Download DODSON, STANLEY Predicting crustacean zooplankton species

Limnol. Ocennogr., 37(4), 1992, 848-856
Q 1992, by the American Society of Limnology
and Oceanography,
Inc.
Predicting crustacean zooplankton species richness
Stanley Dodson
Department of Zoology, University of Wisconsin, Madison 53706
Data from 66 North American lakes were collected to construct a model for predicting the
number of crustacean zooplankton species expected in a lake. The chosen lakes have a range from
4 m* to 80 x lo9 m2 surface area, range from ultra-oligotrophic to hypereutrophic, and have
zooplankton species lists based on several years of observation. The number of crustacean zooplankton species in a lake is significantly correlated with lake size, average rate of photosynthesis
(parabolic function), and the number of lakes within 20 km. A multiple linear regression model,
using these three independent variables, explains -75% of the variation in log species richness.
Prediction of species richness was not enhanced by knowledge of lake depth, salinity, elevation,
latitude, longitude, or distance to the nearest lake. The North American species-area curve is
statistically different from and steeper than the corresponding European curve.
The question of “how many species of
zooplankton can one expect to find in a
lake?’ can be approached on a number of
theoretical and empirical levels. One approach is to develop a model for predicting
the number of species using regression analysis of common limnological
parameters.
Thle equilibrium
hypothesis of island biogeography (MacArthur
and Wilson 1967)
provides a framework for such a model, if
lakes and ponds are viewed as islands in a
terrestrial landscape. The equilibrium
hypothesis is based on the observation that,
in general, more species occur on larger islands. The number of species on an island
(or in a lake) is predicted by a power function, characterized by the linear relationship
between the log number species and log island area. There are three major hypotheses
for why species-area relationships exist (Angermeier and Schlosser 1989). More species
on larger islands may be due to lower extincti’on rates and higher immigration rates
than on smaller islands, to more subdivisions (niches and resources) on larger islands, or to sampling phenomena.
A systematic large-scale study of number
of species in lakes has two advantages. First,
--Acknowledgments
I thank all those who contributed unpublished data;
they are acknowledged elsewhere (supplemental material available on request: Dodson 1992). The manuscript benefited from suggestions from Virginia Dodson and Tim Moermond.
This study was supported by NSF grant BSR 8805805.
previous studies of the relationship between
species number and area have by and large
been done on islands, using terrestrial organisms such as birds, lizards, plants, ants,
and spiders (Connor and McCoy 1979;
Schoener 1986). Thus, a study of zooplankton in lakes provides a comparison from a
new environment with a different group of
organisms. Second, ecological studies of
freshwater habitats stress the measurement
of primary productivity.
Thus, a study of
zooplankton in lakes allows for an analysis
of the relationships of lake size and productivity on species richness.
Previous studies suggest that species of
either pelagic or littoral microcrustaceans
do have a species-area curve. Browne (198 1)
found a significant species-area power curve
for crustacean zooplankton in 13 lakes in
central New York. Dodson (199 1) found a
similar relationship for 32 European lakes.
Fryer (1985) reported that, for 207 lowland
water bodies in Yorkshire, England, large
water bodies generally had the most diverse
faunas of pelagic and littoral crustaceans.
My purpose here is to extend the analysis
of species richness to a data set of the crustacean zooplankton of 66 well-studied lakes
in North America. The species list was limited to pelagic crustaceans, because they are
reported more often and with greater precision than other aquatic organisms, including insects, rotifers, and protozoans.
Crustacean zooplankton species richness is
compared to physical, biological, and geographic variables in order to first identify
848
Zooplankton
species richness
independent variables correlated with spe-‘
ties richness, and then to develop a multiple
linear regression model for predicting species richness. The availability of estimates
of productivity rates for lakes makes it possible to compare the relationship of habitat
size and productivity to species richness. In
addition, cartographic data are used to test
for a relationship between species richness
and distance to the nearest lake and the
number of lakes within 10 and 20 km. Interpretation of correlations between species
richness and various factors is from the perspective of the three hypotheses concerning
the species-areacurve.
