摘要
在分析1981—1990年间我国东北地区大针茅(Stipa grandis)、克氏针茅(Stipa krylovii)、贝加尔针茅(Stipa baicalensis)、小针茅(Stipa klemenzii)和短花针茅(Stipa breviflora)5种针茅的分布数据和相应的物种分布区气候数据,以及2041—2050年的气候预测数据的基础上,采用数据统计处理与分析的方法,提出了针茅分布关于气候因子的随机预测模型。在Ar GIS9.3界面下,应用Matlab进行程序设计与运行,获得了2041—2050年间5种针茅的最适分布区、次适分布区和可适分布区的预测图。进一步从5种针茅的3种适应分布区域预测图的动态分析表明:除短花针茅外,它们的分布区域都有向北漂移现象,且大针茅和贝加尔针茅都向东北方向漂移;克氏针茅和小针茅既有向东北方向,也有向西北方向漂移;而短花针茅的最适应分布区域、次适应分布区域和可适应分布区域比较稳定,仍在原分布区内。同时大针茅和贝加尔针茅的最适分布区、次适分布区和可适应分布区域预测图之间都有很多重叠部分,这也反映了这两种针茅具有相近或相似的生态位。
Based on the distribution data of Stipa grandis,Stipa baicalensis,Stipa krylovii,Stipa klemenzii and Stipa breviflora from 1981 to 1990 years, their climate data in distribution area and the climate prediction data in the Northeast China from 2041 to 2050,the forecasting distributions of 5 kinds Stipa are obtained,including their optimal adaptive distribution, medium adaptable distribution and general adaptive distribution. The stochastic prediction model for Stipa on the climate factors is built by using the rigorous theory and methods of the statistical analysis and data processing, and the corresponding algorithms for computing the forecasting distributions of 5 kinds Stipa is given by using Matlab under ArGIS9.3. The results show that their distribution areas drift to the north except Stipa breviflora,where Stipa grandis and Stipa baicalensis drift to the northeast,Stipa krylovii and Stipa klemenzii drift not only to the northeast but also to the northwest. And the distribution area of Stipa breviflora is relatively stable, and still in its original distribution area. Furthermore,Stipa grandis and Stipa baicalensis have many overlapping between their distribution areas on three kinds of adaptation prediction map,which indicate that they have similar ecological niche and strong competitiveness.
出处
《江南大学学报（自然科学版）》
CAS
2015年第3期357-363,共7页
Joural of Jiangnan University (Natural Science Edition)
基金
国家自然科学基金项目(11371174)
环保部公益性行业科研专项项目(200909070)
中央高校基本科研业务费专项项目(JUSRP51317B)
关键词
气候因子
针茅
物种分布区
数据统计
随机预测模型
climate factor, Stipa, species distribution area, data statistics, stochastic prediction model
