Measuring biodiversity from DNA in the air
Bibliographic record
Abstract
The crisis of declining biodiversity 1 exceeds our current ability to monitor changes in ecosystems. Rapid terrestrial biomonitoring approaches are essential to quantify the causes and consequences of global change. Environmental DNA 2 has revolutionized aquatic ecology, 3 permitting population monitoring 4 and remote diversity assessments matching or outperforming conventional methods of community sampling. 3–5 Despite this model, similar methods have not been widely adopted in terrestrial ecosystems. Here, we demonstrate that DNA from terrestrial animals can be filtered, amplified, and then sequenced from air samples collected in natural settings representing a powerful tool for terrestrial ecology. We collected air samples at a zoological park, where spatially confined non-native species allowed us to track DNA sources. We show that DNA can be collected from air and used to identify species and their ecological interactions. Air samples contained DNA from 25 species of mammals and birds, including 17 known terrestrial resident zoo species. We also identified food items from air sampled in enclosures and detected taxa native to the local area, including the Eurasian hedgehog, endangered in the United Kingdom. Our data demonstrate that airborne eDNA concentrates around recently inhabited areas but disperses away from sources, suggesting an ecology to airborne eDNA and the potential for sampling at a distance. Our findings demonstrate the profound potential of air as a source of DNA for global terrestrial biomonitoring.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".