Moya-Lara¤o / Rowntree / Woodward Eco-Evolutionary Dynamics

1. Auflage 2014
ISBN: 978-0-12-801433-2
Verlag: Elsevier Science & Techn.
Format: EPUB
Kopierschutz: 6 - ePub Watermark

E-Book, Englisch, Band Volume 50, 392 Seiten

Reihe: Advances in Ecological Research

The theme of this volume is to discuss Eco-evolutionary Dynamics. - Updates and informs the reader on the latest research findings - Written by leading experts in the field - Highlights areas for future investigation

Autoren/Hrsg.

Moya-Lara¤o, Jordi

Rowntree, Jennifer

Woodward, Guy

Inhaltsverzeichnis

1;Front Cover;1
2;Eco-Evolutionary Dynamics;4
3;Copyright;5
4;Contents;6
5;Contributors;10
6;Preface;14
6.1;References;21
7;Chapter One: Do Eco-Evo Feedbacks Help Us Understand Nature? Answers From Studies of the Trinidadian Guppy;24
7.1;1. Introduction;25
7.2;2. Operational Framework;27
7.3;3. Population Biology of Guppies;30
7.3.1;3.1. Natural history and evolution;30
7.3.2;3.2. The importance of density regulation;34
7.4;4. Experimental Studies of Eco-Evo Dynamics;36
7.4.1;4.1. Hypotheses for eco-evo feedbacks in the evolution of LP guppies;36
7.4.2;4.2. Artificial streams: Retrospective studies of guppy evolution;38
7.4.2.1;4.2.1. LP and HP exert different direct and indirect effects on their ecosystems and the indirect effects create eco-evo f ...;38
7.4.2.2;4.2.2. The fitness advantage of HP `superguppies evaporates at high densities (Bassar et al., 2013);44
7.4.2.3;4.2.3. The interactions between guppies and Rivulus can help drive the evolution of the LP phenotype (Palkovacs et al., 2009);45
7.4.3;4.3. Interactions between guppies and Rivulus;46
7.4.4;4.4. Focal streams: Prospective studies of evolution;48
7.4.4.1;4.4.1. Experimental introductions of guppy populations;48
7.4.4.2;4.4.2. Ecological consequences of canopy manipulations;52
7.4.4.3;4.4.3. The impact of guppies on Rivulus;54
7.4.4.4;4.4.4. The impact of guppies on Invertebrates;54
7.4.4.5;4.4.5. Do guppies change the structure of natural ecosystems?;55
7.4.4.6;4.4.6. Guppy evolution;55
7.4.4.7;4.4.7. Future work on guppies and their ecosystem;56
7.5;5. Conclusions;57
7.6;References;59
8;Chapter Two: Eco-Evolutionary Dynamics in a Three-Species Food Web with Intraguild Predation: Intriguingly Complex;64
8.1;1. Introduction;65
8.2;2. Methods;70
8.2.1;2.1. Study species and setting up the experimental community;70
8.2.2;2.2. Controlling the initial genetic variation in prey populations;71
8.2.3;2.3. Community dynamics experiment;72
8.2.4;2.4. Data smoothing;74
8.2.5;2.5. Estimating predictability of predator dynamics;74
8.2.6;2.6. Models for community and eco-evolutionary dynamics;76
8.3;3. Results;77
8.3.1;3.1. Two-species (single predator, single prey) experiments;77
8.3.2;3.2. Three-species (two predators, single prey) experiments with prey defence evolution;79
8.3.3;3.3. Prey evolution and the predictability of population dynamics;82
8.3.4;3.4. Canard cycles and regime shifts in eco-evolutionary dynamics;84
8.4;4. Discussion and Conclusions;91
8.5;Acknowledgements;94
8.6;References;94
9;Chapter Three: Eco-Evolutionary Spatial Dynamics: Rapid Evolution and Isolation Explain Food Web Persistence;98
9.1;1. Introduction;99
9.1.1;1.1. Food webs and eco-evolutionary dynamics;99
9.1.2;1.2. Space, the next frontier;101
9.1.3;1.3. Merging space, food webs and evolution;102
9.1.4;1.4. Soil food webs as a model system;103
9.1.5;1.5. Aims: A few examples of hypothesis testing using Weaver;103
9.2;2. Materials and Methods;105
9.3;3. Results;115
9.3.1;3.1. Connectance and food web persistence;115
9.3.2;3.2. Genetic variation and food web persistence;117
9.3.3;3.3. Island distance and food web persistence;119
9.3.4;3.4. Predator top-down control on prey diversity;119
9.3.5;3.5. Multi-trophic spatio-temporal dynamics during a 500-day simulation;121
9.3.6;3.6. Relatively long-term micro-evolution (500 days) in a persistent web;126
9.3.7;3.7. Evolutionary dynamics of potworms in the presence and absence of predators;128
9.3.8;3.8. Results summary;128
