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作 者:文雪 严勇[1] 和梅香 冉江洪[1] 马晓龙 邱瑞军 王妮 WEN Xue;YAN Yong;HE Meixiang;RAN Jianghong;MA Xiaolong;QIU Ruijun;WANG Ni(Key Laboratory of Bio-Resource and Eco-Environment of Ministry of Education,College of Life Sciences,Sichuan University,Chengdu 610065,China;Administration of Heizhugou National Nature Reserve,Ebian,Sichuan Province 614300,China;Yulin Desert Forest Park Management Center,Yulin,Shaanxi Province 719000,China)
机构地区:[1]四川大学生命科学学院,生物资源与生态环境教育部重点实验室,成都610065 [2]四川黑竹沟国家级自然保护区管理局,四川峨边614300 [3]榆林沙地森林公园管理中心,陕西榆林719000
出 处:《四川动物》2020年第1期68-74,共7页Sichuan Journal of Zoology
摘 要:种群数量是物种的重要生态学基础资料,合适的密度调查方法是数量估算的基础。2016年4-5月,采用广泛应用于鸡形目Galliformes鸟类种群密度调查的样线法和样点法,调查了四川黑竹沟国家级自然保护区3种鸡形目鸟类(白腹锦鸡Chrysolophus amherstiae、红腹角雉Tragopan temminckii和血雉Ithaginis cruentus)的种群密度。样线法和样点法估算的雄体密度分别是:白腹锦鸡1.20只/km^2和(6.31±0.98)只/km^2,红腹角雉5.41只/km^2和(0.39±0.17)只/km^2,血雉3.01只/km^2和(5.97±2.70)只/km^2。除红腹角雉外,样点法估算的白腹锦鸡、血雉种群密度均大于样线法。建议针对不同鸡形目鸟类采用不同的调查方法,并尽量扩大样本数量,从而提高调查结果的准确性。Population size is an important basic ecological character of species, and thus applying appropriate density survey method is fundamental to estimate the quantity. In this study, 2 commonly used methods(line transect and point count) were used to estimate the population densities of 3 Galliformes species of calling males(Chrysolophus amherstiae, Tragopan temminckii, and Ithaginis cruentus) in the Heizhugou National Nature Reserve(Sichuan province) between April and May in 2016. The results of line transect method and point count method showed that the population densities of the 3 species were 1.20 individuals/km^2 and(6.31±0.98) individuals/km^2, 5.41 individuals/km^2 and(0.39±0.17) individuals/km^2, and 3.01 individuals/km2 and(5.97±2.70) individuals/km^2, respectively. This result suggested that except for T. temminckii, the densities of the other 2 species estimated by using point count method are better than by using line transect method. Therefore, it is recommended that different methods should be used for estimating the population densities of different pheasants, and large sample size can improve the accuracy of predictive results.
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