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应用生态学报 ›› 2018, Vol. 29 ›› Issue (12): 4217-4225.doi: 10.13287/j.1001-9332.201812.009

• 综合评述 • 上一篇    下一篇

揭示群落结构及其环境响应的联合物种分布模型的研究进展

朱媛君,山丹,张晓,刘艳书,时忠杰,杨晓晖*   

  1. 中国林业科学研究院荒漠化研究所, 北京 100091
  • 收稿日期:2018-05-10 修回日期:2018-09-21 出版日期:2018-12-20 发布日期:2018-12-20
  • 作者简介:朱媛君, 男, 1992年生, 博士. 主要从事荒漠生态学与植被生态学研究. E-mail: neidazhuyuanjun@163.com
  • 基金资助:

    本文由国家重点研发计划项目(2016YFC0500908)和国家自然科学基金项目(31670715,41471029,41701249)资助

Advances in joint species distribution models to reveal community structure and its environmental response

ZHU Yuan-jun, SHAN Dan, ZHANG Xiao, LIU Yan-shu, SHI Zhong-jie, YANG Xiao-hui*   

  1. Institution of Desertification Studies, Chinese Academy of Forestry, Beijing 100091, China
  • Received:2018-05-10 Revised:2018-09-21 Online:2018-12-20 Published:2018-12-20
  • Supported by:

    This work was supported by the National Key Research and Development Program of China (2016YFC0500908) and the National Natural Science Foundation of China (31670715, 41471029, 41701249).
    2018-05-10 Received, 2018-09-21 Accepted.

摘要: 物种分布模型通常用于基础生态和应用生态研究,用来确定影响生物分布和物种丰富度的因素,量化物种与非生物条件的关系,预测物种对土地利用和气候变化的反应,并确定潜在的保护区.在传统的物种分布模型中,生物的相互作用很少被纳入,而联合物种分布模型(JSDMs)作为近年提出的一种新的可行方法,可以同时考虑环境因素和生物交互作用,因而成为分析生物群落结构和种间相互作用过程的有力工具.JSDMs以物种分布模型(SDMs)为基础,通常采用广义线性回归模型建立物种对环境变量的多变量响应,以随机效应的形式获取物种间的关联,同时结合隐变量模型(LVMs),并基于Laplace近似和马尔科夫蒙脱卡罗模拟的最大似然估计或贝叶斯方法来估算模型参数.本文对JSDMs的产生及理论基础进行归纳总结,重点介绍了不同类型JSDMs的特点及其在现代生态学中的应用,阐述了JSDMs的应用前景、使用过程中存在的问题及发展方向.随着对环境因素与多物种种间关系研究的深入,JSDMs将是今后物种分布模型研究的重点.

Abstract: Species distribution models are commonly used in basic and applied ecological research to examine the factors driving the distribution and abundance of organisms. They are employed to quantify species’ relationships with abiotic conditions, to predict species’ response to land-use and climatic change, and to identify potential conservation areas. Biotic interactions have been rarely included in traditional species distribution models, wherein joint species distribution models (JSDMs) emerge as a feasible approach to simultaneously incorporate environmental factors and interspecific interactions, making it a powerful tool for analyzing the structure and assembly of biotic communities. Generally, the JSDMs are based on species distribution models (SDMs), with the abundance or occurrence of multiple species as response variables and environmental factors, species associations and species traits being incorporated in the modeling framework. These models commonly use generalized linear regression methods (GLM) to relate multivariate response to environmental variables, and capture species associations in the form of random effects. The limitation has been overcome by the introduction of latent variable models (LVMs). Typically, the model parameters are estimated using maximum likelihood estimation or Bayesian methods implemented by Laplace Approximation and Markov Chain Monte Carlo (MCMC) simulations, respectively. In this review, the generation and theoretical basis of JSDMs were summarized. The characteristics of different types of JSDMs and their applications in modern ecology were emphatically introduced. The problems and prospects of JSDMs were discussed. With the in-depth study of the relationship between environmental factors and multi-species interactions, JSDMs would be the focus of future studies of species distribution model.