Date Published: March 11, 2019
Publisher: Public Library of Science
Author(s): Yanping Zhou, Zeyong Lei, Fengyan Zhou, Yangang Han, Deliang Yu, Yansong Zhang, Bao Yang.
Tree height growth is sensitive to climate change; therefore, incorporating climate factors into tree height prediction models can improve our understanding of this relationship and provide a scientific basis for plantation management under climate change conditions. Mongolian pine (Pinus sylvestris var. mongolica) is one of the most important afforestation species in Three-North Regions in China. Yet our knowledge on the relationship between height growth and climate for Mongolian pine is limited. Based on survey data for the dominant height of Mongolian pine and climate data from meteorological station, a mixed-effects Chapman-Richards model (including climate factors and random parameters) was used to study the effects of climate factors on the height growth of Mongolian pine in Zhanggutai sandy land, Northeast China. The results showed that precipitation had a delayed effect on the tree height growth. Generally, tree heights increased with increasing mean temperature in May and precipitation from October to April and decreased with increasing precipitation in the previous growing season. The model could effectively predict the dominant height growth of Mongolian pine under varying climate, which could help in further understanding the relationship between climate and height growth of Mongolian pine in semiarid areas of China.
Forests are important components of terrestrial ecosystems. They cover ~30% of the global land surface and play an important role in maintaining global carbon balance and biodiversity . To improve the ecological, economic, and social benefits of forests and realize the sustainable development, it is necessary to understand the growth law of trees and to accurately predict tree growth. Both height and diameter at breast height are affected by the genetic characteristics of trees and local environmental factors . Climate is the most influential factor affecting the growth and distribution of trees . As the structural function and growth patterns of forests have been altered by global climate change, an enhanced tree growth prediction model can help provide a basis for sustainable forest management. New growth models that incorporate climate attributes as a pivotal part of independent variables are necessary . The growth–climate relationship is normally assessed through an analysis of secondary growth via diverse tree-ring proxies, but the relationship between primary (height) growth and climate has rarely been studied because of difficulty in data collection [3,5]. Given that tree height is an important indicator of forest dynamics, detecting and quantifying the relationship between tree growth and the surrounding environmental or climatic factors can help determine the impact of external environments on tree growth. This approach also provides a basis for more effective predictions of forest dynamics under future changing climate scenarios to better inform plantation management. Dominant tree height, for example, has been used to calculate the site index for even-aged and pure forests . The dominant height growth model with climate factors is used to help predict the site index under future climate change and to analyze the adaptation strategies of trees .
Based on the biological Chapman-Richards model, a climate-sensitive dominant height growth model for Mongolian pine was established. Temperature and precipitation are the limiting factors for tree growth [33,34]. MTM, PNP, and PGP significantly affected the height growth of Mongolian pine in Zhanggutai and the three climatic factors all had significant effects on the asymptotic parameters of the Chapman-Richards model. MTM and PNP were positively correlated with tree height, while PGP was negatively correlated. Using the dominant tree height model with climatic factors clearly improved the performance of the model.
This study quantified the effects of climate factors on the height growth of Mongolian pine, of which mean temperature in May (MTM), precipitation from October to April (PNP), and precipitation in the previous growing season (PGP) were found to be the most influential. Tree height was positively correlated with MTM and PNP and negatively correlated with PGP. Incorporating random parameters into the model improved the model performance and reflected differences between different sites. Therefore, our model can be used to predict the height growth of Mongolian pine under changing climate.