植物生态

气候变化背景下的天山云杉潜在分布区预测

  • 周杰 ,
  • 王旭虎 ,
  • 杜维波 ,
  • 周晓雷 ,
  • 杨洁 ,
  • 张晓玮
展开
  • 1.甘肃农业大学林学院,甘肃 兰州 730070
    2.甘肃农业大学草业学院,甘肃 兰州 730070
周杰(2001-),男,硕士研究生,主要从事区域生态系统功能研究. E-mail: zj@st.gsau.edu.cn
张晓玮. E-mail: zhangxw@gasu.edu.cn

收稿日期: 2024-01-18

  修回日期: 2024-02-16

  网络出版日期: 2024-08-01

基金资助

国家自然科学基金项目(31860197);甘肃农业大学人才引进科研启动基金项目(GAU-KYQD-2020-13);甘肃农业大学人才引进科研启动基金项目(GAU-KYQD-2021-36);甘肃农业大学人才引进科研启动基金项目(GAU-KYQD-2021-39)

Prediction of potential distribution area of Picea schrenkiana under the background of climate change

  • ZHOU Jie ,
  • WANG Xuhu ,
  • DU Weibo ,
  • ZHOU Xiaolei ,
  • YANG Jie ,
  • ZAHNG Xiaowei
Expand
  • 1. College of Forestry of Gansu Agricultural University, Lanzhou 730070, Gansu, China
    2. College of Pratacultural Science of Gansu Agricultural University, Lanzhou 730070, Gansu, China

Received date: 2024-01-18

  Revised date: 2024-02-16

  Online published: 2024-08-01

摘要

天山云杉(Picea schrenkiana)是天山地区最主要的树种之一,为天山山地的水土保持和水源涵养发挥着重要的作用。本文基于气候相似性原理以最大熵(MaxEnt)模型为基础结合ArcGIS预测当前(2000—2020年)与2020—2040年、2040—2060年两个时段3种气候情景低温室气体排放条件(SSP1-2.6)、中温室气体排放条件(SSP3-7.0)和高温室气体排放条件(SSP5-8.5)下天山云杉的潜在分布范围,并分析影响天山云杉分布的主要环境因子。结果表明:(1) MaxEnt模型对天山云杉的分布区预测可信度高,所有模型AUC值均大于0.99。温度(等温性、季节性温度变异、年平均温度)与降水(最冷季度的降水量、最湿月的降水量、最干季度的降水量)是影响天山云杉潜在分布的主导因子;其中温度为当前主要的影响因子,最冷季度的降水量为未来的主要影响因子。(2) 当前时期天山云杉的适生区主要分布在新疆、青海、内蒙古、西藏、甘肃、宁夏、陕西、四川等地区的山区,总适生区面积为299.17×104 km2,高适区面积为49.45×104 km2。未来各情景下天山云杉的潜在适生区面积变化不大且分布仍以这些地区为主,但高适生区较当前均表现增加。除2020—2040年SSP5-8.5情景下天山云杉的适宜分布向东南方向迁移外,其他情景天山云杉的适宜分布有向西扩展的趋势。

本文引用格式

周杰 , 王旭虎 , 杜维波 , 周晓雷 , 杨洁 , 张晓玮 . 气候变化背景下的天山云杉潜在分布区预测[J]. 干旱区研究, 2024 , 41(7) : 1167 -1176 . DOI: 10.13866/j.azr.2024.07.08

Abstract

Picea schrenkiana, one of the most important tree species in the Tianshan Mountains, plays an important role in soil and water conservation in this region. In this study, the potential distribution and dominant climatic factors of current and three climate change scenarios (i.e., low, the medium, and the high greenhouse gas emission scenarios; SSP1-2.6, SSP3-7.0 and SSP5-8.5) in two future time periods (2020-2040 and 2040-2060) were modeled using the maximum entropy model (MaxEnt). The results were: (1) AUC values of the MaxEnt model were all greater than 0.99, indicating that the model had high reliability for predicting the distribution region of P. schrenkiana. The results from the jackknife test and climate factor response curves revealed that isotherm, seasonal temperature variation, annual mean temperature, precipitation in the coldest quarter, precipitation in the wettest month, and precipitation in the driest quarter were the main factors affecting the potential distribution of P. schrenkiana. Overall, temperature is key factor affecting the potential distribution at present, and precipitation, especially precipitation in the coldest quarter, will be the key factor in the future. (2) At present, the potential distribution of P. schrenkiana is mainly in the mountainous regions of the Xinjiang, Qinghai, Inner Mongolia, Xizang, Gansu, Ningxia, Shanxi, and Sichuan provinces. The total potential area of P. schrenkiana is 299.17×104 km2 at present, and the area of highest suitability is 49.45×104 km2. The potential distribution of P. schrenkiana in future scenarios is still dominated by its currently simulated distribution regions, meaning that the simulated potential area of P. schrenkiana does not significantly change under the different future scenarios. However, the most suitable regions tended to be larger than currently. The potential distribution of P. schrenkiana tended to expand to the west in all scenarios, apart from a migration to the southeast that was predicted under the SSP5-8.5 scenario from 2020 to 2040.

