Following the appointment of the new Cabinet, the Forest Sector now reports to the ministère des Ressources naturelles et des Forêts, while the Wildlife and Parks Sectors report to the ministère de l'Environnement, de la Lutte aux changements climatiques, de la Faune et des Parcs. Adjustments will be made to the website over time.

Back to publications

Development of tree recruitment models for 10 species groups in the sugar maple-dominated mixed forests of eastern Canda

Summary

Published in Canadian Journal of Forest Research 53: 134-150. https://doi.org/10.1139/cjfr-2022-0111

Individual tree recruitment is an important element needed to understand stand dynamics, as it influences both stand composition and productivity. Forest growth simulators usually include recruitment models. The quality of recruitment predictions can have long-term impacts on estimations of forest growth, ecosystem health and the commercial utility of managed forests. The main objective of this study was to develop a recruitment model for commercial-size trees (i.e., trees with a diameter at breast height > 9 cm) of 10 species groups using different dendrometric and environmental variables. The resulting model will be included in a growth simulator used to support forest management planning. We hypothesized that accounting for sapling density as a covariate would improve the recruitment model’s predictive performance. Using empirical data from periodically measured permanent sample plots (1982–2019) located throughout the managed mixed hardwood forests of Quebec, we constructed models with and without sapling-related covariates and compared them on the basis of cross-validation model performance statistics. Our results show that including sapling density significantly improved model performance. From this, we conclude that adding sapling density as a covariate can significantly improve a recruitment model’s predictive power for eastern mixed hardwood forests.

Sector(s): 

Forests

Categorie(s): 

Scientific Article

Theme(s): 

Forest Growth and Yield Modelling, Forestry Research, Forests

Author(s)

RIJAL, Baburam, Hugues POWER, Isabelle AUGER, François GUILLEMETTE, Steve BÉDARD and Robert SCHNEIDER

Year of publication :

2023

Format :

PDF

Keywords :

modélisation de la croissance et du rendement des forêts, forest growth and yield modelling, recrutement, érablières, modèles de croissance, SaMARE, régénération, model comparison, negative binomial distribution, northern hardwoods, Quebec, zero-inflated model

Partagez