Using <scp>eDNA</scp> to Assess Freshwater Bacterial Diversity Along a Forest–Non‐Forest Gradient in the Afrotropics
Bibliographic record
Abstract
ABSTRACT Healthy ecosystems are critical for maintaining ecosystem services and water security; yet many freshwater ecosystems have been subject to environmental degradation. Impacts are often greatest in water‐scarce and developing regions, including across much of Sub‐Saharan Africa, where many people lack access to basic drinking water. However, environmental monitoring programmes to track ecosystem health are generally lacking across this region due to limited resources and funding. Recent advances in environmental DNA (eDNA) methods offer an increasingly cost‐effective and information‐rich solution. Here, we explore the potential of eDNA as a tool for ecological monitoring of freshwater ecosystems in Uganda, East Africa. We sampled eDNA to quantify the bacterial diversity of rivers, streams, and swamps across a gradient of human disturbance in and around Kibale National Park, using off‐the‐shelf sampling methods that require minimal pre‐existing infrastructure. We found distinct bacterial communities between intact and degraded habitats, but the bacterial community in rivers converged when flowing through intact forest. We identified several taxa with differential abundances that might serve as potential bioindicators of degraded ecosystems, and showed that a machine learning tool trained on eDNA can accurately differentiate between intact and degraded habitats. Our proof‐of‐concept study demonstrates the potential of eDNA as a practical and cost‐effective biomonitoring tool for freshwater ecosystems in resource‐limited regions, including Sub‐Saharan Africa. We also highlight the potential benefits of protected forest in modulating bacterial composition in freshwater ecosystems.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".