JOURNAL ARTICLE
The importance of rodents to a specialist carnivore in an industrialized site.
Published In: Mammalia: International Journal of the Systematics, Biology & Ecology of Mammals, 2024, v. 88, n. 6. P. 525 1 of 3
Database: Academic Search Ultimate 2 of 3
Authored By: Ravhuanzwo, Fortune; Loock, Daan J.E.; Swanepoel, Lourens H. 3 of 3
Abstract
Specialist carnivores are often among the first species disappearing from transformed, human-dominated landscapes. However, some carnivore species can exploit abundant food sources in human-dominated landscapes. In this study, we investigated the diet of a specialist carnivore, the serval (Leptailurus serval), inhabiting artificial and natural landscapes surrounding a petrochemical plant in the Highveld of Mpumalanga, South Africa. From 2013 to 2018, for each year, we collected and analysed a total of 264 scat samples. We found that rodent species dominated the serval diet, while other prey items like birds, insects, and unidentified prey contributed little to the diet. In terms of biomass consumed, Otomys sp (56.94 %), Mastomys sp (19.12 %), and Rhabdomys sp (8.68 %) were the most important rodent prey. We further found that biomass consumed is only affected by species, not season or species–season interactions. Our results concur with previous studies that serval is primarily a rodent specialist and that specialisation holds even in human-altered landscapes. [ABSTRACT FROM AUTHOR]
Additional Information
- Source:Mammalia: International Journal of the Systematics, Biology & Ecology of Mammals. 2024/11, Vol. 88, Issue 6, p525
- Document Type:Article
- Subject Area:Earth and Atmospheric Sciences
- Publication Date:2024
- ISSN:0025-1461
- DOI:10.1515/mammalia-2023-0079
- Accession Number:180807253
- Copyright Statement:Copyright of Mammalia: International Journal of the Systematics, Biology & Ecology of Mammals is the property of De Gruyter 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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