Ecology, 69(5), 1988, pp. 1393-1400
© 1988 by the Ecological Society of America
SPATIAL HETEROGENEITY IN FRESHWATER ZOOPLANKTON:
VARIATION WITH BODY SIZE, DEPTH, AND SCALE1
Bernadette Pinel-Alloul, John A. Downing, Martin Perusse, and Gabriella Codin-Blumer
Departement de Sciences Biologiques, Universite de Montreal, Montreal, QuSbec, Canada H3C 3J7
Abstract. The effects of body size, depth, and sampling scale on spatial heterogeneity
were examined in the zooplankton community of a small lake. Analyses were performed
by regression analysis of 27 sets (3 scales x 3 depths x 3 dates) of replicate (« = 4) samples
of the natural zooplankton (cladocerans, copepods, rotifers) community of Lake Cromwell,
Quebec, Canada. Spatial heterogeneity was measured as the variance among the four
randomly arranged replicate samples of taxa taken at each scale-depth-date combination.
The spatial distribution of populations of zooplankton in this community was found
to be typical of the spatial heterogeneity encountered in other freshwater and marine
ecosystems. The effect of population density on spatial heterogeneity was comparable to
that found for other flora and fauna. Small animals were more heterogeneous than large
ones, and populations sampled on large spatial scales or at greater depths showed greatest
spatial variability. These effects were stable over the season. Few taxa or life stages diverged
significantly from these trends.
Key words: aggregation; body size; cladocerans; copepods; depth; freshwater; rotifers; sampling
theory; scale; spatial heterogeneity; variance; zooplankton.
Introduction
In spite of the long-standing theoretical and practical
importance of spatial distribution to ecology (Lussenhop 1974, Sih 1984, Taylor 1984), experimental stud
ies of factors that give rise to heterogeneous spatial
distributions in nature have received little attention
(Gilinsky 1984). The study of zooplankton has been
central to the development of ecological thought about
spatial distribution for more than a century (Hensen
1884, Haeckel 1891). The etymological derivation of
the term "plankton" (noun of irXayKTos = wandering,
drifting) and its early use (Hensen 1884) imply that
zooplankton are randomly or involuntarily distributed
(Ruttner 1953:106, 258), an idea that persists today
(e.g., Taylor 1984:323). On the other hand, early in
vestigations of the spatial distribution of freshwater
zooplankton (e.g., Moberg and Young 1918) and more
recent research (e.g., Colebrook 1960, Dumont 1967,
Klemetsen 1970, George 1974, Nieet al. 1980, Malone
and McQueen 1983) have concluded that zooplankton
spatial distributions are as strongly heterogeneous as
other terrestrial and aquatic plants and animals. The
spatial heterogeneity ofanimals in general (Taylor 1984)
and marine and freshwater zooplankton populations
in particular (Downing et al. 1987) is known to vary
significantly with population density. Little other quan
titative information is available about other ecological
factors having systematic effects on spatial heteroge
neity in natural zooplankton populations. In fact, sev
eral authors have recently decried the general rarity of
quantitative data on the degree of heterogeneity to be
1 Manuscript received 29 September 1986; revised 5 Jan
uary 1988; accepted 31 January 1988.
found in natural populations (e.g., Taylor 1984) and
the lack of experimental evidence of its cause (Hanski
1983, Gilinsky 1984, Vodopich and Cowell 1984).
