JOURNAL ARTICLE

How does anthropogenic food influence the trophic ecology of Rocky Mountain Red Fox?

  • Published In: Journal of Mammalogy, 2025, v. 106, n. 1. P. 59 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Burkholder, Emily N; Stephenson, John; Hegg, Sarah; Gustine, David; Robinson, Tim; Holbrook, Joseph D 3 of 3

Abstract

This article investigates how seasonal and spatial variation in anthropogenic food availability, along with demographic factors, influence the diet and trophic ecology of the Rocky Mountain Red Fox (Vulpes vulpes macroura) in Grand Teton National Park, Wyoming. Using stable isotope analysis of carbon (δ¹³C) and nitrogen (δ¹⁵N) from hair and whisker samples collected between 2016 and 2021, the study found that foxes rely more on anthropogenic food during winter months despite lower human visitation, likely due to natural food scarcity, while summer diets include more natural food. Adults exhibited higher δ¹³C values than juveniles, suggesting greater exploitation of anthropogenic food possibly linked to learned behavior or dominance, and foxes with home ranges containing higher densities of human features showed increased reliance on anthropogenic food year-round. These findings highlight individual dietary variation and the influence of human activity on wildlife trophic interactions within a protected area, providing insights relevant for managing human–fox conflicts and conserving ecosystem processes in national parks.

Additional Information

  • Source:Journal of Mammalogy. 2025/02, Vol. 106, Issue 1, p59
  • Document Type:Article
  • Subject Area:History
  • Publication Date:2025
  • ISSN:0022-2372
  • DOI:10.1093/jmammal/gyae108
  • Accession Number:182609379
  • Copyright Statement:Copyright of Journal of Mammalogy 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.)

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