干旱区研究

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2025 , Vol. 42 >Issue 3: 511 - 522

DOI: https://doi.org/10.13866/j.azr.2025.03.11

植物生态

气候变化下沙棘在新疆潜在分布格局的变化

  • 罗磊 ,
  • 李曦光 ,
  • 李萧婷 ,
  • 王磊 ,
  • 王蕾
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  • 1.新疆林业科学院资源信息研究所，新疆 乌鲁木齐 830063
    2.新疆林木资源与利用国家林草局重点实验室，新疆 乌鲁木齐 830063
    3.新疆林果树种选育与栽培重点实验室，新疆 乌鲁木齐 830063
    4.新疆维吾尔自治区林果产业发展中心，新疆 乌鲁木齐 830063
罗磊（1981-），女，研究员，主要从事数字林业方面的研究工作. E-mail: 23799341@qq.com
王蕾. E-mail: 39282573@qq.com

收稿日期: 2024-06-25

  修回日期: 2024-08-26

  网络出版日期: 2025-03-17

基金资助

自治区重点研发计划项目“基于“空天地”多源遥感监测技术的林果资源数据体系建设”(2023B02004-1);自治区重点研发计划项目“冻害预报、预警技术体系和风险区划研究”(2023B02026-3)

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Potential distribution pattern of Hippophae rhamnoides in Xinjiang under climate change predicted using the MaxEnt model

  • LUO Lei ,
  • LI Xiguang ,
  • LI Xiaoting ,
  • WANG Lei ,
  • WANG Lei
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  • 1. Institute of Resource Information, Xinjiang Academy of Forestry Sciences, Urumqi 830063, Xinjiang, China
    2. Key Laboratory of Forest Resources and Utilization in Xinjiang of National Forestry and Grassland Administration, Urumqi 830063, Xinjiang, China
    3. Key Laboratory of Fruit Tree Species Breeding and Cultivation in Xinjiang, Urumqi 830063, Xinjiang, China
    4. Xinjiang Uygur Autonomous Region Forest Fruit Industry Development Center, Urumqi 830063, Xinjiang, China

Received date: 2024-06-25

  Revised date: 2024-08-26

  Online published: 2025-03-17

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摘要

本研究基于MaxEnt模型，结合沙棘在新疆分布点数据、历史（末次盛冰期、全新世中期）、当前、未来时期（2050s、2070s）气候数据以及海拔数据，模拟沙棘在不同气候场景下的潜在地理分布区域。分析影响沙棘在新疆自然分布的主要气候环境因子，阐述了沙棘分布区的迁移变化。结果表明：沙棘适宜区环境因子为年平均温度在-1~8 ℃，最湿季度降水量在50~110 mm，最暖月最高气温在22~32 ℃，最干月降水量为5~13 mm，适宜沙棘生存的海拔主要为500~1000 m。在当前气候环境下，沙棘在南北疆均有分布，主要分布于新疆北部及西南部地区。未来气候场景下沙棘分布区质心均向东北方向迁移。研究结果可为沙棘资源发展规划提供理论数据支持。

关键词： 沙棘; 最大熵模型; 气候变化; 适生区; 新疆

本文引用格式

罗磊 , 李曦光 , 李萧婷 , 王磊 , 王蕾 . 气候变化下沙棘在新疆潜在分布格局的变化[J]. 干旱区研究, 2025 , 42(3) : 511 -522 . DOI: 10.13866/j.azr.2025.03.11

Abstract

Hippophae rhamnoides is a plant that is light-loving, cold-resistant, heat-resistant, wind/sand-resistant, thrives in arid climates, and has high nutritional value. This study combines data on the distribution of sea buckthorn in Xinjiang, historical (last glacial period, mid-Holocene), current, and future (2050s, 2070s) climate data, and altitude data to simulate the potential geographical distribution of Hippophae rhamnoides under different climate scenarios based on the MaxEnt model. The main climatic and environmental factors affecting the natural distribution of sea buckthorn in Xinjiang were analyzed, and its distribution and migration were expounded. The results showed that the AUC values of each climate scenario exceeded 0.9, indicating high accuracy of the model prediction. The main environmental factors in the areas suitable for sea buckthorn are annual average temperature of -1 to 8 ℃, precipitation in the warmest quarter of 50-110 mm, highest temperature in the warmest month of 22-32 ℃, and precipitation in the driest month of 5-13 mm. In terms of altitude, the main range suitable for sea buckthorn survival is 500-1000 m. In the current climate, sea buckthorn is distributed in both northern and southern Xinjiang, particularly in northern and southwestern Xinjiang. Analysis of the centroid migration of the distribution of sea buckthorn under future climate scenarios revealed migration to the northeast. The results of this study can provide theoretical support for the development of plans to maximize the use of sea buckthorn resources.

