Paired environmental <scp>DNA</scp> and dive surveys provide distinct but complementary snapshots of marine biodiversity in a temperate fjord
Bibliographic record
Abstract
Abstract Marine biodiversity is a key indicator of ecosystem health and can be assessed using a variety of methods, including environmental DNA (eDNA) sampling. However, the ecology of eDNA in physically dynamic nearshore environments remains uncertain, particularly with regards to how eDNA stratifies with depth. Here, we paired eDNA sampling with dive surveys at six sites in Knight Inlet, British Columbia, Canada. eDNA samples were collected from the surface, midwater column and bottom (8–25 m depth) at each site, while dive surveys focused on the bottom (benthic) habitat. Amplicon sequencing using the mitochondrial 12S rRNA gene (targeting fish) and the COI gene (targeting marine invertebrates and algae) resolved significant differences in community composition in surface waters compared with midwater and bottom. Differences by depth were greater than differences across sites, with surface waters dominated by salmon (Oncorhynchus spp.) and rotifer DNA, and midwater and bottom samples largely dominated by Pacific herring, copepods, and mussels. eDNA samples collected at the surface, therefore, may not accurately capture benthic communities, particularly in systems with high levels of freshwater input such as coastal temperate fjords. Over small spatial scales, particularly in systems with strong stratification, adding samples from different depths may be more effective at maximizing inferred diversity rather than sampling more sites. In general, there was low overlap in species detection between dive and eDNA surveys (less than 10% for each taxonomic group – fish, invertebrates, and algae). However, we observed clear strengths for each method – dive surveys provided better taxonomic resolution, while eDNA resolved greater total diversity. These results suggest that the two survey methods can be used in tandem to provide distinct and complementary snapshots of marine biodiversity in the nearshore environment.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".