Global genetic diversity and historical demography of the Bull Shark
Bibliographic record
Abstract
Abstract Aim Biogeographic boundaries and genetic structuring have important effects on the inferences and interpretation of effective population size ( N e ) temporal variations, a key genetics parameter. We reconstructed the historical demography and divergence history of a vulnerable coastal high‐trophic shark using population genomics and assessed our ability to detect recent bottleneck events. Location Western and Central Indo‐Pacific (IPA), Western Tropical Atlantic (WTA) and Eastern Tropical Pacific (EPA). Taxon Carcharhinus leucas (Müller & Henle, 1839). Methods A DArTcap™ approach was used to sequence 475 samples and assess global genetic structuring. Three demographic models were tested on each population, using an ABC‐RF framework coupled with coalescent simulations, to investigate within‐cluster structure. Divergence times between clusters were computed, testing multiple scenarios, with fastsimcoal . N e temporal variations were reconstructed with STAIRWAYPLOT. Coalescent simulations were performed to determine the detectability of recent bottleneck under the estimated historical trend for datasets of this size. Results Three genetic clusters corresponding to the IPA, WTA and EPA regions were identified, agreeing with previous studies. The IPA presented the highest genetic diversity and was consistently identified as the oldest. No significant within‐cluster structuring was detected. N e increased globally, with an earlier onset in the IPA, during the last glacial period. Coalescent simulations showed that weak and recent bottlenecks could not be detected with our dataset, while old and/or strong bottlenecks would erase the observed ancestral expansion. Main Conclusions This study further confirms the role of marine biogeographic breaks in shaping the genetic history of large mobile marine predators. N e historical increases in N e are potentially linked to extended coastal habitat availability. The limited within‐cluster population structuring suggests that N e can be monitored over ocean basins. Due to insufficient amount of available genetic data, it cannot be concluded whether overfishing is impacting Bull Shark genetic diversity, calling for whole‐genome sequencing.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 teacher head, 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".