Proper environmental DNA metabarcoding data transformation reveals temporal stability of fish communities in a dendritic river system
Bibliographic record
Abstract
Abstract Protecting freshwater biodiversity is considered an ultimate challenge but depends on reliable surveys of species distribution and abundance which eDNA metabarcoding (environmental DNA metabarcoding) may offer. To do so, a better understanding of the sources of temporal variation among species eDNA abundance and of data transformation in eDNA metabarcoding studies is needed. Here, we show that transformation based on relative abundance is critical to suitable analyses of eDNA metabarcoding data and that Hellinger transformation performed slightly better than other methods. Furthermore, we show that site localities significantly explain eDNA metabarcoding variation, while no variation is explained by time of sampling. This indicates that species communities vary more spatially than temporally within a dendritic system composed of small rivers. We then further documented the community structure in the St. Charles River (Québec City, Canada) and six of its tributaries. This revealed the existence of eight species communities explaining 82.1% of eDNA read variation within this river network. Moreover, variation in environmental variables among sites explained 53.0% of eDNA reads, while sampling events and temporal environmental variation explained no eDNA metabarcoding variation. Altogether, this supports the claim that eDNA metabarcoding is a powerful tool to document and monitor fish communities in watersheds composed of small river dendritic 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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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 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".