9.4;4. Discussion;130
9.4.1;4.1. Ecological dynamics;130
9.4.2;4.2. Evolutionary dynamics: Relatively long-term micro-evolution (500 days) in a persistent web;132
9.4.3;4.3. Evolutionary dynamics of potworms in the presence and absence of predators;134
9.4.4;4.4. Future directions;136
9.4.4.1;4.4.1. The quest for eco-evolutionary patterns: An FRP using global optimization algorithms and approximate Bayesian compu ...;137
9.4.4.2;4.4.2. Engineering food webs for pest control;141
9.5;Acknowledgements;144
9.6;Appendix;144
9.6.1;A.1. Weaver-An IBM platform to simulate eco-evolutionary dynamics in food webs;144
9.6.2;A.2. Space and basal resources-2D and 3D and a chemostat;145
9.6.2.1;A.2.1. Chemostat;146
9.6.3;A.3. Phenotypic ranges with quantitative genetic variation;147
9.6.4;A.4. Animal traits;147
9.6.5;A.5. Predator and prey quantitative genetics with more realistic recombination rates;151
9.6.6;A.6. New adjustments in all mass-dependent equations and plastic traits;152
9.6.6.1;A.6.1. Water body content;152
9.6.6.2;A.6.2. Adjustment of scaling constants in mass- and temperature-dependent equations;152
9.6.6.3;A.6.3. Field metabolic rates;153
9.6.7;A.7. Moulting algorithm;154
9.6.8;A.8. Reproductive algorithm;155
9.6.9;A.9. Restricting and controlling attack rates;156
9.6.10;A.10. Adaptive animal movement;158
9.6.11;A.11. Computational demand and requirements;159
9.7;References;160
10;Chapter Four: Eco-Evolutionary Interactions as a Consequence of Selection on a Secondary Sexual Trait;168
10.1;1. Introduction;169
10.2;2. Methods;173
10.2.1;2.1. Predictions on evolution of fighter expression in response to harvesting;173
10.2.2;2.2. Source population and bulb mite life cycle;173
10.2.3;2.3. Experimental procedure;173
10.2.4;2.4. Statistical analyses;175
10.3;3. Results;176
10.3.1;3.1. Evolution of fighter expression;177
10.3.2;3.2. Effects of fighter expression on population size and structure;180
10.3.3;3.3. Realized sex ratio and plasticity in body size;180
10.4;4. Discussion;182
10.5;Acknowledgements;186
10.6;Appendix. Predicting the Evolution of Fighter Expression;186
10.6.1;A1. Population dynamics;187
10.7;References;189
11;Chapter Five: Eco-Evolutionary Dynamics: Experiments in a Model System;194
11.1;1. Introduction;195
11.2;2. Aims and Scope;196
11.3;3. Model System and Methods;198
11.3.1;3.1. The mite model system and generic methods;198
11.3.2;3.2. General experimental procedures;199
11.3.2.1;3.2.1. Common garden environments;199
11.3.2.2;3.2.2. Life-history assays;200
11.3.2.3;3.2.3. Population dynamic experiments;200
11.4;4. Within and Between Individual Phenotypic Variations;201
11.4.1;4.1. Age- and size-at-maturity reaction norms;201
11.4.2;4.2. Inter-generational parental effects on individual phenotypic variation;203
11.4.3;4.3. Understanding the context dependence of parental effects;206
11.5;5. From Phenotypic Variation to Population Dynamics;207
11.5.1;5.1. Transient population dynamics and parental effects;207
11.6;6. Eco-Evolutionary Population Dynamics-the Full Loop;209
11.6.1;6.1. Methods;212
11.6.1.1;6.1.1. Quantitative methods and statistical analysis;214
11.6.2;6.2. Results-Evolution of population dynamics in variable environments;215
11.6.3;6.3. Results-Life-history responses to harvesting in variable environments;218
11.6.4;6.4. Discussion of evolution of life histories in response to environmental variation and harvesting;220
11.7;7. Summary;222
11.8;References;224
12;Chapter Six: Individual Trait Variation and Diversity in Food Webs;230
12.1;1. Introduction;231
12.2;2. Material and Methods;236
12.2.1;2.1. Stochastic individual trait-based predator-prey model;236
12.2.1.1;2.1.1. Speed of learning and the distribution of predator connectivity;236
12.2.1.2;2.1.2. Profitability and the strength of prey selection for individual predators;238
12.2.1.3;2.1.3. Profitability and the strength of prey selection for connectivity classes;240
12.2.1.4;2.1.4. Dynamics of prey populations driven by predators;241
