Chin J Plant Ecol ›› 2012, Vol. 36 ›› Issue (5): 372-381.DOI: 10.3724/SP.J.1258.2012.00372

• Research Articles • Previous Articles     Next Articles

Relationship between climatic factors and geographical distribution of spruce forests in China

LI He1,2, ZHANG Wei-Kang1,3, WANG Guo-Hong1,*()   

  1. 1State Key Laboratory of Vegetation and Environmental Change, Institute of Botany, Chinese Academy of Sciences, Beijing 100093, China
    2Graduate University of Chinese Academy of Sciences, Beijing 100049, China
    3Xuzhou Normal University, Xuzhou, Jiangsu 221116, China
  • Published:2012-05-04
  • Contact: WANG Guo-Hong

Abstract:

Aims Our objective was to examine the relationship between climatic factors and geographical distribution of spruce forests in China.
Methods We sampled 613 points within the geographical range of Chinese spruce forests, of which 235 points were at the upper altitudinal limit and 228 at the lower altitudinal limit. The elevation for each point was determined using Google Earth while climatic data were from the Chinese meteorological interpolation database. Linear regression, comparison of coefficient of variation (CV) and principal component analysis (PCA) were conducted for data analysis.
Important findings Within the distribution range of Chinese spruce forests, mean values of mean annual air temperature (MAT), mean air temperature of the coldest month (MTCM), mean air temperature of the warmest month (MTWM), growing degree days on a 5 ℃ basis (GDD5) and on a 0 ℃ basis (GDD0), mean annual precipitation (MAP), soil moisture (SM) and aridity index (α) are 3.38 ℃, -9.75 ℃, 14.78 ℃, 1 227.83 ℃·d, 2 271.19 ℃·d, 712.23 mm, 80.02% and 0.50, respectively. Both the upper and lower limits of altitude were significantly correlated with each of the climatic factors. In terms of CV, MAT and MTCM are significantly higher than the other six climatic factors; however, no significant differences were detected among those six. In addition, GDD5 and GDD0 have higher loading on the first principal component, yet MAP and SM have higher loading on the second and third principal component. Major conclusions are that GDD0 and GDD5 are likely the key factors that influence the distribution of Chinese spruce forest, followed by MAP and SM.

Key words: climatic factors, coefficient of variation, principal component analysis, spruce forest in China, upper and lower altitudinal limits, vertical distribution