Mapping Biodiversity Coast‐to‐Coast‐to‐Coast Across Canada's Three Oceans Using eDNA Metabarcoding
Bibliographic record
Abstract
ABSTRACT Marine biodiversity worldwide is rapidly declining, and nowhere is this more evident than in coastal ecosystems where the impacts of climate change and anthropogenic activities concentrate. The ongoing biodiversity crisis affects all components of the marine food web, but data required to monitor biodiversity shifts at continental scales are scarce and taxonomically and spatially heterogeneous. The application of environmental DNA metabarcoding can complement traditional approaches to monitoring marine biodiversity, but its efficiency in detecting large‐scale biogeographic breaks remains to be tested. Using 86 coastal surface water samples collected during the Canada C3 expedition in the summer of 2017, we investigated metazoan biodiversity across Canada's three oceans—North Pacific, Arctic and North Atlantic—using multi‐marker eDNA metabarcoding. The resulting dataset, combining information from seven separate amplicons, identified 1477 unique species ranging from zooplankton to marine mammals. We found that marine coastal biodiversity around Canada separated into four clusters that overlapped with known marine ecoregions, indicating a higher connectivity between the Arctic and Atlantic than between the Arctic and Pacific clusters. However, the detection of Pacific salmon eDNA in the Canadian Arctic suggests that these species may be extending their Pacific distribution range poleward. By comparing the distribution of eDNA with species occurrence recorded in the Ocean Biodiversity Information System (OBIS) for Canada and Alaska coastal waters, we identified 324 “unexpected” species. These results demonstrate the importance of primer selection for species‐specific applications of eDNA metabarcoding and provide a benchmark for further work aimed at validating species identification and map species distribution at large spatial scale. Our results showed that eDNA metabarcoding is a powerful method for monitoring biodiversity shifts at an interoceanic scale. Integrating eDNA into monitoring programs can provide valuable insights into biodiversity changes associated with climate change and contribute to filling gaps in the distribution of species‐at‐risk.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.002 | 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".