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A Bayesian Dirichlet process community occupancy model to estimate community structure and species similarity
Author(s) -
Sollmann Rahel,
Eaton Mitchell Joseph,
Link William A.,
Mulondo Paul,
Ayebare Samuel,
Prinsloo Sarah,
Plumptre Andrew J.,
Johnson Devin S.
Publication year - 2021
Publication title -
ecological applications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.864
H-Index - 213
eISSN - 1939-5582
pISSN - 1051-0761
DOI - 10.1002/eap.2249
Subject(s) - occupancy , dirichlet process , statistics , dirichlet distribution , community structure , mathematics , covariate , cluster analysis , species distribution , bayesian probability , similarity (geometry) , random effects model , cluster (spacecraft) , ecology , computer science , biology , artificial intelligence , medicine , mathematical analysis , meta analysis , habitat , programming language , image (mathematics) , boundary value problem
Community occupancy models estimate species‐specific parameters while sharing information across species by treating parameters as sampled from a common distribution. When communities consist of discrete groups, shrinkage of estimates toward the community mean can mask differences among groups. Infinite‐mixture models using a Dirichlet process (DP) distribution, in which the number of latent groups is estimated from the data, have been proposed as a solution. In addition to community structure, these models estimate species similarity, which allows testing hypotheses about whether traits drive species response to environmental conditions. We develop a community occupancy model (COM) using a DP distribution to model species‐level parameters. Because clustering algorithms are sensitive to dimensionality and distinctiveness of clusters, we conducted a simulation study to explore performance of the DP‐COM with different dimensions (i.e., different numbers of model parameters with species‐level DP random effects) and under varying cluster differences. Because the DP‐COM is computationally expensive, we compared its estimates to a COM with a normal random species effect. We further applied the DP‐COM model to a bird data set from Uganda. Estimates of the number of clusters and species cluster identity improved with increasing difference among clusters and increasing dimensions of the DP; but the number of clusters was always overestimated. Estimates of number of sites occupied and species and community‐level covariate coefficients on occupancy probability were generally unbiased with (near‐) nominal 95% Bayesian Credible Interval coverage. Accuracy of estimates from the normal and the DP‐COM was similar. The DP‐COM clustered 166 bird species into 27 clusters regarding their affiliation with open or woodland habitat and distance to oil wells. Estimates of covariate coefficients were similar between a normal and the DP‐COM. Except sunbirds, species within a family were not more similar in their response to these covariates than the overall community. Given that estimates were consistent between the normal and the DP‐COM, and considering the computational burden for the DP models, we recommend using the DP‐COM only when the analysis focuses on community structure and species similarity, as these quantities can only be obtained under the DP‐COM.