马尾松分布格局对未来气候变化的响应

吴帆, 朱沛煌, 季孔庶

南京林业大学学报(自然科学版) ›› 2022, Vol. 46 ›› Issue (2) : 196-204.

PDF(22765 KB)
PDF(22765 KB)
南京林业大学学报(自然科学版) ›› 2022, Vol. 46 ›› Issue (2) : 196-204. DOI: 10.12302/j.issn.1000-2006.202008019
研究论文

马尾松分布格局对未来气候变化的响应

作者信息 +

Responses of masson pine(Pinus massoniana) distribution patterns to future climate change

Author information +
文章历史 +

摘要

【目的】分析并预测未来气候变化对马尾松适宜分布范围,探讨影响马尾松潜在地理分布的主要气候因子,为马尾松潜在分布区种质资源管理和保护提供参考与指导。【方法】以中国数字植物标本馆记录的马尾松分布数据为基础,利用MaxEnt模型及地理信息系统ArcGIS 10.3软件探讨马尾松当前地理分布特征及其潜在分布区,并针对代表性浓度路径(RCP) 2.6及RCP 8.5两种气候情景下未来(2050年和2070年)马尾松适宜分布范围及主要气候因子进行分析。【结果】当前马尾松高适生区覆盖的地区主要分布于秦岭—淮河线以南。浙江、福建、江西、湖北西南部、湖南、重庆、四川东南部、贵州北部、广西中部、广东北部等地区为马尾松主要分布区,海南、云南及台湾等地为零星分布区。在未来气候情景下,马尾松适生区向我国北部地区迁移,包括河南西部、山东半岛、辽东半岛、河北东部、山西南部等地区,而在云南南部零星地区不会再有马尾松自然分布。在未来两种气候情景4个条件中,相同RCP情景下,不同年限各适生区之间的差异并不明显,变化趋势大致相同;但相同年限的不同RCP情景对应的各适生区面积变化存在明显区别,RCP 8.5的影响要高于RCP 2.6。影响马尾松地理分布的主导生物气候变量为年均降水量、最干月降水量及平均气温日较差,且降水较温度的影响更大。【结论】未来气候变化将导致马尾松分布范围进一步扩大,新增分布区主要集中于当前分布区北部。应以当前马尾松适生环境为基础,针对当地气候类型、土壤条件等环境因素合理建立保护区,以便马尾松能够顺利适应新环境。

Abstract

【Objective】The suitable distribution range of masson pine (Pinus massoniana) was analyzed and predicted considering future climate change, and the main climatic factors affecting the potential geographical distribution are discussed in order to provide a reference and a guidance for the management and protection of germplasm resources in the potential distribution area.【Method】Masson pine distribution data recorded according to the Chinese Virtual Herbarium, a MaxEnt model and geographic information system software ArcGIS 10.3 were used to investigate the current distribution characteristics and potential distribution areas. A suitable distribution range and future changes (until 2050 and 2070) under two climate scenarios [representative concentration pathways (RCP) 2.6 and RCP 8.5)] were predicted, and the main climatic factors were analyzed.【Result】At present, the areas covered by masson pine were mainly distributed in the south of the Qinling Mountains-Huaihe River line. Zhejiang, Fujian, Jiangxi, southwest of Hubei, Hunan, Chongqing, southeast of Sichuan, north of Guizhou, central Guangxi, and northern Guangdong were the main distribution areas, whereas Hainan, Yunnan and Taiwan of China were scattered distribution areas. In the future climate scenario, the adaptive area of masson pine will move to the northern part of China, including areas in the west of Henan Province, Shandong Peninsula, Liaodong Peninsula, east of Hebei, and south of Shanxi Province, whereas there will be no natural distribution in scattered areas in the south of Yunnan. In two climate scenarios and under the same RCP, the difference between the adaptive areas of different years was not pronounced, and the variation trend was roughly the same. However, there were significant differences in changes of each adaptive area under different RCP scenarios of the same age, and the impact of RCP 8.5 was higher than that of RCP 2.6. The dominant bioclimate variables affecting the geographical distribution of masson pine were average annual precipitation, precipitation during the driest months, and average daily temperature ranges, and precipitation exerted stronger effects than temperature.【Conclusion】 Future climate change will lead to a further expansion of masson pine distributions, and the new distribution areas will mainly be in the north of the current distribution area. Based on the current habitat of masson pine, a protection zone should be established reasonably according to the local climate type, soil conditions, and other environmental factors, so that masson pine populations can adapt over time to the new environment.

