{"title":"Natural Born Detourers Modern Utility Dog Breeds Show Ancestry-Based Superiority in Social Learning Capacity in a Detour Task","authors":"Péter Pongrácz, Petra Dobos","doi":"10.1111/eva.70151","DOIUrl":null,"url":null,"abstract":"<p>Behaviour has crucial importance in dogs' adaptation to the anthropogenic environment. Functional breed selection, a relatively recent evolutionary event, resulted in strong differences regarding dogs' capacity for observational learning from humans. However, genetic distance among dog breeds has thus far not been connected to their social learning performance. Here we show first evidence that ancestry-based clustering of dog breeds can result in biologically relevant phenotypic differences in their capacity to learn from humans. We analysed a large database of spatial problem-solving (detour) tests, where a representative sample (<i>N</i> = 174) of cooperative and independent working dogs were sorted into 8 ancestry groups based on a genetic cladogram. We analysed whether ancestry would affect individual and social learning-based spatial problem-solving of dog breeds. Our results showed that ancestry groups with today's utility dog breeds performed this task best. Social learning was also prevalent in the ancestry group that collects English herding breeds and sight hounds as well—showing that genetically closely related cooperative and independent working dog breeds can possess similar sociocognitive traits. These results strengthen the notion that the behaviour of dog breeds can provide ecologically valid research opportunities both for proximate and ultimate evolutionary events.</p>","PeriodicalId":168,"journal":{"name":"Evolutionary Applications","volume":"18 8","pages":""},"PeriodicalIF":3.2000,"publicationDate":"2025-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/eva.70151","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Evolutionary Applications","FirstCategoryId":"99","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1111/eva.70151","RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"EVOLUTIONARY BIOLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
Behaviour has crucial importance in dogs' adaptation to the anthropogenic environment. Functional breed selection, a relatively recent evolutionary event, resulted in strong differences regarding dogs' capacity for observational learning from humans. However, genetic distance among dog breeds has thus far not been connected to their social learning performance. Here we show first evidence that ancestry-based clustering of dog breeds can result in biologically relevant phenotypic differences in their capacity to learn from humans. We analysed a large database of spatial problem-solving (detour) tests, where a representative sample (N = 174) of cooperative and independent working dogs were sorted into 8 ancestry groups based on a genetic cladogram. We analysed whether ancestry would affect individual and social learning-based spatial problem-solving of dog breeds. Our results showed that ancestry groups with today's utility dog breeds performed this task best. Social learning was also prevalent in the ancestry group that collects English herding breeds and sight hounds as well—showing that genetically closely related cooperative and independent working dog breeds can possess similar sociocognitive traits. These results strengthen the notion that the behaviour of dog breeds can provide ecologically valid research opportunities both for proximate and ultimate evolutionary events.
期刊介绍:
Evolutionary Applications is a fully peer reviewed open access journal. It publishes papers that utilize concepts from evolutionary biology to address biological questions of health, social and economic relevance. Papers are expected to employ evolutionary concepts or methods to make contributions to areas such as (but not limited to): medicine, agriculture, forestry, exploitation and management (fisheries and wildlife), aquaculture, conservation biology, environmental sciences (including climate change and invasion biology), microbiology, and toxicology. All taxonomic groups are covered from microbes, fungi, plants and animals. In order to better serve the community, we also now strongly encourage submissions of papers making use of modern molecular and genetic methods (population and functional genomics, transcriptomics, proteomics, epigenetics, quantitative genetics, association and linkage mapping) to address important questions in any of these disciplines and in an applied evolutionary framework. Theoretical, empirical, synthesis or perspective papers are welcome.