Data from: Using network analysis to study behavioural phenotypes: an example using domestic dogs

Phenotypic integration describes the complex interrelationships between organismal traits, traditionally focusing on morphology. Recently, research has sought to represent behavioural phenotypes as composed of quasi-independent latent traits. Concurrently, psychologists have opposed latent variable interpretations of human behaviour, proposing instead a network perspective envisaging interrelationships between behaviours as emerging from causal dependencies. Network analysis could also be applied to understand integrated behavioural phenotypes in animals. Here, we assimilate this cross-disciplinary progression of ideas by demonstrating the use of network analysis on survey data collected on behavioural and motivational characteristics of police patrol and detection dogs (Canis lupus familiaris). Networks of conditional independence relationships illustrated a number of functional connections between descriptors, which varied between dog types. The most central descriptors denoted desirable characteristics in both patrol and detection dog networks, with ‘Playful’ being widely correlated and possessing mediating relationships between descriptors. Bootstrap analyses revealed the stability of network results. We discuss the results in relation to previous research on dog personality, and benefits of using network analysis to study behavioural phenotypes. We conclude that a network perspective offers widespread opportunities for advancing the understanding of phenotypic integration in animal behaviour.

表型整合（Phenotypic integration）指生物体各性状间的复杂相互关联，传统研究聚焦于形态学维度。近年来，相关研究尝试将行为表型表征为若干准独立潜在性状的组合。与此同时，心理学研究者反对人类行为的潜变量解释框架，转而提出网络视角，认为行为间的相互关联源自因果依赖关系。网络分析亦可用于解析动物的整合行为表型。

本研究吸纳这一跨学科的思想演进脉络，通过对警用巡逻犬与侦缉犬（Canis lupus familiaris）的行为及动机特征调研数据开展网络分析，展示了该方法的应用价值。条件独立关系网络展现出各特征描述项间的多条功能关联，且这类关联在两类犬种间存在显著差异。在两类犬的网络中，中心性最高的特征描述项均代表理想选育特征，其中“爱玩（Playful）”的关联范围最广，且在各特征描述项间发挥中介作用。Bootstrap分析（Bootstrap analysis）验证了网络结果的稳定性。

我们结合此前犬类人格的相关研究，对本研究结果展开讨论，并分析了利用网络分析研究行为表型的诸多优势。本研究最终得出结论：网络视角为深化动物行为领域的表型整合研究提供了广阔的发展空间。