{"product_id":"population-ecology-in-practice-isbn-9780470674147","title":"Population Ecology in Practice","description":"\u003cp\u003e\u003cb\u003eA synthesis of contemporary analytical and modeling approaches in population ecology\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eThe 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. \u003c\/p\u003e \u003cul\u003e \u003cli\u003eFills a niche for a book that emphasizes applied aspects of population analysis\u003c\/li\u003e \u003cli\u003eCovers many of the current methods being used to analyse population dynamics and structure\u003c\/li\u003e \u003cli\u003eIllustrates the application of specific analytical methods through worked examples based on real datasets\u003c\/li\u003e \u003cli\u003eOffers readers the opportunity to work through examples or adapt the routines to their own datasets using computer code in the R statistical platform\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003e\u003ci\u003ePopulation Ecology in Practice\u003c\/i\u003e 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.\u003c\/p\u003e \u003cp\u003eContributors xvii\u003c\/p\u003e \u003cp\u003ePreface xxi\u003c\/p\u003e \u003cp\u003eAbout the Companion Website xxiii\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart I Tools for Population Biology 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 How to Ask Meaningful Ecological Questions 3\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eCharles J. Krebs\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e1.1 What Problems Do Population Ecologists Try to Solve? 3\u003c\/p\u003e \u003cp\u003e1.2 What Approaches Do Population Ecologists Use? 6\u003c\/p\u003e \u003cp\u003e1.2.1 Generating and Testing Hypotheses in Population Ecology 10\u003c\/p\u003e \u003cp\u003e1.3 Generality in Population Ecology 11\u003c\/p\u003e \u003cp\u003e1.4 Final Thoughts 12\u003c\/p\u003e \u003cp\u003eReferences 13\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 From Research Hypothesis to Model Selection: A Strategy for Robust Inference in Population Ecology 17\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eDennis L. Murray, Guillaume Bastille-Rousseau, Lynne E. Beaty, Megan L. Hornseth, Jeffrey R. Row and Daniel H. Thornton\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e2.1 Introduction 17\u003c\/p\u003e \u003cp\u003e2.1.1 Inductive Methods 18\u003c\/p\u003e \u003cp\u003e2.1.2 Hypothetico-deductive Methods 19\u003c\/p\u003e \u003cp\u003e2.1.3 Multimodel Inference 20\u003c\/p\u003e \u003cp\u003e2.1.4 Bayesian Methods 22\u003c\/p\u003e \u003cp\u003e2.2 What Constitutes a Good Research Hypothesis? 22\u003c\/p\u003e \u003cp\u003e2.3 Multiple Hypotheses and Information Theoretics 24\u003c\/p\u003e \u003cp\u003e2.3.1 How Many are Too Many Hypotheses? 25\u003c\/p\u003e \u003cp\u003e2.4 From Research Hypothesis to Statistical Model 26\u003c\/p\u003e \u003cp\u003e2.4.1 Functional Relationships Between Variables 26\u003c\/p\u003e \u003cp\u003e2.4.2 Interactions Between Predictor Variables 26\u003c\/p\u003e \u003cp\u003e2.4.3 Number and Structure of Predictor Variables 27\u003c\/p\u003e \u003cp\u003e2.5 Exploratory Analysis and Helpful Remedies 28\u003c\/p\u003e \u003cp\u003e2.5.1 Exploratory Analysis and Diagnostic Tests 28\u003c\/p\u003e \u003cp\u003e2.5.2 Missing Data 28\u003c\/p\u003e \u003cp\u003e2.5.3 Inter-relationships Between Predictors 30\u003c\/p\u003e \u003cp\u003e2.5.4 Interpretability of Model Output 31\u003c\/p\u003e \u003cp\u003e2.6 Model Ranking and Evaluation 32\u003c\/p\u003e \u003cp\u003e2.6.1 Model Selection 32\u003c\/p\u003e \u003cp\u003e2.6.2 Multimodel Inference 36\u003c\/p\u003e \u003cp\u003e2.7 Model Validation 39\u003c\/p\u003e \u003cp\u003e2.8 Software Tools 41\u003c\/p\u003e \u003cp\u003e2.9 Online Exercises 41\u003c\/p\u003e \u003cp\u003e2.10 Future Directions 41\u003c\/p\u003e \u003cp\u003eReferences 42\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart II Population Demography 47\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Estimating Abundance or Occupancy from Unmarked Populations 49\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eBrett T. McClintock and Len Thomas\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e3.1 Introduction 49\u003c\/p\u003e \u003cp\u003e3.1.1 Why Collect Data from Unmarked Populations? 