Improving distribution data of threatened species by combining acoustic monitoring and occupancy modelling

摘要: Summary Conservation of threatened species relies on predictions about their spatial distribution; however, it is often difficult to detect in the wild. The combination acoustic monitoring improve detectability and statistical methods account for false-negative detections can distribution estimates. Here, we combine a novel automated species-specific identification approach with occupancy models that imperfect provide more accurate map Elfin Woods Warbler Setophaga angelae, rare, elusive bird species. We also compared three identification/validation approaches determine which provided estimates similar manual validation all recordings. Acoustic data were collected along elevational gradients (95–1074 m a.s.l) El Yunque National Forest, Puerto Rico. detection matrices acquired through validations recordings used create models. Although this has wider than previously reported, depends Palo Colorado forest cover mainly occurs between 600 900 m a.s.l. Unbiased precise developed by using only manually validating 4% recordings. Our draws strength two active areas ecological research: modelling. Our an effective efficient way translate enormous amount information passive devices into meaningful be applied understand