ORIGINAL ARTICLE

doi:10.1111/j.1558-5646.2009.00651.x

A COMPARISON OF COVARIANCE STRUCTUREIN WILD AND LABORATORY MUROID CRANIAHeather A. Jamniczky1,2 and Benedikt Hallgrımsson1

1Department of Cell Biology and Anatomy, University of Calgary, 3330 Hospital Drive NW, Calgary AB T2N 4N1, Canada2E-mail: hajamnic@ucalgary.ca

Received April 4, 2008

Accepted January 14, 2009

Mutations have the ability to produce dramatic changes to covariance structure by altering the variance of covariance-generating

developmental processes. Several evolutionary mechanisms exist that may be acting interdependently to stabilize covariance

structure, despite this developmental potential for variation within species. We explore covariance structure in the crania of labo-

ratory mouse mutants exhibiting mild-to-significant developmental perturbations of the cranium, and contrast it with covariance

structure in related wild muroid taxa. Phenotypic covariance structure is conserved among wild muroidea, but highly variable and

mutation-dependent within the laboratory group. We show that covariance structures in natural populations of related species

occupy a more restricted portion of covariance structure space than do the covariance structures resulting from single mutations

of significant effect or the almost nonexistent genetic differences that separate inbred mouse strains. Our results suggest that

developmental constraint is not the primary mechanism acting to stabilize covariance structure, and imply a more important role

for other mechanisms.

KEY WORDS: Cranial morphology, geometric morphometrics, Muroidea, morphological integration, phenotypic covariance.

The importance of covariance structure patterning, and the re-

sulting integration and observed modularity of the phenotype, for

understanding the evolution of form was recognized early in the

development of evolutionary theory (Darwin 1859). The modern

study of these phenomena was formalized by Olson and Miller

(1958) and has received extensive attention (e.g., Lande 1979;

Cheverud 1982, 1984, 1988; Zelditch 1988; Arnold 1992; Raff,

1996; Wagner 1996; Roff 1997; Steppan 1997a,b; Ackermann and

Cheverud 2000; Marroig and Cheverud 2001; Steppan et al. 2002;

Ackermann 2005; Goswami 2006; Hallgrımsson et al. 2007a,b).

Phenotypic covariance structure among related populations

is the outcome of the action and interaction of several, interdepen-

dent mechanisms including natural selection, developmental con-

straints, gene flow among populations, and genetic drift. Develop-

mental constraints have been widely assumed to be the principal

driver behind covariance structure patterning (e.g., Riska 1986;

Atchley and Hall 1991; Hallgrımsson et al. 2002; Klingenberg

2004), although the relationship between constraints and covari-

ance structure has not always been made explicit. Others have

suggested that selection has a relatively greater role in shap-

ing covariance structure and resulting morphological integration

(e.g., Lande 1980; Cheverud 1984, 1996a; Wagner and Altenberg

1996), and recent theoretical work on genetic covariance (Jones

et al. 2007) suggests that the latter hypothesis may be the more ac-

curate. Gene flow between populations and genetic drift may also

influence covariance structure (Riska 1985; Steppan 1997b). It is

likely that all of these processes are involved to some degree and

vary on a case-by-case basis. Elucidating the relative contribution

of each mechanism is thus a complex task.

Hallgrımsson et al. (2007a) follow previous authors (e.g.,

Atchley et al. 1981; Cheverud 1996a; Zelditch et al. 2006),

and suggest that covariation structure evolves through changes

to the developmental determinants of phenotypic variation.

Hallgrımsson et al. (2007a) are explicit, however, in proposing

that change in the “relative variance” of key developmental pro-

cesses that affect the phenotype unequally is the main factor

that results in alterations to covariance structure and morpholog-

ical integration. Such key processes may include, among others,

1 5 4 0C© 2009 The Author(s). Journal compilation C© 2009 The Society for the Study of Evolution.Evolution 63-6: 1540–1556

COVARIANCE STRUCTURE IN LABORATORY AND WILD MUROIDS

somatic growth, fusion of morphological elements, mesenchymal

condensation and differentiation, and interactions between hard

and soft parts of the skeleton (Hallgrımsson et al. 2007a). If one

or several evolutionary mechanisms maintain covariance struc-

ture by stabilizing the variance of the developmental processes

that produce covariance structure, we would expect that natural

populations of even distantly related species would exhibit simi-

lar covariance structures. Previous studies (e.g., Steppan 1997a,b;

