Systematic studies of deer mice, <i>Peromyscus maniculatus</i> Wagner (Cricetidae, Rodentia): analysis of age and secondary sexual variation in morphometric characters

Koh, Hung Sun; Peterson, Randolph L.

doi:10.1139/z83-343

Cited by 13 publications

(20 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The age of Heteromyidae and Muridae were determined on the basis of molar teeth (Koh and Peterson, 1983). For Thomomys, we used skull symphyses and skull morphology (Daly and Patton, 1986).…”

Section: Study Area and Methodsmentioning

confidence: 99%

Analysis of mammal remains from owl pellets (Tyto alba), in a suburban area in Baja California

Álvarez-Castañeda¹,

Cárdenas²,

Méndez³

2004

Journal of Arid Environments

View full text Add to dashboard Cite

Section: Study Area and Methodsmentioning

confidence: 99%

Analysis of mammal remains from owl pellets (Tyto alba), in a suburban area in Baja California

Álvarez-Castañeda¹,

Cárdenas²,

Méndez³

2004

Journal of Arid Environments

View full text Add to dashboard Cite

“…These measurements are fully described in Koh and Peterson (1983). An additional measurement (greatest length of skull) was used as an index of size to correct for allometric differences.…”

Section: Craniometric Measurementsmentioning

confidence: 99%

“…An additional measurement (greatest length of skull) was used as an index of size to correct for allometric differences. Males and females were pooled for the analysis because there is no secondary sexual dimorphism in skulls of deer mice (Koh and Peterson 1983). Adult specimens only, as determined by toothwear, were considered because significant differences in rates of growth exist among different age classes (Koh and Peterson 1983).…”

Section: Craniometric Measurementsmentioning

confidence: 99%

“…Males and females were pooled for the analysis because there is no secondary sexual dimorphism in skulls of deer mice (Koh and Peterson 1983). Adult specimens only, as determined by toothwear, were considered because significant differences in rates of growth exist among different age classes (Koh and Peterson 1983). For further description of the data, see Landry and Lapointe (2001).…”

Section: Craniometric Measurementsmentioning

confidence: 99%

“…The exclusion of a number of specimens leads directly to a reduction of the total sample size. That reduction might not be problematic for large samples, such as those provided by extensive museum collections (Koh andPeterson 1983, van Zyll de Jong andKirkland 1989). However, population studies often rely on limited samples, and the removal of valid but incomplete information (eg the deletion of 1 individual because of a single missing measurement) can severely hinder the results of the analyses.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Missing data in craniometrics: a simulation study

Gauthier

Landry²,

Lapointe

2003

Acta Theriol

View full text Add to dashboard Cite

J. 2003. Missing data in craniometrics: a simulation study. Acta Theriologica 48: 25-34.Craniometric measurements represent a useful tool for studying the differentiation of mammal populations. However, the fragility of skulls often leads to incomplete data matrices. Damaged specimens or incomplete sets of measurements are usually discarded prior to statistical analysis. We assessed the performance of two strategies that avoid elimination of observations: (1) pairwise deletion of missing cells, and (2) estimation of missing data using available measurements. The effect of these distinct approaches on the computation of inter-individual distances and population differentiation analyses were evaluated using craniometric measurements obtained from insular populations of deer mice Peromyscus maniculatus (Wagner, 1845). In our simulations, Euclidean distances were greatly altered by pairwise deletion, whereas Gower's distance coefficient corrected for missing data provided accurate results. Among the different estimation methods compared in this paper, the regression-based approximations weighted by coefficients of determination (r 2 ) outperformed the competing approaches. We further show that incomplete sets of craniometric measurements can be used to compute distance matrices, provided that an appropriate coefficient is selected. However, the application of estimation procedures provides a flexible approach that allows researchers to analyse incomplete data sets.Département de sciences biologiques, Université de Montréal C. P. 6128, Succ. centre--ville Montréal,

show abstract

Within‐population craniometric variability of insular populations of deer mice, Peromyscus maniculatus, elucidated by landscape configuration

Landry¹,

Lapointe²

2001

Oikos

View full text Add to dashboard Cite

Within‐population genetic variability of twelve insular and four mainland populations of deer mice (Peromyscus maniculatus) was assessed using craniometric characters, and compared to results previously obtained from RAPD data. An index of Craniometric Variance (CVar) was computed from pairwise distances among all specimens. Variations in CVar measures were then compared to landscape variables using a linear regression approach. Our results suggest that CVar decreases in presence of large number of a competitive species (the boreal redback vole, Clethrionomys gapperi; r=−0.527, p<0.037) in deer mouse populations. Island remoteness (r=−0.251, p<0.220) and the geometry of the bank opposite to each island (r=−0.459, p<0.067) were marginally correlated with CVar, but the linear combination of these two variables, forming a composite isolation index, represented the major factor explaining the observed CVar (r=−0.648, p<0.011). Using a multiple regression model, 76.3% of the CVar was explained by a combination of this isolation index and the competitors’ abundance. These results suggest that taking into account landscape barriers as well as the dispersal behavior of small mammals might provide sounder ecological variables than geographical distances alone for predicting within‐population genetic variability in a network of habitat patches.

show abstract

Systematic studies of deer mice, Peromyscus maniculatus Wagner (Cricetidae, Rodentia): analysis of age and secondary sexual variation in morphometric characters

Cited by 13 publications

References 14 publications

Analysis of mammal remains from owl pellets (Tyto alba), in a suburban area in Baja California

Analysis of mammal remains from owl pellets (Tyto alba), in a suburban area in Baja California

Missing data in craniometrics: a simulation study

Within‐population craniometric variability of insular populations of deer mice, Peromyscus maniculatus, elucidated by landscape configuration

Contact Info

Product

Resources

About