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Population Ecology in Practice. Edition No. 1

  • ID: 5224689
  • Book
  • February 2020
  • 448 Pages
  • John Wiley and Sons Ltd
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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.

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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

4.2 Time Series (ARMA) Modeling 83

4.2.1 Time Series Models 83

4.2.2 Autoregressive Moving Average Models 83

4.3 Regression Models with Correlated Errors 87

4.4 Phenomenological Models of Population Dynamics 88

4.4.1 Deterministic Models 89

4.4.1.1 Exponential Growth 89

4.4.1.2 Classic ODE Single-Species Population Models that Incorporate Density Dependence 90

4.4.2 Discrete-Time Population Growth Models with Stochasticity 92

4.5 State-space Modeling 93

4.5.1 Gompertz State-space Population Model 94

4.5.2 Nonlinear and Non-Gaussian State-space Population Models 96

4.6 Software Tools 96

4.7 Online Exercises 97

4.8 Future Directions 97

References 98

5 Estimating Abundance from Capture-Recapture Data 103
J. Andrew Royle and Sarah J. Converse

5.1 Introduction 103

5.2 Genesis of Capture-Recapture Data 104

5.3 The Basic Closed Population Models: M0, Mt, Mb104

5.4 Inference Strategies 105

5.4.1 Likelihood Inference 105

5.4.2 Bayesian Analysis 107

5.4.3 Other Inference Strategies 108

5.5 Models with Individual Heterogeneity in Detection 108

5.5.1 Model Mh 108

5.5.2 Individual Covariate Models 109

5.5.2.1 The Full Likelihood 109

5.5.2.2 Horvitz-Thompson Estimation 110

5.5.3 Distance Sampling 110

5.5.4 Spatial Capture-Recapture Models 110

5.5.4.1 The State-space 112

5.5.4.2 Inference in SCR Models 112

5.6 Stratified Populations or Multisession Models 112

5.6.1 Nonparametric Estimation 112

5.6.2 Hierarchical Capture-Recapture Models 113

5.7 Model Selection and Model Fit 113

5.7.1 Model Selection 113

5.7.2 Goodness-of-Fit 114

5.7.3 What to Do When Your Model Does Not Fit 115

5.8 Open Population Models 115

5.9 Software Tools 116

5.10 Online Exercises 117

5.11 Future Directions 118

References 119

6 Estimating Survival and Cause-specific Mortality from Continuous Time Observations 123
Dennis L. Murray and Guillaume Bastille-Rousseau

