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Data from: Passive acoustic monitoring effectively detects Northern Spotted Owls and Barred Owls over a range of forest conditions

Citation

Duchac, Leila S. et al. (2021), Data from: Passive acoustic monitoring effectively detects Northern Spotted Owls and Barred Owls over a range of forest conditions, Dryad, Dataset, https://doi.org/10.5061/dryad.qnk98sfcd

Abstract

Passive acoustic monitoring using autonomous recording units (ARUs) is a fast-growing area of wildlife research especially for rare, cryptic species that vocalize. Northern Spotted Owl (Strix occidentalis caurina) populations have been monitored since the mid-1980s using mark-recapture methods. To evaluate an alternative survey method, we used ARUs to detect calls of Northern Spotted Owls and Barred Owls (S. varia), a congener that has expanded its range into the Pacific Northwest and threatens Northern Spotted Owl persistence. We set ARUs at 30, 500-ha hexagons (150 ARU stations) with recent Northern Spotted Owl activity and high Barred Owl density within Northern Spotted Owl demographic study areas in Oregon and Washington, and set ARUs to record continuously each night from March-July 2017. We reviewed spectrograms (visual representations of sound) and tagged target vocalizations to extract calls from ~160,000 hours of recordings. Even in a study area with low occupancy rates on historical territories (Washington’s Olympic Peninsula), the probability of detecting a Northern Spotted Owl when it was present in a hexagon exceeded 0.95 after 3 weeks of recording. Environmental noise, mainly from rain, wind, and streams, decreased detection probabilities for both species over all study areas. Using demographic information about known Northern Spotted Owls, we found that weekly detection probabilities of Northern Spotted Owls were higher when ARUs were closer to known nests and activity centers and when owls were paired, suggesting passive acoustic data alone could help locate Northern Spotted Owl pairs on the landscape. These results demonstrate that ARUs can effectively detect Northern Spotted Owls when they are present, even in a landscape with high Barred Owl density, thereby facilitating the use of passive, occupancy-based study designs to monitor Northern Spotted Owl populations.

Methods

This dataset contains two encounter histories and associated environmental covariates for analysis in an occupancy modeling framework. Encounter histories for northern spotted owls and barred owls were generated using passive acoustic monitoring data summarized to a weekly scale. In the encounter histories, dots (.) indicate weeks in which stations were not surveyed, zeros represent weeks in which stations were surveyed but the species was not detected, and ones indicate weeks in which stations were surveyed and the species was detected.

Environmental covariates are untransformed values from field measurements or remote sensed data. Site covariates are those that do not vary through the season (e.g. distance from stream), while survey covariates vary within season (e.g. weekly temperature) and can be used to model within-season heterogeneity in detection probability.