干旱区研究

干旱区研究 >

2024 , Vol. 41 >Issue 7: 1167 - 1176

DOI: https://doi.org/10.13866/j.azr.2024.07.08

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
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  • 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)

收起

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
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  • 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

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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。

关键词： 最大熵（MaxEnt）模型; 气候变化; 潜在分布区; 天山云杉

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.

Key words： maximum entropy (MaxEnt) model; climate change; potential distribution area; Picea schrenkiana

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