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    Resolving Ecosystem Complexity
    Resolving Ecosystem Complexity
    Resolving Ecosystem Complexity
    Ebook335 pages3 hours

    Resolving Ecosystem Complexity

    By Oswald J. Schmitz

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    An ecosystem's complexity develops from the vast numbers of species interacting in ecological communities. The nature of these interactions, in turn, depends on environmental context. How do these components together influence an ecosystem's behavior as a whole? Can ecologists resolve an ecosystem's complexity in order to predict its response to disturbances? Resolving Ecosystem Complexity develops a framework for anticipating the ways environmental context determines the functioning of ecosystems.


    Oswald Schmitz addresses the critical questions of contemporary ecology: How should an ecosystem be conceptualized to blend its biotic and biophysical components? How should evolutionary ecological principles be used to derive an operational understanding of complex, adaptive ecosystems? How should the relationship between the functional biotic diversity of ecosystems and their properties be understood? Schmitz begins with the universal concept that ecosystems are comprised of species that consume resources and which are then resources for other consumers. From this, he deduces a fundamental rule or evolutionary ecological mechanism for explaining context dependency: individuals within a species trade off foraging gains against the risk of being consumed by predators. Through empirical examples, Schmitz illustrates how species use evolutionary ecological strategies to negotiate a predator-eat-predator world, and he suggests that the implications of species trade-offs are critical to making ecology a predictive science.


    Bridging the traditional divides between individuals, populations, and communities in ecology, Resolving Ecosystem Complexity builds a systematic foundation for thinking about natural systems.

    LanguageEnglish
    PublisherPrinceton University Press
    Release dateJul 1, 2010
    ISBN9781400834174
    Resolving Ecosystem Complexity

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      Book preview

      Resolving Ecosystem Complexity - Oswald J. Schmitz

      MONOGRAPHS IN POPULATION BIOLOGY

      EDITED BY SIMON A. LEVIN AND HENRY S. HORN

      A complete series list follows the index

      Resolving Ecosystem Complexity

      OSWALD J. SCHMITZ

      PRINCETON UNIVERSITY PRESS

      Princeton and Oxford

      Copyright © 2010 by Princeton University Press

      Published by Princeton University Press, 41 William Street,

      Princeton, New Jersey 08540

      In the United Kingdom: Princeton University Press,

      6 Oxford Street, Woodstock, Oxfordshire OX20 1TW

      press.princeton.edu

      All Rights Reserved

      Library of Congress Cataloging-in-Publication Data

      Schmitz, Oswald J.

      Resolving ecosystem complexity / Oswald J. Schmitz.

              p. cm.—(Monographs in population biology; 47)

          Includes bibliographical references and index.

          ISBN 978-0-691-12848-1 (hardcover : alk. paper)—ISBN 978-0-691-12849-8 (pbk. : alk.

      paper)   1. Biotic communities.   2. Ecosystem management.   3. Biodiversity conservation.

