Challenges for assessing vertebrate diversity in turbid Saharan water-bodies using environmental DNA
Bibliographic record
Abstract
The Sahara desert is the largest warm desert in the world and a poorly explored area. Small water-bodies occur across the desert and are crucial habitats for vertebrate biodiversity. Environmental DNA (eDNA) is a powerful tool for species detection and is being increasingly used to conduct biodiversity assessments. However, there are a number of difficulties with sampling eDNA from such turbid water-bodies and it is often not feasible to rely on electrical tools in remote desert environments. We trialled a manually powered filtering method in Mauritania, using pre-filtration to circumvent problems posed by turbid water in remote arid areas. From nine vertebrate species expected in the water-bodies, four were detected visually, two via metabarcoding, and one via both methods. Difficulties filtering turbid water led to severe constraints, limiting the sampling protocol to only one sampling point per study site, which alone may largely explain why many of the expected vertebrate species were not detected. The amplification of human DNA using general vertebrate primers is also likely to have contributed to the low number of taxa identified. Here we highlight a number of challenges that need to be overcome to successfully conduct metabarcoding eDNA studies for vertebrates in desert environments in Africa.
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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 | 0.010 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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 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".