Applied Hierarchical Modeling in Ecology: Analysis of distribution, abundance and species richness in R and BUGS

Applied Hierarchical Modeling in Ecology

Last edition Elsevier Applied Hierarchical Modeling in Ecology: Distribution, Abundance, Species Richness offers a new synthesis of the state-of-the-art of hierarchical models for plant and animal distribution, abundance, and community characteristics such as species richness using data collected in metapopulation designs. These types of data are extremely widespread in ecology and its applications in such areas as biodiversity monitoring and fisheries and wildlife management. This first volume explains static models/procedures in the context of hierarchical models that collectively represent a unified approach to ecological research, taking the reader from design, through data collection, and into analyses using a very powerful class of models.

Last Edition

ISBN 13: 9780128013786

Imprint: Elsevier

Language: English

Authors: Marc Kery

Pub Date: 11/2015

Pages: 808

Illus: Illustrated

Weight: 1,810.000 grams

Size: h 191 X 235 mm

Product Type: Hardcover

List Price
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  • • Provides a synthesis of important classes of models about distribution, abundance, and species richness while accommodating imperfect detection
  • • Presents models and methods for identifying unmarked individuals and species
  • • Written in a step-by-step approach accessible to non-statisticians and provides fully worked examples that serve as a template for readers' analyses
  • • Includes companion website containing data sets, code, solutions to exercises, and further information
  • Marc Kery, Population Ecologist, Swiss Ornithological Institute, Switzerland and J. Andrew Royle, Research Statistician, U.S. Geological Survey, Patuxent Wildlife Research Center, Laurel, MD, USA
  • • Dedication • Foreword • Preface • Acknowledgments
  • • Part 1. Prelude
  • o 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
  • o 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
  • o 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
  • o 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
  • o 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
  • o 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
  • o 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
  • o 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
  • o 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
  • o 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
  • o 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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