49\u003c\/p\u003e \u003cp\u003e3.1.2 Relative Indices and Detection Probability 50\u003c\/p\u003e \u003cp\u003e3.1.2.1 Population Abundance 50\u003c\/p\u003e \u003cp\u003e3.1.2.2 Species Occurrence 51\u003c\/p\u003e \u003cp\u003e3.1.3 Hierarchy of Sampling Methods for Unmarked Individuals 52\u003c\/p\u003e \u003cp\u003e3.2 Estimating Abundance (or Density) from Unmarked Individuals 53\u003c\/p\u003e \u003cp\u003e3.2.1 Distance Sampling 53\u003c\/p\u003e \u003cp\u003e3.2.1.1 Classical Distance Sampling 54\u003c\/p\u003e \u003cp\u003e3.2.1.2 Model-Based Distance Sampling 57\u003c\/p\u003e \u003cp\u003e3.2.2 Replicated Counts of Unmarked Individuals 61\u003c\/p\u003e \u003cp\u003e3.2.2.1 Spatially Replicated Counts 61\u003c\/p\u003e \u003cp\u003e3.2.2.2 Removal Sampling 63\u003c\/p\u003e \u003cp\u003e3.3 Estimating Species Occurrence under Imperfect Detection 64\u003c\/p\u003e \u003cp\u003e3.3.1 Single-Season Occupancy Models 65\u003c\/p\u003e \u003cp\u003e3.3.2 Multiple-Season Occupancy Models 66\u003c\/p\u003e \u003cp\u003e3.3.3 Other Developments in Occupancy Estimation 68\u003c\/p\u003e \u003cp\u003e3.3.3.1 Site Heterogeneity in Detection Probability 68\u003c\/p\u003e \u003cp\u003e3.3.3.2 Occupancy and Abundance Relationships 68\u003c\/p\u003e \u003cp\u003e3.3.3.3 Multistate and Multiscale Occupancy Models 68\u003c\/p\u003e \u003cp\u003e3.3.3.4 Metapopulation Occupancy Models 69\u003c\/p\u003e \u003cp\u003e3.3.3.5 False Positive Occupancy Models 70\u003c\/p\u003e \u003cp\u003e3.4 Software Tools 70\u003c\/p\u003e \u003cp\u003e3.5 Online Exercises 71\u003c\/p\u003e \u003cp\u003e3.6 Future Directions 71\u003c\/p\u003e \u003cp\u003eReferences 73\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Analyzing Time Series Data: Single-Species Abundance Modeling 79\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eSteven Delean, Thomas A.A. Prowse, Joshua V. Ross and Jonathan Tuke\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e4.1 Introduction 79\u003c\/p\u003e \u003cp\u003e4.1.1 Principal Approaches to Time Series Analysis in Ecology 80\u003c\/p\u003e \u003cp\u003e4.1.2 Challenges to Time Series Analysis in Ecology 82\u003c\/p\u003e \u003cp\u003e4.2 Time Series (ARMA) Modeling 83\u003c\/p\u003e \u003cp\u003e4.2.1 Time Series Models 83\u003c\/p\u003e \u003cp\u003e4.2.2 Autoregressive Moving Average Models 83\u003c\/p\u003e \u003cp\u003e4.3 Regression Models with Correlated Errors 87\u003c\/p\u003e \u003cp\u003e4.4 Phenomenological Models of Population Dynamics 88\u003c\/p\u003e \u003cp\u003e4.4.1 Deterministic Models 89\u003c\/p\u003e \u003cp\u003e4.4.1.1 Exponential Growth 89\u003c\/p\u003e \u003cp\u003e4.4.1.2 Classic ODE Single-Species Population Models that Incorporate Density Dependence 90\u003c\/p\u003e \u003cp\u003e4.4.2 Discrete-Time Population Growth Models with Stochasticity 92\u003c\/p\u003e \u003cp\u003e4.5 State-space Modeling 93\u003c\/p\u003e \u003cp\u003e4.5.1 Gompertz State-space Population Model 94\u003c\/p\u003e \u003cp\u003e4.5.2 Nonlinear and Non-Gaussian State-space Population Models 96\u003c\/p\u003e \u003cp\u003e4.6 Software Tools 96\u003c\/p\u003e \u003cp\u003e4.7 Online Exercises 97\u003c\/p\u003e \u003cp\u003e4.8 Future Directions 97\u003c\/p\u003e \u003cp\u003eReferences 98\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Estimating Abundance from Capture-Recapture Data 103\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eJ. Andrew Royle and Sarah J. Converse\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e5.1 Introduction 103\u003c\/p\u003e \u003cp\u003e5.2 Genesis of Capture-Recapture