Resource heterogeneity and fish species diversity in ...

Resource heterogeneity and fish species diversity in lakes JOHNMcA. EADIE'AND ALLENKEAST Depurtment of Biology, Queen's Universih., Kingston, Ont., Canada K7L 3N6

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Received August 2. 1983 1984. Resource heterogeneity and fish species diversity in lakes. Can. J . Zool. 62: 1689- 1695. EADIE,J. McA., and A. KEAST. We tested the hypothesis that fish species diversity would be positively related to resource diversity in a series of northern and southeastern Ontario lakes. At a macrospatial scale (among lakes), the number of fish species per lake was significantly correlated with two indices of habitat heterogeneity: lake area and the shoreline development factor. At a microspatial scale (among habitats within lakes), detailed analyses revealed several significant correlations between fish species diversity and resource heterogeneity, although some effects of latitude were apparent. Fish species diversity in northern Ontario lakes was positively correlated with the diversity of invertebrate prey (benthos and zooplankton), but not with measures of physical habitat complexity. Species diversity in southern Ontario lakes was positively related to several measures of habitat heterogeneity. Reduction of redundancy in the habitat variables by principal components analysis, followed by multiple regression analyses, showed that fish species diversity in southern lakes was best predicted by substrate diversity and vertical vegetation complexity. Differences in the response of northern and southern fish communities to habitat structure likely relate to glacial history and to characteristics of the different habitats. Our results are consistent with earlier studies and suggest that species diversity - resource diversity relationships, previously reported for terrestrial communities, also apply in aquatic environments.

J. McA., et A. KEAST.1984. Resource heterogeneity and fish species diversity in lakes. Can. J. Zool. 62: 1689- 1695. EADIE, Nous avons mis a I'epreuve I'hypothese selon laquelle la diversite des especes de poissons est en correlation positive avec la diversite des ressources dans une serie de lacs du Nord et du Sud-Est de I'Ontario. A I'ichelle macrospatiale (en considerant la totalit6 des lacs), le nombre d'especes de poissons par lac,est en correlation positive avec deux indices de I'heterogeneite de l'habitat: la surface du lac et le developpement de la rive. A l'echelle microspatiale (en considerant les divers habitats d'un meme lac), des analyses detaillees ont mis en evidence plusieurs correlations significatives entre la diversite des especes et I'heterogeneite des ressources, mais l'influence de la latitude est egalement apparente. La diversite des especes de poissons dans les lacs du Nord de I'Ontario est directement relike a la diversite des invertCbr6s qui leur servent de proies (benthos et zooplancton), mais n'est pas en correlation positive avec certaines mesures de la complexite de I'environnement physique. Dans les lacs du Sud, la diversite des especes est fonction directe de plusieurs caract6ristiques de I'heterogeneite du milieu. L'analyse des composantes principales a permis de reduire la redondance de certaines variables ecologiques; la calcul des regressions multiples a par la suite mis en evidence que la diversite dans le substrat et la complexite verticale de la vegetation sont les facteurs qui permettent le mieux de predire la diversite des especes de poissons. Les communautes des lacs du Nord et celles des lacs du Sud ne rkagissent pas de la meme facon a la structure de l'habitat et cette difference est probablement reliee a I'histoire des glaciations et aux caracteristiques des divers habitats. Nos resultats rejoignent ceux d'etudes anterieures et permettent de croire que les correlations diversite specifique - diversite des ressources qui ont ete trouvees chez les communautes terrestres prevalent aussi en milieux aquatiques. [Traduit par le journal]

Introduction Competition theory predicts that increased species diversity can be accommodated by ( i ) increased niche packing (i.e., finer division of available resources) or (ii) increased resource diversity (MacArthur 1972; Cody 1974). Wiens ( 1977) argued that competitively induced niche changes would be rare in most communities because of inherent environmental variability. Rotenberry (1978, p. 697) instead suggested that, in variable environments, increased resource heterogeneity would be more important than competitive interrelationships in accommodating diverse assemblages of species. Positive relationships between resource diversity and species diversity have been demonstrated for a number of terrestrial organisms (e.g., MacArthur and MacArthur 1961; Pianka 1967; Rosenzweig and Winakur 1969; Tomoff 1974; Roth 1976). However, there have been few attempts to investigate these relationships in lacustrine systems, despite the fact that lake habitats are in many ways analogous to terrestrial ones. We predicted that fish species diversity in lakes would be 'Author to whom reprint requests should be sent at his present address: Department of Zoology and Institute of Animal Resource Ecology, University of British Columbia, Vancouver, B.C., Canada V6T 2A9.

