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A Hierarchical Bayesian Approach to Ecological Count Data: A Flexible Tool for Ecologists

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  • James A Fordyce
  • Zachariah Gompert
  • Matthew L Forister
  • Chris C Nice

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.

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  • James A Fordyce & Zachariah Gompert & Matthew L Forister & Chris C Nice, 2011. "A Hierarchical Bayesian Approach to Ecological Count Data: A Flexible Tool for Ecologists," PLOS ONE, Public Library of Science, vol. 6(11), pages 1-7, November.
    • Handle: RePEc:plo:pone00:0026785
    DOI: 10.1371/journal.pone.0026785
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    Cited by:

    1. Nicolas Jouvin & Pierre Latouche & Charles Bouveyron & Guillaume Bataillon & Alain Livartowski, 2021. "Greedy clustering of count data through a mixture of multinomial PCA," Computational Statistics, Springer, vol. 36(1), pages 1-33, March.

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