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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.
Contributors xvii
Preface xxi
About the Companion Website xxiii
Part I Tools for Population Biology 1
1 How to Ask Meaningful Ecological Questions 3Charles 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 17Dennis 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 49Brett 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 79Steven 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 103J. 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 123Dennis 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 157Brett 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 193Stéphane 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 215Michael 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 237Eloy 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 263Jeffrey 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 299Marie-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 315Jon 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 333Joshua 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 359Daniel 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 391Clément 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
Our motivation as editors for assembling a book on current methods in population ecology arose from our ongoing interactions with graduate students and professionals in the fields of ecology, conservation biology, and wildlife management. Over the past several decades, research in population ecology has developed at a rapid pace, from a largely descriptive field dominated by observation and description, to a mature discipline that emphasizes innovative and robust analyses of ecological patterns and processes. Many recent advances have been driven by persistent knowledge gaps, not the least of which are urgent questions about the key drivers of population dynamics and their ecological relevance in the face of ongoing global environmental change. Increasingly, population ecologists have recognized that key questions in ecology and evolutionary biology must be investigated using the data and analytical methods that allow researchers to make robust inferences about causality. At the same time, advances in satellite or GPS-based telemetry, noninvasive genetic sampling, automated field photography, and other new technologies have revolutionized our ability to collect new data on the occurrence, abundance, and distributions of rare or elusive organisms under natural conditions. Emerging technologies have opened up new possibilities for data collection, but many have also required development of innovative approaches for data analysis. In some cases, new quantitative approaches have been adopted directly from other fields of research but some types of data have required the development of entirely novel analytical tools. Improvements in the capacity of personal and cloud-based computing, availability of Program R and other freeware statistical packages, and online resources for learning and troubleshooting new statistical procedures have led to tremendous improvements in the potential capacity for data analysis in population ecology. Developments in population ecology have paralleled improvements in data quality and analysis in genomics, data sciences, and other scientific disciplines. Still, population ecology has been transformed in recent decades so that our current ability to answer longstanding and elusive questions greatly surpasses what could have been imaginable even a short time ago.
Development of new tools for ecological analysis has been exciting to witness but presents a challenge for both new and seasoned ecologists who would like to stay current with available technologies and analytical approaches. During our own formative years as graduate students a few decades ago, the prevailing quantitative methods for data analysis consisted mainly of statistical tests in a frequentist framework that were originally designed for analysis of data from controlled experiments and balanced study designs. Basic tests like analysis of variance and regression were familiar because of their extensive coverage in undergraduate courses, or else they were readily adopted following focused reading or trial and error. Even specialized techniques like population estimation or habitat selection analysis were mostly accessible using off-the-shelf analytical approaches. Accordingly, at the time most ecologists were not unduly challenged to conduct data analysis that met contemporary standards. However, ecological systems are rarely governed by factors that conform to controlled conditions, and therefore ecological research rarely yields field data that truly fits standard assumptions of independence, normality, and lack of bias. Moreover, the sheer volume, structure, and complexity of ecological data collected in many field studies preclude standard statistical approaches. New quantitative methods in ecology often deviate substantially from the standard approaches that form the basis of undergraduate training in statistics, and ecologists may be left scrambling to correctly identify and implement an appropriate analytical technique. The correct application of contemporary methods for data analysis is increasingly a prerequisite for publication and for implementation of effective management policy in ecology.
Our edited volume is primarily aimed at graduate students and early career professionals who may be embarking on their first attempt to analyze ecological data using contemporary methods. We aimed to assemble a series of chapters that review the state of knowledge in the core areas of population ecology, and our selection of topics and authors was purposeful to cover the main areas by experts in the field. Our final submissions included 16 manuscripts from 39 contributors working in 8 different countries. Our aim was for every chapter to serve as a stand-alone assessment for different topics in population ecology, including pros and cons of related quantitative methods, basic assumptions and limitations when deriving inference from a given approach, and some of the potential pitfalls in the application of available techniques. Perhaps unavoidably, the chapters include some bias toward methods that are especially relevant for use with wildlife species and for using data that have been collected through newer monitoring technologies. Nevertheless, many of the general concepts and approaches covered in our contributed chapters have broad relevance to a diversity of research questions and study systems.
The 16 chapters of our book are organized into five sections. The first section begins with two chapters that provide a framework for asking relevant questions in ecology, including how research studies can be best designed to derive robust inference. The second section assembles five chapters covering a variety of analytical approaches in population demography and population time series analysis; these topics normally form the requisite basis of most investigations into population status and trend. The analytical approaches differ in whether they are based on closed or open population models, use encounter histories from marked or unmarked individuals, or control for situations where detection may be perfect or imperfect. The third section highlights population-level analysis, including newer approaches that use integrative and individual-based models to understand population drivers and forecast their potential change. The fourth section includes five chapters that address genetic and spatial approaches in population analysis, covering topics like home range and resource selection analysis and species distribution modeling.
This volume is intended to provide an overview for researchers using a variety of analytical tools and platforms. Importantly, the R statistical software platform has been transformative to data analysis in ecology, and to that end the final chapter provides an ecologically focused overview of basic nomenclature and data management using R software. Chapters are supported by a compendium of online exercises in R that provide worked through examples that reinforce topics covered in individual chapters. The intent is for exercises to provide readers with both the necessary background to implement more common analytical approaches, as well as sample code in R that can be adapted to start their own data analysis. All online exercises can be accessed from the publisher's website (www.wiley.com/go/MurrayPopulationEcology).
Our edited book would not have seen the light of day without the significant efforts of a number of people to whom we are indebted. We thank Guillaume Chapron, who began this journey with us and helped start the editorial process of selecting topics for the different chapters and inviting contributors. We thank all of the contributors who contributed their work to this volume for sharing with us a vision for the book, mostly adhering to our editorial requests, peer-reviewing each other's chapters, and for working hard to improve the quality of their chapters. Working on an edited volume can provide new appreciation of the old adage that a caravan is only as fast as the slowest camel. We thank the contributors for their sustained efforts and commitment, but especially for their patience in graciously accepting delays that arose while two slow camels worked to keep the editorial process on track. Special thanks to all of the external reviewers who provided anonymous reviews of chapters, including the many graduate students who served as test groups for the chapters and the online exercises. The students provided many useful comments that helped calibrate the volume for its intended audience. We also highlight the valuable contribution by Pat Heney, who standardized and tested all the online exercises prior to their release. Likewise, a debt of gratitude is owed to Sam Sonnega for help with indexing the complete volume. The staff at Wiley-Blackwell, especially Anupama Sreekanth, Kavitha Chandrasekar and Emma Cole, provided valuable assistance in support of our vision for the book. Last, we thank H. Dean Cluff for being an initial source of inspiration and for an exploding can of sardines.
Our hope is that our edited book will contribute to a growing body of literature that guides researchers in the rigorous analysis of ecological data. The current state of our planet, and of the species and ecosystems that have captivated the fascination of population ecologists for decades, are under grave peril. The quantitative methods described in this volume provide a valuable set of tools for addressing some of the current and emerging environmental problems that will command humanity's attention for the foreseeable future. Our book will be a success if it provides a new generation of early career researchers with the necessary tools to tackle some of these problems.
In recognition of the daunting environmental...
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