地球科学进展

地球科学进展 >

2020 , Vol. 35 >Issue 12: 1292 - 1305

DOI: https://doi.org/10.11867/j.issn.1001-8166.2020.110

生态学研究

物种分布模型面临的挑战与发展趋势

  • 郭彦龙 ,
  • 赵泽芳 ,
  • 乔慧捷 ,
  • 王然 ,
  • 卫海燕 ,
  • 王璐坤 ,
  • 顾蔚 ,
  • 李新
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  • 1.中国科学院青藏高原研究所国家青藏高原科学数据中心，北京 100101
    2.北京师范大学地理科学学部，北京 100875
    3.中国科学院动物研究所，北京 100101
    4.中国科学院大学，北京 100049
    5.陕西师范 大学地理科学与旅游学院，陕西 西安 710119
    6.陕西师范大学生命科学学院，陕西 西安 710119
郭彦龙(1987-)，男，山西朔州人，博士后，主要从事物种分布以及生态学模型研究. E-mail: guoyl@itpcas.ac.cn

收稿日期: 2020-10-05

  修回日期: 2020-11-25

  网络出版日期: 2021-02-09

基金资助

国家自然科学基金项目“气候变化对青藏高原高寒草地植物物种迁移以及物种多样性的影响预测”(41901068);“使用互相映射的现实与虚拟场景对生态位模型适用性的比较研究”(31772432)

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Challenges and Development Trend of Species Distribution Model

  • Yanlong Guo ,
  • Zefang Zhao ,
  • Huijie Qiao ,
  • Ran Wang ,
  • Haiyan Wei ,
  • Lukun Wang ,
  • Wei Gu ,
  • Xin Li
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  • 1.National Tibetan Plateau Data Center，Institute of Tibetan Plateau Research，Chinese Academy of Sciences，Beijing 100101，China
    2.Faculty of Geographical Science，Beijing Normal University，Beijing 100875，China
    3.Institute of Zoology，Chinese Academy of Sciences，Beijing 100101，China
    4.University of the Chinese Academy of Sciences，Beijing 100049，China
    5.School of Geography and Tourism，Shaanxi Normal University，Xi'an 710119，China
    6.College of Life Sciences，Shaanxi Normal University，Xi'an 710119，China
Guo Yanlong (1987-), male, Shuozhou City, Shanxi Province, Postdoctoral. Research areas include species distribution and ecological model. E-mail: guoyl@itpcas.ac.cn

Received date: 2020-10-05

  Revised date: 2020-11-25

  Online published: 2021-02-09

Supported by

the National Natural Science Foundation of China "Predicting the impacts of climate change on plant species migration and species diversity in alpine grassland of Qinghai-Tibet Plateau"(41901068);"A comparative study on ecological niche modelling using real and virtual scenarios"(31772432)

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摘要

物种分布模型是基于物种已知分布点位数据以及环境因子数据估计物种在特定时空条件下的地理分布的模型工具，是当下生物地理学研究的热点领域，但是对模型构建的背景知识缺乏理解而造成的模型滥用阻碍了其合理的应用与发展。从模型构建和模型应用两个方面，探讨了物种分布模型研究目前所面临的挑战和未来的发展方向，重点展示了我国物种分布模型发展现状，展望了其发展前景。在物种分布模型建模实践中，模型结果的合理性和精确性受建模方法的选择、模型参数设置、模型输入数据等带来的不确定性以及模型外推程度的影响。因此在相关研究中应理解模型的生态学和地学背景，重视模型的统计学规范，合理地选择模型算法。探讨了物种分布模型的重点发展方向，包括：探讨造成物种当前分布格局历史地理原因以及物种生态位演化造成的可能影响；结合谱系生物地理学和景观遗传学原理识别生物地理屏障和避难所、反演物种历史扩散路径；将物种相关作用等生态过程有效的纳入到模型框架中；合理地利用地学海量数据（如遥感数据）提高模型精度等。

关键词： 物种分布模型; 模型选择; 建模流程; 生态位理论; 生物地理学

本文引用格式

郭彦龙 , 赵泽芳 , 乔慧捷 , 王然 , 卫海燕 , 王璐坤 , 顾蔚 , 李新 . 物种分布模型面临的挑战与发展趋势[J]. 地球科学进展, 2020 , 35(12) : 1292 -1305 . DOI: 10.11867/j.issn.1001-8166.2020.110

Abstract

Species Distribution Models （SDMs） are numerical tools that combine observations of species occurrence or abundance with environmental variables database. SDMs tend to be used to simulate the potential distribution of species in a larger scale， under the confirming modeling conditions， which can be extrapolated in space and time. SDMs are now widely used in the fields of climate change biology， landscape ecology and conservation biology， and they are one of the most important tools in current biophysics research. This paper systematically discusses the current challenges and future development of species distribution model from two aspects： i.e.， model building and model application. The development status and prospect of species distribution models in China are reviewed. The main viewpoints and conclusions are as follows： In the modeling practice of species distribution model， the rationality and accuracy of model results depend on many factors， including the choice of environmental variables and model algorithms， spatial and temporal scale， the interaction between environmental and geographical factors and the model extrapolation degree. Hence， researchers should understand the theoretical basis of the model， attach importance to the standardization of statistics in the process of modeling， choose and design a reasonable model algorithm. In future research， we need to consider the historical and geographical reasons for the current distribution pattern and the possible impacts of species niche shift， as well as the principles of landscape genetics and phylogeography， which should be effectively incorporated into the model framework. Meanwhile， how to use geoscience big data （remote sensing data） effectively is also one of the challenges for the development of the models.

Key words： Species distribution model; Model selection; Modeling process; Niche theory; Biogeography.

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