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ECOGRAPHY 26: 715–722, 2003

Relationships between spatial environmental heterogeneity and plant species diversity on a limestone pavement Jeremy T. Lundholm and Douglas W. Larson

Lundholm, J. T. and Larson, D. W. 2003. Relationships between spatial environmental heterogeneity and plant species diversity on a limestone pavement. – Ecography 26: 715– 722. No empirical studies have examined the relationship between diversity and spatial heterogeneity across unimodal species richness gradients. We determined the relationships between diversity and environmental factors for 144 0.18 m2 plots in a limestone pavement alvar in southern Ontario, Canada, including within-plot spatial heterogeneity in soil depth, microtopography and microsite composition. Species richness was unimodally related to mean soil depth and relative elevation. Microsite heterogeneity and soil depth heterogeneity were positively correlated with species richness, and the richness peaks of the unimodal gradients correspond to the maximally spatially heterogeneous plots. The best predictive models of species richness and evenness, however, showed that other factors, such as ramet density and flooding, are the major determinants of diversity in this system. The findings that soil depth heterogeneity had effects on diversity when the effects of mean soil depth were factored out, and that unimodal richness peaks were associated with high spatial heterogeneity in environmental factors represent significant contributions to our understanding of how spatial heterogeneity might contribute to diversity maintenance in plant communities. J. T. Lundholm ( [email protected]) and D. W. Larson, Dept of Botany, Uni6. of Guelph, Guelph, ON, Canada N1G 2W1.

Heterogeneity in environmental factors has long been theorized to contribute to the maintenance of diversity in plant communities (Ricklefs 1977, Grime 1979, Tilman 1982, Tilman and Pacala 1993, Palmer 1994). Unimodal relationships between plant species richness and aboveground biomass are common in herbaceous communities (Grace 1999). One explanation for such patterns is that spatial heterogeneity in resource availability is maximal at intermediate productivity, leading to the prevention of competitive exclusion between plant species (Tilman 1982), but there have been no studies that have quantified spatial heterogeneity in environmental factors across unimodal richness gradients (Abrams 1995). The first step toward testing the hypothesis that spatial heterogeneity can maintain diversity would be to compare diversity in areas that differ in spatial heterogeneity. Nevertheless, field stud-

ies that measure the correlations between diversity and small-scale environmental heterogeneity are rare (Grace 1999, Wilson 2000). In this study, we aim to quantify the relationships between diversity and spatial heterogeneity in environmental factors across natural diversity gradients in a habitat known to have strong heterogeneity gradients (Catling et al. 1975), high local diversity (Belcher et al. 1992, Schaefer and Larson 1997) and high conservation importance (Reschke et al. 1999). Plant species are differentiated in their competitive ability or other measures of performance along gradients of soil moisture (Silvertown et al. 1999), microtopography (Zedler and Zedler 1969, Bratton 1976, Beatty 1984), microsite composition (Harper et al. 1965, Silvertown 1981, Zamfir 2000), and soil depth (Belcher et al. 1992, Reynolds et al. 1997) in herbaceous

Accepted 28 April 2003 Copyright © ECOGRAPHY 2003 ISSN 0906-7590 ECOGRAPHY 26:6 (2003)

