Data from: Integrating over uncertainty in spatial scale of response within multispecies occupancy models yields more accurate assessments of community composition

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 scales at which landscapes affect species
remains a persistent challenge, hindering our ability to understand how
environmental gradients shape communities. This problem is amplified by
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 levels of uncertainty in
parameter estimates across a broad range of simulation conditions. An
ssMSOM can be run in a matter of minutes, as opposed to the many hours
required to run normal multi-species occupancy models at all queried
spatial scales, and then conduct model selection—a problem that up to now
has prohibited scale of response from being rigorously evaluated in an
occupancy framework. Alternatives to the ssMSOM, such as GLM based
approaches frequently fail to detect the correct spatial scale and
magnitude of response, and are often falsely confident by favoring the
incorrect parameter estimates, especially as species’ detection
probabilities deviate from perfect. We further show how trait information
can be leveraged to understand how individual species’ scales of response
vary within communities. 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.