2025年04月23日 星期三

广西师范大学学报（自然科学版） ›› 2024, Vol. 42 ›› Issue (6): 215-225.doi: 10.16088/j.issn.1001-6600.2023102502

• “污水处理”专栏 • 上一篇    下一篇

基于MaxEnt模型的中国黄杉潜在适宜生境变化分析

农小霞1,2★, 郁华英1,2★, 向盈盈1,2, 杨盼宇1,2*, 张启伟1,2*   

  1. 1.珍稀濒危动植物生态与环境保护教育部重点实验室(广西师范大学),广西 桂林 541006;
    2.广西师范大学 生命科学学院,广西 桂林 541006
  • 收稿日期:2023-10-25 修回日期:2024-01-03 出版日期:2024-12-30 发布日期:2024-12-30
  • 通讯作者: 杨盼宇(1987—), 女, 湖南岳阳人, 广西师范大学副教授, 博士。E-mail: yangpanyu0904@sina.com;张启伟(1986—), 男, 四川成都人, 广西师范大学助理研究员, 博士。E-mail: zqw_21@163.com
  • 作者简介:★该2位作者对本文的贡献相等
  • 基金资助:
    国家自然科学基金(32260265)

Analysis of Potential Suitable Habitat Change of Pseudotsuga sinensis Based on MaxEnt Model

NONG Xiaoxia1,2★, YU Huaying1,2★, XIANG Yingying1,2, YANG Pangyu1,2*, ZHANG Qiwei1,2*   

  1. 1. Key Laboratory of Ecology of Rare and Endangered Species and Environmental Protection (Guangxi Normal University), Ministry of Education, Guilin Guangxi 541006, China;
    2. College of Life Sciences, Guangxi Normal University, Guilin Guangxi 541006, China
  • Received:2023-10-25 Revised:2024-01-03 Online:2024-12-30 Published:2024-12-30

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摘要： 黄杉Pseudotsuga sinensis是中国特有植物,既是宝贵的经济树种也是山地生态系统中重要的组成成分,为国家 Ⅱ 级重点保护物种,但近年来由于非法采伐导致种群数量急剧下降,因而开展其相关保护研究迫在眉睫。本研究采用MaxEnt模型对黄杉在未来SSP126和SSP585这2种极端温室气体排放情景下的分布情况和空间格局变化进行预测,探究影响其分布的主要环境因子和动态变迁情况,并结合主要分布地年龄结构评估黄杉未来生存前景,以期为未来气候变化背景下黄杉种质资源的保护和合理利用提供理论基础。结果表明:1)最干月降水量、年降水量、海拔和最冷月最低温度是影响黄杉分布的关键因子,其贡献率分别是40.8%、24.7%、15.8%、7.2%;2)当前黄杉适生区主要分布在台湾和大陆西南地区,核心分布区集中在云贵高原的高山地带;3)未来不同情景下的不同时期黄杉适生区分布呈现出北移的趋势,适生区面积有小幅增长,但是核心高适生区分布条带发生断裂,呈现出一定程度的破碎化分布;此外,多数重点保护种群的适生区情况不变,但个别种群适生区缩减,种群存在消失的潜在风险,应该对其进行重点保护。

