Responses of foraminifera communities to aquaculture‐derived organic enrichment as revealed by environmental <scp>DNA</scp> metabarcoding
Bibliographic record
Abstract
Current monitoring methods to assess benthic impacts of marine finfish aquaculture are based on complex biological indices and/or geochemistry data. The former requires benthic macrofauna morpho-taxonomic characterization that is time- and cost-intensive, while the latter provides rapid assessment of the organic enrichment status of sediments but does not directly measure biotic impacts. In this study, sediment samples were collected from seven stations at six salmon farms in British Columbia, Canada, and analyzed for geochemical parameters and by eDNA metabarcoding to investigate linkages between geochemistry and foraminifera. Sediment texture across farm sites ranged from sand to silty loam, while the maximum sediment pore-water sulphide concentration at each site ranged from 1,000 to 13,000 μM. Foraminifera alpha diversity generally increased with distance from cage edge. Adonis analyses revealed that farm site explained the most variation in foraminifera community, followed by sediment type, enrichment status, and distance from cage edge. Farm-specific responses were observed in diversity analyses, taxonomic difference analyses, and correlation analyses. Results demonstrated that species diversity and composition of foraminifera characterized by eDNA metabarcoding generated signals consistent with benthic biodiversity being impacted by finfish farming activities. This substantiates the validity of eDNA metabarcoding for augmenting current approaches to benthic impact assessments by providing more cost-effective and practicable biotic measures than traditional morpho-taxonomy. To capitalize on this potential, further work is needed to design a new nomogram that combines eDNA metabarcoding data and geochemistry data to enable accurate monitoring of benthic impacts of fish farming in a time- and cost-efficient way.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.004 |
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; both teacher heads agree on what is shown here.
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".