Position
8
0
1152
1160
1168
1176
1184
1192
1200
1208
1216
1224
1232
1240
1248
1256
1264
1272
1280
1288
1296
1304
1312
1320
1328
1336
1344
1352
1360
1368
1376
1384
1392
1400
1408
1416
1424
1432
1440
1448
1456
1464
1472
272
-------
Contents of SAS Member EPAZOOP.EPAMSPAR (continued)
Number of Observations: 147
MEMTYPE: Data
Alphabetical List of Variables and Attributes:
Number of Variables: 286
#
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
Variable
PRAR42
PRAR43
PRAR44
PRAR45
PRAR46
PRAR47
PRAR48
PRAR49
PRAR50
PRAR51
PRAR52
PRAR53
PRAR54
PRAR55
PRAR56
PRAR57
PRAR58
PRAR59
PRAR60
PRAR61
PRAR62
PRAR63
PRAR64
PRAR65
PRAR66
PRAR67
PRAR68
PRAR69
PRAR70
PRAR71
PRAR72
PRAR73
PRAR74
PRAR75
PRAR76
PRAR77
PRAR78
PRAR79
PRAR80
PRAR81
PRAR82
PRAR83
PRAR84
Type
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
Length
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
Position
1480
1488
1496
1504
1512
1520
1528
1536
1544
1552
1560
1568
1576
1584
1592
1600
1608
1616
1624
1632
1640
1648
1656
1664
1672
1680
1688
1696
1704
1712
1720
1728
1736
1744
1752
1760
1768
1776
1784
1792
1800
1808
1816
273
-------
Number of Observations: 147
MEMTYPE: Data
Alphabetical List of Variables and Attributes:
Contents of SAS Member EPAZOOP.EPAMSPAR (continued)
Number of Variables: 286
#
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
Variable
PRAR85
PRAR86
PRAR87
PRAR88
PRAR89
PRAR90
PRAR91
PRAR92
PRAR93
PRAR94
PRAR95
PRAR96
PRAR97
PRAR98
PRAR99
PRAR100
PRAR101
PRAR102
PRAR103
PRAR104
PRAR105
PRAR106
PRAR107
PRAR108
PRAR109
PRAR110
PRAR1 1 1
PRAR112
PRAR113
PRAR114
PRAR115
PRAR116
PRAR117
PRAR118
PRAR119
PRAR120
PRAR121
PRAR122
PRAR123
PRAR124
PRAR125
PRAR126
PRAR127
Type
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
Length
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
Position
1824
1832
1840
1848
1856
1864
1872
1880
1888
1896
1904
1912
1920
1928
1936
1944
1952
1960
1968
1976
1984
1992
2000
2008
2016
2024
2032
2040
2048
2056
2064
2072
2080
2088
2096
2104
2112
2120
2128
2136
2144
2152
2160
274
-------
Contents of SAS Member EPAZOOP.EPAMSPAR (continued)
Number of Observations: 147
MEMTYPE: Data
Alphabetical List of Variables and Attributes:
Number of Variables: 286
#
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
Variable
PRAR128
PRAR129
PRAR130
PRAR131
PRAR132
PRAR133
PRAR134
PRAR135
PRAR136
PRAR137
PRAR138
PRAR139
PRAR140
PRAR141
PRAR142
RDAR1
RDAR2
RDAR3
RDAR4
RDAR5
RDAR6
RDAR7
RDAR8
RDAR9
RDAR10
RDAR11
RDAR12
RDAR13
RDAR14
RDAR15
RDAR16
RDAR17
RDAR18
RDAR19
RDAR20
RDAR21
RDAR22
RDAR23
RDAR24
RDAR25
RDAR26
RDAR27
RDAR28
Type
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
Length
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
Position
2168
2176
2184
2192
2200
2208
2216
2224
2232
2240
2248
2256
2264
2272
2280
16
24
32
40
48
56
64
72
80
88
96
104
112
120
128
136
144
152
169
168
176
184
192
200
208
216
224
232
275
-------
Number of Observations: 147
MEMTYPE: Data
Alphabetical List of Variables and Attributes:
Contents of SAS Member EPAZOOP.EPAMSPAR (continued)
Number of Variables: 286
#
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
Variable
RDAR29
RDAR30
RDAR31
RDAR32
RDAR33
RDAR34
RDAR35
RDAR36
RDAR37
RDAR38
RDAR39
RDAR40
RDAR41
RDAR42
RDAR43
RDAR44
RDAR45
RDAR46
RDAR47
RDAR48
RDAR49
RDAR50
RDAR51
RDAR52
RDAR53
RDAR54
RDAR55
RDAR56
RDAR57
RDAR58
RDAR59
RDAR60
RDAR61
RDAR62
RDAR63
RDAR64
RDAR65
RDAR66
RDAR67
RDAR68
RDAR69
RDAR70
RDAR71
Type
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
Length
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
Position
240
248
256
264
272
280
288
296
304
312
320
328
336
344
352
360
368
376
384
392
400
408
416
424
432
440
448
456
464
472
480
488
496
504
512
520
528
536
544
552
560
568
576
276
-------
Contents of SAS Member EPAZOOP.EPAMSPAR (continued)
Number of Observations: 147
MEMTYPE: Data
Alphabetical List of Variables and Attributes:
Number of Variables: 286
#
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
Variable
RDAR72
RDAR73
RDAR74
RDAR75
RDAR76
RDAR77
RDAR78
RDAR79
RDAR80
RDAR81
RDAR82
RDAR83
RDAR84
RDAR85
RDAR86
RDAR87
RDAR88
RDAR89
RDAR90
RDAR91
RDAR92
RDAR93
RDAR94
RDAR95
RDAR96
RDAR97
RDAR98
RDAR99
RDAR100
RDAR101
RDAR102
RDAR103
RDAR104
RDAR105
RDAR106
RDAR107
RDAR108
RDAR109
RDAR110
RDAR111
RDAR112
RDAR113
RDAR114
Type
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
Length
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
Position
584
592
600
608
616
624
632
640
648
656
664
672
680
688
696
704
712
720
728
736
744
752
760
768
776
784
792
800
808
816
824
832
840
848
856
864
872
880
888
896
904
912
920
277
-------
Number of Observations: 147
MEMTYPE: Data
Alphabetical List of Variables and Attributes:
Contents of SAS Member EPAZOOP.EPAMSPAR (continued)
Number of Variables: 286
#
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
Variable
RDAR115
RDAR116
RDAR117
RDAR118
RDAR119
RDAR120
RDAR121
RDAR122
RDAR123
RDAR124
RDAR125
RDAR126
RDAR127
RDAR128
RDAR129
RDAR130
RDAR131
RDAR132
RDARR133
RDAR134
RDAR135
RDAR136
RDAR137
RDAR138
RDAR139
RDAR140
RDAR141
RDAR142
Type
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
Length
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
Position
928
936
944
952
960
968
976
984
992
1000
1008
1016
1024
1032
1040
1048
1056
1064
1072
1080
1088
1096
1104
1112
1120
1128
1136
1144
278
-------
Contents of SAS Member EPAZOOP.EPACGPCA
Number of Observations: 147
MEMTYPE: Data
Alphabetical List of Variables and Attributes:
Number of Variables: 194
#
42
29
31
92
16
33
108
98
99
100
101
102
103
104
105
106
107
123
124
125
126
127
128
129
130
131
132
133
134
34
55
24
8
12
135
136
137
138
139
140
141
142
143
144
Variable
ALDI98
ALKA11
ANCAT98
BTMP
C0151D
CA98
CHEMGRP
CHEMP1
CHEMP2
CHEMP3
CHEMP4
CHEMP5
CHEMP6
CHEMP7
CHEMP8
CHEMP9
CHEMP10
CL1
CL2
CL3
CL4
CL5
CL6
CL7
CL8
CL9
CL10
CL11
CL12
CL98
CLSTR99
COLOR02
CONMH1D
CON B1D
COPT
COP2
COP3
COP4
COPS
COP6
COP7
COPS
COP9
COP10
Type
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
Length
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
Position Format
354 F8.4
250 F8.1
266 F8.4
738
146 F8.
282 F8.4
866
786
794
802
810
818
826
834
842
850
858
986
994
1002
1010
1018
1026
1034
1042
1050
1058
1066
1074
290 F8.4
446 F8.
210 F8.
82 F8.
114 F8.
1082
1090
1098
1106
1114
1122
1130
1138
1146
1154
Label
Labile monomeric Al (/tfj/L)
Alkalinity (/zeq/L) form 1 1
Cations/ Anions ratio
Conductivity at 1 .5 m (/iS/cm) form 1 1
Calcium (jueq/L)
Chloride (/*eq/L)
Phase II cluster (1, 2, or 3)
Color (PCU) form 2
Conductivity at mid-hyp (jjS/cm) form 1 1
Conductivity at btm 1 .5 m (/.iS/cm) form 1 1
279
-------
Number of Observations: 147
MEMTYPE: Data
Alphabetical List of Variables and Attributes:
Contents of SAS Member EPAZOOP.EPACGPCA (continued)
Number of Variables: 194
#
145
146
21
88
87
59
44
28
9
17
13
57
6
26
40
185
186
187
188
189
190
191
192
193
194
32
45
46
56
37
5
1
2
60
61
62
3
48
63
64
161
162
163
164
Variable
COP11
COP12
DIC02
DIFCON
DIFDO
DIFTMP
DISM99
DOC11
DOMH1D
DO 151 D
DO B1D
DPAER
DPSIT1D
FE11
FTL98
GENP1
GENP2
GENP3
GENP4
GENP5
GENP6
GENP7
GENP8
GENP9
GENP10
HC0398
HDEP99
HYTYP99
INDAER
K98
LAKEID
LAKE ID
LAKNA1D
LALDI98
LALKA11
LANCAT98
LAT99
LATDD99
LC0151D
LCA98
LCL1
LCL2
LCL3
LCL4
Type
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
CHAR
CHAR
NUM
NUM
CHAR
CHAR
NUM
NUM
NUM
CHAR
NUM
NUM
NUM
NUM
NUM
NUM
NUM
Length
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
9
4
8
8
7
30
8
8
8
10
8
8
8
8
8
8
8
Position
1162
1170
186
706
698
474
370
242
90
154
122
458
66
226
338
1482
1490
1498
1506
1514
1522
1530
1538
1546
1554
274
378
386
454
314
58
0
7
482
490
498
37
403
506
514
1290
1298
1306
1314
Format
F8.3
F8.
