JOURNAL ARTICLE

Temporal stability in songs across the breeding range of Geothlypis philadelphia (Mourning Warbler) may be due to learning fidelity and transmission biases.

  • Published In: Ornithology (Oxford University Press), 2025, v. 142, n. 1. P. 1 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Pitocchelli, Jay; Albina, Adam; Bentley, R Alexander; Guerra, David; Youngblood, Mason 3 of 3

Abstract

This article focuses on the long-term geographic and temporal stability of song variation in the Mourning Warbler (Geothlypis philadelphia) across its breeding range over 36 years. Four distinct regiolects—Western, Eastern, Nova Scotia, and Newfoundland—were consistently identified from 1983 to 2019, each characterized by unique syllable types with high persistence, while entire songs showed more turnover. Agent-based modeling and simulation-based inference revealed that strong content bias, likely favoring more complex syllables, combined with low innovation rates and high learning fidelity, best explains the stability of these regiolects and syllable types. Analyses of physical song parameters and habitat characteristics using Landsat data provided little evidence supporting the acoustic adaptation hypothesis, indicating that song variation is not strongly shaped by breeding habitat vegetation density. Overall, the study highlights cultural evolutionary processes maintaining stable regional song dialects in Mourning Warblers despite ongoing minor changes in syllable variants.

Additional Information

  • Source:Ornithology (Oxford University Press). 2025/01, Vol. 142, Issue 1, p1
  • Document Type:Article
  • Subject Area:Applied Sciences
  • Publication Date:2025
  • ISSN:2732-4613
  • DOI:10.1093/ornithology/ukae046
  • Accession Number:182904886
  • Copyright Statement:Copyright of Ornithology (Oxford University Press) 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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