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通过新热带界雀形目鸟类(粗腿小霸鹟)的鸣声对分类群进行聚类的均匀流形逼近与投影

Uniform Manifold Approximation and Projection for Clustering Taxa through Vocalizations in a Neotropical Passerine (Rough-Legged Tyrannulet, ).

作者信息

Parra-Hernández Ronald M, Posada-Quintero Jorge I, Acevedo-Charry Orlando, Posada-Quintero Hugo F

机构信息

Institución Educativa Normal Superior Fabio Lozano Torrijos, Falan, Tolima 732001, Colombia.

Asociación Tolimense de Ornitología, Ibagué, Tolima 730005, Colombia.

出版信息

Animals (Basel). 2020 Aug 12;10(8):1406. doi: 10.3390/ani10081406.

Abstract

Vocalizations from birds are a fruitful source of information for the classification of species. However, currently used analyses are ineffective to determine the taxonomic status of some groups. To provide a clearer grouping of taxa for such bird species from the analysis of vocalizations, more sensitive techniques are required. In this study, we have evaluated the sensitivity of the Uniform Manifold Approximation and Projection (UMAP) technique for grouping the vocalizations of individuals of the Rough-legged Tyrannulet complex. Although the existence of two taxonomic groups has been suggested by some studies, the species has presented taxonomic difficulties in classification in previous studies. UMAP exhibited a clearer separation of groups than previously used dimensionality-reduction techniques (i.e., principal component analysis), as it was able to effectively identify the two taxa groups. The results achieved with UMAP in this study suggest that the technique can be useful in the analysis of species with complex in taxonomy through vocalizations data as a complementary tool including behavioral traits such as acoustic communication.

摘要

鸟类的鸣声是物种分类的丰富信息来源。然而,目前使用的分析方法在确定某些类群的分类地位时效果不佳。为了通过鸣声分析为这类鸟类提供更清晰的分类群分组,需要更灵敏的技术。在本研究中,我们评估了统一流形近似和投影(UMAP)技术对粗腿小霸鹟复合体个体鸣声进行分组的敏感性。尽管一些研究表明存在两个分类群,但该物种在以往研究中分类时存在分类困难。UMAP比之前使用的降维技术(即主成分分析)表现出更清晰的组间分离,因为它能够有效识别这两个分类群。本研究中UMAP取得的结果表明,该技术作为一种包括声学通讯等行为特征的补充工具,可用于通过鸣声数据对分类复杂的物种进行分析。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d81f/7460062/c4d52887e186/animals-10-01406-g001.jpg

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