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plant communities. Essential resources for plant growth are spatially heterogeneous at scales B 1 m (Bell and Lechowicz 1991, Lechowicz and Bell 1991, Jackson and Caldwell 1993, Ryel et al. 1996, Kleb and Wilson 1999, Fitter et al. 2000), thus co-occurring individuals may experience functionally different conditions at very small spatial scales. Since small-scale variability in environmental factors is common and plants are differentiated in their responses to average or typical levels of these environmental factors, it should be a relatively simple matter to examine correlations between environmental heterogeneity and species richness or other indices of coexistence. Yet very few studies have examined the relationships between heterogeneity and plant diversity at small scales over which interspecific interactions and competitive exclusion are thought to be important in the maintenance of diversity (Shmida and Wilson 1985). Of the few studies that compared areas B 25 m2 that differed in spatial heterogeneity, experiments in old fields have revealed no correlations between spatial heterogeneity in soil nutrients or light and diversity (Collins and Wein 1998, Stevens and Carson 2002), greenhouse experiments using grassland and wetland communities have revealed positive correlations between diversity and spatial heterogeneity in soil characteristics and microtopography (Fitter 1982, Vivian-Smith 1997) or no correlations (Grime et al. 1987), and field studies in grasslands have shown positive correlations between spatial heterogeneity in patch type (disturbed vs undisturbed) and plant diversity (Wilson 2000). Limestone pavements (plant communities on shallow soils over limestone bedrock) occur within habitats known locally as alvars in Sweden, Estonia and North America. These systems are globally imperiled and have been intensively studied with respect to their community composition (Krahulec and van der Maarel 1986, van der Maarel 1988, Bakker et al. 1996) and dynamics (Rusch 1992, Rusch and van der Maarel 1992, Pa¨ rtel and Zobel 1995, Kalamees and Zobel 2002). In general, these habitats are characterized as low in productivity but high in plant diversity and rare species (Catling et al. 1975, van der Maarel 1988, Schaefer and Larson 1997). Van der Maarel and Sykes (1993) hypothesized that in spatially homogeneous alvars, the exceptionally high levels of species richness on them are maintained by a high rate of turnover of ecologically equivalent species. Gigon and Leutert (1996), however, argue that in grasslands where different kinds of microsites co-occur at small spatial scales, microsite heterogeneity might promote diversity, but this has not been studied quantitatively. Likewise, North American alvars are spatially heterogeneous in soil depth (Belcher et al. 1992) and microsite composition (Catling et al. 1975, Catling and Brownell 1995) but these variables have not been quantified and related to plant species diversity. Belcher et al. (1992) and Zamfir (2000) showed that 716

individual species are differentiated in their responses to factors such as soil depth and germination substrate. Thus the potential exists for heterogeneity in either of these factors to promote coexistence amongst potential competitors interacting at very small scales. If heterogeneity promotes coexistence, then we would expect to see positive relationships between species diversity and measures of spatial habitat heterogeneity. In this paper we present work that tests this prediction. To accomplish this, we chose a site with large diversity gradients that represent the broadest possible range of conditions within limestone pavements in the region. We compared plant species diversity among 0.18 m2 plots that had varying levels of spatial heterogeneity in soil depth, microtopography and microsite composition. We asked two main questions. First, what are the relationships between within-plot heterogeneity and diversity? Second, does high spatial heterogeneity coincide with the richness peak in unimodal richness gradients?

Methods Site description We studied a limestone pavement alvar in Bruce Peninsula National Park, Ontario, Canada (45°12%N, 80°37%W). The site has B10% tree cover and \50% exposed rock, thus is classified as alvar pavement (Brownell and Riley 2000) and its vegetation composition and structure is typical of the alvars studied in Schaefer and Larson (1997). Unlike the alvars of northern Europe, the ones studied here do not seem to represent a successional continuum from alvar to forest. Open patches of alvar pavement with similar species composition exist in areas burned 90 yr ago as well as areas that have not been disturbed for over 500 yr (Schaefer and Larson 1997). The bedrock is dolomite, a harder form of limestone containing magnesium. The study area comprised a 50 ×210 m rectangle dominated by limestone pavement, bounded by a pond on one side and a ridge with scattered coniferous trees on the other. The rectangle enclosed the entire local elevation gradient: beyond the ridge, elevation again decreased. Tree and shrub cover was low throughout the site but peaked at the highest elevations. Dominant herbaceous species included Scirpus cespitosus, Schizachyrium scoparium, Rhynchospora capillacea, Calamintha arkansana, Hedyotis longifolia and Cladium mariscoides. Trees were mostly Thuja occidentalis (nomenclature follows Newmaster et al. (1998)). Ground cover was highly variable in space and included bare soil, bare rock, granitic and calcareous glacial deposits, algal mats, mosses, lichens and higher plants. The lowest elevations were flooded for most of the growing season. Soil chemistry is comparable to other alvar sites in the region (Schaefer and Larson 1997). ECOGRAPHY 26:6 (2003)

