Developing an automated iterative near-term forecasting system for an ecological study
Developing an automated iterative near-term forecasting system for an ecological study
复制标题
为生态研究开发自动迭代近期预测系统
DOI：
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发表时间：
2018
期刊：
影响因子：
--
通讯作者：
S. Ernest
中科院分区：
文献类型：
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作者：
Ethan P. White;G. Yenni;Shawn D. Taylor;Erica M. Christensen;Ellen K. Bledsoe;Juniper L. Simonis;S. Ernest
大多数对生态系统未来状态的预测都是一次性进行的，从未更新或评估。因此，许多可用的生态预测并不是基于最新的数据，并且由于缺乏对预测效果的反馈，生态预测模型的科学进步被减缓。迭代近期生态预测涉及当新数据可用时对生态系统进行每日到年度规模的重复预测，并对结果预测进行定期评估。我们展示了如何构建生态学的自动迭代近期预测系统，方法是在门户项目中构建一个对啮齿动物丰度进行每月预测的系统，这是一项利用 40 多年月度数据进行的长期研究。该系统自动执行将原始数据转换为新预测的六个阶段的大部分方面：数据收集、数据共享、数据操作、建模和预测、归档以及预测呈现。预测系统使用 R 代码来处理数据、拟合模型、进行预测以及存档和呈现这些预测。使用持续集成（软件开发工具）将生成的管道自动化，每周运行一次整个管道。网络基础设施旨在实现长期可维护性，并允许轻松添加新模型。构建这个预测系统需要一支具有从现场经验到软件开发等专业知识的团队。自动化近期迭代预测系统将使生态预测科学发展得更快，并为保护和管理提供最新的预测。这些预测系统还将允许快速实施新的自然系统模型并与现有模型进行比较，从而加速基础科学的发展。利用现有技术和具有不同技能的团队，生态学家可以构建自动预测系统并利用它们来增进我们对自然系统的理解。
Most forecasts for the future state of ecological systems are conducted once and never updated or assessed. As a result, many available ecological forecasts are not based on the most up-to-date data, and the scientific progress of ecological forecasting models is slowed by a lack of feedback on how well the forecasts perform. Iterative near-term ecological forecasting involves repeated daily to annual scale forecasts of an ecological system as new data becomes available and regular assessment of the resulting forecasts. We demonstrate how automated iterative near-term forecasting systems for ecology can be constructed by building one to conduct monthly forecasts of rodent abundances at the Portal Project, a long-term study with over 40 years of monthly data. This system automates most aspects of the six stages of converting raw data into new forecasts: data collection, data sharing, data manipulation, modeling and forecasting, archiving, and presentation of the forecasts. The forecasting system uses R code for working with data, fitting models, making forecasts, and archiving and presenting these forecasts. The resulting pipeline is automated using continuous integration (a software development tool) to run the entire pipeline once a week. The cyberinfrastructure is designed for long-term maintainability and to allow the easy addition of new models. Constructing this forecasting system required a team with expertise ranging from field site experience to software development. Automated near-term iterative forecasting systems will allow the science of ecological forecasting to advance more rapidly and provide the most up-to-date forecasts possible for conservation and management. These forecasting systems will also accelerate basic science by allowing new models of natural systems to be quickly implemented and compared to existing models. Using existing technology, and teams with diverse skill sets, it is possible for ecologists to build automated forecasting systems and use them to advance our understanding of natural systems.
影响因子：
2.7
作者：
Hampton, Stephanie E.;Anderson, Sean S.;Zimmerman, Naupaka
通讯作者：
Zimmerman, Naupaka
预测气候变化对大空间范围内植物种群的影响
影响因子：
2.7
作者：
Tredennick, Andrew T.;Hooten, Mevin B.;Aldridge, Cameron L.;Homer, Collin G.;Kleinhesselink, Andrew R.;Adler, Peter B.
通讯作者：
Adler, Peter B.
影响因子：
9.8
作者：
Wilson G;Aruliah DA;Brown CT;Chue Hong NP;Davis M;Guy RT;Haddock SH;Huff KD;Mitchell IM;Plumbley MD;Waugh B;White EP;Wilson P
通讯作者：
Wilson P