correlated with resource diversity for two reasons. First, fishes utilize different prey items and partition habitat in both vertical and horizontal dimensions (Keast 1978; Werner et al. 1977; 1978). Substrate and vegetation characteristics also vary vertically and horizontally among littoral zone habitats, and different plants and bottom types harbour different kinds of invertebrate prey (Rosine 1955; Gerking 1957; MacLachlan 1962; Allan 1975). Consequently, increased substrate and vegetation diversity might be expected to support a greater variety of fish consumers. Second, at least two studies have shown that fish species diversity is related to habitat heterogeneity in some streams (Gorman and Karr 1978) and lakes (Tonn and Magnusson 1982). Results of a previous analysis (Eadie 1982) suggested that resource diversity might also be an important determinant of fish species diversity in Ontario lakes, although no attempt was made to specifically test this hypothesis. We therefore initiated the present study to test the prediction that fish species diversity in Ontario lakes would be correlated with resource diversity. We explored these relationships at both a macrospatial (among lakes) and microspatial (among habitats within lakes) scale. To test the generality of our results, and to examine for possible latitudinal effects, we compared a series of communities in northern Ontario to a series in southeastern Ontario.

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Materials and methods Macrospatial analyses We searched for general relationships at the macrospatial scale by examining the number of fish species per lake as a function of two indices of habitat diversity: lake area and the shoreline development factor (Barbour and Brown 1974; Kent and Wong 1982). Data for 70 lakes in northern Ontario were obtained from lake survey files of the Ontario Ministry of Natural Resources (Kirkland Lake District). From these reports, we recorded the number of fish species in each lake, the lake area, and the shoreline development factor (SLDF) (survey methods are given in Martin and Olver 1976 The shoreline development + 4 .rrA where L is shoreline length factor, calculated as SLDF = L/ l and A is lake area, indexes the irregularity of the shoreline controlling for lake size effects. Curves were fit to these data using geometric mean regression (Ricker 1973). Microspatial analyses The effects of microspatial habitat heterogeneity were examined via intensive studies on three lakes in northern Ontario (Elk, Mountain, and Penassi lakes, Timmiskaming Co.) and seven lakes in southeastern Ontario (Redhorse, Singleton, Gananoque, Grippen, Graham, Lyndhurst, and Lower Beverly lakes, Leeds Co.) (Fig. 1). Five sites were studied in each of three seasons (May, July, September) in the northern Ontario lakes. Habitat structure and fish species composition changed markedly within study sites over this period, so we consider each site in each season separately. Twenty-three sites were studied in July for the southeastern Ontario lakes. All the study lakes are mesotrophic. Mean depths of northern Ontario lakes ranged from 3 to 11 m. The inshore vegetation was predominantly Scirpus, Equisetum, Vallisneria, and Potamogeton sp. Southeastern Ontario lakes had mean depths of 2- 12 m and dominant vegetation included Chara ,Elodea, Mvriophyllum , and Potamogeton . Habitat analysis At each site a 100-m2 sampling grid was established in homogeneous habitat. Plant cover and substrate composition were sampled using 20 randomly located 0.75-m2 quadrats. The percent cover of each plant species, the height of the submergent plant nearest to the center of the quadrat, and the percent cover of each of eight substrate types was estimated. Water depth was measured at the center of the quadrat and water temperature was obtained from the middle of the sample grid. Substrate diversity and plant species diversity were calculated using

where Pi is the proportion of the ith resource category (Hill 1973; Peet 1974). Foliage height diversity was estimated using two methods. In method 1, five categories of plant heights were delineated (0-0.25, 0.25-0.50, 0.50-0.75. 0.75- 1.0, and > I .0 m) and proportions of plant heights that fell into each of these categories were calculated. These proportions were then substituted into the above formula. In method 2, a modified vegetation profile board was used (Nudds 1977). Within each quadrat, the profile board was placed in the middle of the quadrat and the proportion of plant cover at each of the five height categories was estimated according to the relative visibility of the profile board. A diversity index was calculated for each quadrat and then averaged over all quadrats for each site. The two methods were highly correlated ( r = 0.87, P < 0.001, N = 23) and only method 1 was used in the northern Ontario lakes. Horizontal vegetation patchiness of each grid was calculated by dividing the standard deviation of plant cover by the mean value. Invertebrate sampling The availability of invertebrate prey resources was assessed only for the northern lakes because of time limitations. The benthic fauna were sampled using a 0.25-m push net (description in Keast 1965). Twenty random 2-m (0.5-m2) samples were taken at each site during each season. The total biomass of each sample was measured and in-

