Improving an endangered marine species distribution using reliable and localized environmental DNA detections combined with trawl captures
Bibliographic record
Abstract
The description of marine fish distributions generally relies on trawl survey observations. For rare species, sporadic catches necessitate the combination of multiannual trawl surveys to accurately describe the distribution, limiting short term monitoring. Recent studies suggest that combining traditional capture methods and environmental DNA (eDNA) detections enhance rare species' occurrence. In this study, the reliability and localization of eDNA detections (single- and multi-species) of an endangered marine species, the Atlantic wolffish Anarhichas lupus, was assessed during fine scale surveys. eDNA was detected at all six stations sampled with Niskin bottles over caves housing one or two A. lupus. Detections from samples collected with syringes by divers along a 15 m transect perpendicular to each cave were limited to the fish cave entrance. Trawl-captures and eDNA detections were then combined to test if the species distribution is improved for broad scale surveys. The station-based frequency of species occurrence was 13% with trawl captures and increased to 23% when combined with eDNA detections. Single-species detections were generally more sensitive than multi-species detections. Our results showed that a rare marine species distribution improves combining traditional methods and eDNA detections in oceanographic surveys. Strategies for integrating optimal eDNA detections in marine surveys are discussed.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".