Bacterial diversity in the waterholes of the Kruger National Park: an eDNA metabarcoding approach
Bibliographic record
Abstract
Bacteria are essential components of natural environments. They contribute to ecosystem functioning through roles as mutualists and pathogens for larger species, and as key components of food webs and nutrient cycles. Bacterial communities respond to environmental disturbances, and the tracking of these communities across space and time may serve as indicators of ecosystem health in areas of conservation concern. Recent advances in DNA sequencing of environmental samples allow for rapid and culture-free characterization of bacterial communities. Here we conduct the first metabarcoding survey of bacterial diversity in the waterholes of the Kruger National Park, South Africa. We show that eDNA can be amplified from waterholes and find strongly structured microbial communities, likely reflecting local abiotic conditions, animal ecology, and anthropogenic disturbance. Over timescales from days to weeks we find increased turnover in community composition, indicating bacteria may represent host-associated taxa of large vertebrates visiting the waterholes. Through taxonomic annotation we also identify pathogenic taxa, demonstrating the utility of eDNA metabarcoding for surveillance of infectious diseases. These samples serve as a baseline survey of bacterial diversity in the Kruger National Park, and in the future, spatially distinct microbial communities may be used as markers of ecosystem disturbance, or biotic homogenization across the park.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".