Genotyping‐in‐thousands by sequencing (GT‐seq) of noninvasive faecal and degraded samples: A new panel to enable ongoing monitoring of Canadian polar bear populations
Bibliographic record
Abstract
Genetic monitoring using noninvasive samples provides a complement or alternative to traditional population monitoring methods. However, next-generation sequencing approaches to monitoring typically require high quality DNA and the use of noninvasive samples (e.g., scat) is often challenged by poor DNA quality and contamination by nontarget species. One promising solution is a highly multiplexed sequencing approach called genotyping-in-thousands by sequencing (GT-seq), which can enable cost-efficient genomics-based monitoring for populations based on noninvasively collected samples. Here, we develop and validate a GT-seq panel of 324 single nucleotide polymorphisms (SNPs) optimized for genotyping of polar bears based on DNA from noninvasively collected faecal samples. We demonstrate (1) successful GT-seq genotyping of DNA from a range of sample sources, including successful genotyping (>50% loci) of 62.9% of noninvasively collected faecal samples determined to contain polar bear DNA; and (2) that we can reliably differentiate individuals, ascertain sex, assess relatedness, and resolve population structure of Canadian polar bear subpopulations based on a GT-seq panel of 324 SNPs. Our GT-seq data reveal spatial-genetic patterns similar to previous polar bear studies but at lesser cost per sample and through use of noninvasively collected samples, indicating the potential of this approach for population monitoring. This GT-seq panel provides the foundation for a noninvasive toolkit for polar bear monitoring and can contribute to community-based programmes - a framework which may serve as a model for wildlife conservation and management for species worldwide.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".