Decision-Making in a Complex Social Environment: Developing Methodological Approaches Using Innovative Technologies

在这个项目中，我将使用自动跟踪技术和机器学习来测试个人投资和决策如何受到社会环境的影响。为了实现这一点，我将开发一种自动检测野生松鼠种群决策的方法，并将其与允许我构建社交网络(量化社会关系)和空间网络(记录家园范围)的技术方法相结合。研究系统I将在埃克塞特大学校园(由我的首席导师丽莎·利弗博士于2005年建立)东部灰松鼠的长期野外现场进行我的项目。东部灰松鼠是分散的捕猎者，一年中相当一部分时间依赖于储存的食物[2]。因此，缓存决策对个体生存具有重要意义。松鼠通常被描述为不合群，但两性共同筑巢，雌性松鼠表现出出生时的亲友行为，并有重叠的家园范围[12]。校园人群中的个体习惯于人类，经常被困住并标记坑标签，这样个体就会受到多年的监控。这个诱捕协议将允许我相对容易地安装GPS项圈和加速计。

In this project, I will use automated tracking technology and machine learning to test how individual investments and decision-making are influenced by the social environment. To achieve this, I will develop a method for automated detection of decision-making in a wild population of squirrels, and combine this with technological methods that allow me to construct social networks (quantifying social relationships) and spatial networks (documenting home ranges). Study system I will conduct my project on the long-term field site of the eastern grey squirrels at the University of Exeter campus (established in 2005 by my proposed primary supervisor, Dr Lisa Leaver). Eastern grey squirrels are scatter-cachers relying on cached food for considerable portions of the year[2]. Thus, caching decisions are of significant importance for individual survival. Squirrels are often described as asocial but both sexes nest communally and females show natal philopatry and have overlapping home ranges[12]. Individuals in the campus population are habituated to humans and routinely trapped and marked with PIT tags, such that individuals are monitored across multiple years. This trapping protocol will allow me to fit GPS collars and accelerometers with relative ease.