Estimating thresholds to optimal harvest rate for long-lived, low-fecundity sharks accounting for selectivity and density dependence in recruitment
Bibliographic record
Abstract
Deepwater dogsharks (Order Squaliformes) are thought to be particularly vulnerable to overfishing due to life history strategies that place them at the lower end of the shark productivity spectrum. Sharks frequently have relatively low value in multispecies fisheries, where management is usually aimed at maintaining harvest of more valuable and productive teleosts. This results in low priority being given to data collection for sharks and hampers identification of appropriate harvest strategies. Here an age-structured model with maximum sustainable harvest rate (U MSY ) as leading productivity parameter is systematically applied to show that for certain growth and reproductive schedules that apply to some sharks, the range of possible values that can be taken by U MSY can become very small. The model was applied to 12 Australian dogshark species and was used to show that U MSY is highly constrained under some selectivity schedules. Results were consistent with estimates of the intrinsic rate of growth obtained using a demographic model, suggesting that there may be more certainty about U MSY than expected for many shark species, given uncertainty in recruitment parameters. The approach could be used to inform policy for some sharks and may be useful in the development of informative Bayesian priors for assessment models.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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".