Data 104\u003c\/p\u003e \u003cp\u003e5.3 The Basic Closed Population Models: \u003ci\u003eM\u003csub\u003e0\u003c\/sub\u003e\u003c\/i\u003e, \u003ci\u003eM\u003csub\u003et\u003c\/sub\u003e\u003c\/i\u003e, \u003ci\u003eM\u003csub\u003eb\u003c\/sub\u003e\u003c\/i\u003e104\u003c\/p\u003e \u003cp\u003e5.4 Inference Strategies 105\u003c\/p\u003e \u003cp\u003e5.4.1 Likelihood Inference 105\u003c\/p\u003e \u003cp\u003e5.4.2 Bayesian Analysis 107\u003c\/p\u003e \u003cp\u003e5.4.3 Other Inference Strategies 108\u003c\/p\u003e \u003cp\u003e5.5 Models with Individual Heterogeneity in Detection 108\u003c\/p\u003e \u003cp\u003e5.5.1 Model \u003ci\u003eM\u003csub\u003eh\u003c\/sub\u003e \u003c\/i\u003e108\u003c\/p\u003e \u003cp\u003e5.5.2 Individual Covariate Models 109\u003c\/p\u003e \u003cp\u003e5.5.2.1 The Full Likelihood 109\u003c\/p\u003e \u003cp\u003e5.5.2.2 Horvitz-Thompson Estimation 110\u003c\/p\u003e \u003cp\u003e5.5.3 Distance Sampling 110\u003c\/p\u003e \u003cp\u003e5.5.4 Spatial Capture-Recapture Models 110\u003c\/p\u003e \u003cp\u003e5.5.4.1 The State-space 112\u003c\/p\u003e \u003cp\u003e5.5.4.2 Inference in SCR Models 112\u003c\/p\u003e \u003cp\u003e5.6 Stratified Populations or Multisession Models 112\u003c\/p\u003e \u003cp\u003e5.6.1 Nonparametric Estimation 112\u003c\/p\u003e \u003cp\u003e5.6.2 Hierarchical Capture-Recapture Models 113\u003c\/p\u003e \u003cp\u003e5.7 Model Selection and Model Fit 113\u003c\/p\u003e \u003cp\u003e5.7.1 Model Selection 113\u003c\/p\u003e \u003cp\u003e5.7.2 Goodness-of-Fit 114\u003c\/p\u003e \u003cp\u003e5.7.3 What to Do When Your Model Does Not Fit 115\u003c\/p\u003e \u003cp\u003e5.8 Open Population Models 115\u003c\/p\u003e \u003cp\u003e5.9 Software Tools 116\u003c\/p\u003e \u003cp\u003e5.10 Online Exercises 117\u003c\/p\u003e \u003cp\u003e5.11 Future Directions 118\u003c\/p\u003e \u003cp\u003eReferences 119\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Estimating Survival and Cause-specific Mortality from Continuous Time Observations 123\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eDennis L. Murray and Guillaume Bastille-Rousseau\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e6.1 Introduction 123\u003c\/p\u003e \u003cp\u003e6.1.1 Assumption of No Handling, Marking or Monitoring Effects 125\u003c\/p\u003e \u003cp\u003e6.1.2 Cause of Death Assessment 125\u003c\/p\u003e \u003cp\u003e6.1.3 Historical Origins of Survival Estimation 126\u003c\/p\u003e \u003cp\u003e6.2 Survival and Hazard Functions in Theory 127\u003c\/p\u003e \u003cp\u003e6.3 Developing Continuous Time Survival Datasets 130\u003c\/p\u003e \u003cp\u003e6.3.1 Dataset Structure 131\u003c\/p\u003e \u003cp\u003e6.3.2 Right-censoring 133\u003c\/p\u003e \u003cp\u003e6.3.3 Delayed Entry and Other Time Considerations 133\u003c\/p\u003e \u003cp\u003e6.3.4 Sampling Heterogeneity 134\u003c\/p\u003e \u003cp\u003e6.3.5 Time-dependent Covariates 135\u003c\/p\u003e \u003cp\u003e6.4 Survival and Hazard Functions in Practice 135\u003c\/p\u003e \u003cp\u003e6.4.1 Mayfield and Heisey–Fuller Survival Estimation 135\u003c\/p\u003e \u003cp\u003e6.4.2 Kaplan–Meier Estimator 136\u003c\/p\u003e \u003cp\u003e6.4.3 Nelson–Aalen Estimator 138\u003c\/p\u003e \u003cp\u003e6.5 Statistical Analysis of Survival 138\u003c\/p\u003e \u003cp\u003e6.5.1 Simple Hypothesis Tests 138\u003c\/p\u003e \u003cp\u003e6.5.2 Cox Proportional Hazards 139\u003c\/p\u003e \u003cp\u003e6.5.3 Proportionality of Hazards 140\u003c\/p\u003e \u003cp\u003e6.5.4 Extended CPH 142\u003c\/p\u003e \u003cp\u003e6.5.5 Further Extensions 143\u003c\/p\u003e \u003cp\u003e6.5.6 Parametric Models 143\u003c\/p\u003e \u003cp\u003e6.6 Cause-specific Survival Analysis 144\u003c\/p\u003e \u003cp\u003e6.6.1 The Case for Cause-specific Mortality Data 144\u003c\/p\u003e \u003cp\u003e6.6.2 Cause-specific Hazards and Mortality Rates 145\u003c\/p\u003e \u003cp\u003e6.6.3 Competing Risks Analysis 146\u003c\/p\u003e \u003cp\u003e6.6.4 Additive Versus Compensatory