摘要
20 0 3年 1～ 4月 ,对分布于四川省甘孜藏族自治州稻城县著杰寺周围的白马鸡 (Crossoptiloncrossoptilon)群体的夜栖地特征进行了分析。随机跟踪黄昏时发现的白马鸡群体直至其上树夜栖来确定夜栖地的位置。通过系统取样的样线法调查整个研究区域环境变量的特征。共获得 1 72个 2 0 0m×2 0 0m的栅格 ,栅格中有夜栖地出现的定义为活动栅格 (37个 ) ,赋值为 1 ;反之为非活动栅格 (1 35个 ) ,赋值为 0。通过逻辑斯蒂回归建立白马鸡群体夜栖地选择的预测模型。该模型的数学表达式为Ln[P/(1 -P) ]=- 3 938+0 0 83×坡度 +0 0 37×乔木盖度 +0 1 1 6×乔木高度 - 0 0 0 3×草本盖度 (P为白马鸡群体夜栖地的出现概率 )。模型表明坡度、乔木盖度、乔木高度和草本盖度显著影响白马鸡群体的夜栖地选择。白马鸡群体的夜栖地选择与坡度、乔木盖度和乔木高度正相关 ,与草本盖度负相关。该预测模型具有较高的预测准确性。
From January to April 2003,we studied the roosting site characteristics of Crossoptilon crossoptilon populations around Zhujie Monastery in Daocheng Country,Sichuan Province.We randomly tracked the populations we met before dusk until they roosted in woods,by which we could decide the accurate positions of the bird's roosting sites.Line transects of systematic sampling were used to survey the fundamental characteristics of the environmental variables in the study area.We obtained 172 grids (200 m× 200 m) including 37 active grids (with roosting sites) valued 1 and 135 inactive grids (without roosting site) valued 0.We used logistic multiple regression to derive the predictive model of wintering roosting-site selection for the flocks.The predictive model could be expressed well by the following equation:Ln[P/(1-P)]=-3.938+0.083×degree of slope+0.037×tree cover+0.116×tree height-0.003×herb cover (P was the probability of the occurrence of C.crossoptilon Populations roosting-site).Results showed that the variables of degree of slope,wood cover,wood height and herb cover were the most important factors affecting the occurrence of the roosting sites for the Populations.The occurrence of the roosting sites was positively related to degree of slope,wood cover and wood height,and negatively related to herb cover.The model could accurately predict the occurrence of the roosting site of C.crossoptilon Populations.
出处
《生态学杂志》
CAS
CSCD
北大核心
2005年第2期153-158,共6页
Chinese Journal of Ecology
基金
国家自然科学基金重点项目 (3 0 3 3 0 0 5 0 )
芝加哥动物学会
芝加哥濒危物种贸易公约基金资助项目 (CZS)。
关键词
白马鸡
群体
夜栖地
逻辑斯蒂回归
模型
C.crossoptilon,Crossoptilion crossoptilion,Populsyiond,roosting-site,logistic regression,model.
