Three-dimensional partitioning of resources by congeneric forest predators with recent sympatry
Scientific Reports
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Abstract
Coexistence of ecologically similar species can be maintained by partitioning along one or more niche axes. Three-dimensional structural complexity is central to facilitating resource partitioning between many forest species, but is underrepresented in field-based studies. We examined resource selection by sympatric northern spotted owls (Strix occidentalis caurina), a threatened species under the US Endangered Species Act, and nonnative barred owls (S. varia) in western Oregon, USA to explore the relative importance of canopy heterogeneity, vertical complexity of forest, and abiotic features to resource selection and identify potential differences that may facilitate long-term coexistence. We predicted that within home range selection of understory densities, measured with airborne lidar, would differ between species based on proportional differences in arboreal and terrestrial prey taken by each owl species. We used discrete choice models and telemetry data from 41 spotted owls and 38 barred owls monitored during 2007–2009 and 2012–2015. Our results suggested that while both species used tall canopy areas more often than low canopy areas, spotted owls were more commonly found in areas with lower tree cover, more developed understory, and steeper slopes. This is the first evidence of
fine-scale partitioning based on structural forest properties by northern spotted owls and barred owls.
Study Area
| Publication type | Article |
|---|---|
| Publication Subtype | Journal Article |
| Title | Three-dimensional partitioning of resources by congeneric forest predators with recent sympatry |
| Series title | Scientific Reports |
| DOI | 10.1038/s41598-019-42426-0 |
| Volume | 9 |
| Year Published | 2019 |
| Language | English |
| Publisher | Nature |
| Contributing office(s) | Forest and Rangeland Ecosystem Science Center |
| Description | Article 6036; 10 p. |
| First page | 1 |
| Last page | 10 |
| Country | United States |
| State | Oregon |
| Google Analytic Metrics | Metrics page |