  The occurrence and distributions of wildlife populations and communities are
shifting as a result of global changes. To evaluate whether these shifts are
negatively impacting biodiversity processes, it is critical to monitor the
status, trends, and effects of environmental variables on entire communities.
However, modeling the dynamics of multiple species simultaneously can require
large amounts of diverse data, and few modeling approaches exist to
simultaneously provide species and community level inferences. We present an
"integrated community occupancy model" (ICOM) that unites principles of data
integration and hierarchical community modeling in a single framework to
provide inferences on species-specific and community occurrence dynamics using
multiple data sources. We use simulations to compare the ICOM to previously
developed hierarchical community occupancy models and single species integrated
distribution models. We then apply our model to assess the occurrence and
biodiversity dynamics of foliage-gleaning birds in the White Mountain National
Forest in the northeastern USA from 2010-2018 using three independent data
sources. Simulations reveal that integrating multiple data sources in the ICOM
increased precision and accuracy of species and community level inferences
compared to single data source models, although benefits of integration were
dependent on data source quality (e.g., amount of replication). Compared to
single species models, the ICOM yielded more precise species-level estimates.
Within our case study, the ICOM had the highest out-of-sample predictive
performance compared to single species models and models that used only a
subset of the three data sources. The ICOM offers an attractive approach to
estimate species and biodiversity dynamics, which is additionally valuable to
inform management objectives of both individual species and their broader
communities.