Mortality 147\u003c\/p\u003e \u003cp\u003e6.7 Software Tools 149\u003c\/p\u003e \u003cp\u003e6.8 Online Exercises 149\u003c\/p\u003e \u003cp\u003e6.9 Future Directions 149\u003c\/p\u003e \u003cp\u003eReferences 151\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Mark-Recapture Models for Estimation of Demographic Parameters 157\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eBrett K. Sandercock\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e7.1 Introduction 157\u003c\/p\u003e \u003cp\u003e7.2 Live Encounter Data 158\u003c\/p\u003e \u003cp\u003e7.3 Encounter Histories and Model Selection 159\u003c\/p\u003e \u003cp\u003e7.4 Return Rates 163\u003c\/p\u003e \u003cp\u003e7.5 Cormack–Jolly–Seber Models 164\u003c\/p\u003e \u003cp\u003e7.6 The Challenge of Emigration 164\u003c\/p\u003e \u003cp\u003e7.7 Extending the CJS Model 167\u003c\/p\u003e \u003cp\u003e7.8 Time-since-marking and Transient Models 167\u003c\/p\u003e \u003cp\u003e7.9 Temporal Symmetry Models 168\u003c\/p\u003e \u003cp\u003e7.10 Jolly–Seber Model 169\u003c\/p\u003e \u003cp\u003e7.11 Multilevel Models 169\u003c\/p\u003e \u003cp\u003e7.12 Spatially Explicit Models 170\u003c\/p\u003e \u003cp\u003e7.13 Robust Design Models 170\u003c\/p\u003e \u003cp\u003e7.14 Mark-resight Models 171\u003c\/p\u003e \u003cp\u003e7.15 Young Survival Model 172\u003c\/p\u003e \u003cp\u003e7.16 Multistate Models 173\u003c\/p\u003e \u003cp\u003e7.17 Multistate Models with Unobservable States 175\u003c\/p\u003e \u003cp\u003e7.18 Multievent Models with Uncertain States 176\u003c\/p\u003e \u003cp\u003e7.19 Joint Models 177\u003c\/p\u003e \u003cp\u003e7.20 Integrated Population Models 178\u003c\/p\u003e \u003cp\u003e7.21 Frequentist vs. Bayesian Methods 178\u003c\/p\u003e \u003cp\u003e7.22 Software Tools 179\u003c\/p\u003e \u003cp\u003e7.23 Online Exercises 180\u003c\/p\u003e \u003cp\u003e7.24 Future Directions 180\u003c\/p\u003e \u003cp\u003eReferences 180\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart III Population Models 191\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Projecting Populations 193\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eStéphane Legendre\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e8.1 Introduction 193\u003c\/p\u003e \u003cp\u003e8.2 The Life Cycle Graph 194\u003c\/p\u003e \u003cp\u003e8.2.1 Description 194\u003c\/p\u003e \u003cp\u003e8.2.2 Construction 194\u003c\/p\u003e \u003cp\u003e8.3 Matrix Models 198\u003c\/p\u003e \u003cp\u003e8.3.1 The Projection Equation 198\u003c\/p\u003e \u003cp\u003e8.3.2 Demographic Descriptors 200\u003c\/p\u003e \u003cp\u003e8.3.3 Sensitivities 200\u003c\/p\u003e \u003cp\u003e8.4 Accounting for the Environment 202\u003c\/p\u003e \u003cp\u003e8.5 Density Dependence 203\u003c\/p\u003e \u003cp\u003e8.5.1 Density-dependent Scalar Models 203\u003c\/p\u003e \u003cp\u003e8.5.2 Density-dependent Matrix Models 203\u003c\/p\u003e \u003cp\u003e8.5.3 Parameterizing Density Dependence 204\u003c\/p\u003e \u003cp\u003e8.5.4 Density-dependent Sensitivities 204\u003c\/p\u003e \u003cp\u003e8.6 Environmental Stochasticity 204\u003c\/p\u003e \u003cp\u003e8.6.1 Models of the Environment 204\u003c\/p\u003e \u003cp\u003e8.6.2 Stochastic Dynamics 205\u003c\/p\u003e \u003cp\u003e8.6.3 Parameterizing Environmental Stochasticity 208\u003c\/p\u003e \u003cp\u003e8.7 Spatial Structure 208\u003c\/p\u003e \u003cp\u003e8.8 Demographic Stochasticity 209\u003c\/p\u003e \u003cp\u003e8.8.1 Branching Processes 209\u003c\/p\u003e \u003cp\u003e8.8.2 Two-sex Models 210\u003c\/p\u003e \u003cp\u003e8.9 Demographic Heterogeneity 210\u003c\/p\u003e \u003cp\u003e8.9.1 Integral Projection Models 211\u003c\/p\u003e \u003cp\u003e8.10 Software Tools 212\u003c\/p\u003e \u003cp\u003e8.11 Online Exercises 212\u003c\/p\u003e \u003cp\u003e8.12 Future Directions 212\u003c\/p\u003e \u003cp\u003eReferences 212\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Combining Counts of Unmarked Individuals and Demographic