Machine learning approaches to explore important features behind bird flight modes
This work addresses a challenge in ornithology for understanding bird flight adaptations, but it is incremental as it applies existing machine learning methods to a specific biological dataset.
The study tackled the problem of evaluating the contribution of morphological and physiological features to bird flight styles by analyzing phenotypic data from 635 migratory bird species, using Feature Importance and SHAP values to quantify feature importance and construct weighted distance matrices and NJ trees, revealing similarities and differences compared to traditional methods.
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.