R Package for Predicting Submerged Aquatic Vegetation in the Great Lakes

Fisheries and Oceans Canada (DFO) is developing decision-support tools to manage freshwater fish habitat. The Fish Ecology Science Lab created models to predict the presence and cover of submerged aquatic vegetation (SAV) in the Laurentian Great Lakes, using factors like fetch and depth. SAV is a key component of fish habitat and is often affected by in-water works or habitat restoration efforts. At inSileco, we developed an R package that implements DFO’s random forest model to make these predictions accessible, reproducible, and easy to integrate into existing workflows. The package was designed to support future development within DFO by standardizing inputs and outputs, facilitating integration with other tools, and ensuring that methods can be reused or extended as new needs arise.