Population ecology in practice:
Gespeichert in:
| Format: | Buch |
|---|---|
| Sprache: | English |
| Veröffentlicht: |
Hoboken, NJ
Wiley Blackwell
2020
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| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | Includes bibliographical references and index |
| Beschreibung: | xxiii, 424 Seiten Illustrationen, Diagramme |
| ISBN: | 9780470674147 |
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MARC
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| 245 | 1 | 0 | |a Population ecology in practice |c edited by Dennis L. Murray (Trent University, Peterborough, Ontario, Canada), Brett K. Sandercock (Norwegian Institute of Nature Research, Trondheim, Trøndelag, Norway) |
| 264 | 1 | |a Hoboken, NJ |b Wiley Blackwell |c 2020 | |
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| 776 | 0 | 8 | |i Erscheint auch als |n Online-Ausgabe |t Population ecology in practice |d Hoboken, NJ, USA : John Wiley & Sons, NJ, USA : John Wiley & Sons, Ltd, [2019] |z 978-1-119-57462-0 |
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Datensatz im Suchindex
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| adam_text | Contents Contributors xvii Preface xxi About the Companion Website Part I xxiii Tools for Population Biology 1 1 How to Ask Meaningful Ecological Questions Charles J. Krebs 1.1 1.2 1.2.1 1.3 1.4 What Problems Do Population Ecologists Try to Solve? 3 What Approaches Do Population Ecologists Use? 6 Generating and Testing Hypotheses in Population Ecology 10 Generality in Population Ecology 11 Final Thoughts 12 References 13 2 From Research Hypothesis to Model Selection: A Strategy for Robust Inference in Population Ecology Dennis L. Murray, Guillaume Bastille-Rousseau, Lynne E. Beaty, Megan L Hornseth, Jeffrey R. Row and Daniel H. Thornton Introduction 17 Inductive Methods 18 Hypothetico-deductive Methods 19 Multimodel Inference 20 Bayesian Methods 22 2.1 2.1.1 2.1.2 2.1.3 2.1.4 2.2 2.3 2.3.1 2.4 2.4.1 2.4.2 2.4.3 2.5 2.5.1 2.5.2 2.5.3 2.5.4 2.6 2.6.1 2.6.2 2.7 2.8 3 What Constitutes a Good Research Hypothesis? 22 Multiple Hypotheses and Information Theoretics 24 How Many Are Too Many Hypotheses? 25 From Research Hypothesis to Statistical Model 26 Functional Relationships Between Variables 26 Interactions Between Predictor Variables 26 Number and Structure of Predictor Variables 27 Exploratory Analysis and Helpftil Remedies 28 Exploratory Analysis and Diagnostic Tests 28 Missing Data 28 Inter-relationships Between Predictors 30 Interpretability of Model Output 31 Model Ranking and Evaluation 32 Model Selection 32 Multimodel Inference 36 Model Validation 39 Software Tools 41 17
viii ļ Contents 2.9 2.10 Online Exercises 41 Future Directions 41 References 42 Part I) 3 3.1 3.1.1 3.1.2 3.1.2.1 3.1.2.2 3.1.3 3.2 3.2.1 3.2.1.1 3.2.1.2 3.2.2 3.2.2.1 3.2.2.2 3.3 3.3.1 3.3.2 3.3.3 3.3.3.1 3.3.3.2 3.3.3.3 3.3.3.4 3.3.3.5 3.4 3.5 3.6 4 4.1 4.1.1 4.1.2 4.2 4.2.1 4.2.2 4.3 4.4 4.4.1 4.4.1.1 4.4.1.2 4.4.2 4.5 4.5.1 4.5.2 Population Demography 47 Estimating Abundance or Occupancy from Unmarked Populations 49 Brett T. McClintock and Len Thomas Introduction 49 Why Collect Data from Unmarked Populations? 49 Relative Indices and Detection Probability 50 Population Abundance 50 Species Occurrence 51 Hierarchy of Sampling Methods for Unmarked Individuals 52 Estimating Abundance (or Density) from Unmarked Individuals 53 Distance Sampling 53 Classical Distance Sampling 54 Model-Based Distance Sampling 57 Replicated Counts of Unmarked Individuals 61 Spatially Replicated Counts 61 Removal Sampling 63 Estimating Species Occurrence under Imperfect Detection 64 Single-Season Occupancy Models 65 Multiple-Season Occupancy Models 66 Other Developments in Occupancy Estimation 68 Site Heterogeneity in Detection Probability 68 Occupancy and Abundance Relationships 68 Multistate and Multiscale Occupancy Models 68 Metapopulation Occupancy Models 69 False Positive Occupancy Models 70 Software Tools 70 Online Exercises 71 Future Directions 71 References 73 79 Steven Delean, Thomas A.A. Prowse, Joshua V. Ross and Jonathan Tuke Introduction 79 Principal Approaches to Time Series Analysis in Ecology 80 Challenges to Time Series Analysis in Ecology 82 Time Series (ARMA) Modeling 83 Time
Series Models 83 Autoregressive Moving Average Models 83 Regression Models with Correlated Errors 87 Phenomenological Models of Population Dynamics 88 Deterministic Models 89 Exponential Growth 89 Classic ODE Single-Species Population Models that Incorporate Density Dependence Discrete-Time Population Growth Models with Stochasticity 92 State-space Modeling 93 Gompertz State-space Population Model 94 Nonlinear and Non-Gaussian State-space Population Models 96 Analyzing Time Series Data; Single-Species Abundance Modeling 90
