On the potential strength and consequences for nonrandom gene flow caused by local adaptation in flowering time
Bibliographic record
Abstract
Gene flow is generally considered a random process, that is the loci under consideration have no effect on dispersal success. Edelaar and Bolnick (Trends Ecol Evol, 27, 2012 659) recently argued that nonrandom gene flow could exert a significant evolutionary force. It can, for instance, ameliorate the maladaptive effects of immigration into locally adapted populations. I examined the potential strength for nonrandom gene flow for flowering time genes, a trait frequently found to be locally adapted. The idea is that plants that successfully export pollen into a locally adapted resident population will be a genetically biased subset of their natal population - they will have resident-like flowering times. Reciprocally, recipients will be more migrant-like than the resident population average. I quantified the potential for biased pollen exchange among three populations along a flowering time cline in Brassica rapa from southern California. A two-generation line cross experiment demonstrated genetic variance in flowering time, both within and among populations. Calculations based on the variation in individual flowering schedules showed that resident plants with the most migrant-like flowering times could expect to have up to 10 times more of the their flowers pollinated by immigrant pollen than the least migrant-like. Further, the mean flowering time of the pollen exporters that have access to resident mates differs by up to 4 weeks from the mean in the exporters' natal population. The data from these three populations suggest that the bias in gene flow for flowering time cuts the impact on the resident population by as much as half. This implies that when selection is divergent between populations, migrants with the highest mating success tend to be resident-like in their flowering times, and so, fewer maladaptive alleles will be introduced into the locally adapting gene pool.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".