AI Article Synopsis

  • Traditional classification of animals, particularly birds, often relies on physical traits like plumage color, but this can lead to misclassification.
  • Recent studies using bioacoustic and genomic data have challenged these traditional methods, revealing errors in genus boundaries within the flycatcher subfamily Niltavinae.
  • The research demonstrates that song characteristics are crucial for accurate classification, prompting a reorganization of genera and a reconsideration of biogeographical limits based on integrative datasets rather than solely on morphology.

Article Abstract

Traditional classification of many animals, including birds, has been highly dependent on external morphological characters like plumage coloration. However, both bioacoustics and genetic or genomic data have revolutionized our understanding of the relationships of certain lineages and led to sweeping taxonomic re-organizations. In this study, we present a case of erroneous delimitation of genus boundaries in the species-rich flycatcher subfamily Niltavinae. Genera within this subfamily have historically been delineated based on blue versus brown male body plumage until recent studies based on a few mitochondrial and nuclear loci unearthed several cases of generic misclassification. Here we use extensive bioacoustic data from 43 species and genomic data from 28 species for a fundamental reclassification of species in the Niltavinae. Our study reveals that song is an important trait to classify these birds even at the genus level, whereas plumage traits exhibit ample convergence and have led to numerous historic misattributions. Our taxonomic re-organization leads to new biogeographic limits of major genera, such that the genus Cyornis now only extends as far east as the islands of Sulawesi, Sula, and Banggai, whereas Eumyias is redefined to extend far beyond Wallace's Line to the islands of Seram and Timor. Our conclusions advise against an over-reliance on morphological traits and underscore the importance of integrative datasets.

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Source
http://dx.doi.org/10.1016/j.ympev.2023.107999DOI Listing

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