Environmental DNA filtration techniques affect recovered biodiversity
Bibliographic record
Abstract
Freshwater metazoan biodiversity assessment using environmental DNA (eDNA) captured on filters offers new opportunities for water quality management. Filtering of water in the field is a logistical advantage compared to transport of water to the nearest lab, and thus, appropriate filter preservation becomes crucial for maximum DNA recovery. Here, the effect of four different filter preservation strategies, two filter types, and pre-filtration were evaluated by measuring metazoan diversity and community composition, using eDNA collected from a river and a lake ecosystem. The filters were preserved cold on ice, in ethanol, in lysis buffer and dry in silica gel. Our results show that filters preserved either dry or in lysis buffer give the most consistent community composition. In addition, mixed cellulose ester filters yield more consistent community composition than polyethersulfone filters, while the effect of pre-filtration remained ambiguous. Our study facilitates development of guidelines for aquatic community-level eDNA biomonitoring, and we advocate filtering in the field, using mixed cellulose ester filters and preserving the filters either dry or in lysis buffer.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".