a hierarchical bayesian approach to ecological count data a flexible tool for ecologists分层贝叶斯方法为生态学家生态计数数据灵活的工具.pdfVIP

A Hierarchical Bayesian Approach to Ecological Count Data: A Flexible Tool for Ecologists 1 2 3 4 James A. Fordyce *, Zachariah Gompert , Matthew L. Forister , Chris C. Nice 1 Department of Ecology and Evolutionary Biology, University of Tennessee, Knoxville, Tennessee, United States of America, 2 Department of Botany, Program in Ecology, University of Wyoming, Laramie, Wyoming, United States of America, 3 Department of Biology, University of Nevada, Reno, Nevada, United States of America, 4 Department of Biology, Population and Conservation Biology Program, Texas State University, San Marcos, Texas, United States of America 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. Citation: Fordyce JA, Gompert Z, Forister ML, Nice CC (2011) A Hierarchical Bayesian Approach to Ecological Count Data: A Flexible Tool for Ecologists. PLoS ONE 6(11): e26785. doi:10.1371/journal.pone.0026785 Editor: Enrico Scalas, Universita’