Hierarchical modeling and inference in ecology the analysis of data from populations, metapopulations and communities

A guide to data collection, modeling and inference strategies for biological survey data using Bayesian and classical statistical methods. This book describes a general and flexible framework for modeling and inference in ecological systems based on hierarchical models, with a strict focus on the us...

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Bibliographic Details
Main Author: Royle, J. Andrew
Other Authors: Dorazio, Robert M.
Format: eBook
Language:English
Published: Amsterdam Academic 2008, 2008
Edition:1st ed
Subjects:
Online Access:
Collection: Elsevier ScienceDirect eBooks - Collection details see MPG.ReNa
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245 0 0 |a Hierarchical modeling and inference in ecology  |h [electronic resource]  |h Elektronische Ressource  |b the analysis of data from populations, metapopulations and communities  |c J. Andrew Royle and Robert M. Dorazio 
250 |a 1st ed 
260 |a Amsterdam  |b Academic  |c 2008, 2008 
300 |a online resource (xviii, 444 p., [8] p. of plates)  |b ill. (some col.), maps (some col.) 
505 0 |a Includes bibliographical references (p. 417-437) and index 
505 0 |a R-tutorial, Sample R-functions for implementing several methods -- Appendix2: WinBUGS tutorial and R2WinBUGS package -- Appendix3:Sample WinBUGS and R-scripts for examples used in book 
653 |a Spatial ecology / Mathematical models 
653 |a SCIENCE / Life Sciences / Ecology / bisacsh 
653 |a Electronic books 
653 |a NATURE / Ecosystems & Habitats / Wilderness / bisacsh 
653 |a SCIENCE / Environmental Science (see also Chemistry / Environmental) / bisacsh 
653 |a Spatial ecology / Computer simulation 
653 |a NATURE / Ecology / bisacsh 
700 1 |a Dorazio, Robert M. 
041 0 7 |a eng  |2 ISO 639-2 
989 |b ESD  |a Elsevier ScienceDirect eBooks 
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082 0 |a 577.015118 
520 |a A guide to data collection, modeling and inference strategies for biological survey data using Bayesian and classical statistical methods. This book describes a general and flexible framework for modeling and inference in ecological systems based on hierarchical models, with a strict focus on the use of probability models and parametric inference. Hierarchical models represent a paradigm shift in the application of statistics to ecological inference problems because they combine explicit models of ecological system structure or dynamics with models of how ecological systems are observed. The principles of hierarchical modeling are developed and applied to problems in population, metapopulation, community, and metacommunity systems. The book provides the first synthetic treatment of many recent methodological advances in ecological modeling and unifies disparate methods and procedures. The authors apply principles of hierarchical modeling to ecological problems, including * occurrence or occupancy models for estimating species distribution * abundance models based on many sampling protocols, including distance sampling * capture-recapture models with individual effects * spatial capture-recapture models based on camera trapping and related methods * population and metapopulation dynamic models * models of biodiversity, community structure and dynamics * Wide variety of examples involving many taxa (birds, amphibians, mammals, insects, plants) * Development of classical, likelihood-based procedures for inference, as well as Bayesian methods of analysis * Detailed explanations describing the implementation of hierarchical models using freely available software such as R and WinBUGS * Computing support in technical appendices in an online companion web site