Absolute fish population censuses in ponds demonstrate eDNA metabarcoding provides biodiversity estimates comparable to conventional sampling methods
Bibliographic record
Abstract
Freshwater fishes are experiencing an unprecedented decline. Effective strategies for estimating the composition of fish communities are crucial for conservation efforts. While conventional physical collection methods can be effective, environmental DNA analysis has emerged as a potential alternative. Systematic comparisons of these methods are critical to evaluate their relative effectiveness. We collected water samples from 14 ponds near Hamilton, ON, which were analyzed for fish eDNA using two universal metabarcoding primers, and sampled using conventional methods (electrofishing and/or seining). A subset of ponds were then drained to obtain population census counts, facilitating a standardized comparison of false negatives and positives across conventional and eDNA sampling. We found no significant difference between survey methods in the number of species detected, although eDNA was more effective at detecting species at extremely low abundances, but was prone to false positives. We estimate that 11 eDNA samples should be sufficient to quantify fish community biodiversity in similar ecosystems. eDNA represents a viable alternative or complement to conventional methods, reinforcing its potential for enhancing aquatic ecosystem management.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.024 | 0.016 |
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".