Several factors may cause spatial heterogeneity in
natural populations. In freshwater zooplankton as in
other communities, spatial heterogeneity may result
from both physical (George and Edwards 1976, Riley
1976, Watson 1976, Patalas 1981) and behavioral pro
cesses (Ringelberg 1969, Siebeck 1969, Stavn 1970,
Dagg 1977). Data on the effects of specific variables on
spatial heterogeneity are difficult to collect in nature,
and general tendencies need illumination. The role of
body size is not clear, for example. If intraspecific com
petition is important in structuring spatial pattern then
larger organisms might be less aggregated on any given
spatial scale because their rates of locomotion are great
er (Peters 1983) and thus they can be more widely
spaced, yet still find mates. On the other hand, if en
vironmental forces tend to randomize populations (e.g.,
turbulent mixing, random resource distribution), then
larger, more mobile organisms might be capable of
maintaining larger social aggregations. Calculations
based on allometric relationships between body size,
spatial density, and home-range size (Peters 1983,
Calder 1984) suggest that larger animals do form larger
aggregations. There is also disagreement about the ef
fects of physical factors on zooplankton heterogeneity
because surface populations may be most readily ag
gregated by wind-driven water motion (e.g., Langford
and Jermolajev 1966, Malone and McQueen 1983),
while populations in less turbulent waters at greater
depths have been observed to be most heterogeneous
(Dumont 1967). Several authors dating from Grieg-
Smith (1952) have reasoned that samples distributed
BERNADETTE PINEL-ALLOUL ET AL.
1394
Ecology, Vol. 69, No. 5
2500 m2
10 000 m2
-45°59'
100 m;
*-
50
III
CURRENT DIRECTION
BEAVER
DAM
Fig. 1.
Morphometry of Lake Cromwell, Quebec, Canada, showing depth contours in metres. The squares and rectangle
(heavy solid lines) indicate the spatial scales over which samples were distributed randomly.
over a large area should cover more habitat variability
Methods
and therefore spatial variation in populations should
be greater over large areas than small ones. Seasonal
The above hypotheses were tested by collecting four
variation in spatial distribution might be induced by
horizontally replicated samples of various taxa and
structuring variables such as competition (Diamond
sizes of zooplankton on three spalial scales, at three
1979. Levin 1984), predation (e.g., Anscombe 1950,
different depths, on three dates in Lake Cromwell (Que
Sih 1984), reproduction (eg., Cole 1946. Cowie and
bec; 45°49' N, 74° W: Paquctte and Pincl-Alloul 1982).
Krebs 1979), or habitat structure (e.g.. Cassic 1959a,
Sampling sites within each sampling scale (100, 2500,
b, Corkett and McLaren 1978), which vary seasonally.
10 000 ma; Fig. 1) were determined using a coordinate
Taxonomy might also play a role, as many suggest that
system and a random number table. Replicate samples
spatial heterogeneity is an evolved species trait (e.g..
were taken at
Taylor and Taylor 1977, Taylor 1984), although Win-
hypolimnion) at each sampling site using a 31.25 L(50
1. 3.5, and 7 m depths (epi-, meta-,
sor and Clarke (1940) found little difference in spatial
cm high. 25 cm wide, 25 cm deep) plexiglass plankton
variation among marine zooplanklon taxa. Thus, the
trap (Patalas 1954, Schindler 1969) with a 50-^m Nitcx
conflicting evidence suggests that spatial heterogeneity
plankton net, This plankton trap was chosen because
may not be a simple function of density alone, but may
all samples arc of identical volume, and parallel com
vary with several characteristics of the organism, en
parisons show that pure sampling errors are small and
vironment, and sampling scheme.
sampling efficiencies are comparatively high, especially
The purpose of this research was to use easily sam
for species that usually show the strongest sampler
pled freshwater zooplankton as a study community to
avoidance reactions (di Bernardi 1984). Sampling was
test the hypotheses thai spatial heterogeneity varies
repeated on 14 June. 11 July, and 14 August 1985. All
systematically with body size, spatial scale of obser
samples were taken between 1000 and 1300 to avoid
vation, and depth, and that it varies both seasonally
diurnal migrations.
and among laxonomic classifications.