参考文献

[1] 张爱平, 王毅, 熊勤犁, 等. 末次间冰期以来3种云杉属植物的历史分布变迁及避难所[J]. 应用生态学报, 2018, 29(7): 2411-2421.
  [Zhang Aiping, Wang Yi, Xiong Qinli, et al. Distribution changes and refugia of three spruce taxa since the last interglacial[J]. Chinese Journal of Applied Ecology, 2018, 29(7): 2411-2421.]
[2] 方精云. 我国森林植被带的生态气候学分析[J]. 生态学报, 1991, 11(4): 377-387.
  [Fang Jingyun. Ecolomatological analysis of the forest zones in China[J]. Acta Ecologica Sinica, 1991, 11(4): 377-387.]
[3] 曹雪萍, 王婧如, 鲁松松, 等. 气候变化情景下基于最大熵模型的青海云杉潜在分布格局模拟[J]. 生态学报, 2019, 39(14): 5232-5240.
  [Cao Xueping, Wang Jingru, Lu Songsong, et al. Simulation of the potential distribution patterns of Picea crassifolia in climate change scenarios based on the maximum entropy (Maxent) model[J]. Acta Ecologica Sinica, 2019, 39(14): 5232-5240.]
[4] 钱灵颖, 黄智洵, 杨盛昌, 等. 厦门市重点保护植物空间优先保护格局研究[J]. 生态学报, 2021, 41(11): 4367-4378.
  [Qian Lingying, Huang Zhixun, Yang Shengchang, et al. Study on spatial conservation priorty pattern of key protected plants in Xiamen[J]. Acta Ecologica Sinica, 2021, 41(11): 4367-4378.]
[5] 管凤荣. 结合物种分布模型的蜀柏毒蛾在四川的发生分布及影响因子分析[D]. 成都: 四川农业大学, 2018.
  [Guan Fengrong. The Distribution and Analysis of Influence Factors of Parocneria orienta in Sichuan with Species Ditribution Model[D]. Chengdu: Sichuan Agricultural University, 2018.]
[6] 徐文力, 李庆康, 杨潇, 等. 气候变化情景下西藏入侵植物印加孔雀草的潜在分布预测[J]. 生态学报, 2022, 42(17): 7266-7277.
  [Xu Wenli, Li Qingkang, Yang Xiao, et al. Prediction of potential distribution of the invasive plant Tagetes minuta L. (Wild Marigold) in Tibet under climate change[J]. Acta Ecologica Sinica, 2022, 42(17): 7266-7277.]
[7] Zhang K, Yao L, Meng J, et al. Maxent modeling for predicting the potential geographical distribution of two peony species under climate change[J]. Science of The Total Environment, 2018, 634: 1326-1334.
[8] 邢丁亮, 郝占庆. 最大熵原理及其在生态学研究中的应用[J]. 生物多样性, 2011, 19(3): 295-302.
  [Xing Dingliang, Hao Zhanqing. The principle of maximum entropy and its applications in ecology[J]. Biodiversity Science, 2011, 19(3): 295-302.]
[9] 吴艳, 王洪峰, 穆立蔷. 物种分布模型的研究进展与展望[J]. 高师理科学刊, 2022, 42(5): 66-70.
  [Wu Yan, Wang Hongfeng, Mu Liqiang. Research progress and prospect of species distribution models[J]. Journal of Science of Teachers’ College and University, 2022, 42(5): 66-70.]
[10] 刘婷, 曹家豪, 齐瑞, 等. 基于GIS和MaxEnt模型分析气候变化背景下紫果云杉的潜在分布区[J]. 西北植物学报, 2022, 42(3): 481-491.
  [Liu Ting, Cao Jiahao, Qi Rui, et al. Research of potential geographical distribution of Picea purpurea based on GIS and MaxEnt under different climate conditions[J]. Acta Botanica Boreali-Occidentalia Sinica, 2022, 42(3): 481-491.]
[11] 张世林, 高润红, 高明龙, 等. 气候变化背景下中国樟子松潜在分布预测[J]. 浙江农林大学学报, 2023, 40(3): 560-568.
  [Zhang Shilin, Gao Runhong, Gao Minglong, et al. Prediction of the potential distribution pattern of Pinus sylvestris var. mongolica in China under climate change[J]. Journal of Zhejiang Agricultural and Forestry University, 2023, 40(3): 560-568.]
