Multi-marker DNA metabarcoding reflects tardigrade diversity in different habitats
Bibliographic record
Abstract
Like meiofauna in general, tardigrades are often neglected in ecological and environmental surveys. Tardigrades occur in all parts of the world, from deep marine sediments to alpine environments, and are present in most ecosystems. They are therefore potentially good candidates for biomonitoring programs. However, sampling of these minute animals is both tedious and time-consuming, impeding their inclusion in large-scale ecological surveys. In this study we argue that using a multi-marker metabarcoding approach on environmental DNA (eDNA) partly can overcome this barrier. Samples of moss, lichens, and leaf litter were investigated both by morphology-based methods and DNA metabarcoding, and the results were compared in terms of tardigrade diversity and community composition of the sampled microhabitats. DNA metabarcoding using three markers detected more species of tardigrades than identification by morphology in most samples. Also, metabarcoding detected the same community differences and microhabitat distribution patterns as morphology-based methods. In general, metabarcoding of litter samples was unreliable, with only one out of three markers consistently amplifying and detecting tardigrades. The low availability of tardigrade reference sequences in public databases restricts the taxonomic resolution in eDNA surveys, but this impediment is partly circumvented by utilizing multiple markers.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".