Complex population dynamics: a theoretical / empirical synthesis / Peter Turchin

Incluye bibliografía: páginas 405-436 e indice: páginas 347-450

Preface.. Mathematical Symbols.. Part I.. Theory.. 1. Introduction.. 1.1 At the Sources.. 1.1.1 The Puzzle of Population Cycles.. 1.1.2 Modeling Nature.. 1.1.3 The Balance of Nature.. 1.2 General Philosophy of the Approach.. 1.2.1 Defining the Phenomenon to Be Explained.. 1.2.2 Formalizing Hypotheses as Mathematical Models.. 1.2.3 Contrasting Models with Data.. 2. Population Dynamics from First Principles.. 2.1 Introduction.. 2.2 Exponential Growth.. 2.2.1 Derivation of the Exponential Model.. 2.2.2 Comparison with the Law of Inertia.. 2.2.3 “Laws”: Postulates, Theorems, Empirical Generalizations?.. 2.3 Self-Limitation.. 2.3.1 Upper and Lower Density Bounds.. 2.3.2 Formalizing the Notion of Self-Limitation.. 2.3.3 The Logistic Model.. 2.4 Consumer-Resource Oscillations.. 2.4.1 Three More Postulates.. 2.4.2 The Lotka-Volterra Predation Model.. 2.5 Process Order.. 2.6 Synthesis.. 3. Single-Species Populations.. 3.1 Models without Population Structure.. 3.1.1 Continuous-Time Models.. 3.1.2 Discrete-Time Models.. 3.1.3 Delayed Differential Models.. 3.2 Exogenous Drivers.. 3.2.1 Stochastic Variation.. 3.4 Second-Order Models.. 3.4.1 Maternal Effect Hypothesis.. 3.4.2 Kin Favoritism Model.. 3.5 Synthesis.. 4. Trophic Interactions.. 4.1 Responses of Predators to Fluctuations in Prey Density.. 4.1.1 Functional Response.. 4.1.2 Aggregative Response.. 4.1.3 Numerical Response.. 4.2 Continuous-Time Models.. 4.2.1 Generalized Lotka-Volterra Models.. 4.2.2 Models Not Conforming to the LV Framework.. 4.2.3 Anatomy of a Predator-Prey Cycle.. 4.2.4 Generalist Predators.. 4.3 Discrete-Time Models: Parasitoids.. 4.3.1 Functional and Numerical Responses.. 4.3.2 Dynamical Models.. 4.4 Grazing Systems.. 4.4.1 Grazer’s Functional Response.. 4.4.2 Dynamics of Vegetation Regrowth.. 4.4.3 Dynamics of Grazer-Vegetation Interactions.. 4.4.4 Plant Quality.. 4.5 Pathogens and Parasites.. 4.5.1 Transmission Rate.. 4.5.2 Microparasitism Models.. 4.5.3 Macroparasitism Models.. 4.6 Tritrophic Models.. 4.7 Synthesis.. 5. Connecting Mathematical Theory to Empirical Dynamics.. 5.1 Introduction.. 5.2 Qualitative Types of Deterministic Dynamics.. 5.2.1 Attractors.. 5.2.2 Sensitive Dependence on Initial Conditions.. 5.3 Population Dynamics in the Presence of Noise.. 5.3.1 Simple Population Dynamics.. 5.3.2 Stable Periodic Oscillations.. 5.3.3 Chaotic Oscillations.. 5.3.4 Quasi-Chaotic Oscillations.. 5.3.5 Regular Exogenous Forcing.. 5.3.6 Synthesis.. 5.4 Population Regulation.. 5.4.1 Definition of Density Dependence.. 5.4.2 Regulation: Evolution of the Concept.. 5.4.3 The Stationarity Definition of Regulation.. 5.4.4 Beyond Stationarity: Stochastic Boundedness.. 5.4.5 Synthesis

