Multivariate studies of breeding bird communities have used principal component analysis (PCA) or several-group (three or more groups) discriminant function analysis (DFA) to ordinate bird species on vegetational continua (Cody 1968, James 1971, Whitmore 1975). In community studies, high resolution of habitat requirements for individual species is not always possible with either PCA or several-group DFA. When habitat characteristics of several species are examined with a DFA the resultant axes optimally discriminate among all species simultaneously. Hence, the characteristics assigned to a particular species reflect in part the presence of other species in the analyses. A better resolution of each species' habitat requirements may be obtained from a two-group DFA, wherein habitats selected by a species are discriminated from all other available habitats.

Analyses using two-group DFAs to compare habitat used by a species with habitat unused by the same species have the potential to provide an optimal frame of reference from which to examine habitat variables (Martinka 1972, Conner and Adkisson 1976, Whitmore 1981). Mathematically (DFA) it is possible to maximally separate two groups of multivariate observations with a single axis (Harner and whitmore 1977). A line drawn in three or n-dimensional space can easily be positioned to intersect two multivariate means (centroids). If three or more centroids for species are analyzed simultaneously, a single line can no longer intersect all centroids unless a perfectly linear relationship exists for the species being examined. The probability of such an occurrence is extremely low. Thus, a high degree of resolution can be realized when a two-group DFA is used to determine habitat parameters important to individual species. We have used two-group DFA to identify vegetation variable important to 12 common species of songbirds in East Texas.