How to barcode (almost all) freshwater biodiversity
Bibliographic record
Abstract
ABSTRACT Freshwater ecosystems are complex, diverse and face a variety of imminent threats that have led to changes in both ecosystem structure and function. It is urgent that we develop and standardize monitoring tools allowing for rapid and comprehensive assessment of freshwater communities to understand their changing dynamics and to 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 only. In this study, we sought to 1) identify thoroughly validated, cost-efficient primer pair combinations that maximize detection of broad swaths of freshwater diversity, and 2) facilitate future primer pair selection by creating a free online and user-friendly tool. We first evaluated the completeness of public reference sequence databases and the efficiency of 14 primer pairs using an in silico approach, and then performed eDNA surveys using five mock communities (mix of DNA from tissues), water samples from aquarium samples with known taxonomic composition, and finally water samples from freshwater systems in Eastern Canada. We highlight the power of eDNA-based metabarcoding for reconstructing freshwater communities, including prey, parasite, pathogen, invasive, and declining species. Our work reveals the importance of the marker choice on species resolution, as well as the importance of degenerate primers, the length of the target fragment and the filtering parameters on detection success in water eDNA samples. Our new online tool SNIPe revealed that 13 to 14 primer pairs are necessary to recover 100% of the species in water samples (aquarium and natural systems), but four primer pairs are sufficient to recover almost 75% of taxa with little overlap. These results highlight the usefulness of eDNA for freshwater monitoring and should prompt more studies on tools to survey all-inclusive communities.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.015 |
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".