Prediction of the body mass of the bank voleMyodes glareolusfrom skull measurements

Abstract

Regression equations were computed for determining body mass from skull measurements from 376 bank vole (Myodes glareolus) individuals trapped in seven sites of Lithuania during 1999-2005. The obtained linear and multiple regressions explained 38-58% of the body mass variation. The applicability of regressions was checked on 92 additional specimens. The error of the prediction of the body mass of bank voles was 1.2-4.4%. The obtained equations improve the accuracy of evaluation of biomass consumed and widen possibilities for the analysis of material containing small mammal skull remains. The method is applicable to investigations on the feeding ecology of owls and other myophagous birds.

Sexual dimorphism, asymmetry, and the effect of reproduction on pelvis bone in the bank vole, Myodes glareolus

“…Several studies have focused on the prediction of body weight based on measurements of the skull in the bank vole (e.g. Balčiauskienė and Balčiauskas 2009b), but not such estimations exist for pelvis bones. However, using pelvis measurements is a potentially very useful method to analyse the population structure of prey, because pelvis bones typically persist well in predators' digestive tracks and, therefore, can be used later to take detailed measurements (Raczyński and Ruprecht 1974).…”

Biometric Analysis of Cranial and Somatic Features in the Pannonian Root Vole

“…The relationship between rodent body weight and age, and also cranial and body measurements, was analysed several decades ago [ 15 , 16 ], where they discovered significant correlation between different craniometric measurements and the body weight of small mammals [ 17 , 18 , 19 ]. Regression equations have been suggested as instruments to measure the body weight of small mammals consumed by various predators [ 22 , 23 , 24 , 25 , 26 ]. Pagels and Blem [ 16 ] estimated small mammal body weight from the dimensions of their skulls.…”

“…They found regression to be routinely better for estimating body weight from cranial dimensions, provided all prey specimens are adults, and when calculating weight from individual skeletal measurements. Balčiauskienė and Balčiauskas [ 26 ] wrote that regression equations derived from cranial measurements can predict the body weight of bank voles, which explained 38–58% of body weight variability. They found three mandibular features—LMd, ML, and AMdm—and three maxillary features—LaZ, LD, and FI—to correlate better to bank vole body weight.…”

“…Analysing the relationship between rodent, including some root vole sub-species, body weight and their cranial or body traits has been a widely used approach in order to estimate body weight and biomass of mammals consumed by predators [ 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 ]. Borowski et al [ 25 ] investigated the relationships between the biometrics of cranial traits and the body weight of Alexandromys oeconomus stimmingi , while Balčiauskas and Balčiauskiené [ 27 ] estimated body weight from 26 cranial and pelvic features of A. oeconomus stimmingi .…”

Growth of the bank voleMyodes glareolusin the non-vegetative period in NE Lithuania

“…The following skull (8 mandibular and 9 cranial) characters were measured: X 1 -total length of mandible at processus articularis excluding incisors; X 2length of mandible excluding incisors; X 3 -height of mandible at, and including, first molar; X 4 -maximum height of mandible excluding coronoid process; X 5coronoid height of mandible; X 6 -length of mandibular diastema; X 7 -length of mandibular tooth row; X 8 -length of molar M 1 ; X 9 -length of nasalia; X 10breadth of braincase measured in the widest part; X 11 -zygomatic skull width; X 12 -length of cranial (upper) diastema; X 13 -zygomatic arc length; X 14 -length of foramen incisivum; X 15 -length of maxillary toothrow; X 16 -length of molar M 1 ; X 17 -incisor width across both upper incisors (Balčiauskienė, 2006(Balčiauskienė, , 2007Balčiauskienė & Balčiauskas, 2009).…”