The multiple states of environmental DNA and what is known about their persistence in aquatic environments
Bibliographic record
Abstract
Increased use of environmental DNA (eDNA) analysis for indirect species detection has spurred the need to understand eDNA persistence in the environment.Understanding the persistence of eDNA is complex because it exists in a mixture of different states (e.g., dissolved, particle-adsorbed, intra-cellular and organellar), and each state is expected to have a specific decay rate that depends on environmental parameters.Thus, improving knowledge about eDNA conversion rates between states and the reactions that degrade eDNA in different states is needed.Here, we outline how water chemistry and suspended mineral particles likely affect conversion among each eDNA state and indicate how environmental parameters affect persistence of states in the water column.Based on deducing these controlling parameters, we conducted a meta-analysis of the eDNA literature to assess whether we could already derive a general understanding of eDNA states persisting in the environment.However, we found that these parameters are often not being measured or reported when measured, and in many cases very few experimental data exists from which to draw conclusions.Therefore, further study of how environmental parameters affect eDNA state conversion and eDNA decay in aquatic environments is needed.We recommend analytic controls that can be used during the processing of water to assess potential losses of different eDNA states if all were present in a water sample and outline future experimental work that would help determine the dominant eDNA states in water.
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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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".