Detection of freshwater mussels (Unionidae) using environmental DNA in riverine systems
Bibliographic record
Abstract
Abstract Environmental DNA (eDNA) methods are being developed for use in conservation biology to improve upon conventional species survey techniques. Validation of eDNA methods in different environmental contexts is required if they are to be widely adopted. One potential application of eDNA methods is for the detection of freshwater mussels (Bivalvia: Unionidae), which are among the most imperiled species in North America. Conventional unionid survey methods are highly invasive and can be difficult to conduct due to issues with morphological identification and their cryptic use of habitat. eDNA methods can potentially provide a non‐invasive, extremely specific, and highly sensitive alternative. Here, we examine the effectiveness of eDNA methods at detecting an imperiled unionid, the wavy‐rayed lampmussel (Lampsilis fasciola), in lotic systems with moderate discharge. We developed a novel qPCR assay for the detection of L. fasciola eDNA, which included a custom internal positive control to check for PCR inhibition. We used different experimental densities of caged L. fasciola specimens as a point source of eDNA within two rivers of the Grand River watershed in Southern Ontario. Sampling occurred at set distances downstream of the cage using purpose‐built sampling equipment. Detection was obtained at the cage (i.e., point of eDNA shedding) but not downstream at distances ≥10 m during stream discharges of approximately 1,632–2,332 L/s. The results indicate that eDNA is diluted rapidly in rivers with moderate discharge and that high‐resolution spatial sampling efforts may be necessary to obtain meaningful eDNA‐based distribution data of unionids, and other sessile organisms, present at low density in lotic systems.
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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 | 0.001 | 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 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".