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.

越来越有可能对际气候变化和土地使用变化的预测产生所需的准确信息，以预测生态系统适应人类引起的变化（例如气候变异性，土地利用变化）。因此，我们必须开发能够预测生态系统适应这些变化的新理论，建模工具和数据产品，并且可以预料到非线性阈值会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衡量和预测系统的敏感性和可能的​​适应性。该奖项将提供基于观察到的生态过程网络中信息流的信息流，使用霓虹灯和现有观察网络的数据来开发复杂系统的理论方法，该数据将应用于（1）（1）（1）（1）通过在多层型过程和区域生态过程之间在局部和区域生态过程之间进行2次接种的局限性，预测非线性过渡阈值，通过在多个局部耦合之间进行局部和区域生态过程，并直接在观察的反馈界面上，（且（美国的区域生态系统将在特定气候变量的增量变化中变化。现有的观察网络（例如FluxNet，LTER，NEON）和国家物候网（NPN）的数据将用于预测美国对特定类型的强迫类型的局部和区域生态系统的敏感性。这种新的理论方法能够直接量化对强迫变化的生态系统敏感性，以及非线性反馈模式如何帮助预测可能的生态系统过渡阈值。更广泛的影响：拟议的工作将创建并广泛传播一组工具和数据产品，这些工具和数据产品将使美国各地的生态学家能够应用过程网络理论和信息理论方法来分析生态系统规模的环境天文台数据。这种概念的方法将是环境观察者使用的“数据分析工具箱”的宝贵贡献，以解释生态变化及其对这些生态系统代表重要生命支持的人类社会的影响。研究生和博士后学者将接受这些高级定量方法在生态学和多量表生态系统数据的合成分析中的创建和应用培训。基于理论的模型的产品将在原始数据收集与环境理解之间形成一个关键的分析桥梁，而环境理解是由历史上没有前所未有的和非线性气候变化影响的未来驱动的知情资源管理和政策制定所必需的。