SFB 1537: ECOSENSE – Multiscale quantification of spatio-temporal dynamics of ecosys-tem processes by smart autonomous sensor networks

全球气候变化威胁着全球生态系统的运行。森林生态系统对碳汇特别重要。然而，反复出现的压力，如热浪、洪水和干旱，甚至越来越危及中欧森林，对其碳汇能力、抗旱能力和可持续性可能产生连锁影响。对于这些复杂系统中驱动土壤-植物-大气相互作用的多种过程的影响，人们普遍缺乏了解，对未来变化的不确定性极高。因此，预测森林对气候变化的反应将需要更好地了解碳和水在各种时间和空间尺度上的循环，从几分钟到四季，从树叶到生态系统，覆盖大气层、生物圈、土壤圈和水圈。许多相关的过程发生在小尺度和高度的空间异质性，它们的相互作用和反馈环可能是放大或抑制系统对应激的反应的关键角色。目前，我们缺乏适当的测量、数据和建模工具，以便在较高的时空覆盖率下对相关过程进行全面、实时的量化。此外，气候影响是高度不可预测的，因此未来的研究将需要新的移动、易于部署和具有成本效益的方法。我们的跨学科研究项目EcoSense将在下一代生态系统研究评估中调查所有相关规模。我们的愿景是基于对分层过程相互作用的理解，检测和预测生态系统功能的关键变化。为此，EcoSense将开发、实施和测试一种新的多功能、分布式、经济高效、自主的智能传感器网络，该网络基于为偏远和恶劣森林环境中的特定需求量身定做的新型微型传感器。他们将以最小的侵入性方式测量自然复杂结构森林中生态系统状态和通量的时空动态。测量数据将被实时传输到一个复杂的数据库中，该数据库可用于过程分析、深度学习方法以及针对当前和预测应用的增强模拟模型。EcoSense将通过i)确定森林碳和水交换的非生物和生物过程的等级和相互作用，ii)提供对环境应激源的复杂生态系统反应的深刻理解，从而为综合生态系统研究开辟新的可怕领域，从而使iii)能够预测生态系统功能和可持续性的基于过程的变化。我们的新型EcoSense工具包在受控气候极端实验中进行了测试和验证，我们的EcoSense森林将为在广阔和偏远的生态系统中进行快速评估开辟新的视野。

Global climate change threatens ecosystem functioning worldwide. Forest ecosystems are particularly important for carbon sequestration. However, recurrent stresses, such as heat waves, floods, and droughts, increasingly endanger even central European forests, with potentially cascading effects on their carbon sink capacity, drought resilience, and sustainability. Knowledge on the impact on the multitude of processes driving soil-plant-atmosphere interactions within these complex systems is widely lacking and uncertainty about future changes extremely high. Thus, forecasting forest response to climate change will require an improved process understanding of carbon and water cycling across various temporal and spatial scales, from minutes to seasons, from leaves to ecosystem, covering the atmosphere, biosphere, pedosphere and hydrosphere. Many relevant processes occur at small scales and high spatial heterogeneity and their interactions and feed-back loops can be key players to amplify or dampen a system’s response to stress. Currently, we are lacking the appropriate measuring, data and modelling tools allowing for comprehensive, real time quantification of relevant processes at high spatio-temporal coverage. Moreover, climate impacts are highly unpredictable, and thus future research will require novel mobile, easy deployable, and cost-efficient approaches.Our interdisciplinary research project ECOSENSE will investigate all relevant scales in a next generation ecosystem research assessment. Our vision is to detect and forecast critical changes in ecosystem functioning based on the understanding of hierarchical process interaction. To do so ECOSENSE will develop, implement, and test a new versatile, distributed, cost-effective, autonomous, intelligent sensor network based on novel microsensors tailored to the specific needs in remote and harsh forest environments. They will measure the spatio-temporal dynamics of ecosystem states and fluxes in a minimally invasive manner in naturally complex structured forests. Measured data will be transferred in real-time into a sophisticated database which can be explored for process analysis, deep learning approaches, and enhanced simulation models for now- and forecasting applications. ECOSENSE will open new horrizons for integrative ecosystem research by i) identifying hierarchies and interactions of abiotic and biotic processes of forest carbon and water exchange, ii) provide a profound understanding of complex ecosystem responses to environmental stressors enabling the iii) prediction of process-based alterations in ecosystem functioning and sustainability. Our novel ECOSENSE Toolkit, tested and validated in controlled climate extreme experiments, and our ECOSENSE Forest, will open new horizons for rapid assessment in vast and remote ecosystems.