Abstract
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 moderate overall accuracy (62%), with class-specific performance metrics that exceeded null expectations for birds that survived migration. Although no traits had consistent univariate effects on model prediction, the traits that contributed most to classification were migration year, sex, wing shape, and fall migratory orientation. When considering pairwise combinations of traits, the strongest effects were driven by interactions with migration year, suggesting that temporal variation strongly modulates the predictive contribution of all other variables. These results underscore the challenges of predicting complex and inherently stochastic ecological outcomes such as survival. Nonetheless, the traits most consistently associated with survival provide promising targets for future investigation, and our approach offers a roadmap for similar efforts to link phenotypic variation with fitness in natural populations.
Funding
Funding was provided by NSF (IOS-2143004) and NIH (1R35GM151012) grants awarded to KED.
Author information
Authors and Affiliations
Corresponding authors
Ethics declarations
Competing interests
The authors declare no competing interests.
Additional information
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary Information
Below is the link to the electronic supplementary material.
Supplementary Material 1 (download DOCX )
Rights and permissions
Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
Reprints and permissions
About this article
Cite this article
Vastani, S.A., Blain, S.A., Justen, H. et al. Predicting migratory survival in a songbird hybrid zone using machine learning.
Sci Rep (2026). https://doi.org/10.1038/s41598-026-58881-5
Received:
Accepted:
Published:
DOI: https://doi.org/10.1038/s41598-026-58881-5
Source: Ecology - nature.com
