<scp>eDNA</scp> metabarcoding reveals riverine fish community structure and climate associations in northeastern Canada
Bibliographic record
Abstract
Abstract Climate change is a critical threat to northern freshwater ecosystems, yet many remote areas are data deficient in terms of biodiversity information. Generating community composition data through collection of environmental DNA (eDNA) is less labor‐intensive than traditional sampling methods and is being increasingly used in areas that have been historically difficult to sample such as northern freshwater habitats. Here, we employed eDNA metabarcoding using three mitochondrial markers at 174 coastal river sites, sampled over three years (2019–2021) across a broad region in northeastern North America, Newfoundland and Labrador. We characterized current riverine fish community composition, compared it to traditional sampling records, and quantified the influence of climate on variation in fish community composition. The analysis detected 33 fish species across the region (1–13 per location), including three non‐native species, as well as several new possible range expansions. Variance partitioning with redundancy analysis indicated ~56% of the variation in community composition could be explained by spatial and climate factors (~21% and ~7%, respectively, with an additional ~28% shared). A temporal comparison across a subset of locations with both eDNA and historical records (1965–1985) revealed that more species were detected on average with eDNA sampling, and that sampling method explained a small portion of the variation (~4%) in comparison with space (~10%) and climate (~7%). Ultimately, this work is the most complete survey of freshwater and diadromous fishes present in Newfoundland and Labrador to date, highlights new detections of non‐native species including previously unknown diversity for the region, and provides future direction for the application of eDNA analysis in northern riverine habitats.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".