F8.2
F8.2
F8.2
F8.2
F8.1
F8.3
F8.4
F8.4
F8.3
F8.4
F8.4
Label
DIC (mg/L) form 2
Distance from coast (km)
DOC (mg/L) form 1 1
Dis. oxygen (mg/L) at mid-hyp form 1 D
Dis. oxygen (mg/L) at 1 .5 m form 1 D
Dis. oxygen (mg/L) at btm 1 .5 m form 1 D
Site depth (m) form 1 D
Iron (fJQ/L) form 11
Fluoride O^eq/L)
HC03 (/xeq/L)
Hydrogen ion deposition (g/m /yr)
Hydrologic type
Potassium (/ieq/L)
Lake identification number
Lake name form 1 D
Latitude
Latitude (decimal degrees)
280
-------
Contents of SAS Member EPAZOOP.EPACGPCA (continued)
Number of Observations: 147
MEMTYPE: Data
Alphabetical List of Variables and Attributes:
Number of Variables: 194
#
165
166
167
168
169
170
171
172
65
66
173
174
175
176
177
178
179
180
181
182
183
184
67
68
69
90
89
70
71
72
73
74
47
75
76
77
78
49
79
80
4
81
147
Variable
LCL5
LCL6
LCL7
LCL8
LCL9
LCL10
LCL11
LCL12
LCL98
LCOLOR02
LCOP1
LCOP2
LCOP3
LCOP4
LCOP5
LCOP6
LCOP7
LCOP8
LCOP9
LCOP10
LCOP11
LCOP12
LDIC02
LDOC11
LDO 151D
LDPAER
LDPSIT1D
LFE11
LFTL98
LHCO398
LHDEP99
LK98
LKSIZ99
LLKSIZ99
LMG98
LMN11
LNA98
LNGDD99
LNH498
LN0398
LONG99
LPTL11
LR1
Type
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
CHAR
NUM
NUM
Length
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
11
8
8
Position Format Label
1322
1330
1338
1346
1354
1362
1370
1378
522
530
1386
1394
1402
1410
1418
1426
1434
1442
1450
1458
1466
1474
538
546
554
722
714
562
570
578
586
594
395 F8.2 Lake surface area (ha)
602
610
618
626
41 1 F8.4 Longitude (decimal degrees)
634
642
47 Longitude
650
1178
281
-------
Number of Observations: 147
MEMTYPE: Data
Alphabetical List of Variables and Attributes:
Contents of SAS Member EPAZOOP.EPACGPCA (continued)
Number of Variables: 194
#
148
149
150
151
152
153
154
155
156
157
158
159
160
82
83
84
85
86
35
25
38
41
36
18
22
10
14
93
94
95
96
97
30
109
110
111
112
113
114
115
116
117
118
119
Variable
LR2
LR3
LR4
LR5
LR6
LR7
LR8
LR9
LR10
LR11
LR12
LR13
LR14
LSCEME98
LSIO21 1
LSO498
LT0151D
LTUR02
MG98
MN11
NA98
NH498
NO398
PH0151D
PH02
PHMH1D
PH B1D
PRJN31
PRIN32
PRIN33
PRIN34
PRIN35
PTL11
R1
R2
R3
R4
R5
R6
R7
R8
R9
R10
R11
Type
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
NUM
Length
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
Position Format
1186
1194
1202
1210
1218
1226
1234
1242
1250
1258
1266
1274
1282
658
666
674
682
690
298 F8.4
218 F8.3
322 F8.4
346 F8.4
306 F8.4
162 F8.2
194 F8.2
98 F8.2
130 F8.2
746
754
762
770
778
258 F8.4
874
882
890
898
906
914
922
930
938
946
954
Label
Magnesium (/xeq/L)
Manganese (pg/L) form 1 1
Sodium (A*eq/L)
Ammonium (jueq/L)
Nitrate (/xeq/L)
pH at 1 .5 m form 1 D
Station pH form 2
pH at mid-hyp form 1 D
pH at btm 1 .5 m form 1 D
Total phosphorus (/xg/L) form 1 1
282
-------
Number of Observations: 147
MEMTYPE: Data
Alphabetical List of Variables and Attributes:
Contents of SAS Member EPAZOOP.EPACGPCA (continued)
Number of Variables: 194
#
120
121
122
58
50
52
43
27
20
39
51
15
7
11
91
23
53
54
19
Variable
R12
R13
R14
RANAER
RT99
SBRGN99
SECME98
SIO21 1
SITETYP
SO498
ST99
T0151D
TMPMH1D
IMP 81 D
TTMP
TUR02
WALA99
WSHED99
WTRSH ID
Type
NUM
NUM
NUM
NUM
NUM
CHAR
NUM
NUM
CHAR
NUM
CHAR
NUM
NUM
NUM
NUM
NUM
NUM
NUM
CHAR
Length
8
8
8
8
8
1
8
8
9
8
2
8
8
8
8
8
8
8
7
Position
962
970
978
466
419
429
362
234
177
330
427
138
74
106
730
202
430
438
170
Format
F8.3
F8.1
F8.3
F8.4
F8.1
F8.1
F8.1
F8.2
F8.2
F8.3
Label
Residence time (yr)
NSWS subregion
Mean: Secchi disk disappear/reappear
Silica (mg/L) form 1 1
Sampling site or type code
Sulfate Gueq/L)
State (2-letter abbreviation)
Temperature at 1.5 m (deg C) form 1D
Temperature at miid-hyp (deg C) form 1 D
Temperature at btm 1.5 m (deg C) form 1D
Turbidity (NTU) form 2
Watershed area/Lake area
Watershed area (ha)
283
-------
APPENDIX D
List of All Species and Their Summary Statistics for Abundance
(Untransformed) Found in All 147 ELS-II Lakes, and
a Separate Listing for Each Chemistry Cluster
Variable
N
Mean
Standard
Deviation
Sum
Variance
C.V.
ROTIFERA
Summary Statistics for All ELS-II Lakes
Keratella earlinae
K. cochlearis hispida
K. crassa
K. taurocephala
K. cochlearis-cochlearis
K. hiemalis
K. irregularis
K. ticinensis
Keratella c. robusta
K. serrulata
Kellicottia longispina
K. bostoniensis
Notholco labis
N. squamula
Brachionus urceolaris
B. quadridentatus
Euchlanis dilatata
E. pellucida
Platyias patulus
Mytilina spp.
Lecane luna
L. flexilis
L mira
L. tudicola
L. ungulata
Monostyla lunaris
Trichocerca multicrinis
T. cylindrica
T. pusilla
T. porcellus
T. similis
T. rousseleti
T. lata
T. elongata
Gastropus hyptopus
G. stylifer
Ascomorpha ovalis
A. saltans
A. ecaudis
Asplanchna priodonta
Asplanchna sp.
Polyarthra vulgaris
P. euryptera
P. remata
P. major
P. dolichoptera
147
147
147
147
147
147
147
147
147
147
147
147
147
147
147
147
147
147
147
147
147
147
147
147
147
147
147
147
147
147
147
147
147
147
147
147
147
147
147
147
147
147
147
147
147
147
0.07693878
32.63086168
104.69653061
119.87917234
34.09519274
0.24732426
0.12619048
0.00117914
0.34467120
0.00702948
50.18235828
36.61841270
0.00426304
0.03081633
0.00006803
0.00006803
0.61907029
0.24294785
0.02120181
0.00006803
0.18197279
0.02780045
0.00408163
0.01455782
0.00759637
0.12192744
17.12256236
22.78113379
0.14383220
0.00006803
0.00274376
0.02929705
0.00006803
0.04882086
0.22643991
1.04336735
0.23104308
0.00573696
0.66918367
9.79875283
0.03888889
25.87945578
21.27784580
5.82875283
5.70546485
0.01968254
0.66607868
251.44676749
344.26949798
400.38988849
147.52463505
1.38745448
1.52997821
0.01429629
4.17891623
0.08522790
245.30988902
150.80251846
0.05168660
0.31483962
0.00082479
0.00082479
7.47792199
2.94558609
0.23223451
0.00082479
1.64257750
0.24232056
0.03617309
0.17650423
0.09210111
0.96534644
65.24811675
80.63220480
1.62141417
0.00082479
0.03162687
0.32491639
0.00082479
0.36532898
2.23457478
5.92340972
1.73811173
0.06161670
3.01426291
38.51315372
0.47150272
113.56936812
76.79368086
19.15899781
38.59259590
0.23863811
11.3100000
4796.7366667
15390.3900000
17622.2383333
5011.9933333
36.3566667
18.5500000
0.1733333
50.6666667
1.0333333
7376.8066667
5382.9066667
0.6266667
4.5300000
0.0100000
0.0100000
91.0033333
35.7133333
3.1166667
0.0100000
26.7500000
4.0866667
0.6000000
2.1400000
1.1166667
17.9233333
2517.0166667
3348.8266667
21.1433333
0.0100000
0.4033333
4.3066667
0.0100000
7.1766667
33.2866667
153.3750000
33.9633333
0.8433333
98.3700000
1440.4166667
5.7166667
3804.2800000
3127.8433333
856.8266667
838.7033333
2.8933333
0.443661
63225.476883
118521.487236
160312.062805
21763.517945
1.925030
2.340833
0.000204
17.463341
0.007264
60176.941651
22741.399574
0.002672
0.099124
0.000001
0.000001
55.919317
8.676477
0.053933
0.000001
2.698061
0.058719
0.001308
0.031154
0.008483
0.931894
4257.316739
6501.552452
2.628984
0.000001
0.001000
0.105571
0.000001
0.133465
4.993324
35.086783
3.021032
0.003797
9.085781
1483.263010
0.222315
12898.001374
5897.269420
367.067197
1489.388458
0.056948
865.726
770.580
328.826
333.995
432.685
560.986
1212.436
1212.436
1212.436
1212.436
488.837
411.822
1212.436
1021.665
1212.436
1212.436
1207.928
1212.436
1095.352
1212.436
902.650
871.642
886.241
1212.436
1212.436
791.738
381.065
353.943
1127.296
1212.436
1152.682
1109.041
1212.436
748.305
986.829
567.720
752.289
1074.030
450.439
393.041
1212.436
438.840
360.909
328.698
676.415
1212.436
284
-------
Variable
N
Mean
Standard
Deviation
Sum
Variance
C.V.
Summary Statistics for All ELS-II Lakes (continued)
ROTIFER A (continued)
Synchaeta pectinata
S. kitti
S. oblonga
Ploesoma truncatum
P. lenticularie
P. hudsoni
P. triacanthum
Filinia spp.
F. terminalis
F. longiseta
Hexarthra mira
Conochilus unicornis
C. hippocrepis
Conochiloides dossarius
C. natans
Collotheca pelagica
C. mutabilis
Unidentified rotifera
CLADOCERA
Leptodora kindtii
Diaphanosoma birgei
D. brachyurum
Sida crystallina
Holopedium gibberum
Bosmina longirostris
Eubosmina hagmann
E. tubicen
E. longispina
Chydorus brevalibris
C. sphaericus
Chydorus sp.
Alona setulosa
A. guttata
A. circumfimbrata
A. barbula
Alona sp.