关键词: 黄杉, 气候变化, MaxEnt, 潜在适生区, 分布格局, 年龄结构

Abstract: Pseudotsuga sinensis is an endemic plant in China, which is not only a valuable economic tree but also an important component of the mountain ecosystem, and is National Level II Key Protected Species in China. However, in recent years, due to logging activities, its number has declined sharply, so it is urgent to carry out related research on its protection. In this study, MaxEnt model was used to simulate the potential distribution and spatial pattern changes of the suitable areas of P. sinensis under the two extreme greenhouse gas emission scenarios of SSP126 and SSP585 in the future, to explore the main environmental factors influencing its distribution and its dynamic changes, and to evaluate the future survival prospects of P. sinensis combined with the age structure of the main distribution areas so as to provide a theoretical basis for the protection and rational utilization of P. sinensis germplasm resources under the background of climate change in the future. The results indicated that: 1) Precipitation in the driest month, annual precipitation, elevation and the lowest temperature in the coldest month were the key factors affecting the distribution of P. sinensis, and their contribution rates were 40.8%, 24.7%, 15.8% and 7.2%, respectively; 2) At present, the suitable habitat areas of P. sinensis were mainly distributed in Taiwan and southwest China. The core distribution areas were concentrated in the alpine area of Yunnan-Guizhou Plateau; 3) In the future, under different scenarios and at different periods, the distribution of suitable areas of P. sinensis showed a trend of northward migration, and the area of suitable areas increasd slightly, but the distribution strip of the core high suitable area was fractured, showing a certain degree of fragmentation distribution. In addition, the suitable areas of most key protected populations remained unchanged, but the suitable areas of some populations were reduced. These populations are at potential risk of extinction and should be prioritized for protection.

Key words: Pseudotsuga sinensis, climate change, MaxEnt, potential habitat area, distribution pattern, age structure