Data Using Integrated Population Models 215\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eMichael Schaub\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e9.1 Introduction 215\u003c\/p\u003e \u003cp\u003e9.2 Construction of Integrated Population Models 216\u003c\/p\u003e \u003cp\u003e9.2.1 Development of a Population Model 216\u003c\/p\u003e \u003cp\u003e9.2.2 Construction of the Likelihood for Different Datasets 218\u003c\/p\u003e \u003cp\u003e9.2.3 The Joint Likelihood 220\u003c\/p\u003e \u003cp\u003e9.2.4 Fitting an Integrated Population Model 221\u003c\/p\u003e \u003cp\u003e9.3 Model Extensions 223\u003c\/p\u003e \u003cp\u003e9.3.1 Environmental Stochasticity 223\u003c\/p\u003e \u003cp\u003e9.3.2 Direct Density Dependence 224\u003c\/p\u003e \u003cp\u003e9.3.3 Open Population Models and Other Extensions 226\u003c\/p\u003e \u003cp\u003e9.3.4 Alternative Observation Models 226\u003c\/p\u003e \u003cp\u003e9.4 Inference About Population Dynamics 227\u003c\/p\u003e \u003cp\u003e9.4.1 Retrospective Population Analyses 227\u003c\/p\u003e \u003cp\u003e9.4.2 Population Viability Analyses 227\u003c\/p\u003e \u003cp\u003e9.5 Missing Data 229\u003c\/p\u003e \u003cp\u003e9.6 Goodness-of-fit and Model Selection 230\u003c\/p\u003e \u003cp\u003e9.7 Software Tools 230\u003c\/p\u003e \u003cp\u003e9.8 Online Exercises 231\u003c\/p\u003e \u003cp\u003e9.9 Future Directions 231\u003c\/p\u003e \u003cp\u003eReferences 232\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Individual and Agent-based Models in Population Ecology and Conservation Biology 237\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eEloy Revilla\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e10.1 Individual and Agent-based Models 237\u003c\/p\u003e \u003cp\u003e10.1.1 What an IBM is and What it is Not 238\u003c\/p\u003e \u003cp\u003e10.1.2 When to Use an Individual-based Model 238\u003c\/p\u003e \u003cp\u003e10.1.3 Criticisms on the Use of IBMs: Advantages or Disadvantages 239\u003c\/p\u003e \u003cp\u003e10.2 Building the Core Model 239\u003c\/p\u003e \u003cp\u003e10.2.1 Design Phase: The Question and the Conceptual Model 239\u003c\/p\u003e \u003cp\u003e10.2.2 Implementation of the Core Model 240\u003c\/p\u003e \u003cp\u003e10.2.3 Individuals and Their Traits 240\u003c\/p\u003e \u003cp\u003e10.2.4 Functional Relationships 244\u003c\/p\u003e \u003cp\u003e10.2.5 The Environment and Its Relevant Properties 244\u003c\/p\u003e \u003cp\u003e10.2.6 Time and Space: Domains, Resolutions, Boundary Conditions, and Scheduling 244\u003c\/p\u003e \u003cp\u003e10.2.7 Single Model Run, Data Input, Model Output 246\u003c\/p\u003e \u003cp\u003e10.3 Protocols for Model Documentation 247\u003c\/p\u003e \u003cp\u003e10.3.1 The Overview, Design Concepts, and Details Protocol 249\u003c\/p\u003e \u003cp\u003e10.4 Model Analysis and Inference 249\u003c\/p\u003e \u003cp\u003e10.4.1 Model Debugging and Checking the Consistency of Model Behavior 249\u003c\/p\u003e \u003cp\u003e10.4.2 Model Structural Uncertainty and Sensitivity Analyses 252\u003c\/p\u003e \u003cp\u003e10.4.3 Model Selection, Validation, and Calibration 254\u003c\/p\u003e \u003cp\u003e10.4.4 Answering your Questions 256\u003c\/p\u003e \u003cp\u003e10.5 Software Tools 257\u003c\/p\u003e \u003cp\u003e10.6 Online Exercises 257\u003c\/p\u003e \u003cp\u003e10.7 Future Directions 257\u003c\/p\u003e \u003cp\u003eReferences 258\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart IV Population Genetics and Spatial Ecology 261\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 Genetic Insights into Population Ecology 263\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eJeffrey R. Row and Stephen C. Lougheed\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e11.1 Introduction 263\u003c\/p\u003e \u003cp\u003e11.2 Types of Genetic Markers 264\u003c\/p\u003e \u003cp\u003e11.2.1 Mitochondrial DNA 264\u003c\/p\u003e \u003cp\u003e11.2.2 Nuclear Introns 265\u003c\/p\u003e \u003cp\u003e11.2.3 