Key words： Hippophae rhamnoides; maximum entropy model; climatic change; suitable area; Xinjiang

参考文献

[1] 张秀芸, 伍文慧, 梁英梅. 落叶松枯梢病在中国的适生性分析[J]. 生态学报, 2024, 44(7): 1-11.
  [Zhang Xiuyun, Wu Wenhui, Liang Yingmei. Prediction of potential suitable distribution of shoot blight of larch (Neofusicoccum laricinum) in China[J]. Acta Ecologica Sinica, 2024, 44(7): 1-11.]
[2] 蔡琦琪, 安明态, 余江洪, 等. 气候变化下华南五针松在中国的适生区预测[J]. 陕西师范大学学报(自然科学版), 2024, 52(1): 90-102.
  [Cai Qiqi, An Mingtai, Yu Jianghong, et al. Suitable habitat prediction of Pinus kwangtungensis in China under climate change[J]. Journal of Shaanxi Normal University (Natural Sience Edition), 2024, 52(1): 90-102.]
[3] 张雅茜, 王淋, 包福海, 等. 应用最大熵模型预测的欧李潜在适生区分布及气候变化对其的影响[J]. 东北林业大学学报, 2023, 51(11): 54-62.
  [Zhang Yaqian, Wang Lin, Bao Fuhai, et al. Distribution of potential suitable areas of Cerasus humilis by using Maxent model and effect of climate change on it[J]. Journal of Northeast Forestry University, 2023, 51(11): 54-62.]
[4] 应邦肯, 田阔, 郭浩宇, 等. 基于MaxEnt模型预测未来气候变化环境下红树秋茄(Kandelia obovata)在中国潜在适生区的变化[J]. 生态学报, 2024, 44(1): 224-234.
  [Ying Bangken, Tian Kuo, Guo Haoyu, et al. Predicting potential suitable habitats of Kandelia obovata in China under future climatic scenarios based on MaxEnt model[J]. Acta Ecologica Sinica, 2024, 44(1): 224-234.]
[5] 陈瑜, 杨毅哲, 陈丽丽, 等. 基于生态位模型的多花黑麦草入侵风险分析[J]. 陕西师范大学学报(自然科学版), 2024, 52(1): 70-78.
  [Chen Yu, Yang Yizhe, Chen Lili, et al. Analysis of the invasion risk for Lolium multiflorum Lam. based on niche model[J]. Journal of Shaanxi Normal University (Natural Science Edition), 2024, 52(1): 70-78.]
[6] 荣文文, 黄祥, 牛攀新, 等. 基于最大熵模型的中药材木贼麻黄潜在适生区[J]. 生态学报, 2023, 43(20): 8631-8646.
  [Rong Wenwen, Huang Xiang, Niu Panxin, et al. Potentially suitable areas for traditional Chinese medicinal material Ephedra equisetina based on MaxEnt model[J]. Acta Ecologica Sinica, 2023, 43(20): 8631-8646.]
[7] 陈伊能, 刘志刚, 于婷, 等. 基于MaxEnt模型预测梅的潜在适生区分布[J]. 中国野生植物资源, 2024, 43(1): 107-113, 126.
  [Chen Yineng, Liu Zhigang, Yu Ting, et al. Prediction of potential distribution of Prunus mume based on MaxEnt model[J]. Chinese Wild Plant Resources, 2024, 43(1): 107-113,126.]
[8] 姚政宇, 韩其飞, 林彬. 基于最大熵模型的新疆主要有毒杂草分布区预测[J]. 生态学报, 2023, 43(12): 5096-5109.
  [Yao Zhenyu, Han Qifei, Lin Bin. Prediction of distribution area of main noxious and miscellaneous weeds in Xinjiang based on MaxEnt model[J]. Acta Ecologica Sinica, 2023, 43(12): 5096-5109.]
[9] 刘小燕. 基于最大熵模型的白鹤栖息地适宜性评价研究[D]. 南京: 南京林业大学, 2023.
  [Liu Xiaoyan. Study on Habitat Suitability Assessment of Grus leucogeranus based on MaxEnt Model[D]. Nanjing: Nanjing Forestry University, 2023.]
[10] 唐杨欣, 皮杰, 刘新华, 等. 基于最大熵模型预测气候变化下河蚬在中国的潜在分布[J]. 生态学报, 2023, 43(10): 4250-4259.
  [Tang Yangxin, Pi Jie, Liu Xinhua, et al. Predicting potential distribution of Corbicula fuminea under climate change scenarios using MaxEnt model[J]. Acta Ecologica Sinica, 2023, 43(10): 4250-4259.]
[11] 张嘉容. 南太平洋长鳍金枪鱼栖息地预测模型建构研究[D]. 上海: 上海海洋大学, 2020.
  [Zhang Jiarong. Research on the Habitat Distribution Model of Albacore (Thunnus alalunga) in the South Pacific[D]. Shanghai: Shanghai Ocean University, 2020.]
[12] 徐强, 顾渝娟, 李盼畔, 等. 基于最大熵模型预测小火蚁在我国的适生区[J]. 植物保护, 2023, 49(4): 101-107.
  [Xu Qiang, Gu Yujuan, Li Panpan, et al. Potential geographical distribution of the little fire ant, Wasmannia auropunctata (Hymenoptera: Formicidae) in China based on MaxEnt model[J]. Plant Protection, 2023, 49(4): 101-107.]
