Migration ameliorates the deleterious effects of genomic offset to environment on population growth in a free-living bird
Bibliographic record
Abstract
Environmental change reshapes species’ distributions and abundances globally, yet the numerous ways genetic variation, life-history, and climate change influence population dynamics remain poorly understood. Using 316 whole-genomes from 21 subspecies of resident and migrant song sparrows (Melospiza melodia) across western North America, we tested whether population growth was predicted by genomic offset—the mismatch between a population’s current climate-adapted genomic composition and that predicted as optimal under future or spatially distinct climates. Genomic offset explained 59% of the variation in population growth from 2001-11 to 2012-22. While genomic offsets were similar in migrant and resident populations, residents declined, especially where thermal and/or hydric stress increased, whereas migrants increased where climatic limits on population growth were ameliorated. Our results support that genomic offset can predict the dynamics of locally adapted species, highlight the potential utility of such models in conservation, and suggest seasonal migration may mitigate maladaptation to environmental change in mobile animals.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".