After collection, organisms were narcotized with car-
October 1988
ZOOPLANKTON SPATIAL HETEROGENEITY
bonated water, and concentrated and preserved with
/•
4% formaldehyde. Preserved sample volumes were ad
from mixed samples using a pipette with enlarged
CD
opening, and zooplankton were identified and counted
c
wheel (Ward 1955). A constant 10% of each sample
was counted using this subsampling routine. Regres
sion analyses of counts revealed no significant differ
ences between full-sample and subsample estimates (P
< .05), and x2 tests showed that subsampling did not
significantly alter assessments of community structure
(Loubier 1983). Subsampling error was thus assumed
to be constant and negligible. Zooplankton species were
identified according to Brooks (1959), Chengalath et
al. (1971), and Smith and Fernando (1978). Crustacean
body masses were estimated by drying (60°C, 8 h) a
known number of specimens and weighing (±0.1 jug)
u
5-
"Z
4-
o
0)
_c
3-
"5.
E
2-
to
1 -
o
O)
3
0-1 -
-2-1
by biovolume (Bottrell et al. 1976, Ruttner-Kolisko
1977). This process resulted in the collection of 108
zooplankton samples corresponding to a three-factor
(scale, depth, date) nested design with each cell con
/
.^X
/
/
■ *&
/
\&T
f
_^^^^^1 ■
.JleTf
■V'b ■■!■
I
-
1
Log mean density
on a Cahn microbalance (Loubier 1983, Pinel-Alloul
and Methot 1984). Rotifer body masses were estimated
Downing et al. (1987)
6-
justed to 100 mL. Ten-millilitre subsamples were taken
under 25-40 x magnification using an acrylic counting
1395
Fig. 2. Relationship between Iog1052 and \o%wX densities
of zooplankton taxa collected in Lake Cromwell. (Densities
were measured as number of individuals per sample.) The
solid line is the relationship predicted by Downing etal. (1987),
on the basis of published marine and freshwater data.
taining four replicate samples of the zooplankton com
munity (i.e., 4 replicates x 3 scales x 3 depths x 3
analysis (equivalent to ANCOVA) to test the hypoth
dates).
esis that all sampling scales, dates, depths, and body
The variation among taxon-density estimates made
sizes yield equal variances, by employing mean density
using the four randomly placed replicate samples yields
as a covariable. The analysis makes no assumptions
an estimation of the spatial variation in the population.
about Taylor's b as an index of spatial heterogeneity
Our hypotheses were tested by calculating the mean
(cf. Downing 1986) and avoids the confounding effect
(X) and variance (s2; n — 1 weighting) of each taxon
of mean density on common indices of spatial heter
within each scale-depth-date combination, then using
ogeneity.
s2 as the dependent variable in a multiple regression
analysis. Both Xand s2 are expressed on a per-subsam-
ple basis with no normalization to volume. The mul
Mean zooplankton taxon densities (X) covered a range
from 0.25 to 687 organisms per subsample, and vari
tiple regression model
log10s2 = a + b Iog10;r + cS + dD + eZ + flogl0W
+ (error),
Results and Discussion
(1)
ances (s2) ranged from 0.25 to 107 300. Body sizes of
zooplankters ranged from 0.12 to 23.4 jug/organism.
Only the 458 sets of observations where X > 1 were
where S is the coded spatial scale (1 = 100 m2, 2 =
considered in the analysis to avoid pseudorandom dis
2500 m2, 3 = 10 000 m2), D is the coded date (1 =
tributions (J. A. Downing, personal observation). Only
June, 2 = July, 3 = August), Z is the coded depth (1
20 of the 40 taxa collected were abundant enough to
= epilimnion, 2 = metalimnion, 3 = hypolimnion), W
be included in the analyses (11 rotifers, 3 cladocerans,
is the body mass (micrograms dry mass per organism),
4 cyclopoid copepods, and 2 calanoid copepods). Dom
and a-faxt fitted constants, was fitted to the data (Draper
t value associated with each regression coefficient. The
inant rotifers were Conochilus sp. and Keratella cochlearis. Bosmina longirostris, Holopedium gibberum, and
Diaphanosoma sp. were the dominant cladocerans.
Dominant copepods were the cydopoids Tropocydops
significance of interaction terms was tested similarly,
prasinus, Mesocydops edax, Diacydops bicuspidatus
and the order of the coded variables for scale, date,
thomasi, and Cydops scutifer, and the calanoid Skis-
and depth was verified by residual analysis (Gujarati
todiaptomus oregonensis.
