植物生态学报 ›› 2019, Vol. 43 ›› Issue (4): 273-283.doi: 10.17521/cjpe.2018.0237

• 综述 •    下一篇

中国植物分布模拟研究现状

刘晓彤1,袁泉1,倪健1,2,*()   

  1. 1 浙江师范大学化学与生命科学学院, 浙江金华 321004
    2 浙江金华山亚热带森林生态系统野外科学观测研究站, 浙江金华 321004
  • 收稿日期:2018-09-25 修回日期:2019-03-20 出版日期:2019-04-20 发布日期:2019-04-23
  • 通讯作者: 倪健 ORCID:0000-0001-6198-4849 E-mail:nijian@zjnu.edu.cn
  • 基金资助:
    国家自然科学基金(41471049)

Research advances in modelling plant species distribution in China

LIU Xiao-Tong1,YUAN Quan1,NI Jian1,2,*()   

  1. 1 College of Chemistry and Life Sciences, Zhejiang Normal University, Jinhua, Zhejiang 321004, China
    2 Jinhua Mountain Observation and Research Station for Subtropical Forest Ecosystems, Jinhua, Zhejiang 321004, China
  • Received:2018-09-25 Revised:2019-03-20 Online:2019-04-20 Published:2019-04-23
  • Contact: NI Jian ORCID:0000-0001-6198-4849 E-mail:nijian@zjnu.edu.cn
  • Supported by:
    Supported by the National Natural Science Foundation of China(41471049)

摘要:

在过去的20年里, 物种分布模型已广泛应用于动植物地理分布的模拟研究。该文以植物物种分布模拟为例, 利用中国知网、维普网以及Web of Science文献数据库的检索与统计, 分析了2000-2018年间, 中国研究人员利用各种物种分布模型对植物物种分布模拟研究的发文量、模拟模型、物种类型、数据来源、研究目的等信息。最终共收集到366篇有效文献, 分析表明2011年以来中国的物种分布模型应用发展迅速, 且以最近5年最为迅猛, 在生态学、中草药业、农业和林业等行业部门应用广泛。在使用的33种模型中, 应用最广的为最大熵模型(MaxEnt)。有一半研究的环境数据仅包含气候数据, 另一半研究不仅包含气候数据还包括地形与土壤等数据; 环境及物种数据的来源多样, 国际及本土数据库均得到使用。模拟涉及有明确清单的562个植物种, 既有木本植物(52.7%), 也有草本植物(41.8%), 其中中草药、果树、园林植物、农作物等占比较高。研究目的主要集中在过去、现在和未来气候变化对植物种分布的影响及预测, 以及物种分布评估与生物多样性评价(包括入侵植物风险评估)两大方面。预测物种潜在分布范围与气候变化影响等基础研究, 与模拟物种适生区与推广种植等应用研究并重, 物种分布模型在生态学与农业、林业和中草药业等多学科、多行业开展多种应用, 多物种、多模型和多来源数据共同参与模拟与比较, 开发新的机理性物种分布模型, 拓展新的物种分布模拟应用领域, 是今后研究的重点发展方向。

关键词: 物种分布模型, 气候变化, 生物多样性保护, 潜在分布区, 最大熵模型

Abstract:
Species distribution models (SDMs) have been extensively used in simulations of geographical distribution of animal and plant species during the past 20 years. Taking the simulation of plant species distribution as an example, we used both the digitized and library databases including the China National Knowledge Infrastructure (CNKI), the VIP Chinese Journal Database (VIP) and the Web of Science (WoS) to compile available literatures published from 2000 to 2018. The number of publications, SDMs used, target plant species, data sources, and the purpose of studies about using various SDMs to simulate plant species distribution in China was statistically investigated. In total 366 publications were collected. Further analysis and synthesis showed that the application of SDMs in simulating Chinese plant species distribution has developed rapidly since 2011, especially during the past five years. SDMs have been used in studies of ecology, Chinese traditional medicine, agriculture, and forestry. The Maximum Entropy Model (MaxEnt) is the most widely used model among 33 commonly used SDMs. A half of the studies use climate data only, and another half of the studies use both climate, soil and topography data. The source of both environmental data and plant distribution data are diverse, derived from international and domestic databases. In these studies, researchers have simulated the distribution of 562 plant species, in which 52.7% are woody species and 41.8% are herbaceous species, including a large number of Chinese medicinal plants, fruit trees, garden plants, and crops. Studies aim mainly on two aspects, i.e. the impact of climate change on plant species distribution and their predicted pattern in the past, present, and future climate scenarios, and the assessment of the potential distribution of plant species and biodiversity trends (including the risk of invasive species). In future studies, more attention should be paid to both the basic science on the modelling of potential distribution of plant species and the impact from climate change, and the applied science on the prediction of suitable distribution area of plant species in order to popularize their plantation. More applications of SDMs in multiple disciplines and in multiple industries such as ecology, forestry, crop science and Chinese traditional medicine should be further developed. Joint simulations and inter-comparisons using multiple plant species, more SDMs and multiple data sources of environmental data, as well as the development of new and mechanism SDMs are encouraged. The extension of model applications in new research fields is also needed.

Key words: species distribution models, climate change, biodiversity conservation, potential distribution area, Maximum Entropy Model