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

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 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 rare 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 multispecies 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 multispecies 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.

Measuring the impact of oceanographic indices on species distribution shifts: The spatially varying effect of cold‐pool extent in the eastern Bering Sea

“…However, SDMs have typically focused on including local habitat measurements within regression models for predicting local densities, and these nonlocal mechanisms are rarely included explicitly in SDMs. Alternatively, some SDMs apply a kernel smoother to local covariates prior to inclusion (Chandler and Hepinstall‐Cymerman ; Frishkoff et al ); this smoother captures the response to a spatial average of nearby habitats, but does not allow for complicated dependencies on spatially distant habitats like the SVC model explored here. I therefore argue that SVCs for annual oceanographic indices represent a useful and flexible way to represent habitat selection as animals respond to information about resource availability in years with different oceanographic conditions.…”

Species‐specific responses to habitat conversion across scales synergistically restructure Neotropical bird communities

“…We therefore integrated over the uncertainty in spatial scale directly within the Markov chain Monte Carlo (MCMC) (Frishkoff et al. ). Briefly, along each iteration of the MCMC, a spatial scale ( s ) was drawn from a uniform prior, stretching from the smallest (60 m) to the largest scale (1,500 m).…”

Remnant forest in Costa Rican working landscapes fosters bird communities that are indistinguishable from protected areas

“…Landscape forest cover and edge variables were estimated as the fraction of forest and edge perimeter within 60 to 1,500 m of each sampling the site. The model was allowed to select the most predictive scale (Frishkoff, Mahler, & Fortin, ). Here, 610 m was most predictive; thus, we used a 610‐m buffer radius in all analyses.…”