Implications of life history uncertainty when evaluating status in the Northwest Atlantic population of white shark (<i>Carcharodon carcharias</i>)
Bibliographic record
Abstract
Abstract To effectively protect at‐risk sharks, resource managers and conservation practitioners must have a good understanding of how fisheries removals contribute to changes in abundance and how regulatory restrictions may impact a population trajectory. This means they need to know the number of animals being removed from a population and whether a given number of removals will lead to population increases or declines. For white shark (Carcharodon carcharias), theoretical quantities like the intrinsic rate of population increase or rebound potential (ability to increase in size following decline) are difficult to conceptualize in terms of real‐world abundance changes, which limits our ability to answer practical management questions. To address this shortfall, we designed a simulation model to evaluate how our understanding of longevity and life history variability of white shark affects our understanding of population trends in the Northwest Atlantic. Then, we quantified the magnitude of removals that could have caused historical population declines, compared these to biologically based reference points, and explored the removal scenarios which would result in population increase. Our results suggest that removals on the order of 100s of juveniles per year could have resulted in population‐level declines in excess of 60% during the 1970s and 1980s. Conservation actions implemented since the 1990s would have needed to be nearly 100% effective at preventing fishing mortality in order for the population to double in abundance over the last 30 years. Total removals from all fleets needed to be exceptionally small to keep them below biological reference points for white shark in the Northwest Atlantic. The population's inherent vulnerability to fishing pressure reaffirms the need for restrictive national and international conservation measures, even under a situation of abundance increase.
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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 | 0.006 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".