Supporting information.

<p>Additional figures and tables: Fig A–Loading of PCA results for PC1 and PC2; Fig B–mtDNA cytb maximum-likelihood tree; Fig C–Clusters determined by partitioning around medoid (PAM) using R package ‘cluster’; Fig D–<i>Setophaga townsendi</i> regiolect; Fig E–<i>Icterus</i> and <i>Sturnella</i> additional analyses; Fig F–Spearman’s correlation between mtDNA divergence and song divergence quantified by PC1, PC2, PAM dimension 1, and PAM dimension 2; Table A–Summary of within-taxa regiolect detection in each counterpart; Table B–Dissimilarity matrices produced by PAM; Table C–The principal components 1–6 for each between-taxa comparison; Table D–The loading of principal components 1 & 2 for each variable measured for each between-taxa comparison.</p> <p>(PDF)</p>

Publication Details

Journal
Figshare
Published
2024-12-26
DOI
https://doi.org/10.1371/journal.pone.0312706.s001
Primary Topic
Animal Vocal Communication and Behavior
Type
article
Field-Weighted Citation Impact
0.00
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Supporting information.

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Animal Vocal Communication and Behavior
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Supporting information.

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Abstract

<p>Additional figures and tables: Fig A–Loading of PCA results for PC1 and PC2; Fig B–mtDNA cytb maximum-likelihood tree; Fig C–Clusters determined by partitioning around medoid (PAM) using R package ‘cluster’; Fig D–<i>Setophaga townsendi</i> regiolect; Fig E–<i>Icterus</i> and <i>Sturnella</i> additional analyses; Fig F–Spearman’s correlation between mtDNA divergence and song divergence quantified by PC1, PC2, PAM dimension 1, and PAM dimension 2; Table A–Summary of within-taxa regiolect detection in each counterpart; Table B–Dissimilarity matrices produced by PAM; Table C–The principal components 1–6 for each between-taxa comparison; Table D–The loading of principal components 1 & 2 for each variable measured for each between-taxa comparison.</p> <p>(PDF)</p>

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Animal Vocal Communication and Behavior
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