[12] 张艳芳. 基于MaxEnt模型预测花烟草全球潜在适生区的研究[D]. 泰安: 山东农业大学, 2023.
  [Zhang Yanfang. Prediction of Global Potential Suitable Habitats of Nicotiana alata Link et Otto Based on MaxEnt Model[D]. Tai’an: Shandong Agricultural University, 2023.]
[13] 李晓辰, 贡璐, 魏博, 等. 气候变化对新疆雪岭云杉潜在适宜分布及生态位分化的影响[J]. 生态学报, 2022, 42(10): 4091-4100.
  [Li Xiaochen, Gong Lu, Wei Bo, et al. Effects of climate change on potential distribution and niche differentiation of Picea schrenkiana in Xinjiang[J]. Acta Ecologica Sinica, 2022, 42(10): 4091-4100.]
[14] 刘梦婷, 王振锡, 王雅佩, 等. 新疆天山云杉林群落分布格局及环境解释[J]. 林业科学研究, 2019, 32(6): 90-98.
  [Liu Mengting, Wang Zhenxi, Wang Yapei, et al. Plant communities pattern of Picea tianschanica forest and their interrelations with invironmental factors in Tianshan area[J]. Forestry Research, 2019, 32(6): 90-98.]
[15] 范晓聪. 天山云杉ABI5的克隆和原核表达[D]. 杭州: 浙江农林大学, 2021.
  [Fan Xiaocong. Cloning and Prokaryotic Expression of ABI5 From Picea schrenkiana[D]. Hangzhou: Zhejiang Agricultural and Forestry University, 2021.]
[16] 周小东, 常顺利, 王冠正, 等. 天山北坡中段雪岭云杉径向生长对气候变化的响应[J]. 干旱区研究, 2023, 40(8): 1215-1228.
  [Zhou Xiaodong, Chang Shunli, Wang Guanzheng, et al. Radial growth response of Picea schrenkiana to climate change in the middlesection of the northern slope of the Tianshan Mountains[J]. Arid Zone Research, 2023, 40(8): 1215-1228.]
[17] 李宗英, 罗庆辉, 许仲林. 西天山雪岭云杉林分密度对森林生物量分配格局和异速生长的影响[J]. 干旱区研究, 2021, 38(2): 545-552.
  [Li Zongying, Luo Qinghui, Xu Zhonglin. Effects of stand density on the biomass allocation and tree height-diameter allometric growth of Picea schrenkiana forest on the northern slope of the western Tianshan Mountains[J]. Arid Zone Research, 2021, 38(2): 545-552.]
[18] 王燕, 赵士洞. 天山云杉林生物生产力的地理分布[J]. 植物生态学报, 2000(2): 186-190.
  [Wang Yan, Zhao Shidong. Productivity pattern of Picea schrenkiana var. Tianschanica forest[J]. Chinese Journal of Plant Ecology, 2000(2): 186-190.]
[19] 张晓玮, 蒋玉梅, 毕阳, 等. 基于MaxEnt模型的中国沙棘潜在适宜分布区分析[J]. 生态学报, 2022, 42(4): 1420-1428.
  [Zhang Xiaowei, Jiang Yumei, Bi Yang, et al. Identification of potential distribution area for Hippophae rhamnoides subsp. sinensis by the MaxEnt model[J]. Acta Ecologica Sinica, 2022, 42(4): 1420-1428.]
[20] 胡永云. 复杂气候系统和全球变暖[J]. 物理, 2022, 51(1): 10-15.
  [Hu Yongyun. The complex climate system and global warming[J]. Physics, 2022, 51(1): 10-15.]
[21] 李建宇, 陈燕婷, 郭燕青, 等. 基于MaxEnt预测未来气候条件下钻叶紫菀在中国的潜在适生区[J]. 植物保护, 2023, 49(2): 92-102.
  [Li Jianyu, Chen Yanting, Guo Yanqing, et al. Potential suitabie areas of Symphyotrichum subulatum based on MaxEnt under future climate scenarios[J]. Plant Protection, 2023, 49(2): 92-102.]
[22] 陈新美, 雷渊才, 张雄清, 等. 样本量对MaxEnt模型预测物种分布精度和稳定性的影响[J]. 林业科学, 2012, 48(1): 53-59.
  [Chen Xinmei, Lei Yuancai, Zhang Xiongqing, et al. Impact of sample size and spatial distribution on species distribution model[J]. Scientia Silvae Sinicae, 2012, 48(1): 53-59.]