Alonella acutirostris
Kurzia laissima
Acroperus harpae
Eurycercus lamellatus
Graptoleberis testudinaria
Polyphemus pediculus
Daphnia catawba
D. galeata mendotae
D. rosea
D. ambigua
D. pulex
D. parvula
D. schodleri
D. retrocurva
D. longiremus
147
147
147
147
147
147
147
147
147
147
147
147
147
147
147
147
147
147
147
147
147
147
147
147
147
147
147
147
147
147
147
147
147
147
147
147
147
147
147
147
147
147
147
147
147
147
147
147
147
147
5.03816327
0.44950113
0.07605442
2.88964853
0.00260771
0.79181406
0.03077098
0.04301587
1.16809524
0.43467120
22.96557823
86.96866213
1.60646259
4.67453515
17.10789116
0.14986395
0.57712018
0.02247166
0.03375283
29.20301587
0.01514739
0.11045351
10.02201814
41.31733560
2.88492063
8.73029478
2.90606576
0.75437642
0.36419501
0.01603175
0.00013605
0.01034014
0.00823129
0.00006803
0.02412698
0.04362812
0.00006803
0.00006803
0.00006803
0.00013605
0.09213152
12.96732426
0.61628118
0.11981859
2.21098639
0.55009070
4.86845805
0.39358277
0.55569161
0.36111111
27.08630427
3.60722129
0.92211086
10.53031322
0.03161680
6.33316663
0.35896881
0.52153974
13.80585703
3.07953133
254.80250912
689.58796170
15.59927626
25.02111786
196.13713999
1.26842661
3.54396792
0.17274491
0.20615988
74.91000499
0.18365237
0.63317814
34.12053368
153.65058605
25.48957954
23.90060867
15.04752201
6.22697502
3.36616317
0.19437459
0.00116242
0.11106906
0.09814513
0.00082479
0.17127169
0.52813303
0.00082479
0.00082479
0.00082479
0.00116242
0.52438469
36.20455688
2.96126548
1.21808167
12.16791264
3.25837770
18.13778087
2.56545153
4.09927439
3.22096560
740.6100000
66.0766667
11.1800000
424.7783333
0.3833333
116.3966667
4.5233333
6.3233333
171.7100000
63.8966667
3375.9400000
12784.3933333
236.1500000
687.1566667
2514.8600000
22.0300000
84.8366667
3.3033333
4.9616667
4292.8433333
2.2266667
16.2366667
1473.2366667
6073.6483333
424.0833333
1283.3533333
427.1916667
110.8933333
53.5366667
2.3566667
0.0200000
1.5200000
1.2100000
0.0100000
3.5466667
6.4133333
0.0100000
0.0100000
0.0100000
0.0200000
13.5433333
1906.1966667
90.5933333
17.6133333
325.0150000
80.8633333
715.6633333
57.8566667
81.6866667
53.0833333
733.667879
13.012045
0.850288
110.887497
0.001000
40.109000
0.128859
0.272004
190.601688
9.483513
64924.318656
475531.584499
243.337420
626.056339
38469.777682
1.608906
12.559709
0.029841
0.042502
5611.508847
0.033728
0.400915
1164.210819
23608.502592
649.718665
571.239095
226.427919
38.775218
11.331055
0.037781
0.000001
0.012336
0.009632
0.000001
0.029334
0.278924
0.000001
0.000001
0.000001
0.000001
0.274979
1310.769939
8.769093
1.483723
148.058098
10.617025
328.979095
6.581542
16.804051
10.374619
537.623
802.494
1212.436
364.415
1212.436
799.830
1166.583
1212.436
1181.912
708.474
1109.497
792.915
971.033
535.264
1146.472
846.385
614.078
768.724
610.793
256.515
1212.436
573.253
340.456
371.879
883 545
273.766
517.797
825.447
924.275
1212.436
854.380
1074.155
1192.342
1212.436
709.876
1210.534
1212.436
1212.436
1212.436
854.380
569.170
279.198
480.506
1016.605
550.339
592.335
373.557
651.820
737.689
891.960
285
-------
Variable
CLADOCERA (continued)
D. dubia
Scapholebris mucronata
Ceriodaphnia reticulata
C. lacustris
C. affinis/dubia
C. quadrangula
Ceriodaphnia sp.
COPEPODA
Epischura lacustris
E. nordenskioldi
Epischura spp.
Aglaodiaptomus leptopus
A. spatulocrenatus
Leptodiaptomus minutus
L sicilis
Skistodiaptomus oregonensis
S. reighhardi
S. pygmaeus
Skistodiaptomus spp.
Onychodiaptomus birgei
Unknown sp. calanoida
Tropocyclops sp. 1
Mesocyclops edax
Tropocyclops sp. 2
T. prasinus-mexicanus
T. prasinus
Cyclops scutifer
C. strenus strenuus
C. vernalis
C. bicuspidatus thomasi
Cyclops sp.
Orthocyclops modestus
Eucyclops speratus
E. agilus
E. prionophonis
Ectocyclops phaleratus
Macrocyclops albidus
Unknown sp. cyclopoida
Ergasilus chautauquaensis
Nauplii
MISCELLANEOUS
Chaoborus punctipennis
C. americanus
C. flavicens
Chaoborus spp.
Mites 1
Mites 2
Ostracoda
N Mean
Summary Statistics
147 0.64962585
147 0.01258503
147 0.43185941
147 0.30224490
147 6.05038549
147 1.07138322
147 0.00006803
147 0.49113379
147 0.40180272
147 0.01435374
147 0.25773243
147 0.95744898
147 73.73926304
147 0.07256236
147 0.94816327
147 3.09308390
147 6.41460317
147 0.60072562
147 0.36589569
147 0.04036281
147 0.01365079
147 27.21916100
147 0.05414966
147 17.58734694
147 11.23541950
147 1.03519274
147 0.00276644
147 0.08086168
147 0.85950113
147 0.92269841
147 0.53623583
147 0.02993197
147 0.01462585
147 0.09068027
147 0.01455782
147 0.03437642
147 4.86442177
147 0.00446712
Standard
Deviation
for All ELS-II
3.17405257
0.15258543
2.35642588
3.30437515
38.12871578
12.60907155
0.00082479
2.14340759
1.95729450
0.13613585
1.44454461
9.20436699
129.40992807
0.87977184
4.66587818
28.50755876
22.37350552
6.70326279
3.43794519
0.37774830
0.16550708
55.05663125
0.65652973
47.98196678
66.82051728
4.47980091
0.03354130
0.86849448
4.14518039
5.95812811
3.01495151
0.20063426
0.17732901
0.82672871
0.17650423
0.33511051
23.10427219
0.05168912
147211.85553288 31142.51610418
147 2.37863946
147 0.19510204
147 0.04809524
147 0.02176871
147 0.11637188
147 0.00938776
147 0.11625850
6.75392556
1.28203524
0.58312377
0.26393155
0.38799526
0.08377510
1.17648542
Sum
Lakes (continued)
95.4950000
1.8500000
63.4833333
44.4300000
889.4066667
157.4933333
0.0100000
72.1966667
59.0650000
2.1100000
37.8866667
140.7450000
10839.6716667
10.6666667
139.3800000
454.6833333
942.9466667
88.3066667
53.7866667
5.9333333
2.0066667
4001.2166667
7.9600000
2585.3400000
1651.6066667
152.1733333
0.4066667
11.8666667
126.3466667
135.6366667
78.8266667
4.4000000
2.1500000
13.3300000
2.1400000
5.0533333
715.0700000
0.6566667
31142.7633333
349.6600000
28.6800000
7.0700000
3.2000000
17.1066667
1.3800000
17.0900000
Variance
10.074610
0.023282
5.552743
10.918895
1453.798967
158.988685
0.000001
4.594196
3.831002
0.018533
2.086709
84.720372
16746.929482
0.773998
21.770419
812.680906
500.573749
44.933732
11.819467
0.142694
0.207393
3031.232645
0.431031
2302.269136
4464.981530
20.068616
0.001125
0.754283
17.182520
35.499291
9.089933
0.040254
0.031446
0.683480
0.031154
0.112299
533.807394
0.002672
49959.448828
45.615510
1.643614
0.340033
0.069660
0.150540
0.007018
1.384118
C.V.
488.597
1212.436
545.647
1093.277
630.187
1176.896
1212.436
436.420
487.128
948.435
560.482
961.343
175.497
1212.436
492.096
921.655
348.790
1115.861
939.597
935.882
1212.436
202.272
1212.436
272.821
594.731
432.750
1212.436
1074.050
482.277
645.729
562.244
670.301
1212.436
911.696
1212.436
974.827
474.964
1157.102
105.504
283.941
657.110
1212.436
1212.436
333.410
892.387
1011.956
286
-------
Variable
N
Mean
Standard
Deviation
Sum
Variance
C.V.
ROTIFERA
Summary Statistics for CHEMGRP 1 Lakes
Keratella earlinae
K. cochlearis hispida
K. crassa
K. taurocephala
K. cochlearis-cochlearis
K. hiemalis
K. irregularis
K. ticinensis
Keratella c. robusta
K. serrulata
Kellicottia longispina
K. bostoniensis
Notholco labis
N. squamula
Brachionus urceolaris
B. quadridentatus
Euchlanis dilatata
E. pellucida
Platyias patulus
Mytilina spp.
Lecane luna
L. flexilis
L. mira
L. tudicola
L. ungulata
Monostyla lunaris
Trichocerca multicrinis
T. cylindrica
T. pusilla
T. porcellus
T. similis
T. rousseleti
T. lata
T. elongata
Gastropus hyptopus
G. stylifer
Ascomorpha ovalis
A. saltans
A. ecaudis
Asplanchna priodonta
Asplanchna sp.
