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Books in Statistical ecology

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Numerical Ecology

  • 3rd Edition
  • Volume 24
  • July 3, 2012
  • P. Legendre + 1 more
  • English
  • Paperback
    9 7 8 - 0 - 4 4 4 - 5 3 8 6 8 - 0
  • eBook
    9 7 8 - 0 - 4 4 4 - 5 3 8 6 9 - 7
The book describes and discusses the numerical methods which are successfully being used for analysing ecological data, using a clear and comprehensive approach. These methods are derived from the fields of mathematical physics, parametric and nonparametric statistics, information theory, numerical taxonomy, archaeology, psychometry, sociometry, econometry and others.

Introduction to WinBUGS for Ecologists

  • 1st Edition
  • June 17, 2010
  • Marc Kéry
  • English
  • Paperback
    9 7 8 - 0 - 1 2 - 3 7 8 6 0 5 - 0
  • eBook
    9 7 8 - 0 - 1 2 - 3 7 8 6 0 6 - 7
Introduction to WinBUGS for Ecologists introduces applied Bayesian modeling to ecologists using the highly acclaimed, free WinBUGS software. It offers an understanding of statistical models as abstract representations of the various processes that give rise to a data set. Such an understanding is basic to the development of inference models tailored to specific sampling and ecological scenarios. The book begins by presenting the advantages of a Bayesian approach to statistics and introducing the WinBUGS software. It reviews the four most common statistical distributions: the normal, the uniform, the binomial, and the Poisson. It describes the two different kinds of analysis of variance (ANOVA): one-way and two- or multiway. It looks at the general linear model, or ANCOVA, in R and WinBUGS. It introduces generalized linear model (GLM), i.e., the extension of the normal linear model to allow error distributions other than the normal. The GLM is then extended contain additional sources of random variation to become a generalized linear mixed model (GLMM) for a Poisson example and for a binomial example. The final two chapters showcase two fairly novel and nonstandard versions of a GLMM. The first is the site-occupancy model for species distributions; the second is the binomial (or N-) mixture model for estimation and modeling of abundance.

Bayesian Inference

  • 1st Edition
  • August 4, 2009
  • William A Link + 1 more
  • English
  • Hardback
    9 7 8 - 0 - 1 2 - 3 7 4 8 5 4 - 6
  • eBook
    9 7 8 - 0 - 0 8 - 0 8 8 9 8 0 - 1
This text is written to provide a mathematically sound but accessible and engaging introduction to Bayesian inference specifically for environmental scientists, ecologists and wildlife biologists. It emphasizes the power and usefulness of Bayesian methods in an ecological context. The advent of fast personal computers and easily available software has simplified the use of Bayesian and hierarchical models . One obstacle remains for ecologists and wildlife biologists, namely the near absence of Bayesian texts written specifically for them. The book includes many relevant examples, is supported by software and examples on a companion website and will become an essential grounding in this approach for students and research ecologists.

Hierarchical Modeling and Inference in Ecology

  • 1st Edition
  • July 3, 2008
  • J. Andrew Royle + 1 more
  • English
  • Hardback
    9 7 8 - 0 - 1 2 - 3 7 4 0 9 7 - 7
  • eBook
    9 7 8 - 0 - 0 8 - 0 5 5 9 2 5 - 4
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