气候变化下栓皮栎潜在地理分布格局及其主导气候因子

栓皮栎(Quercus variabilis)是东亚天然分布最广泛的树种之一。利用最大熵(Maxent)模型对现实气候条件下栓皮栎在东亚地区的潜在分布及其分布的主导气候因子进行分析,同时结合全新世中期(6000年前)和未来气候(2050年)来模拟和预测气候变化背景下栓皮栎潜在分布格局的变化。结果表明:现实气候条件下东亚栓皮栎适生区(适宜生境和低适宜生境)面积占总研究区面积的21.88%,主要集中在东亚南部区域,在我国北起陕西中部、山西和河北南部边缘、山东,西起甘肃东部边缘、四川中东部、云南、西藏东部边缘,一直到东部沿海区域,同时在朝鲜半岛南部和日本中南部也有分布,其中适宜生境面积占研究区总面积的5.69%,主要集中在秦岭山脉、大巴山脉、伏牛山、云南的云贵高原、罗霄山脉、南岭山脉、武夷山和台湾岛;气候变化情景下,栓皮栎的适生区分布面积变化较小,但其适宜生境的分布范围却发生了较大的变化,随着全球气候的波动性变化,适宜生境分布范围逐渐向西部秦岭山脉、大巴山脉、四川、重庆和云贵高原等区域集中,并使该分布中心的适宜生境面积逐渐扩大;影响栓皮栎分布的主要气候因子为最冷月的最低气温(Bio6)、最冷季平均气温(Bio11)和年降水量(Bio12),三者的贡献率分别为48.6%、21.4%和14.2%。

Impact of climate change on the potential geographical distribution pattern and dominant climatic factors of Quercus variabilis

In order to predict the impact of climate change on the distribution of Quercus variabilis, one of the most widely distributed species in East Asia, the maximum entropy model (Maxent) was used to analyze the potential distribution under past (Mid Holocene, 6000 years ago), current (1950-2000) and future (2050) climate conditions. The jackknife method was used to reveal the contribution of the dominant climatic factors of Q. variabilis. The results showed that the potential distribution of Q. variabilis under the current climate conditions centered on the south of East Asia, comprising up to 21.88% of the total studied area. The distribution range was mostly located in central and south of China, as well as south of the Korean peninsula, and central to south of Japan. Suitable habitats, comprising 5.69% of the studied area, were mainly centered on Qinling, Daba, Funiu, Luoxiao, Nanling, and Wuyi Mountains, the Yunnan-Guizhou Plateau, and Taiwan. The climate change scenarios showed that the areas of the suitable habitats have changed slightly, while significantly for the distribution range. With changes in global climate volatility, the distribution centers of Q. variabilis are gradually concentrating in the regions of Qinling and Daba Mountains and Yunnan-Guizhou Plateau, and the areas of suitable habitats are gradually expanding. Minimum air temperature of the coldest month (Bio6), mean air temperature of the coldest quarter (Bio11), and annual precipitation (Bio12) were found to be dominant in influencing the geographic distribution of Q. variabilis, with contribution rates of 48.6%, 21.4%, and 14.2%, respectively.