Comparative Decay Dynamics and Detectability of <scp>eDNA</scp> and <scp>eRNA</scp> in Connected and Isolated Freshwater Mesocosms Using Digital <scp>PCR</scp>
Bibliographic record
Abstract
Efficient use of environmental nucleic acids (eNAs) in freshwater biodiversity monitoring requires understanding their degradation and detectability in interconnected ecosystems. We employed a novel field-scale assay to compare environmental DNA (eDNA) and environmental RNA (eRNA) decay rates and detectability across four genetic markers (16S, 18S, COI and LDHA) in connected and isolated 1000-L mesocosms containing natural planktonic assemblages. This design provides ecologically relevant and complex settings to assess how connectivity influences the detectability of eNA over time. Isolated and head mesocosms were spiked with eNAs from cultured Daphnia pulex, absent from the water source, while downstream mesocosms received eNAs via unidirectional water transfers. Using digital PCR (dPCR), we captured fine-scale temporal patterns across mitochondrial and nuclear markers and transcript types (mRNA and rRNA), an approach rarely combined in previous research. eRNA degraded significantly faster than eDNA across markers and mesocosm types. Among RNA types, mRNA (COI, LDHA) degraded faster than rRNA (16S, 18S). eRNA followed a uniform monophasic decay pattern, whereas eDNA displayed biphasic decay for nuclear markers and monophasic decay for mitochondrial markers. eNA decay rates in this field-relevant mesocosm network exceeded those from laboratory scale. While decay rates remained consistent across networks, detectability declined with dilution. Even after a 10,000-fold dilution, both eNAs were detected in terminal mesocosms, demonstrating effective transport across the network. Although RNA degrades rapidly, high detectability was achieved across diverse dilutions using dPCR, highlighting eRNA's potential for detecting active biological communities in freshwater systems.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".