基于MaxEnt模型的黄腹角雉潜在生境预测

doi:10.16088/j.issn.1001-6600.2022092101

摘要/Abstract

摘要： 保护濒危野生动植物是推进生态文明建设的重点任务之一,是进行生物资源深入研究、开发和制定保护战略的基础。由于栖息地的破坏,中国特有种黄腹角雉Tragopan caboti被列为易危等级。本研究基于筛选的298个黄腹角雉分布点和12个环境因子,利用MaxEnt模型预测该物种的潜在适宜生境。结果发现:1)影响黄腹角雉分布的主要环境因素是降水、气温和植被类型;2)黄腹角雉的潜在生境总面积约66.76×10⁴ km²,低适生生境面积约33.38×10⁴ km²,中适生生境面积约20.04×10⁴ km²,高适生生境面积约13.34×10⁴ km²;3)黄腹角雉潜在高适宜生境的分布与其分布密度高度吻合,主要集中在闽北地区和桂东北地区。因此,建议加强对闽北地区和桂东北地区等高适宜生境的保护力度。

关键词: 黄腹角雉, 环境因子, 潜在生境分布, MaxEnt模型

Abstract: Protecting the habitats of endangered wildlife is the basis for intensive research, development and protection of biological resources, which is one of the key tasks in promoting the construction of ecological civilization. The endemic birds, Tragopan caboti has been included in the list of vulnerable species in the International Union for Conservation of Nature for habitat loss and fragmentation. Based on 298 distribution screened records and 12 environmental factors, the potentially suitable habitats of the T. caboti was predicted using MaxEnt model. The results showed that: 1) The distribution of the T. caboti was mainly affected by precipitation, atmospheric temperature and vegetation types. 2) The total potentially habitats area of the T. caboti was 66.76×10⁴ km². Specifically, the low suitable habitats, medium suitable habitats and high suitable habitats were about 33.38×10⁴ km², 20.04×10⁴ km², 13.34×10⁴ km², respectively. 3) The distribution of potential habitats had a high overlap with the actual distribution density. Furthermore, the high suitable habitats were mainly concentrated in Northern Fujian and Northeast Guangxi. Therefore, great attention should be paid to the conservation of T. caboti’s highly-suitable habitat, such as in northern Fujian and northeast Guangxi, China.

Key words: Tragopan caboti, environment factor, distribution of potential habitats, MaxEnt model

中图分类号: Q958

庞丽芳, 庾太林. 基于MaxEnt模型的黄腹角雉潜在生境预测[J]. 广西师范大学学报（自然科学版）, 2023, 41(5): 123-133.

PANG Lifang, YU Tailin. Prediction of Potential Habitat for Tragopan caboti Based on MaxEnt Model[J]. Journal of Guangxi Normal University(Natural Science Edition), 2023, 41(5): 123-133.

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