Simulation code for "When does the probability of evolutionary rescue increase with the strength of selection despite a potential demographic cost?"
Bibliographic record
Abstract
This data repository contains simulation code associated with the paper in "Xu, K., & Osmond, M. M. (2025). When does the probability of evolutionary rescue increase with the strength of selection despite a potential demographic cost? The American Naturalist." Paper abstract: Populations may be rescued from extinction via sufficiently rapid adaptive evolution. Evolution is faster with stronger selection but this may come with a demographic cost, creating opposing effects on evolutionary rescue. The outcome of this trade-off influences the optimal strategy for avoiding herbicide/drug resistance evolution. Here we examine the effect of stronger selection on rescue when demography and selection covary, across four models of evolutionary rescue. We find that stronger selection cannot facilitate rescue in two quite different population genetic models unless selection is associated with higher absolute fitness of rescue homozygotes. Similarly, in a quantitative genetic model of rescue under an abrupt environmental shift, stronger selection accelerates evolution but leaves maximum fitness unchanged and cannot facilitate rescue. We also explore a quantitative genetic model of rescue in a gradually changing environment, generalizing the finding that an intermediate selection strength maximizes survival at steady-state to a wider class of fitness functions. This data repository consist of three code scripts in R and this README document, with the following code filenames: 1. simulation_rescue_rare_allele.RCode to generate simulation results in Figure 1c and 1f when rescue occurs via the establishment of initially rare allele 2. simulation_rescue_common_allele.RCode to generate both theoretical and simulation results in Figure 2 when rescue occurs via the sweeping of an initially common adaptive allele. 3. simulation_rescue_quantitative_trait.RCode to generate simulation results in Figure 4 when rescue occurs via the evolution of a quantitative trait under an abrupt environmental shift.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.156 | 0.035 |
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".