A synthesis of contemporary analytical and modeling approaches in population ecology The book provides an overview of the key analytical approaches that are currently used in demographic, genetic, and spatial analyses in population ecology. The chapters present current problems, introduce advances in analytical methods and models, and demonstrate the applications of quantitative methods to ecological data. The book covers new tools for designing robust field studies; estimation of abundance and demographic rates; matrix population models and analyses of population dynamics; and current…mehr
A synthesis of contemporary analytical and modeling approaches in population ecology
The book provides an overview of the key analytical approaches that are currently used in demographic, genetic, and spatial analyses in population ecology. The chapters present current problems, introduce advances in analytical methods and models, and demonstrate the applications of quantitative methods to ecological data. The book covers new tools for designing robust field studies; estimation of abundance and demographic rates; matrix population models and analyses of population dynamics; and current approaches for genetic and spatial analysis. Each chapter is illustrated by empirical examples based on real datasets, with a companion website that offers online exercises and examples of computer code in the R statistical software platform. _ Fills a niche for a book that emphasizes applied aspects of population analysis _ Covers many of the current methods being used to analyse population dynamics and structure _ Illustrates the application of specific analytical methods through worked examples based on real datasets _ Offers readers the opportunity to work through examples or adapt the routines to their own datasets using computer code in the R statistical platform
Population Ecology in Practice is an excellent book for upper-level undergraduate and graduate students taking courses in population ecology or ecological statistics, as well as established researchers needing a desktop reference for contemporary methods used to develop robust population assessments.Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
DENNIS L. MURRAY, PHD, is Professor of Biology at Trent University and holds the position of Canada Research Chair in Integrative Wildlife Conservation, Bioinformatics, and Ecological Modeling. BRETT K. SANDERCOCK, PHD, is a Senior Research Scientist in the Department of Terrestrial Ecology at the Norwegian Institute for Nature Research.
Inhaltsangabe
Contributors xvii
Preface xxi
About the Companion Website xxiii
Part I Tools for Population Biology 1
1 How to Ask Meaningful Ecological Questions 3 Charles J. Krebs
1.1 What Problems Do Population Ecologists Try to Solve? 3
1.2 What Approaches Do Population Ecologists Use? 6
1.2.1 Generating and Testing Hypotheses in Population Ecology 10
1.3 Generality in Population Ecology 11
1.4 Final Thoughts 12
References 13
2 From Research Hypothesis to Model Selection: A Strategy for Robust Inference in Population Ecology 17 Dennis L. Murray, Guillaume Bastille-Rousseau, Lynne E. Beaty, Megan L. Hornseth, Jeffrey R. Row and Daniel H. Thornton
2.1 Introduction 17
2.1.1 Inductive Methods 18
2.1.2 Hypothetico-deductive Methods 19
2.1.3 Multimodel Inference 20
2.1.4 Bayesian Methods 22
2.2 What Constitutes a Good Research Hypothesis? 22
2.3 Multiple Hypotheses and Information Theoretics 24
2.3.1 How Many are Too Many Hypotheses? 25
2.4 From Research Hypothesis to Statistical Model 26
2.4.1 Functional Relationships Between Variables 26
2.4.2 Interactions Between Predictor Variables 26
2.4.3 Number and Structure of Predictor Variables 27
2.5 Exploratory Analysis and Helpful Remedies 28
2.5.1 Exploratory Analysis and Diagnostic Tests 28
2.5.2 Missing Data 28
2.5.3 Inter-relationships Between Predictors 30
2.5.4 Interpretability of Model Output 31
2.6 Model Ranking and Evaluation 32
2.6.1 Model Selection 32
2.6.2 Multimodel Inference 36
2.7 Model Validation 39
2.8 Software Tools 41
2.9 Online Exercises 41
2.10 Future Directions 41
References 42
Part II Population Demography 47
3 Estimating Abundance or Occupancy from Unmarked Populations 49 Brett T. McClintock and Len Thomas
3.1 Introduction 49
3.1.1 Why Collect Data from Unmarked Populations? 49
