Environmental conditions are changing rapidly in response to anthropogenic stressors. These changes can influence the occurrence, distribution, richness, and evenness of species at local, regional, and even continental scales. Determining how species diversity may be altered in response to these environmental changes is fundamental to understanding community dynamics and for prioritizing conservation and management actions. Traditional approaches for studying species diversity estimate richness from observed counts using various regression procedures relating to local habitat characteristics (e.g., species accumulation curves). Yet, a community will not respond homogeneously with respect to habitat covariates because species differ in their responses to landscape characteristics. By modeling the aggregate variable, the net effect of species richness responses to landscape heterogeneity must necessarily be biased, and one would expect to produce models with fewer and less important covariate effects. Furthermore, species are not observed perfectly and rare species tend to be undercounted because detection during sampling is often positively correlated with local abundance. Yet, rare species are often comparatively more dynamic and specialized potentially leading to heightened responses to anthropogenic changes. Failure to accurately account for all species, and especially rare species, in a community can thus lead to incorrect inferences on patterns of community diversity.
Recent developments in multi-species modeling have improved the ability to make inferences about community distributions and structure based on species-specific models of occurrence or abundance, integrated within a hierarchical modeling framework. This modeling framework offers advantages to inference about species occupancy, distributions, and richness over typical approaches by accounting for both species-level effects and the aggregated effects of landscape composition on a community as a whole, thus leading to increased precision of localized species diversity by improving estimates for all species, including those that are rare or were observed infrequently. I present the underlying principles for multi-species modeling and demonstrate the utility of the approach to studying the effects of anthropogenic changes to wildlife diversity. This analysis framework can be used to investigate the impacts of management actions as well as land-use changes on species or assemblage richness, and to understand trade-offs in species-specific distribution patterns associated with landscape variability.