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林业科学 ›› 2021, Vol. 57 ›› Issue (10): 102-110.doi: 10.11707/j.1001-7488.20211010

• 论文与研究报告 • 上一篇    下一篇

气候变化对我国特有濒危动物褐马鸡潜在生境的影响

李宏群1,韩培士2,牛常会2,袁晓青2,邢立刚1   

  1. 1. 长江师范学院现代农业与生物工程学院 重庆 408100
    2. 延安市黄龙山国有林管理局 延安 715700
  • 收稿日期:2019-12-22 出版日期:2021-10-25 发布日期:2021-12-11
  • 基金资助:
    国家自然科学基金项目(31870515);重庆市高校优秀成果转化资助项目(KJZH17132);重庆市科委的基础研究与前沿探索项目(cstc2018jcyjAX0557)

Impact of Climate Change on the Potential Habitat of Brown-Eared Pheasant (Crossoptilon mantchuricum), An Endemic and Endangered Animals to China

Hongqun Li1,Peishi Han2,Changhui Niu2,Xiaoqing Yuan2,Ligang Xing1   

  1. 1. School of Modern Agriculture and Bioengineering, Yangtze Normal University Chongqing 408100
    2. Yan'an Huanglongshan National Forestry Administration Yan'an 715700
  • Received:2019-12-22 Online:2021-10-25 Published:2021-12-11

摘要:

目的: 预测现在和未来气候情景下我国特有濒危动物褐马鸡潜在生境分布的变化,为制定有关保护政策提供科学依据。方法: 基于褐马鸡152个分布点和26个环境变量数据,利用MaxEnt模型模拟褐马鸡在我国的潜在地理分布,并基于该模型预测国际政府间气候变化专门委员会(IPCC)发布的RCP4.5和RCP8.5气候情景下21世纪50和70年代褐马鸡潜在分布范围。结果: 所有训练集与验证集为AUC均大于0.8,所构建模型的预测准确性达到优良标准。当前褐马鸡潜在分布生境的主要影响因子(贡献率)依次为年降水量(15.4%)、昼夜温差月均值(15.3%)、植被类型(9.7%)、最干季降水量(9.1%)、最湿季降水量(8.7%)、到道路距离(8.2%)和到水源距离(7.8%)等,累计贡献率达74.2%,且各因子阈值分别为525~580 mm、8.2~10.8℃、植被类型偏向于阔叶林和针阔混交林、12.4~17.1 mm、310~340 mm、0~12.5 m和大于270 m、0~20 m和105~220 m。褐马鸡的适宜生境主要分布在陕西黄龙山、山西吕梁山、河北小五台山和北京百花山,其适宜、中度适宜和不适宜区面积占研究区面积比例分别为6.45%、19.92%和73.62%。与当前情景下相比,未来褐马鸡可生存生境表现为增加趋势,且适宜和中度适宜生境变化趋势相同,而21世纪50和70年代之间几乎不变。结论: 褐马鸡适宜生境主要分布在陕西黄龙山、山西吕梁山、河北小五台山和北京百花山,未来可生存生境表现为增加趋势。影响褐马鸡分布的主要因子为年降水量、昼夜温差月均值、植被类型、最干季降水量、最湿季降水量、到道路距离和到水源距离等。建议对褐马鸡适宜区加强重点保护,对中度适宜区加强保护和管理,同时也要加强常绿针叶林木的栽植和人口控制。

关键词: 褐马鸡, MaxEnt模型, 刀切法, 主导气候因子, 气候变化

Abstract:

Objective: Global climate change is one of the main factors causing biodiversity reduction and species extinction. The research on the potential habitat changes of brown-eared pheasants, a species endemic and endangered to China under the present and future climate change scenarios has great significance for establishing relevant conservation policies. Method: Based on the data at 152 distribution locations and 26 high-resolution environmental variables, the potential habitat of brown-eared pheasant was analyzed by using MaxEnt model under the current conditions, and the future distributions were also simulated for the periods of 2050s and 2070s under the climate change scenarios of RCP4.5 and RCP8.5 predicted in the Special Report of Intergovernmental Panel on Climate Change (IPCC). Result: The AUC values for all training and testing model were greater than 0.8, indicating that MaxEnt model is good in predicting its potential habitat. At present, the contribution rates of the dominate factors to brown-eared pheasant habitat were annual precipitation (15.4%), mean diurnal range (15.3%), vegetation types (9.7%), precipitation in the driest season (9.1%) and in the wettest season (8.7%), distance to road (8.2%) and water source (7.8%), with their cumulative contributions of 74.2%. The thresholds of each factor were 525-580 mm, 8.2-10.8℃, preference for broadleaved forest and mixed forest, 12.4-17.1 mm, 310-340 mm, 0~2.5 km, 0~0.63 km and more than 10 km, respectively. The suitable habitats of brown-eared pheasant were mainly distributed in Huanglong Mountain of Shaanxi Province, Lüliang Mountain of Shanxi Province, Xiaowutai Mountain in Hebei Province and Baihua Mountain of Beijing. The proportion of suitable, moderate and unsuitable areas was 6.45%, 19.92% and 73.62%, respectively. Compared with that in the current condition, the livable habitat of the pheasant shows a increase trend in the future, and the suitable and moderately suitable habitat has the same trend. Meanwhile, it is almost unchanged between 2050s and 2070s. Conclusion: The potential distribution areas of the pheasant in this study area are mainly in Huanglong Mountain of Shaanxi Province, Lüliang Mountain of Shanxi Province, Xiaowutai Mountain in Hebei Province and Baihua Mountain of Beijing City. The livable habitats of the pheasant will increase in the future. The dominate factors affecting the distribution of brown-eared pheasant are annual precipitation, mean diurnal range, vegetation types, precipitation of the driest and the wettest quarter, distance to road and water source. This study reminds us to strengthen the protection and management of the suitable and moderate areas, and meanwhile, strengthen evergreen coniferous forest planting and population control in the reserve.

Key words: Crossoptilon mantchuricum, MaxEnt model, Jackknife test, dominate factor, climate change

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