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# From Populations to Ecosystems: Theoretical Foundations for a New Ecological Synthesis (MPB-46)

## NOOK BookCourse Book (eBook - Course Book)

^{$}34.99

## Overview

The major subdisciplines of ecology--population ecology, community ecology, ecosystem ecology, and evolutionary ecology--have diverged increasingly in recent decades. What is critically needed today is an integrated, real-world approach to ecology that reflects the interdependency of biodiversity and ecosystem functioning. *From Populations to Ecosystems* proposes an innovative theoretical synthesis that will enable us to advance our fundamental understanding of ecological systems and help us to respond to today's emerging global ecological crisis.

Michel Loreau begins by explaining how the principles of population dynamics and ecosystem functioning can be merged. He then addresses key issues in the study of biodiversity and ecosystems, such as functional complementarity, food webs, stability and complexity, material cycling, and metacommunities. Loreau describes the most recent theoretical advances that link the properties of individual populations to the aggregate properties of communities, and the properties of functional groups or trophic levels to the functioning of whole ecosystems, placing special emphasis on the relationship between biodiversity and ecosystem functioning. Finally, he turns his attention to the controversial issue of the evolution of entire ecosystems and their properties, laying the theoretical foundations for a genuine evolutionary ecosystem ecology.

*From Populations to Ecosystems* points the way to a much-needed synthesis in ecology, one that offers a fuller understanding of ecosystem processes in the natural world.

## Product Details

ISBN-13: | 9781400834167 |
---|---|

Publisher: | Princeton University Press |

Publication date: | 07/01/2010 |

Series: | Monographs in Population Biology |

Sold by: | Barnes & Noble |

Format: | NOOK Book |

Pages: | 320 |

File size: | 6 MB |

## About the Author

**Michel Loreau**is professor and Canada Research Chair in theoretical ecology at McGill University.

## Read an Excerpt

#### From Populations to Ecosystems

**Theoretical Foundations for a New Ecological Synthesis**

**By Michel Loreau**

** PRINCETON UNIVERSITY PRESS **

**Copyright © 2010**

**Princeton University Press**

All right reserved.

All right reserved.

**ISBN: 978-0-691-12269-4**

#### Chapter One

**Population and Ecosystem Approaches in Ecology**

Building a theory that merges population, community, and ecosystem ecology requires at the very least that the fundamental descriptions of reality provided by the various subdisciplines be compatible with each other. But meeting this basic requirement is far from being a trivial issue given the widely different conceptual foundations and formalisms used by population and community ecology on the one hand and by ecosystem ecology on the other. In this introductory chapter, I first briefly revisit the foundations and formalisms of the population and ecosystem approaches in ecology. I then show how mass and energy budgets can bridge the gap between them. Last, I present a minimal ecosystem model to illustrate how an approach based on mass and energy budgets can be used to build simple models that combine the flexibility of demographic models and the physical realism of ecosystem models. The approach developed in this chapter will be the basis for most of the models presented in the rest of the book.

THE FORMALISM OF POPULATION DYNAMICS: EXPONENTIAL AND DENSITY-DEPENDENT GROWTH

A population is a set of organisms from the same biological species in a given area. Since all individuals belonging to the same species are very similar to each other when considered over a whole life cycle, classical approaches to population ecology ignore variability among individuals and assume that these are identical. As a consequence, population dynamics focuses on changes in the number or density of individuals that make up the population. Thus, population ecology fundamentally has a demographic approach to reality, in which the basic unit of measurement is the individual.

Population dynamics is implicitly or explicitly based on the following balance equation, which tracks the fate of individuals from time *t* to time *t* + 1:

[*N.sub.t*+1] = *[N.sub.t] + B + I + D + E.* (1.1)

In this equation, Nt is the number of individuals at time *t*, and *B, I, D,* and *E* are the numbers of births, immigrants, deaths, and emigrants, respectively, during the time interval from *t* to *t* + 1. The time unit is arbitrary; it may be a day, a year, or a generation, depending on the kind of organisms considered. This demographic balance equation simply states that the population at time *t* + 1 is the population at time t, plus the individuals that have been added to the population by birth or immigration, minus the individuals that have been removed from the population by death or emigration.

