Community eDNA metabarcoding as a detection tool for documenting freshwater mussel (Unionidae) species assemblages
Bibliographic record
Abstract
Abstract Documenting species occurrences and habitat occupancy of unionid mussels can often be challenging. Environmental DNA (eDNA) has been shown to be a reliable tool for detecting unionids with comparable or greater sensitivity than conventional sampling and has the added advantages of not disturbing individuals or occupied habitats. However, single‐species eDNA assays are limited to targeting individual species of interest and are functionally blind to the presence of other species. Community eDNA assays have the potential to characterize local species assemblages simultaneously but are currently less extensively developed and implemented than single‐species eDNA testing. We tested the effectiveness of community eDNA markers to identify unionid species assemblages, using two overlapping conserved primers that target the maternal mitochondrial 16S rDNA region. Both primer sets were optimized using three mock communities and successfully amplified 62.5%–81.6% of species with largely consistent results between the primer sets. Following optimization, eDNA from water samples from 24 reference sites with known mussel communities was amplified and sequenced to quantify species richness and diversity within and among sites. Metabarcoding results from the monitoring sites largely mirrored those from the mock communities, with >80% of species detections identified by both assays. The results were broadly consistent with species data from quadrat‐based manual field surveys, although both community eDNA and conventional sampling detected some species that the other method did not. These results demonstrate that community eDNA assays using conserved primers and next‐generation sequencing have the potential to simultaneously target eDNA from multiple unionid species and provide a powerful tool for complementing or augmenting conventional field surveys to characterize and monitor unionid species assemblages.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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; both teacher heads agree on what is shown here.
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".