地球科学进展 ›› 2020, Vol. 35 ›› Issue (12): 1292 -1305. doi: 10.11867/j.issn.1001-8166.2020.110

生态学研究 上一篇    下一篇

物种分布模型面临的挑战与发展趋势
郭彦龙 1( ),赵泽芳 2,乔慧捷 3,王然 3, 4,卫海燕 5,王璐坤 5,顾蔚 6,李新 1( )   
  1. 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
  • 收稿日期:2020-10-05 修回日期:2020-11-25 出版日期:2020-12-10
  • 通讯作者: 李新 E-mail:guoyl@itpcas.ac.cn;xinli@itpcas.ac.cn
  • 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)

Challenges and Development Trend of Species Distribution Model

Yanlong Guo 1( ),Zefang Zhao 2,Huijie Qiao 3,Ran Wang 3, 4,Haiyan Wei 5,Lukun Wang 5,Wei Gu 6,Xin Li 1( )   

  1. 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
  • Received:2020-10-05 Revised:2020-11-25 Online:2020-12-10 Published:2021-02-09
  • Contact: Xin Li E-mail:guoyl@itpcas.ac.cn;xinli@itpcas.ac.cn
  • About author:Guo Yanlong (1987-), male, Shuozhou City, Shanxi Province, Postdoctoral. Research areas include species distribution and ecological model. E-mail: guoyl@itpcas.ac.cn
  • 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)

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。

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.

中图分类号: 

图1 物种分布模型相关研究年度文章数(数据来源于Web of Science和中国知网)
Fig.1 Total number of science papers about species distribution model every year (data from Web of Science & CNKI)
表1 常见物种分布模型算法的优缺点
Table 1 The advantages and disadvantages of the popular species distribution models
类型 代表模型算法 采样点数据需求 优点 缺点
包络算法

表面分布区分室模型(Surface Range Envelope, SRE)[ 31 ]

栖息地模型(HABITAT)[ 32 ]

物种分布数据 算法简单,易于使用 结果为二元数据(存在/不存在);对异常值敏感;气候变量的作用等同;模型结果无法给出分布细节;精度较低
距离算法

生态位因子分析模型(Ecological Niche Factor Analysis,ENFA)[ 26 ]

Gower距离(DOMAIN)[ 33 ]

马氏距离(Mahalanobis Distance, MD)[ 34 ]

物种分布数据 简单的规则和假设;在环境空间中推导出简单生态位特征,如生态位和生态位宽度;ENFA可以获取影响物种分布的主要限制因子 精度较低;无法处理定性的环境因子(分类变量);受取样点分布特征的影响较大
回归算法

广义线性模型(Generalized Linear Model, GLM)[ 35 ];

广义相加模型(Generalized Additive Model, GAM)[ 35 ];

多元自回归样条模型(Multiple Adaptive Regression Splines, MARS)[ 36 ]

物种分布与不分布数据

物种丰度数据

生物量

针对不同的因变量,分布形式可以有不同的处理方式;适用于响应变量是数值变量的情况;模型解释能力较好,通过回归方程显示的表达环境因子与建模目标的关系 无法处理定性的环境因子;精度依赖样本数量的大小
分类算法

分类树分析(Classification Tree Analysis, CTA)[ 37 ]

柔性判别分析(Flexible Discriminant Analysis, FDA)[ 16 , 38 ]

物种分布与不分布数据 不需要预先假设响应变量与预测变量之间的关系,有效的处理非线性关系;不易受少数异常数据影响;强大的统计解析功能 分类节点的生成只与数据特征有关,没有生态学意义;模型结果为离散值
常用机器学习算法

人工神经网络 (Artificial Neural Network, ANN)[ 39 ]

支持向量机(Support Vector Machine, SVM)[ 40 ]

随机森林(Random Forest, RF)[ 20 ]

推进式回归树(Boosted Regression Tree, BRT)[ 16 , 38 ]

物种分布数据;物种分布与不分布数据 精度较高;模拟结果比较收敛,提供了生境分布的细节,具有较好的空间表现 模型精度需要大数据量保证;可移植性差;不能提供清晰的统计学原理;存在过拟合等风险;计算成本高
最大熵 MaxEnt[ 41 ] 物种分布—背景数据(最大熵模型);物种分布与不分布数据(判别最大熵) 预测结果精度较高;在样本量相对较小的情况下能够取得较好的建模效果;模型可以仅依靠物种存在点数据建模;在统一建模框架下可以处理连续环境变量与分类环境变量;MaxEnt软件是免费的并且有友好的用户界面 由于其模型界面良好的封装性,无法调整相应程序;模型的时空外推能力仅在低阈值情况下较好;在较小的样本量情况下得出的结论可能对物种生态位模拟不完整,造成模拟结果失真;友好的模型界面也会造成模型的滥用
模糊数学

模糊物元模型(Fuzzy Matter Element, FME)[ 27 , 42 , 43 ];

模糊神经网络(Fuzzy Neural Networks, FNN)[ 44 ]

物种分布数据;

物种丰度数据;

生物量;

目标物种成分含量数据

专家经验与实际采样点统计信息融入到隶属函数中,以实现有限采样点基础上的物种分布模型建模;针对复杂系统建模具有较高预测能力;可以有效地在空间上预测物种的某种特性(生物量,有效成分含量)的分布 针对不同的建模目标需要额外的采样点数据信息(生物量和有效成分含量等);完全由专家经验确定的隶属函数具有主观性
贝叶斯 网络 贝叶斯网络模型(Bayesian networks, BN)[ 45 ] 物种分布数据 采用变量之间的概率关系的图形模型来构建模型;概率关系可以由统计数据得到也可以由专家经验生成;可以有效地整合专家知识;利用有限采样点数据构建稳健(robust)模型;建模过程有严格的生态学意义 以有向无循环图(Directed Acyclic Graph,DAG)相关节点(变量)之间的概率关系,在缺乏足够的专家知识或实验数据时,模型的构建存在随机性以及不确定性;只使用离散变量,会导致信息损失
图2 基于物种分布模型的物种保护决策过程(据参考文献[ 80 ]修改)
Fig.2 Decision-making process for species conservation and management based on species distribution model modified after reference [ 80 ])
图3 物种分布模型相关研究中文文章题目词云图
Fig.3 Word cloud of Chinese article title in research on species distribution model
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