Occupancy Estimation and Modeling : Inferring Patterns and Dynamics of Species Occurrence.

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: MacKenzie, Darryl I.
Otros Autores: Nichols, James D., Royle, J. Andrew, Pollock, Kenneth H., Bailey, Larissa, Hines, James E.
Formato: Electrónico eBook
Idioma:Inglés
Publicado: San Diego : Elsevier Science, 2005.
Edición:2nd ed.
Temas:
Animal populations > Estimates.
Animal populations > Mathematical models.
Animaux > Populations > Estimation.
Animaux > Populations > Modèles mathématiques.
Animal populations > Estimates
Animal populations > Mathematical models
Electronic books.
Acceso en línea:Texto completo
Tabla de Contenidos:
  • 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
  • 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
  • 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
  • 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
  • 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

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