Polyarthra vulgaris
P. euryptera
P. remata
P. major
P. dolichoptera
Synchaeta pectinata
S. kitti
S. oblonga
Ploesoma truncatum
P. lenticularie
P. hudsoni
P. triaoanthum
48
48
48
48
48
48
48
48
48
48
48
48
48
48
48
48
48
48
48
48
48
48
48
48
48
48
48
48
48
48
48
48
48
48
48
48
48
48
48
48
48
48
48
48
48
48
48
48
48
48
48
48
48
0.00000000
5.83333333
116.29013889
276.16857639
2.37277778
0.07138889
0.00000000
0.00000000
0.00000000
0.00000000
17.92562500
1.80000000
0.01305556
0.09437500
0.00000000
0.00000000
1.88888889
0.00000000
0.00000000
0.00000000
0.00000000
0.00000000
0.00000000
0.00000000
0.00000000
0.00041667
8.31104167
0.67118056
0.00000000
0.00000000
0.00000000
0.00000000
0.00000000
0.0954861 1
0.00000000
1.21368056
0.04687500
0.00000000
0.10687500
2.45243056
0.00000000
1.43055556
2.08930556
5.26659722
0.00000000
0.00000000
3.40395833
0.00000000
0.23291667
1.12166667
0.00798611
0.77194444
0.09062500
0.00000000
35.54544182
520.97633593
630.35881891
11.23871434
0.27027858
0.00000000
0.00000000
0.00000000
0.00000000
105.11320407
6.19809274
0.09045154
0.54935516
0.00000000
0.00000000
13.08660610
0.00000000
0.00000000
0.00000000
0.00000000
0.00000000
0.00000000
0.00000000
0.00000000
0.00201941
23.48191660
3.26569703
0.00000000
0.00000000
0.00000000
0.00000000
0.00000000
0.60815362
o.oooooood
7.56052043
0.27351352
0.00000000
0.74045172
10.76218906
0.00000000
4.04400364
7.74462855
16.20762101
0.00000000
0.00000000
14.27777793
0.00000000
1.61369400
6.61125479
0.05532940
5.34818799
0.62786842
0.0000000
280.0000000
5581.9266667
13256.0916667
113.8933333
3.4266667
0.0000000
0.0000000
0.0000000
0.0000000
860.4300000
86.4000000
0.6266667
4.5300000
0.0000000
0.0000000
90.6666667
0.0000000
0.0000000
0.0000000
0.0000000
0.0000000
0.0000000
0.0000000
0.0000000
0.0200000
398.9300000
32.2166667
0.0000000
0.0000000
0.0000000
0.0000000
0.0000000
4.5833333
0.0000000
58.2566667
2.2500000
0.0000000
5.1300000
117.7166667
0.0000000
68.6666667
100.2866667
252.7966667
0.0000000
0.0000000
163.3900000
0.0000000
11.1800000
53.8400000
0.3833333
37.0533333
4.3500000
0.00000
1263.47843
271416.34260
397352.24057
126.30870
0.07305
0.00000
0.00000
0.00000
0.00000
11048.78567
38.41635
0.00818
0.30179
0.00000
0.00000
171.25926
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
551.40041
10.66478
0.00000
0.00000
0.00000
0.00000
0.00000
0.36985
0.00000
57.16147
0.07481
0.00000
0.54827
115.82471
0.00000
16.44305
59.97927
262.68698
0.00000
0.00000
203.85494
0.00000
2.60401
43.70869
0.00306
28.60311
0.39422
609.350
447.997
228.251
473.652
378.600
586.385
344.338
692.820
582.098
692.820
484.658
282.539
486.560
636.903
622.942
583.496
692.820
438.838
283.457
370.680
307.744
419.446
692.820
589.414
692.820
692.820
692.820
287
-------
Variable
N
Mean
Standard
Deviation
Sum
Variance
C.V.
ROTIFERA (continued)
Summary Statistics for CHEMGRP 1 Lakes (continued)
Filinia spp.
F. terminalis
F. longiseta
Hexarthra mira
Conochilus unicornis
C. hippocrepis
Conochiloides dossarius
C. natans
Collotheca
C. mutabilis
Unidentified rotifera
CLADOCERA
Leptodora kindtii
Diaphanosoma birgei
D. brachyurum
Sida crystalline
Holopedium gibberum
Bosmina longirostris
Eubosmina hagmanni
E. tubicen
E. longispina
Chydorus brevalibris
C. sphaericus
Chydorus sp.
Alona setulosa
A. guttata
A. circumfimbrata
A. barbula
Alona sp.
Alonella acutirostris
Kurzia laissima
Acroperus harpae
Eurycercus lamellatus
Graptoleberis testudinaria
Polyphemus pediculus
Daphnia catawba
D. galeata mendotae
D. rosea
D. ambigua
D. pulex
D. parvula
D. schodleri
D. retrocurva
0. longiremis
D. dubia
Scapholebris mucronata
Ceriodaphnia reticulata
C. lacustris
C. affinis/dubia
C. quadrangula
Ceriodaphnia sp.
48
48
48
48
48
48
48
48
48
48
48
48
48
48
48
48
48
48
48
48
48
48
48
48
48
48
48
48
48
48
48
48
48
48
48
48
48
48
48
48
48
48
48
48
48
48
48
48
48
48
0.13173611
0.00000000
0.00000000
1.52604167
184.51687500
0.00000000
1.36875000
50.67555556
0.01173611
0.01277778
0.03284722
0.00041667
41.02003472
0.00000000
0.00000000
9.66270833
73.88104167
7.4504861 1
11.00993056
8.12677083
0.04965278
0.00395833
0.04909722
0.00020833
0.02784722
0.02520833
0.00000000
0.03770833
0.00000000
0.00020833
0.00020833
0.00000000
0.00020833
0.20465278
3.85916667
0.00020833
0.06597222
5.51388889
1.05312500
1.31416667
0.51902778
0.00000000
0.00000000
0.00020833
0.00000000
0.00062500
0.00000000
0.02291667
0.02625000
0.00000000
0.91269455
0.00000000
0.00000000
7.55511655
1165.85122700
0.00000000
9.48297817
343.11969370
0.08131016
0.06852704
0.22757223
0.00288675
113.11002868
0.00000000
0.00000000
41.01469784
250.66667466
43.61752439
30.01501247
25.61287169
0.26982810
0.02742414
0.34015553
0.00144338
0.19293121
0.17171215
0.00000000
0.22913359
0.00000000
0.00144338
0.00144338
0.00000000
0.00144338
0.82307781
9.92319192
0.00144338
0.45706896
20.76897700
5.20085765
8.02630603
2.93782184
0.00000000
0.00000000
0.00144338
0.00000000
0.00433013
0.00000000
0.10882268
0.18186533
0.00000000
6.3233333
0.0000000
0.0000000
73.2500000
8856.8100000
0.0000000
65.7000000
2432.4266667
0.5633333
0.6133333
1.5766667
0.83301
0.00000
0.00000
57.07979
1359209.08350
0.00000
89.92688
117731.12420
0.00661
0.00784
0.05179
692.820
495.079
631.840
692.820
677.091
692.820
692.820
692.820
0.00288675
113.11002868
0.00000000
0.00000000
41.01469784
250.66667466
43.61752439
30.01501247
25.61287169
0.26982810
0.02742414
0.34015553
0.00144338
0.19293121
0.17171215
0.00000000
0.22913359
0.00000000
0.00144338
0.00144338
0.00000000
0.00144338
0.82307781
9.92319192
0.00144338
0.45706896
20.76897700
5.20085765
8.02630603
2.93782184
0.00000000
0.00000000
0.00144338
0.00000000
0.00433013
0.00000000
0.10882268
0.18186533
0.00000000
0.0200000
1968.9616667
0.0000000
0.0000000
463.8100000
3546.2900000
357.6233333
528.4766667
390.0850000
2.3833333
0.1900000
2.3566667
0.0100000
1.3366667
1.2100000
0.0000000
1.8100000
0.0000000
0.0100000
0.0100000
0.0000000
0.0100000
9.8233333
185.2400000
0.0100000
3.1666667
264.6666667
50.5500000
63.0800000
24.9133333
0.0000000
0.0000000
0.0100000
0.0000000
0.0300000
0.0000000
1.1000000
1.2600000
0.0000000
0.00001
12793.87859
0.00000
0.00000
1682.20544
62833.78179
1902.48843
900.90097
656.01920
0.07281
0.00075
0.11571
0.00000
0.03722
0.02949
0.00000
0.05250
0.00000
0.00000
0.00000
0.00000
0.00000
0.67746
98.46974
0.00000
0.20891
431.35041
27.04892
64.42159
8.63080
0.00000
0.00000
0.00000
0.00000
0.00002
0.00000
0.01184
0.03308
0.00000
692.820
275.743
424.464
339.284
585.432
272.618
315.167
543.430
692.820
692.820
692.820
692.820
681.172
607.647
692.820
692.820
692.820
402.183
257.133
692.820
692.820
376.667
493.850
610.753
566.024
,
692.820
692.820
474.863
692.820
288
-------
Variable
N
Mean
Standard
Deviation
Sum
Variance
C.V.
COPEPODA
Summary Statistics for CHEMGRP 1 Lakes (continued)
Epischura lacustris
E. nordenskioldi
Epischura spp.
Aglaodiaptomus leptopus
A. spatulocrenatus
Leptodiaptomus minutus
L sicilis
Skistodiaptomus oregonensis
S. reighardi
S. pygmaeus
Skistodiaptomus spp.
Onychodiaptomus birgei
Unknown sp. calanoida
Tropocyclops sp. 1
Mesocyclops edax
Tropocyclops sp. 2
T. prasinus-mexicanus
T. prasinus
Cyclops scutifer
C. strenus strenuus
C. vernalis
C. bicuspidatus thomasi
Cyclops sp.
Orthocyclops modestus
Eucyclops speratus
E. agilus
E. prionophonis
Ectocyclops phaleratus
Macrocyclops albidus
Unknown sp. cyclopoida
Ergasilus chautauquaensis
Nauplii
MISCELLANEOUS
Chaoborus punctipennis
C. americanus
C. flavicens
Chaoborus spp.
Mites 1
Mites 2
Ostracoda
48
48
48
48
48
48
48
48
48
48
48
48
48
48
48
48
48
48
48
48
48
48
48
48
48
48
48
48
48
48
48
48
48
48
48
48
48
48
48
0.00020833
0.95350694
0.00000000
0.72208333
0.13902778
151.56736111
0.00000000
0.00000000
7.42805556
6.01986111
0.00000000
0.00000000
0.00000000
0.00000000
34.04208333
0.00000000
0.87736111
10.71000000
0.57659722
0.00000000
0.00000000
0.27395833
0.00000000
0.80354167
0.03375000
0.00000000
0.00000000
0.00000000
0.09381944
1.95229167
0.00000000
198.62291667
2.14638889
0.59750000
0.00000000
0.06666667
0.08152778
0.00000000
0.00020833
0.00144338
3.21995056
0.00000000
2.43636182
0.75739380
165.97886088
0.00000000
0.00000000
49.14902306
23.97653830
0.00000000
0.00000000
0.00000000
0.00000000
84.54789661
0.00000000
2.98893293
55.47078391
2.19974246
0.00000000
0.00000000
1.30273284
0.00000000
3.56358947
0.16921125
0.00000000
0.00000000
0.00000000
0.58061555
9.18361371
0.00000000
184.53335254
9.50292519
2.20457417
0.00000000
0.46188022
0.21566649
0.00000000
0.00144338
0.0100000
45.7683333
0.0000000
34.6600000
6.6733333
7275.2333333
0.0000000
0.0000000
356.5466667
288.9533333
0.0000000
0.0000000
0.0000000
0.0000000
1634.0200000
0.0000000
42.1133333
514.0800000
27.6766667
0.0000000
0.0000000
13.1500000
0.0000000
38.5700000
1.6200000
0.0000000
0.0000000
0.0000000
4.5033333
93.7100000
0.0000000
9533.9000000
103.0266667
28.6800000
0.0000000
3.2000000
3.9133333
0.0000000
0.0100000
0.00000
10.36808
0.00000
5.93586
0.57365
27548.98226
0.00000
0.00000
2415.62647
574.87439
0.00000
0.00000
0.00000
0.00000
7148.34682
0.00000
8.93372
3077.00787
4.83887
0.00000
0.00000
1.69711
0.00000
12.69917
0.02863
0.00000
0.00000
0.00000
0.33711
84.33876
0.00000
34052.55820
90.30559
4.86015
0.00000
0.21333
0.04651
0.00000
0.00000
692.820
337.696
337.407
544.779
109.508
661.667
398.291
248.363
340.673
517.934
381.504
475.522
443.485
501.367
618.865
470.402
92.906
442.740
368.966
692.820
264.531
692.820
289
-------
Variable
N
Mean
Standard
Deviation
Sum
Variance
C.V.
