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Abstract:
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In ecological research, it is of interest to study the mechanism of how environmental variables are related to species abundance and richness. The observed species abundances often contain excessive number of zeroes due to limitation of field sampling; hence the observed species richness is often smaller than the actual species richness in the study region. Even though a lot of environmental variables, e.g. climate, geography feature land use and land cover, are observed in such study with the species abundance measurements, the species abundance might respond to only a small portion of these environmental variables. Identification of these variables would be crucial to study the mechanism of how species abundance is related to environment change and how diversity partition in a region responds to these changes. This study aims at developing a Bayesian hierarchical approach, which can handle the zero-inflation and sparsity simultaneously. Bayesian shrinkage priors are used to detect signals (relevant environmental variables) and avoid picking up the redundant predictors. The proposed approach is used to analyze the butterfly occurrence data collected in O
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