植物生态学报 ›› 2012, Vol. 36 ›› Issue (9): 923-934.DOI: 10.3724/SP.J.1258.2012.00923

• 研究论文 •    下一篇

长白山不同演替阶段针阔混交林群落物种多度分布格局

闫琰, 张春雨, 赵秀海*()   

  1. 北京林业大学森林资源与生态系统过程北京市重点实验室, 北京 100083
  • 收稿日期:2012-03-30 接受日期:2012-06-01 出版日期:2012-03-30 发布日期:2012-09-06
  • 通讯作者: 赵秀海
  • 作者简介: (E-mail: zhaoxh@bjfu.edu.cn)

Species-abundance distribution patterns at different successional stages of conifer and broad-leaved mixed forest communities in Changbai Mountains, China

YAN Yan, ZHANG Chun-Yu, ZHAO Xiu-Hai*()   

  1. Key Laboratory for Forest Resources & Ecosystem Processes of Beijing, Beijing Forestry University, Beijing 100083, China
  • Received:2012-03-30 Accepted:2012-06-01 Online:2012-03-30 Published:2012-09-06
  • Contact: ZHAO Xiu-Hai

摘要:

为解释长白山温带森林群落构建和物种多度格局的形成过程, 该文以不同演替阶段的针阔混交林监测样地数据为基础, 采用中性理论模型、生物统计模型(对数正态分布模型)和生态位模型(Zifp模型、分割线段模型、生态位优先模型)拟合森林群落物种多度分布, 并用χ 2检验、Kolmogorov-Smirnov (K-S)检验和赤池信息准则(AIC)选择最佳拟合模型。结果显示: 中性模型能很好地预测长白山温带森林不同演替阶段植物群落的物种多度分布。在10 m × 10 m尺度上, 5种模型均可被χ 2检验和K-S检验接受, 但中性模型拟合效果不如对数正态分布模型、Zifp模型、分割线段模型和生态位优先模型, 表明小尺度上中性过程和生态位过程均能解释群落物种多度分布, 但生态位过程的解释能力相对较大。而在中大尺度上(30 m × 30 m、60 m × 60 m和90 m × 90 m), 中性模型为最优拟合模型, 并且随着研究尺度增加, 生态位模型和生物统计模型逐渐被χ 2检验拒绝, 表明中性过程在长白山针阔混交林群落物种多度分布格局形成中的作用随着研究尺度增加而逐渐增大。该文证实了中性过程在长白山温带针阔混交林群落结构形成中具有重要作用, 但未否认生态位机制在群落构建中的贡献。因此, 温带森林群落构建过程中中性理论和生态位理论并非相互矛盾, 而是相互融合的。在研究森林群落物种多度分布时, 应重视取样尺度和演替阶段的影响, 并采用多种模型进行拟合。

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关键词: 中性理论模型, 生态位模型, 取样尺度, 统计模型, 温带森林

Abstract:

Aims Our objective was to explain processes that dominate species-abundance distribution pattern and mechanism of community assembly in temperate forests.
Methods We used three 5.2-hm 2 permanent plots established in secondary Populus davidiana-Betula platyphylla forest, secondary conifer and broad-leaved mixed forest and Tilia amurensis-Pinus koraiensis mixed forest in Changbai Mountains. Within each plot, we randomly selected 500 subplots within 260 m × 200 m at the scales of 10 m × 10 m, 30 m × 30 m, 60 m × 60 m and 90 m × 90 m. We calculated the mean value of species-abundance distributions taken from the 500 subplots as the observed species-abundance distribution. We estimated the fitted species-abundance distributions by neutral, log-normal, Zipf, broken stick and niche preemption models at different scales. Simulation effects were tested by Chi-square test, Kolmogorov-Smirnov (K-S) test and Akaike Information Criterion (AIC). For the neutral model, we first estimated two parameters θ and m and then simulated 600 species-abundance distributions. The average of these 600 species-abundance distributions was the best-fit result of the neutral model. We employed the 95% confidence envelopes that were approximated by the 2.5 and 97.5 percentiles of the abundances of species of rank i = 1 to S over the 600 simulations to test goodness-of-fit for the neutral model. All of the computations were conducted in R 2.14.1 with UNTB and VAGEN packages.
Important findings The neutral model fit species-abundance distribution at different successional stages of conifer and broad-leaved mixed temperate forest communities. All five models fit the observed value at the 10 m × 10 m sampling scale, and the goodness of fit of the log-normal, Zipf, broken stick and niche preemption models were better than that of the neutral model. That means at small sampling scale the species-abundance distribution is dominated by neutral process and niche process; however, the niche process is much more important. At other sampling scales (30 m × 30 m, 60 m × 60 m, 90 m × 90 m), the neutral model was the best-fit model. As the sampling scales increased, other models gradually dropped out. At the sampling scale of 90 m × 90 m, none of the models fit well, except for the neutral model. This suggests for north temperate forests in the Changbai Mountains that the random process represented by neutral model is the main ecological process that determines the species-abundance distribution pattern at middle and large sampling scales and that the species-abundance distributions at different sampling scales are likely dominated by different ecological processes.

Key words: neutral theory model, niche model, sample scale, statistical model, temperate forest