ROTIFER A
Summary Statistics for CHEMGRP 2 Lakes
Keratella earlinae
K. cochlearis hispida
K. crassa
K. taurocephala
K. cochlearis-cochlearis
K. hiemalis
K. irregularis
K. ticinensis
Keratella c. robusta
K. serrulata
Kellicottia longispina
K. bostoniensis
Notholco labis
N. squamula
Brachionus urceolaris
B. quadridentatus
Euchlenis dilatata
E. pellucida
Platyias petulus
Mytilina spp.
Lecane luna
L. flexilis
L mira
L. tudicola
L. ungulata
Monostyla lunaris
Trichocerca multicrinis
T. cylindrica
T. pusilla
T. porcellus
T. similis
T. rousseleti
T. lata
T. elongata
Gastropus hyptopus
G. stylifer
Ascomorpha ovalis
A. saltans
A. ecaudis
Asplanchna priodonta
Asplanchna sp.
Polyarthra vulgaris
P. euryptera
P. remata
P. major
P. dolichoptera
Synchaeta pectinata
S. kitti
S. oblonga
Ploesoma truncatum
P. lenticularie
P. hudsoni
P. triacanthum
53 0.00000000
53 81.54779874
53 137.66666667
53 78.31735843
53 43.49031447
53 0.53037736
53 0.35000000
53 0.00000000
53 0.95597484
53 0.01949686
53 86.27484277
53 90.97540881
53 0.00000000
53 0.00000000
53 0.00000000
53 0.00000000
53 0.00000000
53 0.67383648
53 0.05880503
53 0.00000000
53 0.00528302
53 0.07710692
53 0.00761006
53 0.04037736
53 0.00000000
53 0.17974843
53 17.38377358
53 45.80320755
53 0.37018868
53 0.00018868
53 0.00037736
53 0.00000000
53 0.00018868
53 0.01490566
53 0.01477987
53 1.77930818
53 0.24534591
53 0.00169811
53 1.27232704
53 12.49213836
53 0.10786164
53 44.85075472
53 35.18113208
53 8.87012579
53 1.18031447
53 0.00000000
53 1.59100629
53 1.21314465
53 0.00000000
53 4.95805031
53 0.00000000
53 1.49704403
53 0.00000000
0.00000000
415.01723871
274.79963817
234.08999432
98.33248606
2.18570156
2.54803846
0.00000000
6.95960191
0.14193925
386.62627755
242.05182486
0.00000000
0.00000000
0.00000000
0.00000000
0.00000000
4.90560361
0.38622936
0.00000000
0.03846096
0.40123636
0.05401953
0.29395161
0.00000000
1.15362729
29.66312895
100.84620416
2.69361459
0.00137361
0.00192380
0.00000000
0.00137361
0.08536649
0.10481671
6.73246997
1.37867246
0.01104812
4.22831969
23.66558163
0.78524456
173.09780723
94.19898992
25.09696915
5.79216592
0.00000000
5.14444255
5.96188311
0.00000000
14.68011785
0.00000000
9.25737594
0.00000000
0.0000000
4322.0333333
7296.3333333
4150.8200000
2304.9866667
28.1100000
18.5500000
0.0000000
50.6666667
1.0333333
4572.5666667
4821.6966667
0.0000000
0.0000000
0.0000000
0.0000000
0.0000000
35.7133333
3.1166667
0.0000000
0.2800000
4.0866667
0.4033333
2.1400000
0.0000000
9.5266667
921.3400000
2427.5700000
19.6200000
0.0100000
0.0200000
0.0000000
0.0100000
0.7900000
0.7833333
94.3033333
13.0033333
0.0900000
67.4333333
662.0833333
5.7166667
2377.0900000
1864.6000000
470.1166667
62.5566667
0.0000000
84.3233333
64.2966667
0.0000000
262.7766667
0.0000000
79.3433333
0.0000000
0.000000
172239.308429
75514.841138
54798.125442
9669.277815
4.777291
6.492500
0.000000
48.436059
0.020147
149479.878495
58589.085920
0.000000
0.000000
0.000000
0.000000
0.000000
24.064947
0.149173
0.000000
0.001479
0.160991
0.002918
0.086408
0.000000
1.330856
879.901219
10169.956894
7.255560
0.000002
0.000004
0.000000
0.000002
0.007287
0.010987
45.326152
1.900738
0.000122
17.878687
560.059754
0.616609
29962.850868
8873.449702
629.857861
33.549186
0.000000
26.465289
35.544050
0.000000
215.505860
0.000000
85.699009
0.000000
508.925
199.612
298.899
226.102
412.103
728.011
728.01 1
728.011
448.133
266.063
728.011
656.796
728.011
520.364
709.843
728.01 1
641.801
170.637
220.173
727.633
728.011
509.808
728.01 1
572.712
709.185
378.376
561.930
650.612
332.330
189.444
728.011
385.942
267.754
282.938
490.731
323.345
491.440
296.087
618.377
290
-------
Variable
N
Mean
Standard
Deviation
Sum
Variance
C.V.
ROTIFERA (continued)
Summary Statistics for CHEMGRP 2 Lakes (continued)
Filinia spp.
F. terminalis
F. longiseta
Hexarthra mira
Conochilus unicornis
C. hippocrepis
Conochiloides dossarius
C. natans
Collotheca pelagica
C. mutabilis
Unidentified rotifera
CLADOCERA
Leptodora kindtii
Diaphanosoma birgeii
D. brachyurum
Sida crystalline
Holopedium gibberum
Bosmina longirostris
Eubosmina hagmanni
E. tubicen
E. longispina
Chydorus brevalibris
C. sphaericus
Chydorus sp.
Alona setulosa
A. guttata
A. circumfimbrata
A. barbula
Alona sp.
Alonella acutirostris
Kurzia laissima
Acroperus harpae
Eurycercus lamellatus
Graptoleberis testudinaria
Polyphemus pediculus
Daphnia catawba
D. galeata mendotae
0. rosea
D. ambigua
D. pulex
D. parvula
D. schodleri
D. retrocurva
D. longiremis
D. dubia
Scapholebris mucronata
Ceriodaphnia reticulata
C. lacustris
53
53
53
53
53
53
53
53
53
53
53
53
53
53
53
53
53
53
53
53
53
53
53
53
53
53
53
53
53
53
53
53
53
53
53
53
53
53
53
53
53
53
53
53
53
53
53
0.00000000
0.02402516
0.92685535
4.09836478
11.89176101
3.50295597
8.95528302
0.15201258
0.23899371
1.28522013
0.00000000
0.01459119
31.25025157
0.00000000
0.07245283
11.77125786
27.84150943
1.22867925
11.03000000
0.47238994
0.04075472
0.01270440
0.00000000
0.00018868
0.00018868
0.00000000
0.00018868
0.00018868
0.12100629
0.00000000
0.00000000
0.00018868
0.00018868
0.01660377
7.17855346
0.02584906
0.00000000
0.46446541
0.29427673
9.67874214
0.24610063
1.25823899
0.21201258
0.49635220
0.00000000
0.02849057
0.08339623
0.09239276
54.13593327
0.00000000
0.27051561
30.96438252
58.99265685
8.68147963
26.19134490
2.15550383
0.29390507
0.09248945
0.00000000
0.00137361
0.00137361
0.00000000
0.00137361
0.00137361
0.87954013
0.00000000
0.00000000
0.00137361
0.00137361
0.08632202
21.16923478
0.18818397
0.00000000
2.09164863
1.61400553
26.01710163
1.78604799
6.66608926
1.32127760
1.92631532
0.00000000
0.20463284
0.37258768
0.7733333
1656.2633333
0.0000000
3.8400000
623.8766667
1475.6000000
65.1200000
584.5900000
25.0366667
2.1600000
0.6733333
0.0000000
0.0100000
0.0100000
0.0000000
0.0100000
0.0100000
6.4133333
0.0000000
0.0000000
0.0100000
0.0100000
0.8800000
380.4633333
1.3700000
0.0000000
24.6166667
15.5966667
512.9733333
13.0433333
66.6866667
11.2366667
26.3066667
0.0000000
1.5100000
4.4200000
0.000000
0.015209
22.842307
700.665117
1166.059634
650.347130
1533.338354
0.469009
3.027254
30.716004
0.000000
0.008536
2930.699271
0.000000
0.073179
958.792985
3480.133562
75.368089
685.986547
4.646197
0.086380
0.008554
0.000000
0.000002
0.000002
0.000000
0.000002
0.000002
0.773591
0.000000
0.000000
0.000002
0.000002
0.007451
448.136501
0.035413
0.000000
4.374994
2.605014
676.889577
3.189967
44.436746
1.745775
3.710691
0.000000
0.041875
0.138822
513.310
515.654
645.869
287.154
728.01 1
437.260
450.517
728.011
431.226
633.209
173.234
373.368
263.051
211.887
706.570
237.456
456.298
721.156
728.011
728.011
728.01 1
728.011
728.01 1
726.855
728.011
728.011
519.894
294.896
728.01 1
450.335
548.465
268.807
725.739
529.795
623.207
388.094
718.248
446.768
291
-------
Variable
N
Mean
Standard
Deviation
Sum
Variance
C.V.
Summary Statistics for CHEMGRP 2 Lakes (continued)
COPEPODA
C. affinis/dubia
C. quadrangula
Ceriodaphnia sp.
Epischura lacustris
E. nordenskioldi
Epischura spp.
Aglaodiaptomus leptopus
A. spatulocrenatus
Leptodiaptomus minutus
L. sicilis
Skistodiaptomus oregonensis
S. reighardi
S. pygmaeus
Skistodiaptomus spp.
Onychodiaptomus birgei
Unknown sp. calanoida
Tropocyclops sp. 1
Mesocyclops edax
Tropocyclops sp. 2
T. prasinus-mexicanus
T. prasinus
Cyclops scutifer
C. strenus strenuus
C. vernalis
C. bicuspidatus thomasi
Cyclops sp.
Orthocyclops modestus
Eucyclops speratus
E. agilus
E. prionophonis
Ectocyclops phaleratus
Macrocyclops albidus
Unknown sp. cyclopoida
Ergasilus chautauquaensis
Nauplii
MISCELLANEOUS
Chaoborus punctipennis
C. americanus
C. flavicens
Chaoborus spp.
