Integrating High Resolution Monitoring and Trait-based Modelling to Understand and Predict Phytoplankton Dynamics (AQUASCOPE)

浮游生物群落动态受自下而上（水体物理化学）和自上而下（天敌）驱动因素的控制。然而，在自然群落中，这些影响对分类群组成和相对丰度的相对重要性和方向尚未得到很好的确定：它们在时间和空间上存在差异，并且还取决于性状介导的生理和生态相互作用。该项目的目标是量化相互作用的控制（如温度、湍流、营养供应、食草动物身份、密度和猎物选择性）对湖泊浮游植物的影响，在分类学和基于大小的类别中，为了（重新）设计基于特征的理论和数据驱动模型，这些模型将允许准确预测浮游生物食物网的变化——因此，生态系统过程和服务——跨越空间和时间的环境梯度。该项目包括三个相互关联的工作包（WPs）： 1)应用现场监测的新方法，2)数据分析（探索模式，假设检验和驱动因素分析），以及3)基于特征的建模（开发和测试理论，预测空间和时间）。该项目将评估水下成像作为研究和常规湖泊浮游生物监测的新工具。获得的数据将使我们能够完善群落生态学的概念和理论，特别是化学、物理和物种相互作用如何利用基于特征的框架，在时间和空间上塑造浮游植物群落的动态。定量了解控制群落结构和丰度的驱动因素和机制将使我们能够预测浮游生物多样性在环境梯度和藻华中的变化。

Plankton community dynamics are controlled by bottom-up (water physics and chemistry) and top-down (natural enemies) drivers. However, the relative importance and direction of these effects on taxa composition and relative abundances are not well established in natural communities: they vary in time and space and also depend on trait-mediated physiological and ecological interactions. The goal of this project is to quantify the effects of interacting controls (e.g. temperature, turbulence, nutrient supply, grazer identity, density and prey selectivity) on lake phytoplankton, within taxonomic and size-based categories, in order to (re)design trait-based theoretical and data-driven models that will allow accurate prediction of plankton food-web changes ---and therefore ecosystem processes and services-- across environmental gradients in space and time. This project features three interconnected work packages (WPs): 1) application of new methods for in situ monitoring, 2) data analysis (exploration of patterns, hypothesis testing and analysis of drivers), and 3) trait-based modelling (development and testing of theories, predictions over space and time). This project will evaluate underwater imaging as a new tool for research and routine lake plankton monitoring. The data to be obtained will allow us to refine concepts and theories in community ecology, particularly of how chemistry, physics and species interactions can the shape dynamics of phytoplankton communities over time and space, using a trait-based framework. Quantitative understanding of drivers and mechanisms that control community structure and abundances will allow us to make forecasts of changes in plankton biodiversity across environmental gradients, and of algal blooms.