The spatial heterogeneity of zooplankton in Lake
and Smith 1981). The significance of the effect of each
of the variables was judged by the significance of the
1978). Logarithmic transformations were used to lin
earize the effects of X and W, and to stabilize the vari
Cromwell agrees well with that found in other geo
ance of s2. Residuals (observed-predicted) were cal
graphic areas. Downing et al. (1987), analyzing pub
culated and analyzed using normal ANOVA to test for
lished data from several marine and freshwater eco
divergence of various taxa from general equations fit
systems, found that s2 of replicate zooplankton samples,
ted to Eq. 1. This analysis uses multiple regression
calculated on a per-litre basis varies as:
BERNADETTE PINEL-ALLOUL ET AL.
1396
Table 1. Multiple regression analysis of data on the spatial
heterogeneity ofdensity ofzooplankton taxa in Lake Crom
well (n = 458, F= 412, P <£ .0001).*
Variable
logm mean density
logm body mass
Coeff.
t value
35.8
-3.5
<.0001
.0006
0.091
3.4
3.0
-0.4
.0009
.0030
.6621
Depth (1,2, 3)
Date (1,2, 3)
in Lake Cromwell also agrees well with values found
for other aquatic organisms such as the profundal
benthos, and macrophyte biomass (Downing and An
derson 1985). The exponent of 1.53 is also within the
range of 1.4-1.9 reported for the stream benthos (Mor-
Coded vari ablest
Sampling scale
(1,2,3)
measures of spatial heterogeneity. The exponent found
(Downing 1979) and littoral (Downing and Cyr 1985)
P
1.528
-0.097
Ecology, Vol. 69, No. 5
in 1985).
0.082
-0.012
Larger zooplankton were less heterogeneous than
smaller ones (Table 2). A population of large zooplank
ton («30 tig) would have half the s2 of a population of
* The dependent variable is log,oS2 calculated among the
four randomly distributed replicate samples taken at each
scale-depth-date combination. The coefficient is the regres
sion coefficient associated with each independent variable;
the t value and associated probability (P) indicate the signif
icance of each variable in the prediction of log,<>s2.
t The coded variables are arranged in the order that ac
counted for the greatest variation in s1. Sampling scale: 1 =
100 m2, 2 = 2500 m2, 3 = 10 000 m2; Sample depth: 1 = 1
m, 2 = 3.5 m, 3 = 7 m; Date: 1 = June, 2 = July, 3 = August.
small organisms («0.1 ng) regardless of density, depth,
or sampling scale, k of the negative binomial distri
bution (Elliott 1977) would be 16.5 for a population
of 30-^g organisms at average density (26 organisms/
sample) in the epilimnion on a spatial scale of 100 m2.
The corresponding k for a population of small organ
isms (0.1 Mg) would be 7.5. This finding is in conflict
with the allometric analyses of Calder (1984) and Pe
ters (1983). Both of these analyses use empirical allo
(2)
metric relationships between body size, density, and
home-range size to predict the relationship between
where XL is the mean population density (as number
body size and the number of conspecifics found within
per litre) and V is the sample volume in litres. Zoo
an average home-range area. Subsequent interpreta
plankton s2 in Lake Cromwell rises as a power function
tions then equate this average number of organisms
of X, and s2 values agree well with the predictions of
with herd or aggregation size. Although the number of
Downing et al. (1987) (Fig. 2). This suggests that Eq.
organisms per home range may correspond to the num
2 makes good predictions in both space and time as
ber per group in some organisms (e.g., marmot, prong-
no data from Lake Cromwell were used in the concep
horn, wapiti), this is not necessarily so in nonrandomly
tion of Eq. 2, and our data were collected after the
analysis of Downing et al. was completed. In addition,
it appears that the degree ofspatial heterogeneity found
in Lake Cromwell is typical of that found in other
freshwater and marine zooplankton communities.
distributed species that do not form obvious social
Multiple regression analysis (Table 1) shows that
groupings (e.g., zooplankton, benthos, terrestrial in
sects). Spatial aggregation in zooplankton may be me
diated by predation, reproduction, and competition.
