Assessing shifting technology in genetic monitoring of the North American plains bison Federal conservation herds
Bibliographic record
Abstract
Abstract Human expansion is a major driver of both declining wildlife abundance and the contraction of species’ distributions, increasing the risk of genetic erosion and the need for genetic monitoring. Rapidly advancing technology has expanded the types of genetic data that are available for wildlife conservation. However, inferences from different genetic markers could result in different management decisions and, thus, must be considered carefully. Rebounding from near extinction in the early 1900s, the majority of North American plains bison (Bison bison bison) are managed as small and isolated herds. Microsatellite-based analyses have been used to inform management of the US Federal bison conservation herds since the early 2000s. Transitioning from monitoring with tens of multiallelic loci (e.g., microsatellite loci) to thousands of biallelic loci (e.g., single nucleotide polymorphisms [SNPs]) could increase genotyping efficiency and improve the precision of population genetic inference but would require an understanding of the inferential differences between genetic marker types. We compared microsatellite-based measures of genetic diversity, differentiation, and population structure for 20 bison conservation herds (17 Federal, 1 Tribal, 2 Canadian) to inference from SNP-based analyses for the Tribal herd and 15 of the Federal herds. Data from both genetic marker types found that all herds have remarkably high genetic diversity given the severity of the bottleneck from which these populations recovered, and that population structure was consistent with founding histories. Importantly, SNPs had greater power to describe differences in genetic diversity and groups of related herds, but only if analyses are based on 250 or more loci. Overall, we found that microsatellite and SNP data can provide comparable conservation insight, but SNPs must be carefully selected to ensure continuity in genetic monitoring and to achieve the increased precision in genetic diversity and differentiation among herds that we observed in this study.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".