Mobilizing Evidence-based Knowledge for Sustainable Wetlands Co-management and Co-governance amidst increasing Anthropogenic and Environmental Stressors: Key Lessons from Mityana District, Uganda
Bibliographic record
Abstract
• Two wetland systems crisscross Mityana district (Lake Wamala and River Mayanja) and form a part of the Lake Victoria basin catchment. • Limited research and knowledge of the critical wetland values among most rural communities. • Complex anthropogenic threats are primary threat drivers and loss of culturally valuable wetlands leads to socio-cultural-ecological grief. • Sedentary communities along wetlands have untapped social science knowledge of wetlands governance and management. • The co-developed Sustainable Wetlands Management Action Pathway (SWeMAP) provides seven (7) critical social science insights for sustainable wetlands governance and management across geographies. Wetlands (covering about 1.5–1.6 billion hectares globally), are critical biodiversity and livelihood hotspots. Wetlands further replenish the global economy with $47.4 trillion/year worth of ecosystem services. By jealously guarding wetlands, progress toward sustainable development goals, and livelihood welfare are possible. Unfortunately, despite the promulgation of wetland governance mechanisms, 35 percent of the global natural wetlands have been lost since the 1970s. This could be worse in undocumented or explored wetland zones situated in remote tropical regions. In this study, we bring to the fore insights from 286 documents sourced from Scopus and engagements from 105 citizens in Mityana, to (i) map wetlands (including the current vulnerabilities and threats), and (ii) co-develop a wetlands management action pathway that could create sustainable co-management possibilities and sustainable livelihood futures. Findings revealed that although research on wetlands has increased for the last 31 years, since 2021, it has plummeted. In Uganda, wetland research and scholarship is predominantly situated around the Lake Victoria region. Most research focuses on natural or biological sciences. Emerging policy themes and trending research topics are shifting from key wetland management paradigms. From a total of 105 sampled wetlands scattered across fourteen (14) sub-counties in the Mityana district, critical wetland issues were unraveled. Mityana is crossed by two wetland systems (Lake Wamala and River Mayanja dominated by permanent papyrus and seasonal swamps respectively. Wetlands offer unique livelihood, cultural assets/capitals, and ecological benefits (including cultural/aesthetics meaning). An unfathomable rate of degradation is evident. Anthropogenic factors are the predominant threat drivers, especially eucalyptus planting. The loss of culturally valuable wetlands has increased socio-cultural-ecological grief, such as around Lake Wamala. Micro-level management actions are increasing, albeit mainly around accessible permanent wetlands. Most riparian wetland sedentary populations expressed willingness and interest in the co-management and governance of community wetlands. More robust actions and pathways are needed to create avenues for community co-management. The co-developed the sustainable wetlands management action pathway (SWeMAP) provides seven (7) coherent steps, including critical social science insights that could aid sustainable wetlands governance and management across geographies. As wetlands in Uganda have been gazetted as critical to sustainable development, the urgent co-development and financing of micro-level wetland action plans, including situational inventories could help create avenues for sustainable wetlands management.
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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 | 0.025 | 0.082 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".