Preface to the Princeton Landmarks in Biology Edition vii Preface xi Symbols Used xiii 1. The Importance of Islands 3 2. Area and Number of Speicies 8 3. Further Explanations of the Area-Diversity Pattern 19 4. The Strategy of Colonization 68 5. Invasibility and the Variable Niche 94 6. Stepping Stones and Biotic Exchange 123 7. Evolutionary Changes Following Colonization 145 8. Prospect 181 Glossary 185 References 193 Index 201

The Theory of Island Biogeography

Predation has long been implicated as a major selective force in the evolution of several morphological and behavioral characteristics of animals. The importance of predation during evolutionary ti...

Behavioral decisions made under the risk of predation: a review and prospectus

▪ Abstract The focus of most ideas on diversity maintenance is species coexistence, which may be stable or unstable. Stable coexistence can be quantified by the long-term rates at which community members recover from low density. Quantification shows that coexistence mechanisms function in two major ways: They may be (a) equalizing because they tend to minimize average fitness differences between species, or (b) stabilizing because they tend to increase negative intraspecific interactions relative to negative interspecific interactions. Stabilizing mechanisms are essential for species coexistence and include traditional mechanisms such as resource partitioning and frequency-dependent predation, as well as mechanisms that depend on fluctuations in population densities and environmental factors in space and time. Equalizing mechanisms contribute to stable coexistence because they reduce large average fitness inequalities which might negate the effects of stabilizing mechanisms. Models of unstable coexitence...

/pdf/mechanisms-of-maintenance-of-species-diversity-340h1nopps.pdf

Mechanisms of Maintenance of Species Diversity

Every form of behavior is shaped by trial and error. Such stepwise adaptation can occur through individual learning or through natural selection, the basis of evolution. Since the work of Maynard Smith and others, it has been realized how game theory can model this process. Evolutionary game theory replaces the static solutions of classical game theory by a dynamical approach centered not on the concept of rational players but on the population dynamics of behavioral programs. In this book the authors investigate the nonlinear dynamics of the self-regulation of social and economic behavior, and of the closely related interactions among species in ecological communities. Replicator equations describe how successful strategies spread and thereby create new conditions that can alter the basis of their success, i.e., to enable us to understand the strategic and genetic foundations of the endless chronicle of invasions and extinctions that punctuate evolution. In short, evolutionary game theory describes when to escalate a conflict, how to elicit cooperation, why to expect a balance of the sexes, and how to understand natural selection in mathematical terms.

 Comprehensive treatment of ecological and game theoretic dynamics
 Invasion dynamics and permanence as key concepts
 Explanation in terms of games of things like competition between species