Our data suggest that the more aggregated small zoo
plankton would be encountered at lower average rates
several factors in addition to X account for significant
by randomly searching predators than would large or
variation in zooplankton heterogeneity. The regression
ganisms (e.g., May 1978). In addition, the nearest dis
coefficients show that body size, sampling scale, and
tance to a conspecific is much greater in large organisms
depth all account for significant variation in s2. Sam
pling date is not a significant predictor of s2, suggesting
than small. This spatial arrangement may allow small
that variance relations had little seasonal component.
large organisms to decrease intraspecific competition.
All interaction terms were found nonsignificant (P >
organisms to avoid predators and locate mates, and
Spatial heterogeneity increased rapidly with the area
.05), and analysis of residuals showed that coded vari
over which sampling effort was distributed (Table 2).
ables (i.e., scale, depth, date) were arranged in the order
accounting for the greatest variation in s2. Table 2 shows
The increase in spatial scale from 100 m2 to 1 ha re
the regression equation (R2 = 0.82, F = 516, n = 458)
population of 10-ju.g animals in the epilimnion at the
sulted in a > 50% increase in sampling variance for a
considering only the variables accounting for signifi
cant variation in s2 (Table 1).
The effect of zooplankton density on spatial heter
ogeneity is similar to that found in other research. The
exponent associated with X is 1.53, slightly lower than
the value of 1.622 reported by Downing et al. (1987)
and very close to the value of 1.57 taken from the
plankton literature (Cassie 1963). The standard error
of this exponent is 0.04, and the difference between
this exponent and that found by Downing et al. (1987)
is statistically significant (P < .05). Downing (1986)
has shown, however, that such exponents are biased
Table 2.
Multiple regression equation for the prediction of
logl052 in Lake Cromwell. Only variables that accounted
for significant variation were retained. All variables are as
in Table 1.
Variable
log,,, mean density
log,n body mass
Sampling scale
(1,2,3)
Depth (1,2, 3)
Intercept
Coeff.
/ value
P
1.530
-0.096
36.2
-3.5
<.0001
0.090
0.083
3.4
3.0
.0009
.0026
-0.364
.0007
ZOOPLANKTON SPATIAL HETEROGENEITY
October 1988
Table 3.
Test for differences in fit of taxonomic groups to
equation in Table 2*
Test
One-way ANOVA
plankton predators such as Chaoborus are present all
summer.
Mean residual
variance
Group
Calanoids
Cyclopoids
Cladocerans
Rotifers
1397
104
95
37
222
-0.058
-0.036
-0.033
0.048
P of heterogeneity
0.198
* The mean residual is calculated as the average of observed
logl0s2 less the log,0s2 predicted using Table 2. n is the number
of s2 estimates available for each taxonomic group. The test
for heterogeneity among groups was performed using the mean
residual as the dependent variable in a one-way analysis of
variance.
A reviewer has suggested that actual spatial heter
ogeneity might be better analyzed by subtracting the
probable random error due to sampling (i.e., s2 = X)
from each s2 estimate, then using these "corrected"
data in an analysis of variance. This approach would
have some practical problems in that it assumes that
s2 due to sampling is always exactly equal to the mean,
which is doubtful, and it causes all observations for
which s2 < Xto be ignored in the analysis because the
logarithm of values <0 is undefined. This would have
caused us to delete 58 values, and would have weak
ened our analysis. In addition, for real data, s2 « Xb
where b > 1, and therefore values of s2 corrected by
simple subtraction of X would have varied significantly
with X. This would have lead to confounding of effects
average population density of 26 organisms/sample.
in an ANOVA because some of the variables were
s2/X is usually > 1 and increases with increased scale.
