Applying eDNA metabarcoding to assess invertebrate biodiversity of eelgrass ( <i>Zostera marina</i> ) meadows across Nova Scotia
Bibliographic record
Abstract
Eelgrass ( Zostera marina) meadows are a common feature of Atlantic coastlines, forming productive marine communities that are valued for their ecosystem services. Long-term adaptive management of these sensitive ecosystems and conservation of the biodiversity they support requires tools to evaluate and monitor patterns of diversity. Environmental DNA (eDNA) metabarcoding is a noninvasive and cost-effective approach for estimating aquatic biodiversity, with significant potential for broad-scale monitoring of complex habitats like seagrass communities. In this study, we utilized eDNA metabarcoding to characterize invertebrate biodiversity in eelgrass meadows along a latitudinal gradient in the Northwest Atlantic and compared results to historical surveys of eelgrass in the region. Across 17 sites, 138 metazoan invertebrate taxa were detected with a taxonomic probability assignment > 95%. eDNA was successful at capturing regional patterns of community structure and detecting 20 of 26 common invertebrate taxa based on published records, as well as 80 species not historically recorded. These results emphasize the potential of eDNA to augment eelgrass ecosystem monitoring by enabling efficient, noninvasive cataloguing of biodiversity, including cryptic species that evade conventional sampling.
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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 | 0.000 |
| 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".