Patterns in marine surface fish biodiversity and community composition detected by different eDNA metabarcoding sampling methods
Bibliographic record
Abstract
Abstract Understanding marine surface fish diversity is crucial for ecosystem management. However, the traditional sampling methods are often invasive, costly, or unsuitable for certain species or locations. Environmental DNA (eDNA) metabarcoding provides a non-invasive and relatively cheap alternative to explore patterns of diversity. It is important to recognize that, eDNA-based inference can vary across sampling methods, potentially impacting the validity of biodiversity assessments. To evaluate and compare the effectiveness of three eDNA sampling methods—ship-bottom intake (4.5 m), Niskin bottles (5 or 10 m), and bucket (0 m)—for assessing fish diversity and fish community composition in the western North Pacific near Japan, we analyzed fish communities from 83 stations sampled during nine research cruises. Taxonomic analysis revealed that each method detected over 324 taxa, contributing to a total of 465 taxa. Hierarchical clustering generally identified similar species composition across methods at a station. The exception was when intake samples, collected at different times, diverged from bucket and Niskin samples at the same station. Hill’s number rarefaction and extrapolation curves across all clusters showed similar results among methods, with exceptions in a few clusters where bucket samples exhibited higher biodiversity indices than intake and Niskin samples. Non-metric multidimensional scaling indicated significant relationships between cluster composition and environmental factors like temperature, salinity, and chlorophyll-a. Some clusters were also controlled by integrated seasonal factors. Overall, fish community composition was convincingly similar among methods. This finding suggests that any of these eDNA sampling methods can be effective and may be prioritized based on logistical considerations.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 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".