Ackermann and Cheverud 2000; Marroig and Cheverud 2001;

Goswami 2006) have indeed found that covariance structure is

broadly shared among related taxa. Interestingly, however, these

studies also show that correlation between covariance structures

is not a reliable predictor of phylogenetic distance among taxa,

hinting at the action of microevolutionary forces within a con-

served phenotypic covariance structure. Young and Hallgrımsson

(2005) found that selection for functional specialization in mam-

malian limbs results in reduced covariance between limbs, further

indicating that the effects of selection on common developmental

factors may play a larger role in shaping covariance structure that

has a tendency to be conserved despite the evolution of disparate

morphologies.

If evolutionary mechanisms have a prominent role in stabiliz-

ing the variance of developmental processes, then it follows that

populations in which the effects of such stabilizing mechanisms

are greatly reduced may exhibit dramatic changes to covariance

structure, even as the result of single mutations, if these mutations

Table 1. Laboratory mice used in this study. The designation “normal” refers to laboratory strains whose phenotypes do not differ

substantially from those of wild Mus musculus.

Strain Abbreviation Genotype n Craniofacial phenotype

C57BL/6J C57 Wild-type 50 NormalCBA/J CBA Wild-type 20 NormalDBA/J DBA Wild-type 20 NormalFVB/J FVB Wild-type 20 NormalA/WySnJ AWS Wild-type 20 20% frequency of spontaneous cleft lip (Juriloff 1982), caused by

hypermorphic mutation of Wnt9b (Juriloff et al. 2006).C57BL/6J BMO Papps2-/Papps2- 30 Dramatic reduction in the size of the chondrocranium, doming of the

neurocranium and shortening of the face (Hallgrimsson et al. 2006,2007a).

C57BL/6J CRF Crf4/Crf4 20 Complex alteration in craniofacial shape, which includes reduction insize of the face. Facial prominence outgrowth is also reducedduring face formation (Boughner et al. 2008).

C57BL/6J LTL Ghrhr-/Ghrhr- 20 “Little mice”; Pituitary hormone and prolactin deficiency; small size(Eicher and Beamer 1976).

C57BL/6J∗Balb6 MCP Mceph/Mceph 21 Approximately 25–30% increase in the size of the brain by 90 days ofage (Diez et al. 2003).

C57BL/6J∗129 PTN Cre (fl/fl) 25 Overgrowth of the cartilage including increased posterior basicraniallength and increased facial length (Lieberman et al. 2008).

Mixed NIP Nipbl (±) 25 Reduction in cranial size and alteration of facial shape (Krantz et al.2004).

alter the variance of key developmental processes. Morphological

integration may also be affected, depending upon the relative im-

portance of the developmental process in question in determining

covariance structure (Hallgrımsson et al. 2007a).

Herein we examine the effects of developmental perturba-

tions on phenotypic covariance patterns in laboratory mouse cra-

nia, and contrast these with patterns of covariance in a controlled

sample of related natural populations of muroid rodent taxa. We

predict that the laboratory mouse group will show evidence of

disrupted covariance structure resulting from reduced levels of

selection acting on this structure. We further predict that muta-

tions that alter the variance of developmental processes in the

laboratory mouse group will result in substantial changes to de-

gree of integration among parts of the skull.

MethodsSAMPLE COMPOSITION

The laboratory mouse sample was composed of adult individ-

uals from four strains of laboratory mice that exhibit a normal

craniofacial phenotype, and seven groups that exhibit consider-

able variation in cranial shape resulting from both known and

unknown developmental perturbations (Table 1), while remain-

ing more similar to each other in gross morphology than different

species of muroid rodents to each other. These mice have been

the focus of previous investigations, and detailed descriptions of

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H. A. JAMNICZKY AND B. HALLGRIMSSON

Table 2. Muroid rodents used in this study. Specimens are housed

at the Field Museum of Natural History (FMNH), Chicago, Illinois.

Species Taxon n Locality

Calomys callosus Cricetidae 57 BoliviaEliurus majori Nesomyidae 47 MadagascarMus musculus praetextus Murinae 48 EgyptPraomys delectorum Murinae 60 TanzaniaRattus tanezumi Murinae 49 Philippine

IslandsRhabdomys pumilio Murinae 49 Tanzania

each sample may be found elsewhere (Hallgrımsson et al. 2006;

Lieberman et al. 2008; see Table 1).

