用机器学习分析635种候鸟的飞行方式，找出关键身体特征。
Machine learning approaches to explore important features behind bird flight modes
- 基于635种鸟类的体型数据，用特征重要性与SHAP值量化各特征影响。
- 发现不同飞行模式的关键特征权重分布差异显著，尤其在聚类结果中体现。
- 适合研究生物演化、形态适应与机器学习交叉的学者参考。
鸟类展现出多种飞行方式，主要分为拍翼飞行（快速上下扑动翅膀）和滑翔飞行（展开翅膀滑行）。每种鸟类通常采用特定的飞行方式，这被认为与形态和生理适应有关。然而，评估各因素对飞行方式差异的贡献仍具挑战。本研究利用635种迁徙鸟类的表型数据（如体重、翼展、繁殖期等），通过特征重要性与SHAP值量化各特征相对重要性，并构建加权L1距离矩阵，生成邻接树（NJ树）。与传统系统发育逻辑回归相比，顶峰特征排名相似，但整体权重分布及聚类模式存在差异。结果凸显了从相关表型特征构建生物学意义距离矩阵的复杂性，同时表明这些加权方法具有互补性，提示多维度方法在评估特征贡献方面的潜力。
原文摘要 · Abstract (English)
Birds exhibit a variety of flight styles, primarily classified as flapping, which is characterized by rapid up-and-down wing movements, and soaring, which involves gliding with wings outstretched. Each species usually performs specific flight styles, and this has been argued in terms of morphological and physiological adaptation. However, it remains a challenge to evaluate the contribution of each factor to the difference in flight styles. In this study, using phenotypic data from 635 migratory bird species, such as body mass, wing length, and breeding periods, we quantified the relative importance of each feature using Feature Importance and SHAP values, and used them to construct weighted L1 distance matrices and construct NJ trees. Comparison with traditional phylogenetic logistic regression revealed similarity in top-ranked features, but also differences in overall weight distributions and clustering patterns in NJ trees. Our results highlight the complexity of constructing a biologically useful distance matrix from correlated phenotypic features, while the complementary nature of these weighting methods suggests the potential utility of multi-faceted approaches to assessing feature contributions.
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