A New Theory and Data Product Quantifying Ecosystem Sensitivity to Climate Change

量化生态系统对气候变化敏感性的新理论和数据产品

• 批准号: 1241960
• 负责人: Benjamin Ruddell
• 金额: $ 29.26万
• 依托单位: Arizona State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-03-01 至 2017-03-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1241960&HistoricalAwards=false
• 关键词: New; Theory; Data; Product; Quantifying

It is increasingly likely that predictions of decadal climate change and land use change will yield the accurate information needed to anticipate ecosystem adaptation to human-induced change (e.g. climate variability, land use change). It is therefore essential that we develop new theories, modeling tools, and data products that are capable of predicting ecosystem adaptation to these changes, and that can anticipate how possible nonlinear thresholds will affect ecosystem structure, function, and services. A new theoretical concept is proposed to measure and predictecosystem sensitivity and likely adaptation. This award will provide funds to develop complex-systems theoretical approach based on information flow in observed ecological process networks, using data from NEON and existing observational networks, which will be applied to, (1) predict nonlinear transition thresholds in the multiscalar couplings between local and regional ecosystem processes by observing feedback couplings in observed ecosystem process networks, and (2) directly measure the current sensitivity of regional ecosystems in the USA to incremental changes in specific climate variables. Data from existing observational networks (e.g. FLUXNET, LTER, NEON), and the National Phenology Network (NPN) will be used to predict the sensitivity of local and regional ecosystems across the USA to specific types of forcings. This new theoretical approach is able to directly quantify ecosystem sensitivity to changes in forcings, and how nonlinear feedback patterns can help predict possible ecosystem transition thresholds. Broader Impacts: The proposed work will create and widely disseminate a set of tools and data products that will allow ecologists across the USA to apply process network theory and information theoretic approaches to analyze ecosystem-scale environmental observatory data. This conceptual approach will be a valuable contribution to the "data analysis toolbox" being used by environmental observatories to explain ecological change and its impact on human societies for which these ecosystems represent vital life support. Graduate students and a postdoctoral scholar will be trained in the creation and application of these advanced quantitative methods in ecology and in the synthetic analysis of multi-scalar ecosystem data. The products of the theory-based models will form a critical analytical bridge between raw data collection and the environmental understanding necessary for informed resource management and policy-making in a future driven by historically unprecedented and nonlinear climate change impacts.

对十年间气候变化和土地利用变化的预测将越来越有可能产生预测生态系统对人类引起的变化（例如气候变化、土地利用变化）的适应所需的准确信息。因此，我们必须开发新的理论、建模工具和数据产品，这些理论、建模工具和数据产品能够预测生态系统对这些变化的适应，并能够预测非线性阈值将如何影响生态系统的结构、功能和服务。提出了一个新的理论概念来测量和预测生态系统的敏感性和可能的​​适应。该奖项将提供资金，利用来自 NEON 和现有观测网络的数据，开发基于观测到的生态过程网络中的信息流的复杂系统理论方法，该方法将应用于：（1）预测局部之间多标量耦合中的非线性过渡阈值通过观察观测到的生态系统过程网络中的反馈耦合来评估区域生态系统过程，以及（2）直接测量美国区域生态系统当前对特定气候变量增量变化的敏感性。来自现有观测网络（例如 FLUXNET、LTER、NEON）和国家物候网络 (NPN) 的数据将用于预测美国当地和区域生态系统对特定类型强迫的敏感性。这种新的理论方法能够直接量化生态系统对强迫变化的敏感性，以及非线性反馈模式如何帮助预测可能的生态系统转变阈值。更广泛的影响：拟议的工作将创建并广泛传播一套工具和数据产品，使美国各地的生态学家能够应用过程网络理论和信息论方法来分析生态系统规模的环境观测数据。这种概念方法将对环境观测站用来解释生态变化及其对人类社会的影响的“数据分析工具箱”做出宝贵的贡献，而这些生态系统代表着重要的生命支持。研究生和博士后学者将接受有关生态学中这些先进定量方法的创建和应用以及多标量生态系统数据的综合分析的培训。基于理论的模型的产品将在原始数据收集和环境理解之间形成一座关键的分析桥梁，这对于未来在历史上前所未有的非线性气候变化影响的驱动下进行明智的资源管理和政策制定是必需的。