Validating environmental DNA metabarcoding for marine fishes in diverse ecosystems using a public aquarium
Bibliographic record
Abstract
Abstract Environmental DNA metabarcoding has been widely touted as a powerful tool for monitoring biodiversity in marine ecosystems. However, this method still requires thorough validation and standardization before it can be widely applied for ecological monitoring. The potential utility of environmental DNA metabarcoding is greatest in systems with high levels of diversity, yet environmental DNA metabarcoding has typically been validated in closed systems with relatively low levels of diversity. Additionally, the use of a multiple marker approach has been minimally explored under controlled, closed systems in the literature. Using a pilot study, we assess the ability of eDNA metabarcoding to capture biodiversity in a highly diverse closed system at the Ripley's Aquarium of Canada in Toronto, Ontario. Our pilot study highlights several key knowledge gaps that must be addressed before metabarcoding can be employed for widespread use in ecological monitoring. We found that environmental DNA metabarcoding recovered just over 50% of target species and 80% of target genera within a closed marine system containing 107 species and 44 genera using previously published markers for COI, 12S, and 16S. Additionally, COI and 12S were found to identify fewer target species than 16S, with COI generating the most off‐target identifications, but maximum detection success was achievable by combining all three markers. We discuss numerous key limitations which currently present barriers to the application of eDNA metabarcoding for studying highly diverse marine systems. These include marker selection, data validation and confidence, and the complexity of abundant and diverse novel systems. This study highlights important, yet incompletely resolved, challenges of environmental DNA metabarcoding for the detection of marine fishes in a diverse closed system environment using a multiple marker approach.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".