A Multi-Taxa Approach to Estuarine Biomonitoring: Assessing Vertebrate Biodiversity and Ecological Continuity using Environmental DNA Metabarcoding in the Rance River (Brittany, France)
Bibliographic record
Abstract
Abstract Estuaries are ecologically vital yet highly impacted ecosystems that serve as transitional zones between land and sea. Monitoring their biodiversity is essential but challenging due to their dynamic nature and the transient presence of many species. Traditionally actinopterygian monitoring in these systems still relies on conventional and intrusive methods such as gill nets and trawls. Environmental DNA (eDNA) metabarcoding offers a non-invasive, multi-taxa alternative that can complement these traditional approaches. Here, we applied an eDNA-based metabarcoding approach to characterize vertebrate diversity in the Rance Estuary, located in the Brittany Region of France. Water samples were collected from five stations spanning marine to freshwater environments. Special attention was given to two stations located upstream and downstream of the tidal power plant (TPP) dam to assess its potential impact on ecological continuity. We detected a total of 124 distinct vertebrate MOTUs—comprising actinopterygians, birds, mammals, and amphibians. Taxonomic composition followed the estuarine gradient, with Jaccard dissimilarity increasing with distance from the sea and largely driven by species turnover. While taxonomic and phylogenetic diversity remained relatively stable across the vertebrate community, functional diversity revealed an increasing terrestrial influence. For actinopterygians, taxonomic diversity decreased upstream, whereas phylogenetic and functional diversity indicated fine-scale structuring, even among nearby stations. This approach enabled the development of biodiversity metrics and facilitated comparisons with previous actinopterygian monitoring surveys in the same area based on conventional methods (scientific fishing using nets and dredges). Our results emphasise the potential of eDNA for holistic estuarine biomonitoring and establish a valuable baseline for future non-invasive assessments.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 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".