Microsatellites 265\u003c\/p\u003e \u003cp\u003e11.2.4 Single Nucleotide Polymorphisms 265\u003c\/p\u003e \u003cp\u003e11.2.5 Next-generation Sequencing 265\u003c\/p\u003e \u003cp\u003e11.3 Quantifying Population Structure with Individual-based Analyses 266\u003c\/p\u003e \u003cp\u003e11.3.1 Bayesian Clustering 267\u003c\/p\u003e \u003cp\u003e11.3.2 Multivariate Analysis of Genetic Data Through Ordinations 269\u003c\/p\u003e \u003cp\u003e11.3.3 Spatial Autocorrelation Analysis 271\u003c\/p\u003e \u003cp\u003e11.3.4 Population-level Considerations 273\u003c\/p\u003e \u003cp\u003e11.4 Estimating Population Size and Trends 273\u003c\/p\u003e \u003cp\u003e11.4.1 Estimating Census Population Size 277\u003c\/p\u003e \u003cp\u003e11.4.2 Estimating Contemporary Effective Population Size with One Sample Methods 277\u003c\/p\u003e \u003cp\u003e11.4.3 Estimating Contemporary Effective Population Size with Temporal Sampling 279\u003c\/p\u003e \u003cp\u003e11.4.4 Diagnosing Recent Population Bottlenecks 280\u003c\/p\u003e \u003cp\u003e11.5 Estimating Dispersal and Gene Flow 281\u003c\/p\u003e \u003cp\u003e11.5.1 Estimating Dispersal and Recent Gene Flow 282\u003c\/p\u003e \u003cp\u003e11.5.2 Estimating Sustained Levels of Gene Flow 282\u003c\/p\u003e \u003cp\u003e11.5.3 Network Analysis of Genetic Connectivity 283\u003c\/p\u003e \u003cp\u003e11.6 Software Tools 284\u003c\/p\u003e \u003cp\u003e11.6.1 Individual-based Analysis 284\u003c\/p\u003e \u003cp\u003e11.6.2 Population-based Population Size 285\u003c\/p\u003e \u003cp\u003e11.6.3 Dispersal and Gene Flow 286\u003c\/p\u003e \u003cp\u003e11.7 Online Exercises 286\u003c\/p\u003e \u003cp\u003e11.8 Future Directions 286\u003c\/p\u003e \u003cp\u003eGlossary 287\u003c\/p\u003e \u003cp\u003eReferences 289\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12 Spatial Structure in Population Data 299\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eMarie-Josée Fortin\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e12.1 Introduction 299\u003c\/p\u003e \u003cp\u003e12.2 Data Acquisition and Spatial Scales 302\u003c\/p\u003e \u003cp\u003e12.3 Point Data Analysis 302\u003c\/p\u003e \u003cp\u003e12.4 Abundance Data Analysis 304\u003c\/p\u003e \u003cp\u003e12.5 Spatial Interpolation 306\u003c\/p\u003e \u003cp\u003e12.6 Spatial Density Mapping 308\u003c\/p\u003e \u003cp\u003e12.7 Multiple Scale Analysis 308\u003c\/p\u003e \u003cp\u003e12.8 Spatial Regression 311\u003c\/p\u003e \u003cp\u003e12.9 Software Tools 312\u003c\/p\u003e \u003cp\u003e12.10 Online Exercises 312\u003c\/p\u003e \u003cp\u003e12.11 Future Directions 312\u003c\/p\u003e \u003cp\u003eGlossary 312\u003c\/p\u003e \u003cp\u003eReferences 313\u003c\/p\u003e \u003cp\u003e\u003cb\u003e13 Animal Home Ranges: Concepts, Uses, and Estimation 315\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eJon S. Horne, John Fieberg, Luca Börger, Janet L. Rachlow, Justin M. Calabrese and Chris H. Fleming\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e13.1 What is a Home Range? 315\u003c\/p\u003e \u003cp\u003e13.1.1 Quantifying Animal Home Ranges as a Probability Density Function 316\u003c\/p\u003e \u003cp\u003e13.1.2 Why Estimate Animal Home Ranges? 318\u003c\/p\u003e \u003cp\u003e13.2 Estimating Home Ranges: Preliminary Considerations 319\u003c\/p\u003e \u003cp\u003e13.3 Estimating Home Ranges: The Occurrence Distribution 321\u003c\/p\u003e \u003cp\u003e13.3.1 Minimum Convex Polygon 321\u003c\/p\u003e \u003cp\u003e13.3.2 Kernel Smoothing 322\u003c\/p\u003e \u003cp\u003e13.3.3 Models Based on Animal Movements 323\u003c\/p\u003e \u003cp\u003e13.3.4 Estimation from a One-dimensional Path 324\u003c\/p\u003e \u003cp\u003e13.4 Estimating Home Ranges: The Range Distribution 324\u003c\/p\u003e \u003cp\u003e13.4.1 Bivariate Normal Models 324\u003c\/p\u003e \u003cp\u003e13.4.2 The Synoptic Model 324\u003c\/p\u003e \u003cp\u003e13.4.3 Mechanistic Models 325\u003c\/p\u003e \u003cp\u003e13.4.4 Kernel Smoothing 