Undergraduate Honors Thesis

 

Discrimination Between Microtus Longicaudus and Microtus Montanus Using Cranial Morphology, External Measurements, DNA Sequencing, and Discriminate Function Analysis Public Deposited

https://scholar.colorado.edu/concern/undergraduate_honors_theses/vh53ww08w
Abstract
  • Montane and Long-tailed voles (Microtus montanus and Microtus longicaudus) are notoriously difficult to distinguish through external characteristics and measurements in the field, particularly among juveniles. Being able to accurate identify these species is important, due to their central role in ecosystems (as keystone species) and as indicator species of climate change (due to their high sensitivity to climate change). The present study assessed three methods for distinguishing the two species: cranial (skull-based) and external measurements, the presence of additional skull characteristics, and DNA sequencing through examining 372 specimens in the University of Colorado Natural History Museum collection and 52 specimen from recent collection by the McCain lab (2010-2011). Three external measurements (including total specimen length, tail length, and hind foot length), nine cranial measurements, and four skull characteristics were employed. A subset of the sample population (M. longicaudus, n = 5; M. montanus, n = 17) was verified with genetic methods. Through assessing combinations of external and cranial measurements using discriminant function analysis, I determined which would best identify species regardless of age. Models with only external measurements left many juveniles misclassified (4.6% misclassified), models with only cranial measurements were more accurate (0.5% misclassified), and models with all twelve characters were most accurate (0% misclassified). All specimens were correctly identified using a best-fit model of three measurements (tail length and the breadth of two specific skull bone formations; 0% misclassified). This model correctly classified the remaining 28 juvenile specimens of unknown identity with an average fit of 99.8%.
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  • 2013-03-10
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  • 2019-12-02
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