Collecting baleen whale blow samples by drone: A minimally intrusive tool for conservation genetics
Bibliographic record
Abstract
In coastal British Columbia, Canada, marine megafauna such as humpback whales (Megaptera novaeangliae) and fin whales (Balaenoptera physalus velifera) have been subject to a history of exploitation and near extirpation. While their populations have been in recovery, significant threats are posed to these vulnerable species by proposed natural resource ventures in this region, in addition to the compounding effects of anthropogenic climate change. Genetic tools play a vital role in informing conservation efforts, but the associated collection of tissue biopsy samples can be challenging for the investigators and disruptive to the ongoing behaviour of the targeted whales. Here, we evaluate a minimally intrusive approach based on collecting exhaled breath condensate, or respiratory 'blow' samples, from baleen whales using an unoccupied aerial system (UAS), within Gitga'at First Nation territory for conservation genetics. Minimal behavioural responses to the sampling technique were observed, with no response detected 87% of the time (of 112 UAS deployments). DNA from whale blow (n = 88 samples) was extracted, and DNA profiles consisting of 10 nuclear microsatellite loci, sex identification and mitochondrial (mt) DNA haplotypes were constructed. An average of 7.5 microsatellite loci per individual were successfully genotyped. The success rates for mtDNA and sex assignment were 80% and 89% respectively. Thus, this minimally intrusive sampling method can be used to describe genetic diversity and generate genetic profiles for individual identification. The results of this research demonstrate the potential of UAS-collected whale blow for conservation genetics from a remote location.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".