DOI: 10.5061/dryad.4b8gthtfn
title: Data from: Large trees and forest heterogeneity facilitate prey capture by California spotted owls

description: Predators are among the most threatened animal groups globally, with prey
 declines contributing to their endangerment. However, assessments of the
 habitat conditions that influence the successful capture of different prey
 species are rare, especially for small, cryptic predators. Accordingly,
 most predator conservation plans are based on the relative importance of
 habitats inferred from coarse-scale studies that do not consider
 habitat features contributing to hunting success, which can vary among
 prey species. To address this limitation, we integrated high-resolution
 GPS tracking and nest video monitoring to characterize habitat features at
 prey capture locations during the nestling provisioning stage for the
 Spotted Owl (Strix occidentalis) a small, cryptic predator that has been
 at the center of a decades-long forest management conflict in western
 North America. When all prey species were considered collectively, males
 provisioning nests tended to capture prey: (i) in areas with more
 large-tree forest, (ii) in areas with more medium trees/medium canopy
 forest, and (iii) at edges between conifer and hardwood forests. However,
 when we considered the owl’s two key prey species separately, males
 captured woodrats (Neotoma fuscipes) and Humboldt flying squirrels
 (Glaucomys oregonensis) in areas with markedly different habitat features.
 Our study provides clarity for forest management in mixed-ownership
 landscapes because different prey species achieve high densities in
 different habitat types. Specifically, our results suggest that promoting
 large trees, increasing forest heterogeneity, and creating canopy gaps in
 forests with medium trees/high canopy cover could benefit Spotted Owls and
 their prey, which has the ancillary benefit of enhancing forest
 resilience. Combining high-resolution GPS tagging with video-based
 information on prey deliveries to breeding sites can strengthen
 conservation planning for small predators by more rigorously defining
 those habitat features that are associated with successful prey
 acquisition.
description: Please see
 the <em>Methods </em>section below for the specific methods
 (copied from the manuscript). Overall, these data include 4 shapefiles
 detailed below. Please note that data were spatially shifted due to the
 sensitive nature of the California spotted owl.
 Nest_locations_shifted Point file showing the location of the spotted owl
 nest Attributes include: Site
 – unique site identifier for each tagged bird
 P_KDE_publc - estimated proportion of public land in that
 bird's KDE GPS_points_shifted Point file showing the location of all GPS tag
 locations Attributes include: Site
 – unique site identifier for each tagged bird
 Year
 – year that the GPS point was recorded Month – year that the GPS point was
 recorded Day
 – year that the GPS point was recorded Hour
 – year that the GPS point was recorded Minute – year that the GPS point was
 recorded Capture_polygons_shifted Polygon file showing the used and available capture
 polygons and their associated prey, split by vegetation
 type Attributes include: Site
 – unique site identifier for each tagged bird
 PolyID – the capture polygon identifier (unique to each
 used capture polygon, and serving as a link to all available
 polygons) Rand_ID – a used/random identifier
 Area_sqm – the area of each polygon in square
 meters Veg_ID – the numeric vegetation type
 ID VegClass – a text description of the numeric
 Veg_ID Prey_comm – the common name of the prey associated with
 that polygon Prey_sci – the scientific name of the prey associated with
 that polygon Buffered_capture_polygons_shifted
 Polygon file showing the 50 m buffered used and available
 capture polygons and their associated prey, split by vegetation
 type Attributes include: Site
 – unique site identifier for each tagged bird
 PolyID – the capture polygon identifier (unique to each
 used capture polygon, and serving as a link to all available
 polygons) Rand_ID – a used/random identifier
 Area_sqm – the area of each polygon in square
 meters Veg_ID – the numeric vegetation type
 ID VegClass – a text description of the numeric
 Veg_ID Prey_comm – the common name of the prey associated with
 that polygon Prey_sci – the scientific name of the prey associated with
 that polygon 
 <em> </em> <em>Methods – copied directly from the
 manuscript</em> <strong>2.2 | GPS
 tagging</strong>
 We
 located territorial breeding Spotted Owls as part of our routine
 monitoring surveys conducted annually on the EDSA and SSA (e.g.,
 Hobart et al., 2019b; Roberts et al.,
 2017). Briefly, owls were located during
 call-based surveys at night and found during dawn/dusk surveys the
 following day to ascertain their reproductive status and locate nests
 (Franklin et al. 1996).
 Owls were fed live mice during follow-up surveys, which breeding owls then
 typically delivered to nest sites (Franklin et al. 1996).
 We
 captured 15 nesting males (5 in 2019 and 10 in 2020) for GPS tagging using
 noose poles and “hand capture” (Wood et al. 2021).
