Macrosystems EDDIE Module 5 version 2: Introduction to Ecological Forecasting (Instructor Materials)

Ecological forecasting is a tool that can be used for understanding and predicting changes in populations, communities, and ecosystems. Ecological forecasting is an emerging approach which provides an estimate of the future state of an ecological system with uncertainty, allowing society to prepare for changes in important ecosystem services. Ecological forecasters develop and update forecasts using the iterative forecasting cycle, in which they make a hypothesis of how an ecological system works; embed their hypothesis in a model; and use the model to make a forecast of future conditions. When observations become available, they can assess the accuracy of their forecast, which indicates if their hypothesis is supported or needs to be updated before the next forecast is generated. In this Macrosystems EDDIE (Environmental Data-Driven Inquiry & Exploration) module, students will apply the iterative forecasting cycle to develop an ecological forecast for a National Ecological Observation Network (NEON) site. Students will use NEON data to build an ecological model that predicts primary productivity. Using their calibrated model, they will learn about the different components of a forecast with uncertainty and compare productivity forecasts among NEON sites. The overarching goal of this module is for students to learn fundamental concepts about ecological forecasting and build a forecast for a NEON site. Students will work with an R Shiny interface to visualize data, build a model, generate a forecast with uncertainty, and then compare the forecast with observations. The A-B-C structure of this module makes it flexible and adaptable to a range of student levels and course structures. This EDI data package contains instructional materials necessary to teach the module. Intructional materials (instructor manual, introductory presentation for the module, and a presentation to introduce students and instructors to R Shiny) are provided in both pdf and editable formats within a compressed file. The module R Shiny application is available at https://macrosystemseddie.shinyapps.io/module5/. Readers are referred to the module landing page for additional information (https://serc.carleton.edu/eddie/teaching_materials/modules/module5.html) and GitHub repo (https://github.com/MacrosystemsEDDIE/module5) and/or Zenodo data package (Moore et al. 2024; DOI: 10.5281/zenodo.10733117) for the R Shiny application code.

生态预测（Ecological forecasting）是一种可用于理解并预测种群、群落与生态系统变化的工具。生态预测作为一种新兴研究范式，可对生态系统的未来状态进行带不确定性的评估，助力社会针对重要生态系统服务的变化提前做好应对准备。生态预测研究者依托迭代预测循环开发并更新预测结果：首先提出关于生态系统运作机制的假说，将该假说嵌入模型，再利用模型生成未来状态的预测。当获取到观测数据后，研究者可评估预测的准确性，以此判断其假说是否成立，或是需要在生成下一次预测前对假说进行修正。在本Macrosystems EDDIE（环境数据驱动探究与探索，Environmental Data-Driven Inquiry & Exploration）模块中，学生将运用迭代预测循环，为美国国家生态观测网（National Ecological Observation Network, NEON）的某一站点开发生态预测模型。学生将利用NEON数据构建可预测初级生产力的生态模型。借助校准后的模型，学生将了解带不确定性的预测的各个组成部分，并对比不同NEON站点的生产力预测结果。本模块的核心教学目标是让学生掌握生态预测的核心概念，并为NEON站点构建生态预测模型。学生将通过R Shiny交互界面完成数据可视化、模型构建、生成带不确定性的预测，并将预测结果与观测数据进行对比。本模块采用A-B-C式架构，具备良好的灵活性与适配性，可兼容不同学力层次的学生与多种课程设置。本EDI数据包包含了开展本模块教学所需的全部教学资料：教学手册、模块介绍演示文稿，以及面向师生介绍R Shiny的演示文稿，所有资料均以PDF与可编辑格式打包于压缩文件中。本模块的R Shiny应用程序可通过以下链接访问：https://macrosystemseddie.shinyapps.io/module5/。如需获取更多信息，可查阅模块主页（https://serc.carleton.edu/eddie/teaching_materials/modules/module5.html）、GitHub代码仓库（https://github.com/MacrosystemsEDDIE/module5），或Zenodo数据包（Moore等人，2024；DOI: 10.5281/zenodo.10733117）以获取R Shiny应用程序的源代码。