Large-scale patterns in forest growth rates are mainly driven by climatic variables and stand characteristics

Abstract

Comparing the growth rate of natural forest and plantation forest may be useful to better understand rates of carbon sequestration and carbon turnover. However, the large-scale patterns of biomass growth rates in China’s forests are still not well defined. We analyzed the growth rates of forest leaves, branches, stems, and roots across forest communities in China by using data collection, collation, and systematic analysis of published research and our unpublished data. The biomass growth rates in all forests exhibited negative latitudinal trends and negative altitudinal trends, with significant influence from climatic variables and stand characteristics. Stand characteristics explained more variation in growth rates of forest biomass than did climatic variables, and growth rates of forest leaves, branches, stems, and roots varied in relation to climate, stand characteristics, and forest origin. The cross-validated results of stepwise multiple regression (SMR) models and neural network models (NNM) indicated that the prediction accuracy of growth rate of forest biomass by NNM was better than that of the SMR models. Our results improve understanding of the environmental factors affecting Chinese forest growth and inform efforts to model dynamics of carbon accumulation in China’s forests.