中国农业气象 ›› 2025, Vol. 46 ›› Issue (2): 213-225.doi: 10.3969/j.issn.1000-6362.2025.02.008

• 农业生物气象栏目 • 上一篇    下一篇

基于MaxEnt模型的中国显齿蛇葡萄潜在适生区分析

周威,钟艳雯,陈玉贵,李艳   

  1. 1.气象防灾减灾湖南省重点实验室,长沙 410118;2.湖南省气象服务中心,长沙 410118;3.湖南省气象信息中心,长沙 410118;4.中国气象局公共气象服务中心,北京 100081
  • 收稿日期:2024-01-22 出版日期:2025-02-20 发布日期:2025-02-20
  • 作者简介:周威,E-mail:z-will@163.com
  • 基金资助:
    中国气象局公共气象服务中心2023年度创新基金项目(M2023015);湖南省市场监督管理局2023年度第1批地方标准制修订项目计划(319)

Potential Distribution Region for Chinese Ampelopsis grossedentata Based on the MaxEnt Model

ZHOU Wei, ZHONG Yan-wen, CHEN Yu-gui, LI Yan   

  1. 1. Hunan Key Laboratory Meteorological Disaster Prevention and Reduction, Changsha 410118, China; 2. Hunan Provincial Meteorological Service Center, Changsha 410118; 3. Hunan Meteorological Information Center, Changsha 410118; 4. China Meteorological Administration Public Meteorological Service Center, Beijing 100081
  • Received:2024-01-22 Online:2025-02-20 Published:2025-02-20

摘要:

基于中国显齿蛇葡萄现有193个地理分布数据和包括生物气候、海拔、辐射34个环境变量,利用最大熵模型(MaxEnt)模拟2041-21003种气候情景下显齿蛇葡萄潜在分布区及其变化,以期充分利用气候资源,为显齿蛇葡萄的种植、保护和产业发展等提供科学参考。结果表明:(1)MaxEnt模型的模拟准确性较高,AUC值为0.9280.956。(2)影响中国显齿蛇葡萄分布的6个关键环境因子为最干月降水量、最干季降水量、年降水量、5月月平均辐射、海拔和最冷月最低气温,高适生区的阈值分别为30~90mm、130~300mm、1600~2450mm、14000~15200KJ·m2·d1、<1100m和4.0~9.0℃。(3)可持续发展(SSP126)气候情景有利于增加显齿蛇葡萄的高、中适生区面积,2081-2100年显齿蛇葡萄适生区面积最大,为182.2万km2;局部发展(SSP245)气候情景下显齿蛇葡萄适生区面积增加效果低于可持续发展(SSP126)气候情景,但高于常规发展(SSP585)气候情景,主要表现在广东、广西、福建、云南和湖南的高适生区有所退化,四川东南部的中适生区增加;常规发展(SSP585)气候情景下,贵州、福建、广东和江西的部分高适生区逐步退化为中适生区,海南的中适生区退化为低适生区,全国的总适生区面积在2081-2100年为历史最低。(4)2041-2100年,显齿蛇葡萄总适生区面积仅在可持续发展(SSP126)气候情景下增加,在局部发展(SSP245)、常规发展(SSP585)气候情景下呈减少趋势;无论哪种气候情景,总适生区质心一致从目前的湖南、广西交界处向北略偏东方向移动46~80km至湖南省南部。

关键词: 显齿蛇葡萄, MaxEnt模型, 适生区分析

Abstract:

Based on the current 193 geographical distribution data and 34 environmental variables such as bioclimate, altitude, and radiation, the maximum entropy model (MaxEnt) was used to simulate potential distribution areas and change characteristics of Ampelopsis grossedentata under three kinds of future climate scenarios. In order to make full use of climate resources and provide scientific basis for Ampelopsis grossedentata planting, protection and industrial development. The results showed that: (1) the prediction accuracy of the MaxEnt model was relatively high, with an AUC value of 0.928 to 0.956. (2) The six key environmental variables that affect Ampelopsis grossedentata distribution were precipitation in the driest month, precipitation of driest quarter, annual rainfall, monthly average radiation in May, altitude, and minimum temperature of coldest month. The thresholds of key environmental variables in the highly suitable region were 30-90mm, 130-300mm, 1600-2450mm, 14000-15200KJ·m−2·d−1, 1100m and 4.0-9.0, respectively. (3) Under the SSP126 scenario, the area of highly and moderately suitable region for Ampelopsis grossedentata could be increased. The area of suitable region from 2081 to 2100 was the largest at 1.822 million km2. SSP245 scenario was not as effective as SSP126 scenario in increasing the region of suitable region. However, it was stronger than SSP585 scenario, which was mainly reflected by an increase in the highly suitable regions of Guangdong, Guangxi, Fujian, Yunan, and Hunan that had degenerated, and an increase in the moderately suitable regions of southeastern Sichuan. Under the SSP585 scenario, some high-probability regions in Guizhou, Fujian, Guangdong and Jiangxi had gradually degenerated into medium-probability regions, and the middle-probability region in Hainan had degenerated into low-probability regions, and the total number of suitable regions in China from 2081 to 2100 was the lowest in history. (4) From 2041 to 2100, the total suitable region of Ampelopsis grossedentata only increased under the SSP126 climate scenario, and decreased under the SSP245 and SSP585 climate scenarios. Regardless of the climate scenario, the centroid of the total suitable area will move away from the current junction of Hunan and Guangxi, and it would move 46 to 80 km to the south of Hunan, slightly to the north and east.

Key words: Ampelopsis grossedentata, MaxEnt model, Potential distribution