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

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气候变化对地方、州、国家和国际层面生态系统服务的影响预测中发挥着重要作用。然而，目前存在一些障碍，减缓了模型改进的步伐，减少了模型的广泛使用。首先，每个模型使用的软件都是独一无二的，不能很好地与其他模型沟通。其次，因为每个模型都是唯一的，所以管理进入模型的数据、分析模型和可视化结果的工具是不共享的。在这个项目中，正在开发PEcAn（预测生态系统分析仪），为研究人员和土地管理者提供一套通用的软件工具，以有效地处理多种生态系统模型和数据。网络技术将允许远程建模团队共享信息，协同工作，更好地利用公共和私有云和超级计算资源。将开发其他工具来识别模型错误，并将新的和现有的应用程序结合到工作流程中，以提高生态研究的效率，更好地预测生态系统服务，并支持基于证据的决策。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、可重复和负责任。PEcAn是一个开源的生态信息系统，为具有一系列建模背景的生态学家设计，能够更好，更容易地参数化，运行，分析和吸收数据到局部和区域尺度的生态系统模型中。该项目将扩展PEcAn用户社区，合并更多模型，并开发更直观、更易于访问的工具。此外，该项目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有弹性的、可伸缩的、分布式的点对点网络，用于管理建模团队之间以及与更广泛的社区之间的信息流。为了支持更多的模型，数据处理工作流将得到改进，并且将开发用于多模型可视化和基准测试的工具。跨PEcAn网络、云和高性能计算环境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，并使用户以知情的方式为不同的应用选择最佳模型。最后，PEcAn工具将结合到可定制的工作流中，用于实时合成、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上获得，而项目计算机代码可在https://github.com/pecanproject上获得。

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.