Contents 4.6 4.7 4.8 5 5.1 5.2 5.3 5.4 5.4.1 5.4.2 5.43 5.5 5.5.1 5.5.2 5.5.2.1 5.5.2.2 5.5.3 5.5.4 5.5.4.1 5.5.4.2 5.6 5.6.1 5.6.2 5.7 5.7.1 5.7.2 5.7.3 5.8 5.9 5.10 5.11 6 6.1 6.1.1 6.1.2 6.1.3 6.2 6.3 6.3.1 6.3.2 6.3.3 6.3.4 6.3.5 6.4 6.4.1 6.4.2 6.4.3 Software Tools 96 Online Exercises 97 Future Directions 97 References 98 Estimating Abundance from Capture-Recapture Data 103 J. Andrew Royle and Sarah J. Converse Introduction 103 Genesis of Capture-Recapture Data 104 The Basic Closed Population Models: M0, Mt, Mb 104 Inference Strategies 105 Likelihood Inference 105 Bayesian Analysis 107 Other Inference Strategies 108 Models with Individual Heterogeneity in Detection 108 Model Mh 108 Individual Covariate Models 109 The Full Likelihood 109 Horvitz-Thompson Estimation 110 Distance Sampling 110 Spatial Capture-Recapture Models 110 The State-space 112 Inference in SCR Models 112 Stratified Populations or Multisession Models 112 Nonparametric Estimation 112 Hierarchical Capture-Recapture Models 113 Model Selection and Model Fit 113 Model Selection 113 Goodness-of֊Fit 114 What to Do When Your Model Does Not Fit 115 Open Population Models 115 Software Tools 116 Online Exercises 117 Future Directions 118 References 119 Estimating Survival and Cause-specific Mortality from Continuous Time Observations 123 Dennis L Murray and Guillaume Bastille-Rousseau Introduction 123 Assumption of No Handling, Marking or Monitoring Effects 125 Cause of Death Assessment 125 Historical Origins of Survival Estimation 126 Survival and Hazard Functions in Theory 127 Developing Continuous Time
Survival Datasets 130 Dataset Structure 131 Right-censoring 133 Delayed Entry and Other Time Considerations 133 Sampling Heterogeneity 134 Time-dependent Covariates 135 Survival and Hazard Functions in Practice 135 Mayfield and Heisey-Fuller Survival Estimation 135 Kaplan-Meier Estimator 136 Nelson-Aalen Estimator 138 ix
x Contents 6.5 6.5.1 6.5.2 6.5.3 6.5.4 6.5.5 6.5.6 6.6 6.6.1 6.6.2 6.6.3 6.6.4 6.7 6.8 6.9 7 7.1 7.2 7.3 7.4 7.5 7.6 7.7 7.8 7.9 7.10 7.11 7.12 7.13 7.14 7.15 7.16 7.17 7.18 7.19 7.20 7.21 7.22 7.23 7.24 Statistical Analysis of Survival 138 Simple Hypothesis Tests 138 Cox Proportional Hazards 139 Proportionality of Hazards 140 Extended CPH 142 Further Extensions 143 Parametric Models 143 Cause-specific Survival Analysis 144 The Case for Cause-specific Mortality Data 144 Cause-specific Hazards and Mortality Rates 145 Competing Risks Analysis 146 Additive Versus Compensatory Mortality 147 Software Tools 149 Online Exercises 149 Future Directions 149 References 151 Mark-Recapture Models for Estimation of Demographic Parameters Brett K. Sandercock Introduction 157 Live Encounter Data 158 Encounter Histories and Model Selection 159 Return Rates 163 Cormack-Jolly-Seber Models 164 The Challenge of Emigration 164 Extending the CJS Model 167 Time-since-marking andTransient Models 167 Temporal Symmetry Models 168 Jolly-Seber Model 169 Multilevel Models 169 Spatially Explicit Models 170 Robust Design Models 170 Mark-resight Models 171 Young Survival Model 172 Multistate Models 173 Multistate Models with Unobservable States 175 Multievent Models with Uncertain States 176 Joint Models 177 Integrated Population Models 178 Frequentisi vs. Bayesian Methods 178 Software Tools 179 Online Exercises 180 Future Directions 180 References 180 Part III Population Models 8 Projecting Populations 8.1 8.2 8.2.1 Stéphane Legendre Introduction 193 The Life Cycle Graph Description 194 193 194 191 157
8.2.2 8.3 8.3.1 8.3.2 8.3.3 8.4 8.5 8.5.1 8.5.2 8.5.3 8.5.4 8.6 8.6.1 8.6.2 8.6.3 8.7 8.8 8.8.1 8.8.2 8.9 8.9.1 8.10 8.11 8.12 Construction 194 Matrix Models 198 The Projection Equation 198 Demographic Descriptors 200 Sensitivities 200 Accounting for the Environment 202 Density Dependence 203 Density-dependent Scalar Models 203 Density-dependent Matrix Models 203 Parameterizing Density Dependence 204 Density-dependent Sensitivities 204 Environmental Stochasticity 204 Models of the Environment 204 Stochastic Dynamics 205 Parameterizing Environmental Stochasticity 208 Spatial Structure 208 Demographic Stochasticity 209 Branching Processes 209 Two-sex Models 210 Demographic Heterogeneity 210 Integral Projection Models 211 Software Tools 212 Online Exercises 212 Future Directions 212 References 212 9 Combining Counts of Unmarked Individuals and Demographic Data Using Integrated Population Models 9.1 9.2 9.2.1 9.2.2 9.2.3 9.2.4 9.3 9.3.1 9.3.2 9.3.3 9.3.4 9.4 9.4.1 9.4.2 9.5 9.6 9.7 9.8 9.9 Michael Schaub Introduction 215 Construction of Integrated Population Models 216 Development of a Population Model 216 Construction of the Likelihood for Different Datasets 218 The Joint Likelihood 220 Fitting an Integrated Population Model 221 Model Extensions 223 Environmental Stochasticity 223 Direct Density Dependence 224 Open Population Models and Other Extensions 226 Alternative Observation Models 226 Inference About Population Dynamics 227 Retrospective Population Analyses 227 Population Viability Analyses 227 Missing Data 229 Goodness-of-fit and Model Selection 230 Software Tools 230
