Improving geographically extensive acoustic survey designs for modeling species occurrence with imperfect detection and misidentification.

ABSTRACT: Acoustic recording units (ARUs) enable geographically extensive surveys of sensitive and elusive species. However, a hidden cost of using ARU data for modeling species occupancy is that prohibitive amounts of human verification may be required to correct species identifications made from automated software. Bat acoustic studies exemplify this challenge because large volumes of echolocation calls could be recorded and automatically classified to species. The standard occupancy model requires aggregating verified recordings to construct confirmed detection/non-detection datasets. The multistep data processing workflow is not necessarily transparent nor consistent among studies. We share a workflow diagramming strategy that could provide coherency among practitioners. A false-positive occupan

SUBMITTER: Banner KM 

PROVIDER: S-EPMC6024138 | biostudies-literature | 2018 Jun

REPOSITORIES: biostudies-literature