Über die Autorin bzw. den Autor

Oswald J. Schmitz is the Oastler Professor of Population and Community Ecology in the Yale School of Forestry and Environmental Studies.

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"Resolving Ecosystem Complexity presents a modern synthesis of trophic structure and function that addresses some of the most fundamental questions raised by Darwin, Tansley, and Hutchinson. Through rigorous analysis of case studies and data, Schmitz brings to life the importance of direct and indirect interactions on the functioning of ecosystems. This clear and compelling book is a must-read for scientists and educators interested in integrative ecosystem analysis."--Adrien Finzi, Boston University

"This ambitious and inspiring book is a valuable contribution to ecology. Highly synthetic, it melds an overall approach to science with summaries of detailed empirical and theoretical work.Resolving Ecosystem Complexity is remarkably well done and I learned a great deal from reading this important book."--Anurag Agrawal, Cornell University

"This book focuses on the importance of multitrophic interactions in ecology. It illuminates significant points and helped me to think about complex ecosystems more clearly."--Frederick R. Adler, University of Utah

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Resolving Ecosystem Complexity

PRINCETON UNIVERSITY PRESS

Contents

List of Illustrations..............................................................ixList of Tables.....................................................................xiiiPreface............................................................................xv1. Introduction....................................................................12. Conceptualizing Ecosystem Structure.............................................103. Trophic Dynamics: Why Is the World Green?.......................................234. The Green World and the Brown Chain.............................................555. The Evolutionary Ecology of Trophic Control in Ecosystems.......................686. The Whole and the Parts.........................................................997. The Ecological Theater and the Evolutionary Ecological Play.....................125Closing Remarks....................................................................139References.........................................................................143Index..............................................................................167

Chapter One

Ecosystems are paradigmatically among the most complex systems known to science. They contain many different components (e.g., individuals within species populations, species within communities) interacting directly and indirectly in highly interconnected networks (Paine 1980; Schoener 1993; Brown 1995; Yodzis 1995; Levin 1998; Cohen et al. 1990). Moreover, system properties such as trophic structure and functions such as nutrient fluxes and productivity emerge from direct and indirect interactions among the component parts (Brown 1995; Levin 1998). This feature of ecosystems fascinates those who have purely academic interests to develop broad theoretical principles that explain the emergence of complexity (e.g., Holland 1992; Cowan, Pines, and Meltzer 1994; Gell-Mann 1994; Brak 1996; Milo et al. 2002). Complexity theorists, however, treat ecosystems merely as powerful metaphors and accordingly abstract much ecological detail (e.g., treating species as nodes in a network abstracts species' functional traits) to facilitate pattern identification and comparison among myriad physical, chemical, biological, social, and economic systems.

Ecologists too have a fundamental academic interest in resolving ecological complexity (e.g., May 1973; O'Neill et al. 1986; MacMahon et al. 1987; Allen and Hoekstra 1992; Levin 1992, 1998; Turchin 2003). But, that academic interest is tempered by the important practical reality that ecology is increasingly being called upon to offer a leading role in identifying and solving pressing environmental problems (Worster 1994; Lubchenco et al. 1991; Levin 1999; Ludwig, Mangel, and Haddad 2001). There is a huge premium, then, to resolve complexity in ways that enable one to make general predictions about how ecosystems will function in response to myriad natural and human-caused disturbances. Making reliable predictions requires having a solid empirical understanding of how the components fit together to determine whole ecosystem functioning. In this endeavor, ecologists must, to some extent, embrace ecological details because they provide the contexts for discovering the mechanisms leading to different outcomes. The challenge, then, is to develop an empirical research program that can resolve what mechanisms must be understood in order to predict the different outcomes (Levin 1992). This book elaborates such an empirical program.

PHILOSOPHICAL MUSINGS

Ecologists do not rely on a single empirical method to derive understanding of their systems (Hairston 1990). Broadly speaking, they use two different kinds of approaches: experimentation (Hairston 1990) and meta-analyses (Peters 1986; Hedges, Gurevitch, and Curtis 1999; Osenberg, Sarnelle, and Cooper 1999). Experimentation is believed to lead to predictive insights because it uncovers causal relationships (Lehman 1986). Meta-analysis is believed to offer predictive insights whenever the function that is statistically fit to the data explains a good degree of variation in the data set (Peters 1986). These different approaches lead to different understanding of the relationship between the whole system and its component parts and ultimately on the application of that knowledge to solve environmental problems (Lehman 1986; Lawton 1999). Let me illustrate my point with an example.

Suppose we agreed that a reasonable way to characterize ecosystems is by their component plant species and the plant species' trophic linkages with the soil nutrient pool. Suppose that ultimately we wanted to predict how the number of plant species (a measure of plant species diversity) influenced the level of some ecosystem function such as nutrient cycling or primary production. We might then manipulate plant species diversity in a single location and measure the ensuing levels of ecosystem function. Let's further suppose that this experimental protocol was used to evaluate the relationship between plant species diversity and ecosystem function across geographic locations. Such coordinated research could, and indeed often does, reveal different functional relationships in different locations (figure 1.1). At some locations, there could be strong positive relationships between plant species diversity and ecosystem function, as revealed by the steep slope of the regression line. At other locations, flat, almost horizontal lines infer weak if any relationships. Finally, at other locations there could be negative relationships between plant species diversity and function. This leads to a dilemma because we don't know which causal relation to use when making predictions about ecosystem responses to, say, loss of species diversity.

Such an outcome has led to despair that results from experimental ecology are insufficient to make general predictions because the outcomes are entirely context dependent. It is argued that experimentation will never uncover the suite of variables needed to make reliable predictions for all local conditions (Lawton 1999). Instead, it is believed that predictive ability is more likely to come about by combining data from the many study sites and estimating the degree of statistical association between variables of interest (Peters 1986; Lawton 1999). The problem here is that one derives an association, not a causal insight, and so it is merely a "rough" generalization (figure 1.1). Moreover, it is not a meaningful generalization because it abstracts the contingent outcomes among locations. There is no guarantee that, say, boosting plant diversity at any one location will enhance ecosystem function, even though the rough generalization says it ought to. This example illustrates that neither experimental nor meta-analytic approaches necessarily produce predictive insights that can be applied to management if they do not explicitly confront the issue of contingency.

Contingency arises when the nature and strength of ecosystem functioning in different locations are different realizations of the same underlying process. I show in this book how focusing empirical research to explain contingent outcomes can lead to predictive understanding of ecosystem function.

EXPLAINING CONTINGENCY: A WORLDVIEW

I am certainly not the first to suggest that focusing on contingency may...