Long-term Trends in Coral Reef Fish Yields and Exploitation Rates of Commercial Species from Coastal Kenya
Bibliographic record
Abstract
Keywords: coral reef fish, yield, long term trends, commercial fishing, Kenya Analysis of long-term (1978–2001) marine fisheries data showed that Kenyan coralreefs produced an estimated 2–4 metric t/km2/year of demersal fish. A rapid overall decline in landings occurred during the 1990s. Yields (t/km2/year) showed bimodal peaks in 1982 (2.98) and 1991 (2.90). The average total landings dropped by 55% during the last decade following peak landings in 1982. Landings of the commercially important families (e.g., Siganidae, Lethrinidae, Lutjanidae and Serranidae) declined by about 40% during the last decade, with the groupers (Serranidae) showing the steepest (72%) decline. Analysis of landings per administrative district showed a 78% decline in the densely populated Mombasa district between the periods 1983–1991 and 1992–2001. The less populated districts have registered stable (e.g., Kilifi) to increasing (e.g., Kwale) catches over time. An autoregressive moving average (ARIMA) model forecast of landings predicted a gradual decline in catches during the next decade (2002– 2011) with a trend slope of -0.01 t/km2. Length-frequency analysis for the commercially important species indicated above optimum exploitation (E) and fishing mortality (per year) rates for the sky emperor, Lethrinus mahsena (E = 0.64; F = 2.48) and lower but strong rates for the emperor, L. sangueinus (E=0.51; F=0.93). The more abundant and commercially important whitespotted rabbitfish, Siganus sutor, showed equally strong rates (E= 0.56; F = 1.44/year). A precautionary approach in the management of Kenya\'s coral-reef fisheries is recommended. Western Indian Ocean Journal of Marine Sciences Vol.2(2) 2003: 105-116
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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) | 0.002 | 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".