Rethinking dog domestication by integrating genetics, archeology, and biogeography
Bibliographic record
Abstract
The dog was the first domesticated animal but it remains uncertain when the domestication process began and whether it occurred just once or multiple times across the Northern Hemisphere. To ascertain the value of modern genetic data to elucidate the origins of dog domestication, we analyzed 49,024 autosomal SNPs in 1,375 dogs (representing 35 breeds) and 19 wolves. After combining our data with previously published data, we contrasted the genetic signatures of 121 breeds with a worldwide archeological assessment of the earliest dog remains. Correlating the earliest archeological dogs with the geographic locations of 14 so-called "ancient" breeds (defined by their genetic differentiation) resulted in a counterintuitive pattern. First, none of the ancient breeds derive from regions where the oldest archeological remains have been found. Second, three of the ancient breeds (Basenjis, Dingoes, and New Guinea Singing Dogs) come from regions outside the natural range of Canis lupus (the dog's wild ancestor) and where dogs were introduced more than 10,000 y after domestication. These results demonstrate that the unifying characteristic among all genetically distinct so-called ancient breeds is a lack of recent admixture with other breeds likely facilitated by geographic and cultural isolation. Furthermore, these genetically distinct ancient breeds only appear so because of their relative isolation, suggesting that studies of modern breeds have yet to shed light on dog origins. We conclude by assessing the limitations of past studies and how next-generation sequencing of modern and ancient individuals may unravel the history of dog domestication.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".