A recent review identified an important gap in the capability of forest growth models: many cannot provide policy makers, practitioners and scientists with the information needed to evaluate forest responses (growth, carbon balance, water balance etc) to innovative management and novel species mixes (e.g. invasive species A. altissima) under current or future climates. The 3-PG (Physiological Processes Predicting Growth) model is widely used and one of the only process-based models used by practitioners, due to its simplicity and reliability. This project aims to (1) implement minor but critical modifications for uneven-aged forests, (2) validate 3-PG simulations against NFI data and simulations with the tree-level empirical model Massimo, and (3) provide user-friendly documentation and an R package for 3-PG. It can then be used for growth and carbon forecasting, as an outreach tool for forest practitioners and in projects about silviculture, biodiversity and climate change.
2020 - 2020
Dr. Amanda Mathys