Applied Hierarchical Modeling in Ecology Analysis of distribution abundance and species richness in R and BUGS Volume 1 Prelude and Static Models 1st Edition by Marc Kéry, Royle Andrew 0128013788 9780128013786

Applied Hierarchical Modeling in Ecology. Analysis of distribution, abundance and species richness in R and BUGS: Volume 1: Prelude and Static Models 1st Edition by Marc Kéry, Royle J. Andrew – Ebook PDF Instant Download/DeliveryISBN:  0128013788, 9780128013786 

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ISBN-10 :  0128013788  

ISBN-13 :  9780128013786

Author: Marc Kéry, Royle J. Andrew 

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.

Applied Hierarchical Modeling in Ecology. Analysis of distribution, abundance and species richness in R and BUGS: Volume 1: Prelude and Static Models 1st 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

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