Predicting the effects of whale population recovery on Northeast Pacific food webs and fisheries: an ecosystem modelling approach
Bibliographic record
Abstract
Abstract The recovery of whale populations from historical depletion may have the potential to noticeably affect Northeast Pacific ecosystems and fisheries. Surplus production models based on whaling catch records were used to reconstruct the historical abundances of five large whale species in the waters surrounding Haida Gwaii, British Columbia. The results suggest that the local abundances of all five species were vastly higher before the onset of modern whaling. A comparison of ecosystem models representing the states of the local marine food web before and after full whale recovery indicates that abundant whales could consume large proportions of the annual production of their principal prey, ranging up to 87% for Pacific herring ( Clupea pallasii ) and 72% for piscivorous rockfish ( Sebastes spp.). Dynamic modelling of the food web effects of whale recovery, including simulations of simultaneous top‐down and bottom‐up forcing and a Monte Carlo sensitivity analysis, revealed noticeable (∼6–12%) top‐down effects on Pacific herring biomass owing to increased predation by humpback and fin whales. However, these effects cannot explain the magnitude of recent declines in local herring biomass. The dynamic modelling results also suggest that top‐down effects of whale recovery could result in reduced biomasses of large rockfish as a result of predation by sperm whales, as well as potential cascading effects on many demersal fish groups. These findings have numerous practical implications for ecosystem‐based fisheries management and whale conservation strategies in Northeast Pacific waters.
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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 | 0.000 | 0.000 |
| 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 | 0.001 |
| 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".