Online Exercises 231 Future Directions 231 References 232 10 individual and Agent-based Models in Population Ecology and Conservation Biology 10.1 10.1.1 Eloy Revilla Individual and Agent-based Models 237 What an IBM Is and What it Is Not 238 237 215
xii І Contents 10.1.2 10.1.3 10.2 10.2.1 10.2.2 10.2.3 10.2.4 10.2.5 10.2.6 10.2.7 10.3 10.3.1 10.4 10.4.1 10.4.2 10.4.3 10.4.4 10.5 10.6 10.7 When to Use an Individual-based Model 238 Criticisms on the Use of IBMs: Advantages or Disadvantages 239 Building the Core Model 239 Design Phase: The Question and the Conceptual Model 239 Implementation of the Core Model 240 Individuals and Their Traits 240 Functional Relationships 244 The Environment and Its Relevant Properties 244 Time and Space: Domains, Resolutions, Boundary Conditions, and Scheduling 244 Single Model Run, Data Input, Model Output 246 Protocols for Model Documentation 247 The Overview, Design Concepts, and Details Protocol 249 Model Analysis and Inference 249 Model Debugging and Checking the Consistency of Model Behavior 249 Model Structural Uncertainty and Sensitivity Analyses 252 Model Selection, Validation, and Calibration 254 Answering your Questions 256 Software Tools 257 Online Exercises 257 Future Directions 257 References 258 Part IV 11 11.1 11.2 11.2.1 11.2.2 11.2.3 11.2.4 11.2.5 11.3 11.3.1 11.3.2 11.3.3 11.3.4 11.4 11.4.1 11.4.2 11.4.3 11.4.4 11.5 11.5.1 11.5.2 11.5.3 11.6 11.6.1 11.6.2 Population Genetics and Spatial Ecology 261 Genetic Insights into Population Ecology 263 Jeffrey R. Row and Stephen C. Lougheed Introduction 263 Types of Genetic Markers 264 Mitochondrial DNA 264 Nuclear Introns 265 Microsatellites 265 Single Nucleotide Polymorphisms 265 Next-generation Sequencing 265 Quantifying Population Structure with Individual-based Analyses 266 Bayesian Clustering 267 Multivariate Analysis of
Genetic Data Through Ordinations 269 Spatial Autocorrelation Analysis 271 Population-level Considerations 273 Estimating Population Size and Trends 273 Estimating Census Population Size 277 Estimating Contemporary Effective Population Size with One Sample Methods 277 Estimating Contemporary Effective Population Size with Temporal Sampling 279 Diagnosing Recent Population Bottlenecks 280 Estimating Dispersal and Gene Flow 281 Estimating Dispersal and Recent Gene Flow 282 Estimating Sustained Levels of Gene Flow 282 Network Analysis of Genetic Connectivity 283 Software Tools 284 Individual-based Analysis 284 Population-based Population Size 285
11.6.3 լ 1.7 11.8 12 12.1 12.2 12.3 12.4 12.5 12.6 12.7 12.8 12.9 12.10 12.11 13 13.1 13.1.1 13.1.2 13.2 13.3 13.3.1 13.3.2 13.3.3 13.3.4 13.4 13.4.1 13.4.2 13.4.3 13.4.4 13.5 13.6 13.7 13.7.1 13.7.2 14 14.1 14.2 14.2.1 14.2.2 14.2.3 Dispersal and Gene Flow 286 Online Exercises 286 Future Directions 286 Glossary 287 References 289 Spatial Structure in Population Data Marie-Josée Fortin Introduction 299 Data Acquisition and Spatial Scales Point Data Analysis 302 Abundance Data Analysis 304 Spatial Interpolation 306 Spatial Density Mapping 308 Multiple Scale Analysis 308 Spatial Regression 311 Software Tools 312 Online Exercises 312 Future Directions 312 Glossary 312 References 313 299 302 Animal Home Ranges: Concepts, Uses, and Estimation 315 Jon 5. Horne, John Fieberg, Luca Börger, Janet L Rachlow, Justin M. Calabrese and Chris H. Fleming What Is a Home Range? 315 Quantifying Animal Home Ranges as a Probability Density Function 316 Why Estimate Animal Home Ranges? 318 Estimating Home Ranges: Preliminary Considerations 319 Estimating Home Ranges: The Occurrence Distribution 321 Minimum Convex Polygon 321 Kernel Smoothing 322 Models Based on Animal Movements 323 Estimation from a One-dimensional Path 324 Estimating Home Ranges: The Range Distribution 324 Bivariate Normal Models 324 The Synoptic Model 324 Mechanistic Models 325 Kernel Smoothing 326 Software Tools 326 Online Exercises 327 Future Directions 327 Choosing a Home Range Model 327 The Future of Home Range Modeling 327 References 328 Analysis of Resource Selection by Animals 333 Joshua J. Millspaugh, Christopher T.
