«Species richness, species–area curves and Simpson’s paradox Samuel M. Scheiner,1* Stephen B. Cox,2 Michael Willig,2 Gary G. Mittelbach,3 Craig ...»
Evolutionary Ecology Research, 2000, 2: 791–802
Species richness, species–area curves and
Samuel M. Scheiner,1* Stephen B. Cox,2 Michael Willig,2
Gary G. Mittelbach,3 Craig Osenberg4 and Michael Kaspari5
Department of Life Sciences (2352), Arizona State University West, P.O. Box 37100, Phoenix,
AZ 85069, 2Program in Ecology and Conservation Biology, Department of Biological Sciences
and The Museum, Texas Tech University, Lubbock, TX 79409, 3W.K. Kellogg Biological Station, 3700 E. Gull Lake Drive, Michigan State University, Hickory Corners, MI 49060, Department of Zoology, University of Florida, Gainesville, FL 32611 and 5Department of Zoology, University of Oklahoma, Norman, OK 73019, USA
ABSTRACTA key issue in ecology is how patterns of species diversity diﬀer as a function of scale. The scaling function is the species–area curve. The form of the species–area curve results from patterns of environmental heterogeneity and species dispersal, and may be system-speciﬁc.
A central concern is how, for a given set of species, the species–area curve varies with respect to a third variable, such as latitude or productivity. Critical is whether the relationship is scale-invariant (i.e. the species–area curves for diﬀerent levels of the third variable are parallel), rank-invariant (i.e. the curves are non-parallel, but non-crossing within the scales of interest) or neither, in which case the qualitative relationship is scale-dependent. This recognition is critical for the development and testing of theories explaining patterns of species richness because diﬀerent theories have mechanistic bases at diﬀerent scales of action. Scale includes four attributes: sample-unit, grain, focus and extent. Focus is newly deﬁned here. Distinguishing among these attributes is a key step in identifying the probable scale(s) at which ecological processes determine patterns.
Keywords: combining data, productivity, scale, species–area curve, species diversity, species richness.
INTRODUCTIONA key issue in ecology is how patterns of species diversity diﬀer as a function of scale (Brown, 1995; Rosenzweig, 1995; Gaston, 1996). For example, Waide et al. (1999) show that the relationship between species richness and productivity changes depending on the spatial scale over which these variables are measured. Such scale dependency can reveal the operation of important processes that need to be incorporated into any general theory explaining relationships involving species diversity (e.g. Palmer and White, 1994; Pastor * Address all correspondence to Samuel M. Scheiner, National Science Foundation, 4201 Wilson Boulevard, Arlington, VA 22230, USA. e-mail: email@example.com Consult the copyright statement on the inside front cover for non-commercial copying policies.
et al., 1996). In this paper, we explore three issues necessary to examine patterns of species richness and some potential controlling variable, using productivity as our exemplar. (1) How do issues of scale arise as a consequence of the species–area relationship? (2) When is the species–area relationship scale invariant with respect to a third variable? (3) What are the components of scale and how do they aﬀect our view of ecological processes?
SPECIES–AREA RELATIONSHIPSIf we wish to relate species richness (the number of species observed within a speciﬁed area) to some environmental factor, especially if we are comparing or combining data from a variety of sources, we need a function that standardizes estimates of species richness to a common scale. This scaling function is a species–area curve. The species–area relationship is a consequence of two independent phenomena. The total number of individuals increases with area, leading to an increased probability of encountering more species with larger areas, even in a uniform environment (Coleman et al., 1982; Palmer and White, 1994;
Rosenzweig, 1995). If this were the only factor aﬀecting the rate of accumulating species, and if the number of individuals sampled were large enough, then the species–area curve would approach an asymptote at the total number of species in the species pool. The asymptote requires that, at some spatial scale, species distributions are suﬃciently mixed and rare species are suﬃciently abundant that all species will be encountered before the entire space is sampled. Although for terrestrial systems, especially for plants, this curve is plotted with respect to area, it could be plotted with regard to other measures of sampling eﬀort, such as number of net tows for zooplankton.
