Large-Scale Monitoring of Plants through Environmental DNA Metabarcoding of Soil: Recovery, Resolution, and Annotation of Four DNA Markers
Bibliographic record
Abstract
In a rapidly changing world we need methods to efficiently assess biodiversity in order to monitor ecosystem trends. Ecological monitoring often uses plant community composition to infer quality of sites but conventional aboveground surveys only capture a snapshot of the actively growing plant diversity. Environmental DNA (eDNA) extracted from soil samples, however, can include taxa represented by both active and dormant tissues, seeds, pollen, and detritus. Analysis of this eDNA through DNA metabarcoding provides a more comprehensive view of plant diversity at a site from a single assessment but it is not clear which DNA markers are best used to capture this diversity. Sequence recovery, annotation, and sequence resolution among taxa were evaluated for four established DNA markers (matK, rbcL, ITS2, and the trnL P6 loop) in silico using database sequences and in situ using high throughput sequencing of 35 soil samples from a remote boreal wetland. Overall, ITS2 and rbcL are recommended for DNA metabarcoding of vascular plants from eDNA when not using customized or geographically restricted reference databases. We describe a new framework for evaluating DNA metabarcodes and, contrary to existing assumptions, we found that full length DNA barcode regions could outperform shorter markers for surveying plant diversity from soil samples. By using current DNA barcoding markers rbcL and ITS2 for plant metabarcoding, we can take advantage of existing resources such as the growing DNA barcode database. Our work establishes the value of standard DNA barcodes for soil plant eDNA analysis in ecological investigations and biomonitoring programs and supports the collaborative development of DNA barcoding and metabarcoding.
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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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 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".