LTREB: Integrating real-time open data pipelines and forecasting to quantify ecosystem predictability at day to decadal scales

LTREB：集成实时开放数据管道和预测，以量化每日到十年尺度的生态系统可预测性

• 批准号: 2327030
• 负责人: Cayelan Carey
• 金额: $ 45万
• 依托单位: Virginia Polytechnic Institute and State University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-01-01 至 2028-12-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2327030&HistoricalAwards=false
• 关键词: LTREB; Integrating; real; time; open

Many ecosystems are exhibiting increased variability as a result of human activities. This environmental variability poses substantial challenges for managers and decision-makers, who can no longer use historical baselines to guide predictions of future ecosystem conditions. Consequently, advancing the capacity to predict the future for a range of physical, chemical, and biological ecosystem variables that influence water quality is paramount for improving resource management. In response to this need, this Long Term Research in Environmental Biology (LTREB) project will support a field monitoring and data-sharing program at two drinking water supply reservoirs. The ecosystem data that will be collected (which will include water temperature, clarity, chemistry, and phytoplankton, among other variables) will be used to generate and evaluate real-time forecasts (predictions of future ecosystem conditions, and the uncertainty associated with them) at daily to annual scales. Forecasting is a powerful approach for quantifying ecological predictability, as it requires using models that represent our best hypotheses about how ecosystems function to predict ecological conditions into the future. Iteratively evaluating these forecasts as new data are collected will reveal which models perform best in different environmental conditions and identify how far into the future different variables can be accurately predicted, from one day to one decade in advance. This project will enable the testing of fundamental hypotheses about the predictability of ecosystems; develop novel workflows for integrating environmental observations into real-time forecasting and data publishing; and broaden the participation of students from underrepresented groups in environmental data science. Moreover, all forecasts will be disseminated to water utilities in real time, enabling their immediate use as decision-making tools for water management. This LTREB project will represent one of the first systematic analyses of the predictability of ecosystem dynamics, thereby providing valuable information on the gradients and controls of predictability between contrasting ecosystems and among ecosystem variables. Importantly, researchers will be able to compare the performance of different forecast models with competing representations of ecosystem dynamics (e.g., varying driver variables, model structures) to test ecological hypotheses about predictability and examine the controls on ecosystem function. For example, forecast accuracy will be compared between two reservoirs that are similar in all characteristics except for oxygen availability to determine how anoxia, which is increasing in waterbodies globally, alters predictability. In addition, this LTREB project will develop novel FAIR (Findable, Accessible, Interoperable, Reusable) data-publishing workflows in collaboration with the Environmental Data Initiative (EDI) that advance reproducibility in ecology, support environmental data science education, and enable the scaling of ecological forecasting to other sites. Altogether, this project will result in data products for water reservoir physical, chemical, and biological ecosystem variables available in real-time for automated forecasting; a suite of different forecasting models and evaluated forecasts; forecasting and data-publishing workflows and software; and most critically, substantial ecosystem knowledge gained about the predictability of reservoir dynamics.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

由于人类活动的结果，许多生态系统表现出增加的变异性。这种环境变异性对经理和决策者构成了重大挑战，他们无法再使用历史基准来指导对未来生态系统条件的预测。因此，促进预测一系列影响水质的物理，化学和生物生态系统变量的能力对于改善资源管理至关重要。为了应对这一需求，这项在环境生物学（LTREB）项目中的长期研究将支持两个饮用水供应库的现场监测和数据共享计划。将使用将收集的生态系统数据（包括水温，清晰度，化学和浮游植物等变量）将在每天到年度量表生成和评估实时预测（对未来生态系统条件的预测以及与之相关的不确定性）。预测是一种量化生态可预测性的有力方法，因为它需要使用代表我们关于生态系统如何运作以预测未来生态条件的最佳假设的模型。迭代评估这些预测，因为收集了新数据将揭示哪些模型在不同的环境条件下表现最佳，并确定未来的距离可以准确预测到未来的多远，从一天到十年。该项目将对生态系统的可预测性进行基本假设的测试；开发新颖的工作流程，将环境观察结果整合到实时预测和数据发布中；并扩大了代表性不足小组的学生参与环境数据科学。此外，所有预测都将实时传播到水公用事业，从而可以立即用作水管理的决策工具。该LTREB项目将代表生态系统动力学可预测性的首次系统分析之一，从而提供有关对比生态系统和生态系统变量之间可预测性的有价值信息。重要的是，研究人员将能够将不同预测模型的性能与生态系统动力学的竞争表示（例如，不同的驱动器变量，模型结构）进行比较，以测试有关可预测性的生态假设并检查对生态系统功能的控制。例如，除了氧气可用性以确定全球水体增加的缺氧是如何改变可预测性的两个氧化储层之间的预测精度，这些储层的精度将在所有特征上相似。此外，该LTREB项目将与环境数据计划（EDI）合作开发新颖的公平（可访问，可互操作，可重复使用，可重复使用的）数据出版工作流，这些工作流程（EDI）提高了生态学的可重复性，支持环境数据科学教育，并启用对其他站点的生态预测的扩展。总的来说，该项目将为水库物理，化学和生物生态系统变量实时可用于自动化预测，从而为水库物理，化学和生物生态系统变量提供数据产品；一套不同的预测模型，并评估了预测；预测和数据出版工作流和软件；而且，最重要的是，关于储层动力学的可预测性获得的实质性生态系统知识。该奖项反映了NSF的法定任务，并认为使用基金会的知识分子优点和更广泛的影响评估标准，认为值得通过评估来获得支持。