Genetic diversity and structure among subspecies of white-tailed deer in Mexico
Bibliographic record
Abstract
The white-tailed deer (Odocoileus virginianus) occurs over a broad latitudinal range from South America to Canada. Thirty-eight subspecies are recognized, 14 of which occur in Mexico. Genetic studies in Latin America are lacking and the diversity and structure of white-tailed deer in Mexico are unknown. We sampled white-tailed deer from 13 sites in the range of 5 subspecies occurring in Mexico, O. v. texanus, O. v. carminis, O. v. veraecrucis, O. v. sinaloae, and O. v. yucatanensis. We estimated genetic diversity and structure based on 12 microsatellite DNA loci. Observed heterozygosity (HO) was comparable to that of white-tailed deer in the United States (HO = 0.53–0.64), with the exception of O. v. yucatanensis (HO = 0.41). We observed statistically significant genetic structure among all 13 sites (FST = 0.15). Analysis of molecular variance revealed that grouping sites by subspecies (FSC = 0.09) or geographic region (FSC = 0.13–0.14) explained a moderate portion of genetic variation. However, no higher-level group minimized differentiation among populations within the subspecies or regional groups (FST = 0.16–0.20). Pairwise genetic distances among sites were correlated with geographic distance (r2 = 0.38), but some geographically proximate sites were genetically differentiated (FST > 0.20), especially in the Yucatan. Deer in the Yucatan were genetically differentiated from other subspecies and had comparatively lower genetic diversity, consistent with the biogeographic history of the region. Populations of white-tailed deer in Mexico are subject to a range of management challenges. Additional research is needed to understand the effect of management on the diversity and genetic structure of white-tailed deer.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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 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".