Tidal effect on environmental DNA communities in Arctic estuarine and marine ecosystems
Bibliographic record
Abstract
Introduction Arctic marine ecosystems are changing rapidly, largely due to the observed accelerated warming that is associated with ongoing climate change. Environmental DNA (eDNA) combined with metabarcoding has great potential for large-scale biomonitoring of Arctic marine communities. However, important limitations remain, such as understanding the complexity and drivers of spatio-temporal variation in eDNA distribution. Methods In this study, we investigated the effect of tidal dynamics on aquatic metazoan (vertebrates and invertebrates) on eDNA metabarcoding results from nearshore estuarine and marine Arctic ports of Churchill (Manitoba) and Milne Inlet (Nunavut), respectively. We collected and sequenced 54 water samples per port at low, middle and high tide across three days, as well as two depths (surface, bottom), using four universal primer pairs (two primers in the COI gene and two in the 18S rRNA gene). Results We observed a significant transition in the estuarine community structure from low to high tide, whereas the marine community structure was more stable across tides. The eDNA community structure differed between the surface and bottom waters in both the estuarine and marine ecosystems. However, the biodiversity pattern within the water column was significantly different between estuarine and marine ecosystems. Finally, we observed short-term temporal variation of the communities in both systems. Discussion Altogether, our results highlight the short-term temporal dynamic nature of eDNA derived from coastal communities. This variability should be accounted for in eDNA sampling design to ensure robust characterization of coastal communities and long-term time series, particularly for estuarine environments where the effects of tide and depth are more important.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".