生物多样性 ›› 2009, Vol. 17 ›› Issue (1): 30-42.DOI: 10.3724/SP.J.1003.2009.08161

• 论文 • 上一篇    下一篇

北京野鸭湖湿地地表甲虫群落组成与空间分布格局

王玉1, 高光彩1, 付必谦1,*(), 吴专2   

  1. 1 首都师范大学生命科学学院, 北京 100048
    2 北京野鸭湖湿地自然保护区, 北京 102101
  • 收稿日期:2008-07-16 接受日期:2008-12-31 出版日期:2009-01-20 发布日期:2009-01-20
  • 通讯作者: 付必谦
  • 作者简介:* E-mail: fbq1@sina.com
  • 基金资助:
    北京市科学技术委员会项目(D08040600580803)

Composition and spatial distribution pattern of ground-dwelling beetle communities in Yeyahu Wetland, Beijing

Yu Wang1, Guangcai Gao1, Biqian Fu1,*(), Zhuan Wu2   

  1. 1 College of Life Science, Capital Normal University, Beijing 100048
    2 Yeyahu Wetland Nature Reserve, Beijing 102101
  • Received:2008-07-16 Accepted:2008-12-31 Online:2009-01-20 Published:2009-01-20
  • Contact: Biqian Fu

摘要:

2007年4-10月在北京野鸭湖湿地两种保存较好的湿地植被类型和3种主要的退化植被类型中设立了32个样地, 采用陷阱法调查地表甲虫群落的组成, 并在科级水平上探讨了湿地退化及植被类型变化对地表甲虫群落组成的影响。整个采样周期共采集甲虫标本42科, 其中步甲科和隐翅虫科为优势科, 蚁形甲科、肖叶甲科和薪甲科为亚优势科。在所研究的5种植被类型中, 湿地景观保存较好的芦苇(Phragmites communis)带与球穗莎草(Cyperus glomeratus)带的甲虫群落活动密度、科丰富度和Shannon-Wiener多样性指数(H')均无显著差异, 而上述两种植被类型的甲虫活动密度以及球穗莎草带的甲虫科丰富度均显著高于3种退化的植被类型。对地表甲虫群落组成与9个环境因子进行的典范对应分析(CCA)表明, 32个样地在CCA排序图中的分布与植被类型之间存在明显的对应关系, 土壤含水量、植物盖度、植物生物量和枯落物盖度是影响地表甲虫群落组成及空间分布的主要环境因子。相关和回归分析结果也显示, 甲虫群落的活动密度与土壤含水量、植物生物量和植物盖度均极显著或显著正相关, 科丰富度与植物生物量显著正相关, 多样性指数(H')与植物盖度极显著负相关; 其中土壤含水量的变化能够解释甲虫群落活动密度总方差的57%。此外, 通过主成分分析获得了反映土壤含水量、植物生物量和植物盖度综合作用的环境变量WBC (Water-Biomass-Coverage)。依据地表甲虫活动密度与WBC的关系, 可将5种植被类型分为彼此差异极显著的3组。研究结果表明保持良好的湿地景观对于保护湿地甲虫具有重要意义。

关键词: 植被类型, 湿地, 典范对应分析, 主成分分析

Abstract

In this paper, ground-dwelling beetle communities (GDBCs) from 32 sampling sites with five main vegetation types, two well-conserved ones and three degradated ones, were investigated in Yeyahu Wetland of Beijing by pitfall traps from April to December, 2007, and the influence of wetland degradation and vegetation variation on the composition of GDBCs was studied at the family level. A total of 42 families were collected in the whole sampling period. Among these, Carabidae and Staphylinidae were dominant, while Anthicidae, Eumolpidae and Lathridiidae were subdominant. In the five types of vegetation investigated, the activity density, family level richness and the Shannon-Wiener diversity index (H') of GDBCs were not significantly different between the well-conserved Phragmites communis and Cyperus glomeratus ones. However, the beetle activity density in these two well-conserved vegetations, and the family richness in the Cyperus glomeratus one were significantly higher than those of the three degradated ones. It was showed by canonical correspondence analysis (CCA) to the relationship between the composition of GDBCs and nine environmental factors, that there was an obviously correspondance between the distribution of the 32 sampling sites in CCA ordination diagram and the vegetation types, and soil water content, plant coverage, plant biomass, and litter coverage were the major factors affecting on the composition and spatial distribution of GDBCs in the wetland. According to the correlation analysis, we also found that the correlation among beetle activity density and the soil water content, plant biomass and coverage was all significantly positive (P<0.05 or P<0.01), and it was between beetle family richness and plant biomass (P<0.05); while that between the beetle diversity index (H') and plant coverage was significantly negative (P<0.01). Moreover, in a nonlinear regression analysis, the change of soil water content could explain about 57% of the variation in beetle activity density. A synthetic environmental variable, WBC (Water-Biomass-Coverage), which reflects the situation of soil water content, plant biomass and coverage, was obtained by principal component analysis (PCA) of the nine environmental factors. On the basis of relationship between beetle activity density and WBC, the five vegetation types could be divided into three extremely different groups. Our results showed that maintaining appropriate wetland landscapes is vital for beetle protection.

Key words: vegetation type, wetland, canonical correspondence analysis, principal component analysis