[23] 柳晓燕, 李俊生, 赵彩云, 等. 基于MAXENT模型和ArcGIS预测豚草在中国的潜在适生区[J]. 植物保护学报, 2016, 43(6): 1041-1048.
  [Liu Xiaoyan, Li Junsheng, Zhao Caiyun, et al. Prediction of potential suitable area of Ambrosia artemisiifolia L. in China based on MAXENT and ArcGIS[J]. Journal of Plant Protection, 2016, 43(6): 1041-1048.]
[24] 王露露, 伊力哈木·亚尔买买提. 未来气候情景下2种新疆特有树种潜在适生区预测[J]. 北京林业大学学报, 2022, 44(6): 10-22.
  [Wang Lulu, Yilihamu Yaermaimaiti. Prediction of the potential distribution of two endemic tree species in Xinjiang of western China under future climate scenarios[J]. Journal of Beijing Forestry University, 2022, 44(6): 10-22.]
[25] 王晓帆, 段雨萱, 金露露, 等. 基于优化的最大熵模型预测中国高山栎组植物的历史、现状与未来分布变化[J]. 生态学报, 2023, 43(16): 6590-6604.
  [Wang Xiaofan, Duan Yuxuan, Jin Lulu, et al. Prediction of historical, present and future distribution of Quercus sect. Heterobalanus based on the optimized MaxEnt model in China[J]. Acta Ecologica Sinica, 2023, 43(16): 6590-6604.]
[26] 赵晓冏, 巩娟霄, 赵莎莎, 等. 样本量及其空间分布对物种分布模型的影响[J]. 兰州大学学报(自然科学版), 2018, 54(2): 208-215.
  [Zhao Xiaojiong, Gong Juanxiao, Zhao Shasha, et al. Impact of sample size and spatial distribution on species distribution model[J]. Journal of Lanzhou University (Natural Sciences), 2018, 54(2): 208-215.]
[27] 李岳峰. 新疆天山雪岭云杉物候变化及其气候影响因子分析[D]. 乌鲁木齐: 新疆大学, 2021.
  [Li Yuefeng. Phenology Changes of Schrenk Spruce Forest and Their Climatic Impact Factor in Tianshan[D]. Urumqi: Xinjiang University, 2021.]
[28] 郯俊岭, 江志红, 马婷婷. 基于贝叶斯模型的中国未来气温变化预估及不确定性分析[J]. 气象学报, 2016, 74(4): 583-597.
  [Tan Junling, Jiang Zhihong, Ma Tingting. Projections of future climate change and uncertainty over China based on bayesian model averaging[J]. Acta Meteorologica Sinica, 2016, 74(4): 583-597.]
[29] 李宁宁, 张爱平, 张林, 等. 气候变化下青藏高原两种云杉植物的潜在适生区预测[J]. 植物研究, 2019, 39(3): 395-406.
  [Li Ningning, Zhang Aiping, Zhang Lin, et al. Predicting potential distribution of two species of spruce in Qinghai-Tibet Plateau under climate change[J]. Bulletin of Botanical Research, 2019, 39(3): 395-406.]
[30] 尹锴. 天山云杉林土壤种子库研究[D]. 乌鲁木齐: 新疆农业大学, 2006.
  [Yin Kai. Study on Soil Seed Bank Picea schrenkiana Forest[D]. Urumqi: Xinjiang Agricultural University, 2006.]
[31] Wu G, Liu X, Chen T, et al. The positive contribution of WUE to the resilience of Schrenk spruce (Picea schrenkiana) to extreme drought in the western Tianshan Mountains, China[J]. Acta Physiologiae Plantarum, 2020, 42: 1-16.
[32] 罗庆辉, 徐泽源, 许仲林. 天山雪岭云杉林生物量估测及空间格局分析[J]. 生态学报, 2020, 40(15): 5288-5297.
  [Luo Qinghui, Xu Zeyuan, Xu Zhonglin. Estimation and spatial pattern analysis of biomass of Picea schrenkiana forests[J]. Acta Ecologica Sinica, 2020, 40(15): 5288-5297.]
[33] 万辛如, 程超源, 白德凤, 等. 气候变化的生态影响及适应对策[J]. 中国科学院院刊, 2023, 38(3): 518-527.
  [Wan Xinru, Cheng Chaoyuan, Bai Defeng, et al. Ecological impacts of climate change and adaption strategies[J]. Bulletin of the Chinese Academy of Sciences, 2023, 38(3): 518-527.]
文章导航

/