Validation of environmental DNA (eDNA) as a detection tool for at‐risk freshwater pearly mussel species (Bivalvia: Unionidae)
Bibliographic record
Abstract
Abstract Documenting the occurrence and habitat occupancy of rare aquatic species is an ongoing challenge for conservation. Characterization of environmental DNA (eDNA) from bulk water samples has emerged as a powerful tool to infer species presence or absence without the need to observe or handle organisms. Previous eDNA studies have yet to develop species‐specific markers that target taxa with many potentially sympatric confamilials. Forty‐one freshwater pearly mussel species (Unionidae) are found in southern Ontario, Canada, with many of these listed as threatened, endangered, or of conservation concern; however, locating populations for protection can be challenging owing to morphological crypsis and species scarcity. Species‐specific eDNA markers were developed to target four unionid species. Following in silico and in vitro validation, markers were validated in the field by comparing eDNA results from water samples to detections based on quadrat sampling. Target species were detected by eDNA sampling at all sites where they had previously been located by quadrat sampling. The paired sampling design showed that species‐specific markers can be designed even within speciose families, and that eDNA detection of mussels is at least as sensitive as quadrat sampling. Furthermore, detection probabilities were not affected by sampling depth, and eDNA concentrations were positively correlated with mussel densities. These findings confirm that eDNA assays are a valuable complement to traditional methods for locating and managing imperilled unionid populations.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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 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".