Detecting community change in Arctic marine ecosystems using the temporal dynamics of environmental DNA
Bibliographic record
Abstract
Abstract Large‐scale biomonitoring of Arctic coastal marine communities is essential to track temporal changes in ecosystems. Despite the potential of environmental DNA (eDNA) as an innovative coastal biomonitoring tool, important questions remain pertaining to its temporal and spatial variation and how this may affect the evaluation of ecosystem changes over time in hydrodynamic ecosystems. In this study, we used eDNA metabarcoding of coastal water samples in two Canadian Arctic ports to evaluate the potential of eDNA to detect temporal transition in marine coastal communities. We sequenced eDNA from approximately 20 surface water samples collected each month (N ≈ 150 samples) covering the transition period between summer and late fall using four different universal primer pairs (two pairs of COI mitochondrial genes and two pairs of 18S rRNA genes). Our results from both primer pairs highlighted a significant transition from the summer to the fall marine community. We also observed a putative link between eDNA peaks of read abundance and timing for different life stages (e.g., spawning and larvae) of several species with the most abundant sequence reads. As such, our results show that temporal variation must be considered in ensuring comprehensive coastal biomonitoring with eDNA. Although much remains to be investigated about the ecology of eDNA, our results contribute to fundamental knowledge on the origin of eDNA and highlight the importance of considering temporal variation in developing guidance for coastal biomonitoring with this approach.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".