NSF Postdoctoral Fellowship in Biology for FY 2011

本行动资助2011年度美国国家科学基金会生物学博士后研究奖学金，生物学与数学和数学的交叉。该奖学金支持奖学金获得者在生物学、数学和地球化学交叉领域的一个主办实验室进行研究和培训计划。辛西娅·张的研究和培训计划的标题是“使用层次贝叶斯模型来理解火山爆发后的社区聚集，并预测对全球变化的反应。”该奖学金的主办机构是华盛顿大学，西雅图和赞助科学家是博士。珍妮克·希尔，里斯·兰伯斯和彼得·古托普。了解自然群落是如何在重大干扰后聚集起来的，可以让我们对生态系统随时间变化的动态本质有独特的了解。在植物群落中，传播策略、生活史和生长性状是影响群落相互作用的决定性因素。随机因素，如随机机会，也可以决定社区如何聚集。在演替过程中，这两种相互作用都会影响群落物种多样性。贝叶斯层次模型具有独特的能力，可以将确定性和随机因素、多尺度和相互作用结合起来。然而，生物学的洞察力对于建立有意义的模型是必要的。基于30年的大型火山爆发后植物定植、组成和性状分布数据，本研究正在生成模型，以进一步了解重大干扰后的群落组装过程，并根据过去的关系预测未来群落对全球变化的响应。培训目标包括发展统计和分析技术方面的专门知识。教育推广包括开发本科和高中课程课程，使用圣海伦山？S研究作为案例研究，并涉及研究生和本科生的研究。更广泛的影响包括向决策者通报该生态系统和类似生态系统的特征，以便未来进行土地管理。这些预测模型可以作为理解生态群落如何应对其他主要干扰以及预测全球变化的理论框架。这项研究的结果正在通过科学和大众讲座、与大自然保护协会和美国林务局的会议以及同行评议期刊上的出版物进行传播。

This action funds an NSF Postdoctoral Research Fellowship in Biology for FY 2011, Intersections of Biology and Mathematical and Math. The fellowship supports a research and training plan in a host laboratory for the Fellow at the intersection of biology with mathematics and geochemistry. The title of the research and training plan for this fellowship to Cynthia Chang is "Using hierarchical Bayesian modeling to understand community assembly after an eruption and predict responses to global change." The host institution for this fellowship is the University of Washington, Seattle and the sponsoring scientists are Drs. Janneke Hille Ris Lambers and Peter Guttorp.Understanding how natural communities assemble following major disturbances gives unique insight into the dynamic nature of ecosystems through time. In plant communities, dispersal strategy, life history, and growth traits are deterministic factors that influence community interactions. Stochastic factors, such as random chance, can also dictate how communities assemble. Both types of interactions influence community species diversity over the course of succession. Bayesian hierarchical modeling has the unique ability to incorporate deterministic and stochastic factors, multiple scales, and interactions. Biological insight, however, is necessary to build meaningful models. Based upon a 30-year dataset on plant colonization, composition, and trait distribution after a major volcanic eruption, this research is generating models that further our understanding of community assembly processes after a major disturbance, as well as predict future community response to global change based on past relationships.Training objectives include developing expertise in statistical and analytical techniques. Educational outreach includes developing undergraduate and high school course curricula that use Mount St. Helen?s research as a case study and involving graduate and undergraduate students with research. The broader impacts include informing policy makers for future land management of the characteristics of this and similar ecosystems. The predictive models can serve as a theoretical framework for understanding how ecological communities may respond to other major disturbances as well as anticipated global change. The findings of this research are being disseminated through scientific and popular talks, meetings with The Nature Conservancy and U.S. Forest Service, and publications in peer-reviewed journals.