A large-scale assessment of eastern whip-poor-will (Antrostomus vociferus) occupancy across a gradient of forest management intensity using autonomous recording units

Conservationists spend considerable resources to create and enhance wildlife habitat. Monitoring how species respond to these efforts helps managers allocate limited resources. However, monitoring efforts often encounter logistical challenges that are exacerbated as geographic extent increases. We used autonomous recording units (ARUs) and automated acoustic classification to mitigate the challenges of assessing Eastern Whip-poor-will (Antrostomus vociferus) response to forest management across the eastern USA. We deployed 1263 ARUs in forests with varying degrees of management intensity. Recordings were processed using an automated classifier and the resulting detection data were used to assess occupancy. Whip-poor-wills were detected at 401 survey locations. Across our study region, whip-poor-will occupancy decreased with latitude and elevation. At the landscape scale, occupancy decreased with the amount of impervious cover, increased with herbaceous cover and oak and evergreen forests, and exhibited a quadratic relationship with the amount of shrub-scrub cover. At the site-level, occupancy was negatively associated with basal area and brambles (Rubus spp.) and exhibited a quadratic relationship with woody stem density. Implementation of practices that create and sustain a mosaic of forest age classes and a diverse range of canopy closure within oak (Quercus spp.) dominated landscapes will have the highest probability of hosting whip-poor-wills. The use of ARUs and a machine learning classifier helped overcome challenges associated with monitoring a nocturnal species with a short survey window across a large spatial extent. Future monitoring efforts that combine ARU-based protocols and mappable fine-resolution structural vegetation data would likely further advance our understanding of whip-poor-will response to forest management.