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

集成高分辨率监测和基于性状的建模来理解和预测浮游植物动态 (AQUASCOPE)

• 批准号: 412375259
• 负责人: Professor Dr. Agostino Merico
• 金额: --
• 依托单位: Leibniz-Zentrum für Marine Tropenforschung (ZMT)
• 依托单位国家: 德国
• 项目类别: Research Grants
• 财政年份: 2019
• 资助国家: 德国
• 起止时间: 2018-12-31 至 2022-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/412375259?language=en
• 关键词: Integrating; Resolution; Monitoring; Trait; based

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.

浮游生物群落动态由自下而上（水物理和化学）和自上而下（天敌）驱动因素控制。然而，这些影响的相对重要性和方向的类群组成和相对丰度没有很好地建立在自然界中：它们在时间和空间上变化，也取决于性状介导的生理和生态相互作用。这个项目的目标是量化交互控制的效果（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，为了（重新）设计基于特征的理论和数据驱动的模型，从而能够准确预测浮游生物食物网的变化-从而预测生态系统的过程和服务-穿越时空的环境梯度该项目包括三个相互关联的工作包：1）现场监测新方法的应用，2）数据分析（模式探索，假设检验和驱动因素分析），以及3）基于特征的建模（理论的开发和测试，对空间和时间的预测）。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社区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在时间和空间上塑造浮游植物群落的动态，使用基于特征的框架。对控制群落结构和丰度的驱动因素和机制的定量理解将使我们能够预测浮游生物多样性在环境梯度中的变化，以及藻类水华。