Adaptive trait dynamics of lake phytoplankton at short time scales

我们提出的项目的目的是提高我们的理解的短期变化的特征多样性的生态群落，以应对周围环境及其对生态系统功能的影响。为此，我们将使用淡水湖泊中的浮游植物群落作为模型系统。通过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访问众多的浮游植物特征。使用这些方法，我们将监测浮游植物群落的特征多样性在格赖芬湖（瑞士）在短时间尺度上，在不同的深度和不同的季节。基于所获得的数据，我们将提取的特征组成和多样性的定量特征，这将使我们能够跟踪生态动态和湖泊浮游植物对环境条件的适应性反应（灵活性）。使用这个框架，我们将测试的假设，动态性状多样性是一个关键的概念，通过生态动态和适应性的灵活性等机制，将环境与生态系统功能。我们的数据分析将通过数学建模来补充，该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特征多样性的适应性变化，以及新兴的生态系统功能。拟议的研究包括：（i）高分辨率采样的特征光谱在湖泊浮游植物，（ii）量化的特征多样性，（iii）分析的适应动力学特征多样性，（iv）的影响特征多样性对生态系统功能，和（v）基于特征的浮游植物多样性建模。该项目的结果有潜在的影响，有效的生态系统管理，提供洞察生态进化反馈之间的性状多样性和生态系统的灵活性在短时间尺度。研究结果对于预测人类活动造成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.