Mites 1
Mites 2
Ostracoda
53
53
53
53
53
53
53
53
53
53
53
53
53
53
53
53
53
53
53
53
53
53
53
53
53
53
53
53
53
53
53
53
53
53
53
53
53
53
53
53
53
53
4.24163522
2.93301887
0.00018868
0.53415094
0.11421384
0.02943396
0.00000000
0.08830189
52.16786164
0.20125786
1.28968553
0.05566038
8.72930818
1.53138365
0.73805031
0.00000000
0.00000000
27.10421384
0.00000000
24.61433962
17.73327044
0.60603774
0.00767296
0.22427673
0.94899371
2.55918239
0.12490566
0.01471698
0.04056604
0.07924528
0.04037736
0.00000000
3.16484277
0.00037736
243.67540881
2.94025157
0.00000000
0.00000000
0.00000000
0.13264151
0.00000000
0.27855346
21.13205543
20.99597378
0.00137361
2.41809842
0.45822733
0.21428248
0.00000000
0.45441736
112.19885793
1.46517935
6.17004392
0.40521366
27.18964998
11.14864124
5.37308739
0.00000000
0.00000000
39.35183259
0.00000000
50.41443198
96.44704463
2.27407322
0.05585996
1.44395652
5.28569945
9.76741955
0.82111260
0.10714124
0.29532521
0.57691437
0.29395161
0.00000000
10.90512233
0.00192380
283.49017339
4,85733325
0.00000000
0.00000000
0.00000000
0.51465587
0.00000000
1.93513295
21.13205543
20.99597378
0.00137361
2.41809842
0.45822733
0.21428248
0.00000000
0.45441736
112.19885793
1.46517935
6.17004392
0.40521366
27.18964998
11.14864124
5.37308739
0.00000000
0.00000000
39.35183259
0.00000000
50.41443198
96.44704463
2.27407322
0.05585996
1.44395652
5.28569945
9.76741955
0.82111260
0.10714124
0.29532521
0.57691437
0.29395161
0.00000000
10.90512233
0.00192380
283.49017339
224.8066667
155.4500000
0.0100000
28.3100000
6.0533333
1.5600000
0.0000000
4.6800000
2764.8966667
10.6666667
68.3533333
2.9500000
462.6533333
81.1633333
39.1166667
0.0000000
0.0000000
1436.5233333
0.0000000
1304.5600000
939.8633333
32.1200000
0.4066667
11.8866667
50.2966667
135.6366667
6.6200000
0.7800000
2.1500000
4.2000000
2.1400000
0.0000000
167.7366667
0.0200000
12914.7966667
446.563767
440.830915
0.000002
5.847200
0.209972
0.045917
0.000000
0.206495
12588.583772
2.146751
38.069442
0.164198
739.277066
124.292201
28.870068
0.000000
0.000000
1548.566728
0.000000
2541.614952
9302.032418
5.171409
0.003120
2.085010
27.938619
95.402485
0.674226
0.011479
0.087217
0.332830
0.086408
0.000000
118.921693
0.000004
80366.678410
498.205
715.849
728.01 1
452.699
401.201
728.01 1
514.618
215.073
728.01 1
478.415
728.01 1
311.475
728.01 1
728.01 1
,
145.187
204.817
543.876
375.236
728.01 1
643.828
556.979
381.662
657.386
728.01 1
728.011
728.01 1
728.011
344.571
509.808
116.339
155.8333333
0.0000000
0.0000000
0.0000000
7.0300000
0.0000000
14.7633333
23.593686
0.000000
0.000000
0.000000
0.264871
0.000000
3.744740
165.201
388.005
694.708
292
-------
Variable
N
Mean
Standard
Deviation
Sum
Variance
C.V.
ROTIFERA
Summary Statistics for CHEMGRP 3 Lakes
Keratella earlinae
K. cochlearis hispida
K. crassa
K. taurocephala
K. cochlearis-cochlearis
K. hiemalis
K. irregularis
K. ticinensis
Keratella c. robusta
K. serrulata
Kellicottia longispina
K. bostoniensis
Notholco labis
Notholco squamula
Brachionus urceolaris
B. quadridentatus
Euchlanis dilatata
E. pellucida
Platyias patulus
Mytilina spp.
Lecane luna
L. flexilis
L. mira
L. tudicola
L ungulata
Monostyla lunaris
Trichocerca multicrinis
T. cylindrica
T. pusilla
T. porcellus
T. similis
T. rousseletl
T. lata
T. elongata
Gastropus hyptopus
G. stylifer
Ascomorpha ovalis
A. saltans
A. ecaudis
Asplanchna priodonta
Asplanchna sp.
Polyarthra vulgaris
P. euryptera
P. remata
P. major
P. dolichoptera
Synchaeta pectinata
S. kitti
S. oblonga
Ploesoma truncatum
P. lenticularie
P. hudsoni
P. triacanthum
46 0.24586957
46 4.23268116
46 54.61152174
46 4.68101449
46 56.37202899
46 0.10478261
46 0.00000000
46 0.00376812
46 0.00000000
46 0.00000000
46 42.25673913
46 10.32195652
46 0.00000000
46 0.00000000
46 0.00021739
46 0.00021739
46 0.00731884
46 0.00000000
46 0.00000000
46 0.00021739
46 0.57543478
46 0.00000000
46 0.00427536
46 0.00000000
46 0.02427536
46 0.18210145
46 26.01623188
46 19.32695652
46 0.03311594
46 0.00000000
46 0.00833333
46 0.09362319
46 0.00000000
46 0.03920290
46 0.70659420
46 0.01771739
46 0.40673913
46 0.01637681
46 0.56101449
46 14.36123188
46 0.00000000
46 29.53311594
46 25.28166667
46 2.91115942
46 16.87275362
46 0.06289855
46 10.71514493
46 0.03869565
46 0.00000000
46 2.35134058
46 0.00000000
46 0.00000000
46 0.00376812
1.18193693
19.26654900
99.03328000
11.58304936
240.0428501 1
0.70765886
0.00000000
0.02555661
0.00000000
0.00000000
90.88602632
23.66783362
0.00000000
0.00000000
0.00147442
0.00147442
0.04963879
0.00000000
0.00000000
0.00147442
2.91918747
0.00000000
0.02899692
0.00000000
0.16464352
1.20921755
109.80068889
90.40442099
0.22460325
0.00000000
0.05651942
0.57996715
0.00000000
0.18584633
3.98054506
0.11866717
2.73112689
0.10957567
2.73279587
62.90328024
0.00000000
78.29940951
90.44420528
12.99224521
67.87599504
0.42659873
45.68434115
0.26244668
0.00000000
7.51124498
0.00000000
0.00000000
0.02555661
11.31000000
194.70333333
2512.13000000
215.32666667
2593.11333333
4.82000000
0.00000000
0.17333333
0.00000000
0.00000000
1943.81000000
474.81000000
0.00000000
0.00000000
0.01000000
0.01000000
0.33666667
0.00000000
0.00000000
0.01000000
26.47000000
0.00000000
0.19666667
0.00000000
1.11666667
8.37666667
1196.74666667
889.04000000
1.52333333
0.00000000
0.38333333
4.30666667
0.00000000
1.80333333
32.50333333
0.81500000
18.71000000
0.75333333
25.80666667
660.61666667
0.00000000
1358.52333333
1162.95666667
133.91333333
776.14666667
2.89333333
492.89666667
1.78000000
0.00000000
108.16166667
0.00000000
0.00000000
0.17333333
1.396975
371.199910
9807.590548
134.167033
57620.569888
0.500781
0.000000
0.000653
0.000000
0.000000
8260.269781
560.166348
0.000000
0.000000
0.000002
0.000002
0.002464
0.000000
0.000000
0.000002
8.521655
0.000000
0.000841
0.000000
0.027107
1.462207
12056.191281
8172.959334
0.050447
0.000000
0.003194
0.336362
0.000000
0.034539
15.844739
0.014082
7.459054
0.012007
7.468173
3956.822664
0.000000
6130.797530
8180.154269
168.798436
4607.150703
0.181986
2087.059027
0.068878
0.000000
56.418801
0.000000
0.000000
0.000653
480.717
455.185
181.341
247.447
425.819
675.359
678.233
.
215.081
229.296
678.233
678.233
678.233
678.233
507.301
678.233
678.233
664.035
422.047
467.763
678.233
678.233
619.470
474.063
563.342
669.778
671.469
669.090
487.117
438.008
265.124
357.746
446.291
402.282
678.233
426.353
678.233
319.445
678.233
293
-------
Variable
N
Mean
Standard
Deviation
Sum
Variance
C.V.
Summary Statistics for CHEMGRP 3 Lakes (continued)
ROTIFERA (continued)
Filinia spp.
F. terminalis
F. longiseta
Hexarthra mira
Conochilus unicornis
C. hippocrepis
Conochiloides dossarius
C. natans
Collotheca pelagica
C. mutabilis
Unidentified rotifera
CLADOCERA
Leptodora kindtii
Diaphanosoma birgei
D. brachyurum
Sida crystalline
Holopedium gibberum
Bosmina longirostris
Eubosmina hagmanni
E. tubicen
E. longispina
Chydorus brevalibris
C. sphaericus
Chydorus sp.
Alona setulosa
A. guttata
A. circumfimbrata
A. barbula
Alona sp.
Alonella acutirostris
Kurzia laissima
Acroperus harpae
Eurycercus lamellatus
Graptoleberis testudinaria
Polyphemus pediculus
Daphnia catawba
D. galeata mendotae
D. rosea
D. ambigua
D. pulex
D. parvula
D. schodleri
D. retrocurva
D. longiremis
D. dubia
Scapholebris mucronata
Ceriodaphnia reticulata
C. lacustris
C. affinis/dubia
C. quadrangula
Ceriodaphnia sp.