Materials and methods
Criteria for the zooplankton
species list -
Methods of selecting lakes and defining species lists are similar to those of Dodson
(199 1). Specieslists for each lake include all
pelagic crustaceans, even rare species. The
following is a brief description of the criteria
used for choosing species; more complete
information, including a justification of the
criteria used, the species lists, and the literature cited, is available on request (Dodson 1992). Each species list includes all pelagic speciesever observed in the lake, based
on a minimum of three samples taken in
different seasons of at least two different
years. This criterion was necessary to minimize underestimating species richness, because of the large seasonal and annual variations in species abundances and because
of potential sampling errors due to horizontal and vertical patchiness (see Dodson
1992 for further discussion of factors that
can influence the number of species found
in a lake at any one time).
Specieslists are often given without a clear
statement of whether the listed crustaceans
are pelagic (typical of the plankton). I attempted to follow common usage (e.g. Patalas 198 1) in deciding which species were
pelagic. Unless the following littoral species
were explicitly mentioned as part of the
plankton, I excluded all species of Simocephalus, all sidids except Diaphanosoma,
Polyphemus, all chydorids except Chydorus
sphaericus, and all macrothricids. Because
I am not convinced they can be distinguished, I counted only one speciesof Cerio-
849
daphnia when both C. quadrangula and C.
pulchella were listed. Among the copepods,
I excluded all species of Macrocyclops, Ectocyclops, Megacyclops, Paracyclops, Microcyclops, and Ergasilus. I counted Eucyclaps speciesas pelagic. All fairy shrimp and
tadpole shrimp were excluded, unless they
were described as being pelagic. No attempt
was made to standardize species names
(taxonomy) between lakes.
Taxonomic problems will affect the species lists. The problem is not with what a
species is called, but with groups of species
that tend to be lumped under one name.
For example, the name “Acanthocyclops
vernalis” is applied to a group of at least
four genetically and morphologically distinct species (e.g. Smith 1981) and the
“Daphnia pulex” group likely contains several cryptic species (e.g. Dodson 1981; Hebert et al. 1989). Thus, the length of a species list will be affected by the taxonomic
taste and skill of the author.
Species richness was log-transformed for
two reasons: first, so the results of this study
will be comparable with other studies of the
equilibrium theory of island biogeography
(e.g. Connor and McCoy 1979); second, because species numbers appear log normal,
with many examples of low or moderate
numbers and a few extremely large values.
The limnological variables (see Dodson
1992) were also log-transformed (except for
latitude and longitude), because of skewed
frequency distributions of the untransformed data.
Criteria for choosing lakes and ponds-
Besides number of crustacean zooplankton
species(the dependent variable), I collected
data on 10 morphometric, physical, biological, and geographic variables (Table 1).
Forty-four North American lakes, ponds,
and reservoirs were identified that had published accounts of morphometry and geography, estimates of annual primary productivity using a 14Ctechnique, and species
lists for crustacean zooplankton based on
more than two visits. Six additional lakes
fit the above criteria, but had estimated
photosynthesis rates based on oxygen methods. Twenty-two lakes had all the data on
morphometry but no estimate of photosynthetic rate. These 22 lakes were included in
850
Dodson
Table 1. Ranges for the 11 parameters used in this study. The list of parameters indicates which ones were
log-transformed, but the ranges are given for the untransformed data. The examples are lakes which show the
extreme values for each parameter.
--_.
Raw data range
(not log-transformed)
Parameters
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
11.
--
log spp. (No.)
log surface area (ni*)
log mean depth (m)
log avg photo. flux (mg C m-2 d-i)
log avg photo. flux (mg C m-3 d-l)
log sp. conductance (pS)
llog elevation (m)
N lat (degrees)
IN long (degrees)
log distance to nearest lake (km)
log No. lakes within 20 km
EX.XtlplCS
l-33
4-82,000,000,000
0.1-613
l-l,500
0.02-43 1
12.6-1,600
0.5-3,433
28.0-74.7
71.6-156.7
0.12-14
2-8.805
the species-area analysis, in order to represent, as evenly as possible, lakes of all
sizes. Thus, the North American data set
consists of 66 lakes, of which 38 meet all
crite:ria (see Dodson 1992).