In the simplest case, assume a closed population (no immigration or emigration), a constant environment, and density-independent growth; i.e., the per capita demographic parameters are independent of population density. In this case, *I = E* = 0, *B = b[N.sub.t]*, and *D = d[N.sub.t]*, where *b* and *d* are constant per capita birth and death rates, respectively. Equation (1.1) then reduces to the familiar equation

*[N.sub.t+1] = [N.sub.t] + b[N.sub.t] - d[N.sub.t]* = [lambda]*[N.sub.t]*, (1.2)

where [lambda] = 1 + *b - d* is the finite rate of increase of the population.

This equation says that population size is multiplied by a factor [lambda] during each time unit. Starting from *t* = 0 and iterating the process over *t* time units yields

*[N.sub.t] = [N*.sub.0][[lambda].sup.*t*]. (1.3)

Thus, the population is predicted to grow geometrically at a rate [lambda] per time unit.

An identical prediction is obtained assuming that demographic processes are continuous instead of discrete in time, which leads to the following differential equation:

*dN/dt* = s*N* - [delta]*N = rN*, (1.4)

where s, [delta], and *r* are instantaneous per capita rates of birth, death, and population growth, respectively. This equation can be integrated to give

*N(t) = [N*.sub.0]*[e.sup.rt]*, (1.5)

which is identical to equation (1.3) with [lambda] = *[e.sup.r]*.

This fundamental equation of population dynamics, which is also known as Malthus's (1798) law, describes the inherent capacity of all organisms for exponential or geometric population growth (figure 1.1). *Exponential population growth* is a remarkably robust property as long as population processes are density-independent. It holds irrespective of spatial and temporal variations in demographic processes and population structure. If demographic processes vary in space or time, the finite and instantaneous population growth rates l and r are simply replaced by appropriate spatial or temporal averages in equations (1.3) and (1.5). If age or stage structure is explicitly considered, the finite population growth rate l is obtained from the projection matrix that describes transitions of individuals among age or stage classes (Caswell 1989). Exponential growth has been shown in numerous laboratory and natural populations under conditions of unlimited resource availability or low population density. The global human population itself is roughly experiencing exponential growth. More details on this topic can be found in theoretical ecology textbooks (e.g., Case 2000).

The propensity of populations to grow exponentially is an expression of the autocatalytic nature of biological systems and represents a fundamental source of instability in ecological systems. Basically, all organisms multiply as much as they can-until something prevents them from continuing to do so. And that is where ecology comes into play. All organisms are embedded in a complex web of interactions with their environment, which includes other organisms as well as abiotic factors. As populations grow, they modify their own environment through these multiple interactions, which feeds back on their capacity to grow further.

Classical population ecology makes the simplest possible assumption regarding these environmental feedbacks: it assumes that they can be reduced to a dependence of demographic processes on the population's own density. As the population grows, it progressively exhausts resources such as space, food, and nutrients, and as a result it decreases its potential to grow further. This convenient assumption eliminates the need to consider the complex web of interactions that organisms maintain with their environment and focuses on their net effect on the population variable under consideration. *Density dependence* is formally defined as a dependence of the per capita population growth rate on population density. In the continuous formalism of equation (1.4), which is the formalism that I shall use in most of this book, density dependence is expressed as

*dN/Ndt = f*(N), (1.6)

with *f'(N)* [less than or equal to] 0; i.e., the per capita growth rate monotonically decreases as population size increases.

The simplest form for the density-dependence function *f(N)* is a linear form, which yields the classical *logistic equation* proposed by Verhulst (1838):

*dN/dt = rN*(1 - *N/K*). (1.7)

In this equation, *r*, which is known as the intrinsic rate of natural increase, represents the maximum instantaneous population growth rate when population density is very low (close to zero), and *K* is known as the carrying capacity.

The logistic equation predicts a sigmoid growth pattern with a nearly exponential growth at low population size and a nearly exponential approach to a stable equilibrium population size equal to the carrying capacity (figure 1.1). This can be seen easily by noting that when population size is very small compared with the carrying capacity (*N* [much less than] *K*), the term in parentheses in equation (1.7) vanishes, and equation (1.4) describing exponential growth is recovered. On the other hand, when *N* approaches *K*, a first-order Taylor expansion of the right-hand side of equation (1.7) around *K* yields

*dn/dt = -rn*, (1.8)

where *n = N - K* is a perturbation from the equilibrium value *K*. Thus, the logistic equation predicts an exponential decline of perturbations in the vicinity of the carrying capacity at the same rate as the exponential growth of population size at low density. In other words, density dependence stabilizes the population by counteracting its inherent tendency toward exponential growth and instability.