46
46
46
46
46
46
46
46
46
46
46
46
46
46
46
46
46
46
46
46
46
46
46
46
46
46
46
46
46
46
46
46
46
46
46
46
46
46
46
46
46
46
46
46
46
46
46
46
46
46
0.00000000
3.70514493
0.32115942
67.07557971
71.68086957
1.09768116
3.19188406
1.61688406
0.19130435
0.35014493
0.03753623
0.09061594
14.51344203
0.04840580
0.26949275
8.38152174
22.86431159
0.02913043
3.70188406
0.26239130
2.31195652
1.14507246
0.00000000
0.00000000
0.00376812
0.00000000
0.00000000
0.03753623
0.00000000
0.00000000
0.00000000
0.00000000
0.00000000
0.06173913
29.14115942
1.93942029
0.31405797
0.77677536
0.31992754
3.03500000
0.43260870
0.32608696
0.90971014
1.50387681
0.04021739
1.34659420
0.86978261
14.42391304
0.01702899
0.00000000
0.00000000
24.67392061
1.96803458
454.83578145
323.79383998
5.54337638
11.39900519
7.94174746
1.29748921
2.06497481
0.20441236
0.35085458
32.90411870
0.32830409
1.08425703
30.09677859
72.49511784
0.19757222
9.06079951
0.94625804
11.04620647
5.98707401
0.00000000
0.00000000
0.02555661
0.00000000
0.00000000
0.19818988
0.00000000
0.00000000
0.00000000
0.00000000
o.ooopoooo
0.39345933
56.89169581
5.07985002
2.13004479
2.73083477
1.66849507
13.18037386
2.^3409493
1.48529172
5.58297508
5.21297517
0.27276762
4.08923359
5.89764567
63.92533674
0.08659891
0.00000000
0.00000000
170.43666667
14.77333333
3085.47666667
3297.32000000
50.49333333
146.82666667
74.37666667
8.80000000
16.10666667
1.72666667
4.16833333
667.61833333
2.22666667
12.39666667
385.55000000
1051.75833333
1.34000000
170.28666667
12.07000000
106.35000000
52.67333333
0.00000000
0.00000000
0.17333333
0.00000000
0.00000000
1.72666667
0.00000000
0.00000000
0.00000000
0.00000000
0.00000000
2.84000000
1340.49333333
89.21333333
14.44666667
35.73166667
14.71666667
139.61000000
19.90000000
15.00000000
41.84666667
69.17833333
1.85000000
61 .94333333
40.01000000
663.50000000
0.78333333
0.00000000
0.000000
608.802358
3.873160
206875.588084
104842.450811
30.729022
129.937319
63.071353
1.683478
4.264121
0.041784
0.123099
1082.681027
0.107784
1.175613
905.816081
5255.5421 1 1
0.039035
82.098088
0.895404
122.018677
35.845055
0.000000
0.000000
0.000653
0.000000
0.000000
0.039279
0.000000
0.000000
0.000000
0.000000
0.000000
0.154810
3236.665052
25.804876
4.537091
7.457459
2.783876
173.722255
8.608913
2.206092
31.169611
27.175110
0.074402
16.721831
34.782224
4086.448678
0.007499
0.000000
665.937
612.791
678.094
451.716
505.008
357.125
491.176
678.233
589.749
544.573
387.189
226.715
678.233
402.333
359.085
317.067
678.233
244.762
360.629
477.786
522.855
678.233
527.996
637.293
195.228
261 .926
678.233
351.560
521.523
434.279
678.233
455.489
613.709
346.636
678.233
303.672
678.060
443.190
508.538
294
-------
Variable
N
Mean
Standard
Deviation
Sum
Variance
C.V.
Summary Statistics for CHEMGRP 3 Lakes (continued)
COPEPODA
46
46
46
46
46
46
46
46
46
46
46
46
46
46
46
46
46
46
46
46
46
46
46
46
46
46
46
46
46
46
46
46
0.95384058
0.15746377
0.01195652
0.07014493
2.81286232
17.38134058
0.00000000
1.54405797
2.06927536
4.15956522
0.15528986
0.31891304
0.12898551
0.04362319
20.23202899
0.17304348
26.92753623
4.29702899
2.00818841
0.00000000
0.00000000
1.36739130
0.00000000
0.73123188
0.04347826
0.00000000
0.19847826
0.00000000
0.01195652
9.86137681
0.01384058
189.00144928
2.76960968
0.93533098
0.08109308
0.47574605
16.39846155
27.53939174
0.00000000
5.01819273
9.02985626
12.55821884
0.74202412
2.16297350
0.67177152
0.29586686
21.89939922
1.17363797
63.86002576
20.60028538
7.25759786
0.00000000
0.00000000
4.58821663
0.00000000
3.89033969
0.29488391
0.00000000
1.34614506
0.00000000
0.08109308
38.32007646
0.09237596
178.16647364
43.87666667
7.22433333
0.55000000
3.22666667
129.39166667
799.54166667
0.00000000
71.02666667
95.18666667
191.34000000
7.14333333
14.67000000
5.93333333
2.00666667
930.67333333
7.96000000
1238.66666667
197.66333333
92.37666667
0.00000000
0.00000000
62.90000000
0.00000000
33.63666667
2.00000000
0.00000000
9.13000000
0.00000000
0.55000000
453.62333333
0.63666667
8694.06666667
7.670738
0.874844
0.006576
0.226334
268.909541
758.418097
0.000000
25.182258
81.538304
157.708861
0.550600
4.678454
0.451277
0.087537
479.583686
1.377426
4078.102890
424.371758
52.672727
0.000000
0.000000
21.051732
0.000000
15.134743
0.086957
0.000000
1.812107
0.000000
0.006576
1468.428260
0.008533
31743.292329
290.364
593.998
678.233
678.233
582.981
158.442
325.000
436.378
301.912
477.832
678.233
520.812
678.233
108.241
678.233
237.155
479.408
361.400
335.545
532.025
678.233
678.233
678.233
388.587
667.428
94.267
46
46
46
46
46
46
46
1.97391304
0.00000000
0.15369565
0.00000000
0.13398551
0.03000000
0.05036232
5.08109681
0.00000000
1.04241463
0.00000000
0.36310296
0.14878935
0.33706154
90.80000000
0.00000000
7.07000000
0.00000000
6.16333333
1.38000000
2.31666667
25.817545
0.000000
1.086628
0.000000
0.131844
0.022138
0.113610
257.412
678.233
271.002
495.965
669.273
295
-------
APPENDIX E
List of, and Summary Statistics for, 38 Genera Found in All 147 ELS-II
Survey Lakes, Plus a Separate Listing for Each Chemistry Cluster
Taxa
Keratella
Kellicottia
Trichocerca
Gastropus
Ascomorpha
Asplanchna
Polyarthra
Synchaeta
Ploesoma
Filinia
Hexarthra
Conochilus
Conochiloides
Collotheca
Leptodora
Diaphanosoma
Sida
Holopedium
Bosmina
Eubosmina
Chydorus
Polyphemus
Daphnia (pulex)
Daphnia (galeata)
Daphnia (parvula)
Ceriodaphnia
Epischura
Agtaodiaptomus
Leptodiaptomus
Skistodiaptomus
Onychodiaptomus
Mesocyclops
Tropocyclops
Cyclops
Orthocyclops
Eucyclops
Ectocyclops
Macrocyclops
Mean
293.33640091
87.16706150
40.30662870
1.27375854
0.91009112
9.88243736
58.95316629
5.58906606
3.72810934
1.65328018
23.07020501
88.97865604
21.88166287
0.71025057
0.03207289
29.32093394
0.11091116
10.06291572
41.50371298
14.33902050
1.13977221
0.09250569
14.03455581
1.61066059
7.66804100
7.89173121
0.89100228
1.21886105
73.92938497
11.08965831
0.36756264
27.35687927
29.00300683
2.87596811
0.53867882
0.13585421
0.01462415
0.03453303
Variance
403325.823841
84927.402250
13646.341784
50.261135
30.844127
1607.320645
26248.187301
839.519167
189.733199
210.988831
70838.087374
510467.696657
39131.993994
14.417133
0.081042
6317.966564
0.825234
1237.452531
25422.894193
1386.720605
54.487609
0.526361
1515.411962
35.146235
552.413763
1681.723054
9.958191
88.224792
18566.946949
1360.146100
13.033093
3211.292762
6643.688492
74.665897
9.929020
0.939693
0.093302
0.192321
C.V.
216.502
334.327
289.822
556.582
610.241
405.683
274.816
518.413
369.473
878.584
1153.671
802.968
904.036
534.599
887.599
271.088
819.055
349.575
384.172
259.702
647.636
784.284
277.375
368.074
306.512
519.643
354.170
770.622
184.312
332.564
982.183
207.144
281.036
300.453
584.956
713.542
2088.696
1269.927
Sum
128774.680000
38266.340000
17694.610000
559.180000
399.530000
4338.390000
25880.440000
2453.600000
1636.640000
725.790000
10127.820000
39061.630000
9606.050000
31 1 .800000
14.080000
12871.890000
48.690000
4417.620000
18220.130000
6294.830000
500.360000
40.610000
6161.170000
707.080000
3366.270000
3464.470000
391.150000
535.080000
32455.000000
4868.360000
161.360000
12009.670000
12732.320000
1262.550000
236.480000
59.640000
6.420000
15.160000
296
-------
Taxa
Mean
Variance
C.V.
Sum
CHEMGRP 3
Keratella
Kellicottia
Trichocerca
Gastropus
Ascomorpha
Asplanchna
Polyarthra
Synchaeta
Ploesoma
Filinia
Hexarthra
Conochilus
Conochiloides
Collotheca
Leptodora
Diaphanosoma
Sida
Holopedium
Bosmina
Eubosmina
Chydorus
Polyphemus
Daphnia (pulex)
Daphnia (galeata)
Daphnia (parvula)
Ceriodaphnia
Epischura
Aglaodiaptomus
Leptodiaptomus
Skistodiaptomus
Onychodiaptomus
Mesocyclops
Tropocyclops
Cyclops
Orthocyclops
Eucyclops
Ectocyclops
Macrocyclops
122.45029851
53.87447761
44.66611940
0.73992537
0.62149254
14.78985075
76.68194030
11.07485075
2.41343284
4.14649254
69.07783582
74.95104478
4.95231343
0.49194030
0.08731343
14.77014925
0.27738806
8.50664179
23.50492537
4.05276119
3.56022388
0.06320896
30.46694030
4.40507463
4.25522388
17.15455224
1.15656716
2.96298507
15.89164179
8.16485075
0.32843284
20.72029851
32.13873134
3.35097015
0.75305970
0.24917910
0.00000000
0.01231343
69238.109713
8955.525814
23815.795221
17.293138
24.048962
4351.142862
23374.390165
2351.271306
59.923433
648.814354
229258.454146
133655.551733
195.381219
5.929745
0.232478
1128.376114
2.513095
976.923238
5571.607742
89.058297
170.171766
0.237314
3902.131327
96.740863
285.406679
4276.984025
10.791367
276.813684
721.208851
259.044712
4.752407
607.518603
4573.700279
73.100630
15.990394
2.285239
0.000000
0.019825
214.888
175.656
345.505
562.016
789.064
446.003
199.378
437.838
320.747
614.298
693.145
487.771
282.250
495.000
552.218
227.427
571.501
367.428
317.564
232.855
366.409
770.695
205.032
223.281
397.018
381.232
284.032
561.519
168.990
197.124
663.759
118.955
210.429
255.147
531.007
606.672
1143.490
16408.3400000
7219.1800000
5985.2600000
99.1500000
83.2800000
1981.8400000
10275.3800000
1484.0300000
323.4000000
555.6300000
9256.4300000
10043.4400000
663.6100000
65.9200000
11.7000000
1979.2000000
37.1700000
1139.8900000
3149.6600000
543.0700000
477.0700000
8.4700000
4082.5700000
590.2800000
570.2000000
2298.7100000
154.9800000
397.0400000
2129.4800000
1094.0900000
44.0100000
2776.5200000
4306.5900000
449.0300000
100.9100000
33.3900000
0.0000000
1.6500000
297
-------
Taxa
Mean
Variance
C.V.
