Leveraging Light Detection and Ranging (LiDAR) to elucidate forest structural conditions that influence eastern whip-poor-will abundance

Eastern North American forests are degraded due to land use history and are threatened by numerous factors that further reduce their structural complexity, which contributes to population declines of many taxa. As such, many agencies and their conservation partners are employing habitat centric conservation efforts. Increased availability of airborne Light Detection and Ranging (LiDAR) data provides an opportunity to quantify fine-scale structural habitat characteristics for forest wildlife. One such species of conservation concern, the eastern whip-poor-will (Antrostomus vociferus), requires diverse forest structural conditions to meet its breeding season habitat requirements. We used airborne LiDAR data and autonomous recording units (ARUs) to identify elements of forest structure that influence whip-poor-will breeding season abundance in Pennsylvania, USA. Specifically, we applied a machine-learning classifier for whip-poor-will song to audio recordings obtained from 851 ARUs that were deployed in forested landscapes and then created daily detection histories to estimate whip-poor-will relative abundance. Whip-poor-wills were detected at 334 survey locations (41 %). Abundance exhibited positive linear relationships with percent forest cover and percent oak forest and a negative linear relationship with percent impervious cover. Whip-poor-will abundance was also influenced by forest structure, with abundance exhibiting a quadratic relationship with two LiDAR-derived covariates; canopy heterogeneity and height within 300 m. Using these results, we predicted whip-poor-will abundance and habitat management potential. Whip-poor-will conservation in our study region will depend on public and private land efforts that maintain heavily forested, oak dominated landscapes that are managed using practices that increase canopy height diversity among and within stands.