      I.   Title.

           QH541S326 2010

           577.8’2—dc22         2009050861

      British Library Cataloging-in-Publication Data is available

      This book has been composed in Times Roman

      Printed on acid-free paper.∞

      Printed in the United States of America

      10 9 8 7 6 5 4 3 2 1

      Contents

      List of Illustrations

      List of Tables

      Preface

      1. Introduction

      Philosophical Musings

      Explaining Contingency: A Worldview

      Contingency and Emergence

      Preparing the Mind for Discovery

      Structure of the Book

      2. Conceptualizing Ecosystem Structure

      Abstracting Complexity

      Whole System vs. Building Blocks Approach

      Defining Species Interaction Modules

      Identifying Interaction Modules in a Grassland Ecosystem

      Conception of Ecosystem Structure

      3. Trophic Dynamics: Why Is the World Green?

      Trophic Control as an Emergent Property of Resource Limitation

      Explaining Contingency in Trophic Control of Ecosystem Function

      The Nature of Resource Limitation and Trophic Control of Food Chains

      The Mechanism Switching Hypothesis of Trophic Control

      Effects of Herbivore Feeding Mode

      Collective Effects of Herbivore Species with Different Feeding Modes

      Plant-Antiherbivore Defense and Strength of Trophic Control

      Herbivore Resource Selection and Ecosystem Function

      Stoichiometry and Herbivore Resource Use

      Resource Selection and Ecosystem Function

      Herbivore Indirect Effects and Engineering of Green Worlds

      Herbivore-Mediated Carnivore Indirect Effects on Ecosystems

      Carnivore Indirect Effects on Plant Diversity

      Carnivore Indirect Effects on Ecosystem Function

      4. The Green World and the Brown Chain

      Conceptualizing Functions along Detritus-Based Chains

      Resource Limitation and Trophic Control

      Trophic Control of Decomposition

      Trophic Control of Mineralization

      Mechanisms of Top-Down Control

      Trophic Coupling between Detritus-Based and Plant-Based Chains

      5. The Evolutionary Ecology of Trophic Control in Ecosystems

      Carnivore Species and the Nature of Trophic Interactions in an Old-Field System

      Carnivore Hunting Mode and the Nature of Trophic Interactions

      The Evolutionary Ecology of Trophic Cascades

      6. The Whole and the Parts

      Developing Predictive Theory for Emergence

      Contingency and Carnivore Diversity Effects on Ecosystems

      Carnivore Diversity and Emergent Effects on Ecosystem Function

      Shifting Down One Trophic Level: Intermediate Species Diversity and Ecosystem Function

      Herbivore Diversity and Mediation of Top-Down Control of Ecosystem Function

      Detritivore Diversity and Mediation of Top-Down Control of Ecosystem Function

      The Basal Trophic Level: Plant Diversity and Ecosystem Function

      Functional Classifications

      Resource Identity Effects on Trophic Interactions

      Moving Forward on Functional Diversity and Ecosystem Function

      7. The Ecological Theater and the Evolutionary Ecological Play

      Phenotypic Variation and State-Dependent Trade-Offs

      Attacked Plants Attract Predators

      Predators That Avoid Predation

      The Nonconsumptive Basis of Trophic Transfer Efficiencies

      Trophic Interactions in a Changing Theater

      Rapid Change in Hunting Strategy

      Landscapes of Fear and Ecosystem Management

      Closing Remarks

      References

      Index

      List of Illustrations

      FIGURE 1.1.   Hypothetical case in which the relationship between a treatment and response variable is deduced by identifying an average trend among data compiled from numerous, local experiments.

      FIGURE 2.1.   Generic conceptualization of ecosystem structure depicting the live plant-based food chain and the detritus-based food chain that are common aboveground components of all ecosystems.

      FIGURE 2.2.   Hypothetical food web comprised of species of carnivores, herbivores, and plants illustrating the idea that envisioning the whole system as a collection of food chain subsystems might reduce complexity.

      FIGURE 2.3.   Results from field experiments examining the interplay among carnivore, focal herbivore species and plants to illustrate how quantifying interaction strengths of carnivores on herbivores and carnivores on plants can lead to identification of a dominant interaction web that is amenable to experimental analysis of ecosystem functioning in the field

      FIGURE 3.1.   Extending classic HSS theory to explain ecosystem function. Carnivore indirect effects on plant community composition and on ecosystem functions determined by the direct causal chain running from predators, through herbivores, through plant community composition.

      FIGURE 3.2.   Qualitative predictions generated from mechanistic theory of food chain interactions in which herbivores face relative and absolute resource limitation.

      FIGURE 3.3.   Results of an experiment that manipulated abiotic conditions and trophic structure to reveal the nature of trophic control of a grassland ecosystem comprised of herbaceous vegetation, a generalist grasshopper herbivore, and a wolf spider carnivore.

      FIGURE 3.4.   Locations of the three grassland ecosystems that were the subject of experiments testing for trophic cascades.

      FIGURE 3.5.   Relationship between the magnitude of direct effects of carnivores on herbivores and indirect effect of carnivores on plant biomass.

      FIGURE 3.6.   Results of an experiment demonstrating the effects of manipulating trophic structure (2-level plants and herbivores vs. 3-level plants, herbivores, and carnivores) and plant antiherbivore defense (Undefended and Defended) on the strength of top-down control.

