A Hierarchical Bayesian Approach to Ecological Count Data: A Flexible Tool for Ecologists
Date
2011-11-21
Authors
Fordyce, James
Gompert, Zachariah
Forister, Matthew L.
Nice, Chris C.
Journal Title
Journal ISSN
Volume Title
Publisher
Public Library of Science
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.
Description
Keywords
plants, eggs, oviposition, bird eggs, probability distribution, astragalus, simulation and modeling, analysis of variance, Biology
Citation
Fordyce, J. A., Gompert, Z., Forister, M. L., & Nice, C. C. (2011). A hierarchical Bayesian approach to ecological count data: A flexible tool for ecologists. PLoS One, 6(11), e26785.
Rights
Rights Holder
© 2011 Fordyce et al.
Rights License
This work is licensed under a Creative Commons Attribution 4.0 International License.