EAGER-NEON: Detecting Disturbance and Ecosystem Response in Continental Observatory Networks

陆地植物和微生物目前向社会提供的最重要的生态系统服务之一是消除排放到大气中的人类产生的二氧化碳(CO2)的大约四分之一。然而，在全球和国家层面，这种所谓的“碳汇”模式的前景仍不明朗。一些证据表明，最近可能是由于美国西部干旱造成的干扰，导致陆地生态系统突然成为大气的碳源，而不是碳汇。随着我们进入碳责任时代，我们必须能够更准确地计算整个北美的生态系统碳生产和消费。在这项研究中，将首次将计算机模拟与NSF霓虹灯观测网络新获得的数据结合起来，以评估霓虹灯数据，并更好地了解这种大规模环境干扰如何影响大陆的碳平衡。该项目还将包括博士后和研究生层面的培训，以及积极学习经验的发展，这将扩大来自代表性不足群体的本科生对科学的参与。该项目的最终目标是确定可能的干扰机制，这些机制已经导致整个美国大陆范围内的生态系统从净碳汇转变为净碳源。在这个项目中，我们将使用卫星数据来识别与不同的生态系统干扰机制(如虫害、干旱、火灾)相关的空间和时间指纹，并使用这些指纹来模拟陆地植被模型中与不同干扰机制相关的随机和机械过程。新获得的霓虹观测数据将被用来作为这些陆地植被模拟的基准，模拟将被用来优化观测网络以进行干扰检测。该项目将在两个方面推动生态系统科学的发展。首先，将开发从全球到大陆再到区域尺度的分层观测网络优化的新算法。其次，将改进陆面模式中的扰动动力学，模拟更大空间尺度上的扰动。

One of the most vital ecosystem services currently provided to society by terrestrial plants and microorganisms is the removal of approximately one-quarter of the human-generated carbon dioixide (CO2) emitted to the atmosphere. However, the future of this so-called "carbon sink" pattern at the global and national level remains uncertain. Some evidence suggests that recent disturbance possibly due to drought in the Western US has caused terrestrial ecosystems to suddenly become sources of carbon to the atmosphere, rather than carbon sinks. As we enter an era of carbon accountability it is imperative that we are able to more accurately account for ecosystem carbon production and consumption across North America. For this study, computer simulation in conjunction with newly acquired data from NSF's NEON observing network will be synthesized, for the first time, to evaluate the NEON data, and better understand how such large-scale environmental disturbance affects the carbon balance of the continent. The project will also involve training at the postdoctoral and graduate student levels, as well as development of active learning experiences that will broaden the participation of undergraduates from underrepresented groups in science.The ultimate goal of this project is to identify possible disturbance mechanisms that have caused a continental scale shift in ecosystems across the US from being a net carbon sink to a net carbon source. In this project we will use satellite data to identify spatial and temporal fingerprints associated with different ecosystem disturbance mechanisms (e.g. insect infestation, drought, fire) and use these fingerprints to simulate the random and mechanistic processes associated with different disturbance mechanisms in land vegetation models. Newly acquired NEON observation data will be used to benchmark these land vegetation simulations and simulations will be used to optimize the observation network for disturbance detections. This project will advance ecosystem science in two ways. First, new algorithms for the optimization of hierarchical observation networks from global to continental to regional scales will be developed. Second, disturbance dynamics in land surface models will be refined, and disturbance over larger spatial scales will be simulated.