Suppr超能文献

基于最大熵模型的气候变化下[物种名称]在中国潜在分布区的识别

[Identification of the potential distribution area of in China under climate change based on the MaxEnt model].

作者信息

Chen Yu-Guang, LE Xin-Gui, Chen Yu-Han, Cheng Wu-Xue, DU Jin-Gui, Zhong Quan-Lin, Cheng Dong-Liang

机构信息

Key Laboratory for Humid Subtropical Eco-geographical Processes, Fujian Normal University, Fuzhou 350007, China.

Fujian Provincial Key Laboratory for Plant Eco-Physiology, Fujian Normal University, Fuzhou 350007, China.

出版信息

Ying Yong Sheng Tai Xue Bao. 2022 May;33(5):1207-1214. doi: 10.13287/j.1001-9332.202205.024.

Abstract

Based on the distribution records of we used the maximum Entropy (MaxEnt) model and geographic information system (GIS) methods, combined with environmental factors such as climate and terrain, to predict the potential distribution areas suitable for under current and future climate scenarios. The results showed that annual precipitation was the most important factor driving the distribution of . Under the current climate scenario, the total area of suitable for growth was about 3.28 million km, accounting for about 34.5% of the total land area of China. Among all the suitable areas, the lowly, intermediately, and highly suitable areas accounted for 18.3%, 29.7% and 52.0% of the total, respectively. Under future climate scenarios, the suitable area of would increase, showing a clear trend of northward expansion in China. A concentrated and contiguous distribution region highly suitable for would appear in the humid subtropical areas of southern China. The model was tested by the receiver operating characteristic curve (ROC). The average area under the curve of ROC of the training set was 0.91, showing high reliability.

摘要

基于[具体名称]的分布记录,我们运用最大熵(MaxEnt)模型和地理信息系统(GIS)方法,结合气候和地形等环境因素,来预测在当前和未来气候情景下适合[具体名称]生长的潜在分布区域。结果表明,年降水量是驱动[具体名称]分布的最重要因素。在当前气候情景下,适合[具体名称]生长的总面积约为328万平方千米,约占中国陆地总面积的34.5%。在所有适宜区域中,低度、中度和高度适宜区域分别占总面积的18.3%、29.7%和52.0%。在未来气候情景下,[具体名称]的适宜面积将会增加,在中国呈现出明显的向北扩张趋势。在中国南方湿润亚热带地区将出现一个高度适宜[具体名称]生长的集中且连续的分布区域。该模型通过受试者工作特征曲线(ROC)进行了检验。训练集的ROC曲线下平均面积为0.91,显示出高可靠性。

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验