Vertebrate environmental DNA from leaf swabs
Bibliographic record
Abstract
Terrestrial vertebrates are threatened by anthropogenic activities around the world. The rapid biodiversity loss that ensues is most intense in the tropics and affects ecosystem functions, such as seed dispersal, or may facilitate pathogen transmission 1 . Monitoring vertebrate distributions is essential for understanding changes in biodiversity and ecosystems and also for adaptive management strategies. Environmental DNA (eDNA) approaches have the potential to play a key role in such efforts. Here, we explore whether eDNA swabbed from terrestrial vegetation in a tropical biodiversity hotspot is a useful tool for vertebrate biomonitoring. By swabbing leaves, we collected eDNA from 24 swabs at three locations in Kibale National Park, Uganda and used two metabarcoding systems to catalog the vertebrate taxa in the samples. We detected 52 wild vertebrate genera, including 26 avian and 24 mammalian genera; 30 of these assignments could be refined to the species level. We detected an average of 7.6 genera per swab. This approach, with its inexpensive and simple collection and DNA extraction, opens the door for inexpensive large-scale vertebrate biomonitoring.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.084 |
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; both teacher heads agree on what is shown here.
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".