An alternative simulation framework to evaluate the sustainability of annual harvest on large forest estates

Abstract

Sustainability is central to forest management. To determine the sustainable annual harvest, practitioners rely on a simulation framework that combines inventory data, growth models, and optimization software. Because this standard simulation framework is based on model predictions aggregated into yield tables, it may not properly capture natural dynamics. In this paper, we designed an alternative simulation framework that does not require aggregated model predictions. However, the growth model must implement a harvest submodel and produce stochastic predictions. To showcase this alternative simulation framework, we used a forest management unit in southwestern Quebec, Canada, and compared our simulation results with those of the standard simulation framework. Our alternative simulation framework showed that the standing volume of most coniferous species would decrease, whereas that of maple species would increase over the 21st century. The annual harvest of one species as determined through the standard simulation framework was found to be unsustainable in the alternative simulation framework. Being much lighter in terms of computation, this alternative simulation framework can be used as a complement to the standard simulation framework, notably for checking if the optimization-based annual harvest is sustainable.