A genotyping‐in‐thousands by sequencing panel to inform invasive deer management using noninvasive fecal and hair samples
Bibliographic record
Abstract
Abstract Studies in ecology, evolution, and conservation often rely on noninvasive samples, making it challenging to generate large amounts of high‐quality genetic data for many elusive and at‐risk species. We developed and optimized a Genotyping‐in‐Thousands by sequencing (GT‐seq) panel using noninvasive samples to inform the management of invasive Sitka black‐tailed deer ( Odocoileus hemionus sitkensis ) in Haida Gwaii (Canada). We validated our panel using paired high‐quality tissue and noninvasive fecal and hair samples to simultaneously distinguish individuals, identify sex, and reconstruct kinship among deer sampled across the archipelago, then provided a proof‐of‐concept application using field‐collected feces on SGang Gwaay, an island of high ecological and cultural value. Genotyping success across 244 loci was high (90.3%) and comparable to that of high‐quality tissue samples genotyped using restriction‐site associated DNA sequencing (92.4%), while genotyping discordance between paired high‐quality tissue and noninvasive samples was low (0.50%). The panel will be used to inform future invasive species operations in Haida Gwaii by providing individual and population information to inform management. More broadly, our GT‐seq workflow that includes quality control analyses for targeted SNP selection and a modified protocol may be of wider utility for other studies and systems where noninvasive genetic sampling is employed.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".