The NEON Ecological Forecasting Challenge
The NEON Ecological Forecasting Challenge
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NEON 生态预测挑战
DOI：
10.1002/fee.2616
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发表时间：
2023
影响因子：
10.3
通讯作者：
Weltzin, Jake F
中科院分区：
文献类型：
--
作者：
Thomas, R Quinn;Boettiger, Carl;Carey, Cayelan C;Dietze, Michael C;Johnson, Leah R;Kenney, Melissa A;McLachlan, Jason S;Peters, Jody A;Sokol, Eric R;Weltzin, Jake F
21世纪的特点仍然是社会所依赖的环境和生态系统服务发生重大变化。预测和应对这些变化需要科学家实时明确地预测未来的情况(Dietze等人。2018年)。与天气和流行病学预测一样，生态预测涉及在收集观测数据之前整合数据和模型，以生成对生态系统未来状态的定量预测。创建预测、用新的观测结果评估它们、更新模型，然后做出新的预测的迭代循环，有可能加速许多生态子学科的学习。这一周期建立在公开可用的数据基础上，通常在收集后不久发布，这在国家生态观测网络(NEON)等生态观测网中越来越常见。为了加速改善生态预测，我们设计并推出了霓虹灯生态预测挑战(以下简称“挑战”)(图1)，这是一个开放平台，供生态和数据科学界在收集霓虹灯数据之前对其进行预测。生态预测界对使用预测来推进理论很感兴趣(Lewis等人。2023年)和翻译自然资源管理的预测(Enquist等人。2017年)。通过分析为一系列生态系统、时空尺度和环境梯度开发的预报目录，科学家可以开始解决生态学中的基本问题。由美国国家科学基金会(NSF)资助的生态预测倡议研究协调网络(EFI-RCN)邀请广泛的生态学社区通过预测霓虹灯数据来帮助建立这个目录。霓虹灯是支持这一挑战的强大平台，因为它提供了标准化数据和报告的不确定性，这些数据涵盖了美国陆地和淡水系统的一系列环境条件和生物组织水平。挑战赛是根据学术界、政府和私营部门通过研讨会和工作组提供的意见而设计的。我们称之为“挑战”，因为尽管它与数据科学比赛有相似之处(Makridakis等人)。2021年)，我们使社区能够做的不仅仅是提交预测--我们还协作开发软件、培训材料和最佳实践。2020年5月，我们在一个有200多名与会者的虚拟会议上推出了挑战赛的设计(彼得斯和托马斯2021)。与会者将利用霓虹灯数据、解决开放式科学问题并有可能支持资源管理决策的五个预测“主题”列为优先事项：(1)淡水温度、溶解氧和叶绿素-a；(2)陆地碳通量和蒸散；(3)植物冠层物候；(4)扁虱种群；(5)甲虫群落。在虚拟会议上确定了主题，之后较小的设计团队制定了详细的主题特定协议。这些协议定义了提交预测的时间(何时以及预测到期的频率)和预测范围(预测延伸到未来的程度)。有了这些协议，团队参与者开发了代码，将霓虹灯数据产品转换为标准化的时间序列，以便建模和评估。同时，组建了EFI-RCN标准工作组，以定义预测提交的格式和跨主题的元数据(Dietze等人。2023年)。同样，EFI-RCN指导委员会与每个设计团队合作，以确保开发的协议符合…
The 21st century continues to be characterized by major changes to the environment and the ecosystem services upon which society depends. Anticipating and responding to these changes requires that scientists explicitly forecast future conditions in real time (Dietze et al. 2018). Ecological forecasting, like weather and epidemiological forecasting, involves integrating data and models to generate quantitative predictions of the future state of ecological systems before observations are collected. The iterative cycle of creating forecasts, evaluating them with new observations, updating the models, and then making new forecasts has the potential to accelerate learning across many ecological subdisciplines. This cycle builds on openly available data, often published soon after collection, as is increasingly common in ecological observatory networks, such as the National Ecological Observatory Network (NEON). To accelerate improvements in ecological forecasting, we designed and launched the NEON Ecological Forecasting Challenge (hereafter,“Challenge”)(Figure 1), an open platform for the ecological and data science communities to forecast NEON data before they are collected. The ecological forecasting community is interested in using forecasts to advance theory (Lewis et al. 2023) and in translating forecasts for natural resource management (Enquist et al. 2017). By analyzing a catalog of forecasts developed for a range of ecological systems, spatiotemporal scales, and environmental gradients, scientists can begin to address fundamental questions in ecology. The Ecological Forecasting Initiative Research Coordination Network (EFI-RCN)–funded by the US National Science Foundation (NSF)–invites the broad ecology community to help build this catalog by forecasting NEON data. NEON is a powerful platform to support such a challenge because it provides standardized data with reported uncertainties that span a range of environmental conditions and levels of biological organization across terrestrial and freshwater systems in the US. The Challenge was designed on input from academic, government, and private sectors through workshops and working groups. We call it a “Challenge” because, despite its similarities to data science competitions (Makridakis et al. 2021), we are empowering the community to do more than just submit forecasts–we are also collaboratively developing software, training materials, and best practices. In May 2020, we launched the Challenge’s design at a virtual conference with over 200 attendees (Peters and Thomas 2021). Attendees prioritized five forecasting “themes” that draw on NEON data, address open science questions, and have potential to support decision making for resource management:(1) freshwater temperature, dissolved oxygen, and chlorophyll-a;(2) terrestrial carbon fluxes and evapotranspiration;(3) plant canopy phenology;(4) tick populations; and (5) beetle communities. Themes were identified at the virtual meeting, after which smaller design teams developed detailed theme-specific protocols. The protocols defined the timing of forecast submissions (when and how often forecasts are due) and forecast horizons (how far forecasts extend into the future). With these protocols in place, team participants developed code to convert NEON data products into standardized time-series that are ready for modeling and evaluation. Simultaneously, the EFI-RCN standards working group was assembled to define the format of forecast submissions and metadata across themes (Dietze et al. 2023). Likewise, the EFI-RCN steering committee worked with each design team to ensure the developed protocols …