Optimising the detection of marine taxonomic richness using environmental DNA metabarcoding: the effects of filter material, pore size and extraction method
Bibliographic record
Abstract
The analysis of environmental DNA (eDNA) using metabarcoding has increased in use as a method for tracking biodiversity of ecosystems. Little is known about eDNA in marine human-modified environments, such as commercial ports, which are key sites to monitor for anthropogenic impacts on coastal ecosystems. To optimise an eDNA metabarcoding protocol in these environments, seawater samples were collected in a commercial port and methodologies for concentrating and purifying eDNA were tested for their effect on eukaryotic DNA yield and subsequent richness of Operational Taxonomic Units (OTUs). Different filter materials [Cellulose Nitrate (CN) and Glass Fibre (GF)], with different pore sizes (0.5 µm, 0.7 µm and 1.2 µm) and three previously published liquid phase extraction methods were tested. The number of eukaryotic OTUs detected differed by a factor of three amongst the method combinations. The combination of CN filters with phenol-chloroform-isoamyl alcohol extractions recovered a higher amount of eukaryotic DNA and OTUs compared to GF filters and the chloroform-isoamyl alcohol extraction method. Pore size was not independent of filter material but did affect the yield of eukaryotic DNA. For the OTUs assigned to a highly successful non-indigenous species, Styelaclava, the two extraction methods with phenol significantly outperformed the extraction method without phenol; other experimental treatments did not contribute significantly to detection. These results highlight that careful consideration of methods is warranted because choice of filter material and extraction method create false negative detections of marine eukaryotic OTUs and underestimate taxonomic richness from environmental samples.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".