Postdoctoral Research Fellowship in Biological Informatic FY 2006

项目名称：“模拟人与野生动物的界面：灵长类生态学与疾病溢出边界”该项目是根据2006年生物信息学博士后研究奖学金项目授予的。这个项目的目标是开发一个空间上明确的、基于主体的框架，研究人类中介的景观变化和灵长类社会生态之间的相互作用。这将提供一种方法来评估由社会混乱和疾病易感性，特别是新的病原体引起的营养压力对灵长类种群的影响。该模型框架将建立一个工具来评估系统中的人-野生动物界面，在这些系统中，人们可能对景观和人类与野生动物的个人行为之间的互动的大规模影响知之甚少。该项目将利用乌干达Kibale国家公园内和周围研究得很好的分类群、灵长类动物和数据丰富的研究系统。基于代理的框架将允许模型中的不同代理在不同的时间和空间尺度上进行交互。私家侦探将在詹姆斯·琼斯的建议下在斯坦福大学开展这项工作。PI在生态学和基于地理信息系统的分析方面的背景将扩展到基于代理的计算框架，并将有助于实现支持保护生态和数学生物学的计算方法的学术职业目标。了解人类是其中参与者的复杂生态系统的空间动力学是实现这一目标的基础。这一培训将加强数量生态学和经验数据处理之间的联系，这些数据处理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.