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    Food Webs
    Food Webs
    Food Webs
    Ebook431 pages4 hours

    Food Webs

    By Kevin S. McCann

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    About this ebook

    Human impacts are dramatically altering our natural ecosystems but the exact repercussions on ecological sustainability and function remain unclear. As a result, food web theory has experienced a proliferation of research seeking to address these critical areas. Arguing that the various recent and classical food web theories can be looked at collectively and in a highly consistent and testable way, Food Webs synthesizes and reconciles modern and classical perspectives into a general unified theory.


    Kevin McCann brings together outcomes from population-, community-, and ecosystem-level approaches under the common currency of energy or material fluxes. He shows that these approaches--often studied in isolation--all have the same general implications in terms of population dynamic stability. Specifically, increased fluxes of energy or material tend to destabilize populations, communities, and whole ecosystems. With this understanding, stabilizing structures at different levels of the ecological hierarchy can be identified and any population-, community-, or ecosystem-level structures that mute energy or material flow also stabilize systems dynamics. McCann uses this powerful general framework to discuss the effects of human impact on the stability and sustainability of ecological systems, and he demonstrates that there is clear empirical evidence that the structures supporting ecological systems have been dangerously eroded.


    Uniting the latest research on food webs with classical theories, this book will be a standard source in the understanding of natural food web functions.

    LanguageEnglish
    PublisherPrinceton University Press
    Release dateNov 21, 2011
    ISBN9781400840687
    Food Webs
    Author

    Kevin S. McCann

    Kevin S. McCann is associate professor of integrative biology at the University of Guelph.

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      Book preview

      Food Webs - Kevin S. McCann

      Index

      Preface

      I started thinking about this book after being approached by Sam Elworthy, who suggested a book based on recent food web theory, a synthesis of sorts. At about the same time, Joe Rasmussen, a friend and colleague at McGill told me that he believed no one understands recent food web theory. Taken together, I felt there was a need for such a book and that a synthesis with more attention to conceptual details was clearly in order. One of the more active research areas of late pertains to the theory of weak interactions, which I and numerous collaborators have played a role in developing. While this theory emphasizes weak interactions, it necessarily also considers the role of interactions of all strengths. This book seeks to illustrate how interaction strength governs the dynamics of food webs. As such, it is a very broadly based synthesis.

      Nonetheless, the book does not cover all areas of food webs exhaustively nor does it attempt to. Further, as a monograph, it necessarily pays attention to work developed both in my laboratory and with my many excellent collaborators. At the end of each chapter I briefly scan empirical evidence. In some cases, this constitutes a review or meta-analysis, but frequently it is really just a cursory look at the emerging data and is done to promote further comparison among theory, experiment, and empirical patterns. Clearly this book is not a full review, as to provide such a review would be well beyond its scope.

      The ideas laid out in this book are best developed with the language of mathematics, specifically dynamical systems. Thus, not surprisingly, this book has a fair amount of mathematics in it. However, I have made attempts to present it in an accessible format by constantly interpreting all mathematical logic within a biological framework. Further, I have tried to present the intuitive side of the results. You will see that much of this biological interpretation employs a bioenergetic framework. It just as easily could have been interpreted in terms of limiting nutrients or other relevant currencies, but this was the language of my Ph.D. advisor, Peter Yodzis, and I have grown accustomed to it.

      For those who understand mathematics, I hope this book will be easy, and for those who do not, I hope they can harness the biological intuition behind these results and so contribute to the development of further food web theory (either theoretically or empirically). At a certain level, theory is about the development of heightened logical intuition, and so whether one understands the mathematics or not is not as important as understanding the underlying concept. It just so happens that some people find it easier to think about things in terms of x’s and y’s, and others in terms of rabbits and lynx. I am convinced that all theory in ecology is potentially accessible to those with a more empirical background; however, I think both theoreticians and empiricists must make an effort to overcome the barriers imposed by history. I find this an exciting time, as it is becoming obvious that researchers of both ilk seem quite ready to delve deeply into each other’s realms and benefit from each other’s different perspectives and knowledge. Biocomplexity initiatives have worked wonders to force collaborations over traditional scientific boundaries, making today’s ecological and evolutionary science a truly dynamical research environment.