Logistic growth has been shown in numerous populations, especially in the laboratory under resource limitation. The reason why the logistic equation works so well under controlled laboratory conditions is simple: the linear density-dependence function in the logistic equation may be viewed as a first-order approximation to any form of density dependence. Logistic growth, however, is much less robust than density-independent exponential growth. Departures from the implicit assumptions of continuous demographic processes, constant environmental conditions, instantaneous operation of density dependence, and lack of population structure, can lead to periodic or chaotic population dynamics under logistic growth. These dynamical behaviors are qualitatively different from the stable equilibrium point predicted by the classical model. Again, more details on this topic can be found in theoretical ecology textbooks (e.g., Case 2000).

A more fundamental problem-from the perspective developed in this book-is that the density dependence included in the logistic equation in the form of the carrying capacity is a phenomenological abstraction. Parameter K is a condensed substitute for a wealth of factors and interactions that limit population growth, such as resources, competitors, mutualists, predators, parasites, and diseases. It is not even possible to disentangle the contributions of birth and death processes to density dependence in equation (1.7) since these are lumped into the parameters r and K.

Despite these limitations, the logistic equation has served as a basis for much of theoretical community ecology. The famous Lotka-Volterra models for interspecific competition or mutualism are direct extensions of the logistic equation in which the density-dependence function *f(N)* in equation (1.6) is simply expanded to become a linear function of the population sizes of other interacting species. The classical Lotka-Volterra model for predation does not include direct density dependence but is built on the same principle; i.e., per capita growth rates are linear functions of population sizes.

Although many refinements and developments have been added to the theoretical corpus of community ecology, community ecology is largely an outgrowth of population ecology in its conceptual and methodological foundations. Most dynamical models in community ecology are based on a demographic approach that implicitly takes into account demographic balance constraints of the kind encapsulated in equation (1.1), but they ignore explicit physical constraints such as mass and energy balance (although there are exceptions, of course). As a result, community ecology has a strong focus on the structure, dynamics, and complexity of ecological systems, but it generally does not consider their overall functioning.

THE FORMALISM OF ECOSYSTEM FUNCTIONING: MASS AND ENERGY FLOWS

Ecosystem ecology does not have a simple fundamental law equivalent to the Malthusian law of exponential growth in population dynamics. Consequently, the approaches developed to model ecosystems have been somewhat more variable than in population ecology. The simplest and most common approach, however, has been that of compartmental modeling, which was pioneered by Lotka (1925). Ecosystem ecology is mainly concerned with the stocks and fluxes of materials or energy through the system as a whole, and this is explicitly what compartmental models represent. A *compartmental model* describes a set of compartments, the size of which is measured by the stock of materials or energy they contain, which are connected by fluxes of materials or energy. *Mass or energy balance* is explicitly taken into account in the description of these fluxes.

The basic building block of these models is a single-compartment model open to material or energy exchanges with the outside world. As an example borrowed from DeAngelis (1992), take a water body with a constant volume *V* that contains a solute of concentration ITLITL, and through which water flows at a constant rate *q'* per unit time, and let *[C.sub.I]* be the solute concentration in the inflowing water. The principle of conservation of mass states that the rate of change of the mass of solute in the compartment equals the rate at which mass enters that compartment minus the rate at which mass leaves that compartment. Since the mass of solute in the compartment is *CV*, this principle is expressed in the following dynamical equation:

*d(CV)/dt = q'[C.sub.I] - q'C.* (1.9)

Dividing both sides by the constant volume *V* and rescaling the water flow rate as *q = q'/V* yields

*dC/dt = q([C.sub.I] - C)*, (1.10)

which has the solution

*C(t) = [C.sub.I] + (ITL.sub.0] - [C.sub.I])[e.sup.-qt]*. (1.11)

This solution shows that the solute concentration in the water body tends asymptotically to the concentration in the inflowing water (the second term on the right-hand-side tends to zero as time goes to infinity, which leaves *C = [C.sub.I]*) and that the deviation between the initial ([ITLITL.sub.0]) and final (*[C.sub.I]*) concentrations declines exponentially with time at a rate *q*. Thus, this system smoothly approaches a stable equilibrium concentration set by the inflowing water, at a rate governed by water flow. The water flow rate q sets the characteristic time of the system. It measures the rate at which the system approaches its equilibrium, which is one common measure of resilience (DeAngelis 1992). It also determines the mean residence time of the solute in the compartment, also called the turnover time of the system, which is obtained as the ratio of the equilibrium mass of solute (*[C.sub.I]V*) over the equilibrium mass flow of solute (*q'[C.sub.I] = q[C.sub.I]V*), i.e., 1/*q*.