      FIGURE 3.7.   Effects of elk herbivory on Yellowstone National Park grassland ecosystem properties and functions.

      FIGURE 3.8.   Effects of moose herbivory on Isle Royale National Park forest ecosystem properties and functions.

      FIGURE 3.9.   Examples of indirect effects of top carnivores on plant diversity.

      FIGURE 3.10. Effects of trophic level manipulations on composition of the plant community, plant diversity (evenness), and ecosystem functions (nitrogen mineralization rate, net primary productivity).

      FIGURE 4.1.   Conceptualization of the way carnivore trophic effects may propagate in detritus-based food chains to affect important ecosystem functions such as decomposition and elemental mineralization.

      FIGURE 4.2.   Relationship between the magnitude of direct effects of carnivores on detritivores and indirect effect of carnivores on detrital decomposition rates.

      FIGURE 5.1.   Habitat use by grasshoppers in the New England meadow ecosystem in the presence and absence of different spider species.

      FIGURE 5.2.   Top: Effects of spider carnivore identity on the cage density of grasshoppers in experimental old-field interaction webs relative to a no-predator control. Lower: Effects of trophic level manipulations on grass and herb biomass in experimental field mesocosm cages.

      FIGURE 5.3.   Demonstration of movement measurements needed to calculate predator species habitat domain.

      FIGURE 5.4.   Illustration of the habitat domain concept. Prey habitat domain is depicted as a vertical rectangle; predator habitat domain is depicted as a horizontal ellipse.

      FIGURE 5.5.   Synthesis of prey antipredator responses in relation to prey and predator habitat domain and predator hunting mode.

      FIGURE 5.6.   Nature of emergent top-down cascading effects cases involving different prey behavioral responses to predators with different hunting modes and habitat domains.

      FIGURE 5.7.   Food web topologies and indirect effects resulting from consumptive and nonconsumptive effects of carnivores on herbivores.

      FIGURE 5.8.   Relationship between the magnitude of direct effects of carnivores on herbivores and indirect effect of carnivores on plant biomass.

      FIGURE 5.9.   Hypothesized predator indirect effects on plant community composition and on ecosystem functions. Predators can influence ecosystem function via the direct causal chain running from predators, through herbivores, through plant community composition.

      FIGURE 5.10. Effects of manipulating predator hunting mode on the composition of the meadow plant community.

      FIGURE 5.11. Effects of manipulating predator hunting mode on three key ecosystem functions.

      FIGURE 6.1.   Working hypothesis for four contingent multiple carnivore effects on a common prey species derived from an empirical synthesis of multiple carnivore experiments.

      FIGURE 6.2.   Effect of experimentally manipulating the relative abundance of two species of spider carnivores with different functional identities (active hunting, sit-and-wait) on ecosystem properties and functions in a New England grassland.

      FIGURE 6.3.   Indirect effects of carnivorous fish diversity on two stream eco-system attributes: detrital accumulation and algal production.

      FIGURE 6.4.   Effects of trophic manipulations and herbivore diversity on the strength of top-down control in an old-field ecosystem comprised of plants, a generalist grasshopper herbivore whose habitat domain is the entire vegetation canopy, a grass specialist grasshopper whose habitat domain is the grass portion of the canopy, and the sit-and-wait spider predator that resides in the upper canopy.

      FIGURE 6.5.   Effects of trophic manipulations and herbivore identity and diversity on the strength of top-down control in an experimental eelgrass community comprised of eelgrass, algae, and epiphytic plants, various species of isopod and amphipod herbivores, and the blue crab.

      List of Tables

      TABLE 5.1. Summary of studies that explicitly explored the effects of multiple predator species with different hunting modes and habitat domains on the antipredator behavior of a single prey species.

      TABLE 5.2. Summary of studies that explicitly explored the responses of different prey species with different habitat use to the same predator species.

      TABLE 5.3. Predicted predator indirect effects on ecosystem properties and functions precipitated either by predator-caused changes in herbivore density (a consumptive effect) or changes in herbivore adaptive foraging (a nonconsumptive effect).

