Caged fish experiment and hydrodynamic bidimensional modeling highlight the importance to consider 2D dispersion in fluvial environmental DNA studies
Bibliographic record
Abstract
Abstract The analysis of environmental DNA (eDNA) is a powerful tool to increase the efficiency of species detection and monitoring in aquatic ecosystems. Yet, several points remain to be clarified in order to estimate with better precision the distribution and abundance of targeted species, such as the dispersion and dilution of eDNA in large lotic systems. This study aimed to document the dispersion patterns of eDNA in the St. Lawrence River, the largest fluvial system in eastern North America. Caged Brown trout (Salmo trutta) were placed in two different water masses present in this part of the river, the Ottawa River, and water from the outlet of the Laurentian Great Lakes. eDNA detection of the caged fish was performed for two days following cage removal at 53 sampling stations located at 500 upstream, and 10, 100, 500, 1,000, and 5,000 m downstream from the cages. Quantitative PCR analysis using a Brown trout specific assay revealed a positive detection only at downstream stations and up to 5,000 m. To further investigate patterns of dispersion, the relative concentrations of eDNA were predicted using a bidimensional hydrodynamic model, calibrated for downstream advection and lateral mixing of particles (i.e., quantification of 2D dispersion). The detection and the quantities of eDNA obtained by qPCR analyses were compared with the model predictions. Our model which predicts a low lateral mixing and a downstream flow in direct line from the eDNA source best fits the results. We discuss how such studies can improve our capacity to produce more precise estimates of species abundance and distribution in order to better interpret eDNA signals in large lotic systems.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".