Impact of the 2014–2016 marine heatwave on US and Canada West Coast fisheries: Surprises and lessons from key case studies
Bibliographic record
Abstract
Abstract Marine heatwaves are increasingly affecting marine ecosystems, with cascading impacts on coastal economies, communities, and food systems. Studies of heatwaves provide crucial insights into potential ecosystem shifts under future climate change and put fisheries social‐ecological systems through “stress tests” that expose both vulnerabilities and resilience. The 2014–16 Northeast Pacific heatwave was the strongest and longest marine heatwave on record and resulted in profound ecological changes that impacted fisheries, fisheries management, and human livelihoods. Here, we synthesize the impacts of the 2014–2016 marine heatwave on US and Canada West Coast fisheries and extract key lessons for preparing global fisheries science, management, and industries for the future. We set the stage with a brief review of the impacts of the heatwave on marine ecosystems and the first systematic analysis of the economic impacts of these changes on commercial and recreational fisheries. We then examine ten key case studies that provide instructive examples of the complex and surprising challenges that heatwaves pose to fisheries social‐ecological systems. These reveal important insights into improving the resilience of monitoring and management and increasing adaptive capacity to future stressors. Key recommendations include: (1) expanding monitoring to enhance mechanistic understanding, provide early warning signals, and improve predictions of impacts; (2) increasing the flexibility, adaptiveness, and inclusiveness of management where possible; (3) using simulation testing to help guide management decisions; and (4) enhancing the adaptive capacity of fishing communities by promoting engagement, flexibility, experimentation, and failsafes. These advancements are important as global fisheries prepare for a changing ocean.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".