Influence of predation mortality on past and future dynamics of Pacific Herring: implications for stock status and future biomass
Bibliographic record
Abstract
Abstract The recovery of marine mammals from historical over-exploitation in the 1970s represents one of the largest changes in trophic structure in the northeast Pacific Ocean over the last century, for which the impacts on key forage species such as Pacific Herring ( Clupea pallasii ) are poorly understood. This has prompted hypotheses that increasing marine mammal populations are the primary cause for productivity declines for some fish stocks and their lack of recovery to historical abundance levels. In this study, we evaluate such a hypothesis for Pacific Herring by quantifying historical predation rates by key predators including cetaceans (Pacific Humpbacks, Grey Whales), pinnipeds (Stellar Sea Lions, Harbour Seals), and piscivorous fish (Pacific Hake). Predation mortality is quantified via a novel approach that integrates a single-species catch-at-age model with estimates of predator consumption derived from bioenergetic models. We found that predator consumption, largely driven by Humpback Whales, explained increasing Pacific Herring natural mortality rates in recent years and could be used to forecast future mortality. Incorporating higher future natural mortality rates produced higher estimates of current stock status (1.09-1.2B0) based on lower estimates of equilibrium unfished biomass (17.5-20.3 kt). Conversely, models that assumed mortality was more like the historical average had lower stock status (0.63B0) and higher estimates of unfished biomass (32.4 kt). We demonstrate a practical approach for ecosystem modelling that can be used to develop operating model scenarios for management strategy evaluation, improving scientific defensibility by removing an element of analyst choice for future mortality scenarios. We discuss how simpler modifications to single-species model assumptions can be more pragmatic for providing fisheries management advice, while more complex multi-species or ecosystem models might provide more nuanced insights for exploring research questions related to multi-species ecosystems and fisheries interactions.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".