Fishing Effort and Associated Catch per Unit Effort for Small-Scale Fisheries in the Mozambique Channel Region: 1950–2016
Bibliographic record
Abstract
The Mozambique Channel region in East Africa has diverse marine ecosystems and serves as a migratory corridor for economically important species. Local and foreign industrial fisheries operate in the Mozambique Channel, but regional small-scale fisheries are the crucially important fisheries that provide food security, livelihoods, and economic opportunities for rural coastal communities. This study reconstructed and investigated trends in the fishing effort and catch per unit effort (CPUE) of small-scale marine fisheries in four Exclusive Economic Zones (EEZ) that constitute the Mozambique Channel, i.e., Union of Comoros, Madagascar, Mayotte, and Mozambique, from 1950 to 2016. Effective fishing effort for small-scale fisheries in the form of fishing capacity in kWdays (i.e., kilowatt days) was derived using the number, length, motorization (engine power) by fishing vessels, as well as an approximate human-powered equivalent for shore-based fishers without vessels, as well as days of fishing per year. Effective small-scale fishing effort in the Mozambique Channel increased by nearly 60 times from just over 386,000 kWdays in 1950 to over 23 million kWdays in 2016. Correspondingly, the overall small-scale CPUE, based on previously and independently reconstructed catch data declined by 91% in the region as a whole, from just under 175 kg⋅kWday–1 in the early 1950s to just over 15 kg⋅kWday–1 in recent years. All four EEZs showed the strongest declines in the small-scale CPUE in the earlier decades, driven by motorization and growth in vessel numbers impacting effective fishing effort. Increased motorization combined with a substantial growth in overall vessel numbers were the drivers of the increasing fishing effort and decreasing CPUE, and clearly suggest that continuing to increase the fishing capacity of small-scale fisheries in the absence of effective and restrictive management actions may exacerbate overexploitation risk.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".