Using Data to Improve Ecological Forecasts

This module was initially developed by: Lofton, M.E., T.N. Moore, C.C. Carey, and R.Q. Thomas. 13 May 2022. Macrosystems EDDIE: Using Data to Improve Ecological Forecasts. Macrosystems EDDIE Module 7, Version 1. Module development was supported by NSF grants DEB-1926050 and DBI-1933016.

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Algal blooms are increasing in frequency and severity across many freshwater lakes and can substantially impact water quality. One tool that can be useful in helping water resource managers to mitigate the effect of these blooms is an ecological forecast, which generates predictions about how likely a bloom is to occur. Forecasts can give managers time to take pre-emptive actions to prevent blooms, such as applying algicide, or to make plans to reduce the bloom's impact on human health, such as closing recreational beaches. Useful forecasts need to be accurate enough for managers to rely on for decision-making and also must include a representation of forecast uncertainty, so managers can incorporate the probability of an algal bloom event into their management strategy. Recent improvements in sensor technology and an increase in the number of sensors deployed in freshwater lakes have resulted in an increase of available data to help develop forecasting systems for water resource management.

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Learning Goals

By the end of the this module, you will be able to:

  • Define data assimilation
  • Identify and compare the characteristics of different data streams that would make it easier or harder to assimilate them into a forecasting system
  • Describe how forecast uncertainty and accuracy are affected by data assimilation
  • Explain how updating models with data of different variables (e.g., chl-a, water temperature) and frequencies (e.g., daily, weekly) affects ecological forecasts    
  • Describe the trade-offs that are involved when designing an environmental monitoring program for a forecasting system to achieve management goals 

Context for Use

This module is targeted for upper-level undergraduate students studying resource management, ecology, environmental biology, applied statistics, or ecological forecasting. It is part of a four-module series on ecological forecasting as part of theMacrosystems EDDIE project.

The module is designed to be completed in three hours, and may be divided into three one-hour segments. Broadband access is required, and the module is designed to be suitable for either in-person or remote learning.

The module will be tested in a 4000-level Freshwater Ecology course in the Biological Sciences department and a 5000-level Ecological Modeling and Forecasting cross-listed graduate and undergraduate course in the Forest Resources and Environmental Conservation department at Virginia Tech.

Description and Teaching Materials

Currently in development (Summer 2022). Will be posted soon.

Shiny application (beta):

Teaching Notes and Tips

Important Note to Instructors:

The R Shiny app was built using R version 4.1.0. All dependent R packages used in this module are continually being updated, so these module instructions will periodically change to account for changes in the code.

If you have any questions or any problems with this module, please reach out to us at


  • Activity A: Students explore ecological model structure, prepare inputs to the ecological model and generate an ecological forecast.
  • Activity B: Students assimilate data into their forecast, explore how observation uncertainty affect data assimilation, and explore how data assimilation frequency affects forecast output.
  • Activity C: Students use their understanding of data assimilation to design an environmental monitoring system to inform ecological forecasts for a reservoir management scenario.

References and Resources

National Ecological Observatory Network (NEON) Data Portal - this is where we will retrieve data used in this module.