Quantifying the effect of water quality on eDNA degradation using microcosm and bioassay experiments
Bibliographic record
Abstract
Abstract Environmental DNA (eDNA) is often used to determine the presence and absence of species in a specific environment, be it air, water, or soil. Numerous environmental conditions are known to directly alter the rate at which eDNA degrades, including pH, temperature, and UV‐B light exposure. Beyond these, many limnological parameters have not been thoroughly examined for their ability to modify the degradation rate of eDNA. Here we used 20 mL microcosms with water collected from 12 lakes from the Kawartha Highlands near Peterborough Ontario, Canada, to study the decay rates of dissolved Yellow perch ( Perca flavescens ) eDNA. We measured and related rates of eDNA loss to multiple water quality parameters: total dissolved phosphorus, total dissolved nitrogen, size‐fractionated carbon, and chlorophyll‐a levels. Bioassays were also conducted to examine the bacterial role in eDNA degradation using three treatments under natural system conditions: non‐filtered, filtered (0.22 μm), and non‐filtered with added phosphorus (50 μg/L). Each microcosm exhibited a unique rate of degradation with eDNA half‐life (C 0.5 ) ranging from 2.5 to 12.9 h. Chlorophyll‐a levels exhibited a positive linear relationship to the rate of degradation, while all other parameters showed no effect. The bioassays showed a general trend of the filtered treatments exhibiting the lowest rate of degradation, followed by the phosphorus treatments with the non‐filtered treatment containing bacteria exhibiting the highest rate of degradation. Overall, water with an increased level of chlorophyll‐a, in conjunction with elevated bacteria (i.e. non‐filtered bioassay) will exhibit a faster overall rate of eDNA degradation. These results show the necessity to individualize eDNA survey plans to the water body of interest and to account for environmental conditions relating to the microbial processing of eDNA.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".