Sum
CHEMGRP 2
Keratella
Kellicottia
Trichocerca
Gastropus
Ascomorpha
Asplanchna
Polyarthra
Synchaeta
Ploesoma
Filinia
Hexarthra
Conochilus
Conochiloides
Collotheca
Leptodora
Diaphanosoma
Sida
Holopedium
Bosmina
Eubosmina
Chydorus
Polyphemus
Daphnia (pulex)
Daphnia (galeata)
Daphnia (parvula)
Ceriodaphnia
Epischura
Aglaodiaptomus
Leptodiaptomus
Skistodiaptomus
Onychodiaptomus
Mesocyclops
Tropocyclops
Cyclops
Orthocyclops
Eucyclops
Ectocyclops
Macrocyclops
337.54561728
174.11524691
64.22364198
1.76086420
1.81549383
12.36666667
88.51734568
2.75222222
6.33555556
0.93327160
4.02246914
15.10962963
8.93864198
1.49598765
0.01432099
30.81925926
0.07111111
11.65660494
27.35555556
12.51018519
0.05246914
0.01648148
7.94450617
0.72061728
11.19030864
7.15179012
0.66543210
0.08666667
52.96191358
11.39111111
0.72438272
26.73512346
41.71080247
4.26567901
0.12259259
0.13203704
0.03962963
0.00000000
405575.967462
201326.586967
15376.417709
68.415307
61.785704
598.855928
48301.887285
91.772135
384.413221
29.415190
738.155824
1900.390867
1651.442387
33.216142
0.024673
3086.938694
0.133425
1016.073085
3927.309653
775.089061
0.276491
0.021565
454.393835
6.099561
793.434014
915.267184
7.095359
0.328709
13867.506117
856.838959
31.281204
1673.501715
11038.769722
130.971133
0.813029
0.610664
0.252831
0.000000
188.670
257.700
193.078
469.733
432.961
197.883
248.287
348.074
309.467
581.136
675.431
288.515
454.632
385.253
1096.830
180.278
513.666
273.458
229.088
222.542
1002.158
890.993
268.318
342.724
251.717
423.018
400.298
661.537
222.349
256.971
772.100
153.014
251.891
268.287
735.510
591.841
1268.806
54682.3900000
28206.6700000
10404.2300000
285.2600000
294.1100000
2003.4000000
14339.8100000
445.8600000
1026.3600000
151.1900000
651 .6400000
2447.7600000
1448.0600000
242.3500000
2.3200000
4992.7200000
11.5200000
1888.3700000
4431.6000000
2026.6500000
8.5000000
2.6700000
1287.0100000
116.7400000
1812.8300000
1158.5900000
107.8000000
14.0400000
8579.8300000
1845.3600000
117.3500000
4331.0900000
6757.1500000
691 .0400000
19.8600000
21.3900000
6.4200000
0.0000000
298
-------
Taxa
Mean
Variance
C.V.
Sum
CHEMGRP 1
Keratella
Kellicottia
Trichocerca
Gastropus
Ascomorpha
Asplanchna
Polyarthra
Synchaeta
Ploesoma
Filinia
Hexarthra
Conochilus
Conochiloides
Collotheca
Leptodora
Diaphanosoma
Sida
Holopedium
Bosmina
Eubosmina
Chydorus
Polyphemus
Daphnia (pulex)
Daphnia (galeata)
Daphnia (parvula)
Ceriodaphnia
Epischura
Aglaodiaptomus
Leptodiaptomus
Skistodiaptomus
Onychodiaptomus
Mesocyclops
Tropocyclops
Cyclops
Orthocyclops
Eucyclops
Ectocyclops
Macrocyclops
403.38426573
19.86356643
9.12671329
1.22216783
0.15482517
2.46958042
8.84790210
3.66230769
2.00615385
0.13265734
1.53671329
185.80720280
52.40825175
0.02468531
0.00041958
41.25853147
0.00000000
9.71580420
74.39769231
26.04972028
0.10342657
0.20608392
5.53559441
0.00041958
6.87580420
0.05013986
0.89769231
0.86713287
152.06776224
13.48888112
0.00000000
34.28013986
11.66839161
0.85650350
0.80916084
0.03398601
0.00000000
0.09447552
677385.96912
11074.21153
702.51003
60.72197
0.97314
118.33714
483.02375
241.88610
80.88576
0.96728
57.34238
1431349.45649
117247.49537
0.03265
0.00001
14585.10514
0.00000
1744.59451
67122.12140
3073.36941
0.40886
1.35645
134.42854
0.00000
511.23196
0.13225
12.43932
8.03056
31042.59243
2967.29438
0.00000
7348.54733
3196.78119
6.82137
14.41284
0.04313
0.00000
0.56920
204.032
529.784
290.410
637.591
637.156
440.491
248.396
424.669
448.303
741.388
492.771
643.888
653.359
732.023
685.531
292.712
429.901
348.236
212.816
618.234
565.142
209.451
479.522
328.841
725.285
392.890
326.804
115.862
403.835
250.068
484.558
304.935
469.180
611.076
798.572
57683.9500000
2840.4900000
1305.1200000
174.7700000
22.1400000
353.1500000
1265.2500000
523.7100000
286.8800000
18.9700000
219.7500000
26570.4300000
7494.3800000
3.5300000
0.0600000
5899.9700000
0.0000000
1389.3600000
10638.8700000
3725.1100000
14.7900000
29.4700000
791.5900000
0.0600000
983.2400000
7.1700000
128.3700000
124.0000000
21745.6900000
1928.9100000
0.0000000
4902.0600000
1668.5800000
122.4800000
115.7100000
4.8600000
0.0000000
13.5100000
299
-------
APPENDIX F
List of Common Species by Genera and Their Code Numbers
ROTIFERA
Keratella earlinae 1000
K. cochlearis hispida 1001
Keratella crassa 1002
Keratella taurocephala 1003
K. cochlearis-cochlearis 1004
Keratella hiemalis 1005
Keratella irregularis 1006
Keratella ticinensis 1007
Keratella c. robusta 1008
Keratella serrulata 1009
Kellicottia longispina 1010
Kellicottia bostoniensis 1011
Notholco labis 1030
Notholco squamula 1031
Brachionus urceolaris 1040
Brachionus quadridentatus 1041
Euchlanis dilatata 1050
Euchlanis pellucida 1051
Platyias patulus 1060
Mytilina spp. 1070
Lecane luna 1101
Lecane flexilis 1102
Lecane mira 1103
Lecane tudicola 1104
Lecane ungulata 1105
Monostyla lunaris 1110
Trichocerca multicrinis 1400
Trichocerca cylindrica 1401
Trichocerca pusilla 1402
Trichocerca porcellus 1403
Trichocerca similis 1404
Trichocerca rousseleti 1405
Trichocerca lata 1406
Trichocerca elongata 1407
Gastropus hyptopus 1500
Gastropus stylifer 1501
Ascomorpha ovalis 1510
Ascomorpha saltans 1511
Ascomorpha ecaudis 1512
Asplanchna priodonta 1800
Asplanchna sp. 1809
Polyarthra vulgaris 1900
Polyarthra euryptera 1901
Polyarthra remata 1902
Polyarthra major 1903
Polyarthra dolichoptera 1904
Synchaeta pectinata 1910
Synchaeta kitti 1911
Synchaeta oblonga 1912
Ploesoma truncatum 1921
Ploesoma lenticularie 1922
Ploesoma hudsoni 1923
Ploesoma triacanthum 1924
Filinia spp. 2100
Filinia terminalis 2101
Filinia longiseta 2102
Hexarthra mira 2200
Conochilus unicornis 2300
Conochilus hippocrepis 2301
Conochiloides dossarius 2310
Conochiloides natans 2311
Collotheca pelagica 3100
Collotheca mutabilis 3101
Unidentified rotifera 3300
CLADOCERA
Leptodora kindtii 4100
Diaphanosoma birgei 5101
Diaphanosoma brachyurum 5102
Sida crystalline 5110
Holopedium gibberum 5201
Bosmina longirostris 5301
Eubosmina hagmanni 5310
Eubosmina tubicen 5311
Eubosmina longispina 5312
Chydorus brevalibris 5501
Chydorus sphaericus 5502
Chydorus sp. 5509
Alona setulosa 5510
Alona guttata 5511
Alona circumfimbrata 5512
Alona barbula 5513
Alona sp. 5519
Alonella acutirostris 5520
Kurzia laissima 5530
Acroperus harpae 5540
Eurycercus lamellatus 5550
Graptoleberis testudinaria 5560
Polyphemus pediculus 5600
Daphnia catawba 5701
Daphnia galeata mendotae 5702
Daphnia rosea 5703
Daphnia ambigua 5704
Daphnia pulex 5705
Daphnia parvula 5706
Daphnia schodleri 5707
Daphnia retrocurva 5708
Daphnia longiremis 5709
Daphnia dubia 5710
Scapholebris mucronata 5801
Ceriodaphnia reticulata 5802
Ceriodaphnia lacustris 5803
C. affinis/dubia 5804
Ceriodaphnia quadrangula 5805
Ceriodaphnia sp. 5809
COPEPODA
Epischura lacustris 6300
Epischura nordenskioldi 6301
Epischura spp. 6309
Aglaodiaptomus leptopus 6401
A. spatulocrenatus 6402
Leptodiaptomus minutus 6411
L sicilis 6412
Skistodiaptomus oregonensis 6421
S. reighardi 6422
S. pygmaeus 6423
Skistodiaptomus spp. 6429
Onychodiaptomus birgei 6431
Unknown sp. calanoida 6500
Tropocyclops sp. 1 7100
Mesocyclops edax 7101
Tropocyclops sp. 2 7110
T. prasinus-mexicanus 7111
T. prasinus 7112
Cyclops scutifer 7121
C. strenus strenuus 7122
C. vernalis 7123
C. bicuspidatus thomasi 7124
Cyclops sp. 7129
Orthocyclops modestus 7131
Eucyclops speratus 7141
E. agilus 7142
E. prionophonis 7143
Ectocyclops phaleratus 7144
Macrocyclops albidus 7160
Unknown sp. cyclopoida 7200
Ergasilus chautauquaensis 7500
Nauplii 8000
MISCELLANEOUS
Chaoborus punctipennis 9100
C. americanus 9101
C. flavicens 9102
Chaoborus spp. 9199
Mites 1 9200
Mites 2 9201
Ostracoda 9300
*U.S. GOVERNMENT PRINTING OFFICE- i992-6"te-003''*072(
300
------- |