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These morphological variables were: TCSA, number of water sprouts on the trunk and length of annual shoot at the distal part of fruiting branches at the bottom of the tree.
Thirty-four morphological variables were recorded as described in Table 1.
After the experiments, detailed measurements of several morphological variables were performed (see below).
Because several morphological variables were measured per wing, we used the conservative Bonferroni correction for multiple tests.
To obtain normality and homoscedascity, arrival date, all flight measures and morphological variables were log transformed before entering them in the statistical analysis.
Five of the 11 morphological variables were significantly different between species: epaxial height, epaxial width, output link, input link, and opening jaw inlever (Table 1).
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For all of the laboratory-raised birds a DFA based on morphological variables was highly significant (Wilk's λ = 0.12, P<0.001) and correctly identified 100% of the individuals in a post-hoc classification based on a prior expectation of 50% correct classification by chance alone.
Our study shows that quantification of several morphological variables is possible using these methods.
Leaf morphological variables are measured and calculated using CorelDRAW 12.0 on the basis of leaf images (Fig. 2) from illustrated species, including the length-to-width/2 ratio, dissection index, and sinus (see Additional file 4).
None of the morphological variables are significantly correlated to the residual genus richness in focused tests (Table 5) although the PC1-4 are present in the top three non-phylogenetic multiple regression models (Table 6) and PC1 enters the top three phylogenetic multiple regression models (Table 7).
Phylogenetically controlled regression analyses of log-transformed root-to-tip ω versus each log-transformed morphological variable were performed using BayesTraits [ 46, 47] and the time-calibrated tree of [ 42] to explicitly test for gene-phenotype associations.
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