阿尔泰山小东沟林区乔木物种丰富度空间分布规律
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承德市环境保护局,承德市环境科学研究院,国家林业局森林生态环境重点实验室,中国林业科学研究院森林生态环境与保护研究所

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国家"十二五"科技支撑课题(2012BAD22B0301)


Spatial distribution of tree species richness in Xiaodonggou forest region of the Altai Mountains, Northwest China
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Chengde Bureau of Environmental Protection of Hebei Chengde,Chengde Bureau of Environmental Protection of Hebei Chengde,Key Laboratory of Forest Ecology and Environment of State Forestry Adinistration Institute of Forest Ecology,Environment and Protection,Chinese Academy of Forestry Beijing

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    摘要:

    对一定区域内生物多样性的合理保护往往需要在景观水平上了解生物多样性分布的总体规律.借助于典型抽样调查和遥感及地理信息系统相结合的方法来预测物种丰富度是定量研究生物多样性宏观分布规律的重要途径.以阿尔泰山小东沟林区为对象,在外业调查的基础上,选取主要气象因子、地形因子和植被指数(NDVI)作为预测变量,利用主成分分析和多元回归分析分别提取主要环境信息和构建物种丰富度预测模型,借助ArcGIS9.1的空间分析功能,得到了阿尔泰山小东沟林区乔木物种丰富度空间分布预测图,并利用残差图评价其精度.将小东沟乔木物种丰富度预测图分别与坡度、坡向和海拔图叠加,分析不同地形条件下乔木物种丰富度的空间分布规律.结果表明:占总研究区面积70.28%的区域,其乔木物种丰富度在3到4种之间.坡度0-5°的地形条件下乔木物种丰富度出现频率最高的数值是3,其余坡度条件下,乔木物种丰富度出现频率最高的数值是4;乔木物种丰富度在西坡和西北坡出现频率最高的数值是3,其余坡向乔木物种丰富度出现频率最高的数值均是4;海拔梯度上,乔木物种丰富度出现频率最高的数值呈现先增加后减少的趋势.残差类型面积统计表明,较强预测水平面积和中等预测水平面积占研究区总面积的94.62%,表明预测效果较好.

    Abstract:

    Biodiversity is the basis of ecosystem functioning. Species richness has been widely used in biodiversity studies. Understanding the spatial distribution of species richness at landscape scale is vitally important in biodiversity conservation and natural resources management. Predicting species richness on large scale could help managers to rationally conserve and utilize natural resources. With the availability of remotely sensed data and the development of geographical information system (GIS) techniques spatial analysis on large scale has been possible. Integrating field sample plot investigation, remote sensing (RS), and geographic information system is a novel way to explore the distribution of species richness at macro spatial scales. The Altai Mountains is one of the magnificent Mountains of Asia, which distributes across Mongolia, China, Kazakhstan, and Russia. In this study, we adopted the above mentioned approach to predict the spatial distribution of tree species richness in Xiaodonggou forest region of the Altai Mountains in Xinjiang, Northwest China. In the south and north slope of the Xiaodonggou forest region, a investigation transect was selected respectively. In each of the transect, we set investigation plots (each was 20 m×20 m in size) at intervals of 50 m along the altitude. All woody plants in the plots with diameter at breast height (DBH)≥1cm were identified and measured. The species richness in each plot was calculated. Normalized difference vegetation index (NDVI) was obtained from ETM+ image. In order to overlay ETM+ image and topographic factor maps, we selected ETM+ image in size of 30 m×30m. The predictor variables include climate, topography, and NDVI. Principle component analysis (PCA) and multiple linear regression were firstly utilized for obtaining the environmental factors and developing the predictive model of species richness distribution. Annual minimum temperature, annual average relative humidity, aspect, slope and NDVI were selected into the predictive model. Tree species richness distribution map was produced by GIS. The residual map was produced by the inverse distance weighted interpolation (IDW) method. The residual map was used to evaluate the validity of the model. In order to analyze variation of species richness with the topographic factors, the spatial distribution map of species richness was overlaid with the slope, aspect and elevation maps, respectively. The results showed that the areas with 3-4 tree species occupied 70.08% of the total study region. In slopes of 0- 5°, the areas with tree species richness of 3 had the highest presence frequency, while in slopes of other ranges, the areas with tree species richness of 4 had the highest presence frequency. In west and northwest aspects, the areas with three tree species had the highest presence frequency, in the other aspects, however, the areas contained four tree species had the highest presence frequency. Along with the altitudinal gradient, the tree species richness showed a unimodal distribution pattern, which is consistent with the hypothesis of mid-domain effect. The statistic results of residual types showed that strongly predicting area and moderately predicting area together reached 94.62% of the total study area, which implied that our predictive model was robust and could be successfully implemented in this forest region.

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井学辉,曹磊,臧润国.阿尔泰山小东沟林区乔木物种丰富度空间分布规律.生态学报,2013,33(9):2886~2895

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