Multi-marker DNA metabarcoding reflects tardigrade diversity in different habitats
Why this work is in the frame
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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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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 it