植物生态学报 ›› 2008, Vol. 32 ›› Issue (1): 80-87.DOI: 10.3773/j.issn.1005-264x.2008.01.009

• 论文 • 上一篇    下一篇

小兴安岭针叶树种在不同尺度上对环境因子的敏感性分析

布仁仓;常 禹;胡远满;李秀珍;贺红士   

  1. (中国科学院沈阳应用生态研究所,沈阳 110016)
  • 出版日期:2008-01-30 发布日期:2008-01-30
  • 通讯作者: 布仁仓

SENSITIVITY OF CONIFEROUS TREES TO ENVIRONMENTAL FACTORS AT DIFFERENT SCALES IN THE SMALL XING’AN MOUNTAINS, CHINA

BU Ren-Cang; CHANG Yu; HU Yuan-Man; LI Xiu-Zhen; HE Hong-Shi   

  1. Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang 110016, China
  • Online:2008-01-30 Published:2008-01-30
  • Contact: BU Ren-Cang

摘要: 小兴安岭地区是我国重要的林区之一,预测该地区针叶树种的分布,在不同尺度上查找针叶树种分布最敏感的环境因子,是不同层次的林业部
门制定森林恢复和植树造林方针的重要科学依据。该文以坡度、坡向、综合地形指数、海拔、坡位指数、年平均温度和年平均降水量作为环境
因子,利用Logistic回归模型对红松(Pinus koraiensis)、兴安落叶松(Larix gmelinii)、冷杉(Abies nephrolepis)、红皮云杉
Picea koraiensis)、鱼鳞云杉(P. jezoensis)和樟子松(Pinus sylvestris var. mo ngolica)的分布进行了预测。并且采用相对运行特征
(Relative operating characteristic, ROC),对模型进行了精度评价。其取值范围为0~1, 如果ROC小于0.7,认为模型具有低精度;如
果大于0.7且小于0.9,则模型具有较好的模拟精度;如果大于0.9,认为模型具有很高的预测精度。对每个树种的模型验证表明只有冷杉的ROC
大于 80%,红松、兴安落叶松和云杉的ROC在70%~80%之间,而樟子松的为67.9%。之后,把预测模型应用到丰林保护区,揭示局域尺度上树种
分布最敏感的环境因子。经过树种分布预测图与环境因子之间的相关分析发现,在区域尺度(整个研究区)上,红松、冷杉、云杉和樟子松对
年降水量最为敏感,而兴安落叶松对坡度最敏感。在局域尺度(丰林保护区)上,红松分布对坡度最敏感,冷杉和云杉对海拔最敏感,兴安落
叶松对坡位最敏感。在不同尺度上,树种最敏感的环境因子的转移,引起了在不同尺度上树种分布类型的变化。红松在区域尺度上聚集分布
(ROC=78.6%),而在局域尺度上其聚集程度有所减弱(ROC=74.4%),红松的分布范围增加。在区域尺度上,云杉和冷杉聚集分布,但在局域
尺度上,它们的分布接近随机分布类型(ROC<60%),它们在丰林保护区内分布面积较大。与以上3个树种相反,兴安落叶松的ROC从71.7%增加
到了82.0%,在区域尺度上聚集分布的兴安落叶松,在局域尺度上更加聚集,其分布范围局限于某个特定环境(谷底)。总的来说,在区域尺度
上,多数树种分布对气候因子最为敏感,在局域尺度上,对地理因子最为敏感。不同树种对不同环境因子的敏感性,揭示了树种空间分布格局
和分异规律。

Abstract:

Aims Our objective is to predict the distribution of coniferous species and determine species' sensitivity to environmental

factors in the Small Xing'an Mountains, China. This information is important for forest regeneration and biodiversity

conservation and is useful to policy makers for forest management at different scales.
Methods Slope, aspect, compound topographic index, elevation, topographic position index and average annual temperature and

precipitation were selected for use in Logistic regression to predict the occurrence of Korean pine (Pinus koraiensis), larch

(Larix gmelinii), fir (Abies nephrolepis), spruces (Picea koraiensis and P. jezoensis) and Mongol scotch pine's (Pinus

sylvestris var. mongolica). Relative operating characteristic (ROC) was used to evaluate the Logistic models. Because

Logistic regression is difficult to explain, we used the correlation coefficient between species distribution and

environmental factors to explore the sensitivity of species to environmental factors.
Important findings The fir model had best fitness (ROC >80%), and Korean pine, spruces and larch had good fitness (ROC =

60%-80%). The predictions of Mongol Scotch pine, a rare species, had low accuracy. At the regional scale, Korean pine, fir,

spruces and Mongol Scotch pine were the most sensitive to average annual precipitation, and larch was the most sensitive to

slope. At local scale, Korean pine was the most sensitive to slope, fir and spruces to elevation and larch to slope position.

In addition, this shift of the most important factor in species occurrence at different scale was accompanied by a change of

distribution type. Korean pine w as more clustered at regional scale (ROC=78.6%) than at local scale (74.4%), and its

proportion increased in Fenglin Natural Reserve. Fir and spruces clustered at regional scale, but approached random

distribution at local scale (ROC<60 %), i.e., they occurred everywhere. Larch, the dominant species in the north, became more

clustered at local scale (ROC increased from 71.7 to 82.0%) and was restricted to sites such as valleys. Scaling down from

regional to local scale resulted in the changes of the most important factors affecting species distribution. Most species

were sensitive to climatic factors at regional scale and geographic factors at local scale; however, larch was always the

most sensitive to geographic factors due to the temperature inversion in the region.