title: A hierarchical bayesian approach to ecological count data: a flexible tool for ecologists.
abstract: Many ecological studies use the analysis of count data to arrive at biologically meaningful inferences. Here, we introduce a hierarchical bayesian approach to count data. This approach has the advantage over traditional approaches in that it directly estimates the parameters of interest at both the individual-level and population-level, appropriately models uncertainty, and allows for comparisons among models, including those that exceed the complexity of many traditional approaches, such as ANOVA or non-parametric analogs. As an example, we apply this method to oviposition preference data for butterflies in the genus Lycaeides. Using this method, we estimate the parameters that describe preference for each population, compare the preference hierarchies among populations, and explore various models that group populations that share the same preference hierarchy.
authors: Department of Ecology and Evolutionary Biology, University of Tennessee, Knoxville, Tennessee, United States of America. jfordyce@utk.edu
authors: James A
authors: JA
authors: Fordyce
authors: Zachariah
authors: Z
authors: Gompert
authors: Matthew L
authors: ML
authors: Forister
authors: Chris C
authors: CC
authors: Nice
mesh_terms: D000818:Animals; D001499:Bayes Theorem; D002080:Butterflies / Q000502:physiology; D002755:Choice Behavior; D003198:Computer Simulation; D004463:Ecology / Q000379:methods*; D005260:Female; D006790:Host-Parasite Interactions; D015233:Models, Statistical; D011157:Population Dynamics