Applied Hierarchical Modeling in Ecology

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Table of Contents

• Part 1. Prelude
  • Chapter 1. Distribution, Abundance, and Species Richness in Ecology
    • 1.1. Point Processes, Distribution, Abundance, and Species Richness
    • 1.2. Meta-population Designs
    • 1.3. State and Rate Parameters
    • 1.4. Measurement Error Models in Ecology
    • 1.5. Hierarchical Models for Distribution, Abundance, and Species Richness
    • 1.6. Summary and Outlook
    • Exercises
      
  • Chapter 2. What Are Hierarchical Models and How Do We Analyze Them?
    • 2.1. Introduction
    • 2.2. Random Variables, Probability Density Functions, Statistical Models, Probability, and Statistical Inference
    • 2.3. Hierarchical Models (HMs)
    • 2.4. Classical Inference Based on Likelihood
    • 2.5. Bayesian Inference
    • 2.6. Basic Markov Chain Monte Carlo (MCMC)
    • 2.7. Model Selection and Averaging
    • 2.8. Assessment of Model Fit
    • 2.9. Summary and Outlook
    • Exercises
      
  • Chapter 3. Linear Models, Generalized Linear Models (GLMs), and Random Effects Models: The Components of Hierarchical Models
    • 3.1. Introduction
    • 3.2. Linear Models
    • 3.3. Generalized Linear Models (GLMs)
    • 3.4. Random Effects (Mixed) Models
    • 3.5. Summary and Outlook
    • Exercises
      
  • Chapter 4. Introduction to Data Simulation
    • 4.1. What Do We Mean by Data Simulation, and Why Is It So Tremendously Useful?
    • 4.2. Generation of a Typical Point Count Data Set
    • 4.3. Packaging Everything in a Function
    • 4.4. Summary and Outlook
    • Exercises
      
  • Chapter 5. Fitting Models Using the Bayesian Modeling Software BUGS and JAGS
    • 5.1. Introduction
    • 5.2. Introduction to BUGS Software: WinBUGS, OpenBUGS, and JAGS
    • 5.3. Linear Model with Normal Response (Normal GLM): Multiple Linear Regression
    • 5.4. The R Package rjags
    • 5.5. Missing values (NAs) in a Bayesian Analysis
    • 5.6. Linear Model with Normal Response (Normal GLM): Analysis of Covariance (ANCOVA)
    • 5.7. Proportion of Variance Explained (R2)
    • 5.8. Fitting a Model with Nonstandard Likelihood Using the Zeros or the Ones Tricks
    • 5.9. Poisson GLM
    • 5.10. GoF Assessment: Posterior Predictive Checks and the Parametric Bootstrap
    • 5.11. Binomial GLM (Logistic Regression)
    • 5.12. Moment-Matching in a Binomial GLM to Accommodate Underdispersion
    • 5.13. Random-Effects Poisson GLM (Poisson GLMM)
    • 5.14. Random-Effects Binomial GLM (Binomial GLMM)
    • 5.15. General Strategy of Model Building with BUGS
    • 5.16. Summary and Outlook
    • Exercises
      
    
    
• Part 2. Models for Static Systems
  • Chapter 6. Modeling Abundance with Counts of Unmarked Individuals in Closed Populations: Binomial N-mixture Models
    • 6.1. Introduction to the Modeling of Abundance
    • 6.2. An Exercise in Hierarchical Modeling: Derivation of Binomial N-mixture Models from First Principles
    • 6.3. Simulation and Analysis of the Simplest Possible N-mixture Model
    • 6.4. A Slightly More Complex N-mixture Model with Covariates
    • 6.5. A Very General Data Simulation Function for N-mixture Models: simNmix
    • 6.6. Study Design, Bias, and Precision of the Binomial N-mixture Model Estimator
    • 6.7. Study of Some Assumption Violations Using Function simNmix
    • 6.8. Goodness-of-Fit (GoF)
    • 6.9. Abundance Mapping of Swiss Great Tits with unmarked
    • 6.10. The Issue of Space, or: What Is Your Effective Sample Area?
    • 6.11. Bayesian Modeling of Swiss Great Tits with BUGS
    • 6.12. Time-for-Space Substitution
    • 6.13. The Royle-Nichols Model and Other Nonstandard N-mixture Models
    • 6.14. Multiscale N-mixture Models
    • 6.15. Summary and Outlook
    • Exercises
      
