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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 First, --Acknowledgments I thank all those who contributed unpublished data; they are acknowledged elsewhere (supplemental material available on request: Dodson 1992).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. References ANGERMEIER,P. L., AND I. J. SCHLOSSER. 1989. Species-area relationships for stream fishes. Ecology 70: 1450-1462. BLACK, R. W., II, AND N. G. HAIRSTON, JR. 1988. 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