The Development and Application of Targeted eDNA Metabarcoding for Monitoring Freshwater and Marine AIS
Bibliographic record
Abstract
Species invasions are of critical concern due to their significant impacts on ecosystems and social economies, of which aquatic invasive species (AIS) often pose significant challenges in their control and management, notably because of difficulties in early detection. Environmental DNA (eDNA) provides a promising tool in advancing the detection of newly introduced aquatic organisms because of its high sensitivity and ease of use compared to traditional capture-based methods. Although eDNA-based methods are increasingly used worldwide, especially in aquatic ecosystems, most studies focus on a limited number of target species despite a pressing need for broad taxonomic monitoring for conservation and management. In this thesis, I developed and applied an approach that capitalizes on a combination of high-sensitivity PCR primer sets and high-throughput sequencing (HTS) to detect 69 aquatic invasive species. This hybrid approach is defined as “targeted metabarcoding”. The sensitivity of the 128 primer sets ranged between 2.8 × 10–4 ng and 4.8 ng, and the inclusion of interfering plankton eDNA reduced the sensitivity by an average of approximately an order of magnitude. My targeted metabarcoding resulted in the detection of > 97% of the AIS spiked into eDNA samples, and the number of HTS reads had a significantly (P < 0.002) positive relationship with the amount of spiked DNA. I then applied this approach to eDNA collected at eight Canadian ports or harbors to detect potential invaders; 38.6% of anticipated species from our 69 were detected. This fast, high-sensitive, and relative cost-saving approach can be used to detect AIS at early invasion stages, which will contribute to routine aquatic invasive species detection globally.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".