生物多样性 ›› 2015, Vol. 23 ›› Issue (5): 641-648.DOI: 10.17520/biods.2015089

所属专题: 森林动态监测样地专题

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长白山阔叶红松林草本层物种多度分布格局及其季节动态

张姗1,2, 蔺菲1, 原作强1, 匡旭1,2, 贾仕宏1,2, 王芸芸1,2, 索炎炎1,2, 房帅1,2, 王绪高1, 叶吉1, 郝占庆1,*()   

  1. 1 中国科学院沈阳应用生态研究所森林与土壤生态国家重点实验室, 沈阳 110016
    2 中国科学院大学, 北京 100049
  • 收稿日期:2015-04-11 接受日期:2015-05-28 出版日期:2015-09-20 发布日期:2015-10-12
  • 通讯作者: 郝占庆
  • 基金资助:
    国家科技基础性工作专项(2012FY112000)和国家自然科学基金(41301057, 31370444)

Herb layer species abundance distribution patterns in different seasons in an old-growth temperate forest in Changbai Mountain, China

Shan Zhang1,2, Fei Lin1, Zuoqiang Yuan1, Xu Kuang1,2, Shihong Jia1,2, Yunyun Wang1,2, Yanyan Suo1,2, Shuai Fang1,2, Xugao Wang1, Ji Ye1, Zhanqing Hao1,*()   

  1. 1 State Key Laboratory of Forest and Soil Ecology, Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang 110016
    2 University of Chinese Academy of Sciences, Beijing 100049
  • Received:2015-04-11 Accepted:2015-05-28 Online:2015-09-20 Published:2015-10-12
  • Contact: Hao Zhanqing

摘要:

草本层是森林生态系统的重要组成部分, 对维持森林生物多样性具有重要意义。本文以长白山阔叶红松(Pinus koraiensis)林25 ha固定监测样地为研究平台, 运用不同的统计模型(对数正态模型和对数级数模型)及机理模型(包括生态位模型: 断棍模型和生态位优先占领模型; 中性模型: 复合群落零和多项式模型和Volkov模型), 对不同季节草本物种多度分布进行拟合。采用Kolmogorov-Smirnov和AIC检验确定最优模型, 以揭示草本层物种多度分布格局随季节的变化规律, 探讨草本层物种组成与结构背后的生态学过程。结果表明: (1)草本层物种多度分布季节差异明显。春季各多度级物种数差异不大, 夏季中间种较多, 秋季则是稀有种较多; (2)模型拟合结果显示, 不同季节草本层物种多度分布的最优拟合模型相近。统计模型中对数级数模型表现最优, 机理模型中中性模型的拟合效果优于生态位模型。复合群落零和多项式模型较好地拟合了春夏季草本物种多度分布, Volkov模型较好地拟合了秋季草本物种多度分布。综上所述, 尽管长白山阔叶红松林草本植物不同季节的物种多度分布格局不尽一致, 但其背后的构建机制相似, 中性随机过程在草本层物种多样性维持过程中显得更为重要。

关键词: 森林草本层, 生态位过程, 中性过程, 统计模型, 模型拟合

Abstract:

The herbaceous layer is an important component of forest ecosystems and plays an important role in maintaining forest biodiversity. To understand the mechanisms shaping the forest herb community patterns over multiple growing seasons, we used herbaceous data collected in a 25 ha broad-leaved Korean pine (Pinus koraiensis) mixed forest plot in Changbai Mountain, Northeast China and fitted species abundance distributions (SADs) using different models. We used both pure statistical models including log-normal, log-series, and mechanistic models, including two niche models (broken-stick and niche preemption) and two neutral models (metacommunity zero-sum multinomial distribution and Volkov model). Further, we applied the AIC and Kolmogorov-Smirnov tests to compare the goodness-of-fit of these models. Our results showed: (1) The observed SADs of the herb layer varied by season. While there were similar proportions of rare and common species in spring, there were more species with moderate abundances in summer and more rare species in autumn. (2) The best-fitting models of SADs were similar in different seasons. In our analyses, the log-series model was the best pure statistical model across the three seasons. For the mechanistic models, neutral models performed better at explaining patterns of SADs than niche models. The metacommunity zero-sum multinomial distribution model was the best model in spring and summer and the Volkov model was the best one in autumn. This indicates that stochastic processes may play a dominant role in maintaining the herb species abundance distributions. Our study showed that although the SAD patterns varied over growing seasons for the herb layer in the broad-leaved Korean pine mixed forest, the underlying mechanisms governing these patterns are similar and neutral models always perform better than niche models in fitting the SADs.

Key words: forest herb layer, niche process, neutral process, statistical model, model fitting