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北京小龙门森林鸟类群落划分与生态分析
引用本文:孙忻,王丽. 北京小龙门森林鸟类群落划分与生态分析[J]. 生态学杂志, 2001, 20(5): 25-31
作者姓名:孙忻  王丽
作者单位:中国科学院动物研究所
摘    要:森林鸟类是森林陆栖脊椎动物中种类和数量最丰富的动物类群 ,它们对于促进森林天然更新、传播植物种子及花粉、控制森林害虫和害兽等方面都起着重要作用。关于小龙门林区鸟类群落结构的研究已有报道[1 ] ,但是对于小龙门林区鸟类群落的划分未见有专门报道 ,我们于 1 992年 1 0月~1 993年 2月 ,1 993年 4~ 1 2月 ,1 994年 2~ 8月对北京小龙门林区所有 8条自然沟和 1条公路进行了调查 ,目的在于了解小龙门林区不同林型、海拔等区域的鸟类种类组成和数量状况 ,探讨不同区域的鸟类群落的划分及其影响因素。1 研究地点及方法小龙门林区位于北…

关 键 词:北京 森林鸟类 种类组成 群落划分 生态分析
文章编号:1000-4890(2001)05-0025-07
修稿时间:2000-11-10

Ecological Analysis andClassification of Forest Bird Communities at Xiaolongmen,Beijing
Abstract. Ecological Analysis andClassification of Forest Bird Communities at Xiaolongmen,Beijing[J]. Chinese Journal of Ecology, 2001, 20(5): 25-31
Authors:Abstract
Abstract:During 1992 to 1994,bird communities were investigatcd at Xiaolongmen forest area in Beijing,which includs 6 forest types and 7 altitude zones.Totally 69 birds (8 orders and 21 families) were recorded for 3842 times.The Yellow-bellied Tit (Parus venustulus) is the only dominant species in our study area,with the record times 13.12% of the total.Bird community classification was based on fuzzy cluster analysis.Four bird communities were classified according to the forest type:Ⅰ.mixing forest,Ⅱ.conifer forest,Ⅲ.Quercus liaotungensis forest and IV.shrubland.As for the altitude in our study area,which is from 1070m to 1763m,seven zones were divided.Two main bird communities exist.Ⅴ.1070~1450m and VI.1450~1763m.Species diversity and similarity indices of each bird community were calculated.The results indicated that indices for group Ⅳ was the lowest with obvious independence,while other three forest bird communities (Ⅰ-Ⅲ) were more stable,Group Ⅱ has the highest bird species diversity indices and most species.At Xiaolongmen forest,the most abundant bird area was the mixing forest at the altitude between 1070m to 1450m,with 67 birds recorded (97.10% of total bird species number).The birds around 1450m to 1763m were relatively less,with the species number and density 26.87% and 10.12% of the total,respectively.
Keywords:bird community classification  fuzzy cluster analysis  Xiaolongmen  Beijing.
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