中图分类号:  Q948

[1] WEI X X, YANG Z Y, LI Y, et al. Molecular phylogeny and biogeography of Pseudotsuga(Pinaceae): Insights into the floristic relationship between Taiwan and its adjacent areas[J]. Molecular Phylogenetics and Evolution, 2010, 55(3): 776-785. DOI: 10.1016/j.ympev.2010.03.007.
[2] 李楠. 黄杉属植物地理学的研究[J]. 植物研究, 1993(4): 404-411.
[3] 冯倩. 黄杉[J]. 百科知识, 2013(12): 39-40.
[4] HUANG T, YING S H, LI J Y, et al. Phytochemical and biological studies on rare and endangered plants endemic to China. Part XV. Structurally diverse diterpenoids and sesquiterpenoids from the vulnerable conifer Pseudotsuga sinensis[J]. Phytochemistry, 2020, 169: 112184. DOI: 10.1016/j.phytochem.2019.112184.
[5] 罗荣飞. 宣威市黄杉资源现状及保护利用建议[J]. 现代农业科技, 2022(17): 135-137, 142. DOI: 10.3969/j.issn.1007-5739.2022.17.034.
[6] KEHIMKAR I. The IUCN red list of threatened speciesTM [M].[S.l.] :[s.n.] , 2020.
[7] RUMPF S B, GRAVEY M, BRÖNNIMANN O, et al. From white to green: Snow cover loss and increased vegetation productivity in the European Alps[J]. Science, 2022, 376(6597): 1119-1122. DOI: 10.1126/science.abn6697.
[8] YE X Z, ZHANG M Z, YANG Q Y, et al. Prediction of suitable distribution of a critically endangered plant Glyptostrobus pensilis[J]. Forests, 2022, 13(2): 257. DOI: 10.3390/f13020257.
[9] 易进海, 张国林, 李伯刚. 黄杉化学成分的研究[J]. 药学学报, 2002,37(5): 352-354. DOI: 10.3321/j.issn:0513-4870.2002.05.008.
[10] 李玲丽, 徐耀粘, 周天阳, 等. 湖南八大公山黄杉种群结构和群落特征研究[J]. 植物科学学报, 2021, 39(2): 111-120. DOI: 10.11913/PSJ.2095-0837.2021.20111.
[11] 李艳, 鲁顺保, 刘晓燕, 等. 江西三清山华东黄杉种群遗传多样性研究[J]. 江西农业大学学报, 2009, 31(4): 685-689. DOI: 10.3969/j.issn.1000-2286.2009.04.020.
[12] 李继品, 岳彩荣, 孟翠萍, 等. 基于GIS的黄杉生长适宜性评价研究:以滇东北为例[J]. 林业调查规划, 2023, 48(4): 13-23. DOI: 10.3969/j.issn.1671-3168.2023.04.002.
[13] 李望军, 冯图, 周瑞伍, 等. 基于MaxEnt模型的贵州省天然黄杉林的潜在分布预测研究[J]. 西部林业科学, 2019, 48(3): 47-52. DOI: 10.16473/j.cnki.xblykx1972.2019.03.008.
[14] 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.
[15] RATHORE M K, SHARMA L K. Efficacy of species distribution models (SDMs) for ecological realms to ascertain biological conservation and practices[J]. Biodiversity and Conservation, 2023, 32(10): 3053-3087. DOI: 10.1007/s10531-023-02648-1.
[16] 庞丽芳, 庾太林. 基于MaxEnt模型的黄腹角雉潜在生境预测[J]. 广西师范大学学报(自然科学版), 2023, 41(5): 123-133. DOI: 10.16088/j.issn.1001-6600.2022092101.
[17] RIAHI K, VAN VUUREN D P, KRIEGLER E, et al. The Shared Socioeconomic Pathways and their energy, land use, and greenhouse gas emissions implications: An overview[J]. Global Environmental Change, 2017, 42: 153-168. DOI: 10.1016/j.gloenvcha.2016.05.009.
[18] DORMANN C F. Effects of incorporating spatial autocorrelation into the analysis of species distribution data[J]. Global Ecology and Biogeography, 2007, 16(2): 129-138. DOI: 10.1111/j.1466-8238.2006.00279.x.
[19] FICK S E, HIJMANS R J. WorldClim 2: new 1-km spatial resolution climate surfaces for global land areas[J]. International Journal of Climatology, 2017, 37(12): 4302-4315. DOI: 10.1002/joc.5086.
[20] TANG X G, YUAN Y D, LI X M, et al. Maximum entropy modeling to predict the impact of climate change on pine wilt disease in China[J]. Frontiers in Plant Science, 2021, 12: 652500. DOI: 10.3389/fpls.2021.652500.
[21] FLETCHER R J, Jr., HEFLEY T J, ROBERTSON E P, et al. A practical guide for combining data to model species distributions[J]. Ecology, 2019, 100(6): e02710. DOI: 10.1002/ecy.2710.
[22] 张殷波, 刘彦岚, 秦浩, 等. 气候变化条件下山西翅果油树适宜分布区的空间迁移预测[J]. 应用生态学报, 2019, 30(2): 496-502. DOI: 10.13287/j.1001-9332.201902.040.
[23] 何斌, 张萍, 李青, 等. 珍稀濒危植物黄杉种群结构与空间分布格局[J]. 热带亚热带植物学报, 2022, 30(4): 461-471. DOI: 10.11926/jtsb.4507.
