Disentangling Timing of Admixture, Patterns of Introgression, and Phenotypic Indicators in a Hybridizing Wolf Population
Bibliographic record
Abstract
Hybridization is a natural or anthropogenic process that can deeply affect the genetic make-up of populations, possibly decreasing individual fitness but sometimes favoring local adaptations. The population of Italian wolves (Canis lupus), after protracted demographic declines and isolation, is currently expanding in anthropic areas, with documented cases of hybridization with stray domestic dogs. However, identifying admixture patterns in deeply introgressed populations is far from trivial. In this study, we used a panel of 170,000 SNPs analyzed with multivariate, Bayesian and local ancestry reconstruction methods to identify hybrids, estimate their ancestry proportions and timing since admixture. Moreover, we carried out preliminary genotype-phenotype association analyses to identify the genetic bases of three phenotypic traits (black coat, white claws, and spur on the hind legs) putative indicators of hybridization. Results showed no sharp subdivisions between nonadmixed wolves and hybrids, indicating that recurrent hybridization and deep introgression might have started mostly at the beginning of the population reexpansion. In hybrids, we identified a number of genomic regions with excess of ancestry in one of the parental populations, and regions with excess or resistance to introgression compared with neutral expectations. The three morphological traits showed significant genotype-phenotype associations, with a single genomic region for black coats and white claws, and with multiple genomic regions for the spur. In all cases the associated haplotypes were likely derived from dogs. In conclusion, we show that the use of multiple genome-wide ancestry reconstructions allows clarifying the admixture dynamics even in highly introgressed populations, and supports their conservation management.
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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 | 0.000 | 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 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".