Effects of Nile tilapia (Oreochromis niloticus) cage aquaculture on water quality in the world’s largest tropical lake
Bibliographic record
Abstract
Cage aquaculture in tropical lakes is an efficient means of generating fish protein to ensure food security and support livelihoods. However, it has been repeatedly criticized as injurious to lake ecosystems due to eutrophication and other problems, and therefore ill-advised. This study examined the impacts of Nile tilapia ( Oreochromis niloticus ) cage farming on water quality across spatial scales in the world’s largest tropical lake, Lake Victoria. The primary objectives were to determine if water quality patterns differed between cage and control stations along a background limnological gradient, and to identify the water quality variables that contributed most to these differences. We postulated that the spatial arrangement of sites in the lake would drive overarching patterns of water quality variation, while cage aquaculture would have additional impacts on these parameters across the spatial gradient. Water column samples were collected from both cage and control stations at four locations along a known gradient in limnology, across 2 years. The results revealed significant differences in water quality between cage and control stations at all four sites, with dissolved oxygen, pH, and silica contributing the most to these differences. These findings highlight the need for site-specific management strategies to both mitigate the environmental impacts of cage aquaculture in tropical lakes and ensure continuity of aquaculture production. Our study provides an approach that can be applied to tropical lakes and reservoirs around the world. Effective nutrient management and continuous monitoring are recommended to support sustainable aquaculture in ecosystems under stress from growing lakeside populations.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".