Seas the DNA? Limited detection of cetaceans by low‐volume environmental DNA transect surveys
Bibliographic record
Abstract
Abstract Environmental DNA (eDNA) has begun to show promise as a robust and reproducible tool for monitoring cetaceans in coastal and offshore waters. Some limiting factors preventing the wider application of eDNA for cetacean monitoring includes lack of species‐specific qPCR assays and limited in situ validation. In this study, we determined 15 monitoring stations within cetacean hotspots in Chatham Sound (British Columbia, Canada), from which we collected a combination of visual and acoustic data, and low‐volume eDNA samples (equivalent to ~250 mL seawater). We designed novel eDNA assays for gray whale and Dall's porpoise and validated existing assays for harbor porpoise, killer whale, and humpback whale. Overall, we collected a total of 120 paired eDNA samples across four sampling intervals, 60 preserved with absolute ethanol and 60 preserved with propylene glycol antifreeze. Positive rates for visual (18%) and acoustic (4%) detections were higher than the eDNA detection rate (<3%), with only one sample (antifreeze‐preserved) producing a positive detection for humpback whales at one of the stations. We discuss factors which could have influenced the lack of detections and highlight the need for higher sample volumes and species‐specific sample approaches to improve detection success and confidence in eDNA applicability for cetacean monitoring.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".