Librería Biogea APPLIED HIERARCHICAL MODELING IN ECOLOGY. ANALYSIS OF DISTRIBUTION, ABUNDANCE AND SPECIES RICHNESS IN R AND BUGS

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APPLIED HIERARCHICAL MODELING IN ECOLOGY
Título:
APPLIED HIERARCHICAL MODELING IN ECOLOGY. ANALYSIS OF DISTRIBUTION, ABUNDANCE AND SPECIES RICHNESS IN R AND BUGS
Subtítulo:
Autor:
MARC KERY J. ROYLE
Editorial:
ACADEMIC PRESS
Año de edición:
2015
Materia
MÉTODOS
ISBN:
978-0-12-801378-6
Páginas:
808
Tamaño:
190 x 235
Disponibilidad:
Reimpresión
89,99 € Comprar

Sinopsis

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

Readership

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

Índice

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