 Females were not tagged because they spend most of their time incubating
 eggs and brooding nestlings while nesting and relatively little time
 foraging (Forsman et al. 1984).
 Males were selected
 opportunistically for tagging based on the accessibility of the nest for
 video-monitoring (see below) throughout the nesting months (May – July)
 and the likelihood of recapture to remove transmitters. We affixed GPS
 tags (Alle-300, Ecotone, Poland) weighing 10 grams and with remote
 downloading capability as tail mounts following methods described in
 Kramer et al. (2021)
 (Fig. 1). We programmed tags to collect locations at two-minute intervals
 to characterize Spotted Owl nocturnal movements and ultimately identify
 prey capture locations (described below). We recaptured five individuals
 for a second deployment to increase the number of observations of prey
 captures. Following the final deployment, we attempted to recapture all
 owls to remove GPS tags; two individuals that were not recaptured were
 expected to molt during that season or the following season, thus shedding
 the GPS tag.  <strong>2.3 | Nest video
 monitoring</strong> We
 monitored prey deliveries using infrared (IR) video cameras placed at the
 nest sites of the 15 GPS-tagged males concurrent with the collection of
 GPS locations. To do so, we climbed a nearby adjacent tree (10-50 m from
 the nest tree) using a single rope technique and secured a video camera
 across from the nest tree (Fig. 1). We monitored nests using AXIS
 Q1786 – LE 4 megapixel outdoor infrared video cameras that continuously
 recorded high quality video throughout the nocturnal foraging period that
 coincided with GPS tag data collection (2000 to 0630 Pacific Daylight
 Time). We reviewed each video to detect and identify the different prey
 species delivered to each nest. <strong> 2.4 | Identifying prey capture locations
 </strong> We
 located successful foraging sites (hereafter ‘prey capture locations’) by
 visually identifying tight clusters of GPS points followed immediately by
 a straight flight path back to the nest tree (Marsh et al. 2014, Wood et al. 2021).
 We defined a prey capture
 location as a cluster of up to ten GPS points (within the 20 minutes prior
 to movement back to the nest), all within 250 meters of one another based
 on that likely being the maximum hunting radius of a perched owl
 (Whitmore, personal observation). If the two farthest points were greater
 than 250 meters apart, we removed the oldest point (the first point that
 defined the prey capture location) and continued to remove the points
 until they were all within 250 meters of one another. Each cluster of
 points was transformed into a minimum convex polygon (mean = 0.38 ha,
 range = 0.002 - 2.713 ha) surrounding the cluster and
 assumed to represent the prey capture location. Although this approach,
 including the criterion of &lt;250 m among pairs of points, was
 somewhat subjective in that it was based on field observations, it was
 repeatable and typically yielded tight, defined clusters of points
 (compared to the distribution of owl GPS points overall) followed by
 distinct straight-line movements back to the nest site (Fig. 2). We then
 matched the time stamp of the video with the time stamp of the GPS return
 to the nest to link the species of prey to its putative capture location
 (Fig. 2). The area contained by the
 minimum convex polygon of these GPS points that matched with a prey
 delivery caught on video was defined as a prey capture location. Three prey capture locations
 were excluded from the analysis because they likely represented the
 retrieval of prey from a cache site. We suspected these deliveries to be
 from a cache location because of their close proximity to the nest site
 (within 50 m) and the early timing of the delivery (all three instances
 were the first delivery of the night and occurred before sunset), which
 both indicated a cache delivery (Whitmore, personal
 observation). <strong> 2.5 | Vegetation classification
 </strong><em> </em> We
 visually interpreted aerial imagery from the National Agriculture Imagery
 Program (NAIP) collected in 2018 and 2020 to characterize vegetation
 conditions within prey capture locations (as well as random locations, see
 below) following methods developed and described by Tempel et al. (2014)
 that typically results in &gt;80% classification accuracy.
 Specifically, we considered ten possible vegetation classes based on
 species composition, canopy cover, and the size of the dominant trees that
 was largely based on the California Wildlife Habitat Relationships system
 (Mayer and Laudenslayer 1988)
 (Table 1). Areas classified as hardwood typically represented patches of
 black oaks, with tanoaks in the understory typically being obscured by
 overstory conifer trees and thus not measured. We digitized polygons
 around relatively homogenous areas of vegetation with a minimum mapping
 unit of 20 m<sup>2</sup> and then classified the area within
 the polygon as one of the ten vegetation classes.
description: ArcGIS Pro was used to make the files, but they can be opened
 with any GIS software such as QGIS.