An optimal proportion of mixing broad-leaved forest for enhancing the effective productivity of moso bamboo.
Moso bamboos (Phyllostachys edulis) are important forestry plants in southern China, with substantial roles to play in regional economic and ecological systems. Mixing broad-leaved forests and moso bamboos is a common management practice in China, and it is fundamental to elucidate the interactions between broad-leaved trees and moso bamboos for ensuring the sustainable provision of ecosystem services. We examine how the proportion of broad-leaved forest in a mixed managed zone, topology, and soil profile affects the effective productivity of moso bamboos (i.e., those with significant economic value), using linear regression and generalized additive models. Bamboo's diameter at breast height follows a Weibull distribution. The importance of these variables to bamboo productivity is, respectively, slope (25.9%), the proportion of broad-leaved forest (24.8%), elevation (23.3%), gravel content by volume (16.6%), slope location (8.3%), and soil layer thickness (1.2%). Highest productivity is found on the 25° slope, with a 600-m elevation, and 30% broad-leaved forest. As such, broad-leaved forest in the upper slope can have a strong influence on the effective productivity of moso bamboo, ranking only after slope and before elevation. These factors can be considered in future management practice.
Collaborative Innovation Center of Sustainable Forestry in Southern China of Jiangsu Province, Bamboo Research Institute, Nanjing Forestry University Nanjing, 210037, China.
Xiao-Fei
XF
Cheng
Collaborative Innovation Center of Sustainable Forestry in Southern China of Jiangsu Province, Bamboo Research Institute, Nanjing Forestry University Nanjing, 210037, China.
Pei-Jian
PJ
Shi
Centre for Invasion Biology, Department of Mathematical Sciences, Stellenbosch University Matieland, 7602, South Africa ; Mathematical and Physical Biosciences, African Institute for Mathematical Sciences Cape Town, 7945, South Africa.
Cang
C
Hui
Collaborative Innovation Center of Sustainable Forestry in Southern China of Jiangsu Province, Bamboo Research Institute, Nanjing Forestry University Nanjing, 210037, China.
Fu-Sheng
FS
Wang
Collaborative Innovation Center of Sustainable Forestry in Southern China of Jiangsu Province, Bamboo Research Institute, Nanjing Forestry University Nanjing, 210037, China.
Guo-Hua
GH
Liu
Ecological Complexity and Modeling Laboratory, Department of Botany and Plant Sciences, University of California Riverside, California, 92521-0124.
Bai-Lian
BL
Li
Biomass; environmental factors; generalized additive model; linear regression model; productivity