FIG. 1 . Location of the study lakes in northern Ontario (upper inset) and southeastern Ontario (lower inset). These are ( 1 ) Penassi Lake, (2) Elk Lake, (3) Mountain Lake, (4) Grippen Lake, (5) Lower Beverly Lake. (6) Lyndhurst Lake, (7) Singleton Lake. (8) Redhorse Lake, (9) Gananoque Lake, (10) Graham Lake.

vertebrates were sorted to family or order. Zooplankton were sampled with a 30-L Schindler trap. Twenty samples were obtained at each site during each season. Numbers of individuals in each genus of zooplankton were counted for each sample. Most samples contained relatively few individuals and so were counted in their entirety. Samples with large numbers of individuals were diluted to 250 mL, homogeneously mixed in a fluted flask, and three 10-mL subsamples were analysed. Benthic diversity and zooplankton diversity were calculated using the above formula where Pi is the proportion of each taxa. Fish samples Fish communities at each site were sampled using a 16 m x 2 m bag seine net with a 0.5-cm mesh size. Preliminary analyses showed that the cumulative species curve become asymptotic after two to three seine hauls. We conducted a minimum of four seine hauls per site in northern Ontario lakes and a minimum of two (usually three to four) seine hauls in southern Ontario lakes. Each haul encompassed the entire study grid (100 m2). All fish captured were counted, measured (length), and released with the exception of 20-30 specimens retained from each northern site for other analyses. Seven species of fish were common in the littoral zone of northern Ontario lakis while 12 species were commonly collected in the littoral zone of southern Ontario lakes. Species lists are provided in the Appendix. Two indices of fish species diversity were determined for each study site: ( i ) species richness ( R ) , the total number of species, and (ii) Simpson's diversity index (Hill 1973), calculated as

EADlE AND KEAST

TABLE 1. Correlations of fish species diversity (FSD) and species richness (R) with measures of habitat heterogeneity for 15 "sites" in northern Ontario


FSD

R

Plant cover Plant species diversity Plant height Foliage height diversity Substrate diversity Water depth Benthos diversity Zooplankton diversity

1.0

Lake

1.00

Area

1000

[~cres)

1

Benthic Shoreline Development Factor FIG. 2. The relationship between the number of fish species per lake and ( a ) lake area and (b) the shoreline development factor.

[2] FSD

=

~/C(P,)~

where P, is the proportion of all individuals caught belonging to the ith species. Richness ( R ) measures the absolute number of species in each site whereas Simpson's index (FSD) "weights" each species by its relative abundance and so can be interpreted as the number of equally common species (Hill 1973).

Results Lake area, shoreline development factor, and fish species diversity The number of fish species per lake in northern Ontario was significantly correlated with lake area (Fig. 2a), and with the shoreline development factor (Fig. 2b). The slope of the fish species - lake area curve (0.39) was comparable to that reported for other isolated habitats (Barbour and Brown 1974; Connor and McCoy 1979). Habitat diversity, prey diversity and fish species diversity Northern sites There were few significant correlations between overall fish species diversity (FSD) or species richness ( R ) and measures

2

3 4 Prey D i v e r s i t y

FIG. 3. The relationship between fish species diversity and benthic invertebrate prey diversity in northern Ontario lakes.

