Effect of eDNA metabarcoding temporal sampling strategies on detection of coastal biodiversity
Bibliographic record
Abstract
Introduction Environmental DNA (eDNA) metabarcoding of water is increasingly being used to monitor coastal biodiversity shifts. However, we have limited knowledge of whether samples collected during discreet temporal periods depict holistic ecosystem changes over longer time spans. Methods Here, we show how eDNA community structure varies across repeated sampling events at different temporal scales ranging from years to months to days at an Arctic coastal site, Churchill (Canada), using metabarcoding analyses of water eDNA samples with four universal primer pairs (two primers in COI and two in the 18S rRNA). Results Daily variations were highly dynamic and less structured, likely due to the stochastic nature of estuarine ecosystems, but there was a clear annual consistency in eDNA communities with a high proportion of shared taxa between years. However, monthly sampling was the most efficient for capturing holistic biodiversity. Discussion We provide recommendations for optimal eDNA metabarcoding sampling design based on our observations. The study underscores the importance of understanding biological and physical factors altering eDNA detection to improve the efficiency of detecting and interpreting long-term eDNA changes.
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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 | 0.007 | 0.018 |
| 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 | 0.001 | 0.001 |
| 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".