Sampling design We oriented a 210 m transect to bisect the site rectangle along the long axis. We selected 72 plots on each side of this transect in order to represent the entire elevation gradient on the site (144 plots total). Every 3 m along this transect two plots were placed at a random distance perpendicular to the transect (one on each side of the transect). Each plot was a permanently marked 30× 60 cm rectangle (0.18 m2) oriented parallel to the short axis of the site rectangle. If the plot fell upon a shrub above 0.5 m in height a new plot was chosen along the same line. Plots that fell under tree canopy (tree height \1.5 m) were included.

Dependent variables Plots were divided into 72 5 × 5 cm subplots. The number of stems of each vascular plant species was counted in each subplot, including seedlings and adults. For graminoid species, the number of tillers (ramets) was counted. The aerial cover of each species in each subplot was estimated and assigned a quartile cover class (0 =absent, 1 = 0–25%, 2 = 25 –50%, 3 = 50– 75%, 4 = 75–100%). The density and cover of each species in each subplot were summed to give total density and an estimate of percent cover for each species in each plot. In order to determine the relationships between diversity and environmental factors the total number of species rooted in a plot (species richness) and evenness (Evar: Smith and Wilson (1996),

using relative cover of each species in each plot as a measure of relative abundance) were used as diversity measures. To factor out the effects of the number of individuals sampled on species richness, we also estimated the number of species per 50 ramets in each plot as a density-independent measure of diversity (e.g. Pa¨ rtel and Zobel 1995, Zobel and Liira 1997). Using the number of stems and number of species in each sub-plot, we were able to calculate the number of stems and species for larger, overlapping squares (72 × 0.0025 m2, 55 ×0.01 m2, 40 ×0.0225 m2, 27 ×0.04 m2, 16 × 0.0625 m2 and 7 ×0.09 m2 plots). By calculating the mean density and richness for each size of square, a species-density curve was created for each plot. By interpolating along this curve the number of species/50 ramets was calculated for each plot. Plant sampling was carried out from June to August 1999.

Independent variables Environmental factors in each plot were characterized in order to determine which variables best predicted vascular plant diversity (Table 1). Measures of spatial heterogeneity in three variables were calculated: soil depth, microtopography and microsite type (Table 1). Measures of soil moisture content, water depth, vascular plant cover and density, and the relative abundance of different types of microsite were also calculated for each plot (Table 1).

Table 1. Independent variables measured in each plot. Short name

Description

mean soil depth

mean within-plot soil depth (cm) calculated from the depth measured in the center of each of 72 subplots soil depth CV within-plot coefficient of variation of soil depth (%) soil depth spatial heterogeneity within-plot variance of soil depth multiplied by within-plot fractal dimension of soil depth (Armstrong 1986, Palmer 1992) microelevation mean within-plot elevation above lowest point in plot (cm) calculated using elevation of center of each of 72 subplots microtopographic CV coefficient of variation of within-plot elevation above lowest point in plot (%) microtopographic spatial within-plot variance of elevation above lowest point multiplied by within-plot fractal heterogeneity dimension (Armstrong 1986, Palmer 1992) (all fractal dimensions calculated with GS+ (ver. 5.1.1, Anon. 2001)) water depth maximum water depth in plot between June and August 1999 (cm) mean soil moisture mean gravimetric soil moisture content at surface measured in the center of each plot in June and July 1999 (gwater/gsoil) (when differences in moisture content among plots were likely to be maximal due to drought in plots at higher topographic positions) minimum soil moisture minimum gravimetric soil moisture content (June–July 1999) (gwater/gsoil) plant cover cover of vascular plants (visually estimated at phenological maximum at end of July) (%) density total number of ramets counted in plot (herbaceous species and small shrubs) X cover number of subplots occupied by microsite type X/72 subplots, microsite types: litter = dead vascular plant litter, woody debris =woody plant litter \1 cm diameter, moss, liverwort, lichen, rock = bedrock, till =rock moveable by hand \5 cm in any dimension, gravel = moveable rock B5 cm in all dimensions, soil = bare soil, algal mat= colonial algae (mostly Nostoc sp.) (%) microsite heterogeneity number of distinct types of microsite in plot