*(Continues...)*

Excerpted fromFrom Populations to EcosystemsbyMichel LoreauCopyright © 2010 by Princeton University Press. Excerpted by permission.

All rights reserved. No part of this excerpt may be reproduced or reprinted without permission in writing from the publisher.

Excerpts are provided by Dial-A-Book Inc. solely for the personal use of visitors to this web site.

## First Chapter

#### From Populations to Ecosystems

**Theoretical Foundations for a New Ecological Synthesis**

**By Michel Loreau**

** PRINCETON UNIVERSITY PRESS **

**Copyright © 2010**

**Princeton University Press**

All right reserved.

All right reserved.

**ISBN: 978-0-691-12269-4**

#### Chapter One

**Population and Ecosystem Approaches in Ecology**

Building a theory that merges population, community, and ecosystem ecology requires at the very least that the fundamental descriptions of reality provided by the various subdisciplines be compatible with each other. But meeting this basic requirement is far from being a trivial issue given the widely different conceptual foundations and formalisms used by population and community ecology on the one hand and by ecosystem ecology on the other. In this introductory chapter, I first briefly revisit the foundations and formalisms of the population and ecosystem approaches in ecology. I then show how mass and energy budgets can bridge the gap between them. Last, I present a minimal ecosystem model to illustrate how an approach based on mass and energy budgets can be used to build simple models that combine the flexibility of demographic models and the physical realism of ecosystem models. The approach developed in this chapter will be the basis for most of the models presented in the rest of the book.

THE FORMALISM OF POPULATION DYNAMICS: EXPONENTIAL AND DENSITY-DEPENDENT GROWTH

A population is a set of organisms from the same biological species in a given area. Since all individuals belonging to the same species are very similar to each other when considered over a whole life cycle, classical approaches to population ecology ignore variability among individuals and assume that these are identical. As a consequence, population dynamics focuses on changes in the number or density of individuals that make up the population. Thus, population ecology fundamentally has a demographic approach to reality, in which the basic unit of measurement is the individual.

Population dynamics is implicitly or explicitly based on the following balance equation, which tracks the fate of individuals from time *t* to time *t* + 1:

[*N.sub.t*+1] = *[N.sub.t] + B + I + D + E.* (1.1)

In this equation, Nt is the number of individuals at time *t*, and *B, I, D,* and *E* are the numbers of births, immigrants, deaths, and emigrants, respectively, during the time interval from *t* to *t* + 1. The time unit is arbitrary; it may be a day, a year, or a generation, depending on the kind of organisms considered. This demographic balance equation simply states that the population at time *t* + 1 is the population at time t, plus the individuals that have been added to the population by birth or immigration, minus the individuals that have been removed from the population by death or emigration.

In the simplest case, assume a closed population (no immigration or emigration), a constant environment, and density-independent growth; i.e., the per capita demographic parameters are independent of population density. In this case, *I = E* = 0, *B = b[N.sub.t]*, and *D = d[N.sub.t]*, where *b* and *d* are constant per capita birth and death rates, respectively. Equation (1.1) then reduces to the familiar equation

*[N.sub.t+1] = [N.sub.t] + b[N.sub.t] - d[N.sub.t]* = [lambda]*[N.sub.t]*, (1.2)

where [lambda] = 1 + *b - d* is the finite rate of increase of the population.

This equation says that population size is multiplied by a factor [lambda] during each time unit. Starting from *t* = 0 and iterating the process over *t* time units yields

*[N.sub.t] = [N*.sub.0][[lambda].sup.*t*]. (1.3)

Thus, the population is predicted to grow geometrically at a rate [lambda] per time unit.

An identical prediction is obtained assuming that demographic processes are continuous instead of discrete in time, which leads to the following differential equation:

*dN/dt* = s*N* - [delta]*N = rN*, (1.4)

where s, [delta], and *r* are instantaneous per capita rates of birth, death, and population growth, respectively. This equation can be integrated to give

*N(t) = [N*.sub.0]*[e.sup.rt]*, (1.5)

which is identical to equation (1.3) with [lambda] = *[e.sup.r]*.

