State and federal agencies spend considerable time and resources to enhance and create habitat for wildlife. Understanding how target and non-target species respond to these efforts can help direct the allocation of limited conservation resources. However, monitoring species response to habitat management comes with several logistical challenges that are exacerbated as the area of geographic focus increases. I used autonomous recording units (ARUs) to mitigate these challenges when assessing Eastern Whip-poor-will (Antrostomus vociferus) response to forest management. I deployed 1,265 ARUs across managed and unmanaged public and private forests from western North Carolina to southern Maine. I then applied a machine learned classifier to all recordings to create whip-poor-will daily detection histories for each survey location. I used detection data and generalized linear models to examine regional, landscape, and site factors that influenced whip-poor-will occurrence. Whip-poor-wills were detected at 399 (35%) survey locations. At the regional scale, occupancy decreased with latitude and elevation. At the landscape scale, occupancy was negatively associated with the amount of impervious cover within 500m, and was positively associated with the amount of oak forest and evergreen forest cover within 1,750m. Additionally, whip-poor-will occupancy exhibited a quadratic relationship with the amount of shrub/scrub cover within 1,500m. At the site-level, occupancy was negatively associated with increased basal area and exhibited a quadratic relationship with woody stem density. Whip-poor-will populations can benefit from the implementation of forestry practices that create and sustain early successional forests within forested landscapes, especially those dominated by oak forest types. The use of ARUs helped overcome several challenges associated with intensive broad-scale monitoring efforts for a species with a limited survey window, but also presented new challenges associated with data management, storage, and analyses.
| Identifer | oai:union.ndltd.org:UMASS/oai:scholarworks.umass.edu:masters_theses_2-2413 |
| Date | 14 November 2023 |
| Creators | Larkin, Jeffery T. |
| Publisher | ScholarWorks@UMass Amherst |
| Source Sets | University of Massachusetts, Amherst |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Masters Theses |
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