The skulls of adult individuals from six wild-caught muroid

species from specific localities, housed in the collections of the

Field Museum of Natural History (FMNH), were imaged for this

study (Table 2, Appendix 1). The group included four murines

(Mus musculus, Rattus tanezumi, Praomys delectorum, Rhab-

domys pumilio), a cricetid (Calomys callosus), and a nesomyid

(Eliurus majori).

Figure 1. Landmarks collected for this study. Landmarks were col-

lected bilaterally but are shown on the right side only for clarity.

See Table 3 for landmark descriptions.

DATA COLLECTION AND STANDARDIZATION

Data for this study were collected in two different ways due to the

differing nature of the wild and laboratory samples. The nature

of the experimental design, as discussed above, necessitated the

use of a portable imaging system able to process moderately large

numbers of specimens in a relatively short period of time. Flatbed

scanners can be used in place of camera-based systems in the case

of two-dimensional objects, and along with portability convey the

added benefit of reduced parallax when compared with camera-

based systems (Schubert 2000; Young and Hallgrımsson 2005).

Because this study focused on the palatal aspect of these skulls, a

two-dimensional approach was applicable. Multiple skulls were

placed in a standardized position on a flatbed scanner (Canon

CanoScan 8600F, Canon USA, Lake Success, NY) and scanned

as one image in 48-bit color at 4800 dpi (TIFF format). Raw scans

were converted into individual images for each specimen using

custom software written in Java (ImageGridder, N. Jones pers.

comm. 2007). Twenty-six two-dimensional landmarks (Fig. 1,

Table 3) were collected from these images using TpsDig ver-

sion1.40 (Rohlf 2004).

For the laboratory mouse sample, each skull was micro-CT

scanned (Scanco Viva-CT40, Scanco Medical AG, Basserdorf,

Switzerland) at 35 μm resolution (70kv, 160 μA, 500 projections).

Fifty-two three-dimensional landmarks were originally collected

from these images using Analyze 5.0 (Biomedical Imaging Re-

source 2003). These landmarks were oriented by Procrustes su-

perimposition using MorphoJ (Klingenberg 2008), after which

the z-dimension was removed from the dataset and the same 26

landmarks digitized on the images of wild crania were extracted,

Table 3. Landmarks collected in this study. Each landmark was

digitized bilaterally for a total of 26 datapoints per specimen. See

Figure 2 for anatomical positions.

Number Description

1 Anterior margin of incisive foramen2 Anterior margin of inferior zygomatic arch3 Premaxilla–maxilla junction along margin of

incisive foramen4 Point of maximum curvature on posterior margin

of malar process of maxilla5 Anterior superior alveolus6 Posterior margin of incisive foramen7 Posterior superior alveolus8 Lateral palatal-pterygoid junction9 Point of maximum curvature on anterior margin

of zygomatic process of temporal10 Anterior edge of foramen ovale11 Anterior edge of interior auditory bulla12 Occipital-auditory-sphenoid junction13 Occipital-auditory junction

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resulting in an identical set of 26 two-dimensional landmarks for

the laboratory mouse crania (Fig. 1, Table 3).

Each species within the wild sample, and each group within

the laboratory sample, was then subjected to Procrustes superim-

position, and the resulting coordinates were regressed on centroid

size. The residuals from this procedure were used to standard-

ize each group to mean centroid size using Standard6 (Sheets

2001), and thus remove allometric shape variation related to size.

Only the symmetric component of these data was used for further

analysis.

DATASET COMPARISON

A separate dataset composed of the skulls of 20 adult BALB/c

mice was subjected to both the flatbed scanner and micro-CT scan-

ner methods of data collection described above, to test whether

these procedures generate equivalent datasets that may be directly

compared. Matrix repeatability for each dataset was determined,

and the correlation between the two datasets was assessed as de-

scribed below, using both standard matrix correlation and random

skewers. A principal components analysis (PCA) was performed

to examine variation within and between these datasets, and a

Procrustes analysis of variance (Procrustes ANOVA [Klingenberg

and McIntyre 1998]) was used to assess differences between the

two datasets. The results of this test indicated that the following

statistical analyses be performed separately for laboratory and

wild groups (see Results and Discussion).