/pdf/evolutionary-games-and-population-dynamics-pax1uk4ywa.pdf

Evolutionary games and population dynamics

Preface 1. Decision-Theoretic Foundations 1.1 Game Theory, Rationality, and Intelligence 1.2 Basic Concepts of Decision Theory 1.3 Axioms 1.4 The Expected-Utility Maximization Theorem 1.5 Equivalent Representations 1.6 Bayesian Conditional-Probability Systems 1.7 Limitations of the Bayesian Model 1.8 Domination 1.9 Proofs of the Domination Theorems Exercises 2. Basic Models 2.1 Games in Extensive Form 2.2 Strategic Form and the Normal Representation 2.3 Equivalence of Strategic-Form Games 2.4 Reduced Normal Representations 2.5 Elimination of Dominated Strategies 2.6 Multiagent Representations 2.7 Common Knowledge 2.8 Bayesian Games 2.9 Modeling Games with Incomplete Information Exercises 3. Equilibria of Strategic-Form Games 3.1 Domination and Ratonalizability 3.2 Nash Equilibrium 3.3 Computing Nash Equilibria 3.4 Significance of Nash Equilibria 3.5 The Focal-Point Effect 3.6 The Decision-Analytic Approach to Games 3.7 Evolution. Resistance. and Risk Dominance 3.8 Two-Person Zero-Sum Games 3.9 Bayesian Equilibria 3.10 Purification of Randomized Strategies in Equilibria 3.11 Auctions 3.12 Proof of Existence of Equilibrium 3.13 Infinite Strategy Sets Exercises 4. Sequential Equilibria of Extensive-Form Games 4.1 Mixed Strategies and Behavioral Strategies 4.2 Equilibria in Behavioral Strategies 4.3 Sequential Rationality at Information States with Positive Probability 4.4 Consistent Beliefs and Sequential Rationality at All Information States 4.5 Computing Sequential Equilibria 4.6 Subgame-Perfect Equilibria 4.7 Games with Perfect Information 4.8 Adding Chance Events with Small Probability 4.9 Forward Induction 4.10 Voting and Binary Agendas 4.11 Technical Proofs Exercises 5. Refinements of Equilibrium in Strategic Form 5.1 Introduction 5.2 Perfect Equilibria 5.3 Existence of Perfect and Sequential Equilibria 5.4 Proper Equilibria 5.5 Persistent Equilibria 5.6 Stable Sets 01 Equilibria 5.7 Generic Properties 5.8 Conclusions Exercises 6. Games with Communication 6.1 Contracts and Correlated Strategies 6.2 Correlated Equilibria 6.3 Bayesian Games with Communication 6.4 Bayesian Collective-Choice Problems and Bayesian Bargaining Problems 6.5 Trading Problems with Linear Utility 6.6 General Participation Constraints for Bayesian Games with Contracts 6.7 Sender-Receiver Games 6.8 Acceptable and Predominant Correlated Equilibria 6.9 Communication in Extensive-Form and Multistage Games Exercises Bibliographic Note 7. Repeated Games 7.1 The Repeated Prisoners Dilemma 7.2 A General Model of Repeated Garnet 7.3 Stationary Equilibria of Repeated Games with Complete State Information and Discounting 7.4 Repeated Games with Standard Information: Examples 7.5 General Feasibility Theorems for Standard Repeated Games 7.6 Finitely Repeated Games and the Role of Initial Doubt 7.7 Imperfect Observability of Moves 7.8 Repeated Wines in Large Decentralized Groups 7.9 Repeated Games with Incomplete Information 7.10 Continuous Time 7.11 Evolutionary Simulation of Repeated Games Exercises 8. Bargaining and Cooperation in Two-Person Games 8.1 Noncooperative Foundations of Cooperative Game Theory 8.2 Two-Person Bargaining Problems and the Nash Bargaining Solution 8.3 Interpersonal Comparisons of Weighted Utility 8.4 Transferable Utility 8.5 Rational Threats 8.6 Other Bargaining Solutions 8.7 An Alternating-Offer Bargaining Game 8.8 An Alternating-Offer Game with Incomplete Information 8.9 A Discrete Alternating-Offer Game 8.10 Renegotiation Exercises 9. Coalitions in Cooperative Games 9.1 Introduction to Coalitional Analysis 9.2 Characteristic Functions with Transferable Utility 9.3 The Core 9.4 The Shapkey Value 9.5 Values with Cooperation Structures 9.6 Other Solution Concepts 9.7 Colational Games with Nontransferable Utility 9.8 Cores without Transferable Utility 9.9 Values without Transferable Utility Exercises Bibliographic Note 10. Cooperation under Uncertainty 10.1 Introduction 10.2 Concepts of Efficiency 10.3 An Example 10.4 Ex Post Inefficiency and Subsequent Oilers 10.5 Computing Incentive-Efficient Mechanisms 10.6 Inscrutability and Durability 10.7 Mechanism Selection by an Informed Principal 10.8 Neutral Bargaining Solutions 10.9 Dynamic Matching Processes with Incomplete Information Exercises Bibliography Index

博弈论 : 矛盾冲突分析 = Game theory : analysis of conflict

A technique for using patch giving up densities to investigate habitat preferences, predation risk, and interspecific competitive relationships is theoretically analyzed and empirically investigated. Giving up densities, the density of resources within a patch at which an individual ceases foraging, provide considerably more information than simply the amount of resources harvested. The giving up density of a forager, which is behaving optimally, should correspond to a harvest rate that just balances the metabolic costs of foraging, the predation cost of foraging, and the missed opportunity cost of not engaging in alternative activities. In addition, changes in giving up densities in response to climatic factors, predation risk, and missed opportunities can be used to test the model and to examine the consistency of the foragers' behavior. The technique was applied to a community of four Arizonan granivorous rodents (Perognathus amplus, Dipodomys merriami, Ammospermophilus harrisii, and Spermophilus tereticaudus). Aluminum trays filled with 3 grams of millet seeds mixed into 3 liters of sifted soil provided resource patches. The seeds remaining following a night or day of foraging were used to determine the giving up density, and footprints in the sifted sand indicated the identity of the forager. Giving up densities consistently differed in response to forager species, microhabitat (bush versus open), data, and station. The data also provide useful information regarding the relative foraging efficiencies and microhabitat preferences of the coexisting rodent species.

http://max2.ese.u-psud.fr/epc/conservation/PDFs/HIPE/Brown1988.pdf

Patch use as an indicator of habitat preference, predation risk, and competition

Mammalian predator-prey systems are behaviorally sophisticated games of stealth and fear. But, traditional mass-action models of predator prey dynamics treat individuals as behaviorally unresponsive “molecules” in Brownian motion. Foraging theory should provide the conceptual framework to envision the interaction. But, current models of predator feeding behavior generally envision a clever predator consuming large numbers of sessile and behaviorally inert prey (e.g., kangaroo rats, Dipodomys , collecting seeds from food patches). Here, we extend foraging theory to consider a predator-prey game of stealth and fear and then embed this game into the modeling of predator-prey population dynamics. The melding of the prey and predator's optimal behaviors with their population and community-level consequences constitutes the ecology of fear. The ecology of fear identifies the endpoints of a continuum of N-driven (population size) versus μ-driven (fear) systems. In N-driven systems, the major direct dynamical feedback involves predators killing prey, whereas μ-driven systems involve the indirect effects from changes in fear levels and prey catchability. In μ-driven systems, prey respond to predators by becoming more vigilant or by moving away from suspected predators. In this way, a predator (e.g., mountain lion, Puma concolor ) depletes a food patch (e.g., local herd of mule deer, Odocoileus hemionus ) by frightening prey rather than by actually killing prey. Behavior buffers the system: a reduction in predator numbers should rapidly engender less vigilant and more catchable prey. The ecology of fear explains why big fierce carnivores should be and can be rare. In carnivore systems, ignore the behavioral game at one's peril.

