Gene flow improves fitness at a range edge under climate change
Bibliographic record
Abstract
Abstract Populations at the margins of a species' geographic range are often thought to be poorly adapted to their environment. According to theoretical predictions, gene flow can inhibit these range edge populations if it disrupts adaptation to local conditions. Alternatively, if range edge populations are small or isolated, gene flow can provide beneficial genetic variation and may facilitate adaptation to environmental change. We tested these competing predictions in the annual wildflower Clarkia pulchella using greenhouse crosses to simulate gene flow from sources across the geographic range into two populations at the northern range margin. We planted these between-population hybrids in common gardens at the range edge and evaluated how genetic differentiation and climatic differences between edge populations and gene flow sources affected lifetime fitness. During an anomalously warm study year, gene flow from populations occupying historically warm sites improved fitness at the range edge and plants with one or both parents from warm populations performed best. The effects of the temperature provenance of gene flow sources were most apparent at early life history stages, but precipitation provenance also affected reproduction. We also found benefits of gene flow that were independent of climate: after climate was controlled for, plants with parents from different populations performed better at later lifestages than those with parents from the same population, indicating that gene flow may improve fitness via relieving homozygosity. Further supporting this result, we found that increasing genetic differentiation of parental populations had positive effects on fitness of hybrid seeds. Gene flow from warmer populations, when it occurs, is likely to contribute adaptive genetic variation to populations at the northern range edge as the climate warms. On heterogeneous landscapes, climate of origin may be a better predictor of gene flow effects than geographic proximity.
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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 | 0.000 |
| 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 | 0.000 | 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".