Comparison of environmental DNA and SCUBA diving methods to survey keystone rockfish species on the Central Coast of British Columbia, Canada
Bibliographic record
Abstract
The rocky reefs of British Columbia’s (BC) coast are a productive ecosystem, home to 38 rockfish species (Genus: Sebastes) that are culturally and economically important. Quantitatively assessing rockfish populations is vital to support conservation and stock assessment needs. Self-contained underwater breathing apparatus (SCUBA) diving surveys are a commonly used monitoring method in BC. However, this resource-intensive approach is challenging, particularly for cryptic or deeper species. Herein, we compared environmental DNA (eDNA) detection methods with SCUBA diving surveys to capture overall rockfish biodiversity. We employed two eDNA methods: 1) a targeted quantitative real-time polymerase chain reaction (qPCR) approach to monitor species of particular importance to First Nations collaborators and decision makers, and 2) a metabarcoding approach for assessing community composition using the previously published MiSebastes assay. Both approaches are confounded by the little DNA sequence divergence among species and high sequence variation within species. Overcoming these challenges using a whole mitochondrial approach with the mtGrasp and unikseq pipelines, we generated highly useful eDNA tools. We found that eDNA methods were highly comparable to dive surveys, as both methods indicated a similar ecological reality, including species detections and distributions. Though there are certain species that cannot be distinguished by the MiSebastes assay, eDNA metabarcoding still detected more rockfish species overall. Both eDNA methods show potential for use alongside conventional methods for scalable incorporation into community-based monitoring programs.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".