      Preface

      A few years after I arrived at Yale I was interviewed by a local newspaper reporter (the newsworthy event was the creation of the new Yale Department of Ecology and Evolutionary Biology) who asked me what I hoped to accomplish over my career. I can’t recall exactly what long-winded answer I gave but it ended up being distilled in print as Professor Schmitz has the lofty goal of figuring out how Nature works. There is dual meaning in the reporter’s representation of what I said. You could take it to mean (somewhat cynically as the reporter did) that by the end of my career I’ll be able to explain how Nature works. Or, you could take it to mean that over the course of my career I’ll develop ways of looking at Nature that lead to insights about how it might work. The former is about reaching an end; the latter is about developing the means to reach an end. I meant the latter.

      This book, audaciously titled Resolving Ecosystem Complexity, embodies the same duality of meaning. In writing the book, however, my intention is to deal with the second meaning, the enterprise of discovery rather than discovery itself. To me, the journey of discovery is the far more fascinating and rewarding because it means that as scientists we get to stay in a childlike state of wonder and curiosity for one’s entire professional career—there is always something fresh and new to discover.

      Don’t get me wrong, though. There is nothing like a Eureka moment when you realize that you might fully understand a piece of a grand puzzle. And indeed, the process of scientific discovery is much like building an intricate jigsaw puzzle whose image emerges ever more clearly as the myriad pieces are set into their proper place.

      But, we can complete that puzzle slowly by simply going through the mechanics of trying to fit each small piece with other pieces and thus assemble small subsections, marveling as the picture incrementally emerges. Or, we can devise ways to categorize the pieces according to a priori rules (say, identifying color patterns or identifying pieces that make corners vs. centers) and then put all the those pieces together with a mind’s eye toward what we imagine the bigger emerging picture is going to be. The first approach emphasizes simple brute force trial and error experimentation. The second approach relies on devising an a priori [world] view of the structure of the grand puzzle and proceeds to fill in the pieces according to this view. The first represents a tactical approach to resolving the puzzle; the second is more strategic. Both approaches are appropriate and relevant. My preference, however, is to engage in the latter.

      The point of this book is to relate how I have gone about developing my conceptualization of the grand puzzle and how I have developed the rules for fitting the pieces of the puzzle together. The general theme of the book is to convey ideas and insights gained through detailed study of my own empirical system and show how they can be extended to explain the functioning of other systems. This is not to suggest that I have had, from the start, the correct conception of the big picture. It is an undeniable fact of science that we will find many holes in our simple conceptions; they are deficient because we have not resolved the mechanistic details needed to obtain a clear picture. So, we revise our conceptions by systematically adding more and more detail as we learn how it fits in. Such is the enterprise of discovery I was talking about above.

      This book represents the culmination, thus far, of experience and insights gained through this enterprise over the last seventeen years. These insights were not developed in isolation, and I am grateful to the companionship of those who have helped with both the empirical discoveries and the development of the ideas presented in the book. To this end, I wish to expresses my deepest thanks to Peter Abrams, Brandon Barton, Andrew Beckerman, Michael Booth, Heinrich zu Dohna, Jason Grear, Peter Hambäck, Dror Hawlena, Holly Jones, Libby Jones, Liz Kalies, Kelsey Kidd, Vlastimil Krivan, Joohyoung Lee, Maya Leonard-Cahn, Charlie Liu, Barney Luttbeg, Marc Mangel, Mark McPeek, Kate O’Brien, Ofer Ovadia, Bobbi Peckarsky, David Post, Mary Power, Pete Raymond, Kris Rothley, Locke Rowe, Angie Rutherford, Dave Skelly, Lauge Sokol-Hessner, Blake Suttle, Geoff Trussell, Maria Uriarte, Günter Wagner, and Earl Werner. Several colleagues have also kindly read parts of or the entire book and offered very critical comments for its improvement. For this I wish to thank Fred Adler, Anurag Agrawal, Dan Gruner, Bob Holt, and Dean Pearson.

      Oswald Schmitz

      New Haven, CT

      CHAPTER 1

      Introduction

      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.

      FIGURE 1.1. Hypothetical case in which the relationship between a treatment and response variable is deduced by identifying

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