STATISTICAL ANALYSES

Procrustes-transformed, standardized data were subjected to

canonical variates analysis (CVA) to explore variation in mean

landmark configuration between the different strains and species

examined. This technique retrieves linear combinations of

variables that best discriminate among groups, and allows

visualization of particular shape changes responsible for this

discrimination.

Covariance matrices among landmarks were estimated

from the transformed, standardized data and explored using

several techniques. Covariance matrix repeatability (t) was

estimated following Marroig and Cheverud (2001), to ascertain

the proportion of total variance in each sample that can be

ascribed to individual variation rather than measurement error,

given that some degree of error is always present in covariance

matrix estimation (Cheverud 1996b). The original datasets were

resampled and covariance matrices re-estimated 1000 times,

and the mean matrix correlation between these and the original

datasets was taken as an estimate of t.

Matrix correlations were computed for all possible pairwise

comparisons between species and strains, and significance of

these correlations was assessed using Mantel’s test (1000 repli-

cates). Matrix correlations were then adjusted to account for small

sample size following Marroig and Cheverud (2001), using the

formula Radj = Robs/Rmax. Maximum matrix correlation (Rmax)

was estimated using the formula Rmax = (tatb)1/2. Adjusted cor-

relations were compared using ANOVA to examine patterns of

relationship among covariance matrices. We employed this test-

ing procedure so that our results may be compared to previous

studies in the morphometric literature, but we note that the Man-

tel’s test is not a true significance test as it is commonly applied to

covariance matrices (see Mitteroecker and Bookstein [2007] for

discussion of this issue).

Covariance matrices were further compared using principal

coordinates analysis (PCO), as described by Debat et al. (2006).

The distance between each pair of covariance matrices for each

group (laboratory and wild) was defined as one minus the matrix

correlation between the two, and the matrix of distances obtained

from this transformation was then subjected to PCO, also known

as metric multidimensional scaling, to determine the existence of

clustering patterns among matrices of each group.

Covariance matrices were also compared using the method

of random skewers (Cheverud et al. 1983; Cheverud 1996b). This

technique computes the response of each covariance matrix to a

random selection vector, and then compares these responses to

determine covariance matrix similarity. In the present analysis,

1000 random skewers were calculated and the responses were

averaged to determine similarity between covariance matrices in

each group.

The coefficient of alienation (ca; Zar 1999) was computed

from the coefficient of determination (ca = √[1 − r2]) from each

comparison between covariance matrices, as a metric of distance

among covariance matrices within available covariance structure

space. Coefficients of alienation were normalized using Fisher’s

z-transformation and are hereinafter referred to as “covariance

distances.” Covariance distance was regressed on the Procrustes

distance between the mean shape of each taxon involved in each

comparison, resulting in a scatterplot of all possible pairwise com-

parisons within groups showing the relationship between morpho-

logical distance and covariance distance for each comparison.

The variance of eigenvalues (VE) of each covariance matrix,

scaled to the total sample variance (the sum of the eigenvalues of

the covariance matrix), was used to assess the degree of overall

integration within each group (Wagner 1984, 1990). Significance

of differences in eigenvalues between species/strains was assessed

following Manly (1991), by resampling species data and recom-

puting VE (1000 replicates).

Finally, a second analysis was carried out, using a restricted

set of untransformed interlandmark distances extracted from the

wild sample, to assess the potential effects of Procrustes super-

imposition on subsequently observed matrix correlations and in-

tegration. A set of 11 interlandmark distances (Table 4) was cho-

sen to minimize the number of skull elements included in each

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H. A. JAMNICZKY AND B. HALLGRIMSSON

Table 4. Interlandmark distances collected in this study. See

Table 3 for landmark descriptions, and Figure 2 for anatomical

positions of landmarks.

Numbers Description

1–3 Premaxilla3–6 Maxilla2–4 Anterior zygomatic arch2–5 Maxilla5–7 Tooth row7–8 Maxilla8–9 Lateral skull8–10 Anterior basicranium

11–12 Auditory bulla12 (L)–12 (R) Middle basicranium (across midline)12–13 Basioccipital

distance and thus minimize the number of developmental pro-

cesses potentially affecting each distance. These distances were

used to generate covariance matrices, and these matrices were

subjected to all of the procedures outlined above with the excep-

tion of CVA. The results of these analyses were then compared to

those obtained using Procrustes-transformed data.

