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北京山区栓皮栎林优势种群点格局分析
引用本文:樊登星,余新晓.北京山区栓皮栎林优势种群点格局分析[J].生态学报,2016,36(2):318-325.
作者姓名:樊登星  余新晓
作者单位:北京林业大学水土保持学院, 北京 100083;水土保持与荒漠化防治教育部重点实验室, 北京 100083,北京林业大学水土保持学院, 北京 100083;水土保持与荒漠化防治教育部重点实验室, 北京 100083
基金项目:国家自然科学基金(41430747,41171028);国家科技支撑项目(2011BAD38B05);国家林业公益性行业科研专项(201104005)
摘    要:采用点格局分析方法对北京山区栓皮栎林主要种群和不同发育阶段种群的空间分布格局及相互关系进行分析。结果表明:(1)群落物种组成简单,栓皮栎和油松是群落的优势种群,优势种群以中径木为主,缺乏可供更新的幼苗,种群呈现出衰退趋势;(2)在研究尺度内,栓皮栎、油松单种的空间分布格局以聚集型分布为主,随着尺度的增大呈现出随机分布和均匀分布的趋势;栓皮栎和油松呈现显著负相关;(3)优势种群不同发育阶段的空间分布格局与研究尺度紧密相关,栓皮栎小树和中树以聚集型分布为主、大树呈随机分布,而油松不同发育阶段均以随机分布为主;(4)栓皮栎、油松种群不同发育阶段之间的空间关联主要发生在小尺度范围内,小树和中树以正关联为主,而中树和大树、小树和大树以负关联为主;随着研究尺度的增大趋于无关联。

关 键 词:栓皮栎林  径级结构  点格局分析  空间分布格局  空间关联性
收稿时间:2014/2/6 0:00:00
修稿时间:2015/10/8 0:00:00

Spatial point pattern analysis of Quercus variabilis and Pinus tabulaeformis populations in a mountainous area of Beijing
FAN Dengxing and YU Xinxiao.Spatial point pattern analysis of Quercus variabilis and Pinus tabulaeformis populations in a mountainous area of Beijing[J].Acta Ecologica Sinica,2016,36(2):318-325.
Authors:FAN Dengxing and YU Xinxiao
Institution:School of Soil and Water Conservation, Beijing Forestry University, Beijing 100083, China;Key Laboratory of Soil and Water Conservation and Desertification Combating, Ministry of Education, Beijing 100083, China and School of Soil and Water Conservation, Beijing Forestry University, Beijing 100083, China;Key Laboratory of Soil and Water Conservation and Desertification Combating, Ministry of Education, Beijing 100083, China
Abstract:Spatial pattern and spatial association analysis are important methods to study the population characteristics and community structure underlying ecological processes. In this study, spatial point pattern analysis was conducted to examine the spatial distribution pattern of and the spatial associations between the different developmental stages of the Quercus variabilis and Pinus tabulaeformis populations in a mountainous area of Beijing. Only 11 tree populations were observed in the community, in which Q. variabilis and P. tabulaeformis were the dominantspecies. Intermediate-diameter individuals of both species were most prevalent. The populations lacked regenerative saplings, indicating that the populations are in decline. At the study scale, all Q. variabilis and P. tabuliformis individuals were significantly clustered, and as the scale increased, the distribution became more random and regular. The distribution pattern of Q. variabilis populations varied with the spatial scale; they were mainly aggregated at the small scale(0-39 m) and randomly distributed at the large scale(39-50m).The distribution pattern of P. tabuliformis populations was mainly aggregated at the small scale(0-34m) and randomly distributed at the large scale(34-50m).The spatial association between Q. variabilis and P. tabuliformis was significantly negative. The distribution patterns of Q. variabilis and P. tabulaeformis populations at different developmental stages were closely related to the scale. The spatial distribution patterns of young and middle-aged Q. variabilis individuals were significantly clustered, where as those of older trees were random. The spatial distribution pattern of P.tabuliformis was significantly random at all scales. Spatial association of Q. variabilis and P. tabulaeformis at different developmental stages was primarily observed at the small scale. The association between middle-aged and young trees was significantly positive at the medium and small scales. However, the spatial association between young, middle-aged, and old trees was negative, and gradually disappeared as the scale increased. These data suggest that the spatial distribution and spatial associations of Q. variabilis and P. tabulaeformis populations were mainly affected by environmental heterogeneity, the biological properties of the populations(especially the seed dispersion mechanism, intraspecific relationships, and interspecific relationships), the differing developmental stages, and changes in spatial scale.
Keywords:Quercus variabilis  DBH class structure  point pattern analysis  spatial distribution  spatial association
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