Ecological and Economic Impacts of Ocean Deoxygenation on Pacific Halibut Fisheries: A Multidisciplinary Assessment of Projected Losses
Bibliographic record
Abstract
The ocean has been losing oxygen content since at least the last century, and the rate of decline is accelerated by climate-induced changes like ocean warming, acidification, and anthropogenic driven nutrient load. Oxygen loss alters marine ecosystems and life, and consequently, human-wellbeing based on fisheries. Since marine fishes are known to be the most sensitive to deoxygenation, their mortality, growth, and abundance are affected by dissolved oxygen levels. Hence, fisheries relying on fish stocks are also impacted, with significant economic repercussions for society. The ability to estimate the economic impacts of deoxygenation on fisheries is hampered by the uncertain link between biogeochemical pathways and socio-economic impacts. Thus, we conducted a multidisciplinary modeling approach integrating ecological (metabolic rate index), biological (stock biomass dynamics), and economic (economic rent based on MSY) components to assess the economic ramifications of ocean deoxygenation (2020-2100) on the British Columbia Pacific halibut fishery.We find that under conditions of up to 40% reduction in dissolved oxygen and a 30% increase in temperature by 2100, Pacific halibut biomass could decrease by 66% ~ 89%. This significant decrease in catch could lead to an estimated cumulative economic loss of around $100 million by 2100. Ripple effects throughout the supply chain, including secondary (processing) and tertiary industries (distribution, marketing), could cause an additional economic impact loss of up to $197 million by 2100. Therefore, our study suggests it is crucial for the Pacific Halibut Commission and fisheries to incorporate adaptation and mitigation strategies to counteract ongoing deoxygenation and warming.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".