NSF Postdoctoral Fellowship in Biology FY 2010

这一行动为2010财年美国国家科学基金会博士后研究奖学金提供资金。该奖学金支持蒂娜·韦的一项研究和培训计划，该计划的标题是《动物社会复杂性的网络和统计分析》。这项研究的主办机构是加州大学戴维斯分校，赞助科学家是安迪·谢博士。社会结构，即群体成员之间的社会互动模式，被认为在决定个人特征如何影响交配成功方面发挥着重要作用。然而，对社会结构如何影响交配成功还缺乏全面的理解。社交网络分析提供了一种量化的方法来衡量复杂的互动模式。这个项目使用了一个大型的行为数据集，通过实验操纵的、复制的溪水步行者(Aquarius Remigis)的社会群体来检验社会网络结构对交配成功的影响。具体地说，该项目解决了以下问题：1)多个阶段的社会网络特征对交配成功的相对影响是什么？2)群体网络结构是如何从个体特征中产生并影响繁殖成功的？3)网络动态如何影响交配成功，个体如何对网络变化做出反应？4)环境变化如何改变网络结构与个体成功之间的关系？这项工作解决了难以分析的真实系统的性质(多层次的复杂相互作用和时间动力学)，并正在产生一种可广泛应用于许多社会系统的预测框架和分析方法。培训目标包括发展网络和统计分析、计算机编程和科学推广方面的技能。项目目标包括开发和传播研究动物社会复杂性所需的新分析工具，同时促进行为生态学、网络分析和统计学专家之间的跨学科合作。这个项目加深了我们对交配成功、社会复杂性和进化的理解。预计的结果包括一个研讨会和免费的在线教程，旨在向行为生态学家传授基本的社交网络分析，这些分析可以应用于各种问题和系统。

This action funds an NSF Postdoctoral Research Fellowship for FY 2010. The fellowship supports a research and training plan entitled "Network and statistical analyses of animal social complexity" for Tina Wey. The host institution for this research is University of California Davis and the sponsoring scientist is Dr. Andy Sih.Social structure, the pattern of social interaction among members of a group, is thought to play an important role in determining how individual traits influence mating success. However, a comprehensive understanding of how social structure influences mating success is lacking. Social network analysis offers a quantitative approach to measuring complex patterns of interaction. This project uses a large behavioral dataset on experimentally manipulated, replicated social groups of stream water striders (Aquarius remigis) to examine the effect of social network structure on mating success. Specifically, the project addresses the following questions: 1) What are the relative effects of social network traits at multiple stages on mating success? 2) How does group network structure emerge from individual traits and influence reproductive success? 3) How do network dynamics influence mating success, and how do individuals respond to network change? 4) How does environmental variation alter the relationship between network structure and individual success? This work addresses properties of real systems (complex interactions at multiple levels and temporal dynamics) that have been difficult to analyze and is producing a predictive framework and analytical methodology that can be broadly applied to many social systems.Training objectives include developing skills in network and statistical analysis, computer programming, and scientific outreach. Project goals include developing and disseminating new analytical tools needed to study animal social complexity, while fostering interdisciplinary collaboration between experts in behavioral ecology, network analysis, and statistics. This project advances our understanding of mating success, social complexity and evolution. Expected outcomes include a workshop and free online tutorial designed to teach behavioral ecologists basic social network analysis that can be applied to diverse questions and systems.