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基于 MaxEnt 模型的湘西土家族苗族自治州 中华秋沙鸭栖息地适宜性评价∗
米邵平1, 郑小君2, 康剑3
1.湘西土家族苗族自治州林业科学研究所;2.泸溪县林业局;3.中国科学院华南植物园
摘要:
为了评估物种栖息地适宜性, 提出生物多样性保护和栖息地管理策略, 研究将湘西土家族苗 族自治州作为研究区域, 结合 2021 年该地区国家一级重点保护野生动物中华秋沙鸭 Mergus squamatus 的 实地观测记录, 利用 MaxEnt 模型对该物种的栖息地适宜性进行了评估, 并进一步通过叠加分析, 展示了 该物种在实际保护过程中存在的空间空缺, 以此提出了未来在该地区保护中华秋沙鸭的建议。 结果显示, 中华秋沙鸭的平均 AUC (Area under curve) 值为 0. 890±0. 102, 表明利用 MaxEnt 模型得到的预测结果具 有较高的可信度。 海拔、 坡度和年降水量是影响该物种分布的主要因素。 该物种主要分布于东南部等海 拔较低, 水源充沛的地方。 湘西州境内中华秋沙鸭的高适宜区面积约为 1 948 km 2, 建议在该范围进行相应的保护试点建设, 从而进一步加强对该地区中华秋沙鸭的保护。
关键词:  中华秋沙鸭  栖息地  湘西土家族苗族自治州  MaxEnt 模型
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Habitat Suitability Evaluation of Mergus squamatus in Xiangxi Tujia and Miao Autonomous Prefecture based on MaxEnt Model
mi shaoping1, zheng xiaojun2, kang jian3
1.Xiangxi Tujia and Miao Autonomous Prefecture Forestry Science Research Institute;2.Luxi Forestry Bureau;3.South China Botanical Garden,Chinese Academy of Sciences
Abstract:
To evaluate the habitat suitability of species, biodiversity conservation and habitat management strategies are proposed. In this study, Xiangxi Tujia and Miao Autonomous Prefecture was considered as study areas, using the MaxEnt model to evaluate the habitats suitability of Mergus squamatus, a national Class I key protected animal, combined with local observation records in 2021. Meanwhile, the conservation vacancies in conservation were identified by overlay analysis, which proposed suggestions for future M. squamatus protection in this area. The results showed that the area under curve ( AUC) value for the target species was 0. 890 ± 0. 102, indicating that the model used in this study was reliable. The dominate cues affecting the distribution of M. squamatus were elevation, slope and annual precipitation. The most suitable areas for this species were in the southeastern part of the lower elevation, where the water source is abundant, which amounts to 1 948 km 2. Pilot construction should be developed in these areas, which could further strengthen the regional protection of M. squamatus.
Key words:  Mergus squamatus  habitat  Xiangxi Tujia and Miao Autonomous Prerecture  MaxEnt model