Contrasting strengths of eDNA and electrofishing compared to historic records for assessing fish community diversity and composition
Bibliographic record
Abstract
In times of rapid environmental changes, baseline biodiversity data are crucial for management. In freshwaters, fish inventories are commonly based on the capture and morphological identification of specimens. The sampling of environmental DNA (eDNA) provides an alternative to assess diversity across large catchments. Here, we used extensive historic data of fish communities collected across 89 river sites in all major catchments of Switzerland and compared their diversity and community composition to a single campaign of eDNA and electrofishing, respectively. Locally, we found that eDNA provided diversity estimates similar to the integrated historic richness, while the electrofishing campaign captured a significantly lower local richness. Fish species locally recorded by electrofishing were nested (Jaccard's dissimilarity index) within the respective eDNA community for most sites. Finally, eDNA sequence reads positively correlated with the overall electrofishing biomass. Despite the congruences, the eDNA data did not correlate well with the electrofishing water quality index. Overall, eDNA was more accurately assessing overall diversity than a simultaneous electrofishing campaign, but yet cannot be directly used to calculate fish-based water quality indices.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".