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National Center for Ecological Analysis and Synthesis, Santa Barbara, CA 93101-5504, 2 University of Oslo, Division of Zoology, Department of Biology, Oslo, Norway, 3 Department ofEcology, Evolution, and Marine Biology, University of California at Santa Barbara, Santa Barbara, CA 93106, 4Section of Ecology and Systematics, Cornell University, Ithaca, NY 14853, 5Department of Zoology, University of Wisconsin at Madison, Madison, WI 53706, 6Department of Biology, York University, Toronto, ON Canada M3J 1P3.
Oikos, in press.
Abstract Community variability has a dual nature.On the one hand, there is compositional variability, changes in the relative abundance of component species. On the other hand, there is aggregate variability, changes in summary properties such as total abundance, biomass, or production. Although these two aspects of variability have received much individual attention, few studies have explicitly related the compositional and aggregatevariability of natural communities. In this paper, we show how simultaneous consideration of both aspects of community variability might advance our understanding of ecological communities. We use the distinction between compositional and aggregate variability to develop an organizational framework for describing patterns of variability in natural communities. At their extremes, compositional and aggregatevariability combine in four different ways: (1) stasis, low compositional and low aggregate variability; (2) synchrony, low compositional and high aggregate variability; (3) asynchrony, high compositional and high aggregate variability; and (4) compensation, high compositional and low aggregate variability. Each of these patterns has been observed in natural communities, and can be linked to asuite of abiotic and biotic mechanisms. We give examples of the potential relevance of variability patterns to applied ecology, and describe the methodological development needed to make meaningful comparisons of aggregate and compositional variability across communities. Finally, we provide two numerical examples of how our approach can be applied to natural communities.
Ecological communitiesvary through time. Everyone, scientist and layman alike, is aware of this fact, and it is not surprising that community variability has been a focus of many theoretical and empirical studies (May 1974, McNaughton 1977, Connell and Sousa 1983, Pimm 1984, 1991). To date, ecologists have focused primarily on the causes and consequences of variability. For example, studies have explored theenvironmental forces that drive biotic variability (Chesson 1990); changes in variability among taxa and across gradients of productivity, latitude, and elevation (Connell and Sousa 1983, Duarte 1989, Crowley and Johnson 1992); and the implications of variability for population and community persistence (Pimm 1991). However, ecologists have placed little emphasis on variability as a source of informationabout community dynamics; the challenge is to learn how to decipher that message. Therefore, we need conceptual models for describing patterns of variability and empirical approaches for relating these patterns to ecological mechanisms. A starting point for learning from variability is to consider that there are two main dimensions of community variability. On the one hand there is compositionalvariability, changes in the relative abundance of component species. It is clear that the species composition of some communities changes more through time than the species composition of others. For example, the community composition of annual herbs on a forest floor changes a great deal more from year to year than the community composition of the trees. On the other hand there is aggregate...