The long-term study of bat communities often depends on a diverse set of sampling methodologies that are chosen based on the species or habitat management priorities of the research project. Integrating the data from a diverse set of methodologies (such as acoustic monitoring and mist net sampling) would improve our ability to characterize changes in community structure or composition over time, such as one would expect following an emergent infectious disease such as white-nose syndrome. We developed a Bayesian state-space model to integrate these disparate data into a common currency (relative abundance). We collected both acoustic monitoring and mist net capture data over an 8-y period (2006–2014) to document shifts in the bat community in central New England, USA, in response to the onset of white-nose syndrome in 2009. The integrated data model shows a significant decline in the abundance of little brown bat Myotis lucifugus, northern long-eared bat Myotis septentrionalis, and hoary bat Lasiurus cinereus, and an increase in abundance of the eastern small-footed bat Myotis leibii and the eastern red bat Lasiurus borealis. There was no evidence for a change in abundance in the big brown bat Eptesicus fuscus since the onset of white-nose syndrome. The consistency of this model with regional estimates of decline over the same time period support the validity of our relative abundance estimate. This model provides the opportunity to quantify shifts in other communities where multiple sampling methodologies were employed, and therefore provides natural resource managers with a robust tool to integrate existing sampling data to quantify changes in community composition that can inform conservation and management recommendations.
