Postdoctoral Research Fellowship in Biological Informatic FY 2006

项目名称：“人类与野生动物界面建模：灵长类动物生态学和疾病溢出边界” 该项目是在2006年生物信息学博士后研究奖学金计划下获得的。本计画的目标是发展一个空间上明确的，以代理人为基础的人类介导的景观变化和灵长类动物社会生态学之间的互动框架。这将提供一种手段来评估社会破坏和疾病，特别是新的病原体的易感性引起的营养压力对灵长类动物种群的影响。该模型框架将建立一个工具，以评估系统中的人类-野生动物界面，其中景观与人类和野生动物的个体行为之间的相互作用的大规模影响可能知之甚少。该项目将利用乌干达基巴莱国家公园内和周围的一个研究非常充分的分类群--灵长类动物和一个数据丰富的研究系统。基于代理的框架将允许不同的代理在不同的尺度，时间和空间的模型中的相互作用。 PI将在James Jones的建议下在斯坦福大学开展工作。 PI在生态学和基于GIS的分析的背景将扩展到基于代理的计算框架，并将有助于支持保护生态学和数学生物学的计算方法的学术生涯目标。理解人类作为行为者的复杂生态系统的空间动态对于实现这一目标至关重要。 该培训将加强定量生态学和经验数据操作之间的联系，可用于回答保护生态学中的重要问题。

Project Title: "Modeling the human-wildlife interface: primate ecology and the disease spillover boundary" This project is awarded under the Postdoctoral Research Fellowships in Biological Informatics Program for 2006. The goal of this project is to develop a spatially explicit, agent-based framework of interactions between human mediated landscape change and primate social ecology. This will provide a means to assess impacts on primate population from nutritional stress induced by social disruption and susceptibility to disease, especially novel pathogens. The model framework will establish a tool to assess the human-wildlife interface in systems where the large scale impacts of interaction between the landscape and individual behaviors of humans and wildlife may be poorly understood. This project will make use of a very well studied taxon, primates, and a data rich study system in and around Kibale National Park, Uganda. The agent-based framework will allow for interactions of different agents in the model at different scales, both temporal and spatial. The PI will carry out the work at Stanford University under the advice of James Jones. The PI's background in ecology and GIS-based analyses will be expanded to agent-based computational frameworks, and will contribute to a academic career goal supporting a computational approach to conservation ecology and mathematical biology. Understanding spatial dynamics of complex ecosystems in which humans are actors is fundamental to that goal. This training will reinforce the links between quantitative ecology and empirical data manipulation which can be used to answer important questions in conservation ecology.