Speciation without gene‐flow in hybridizing deer
Bibliographic record
Abstract
Under the ecological speciation model, divergent selection acts on ecological differences between populations, gradually creating barriers to gene flow and ultimately leading to reproductive isolation. Hybridisation is part of this continuum and can both promote and inhibit the speciation process. Here, we used white-tailed (Odocoileus virginianus) and mule deer (O. hemionus) to investigate patterns of speciation in hybridizing sister species. We quantified genome-wide historical introgression and performed genome scans to look for signatures of four different selection scenarios. Despite ample modern evidence of hybridisation, we found negligible patterns of ancestral introgression and no signatures of divergence with gene flow, rather localized patterns of allopatric and balancing selection were detected across the genome. Genes under balancing selection were related to immunity, MHC and sensory perception of smell, the latter of which is consistent with deer biology. The deficiency of historical gene-flow suggests that white-tailed and mule deer were spatially separated during the glaciation cycles of the Pleistocene and genome wide differentiation accrued via genetic drift. Dobzhansky-Muller incompatibilities and selection against hybrids are hypothesised to be acting, and diversity correlations to recombination rates suggests these sister species are far along the speciation continuum.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".