论文标题
使用倍数标记的运动数据检测动态社交网络的变化
Detecting changes in dynamic social networks using multiply-labeled movement data
论文作者
论文摘要
动物种群的社会结构通常会影响运动，并告知研究人员的行为倾向。动物社交网络可以通过运动数据研究；但是，现代数据来源可能会有识别问题，从而导致多重标记的个体。由于所有可用的社会运动模型都依赖于唯一的标签，因此我们以使用潜在社交网络的方式扩展了现有的贝叶斯分层运动模型，并适应了多重标记的运动数据（MLMD）。我们将模型应用于Risso海豚（Grampus Griseus）的无人机测量运动数据，并估算声纳暴露对海豚社会结构的影响。我们提出的框架可以应用于MLMD，以进行各种社会运动应用。
The social structure of an animal population can often influence movement and inform researchers on a species' behavioral tendencies. Animal social networks can be studied through movement data; however, modern sources of data can have identification issues that result in multiply-labeled individuals. Since all available social movement models rely on unique labels, we extend an existing Bayesian hierarchical movement model in a way that makes use of a latent social network and accommodates multiply-labeled movement data (MLMD). We apply our model to drone-measured movement data from Risso's dolphins (Grampus griseus) and estimate the effects of sonar exposure on the dolphins' social structure. Our proposed framework can be applied to MLMD for various social movement applications.
