NSF Postdoctoral Fellowship in Biology FY 2014

NSF生物学博士后奖学金将研究和培训部分结合在一起，为年轻科学家在新兴领域的职业生涯做好准备，这些领域的生物学与其他科学学科交叉，在这种情况下与数学和统计学交叉。院士们有望引领国家--S未来的科学劳动大军。艾琳·K·埃廷格的这一奖学金支持通过提出生态问题来解决植物表现的研究和培训，是不是在气温变暖的情况下尽早开花更好？它使用统计工具将时间安排与工厂绩效联系起来。该奖学金的主办机构是塔夫茨大学和哈佛大学；赞助科学家是伊丽莎白·克龙博士、伊丽莎白·沃尔科维奇博士和卢克·伯恩博士。培训目标包括学习新的建模技术，包括通过调查全球某些地区许多春季事件(称为物候)的时间及其对植物的影响，学习新的建模技术，包括贝叶斯分层建模的强大统计方法。研究结果有望帮助自然资源管理者评估物候是否是优先考虑可能受到环境变化威胁的物种或种群的有用指标。他们可以确定，如果一个具有保护或经济价值的种群没有随着最近的变暖而改变其开花或落叶的时间，它是否可能大量减少。公众对保护组织的接触可能会让他们有兴趣在他们控制的土地上进行物候监测。在过去一个世纪气温上升的地区，物候发生了变化：许多植物物种提前开花或开叶。这项研究将贝叶斯分层模型应用于环境监测和实验的数据集，这些数据记录了叶片爆裂、开花和/或结果日期随着气候变暖而发生变化的程度，以确定物候变化如何影响植物表现和种群增长率。贝叶斯分层方法很少应用于生态学中的荟萃分析，尽管它们在适应不确定性、方差、来自多个来源的数据和小数据集方面具有优势。尽管它们越来越多地被用于生态学研究，但它们仍然存在争议，但如果使用得当，效果会很好。

NSF Postdoctoral Fellowships in Biology combine research and training components to prepare young scientists for careers in emerging areas where biology intersects with other scientific disciplines, in this case with mathematics and statistics. The Fellows are expected to lead the nation?s scientific workforce of the future. This fellowship to Ailene K. Ettinger supports research and training to address plant performance by asking the ecological question, is it better to flower early as temperatures warm? It uses statistical tools to link timing with plant performance. The host institutions for this fellowship are Tufts University and Harvard University; the sponsoring scientists are Drs. Elizabeth Crone,Elizabeth Wolkovich, and Luke Bornn. Training goals include learning new modeling techniques, including the powerful statistical approach of Bayesian hierarchical modeling, through investigating the timing of many spring events, known as phenology, in some regions of the globe and the resulting effects on plants. Research results promise to assist natural resource managers in assessing whether phenology is a useful indicator for prioritizing species or populations that may be threatened by environmental change. They can determine if it is likely that a population of conservation or economic interest will decline in abundance if it has not shifted its flowering or leafing time with recent warming. Public outreach to conservation groups may interest them in conducting phenological monitoring on land they control.In regions where temperatures have increased over the past century, phenology has shifted: numerous plant species are flowering or leafing earlier. This research applies Bayesian hierarchical models to datasets of environmental monitoring and experiments that document the extent to which leaf-burst, flowering, and/or fruiting dates have shifted with warming to determine how shifts in phenology affect plant performance and population growth rates. Bayesian hierarchical methods are rarely applied to meta-analyses in ecology, despite the advantages they offer for accommodating uncertainty, variance, data from multiple sources, and small datasets. Although increasingly used in ecological studies, they remain controversial but powerful when used appropriately.