weakly correlated with X. In this study, we were not
Zooplankton populations therefore attain a random
interested in the effects of X on spatial heterogeneity
distribution (i.e., s2 = X)ona spatial scale smaller than
per se, but were only interested in an unbiased assess
100 m2. Extrapolation of the equation in Table 2 sug
ment of the effects of body size, depth, sampling scale,
gests that zooplankton at the average population den
and season, given the effect of X. Thus, we avoided
sity were randomly distributed in Lake Cromwell on
these assumptions and difficulties by using Jasa co-
a spatial scale between 0.01 and 0.0001 m2. Thus, fac
variable in an ANCOVA of unconnected s2's, thereby
tors generating spatial heterogeneity in zooplankton
employing a statistically determined correction for X.
appear to operate on a very fine scale. This may cor
For our analyses to be confounded by the effect of pure
respond to the stochastic, vectorial, and social patterns
sampling error, pure sampling error would have to vary
suggested by Hutchinson (1953) and Haury et al. (1978)
systematically with body size, depth, and spatial scale.
in micro- (0.1-5 m) and fine (5-103 m) scales. A prac
There is no evidence of this.
tical application of this knowledge is that more zoo
There is little apparent taxon specificity of spatial
plankton samples must be taken from a large sampling
heterogeneity in this community. Most taxa and life
area to obtain a given level of precision on population
stages of zooplankton fitted the regression in Table 2
estimates (cf. Downing et al. 1987). In addition, as seen
well. ANOVA of the residuals (Table 3) shows no sig
by Grieg-Smith (1952) the effect of sampling scale may
nificant differences among broad taxonomic groups (a
not be monotonic, and s2 maxima may be found where
= .05). Within each of these broad groups except the
sampling scale surpasses patch dimensions. Such an
rotifers, some taxa and life stages diverged significantly
effect might have been seen in our study had more
from the overall pattern, however (Table 4). Densities
spatial scales been investigated or had the range of
of the cladoceran Holopedium gibberum seemed some
scales been greater.
what more heterogeneous than those of Bosmina lon-
Physical characteristics of the environment also had
girostris, although differences were only weakly signif
a significant effect on spatial heterogeneity. We found
icant (.05 > P > .01; Table 5). Calanoid nauplii, a
that the spatial heterogeneity of zooplankton increased
planktonic dispersal stage, were much less heteroge
with depth (Table 2). Although many researchers have
neous than adults and copepodids (Table 6). Within
suggested that turbulence and wave action tend to ag
gregate plankton in surface waters (e.g., George and
Edwards 1973, Riley 1976, Smith et al. 1976, George
Table 4. Results of F tests for heterogeneity in mean resid
ual variance among taxa within broad taxonomic groups.*
and Heaney 1978, Alldredge and Hammer 1980, HeaNum
ber of
ney and Tailing 1980, Incze and Yentsch 1981), the
data agree with Dumont (1967) showing that deeper
populations are more heterogeneous than shallow ones.
Our findings may either indicate that surface turbu
lence has a randomizing effect on spatial distribution
or that predation pressure may be higher in deeper
waters yielding aggregation as a predation avoidance
behavior (e.g., Rasmussen and Downing 1988). Either
of these may be possible because Lake Cromwell is
probably too small for wind rowing to be common and
Group
Cladocerans
taxa
3
36
3.95
.0286
5
7
11
104
95
223
3.42
7.99
<.0001
Copepods
Calanoids
Cyclopoids
Rotifers
1.77
.0114
.0672
* Mean residuals are calculated as in Table 3. The four
separate tests were performed as one-way analyses of vari
ance, considering taxa as treatments.
BERNADETTE PINEL-ALLOUL ET AL.
1398
Table 5. Variations in fit of cladoceran taxa to the equation
in Table 2.*
Ecology, Vol. 69, No. 5
Table 7. Variations in fit of cyclopoid copepod taxa and life
stages to the equation in Table 2. All variables are as in
Table 5.
Mean
n
Taxon
Bosmina longirostris
27
Diaphanosoma sp.