This fundamental equation of population dynamics, which is also known as Malthus's (1798) law, describes the inherent capacity of all organisms for exponential or geometric population growth (figure 1.1). *Exponential population growth* is a remarkably robust property as long as population processes are density-independent. It holds irrespective of spatial and temporal variations in demographic processes and population structure. If demographic processes vary in space or time, the finite and instantaneous population growth rates l and r are simply replaced by appropriate spatial or temporal averages in equations (1.3) and (1.5). If age or stage structure is explicitly considered, the finite population growth rate l is obtained from the projection matrix that describes transitions of individuals among age or stage classes (Caswell 1989). Exponential growth has been shown in numerous laboratory and natural populations under conditions of unlimited resource availability or low population density. The global human population itself is roughly experiencing exponential growth. More details on this topic can be found in theoretical ecology textbooks (e.g., Case 2000).

The propensity of populations to grow exponentially is an expression of the autocatalytic nature of biological systems and represents a fundamental source of instability in ecological systems. Basically, all organisms multiply as much as they can-until something prevents them from continuing to do so. And that is where ecology comes into play. All organisms are embedded in a complex web of interactions with their environment, which includes other organisms as well as abiotic factors. As populations grow, they modify their own environment through these multiple interactions, which feeds back on their capacity to grow further.

Classical population ecology makes the simplest possible assumption regarding these environmental feedbacks: it assumes that they can be reduced to a dependence of demographic processes on the population's own density. As the population grows, it progressively exhausts resources such as space, food, and nutrients, and as a result it decreases its potential to grow further. This convenient assumption eliminates the need to consider the complex web of interactions that organisms maintain with their environment and focuses on their net effect on the population variable under consideration. *Density dependence* is formally defined as a dependence of the per capita population growth rate on population density. In the continuous formalism of equation (1.4), which is the formalism that I shall use in most of this book, density dependence is expressed as

*dN/Ndt = f*(N), (1.6)

with *f'(N)* [less than or equal to] 0; i.e., the per capita growth rate monotonically decreases as population size increases.

The simplest form for the density-dependence function *f(N)* is a linear form, which yields the classical *logistic equation* proposed by Verhulst (1838):

*dN/dt = rN*(1 - *N/K*). (1.7)

In this equation, *r*, which is known as the intrinsic rate of natural increase, represents the maximum instantaneous population growth rate when population density is very low (close to zero), and *K* is known as the carrying capacity.

The logistic equation predicts a sigmoid growth pattern with a nearly exponential growth at low population size and a nearly exponential approach to a stable equilibrium population size equal to the carrying capacity (figure 1.1). This can be seen easily by noting that when population size is very small compared with the carrying capacity (*N* [much less than] *K*), the term in parentheses in equation (1.7) vanishes, and equation (1.4) describing exponential growth is recovered. On the other hand, when *N* approaches *K*, a first-order Taylor expansion of the right-hand side of equation (1.7) around *K* yields

*dn/dt = -rn*, (1.8)

where *n = N - K* is a perturbation from the equilibrium value *K*. Thus, the logistic equation predicts an exponential decline of perturbations in the vicinity of the carrying capacity at the same rate as the exponential growth of population size at low density. In other words, density dependence stabilizes the population by counteracting its inherent tendency toward exponential growth and instability.

Logistic growth has been shown in numerous populations, especially in the laboratory under resource limitation. The reason why the logistic equation works so well under controlled laboratory conditions is simple: the linear density-dependence function in the logistic equation may be viewed as a first-order approximation to any form of density dependence. Logistic growth, however, is much less robust than density-independent exponential growth. Departures from the implicit assumptions of continuous demographic processes, constant environmental conditions, instantaneous operation of density dependence, and lack of population structure, can lead to periodic or chaotic population dynamics under logistic growth. These dynamical behaviors are qualitatively different from the stable equilibrium point predicted by the classical model. Again, more details on this topic can be found in theoretical ecology textbooks (e.g., Case 2000).

