Collaborative Research: ABI Development: The PEcAn Project: A Community Platform for Ecological Forecasting

合作研究：ABI 开发：PEcAn 项目：生态预测社区平台

• 批准号: 1457897
• 负责人: Ankur Desai
• 金额: $ 33.33万
• 依托单位: University of Wisconsin-Madison
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-07-15 至 2020-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1457897&HistoricalAwards=false
• 关键词: Collaborative; Research; ABI; Development; PEcAn

Computer simulations play an essential role in ecological research, the management of national forests and other public and private land resources, and projections of climate change impacts on ecosystem services at the local, state, national, and international level. However, at the moment, there are a number of barriers slowing the pace of model improvement and reducing their wider use. First, the software for using each model is unique and does not communicate well with other models. Second, because each model is unique, the tools to manage data going into models, analyze models, and visualize results are not shared. In this project PEcAn (Predictive Ecosystem Analyzer) is being developed to provide a common set of software tools for researchers and land managers to effectively work with multiple ecosystem models and data. Web technologies will be used to allow distant modeling teams to share information, work together, and better use public and private cloud and supercomputing resources. Other tools will be developed to identify model errors and combine new and existing applications into workflows to make ecological research more efficient, better forecast ecosystem services, and support evidence-based decision making. The PEcAn team will also develop training tools for new users and work with the scientific community to add more models to PEcAn. PEcAn will make ecological research more transparent, repeatable, and accountable.PEcAn is an open-source ecoinformatics system designed for ecologists with a range of modeling backgrounds to be able to better and more easily parameterize, run, analyze, and assimilate data into ecosystem models at local and regional scales. This project will expand the PEcAn user community, incorporate more models, and develop tools that are more intuitive and accessible. Further, the project intends to transform tools for managing the flows of information into and out of ecosystem models into a resilient, scalable, and distributed peer-to-peer network for managing the flow of this information among modeling teams and with the broader community. To support a larger number of models, data processing workflows will be improved and tools will be developed for multi-model visualization and benchmarking. Applications that distribute analyses across the PEcAn network, cloud, and high-performance computing environments will be used to better understand model structural error using data mining approaches. Models will benchmarked over a range of environmental conditions, allowing model improvement to be tracked and users to select the best models for different applications in an informed manner. Finally, PEcAn tools will be combined into customizable workflows for real-time synthesis, forecasting, and decision support. By allowing modelers to focus on science rather than informatics, and allowing ecologists to easily compare their data to models, PEcAn will greatly accelerate the pace of model improvement and hypothesis testing. These activities are essential for improving ecosystem models and reducing uncertainty of the impacts of climate change on ecosystems and carbon cycle-climate feedbacks. Project information and results are available at http://pecanproject.org while project computer code is available at https://github.com/pecanproject.

计算机模拟在生态研究、国家森林和其他公共和私人土地资源的管理以及预测气候变化对地方、州、国家和国际各级生态系统服务的影响方面发挥着至关重要的作用。然而，目前，有一些障碍减缓了模型改进的步伐，并减少了其更广泛的使用。首先，使用每个模型的软件都是唯一的，不能与其他模型很好地沟通。其次，由于每个模型都是唯一的，因此用于管理进入模型的数据、分析模型和可视化结果的工具并不共享。在该项目中，正在开发PEcAn（预测生态系统分析仪），为研究人员和土地管理人员提供一套通用的软件工具，以便有效地使用多种生态系统模型和数据。网络技术将被用于允许远程建模团队共享信息，共同工作，并更好地使用公共和私有云以及超级计算资源。将开发其他工具，以确定模型错误，并将联合收割机新的和现有的应用程序结合到工作流程中，使生态研究更有效，更好地预测生态系统服务，并支持循证决策。PEcAn团队还将为新用户开发培训工具，并与科学界合作，为PEcAn添加更多模型。PEcAn将使生态研究更加透明，可重复和可问责。PEcAn是一个开源的生态信息学系统，专为具有各种建模背景的生态学家设计，以便能够更好，更容易地将数据参数化，运行，分析和同化到本地和区域尺度的生态系统模型中。该项目将扩展PEcAn用户社区，整合更多模型，并开发更直观和更易于访问的工具。此外，该项目还打算将用于管理进出生态系统模型的信息流的工具转换为一个弹性，可扩展和分布式的点对点网络，用于管理建模团队之间以及更广泛的社区之间的信息流。为了支持更多的模型，将改进数据处理工作流程，并开发多模型可视化和基准测试工具。在PEc网络、云和高性能计算环境中分发分析的应用程序将用于使用数据挖掘方法更好地理解模型结构错误。模型将在一系列环境条件下进行基准测试，从而可以跟踪模型的改进情况，用户可以在知情的情况下为不同的应用选择最佳模型。最后，PEcAn工具将被组合到可定制的工作流程中，以实现实时合成、预测和决策支持。通过允许建模者专注于科学而不是信息学，并允许生态学家轻松地将他们的数据与模型进行比较，PEcAn将大大加快模型改进和假设检验的步伐。这些活动对于改进生态系统模型和减少气候变化对生态系统和碳循环-气候反馈影响的不确定性至关重要。项目信息和结果可在http://pecanproject.org上查阅，项目计算机代码可在https://github.com/pecanproject上查阅。

项目成果

Toward a Social-Ecological Theory of Forest Macrosystems for Improved Ecosystem Management

• DOI: 10.3390/f9040200
• 发表时间: 2018-04-01
• 期刊: FORESTS
• 影响因子: 2.9
• 作者: Kleindl, William J.;Stoy, Paul C.;Wood, David J. A.
• 通讯作者: Wood, David J. A.