The evolution and fate of diversity under hard and soft selection - all data and code
Bibliographic record
Abstract
How genetic variation arises and persists over evolutionary time despite the depleting effects of natural selection remains a long-standing question. Here, we investigate the impacts of two extreme forms of population regulation – at the level of the total, mixed population (hard selection) and at the level of local, spatially distinct patches (soft selection) – on the emergence and fate of diversity under strong divergent selection. We find that while the form of population regulation has little effect on rates of diversification it can modulate the long-term fate of genetic variation, diversity being more readily maintained under soft selection compared to hard selection. The mechanism responsible for coexistence is negative frequency dependent selection which, while present initially under both forms of population regulation, persists over the long-term only under soft selection. Importantly, coexistence is robust to continued evolution of niche specialist types under soft selection but not hard selection. These results suggest that soft selection could be a general mechanism for the maintenance of ecological diversity over evolutionary time scales.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.016 |
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".