For all lakes, “surface area” indicates the
maximum area, and other measurements
are i.n relation to the maximum surface area.
Average depth was not given for a~few small
ponds, so I estimated it as half the maximum depth.
Estimates of average daily rate of photosynthesis (primary productivity)
are in
most cases from data produced by the 14C
technique. In a few cases (marked by asterisks in the supplementary material of Dodson 1992), the rate of photosynthesis is estim;ated from diurnal oxygen measurements.
Averages were used when data were available for > 1 yr.
This study is restricted to lakes in which
species ,richness was not reduced by physical
--
Martin, Superior
Martin, Superior
Tu-Vu 11, Great Slave
Mexican Cut L12, St. George
Great Slave, Cornell 246
Little Rock, Texoma
Imikpuk, Mexican Cut Ll
Thonotosassa, Char
Little Bullhead, Imikpuk
Great Slave, Crater
Utah Stock, Great Slave
factors such as high salinity (Galat and Robinson 1983) or extreme ]pH (e.g. Frost and
Montz 1988). The lakes have specific con
ductance values at or below - 1,600 $S and
.pH values between -6 and 9.5. The species
list for Little Rock Lake, which is being
acidified, is based on pretreatment samples
and on the untreated half of the lake.
For each lake, the distance to the nearest
neighbor lake and the number of lakes within 10 and 20 km was estimated from
1 : 250,000 scale U.S. Geological Survey topographic maps. I included all water bodies
indicated in blue on the maps: lakes, ponds,
marshes, temporary ponds, and waterholes.
Hereafter, they are all called “lakes.” At this
scale, the least distance shown on the maps
is -0.125 km and the smallest lakes have
- 12,000 m2 of surface area. I counted all
lakes shown within O-10 km and lo-20 km
of the shoreline. For lakes < 10 km2, I used
a circular bullseye printed on a clear plastic
Table 2. Correlation coefficients (r-values) for the data of Dodson (1992). Parameters defined in Table 1.
Numbers above the diagonal refer to sample size; numbers below the diagonal are r-values. Asterisks: *-P <
0.05; **--P < 0.01.
-=
-Parameters
--
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
11.
--
log spp.
log area
log depth
log flux m-2
log flux mm3
log cond.
log elev.
N lat
W long
log distance
log lakes
I
0.75**
0.60**
0.50**
0.22
-0.05
-0.19
0.09
-0.22
0.57**
0.56**
2
3
4
5
6
66
66
66
44
44
44
44
44
44
44
48
48
48
35
35
0X3**
0.35**
-0.25
0.05
-0.08
0.09
-0.11
0.46**
0.34**
-0.41**
-0.36
-0.18
0.08
0.08
0.13
0.40**
0.51**
0.70**
0.43**
-0.12
-0.57**
-0.67**
-0.03
0.26
0.29
--0.08
--0.39**
-0.48**
-0.10
0.28
-
-0.31*
-0.08
-0.21
0.02
-0.12
-
851
Zooplankton species richness
sheet which could be centered over the lake.
For larger lakes, I moved a ruler along the
lakeshore, keeping the ruler perpendicular
to the shore. Because small lakes are noi
indicated on the maps, this method misses
a number, perhaps the majority, of water
bodies that are actually within 20 km of a
lake. The assumption is that the number
counted on the map is proportional to the
actual number.
Comparisons between North American
and European data were made with the data
of Dodson (199 1). I increased the data set
(see Dodson 1992) by adding two energetically extreme lakes (Tjeukemeer, hypereutrophic, and La Caldera, ultra-oligotrophic) to the 32 European lakes cited by
Dodson (199 1).
