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Plant Diversity ›› 2013, Vol. 35 ›› Issue (5): 647-655.DOI: 10.7677/ynzwyj201312127

• 研究论文 • 上一篇    下一篇

应用BioMod集成多种模型研究物种的空间分布——以铁杉在中国的潜在分布为例

 毕迎凤1、2, 许建初3, 李巧宏1, Antoine Guisan4, Wilfried Thuiller5   

  1. 1 中国科学院昆明植物研究所资源植物与生物技术所级重点实验室,云南 昆明650201;2 中国科学院大学,
    北京100049;3 中国科学院昆明植物研究所山地生态系统研究中心,云南 昆明650201;
    4 瑞士洛桑大学生物医学学院,瑞士;5 法国格勒诺布尔第一大学高山生态实验室,法国
  • 收稿日期:2012-10-19 出版日期:2013-09-25 发布日期:2013-02-26
  • 基金资助:

    中国科学院知识创新工程重要方向项目——西南野生生物资源的挖掘与利用 (KSCX2-EW-J-24)

Applying BioMod for ModelEnsemble in Species Distributions: a Case Study for Tsuga chinensis in China

 BI  Ying-Feng-1、2, HU  Jian-Chu-3, LI  Qiao-Hong-1, Antoine  Guisan4, Wilfried  Thuiller5   

  1. 1 Key Laboratory of Economic Plants and Biotechnology, Kunming Institute of Botany, Chinese Academy of Sciences, Kunming
    650201, China; 2 University of Chinese Academy of Sciences, Beijing 100049, China; 3 Centre for Mountain Ecosystem Studies,
    Kunming Institute of Botany, Chinese Academy of Sciences, Kunming 650201, China; 4 Département d′Ecologie
    et Evolution Faculté de Biologie et Médecine Université, de Lausanne CH1015 Lausanne, Switzerland;
    5 Laboratoire d’Ecologie Alpine, University Grenoble 1J. Fourier, France
  • Received:2012-10-19 Online:2013-09-25 Published:2013-02-26
  • Supported by:

    中国科学院知识创新工程重要方向项目——西南野生生物资源的挖掘与利用 (KSCX2-EW-J-24)

摘要:

新型统计方法和多源、多尺度空间信息数据的产生促进了物种空间分布模型的快速发展。不同的物种空间分布模型在生态学理论的运用以及前提假设上存在差异。选用不同的模型方法和输入数据会带来预测结果的不确定性。对比并集成多个物种空间分布模型,同时利用多组输入数据可降低预测的不确定性,提高物种分布模拟的精度。本文以中国特有种铁杉(Tsuga chinensis)为例,运用基于R语言开发的BioMod软件包对比9个物种空间分布模型对铁杉的模拟效果。最后以曲线下面积(ROC)为权重集成9个模型的模拟结果,产生和筛选最佳的铁杉潜在空间分布图。研究发现随机森林模型(RF)的模拟效果最好,其次是多元适应回归样条函数模型(MARS)和广义相加模型(GAM),模拟效果最差的是表面分布区分室模型(SRE)。模型集成结果显示,最适宜铁杉分布的区域集中在中国的西南及四川盆地周围,其次零星分散于华南和台湾部分地区。这一结果与前人对铁杉自然分布的描述和研究结果较为吻合。研究进一步表明,通过模型的集成能有效地降低由于单个模型所带来的模拟结果不确定性,从而提高模拟的精度和效果。

关键词: 物种空间分布模型, 铁杉, 模型集成, 分布区, BioMod

Abstract:

 The integration of new statistical techniques and increasing availability of multisources and multiscale data sets promote the development of species distribution modeling. Yet, choice of data sets, different model types and their underlying ecological theories and assumptions can cause uncertainty in model predictions. In order to decrease prediction uncertainty, studies using model ensemble are gaining in popularity. In this paper we apply the BioMod package developed under R environment to predict the spatial distribution of Tsuga chinensis using nine different models. Our aims were to evaluate model performance, select explanatory variables, and assemble the best predictive output. Random Forest, MARS and GAM performed the best amongst the nine models compared, while SRE was the worst. The ensemble models predicted that the areas of high probability for T.chinensis presence lie mainly in Southwest China and the periphery of the Sichuan basin, and are also distributed sporadically in South China and Taiwan. These predictions reflect the actual distribution pattern of T.chinensis, and show high agreement with other analyses. The application of BioMod for model ensemble lowers uncertainty and improves the prediction performance.

Key words: Species distribution model Tsuga chinensis, Model assembly, Biogeography, BioMod

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