https://www.jstor.org/stable/pdfplus/1383287.pdf

The Ecology of Fear: Optimal Foraging, Game Theory, and Trophic Interactions

We review the concepts and research associated with measuring fear and its consequences for foraging. When foraging, animals should and do demand hazardous duty pay. They assess a foraging cost of predation to compensate for the risk of predation or the risk of catastrophic injury. Similarly, in weighing foraging options, animals tradeoff food and safety. The foraging cost of predation can be modelled, and it can be quantitatively and qualitatively measured using risk titrations. Giving-up densities (GUDs) in depletable food patches and the distribution of foragers across safe and risky feeding opportunities are two frequent experimental tools for titrating food and safety. A growing body of literature shows that: (i) the cost of predation can be big and comprise the forager’s largest foraging cost, (ii) seemingly small changes in habitat or microhabitat characteristics can lead to large changes in the cost of predation, and (iii) a forager’s cost of predation rises with risk of mortality, the forager’s energy state and a decrease in its marginal value of energy. In titrating for the cost of predation, researchers have investigated spatial and temporal variation in risk, scale-dependent variation in risk, and the role of predation risk in a forager’s ecology. A risk titration from a feeding animal often provides a more accurate behavioural indicator of predation risk than direct observations of predator-inflicted mortality. Titrating for fear responses in foragers has some well-established applications and holds promise for novel methodologies, concepts and applications. Future directions for expanding conceptual and empirical tools include: what are the consequences of foraging costs arising from interference behaviours and other sources of catastrophic loss? Are there alternative routes by which organisms can respond to tradeoffs of food and safety? What does an animal’s landscape of fear look like as a spatially explicit map, and how do various environmental factors affect it? Behavioural titrations will help to illuminate these issues and more.

https://onlinelibrary.wiley.com/doi/pdf/10.1111/j.1461-0248.2004.00661.x

Hazardous duty pay and the foraging cost of predation

Seed size, dormancy, and dispersal share three population-dynamic functions in temporally and spatially varying environments: risk reduction or bet hedging, escape from crowding, and escape from sib competition. Here we developed a model to explore the ways they may interact to reduce risk. We have shown that the risk-reducing properties of these three seed traits evolve only in response to global temporal variance. Thus, to understand how selection impinges on the seed traits, creating fitness interactions, we must understand the factors contributing to global temporal variance and how they are mitigated by the various seed traits. Since the traits interact to reduce variance, arbitrarily fixing any one trait at different values alters the fitness-maximizing values of the others, resulting in trade-offs among traits. We explore how changes in the number of independent environmental patches, the probability of favorable conditions, the radius of dispersal, and spatial and temporal autocorrelation of envir...

The Selective Interactions of Dispersal, Dormancy, and Seed Size as Adaptations for Reducing Risk in Variable Environments

Foraging is fundamental to animal survival and reproduction, yet it is much more than a simple matter of finding food; it is a biological imperative. Animals must find and consume resources to succeed, and they make extraordinary efforts to do so. For instance, pythons rarely eat, but when they do, their meals are large - as much as 60 percent larger than their own bodies. The snake's digestive system is normally dormant, but during digestion metabolic rates can increase fortyfold. A python digesting quietly on the forest floor has the metabolic rate of a thoroughbred in a dead heat. This and related foraging processes have broad applications in ecology, cognitive science, anthropology, and conservation biology - and they can be further extrapolated in economics, neurobiology, and computer science. "Foraging" is the first comprehensive review of the topic in more than twenty years. A monumental undertaking, this volume brings together twenty-two experts from throughout the field to offer the latest on the mechanics of foraging, modern foraging theory, and foraging ecology. The fourteen essays cover all the relevant issues, including cognition, individual behavior, caching behavior, parental behavior, anti-predator behavior, social behavior, population and community ecology, herbivory, and conservation. Considering a wide range of taxa, from birds to mammals to amphibians, "Foraging" will be the definitive guide to the field.

Joel S. Brown

Papers

Patch use as an indicator of habitat preference, predation risk, and competition

The Ecology of Fear: Optimal Foraging, Game Theory, and Trophic Interactions

Hazardous duty pay and the foraging cost of predation

The Selective Interactions of Dispersal, Dormancy, and Seed Size as Adaptations for Reducing Risk in Variable Environments

Foraging : behavior and ecology