NSF Postdoctoral Fellowship in Biology FY 2010

这一行动为2010财年美国国家科学基金会博士后研究奖学金提供资金。该奖学金支持一项名为《浮游生物物候学：物种层面的变化如何在湖泊生态系统中传播》的研究和培训计划。为了安妮卡·沃尔特斯。这项研究的主办机构是华盛顿大学，赞助科学家是丹尼尔·E·辛德勒。生态动态受气候条件的影响很大。在湖泊中，较暖的水温可能会改变关键物理过程的时间，包括春季冰层破裂和热分层，这可能会影响淡水浮游生物的物候。挑战在于了解气候驱动的影响，例如浮游植物物种丰度峰值时间的变化，是如何通过复杂的群落传播的。物种间的相互作用可能会导致物种层面的影响迅速减弱或广泛传播，从而在生态系统层面产生影响。这项研究利用了48年来华盛顿湖浮游植物和浮游动物物种丰度的数据集，该湖的水温上升了1.4摄氏度。其目的是探索浮游生物对气候变暖的响应在物种水平、群落和生态系统水平之间的变化。对应对措施如何“放大”的理解可以为检测与气候变化相关的生态系统影响的工作提供信息。培训目标包括发展统计分析和建模方面的高级技能。该项目将利用各种时间序列分析技术，包括动态线性模型、多变量自回归模型和频谱分析。这项研究有助于我们对气候变化的理解，并为指导本科生提供机会。这项研究支持华盛顿湖的长期数据收集，结果将在当地湖泊协会会议上公布，此外还将通过国家科学会议和出版物公布。

This action funds an NSF Postdoctoral Research Fellowship for FY 2010. The fellowship supports a research and training plan entitled "Plankton phenology: how shifts at the species level propagate through a lake ecosystem." for Annika Walters. The host institution for this research is The University of Washington, and the sponsoring scientist is Daniel E. Schindler. Ecological dynamics are strongly influenced by climatic conditions. In lakes, warmer water temperatures can shift the timing of key physical processes, including spring ice-breakup and thermal stratification, which can affect the phenology of freshwater plankton species. The challenge is to understand how climate driven effects, for example shifts in the timing of peak abundance for a phytoplankton species, propagate through complex communities. Inter-specific interactions may cause species level impacts to quickly dampen out or to transmit broadly to have effects at the ecosystem level. This study utilizes a 48 year dataset of phytoplankton and zooplankton species abundances from Lake Washington, which has experienced a 1.4 °C increase in water temperature. The aim is to explore variation between species level and aggregate community and ecosystem level responses of plankton to warming. An understanding of how responses "scale up" can inform efforts to detect ecosystem impacts associated with climate change.Training goals include developing advanced skills in statistical analysis and modeling. The project will utilize a variety of time-series analysis techniques including dynamic linear models, multivariate autoregressive models, and spectral analysis. The study contributes to our understanding of climate change and provides opportunities for mentoring undergraduate students. The research supports long-term data collection at Lake Washington and the results will be presented at local lake association meetings, in addition to national scientific meetings and through publications.