Answers blowing in the wind: Detection of birds, mammals, and amphibians with airborne environmental <scp>DNA</scp> in a natural environment over a yearlong survey
Bibliographic record
Abstract
Abstract Analysis of environmental DNA (eDNA) from passively collected airborne dust has demonstrated broad success for sensitive and robust detection of plants. Recent experiments at small spatial scales have suggested that animals can also be detected using airborne eDNA. However, airborne eDNA analysis has never been used for a long‐term whole‐community assessment of a natural terrestrial community or with passive dust collectors. We conducted a metabarcoding survey targeting vertebrate eDNA from dust carried in the air on an approximately 130‐acre shortgrass prairie passively collected over the course of a year. Our survey detected a wide variety of animal forms including an amphibian species, several bird species, and both small and large mammals. We found that airborne eDNA signals changed with known patterns of animal activity, wind speed, and rainfall. Overall, we demonstrate that passively collected airborne dust carries eDNA from terrestrial animals and could be used to detect a wide variety of terrestrial vertebrate species in a natural environment with minimal effort. To develop this as a valuable monitoring tool, research needs to focus on the ecology of eDNA carried in the air, which includes the origin, state, transport, dispersal, and fate of eDNA in the environment.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 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".