Evolutionary rescue in a consumer-resource system depends on the affected ecological traits and the population’s resident life-history
Bibliographic record
Abstract
Abstract With evolutionary rescue, a population that is declining due to an environmental change adapts to its environment, avoiding extinction. Previous theoretical work has explored the effects of negative density-dependence on rescue, showing that it may aid or hinder persistence. However, these models typically assume that it is only population intrinsic growth rates, r , or carrying capacity, K , that are negatively affected, and do not model density-dependence explicitly. Here, we analyze evolutionary rescue in a consumer-resource species following an abrupt environmental change, characterizing how rescue is dependent on the ecological effects of the environmental change on the consumer, which differently affect r and K through subsequent interactions with an explicit non-substitutable resource species. We derive approximate analytical predictions for the fixation probabilities of beneficial alleles, mutational supply, and times to extinction, which work well when selection is weak and individual turnover rates are low. We demonstrate that consumer rescue is dependent on the ecological effect of the environmental change, the resident life-history of the population, and the genetic architectures of evolving traits (monogenic versus polygenic). Our model suggests that measurements of intrinsic growth rates alone will be insufficient to predict rescue probabilities. This work extends our understanding of the interplay between ecology and evolution in influencing population persistence.
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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 | 0.002 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".