Part II.. Data.. 6. Empirical Approaches: An Overview.. 6.1 Introduction.. 6.2 Analysis of Population Fluctuations.. 6.2.1 The Structure of Density Dependence.. 6.2.2 Probes: Quantitative Measures of Time-Series Patterns.. 6.2.3 Phenomenological versus Mechanistic Approaches.. 6.3 Experimental Approaches.. 7. Phenomenological Time-Series Analysis.. 7.1 Basics.. 7.1.1 Variance Decomposition.. 7.1.2 Data Manipulations Prior to Analysis.. 7.1.3 Diagnostic Tools.. 7.2 Fitting Models to Data.. 7.2.1 General Framework.. 7.2.2 Choosing the Base Lag.. 7.2.3 Functional Forms.. 7.2.4 Model Selection by Cross-Validation.. 7.3 Synthesis.. 8. Fitting Mechanistic Models.. 8.1 Model Selection.. 8.2 Analysis of Ancillary Data.. 8.3 One-Step-Ahead Prediction.. 8.4 Trajectory Matching.. 8.5 Fitting by Nonlinear Forecasting.. Part III.. Casestudies.. 9. Larch Budmoth.. 9.1 Introduction.. 9.2 Analysis of Time-Series Data.. 9.3 Hypotheses and Models.. 9.3.1 Plant Quality.. 9.3.2 Parasitism.. 9.3.3 Putting It All Together: A Parasitism–Plant Quality Model.. 9.4 Synthesis.. 10. Southern Pine Beetle.. 10.1 Introduction.. 10.2 Analysis of Time-Series Data.. 10.3 Hypotheses and Models.. 10.3.1 General Review of Hypotheses.. 10.3.2 Interaction with Hosts.. 10.3.3 Interaction with Parasitoids.. 10.3.4 The Predation Hypothesis.. 10.4 An Experimental Test of the Predation Hypothesis.. 10.4.1 Rationale.. 10.4.2 Results.. 10.5 Synthesis.. 11. Red Grouse.. 11.1 Numerical Patterns.. 11.2 Hypotheses and Models.. 11.2.1 Overview.. 11.2.2 Parasite-Grouse Hypothesis.. 11.2.3 Kin Favoritism Hypothesis.. 11.3 Experiments.. 11.3.1 Density Manipulation.. 11.3.2 Parasite Manipulation.. 11.4 Synthesis.. 12. Voles and Other Rodents.. 12.1 Introduction.. 12.2 Analysis of Time-Series Data.. 12.2.1 Methodological Issues.. 12.2.2 Numerical Patterns.. 12.3 Hypotheses and Models.. 12.3.1 Maternal Effect Hypothesis.. 12.3.2 Interaction with Food.. 12.3.3 Predation.. 12.4 Fitting the Predation Model by NLF.. 12.5 Lemmings.. 12.5.1 Numerical Patterns.. 12.5.2 Testing Alternative Trophic Hypotheses.. 12.5.3 Lemming-Vegetation Dynamics at Barrow.. 12.6 Synthesis.. 12.6.1 Summary of Findings.. 12.6.2 Toward a General Trophic Theory of Rodent Dynamics.. 13. Snowshoe Hare.. 13.1 Introduction.. 13.2 Numerical Patterns.. 13.3 Models.. 13.4 Experiments.. 13.5 Synthesis.. 14. Ungulates.. 14.1 Introduction.. 14.2 Interaction with Food.. 14.3 Interaction with Predators.. 14.4 Numerical Dynamics.. 14.5 Synthesis.. 15. General Conclusions.. 15.1 What Mechanisms Drive Oscillations in Nature?.. 15.2 Structure of Density Dependence.. 15.3 What about Chaos?.. 15.4 Population Ecology: A Mature Science.. Glossary.. References.. Index

Glosario: páginas 397-404

Why do organisms become extremely abundant one year and then seem to disappear a few years later? Why do population outbreaks in particular species happen more or less regularly in certain locations, but only irregularly (or never at all) in other locations? Complex population dynamics have fascinated biologists for decades. By bringing together mathematical models, statistical analyses, and field experiments, this book offers a comprehensive new synthesis of the theory of population oscillations. Peter Turchin first reviews the conceptual tools that ecologists use to investigate population oscillations, introducing population modeling and the statistical analysis of time series data. He then provides an in-depth discussion of several case studies--including the larch budmoth, southern pine beetle, red grouse, voles and lemmings, snowshoe hare, and ungulates--to develop a new analysis of the mechanisms that drive population oscillations in nature. Through such work, the author argues, ecologists can develop general laws of population dynamics that will help turn ecology into a truly quantitative and predictive science. Complex Population Dynamics integrates theoretical and empirical studies into a major new synthesis of current knowledge about population dynamics. It is also a pioneering work that sets the course for ecology's future as a predictive science. eng