Comparing lethal and non‐lethal methods of active population control for harbor seals in British Columbia
Bibliographic record
Abstract
Abstract Pinniped populations around the world increased rapidly after hunting and culling during the nineteenth and twentieth centuries ended. Some believe that pinnipeds are now preventing the recovery of certain fish populations, and that controlling pinniped population abundance using lethal measures such as harvesting or by non‐lethal means like contraception could recover fish populations. It is unclear, however, how effective and how long it would take for such methods of population control to bring numbers of pinnipeds down to target levels. We used sex‐ and age‐structured population models to estimate how quickly harbor seal (Phoca vitulina) abundance in British Columbia, Canada, could be reduced by 50%, through combinations of lethal removals and sterilization of adult females. Models were fit to seal abundance, demographic, and harvest data collected between 1879 and 2014. Simulation modeling suggests reliance on contraception exclusively is unlikely to reduce the current harbor seal population (numbering ~100,000) by 50% within 25 years, and would result in more variable outcomes, compared to lethal removals. Contraception could be combined with harvesting to maintain a target abundance of harbor seals (although captive studies with harbor seals are needed to confirm the efficacy of contraception). Our simulation modeling approach provides a useful framework to assess how non‐lethal measures could be integrated into policies that promote active population control of harbor seal numbers.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 | 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".