<scp>eDNA</scp> Metabarcoding to Monitor Fish Communities in a Large River Floodplain
Bibliographic record
Abstract
ABSTRACT Freshwater ecosystems are highly biodiverse and provide essential services that support both ecosystem health and economic sustainability. Despite their ecological significance, these ecosystems are disproportionately affected by the global biodiversity crisis. Large river floodplains constitute a fundamental component of freshwater ecosystems, sustaining fish biodiversity, growth, and reproduction. Yet, these floodplains face mounting threats from anthropogenic pressures, including physical modifications and land conversion for agriculture. In this context, there is an urgent need for scalable biomonitoring methods to more effectively assess floodplain ecosystems, which present methodological challenges due to their heterogeneous and dynamic nature. Traditional fish monitoring methods, however, are often invasive, costly, and resource‐intensive. In contrast, environmental DNA (eDNA) metabarcoding presents a noninvasive, cost‐effective, and scalable alternative. This study compares eDNA metabarcoding and electrofishing for fish community biomonitoring in the floodplain of Lake St. Pierre, the largest floodplain habitat along the St. Lawrence River. We assessed the effectiveness of these methods in monitoring fish community diversity and composition, as well as the influence of floodplain sectors and a gradient of land use from natural wetlands to annual (row) crops. eDNA metabarcoding detected a broader range of species than electrofishing, while both methods consistently identified abundant species. The two methods yielded uncorrelated diversity indices and distinct community compositions. Fish eDNA community composition was strongly associated with floodplain sectors, whereas land use within these sectors had a weaker influence on community diversity and composition. Our findings highlight eDNA metabarcoding as a valuable tool for characterizing broad patterns of fish communities in floodplain ecosystems. This method provides an additional tool to traditional methods for monitoring and conserving threatened floodplain habitats. However, careful consideration of study scale is essential to ensure effective conservation outcomes in these hydrologically dynamic environments.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 teacher head, 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".