Genotyping-in-thousands by sequencing (GT-seq) of non-invasive fecal and degraded samples: a new panel to enable ongoing monitoring of Canadian polar bear populations
Bibliographic record
Abstract
Genetic monitoring using non-invasive 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 non-invasive samples (e.g. scat) is often challenged by poor DNA quality and contamination by non-target 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 non-invasively 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 non-invasively collected fecal samples. We demonstrate 1) successful GT-seq genotyping of DNA from a range of sample sources, including successful genotyping of 85.7% of non-invasively collected fecal 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 similar spatial-genetic patterns as previous polar bear studies but at lesser cost per sample and using non-invasively collected samples, indicating the potential of this approach for population monitoring. This GT-seq panel provides the foundation for a non-invasive toolkit for polar bear monitoring and contribute to community-based programs – a framework which may serve as a model for wildlife management and contribute to conservation and policy for species 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".