关键词

马尾松 / 气候变化 / MaxEnt模型 / 代表性浓度路径 / 潜在地理分布 / 主导生物气候因子

Key words

masson pine(Pinus massoniana) / climate change / MaxEnt model / representative concentration pathways / potential geographic distribution / dominant bioclimate factor

引用本文

导出引用
吴帆, 朱沛煌, 季孔庶. 马尾松分布格局对未来气候变化的响应[J]. 南京林业大学学报(自然科学版). 2022, 46(2): 196-204 https://doi.org/10.12302/j.issn.1000-2006.202008019
WU Fan, ZHU Peihuang, JI Kongshu. Responses of masson pine(Pinus massoniana) distribution patterns to future climate change[J]. JOURNAL OF NANJING FORESTRY UNIVERSITY. 2022, 46(2): 196-204 https://doi.org/10.12302/j.issn.1000-2006.202008019
中图分类号: S791.24   

参考文献

[1]
高文强, 王小菲, 江泽平, 等. 气候变化下栓皮栎潜在地理分布格局及其主导气候因子[J]. 生态学报, 2016, 36(14): 4475-4484. DOI: 10.5846/stxb201412012387.
GAO W Q, WANG X F, JIANG Z P, et al. Impact of climate change on the potential geographical distribution pattern and dominant climatic factors of Quercus variabilis[J]. Chin J Plant Ecol, 2016, 36(14): 4475-4484.
[2]
BELLARD C, BERTELSMEIER C, LEADLEY P, et al. Impacts of climate change on the future of biodiversity[J]. Ecol Lett, 2012, 15(4): 365-377. DOI: 10.1111/j.1461-0248.2011.01736.x.
[3]
沈永平, 王国亚. IPCC第一工作组第五次评估报告对全球气候变化认知的最新科学要点[J]. 冰川冻土, 2013, 35(5): 1068-1076.
SHEN Y P, WANG G Y. Key Finding and assessment results of IPCC WGI fifth assessmentreport[J]. J Sociology and Geocryology, 2013, 35(5): 1068-1076. DOI: 10.7522/j.issn.1000-0240.2013.0120.
[4]
PHILLIPS S J, ANDERSON R P, SCHAPIRE R E. Maximum entropy modeling of species geographic distributions[J]. Ecol Model, 2006, 190(3/4): 231-259. DOI: 10.1016/j.ecolmodel.2005.03.026.
[5]
MEROW C, SMITH M J, SILANDER J A Jr. A practical guide to MaxEnt for modeling species distributions: what it does, and why inputs and settings matter[J]. Ecography, 2013, 36(10): 1058-1069. DOI: 10.1111/j.1600-0587.2013.07872.x.
[6]
王梦琳, 范靖宇, 李敏, 等. 入侵害虫蔗扁蛾在我国的潜在分布区[J]. 生物安全学报, 2017, 26(2): 129-133.
WANG M L, FAN J Y, LI M, et al. Potential geographical distribution of the introduced banana moth, Opogona sacchari (Lepidoptera: Tineidae) in China[J]. J Biosaf, 2017, 26(2): 129-133. DOI: 10.3969/j.issn.2095-1787.2017.02.004.
[7]
中国科学院中国植物志编辑委员会. 中国植物志(第七卷)[M]. 北京: 科学出版社, 1978.
[8]
冯源恒, 杨章旗, 李火根, 等. 马尾松育种进程中的遗传增益与遗传多样性变化[J]. 南京林业大学学报(自然科学版), 2018, 42(5): 196-200.
FENG Y H, YANG Z Q, LI H G, et al. Changes of genetic gain and genetic diversity in the breeding process of Pinus massoniana[J]. J Nanjing For Univ (Nat Sci Ed), 2018, 42(5): 196-200. DOI: 10.3969/j.issn.1000-2006.201706022.
[9]
尹焕焕, 刘青华, 周志春, 等. 马尾松产脂性状与生长性状的无性系变异及相关性[J]. 林业科学, 2018, 54(12): 82-91.
