Biogeographic multi-species occupancy models for large-scale survey data

Abstract

Ecologists often seek to infer patterns of species occurrence or community structure from survey data. Hierarchical models, including multi-species occupancy models (MSOMs), can improve inference by pooling information across multiple species via random effects. Originally developed for local-scale survey data, MSOMs are increasingly applied to larger spatial scales that transcend major abiotic gradients and dispersal barriers. At biogeographic scales, the benefits of partial pooling in MSOMs trade off against the difficulty of incorporating sufficiently complex spatial effects to account for biogeographic variation in occupancy across multiple species simultaneously.We show how this challenge can be overcome by incorporating pre-existing range information into MSOMs, yielding a ‘biogeographic multi-species occupancy model’ (bMSOM). We illustrate the bMSOM using two published datasets: Parulid warblers in the United States Breeding Bird Survey, and entire avian communities in forests and pastures of Colombia’s West Andes.Compared to traditional MSOMs, the bMSOM provides dramatically better predictive performance at lower computational cost. The bMSOM avoids severe spatial biases in predictions of the traditional MSOM and provides principled species-specific inference even for never-observed species.Incorporating pre-existing range data enables principled partial pooling of information across species in large-scale MSOMs. Our biogeographic framework for multi-species modeling should be broadly applicable in hierarchical models that predict species occurrences, whether or not false-absences are modeled in an occupancy framework.

High sensitivity of tropical forest birds to deforestation at lower altitudes

“…For each forest‐dependency class, this corresponds to two coefficients, one for each range half, so that sensitivity to habitat loss was not constrained to have had the same effect at upper as at lower range margins. We additionally included two terms, $$$ {\upbeta}_{7,\mathrm{dep}\left[k\right]}\times \mathrm{range}\ {\mathrm{breadth}}_k $$$, where range breadth is the breadth of the k th species’ elevational range, and $$$ {\upbeta}_{8,\mathrm{dep}\left[k\right]}\times \mathrm{range}\ {\mathrm{breadth}}_k\times {\mathrm{habitat}}_i $$$, the interaction between range breadth and habitat, in case species with narrower elevational ranges tend to be systematically more or less sensitive to loss of habitat (Socolar et al, 2021).…”

“…This scaling placed species ranges on a common scale, allowing us to fit shared range‐shape parameters across species. To minimize computational effort, rather than model large numbers of all‐0 detection histories that arise at elevations far outside a species' elevational distribution, for each species we clipped the data set to only include points within ±3 units of scaled elevation (note that the most extreme detection was at a scaled elevation of 2) (Socolar et al, 2021 ).…”

“…For each forest‐dependency class, this corresponds to two coefficients, one for each range half, so that sensitivity to habitat loss was not constrained to have had the same effect at upper as at lower range margins. We additionally included two terms, , where range breadth is the breadth of the k th species’ elevational range, and , the interaction between range breadth and habitat, in case species with narrower elevational ranges tend to be systematically more or less sensitive to loss of habitat (Socolar et al, 2021 ).…”