Optimal risk-taking theory applied to marine conservation: harbour seals in Prince William Sound
Bibliographic record
Abstract
I sought theoretical insight on synergistic effects of resources and predators that are potentially relevant to the decline of harbour seals in Prince William Sound and to indirect effects of fisheries. Simulations predicted that compensatory foraging effort by seals will mitigate potential loss of energy reserves when resources decline, but only at the cost of higher predation rates, even if predator densities remain constant. A second study predicted net energy gain and predation risk per foraging dive, parameterising an analytical model with field data on seal behaviour, resource distributions, and use of depth by Pacific sleeper sharks and killer whales. Analyses suggested that risk of mortality from sharks and net energetic gain were greatest when seals foraged in deep strata, and empirical data showed individual variation in use of these strata. Plots of the individuals’ predicted energy gain against predicted predation risk fit best when relative danger from sharks was assumed to be much greater than that from killer whales. The first two studies combined suggest that, theoretically, overfishing of near-surface fatty fishes might increase shark predation rates on seals. A third model predicted an asymmetric trophic cascade in which indirect effects of sleeper sharks on resources were mediated by seal avoidance of riskier strata. Risk management by seals is predicted to reduce mortality on the dangerous resource (deep pollock) while increasing mortality on the safer resource (shallow herring), and the bycatch of sharks altered this dynamic. Although empirical data are lacking to test most predictions and various assumptions, the three models derive from first principles of behavioural ecology and provide a rigorous basis for predicting indirect effects of fisheries. Further, overfishing of sharks and of resources used by marine mammals are pressing global problems which cannot be addressed by empirical studies alone; indirect interactions between species are too complex to be elucidated without theoretical guidance and rapid exploitation often outpaces the acquisition of data relevant to conservation. Thus, theory presented here is important for assessing the potential damage wrought by different fishery scenarios, informing decisions that attempt to optimise exploitation and conservation, and guiding empirical research.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 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".