Genetic population structure and demography of an apex predator, the tiger shark <i>Galeocerdo cuvier</i>
Bibliographic record
Abstract
Abstract Population genetics has been increasingly applied to study large sharks over the last decade. Whilst large shark species are often difficult to study with direct methods, improved knowledge is needed for both population management and conservation, especially for species vulnerable to anthropogenic and climatic impacts. The tiger shark, Galeocerdo cuvier, is an apex predator known to play important direct and indirect roles in tropical and subtropical marine ecosystems. While the global and Indo‐West Pacific population genetic structure of this species has recently been investigated, questions remain over population structure and demographic history within the western Indian (WIO) and within the western Pacific Oceans (WPO). To address the knowledge gap in tiger shark regional population structures, the genetic diversity of 286 individuals sampled in seven localities was investigated using 27 microsatellite loci and three mitochondrial genes (CR, COI, and cytb). A weak genetic differentiation was observed between the WIO and the WPO, suggesting high genetic connectivity. This result agrees with previous studies and highlights the importance of the pelagic behavior of this species to ensure gene flow. Using approximate Bayesian computation to couple information from both nuclear and mitochondrial markers, evidence of a recent bottleneck in the Holocene (2,000–3,000 years ago) was found, which is the most probable cause for the low genetic diversity observed. A contemporary effective population size as low as 111 [43,369] was estimated during the bottleneck. Together, these results indicate low genetic diversity that may reflect a vulnerable population sensitive to regional pressures. Conservation measures are thus needed to protect a species that is classified as Near Threatened.
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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) | 0.001 | 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".