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.