12.2.2;2.2. Guadalquivir estuary food web as a case study: Sampling of gut contents and resource distribution;242
12.2.3;2.3. Fitting the model to the data: Speed of learning (a) and the distribution of predator connectivity;243
12.2.4;2.4. Choosing among alternative models: Strength of prey selection (O);244
12.2.4.1;2.4.1. Negative, neutral, and positive density-dependent prey selection;244
12.2.4.2;2.4.2. Occam factor;248
12.2.4.3;2.4.3. Bayes factor;248
12.3;3. Results;249
12.3.1;3.1. Rate of learning and the distribution of predator connectivity;249
12.3.2;3.2. Variation in the strength of prey selection among individuals;250
12.4;4. Discussion;251
12.4.1;4.1. Intra-specific variation in prey selection;252
12.4.2;4.2. Connecting the strength of prey selection and diversity in food webs;253
12.4.3;4.3. Connecting trait variation with eco-evolutionary food web dynamics;254
12.4.4;4.4. Conclusion;256
12.5;Acknowledgements;256
12.6;Appendix. Sampling Methods;256
12.7;Samplings and Populations Across Environmental Gradients;257
12.8;References;261
13;Chapter Seven: Community Genetic and Competition Effects in a Model Pea Aphid System;266
13.1;1. Introduction;267
13.1.1;1.1. Competition as an eco-evolutionary force;268
13.1.2;1.2. Host-plant effects on aphid fitness and behaviour;269
13.2;2. Materials and Methods;270
13.2.1;2.1. Species and genotype descriptions;270
13.2.2;2.2. Experimental conditions;271
13.2.3;2.3. Measures of fitness;272
13.2.4;2.4. Aphid behaviour;272
13.2.5;2.5. Statistical analysis;272
13.3;3. Results;273
13.3.1;3.1. The effect of competition on P127;273
13.3.2;3.2. Interspecific competition;274
13.3.3;3.3. Intraspecific competition;275
13.3.4;3.4. Mixed competition;276
13.4;4. Discussion;277
13.4.1;4.1. Comparative strength of competition;278
13.4.2;4.2. Relatedness and competition;279
13.4.3;4.3. Interspecific competition;279
13.4.4;4.4. Intraspecific competition;281
13.4.5;4.5. Mixed competition;282
13.4.6;4.6. Consequences for agriculture;283
13.4.7;4.7. Relative importance of effects;283
13.4.8;4.8. Conclusion;284
13.5;Acknowledgements;284
13.6;References;284
14;Chapter Eight: Genetic Correlations in Multi-Species Plant/Herbivore Interactions at Multiple Genetic Scales: Implications ...;290
14.1;1. Introduction;291
14.2;2. Methods;294
14.2.1;2.1. Genetic hierarchy of E. globulus;294
14.2.2;2.2. Field trials and damage assessments;296
14.2.2.1;2.2.1. Summary of field trials and damage assessments;296
14.2.2.2;2.2.2. Four far north-western Tasmania field trials (Togari, Salmon River 1, Salmon River 2 and Temma): Mammalian herbivor ...;297
14.2.2.3;2.2.3. Massy Greene field trial: Sawfly damage;298
14.2.2.4;2.2.4. Oigles Road field trial: Leaf beetle agricola oviposition and larvae damage;300
14.3;3. Statistical Analysis;300
14.3.1;3.1. Juvenile foliage analysis;300
14.3.2;3.2. Adult foliage analysis;302
14.4;4. Results;303
14.5;5. Discussion;309
14.6;Acknowledgements;315
14.7;References;315
15;Chapter Nine: When Ranges Collide: Evolutionary History, Phylogenetic Community Interactions, Global Change Factors, and R ...;320
15.1;1. Introduction;321
15.2;2. Methods;325
15.2.1;2.1. Experimental methods and study species;325
15.2.1.1;2.1.1. Design, study species, and global change manipulations;325
15.2.1.2;2.1.2. Plant propagation and greenhouse methods;325
15.3;3. Statistical Analyses;328
15.3.1;3.1. Evolutionary basis to range size;328
15.3.2;3.2. Interactive effects of evolutionary history, phylogenetic similarity, CO2, and N;328
15.3.3;3.3. Relative effect sizes of phylogenetic similarity, CO2 manipulation, and N fertilization;329
15.4;4. Results;329
15.4.1;4.1. Evolutionary basis to range size;329
15.4.2;4.2. Interactive effects of evolutionary history, phylogenetic similarity, CO2, and N;331
15.4.3;4.3. Relative effect sizes of phylogenetic similarity, CO2 manipulation, and N fertilization;340
15.5;5. Discussion;342
15.6;Acknowledgements;345
15.7;References;370
16;Index;374
17;Advances in Ecological Research Volume 1-50;380
17.1;Cumulative List of Titles;380