      I owe a great debt to Joe Rasmussen, who has talked to me frequently about various and sundry ideas, but much of this talk has attempted to better train me in ecosystem ecology. I also owe a great debt to my graduate and postdoctoral researchers, James Umbanhowar, Tim Bartley, Gabriel Gellner, Tyler Tunney, Jason Rip, and Colette Ward, who have discussed various of these ideas with me in earlier manifestations. Often one learns a lot from the new ideas that their less focused approach allows. Gabriel Gellner deserves special mention, as he has pushed me to consider community matrices all over again, and many of the ideas related to this comes largely from his pushing me along this axis. He led me to rediscover the excellent work of Daniel Haydon, who employed Gershgorin discs to interpret matrix food web theory—a highly informative and, in my opinion, undercited set of novel contributions. Neil Rooney, a friend and postdoctoral researcher, played a large conceptual role in development of the later chapters. This work originates historically from my early training from Peter Yodzis, Don DeAngelis, and Alan Hastings, all of whom have had an enormous influence on my work—they have collectively formed my scientific perspective, and I feel extremely lucky to have worked with such great mentors. I obviously would not have been able to put this book together without the patience and understanding of my wife, Amy, and my children, Caden, Bronwyn, and Robyn. Amy allows me the freedom to pursue all my interests and does so without question. I thank my parents, without whose support I would never have been in a position to write a book like this in the first place. From day one they had no issue with my pursuing something that seemed unimaginable to them (You are getting a Ph.D. in zoology. They have careers for that?). Finally, I am grateful to the people who ultimately support scientific research. I am truly blessed to have the opportunity to ponder daily the workings of one of nature’s most beautiful entities, the food web.

      PART 1

      THE PROBLEM AND THE APPROACH

      CHAPTER ONE

      The Balance of Nature: What Is It and

      Why Care?

      1.1 BALANCING A NOISY SYSTEM

      Each spring as the sun begins to strengthen again, I walk the trail that surrounds our house. Unfailingly, I am met by the steady green carpet of plants, the chorus of songbirds, the scurrying of squirrels, and the occasional hawk presiding over the forest floor. On any spring night I may find myself awakened by the unmistakable cry of coyotes to find that the night is alive with the peeping and buzzing of frogs and insects. At a very rough observational level, the main groups of players that make up this localized food web (i.e., plants, herbivores, predators) appear to be consistently present from year to year. If these observations are correct, we can say that this system is stable in the sense that the species assemblage persists intact over ecological time scales. We as casual observers have grown to expect this consistency. Much of this book is concerned with this aspect of stability, often called persistence in the ecological literature. I would argue that this persistence-based notion of stability has fascinated humans throughout history precisely because they have casually observed this pattern for such a long time and depended upon it for their survival.

      Despite the pleasing notion that the world’s ecosystems harbor a great steadiness or a perfect balance, the more detailed observer is uncomfortable with this statement. Most biologists today would in fact be quick to argue that if we can expect anything in ecological systems, we can expect change (Levin, 2003). As an example, if we look closer at the lush green carpet of plants, we may find there are far more goldenrods this year than last year. Upon further inspection we may also find that last year’s summer storms, more frequent than normal, knocked down many large silver maples, leaving behind forest gaps and impressive new understory growth. Thus, the level below the apparent consistency harbors considerable variation within the plant species themselves. This same phenomenom is true for animals. Some years may see enormous insect outbreaks, followed several years later by the increased presence of insectivorous species that during breeding capitalized on the pulse of abundant prey. Simply put, ecosystems are dynamic entities, waxing and waning at a variety of temporal and spatial scales. As we focus in on them, this beautiful, endless, dynamic mosaic appears to be everywhere, and yet, amazingly, the net result at a more macroscopic level (e.g., that of the casual walking observer) is a complex system that harbors some degree of stasis (i.e., a similar assemblage).

      There is something calming to the human mind about this consistency in species assemblage, and there is also something unsettling when things do change dramatically in an ecosystem. In her 1962 book, Silent Spring, Rachel Carson (1962) documented the loss of birds to DDT. Silent spring referred to the fact that our human expectation of nature (i.e., the sound of birds in spring) had been abruptly altered. Although many challenged her scientific assertions, the book and its ideas endured, trumpeting in the modern environmental movement. Carson’s ideas were tangible in that both scientists and lay people were able to observe the loss of a major group of species as a result of a human activity. In a sense, the consistency of our forests, our parks, and our backyards had been threatened.

      This loose, persistence-based definition of stability is at the heart of most of the more mathematically rigorous definitions that ecologists have historically employed. Variability, or the coefficient of variation, CV (variation/mean), is a common measure of stability in both experiments and recent theory. The logical argument behind this metric of stability is that the more variable a species’ population dynamics are, the more likely that species is to attain dangerously low densities. High variability, all else equal, therefore implies a greater risk of local extinction.

