An accessible method for implementing hierarchical models with spatio-temporal abundance data.

ABSTRACT: A common goal in ecology and wildlife management is to determine the causes of variation in population dynamics over long periods of time and across large spatial scales. Many assumptions must nevertheless be overcome to make appropriate inference about spatio-temporal variation in population dynamics, such as autocorrelation among data points, excess zeros, and observation error in count data. To address these issues, many scientists and statisticians have recommended the use of Bayesian hierarchical models. Unfortunately, hierarchical statistical models remain somewhat difficult to use because of the necessary quantitative background needed to implement them, or because of the computational demands of using Markov Chain Monte Carlo algorithms to estimate parameters. Fortunately, new tool

SUBMITTER: Ross BE 

PROVIDER: S-EPMC3500297 | biostudies-literature | 2012

REPOSITORIES: biostudies-literature