NSF Postdoctoral Fellowship in Biology FY 2016

2016 财年 NSF 生物学博士后奖学金

• 批准号: 1612153
• 负责人: Michael Landis
• 金额: $ 13.8万
• 依托单位: Landis Michael
• 依托单位国家: 美国
• 项目类别: Fellowship Award
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-01-01 至 2018-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1612153&HistoricalAwards=false
• 关键词: NSF; Postdoctoral; Fellowship; Biology; FY

This action funds an NSF Postdoctoral Research Fellowship in Biology for FY 2016, Research Using Biological Collections. The fellowship supports a research and training plan for the Fellow to take transformative approaches to grand challenges in biology that employ biological collections in highly innovative ways. The title of the research plan for this fellowship to Michael Landis is "Modeling ecological niche and geographical range co-evolution in the nightshade family (Solanaceae)." The host institution for this fellowship is Yale University, and the sponsoring scientist is Michael Donoghue. The goal of this research project is to develop a model of geographic range and ecological niche evolution, and apply the model to a family of ecologically diverse and geographically widespread plants, the nightshade family (Solanaceae). Adaptation to novel ecological conditions is common throughout the tree of life, and understanding how, when, and why ecological adaptation occurs has far-reaching applications in areas such as conservation biology, epidemiology, and agricultural science. Because species tend to conserve the ecological niche of their ancestors, and because species adapt to the local conditions found within or abutting their geographical ranges, analyzing this process requires a unified approach that incorporates ecological, geographical, and evolutionary perspectives. Advanced statistical models and inference approaches can be a powerful means of reconstructing evolutionary histories, especially in absence of hard evidence such as fossils. To this end, the Fellow is producing software to estimate how groups of closely related species evolve in response to ecological and geographical factors. Applying the method to environments everywhere from the Atacama Desert to the Tibetan Plateau, the Fellow is characterizing how plants in the Solanaceae family colonized Earth's most extreme environments. To inform the method, the Fellow is developing 'pipeline' tools to integrate molecular, geographical, and ecological data from online biological databases. These data will be supplemented with museum specimens from several major national botanical collections, including the Missouri Botanical Garden, the New York Botanic Garden, and the Harvard University Herbaria, and the Yale Peabody Museum.The Fellow is gaining valuable experience in conducting empirical research, a significant departure from his dissertation research, which was theoretical and consisted entirely of modeling. By working with the curators and collections of various museums, the Fellow is learning how to diagnose morphological characters using preserved and fossilized specimens. The Fellow is leading workshops in statistical phylogenetics, thereby allowing him improve his teaching skills and develop new collaborations. To advance his mentoring skills, the Fellow is training biology undergraduates through the Yale Science, Technology and Research Scholars (STARS) program, focusing on computational and statistical skills. Through community outreach programs, the Fellow is refining his ability to communicate scientific findings in plant evolution to high school students (Yale Science Diplomats) and amateur botanists (Green Café). Software produced during the fellowship is open-source and freely available to the research community.

本行动资助美国国家科学基金会2016年度生物学博士后研究奖学金，利用生物馆藏进行研究。该奖学金支持一项研究和培训计划，使研究员能够以高度创新的方式采用生物收集的变革性方法来应对生物学方面的重大挑战。Michael Landis的这项研究计划的题目是“模拟茄科植物的生态位和地理范围的共同进化”。这个奖学金的主办机构是耶鲁大学，赞助科学家是Michael Donoghue。本研究项目的目标是建立一个地理范围和生态位演化的模型，并将该模型应用于生态多样性和地理分布广泛的植物——茄科植物。对新的生态条件的适应在整个生命之树中是普遍的，理解生态适应是如何、何时以及为什么发生的，在保护生物学、流行病学和农业科学等领域具有深远的应用。由于物种倾向于保护其祖先的生态位，并且由于物种适应其地理范围内或附近的当地条件，因此分析这一过程需要一种结合生态学，地理学和进化观点的统一方法。先进的统计模型和推理方法可以成为重建进化史的有力手段，特别是在缺乏化石等确凿证据的情况下。为此，研究员正在开发软件，以估计密切相关的物种群体如何在生态和地理因素的影响下进化。为了使该方法更有效，该研究员正在开发“管道”工具，以整合来自在线生物数据库的分子、地理和生态数据。这些数据将由来自几个主要国家植物收藏的博物馆标本补充，包括密苏里植物园、纽约植物园、哈佛大学植物标本馆和耶鲁皮博迪博物馆。研究员在进行实证研究方面获得了宝贵的经验，这与他的论文研究有很大的不同，他的论文研究是理论的，完全由建模组成。通过与各博物馆的馆长和藏品合作，研究员正在学习如何使用保存和化石标本诊断形态学特征。该研究员在统计系统发育方面领导讲习班，从而使他能够提高他的教学技能并发展新的合作。为了提高他的指导技能，这位研究员正在通过耶鲁大学科学、技术和研究学者（STARS）计划培训生物学本科生，重点是计算和统计技能。通过社区外展项目，该研究员正在提高他向高中生（耶鲁科学外交官）和业余植物学家（绿色咖啡）传达植物进化科学发现的能力。在奖学金期间产生的软件是开源的，并免费提供给研究社区。