JOURNAL ARTICLE

Divergent effect of predator presence on gut morphology shows parallel patterns in congeneric species.

  • Published In: Biological Journal of the Linnean Society, 2025, v. 144, n. 1. P. 1 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Gorini-Pacheco, Bruno; Mazzoni, Rosana; Marques, Piatã; Neres-Lima, Vinicius; Zandonà, Eugenia 3 of 3

Abstract

This article investigates how predation pressure influences parallel phenotypic divergence in gut morphology in two congeneric poeciliid fish species, *Phalloceros harpagos* and *Phalloceros anisophallos*. By comparing populations from high predation (HP) and low predation (LP) environments with similar abiotic conditions, the study isolates predation effects on trophic traits. Results show that HP populations have lower fish densities but higher per-capita availability of high-quality resources (macroinvertebrates), correlating with shorter gut lengths indicative of a more carnivorous diet, while LP populations exhibit longer guts associated with consumption of lower-quality food such as algae. The parallel divergence in gut morphology across both species suggests that predation indirectly shapes resource availability and diet, driving convergent ecological adaptations. The study highlights the role of both evolutionary processes and phenotypic plasticity in these patterns and recommends further research to disentangle genetic versus plastic contributions to gut morphology variation.

Additional Information

  • Source:Biological Journal of the Linnean Society. 2025/01, Vol. 144, Issue 1, p1
  • Document Type:Article
  • Subject Area:Biology
  • Publication Date:2025
  • ISSN:0024-4066
  • DOI:10.1093/biolinnean/blae119
  • Accession Number:182368252
  • Copyright Statement:Copyright of Biological Journal of the Linnean Society 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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