Expanding Monitoring Capacity for Potential Invasive Species in Arctic Canada With Environmental <scp>DNA</scp> Metabarcoding
Bibliographic record
Abstract
To date, environmental conditions have been enough to act as an effective barrier to prevent non-indigenous species from arriving and establishing in Arctic Canada. However, rapidly changing climatic conditions are creating more suitable habitats for non-indigenous species to potentially establish and become invasive. Concurrently, shipping traffic in parts of Arctic Canada has increased by over 250% since 1990, providing an effective vector for transporting non-indigenous species to the region. Arctic Canada has been historically undersampled, so Arctic biota inventories are incomplete, hampering efforts to establish if a species is new to the region (and potentially invasive) or newly discovered. In this study, we utilize environmental DNA (eDNA) metabarcoding and ships of opportunity to assess eukaryotic community composition and potential invasives along one of the busiest shipping routes, the Northwest Passage. One liter seawater samples were collected in triplicate at 27 locations, targeting touristic hotspots frequently visited by passenger vessels. Eukaryotic DNA was amplified from the 18S rRNA V9 and COI regions, resulting in 126 unique Amplicon Sequence Variants (ASVs) detected with COI and 391 ASVs with 18S, providing an important snapshot of current community composition. Copepods, dinoflagellates, and diatoms were the most abundant taxonomic groups, correlating well with previous net sampler surveys, validating the efficacy of eDNA for biodiversity surveillance. We also report the first detections of a prolific invasive species, the bay barnacle (Amphibalanus improvisus), in Arctic Canada. Further work is currently in progress to establish whether these detections represent transient barnacle larvae or sessile adults capable of recruiting and reproducing. Our study demonstrates the utility of eDNA for the detection of non-indigenous species in a data-poor area, which, if combined with citizen science initiatives and local communities, could provide a vital monitoring tool for the detection of new invasives in this rapidly changing area.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| 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".