All statistical analyses were performed in R version 2.6.2

(R Development Core Team 2008) with the exception of the

micro-CT scanned

flatbed scanned

-0.06

-0.04

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0.00

0.02

0.04

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PC1

PC

2

Figure 2. Bivariate plot of PCA results for dataset comparison test. Mice used for this test are BALB/c, and are not included in subsequent

analyses.

matrix correlations, which were performed in Microsoft Ex-

cel, using the algorithm included in PopTools version 3.0.2

(Hood 2008), and the random skewers analysis, which was per-

formed using Skewers (Revell 2007). PCA, CVA, and Procrustes

ANOVA were performed in MorphoJ version 1.00i (Klingenberg

2008).

ResultsDATASET COMPARISON

Matrix repeatability is 0.811 for landmarks collected from micro-

CT scanned images, and 0.822 for landmarks collected from

flatbed scanned images, both taken from the same set of skulls.

The raw correlation between the two matrices, obtained using

standard matrix correlations, is 0.354, and the adjusted correlation

is 0.416, indicating that the numerical values of the components of

the covariance matrices are different and somewhat measurement-

dependent. Random skewers analysis retrieved a highly signifi-

cant (P < 0.0001) vector correlation of 0.474. The first principal

component (PC1) extracted from these data accounts for 56%

of the total variance, and is the only principal component of the

23 components extracted to reliably separate the two datasets

(Fig. 2). We interpret PC1 as representing a combination of par-

allax error and out-of-plane rotation differences between the two

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Calomys

Rhabdomys

Rattus

Mus

Eliurus

Praomys

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AWS

FVB

C57

BMO

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MCP

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B

Figure 3. (A) Bivariate plot of CVA results for laboratory mice. Wireframes depict variation along CV axes from the average shape,

shown at top right. See Table 2 for abbreviations. (B) Bivariate plot of CVA results for wild muroids. Wireframes depict variation along

CV axes from the average shape, shown at top right.

datasets, resulting from the different data collection techniques

employed. Procrustes ANOVA revealed that data collection tech-

nique is a significant effect (P < 0.0001). These results preclude

comparisons between the numerical values of covariance matri-

ces estimated from flatbed and micro-CT scanning, but indicate

that the nature and patterning of the covariance structure revealed

within each dataset are comparable and informative. In all of the

statistical analyses of covariance matrices that follow, the wild

(flatbed scanned) and laboratory (micro-CT scanned) datasets are

treated separately.

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H. A. JAMNICZKY AND B. HALLGRIMSSON

Table 5. Laboratory mouse covariance matrix similarity. Matrix repeatability (t) is given in bold along the diagonal, with raw correlations

below and adjusted correlations above. All correlations are highly significant (P≤0.001). See Table 2 for abbreviations.

AWS BMO CRF LTL MCP NIP PTN C57 CBA DBA FVB

AWS 0.830 0.383 0.495 0.598 0.670 0.525 0.451 0.683 0.554 0.393 0.570BMO 0.341 0.956 0.736 0.410 0.369 0.543 0.790 0.342 0.517 0.501 0.348CRF 0.446 0.711 0.976 0.427 0.370 0.580 0.663 0.375 0.550 0.485 0.416LTL 0.497 0.365 0.384 0.830 0.553 0.392 0.447 0.584 0.655 0.508 0.483MCP 0.564 0.333 0.337 0.465 0.854 0.577 0.430 0.595 0.544 0.394 0.630NIP 0.439 0.488 0.526 0.328 0.489 0.842 0.665 0.584 0.502 0.511 0.556PTN 0.397 0.746 0.633 0.393 0.384 0.590 0.933 0.362 0.545 0.531 0.397C57 0.602 0.323 0.358 0.515 0.532 0.518 0.339 0.936 0.489 0.350 0.540CBA 0.459 0.460 0.494 0.542 0.457 0.418 0.478 0.430 0.825 0.600 0.487DBA 0.327 0.447 0.437 0.422 0.332 0.428 0.468 0.309 0.498 0.833 0.427FVB 0.472 0.310 0.374 0.400 0.530 0.464 0.349 0.476 0.402 0.355 0.828

STATISTICAL ANALYSES

Ten canonical variates were extracted from the laboratory mouse

dataset, of which the first seven account for 94% of the variation

present in the dataset. Variation along CV1 is manifested primarily

in the length of the rostrum, whereas variation along CV2 is

manifested primarily in the breadth of the middle region of the

skull (Fig. 3A). No CVs were able to reliably discriminate among

the different groups of laboratory mice.

