Comparing eDNA metabarcoding and species collection for documenting Arctic metazoan biodiversity
Bibliographic record
Abstract
Abstract Background Arctic biodiversity has long been poorly documented and is now facing rapid transformations due to ongoing climate change and other impacts, including shipping activities. These changes are placing marine coastal invertebrate communities at greater risk, especially in sensitive areas such as commercial ports. Preserving biodiversity is a significant challenge, going far beyond the protection of charismatic species and involving suitable knowledge of the spatiotemporal organization of species. Therefore, knowledge of alpha, beta, and gamma biodiversity is of great importance to achieve this objective, particularly when partnered with new cost‐effective approaches to monitor biodiversity. Method and results This study compares metabarcoding of COI mitochondrial and 18S rRNA genes from environmental DNA (eDNA) water samples with standard invertebrate species collection methods to document community patterns at multiple spatial scales. Water samples (250 ml) were collected at three different depths within three Canadian Arctic ports: Churchill, MB; Iqaluit, NU; and Deception Bay, QC. From these samples, 202 genera distributed across more than 15 phyla were detected using eDNA metabarcoding, of which only 9%–15% were also identified through species collection at the same sites. Significant differences in taxonomic richness and community composition were observed between eDNA and species collections at both local and regional scales. This study shows that eDNA dispersion in the Arctic Ocean reduces beta diversity in comparison with species collections while emphasizing the importance of pelagic life stages for eDNA detection. Conclusion The study also highlights the potential of eDNA metabarcoding to assess large‐scale Arctic marine invertebrate diversity while emphasizing that eDNA and species collection should be considered as complementary tools to provide a more holistic picture of coastal marine invertebrate communities.
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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 | 0.003 | 0.004 |
| 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 | 0.001 |
| 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".