Morphological Variation in the Dentition and Skull of the Australian Ghost Bat, Macroderma-Gigas (Microchiroptera, Megadermatidae)

1990 ◽  
Vol 38 (3) ◽  
pp. 263 ◽  
Author(s):  
S Hand ◽  
A York

Morphological variation in the dentition and some cranial characters of the Australian ghost bat, Macroderma gigas, is reviewed by means of univariate and multivariate analyses. Specimens examined are drawn from existing populations across northern Australia; also included for parts of this study are mummified remains from southern central South Australia and late Pleistocene subfossil specimens from south-western Western Australia. No clear-cut geographic pattern in morphological variation in M. gigas is indicated by multivariate anlysis (i.e. principal components analysis), although there is some evidence for clinal variation from univariate analysis (i.e. Scheffe's multiple-comparions procedure). Northern Australian ghost bats (with the exception of north-eastern Australian indiv~duals) tend to be smaller than their southern counterparts. Sexual dimorphism appears to be low. Independent patterns of covariation among characters are extracted by principal components analysis: cheek tooth widths cluster separately from lengths; lengths and widths of the same teeth cluster separately from those of occluding teeth; and cranial measurements cluster separately from tooth measurements. Patterns in the data suggest that the number of characters needed to be examined in future morphometric studies of the vulnerable ghost bat can be significantly reduced.

1988 ◽  
Vol 2 (1) ◽  
pp. 55 ◽  
Author(s):  
A Sokol

The present study was directed at clarifying the taxonomy of the destructor group of the genus Cherax. This group was defined by Riek (1969) to include four species: C. destructor Clark, C. albidus Clark, C. davisi Clark and C. esculus Riek. Approximately 1600 specimens representing over 80 localities were examined, including specimens from three outgroup species; C. rotundus, C. punctatus and C. dispar. Variation in 16 metric and 30 multistate characters was analysed by bivariate (analysis of covariance) and multivariate (principal components analysis) techniques. None of the taxonomic analyses supported the distinction of C. davisi or C. esculus from C. destructor, which suggests that the two former species be synonymised with the last. By contrast, C. albidus was found to be morphologically distinct. The pattern and timing of speciation of C. albidus and C. destructor are unclear but may relate to the increase in aridity in inland Australia during the late Tertiary. The analyses also indicated that heterochrony may underly the morphological divergence of these two species.


1983 ◽  
Vol 61 (6) ◽  
pp. 1692-1717 ◽  
Author(s):  
William J. Crins ◽  
Peter W. Ball

The Carex pensylvanica complex consists of four North American taxa. Morphological variation patterns within the complex were examined using principal-components analysis and discriminant-functions analysis. These results indicate that two eastern species, C. lucorum Willdenow ex Link, and C. pensylvanica Lamarck, and one western species, C. inops Bailey, should be recognized. The latter species comprises two subspecies, C. inops subsp. inops and C. inops subsp. heliophila (Mackenzie) Crins, comb. nov. Cytological and geographical evidence lend support to this classification. A key and distribution maps for the taxa are provided.


1980 ◽  
Vol 19 (04) ◽  
pp. 205-209
Author(s):  
L. A. Abbott ◽  
J. B. Mitton

Data taken from the blood of 262 patients diagnosed for malabsorption, elective cholecystectomy, acute cholecystitis, infectious hepatitis, liver cirrhosis, or chronic renal disease were analyzed with three numerical taxonomy (NT) methods : cluster analysis, principal components analysis, and discriminant function analysis. Principal components analysis revealed discrete clusters of patients suffering from chronic renal disease, liver cirrhosis, and infectious hepatitis, which could be displayed by NT clustering as well as by plotting, but other disease groups were poorly defined. Sharper resolution of the same disease groups was attained by discriminant function analysis.


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