[24] 李明刚, 谢双喜. 黔北喀斯特山地黄杉林群落及种群结构研究[J]. 天津农业科学, 2015, 21(9): 150-153, 166. DOI: 10.3969/j.issn.1006-6500.2015.09.035.
[25] 文剑然. 中国西南地区孑遗植物黄杉的森林类型、群落结构与更新动态[D]. 昆明: 云南大学, 2022.
[26] 陈文红, 税玉民, 王文, 等. 云南易门翠柏和黄杉的群落调查及保护[J]. 云南植物研究, 2001,23(2): 189-200. DOI: 10.3969/j.issn.2095-0845.2001.02.009.
[27] 孟广涛, 柴勇, 方向京, 等. 滇东北黄杉种群数量动态的初步研究[J]. 西北林学院学报, 2008,23(6): 54-59.
[28] 徐期瑚, 喻勋林. 湖南阳明山黄杉群落特征的初步研究[J]. 福建林业科技, 2010, 37(2): 10-14. DOI: 10.3969/j.issn.1002-7351.2010.02.003.
[29] 庞鑫. 珍贵观赏植物西昌黄杉种群特征及保护利用策略研究[D]. 成都: 成都理工大学, 2023.
[30] 熊斌梅. 七姊妹山自然保护区黄杉林群落学特征研究[D]. 武汉: 湖北大学, 2017.
[31] 朱华. 论中国海南岛的生物地理起源[J]. 植物科学学报, 2020, 38(6): 839-843. DOI: 10.11913/PSJ.2095-0837.2020.60839.
[32] 张茂钦. 中国的花旗松:黄杉[J]. 云南林业, 1982(3): 29-30.
[33] 徐宏范, 张合理, 尚华明, 等. 自然分布北界的大巴山黄杉树轮气候响应分析[J]. 生态与农村环境学报, 2021, 37(6): 761-768. DOI: 10.19741/j.issn.1673-4831.2020.0928.
[34] 何斌, 李青, 陈群利, 等. 黔西北黄杉群落物种多样性的海拔梯度格局[J]. 生态环境学报, 2021, 30(6): 1111-1120. DOI: 10.16258/j.cnki.1674-5906.2021.06.001.
[35] SIGDEL S R, WANG Y F, CAMARERO J J, et al. Moisture-mediated responsiveness of treeline shifts to global warming in the Himalayas[J]. Global Change Biology, 2018, 24(11): 5549-5559. DOI: 10.1111/gcb.14428.
[36] PAULI H, GOTTFRIED M, REITER K, et al. Signals of range expansions and contractions of vascular plants in the high Alps: observations (1994—2004) at the GLORIA master site Schrankogel, Tyrol, Austria[J]. Global Change Biology, 2007, 13(1): 147-156. DOI: 10.1111/j.1365-2486.2006.01282.x.
[37] 黄晓远, 李谢辉. 基于CMIP6的西南暴雨洪涝灾害风险未来预估[J]. 应用气象学报, 2022, 33(2): 231-243.
[38] YANG S L, DING Z L, LI Y Y, et al. Warming-induced northwestward migration of the East Asian monsoon rain belt from the Last Glacial Maximum to the mid-Holocene[J]. Proceedings of the National Academy of Sciences of the United States of America, 2015, 112(43): 13178-13183. DOI: 10.1073/pnas.1504688112.
[39] ALLEN C D, MACALADY A K, CHENCHOUNI H, et al. A global overview of drought and heat-induced tree mortality reveals emerging climate change risks for forests[J]. Forest Ecology and Management, 2010, 259(4): 660-684. DOI: 10.1016/j.foreco.2009.09.001.
[40] 胡文彬. 贵州湄潭黄杉生态系统保护性评价及保护构想[J]. 福建林业科技, 2015, 42(1): 173-176. DOI: 10.13428/j.cnki.fjlk.2015.01.037.
[41] DAVIS E L, BROWN R, DANIELS L, et al. Regional variability in the response of alpine treelines to climate change[J]. Climatic Change, 2020, 162(3): 1365-1384. DOI: 10.1007/s10584-020-02743-0.
[42] THOMAS Z A, MOONEY S, CADD H, et al. Late Holocene climate anomaly concurrent with fire activity and ecosystem shifts in the eastern Australian highlands[J]. Science of the Total Environment, 2022, 802: 149542. DOI: 10.1016/j.scitotenv.2021.149542.
[43] MA T X, LIANG Y, LI Z Y, et al. Age-related patterns and climatic driving factors of drought-induced forest mortality in Northeast China[J]. Agricultural and Forest Meteorology, 2023, 332: 109360. DOI: 10.1016/j.agrformet.2023.109360.
[44] LIU Y Y, WANG A Y, AN Y N, et al. Hydraulics play an important role in causing low growth rate and dieback of aging Pinus sylvestris var. mongolica trees in plantations of Northeast China[J]. Plant, Cell & Environment, 2018, 41(7): 1500-1511. DOI: 10.1111/pce.13160.
[45] ZHANG Y J, MEINZER F C, HAO G Y, et al. Size-dependent mortality in a Neotropical savanna tree: the role of height-related adjustments in hydraulic architecture and carbon allocation[J]. Plant Cell and Environment, 2009, 32(10): 1456-1466. DOI: 10.1111/j.1365-3040.2009.02012.x.
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