Eco‐evolutionary effects on population recovery following catastrophic disturbance
Bibliographic record
Abstract
Fine-scale genetic diversity and contemporary evolution can theoretically influence ecological dynamics in the wild. Such eco-evolutionary effects might be particularly relevant to the persistence of populations facing acute or chronic environmental change. However, experimental data on wild populations is currently lacking to support this notion. One way that ongoing evolution might influence the dynamics of threatened populations is through the role that selection plays in mediating the 'rescue effect', the ability of migrants to contribute to the recovery of populations facing local disturbance and decline. Here, we combine experiments with natural catastrophic events to show that ongoing evolution is a major determinant of migrant contributions to population recovery in Trinidadian guppies (Poecilia reticulata). These eco-evolutionary limits on migrant contributions appear to be mediated by the reinforcing effects of natural and sexual selection against migrants, despite the close geographic proximity of migrant sources. These findings show that ongoing adaptive evolution can be a double-edged sword for population persistence, maintaining local fitness at a cost to demographic risk. Our study further serves as a potent reminder that significant evolutionary and eco-evolutionary dynamics might be at play even where the phenotypic status quo is largely maintained generation to generation.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".