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生态学杂志 ›› 2025, Vol. 44 ›› Issue (5): 1636-1643.doi: 10.13292/j.1000-4890.202505.005

• 研究报告 • 上一篇    下一篇

翅柄假脉蕨分布格局对未来气候变化的响应

岑润琳1,2,叶童欣2,张宪春2,海老原淳3,沈羽1,4*,张开梅1,3*
  

  1. 1南京林业大学, 南方现代林业协同创新中心, 南京林业大学生命科学学院, 中国南京 210037; 2中国科学院植物研究所, 植物多样性与特色经济作物重点实验室, 中国北京 100093; 3植物研究部, 国立科学博物馆, 日本筑波 305-0005; 4江苏洪泽湖湿地生态系统国家定位观测研究站, 中国洪泽 223100)

  • 出版日期:2025-06-10 发布日期:2025-05-14

Responses of Crepidomanes latealatum distribution patterns to future climate change.

CEN Runlin1,2, YE Tongxin2, ZHANG Xianchun2, ATSUSHI Ebihara3, SHEN Yu1,4*, ZHANG Kaimei1,3*   

  1. (1Co-Innovation Center for the Sustainable Forestry in Southern China, College of Life Sciences, Nanjing Forestry University, Nanjing 210037, China; 2State Key Laboratory of Plant Diversity and Specialty Crops, Institute of Botany, Chinese Academy of Sciences, Beijing 100093, China; 3Department of Botany, National Museum of Nature and Science, Tsukuba-shi 305-0005,Japan; 4National Positioning Observation Station of Hung-tse Lake Wetland Ecosystem in Jiangsu Province, Hongze 223100,China).

  • Online:2025-06-10 Published:2025-05-14

摘要: 翅柄假脉蕨(Crepidomanes latealatum (Bosch) Copel.)是一种对环境变化特别敏感的植物。研究气候变化情景下翅柄假脉蕨潜在适生区的变化,对保护其野生资源具有重要意义。本研究基于翅柄假脉蕨的1276条地理分布数据和19个气候环境因子数据,利用最大熵模型(MaxEnt)并结合地理信息系统软件(ArcGIS),预测当前(1970—2000年)和未来(2021—2040、2041—2060、2061—2080、2081—2100年)不同气候情景下(SSP126、SSP245、SSP370和SSP585)翅柄假脉蕨在全球的潜在地理分布,并确定影响翅柄假脉蕨分布的主要环境变量。结果表明:MaxEnt模型对翅柄假脉蕨潜在分布区的预测具有很高的准确度,其训练集和测试集的AUC值分别为0.962和0.966;影响翅柄假脉蕨潜在分布的主要环境因子是最热季降水量、最干月降水量、年平均气温和气温季节性变动系数;翅柄假脉蕨主要分布在3°S—40°N,95°E—140°E;MaxEnt模型预测当前条件下翅柄假脉蕨的适生区主要分布在亚洲的大部分地区、非洲中西部、巴西东南部,总适生区面积为9.47×106 km2,高适生区面积为1.59×106 km2;在未来气候情景下,翅柄假脉蕨在东亚、非洲热带地区的适生区面积将有所增加,而欧美等温带地区的适生区面积则会减少;此外,在不同气候情景下,随时间的推移,翅柄假脉蕨的适宜生境面积总体呈现先增加后减少的趋势。在全球气候变化背景下,翅柄假脉蕨的生存存在较大挑战。应以当前翅柄假脉蕨适生环境为基础,针对当地气候类型、土壤条件等环境因素合理建立保护区,以便翅柄假脉蕨能够顺利适应新环境。


关键词: 翅柄假脉蕨, 最大熵模型, 气候变化, 地理分布预测, 响应

Abstract: Crepidomanes latealatum is sensitive to environmental changes. It is important to study the changes in the potential distribution areas of C. latealatum under climate change scenarios for the conservation of its wild resources. Using MaxEnt and ArcGIS software, we predicted changes in the spatial distribution pattern of C. latealatum in the potential distribution area of the world under different climate change scenarios (SSP126, SSP245, SSP370 and SSP585) in the current (1970-2000) and future (2021-2040, 2041-2060, 2061-2080, 2081-2100) based on 1276 geographical distribution data of C. latealatum, combined with 19 climatic factors. We further identified the main environmental variables affecting the distribution of C. latealatum. The MaxEnt model predicted the potential distribution areas of C. latealatum with high accuracy, with the AUC values of the training and testing sets being 0.962 and 0.966, respectively. The main environmental factors affecting its potential distribution were the warmest quarter, driest month precipitation, annual mean temperature, and temperature seasonality. C. latealatum is mainly distributed in 3°S-40°N, 95°E-140°E. The MaxEnt model predicted that under the current conditions, the suitable area of C. latealatum was mainly distributed in most parts of Asia, central and western Africa, and southeast Brazil, with a total suitable area of about 9.47×106 km2 and a highly suitable area of about 1.59×106 km2. Under the future climate scenario, the area of suitable areas in tropical East Asia and Africa will increase, while the area of suitable areas in temperate regions such as Europe and America will decrease. In addition, the area of suitable habitat for C. latealatum showed a general trend of first increasing and then decreasing over time under different climate scenarios. Under the background of global climate change, there are great challenges for the conservation of C. latealatum. Based on the current habitat of C. latealatum, protection zones should be established reasonably according to the local climate types, soil conditions, and other environmental factors, to ensure the adaptation of C. latealatum.


Key words: Crepidomanes latealatum, MaxEnt, climate change, prediction of geographical distribution, response