Enhancing metabarcoding of freshwater biotic communities: A new online tool for primer selection and exploring data from 14 primer pairs
Bibliographic record
Abstract
Abstract Freshwater ecosystems are complex, diverse, and face multiple imminent threats that have led to changes in both structure and function. It is urgent that we develop and standardize monitoring tools that allow for rapid and comprehensive assessment of freshwater communities to understand their changing dynamics and inform conservation. Environmental DNA surveys offer a means to inventory and monitor aquatic diversity, yet most studies focus on one or a few taxonomic groups because of technical challenges. In this study, we (1) create an eDNA metabarcoding dataset (natural water bodies) with 14 validated primer pairs; (2) create a free online, user‐friendly tool for primer selection that can be used for any metabarcoding data (SNIPe); and (3) using SNIPe, explore our dataset to derive subsets of informative, cost‐effective primer pairs that maximize detection of freshwater diversity. We first evaluated the completeness of public reference sequence databases and the efficiency of 14 primer pairs in silico, in vitro on five mock communities (mix of DNA from tissues of select taxa), in vivo on water samples from aquarium samples with known taxonomic composition, and finally in vivo on water samples from freshwater systems in Eastern Canada. Results from analyses using SNIPe revealed that 13 or 14 primer pairs are necessary to recover 100% of the species in water samples (natural systems), but that four primer pairs are sufficient to recover almost 75% of taxa with little overlap. Our work highlights the power of eDNA metabarcoding for reconstructing freshwater communities, including prey, parasite, pathogen, invasive, and declining species. It also emphasizes the importance of marker choice on species resolution, and primer characteristics and filtering parameters on detection success and accuracy of biodiversity estimates. Together, these results highlight the usefulness of eDNA for freshwater monitoring and should prompt more studies of tools to survey all communities.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".