Utilizing environmental DNA and imaging to study the deep-sea fish community of Takuyo-Daigo Seamount
Bibliographic record
Abstract
The increase in interest of mining at seamounts means there is a critical need to establish baseline inventories through environmental survey, with the aim of promoting the conservation and stewardship of these remote habitats. To efficiently evaluate fish biodiversity around a seamount, we compared environmental DNA (eDNA) methods using seawater and sponge samples against methods using imagery obtained with a remotely operated vehicle (ROV) and a free-fall deep-sea camera lander called the Edokko Mark I on the Takuyo-Daigo Seamount (153.0°E, 23.5°N) in the northwestern Pacific Ocean. We detected a total of 18 fish families by these methods. The fish fauna detected on the seamount included many families commonly found in deep-sea areas and were similar to the fish fauna of other seamounts located at similar latitudes in the northwestern Pacific. Significant differences in the patterns of detection of fish families between the eDNA and imaging methods is attributed to the differing powers of detection of some fish groups between methods (related to primer compatibility and fish size). For deep-sea fish, the difference in fish composition at the family level between seawater and sponge eDNA methods was not significant, but the difference between Edokko Mark I and ROV methods was significant; the latter difference is likely due to whether or not bait is used to attract fish. Although the eDNA workflow implemented here requires improvements, the use of eDNA and imaging methods in combination provided better insight into the biodiversity of deep-sea fishes in the deep-sea around a seamount, where our knowledge of the fish fauna has been extremely limited. Our recovery of eDNA from seawater and sponge samples around the seamount demonstrates the potential of these methods for facilitating environmental baseline surveys and impact assessments of mining activities to obtain results not previously possible with the use of visual methods only.
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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 | 0.001 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 teacher head, 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".