Climate change adaptation in fisheries
Bibliographic record
Abstract
Abstract We conducted a global systematic literature review of climate change adaptation in fisheries. We addressed three specific questions: (i) What are fisheries adapting to? (ii) How are fisheries adapting? and (iii) What research gaps need to be addressed? We identified, characterized and examined case studies published between 1990 and 2019 that lie at the intersection of the domains of climate change, adaptation and fisheries. We characterized the documented climate change effects in fisheries that are being adapted to multiple stressors, general climate impacts, extreme events, ocean conditions, marine system shifts, climate variability, fishery dynamics, species distribution and atmospheric warming. Three categories of adaptive responses came to light: coping mechanisms (e.g. changing fishing location, use of traditional knowledge); adaptive strategies (e.g. livelihood diversification, incorporation of technology); and management responses (e.g. adaptive management, adaptation planning). We identified key potential areas for future research, including studies on the limits and barriers for adaptation, studies using specific conceptual and methodological approaches, and studies focussing on the top‐producing countries such as China, Indonesia, Peru and Russia. This analysis gives broader insights to the fisheries industry and to climate change adaptation research to proceed in the face of new global challenges.
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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 | 0.009 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.010 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".