Adaptive trait dynamics of lake phytoplankton at short time scales

我们提出的项目旨在提高我们对生态群落性状多样性响应环境的短期变化及其对生态系统功能的影响的理解。为此，我们将使用淡水湖中的浮游植物群落作为模型系统。流式细胞术监测技术的最新进展允许以前所未有的时间分辨率同时获取大量浮游植物的特征。利用这些方法对瑞士格赖芬湖不同季节、不同深度、短时间尺度的浮游植物群落特征多样性进行了监测。基于所获得的数据，我们将提取特征组成和多样性的定量特征，这将使我们能够跟踪湖泊浮游植物的生态动态和对环境条件的适应性响应（灵活性）。利用这一框架，我们将检验动态性状多样性是通过生态动力学和适应性灵活性等机制将环境与生态系统功能联系起来的关键概念的假设。我们的数据分析将通过数学模型加以补充，数学模型提供了外部干扰相互作用的机制基础，由此产生的生态动态和性状多样性的适应性变化，以及新兴的生态系统功能。拟开展的研究包括：(1)湖泊浮游植物性状光谱的高分辨率采样，(2)性状多样性的量化，(3)性状多样性的适应动态分析，(4)性状多样性对生态系统功能的影响，以及(5)基于性状的浮游植物多样性建模。研究结果揭示了短时间尺度上性状多样性与生态系统灵活性之间的生态进化反馈关系，对有效的生态系统管理具有潜在影响。研究结果对预测人类活动导致的生物多样性丧失和环境变化的功能后果具有重要意义。

Our proposed project is designed to improve our understanding of the short-term changes of trait diversity in ecological communities in response to the ambient environment and its influence on ecosystem functioning. For this we will use phytoplankton communities in freshwater lakes as a model system. Recent advances in monitoring techniques by flow cytometry allow to simultaneous access a multitude of phytoplankton traits at an unprecedented temporal resolution. Using these methods we will monitor the trait diversity of phytoplankton communities at Lake Greifensee (Switzerland) at short time scales and at different depth and different seasons. Based on the obtained data we will extract quantitative characteristics of the trait-composition and -diversity, which will allow us to track the ecological dynamics and the adaptive response (flexibility) of lake phytoplankton to ambient conditions. Using this framework we will test the hypothesis that dynamic trait diversity is a key concept that links the environment to ecosystem functions through mechanisms such as ecological dynamics and adaptive flexibility. Our data-analysis will be complemented by mathematical modeling which provides a mechanistic underpinning of the interaction of external disturbances, the resulting ecological dynamics and adaptive changes in trait-diversity, and the emerging ecosystem functions. The proposed research includes (i) high-resolution sampling of trait-spectra in lake phytoplankton, (ii) quantification of trait diversity, (iii) analysis of the adaptive dynamics of trait diversity, (iv) the effect of trait diversity on ecosystem functioning, and (v) trait-based modeling of phytoplankton diversity. The results of the project have potential impact for effective ecosystem management by providing insights into eco-evolutionary feedbacks between trait-diversity and the ecosystem-flexibility at short time scales. The results will be valuable for predicting the functional consequences of biodiversity loss and environmental change caused by humans.