Machine learning approaches to explore important features behind bird flight modes

TL;DR

This study applies machine learning techniques to phenotypic data of 635 bird species to identify key features influencing flight styles, revealing complex feature contributions and the potential of multi-method approaches.

Contribution

It introduces a novel application of feature importance and SHAP values to phenotypic data for understanding bird flight modes, comparing results with traditional phylogenetic methods.

Findings

Identified key phenotypic features influencing flight styles.

Demonstrated differences between machine learning and traditional methods.

Highlighted the complexity of phenotypic feature contributions.

Abstract

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.