6.1 Introduction 123

6.1.1 Assumption of No Handling, Marking or Monitoring Effects 125

6.1.2 Cause of Death Assessment 125

6.1.3 Historical Origins of Survival Estimation 126

6.2 Survival and Hazard Functions in Theory 127

6.3 Developing Continuous Time Survival Datasets 130

6.3.1 Dataset Structure 131

6.3.2 Right-censoring 133

6.3.3 Delayed Entry and Other Time Considerations 133

6.3.4 Sampling Heterogeneity 134

6.3.5 Time-dependent Covariates 135

6.4 Survival and Hazard Functions in Practice 135

6.4.1 Mayfield and Heisey–Fuller Survival Estimation 135

6.4.2 Kaplan–Meier Estimator 136

6.4.3 Nelson–Aalen Estimator 138

6.5 Statistical Analysis of Survival 138

6.5.1 Simple Hypothesis Tests 138

6.5.2 Cox Proportional Hazards 139

6.5.3 Proportionality of Hazards 140

6.5.4 Extended CPH 142

6.5.5 Further Extensions 143

6.5.6 Parametric Models 143

6.6 Cause-specific Survival Analysis 144

6.6.1 The Case for Cause-specific Mortality Data 144

6.6.2 Cause-specific Hazards and Mortality Rates 145

6.6.3 Competing Risks Analysis 146

6.6.4 Additive Versus Compensatory Mortality 147

6.7 Software Tools 149

6.8 Online Exercises 149

6.9 Future Directions 149

References 151

7 Mark-Recapture Models for Estimation of Demographic Parameters 157
Brett K. Sandercock

7.1 Introduction 157

7.2 Live Encounter Data 158

7.3 Encounter Histories and Model Selection 159

7.4 Return Rates 163

7.5 Cormack–Jolly–Seber Models 164

7.6 The Challenge of Emigration 164

7.7 Extending the CJS Model 167

7.8 Time-since-marking and Transient Models 167

7.9 Temporal Symmetry Models 168

7.10 Jolly–Seber Model 169

7.11 Multilevel Models 169

7.12 Spatially Explicit Models 170

7.13 Robust Design Models 170

7.14 Mark-resight Models 171

7.15 Young Survival Model 172

7.16 Multistate Models 173

7.17 Multistate Models with Unobservable States 175

7.18 Multievent Models with Uncertain States 176

7.19 Joint Models 177

7.20 Integrated Population Models 178

7.21 Frequentist vs. Bayesian Methods 178

7.22 Software Tools 179

7.23 Online Exercises 180

7.24 Future Directions 180

References 180

Part III Population Models 191

8 Projecting Populations 193
Stéphane Legendre

8.1 Introduction 193

8.2 The Life Cycle Graph 194

8.2.1 Description 194

8.2.2 Construction 194

8.3 Matrix Models 198

8.3.1 The Projection Equation 198

8.3.2 Demographic Descriptors 200

8.3.3 Sensitivities 200

8.4 Accounting for the Environment 202

8.5 Density Dependence 203

8.5.1 Density-dependent Scalar Models 203

8.5.2 Density-dependent Matrix Models 203

8.5.3 Parameterizing Density Dependence 204

8.5.4 Density-dependent Sensitivities 204

8.6 Environmental Stochasticity 204

8.6.1 Models of the Environment 204

8.6.2 Stochastic Dynamics 205

8.6.3 Parameterizing Environmental Stochasticity 208

8.7 Spatial Structure 208

8.8 Demographic Stochasticity 209

8.8.1 Branching Processes 209

8.8.2 Two-sex Models 210

8.9 Demographic Heterogeneity 210

8.9.1 Integral Projection Models 211

8.10 Software Tools 212

8.11 Online Exercises 212

8.12 Future Directions 212

References 212

9 Combining Counts of Unmarked Individuals and Demographic Data Using Integrated Population Models 215
Michael Schaub

9.1 Introduction 215

9.2 Construction of Integrated Population Models 216

9.2.1 Development of a Population Model 216

9.2.2 Construction of the Likelihood for Different Datasets 218

9.2.3 The Joint Likelihood 220

9.2.4 Fitting an Integrated Population Model 221

9.3 Model Extensions 223

9.3.1 Environmental Stochasticity 223

9.3.2 Direct Density Dependence 224

9.3.3 Open Population Models and Other Extensions 226

9.3.4 Alternative Observation Models 226

9.4 Inference About Population Dynamics 227

9.4.1 Retrospective Population Analyses 227

9.4.2 Population Viability Analyses 227

9.5 Missing Data 229

9.6 Goodness-of-fit and Model Selection 230

9.7 Software Tools 230

9.8 Online Exercises 231

9.9 Future Directions 231

References 232

10 Individual and Agent-based Models in Population Ecology and Conservation Biology 237
Eloy Revilla

10.1 Individual and Agent-based Models 237

