Sampling intensity and temporal persistence of airborne eDNA in partially enclosed spaces
Bibliographic record
Abstract
Airborne environmental DNA (eDNA) has shown promise as a terrestrial biomonitoring tool and its ecological applications are expanding. Despite its growing use, airborne eDNA does not yet have the extensive body of supporting research like its aquatic counterpart, with considerable uncertainty remaining concerning how airborne eDNA behaves, with regards to signal duration, and how much sampling effort is needed to capture DNA in a given airspace. By using airborne eDNA in a semi-controlled environment which acted as an artificial roost where bat species and their abundances were known, we estimated the sampling intensity (both the number of samples and number of sampling events) required to capture bat diversity of a given airspace, as well as signal persistence of airborne eDNA. Together these data provide a temporal scale for airborne eDNA measurements. The majority of species richness was detected using as little as 4 samplers in this enclosed space and the greater the number of sampling events, the fewer samplers were needed. Both air movement and the type of environment (i.e., enclosed space, open area etc.) are likely to impact detection and need to be considered during study design. eDNA also appeared to settle out of the air quickly, suggesting that detections likely reflect recent activity, which also has important implication for rare species which may only have a narrow window for detection. Our results add to the growing body of literature that indicate airborne eDNA can be a useful biosurvey method, especially for rapid surveys in communities with high turnover rates.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".