  • Chapter 7. Modeling Abundance Using Multinomial N-Mixture Models
    • 7.1. Introduction
    • 7.2. Multinomial N-Mixture Models in Ecology
    • 7.3. Simulating Multinomial Observations in R
    • 7.4. Likelihood Inference for Multinomial N-Mixture Models
    • 7.5. Example 1: Bird Point Counts Based on Removal Sampling
    • 7.6. Bayesian Analysis in BUGS Using the Conditional Multinomial (Three-Part) Model
    • 7.7. Building Custom Multinomial Models in unmarked
    • 7.8. Spatially Stratified Capture-Recapture Models
    • 7.9. Example 3: Jays in the Swiss MHB
    • 7.10. Summary and Outlook
    • Exercises
      
  • Chapter 8. Modeling Abundance Using Hierarchical Distance Sampling
    • 8.1. Introduction
    • 8.2. Conventional Distance Sampling
    • 8.3. Bayesian Conventional Distance Sampling
    • 8.4. Hierarchical Distance Sampling (HDS)
    • 8.5. Bayesian HDS
    • 8.6. Summary
    • Exercises
      
  • Chapter 9. Advanced Hierarchical Distance Sampling
    • 9.1. Introduction
    • 9.2. Distance Sampling (DS) with Clusters, Groups, or Other Individual Covariates
    • 9.3. Time-Removal and DS Combined
    • 9.4. Mark-Recapture/Double-Observer DS
    • 9.5. Open HDS Models: Temporary Emigration
    • 9.6. Open HDS Models: Implicit Dynamics
    • 9.7. Open HDS Models: Modeling Population Dynamics
    • 9.8. Spatial Distance Sampling: Modeling Within-Unit Variation in Density
    • 9.9. Summary
    • Exercises
      
  • Chapter 10. Modeling Static Occurrence and Species Distributions Using Site-occupancy Models
    • 10.1. Introduction to the Modeling of Occurrence—Including Species Distributions
    • 10.2. Another Exercise in Hierarchical Modeling: Derivation of the Site-Occupancy Model
    • 10.3. Simulation and Analysis of the Simplest Possible Site-Occupancy Model
    • 10.4. A Slightly More Complex Site-Occupancy Model with Covariates
    • 10.5. A General Data Simulation Function for Static Occupancy Models: simOcc
    • 10.6. A Model with Lots of Covariates: Use of R Function model.matrix with BUGS
    • 10.7. Study Design, and Bias and Precision of Site-Occupancy Estimators
    • 10.8. Goodness-of-Fit
    • 10.9. Distribution Modeling and Mapping of Swiss Red Squirrels
    • 10.10. Multiscale Occupancy Models
    • 10.11. Space-for-Time Substitution
    • 10.12. Models for Data along Transects: Poisson, Exponential, Weibull, and Removal Observation Models
    • 10.13. Occupancy Modeling of a Community of Species
    • 10.14. Modeling Wiggly Covariate Relationships: Penalized Splines in Hierarchical Models
    • 10.15. Summary and Outlook
    • Exercises
      
  • Chapter 11. Hierarchical Models for Communities
    • 11.1. Introduction
    • 11.2. Simulation of a Metacommunity
    • 11.3. Metacommunity Data from the Swiss Breeding Bird Survey MHB
    • 11.4. Overview of Some Models for Metacommunities
    • 11.5. Community Models That Ignore Species Identity
    • 11.6. Community Models that Fully Retain Species Identity
    • 11.7. The Dorazio/Royle (DR) Community Occupancy Model with Data Augmentation (DA)
    • 11.8. Inferences Based on the Estimated Z Matrix: Similarity among Sites and Species
    • 11.9. Species Richness Maps and Species Accumulation Curves
    • 11.10. Community N-mixture (or Dorazio/Royle/Yamaura - DRY) Models
    • 11.11. Summary and Outlook
    • Exercises
      
    
    
• Summary and Conclusion
• References
• Author Index
• Subject Index
  

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