Rota, Robert A. Gitzen, Robert A. Montgomery, Thomas W. Bonnot, Jenőid L Belant, Christopher R. Ayers, Dylan C. Keşler, David A. Eads and Catherine M. Bodinof Jaehowski Introduction 333 Definitions 335 Terminology and Currencies of Use and Availability 335 Use-availability, Paired Use-availability, Use and Non-use (Case-control), and Use-only Designs 336 Differences Between RSF, RSPF, and RUF 336
xiv I Contents 14.3 14.3.1 14.3.2 14.3.3 14.3.4 14.3.5 14.3.6 14.3.7 14.4 14.4.1 14.4.2 14.4.3 14.4.3.1 14.4.3.2 14.4.3.3 14.4.4 14.4.5 14.4.6 14.4.7 14.4.8 14.5 14.6 14.7 Considerations in Studies of Resource Selection 338 Two Important Sampling Considerations: Selecting Sample Units and Time of Day 338 Estimating the Number of Animals and Locations Needed 338 Location Error and Fix Rate Bias Resource Selection Studies 339 Consideration of Animal Behavior in Resource Selection Studies 339 Biological Seasons in Resource Selection Studies 340 Scaling in Resource Selection Studies 340 Linking Resource Selection to Fitness 341 Methods of Analysis and Examples 342 Compositional Analysis 342 Logistic Regression 343 Sampling Designs for Logistic Regression Modeling 344 Random Sampling of Units within the Study Area 344 Random Sampling of Used and Unused Units 344 Random Sample of Used and Available Sampling Units 345 Discrete Choice Models 346 Poisson Regression 347 Resource Utilization Functions 348 Ecological Niche Factor Analysis 348 Mixed Models 349 Software Tools 349 Online Exercises 350 Future Directions 350 References 351 15 Species Distribution Modeling 359 Daniel H. Thornton and Michael 11. Peers Introduction 359 Relationship of Distribution to Other Population Parameters 362 Species Distribution Models and the Niche Concept 363 Building a Species DistributionModel 366 Species Data 366 Environmental Data 368 Model Fitting 368 Interpretation of Model Output 371 Model Accuracy 372 Common Problems when Fitting Species Distribution Models 374 Overfitting 374 Sample Selection
Bias 375 Background Selection 376 Extrapolation 377 Violation of Assumptions 378 Recent Advances 378 Incorporating Dispersal 378 Incorporating Population Dynamics 379 Incorporating Biotic Interactions 379 Software Tools 381 Fitting and Evaluation of Models 381 Incorporating Dispersal or Population Dynamics 381 Online Exercises 381 Future Directions 381 References 383 15.1 15.1.1 15.1.2 15.2 15.2.1 15.2.2 15.2.3 15.2.4 15.2.5 15.3 15.3.1 15.3.2 15.3.3 15.3.4 15.3.5 15.4 15.4.1 15.4.2 15.4.3 15.5 15.5.1 15.5.2 15.6 15.7
Contents Part V 16 16.1 16.1.1 16.1.2 16.1.3 16.1.4 16.2 16.2.1 16.2.2 16.2.3 16.2.4 16.2.5 16.2.6 16.2.7 16.2.8 16.2.9 16.3 16.4 Software Tools 389 The R Software for Data Analysis and Modeling 391 Clément Calenge 391 An Introduction to R 391 The Nature of the R Language 391 Qualities and Limits 392 R for Ecologists 392 R is an Environment 393 Basics of R 393 Several Basic Modes of Data 394 Several Basic Types of Objects 395 Finding Help and Installing New Packages 398 How to Write a Function 400 The for loop 401 The Concept of Attributes and S3 Data Classes 402 Two Important Classes: The Class factor and the Class data. frame Drawing Graphics 406 S4 Classes: Why It Is Useful to Understand Them 407 Online Exercises 410 Final Directions 410 References 411 Index 413 404 XV
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Contents Contributors xvii Preface xxi About the Companion Website Part I xxiii Tools for Population Biology 1 1 How to Ask Meaningful Ecological Questions Charles J. Krebs 1.1 1.2 1.2.1 1.3 1.4 What Problems Do Population Ecologists Try to Solve? 3 What Approaches Do Population Ecologists Use? 6 Generating and Testing Hypotheses in Population Ecology 10 Generality in Population Ecology 11 Final Thoughts 12 References 13 2 From Research Hypothesis to Model Selection: A Strategy for Robust Inference in Population Ecology Dennis L. Murray, Guillaume Bastille-Rousseau, Lynne E. Beaty, Megan L Hornseth, Jeffrey R. Row and Daniel H. Thornton Introduction 17 Inductive Methods 18 Hypothetico-deductive Methods 19 Multimodel Inference 20 Bayesian Methods 22 2.1 2.1.1 2.1.2 2.1.3 2.1.4 2.2 2.3 2.3.1 2.4 2.4.1 2.4.2 2.4.3 2.5 2.5.1 2.5.2 2.5.3 2.5.4 2.6 2.6.1 2.6.2 2.7 2.8 3 What Constitutes a Good Research Hypothesis? 