326\u003c\/p\u003e \u003cp\u003e13.5 Software Tools 326\u003c\/p\u003e \u003cp\u003e13.6 Online Exercises 327\u003c\/p\u003e \u003cp\u003e13.7 Future Directions 327\u003c\/p\u003e \u003cp\u003e13.7.1 Choosing a Home Range Model 327\u003c\/p\u003e \u003cp\u003e13.7.2 The Future of Home Range Modeling 327\u003c\/p\u003e \u003cp\u003eReferences 328\u003c\/p\u003e \u003cp\u003e\u003cb\u003e14 Analysis of Resource Selection by Animals 333\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eJoshua 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\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e14.1 Introduction 333\u003c\/p\u003e \u003cp\u003e14.2 Definitions 335\u003c\/p\u003e \u003cp\u003e14.2.1 Terminology and Currencies of Use and Availability 335\u003c\/p\u003e \u003cp\u003e14.2.2 Use-availability, Paired Use-availability, Use and Non-use (Case-control), and Use-only Designs 336\u003c\/p\u003e \u003cp\u003e14.2.3 Differences Between RSF, RSPF, and RUF 336\u003c\/p\u003e \u003cp\u003e14.3 Considerations in Studies of Resource Selection 338\u003c\/p\u003e \u003cp\u003e14.3.1 Two Important Sampling Considerations: Selecting Sample Units and Time of Day 338\u003c\/p\u003e \u003cp\u003e14.3.2 Estimating the Number of Animals and Locations Needed 338\u003c\/p\u003e \u003cp\u003e14.3.3 Location Error and Fix Rate Bias Resource Selection Studies 339\u003c\/p\u003e \u003cp\u003e14.3.4 Consideration of Animal Behavior in Resource Selection Studies 339\u003c\/p\u003e \u003cp\u003e14.3.5 Biological Seasons in Resource Selection Studies 340\u003c\/p\u003e \u003cp\u003e14.3.6 Scaling in Resource Selection Studies 340\u003c\/p\u003e \u003cp\u003e14.3.7 Linking Resource Selection to Fitness 341\u003c\/p\u003e \u003cp\u003e14.4 Methods of Analysis and Examples 342\u003c\/p\u003e \u003cp\u003e14.4.1 Compositional Analysis 342\u003c\/p\u003e \u003cp\u003e14.4.2 Logistic Regression 343\u003c\/p\u003e \u003cp\u003e14.4.3 Sampling Designs for Logistic Regression Modeling 344\u003c\/p\u003e \u003cp\u003e14.4.3.1 Random Sampling of Units within the Study Area 344\u003c\/p\u003e \u003cp\u003e14.4.3.2 Random Sampling of Used and Unused Units 344\u003c\/p\u003e \u003cp\u003e14.4.3.3 Random Sample of Used and Available Sampling Units 345\u003c\/p\u003e \u003cp\u003e14.4.4 Discrete Choice Models 346\u003c\/p\u003e \u003cp\u003e14.4.5 Poisson Regression 347\u003c\/p\u003e \u003cp\u003e14.4.6 Resource Utilization Functions 348\u003c\/p\u003e \u003cp\u003e14.4.7 Ecological Niche Factor Analysis 348\u003c\/p\u003e \u003cp\u003e14.4.8 Mixed Models 349\u003c\/p\u003e \u003cp\u003e14.5 Software Tools 349\u003c\/p\u003e \u003cp\u003e14.6 Online Exercises 350\u003c\/p\u003e \u003cp\u003e14.7 Future Directions 350\u003c\/p\u003e \u003cp\u003eReferences 351\u003c\/p\u003e \u003cp\u003e\u003cb\u003e15 Species Distribution Modeling 359\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eDaniel H. Thornton and Michael J.L. Peers\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e15.1 Introduction 359\u003c\/p\u003e \u003cp\u003e15.1.1 Relationship of Distribution to Other Population Parameters 362\u003c\/p\u003e \u003cp\u003e15.1.2 Species Distribution Models and the Niche Concept 363\u003c\/p\u003e \u003cp\u003e15.2 Building a Species Distribution Model 366\u003c\/p\u003e \u003cp\u003e15.2.1 Species Data 366\u003c\/p\u003e \u003cp\u003e15.2.2 Environmental Data 368\u003c\/p\u003e \u003cp\u003e15.2.3 Model Fitting 368\u003c\/p\u003e \u003cp\u003e15.2.4 Interpretation of Model Output 371\u003c\/p\u003e \u003cp\u003e15.2.5 Model Accuracy 372\u003c\/p\u003e \u003cp\u003e15.3 Common Problems when Fitting Species Distribution Models 374\u003c\/p\u003e \u003cp\u003e15.3.1 Overfitting 374\u003c\/p\u003e \u003cp\u003e15.3.2 Sample Selection Bias 375\u003c\/p\u003e \u003cp\u003e15.3.3 Background Selection 376\u003c\/p\u003e \u003cp\u003e15.3.4 Extrapolation 377\u003c\/p\u003e \u003cp\u003e15.3.5 Violation of Assumptions 378\u003c\/p\u003e \u003cp\u003e15.4 Recent Advances 378\u003c\/p\u003e \u003cp\u003e15.4.1 Incorporating Dispersal 378\u003c\/p\u003e \u003cp\u003e15.4.2 Incorporating Population Dynamics 379\u003c\/p\u003e \u003cp\u003e15.4.3 Incorporating Biotic Interactions 379\u003c\/p\u003e \u003cp\u003e15.5 Software Tools 381\u003c\/p\u003e \u003cp\u003e15.5.1 Fitting and Evaluation of Models 381\u003c\/p\u003e \u003cp\u003e15.5.2 Incorporating Dispersal or Population Dynamics 381\u003c\/p\u003e \u003cp\u003e15.6 Online Exercises 381\u003c\/p\u003e \u003cp\u003e15.7 Future Directions 381\u003c\/p\u003e \u003cp\u003eReferences 383\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart V Software Tools 389\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e16 The R Software for Data Analysis and Modeling 391\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eClément Calenge 391\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e16.1 An Introduction to R 391\u003c\/p\u003e \u003cp\u003e16.1.1 The Nature of the R Language 391\u003c\/p\u003e \u003cp\u003e16.1.2 Qualities and Limits 392\u003c\/p\u003e \u003cp\u003e16.1.3 R for Ecologists 392\u003c\/p\u003e \u003cp\u003e16.1.4 R is an Environment 393\u003c\/p\u003e \u003cp\u003e16.2 Basics of R 393\u003c\/p\u003e \u003cp\u003e16.2.1 Several Basic Modes of Data 394\u003c\/p\u003e \u003cp\u003e16.2.2 Several Basic Types of Objects 395\u003c\/p\u003e \u003cp\u003e16.2.3 Finding Help and Installing New Packages 398\u003c\/p\u003e \u003cp\u003e16.2.4 How to Write a Function 400\u003c\/p\u003e \u003cp\u003e16.2.5 The for loop 401\u003c\/p\u003e \u003cp\u003e16.2.6 The Concept of Attributes and S3 Data Classes 402\u003c\/p\u003e \u003cp\u003e16.2.7 Two Important Classes: The Class factor and the Class data.frame 404\u003c\/p\u003e \u003cp\u003e16.2.8 Drawing Graphics 406\u003c\/p\u003e \u003cp\u003e16.2.9 S4 Classes: Why It is Useful to Understand Them 407\u003c\/p\u003e \u003cp\u003e16.3 Online Exercises 410\u003c\/p\u003e \u003cp\u003e16.4 Final Directions 410\u003c\/p\u003e \u003cp\u003eReferences 411\u003c\/p\u003e \u003cp\u003eIndex 413\u003c\/p\u003e   \u003cp\u003e\u003cb\u003eDENNIS L. MURRAY, P\u003csmall\u003eH\u003c\/small\u003eD,\u003c\/b\u003e is Professor of Biology at Trent University and holds the position of Canada Research Chair in Integrative Wildlife Conservation, Bioinformatics, and Ecological Modeling. \u003c\/p\u003e\u003cp\u003e\u003cb\u003eBRETT K. SANDERCOCK, P\u003csmall\u003eH\u003c\/small\u003eD,\u003c\/b\u003e is a Senior Research Scientist in the Department of Terrestrial Ecology at the Norwegian Institute for Nature Research. \t   \u003c\/p\u003e\u003cp\u003e\u003cb\u003eA SYNTHESIS OF CONTEMPORARY ANALYTICAL AND MODELING APPROACHES IN POPULATION ECOLOGY\u003c\/b\u003e \u003c\/p\u003e\u003cp\u003eThe 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. \u003c\/p\u003e\u003cul\u003e \u003cli\u003eFills a niche for a book that emphasizes applied aspects of population analysis\u003c\/li\u003e \u003cli\u003eCovers many of the current methods being used to analyse population dynamics and structure\u003c\/li\u003e \u003cli\u003eIllustrates the application of specific analytical methods through worked examples based on real datasets\u003c\/li\u003e \u003cli\u003eOffers readers the opportunity to work through examples or adapt the routines to their own datasets using computer code in the R statistical platform\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003e\u003ci\u003ePopulation Ecology in Practice\u003c\/i\u003e 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.\u003c\/p\u003e","brand":"Wiley-Blackwell","offers":[{"title":"Default Title","offer_id":47989815345381,"sku":"NP9780470674147","price":85.0,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9780470674147.jpg?v=1761785562","url":"https:\/\/k12savings.com\/products\/population-ecology-in-practice-isbn-9780470674147","provider":"K12savings","version":"1.0","type":"link"}