| Description |
1 online resource (668 pages) |
| Contents |
Front Cover -- Occupancy Estimation and Modeling -- Copyright -- Contents -- Preface -- Acknowledgments -- Part I Background and Concepts -- 1 Introduction -- 1.1 Operational De nitions -- 1.2 Sampling Animal Populations and Communities: General Principles -- 1.2.1 Why? -- 1.2.2 What? -- 1.2.3 How? -- 1.3 Inference About Dynamics and Causation -- 1.3.1 Generation of System Dynamics -- 1.3.2 Statics and Process vs. Pattern -- 1.4 Discussion -- 2 Occupancy Applications -- 2.1 Geographic Range -- 2.2 Habitat Relationships and Resource Selection |
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2.3 Metapopulation Dynamics2.3.1 Inference Based on Single-Season Data -- 2.3.2 Inference Based on Multiple-Season Data -- 2.4 Large-Scale Monitoring -- 2.5 Multi-Species Occupancy Data -- 2.5.1 Inference Based on Static Occupancy Patterns -- 2.5.2 Inference Based on Occupancy Dynamics -- 2.6 Paleobiology -- 2.7 Disease Dynamics -- 2.8 Non-Ecological Applications -- 2.9 Discussion -- 3 Fundamental Principals of Statistical Inference -- 3.1 De nitions and Key Concepts -- 3.1.1 Random Variables, Probability Distributions, and the Likelihood Function |
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3.1.2 Expected Values and Variance3.1.3 Introduction to Methods of Estimation -- 3.1.4 Properties of Point Estimators -- Bias -- Precision (Variance and Standard Error) -- Accuracy (Mean Squared Error) -- 3.1.5 Computer Intensive Methods -- 3.2 Maximum Likelihood Estimation Methods -- 3.2.1 Maximum Likelihood Estimators -- 3.2.2 Properties of Maximum Likelihood Estimators -- 3.2.3 Variance, Covariance (and Standard Error) Estimation -- 3.2.4 Con dence Interval Estimators -- 3.2.5 Multiple Maxima -- 3.2.6 Observed and Complete Data Likelihood |
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3.3 Bayesian Estimation3.3.1 Theory -- 3.3.2 Computing Methods -- 3.4 Modeling Predictor Variables -- 3.4.1 The Logit Link Function -- 3.4.2 Interpretation -- 3.4.3 Estimation -- 3.5 Hypothesis Testing -- 3.5.1 Background and De nitions -- 3.5.2 Likelihood Ratio Tests -- 3.5.3 Goodness of Fit Tests -- 3.6 Model Selection -- 3.6.1 Akaike's Information Criterion (AIC) -- 3.6.2 Goodness of Fit and Overdispersion -- 3.6.3 Quasi-AIC -- 3.6.4 Model Averaging and Model Selection Uncertainty -- 3.6.5 Bayesian Assessment of Model Fit |
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3.6.6 Bayesian Model Selection3.7 Discussion -- Part II Single-Species, Single-Season Occupancy Models -- 4 Basic Presence/Absence Situation -- 4.1 The Sampling Situation -- 4.2 Estimation of Occupancy if Probability of Detection Is 1 or Known Without Error -- 4.3 Two-Step Ad Hoc Approaches -- 4.3.1 Geissler-Fuller Method -- 4.3.2 Azuma-Baldwin-Noon Method -- 4.3.3 Nichols-Karanth Method -- 4.4 Model-Based Approach -- 4.4.1 Building a Model -- Observed Data Likelihood -- Complete Data Likelihood -- 4.4.2 Estimation |
| Notes |
""4.4.3 Constant Detection Probability Model"" |
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Print version record |
| Subject |
Animal populations -- Estimates.
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Animal populations -- Mathematical models
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Animal populations -- Estimates
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Animal populations -- Mathematical models
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| Form |
Electronic book
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| Author |
Nichols, James D
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Royle, J. Andrew
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Pollock, Kenneth H
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Bailey, Larissa
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Hines, James E
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| ISBN |
9780124072459 |
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0124072453 |
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