Small-scale fisheries catch and fishing effort in the Socotra Archipelago (Yemen) between 1950 and 2019
Bibliographic record
Abstract
The Socotra Archipelago (Yemen), a group of four islands off the north-eastern tip of Africa in the western Indian Ocean, has a population that relies heavily on small-scale fishing for livelihoods and food security. However, the reporting of fisheries catches by Yemen has consistently been incomplete, with artisanal (small-scale, commercial) catches underreported and small-scale non-commercial subsistence and recreational catches not reported at all. Here, we reconstruct the total small-scale catches and fishing effort from the waters of the Socotra Archipelago for 1950 to 2019, and derive catch-per-unit-effort (CPUE) estimates for these fisheries. The catch officially reported by the Food and Agriculture Organization on behalf of Yemen that was assumed taken from the archipelago is thought to be around 20% of the total reconstructed catch for the archipelago. The reconstructed small-scale catch increased from ~1,500 t in 1950 to an all-time peak of 12,000 t in 2000 before declining to 3,300 t by 2014. Thereafter, catches increased again slightly to just over 3,700 t·year -1 by 2019. Artisanal catches accounted for around 70% of total small-scale catches prior to 2010, but made up only around 46% by 2019. Conversely, subsistence catches increased from ~1,000 t in 2010 to ~2,000 t in 2019, and accounted for 54% of total catches by 2019. Small-scale fishing effort increased by over 1000% since 1950 and reached over 11 million kWdays by 2019. The CPUE derived for small-scale fisheries declined by 78% since 1950, from 1.4 kg·kWday -1 to 0.3 kg·kWday -1 in 2019, with most of the decline occurring after 2000. Our findings suggest resource overexploitation, and may assist efforts to more sustainably manage the Socotra Archipelago’s fish stocks. Small-scale fisheries support food and nutrient security of the local population, not least during political and humanitarian crises such as in Yemen.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".