A genomic test of sex-biased dispersal in white sharks
Bibliographic record
Abstract
Mitonuclear discordance has been observed in several shark species. Female philopatry has often been invoked to explain such discordance but has never been explicitly tested. Here, we focus on the white shark, for which female philopatry has been previously proposed, and produced a chromosome-level genome, high-coverage whole-genome autosomal, and uniparental datasets to investigate mitonuclear discordance. We first reconstructed the historical population demography of the species based on autosomal data. We show that this species once comprised a single panmictic population, which experienced a steady decline until recent times when it fragmented into at least three main autosomal genetic groups. Mitochondrial data depict a strikingly different picture, inconsistent with the spatial distribution of autosomal diversity. Using the demographic scenario established from autosomal data, we performed coalescent and forward simulations to test for the occurrence of female philopatry. Coalescent simulations showed that the model can reproduce the autosomal variability, confirming its robustness. A forward simulation framework was further built to explicitly account for a sex-biased reproduction model and track both autosomal and uniparental markers (Y chromosome and mitochondrial DNA). While our model generates data that are consistent with the observed Y chromosome variation, the mitochondrial pattern is never reproduced even under extreme female philopatry (no female migration), strongly suggesting that demography alone cannot explain the mitonuclear discordance. Our framework could, and perhaps should, be extended to other shark species where philopatry has been suggested. It is possible that the proposed widespread occurrence of female philopatry in sharks should be revisited.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".