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• W2617401596 abstract "Abstract Species abundance and community composition are affected not only by the local environment, but also by broader landscape and regional context. Yet determining the spatial scale at which landscapes affect species remains a persistent challenge that hinders ecologists’ abilities to understand how environmental gradients influence species presence and shape entire communities, especially in the face of data deficient species and imperfect species detection. Here we present a Bayesian framework that allows uncertainty surrounding the ‘true’ spatial scale of species’ responses ( i.e., changes in presence/absence) to be integrated directly into a community hierarchical model. This scale selecting multi-species occupancy model (ssMSOM) estimates the scale of response, and shows high accuracy and correct type I error rates across a broad range of simulation conditions. In contrast, ensembles of single species GLMs frequently fail to detect the correct spatial scale of response, and are often falsely confident in favoring the incorrect spatial scale, especially as species’ detection probabilities deviate from perfect. Integrating spatial scale selection directly into hierarchical community models provides a means of formally testing hypotheses regarding spatial scales of response, and more accurately determining the environmental drivers that shape communities." @default.
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• W2617401596 date "2017-05-29" @default.
• W2617401596 modified "2023-10-16" @default.
• W2617401596 title "Integrating over uncertainty in spatial scale of response within multispecies occupancy models yields more accurate assessments of community composition" @default.
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• W2617401596 doi "https://doi.org/10.1101/143669" @default.
• W2617401596 hasPublicationYear "2017" @default.
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