Opportunities and Challenges for Canada’s Mariculture Under Climate Change: A Regional and Sectoral Outlook
Bibliographic record
Abstract
Mariculture is an important component of the seafood industry in Canada, but climate change poses complex challenges and opportunities. Using the Global Mariculture Production Model (GOMAP), we assessed projected changes in production potential, farm-gate prices, and employment for key 13 species across the Atlantic and Pacific coasts. Projections were modelled under two climate scenarios (SSP1-2.6 and SSP5-8.5) and three production scenarios. Results show substantial regional disparities. The Atlantic region is expected to see modest gains under SSP1-2.6 but steep declines under SSP5-8.5 due to climate stress and habitat loss. In contrast, Pacific Canada; particularly central and northern areas show strong growth potential under SSP5-8.5, especially for climate-resilient finfish like Coho salmon and Steelhead trout. However, these gains depend on policy, infrastructure, and spatial planning that address conflicts with conservation, Indigenous uses, and other priorities. Farm-gate prices are projected to rise, potentially increasing producer revenue but threatening affordability. Employment outcomes diverge: Pacific Canada may benefit under high-emissions scenarios, while the Atlantic faces consistent job losses. Our findings highlight the need for adaptive governance and investment in resilient systems to ensure mariculture remains a sustainable, equitable, and climate-resilient food source for Canada.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".