YIN H H, LIU Q H, ZHOU Z C, et al. Genetic variation among clones of masson pine (Pinus massoniana) for growth, oleoresin traits and their correlations[J]. Sci Silvae Sin, 2018, 54(12): 82-91. DOI: 10.11707/j.1001-7488.20181209.
[10]
谭健晖, 黄永利, 冯源恒, 等. 15年和22年马尾松纸浆材优良家系选择[J]. 中南林业科技大学学报, 2017, 37(2): 9-13.
TAN J H, HUANG Y L, FENG Y H, et al. Selection of excellent pulpwood families of masson pine between 15 and 22 years old[J]. J Central South Univ For Technol, 2017, 37(2): 9-13. DOI: 10.14067/j.cnki.1673-923x.2017.02.002.
[11]
刘彬, 刘青华, 周志春, 等. 基于高通量转录组测序筛选马尾松抗松材线虫病相关基因[J]. 林业科学研究, 2019, 32(5): 1-10.
LIU B, LIU Q H, ZHOU Z C, et al. Identification of candidate constitutive expressed resistant genes of pine wilt disease in Pinus massoniana based on high-throughput transcriptome sequencing[J]. For Res, 2019, 32(5): 1-10. DOI: 10.13275/j.cnki.lykxyj.2019.05.001.
[12]
黄红兰, 钟沃谷, 衣德萍, 等. 未来气候变化对我国毛红椿适生区分布格局的影响预测[J]. 南京林业大学学报(自然科学版), 2020, 44(3): 163-170.
HUANG H L, ZHONG W G, YI D P, et al. Predicting the impact of future climate change on the distribution patterns of Toona ciliata var. pubescens in China[J]. J Nanjing For Univ (Nat Sci Ed), 2020, 44(3): 163-170. DOI: 10.3969/j.issn.1000-2006.201812037.
[13]
KHARIN V V, ZWIERS F W, ZHANG X, et al. Changes in temperature and precipitation extremes in the CMIP5 ensemble[J]. Clim Change, 2013, 119(2): 345-357. DOI: 10.1007/s10584-013-0705-8.
[14]
刘超, 霍宏亮, 田路明, 等. 基于MaxEnt模型不同气候变化情景下的豆梨潜在地理分布[J]. 应用生态学报, 2018, 29(11): 3696-3704.
LIU C, HUO H L, TIAN L M, et al. Potential geographical distribution of Pyrus calleryana under different climate change scenarios based on the MaxEnt model[J]. Chin J Appl Ecol, 2018, 29(11): 3696-3704. DOI: 10.13287/j.1001-9332.201811.016.
[15]
张兴旺, 李垚, 谢艳萍, 等. 气候变化对黄山花楸潜在地理分布的影响[J]. 植物资源与环境学报, 2018, 27(4): 31-41.
ZHANG X W, LI Y, XIE Y P, et al. Effect of climate change on potential geographical distribution of Sorbus amabilis[J]. J Plant Resour Environ, 2018, 27(4): 31-41. DOI: 10.3969/j.issn.1674-7895.2018.04.04.
[16]
王卫, 杨俊杰, 罗晓莹, 等. 基于Maxent模型的丹霞山国家级自然保护区极小种群植物丹霞梧桐的潜在生境评价[J]. 林业科学, 2019, 55(8): 19-27.
WANG W, YANG J J, LUO X Y, et al. Assessment of potential habitat for Firmiana danxiaensis, a plant species with extremely small populations in Danxiashan National Nature Reserve based on Maxent Model[J]. Sci Silvae Sin, 2019, 55(8): 19-27. DOI: 10.11707/j.1001-7488.20190803.
[17]
麻亚鸿. 基于最大熵模型(MaxEnt)和地理信息系统(ArcGIS)预测藓类植物的地理分布范围--以广西花坪自然保护区为例[D]. 上海: 上海师范大学, 2013.
MA Y H. ApplyingMaxEnt and ArcGIS to predict mosses geographic distribution range:a case study of Huaping Nature Reserve, Guangxi[D]. Shanghai: Shanghai Normal University, 2013.
[18]
朱炳海. 中国气候[M]. 北京: 科学出版社, 1962.
[19]