      Similarly, resilience, so common to many theoretical equilibrium studies, was argued to be an appropriate measure of stability because a resilient population rapidly returned to near equilibrium densities after a perturbation (Pimm et al., 1991). Thus, if a species is perturbed to low densities, a rapid return time to equilibrium means that this same species quickly rises away from near-zero densities and, in doing so, avoids the threat of local extinction. A slowly returning species, on the other hand, is more likely to be subjected to the vagaries of nature’s noisy world for a much greater time and so has a significantly higher risk of extinction. So even mathematically based definitions, which assume equilibrium, are in a real sense attempting to speak to the consistency of a species assemblage in a variable world. Clearly though, theory needs to further explore this casual assumption, and many researchers have started to look more rigorously at the fascinating interaction between environmental variability and population stability [e.g., see Ives and Carpenter (2007); Ives et al. (2008)].

      There is a long history of ecologists seeking to understand what factors contribute to the stability of ecological communities. Early ecologists pointed to the role diversity plays in stability. This idea remains to some degree today, but most researchers now seek a more explicit understanding of the mechanisms behind stability. It seems likely that if diversity does truly correlate with stability, this is not because of diversity per se but rather because of some fundamental structures embedded in diversity itself (May, 1974b). This biological structure can be at the population scale (e.g., age structure), the community scale (e.g., food web structure), or the ecosystem scale (e.g., size of detrital compartment). The task remains to uncover these fundamental natural structures—a difficult task for sure because the balancing act of nature couples interactions over an enormous range of spatial scales. At local scales (e.g., 1-m² plots), nature’s balance seems amiss, with organisms varying in number in both space and time. At large enough scales, local variance may in fact cancel itself out to become a flatlined equilibrium process. Local variability can beget regional stability.

      Along these lines and within a single trophic level context, Tilman and others (Tilman et al., 1998; Doak et al., 1998) have recognized that species level variation, under various sets of conditions, can ultimately lead to relatively constant competitive communities. Variation at the plant population level, for example, can sum to give a relatively constant plant community as long as not all species increase or decrease together. These researchers, in a sense, changed the stability question by embracing population level variation in density and focusing on the implications of population variability for whole–plant community stability. Once aware of this complex mosaic of spatial and temporal variance, it becomes interesting to consider how this variation itself may play a role in the stability and sustainability of ecosystems. As such, variation in space and/or time may be considered a form of natural structure that organisms have adapted to thrive within. This book will argue frequently that this may indeed be the case. I will also further argue that most human impacts tend to restructure the landscape with broad, homogenizing strokes. Species loss aside, such actions may remove the intricate, detailed spatial and temporal structure that underlies most pristine ecosystems.

      This precise aspect of an ecosystem—the multilayered complex of interacting organisms that transcends small to large spatial and temporal scales—is also the toughest part to study. One can ignore interactions by focusing on an isolated box (e.g., population ecology), yet these scale-dependent connections cannot be easily ignored for large ecological problems like ecosystem stability and function. When ecologists, for example, purposefully separate this scale dependency in controlled microcosm experiments, such as simplified and spatially restricted laboratory universes, these mini-ecosystems often fail rapidly after a few violent oscillations in population dynamics. In aquatic microcosms these spatially simplified worlds almost always end up dominated by bacteria. This experimental result may speak to the notion that ecological systems are enormously dependent on the interconnections that span huge ranges in spatial scale. If so, human actions that leave behind fragmented and less spatially connected ecosystems ought to put ecosystems at grave risk of collapse.

      In summary, there appears to be a balance of nature, but it is highly unlikely that we are talking about a system in equilibrium. Rather, the persistence of highly diverse complex ecological systems is an emergent property of an intensely interactive and variable underlying dynamical system. I would argue that ecologists never saw the balance of nature as a perfect equilibrium process and that to attack the concept interpreted as such is to take down a straw man. Within this more generalized definition of balance, it remains an important task to ask what it is about nature that allows it to maintain itself and how these complex natural entities adapt in the face of such a variable world. These scientific tasks are closely aligned with the applied societal need to understand how human modifications will impact the diversity, sustainability, and functioning of ecological systems.

