. Bulletin of the British Ornithologists' Club. Birds. [, Orn. CI 1986 106(2)] 50. 1 31 o.: 7 22 3 13 8 16 6 43 »,: 51 7 10 19 9 6 7 3 3 12 4 13 8 9 15 1 .98 66 16 8 • 17 27 14 41 Figure 3. Inter-locality variation of bill length in female Dunlin Calidris alpina (see Fig. 2 for explanation). fourth primary (white vane) could be used to separate central Siberian populations from those to the west which had
. Bulletin of the British Ornithologists' Club. Birds. [, Orn. CI 1986 106(2)] 50. 1 31 o.: 7 22 3 13 8 16 6 43 »,: 51 7 10 19 9 6 7 3 3 12 4 13 8 9 15 1 .98 66 16 8 • 17 27 14 41 Figure 3. Inter-locality variation of bill length in female Dunlin Calidris alpina (see Fig. 2 for explanation). fourth primary (white vane) could be used to separate central Siberian populations from those to the west which had less white. My results show that mean scores of less than 7 occur to the west of the Yenesei Valley, and mean scores above 7 occur to the east of the Yenesei Valley (Table 1). Thus Horvath & Keve's statement needs to be modified to include all eastern Palaearctic and Nearctic populations with the central Siberian ones. Multivariate analysis of inter-locality variation Multivariate analysis of geographical variation allows all characters to be examined simultaneously. The technique used here, linear discriminate analysis (LDA, also known as canonical variate analysis), has been used in other studies of geographical variation in birds ( Power 1970, and see Greenwood 1979b.). An individual specimen can be represented as a point in a hyperspace of 6 dimensions, each dimension corresponding to one of the 6 measurements used. A group of specimens with similar measurements would be represented by a cluster of points. The group mean is referred to as the group centroid. LDA creates new axes or canonical variates, each of which has a value indicating the importance of that variate in separating the groups. The relative contributions of the characters to each canonical variate are rep- resented by an 'eigenvector' (latent vector). Because LDA considers all characters together, specimens with data missing for one or more characters had to be omitted from the analysis. A multiva
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Keywords: ., bookcentury1800, bookdecade1890, booksubjectbirds, bookyear1893
