Reconciling individual-based forest growth models with landscape-level studies through a meta-modelling approach

Abstract

Landscape-level studies such as those on forest management planning and carbon accounting rely on large-area growth projections provided by forest growth models. Nowadays, most of these models are individual tree-based models. The detailed input they require and their complexity are a challenge for the integration into a landscape-level study. A possible alternative consists of approximating the complex model through a meta-model. A meta-model mimics the behaviour of the original model, while being simpler in terms of input and computation. In this study, we developed a Bayesian meta-modelling approach that can be used to obtain a simplified growth model from an individual tree-based model. The approach was exemplified through a real-world case study, namely a forest management unit in the province of Quebec, Canada. Using a Markov chain Monte Carlo method, we managed to fit meta-models based on the Chapman–Richards equation or its derivative for the main potential vegetation types. This meta-modelling approach has the advantages of ( i) being an effective method of upscaling, ( ii) providing simple meta-models suitable for landscape-level studies, and ( iii) ensuring a proper error propagation from the original individual tree-based model into the meta-model.