季孔庶. 马尾松人工林培育技术[M]. 北京: 中国农业出版社, 2001.
[20]
全国土壤普查办公室. 中国土壤[M]. 北京: 中国农业出版社, 1998.
[21]
蒙园园, 石林. 矿物质调理剂中铝的稳定性及其对酸性土壤的改良作用[J]. 土壤, 2017, 49(2): 345-349.
MENG Y Y, SHI L. Stability of aluminum in mineral conditioners and amelioration on acid soil[J]. Soils, 2017, 49(2): 345-349. DOI: 10.13758/j.cnki.tr.2017.02.020.
[22]
郭紫伊. 天津滨海及其周边地区土壤盐渍化特征分析[D]. 天津: 天津大学, 2018.
GUO Z Y. Study on soil salinization characteristics of Tianjin coastal region and its surrounding areas[D]. Tianjin: Tianjin University, 2018.
[23]
林年丰, 汤洁. 松嫩平原环境演变与土地盐碱化、荒漠化的成因分析[J]. 第四纪研究, 2005, 25(4): 474-483.
LIN N F, TANG J. Study on the environment evolution and the analysis of causes to land salinization and desertification in Songnen Plain[J]. Quat Sci, 2005, 25(4): 474-483. DOI: 10.3321/j.issn:1001-7410.2005.04.011.
[24]
张春华, 和菊, 孙永玉, 等. 基于MaxEnt模型的毛红椿适生区预测[J]. 林业科学研究, 2018, 31(3): 120-126.
ZHANG C H, HE J, SUN Y Y, et al. Distributional change in suitable areas for T. ciliata var. pubescens based on MaxEnt[J]. For Res, 2018, 31(3): 120-126. DOI: 10.13275/j.cnki.lykxyj.2018.03.016.
[25]
郑维艳, 曹坤芳. 未来气候变化对四种木姜子地理分布的影响[J]. 广西植物, 2020, 40(11): 1584-1594.
ZHENG W Y, CAO K F. Impact of future climate change on potential geogra-phical distribution of four Litsea species in China[J]. Guihaia, 2020, 40(11): 1584-1594. DOI: 10.11931/guihaia.gxzw201904020.
[26]
闻志彬, 张杰, 张明理. 中国特有种天山猪毛菜的地理分布及潜在分布区预测[J]. 植物资源与环境学报, 2016, 25(1): 81-87.
WEN Z B, ZHANG J, ZHANG M L. Geographical distribution and prediction on potential distribution areas of Chinese endemic species: Salsola junatovii[J]. J Plant Resour Environ, 2016, 25(1): 81-87. DOI: 10.3969/j.issn.1674-7895.2016.01.10.
[27]
朱耿平, 刘国卿, 卜文俊, 等. 生态位模型的基本原理及其在生物多样性保护中的应用[J]. 生物多样性, 2013, 21(1): 90-98.
摘要
生态位模型是利用物种已知的分布数据和相关环境变量, 根据一定的算法来推算物种的生态需求, 然后将运算结果投射至不同的空间和时间中来预测物种的实际分布和潜在分布。近年来, 该类模型被越来越多地应用在入侵生物学、保护生物学、全球气候变化对物种分布影响以及传染病空间传播的研究中。然而, 由于生态位模型的理论基础未被深入理解, 导致得出入侵物种生态位迁移等不符合实际的结论。作者从生态位与物种分布的关系、生态位模型构建的基本原理以及生态位模型和生态位的关系等方面探讨了生态位模型的理论基础。非生物的气候因素、物种间的相互作用和物种的迁移能力是影响物种分布的3个主要因素, 它们在不同的空间尺度下作用于物种的分布。生态位模型是利用物种分布点所关联的环境变量来模拟物种的分布, 这些分布点本身关联着该物种和其他物种间的相互作用, 因此生态位模型所模拟的是现实生态位(realized niche)或潜在生态位(potential niche), 而不是基础生态位(fundamental niche)。Grinnell生态位和Elton生态位均在生态位模型中得到反映, 这取决于环境变量类型的选择、所采用环境变量的分辨率以及物种自身的迁移能力。生态位模型在生物多样性保护中的应用主要包括物种的生态需求分析、未知物种或种群的探索和发现、自然保护区的选择和设计、物种入侵风险评价、气候变化对物种分布的影响、近缘物种生态位保守性及基于生态位分化的物种界定等方面。
ZHU G P, LIU G Q, BU W J, et al. Ecological niche modeling and its applications in biodiversity conservation[J]. Biodivers Sci, 2013, 21(1): 90-98. DOI: 10.3724/SP.J.1003.2013.09106.

基金

国家重点研发计划(2017YFD0600304)
江苏高校优势学科建设工程资助项目(PAPD)

编辑: 吴祝华
PDF(22765 KB)

Accesses

Citation

Detail

段落导航
相关文章

/