The second factor that aﬀects the species–area relationship is environmental heterogeneity. As area increases, more types of environments are likely to be encountered. If species are non-uniformly distributed with regard to environments, then the number of species encountered will increase with area. In this instance, the species–area curve will reach an asymptote only if the number of environments reaches an asymptote at some spatial scale. Or, put another way, an asymptote requires that, at some scale, environmental types are suﬃciently mixed and abundant such that all types will be encountered before the entire space is sampled.
The likelihood of both factors leading to asymptotic species–area curves depends on the particular characteristics of the ecological system of interest and the way in which it is sampled (e.g. nested quadrats vs dispersed quadrats). Species mixing in a uniform environment may occur within a single community. However, at biome to continental scales, such mixing is less likely because biogeographic and evolutionary processes – such as speciation events, large-scale movements due to climate change, dispersal barriers, and so on – continually lead to non-equilibrial distributions of species. With regard to environmental heterogeneity, no general answer is possible because the distribution of habitats is systemspeciﬁc. For example, in the open water of a lake, heterogeneity is small and environmental types are likely to be well-mixed throughout the entire lake (e.g. Dodson et al., in press).
Conversely, a mountainous area has a complex pattern of environmental heterogeneity as a consequence of slope, aspect, elevation and soil type. A species–area curve for terrestrial plants in this system may never attain an asymptote given the usual constraints of sampling.
Thus, issues of spatial scale must be resolved within the spatial context of each system under consideration.
Species–area curves and Simpson’s paradox 793
SCALE AND INVARIANCERegardless of the shapes of species–area curves, they can provide a quantitative means to compare diﬀerent systems at a common sampling scale; for example, by comparing species richness among systems at an adjusted area of 10 m2. In addition to providing a standardized measure of species richness, species–area curves also reﬂect the way that diversity is structured spatially and how environmental variables aﬀect richness at diﬀerent spatial scales. If scale ‘matters’, then observed relationships between richness and environmental factors, say productivity, will vary depending on the scale at which systems are compared. We illustrate this with a simple graphic analysis.
Assume that, for each system (or data set), the relationship between the number of species and area can be described by a function, and that this function may vary among diﬀerent systems (for our purposes, the form of these functions need not be speciﬁed in detail). Furthermore, assume that these systems diﬀer in some environmental parameter of interest. Although we focus on productivity as the environmental parameter of interest, the principles apply to any continuous factor. Our goals, then, are to compare how species richness changes with productivity, and to determine whether and how this relationship changes as a function of spatial scale (i.e. area sampled). This relationship is the sum of abiotic responses of the species to the environmental factor and resulting changes in biotic relationships.
Three general models may characterize the interrelationships among species richness, area and productivity. In the simplest additive case, assume that the species–area relationships are parallel. Each data set is deﬁned by the same function, but the elevations of the line diﬀer (Fig. 1A). As a result, the relationship between species richness and productivity will also be invariant to spatial scale, with the relationships between species richness and productivity diﬀering only by a constant. A plot of species richness versus productivity will produce a single pattern at all spatial scales (Fig. 1D), and plots representing diﬀerent scales will produce parallel lines.
An alternative arises when the species–area curves are not parallel, but do not cross within the range of observed values of productivity (Fig. 1B). Such a case might arise if the environmental factor and area have multiplicative rather than additive eﬀects on species richness. When the species–productivity pattern is plotted for areas of diﬀerent size, it remains qualitatively the same, although the quantitative pattern varies (Fig. 1E).
Although we illustrate the problem in terms of diﬀerences in slope, any variation in the shape of the species–area function results in a similar eﬀect. Because most theories concerning the relationship between productivity and diversity only make qualitative predictions (Rosenzweig, 1995), tests of these theories are not aﬀected by the interaction of area and productivity. However, if one wishes to test theories that make quantitative predictions, or if one wishes to use the pattern to design management plans, considerations of scale (area) are critical, even when patterns are qualitatively the same.
