Recovery potential and conservation options for elasmobranchs
Bibliographic record
Abstract
Many elasmobranchs have experienced strong population declines, which have been largely attributed to the direct and indirect effects of exploitation. Recently, however, live elasmobranchs are being increasingly valued for their role in marine ecosystems, dive tourism and intrinsic worth. Thus, management plans have been implemented to slow and ultimately reverse negative trends, including shark-specific (e.g. anti-finning laws) to ecosystem-based (e.g. no-take marine reserves) strategies. Yet it is unclear how successful these measures are, or will be, given the degree of depletion and slow recovery potential of most elasmobranchs. Here, current understanding of elasmobranch population recoveries is reviewed. The potential and realized extent of population increases, including rates of increase, timelines and drivers are evaluated. Across 40 increasing populations, only 25% were attributed to decreased anthropogenic mortality, while the majority was attributed to predation release. It is also shown that even low exploitation rates (2-6% per year) can halt or reverse positive population trends in six populations currently managed under recovery plans. Management measures that help restore elasmobranch populations include enforcement or near-zero fishing mortality, protection of critical habitats, monitoring and education. These measures are highlighted in a case study from the south-eastern U.S.A., where some evidence of recovery is seen in Pristis pectinata, Galeocerdo cuvier and Sphyrna lewini populations. It is concluded that recovery of elasmobranchs is certainly possible but requires time and a combination of strong and dedicated management actions to be successful.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".