This is a Preprint and has not been peer reviewed. The published version of this Preprint is available: https://doi.org/10.1093/ornithology/ukae046. This is version 1 of this Preprint.
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Abstract
We found a stable pattern of geographic variation in songs across the breeding range of the Mourning Warbler over a 36 yr period. The Western, Eastern, Nova Scotia, and Newfoundland regiolects found in 2005-2009 also existed from 1983-1988 and 2017-2019. Each regiolect contained a pool of syllables that were unique and different from the other regiolects. The primary syllable types that defined each regiolect were present throughout the study, but there were changes in the frequencies of variants of these syllable types in each regiolect. We developed an agent-based model of birdsong learning within each regiolect to explore whether these frequency changes were consistent with unbiased copying or two forms of transmission bias: frequency bias and content bias. Strong content bias, possibly for more complex syllables, best models the temporal dynamics across regiolects. In combination with a high estimated learning fidelity, this may explain why regiolects and syllable types were stable for 36 years. We also examined whether variation in physical parameters of song over time could be attributed to acoustic adaptation to breeding habitat, using Landsat variables as a proxy for vegetation characteristics of each male’s breeding territory. The physical parameters of the songs, which changed little over time, revealed no coherent relationships with the Landsat variables and therefore little evidence for acoustic adaptation.
DOI
https://doi.org/10.32942/X2TW5X
Subjects
Animal Sciences, Behavior and Ethology, Ecology and Evolutionary Biology, Life Sciences, Ornithology
Keywords
birdsong, cultural evolution, Geothlypis philadelphia, Mourning Warbler, regiolects
Dates
Published: 2024-09-30 03:45
License
CC BY Attribution 4.0 International
Additional Metadata
Language:
English
Data and Code Availability Statement:
Songs and data are archived in Open Science Framework at https://doi.org/10.17605/OSF.IO/7G4C9. All code and data used in the agent-based modeling analysis can be found at Zenodo (https://doi.org/10.5281/zenodo.10698484) and GitHub repository (https://github.com/masonyoungblood/MourningWarblers).
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