3.1.2 Relative Indices and Detection Probability 50
3.1.2.1 Population Abundance 50
3.1.2.2 Species Occurrence 51
3.1.3 Hierarchy of Sampling Methods for Unmarked Individuals 52
3.2 Estimating Abundance (or Density) from Unmarked Individuals 53
3.2.1 Distance Sampling 53
3.2.1.1 Classical Distance Sampling 54
3.2.1.2 Model-Based Distance Sampling 57
3.2.2 Replicated Counts of Unmarked Individuals 61
3.2.2.1 Spatially Replicated Counts 61
3.2.2.2 Removal Sampling 63
3.3 Estimating Species Occurrence under Imperfect Detection 64
3.3.1 Single-Season Occupancy Models 65
3.3.2 Multiple-Season Occupancy Models 66
3.3.3 Other Developments in Occupancy Estimation 68
3.3.3.1 Site Heterogeneity in Detection Probability 68
3.3.3.2 Occupancy and Abundance Relationships 68
3.3.3.3 Multistate and Multiscale Occupancy Models 68
3.3.3.4 Metapopulation Occupancy Models 69
3.3.3.5 False Positive Occupancy Models 70
3.4 Software Tools 70
3.5 Online Exercises 71
3.6 Future Directions 71
References 73
4 Analyzing Time Series Data: Single-Species Abundance Modeling 79 Steven Delean, Thomas A.A. Prowse, Joshua V. Ross and Jonathan Tuke
4.1 Introduction 79
4.1.1 Principal Approaches to Time Series Analysis in Ecology 80
4.1.2 Challenges to Time Series Analysis in Ecology 82
1 How to Ask Meaningful Ecological Questions 3 Charles J. Krebs
1.1 What Problems Do Population Ecologists Try to Solve? 3
1.2 What Approaches Do Population Ecologists Use? 6
1.2.1 Generating and Testing Hypotheses in Population Ecology 10
1.3 Generality in Population Ecology 11
1.4 Final Thoughts 12
References 13
2 From Research Hypothesis to Model Selection: A Strategy for Robust Inference in Population Ecology 17 Dennis L. Murray, Guillaume Bastille-Rousseau, Lynne E. Beaty, Megan L. Hornseth, Jeffrey R. Row and Daniel H. Thornton
2.1 Introduction 17
2.1.1 Inductive Methods 18
2.1.2 Hypothetico-deductive Methods 19
2.1.3 Multimodel Inference 20
2.1.4 Bayesian Methods 22
2.2 What Constitutes a Good Research Hypothesis? 22
2.3 Multiple Hypotheses and Information Theoretics 24
2.3.1 How Many are Too Many Hypotheses? 25
2.4 From Research Hypothesis to Statistical Model 26
2.4.1 Functional Relationships Between Variables 26
2.4.2 Interactions Between Predictor Variables 26
2.4.3 Number and Structure of Predictor Variables 27
2.5 Exploratory Analysis and Helpful Remedies 28
2.5.1 Exploratory Analysis and Diagnostic Tests 28
2.5.2 Missing Data 28
2.5.3 Inter-relationships Between Predictors 30
2.5.4 Interpretability of Model Output 31
2.6 Model Ranking and Evaluation 32
2.6.1 Model Selection 32
2.6.2 Multimodel Inference 36
2.7 Model Validation 39
2.8 Software Tools 41
2.9 Online Exercises 41
2.10 Future Directions 41
References 42
Part II Population Demography 47
3 Estimating Abundance or Occupancy from Unmarked Populations 49 Brett T. McClintock and Len Thomas
3.1 Introduction 49
3.1.1 Why Collect Data from Unmarked Populations? 49
3.1.2 Relative Indices and Detection Probability 50
3.1.2.1 Population Abundance 50
3.1.2.2 Species Occurrence 51
3.1.3 Hierarchy of Sampling Methods for Unmarked Individuals 52
3.2 Estimating Abundance (or Density) from Unmarked Individuals 53
3.2.1 Distance Sampling 53
3.2.1.1 Classical Distance Sampling 54
3.2.1.2 Model-Based Distance Sampling 57
3.2.2 Replicated Counts of Unmarked Individuals 61
3.2.2.1 Spatially Replicated Counts 61
3.2.2.2 Removal Sampling 63
3.3 Estimating Species Occurrence under Imperfect Detection 64
3.3.1 Single-Season Occupancy Models 65
3.3.2 Multiple-Season Occupancy Models 66
3.3.3 Other Developments in Occupancy Estimation 68
3.3.3.1 Site Heterogeneity in Detection Probability 68
3.3.3.2 Occupancy and Abundance Relationships 68
3.3.3.3 Multistate and Multiscale Occupancy Models 68
3.3.3.4 Metapopulation Occupancy Models 69
3.3.3.5 False Positive Occupancy Models 70
3.4 Software Tools 70
3.5 Online Exercises 71
3.6 Future Directions 71
References 73
4 Analyzing Time Series Data: Single-Species Abundance Modeling 79 Steven Delean, Thomas A.A. Prowse, Joshua V. Ross and Jonathan Tuke
4.1 Introduction 79
4.1.1 Principal Approaches to Time Series Analysis in Ecology 80
4.1.2 Challenges to Time Series Analysis in Ecology 82
4.2 Time Series (ARMA) Modeling 83
4.2.1 Time S
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