Predicting Migratory Survival in a Songbird Hybrid Zone Using Machine Learning
Bibliographic record
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 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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".