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

Reconciling individual-based forest growth models with landscape-level studies through a meta-modelling approach. Fortin, M. & Lavoie, J. Canadian Journal of Forest Research, 52(8):1140–1153, August, 2022.

Paper doi abstract bibtex

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.

@article{fortin_reconciling_2022,
	title = {Reconciling individual-based forest growth models with landscape-level studies through a meta-modelling approach},
	volume = {52},
	issn = {0045-5067, 1208-6037},
	url = {https://cdnsciencepub.com/doi/10.1139/cjfr-2022-0002},
	doi = {10.1139/cjfr-2022-0002},
	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.},
	language = {en},
	number = {8},
	urldate = {2023-06-15},
	journal = {Canadian Journal of Forest Research},
	author = {Fortin, Mathieu and Lavoie, Jean-François},
	month = aug,
	year = {2022},
	keywords = {Political Boundaries},
	pages = {1140--1153},
}

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