Leseproben

Chapter One Do Eco-Evo Feedbacks Help Us Understand Nature? Answers From Studies of the Trinidadian Guppy
Joseph Travis*,1,2; David Reznick*,†,2; Ronald D. Bassar‡; Andrés López-Sepulcre§; Regis Ferriere¶; Tim Coulson||    * Department of Biological Science, Florida State University, Tallahassee, Florida, USA
† Department of Biology, University of California, Riverside, California, USA
‡ Department of Environmental Conservation, University of Massachusetts, Amherst, Massachusetts, USA
§ Laboratoire Ecologie et Evolution, CNRS Unité Mixte de Recherche, École Normale Supérieure, Paris, France
¶ Department of Ecology and Evolutionary Biology, University of Arizona, Tucson, Arizona, USA
|| Department of Zoology, University of Oxford, Oxford, United Kingdom
1 Corresponding author: email address: [e-mail]
2 Sharing credit as joint first authors. Abstract
The bulk of evolutionary ecology implicitly assumes that ecology shapes evolution, rather than vice versa, but there is increasing interest in the possibility of a two-way interaction. Dynamic feedbacks between ecological and evolutionary processes (eco-evo feedbacks) have long been recognized in the theoretical literature, and the observation of rapid evolution has since inspired empiricists to explore the consequences of these feedbacks. Laboratory studies prove that short-term evolutionary change can significantly alter ecological dynamics, particularly in pair-wise interactions. We know far less about whether these reciprocal dynamics are important in more complex natural systems. Here, we outline our approach to that question, focusing on the Trinidadian guppy and the stream ecosystems it inhabits. We summarize results from several types of studies: comparative demography in two types of communities, experiments in mesocosms, common garden laboratory experiments and replicated introduction experiments. The latter were designed as perturbations to the natural steady state that allow us to follow the joint ecological and evolutionary dynamics of guppies and their ecosystem. In each approach, we replicated experiments across multiple independent origins of guppy population types and found that eco–evo feedbacks play major roles in guppy evolution. There are three possible sources for these feedbacks, all of which have some support in our data, which will form the focus of future research efforts. Keywords Density-dependent selection Eco-evo dynamics Eco-evo feedback Frequency-dependent selection Guppies Poecilia reticulata Rivulus hartii 1 Introduction
Feedbacks between ecology and evolution occur when an organism modifies some feature of its environment and, by extension, changes the nature of selection that it experiences (Cameron et al., 2014, chapter 5 of this volume; Ferriere et al., 2004; Kokko and López-Sepulcre, 2007). This change in the nature of selection may elicit a genetic response that alters the impact the organism has on its environment, which can in turn change the nature of selection again, creating a feedback loop that links the dynamics of phenotypes and those of their constituent alleles and genotypes with the dynamics of ecological variables. Understanding the prevalence and importance of these so-called eco-evo feedbacks is important for two reasons. First, they can generate outcomes of simple ecological interactions that differ from those that prevail in the absence of the evolutionary feedback (Hiltunen et al., 2014, chapter 2 of this volume). For example, a selective feedback from predator to prey can cause prey to evolve resistance to the predator, which may in turn stabilize an otherwise unstable system or destabilize an otherwise stable system (Abrams and Matsuda, 1997). Second, these feedbacks may determine which traits evolve and how they do so. The optimal phenotype when there are eco-evo feedbacks can be quite different from that in their absence (MacArthur and Wilson, 1967). In this chapter, we refer to ‘feedbacks between ecology and evolution’ as ‘eco-evo feedbacks’. This convenient shortcut distils a potentially complex process into two phases: an ecological impact of adaptive genetic change and an evolutionary feedback loop that propels further change. To appreciate this, recall that the parameters used to characterize ecological and evolutionary feedbacks in mathematical models of population and genetic dynamics are emergent properties of the interactions among individuals within and