Bayesian data analysis in population ecology: motivations, methods, and benefits

Population Ecology



During the 20th century ecologists largely relied on the frequentist system of inference for the analysis of their data. However, in the past few decades ecologists have become increasingly interested in the use of Bayesian methods of data analysis. In this article I provide guidance to ecologists who would like to decide whether Bayesian methods can be used to improve their conclusions and predictions. I begin by providing a concise summary of Bayesian methods of analysis, including a comparison of differences between Bayesian and frequentist approaches to inference when using hierarchical models. Next I provide a list of problems where Bayesian methods of analysis may arguably be preferred over frequentist methods. These problems are usually encountered in analyses based on hierarchical models of data. I describe the essentials required for applying modern methods of Bayesian computation, and I use real-world examples to illustrate these methods. I conclude by summarizing what I perceive to be the main strengths and weaknesses of using Bayesian methods to solve ecological inference problems.

Publication type Article
Publication Subtype Journal Article
Title Bayesian data analysis in population ecology: motivations, methods, and benefits
Series title Population Ecology
DOI 10.1007/s10144-015-0503-4
Volume 58
Issue 1
Year Published 2016
Language English
Publisher Springer Japan
Publisher location Tokyo, Japan
Contributing office(s) Wetland and Aquatic Research Center
Description 14 p.
First page 31
Last page 44
Public Comments This manuscript was submitted for the special feature based on a symposium in Tsukuba, Japan, held on 11 October 2014.
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