Improved efficiency in eDNA metabarcoding of benthic metazoans by sieving sediments prior to DNA extraction
Bibliographic record
Abstract
Abstract Environmental DNA (eDNA) metabarcoding can be used to rapidly characterize the taxon assemblage of benthic communities in sediments and thus has high potential to complement routine regulatory monitoring of benthic impacts. However, when using DNA extracted directly from subtidal sediments, only a small proportion of metazoan reads are obtained regardless of which available universal primers are used. Developing new metazoan‐specific primers for broad taxonomic amplification is very challenging and may not solve this problem; here, we investigate whether sieving sediments prior to DNA extraction provides a solution. The effect of sieving was tested on 84 sediment samples collected from two salmon farms. Average percentage of metazoan reads was 19.53% and 17.10% in nonsieved samples, and 81.03% and 89.92% in sieved samples at the two sites. Sieving effectively removed Ochrophyta taxa (e.g., seaweed and phytoplankton), but did not remove pelagic metazoans. Average percentage of benthic metazoan reads in sieved samples was 4.1 times of that in nonsieved samples (47.29% versus 11.46%) at one site and 5.7 times (20.03% versus 3.52%) at another. Sieving increased the number of benthic metazoan amplicon sequence variants by 27.67% and 51.30% at the two sites. Relative abundances of only a small fraction of benthic metazoan phyla showed significant differences between sieved and nonsieved samples. These differences can be taken into account when designing a benthic monitoring approach using eDNA metabarcoding. Since sediment sieving can be conducted either in the field or laboratory, and it allows many more samples to be multiplexed on one MiSeq run without compromising sequencing depth of benthic metazoan reads, we suggest this method can greatly increase the efficiency of eDNA metabarcoding for assessing benthic environments.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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; both teacher heads agree on what is shown here.
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".