[13] 闫子怡, 崔亚琴, 游崇娟. 气候变化下栎枯萎病菌在中国的潜在适生区预测[J]. 植物保护学报, 2023, 50(6): 1528-1539.
  [Yan Ziyi, Cui Yaqin, You Chongjuan. Prediction of potential suitable distribution areas of oak wilt pathogen Bretziella fagacearum in China under climate change[J]. Journal of Plant Protection, 2023, 50(6): 1528-1539.]
[14] 杨帆, 赵远征, 张晓明, 等. 基于MaxEent模型的腐烂茎线虫在内蒙古地区适生区预测[J]. 生物安全学报, 2024, 33(2): 161-168.
  [Yang Fan, Zhao Yuanzheng, Zhang Xiaoming, et al. Potential prediction of Ditylenchus destructor in Inner Mongolia based on MaxEnt[J]. Journal of Biosafety, 2024, 33(2): 161-168.
[15] 李曦光, 李萧婷, 王蕾, 等. 基于空间差异的新疆沙棘资源果实品质及营养成分分析[J]. 农业工程学报, 2023, 39(23): 268-275.
  [Li Xiguang, Li Xiaoting, Wang Lei, et al. Fruit quality and medicinal components in Xinjiang sea buckthorn under spatial distribution differences[J]. Transactions of the Chinese Society of Agricultural Engineering, 2023, 39(23): 268-275.]
[16] 王晨曦, 李乾弘, 刘阳洲, 等. 植物乳杆菌发酵对沙棘果汁中苦味物质的转化作用[J]. 食品工业科技, 2024, 45(16): 159-167.
  [Wang Chenxi, Li Qianhong, Liu Yangzhou, et al. Transformation of bitter substances in sea buckthorn juice by fermentation with Lactiplantibacillus plantarum[J]. Science and Technology of Food Industry, 2024, 45(16): 159-167.]
[17] 范丽红. 新疆天山山区与南、北疆气候变化及其影响的比较研究[D]. 乌鲁木齐: 新疆大学, 2006.
  [Fan Lihong. Study on Comparison of the Climatic Variations and Its Effects in Tianshan Mountainous Area, Southern and Northern Xinjiang[D]. Urumqi: Xinjiang University, 2006.]
[18] 陈思明. 互花米草(Spartina alterniflora)潜在分布格局的空间尺度效应[J]. 生态学报, 2023, 43(14): 6058-6068.
  [Chen Siming. Spatial scale effect of potential distribution pattern of Spartina alternifora[J]. Acta Ecologica Sinica, 2023, 43(14): 6058-6068.]
[19] 焦鑫宇, 龙梅, 刘志雄. 应用最大熵模型预测我国野生蕙兰潜在适生区分布及其影响因素[J]. 东北林业大学学报, 2023, 51(7): 96-101, 122.
  [Jiao Xinyu, Long Mei, Liu Zhixiong. Prediction and influencing factors of wild Cymbidium faberi in China using MaxEnt Model[J]. Journal of Northeast Forestry University, 2023, 51(7): 96-101, 122.]
[20] 李尤, 唐雪海, 王雷宏, 等. 基于MaxEnt模型的不同气候情境下白蜡树中国适生区预测[J]. 西北林学院学报, 2021, 36(6): 100-107.
  [Li You, Tang Xuehai, Wang Leihong, et al. Prediction of suitable areas of Fraxinus chinensis in China under different climate scenarios based on MaxEnt[J]. Journal of Northwest Forestry University, 2021, 36(6): 100-107.]
[21] 张春雨. 大气CO2浓度和温度升高影响水稻产量和品质的光合生理机制研究[D]. 大庆: 黑龙江八一农垦大学, 2023.
  [Zhang Chunyu. Studies on the Photosynthetic Physiological Mechanisms of Elevated Atmospheric CO2 Concentration and Temperature Affecting Yield and Quality in Rice[D]. Daqing: Heilongjiang Bayi Agricultural University, 2023.]
[22] Wu Shaohong, Zheng Du, Yin Yunhe, et al. Northward-shift of temperature zones in Chin’s eco-geographcal study under future climate scenario[J]. Journal of Geographical Science, 2010, 20(5): 643-651.
[23] 张山清, 普宗朝, 冯志敏, 等. 气候变暖对新疆棉花种植气候适宜性分区的影响[J]. 沙漠与绿洲气象, 2023, 17(5): 167-174.
  [Zhang Shanqing, Pu Zongchao, Fen Zhimin, et al. Impact of climate warming on cotton cultivation in Xinjiang[J]. Desert and Oasis Meteorology, 2023, 17(5): 167-174.]
[24] 陈程浩, 龙主多杰, 陆徐伟, 等. 基于优化MaxEnt模型的中国紫堇属植物生境适宜性研究[J]. 生态学报, 2023, 43(24): 10345-10362.
  [Chen Chenghao, Longzhuduojie, Lu Xuwei, et al. Habitat suitability of Corydalis based on the optimized MaxEnt model in China[J]. Acta Ecologica Sinica, 2023, 43(24): 10345-10362.]
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