22 Multiple Hypotheses and Information Theoretics 24 How Many Are Too Many Hypotheses? 25 From Research Hypothesis to Statistical Model 26 Functional Relationships Between Variables 26 Interactions Between Predictor Variables 26 Number and Structure of Predictor Variables 27 Exploratory Analysis and Helpftil Remedies 28 Exploratory Analysis and Diagnostic Tests 28 Missing Data 28 Inter-relationships Between Predictors 30 Interpretability of Model Output 31 Model Ranking and Evaluation 32 Model Selection 32 Multimodel Inference 36 Model Validation 39 Software Tools 41 17
viii ļ Contents 2.9 2.10 Online Exercises 41 Future Directions 41 References 42 Part I) 3 3.1 3.1.1 3.1.2 3.1.2.1 3.1.2.2 3.1.3 3.2 3.2.1 3.2.1.1 3.2.1.2 3.2.2 3.2.2.1 3.2.2.2 3.3 3.3.1 3.3.2 3.3.3 3.3.3.1 3.3.3.2 3.3.3.3 3.3.3.4 3.3.3.5 3.4 3.5 3.6 4 4.1 4.1.1 4.1.2 4.2 4.2.1 4.2.2 4.3 4.4 4.4.1 4.4.1.1 4.4.1.2 4.4.2 4.5 4.5.1 4.5.2 Population Demography 47 Estimating Abundance or Occupancy from Unmarked Populations 49 Brett T. McClintock and Len Thomas Introduction 49 Why Collect Data from Unmarked Populations? 49 Relative Indices and Detection Probability 50 Population Abundance 50 Species Occurrence 51 Hierarchy of Sampling Methods for Unmarked Individuals 52 Estimating Abundance (or Density) from Unmarked Individuals 53 Distance Sampling 53 Classical Distance Sampling 54 Model-Based Distance Sampling 57 Replicated Counts of Unmarked Individuals 61 Spatially Replicated Counts 61 Removal Sampling 63 Estimating Species Occurrence under Imperfect Detection 64 Single-Season Occupancy Models 65 Multiple-Season Occupancy Models 66 Other Developments in Occupancy Estimation 68 Site Heterogeneity in Detection Probability 68 Occupancy and Abundance Relationships 68 Multistate and Multiscale Occupancy Models 68 Metapopulation Occupancy Models 69 False Positive Occupancy Models 70 Software Tools 70 Online Exercises 71 Future Directions 71 References 73 79 Steven Delean, Thomas A.A. Prowse, Joshua V. Ross and Jonathan Tuke Introduction 79 Principal Approaches to Time Series Analysis in Ecology 80 Challenges to Time Series Analysis in Ecology 82 Time Series (ARMA) Modeling 83 Time
Series Models 83 Autoregressive Moving Average Models 83 Regression Models with Correlated Errors 87 Phenomenological Models of Population Dynamics 88 Deterministic Models 89 Exponential Growth 89 Classic ODE Single-Species Population Models that Incorporate Density Dependence Discrete-Time Population Growth Models with Stochasticity 92 State-space Modeling 93 Gompertz State-space Population Model 94 Nonlinear and Non-Gaussian State-space Population Models 96 Analyzing Time Series Data; Single-Species Abundance Modeling 90
Contents 4.6 4.7 4.8 5 5.1 5.2 5.3 5.4 5.4.1 5.4.2 5.43 5.5 5.5.1 5.5.2 5.5.2.1 5.5.2.2 5.5.3 5.5.4 5.5.4.1 5.5.4.2 5.6 5.6.1 5.6.2 5.7 5.7.1 5.7.2 5.7.3 5.8 5.9 5.10 5.11 6 6.1 6.1.1 6.1.2 6.1.3 6.2 6.3 6.3.1 6.3.2 6.3.3 6.3.4 6.3.5 6.4 6.4.1 6.4.2 6.4.3 Software Tools 96 Online Exercises 97 Future Directions 97 References 98 Estimating Abundance from Capture-Recapture Data 103 J. Andrew Royle and Sarah J. Converse Introduction 103 Genesis of Capture-Recapture Data 104 The Basic Closed Population Models: M0, Mt, Mb 104 Inference Strategies 105 Likelihood Inference 105 Bayesian Analysis 107 Other Inference Strategies 108 Models with Individual Heterogeneity in Detection 108 Model Mh 108 Individual Covariate Models 109 The Full Likelihood 109 Horvitz-Thompson Estimation 110 Distance Sampling 110 Spatial Capture-Recapture Models 110 The State-space 112 Inference in SCR Models 112 Stratified Populations or Multisession Models 112 Nonparametric Estimation 112 Hierarchical Capture-Recapture Models 113 Model Selection and Model Fit 113 Model Selection 113 Goodness-of֊Fit 114 What to Do When Your Model Does Not Fit 115 Open Population Models 115 Software Tools 116 Online Exercises 117 Future Directions 118 References 119 Estimating Survival and Cause-specific Mortality from Continuous Time Observations 123 Dennis L Murray and Guillaume Bastille-Rousseau Introduction 123 Assumption of No Handling, Marking or Monitoring Effects 125 Cause of Death Assessment 125 Historical Origins of Survival Estimation 126 Survival and Hazard Functions in Theory 127 Developing Continuous Time