among species and between individuals and their abiotic environment. These interactions change as adaptive evolution modifies how individuals respond to the dynamics of ecological variables like per capita food levels or encounter rates with predators. The first phase, the ecological impact, occurs as adaptive evolution modifies the traits of one species. As these traits change, the demography of its population will change (Cameron et al., 2014, chapter 5 of this volume). The altered demography of our focal species can, in turn, affect variables like resource replenishment rates or the demography of a competitor or predator. The result is a change in the ecological dynamics of a system and perhaps its emergent properties (Moya-Laraño et al., 2014, chapter 3 this volume). The effects of adaptive change on stability in predator–prey systems (e.g. Abrams and Matsuda, 1997) illustrate this result. The second phase, the evolutionary feedback loop, may or may not follow. The evolutionary feedback loop will occur if the effects of the focal species on the demography of a competitor or predator provoke an evolutionary response in that second species. The feedback loop can also act directly on the focal species if the effects of the focal species on ecological variables alter the nature of selection on the focal species (Cameron et al., 2014, chapter 5 of this volume). Whether the second phase of the eco-evo feedback propels further evolutionary change in the focal species or another species depends on whether different genotypes respond differently to the effects of these ecological impacts. Thus, when we refer to eco-evo feedbacks, we are focusing on an evolutionary process driven by how genotypes respond to the dynamics of one or more ecological variables. The current explosion of interest in the empirical study of eco-evo feedbacks is relatively new, but the underlying concepts are well established. The principles of population genetics that underlie such interactions, such as frequency- or density-dependent selection (Pimentel, 1961, 1968), are well defined in theory (e.g. Charlesworth, 1971; Clarke, 1972; Cockerham et al., 1972; MacArthur, 1962; Roughgarden, 1971; Smouse, 1976; Wallace, 1975). The connections between these forms of selection and the dynamics of ecological variables, such as population density or the abundance of competitors, predators or pathogens, have also been long recognized in population genetic theory (e.g. Jayakar, 1970; Leon, 1974; Levin and Udovic, 1977; Roughgarden, 1976). These and many subsequent papers since share the common theme of modelling joint ecological and evolutionary dynamics driven by the reciprocal influences of ecological variables and genetic variation. The burgeoning interest in eco-evo feedbacks, however, has been inspired by more recent experiments that have shown feedbacks that are strong enough to make the joint dynamics of ecological and genetic variation visible (Ellner, 2013; Schoener, 2011). Joint dynamics occur when (a) different genetically based phenotypes have different effects on ecological variables and (b) the selection coefficients generated by the feedback loop from the ecological variables to the fitness of those phenotypes are large (Otto and Day, 2007). These feedbacks are best known in a few model ecosystems, such as in the integrated theoretical and empirical work on predator–prey oscillations performed by Hairston, Ellner and colleagues (Ellner, 2013; Hiltunen et al., 2014, chapter 2 of this volume). These laboratory studies provide the proof of concept for the potential importance of eco-evo feedbacks for the outcome of ecological dynamics. There are some well-known case studies from nature that illustrate how such feedbacks affect the trait distributions we observe in natural populations. For example, interactions between hosts and pathogens and the interlocking roles of evolving immunity and cycling population densities are among the earliest and most striking examples of eco-evo feedbacks (Duffy and Sivars-Becker, 2007; Duffy et al., 2009). The question now facing us is whether eco-evo feedbacks play similar prominent roles when we move from strong pair-wise interactions to the more general case of complex ecosystems with many interconnected species (Schoener, 2011). We distil the challenge of understanding the importance of eco-evo feedbacks into two questions. First, how pervasive are reciprocal feedbacks between...