【摘要】 n个群体的p维环境因子主成分分析（PCA）,Z=U’X,恢复原始数据线性表达成。依据λ值,取前1—Υ个主成分,1≤Υ≤p,将p维环境因子降维成1元向量,建立前Υ个主成分的生态梯度轴[EGA（PC_r）],估算误差为d_i。对白榆全分布区20个群体的样点6维环境因子PCA求算的EGA（PC_r）,取r=1,2,3,建立3个生态梯度轴,累计贡献率依次为63.8％,84.3％和96.0％,都能较好的代表环境因子。EGA（PC_r）在揭示群体7个性状的梯度变异中得到验证,并能用于判别种群变异模式。更多还原

【Abstract】 Principal components (Z=U’X) of p-dimensional environmental factors in few populations were turned into a linear expression of primary data(X = UZ). The estimate error was named dj. EGA(PCr)s were calculated according to principal component analysis (PCA) of 6-dimensional environmental factors among the twenty provenances of American elm.The cumulative contributions of 3 .EGA(PCR)s selected according to eigenvalue were 63.8%, 84.3% and 96.0%, respectively. Most information from environmental factors was contained in these 3 GEA(PCr)S EGA(PCr)method was tested well in investigated population gradient variabiltiy of 7 traits and could be used to identify the model of population gradient variation.更多还原