A new Genotyping-in-thousands by sequencing (GT-seq) assay for polar bears (Ursus maritimus): development, validation, and applications
Bibliographic record
Abstract
Traditional wildlife monitoring approaches are often time-consuming and expensive, and fail to partner with and benefit Indigenous communities. This is particularly relevant for polar bears (Ursus maritimus), which lack contemporary range-wide data on population dynamics, are facing large-scale habitat declines due to climate change, and are of socioeconomic and cultural importance to northern communities. Genetic monitoring using non-invasive samples (e.g. scat) can provide a complement or alternative to traditional methods, but requires novel genetic techniques that are optimized for degraded DNA. Thus, my second data chapter focused on developing, optimizing, and validating a Genotyping-in-Thousands by sequencing (GT-seq) panel of 324 single nucleotide polymorphisms for degraded polar bear DNA. My work demonstrated successful genotyping (>50% loci) for a range of DNA sources, including 62.9% of non-invasively collected scat samples determined to contain polar bear DNA, and that GT-seq data can be reliably used to discern individuals, identify sex, assess relatedness, and resolve population structure in Canadian polar bears. To further expand our understanding of polar bear population dynamics and explore the power of GT-seq, I used this new GT-seq assay in my third data chapter to test for male-biased dispersal in four Canadian polar bear subpopulations and whether this increases with population density. My genetic clustering results indicated some fine-scale genetic structure in females from low-density areas, consistent with male-biased dispersal. In contrast, spatial autocorrelation analyses did not reveal spatial patterns consistent with male-biased dispersal, although were likely limited by poor sampling resolution and geographic scale of sampling. My work confirms that GT-seq provides comparable information and resolution to traditional methods of monitoring, but enables greater cost-efficiency and the use of degraded (e.g. non-invasive) samples. Importantly, I demonstrate the power of GT-seq for sex-biased dispersal evaluation and provide the first of such applications. Overall, I conclude that GT-seq in tandem with community-based monitoring programs may improve temporal monitoring of polar bear populations and Arctic ecosystems, actively provide socioeconomic benefits to northern communities, and serve as a model for inclusive, non-invasive wildlife monitoring worldwide.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".