Survival Datasets 130 Dataset Structure 131 Right-censoring 133 Delayed Entry and Other Time Considerations 133 Sampling Heterogeneity 134 Time-dependent Covariates 135 Survival and Hazard Functions in Practice 135 Mayfield and Heisey-Fuller Survival Estimation 135 Kaplan-Meier Estimator 136 Nelson-Aalen Estimator 138 ix
x Contents 6.5 6.5.1 6.5.2 6.5.3 6.5.4 6.5.5 6.5.6 6.6 6.6.1 6.6.2 6.6.3 6.6.4 6.7 6.8 6.9 7 7.1 7.2 7.3 7.4 7.5 7.6 7.7 7.8 7.9 7.10 7.11 7.12 7.13 7.14 7.15 7.16 7.17 7.18 7.19 7.20 7.21 7.22 7.23 7.24 Statistical Analysis of Survival 138 Simple Hypothesis Tests 138 Cox Proportional Hazards 139 Proportionality of Hazards 140 Extended CPH 142 Further Extensions 143 Parametric Models 143 Cause-specific Survival Analysis 144 The Case for Cause-specific Mortality Data 144 Cause-specific Hazards and Mortality Rates 145 Competing Risks Analysis 146 Additive Versus Compensatory Mortality 147 Software Tools 149 Online Exercises 149 Future Directions 149 References 151 Mark-Recapture Models for Estimation of Demographic Parameters Brett K. Sandercock Introduction 157 Live Encounter Data 158 Encounter Histories and Model Selection 159 Return Rates 163 Cormack-Jolly-Seber Models 164 The Challenge of Emigration 164 Extending the CJS Model 167 Time-since-marking andTransient Models 167 Temporal Symmetry Models 168 Jolly-Seber Model 169 Multilevel Models 169 Spatially Explicit Models 170 Robust Design Models 170 Mark-resight Models 171 Young Survival Model 172 Multistate Models 173 Multistate Models with Unobservable States 175 Multievent Models with Uncertain States 176 Joint Models 177 Integrated Population Models 178 Frequentisi vs. Bayesian Methods 178 Software Tools 179 Online Exercises 180 Future Directions 180 References 180 Part III Population Models 8 Projecting Populations 8.1 8.2 8.2.1 Stéphane Legendre Introduction 193 The Life Cycle Graph Description 194 193 194 191 157
8.2.2 8.3 8.3.1 8.3.2 8.3.3 8.4 8.5 8.5.1 8.5.2 8.5.3 8.5.4 8.6 8.6.1 8.6.2 8.6.3 8.7 8.8 8.8.1 8.8.2 8.9 8.9.1 8.10 8.11 8.12 Construction 194 Matrix Models 198 The Projection Equation 198 Demographic Descriptors 200 Sensitivities 200 Accounting for the Environment 202 Density Dependence 203 Density-dependent Scalar Models 203 Density-dependent Matrix Models 203 Parameterizing Density Dependence 204 Density-dependent Sensitivities 204 Environmental Stochasticity 204 Models of the Environment 204 Stochastic Dynamics 205 Parameterizing Environmental Stochasticity 208 Spatial Structure 208 Demographic Stochasticity 209 Branching Processes 209 Two-sex Models 210 Demographic Heterogeneity 210 Integral Projection Models 211 Software Tools 212 Online Exercises 212 Future Directions 212 References 212 9 Combining Counts of Unmarked Individuals and Demographic Data Using Integrated Population Models 9.1 9.2 9.2.1 9.2.2 9.2.3 9.2.4 9.3 9.3.1 9.3.2 9.3.3 9.3.4 9.4 9.4.1 9.4.2 9.5 9.6 9.7 9.8 9.9 Michael Schaub Introduction 215 Construction of Integrated Population Models 216 Development of a Population Model 216 Construction of the Likelihood for Different Datasets 218 The Joint Likelihood 220 Fitting an Integrated Population Model 221 Model Extensions 223 Environmental Stochasticity 223 Direct Density Dependence 224 Open Population Models and Other Extensions 226 Alternative Observation Models 226 Inference About Population Dynamics 227 Retrospective Population Analyses 227 Population Viability Analyses 227 Missing Data 229 Goodness-of-fit and Model Selection 230 Software Tools 230
