The federally threatened northern spotted owl (Strix occidentalis caurina) has been intensively studied across its range, and habitat needs for the species have influenced forest management in northwestern North America for decades. Dense forest canopies are often reported in the scientific literature and agency management plans as an important habitat attribute for spotted owls, though the means of measuring forest canopy and interpreting species requirements vary across studies and more importantly, among management plans. We used light detection and ranging (lidar) measurements of canopy cover, canopy surface heterogeneity, and upper canopy surface connectivity, and an index of the presence of a competitive invasive species, the barred owl (S. varia), in multinomial discrete choice models using a Bayesian framework to evaluate selection of forest cover types by spotted owls in Oregon, USA, 2008–2015. We designated yearly activity centers based on the most biologically significant observation during the nesting season (Mar–Aug), generally centered on the nest tree. Spotted owls selected activity centers with more canopy cover and higher heterogeneity of the canopy surface within 100 m than was available within their territories. The average proportion of canopy cover within 100 m of a spotted owl activity center was 0.79 ± 0.12 (SD; range = 0.34–0.99). The presence of barred owls did not explain variability in selection of spotted owl activity centers, but barred owls might not affect third‐order habitat selection within territories, or our index was too spatially coarse to detect these effects on spotted owl resource selection. We demonstrate that lidar provides researchers and managers with a tool that can accurately measure forest canopies over large areas, and assist in mapping spotted owl habitat.
|Publication Subtype||Journal Article|
|Title||Activity center selection by northern spotted owls|
|Series title||Journal of Wildlife Management|
|Publisher||The Wildlife Society|
|Contributing office(s)||Coop Res Unit Seattle|
|Google Analytics Metrics||Metrics page|