The most interesting challenge arises when species–area curves intersect (Fig. 1C). In this case, one might ﬁnd one relationship between species richness and productivity when measured at one scale and the opposite relationship when measured at a diﬀerent scale (Fig. 1F). Now the scale of measurement is critical, and no single relationship represents a privileged perspective of the pattern.
The diﬀerence between the situation portrayed in Fig. 1B and that in Fig. 1C is one of scale of interest, as the former is equivalent to the right-hand portion of the latter. If 794 Scheiner et al.
Fig. 1. The eﬀects of invariance of the species–area function on the relationship of productivity and diversity. Parts (A), (B) and (C) illustrate species–area curves for four sites (1–4) that diﬀer in productivity. Scale is not indicated, as any monotonic function would show the same eﬀects. Parts (D), (E) and (F) illustrate the relationship between productivity and number of species across the four sites when sampling at a small and large grain size. In (A) and (D) the relationship is scale-invariant; in (B) and (E) the relationship is rank-invariant; in (C) and (F) the relationship is neither scale- nor rankinvariant. The scales on both axes are arbitrary; the y-intercept does not represent an area of zero.
non-parallel curves exist, then crossing is more likely to occur over greater ecological scales such as across community types. Mittelbach et al. (submitted) found that non-monotonic relationships (hump-shaped and U-shaped) are somewhat more common across rather than within community types.
Recognizing such scale-dependencies is important, because it may reveal mechanisms that cause pattern. Thus, it is critical to know whether the curves are scale-invariant (parallel), rank-invariant (non-crossing) or neither. We do not know whether scale invariance is rare or common in nature (but see Lyons and Willig, 1999; Dodson et al., in press). Determining when and where relationships are scale-invariant is a critical and ongoing endeavour (Westoby, 1993; Pickett et al., 1994; Pastor et al., 1996; Rapson et al., 1997).
To demonstrate scale invariance in species–area relationships, we used two sets of data: (1) six old-ﬁelds at the Kellogg Biological Station (KBS) LTER site in Michigan, USA Species–area curves and Simpson’s paradox 795 (K. Gross, unpublished data) and (2) 18 tallgrass prairie watersheds at the Konza LTER site (http://climate.konza.ksu.edu/toc.html) in Kansas, USA. Each data set consisted of surveys of species of vascular plant. The KBS data were collected in each ﬁeld using a transect (20 × 0.5 m) divided into 0.5 m2 quadrats. The Konza data were collected in each of 18 watersheds using a set of twenty 10 m2 quadrats.
Species–area curves for each ﬁeld or watershed were derived empirically (Fig. 2). We illustrate this procedure using a single watershed from the Konza data. Species richness per 10 m2 was calculated as the mean richness of the 20 quadrats for a watershed. For species richness at 20 m2, we ﬁrst compiled all possible pairwise combinations of quadrats. For each pair, the total number of species was determined. Then, species richness was calculated as the mean number of species for all pairs. For the richness at 30 m2, this procedure was repeated using all three-way combinations. This procedure then was repeated and species numbers were determined up to 200 m2 (i.e. all 20 quadrats). The resulting species–area curve was not ﬁt to any mathematical function.
Fig. 2. Estimated species–area curves based on species richnesses calculated from all possible combinations of quadrats. Within each set, the rank-orders of the sample areas do not diﬀer statistically based on the Kendall coeﬃcient of concordance and are rank-invariant within sampling error. (A) Six old-ﬁelds in southern Michigan at the KBS LTER site, each consisting of a belt transect (20 ×
0.5 m) divided into 0.5 m2 quadrats. (B) Eighteen tallgrass prairie watersheds in Kansas at the Konza LTER site, each consisting of twenty 10 m2 quadrats arranged in ﬁve transects.
796 Scheiner et al.