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

由于人类活动，许多生态系统的变异性正在增加。这种环境变异性给管理者和决策者带来了巨大挑战，他们不能再使用历史基线来指导对未来生态系统状况的预测。因此，提高对影响水质的一系列物理、化学和生物生态系统变量的未来预测能力，对于改善资源管理至关重要。为了满足这一需求，环境生物学长期研究（LTREB）项目将支持两个饮用水供应水库的现场监测和数据共享计划。将收集的生态系统数据（包括水温、透明度、化学和浮游植物等变量）将用于生成和评估每日至年度尺度的实时预测（未来生态系统状况预测及其相关不确定性）。预测是量化生态可预测性的一种强有力的方法，因为它需要使用代表我们关于生态系统如何运作的最佳假设的模型来预测未来的生态条件。随着新数据的收集，迭代评估这些预测将揭示哪些模型在不同的环境条件下表现最好，并确定不同变量在未来多久可以准确预测，从一天到十年。该项目将有助于测试关于生态系统可预测性的基本假设;开发新的工作流程，将环境观测纳入实时预测和数据发布;扩大代表性不足群体的学生对环境数据科学的参与。此外，所有预报都将真实的传送给供水公司，使其能够立即用作水管理的决策工具。该LTREB项目将是对生态系统动态可预测性的首批系统分析之一，从而提供关于对比生态系统之间和生态系统变量之间的梯度和可预测性控制的宝贵信息。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态的竞争表示进行比较（例如，不同的驱动变量，模型结构），以测试可预测性的生态假设，并检查对生态系统功能的控制。例如，将比较两个水库的预测准确性，这两个水库除了氧气可用性外，所有特征都相似，以确定全球水体中日益增加的缺氧如何改变可预测性。此外，该LTREB项目将与环境数据倡议（EDI）合作开发新的FAIR（可查找，可扩展，可互操作，可重用）数据发布工作流程，以提高生态学的可重复性，支持环境数据科学教育，并使生态预测扩展到其他站点。总之，该项目将产生用于自动化预测的水库物理、化学和生物生态系统变量的实时数据产品;一套不同的预测模型和经评估的预测;预测和数据发布工作流程和软件;最关键的是，该奖项反映了NSF的法定使命，并已被视为通过使用基金会的知识价值和更广泛的影响审查标准进行评估，

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.