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Analyses Relationships between diversity measures and environmental factors (Table 1) were examined using all-subsets multiple regression with the standardized mean squared error of prediction (Cp) as the selection criterion (Anon. 1989, Philippi 1993). Measures of spatial heterogeneity in soil depth and microtopography were regressed against mean values of these factors and the residuals of these models entered in the multiple regressions described above instead of raw values of heterogeneity measures. This allows for the detection of the effects of environmental heterogeneity independent of the effects of mean levels of factors (Palmer 1991). Independent variables were transformed where necessary to improve linearity and normality. Relative elevation was not used as a predictor in the multiple regressions as it was spatially autocorrelated (determined using Moran’s I (Legendre and Fortin 1989)) and because elevation only indirectly affects richness via the effects of other independent variables that were included in the models. We used a randomization test to determine whether the species richness pattern across the gradient in relative elevation could be attributed to random placement of species ranges (Veech 2000; software from Ecological Archives, Ecological Society of America: E081-010). In order to further examine the relationships between species richness and elevation, separate multiple regressions of environmental factors on richness and richness/50 ramets were performed for three regions: the low elevation plots (0 –0.5 m) where richness increased monotonically with elevation, the intermediate elevation plots (0.5 –1.0 m) where there was no clear relationship between richness and elevation, and the high elevation plots ( \ 1.0 m) where richness decreased monotonically with elevation (Fig. 1a).

Results Evenness was independent of richness (rs = − 0.07, p = 0.38, n =144: richness; rs =0.15, p = 0.85, n = 144: richness/50 stems). Richness and richness per 50 ramets showed similar relationships to the environmental variables measured, except that density was not a component of predictive models of richness per 50 ramets, hence only richness results will be presented. Richness was unimodally related to relative elevation (Fig. 1a). The randomization analyses determined that the richness pattern along the elevation gradient was significantly non-random (p B0.0001). Richness peaked at relatively low soil depths, but was highly variable at low depths and uniformly low at greater soil depths (Fig. 1b). Microsite heterogeneity (ln transformed) and soil depth CV were both positively related to richness in univariate regressions (Fig. 2). Soil depth CV was a 718

Fig. 1. Relationships between a) relative elevation and b) mean soil depth and plant species richness for 144 limestone pavement plots.

component of the best predictive model for species richness over the whole site (Table 2), but microsite heterogeneity was not. The strongest predictors of richness were maximum water depth which was negatively correlated with richness and vascular plant density which was positively correlated with richness (Table 2). Evenness was negatively related to soil depth mean and weakly correlated with microsite heterogeneity, water depth and plant cover (Table 2). At low elevations, richness was negatively related to maximum water depth and soil depth mean and positively related to microsite heterogeneity and plant density (Table 3). At intermediate elevations, richness was negatively related to maximum water depth and positively related to density, plant cover, microsite heterogeneity and soil depth CV (Table 3). At high elevations, richness was negatively related to litter cover and positively related to plant cover and density (Table 3). Microsite heterogeneity and soil depth CV were unimodally related to relative elevation (Fig. 3a) and negatively related to mean soil depth (Fig. 3b).

Discussion The role of environmental heterogeneity in organizing biological diversity has been difficult to assess because ECOGRAPHY 26:6 (2003)

Fig. 2. Relationships between microsite heterogeneity and soil depth CV and plant species richness for 144 limestone pavement plots. Microsite heterogeneity was calculated as the number of distinct microsite types (see Table 1 for details) encountered in a plot. a) microsite heterogeneity (ln transformed), b) soil depth CV (residuals of CV × mean regression).