Statistical procedures-To design a multiple regression model for predicting species
richness, I used a stepwise linear regression
method for choosing a set of predictor variables: the “forward selection procedure” of
Sokal and Rohlf (198 1). The parameter most
strongly correlated with log species richness
was taken as the first independent variable.
Then the residual was correlated with the
remaining parameters. The parameter with
the highest significant correlation with the
first residual was taken as the second independent variable, and so on. The procedure was repeated until none of the remaining
parameters
was significantly
correlated with the remaining residual. The
statistically independent variables were then
used in a multiple linear regression model
to predict log species number.
Table 2. Extended.
7
8
9
10
II
66
66
66
66
66
66
44
44
44
48
44
48
66
66
66
66
44
44
48
66
66
66
66
66
44
44
48
66
66
66
66
66
66
44
44
44
66
66
66
66
-0.50**
-0.20
-0.21
-0.26*
0.56**
0.58**
0.34**
0.22*
0.16
0.61**
z
Ie I.$
$is
x
1.2-
0.8.
0.6.
0.4.
Y = 0.453 + 0.094x
.
r2=0.57
0.21
0)
0
12
8
10
4
6
log Lake Area (m*)
Fig. 1. The species-area curve for crustacean zooplankton of 66 North American lakes.
2
Estimates of parameters (such as the slope,
z) in species-area studies have always been
obtained from model 1 least-squares regression (Connor and McCoy 1979). I continue the tradition, in order to make comparisons between my results and those of
others. In my study, the major objection to
model 1 regression is largely overcome, in
that the error in measurement of lake area
(perhaps 10%) is small compared to the
overall range in lake area.
Results
Log species richness is significantly correlated with five parameters in this study
(column 1, Table 2): log lake surface area,
log mean depth, log of the average photosynthetic flux per m2, log distance to the
nearest lake, and log number of lakes within
20 km of the target lake. The strongest significant correlation is with log lake surface
area (r = 0.75, n = 66), which is therefore
taken as the first independent variable.
The slope of the North American speciesarea curve is 0.09422 (Fig. 1). The speciesarea slope for the 34 European lakes is
0.054 11, which is significantly different from
the 0.9422 slope of the 66 North American
lakes (t-test, Sokal and Rohlf 198 1, ts =
2.23, 98 df, P -C 0.05).
The species-area residual is significantly
correlated with log photosynthetic flux mW3
(r = 0.540, n = 44), log distance to the near-
Do&on
852
v)
- -.
-0.5
-0.6
-0.7
-1
.
-0.5
t
1
0
Y=-0.178+0.274X-0.066X2
r2 = 0.39
.
g
(
-0.4
Y = -0.241 +0.119X
r'r0.26
.
1
0.5
1
1.5
2
2.5
3
log Photosynthesis (mg C m3 d-l)
Fig. 2. The species-energy curve for crustacean
~ooplanlcton
of 44 North American lakes. Species richness is represented as the residual of the species-area
COlT&tiOlL
est lake (r = -0.342, n = 66), and marginally with log number of lakes within 20 km
(r = 0.236, P = 0.0524, n = 66). Thus, log
flux me3 is taken as the second independent
variable. When plotted (Fig. 2), the data appear to have a parabolic distribution. The
parabolic function (flux and flux squared)
improves the correlation somewhat (r =
0.622, n = 44).
The European data, as augmented with
the two additional lakes and including Lake
Baikal, give a similar but not-sign&ant
parabolic correlation (Dodson 1991). If
Lake Baikal is left out, the parabolic correlation is significant, but it is probably a
mistake to consider Bailcal an outlier, because the species-area residual for Baikal
fhlls within the range of points for North
American lakes of similar productivity. In
any case, the coeflkients of the European
and North American curves are not statistically difFerent (t-tests of the regression coefficients).
