首页|基于不同时空的地理分布数据对加拿大一枝黄花的适生区分析研究

基于不同时空的地理分布数据对加拿大一枝黄花的适生区分析研究

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为更准确地预测加拿大一枝黄花(Solidago canadensis L.)在中国的潜在适生区,本研究运用MaxEnt模型,结合该物种自1903-2023年的7类全球地理分布数据及中国当前的地理分布数据,对其潜在适生区进行了预测分析.分析结果显示,随着年份的增加,基于全球分布数据的AUC值从0.980下降至0.710,其中降水量的季节性变化(bio15)和年平均气温(bio1)是影响模型预测的两个主要环境变量,预测的适生区范围有扩大趋势,且与物种实际分布的变化相符合.基于中国的分布数据得到的AUC值高达0.977,但预测的适生区与全球数据预测结果存在显著差异,且与物种在全球的实际分布情况不相符.综上,最终采用截止至2023年在全球范围内的所有分布数据进行适生区预测.建议在贵州、湖北、湖南、安徽、江苏、浙江、河南、重庆、上海、台湾等地区采取高风险级别的防控措施,以有效应对加拿大一枝黄花的入侵威胁.
Analysis of the suitable habitat of Solidago canadensis L.based on different geospatial and tempo-ral distribution data
To more accurately predict the suitable habitat of Solidago canadensis L.in China,this study employed the MaxEnt model,integrating seven categories of global geographical distribution data from 1903 to 2023 and current geographical distribution data within China,to forecast its potential suitable habitat.The analysis revealed that with the increase in years,the AUC value based on global distribu-tion data decreased from 0.980 to 0.710,with seasonal variation in precipitation(bio15)and mean annual temperature(bio1)being the two primary environmental variables influencing the model's predictions.The predicted suitable habitat range showed an expanding trend,which corresponded with the actual changes in species distribution.However,the AUC value based on Chinese distribution data,although high at 0.977,indicated a suitable habitat range that significantly differed from the global data predictions and did not match the species'actual global distribution.Consequently,the final suitable habitat prediction was conducted using all distribution data worldwide up to 2023.It is recommended to take high-risk pre-vention and control measures in Guizhou,Hubei,Hunan,Anhui,Jiangsu,Zhejiang,Henan,Chongqing,Shang-hai,and Taiwan to effectively cope with the invasion threat of S.canadensis.

Solidago canadensisMaxEnt modelhistorical distribution datapotential suitable habitat

张婷、杜伟、陆占军、杨明进、陈炜、胡诗遥、张伟、徐晗

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中国检验检疫科学研究院 北京 100176

山东大学

宁夏回族自治区农业技术推广总站

银川海关

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加拿大一枝黄花 MaxEnt模型 历史分布数据 潜在适生区

2024

植物检疫
中国检验检疫科学研究院

植物检疫

CSTPCD
影响因子:0.498
ISSN:1005-2755
年,卷(期):2024.38(6)