摘要
在1981-1990年我国东北地区的主要森林物种兴安落叶松、白桦和红皮云杉的调查分布区域内气候数据的基础上,采用分区间统计及基于模糊邻近关系的分层聚类和聚类融合等理论和方法进行相关气候数据的特征分析,获得如下结论:这三个森林物种的生长期为5-9月份,而1-4月份与10-12月份为非生长期.这与这三个物种的生物学和生态学特征相吻合.与以往文献中用于物种分布预测的气候因子提取方法不同的是本文采用的方法完全依赖于预测物种在调查分布区内的气候数据,通过数据挖掘与数据处理而获得,而不是通过预测物种的生物学与生态学特征及其在分布区内的气候因子相关性分析得到.研究结论将为未来气候变化对东北地区森林物种分布预测影响的研究提供基础.
Based on the climate data of the main forest species such as Larix gmelinii, Betula platyphylla Suk, Picea koraiensis Nakai in Northeast China from 1981 to 1990, characteristics analysis of the related climate data is carried out by using the theory and methods of interval statistics, hierarchical clustering based on fuzzy proximity relations and clustering fusion technology. The results shows that the growth period of the three forest species is May to September and the non growth period of them is January to April and October to December. The conclusion coincides with the biological characteristics and ecological characteristics of the three species. Extracting climatic factors from climate data is critical to the predictive distribution of species. Climatic factors that were used in the past mostly depend on the biological and ecological characteristics of species and the correlation analysis for climatic factors in the distribution area of species. Instead, our methods only depend on climate data in the distribution area of the predictive species entirely, and it is obtained by data mining and data processing. This conclusion will provide basis for the research about the impact of future climate change on the distribution prediction of the forest species in Northeast China.
出处
《生物数学学报》
2016年第1期118-128,共11页
Journal of Biomathematics
基金
国家自然科学基金(11271163
11371174)
环保部公益性行业科研专项(200909070)
中央高校基本科研业务费专项(JUSRP51317B)资助
关键词
物种分布区
森林物种
气候因子
分层聚类
聚类融合
Species distribution area
Forest species
Climatic factor
Hierarchical cluster- ing
Clustering fusion