基于MaxEnt模型分析不同气候变化情景下的黄心夜合(Michelia martinii)潜在地理分布

doi:10.19741/j.issn.1673-4831.2022.0145

摘要/Abstract

摘要： 黄心夜合(Michelia martinii)是木兰科含笑属乔木,具有很高的观赏价值,是优良的香料和用材树种。然而其野生资源保存状况不容乐观,被1999颁布的《国家重点保护野生植物名录(第一批)》列为Ⅱ级珍稀濒危植物,现在其保护级别为近危(NT)。预测气候变化对分布范围的影响可为黄心夜合的野生资源保存与可持续利用提供科学基础和参考依据。利用ArcGIS 10.4与MaxEnt 3.4.1软件分析了黄心夜合在全国范围内的潜在地理分布,并与DIVA-GIS 7.5软件的Bioclim和Domain模型进行比较,选择了预测结果最佳模型MaxEnt,继而预测了21世纪50和70年代4种气候情景(RCP 2.6、RCP 4.5、RCP 6.0、RCP 8.5)下其适生区分布范围。结果表明,MaxEnt模型预测黄心夜合潜在生境分布的精度最高,受试者特征曲线(receiver operating characteristic,ROC)训练集的线下面积(area under ROC curve,AUC)为0.977;最干月降雨量、海拔、昼夜温差月均值、年均降雨量是影响黄心夜合分布的主要气候因子,其贡献率分别为43.8%、20.7%、15.2%、8.8%。现阶段黄心夜合的高、中、低适生区以贵州省为中心向四周扩散,适生区总面积共约5.7×10⁵ km²。21世纪50和70年代其适生区分布基本格局不变,21世纪50年代除了RCP 8.5情景外,黄心夜合的低、中适生区以及适生区面积总和增加。21世纪70年代,黄心夜合的中、高适生区及总适生区面积均有所减少。未来全球气候变暖会造成黄心夜合的高适生区大幅缩减。未来大部分情景下,黄心夜合的分布质心均有向东北方迁移的趋势。黄心夜合保护的难点在于其适生区域之间相互孤立、相隔较远,加剧了其灭绝的可能。未来对于黄心夜合濒危资源的保护应结合地理分布的预测结果,从野生资源调查、野外回归的迁地保护、就地建立保护区、开展繁殖与栽培技术研究等方面入手。

关键词: 黄心夜合, 气候变化, MaxEnt模型, 地理分布, 适生区

Abstract: Michelia martinii is an arboreal tree belonging to the genus Michelia of Magnolia family. It has high ornamental value and is an excellent tree for spices and wood. However, its wild resources were not well preserved, and it was listed as one of the rare and endangered plants of national grade Ⅱ (1999). Now its protection level is Near Threatened (NT). The prediction of the effects of climate change on its distribution could provide a scientific basis and reference for the conservation and sustainable utilization of this wild resource. In this study, ArcGIS 10.4 and MaxEnt 3.4.1 model were used to analyze the potential distribution of Michelia martinii across the country, which was compared with Bioclim and Domain models of DIVA-GIS 7.5 software. The MaxEnt model with the best prediction results was then selected, and the distribution of suitable areas under four climate scenarios (RCP 2.6, RCP 4.5, RCP 6.0 and RCP 8.5) in the 2050s and 2070s was predicted. The results show that the MaxEnt model could predict the potential habitat distribution accurately, and the AUC (area under ROC Curve) value of the ROC (receiver operating characteristic) training set was 0.977. Precipitation of driest month, altitude, monthly mean temperature diurnal range, and annual precipitation were the main environmental factors affecting the distribution of Michelia martinii, and the contribution rates were 43.8%, 20.7%, 15.2%, 8.8%, respectively. The high, middle and low suitable growth regions of Michelia martinii spread around Guizhou Province, covering a total area of about 5.7×10⁵ km². In the 2050s and 2070s, the distribution pattern of suitable growth regions will remain unchanged. In the 2050s, except for scenario RCP 8.5, the area of low and medium suitable areas and the total area of suitable areas of Michelia martinii under the other three scenarios will be increased. In the 2070s, the areas of medium and high fitness areas and total suitable areas of Michelia martinii will be decreased under all the four scenarios. In the future, global warming will significantly reduce the high suitable area of Michelia martinii. Under most scenarios in the future, the distribution centroid of Michelia martinii will have a trend moving to the northeast. The difficulty of protecting Michelia martinii was that its habitats are isolated and far away from each other, which intensifies the possibility of extinction. In the future, the protection of endangered resources of Michelia martinii should be combined with the prediction results of geographical distribution, from the aspects of the investigation of wild resources, ex situ protection of field return, establishment of protected areas in situ, research on reproduction and cultivation techniques, etc.

Key words: Michelia martinii, climate change, MaxEnt model, geographic distribution, suitable habitat

中图分类号:

Q948.13

宦智群, 耿兴敏, 徐小蓉, 刘维, 祝遵凌, 唐明. 基于MaxEnt模型分析不同气候变化情景下的黄心夜合(Michelia martinii)潜在地理分布[J]. 生态与农村环境学报, 2023, 39(10): 1277-1287.

HUAN Zhi-qun, GENG Xing-min, XU Xiao-rong, LIU Wei, ZHU Zun-ling, TANG Ming. Potential Geographical Distribution of Michelia martinii under Different Climate Change Scenarios Based on MaxEnt Model[J]. Journal of Ecology and Rural Environment, 2023, 39(10): 1277-1287.

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