Warming increases environmental DNA (eDNA) removal rates in flowing waters
Bibliographic record
Abstract
The use of environmental DNA (eDNA) for aquatic conservation is emerging, but its value is limited by our understanding of how environmental factors like temperature impact eDNA persistence. Although elevated temperatures are known to increase eDNA decay in lakes and ponds, no studies have experimentally explored the effect of temperature on eDNA fate in flowing waters where physical removal could obscure the effect of temperature on decay rates. We compared eDNA removal rates in n=12 indoor, recirculating mesocosms under varying water temperatures (20, 23, 26°C) and found that, for small eDNA particles (0.2-1.0μm), removal rates were higher at the warmest temperature (Tukey’s post hoc, p ≤0.03) while removal rates were consistent across temperatures for larger eDNA particles (>1.0μm, Tukey’s, p<0.05). Consequently, smaller eDNA particles were removed faster than larger particles (>1.0μm) at 26 and 23°C (Tukey’s, p<0.001) compared to 20°C (Tukey’s, p=0.01), resulting in an increase in the proportion of the eDNA sample made up of small particles with downstream transport for the two warmer temperatures (beta linear model, p<0.001). This suggests eDNA removal in streams reflects a complex interplay between physical trapping and microbial degradation influenced by temperature. Consequently, differences in temperature between geographic locations, seasons, and climates could impact the fate and interpretation of eDNA, even in flowing waters where physical removal contributes substantially to eDNA fate.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".