Coupled human and natural system dynamics as key to the sustainability of Lake Victoria's ecosystem services
Bibliographic record
Abstract
East Africa's Lake Victoria provides resources and services to millions of people on the lake's shores and abroad.In particular, the lake's fisheries are an important source of protein, employment, and international economic connections for the whole region.Nonetheless, stock dynamics are poorly understood and currently unpredictable.Furthermore, fishery dynamics are intricately connected to other supporting services of the lake as well as to lakeshore societies and economies.Much research has been carried out piecemeal on different aspects of Lake Victoria's system; e.g., societies, biodiversity, fisheries, and eutrophication.However, to disentangle drivers and dynamics of change in this complex system, we need to put these pieces together and analyze the system as a whole.We did so by first building a qualitative model of the lake's social-ecological system.We then investigated the model system through a qualitative loop analysis, and finally examined effects of changes on the system state and structure.The model and its contextual analysis allowed us to investigate system-wide chain reactions resulting from disturbances.Importantly, we built a tool that can be used to analyze the cascading effects of management options and establish the requirements for their success.We found that high connectedness of the system at the exploitation level, through fisheries having multiple target stocks, can increase the stocks' vulnerability to exploitation but reduce society's vulnerability to variability in individual stocks.We describe how there are multiple pathways to any change in the system, which makes it difficult to identify the root cause of changes but also broadens the management toolkit.Also, we illustrate how nutrient enrichment is not a self-regulating process, and that explicit management is necessary to halt or reverse eutrophication.This model is simple and usable to assess system-wide effects of management policies, and can serve as a paving stone for future quantitative analyses of system dynamics at local scales.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".