DFO Monthly GRDI Survey eDNA metabarcoding (COI) in the Bay of Fundy 2019-2021
Bibliographic record
Abstract
Environmental DNA (eDNA) is a non-invasive monitoring approach increasingly used to detect marine organisms; however, misunderstandings of the temporal variability in eDNA detection has limited its integration within management decisions. A clearer understanding of the periodicity (e.g., seasonality) and duration (e.g., weeks, months) of species eDNA detection is essential to optimize sampling design and data interpretation. As such, this study aims to provide a representative assessment of optimal eDNA detection windows across diverse taxonomic groups, primers, and geographic regions using eDNA metabarcoding. Coastal marine presence-absence eDNA data were collected along the Northwest Atlantic coast, in the Bay of Fundy, Scotian Shelf, and Baffin Island. eDNA detection window(s) were defined as unimodal, contiguous months having greater than 75% detection probability and were calculated for each taxon for each primer in each region. Most marine species exhibited short eDNA detection windows (1–2 months). The optimal sampling periods and durations were conserved among closely related species, highlighting the importance of considering biological traits when designing and interpreting eDNA studies. Additionally, primer choice influenced the optimal detection periods, with higher seasonal variation in community composition and detection rates using universal COI and 18S primers compared to fish 16S and 12S primers. These results demonstrate that ignoring seasonal variation may cause false negatives, inefficient sampling, and reduced data comparability across independent studies, emphasizing the need for temporal considerations when designing eDNA studies. Thus, we propose a set of guidelines aimed at the development of optimal sampling designs for coastal ecosystems and the interpretation of trends across datasets.This dataset is for the COI mitochondrial gene in the Bay of Fundy (BoF) only; Results for the 18S, 16S, and 12S markers in the BoF are also available in OBIS.
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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 | 0.001 | 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 | 0.001 | 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".