Using environmental DNA for biomonitoring of freshwater fish communities: Comparison with established gillnet surveys in a boreal hydroelectric impoundment
Bibliographic record
Abstract
Abstract Accurate data characterizing species distribution and abundance are critical for conservation and management of aquatic resources. Inventory methods, such as gillnet surveys, are widely used to estimate distribution and abundance of fish. However, gillnet surveys can be costly in terms of material and human resources, may cause unwanted mortality in the fish communities being studied, and is subject to size and species selection bias. Detecting allochthonous DNA released by species in their environment (i.e., environmental DNA, hereafter eDNA) could be used as a noninvasive and less costly alternative. In this study, we directly compare eDNA metabarcoding and gillnets for monitoring freshwater fish communities in terms of species richness and relative species abundance. Metabarcoding was performed with the 12S Mifish primers. We also used species‐specific quantitative PCR (qPCR) for the most abundant species, the walleye ( Sander vitreus ), to compare estimated relative abundance with metabarcoding and gillnet captures. Water sample collection, prior to gillnet assessment, was performed on 17 sites in the hydroelectric impoundment of the Rupert River (James Bay, Canada), comparing two water filtration methods. After controlling for amplification biases and repeatability, we show that fish communities’ complexity is better represented using eDNA metabarcoding than previously recorded gillnet data and that metabarcoding read count correlates with qPCR ( r = 0.78, p < .001) in reflecting walleye abundance. Finally, based on partial redundancy analysis, we identified alpha chlorophyll, pH, and dissolved oxygen as environmental variable candidates that may influence differences in fish relative abundance between metabarcoding and gillnets. Altogether, our study demonstrates that the proposed eDNA metabarcoding method can be used as an efficient alternative or complementary technique adapted to the biomonitoring of the fish communities in boreal aquatic ecosystems.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".