the scales of variability that are important to plants in the field are not always ones that can be seen with the naked eye. Here we have shown for an exceptionally

species rich limestone pavement community, that higher species richness is associated with greater spatial heterogeneity in microsite composition and soil depth (Fig. 2). In predictive models of richness that incorporated many variables, however, microsite heterogeneity made a relatively small contribution to the variation in richness and only at low and intermediate elevations. Soil depth CV was an important predictor of richness across all elevations and specifically at intermediate elevations, while mean soil depth was only important at the low elevations. While other studies have shown positive relationships between within-plot microtopographic heterogeneity and species richness (Vivian-Smith 1997), we found no significant relationships between these variables in our site. This is likely due to the generally flat profile of the pavement surface. Despite the strong positive relationship between microsite heterogeneity and richness in the univariate models, colinearity between microsite heterogeneity and other important predictors of diversity resulted in weak, or absence of, contributions of microsite heterogeneity to the multivariate models of richness. The importance of spatial heterogeneity thus varied with relative elevation range within the site. The peaks of unimodal species richness gradients were associated with maximum spatial heterogeneity in microsite composition and soil depth. In this habitat, spatial heterogeneity in within-plot microsite composition was variable, with plots dominated by a single type of microsite to plots with up to nine distinct kinds of microsite (Fig. 3a). Since plant species differ in their germination and establishment responses to different kinds of microsites (Harper et al. 1965, Grubb 1977, Silvertown 1981, Zamfir 2000), plots with higher microsite diversity might be predicted to contain safe sites for establishment by more species (Gigon and Leutert 1996). The relationships between microsite heterogeneity and richness were consistent across all elevation classes (Fig. 2), though microsite

Table 2. Relationships between richness and evenness and environmental variables in 144 plots. Two plots had no variability in soil depth (rock only) and were excluded from these analyses. Results for all subsets multiple regressions (richness: F6,135 =67.08, pB0.0001, adjusted R2 = 0.74; evenness: F4,135 =40.63, pB0.0001, adjusted R2 =0.53). See Table 1 for details of environmental variables. Dependent variable

Independent variable

Transformation

Parameter sign

Pr t

Squared Partial Correlation (Type 2)

richness

density water depth plant cover litter cover soil depth CV minimum soil moisture soil depth mean water depth microsite heterogeneity plant cover

ln(x) 1/(x+1)

+ + + − + + − − − −

B0.0001 B0.0001 B0.0001 B0.0001 0.0002 0.006 B0.0001 0.012 0.07 0.09

0.33 0.30 0.12 0.11 0.10 0.06 0.31 0.05 0.02 0.02

evenness


ln(x) ln(x) ln(x) 1/(x+1) ln(x)

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Table 3. Relationships between species richness and environmental variables for 144 limestone pavement plots. Results are from all-subsets multiple regressions (selection = Cp). Separate regressions were performed in three relative elevation classes: low (B0.5 m) (F4,27 = 77.70, pB0.0001, adjusted R2 = 0.91), intermediate (0.5–1.0 m) (F5,52 =25.41, pB0.0001, adjusted R2 =0.69), high (\1.0 m) (F3,49 = 58.97, pB0.0001, adjusted R2 =0.77). See Table 1 for descriptions of independent variables. Elevation

Independent variable

Transformation

Parameter sign

Pr t

Squared Partial Correlation (Type 2)

Low

water depth density soil depth mean microsite heterogeneity water depth density plant cover soil depth CV microsite heterogeneity litter cover density plant cover

1/(x+1) ln(x) 1/(x+1)

+ + + + + + + + + − + +

B0.0001 B0.0001 0.003 0.015 B0.0001 B0.0001 0.0008 0.001 0.02 B0.0001 B00001 0.0001

0.64 0.46 0.28 0.20 0.39 0.36 0.20 0.18 0.10 0.60 0.40 0.29

Intermediate

High

1/(x+1)