A more fundamental problem-from the perspective developed in this book-is that the density dependence included in the logistic equation in the form of the carrying capacity is a phenomenological abstraction. Parameter K is a condensed substitute for a wealth of factors and interactions that limit population growth, such as resources, competitors, mutualists, predators, parasites, and diseases. It is not even possible to disentangle the contributions of birth and death processes to density dependence in equation (1.7) since these are lumped into the parameters r and K.

Despite these limitations, the logistic equation has served as a basis for much of theoretical community ecology. The famous Lotka-Volterra models for interspecific competition or mutualism are direct extensions of the logistic equation in which the density-dependence function *f(N)* in equation (1.6) is simply expanded to become a linear function of the population sizes of other interacting species. The classical Lotka-Volterra model for predation does not include direct density dependence but is built on the same principle; i.e., per capita growth rates are linear functions of population sizes.

Although many refinements and developments have been added to the theoretical corpus of community ecology, community ecology is largely an outgrowth of population ecology in its conceptual and methodological foundations. Most dynamical models in community ecology are based on a demographic approach that implicitly takes into account demographic balance constraints of the kind encapsulated in equation (1.1), but they ignore explicit physical constraints such as mass and energy balance (although there are exceptions, of course). As a result, community ecology has a strong focus on the structure, dynamics, and complexity of ecological systems, but it generally does not consider their overall functioning.

THE FORMALISM OF ECOSYSTEM FUNCTIONING: MASS AND ENERGY FLOWS

Ecosystem ecology does not have a simple fundamental law equivalent to the Malthusian law of exponential growth in population dynamics. Consequently, the approaches developed to model ecosystems have been somewhat more variable than in population ecology. The simplest and most common approach, however, has been that of compartmental modeling, which was pioneered by Lotka (1925). Ecosystem ecology is mainly concerned with the stocks and fluxes of materials or energy through the system as a whole, and this is explicitly what compartmental models represent. A *compartmental model* describes a set of compartments, the size of which is measured by the stock of materials or energy they contain, which are connected by fluxes of materials or energy. *Mass or energy balance* is explicitly taken into account in the description of these fluxes.

The basic building block of these models is a single-compartment model open to material or energy exchanges with the outside world. As an example borrowed from DeAngelis (1992), take a water body with a constant volume *V* that contains a solute of concentration ITLITL, and through which water flows at a constant rate *q'* per unit time, and let *[C.sub.I]* be the solute concentration in the inflowing water. The principle of conservation of mass states that the rate of change of the mass of solute in the compartment equals the rate at which mass enters that compartment minus the rate at which mass leaves that compartment. Since the mass of solute in the compartment is *CV*, this principle is expressed in the following dynamical equation:

*d(CV)/dt = q'[C.sub.I] - q'C.* (1.9)

Dividing both sides by the constant volume *V* and rescaling the water flow rate as *q = q'/V* yields

*dC/dt = q([C.sub.I] - C)*, (1.10)

which has the solution

*C(t) = [C.sub.I] + (ITL.sub.0] - [C.sub.I])[e.sup.-qt]*. (1.11)

This solution shows that the solute concentration in the water body tends asymptotically to the concentration in the inflowing water (the second term on the right-hand-side tends to zero as time goes to infinity, which leaves *C = [C.sub.I]*) and that the deviation between the initial ([ITLITL.sub.0]) and final (*[C.sub.I]*) concentrations declines exponentially with time at a rate *q*. Thus, this system smoothly approaches a stable equilibrium concentration set by the inflowing water, at a rate governed by water flow. The water flow rate q sets the characteristic time of the system. It measures the rate at which the system approaches its equilibrium, which is one common measure of resilience (DeAngelis 1992). It also determines the mean residence time of the solute in the compartment, also called the turnover time of the system, which is obtained as the ratio of the equilibrium mass of solute (*[C.sub.I]V*) over the equilibrium mass flow of solute (*q'[C.sub.I] = q[C.sub.I]V*), i.e., 1/*q*.

*(Continues...)*

Excerpted fromFrom Populations to EcosystemsbyMichel LoreauCopyright © 2010 by Princeton University Press. Excerpted by permission.

All rights reserved. No part of this excerpt may be reproduced or reprinted without permission in writing from the publisher.

Excerpts are provided by Dial-A-Book Inc. solely for the personal use of visitors to this web site.