Online Exercises 231 Future Directions 231 References 232 10 individual and Agent-based Models in Population Ecology and Conservation Biology 10.1 10.1.1 Eloy Revilla Individual and Agent-based Models 237 What an IBM Is and What it Is Not 238 237 215
xii І Contents 10.1.2 10.1.3 10.2 10.2.1 10.2.2 10.2.3 10.2.4 10.2.5 10.2.6 10.2.7 10.3 10.3.1 10.4 10.4.1 10.4.2 10.4.3 10.4.4 10.5 10.6 10.7 When to Use an Individual-based Model 238 Criticisms on the Use of IBMs: Advantages or Disadvantages 239 Building the Core Model 239 Design Phase: The Question and the Conceptual Model 239 Implementation of the Core Model 240 Individuals and Their Traits 240 Functional Relationships 244 The Environment and Its Relevant Properties 244 Time and Space: Domains, Resolutions, Boundary Conditions, and Scheduling 244 Single Model Run, Data Input, Model Output 246 Protocols for Model Documentation 247 The Overview, Design Concepts, and Details Protocol 249 Model Analysis and Inference 249 Model Debugging and Checking the Consistency of Model Behavior 249 Model Structural Uncertainty and Sensitivity Analyses 252 Model Selection, Validation, and Calibration 254 Answering your Questions 256 Software Tools 257 Online Exercises 257 Future Directions 257 References 258 Part IV 11 11.1 11.2 11.2.1 11.2.2 11.2.3 11.2.4 11.2.5 11.3 11.3.1 11.3.2 11.3.3 11.3.4 11.4 11.4.1 11.4.2 11.4.3 11.4.4 11.5 11.5.1 11.5.2 11.5.3 11.6 11.6.1 11.6.2 Population Genetics and Spatial Ecology 261 Genetic Insights into Population Ecology 263 Jeffrey R. Row and Stephen C. Lougheed Introduction 263 Types of Genetic Markers 264 Mitochondrial DNA 264 Nuclear Introns 265 Microsatellites 265 Single Nucleotide Polymorphisms 265 Next-generation Sequencing 265 Quantifying Population Structure with Individual-based Analyses 266 Bayesian Clustering 267 Multivariate Analysis of
Genetic Data Through Ordinations 269 Spatial Autocorrelation Analysis 271 Population-level Considerations 273 Estimating Population Size and Trends 273 Estimating Census Population Size 277 Estimating Contemporary Effective Population Size with One Sample Methods 277 Estimating Contemporary Effective Population Size with Temporal Sampling 279 Diagnosing Recent Population Bottlenecks 280 Estimating Dispersal and Gene Flow 281 Estimating Dispersal and Recent Gene Flow 282 Estimating Sustained Levels of Gene Flow 282 Network Analysis of Genetic Connectivity 283 Software Tools 284 Individual-based Analysis 284 Population-based Population Size 285
11.6.3 լ 1.7 11.8 12 12.1 12.2 12.3 12.4 12.5 12.6 12.7 12.8 12.9 12.10 12.11 13 13.1 13.1.1 13.1.2 13.2 13.3 13.3.1 13.3.2 13.3.3 13.3.4 13.4 13.4.1 13.4.2 13.4.3 13.4.4 13.5 13.6 13.7 13.7.1 13.7.2 14 14.1 14.2 14.2.1 14.2.2 14.2.3 Dispersal and Gene Flow 286 Online Exercises 286 Future Directions 286 Glossary 287 References 289 Spatial Structure in Population Data Marie-Josée Fortin Introduction 299 Data Acquisition and Spatial Scales Point Data Analysis 302 Abundance Data Analysis 304 Spatial Interpolation 306 Spatial Density Mapping 308 Multiple Scale Analysis 308 Spatial Regression 311 Software Tools 312 Online Exercises 312 Future Directions 312 Glossary 312 References 313 299 302 Animal Home Ranges: Concepts, Uses, and Estimation 315 Jon 5. Horne, John Fieberg, Luca Börger, Janet L Rachlow, Justin M. Calabrese and Chris H. Fleming What Is a Home Range? 315 Quantifying Animal Home Ranges as a Probability Density Function 316 Why Estimate Animal Home Ranges? 318 Estimating Home Ranges: Preliminary Considerations 319 Estimating Home Ranges: The Occurrence Distribution 321 Minimum Convex Polygon 321 Kernel Smoothing 322 Models Based on Animal Movements 323 Estimation from a One-dimensional Path 324 Estimating Home Ranges: The Range Distribution 324 Bivariate Normal Models 324 The Synoptic Model 324 Mechanistic Models 325 Kernel Smoothing 326 Software Tools 326 Online Exercises 327 Future Directions 327 Choosing a Home Range Model 327 The Future of Home Range Modeling 327 References 328 Analysis of Resource Selection by Animals 333 Joshua J. Millspaugh, Christopher T.
