Phenological mismatch and the effectiveness of assisted gene flow
Bibliographic record
Abstract
The persistence of narrowly adapted species under climate change will depend on their ability to migrate apace with their historical climatic envelope or to adapt in place to maintain fitness. This second path to persistence can only occur if there is sufficient genetic variance for response to new selection regimes. Inadequate levels of genetic variation can be remedied through assisted gene flow (AGF), that is the intentional introduction of individuals genetically adapted to localities with historic climates similar to the current or future climate experienced by the resident population. However, the timing of reproduction is frequently adapted to local conditions. Phenological mismatch between residents and migrants can reduce resident × migrant mating frequencies, slowing the introgression of migrant alleles into the resident genetic background and impeding evolutionary rescue efforts. Focusing on plants, we devised a method to estimate the frequency of resident × migrant matings based on flowering schedules and applied it in an experiment that mimicked the first generation of an AGF program with Chamaecrista fasciculata, a prairie annual, under current and expected future temperature regimes. Phenological mismatch reduced the potential for resident × migrant matings by 40-90%, regardless of thermal treatment. The most successful migrant sires were the most resident like in their flowering time, further biasing the genetic admixture between resident and migrant populations. Other loci contributing to local adaptation-heat-tolerance genes, for instance-may be in linkage disequilibrium with phenology when residents and migrants are combined into a single mating pool. Thus, introgression of potentially adaptive migrant alleles into the resident genetic background is slowed when selection acts against migrant phenology. Successful AGF programs may require sustained high immigration rates or preliminary breeding programs when phenologically matched migrant source populations are unavailable.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".