The species-area-energy residual is significantly correlated with both log number
of lakes within 20 km (r = 0.514, n = 44)
(Fii 3) and, to a lesser degree, with log distance to the nearest lake (r = -0.442, n 44). Thus, log number of lakes within 20
km is taken as the third independent variable. The species-area-energyresidual is not
significantly correlated with log number of
-0.5 ’
0.5
1
1.5
2
2.5
3
3.5
4
log No. of Lakes within 20 km
Fig. 3. The effect ofnearby lakes on species richness
of crustacean zooplankton of 44 North Amexican lakes.
Species richness is represented by the residual of the
correlations with lake area and photosynthetic rate.
lakes within 10 km (r = 0.033, n = 44) or
with any of the remaining six parameters.
Thus, only three of the nine parameters are
statistically significant independent variables: log sur&e area (X1), log photosynthetic flux m-3 (Xd, and log number of lakes
within 20 km (X3).
These three variables can be combined in
a multiple linear regression model to explafn 75% of the variation in log species
richness (I’):
Y = 0.1073 + 0.0712X1 + 0.2799X,
- 0.0614(X2)* + 0.1428X3.
(1)
Discussion
Stepwise linear regression identified three
statistically significant predictor variables:
log lake area, log photosynthetic flux me3
(as a parabolic function), and log number
of lakes within 20 km. The effect of each
predictor on speciesrichness is discussed in
turn.
Zooplankton species-area curve -The
slopes (z-values) of the North American and
European curves are, respectively, 0.094
(Fig. 1) and 0.054. Connor and McCoy
(1979), in a study of 100 species-areastudies, reported that most z-values were between 0.1 and 0.5. Only 9% were -CO.1.
Browne (198 1) found a z-value of 0.17 for
Zooplankton species richness
crustacean zooplankton of 13 North American lakes. Thus, the zooplankton z-values
of the current study are relatively low.
A low z-value is not particularly meaningful per se. There is no single mechanism
that causes low z-values. However, low
z-values are consistent with high immigration rates, low extinction rates, and a low
rate of increase in additional habitat with
increasing area. The ecology of crustacean
zooplankton includes all three phenomena.
Immigration rates are assumed to be high
for most speciesof crustacean zooplankton.
Fryer (1985) reported that data on colonization of newly formed ponds indicate high
immigration rates for small crustaceans.
Waterfowl, wind, flowing water, and fish
disperse the resistant eggs of zooplankton.
Most speciesofcladocerans have facultative
or obligate asexual life cycles, which allows
a population to be established by a single
propagule. Adjacent lakes-sources for immigrants- tend to be numerous and closer
together than the activity range of waterfowl, the major vectors (Hebert 1986). In
the current data set, the median number of
lakes (>6,000 m2) within 20 km is 46, and
86% of the lakes have 10 or more lakes
within 20 km. Any given lake is likely to
have additional water bodies of ~6,000 km2
within 20 km.
Extinction rates are assumed inversely
proportional to population size (Connor and
McCoy 1979). Local extinction rates of zooplankton populations are probably relatively low compared to populations of terrestrial vertebrates or arthropods because of
the relatively large population sizes of crustacean zooplankton species which occur in
even small habitats (Hutchinson 1965). For
example, a single speciesof zooplankton will
usually achieve a population of 1 animal
liter-‘. Thus, in the smallest lake in the present data set, Martin Pond, with a maximum
volume of -720 liters, there would be
- 1,000 animals. Animals in lakes of moderate size (1 km2) would have populations
of roughly 10 X lo9 individuals.
The slope of the species-area curve also
depends on the rate of addition of habitat
subdivisions in larger areas (e.g. Stevens
1986). The smallest lakes in this study are
unstratified and well lit throughout the wa-
853
ter column. The largest lakes are stratified,
with onshore and offshore habitats further
subdivided into warm and cold water, illuminated and dark water, and cold dark
water near the bottom at great depths. Thus,
larger lakes have more habitats than small
lakes, and different zooplankton species are
adapted to these different habitats (Hutchinson 1967; Stoddard 1987; Watson and
Wilson 1978). Similarly, patterns of species
associations suggestthat open-ocean pelagic
habitats, which are probably similar to largelake pelagic habitats, can be partitioned into
five or more subhabitats (McGowan and
Walker 1979).
