Ecosystem Modeling

In this module, students will generate an ecological forecast for a NEON site and explore how to use ecological data to improve forecast accuracy. This module will introduce students to the concept of data assimilation within an ecological forecast; how data assimilation can be used to improve forecast accuracy; how the level of uncertainty and temporal frequency of observations affects forecast output; and how data assimilation can affect decision-making using ecological forecasts.

In this module, students will generate an ecological forecast for a NEON site and quantify the different sources of uncertainty within their forecast. This module will introduce students to the conceptof uncertainty within an ecological forecast; where uncertainty in a forecast comes from; how uncertainty can be quantified within a forecast; and how uncertainty can be managed.

In this module, students will apply the iterative forecasting cycle to develop an ecological forecast for a NEON site. This module will introduce students to the basic components of an ecological forecast; how a simple forecasting model is constructed; how changes to model inputs affect forecast uncertainty; and how productivity forecasts vary across ecoclimatic regions.

In this module, students will explore real ecological forecast visualizations, identify ways to represent uncertainty, make management decisions using forecast visualizations and learn decision support techniques. Lastly, students will then customize a forecast visualization for a specific forecast user’s decision needs.

Create open-source software for flexible, scalable, robust, and near-real time iterative ecological forecasts in lakes and reservoirs.

Develop stand-alone, modular classroom activities for undergraduate students that use publicly-available, long-term, and high-frequency datasets to explore the core concepts of macrosystems ecology and ecological forecasting while developing quantitative literacy.

Develop an R package that is user-friendly, facilitates model running, sensitivity analysis, calibration and output processing all within the R environment.