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

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

Volume 1:Prelude and Static Models

1st Edition - November 14, 2015

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  • Authors: Marc Kery, J. Royle
  • Hardcover ISBN: 9780128013786
  • eBook ISBN: 9780128014868

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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. Applied Hierarchical Modeling in Ecology, Volume 1 serves as an indispensable manual for practicing field biologists, and as a graduate-level text for students in ecology, conservation biology, fisheries/wildlife management, and related fields.

Key Features

  • 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


graduate students and professionals in ecology, biogeography, conservation biology, fisheries and wildlife management

Table of Contents

    • Dedication
    • Foreword
    • Preface
    • Acknowledgments
    • 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

Product details

  • No. of pages: 808
  • Language: English
  • Copyright: © Academic Press 2015
  • Published: November 14, 2015
  • Imprint: Academic Press
  • Hardcover ISBN: 9780128013786
  • eBook ISBN: 9780128014868

About the Authors

Marc Kery

Marc Kery
Dr. Marc works as a senior scientist at the Swiss Ornithological Institute, Seerose 1, 6204 Sempach, Switzerland. This is a non-profit NGO with about 160 employees dedicated primarily to bird research, monitoring, and conservation. Marc was trained as a plant population ecologist at the Swiss Universities of Basel and Zuerich. After a 2-year postdoc at the (then) USGS Patuxent Wildlife Center in Laurel, MD. During the last 20 years he has worked at the interface between population ecology, biodiversity monitoring, wildlife management, and statistics. He has published more than 100 peer-reviewed journal articles and five textbooks on applied statistical modeling. He has also been very active in teaching fellow biologists and wildlife managers the concepts and tools of modern statistical analysis in their fields in workshops all over the world, something which goes together with his books, which target the same audiences.

Affiliations and Expertise

Senior Scientist,Swiss Ornithological Institute

J. Royle

J. Royle
Dr Royle is a Senior Scientist and Research Statistician at the U.S. Geological Survey's Patuxent Wildlife Research Center. His research is focused on the application of probability and statistics to ecological problems, especially those related to animal sampling and demographic modeling. Much of his research over the last 10 years has been devoted to the development of methods illustrated in our new book. He has authored or coauthored more than 100 journal articles, and co-authored the books Spatial Capture Recapture, Hierarchical Modeling and Inference in Ecology and Occupancy Estimation and Modeling: Inferring Patterns and Dynamics of Species Occurrence, all published by Academic Press.

Affiliations and Expertise

Research Statistician, U.S. Geological Survey, Patuxent Wildlife Research Center, Laurel, MD, USA

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  • EsteveLlop Tue Oct 23 2018

    Applied Hierarchical Modeling in Ecology:

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

  • Alexej S. Fri Mar 16 2018

    My new favorite ecological statistics book

    Applied hierarchical modeling in ecology (AHM) is hands down the best ecological statistics book written to date. It is very accessible to a wide audience, including ecologists that may shy away from statistics and those that may be less familiar with hierarchical models. This book will help you understand important concepts, such as how the observation process influences our interpretation of ecological states (e.g, occupancy, abundance, species richness). The sections on point processes and the relationship between distribution and abundance was amazing! Also, the section on simulating data, will provide you an appreciation of how survey effort and detectability influence our ability to estimate various states. The authors have a unique gift; translating ecological statistics is not easy. Their writing style and interactive approach, using R and BUGS with clear and understandable examples, shows that they understand their audience. Many thanks and looking forward to Volume 2!