Integrative Analysis of Flight Performance Data Using Basic Machine Learning Approaches in Racing Homing Pigeons (Columba livia)

Racing homing pigeons (Columba livia) have been selectively bred for centuries for superior flight capacity. Yet, the quantitative structure of flight performance traits and the extent to which sex influences these parameters remain poorly characterized, par-ticularly in Turkish populations. This study aimed to evaluate flight performance in racing pigeons raised in the South Marmara region of Turkiye using three key kine-matic traits (flight duration, speed, and distance) and to explore the multivariate structure and individual variation of these parameters through an integrative machine learning framework. Data were compiled from 166 individually registered pigeons (77 females, 89 males), totaling 781 race records used for pattern analysis. A composite Flight Performance Score (FPS) was constructed using min–max normalized compo-nent variables, and its internal consistency was assessed via Cronbach's alpha and principal component analysis. Univariate comparisons revealed no statistically signif-icant sex-related differences in any of the three flight parameters (P > 0.05 for all traits). Principal component analysis confirmed substantial overlap between male and female individuals in multivariate trait space, and Random Forest classification failed to dis-criminate between sexes above chance level (accuracy = 0.490; ROC-AUC = 0.500), col-lectively indicating that sex is not a dominant determinant of flight performance in this population. Internal consistency analysis revealed that flight duration, speed, and dis-tance are functionally independent dimensions (Cronbach's α = 0.135; r = −0.749 be-tween duration and speed), with their variance structure being effectively two-dimensional (PC1: 60.1%; PC2: 39.7%), supporting the equal-weighting scheme applied in FPS construction. Pattern analysis of race records identified four biologically distinct flight performance profiles, characterized by differential trade-offs among flight duration, speed, and distance, suggesting that individual-level performance strategy, rather than sex, is the primary axis of variation in this dataset. These findings challenge common breeder assumptions about sex-based differences in performance and highlight the multidimensional, individual-specific nature of flight performance in racing pigeons.