      This book is an attempt to conceptually synthesize our current understanding of one of the big questions in ecology and evolution—What governs the stability of ecological systems? Although we have briefly discussed stability above, it is obviously critical to more rigorously define what we mean by stability. In the remaining sections of this chapter, I first define stability, discuss the role whole systems play in governing stable ecosystem function, and punctuate the stability problem with case studies of examples of instability in ecological systems. This final aspect of the chapter is included to convince the reader that there are already numerous examples of ecological instability and ecosystem collapse.

      1.2 ECOSYSTEM STABILITY AND SUSTAINABILITY

      It is commonly asserted that different definitions of stability often lead to different answers about what governs nature’s stability (Ives and Carpenter, 2007). As an example, although Ives and Carpenter (2007) found differences between a number of stability definitions in terms of whether diversity begets stability, they also found that all definitions that involved dynamics consistently gave the same qualitative answer. I will show throughout this book that dynamical definitions of stability are often consistent, and when they are not, it is informative to consider why, as suggested by Ives and Carpenter (2007). For instance, attributes that are stable in one sense (e.g., a population return forms a large perturbation rapidly) may be destabilizing in some other important sense (e.g., the same rapidly returning population overshoots the equilibrium and oscillates). I will show that this dynamic trade-off (i.e., fast return–big overshoot) is common in both population and consumerresource models and that this result is useful in developing a synthetic theory for stability in more complex food web models.

      I now define some common measures of stability. These definitions require some understanding of common terms, such as equilibrium, used in theory. For those having diffulculty with terminology, it may be worth reading the mathematical review of chapter 2 before reading the stability definitions below.

      1.2.1 PERSISTENCE-BASED METRICS OF STABILITY

      Much of the theory I will discuss in this book relies on the following broad group of stability metrics (Pimm, 1982, 1984; McCann et al., 2000). I am referring to this set of metrics as persistence-based because they are an attempt, through various means, to quantify how likely the system as a whole is able to persist intact. Persistence-based definitions of stability effectively assume that the underlying dynamical system (i.e., an n-member mathematical model) is not changed by a perturbation (i.e., perturbation does not remove a species).

      1.2.1.1 Engineering Resilience

      A measure that assumes system stability increases with decreasing return time to an attracting state (e.g., equilibrium) after a perturbation. The faster the return time, the more stable the system. In this book the term resilience will refer to engineering resilience.

      1.2.1.2 Equilibrium Resilience

      The state being returned to after a perturbation is an equilibrium attractor. Mathematically, it is measured by the inverse of the maximum eigenvalue (i.e., 1/λmax).

      1.2.1.3 Nonequilibrium Resilience

      The state being returned to after a perturbation is a nonequilibrium attractor (e.g., a limit cycle, a chaotic attractor).

      1.2.1.4 Variance Stability

      The variance in population densities over time, usually measured as the coefficient of variation, CV (variance/mean). High variance implies a greater chance of extinction, especially with external perturbations which are common in experimental tests of stability.

      1.2.1.5 Bounded or Minima Stability

      A system is more stable than another system if its global minimum density is bounded further away from zero densities than that of the other system. Here, bounded further away from zero simply implies that the minimum population density is further away from zero and so less likely to go extinct in a variable environment or after a perturbation.

      1.2.1.6 Sustainability

      A system is said to be sustainable if its’ component members are able to persist in the face of a specified perturbation [either a continuous perturbation (i.e., a press perturbation) or a discrete perturbation (i.e., a pulse perturbation)].

      1.2.2 CHANGE-BASED METRICS OF STABILITY

      Persistence-based notions of stability, in some sense, all concern themselves with the likelihood that the n-member system persists over ecological time scales. There are also metrics of stability that assume a system can change. With this assumption, stability metrics become more concerned with quantifying how a perturbation changes the system and the extent of the change. This has become very popular with the realization that ecosystems may flip from one attractor to another after a perturbation [called an alternative state (Scheffer et al., 2001)]. Some common stability metrics of this sort are the following (Holling, 1996).

      1.2.2.1 Alternative State Stability I

      A system is deemed less stable the more alternative states it has. All else equal, the more alternative states, the greater the likelihood a given pertubation will flip the system to an entirely different state.

      This focus on multiple attractors has led Holling to define a related alternative state–based measure of stability (Holling, 1996).

      1.2.2.2 Alternative State Stability II (Holling’s Resilience)

      A measure of the amount of change or disruption (i.e., the size of the perturbation) that is required to transform a system from being maintained by one set of mutually reinforcing processes and structures (a given attractor) to a different set of processes and structures (another attractor with potentially different species).

