Factors underlying migratory timing of a seasonally migrating bird
Bibliographic record
Abstract
Bird migration has fascinated natural historians and scientists for centuries. While the timing of migration is known to vary by species, population, sex, and individual, identifying the cause of this variation can be challenging. Here we investigate factors underlying migratory timing in a long-distance migratory bird, the Common Yellowthroat (Geothlypas trichas), using a population genomic approach. We begin by creating a map of genetic variation across geographic space (a "genoscape") using lcWGS from across the breeding range. We then utilize genetic assays to assign 249 wintering and 1050 northward migrating birds to genetically distinct breeding populations. Additionally, we estimate the expected spring onset date in each predicted breeding region and calculate the remaining migratory distance for northward migrating birds. Our findings indicate that when population genetic structure is not a factor in the analysis, it appears that birds captured early in the season are migrating to breeding grounds where spring arrives later, which contrasts with prior research. However, when we incorporate population structure into our analysis, our results align with predictions, indicating that birds captured earlier in the season are indeed heading to breeding grounds where spring arrives earlier. Further analysis revealed that the disparity between results obtained with and without population genetic structure can be attributed to the fact that individuals from the western genetic group migrate three times the distance to the west, despite breeding at the same latitude. Our findings suggest that categorizing large numbers of migrating birds into genetically distinct groups can reveal population-specific patterns in migratory timing and shed light on the relative contributions of different selective forces responsible for the observed patterns.
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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 | 0.001 |
| 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".