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  • Macrosystems EDDIE Module 5: Introduction to Ecological Forecasting (Instructor Materials)
  • Moore, Tadhg J.; Virginia Tech
    Carey, Cayelan C.; Virginia Tech
    Thomas, R. Quinn; Virginia Tech
  • 2022-03-16
  • Moore, T.J., C.C. Carey, and R.Q. Thomas. 2022. Macrosystems EDDIE Module 5: Introduction to Ecological Forecasting (Instructor Materials) ver 1. Environmental Data Initiative. https://doi.org/DOI_PLACE_HOLDER (Accessed 2024-12-29).

  • 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 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 and the files necessary to teach the module. Readers are referred to the Zenodo data package (Moore et al. 2022; DOI: 10.5281/zenodo.6363500) for the R Shiny application code needed to run the module locally.

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  • DOI PLACE HOLDER

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