Assessing the degradation of environmental <scp>DNA</scp> and <scp>RNA</scp> based on genomic origin in a metabarcoding context
Bibliographic record
Abstract
Abstract Molecular tools of species identification based on eNAs (environmental nucleic acids; environmental DNA [eDNA] and environmental RNA [eRNA]) have the potential to greatly transform biodiversity science. However, the ability of eNAs to obtain “real‐time” biodiversity estimates may be complicated by the differential persistence and degradation dynamics of the molecular template (eDNA or eRNA) and the barcode marker used. Here, we collected water samples over a 28‐day period to comparatively assess species detection using eDNA and eRNA metabarcoding of two distinct barcode markers—a mitochondrial mRNA marker (COI) and a nuclear rRNA marker (18S)—following complete removal of Arthropoda taxa in a semi‐natural freshwater system. Our findings demonstrate that Arthropoda community composition was largely influenced by marker choice, rather than molecular template, individual microcosm, or sampling time point. Furthermore, although eRNA may capture similar species diversity as the established eDNA method, this finding may be marker‐dependent. Although we found little to no difference in decay rates observed among sample groups (COI eDNA, COI eRNA, 18S eDNA, 18S eRNA), this result is likely due to limitations in the ability of eNA‐based metabarcoding to provide a strong correlation between true eNA copy numbers present in the environment and final read counts obtained (following the metabarcoding workflow). Collectively, our findings provide further support for the use of multi‐marker assessments in metabarcoding surveys to unravel the broadest taxonomic diversity possible, highlight the limitations of eNA metabarcoding methods in providing accurate decay rate estimates, as well as establish the need for further comparative studies using both metabarcoding and single‐species detection methods to assess the persistence and degradation dynamics of eNAs for a diverse range of taxa.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".