## Table of Contents

Preface: On Unifying Approaches in Ecology ix

Chapter 1: Population and Ecosystem Approaches in Ecology 1

Chapter 2: The Maintenance and Functional Consequences of Species Diversity 19

Chapter 3: Biodiversity and Ecosystem Functioning 56

Chapter 4: Food Webs, Interaction Webs, and Ecosystem Functioning 79

Chapter 5: Stability and Complexity of Ecosystems: New Perspectives on an Old Debate 123

Chapter 6: Material Cycling and the Overall Functioning of Ecosystems 164

Chapter 7: Spatial Dynamics of Biodiversity and Ecosystem Functioning: Metacommunities and Metaecosystems 196

Chapter 8: Evolution of Ecosystems and Ecosystem Properties 225

Chapter 9: Postface: Toward an Integrated, Predictive Ecology 260

References 269

Index 291

## What People are Saying About This

**Holt**

Loreau has crafted a significant volume that makes an important and timely addition to the literature on ecology. He writes clearly, even when he is grappling with thorny theoretical problems. No other author out there could have carried off the synthesis he provides. I predict that From Populations to Ecosystems will prove to be an invaluable resource for years to come.

— *Robert D. Holt, University of Florida*

**Shahid Naeem**

Understanding global diversity has primarily been the business of evolutionary biologists, ecologists, and, only recently, the new breed of ecosystem/community ecologists, with little synthesis. In From Populations to Ecosystems, we can begin to see how these might all come together. This book is a valuable contribution, and will garner a wide readership.

— *Shahid Naeem, Columbia University*

**Harold Mooney**

Michel Loreau has bravely, and successfully, built a new unified framework that harmonizes the disparate, and often conflicting, fields of population biology, community ecology, and ecosystem ecology. This book provides an exciting stimulus for new efforts to test and verify the rich array of insights that he provides.

— *Harold Mooney, Stanford University*

## Reading Group Guide

Acknowledgments vii

Preface: On Unifying Approaches in Ecology ix

Chapter 1: Population and Ecosystem Approaches in Ecology 1

Chapter 2: The Maintenance and Functional Consequences of Species Diversity 19

Chapter 3: Biodiversity and Ecosystem Functioning 56

Chapter 4: Food Webs, Interaction Webs, and Ecosystem Functioning 79

Chapter 5: Stability and Complexity of Ecosystems: New Perspectives on an Old Debate 123

Chapter 6: Material Cycling and the Overall Functioning of Ecosystems 164

Chapter 7: Spatial Dynamics of Biodiversity and Ecosystem Functioning: Metacommunities and Metaecosystems 196

Chapter 8: Evolution of Ecosystems and Ecosystem Properties 225

Chapter 9: Postface: Toward an Integrated, Predictive Ecology 260

References 269

Index 291

## Interviews

Acknowledgments vii

Preface: On Unifying Approaches in Ecology ix

Chapter 1: Population and Ecosystem Approaches in Ecology 1

Chapter 2: The Maintenance and Functional Consequences of Species Diversity 19

Chapter 3: Biodiversity and Ecosystem Functioning 56

Chapter 4: Food Webs, Interaction Webs, and Ecosystem Functioning 79

Chapter 5: Stability and Complexity of Ecosystems: New Perspectives on an Old Debate 123

Chapter 6: Material Cycling and the Overall Functioning of Ecosystems 164

Chapter 7: Spatial Dynamics of Biodiversity and Ecosystem Functioning: Metacommunities and Metaecosystems 196

Chapter 8: Evolution of Ecosystems and Ecosystem Properties 225

Chapter 9: Postface: Toward an Integrated, Predictive Ecology 260

References 269

Index 291

## Recipe

Preface: On Unifying Approaches in Ecology ix

Chapter 1: Population and Ecosystem Approaches in Ecology 1

Chapter 2: The Maintenance and Functional Consequences of Species Diversity 19

Chapter 3: Biodiversity and Ecosystem Functioning 56

Chapter 4: Food Webs, Interaction Webs, and Ecosystem Functioning 79

Chapter 5: Stability and Complexity of Ecosystems: New Perspectives on an Old Debate 123

Chapter 6: Material Cycling and the Overall Functioning of Ecosystems 164

Chapter 7: Spatial Dynamics of Biodiversity and Ecosystem Functioning: Metacommunities and Metaecosystems 196

Chapter 8: Evolution of Ecosystems and Ecosystem Properties 225

Chapter 9: Postface: Toward an Integrated, Predictive Ecology 260

References 269

Index 291