Rota, Robert A. Gitzen, Robert A. Montgomery, Thomas W. Bonnot, Jenőid L Belant, Christopher R. Ayers, Dylan C. Keşler, David A. Eads and Catherine M. Bodinof Jaehowski Introduction 333 Definitions 335 Terminology and Currencies of Use and Availability 335 Use-availability, Paired Use-availability, Use and Non-use (Case-control), and Use-only Designs 336 Differences Between RSF, RSPF, and RUF 336
xiv I Contents 14.3 14.3.1 14.3.2 14.3.3 14.3.4 14.3.5 14.3.6 14.3.7 14.4 14.4.1 14.4.2 14.4.3 14.4.3.1 14.4.3.2 14.4.3.3 14.4.4 14.4.5 14.4.6 14.4.7 14.4.8 14.5 14.6 14.7 Considerations in Studies of Resource Selection 338 Two Important Sampling Considerations: Selecting Sample Units and Time of Day 338 Estimating the Number of Animals and Locations Needed 338 Location Error and Fix Rate Bias Resource Selection Studies 339 Consideration of Animal Behavior in Resource Selection Studies 339 Biological Seasons in Resource Selection Studies 340 Scaling in Resource Selection Studies 340 Linking Resource Selection to Fitness 341 Methods of Analysis and Examples 342 Compositional Analysis 342 Logistic Regression 343 Sampling Designs for Logistic Regression Modeling 344 Random Sampling of Units within the Study Area 344 Random Sampling of Used and Unused Units 344 Random Sample of Used and Available Sampling Units 345 Discrete Choice Models 346 Poisson Regression 347 Resource Utilization Functions 348 Ecological Niche Factor Analysis 348 Mixed Models 349 Software Tools 349 Online Exercises 350 Future Directions 350 References 351 15 Species Distribution Modeling 359 Daniel H. Thornton and Michael 11. Peers Introduction 359 Relationship of Distribution to Other Population Parameters 362 Species Distribution Models and the Niche Concept 363 Building a Species DistributionModel 366 Species Data 366 Environmental Data 368 Model Fitting 368 Interpretation of Model Output 371 Model Accuracy 372 Common Problems when Fitting Species Distribution Models 374 Overfitting 374 Sample Selection
Bias 375 Background Selection 376 Extrapolation 377 Violation of Assumptions 378 Recent Advances 378 Incorporating Dispersal 378 Incorporating Population Dynamics 379 Incorporating Biotic Interactions 379 Software Tools 381 Fitting and Evaluation of Models 381 Incorporating Dispersal or Population Dynamics 381 Online Exercises 381 Future Directions 381 References 383 15.1 15.1.1 15.1.2 15.2 15.2.1 15.2.2 15.2.3 15.2.4 15.2.5 15.3 15.3.1 15.3.2 15.3.3 15.3.4 15.3.5 15.4 15.4.1 15.4.2 15.4.3 15.5 15.5.1 15.5.2 15.6 15.7
Contents Part V 16 16.1 16.1.1 16.1.2 16.1.3 16.1.4 16.2 16.2.1 16.2.2 16.2.3 16.2.4 16.2.5 16.2.6 16.2.7 16.2.8 16.2.9 16.3 16.4 Software Tools 389 The R Software for Data Analysis and Modeling 391 Clément Calenge 391 An Introduction to R 391 The Nature of the R Language 391 Qualities and Limits 392 R for Ecologists 392 R is an Environment 393 Basics of R 393 Several Basic Modes of Data 394 Several Basic Types of Objects 395 Finding Help and Installing New Packages 398 How to Write a Function 400 The for loop 401 The Concept of Attributes and S3 Data Classes 402 Two Important Classes: The Class factor and the Class data. frame Drawing Graphics 406 S4 Classes: Why It Is Useful to Understand Them 407 Online Exercises 410 Final Directions 410 References 411 Index 413 404 XV |
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| spelling | Population ecology in practice edited by Dennis L. Murray (Trent University, Peterborough, Ontario, Canada), Brett K. Sandercock (Norwegian Institute of Nature Research, Trondheim, Trøndelag, Norway) Hoboken, NJ Wiley Blackwell 2020 xxiii, 424 Seiten Illustrationen, Diagramme txt rdacontent n rdamedia nc rdacarrier Includes bibliographical references and index Demökologie (DE-588)4149059-9 gnd rswk-swf (DE-588)4123623-3 Lehrbuch gnd-content Demökologie (DE-588)4149059-9 s DE-604 Murray, Dennis L. Sonstige (DE-588)1212073290 oth Sandercock, Brett Kevin 1966- Sonstige (DE-588)1020881186 oth Erscheint auch als Online-Ausgabe, PDF 978-1-119-57464-4 Erscheint auch als Online-Ausgabe Population ecology in practice Hoboken, NJ, USA : John Wiley & Sons, NJ, USA : John Wiley & Sons, Ltd, [2019] 978-1-119-57462-0 Digitalisierung UB Regensburg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=032107073&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
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| title | Population ecology in practice |
| title_auth | Population ecology in practice |
| title_exact_search | Population ecology in practice |
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| title_full | Population ecology in practice edited by Dennis L. Murray (Trent University, Peterborough, Ontario, Canada), Brett K. Sandercock (Norwegian Institute of Nature Research, Trondheim, Trøndelag, Norway) |
| title_fullStr | Population ecology in practice edited by Dennis L. Murray (Trent University, Peterborough, Ontario, Canada), Brett K. Sandercock (Norwegian Institute of Nature Research, Trondheim, Trøndelag, Norway) |
| title_full_unstemmed | Population ecology in practice edited by Dennis L. Murray (Trent University, Peterborough, Ontario, Canada), Brett K. Sandercock (Norwegian Institute of Nature Research, Trondheim, Trøndelag, Norway) |
| title_short | Population ecology in practice |
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