10.1.1 What an IBM is and What it is Not 238

10.1.2 When to Use an Individual-based Model 238

10.1.3 Criticisms on the Use of IBMs: Advantages or Disadvantages 239

10.2 Building the Core Model 239

10.2.1 Design Phase: The Question and the Conceptual Model 239

10.2.2 Implementation of the Core Model 240

10.2.3 Individuals and Their Traits 240

10.2.4 Functional Relationships 244

10.2.5 The Environment and Its Relevant Properties 244

10.2.6 Time and Space: Domains, Resolutions, Boundary Conditions, and Scheduling 244

10.2.7 Single Model Run, Data Input, Model Output 246

10.3 Protocols for Model Documentation 247

10.3.1 The Overview, Design Concepts, and Details Protocol 249

10.4 Model Analysis and Inference 249

10.4.1 Model Debugging and Checking the Consistency of Model Behavior 249

10.4.2 Model Structural Uncertainty and Sensitivity Analyses 252

10.4.3 Model Selection, Validation, and Calibration 254

10.4.4 Answering your Questions 256

10.5 Software Tools 257

10.6 Online Exercises 257

10.7 Future Directions 257

References 258

Part IV Population Genetics and Spatial Ecology 261

11 Genetic Insights into Population Ecology 263
Jeffrey R. Row and Stephen C. Lougheed

11.1 Introduction 263

11.2 Types of Genetic Markers 264

11.2.1 Mitochondrial DNA 264

11.2.2 Nuclear Introns 265

11.2.3 Microsatellites 265

11.2.4 Single Nucleotide Polymorphisms 265

11.2.5 Next-generation Sequencing 265

11.3 Quantifying Population Structure with Individual-based Analyses 266

11.3.1 Bayesian Clustering 267

11.3.2 Multivariate Analysis of Genetic Data Through Ordinations 269

11.3.3 Spatial Autocorrelation Analysis 271

11.3.4 Population-level Considerations 273

11.4 Estimating Population Size and Trends 273

11.4.1 Estimating Census Population Size 277

11.4.2 Estimating Contemporary Effective Population Size with One Sample Methods 277

11.4.3 Estimating Contemporary Effective Population Size with Temporal Sampling 279

11.4.4 Diagnosing Recent Population Bottlenecks 280

11.5 Estimating Dispersal and Gene Flow 281

11.5.1 Estimating Dispersal and Recent Gene Flow 282

11.5.2 Estimating Sustained Levels of Gene Flow 282

11.5.3 Network Analysis of Genetic Connectivity 283

11.6 Software Tools 284

11.6.1 Individual-based Analysis 284

11.6.2 Population-based Population Size 285

11.6.3 Dispersal and Gene Flow 286

11.7 Online Exercises 286

11.8 Future Directions 286

Glossary 287

References 289

12 Spatial Structure in Population Data 299
Marie-Josée Fortin

12.1 Introduction 299

12.2 Data Acquisition and Spatial Scales 302

12.3 Point Data Analysis 302

12.4 Abundance Data Analysis 304

12.5 Spatial Interpolation 306

12.6 Spatial Density Mapping 308

12.7 Multiple Scale Analysis 308

12.8 Spatial Regression 311

12.9 Software Tools 312

12.10 Online Exercises 312

12.11 Future Directions 312

Glossary 312

References 313

13 Animal Home Ranges: Concepts, Uses, and Estimation 315
Jon S. Horne, John Fieberg, Luca Börger, Janet L. Rachlow, Justin M. Calabrese and Chris H. Fleming

13.1 What is a Home Range? 315

13.1.1 Quantifying Animal Home Ranges as a Probability Density Function 316

13.1.2 Why Estimate Animal Home Ranges? 318

13.2 Estimating Home Ranges: Preliminary Considerations 319

13.3 Estimating Home Ranges: The Occurrence Distribution 321

13.3.1 Minimum Convex Polygon 321

13.3.2 Kernel Smoothing 322

13.3.3 Models Based on Animal Movements 323

13.3.4 Estimation from a One-dimensional Path 324

13.4 Estimating Home Ranges: The Range Distribution 324

13.4.1 Bivariate Normal Models 324

13.4.2 The Synoptic Model 324

13.4.3 Mechanistic Models 325

13.4.4 Kernel Smoothing 326

13.5 Software Tools 326

13.6 Online Exercises 327

13.7 Future Directions 327

13.7.1 Choosing a Home Range Model 327

13.7.2 The Future of Home Range Modeling 327

References 328

14 Analysis of Resource Selection by Animals 333
Joshua J. Millspaugh, Christopher T. Rota, Robert A. Gitzen, Robert A. Montgomery, Thomas W. Bonnot, Jerrold L. Belant, Christopher R. Ayers, Dylan C. Kesler, David A. Eads and Catherine M. Bodinof Jachowski

