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The occurrence and distributions of wildlife populations and communities areshifting as a result of global changes. To evaluate whether these shifts arenegatively impacting biodiversity processes, it is critical to monitor thestatus, trends, and effects of environmental variables on entire communities.However, modeling the dynamics of multiple species simultaneously can requirelarge amounts of diverse data, and few modeling approaches exist tosimultaneously provide species and community level inferences. We present an"integrated community occupancy model" (ICOM) that unites principles of dataintegration and hierarchical community modeling in a single framework toprovide inferences on species-specific and community occurrence dynamics usingmultiple data sources. We use simulations to compare the ICOM to previouslydeveloped hierarchical community occupancy models and single species integrateddistribution models. We then apply our model to assess the occurrence andbiodiversity dynamics of foliage-gleaning birds in the White Mountain NationalForest in the northeastern USA from 2010-2018 using three independent datasources. Simulations reveal that integrating multiple data sources in the ICOMincreased precision and accuracy of species and community level inferencescompared to single data source models, although benefits of integration weredependent on data source quality (e.g., amount of replication). Compared tosingle species models, the ICOM yielded more precise species-level estimates.Within our case study, the ICOM had the highest out-of-sample predictiveperformance compared to single species models and models that used only asubset of the three data sources. The ICOM offers an attractive approach toestimate species and biodiversity dynamics, which is additionally valuable toinform management objectives of both individual species and their broadercommunities.

Integrated community occupancy models: A framework to assess occurrence and biodiversity dynamics using multiple data sources