基于Cite Space分析植物功能性状对气候变化的响应研究进展

Research progress on the response of plant functional traits to climate change based on CiteSpace analysis

面对全球气候变化的严峻形势,植物功能性状(PFTs)能较为敏感地响应气候变迁,对预测全球气候变化及生态系统影响具有重要指示作用.以Web of Science核心集TM的科学引文索引扩展版SCI-E数据库作为数据来源,利用CiteSpace软件分析得到植物功能性状对气候变化响应的研究进展:(1)从发文量分析1992-2019年植物功能性状对气候变化的响应研究可分为4个发展阶段:初步萌芽期—波动发展期—平稳发展期—高速发展期,呈现出良好的发展态势.(2)在研究力量方面,德国等欧洲国家的科研水平和影响力较高,我国的科研水平虽已有较大提高,但仍需加强与其他国家及机构间合作,提高在本领域的科研影响力;在研究载体方面,从期刊类型及研究领域的分析能展现出本研究领域的多学科交融性,并基于个体或小尺度层面逐步向全球化研究发展,有利于推动本研究领域进一步发展.(3)通过聚类分析形成22个代表性研究,其文献主题词间交叉重叠,联系紧密,按研究内容归纳为5个主题集群,即气候变化诱导林火及引起的干旱对植物的影响,植物功能性状(叶、根、水力性状等)对气候变化的响应与适应,基于植物功能性状对气候变化响应的时空尺度推绎,借助植物功能性状探究气候变化对群落结构和生态系统功能的影响,植物功能性状对气候变化响应的数据获取、分析方法以及模型模拟方法;整体形成一个连贯的研究系统.(4)主题演化分析按照时间顺序分为探索阶段、初步发展阶段、快速发展阶段、突破性阶段4个阶段.该过程反映出国际植物功能性状对气候变化的响应研究由定性分析到科学定量的过程,尺度推绎技术研究逐步增强,并呈现出3个发展趋势:基于北极等特殊生态系统植物功能性状的敏感特性,将其作为今后长期研究工作的重点,有利于及时反映全球气候变化;基于大数据研究,该领域朝着数据全球化收集—定量分析—动态模拟的态势发展,有助于更精确地反向预测过去与未来的气候变化;基于不易测量的功能性状研究,探索其是否对于气候变化的响应更加强烈,更有助于预测全球气候变化.

Plant functional traits(PFTs)can respond to climate change more sensitively;therefore,they have certain indicative roles in predicting global climate change and its impact on the ecosystem.We used the information visualization technology of CiteSpace to present a systematic review of published literature on the response of PFTs to climate change from 1992-2019.In total,1276 references from the SCI-E database in the Web of Science(WoS)core collection were analyzed using the CiteSpace software,bibliometric methods,and document combing methods to determine the hotspots,frontiers,and development trends of the response of PFTs to climate change.According to our analysis,(1)the international research occurring between 1992 to 2019 on the response of PFTs to climate change can be divided into four stages:initial initialization period,fluctuating development period,stable development period,and high-speed development period.(2)In terms of research strength,Germany and other European countries have a high level of scientific research and influence.In contrast,although the scientific research in China has been greatly improved,strengthened cooperation with other countries and institutions is necessary to improve scientific research and influence in this field.Based on the analysis of journal types and research fields,this research field has multi-disciplinary integration and it gradually develops toward globalization based on individual or small-scale levels,which is conducive to further development of this research field.(3)Twenty-two representative studies were formed through cluster analysis,and the literature subject words were overlapping and closely related.According to the research content,this research field can be classified into five subject clusters:effects of forest fires and droughts induced by climate change on plants;response and adaptation of PFTs(leaf,root,hydraulic properties,etc.)to climate change;spatial and temporal scale extrapolation based on the response of PFTs to climate change;using PFTs to explore the effects of climate change on community structure and ecosystem function,methods of data acquisition,analysis,and model simulation of PFTs in response to climate change.(4)The thematic evolution was divided into four stages according to the time sequence:exploration stage,initial development stage,rapid development stage,and breakthrough stage.This process reflects the process from qualitative analysis to scientific quantitative research on the response of PFTs to climate change.The research on scaling technology has been gradually strengthened and presented three development trends:(1)based on the sensitive characteristics of PFTs in special ecosystems such as the Arctic,it should be the focus of long-term research in the future,which is conducive to timely reflection of global climate change;(2)based on big data research,this field is developing toward the trend of data global collection-quantitative analysis-dynamic simulation,which is aids in more accurate reverse prediction of past and extrapolation of future climate change;(3)based on the study of non-measurable functional traits,to explore whether they are more responsive to climate change and more suitable for predicting global climate change.