How continuing mortality affects recovery potential for prohibited sharks: The case of white sharks in South Africa
Bibliographic record
Abstract
It can be difficult to determine whether a prohibition to exploitation ensures effective conservation or recovery for species that remain exposed to fishing effort and other sources of mortality throughout their range. Here we used simulation modeling of four life history scenarios (different productivity and population size) to contextualize potential population response to multiple levels of mortality, using white sharks (Carcharodon carcharias) in South Africa as a case study. The species has been protected since 1991, yet substantial uncertainty about population dynamics persists and recent declines at two aggregation sites have renewed conservation concern. All scenarios indicated that annual removals in the 10s of individuals would substantially limit the potential for and magnitude of any abundance increase following prohibition. Because average known removals from the KwaZulu-Natal Sharks Board’s Bather Protection Program have typically remained higher than these thresholds, they likely eliminated much of the conservation benefit derived from prohibition. The only life history scenario to achieve appreciable increase when simulated removals were similar to published averages assumed maturation occurred at a much younger age than currently understood. Our results demonstrate why general application of life history-based simulations can provide a useful mechanism to evaluate the biological plausibility of life history information and abundance trends, and to explore the scope for population response to recovery actions. For South Africa, our results suggest that even known levels of white shark removals, which likely underestimate total removals within their range, may be sufficient to drive abundance decline and new mitigation measures may be required to ensure population recovery.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".