A Climate-Sensitive Transition Matrix Growth Model for Masson Pine (Pinus massoniana Lamb.) Natural Forests in Hunan Province, South-Central China

Masson pine natural forests are ecologically and economically valuable forest ecosystems extensively distributed across China. However, they have been subject to deforestation due to human disturbance. Moreover, climate change affects the growth, mortality, and recruitment of forests, yet available forest growth models do not effectively analyze the impacts of climate. A climate-sensitive transition matrix model (CM) was developed using data from 330 sample plots collected during the 7th (2004), 8th (2009), and 9th (2014) Chinese National Forest Inventories in Hunan Province. To assess model robustness, two additional models were created using the same data: a non-climate-sensitive transition matrix model (NCM) and a fixed probability transition matrix model (FM). The models were compared using tenfold cross-validation and long-term predictive performance analysis. The cross-validation results did not show any significant differences among the three models, with the FM performing slightly better than the NCM. However, the application of the CM for long-term prediction (over a span of 100 years) under three representative concentration pathways (RCP2.6, RCP4.5, and RCP8.5) revealed distinct dynamics that demonstrated enhanced reliability. This is attributed to the consideration of climate variables that impact forest dynamics during long-term prediction periods. The CM model offers valuable guidance for the management of Masson pine natural forests within the context of changing climatic conditions.

Automated summary

Masson pine natural forests in China are ecologically and economically valuable but are facing deforestation due to human disturbance. Climate change affects forest dynamics, but existing growth models do not effectively analyze climate impacts. A climate-sensitive transition matrix model (CM) was developed using data from forest inventories in Hunan Province, and it outperformed other models in long-term predictions under different climate scenarios. The CM model provides valuable guidance for managing Masson pine forests in the context of climate change.