heterogeneity was not always part of the best regression model for species richness. The relationships between species richness and elevation and mean soil depth were unimodal, although the soil depth/richness curve appears to approximate the ‘‘unimodal envelope’’ described by Grace (1999), where a unimodal curve describes an upper limit to richness. Microsite heterogeneity was maximal at intermediate elevations and relatively low soil depths, which coincide with the greatest species richness. In this habitat type, above-ground biomass is positively correlated with soil depth (Belcher et al. 1992, 1995), thus the richness gradient described here corresponds to patterns of broad occurrence in herbaceous plant communities (Grace 1999). At the lowest elevations, microsite composition was more uniform due to the presence of standing water throughout the growing season which lead to the dominance of algal mats as the main

microsite type. At the highest elevations, the dominant cover type was plant litter, contributed in part by Thuja occidentalis. Soil depth increased at the highest elevations (Fig. 4), likely leading to increased production by woody plants and high levels of litter. These findings support the idea that heterogeneity decreases at higher productivities (Tilman 1982) but here via the increase in dominance of one type of microsite (algal mats or plant litter) that overrides underlying spatial heterogeneity in other factors (Beatty 1984). Our study presents evidence that high spatial heterogeneity is associated with maximal diversity at the peaks of unimodal gradients. Existing general models of herbaceous plant diversity (e.g. Grace 1999) need to include the potential interdependence of biomass and heterogeneity as contributors to the variance in diversity. While our results suggest that microsite availability may limit diversity in the pavements studied here, seed

Fig. 3. Relationships between spatial heterogeneity in microsite composition and soil depth and a) relative elevation, b) mean soil depth.

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further experimental work is warranted that separates the life stages at which this heterogeneity acts, and determines the relative importance of species pool (e.g. Palmer and Dixon 1990, Eriksson 1993, Pa¨ rtel et al. 1996) versus small-scale biotic interactions in producing the relationships between within-plot spatial heterogeneity and plant species richness.

Fig. 4. Relationships between mean soil depth and relative elevation for 144 limestone pavement plots.

addition experiments are necessary to determine whether recruiting seedlings are actually micrositelimited. While other field studies that have controlled for mean resource levels have found small effects (Palmer 1991) or no effects of spatial heterogeneity (Collins and Wein 1998, Stevens and Carson 2002), we found that spatial heterogeneity in soil depth was significantly positively correlated with richness once effects of the mean had been factored out. Limestone pavements are unproductive (DeGruchy 2002) relative to forests and old fields where these other studies were done. Thus the importance of spatial heterogeneity in plant resources may vary across habitats that differ in productivity. Unimodal patterns of species richness are also expected in models where the number of individuals is lower at the extremes than in the middle of a gradient (Oksanen 1996). The current study suggests that sampling effects made a significant contribution to the richness pattern here: species richness increases with density across the entire elevation gradient. Additionally, density was not an important predictor of richness when sampling effects were factored out by using a measure of richness standardized to 50 ramets. The unimodal patterns detected here were thus a function of both sampling effects and environmental factors, including gradients in spatial heterogeneity. This is consistent with other studies in which the effects of the number of individuals sampled (rarefaction or sampling effects) are factored out (Zobel and Liira 1997, Forbes et al. 2001). In conclusion, in a habitat with large gradients in spatial heterogeneity in environmental factors, there were positive relationships between within-plot heterogeneity and plant species richness, but these effects were relatively small compared with the effects of plant density and other environmental factors, such as flooding. Nevertheless, we also showed that unimodal richness gradients were associated with gradients in spatial heterogeneity and that soil depth heterogeneity had positive effects on richness when separated from mean soil depth. In order to determine the role of spatial heterogeneity in maintaining diversity in this system, ECOGRAPHY 26:6 (2003)

Acknowledgements – We thank Margy DeGruchy, Pete Clarke, Cherie-Lee Fietsch, Sabine Girard, Sylvain The´ venet John Gerrath, Kaeli Stark, and Kathryn Kuntz for assistance with field work; the staff of Bruce Peninsula National Park, especially Andrew Promaine; the National Sciences and Engineering Research Council, Canada, Human Resources and Development, Canada and Parks Canada for financial support. We are grateful to Uta Matthes for statistical advice and Meelis Pa¨ rtel and Scott Wilson for critical comments on the manuscript.

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