Five canonical variates (CVs) were extracted from the wild

muroid dataset, of which the first four account for 94% of the

variation present in the dataset. Variation along CV1 is manifested

primarily in the length of the skull (Fig. 3B), and separates E.

majori and R. tanezumi from a cluster that includes R. pumilio, C.

callosus, M. musculus, and P. delectorum. Variation along CV2 is

manifested primarily in the breadth of the skull, and discriminates

R. tanezumi and R.s pumilio from E. majori, C. callosus, M.

musculus, and P. delectorum. The total variance of the laboratory

group is 0.00219, whereas the total variance of the wild group

is 0.00296, indicating that the overall phenotypic variance of the

wild group is higher than that of the laboratory group.

Matrix repeatability ranges from 0.825 to 0.976 (Table 5),

with a mean of 0.877, in the laboratory mouse sample. Adjusted

matrix correlations between groups (Table 5) range from 0.342

(C57 vs. BMO) to 0.790 (PTN vs. BMO), and are all highly

Table 6. Wild muroid covariance matrix similarity. Matrix repeatability (t) is given in bold along the diagonal, with raw correlations

below and adjusted correlations above. All correlations are highly significant (P≤0.001).

Calomys Eliurus Mus Praomys Rattus Rhabdomys

Calomys 0.940 0.608 0.725 0.509 0.687 0.895Eliurus 0.554 0.883 0.595 0.524 0.661 0.484Mus 0.676 0.537 0.923 0.649 0.516 0.682Praomys 0.474 0.473 0.598 0.922 0.453 0.457Rattus 0.628 0.586 0.467 0.411 0.889 0.560Rhabdomys 0.847 0.444 0.640 0.428 0.516 0.954

significant (P ≤ 0.001). Among the wild muroid sample, matrix

repeatability ranges from 0.883 to 0.954 (Table 6), with a mean

of 0.919. The mean matrix repeatabilities of the two groups do

not vary significantly (P = 0.159). Adjusted matrix correlations

between taxa (Table 6) range from 0.453 (R. tanezumi vs. P.

delectorum) to 0.895 (C. callosus vs. R. pumilio), and are all

highly significant (P ≤ 0.001). Comparison of mean covariance

matrix correlations among wild muroids and among laboratory

mice with and without abnormal craniofacial phenotypes (Fig. 4),

reveals that correlations among wild muroids are significantly

higher than those among members of the other groups (mean

adjusted correlation = 0.600; P = 0.039). Matrix correlations

within groups do not correspond to phylogenetic relatedness.

PCO analysis of the laboratory mouse group reveals three

clusters (Fig. 5A): one containing C57, MCP, FVB, and AWS; a

second containing NIP, CRF, PTN, and BMO; and a third, looser

cluster containing LTL, CBA, DBA. This clustering does not

associate with any obvious similarities among cluster members,

although the NIP-CRF-PTN-BMO exhibit the highest integration

of all laboratory samples (see below) and associate weakly with

position along CV axes (Fig. 3A).

Rhabdomys pumilio and C. callosus are weakly associated

by PCO analysis of the wild group, as are R. tanezumi and E.

majori (Fig. 5B). These clusters do not associate with any obvious

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1 2 3 4

0.4

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0.9 *

Adj

uste

d M

atri

x C

orre

lati

on

Comparison

Figure 4. Boxplot of adjusted covariance matrix correlations for all comparisons (whiskers represent range of observed correlations).

Comparisons: 1 = among craniofacial mutant mice (mean correlation 0.527); 2 = between mutants and nonmutant mice (mean correlation

0.504); 3 = among nonmutant mice (mean correlation 0.482); 4 = among wild muroidea (mean correlation 0.600). ∗: P = 0.039.

similarities, although there is very weak association with position

along CV axes (Fig. 3B).

The random skewers analysis indicates that all matrices are

significantly similar for both the laboratory and wild groups

(P ≤ 0.002 for all comparisons). Laboratory vector correlations

(Table 7) range from 0.354 (MCP vs. CRF) to 0.620 (PTN vs.

NIP). Wild vector correlations (Table 8) range from 0.553 (R.

tanezumi vs. P. delectorum) to 0.827 (R. pumilio vs. C. callosus).