Even though highly significant, the species-area relationship shows considerable
scatter about a linear best-fit (Fig. 1). This
scatter, measured by the correlation coefficient of 0.75, is well within the range of
statistics reported for other taxa (Connor
and McCoy 1979). However, the scatter
suggeststhat other aspects of zooplankton
ecology could be used to predict species
richness. The second predictor identified by
stepwise multiple linear regression is average photosynthetic flux.
Zooplankton species-energy curve-Speties-energy curves are uncommon, perhaps
because of the difficulty in obtaining primary productivity estimates for terrestrial
habitats. The species-energycurve for North
American zooplankton is statistically similar to that ofEuropean lakes (Dodson 1991)
and remarkably similar in shape to that of
terrestrial carnivore species (Owen 1988).
Two related lines of theory suggesta parabolic relationship between speciesrichness
and primary productivity: the toxicological
and the competition models. From a toxicology perspective (e.g. Mertz 198 l), the expectation is that any resource can be present
in either insufficient or excessive amounts,
leading to dominance of a community by
one or a few species at the extremes of resource availability. Zooplankton do not
flourish in either distilled water or in the
most productive lakes characterized by permanent blooms of cyanobacteria (Ganapati
1940). A second line of theory was developed by Tilman (1982) in response to the
frequent observation that “nutrient enrichment leads to decreased species diversity.”
854
Dodson
His model is based on the effects of the relative proportion of limiting (essential) resourIces. His model suggests that enriching
one or more resources will result in a reduction of species diversity and that the pattern of resource enrichment
determines
which species become dominant. Competition theory in general suggests that there
are levels of resource so low that only one
or even no species can persist.
Both the toxicological and competition
models predict a species richness peak at
som.e intermediate value of resource abundance, with roughly symmetrical decreases
of species richness toward each extreme.
Neither model predicts the observation that
the zooplankton curves (and the carnivore
curve, Owen 1988) show only a slight falling
awa,y of diversity on the side of high productivity.
Il. is probable that some lakes are too productive to support zooplankton because of
oxygen limitation.
Zooplankton
require a
constant supply of oxygen In the most productive lakes, oxygen can disappear for most
of the night, when zooplankton compete
with algae and bacteria for oxygen (Dodson
and Frey 199 1). I was not able to find a
study of a hypereutrophic
lake for which
there is both an estimate of the rate of productivity and a statement that no zooplankton were present. However, I suspect such
lakes (e.g. sewage ponds) are only slightly
more productive than the most productive
lakes in this study: l-l .5 g C mP2 d-’ based
on an annual average. Thus, the species
richness curve is skewed with the peak near
the right extreme of the productivity range.
Most of the variation in species richness
is accounted for statistically by the area and
productivity
of lakes. The remaining variation (scatter of points in Fig. 2) is significantly correlated with the third predictor,
the number of lakes within 20 km.
Zooplankton species-lakes curve-The
significant correlation between the speciesarea-energy residual and the log number of
lakes within 20 km suggests that immigration sources are a factor in determining specifes richness, This comparison between species richness in a lake and the number of
lakes in the surrounding landscape is a new
application in limnology. The distances 10
and 20 km were picked more or less at random, based on a desire to include a reasonable number of lakes in the adjacent area
for statistical tests, and on a guess at the
maximum range of waterfowl during their
summer residency. Waterfowl flying among
nearby lakes are likely to be major vectors
of zooplankton distribution. The result that
species richness was significantly correlated
with log lakes within 20 km, but not within
10 km, suggests that the scale of the parameter is important. Further studies are needed
to find the optimal distance for this comparison.
The number of lakes within 20 km of a
given lake is only estimated by counting blue
spots on a map. The success of the comparison suggests that it is worth refining in
future limnological studies. The desire for
better data also emphasizes the lack of
knowledge about the lake size-frequency relationship.