Holopedium gibberum
15
27
residual
variance
-0.130
0.067
0.396
Mean
P
<.05
>.05
<.05
Sign
Taxon*
-
ns
+
* Shown are the mean residual variance (calculated as in
Table 3) for each taxon, the significance of the analysis of
variance comparing the residuals of each taxon with the av
erage residual ofall other cladoceran taxa, and a symbol show
ing whether the taxon yielded a greater (+) or lesser (—) re
sidual than the other taxa combined, ns indicates that no
significant difference was found.
cyclopoid copepods, copepodids C1-C3 were the least
heterogeneous in density while C4-C5 and adult Cy
clops scutifer were most heterogeneous (Table 7). Ap
parently, spatial behavior in copepods changes with
age. Taylor (1984) has also found this to be true in
terrestrial insects. Our analysis suggests that the effects
of population density, body size, scale, and depth op
erate differently on only a few taxa and life stages. This
finding disagrees with one school of thought (e.g., Tay
lor and Taylor 1977, Taylor 1984) that suggests that
spatial pattern is species-specific. Such assertions have
been based on the measurement of spatial heteroge
n
Copepodids C1-C3
Tropocydops prasinus
Nauplii
Mesocyclops edax
Diacychps bicuspidatus
Copepodids C4-C5
Cyclops scutifer
26
15
27
8
3
13
3
residual
variance
-0.214
-0.170
-0.107
0.015
0.211
0.420
0.468
P
<.0i
>.05
>.05
a.05
a.05
<.01
<.05
Sign
—
NS
NS
NS
NS
+
+
* Copepodids C1-C3 and C4-C5 are all cyclopoid copep
odids combined, mainly Tropocydops prasinus and Mesocydops edax, which account for 62% and 18% (Loubier 1983)
of the adult cyclopoids, respectively.
spatial distribution. Thermal stratification decreases
mixing in deeper waters, which permits the persistence
of behaviorally induced patterns in plankton distri
bution. Few differences occur among broad taxonomic
categories but some developmental stages seem less
able to maintain heterogeneous distributions or dis
perse actively. The randomness implied by the term
"plankton" does not apply to the zooplankton of Lake
Cromwell. The "wanderers" distribute themselves in
aggregations that vary among organisms and environ
ments.
neity using b'm s2 = aX*, which varies as an artifact
Acknowledgments
of sample number (Downing 1986), and have failed to
Financial support for this research was provided by oper
ating grants to B. Pinel-Alloul and J. A. Downing from the
Natural Sciences and Engineering Research Council of Can
ada, and a team grant from the Minister of Education of the
Province of Quebec (FCAR). We thank P. Ross, S. Pilon, and
the staff of the Station Biologique of the Universite de Mon
treal for technical assistance. This is a publication ofthe Groupe
d'Ecologie des Eaux douces of l'Uni versite de Montreal and
the Centre de recherches ecologiques de Montreal.
account for the systematic effect of body size on spatial
heterogeneity. The relative similarity of spatial hetero
geneity among taxa found in our analysis is more
reliable because our sampling design was constant and
body size was included as a covariable in the analysis.
This study has demonstrated that the degree of spa
tial heterogeneity in freshwater zooplankton popula
tions is not just a function ofdensity alone (cf. Downing
Literature Cited
et al. 1987), but varies with several factors related to
Alldredge, A. L., and W. M. Hammer. 1980. Recurring
aggregation of zooplankton by a tidal current. Estuarine and
Coastal Marine Science 10:31-37.
the organisms' specific behavioral components. Body
size, already known to have ecological significance in
population dynamics and trophodynamics (Peters 1983,
Calder 1984) is also a specific component of plankton
Table 6. Variations in fit of calanoid copepod taxa and life
stages to the equation in Table 2. AH variables are as in
Table 5.
Mean
n
residual
variance
Nauplii
Skistodiaptomus
27
0.331
<.01
oregonensis
20
22
27
8
0.011
0.042
0.055
0.092
>.05
>.05
Taxon*
Copepodids C1-C3
Copepodids C4-C5
Epischura lacustris
Sign
—
NS
NS
>.05
NS
>.05
NS
* Copepodids C1-C3 and C4-C5 are all calanoid copepo
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