Predicting Migratory Survival in a Songbird Hybrid Zone Using Machine Learning.

Extrinsic postzygotic isolation-selection against hybrids between populations that have undergone divergent ecological adaptation-is hypothesized to play a central role in speciation but is notoriously difficult to demonstrate, as it requires evidence that ecologically relevant traits influence hybrid fitness. We addressed this challenge using individual radio tracking and machine learning in a hybrid zone between two songbirds where differences in seasonal migration are thought to serve as extrinsic isolating barriers. Using detection rates as a proxy for survival, we built a random forest classification model to predict survival from a set of genetic, morphological, and behavioural traits. The model achieved moderately high accuracy (72%), with better prediction for birds that did not survive migration. Alongside body condition and study year, the traits that contributed most to classification were genetic ancestry, genetic heterozygosity, and fall orientation. These traits had non-linear effects on survival, with some values actually predicting higher survival in hybrids. Together, these results suggest that a bird's hybrid class-defined by ancestry and heterozygosity-and its initial migratory direction affect migratory survival. Interactions with morphology are important and along with non-linear associations between traits and survival, reflect the complexity of relationships between behavioral traits and fitness. Our model not only provides a complete picture for the role ecological selection on migration plays in speciation, but also supports growing evidence that some hybrids may benefit from admixture.