JOURNAL ARTICLE

Physiological Basis of Convergent Evolution in Animal Communication Systems.

  • Published In: Integrative & Comparative Biology, 2024, v. 64, n. 5. P. 1422 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Anderson, Nigel K; Preininger, Doris; Fuxjager, Matthew J 3 of 3

Abstract

The article focuses on the convergent evolution of elaborate animal communication displays through the interaction of perceptual bias and ritualization, emphasizing mechanistic and physiological underpinnings. Perceptual bias refers to pre-existing sensory or cognitive preferences in receivers that shape signal evolution, often originating from systems used in feeding or predator avoidance, while ritualization describes the evolutionary co-option of non-communicative traits into intentional signals by signalers. Using foot-flagging frogs as a primary case study, the article illustrates how conserved neural feature analyzers (the "worm/antiworm" system) and androgen-sensitive muscle physiology facilitate the repeated independent evolution of this visual display in noisy environments where acoustic signals are less effective. Additional examples from fiddler crabs and lizards suggest that similar processes involving perceptual biases and ritualization contribute to convergent evolution of communication signals across diverse taxa. The authors propose further empirical tests to validate the roles of perceptual bias and ritualization in shaping these convergent displays.

Additional Information

  • Source:Integrative & Comparative Biology. 2024/11, Vol. 64, Issue 5, p1422
  • Document Type:Article
  • Subject Area:Zoology
  • Publication Date:2024
  • ISSN:1540-7063
  • DOI:10.1093/icb/icae091
  • Accession Number:181030421
  • Copyright Statement:Copyright of Integrative & Comparative Biology 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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