Code for individual-based simulations in "Environmental fluctuations can promote evolutionary rescue in high-extinction-risk scenarios"
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Substantial environmental change can force a population onto a path towards extinction, but under some conditions, adaptation by natural selection can rescue the population and allow it to persist. This process, known as evolutionary rescue, is believed to be less likely to occur with greater magnitudes of random environmental fluctuations because environmental variation decreases expected population size, increases variance in population size, and increases evolutionary lag. However, previous studies of evolutionary rescue in fluctuating environments have only considered scenarios in which evolutionary rescue was likely to occur. We extend these studies to assess how baseline extinction risk (which we manipulated via changes in the initial population size, degree of environmental change, or mutation rate) influences the effects of environmental variation on evolutionary rescue following an abrupt environmental change. Using a combination of analytical models and stochastic simulations, we show that autocorrelated environmental variation hinders evolutionary rescue in low-extinction-risk scenarios but facilitates rescue in high-risk scenarios. In these high-risk cases, the chance of a run of good years counteracts the otherwise negative effects of environmental variation on evolutionary demography. These findings can inform the development of effective conservation practices that consider evolutionary responses to abrupt environmental changes.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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 it