Sex and space destabilize intransitive competition within and between species
Bibliographic record
Abstract
Organisms ranging from bacteria and corals to plants and vertebrates can form intransitive competitive networks, in which coexistence can be maintained because no one species or genotype is superior to all others. However, in the simplest case with three competing types, the long-term outcome may not be so clear if two of the three represent the ends of a continuous heritable trait distribution within one species, as has been recently demonstrated empirically in a short-term experiment with plants. Using simulation models of this scenario, results with asexual reproduction confirm previous studies which showed that local interactions promote coexistence. However, with sexual reproduction, genetic variance is reduced because selection fluctuates between favouring the two extremes during population cycles, while sex continually produces intermediates. Sex thus slows the response to selection when it is the strongest and therefore slows the recovery from extreme abundances, creating larger abundance fluctuations. Local interactions do not stabilize dynamics with sex because the resultant spatial patches of one species are genetically heterogeneous, such that particular phenotypes do not benefit from spatial refuges. In sharp contrast to previous models suggesting that sex or local interactions stabilize population dynamics, here sex and local interactions destabilize dynamics and increase extinction risk.
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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 | 0.002 |
| 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 | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".