Improving geographically extensive acoustic survey designs for modeling species occurrence with imperfect detection and misidentification |
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Authors: | Katharine M. Banner Kathryn M. Irvine Thomas J. Rodhouse Wilson J. Wright Rogelio M. Rodriguez Andrea R. Litt |
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Affiliation: | 1. Department of Ecology, Montana State University, Bozeman, Montana, USA;2. U.S. Geological Survey, Northern Rocky Mountain Science Center, Bozeman, Montana, USA;3. U.S. National Park Service, Upper Columbia Basin Network Inventory and Monitoring Program, Bend, Oregon, USA;4. Department of Animal & Rangeland Sciences, Courtesy Faculty, Oregon State University Cascades, Bend, Oregon, USA;5. Human and Ecosystem Resiliency and Sustainability Lab, Oregon State University‐Cascades, Bend, Oregon, USA |
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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 occupancy model is explored that accounts for misclassification errors and enables potential reduction in the number of confirmed detections. Simulations informed by real data were used to evaluate how much confirmation effort could be reduced without sacrificing site occupancy and detection error estimator bias and precision. We found even under a 50% reduction in total confirmation effort, estimator properties were reasonable for our assumed survey design, species‐specific parameter values, and desired precision. For transferability, a fully documented r package, OCacoustic, for implementing a false‐positive occupancy model is provided. Practitioners can apply OCacoustic to optimize their own study design (required sample sizes, number of visits, and confirmation scenarios) for properly implementing a false‐positive occupancy model with bat or other wildlife acoustic data. Additionally, our work highlights the importance of clearly defining research objectives and data processing strategies at the outset to align the study design with desired statistical inferences. |
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Keywords: | bats false‐positive occupancy models manual review monitoring passive animal detectors survey design vetting |
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