The low correlation coefficient and the
low slope of the species-lake line suggests
that the number of sources (lakes within 20
km) is a minor factor in determining species
richness. As discussed above, the slope of
the species-area curve could be due to high
dispersal rates, low extinction rates, and (or)
habitat subdivisions.
If the species-lake
curve suggests that immigration
rate is relatively unimportant,
then low extinction
rates and habitat subdivisions must be more
important determinants of species richness
in lakes.
The multiple linear regressionmodel-The
linear regression model (Eq. 1) based on 66
North American lakes explains - 75% ofthe
variance in species richness for those lakes.
This significant but moderate correlation
suggests that additional independent factors, such as selective predation and lake
age, may also play a role in explaining the
remaining 25% of the variation in species
richness.
Selective predation is known to affect both
population sizes and the identity of species
that occur in a lake. However, it is not yet
known whether predation intensity or selectivity affect zooplan’kton species diversity in a systematic way. Black and Hairston
(1988) proposed that predator type defines
broadly the group of potential prey species
Zooplankton speciesrichness
that can survive in a lake habitat. The zooplankton species that actually occur will be
a subset of this group, depending on any of
a variety of processes including random
chance, historical accidents, dispersal abilities, physiological tolerances, and competitive interactions.
It is likely that, as with the species-energy
relationship, the relationship between species diversity and predation will have a peak
diversity. In general, predators are thought
to increase diversity v&en diversity is low
by preying on the best competitors for limited resources. At high predation intensity,
probably only one or a few prey specialized
in avoiding predation will be able to live.
To test this hypothesis, it will be necessary
to develop a measure of predator intensity.
The effect of predators on zooplankton species richness will depend on the numbers,
ages, and species of predators present, and
it will be necessary to compare different sets
of species from different lakes. Perhaps the
best first approximation
is simply an estimate of the total biomass of fish in several
of the lakes in this data set. Even this crude
estimate is currently not available for more
than a few lakes (e.g. Dodson 1990).
The effect of lake age is difficult to determine in most north temperate lakes, because the lakes tend to be of the same postglacial age. There are no known endemics
in sexual zooplankton species. Both these
problems are alleviated in the tropics, where
lakes have a wide range of ages and where
endemics are reported to occur in at least
some of the larger African rift lakes.
An exciting application of the regression
model will be an analysis of patterns of tropical species diversity. Tropical lakes are generally species-poor relative to lakes of the
temperate zone (Lehman 1988). However,
the regression model makes it possible to
test for effects of lake size, productivity, and
immigration
sources on tropical species
richness. Tropical lakes are often small, have
extremely high productivities
and few adjacent lakes. Each of these factors tends to
reduce species richness.
Extrapolation to the oceans-Although
not statistically valid, the comparison, by
extrapolation,
of zooplankton
lists from
lakes to those of oceans points out a wide
855
discrepancy. Although the species-area relationship may provide a reasonable prediction
of species richness in North
American freshwater lakes (Fig. l), it underestimates the species richness of seas and
oceans. If one makes the debatable assumption that the Red Sea and the North
Pacific gyre are “islands” as far as zooplankton are concerned, then the North American
species-area curve (Fig. 1) can be used to
predict species richness by extrapolation.
The Red Sea, with an area of 4.5 X 10” m2,
is known to have at least 60 species of pelagic copepods and probably - 100 species
of pelagic crustaceans altogether. The North
Pacific Central Water Mass has a surface
area of about 1.5 x 10L3 m2. It is inhabited
by - 325 species of macrozooplankton
(McGowan and Walker 1979), ofwhich perhaps 200 are crustaceans. Extrapolation
from the relationship in Fig. 1 predicts only
35 species in the Red Sea and 49 species in
the North Pacific gyre. Only 66 species are
predicted if the area of the world ocean (3.6
x lOI m2) is used.
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Submitted: 29 March 199.1
Accepted: 3 December 1991
Revised: 30 January 1992