Evidence of adoption, monozygotic twinning, and low inbreeding rates in a large genetic pedigree of polar bears
Bibliographic record
Abstract
Abstract Multigenerational pedigrees have been developed for free-ranging populations of many species, are frequently used to describe mating systems, and are used in studies of quantitative genetics. Here, we document the development of a 4449-individual pedigree for the Western Hudson Bay subpopulation of polar bears (Ursus maritimus) , created from relationships inferred from field and genetic data collected over six generations of bears sampled between 1966 and 2011. Microsatellite genotypes for 22–25 loci were obtained for 2945 individuals, and parentage analysis was performed using the program FRAN z , including additional offspring–dam associations known only from capture data. Parentage assignments for a subset of 859 individuals were confirmed using an independent high-density set of single nucleotide polymorphisms. To account for unsampled males in our population, we performed half-sib–full-sib analysis to reconstruct males using the program COLONY, resulting in a final pedigree containing 2957 assigned maternities and 1861 assigned paternities with only one observed case of inbreeding between close relatives. During genotyping, we identified two independently captured two-year-old males with identical genotypes at all 25 loci, showing—for the first time—a case of monozygotic twinning among polar bears. In addition, we documented six new cases of cub adoption, which we attribute to cub misidentification or misdirected maternal care by a female bereaved of her young. Importantly, none of these adoptions could be attributed to reduced female vigilance caused by immobilization to facilitate scientific handling, as has previously been suggested.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".