<scp>DNA</scp> from dives: Species detection of humpback whales (<i>Megaptera novaeangliae</i>) from flukeprint <scp>eDNA</scp>
Bibliographic record
Abstract
Abstract Northern British Columbia has been identified as an important habitat for several coastal cetacean species, including humpback whales ( Megaptera novaeangliae ). This species is listed as being of “Special Concern” under Canada's Species at Risk Act, partly due to data deficiencies concerning genetic population structure and demographics in British Columbia. Anthropogenic activities threaten North Coast humpback whale populations, with particular concern for the impact of vessel noise, entanglement, and ship strikes. Current methodology (i.e., biopsy sampling) for obtaining cetacean genetic data is invasive, challenging, and costly; therefore, there is an urgency to develop effective and minimally invasive methodologies for efficiently collecting this data. Environmental DNA (eDNA) has been identified as an ideal tool for monitoring the presence and distribution of numerous species within marine ecosystems; however, the feasibility for cetaceans is not yet well established. In this study, we opportunistically collected targeted 1 L seawater eDNA samples from flukeprints when individual humpback whales were observed diving between the years of 2020 and 2022. A total of 93 samples were collected from individual humpback whales identified using a photographic identification catalogue. We successfully detected humpback whale eDNA in 28 samples using novel species‐specific qPCR primers (~500 mL of sample), with relatively equal successful detection between immediate (0 days) and delayed (up to 10 days) sample filtration. Here, we have validated a qPCR assay for detecting humpback whale DNA from flukeprints and highlighted the future optimizations required to improve the potential application of flukeprint eDNA for conservation management.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.010 |
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".