JOURNAL ARTICLE

Fine-scale forest structure, not management regime, drives occupancy of a declining songbird, the Olive-sided Flycatcher, in the core of its range.

  • Published In: Ornithological Applications, 2024, v. 126, n. 2. P. 1 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Hack, Benjamin; Cansler, C Alina; Peery, M. Zachariah; Wood, Connor M 3 of 3

Abstract

This article examines how forest management regimes and fine-scale forest structure influence the occupancy of the Olive-sided Flycatcher (Contopus cooperi), a declining songbird associated with tall, open forests in California's Sierra Nevada. Using passive acoustic monitoring and occupancy modeling, the study found that Olive-sided Flycatcher presence is more strongly related to fine-scale habitat features—specifically, open canopy conditions relative to tree diameter—than to whether lands are managed by the National Park Service (NPS) or the U.S. Forest Service (USFS). Although open forest conditions favored by the species were more common on NPS-managed lands, occupancy was not explained by management regime alone, and some positive association with USFS lands suggested other habitat factors at play. The findings suggest conservation strategies should prioritize creating and maintaining open-canopy forests with large trees through methods such as prescribed fire, mechanical thinning, and the restoration of Indigenous fire management practices to support this and similar species.

Additional Information

  • Source:Ornithological Applications. 2024/05, Vol. 126, Issue 2, p1
  • Document Type:Article
  • Subject Area:Environmental Sciences
  • Publication Date:2024
  • ISSN:2732-4621
  • DOI:10.1093/ornithapp/duad065
  • Accession Number:177085125
  • Copyright Statement:Copyright of Ornithological Applications is the property of Oxford University Press / USA and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Looking to go deeper into this topic? Look for more articles on EBSCOhost.