Climate change impacts on fisheries in West Africa: implications for economic, food and nutritional security
Bibliographic record
Abstract
West Africa was identified as one of the most vulnerable regions to climate change in previous global analyses. Adverse changes in marine resources under climate change may pose significant threats to the livelihoods and well-being of the communities and countries that depend on fisheries for food and income. However, quantitative studies on the potential impact of climate change on fisheries and its subsequent impact on human well-being in West Africa are still scarce. This paper aims to assess the potential impacts of climate change on fisheries and their effects on the economics, food and nutritional security in West Africa. We use a dynamic bioclimatic envelope model to project future distribution and maximum fisheries catch potential of fish and invertebrates in West African waters. Our projections show that climate change may lead to substantial reduction in marine fish production and decline in fish protein supply in this region by the 2050s under the Special Report on Emission Scenarios (SRES) A1B. Combining with economic parameters, we project a 21% drop in annual landed value, 50% decline in fisheries-related jobs and a total annual loss of US$311 million in the whole economy of West Africa. These changes are expected to increase the vulnerability of the region through economics and food security of West Africa 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".