of physical habitat complexity in northern littoral zone fish communities (Table 1). In contrast, there were significant correlations between fish species diversity and benthic invertebrate diversity, and between fish species richness and zooplankton diversity (Table 1). The geometric mean regression of fish species diversity on benthic invertebrate diversity (Fig. 3) indicates that small changes in prey diversity may have larger effects on fish species diversity. Southern sites Fish species diversity in southern littoral zone fish communities was significantly correlated with foliage height diversity (FHD 2), substrate diversity, and water depth (Table 2). Richness was significantly correlated with plant cover, plant height, and foliage height diversity (FHD 1 and FHD 2). The geometric mean regression of fish species diversity on substrate diversity is given in Fig. 4a, while that of FSD on foliage height diversity is shown in Fig. 4b. The slopes of these regressions suggest that changes in foliage height diversity may have greater effects on fish species diversity than comparable changes in substrate diversity. Previous authors have generally reported only bivariate correlations between indices of species diversity and measures of habitat heterogeneity (e.g., see Gorman and Karr 1978). How-

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CAN. J . ZOOL. VOL. 62. 1984

TABLE 2. Correlations of fish species diversity (FSD) and species richness ( R ) with measures of habitat heterogeneity for 23 sites in southeastern Ontario -

-

-


FSD Plant cover 0.03 Horizontal plant patchiness -0.0 1 Plant species diversity -0.01 No. of plant species (richness) 0.03 Plant height 0.20 0.38* Foliage height diversity 2 Foliage height diversity 1 0.22 Substrate diversity 0.5 1 ** Water depth 0.57** Depth diversity -0.10

R -

Substrate

Diversity

TABLE 3. Principal components analysis on measures of habitat heterogeneity for 23 sites in southeastern Ontario. Factor loadings, variance accounted for, and descriptions of each component are given. High loadings are underlined

Plant cover Horizontal plant patchiness Plant species diversity No. of plant species (richness) Plant height Foliage height diversity 1 Foliage height diversity 2 Substrate diversity Water depth Depth diversity % variance explained Cumulative % variance

I

I

I

1

2

3

Foliage Height Diversity ever, one difficulty in interpreting the results of such analyses is that the habitat variables may themselves be correlated, obscuring a clear relationship between species diversity and any single habitat measure. To circumvent this problem and to more clearly elucidate the relationship between fish species diversity and habitat diversity, we conducted a second series of analyses. First, a principal components analysis (Nie et al. 1970) was performed on all 10 habitat variables to reduce redundancy in these variables and to create orthogonal habitat dimensions (Green 1979; Neff and Marcus 1980). Three principal components accounted for over 78% of the variation in the original habitat data (Table 3). The first component (PC 1) accounted for 39.5% of the variation and loaded highest on plant species diversity and richness, plant height, and foliage height diversity. PC2 accounted for 21.2% of the variation and loaded positively on total plant cover and negatively on horizontal plant patchiness and water depth diversity. The third principal component (PC3) loaded highest on water depth and substrate diversity. Thus, three independent habitat dimensions were clearly defined relating to vegetation complexity (PCI), absolute amount of vegetation (PC2), and substrate diversitylwater depth (PC3). We then used multiple regression analyses to partition the effects of each habitat dimension on fish species diversity and

FIG.4. The relationship between fish species diversity and ( a ) substrate diversity and ( h ) foliage height diversity in southeastern Ontario lakes.

species richness. PC scores were computed for principal components 1 to 3 for each site and input as the independent variables (see Neff and Marcus 1980 for a further discussion of this method). Only one habitat component (PC) was significant for each of the regressions (Table 4). PC3 (substrate diversitylwater depth) explained a significant amount of variation in fish species diversity (FSD) while fish species richness ( R ) was significantly related only to vegetation complexity (PC 1). In both cases the absolute amount of vegetation cover (PC2) was the last variable to enter the regression and explained, at best, no more than 1.O% of the total variation in the dependent variable.

Discussion Our analyses at the macrospatial level show that fish species richness is significantly correlated with lake area and the shoreline development factor. Barbour and Brown (1974) argued that the number of fish species in a lake is primarily determined by habitat diversity, and that larger lakes contain a greater variety of habitats. They therefore anticipated a relationship between lake area and fish species diversity. Kent and Wong

EADlE AND KEAST

TABLE 4. Stepwise multiple regression analysis on fish species diversity (FSD) and species richness ( R ) for 23 sites in southeastern Ontario. Three habitat components (PC's) are entered as independent variables R' change

Dependent variable FSD


R

Independent variable

R

R'

(%)

F

P

( 1 ) PC3 (substrate diversity) 0.60 0.36 (2) PC1 (vegetation complexity) 0.61 0.37 0.61 0.37 (3) PC2 (vegetation cover)

35.8 1.4 0.1

1 1.72