Using environmental DNA metabarcoding to map invasive and native invertebrates in two Great Lakes tributaries
Bibliographic record
Abstract
Abstract Background Aquatic invasive species (AIS) threaten ecosystems and native species. Methods To determine spatial distributions of at‐risk native taxa and AIS in two biologically diverse Laurentian Great Lakes tributaries, we extracted environmental DNA (eDNA) from water samples and used a universal PCR primer set targeting the CO1 gene for metabarcoding of selected taxa. We sampled 43 sites for eDNA in each of the Grand and Sydenham rivers in southwestern Ontario. Results We assigned sequences to 49 taxa at the species level and four mollusks to genus level. Detected AIS included two oligochaete worms ( Branchiura sowerbyi , Potamothrix moldaviensis ), a freshwater jellyfish ( Craspedacusta sowerbyi ), a calanoid copepod ( Skistodiaptomus pallidus ), and a bivalve dreissenid mussel ( Dreissena rostriformis bugensis ). All but D. r. bugensis were previously unreported in these tributaries. Detected native mollusks included one globally endangered species the rayed bean ( Villosa fabalis ), one provincially listed threatened species the maple leaf mussel ( Quadrula quadrula ), and several other at‐risk and unique mollusk species of special interest in Ontario, Canada, and the United States (e.g., Sphaerium fabale , Pyganodon grandis ). At several sampling sites in each river, AIS eDNA overlapped with or was near to sites with detections of at‐risk native mollusks. Most AIS and some native taxa demonstrated clustered detection patterns within each river. However, in some cases, independent detections of individual species occurred at individual sites within each river that were relatively far apart. Our findings should be interpreted with some caution due to the limitations of the aquatic “universal” primer set and the availability of comprehensive reference sequence databases. Conclusion Results from eDNA metabarcoding in our study helped reveal invertebrate AIS and at‐risk species distributions and will help direct approaches for conserving biodiversity in each of these Great Lakes tributaries.
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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 | 0.001 |
| Science and technology studies | 0.001 | 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".