Fish community surveys in eelgrass beds using both eDNA metabarcoding and seining: implications for biodiversity monitoring in the coastal zone - Dataset 1 (16S gene)
Bibliographic record
Abstract
Marine Protected Areas (MPAs) have been adopted globally as a tool to combat biodiversity loss and restore marine ecosystems. Successful application of MPAs will be predicated on the ability to monitor biodiversity in a synoptic and noninvasive manner. Environmental DNA (eDNA) methods have important advantages over traditional biodiversity survey methods for monitoring conservation areas. To evaluate the efficacy of eDNA metabarcoding for fish biodiversity monitoring, we sampled 19 coastal eelgrass (Zostera marina) beds in Canada, as eelgrass beds are known for high biodiversity and significant conservation value. At each site, beach seines were used to survey fish and water samples were collected contemporaneously for eDNA metabarcoding. In total, beach seining caught 32 672 individuals across 59 fish taxa, and eDNA detected 129 fish taxa. eDNA captured site-level variation and detected higher species richness at both site and regional levels compared to seining. eDNA abundance had a positive association with capture abundance. Collectively these results highlight how eDNA metabarcoding offers an efficient approach for monitoring fish biodiversity in coastal eelgrass beds, thus providing a valuable and noninvasive tool for MPA planning and coastal monitoring. This dataset is from the eDNA metabarcoding component using the 16S rRNA genetic marker only; results from the 12S marker are also available within GBIF. This work was published by He et al. (2022) in the Canadian Journal of Fisheries and Aquatic Sciences (https://doi.org/10.1139/cjfas-2021-0215). [This dataset was processed using the GBIF Metabarcoding Data Toolkit.]
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".