Collaborative Proposal: MSB-ENSA: The Near-term Ecological Forecasting Initiative

生命系统在全球范围内不断变化，每天都在做出影响其健康和可持续性的关键决策。面对气候变化和其他环境挑战，社会不能再仅仅依靠过去的经验来理解和管理生物世界。这个奖项提出了一个问题，？如何才能像预测天气一样预测生态过程？该项目将开发一种类似于天气预报的业务生态预报能力，在进行预报、进行分析和根据新证据更新预测之间使用迭代循环。这种获得反馈、积累经验、修正模型和方法的迭代过程对于建立预测能力至关重要，也是高度不确定性下任何决策的关键部分。除了使生态学与管理更加相关外，近期预测还经常将具体的定量预测与新数据进行比较，这是对任何科学理论的最强有力的检验之一。该项目将利用国家生态观测站网络收集的生态数据进行近期预测，涵盖广泛的主题：叶物候、土地碳和能量通量、蜱传疾病发病率、小型哺乳动物种群、水产生产力以及土壤微生物多样性和功能。这种广泛的，比较的方法将被用来解决生态学的可预测性的性质，并制定一个总体预测理论和方法的交叉问题。近期生态预测倡议（NEFI）将在三个层面推进生态知识：（1）关于生态系统可预测性的跨主题假设;（2）关于驱动过程和可预测性的紧迫主题内问题;（3）推进工具和技术，使迭代方法能够进行定量假设检验。该项目的总体假设是：（1）生态可预测性更多地由过程误差而不是初始条件误差驱动;（2）跨主题的不确定性来源具有一致的模式;（3）跨主题，空间和时间自相关性呈正相关;（4）空间和时间自相关性与可预测性的极限呈正相关。总体而言，这些问题的答案解决了在何种程度上存在生态可预测性的一般模式，这将促进我们对生态过程的基本理解，并限制预测的实际问题。NEFI的预期成果是：（1）传播有益于社会的数据产品和预测;（2）开发新的工具和网络基础设施，以加强研究和教育;（3）开发新的生态系统。（3）促进教学、培训和学习。具体的NEFI预测，如蜱传疾病风险，水生水华，碳固存和叶物候，与社会直接相关。预测将通过开放的网络基础设施提供，该基础设施将预测传播给公众，并允许其他生态学家提供新的预测。为了制作这些预测，NEFI将开发一个开源的统计软件包，ecoforecastR，这将推动社区目前使用的工具和技术的发展。最后，除了通过该项目直接指导的研究生外，NEFI还将举办一个关于生态预测的年度夏季课程，以培训下一代生态学家。

Living systems are changing worldwide and critical decisions that affect their health and sustainability are being made every day. In the face of climate change and other environmental challenges, society can no longer rely solely on past experience to understand and manage the living world. This award asks the question, ?What would it take to forecast ecological processes the same way we forecast the weather?? This project will development an operational ecological forecasting capability similar to weather forecasting that uses an iterative cycle between making forecasts, performing analyses, and updating predictions in light of new evidence. This iterative process of gaining feedback, building experience, and correcting models and methods is critical for building a forecast capacity, and also a crucial part of any decision making under high uncertainty. In addition to making ecology more relevant to management, near-term forecasts routinely compare specific, quantitative predictions to new data, which is one of the strongest tests of any scientific theory. This project will generate near-term forecasts that leverage ecological data collected by the National Ecological Observatory Network and spanning a wide range of themes: leaf phenology, land carbon and energy fluxes, tick-borne disease incidence, small-mammal populations, aquatic productivity, and soil microbial diversity and function. This broad, comparative approach will be used to address cross-cutting questions about the nature of predictability in ecology and develop an overarching body of forecasting theory and methods. The Near-term Ecological Forecasting Initiative (NEFI) will advance ecological knowledge at three levels: (1) overarching across-theme hypotheses about the predictability of ecological systems; (2) pressing within-theme questions about what drives process and predictability; and (3) advancing the tools and techniques that will enable an iterative approach to quantitative hypothesis testing. The overarching hypotheses of this project are that: (1) ecological predictability is more driven by processes error than initial condition error; (2) there are consistent patterns in the sources of uncertainty across themes; (3) across themes, spatial and temporal autocorrelation are positively correlated; and (4) spatial and temporal autocorrelation are positively correlated with limits of predictability. Overall, the answers to these questions address to what extent there are general patterns to ecological predictability, which would advance both our basic understanding of ecological processes and constrain the practical problem of making forecasts.The expected outcomes of NEFI are to: (1) Disseminate data products and predictions that benefit society; (2) Develop new tools and cyberinfrastructure that enhances research and education; and (3) to promote teaching, training, and learning. Specific NEFI forecasts, such as tick-borne disease risk, aquatic blooms, carbon sequestration, and leaf phenology, are of direct relevance to society. Forecasts will be made available via open cyberinfrastructure that disseminates forecasts to the public and allows other ecologists to contribute new forecasts. To produce these forecasts, NEFI will develop an open-source statistical package, ecoforecastR, which will advance the tools and techniques beyond what is currently used by the community. Finally, in addition to the graduate students directly mentored through the project, NEFI will run an annual summer course on ecological forecasting that will train the next generation of ecologists.