14.1 Introduction 333

14.2 Definitions 335

14.2.1 Terminology and Currencies of Use and Availability 335

14.2.2 Use-availability, Paired Use-availability, Use and Non-use (Case-control), and Use-only Designs 336

14.2.3 Differences Between RSF, RSPF, and RUF 336

14.3 Considerations in Studies of Resource Selection 338

14.3.1 Two Important Sampling Considerations: Selecting Sample Units and Time of Day 338

14.3.2 Estimating the Number of Animals and Locations Needed 338

14.3.3 Location Error and Fix Rate Bias Resource Selection Studies 339

14.3.4 Consideration of Animal Behavior in Resource Selection Studies 339

14.3.5 Biological Seasons in Resource Selection Studies 340

14.3.6 Scaling in Resource Selection Studies 340

14.3.7 Linking Resource Selection to Fitness 341

14.4 Methods of Analysis and Examples 342

14.4.1 Compositional Analysis 342

14.4.2 Logistic Regression 343

14.4.3 Sampling Designs for Logistic Regression Modeling 344

14.4.3.1 Random Sampling of Units within the Study Area 344

14.4.3.2 Random Sampling of Used and Unused Units 344

14.4.3.3 Random Sample of Used and Available Sampling Units 345

14.4.4 Discrete Choice Models 346

14.4.5 Poisson Regression 347

14.4.6 Resource Utilization Functions 348

14.4.7 Ecological Niche Factor Analysis 348

14.4.8 Mixed Models 349

14.5 Software Tools 349

14.6 Online Exercises 350

14.7 Future Directions 350

References 351

15 Species Distribution Modeling 359
Daniel H. Thornton and Michael J.L. Peers

15.1 Introduction 359

15.1.1 Relationship of Distribution to Other Population Parameters 362

15.1.2 Species Distribution Models and the Niche Concept 363

15.2 Building a Species Distribution Model 366

15.2.1 Species Data 366

15.2.2 Environmental Data 368

15.2.3 Model Fitting 368

15.2.4 Interpretation of Model Output 371

15.2.5 Model Accuracy 372

15.3 Common Problems when Fitting Species Distribution Models 374

15.3.1 Overfitting 374

15.3.2 Sample Selection Bias 375

15.3.3 Background Selection 376

15.3.4 Extrapolation 377

15.3.5 Violation of Assumptions 378

15.4 Recent Advances 378

15.4.1 Incorporating Dispersal 378

15.4.2 Incorporating Population Dynamics 379

15.4.3 Incorporating Biotic Interactions 379

15.5 Software Tools 381

15.5.1 Fitting and Evaluation of Models 381

15.5.2 Incorporating Dispersal or Population Dynamics 381

15.6 Online Exercises 381

15.7 Future Directions 381

References 383

Part V Software Tools 389

16 The R Software for Data Analysis and Modeling 391
Clément Calenge 391

16.1 An Introduction to R 391

16.1.1 The Nature of the R Language 391

16.1.2 Qualities and Limits 392

16.1.3 R for Ecologists 392

16.1.4 R is an Environment 393

16.2 Basics of R 393

16.2.1 Several Basic Modes of Data 394

16.2.2 Several Basic Types of Objects 395

16.2.3 Finding Help and Installing New Packages 398

16.2.4 How to Write a Function 400

16.2.5 The for loop 401

16.2.6 The Concept of Attributes and S3 Data Classes 402

16.2.7 Two Important Classes: The Class factor and the Class data.frame 404

16.2.8 Drawing Graphics 406

16.2.9 S4 Classes: Why It is Useful to Understand Them 407

16.3 Online Exercises 410

16.4 Final Directions 410

References 411

Index 413

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Dennis L. Murray Trent University, Canada.

Brett K. Sandercock Norwegian Institute for Nature Research.
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