JOURNAL ARTICLE

A comparison of 2 methods of diet analysis in Neotropical frugivorous bats reveals the hidden side of bat–plant interactions.

  • Published In: Journal of Mammalogy, 2025, v. 106, n. 2. P. 265 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Colorado, Wendy B; Castro-Luna, Alejandro A; Gómez-Gil, Bruno; Andrade-Torres, Antonio; Galindo-González, Jorge; Flores-Estévez, Norma; Palestina, René A 3 of 3

Abstract

This article focuses on comparing DNA metabarcoding using the 18S V9 rDNA region with traditional morphological seed identification to characterize the diet of three Neotropical frugivorous bat species—*Sturnira hondurensis*, *Carollia perspicillata*, and *Artibeus lituratus*—in central Veracruz, Mexico. DNA metabarcoding identified a greater diversity of plant species (20 taxa) and more interaction events than morphological analysis of seeds (10 taxa), revealing that many plant species with large seeds or consumed as pulp only were previously underrepresented. The study demonstrates that metabarcoding provides higher taxonomic resolution (90% species-level identification) and a more complete picture of bat–plant interactions, although it does not indicate the mode of interaction (e.g., frugivory vs. nectarivory) or quantity consumed. These findings highlight the importance of incorporating molecular methods alongside traditional approaches to better understand the ecological roles of frugivorous bats in Neotropical ecosystems.

Additional Information

  • Source:Journal of Mammalogy. 2025/04, Vol. 106, Issue 2, p265
  • Document Type:Article
  • Subject Area:Biology
  • Publication Date:2025
  • ISSN:0022-2372
  • DOI:10.1093/jmammal/gyae103
  • Accession Number:184163603
  • 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.)

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