Fish community assessment with eDNA metabarcoding: effects of sampling design and bioinformatic filtering
Bibliographic record
Abstract
Species richness is a metric of biodiversity that represents the number of species present in a community. Traditional fisheries assessments that rely on capture of organisms often underestimate true species richness. Environmental DNA (eDNA) metabarcoding is an alternative tool that infers species richness by collecting and sequencing DNA present in the ecosystem. Our objective was to determine how spatial distribution of samples and “bioinformatic stringency” affected eDNA-metabarcoding estimates of species richness compared with capture-based estimates in a 2.2 ha reservoir. When bioinformatic criteria required species to be detected only in a single sample, eDNA metabarcoding detected all species captured with traditional methods plus an additional 11 noncaptured species. However, when we required species to be detected with multiple markers and in multiple samples, eDNA metabarcoding detected only seven of the captured species. Our analysis of the spatial patterns of species detection indicated that eDNA was distributed relatively homogeneously throughout the reservoir, except near the inflowing stream. We suggest that interpretation of eDNA metabarcoding data must consider the potential effects of water body type, spatial resolution, and bioinformatic stringency.
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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 | 0.044 | 0.085 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".