MCA: Using multilayer-network analysis to link the social and physical processes that underlie natal dispersal

MCA：使用多层网络分析将出生扩散背后的社会和物理过程联系起来

• 批准号: 2120988
• 负责人: Karen Mabry
• 金额: $ 23.94万
• 依托单位: New Mexico State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2120988&HistoricalAwards=false
• 关键词: MCA; Using; multilayer; network; analysis

How do the physical environment, interactions with others, and an animal’s own characteristics act together to influence that animal’s movements through a landscape? This project will answer this question by bringing together several of the most influential themes in recent animal behavior and ecology research: movement ecology, individual behavioral differences, and social network approaches. Specifically, this project will employ new multilayer interaction network approaches to understand how interactions with other animals and the environment influence movement behavior in wild mice. This project will use animal location data obtained used automated tracking, hormonal and behavioral profiles of individual brush mice (Peromyscus boylii), and information about the physical environment through which mice move. Broader impacts of this project include teaching and mentoring of students, development of educational materials for K-12 students, and the development of animal tracking infrastructure available for use by the wider scientific community. Despite the clear importance of animal dispersal as a central link between individual behavior and larger-scale ecological and evolutionary processes, the causes, consequences, and process of natal dispersal, movement between the birthplace and site of first reproduction, remain relatively enigmatic. The primary focus of this project will be the integration of established and influential paradigms for the study of the movement ecology and individual variation in dispersal behavior with rapidly-advancing multilayer network approaches connecting physical and social processes to develop a truly integrative understanding of individual variation in dispersal through a socially- and physically-heterogeneous landscape. This project will integrate multilayer interaction networks constructed using data obtained from automated tracking of Peromyscus mice with genetic, endocrine, behavioral, and environmental data to develop an integrative understanding of animal dispersal through landscapes that vary in social and ecological conditions through space and time. An additional objective is the development of research infrastructure through the reestablishment of animal tracking capabilities after previous tracking technology was destroyed by wildfire.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

物理环境、与他人的互动以及动物自身的特性如何共同作用，影响动物在景观中的运动？这个项目将通过汇集最近动物行为和生态学研究中最有影响力的几个主题来回答这个问题：运动生态学，个体行为差异和社会网络方法。具体而言，该项目将采用新的多层交互网络方法来了解与其他动物和环境的相互作用如何影响野生小鼠的运动行为。该项目将使用自动跟踪获得的动物位置数据，个体刷鼠（Peromyscus boylii）的激素和行为特征，以及有关小鼠移动的物理环境的信息。该项目的更广泛影响包括学生的教学和辅导，为K-12学生开发教育材料，以及开发可供更广泛科学界使用的动物跟踪基础设施。 尽管动物扩散作为个体行为与大规模生态和进化过程之间的中心联系具有明显的重要性，但纳塔尔扩散的原因、后果和过程，即出生地和第一次繁殖地点之间的运动，仍然是相对神秘的。该项目的主要重点将是整合已建立的和有影响力的范式，用于研究运动生态学和扩散行为中的个体变异，并采用快速推进的多层网络方法，将物理和社会过程连接起来，以通过社会和物理异质性景观对扩散中的个体变异进行真正的综合理解。该项目将利用自动跟踪Peromyscus小鼠获得的数据构建多层相互作用网络，并将其与遗传、内分泌、行为和环境数据相结合，以综合了解动物通过空间和时间上不同的社会和生态条件的景观扩散。另一个目标是通过在以前的跟踪技术被野火摧毁后重建动物跟踪能力来发展研究基础设施。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。