Climate change impacts on Canadian fishing and seafood supply
Bibliographic record
Abstract
Climate change is already affecting ocean conditions, including warming, acidification, deoxygenation and sea-level rise. These changes are affecting marine species globally, with subsequent impacts on marine fisheries, peoples’ livelihoods, and food security. The magnitude of these changes are more prevalent in developing nations and tropical countries, yet the risk of climate impacts on developed nations is not negligible. Shifts in stock distribution and fish abundance under climate change could impact seafood available to Indigenous peoples and tens of thousands of other Canadians. Achieving the Paris Agreement target of limiting atmospheric warming to 1.5°C above pre-industrial levels could mitigate projected declines in fish biomass, with benefits for ocean life, economies and people. In this work, I conduct a systematic literature review on the impacts of climate change on Canada's Pacific marine ecosystems and fisheries, highlighting its critical effects on them. I then examine climate impacts on Canada’s marine fisheries and seafood supply under two greenhouse gas emissions scenarios that correspond to alternative futures with global warming of 1.5° and 3.5°C relative to pre-industrial levels. Finally, I conduct a semi-quantitative assessment of the effectiveness of ocean-based solutions in British Columbia to mitigate climate change and reduce its impacts on the marine ecosystems and fisheries of the Province. My results indicate that the 1.5°C warming scenario could protect marine catches directed to Canada’s seafood supply by up to 11% and reduce Canadian household seafood expenditures by US$ 528 million annually, relative to a 3.5°C global warming scenario. The results also show that the full implementation of marine renewable energy in British Columbia could reduce GHG emissions by ~270 MT per year, filling the gap between current emissions and Canada’s Paris Agreement pledge to reduce emission by 30% below 2005 levels. While the effectiveness of marine renewables to reduce climate change depends on a global achievement of mitigation targets, solutions such as restoring vegetation, marine protected areas and pollution reduction show potential to address climate impacts locally (e.g., ocean acidification and sea-level rise). The findings offer evidence to support the benefits of carbon emissions mitigation in reducing seafood supply vulnerabilities to climate change.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.014 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".