Environmental DNA Metabarcoding Detects Predators at Higher Rates Than Electrofishing
Bibliographic record
Abstract
ABSTRACT There are numerous downsides and risks associated with electrofishing; hence, environmental DNA (eDNA) metabarcoding is becoming increasingly common in aquatic ecological studies. Generally, researchers agree that eDNA metabarcoding is more sensitive than electrofishing, and that eDNA metabarcoding is better at detecting rare species. As predatory species tend to be rarer than prey species, eDNA metabarcoding should hypothetically detect more predator species than electrofishing. Instead of supporting the notion that eDNA must replace electrofishing, or that eDNA and electrofishing must display the same results, the current study aims to establish the strengths and weaknesses of eDNA metabarcoding when compared to electrofishing. eDNA metabarcoding and electrofishing data were collected on three sampling dates at four experimental sites. A RV coefficient analysis confirmed that the eDNA metabarcoding data (RV = 0.395, p = 0.057) are statistically different from the electrofishing data. A paired Wilcoxon signed rank test revealed that eDNA data collection techniques detect more predatory species than electrofishing (p = 0.041). When the analysis was conducted for prey species a statistically significant difference did not occur (p = 0.661). Overall, the results of the study suggest that eDNA metabarcoding does not display the same results as electrofishing due to eDNA metabarcoding detecting predatory species at higher rates. The combined use of eDNA alongside electrofishing can help mitigate electrofishing's bias against predatory species, while electrofishing can address reliability concerns associated with eDNA. This collaborative approach ultimately enhances the accuracy of fish community assessments.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".