Hybridization and the coexistence of species: HZAM-Sym code and data files
Bibliographic record
Abstract
Abstract It is thought that two species can coexist if they use different resources present in the environment, yet this assumes that species are completely reproductively isolated. We model coexistence outcomes for two sympatric species that are ecologically differentiated but have incomplete reproductive isolation. The consequences of interbreeding depend crucially on hybrid fitness. When hybrid fitness is high, just a small rate of hybridization can lead to collapse of two species into one. Low hybrid fitness can cause population declines, making extinction of one or both species likely. High intrinsic growth rates result in higher reproductive rates when populations are below carrying capacity, reducing the probability of extinction and increasing the probability of stable coexistence at moderate levels of assortative mating and hybrid fitness. Very strong but incomplete assortative mating can induce low hybrid fitness via a mating disadvantage to rare genotypes, and this can stabilize coexistence of two species at high but incomplete levels of assortative mating. Given these results and evidence that it may take many millions of years of divergence before related species become sympatric, we postulate that coexistence of closely-related species is more often limited by insufficient assortative mating than by insufficient ecological differentiation.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.451 | 0.165 |
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".