BIO Informatics: PostDoctoral Research Fellowship

摘要DBI-0532785 Jason J.Kolbe此行动为2006财年NSF生物信息学博士后研究奖学金提供资金。该奖学金支持生物学与信息科学、计算科学、数学科学和统计科学交叉的博士后水平的研究和培训。该研究金的目标是为一名青年科学家提供培训，为从事生物信息学的职业做准备，在生物信息学中将把研究和教育结合起来。生物信息学在各个职业层面的培训需求越来越大，预计通过这些奖学金培训的研究员将在培训未来的劳动力方面发挥重要作用。该奖学金的研究和培训计划名为使用生态位模型预测入侵物种的分布。培训目标是：该项目整合自然历史博物馆数据库中地理参考标本的现有数据、基于地理信息系统的环境数据、生物物理模型、生理测量和生物入侵的遗传数据，以预测入侵物种的非本地分布。它还比较了使用三种局部入侵物种-棕色变色蜥蜴、甘蔗蟾蜍和橄榄果蝇的生态位建模方法。这项研究需要精通地理信息系统技术、生态位建模方法、博物馆数据库和生物物理建模。这个项目的主要目标是开发一个理解和管理生物入侵的总体框架，这需要整合来自生态学、进化论和生物多样性信息学的数据和方法。

AbstractDBI- 0532785Jason J. KolbeThis action funds an NSF Postdoctoral Research Fellowship in Biological Informatics for FY 2006. The fellowship supports research and training at the postdoctoral level at the intersection of biology and the informational, computational, mathematical, and statistical sciences. The goal of the fellowship is to provide training to a young scientist in preparation for a career in biological informatics in which research and education will be integrated. There is an increasing need for training in biological informatics at all occupational levels, and it is expected that Fellows trained through these fellowships will play important roles in training the future workforce.The research and training plan for this fellowship is entitled Predicting invasive species distributions using ecological niche models.The training goals are: This project integrates existing data from geo-referenced specimens in natural history museum databases, GIS-based environmental data, biophysical models, physiological measurements, and genetic data from biological invasions to predict the non-native distribution of invasive species. It also compares ecological-niche modeling approaches using three focal invasive species, the brown anole lizard, cane toad, and olive fruit fly. This research requires proficiency with GIS technology, niche modeling methods, museum databases, and biophysical modeling. The main goal of this project is to develop a general framework for understanding and managing biological invasions, which requires integration of data and methods from ecology, evolution, and biodiversity informatics.