A climate-sensitive transition matrix growth model for uneven-aged mixed-species oak forests in North China. (20th October 2020)

• Record Type: Journal Article
• Authors: Du, Xue; Chen, Xinyun; Zeng, Weisheng; Meng, Jinghui
• Is Part Of: Forestry. Volume 94:Number 2(2021)
• Page Start: 258
• Page End: 277
• Publication Date: 2020-10-20
• Subjects: Forests and forestry -- Periodicals; Forests and forestry -- Great Britain -- Periodicals; 634.9
• DOI: 10.1093/forestry/cpaa035
• Languages: English
• ISSNs: 0015-752X

Abstract

Abstract: Oak-dominated forests, economically and ecologically valuable ecosystems, are widely distributed in China. These oak-dominated forests are now generally degraded coppice forests, and are of relatively low quality. Climate change has been shown to affect forest growth, tree mortality, and recruitment, but available forest growth models are lacking to study climate effects. In this study, a climate-sensitive, transition-matrix growth model (CM) was developed for uneven-aged, mixed-species oak forests using data collected from 253 sample plots from the 8th (2010) and 9th (2015) Chinese National Forest Inventory in Shanxi Province, China. To investigate robustness of the model, we also produced a variable transition model that did not consider climate change (NCM), and fixed parameter transition matrix model (FM), using the same data. Short-term and long-term predictive performance of CM, NCM, and FM were compared. Results indicated that for short-term prediction (5 years), there was almost no significant difference among the three predictive models, though CM exhibited slightly better performance. In contrast, for long-term prediction (100 years), CM, under the three representative concentration pathways (RCPs), i.e. RCP2.6, RCP4.5 and RCP8.5, indicated rather different dynamics that were more reliable because climate factors were considered which could significantly influence forest dynamics, especially in long-term prediction intervals. The CM model provides a framework for the management of mixed-species oak forests in the context of climate change.