      1.2.2.3 Resistance Stability

      Resistance is a metric that quantifies the change of a system after a perturbation. The smaller the change of the system after the perturbation, the more resistant the system. This metric is commonly used when we consider a perturbation that changes the structure of the system, such as the complete removal or addition of a species. A relatively common use of this occurs in the network literature where resistance is related to the number of other species extinctions (secondary extinctions) after the removal or addition of another species [e.g., (Dunne et al. 2004)].

      Note: There is no real reason to expect persistence-based metrics to give the same answer as the change-based metrics above. In fact, one may expect the opposite. Let us imagine, for example, a resilient plant monoculture. After a small perturbation in its own density, the resilient monoculture returns rapidly because of its high growth rate. However, this same monoculture can also be enormously sensitive to an invading herbivore (e.g., its high growth rate correlates with its being highly edible) and so have a low resistance. As noted by Ives and Carpenter (2007), it behooves us to begin to consider these mutiple axes of stability and how nature struggles with different aspects of stability.

      1.3 OF FOOD WEBS, STABILITY, AND FUNCTION

      When considered at all scales, from a local patch to the entire biosphere, a food web governs the flux of energy and nutrients throughout our natural world. This flux and its fate ultimately drive a number of critical functions. Ecosystems recycle nutrients, decompose wastes, and produce primary and secondary biomass. All these major functions of an ecosystem ultimately service humans and frequently do so with such a consistency that people doubt ecosystems will ever stop serving them. Some of the services offered to humans are plant food, animal food, water purification, hospitable climate, detoxification, crop pollination, seed dispersal, wood, carbon storage, energy (e.g., water power, wood), and landscape stabilization. It is estimated that the services provided by natural ecosystems are worth more than 33 trillion U.S. dollars each year (Costanza et al., 1997). The actual number is debatable, but there is little doubt that ecosystems service humans in a way that we tend to overlook.

      Stability, in the sense defined above, can be thought of as both a function and a property of an ecosystem. An imbalance of function can be costly to a society. If a fishery that depends on secondary production (e.g., the cod fishery of the North Atlantic) is lean for many years, this causes obvious problems for an economy. Similarly, outbreaks in pests and diseases incur great societal costs to agricultural crops and bodies of water (e.g., lost forestry or fisheries production). Both are examples of either a drastic reduction in a key species (e.g., cod) and/or a drastic increase in an unwanted organism (e.g., mountain pine beetle). They represent, at the least, a temporary loss in the consistency of the assemblage at both a local (e.g., 1-m²) and a regional (e.g., ocean, forest stand) scale such that the system is tipped excessively one way or another. We now consider some examples of such documented instability and collapse that appear to be driven, at least to some extent, by human activity.

      1.4 ECOLOGICAL INSTABILITY AND COLLAPSE

      Rachel Carson’s book Silent Spring was driven by a large-scale human action, the widespread application of DDT (Carson, 1962). One of the odd things about human impact is that it can occur at such enormous spatial and temporal scales. As DDT made bird eggs less viable, this global human impact directly knocked out a group of highly visible species. There was no refuge in space or time from the DDT, so the influence had direct consequences for birds and many other vulnerable species. Many other influences of DDT, though, were likely less direct and less obvious. I am not aware of anyone considering how the reduction in birds and other vulnerable organisms changed the rest of the ecosystem. One can imagine a huge array of potential indirect cascades throughout terrestrial and aquatic food webs. Perhaps nothing so dramatic occurred. It is possible that other organisms competing with birds rose slightly in density with the simultaneous reduction in birds. If so, nature may have had a way of balancing itself in the face of such a massive human perturbation, a way we just did not empirically detect. Unfortunately, we do not know what happened. We simply tend to hope that such human-induced perturbations find a way of working themselves out in a manner that does not influence our way of living.

      Perhaps ecosystems are so robust that they almost always work themselves out without strongly influencing us, or perhaps we humans are just blissfully unaware that we are eroding critical components of an ecological system—the very attributes that allow the system to right itself in the face of our massive earth experiments. Even one counterexample forces us to recognize that instability and collapse are a real possibility. There are, in fact, numerous suggestions that we are tipping the scales of nature’s balance. Below, I cover just a few of the emerging examples to highlight the fact that at least some human actions seem to be intricately linked to a loss in stability that results in the collapse of whole ecosystems.

      1.4.1 NUTRIENTS ON THE LANDSCAPE

      One commonly observed driver of ecosystem collapse is the large-scale anthropogenic

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