Comparison of mean vector correlations among wild muroids and

among laboratory mice with and without abnormal craniofacial

phenotypes (Fig. 6) again demonstrates that wild muroids are sig-

nificantly more similar to each other than are the other groups

(mean adjusted correlation = 0.645; P = < 0.001).

The regression of covariance distance against Procrustes dis-

tance (Fig. 7) reveals that there is a weak but significant relation-

ship between the degree of morphological difference and degree

of change to covariance structure in comparisons between differ-

ent taxa and strains of mice (r2 = 0.216, P = 0.003). The slope

of the regression line for comparisons of wild taxa to each other

is not significant (slope = 0.970; P = 0.505), whereas the slopes

for the regressions of other comparisons are all significant (P ≤0.018).

Examination of the VE, scaled for variance, of each

species/strain covariance matrix reveals a large range of VE within

the sample (Fig. 8). Wild muroids exhibit a conserved integration

pattern, with a narrow range of resampled VE. The BMO, CRF,

and PTN mutants exhibit greater integration than do any of the

wild muroids, wheras the AWS and LTL mutants, along with

the laboratory mice with normal phenotypes (C57, CBA, DBA,

FVB) exhibit comparable integration to that of the wild muroids.

The range of resampled VE is somewhat larger in the labora-

tory mice with both normal and abnormal phenotypes, a phe-

nomenon that may be an artifact of smaller sample sizes (Young

and Hallgrımsson 2005).

Repeating the above analyses using the matrix of interland-

mark distances described in Table 4 did not reveal any substan-

tially different results. Adjusted matrix correlations between taxa

(Table 9) range from 0.148 (E. majori vs. P. delectorum) to

0.871 (C. callosus vs. R. pumilio). All correlations are signifi-

cant (P ≤ 0.04) with the exception of C. callosus vs. E. majori

(0.253; P = 0.168) and E. majori vs. P. delectorum (0.148; P =0.139).

PCO analysis of the distance data reveals a similar clustering

pattern to that reported above for the Procrustes-transformed data,

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Rattus

Mus

Eliurus

Praomys

-0.3 -0.2 -0.1 0.0 0.1 0.2 0.3

-0.3

-0.2

-0.1

0.0

0.1

0.2

PCo1

PC

o2

C57

MCP

FVB

AWS

NIP

LTL

CBADBA

CRF

PTN

BMO

A

B

Figure 5. (A) Bivariate plot of PCO results for laboratory mice. PCo: principal coordinate. See Table 2 for other abbreviations. (B) Bivariate

plot of PCO results for wild muroids. Pco, principal coordinate.

although the taxa plot in different places relative to the PCO axes

as compared to the PCO results for the Procrustes-transformed

data (Fig. 9).

The random skewers analysis of the distance data pro-

duced similar results to that of the Procrustes-transformed data

(Table 10). Vector correlations ranged from 0.458 (R. tanezumi vs.

R. pumilio) to 0.880 (Calomys calosus vs. R. pumilio). All corre-

lations are significant (P ≤ 0.05) with the exception of E. majori

vs. P. delectorum (0.513; P = 0.054) and P. delectorum vs. R.

pumilio (0.458; P = 0.079).

1 5 4 8 EVOLUTION JUNE 2009

COVARIANCE STRUCTURE IN LABORATORY AND WILD MUROIDS

Table 7. Laboratory mouse random skewers matrix correlations. All correlations are highly significant (P≤0.001). See Table 2 for abbre-

viations.

AWS BMO CRF LTL MCP NIP PTN C57 CBA DBA FVB

AWS 1.000BMO 0.440 1.000CRF 0.455 0.657 1.000LTL 0.546 0.429 0.391 1.000MCP 0.611 0.428 0.354 0.530 1.000NIP 0.506 0.516 0.515 0.442 0.546 1.000PTN 0.493 0.696 0.585 0.463 0.468 0.620 1.000C57 0.635 0.428 0.391 0.583 0.592 0.580 0.455 1.000CBA 0.537 0.490 0.477 0.593 0.533 0.512 0.508 0.527 1.000DBA 0.430 0.487 0.436 0.505 0.436 0.496 0.491 0.428 0.561 1.000FVB 0.540 0.393 0.383 0.501 0.574 0.522 0.417 0.557 0.495 0.457 1.000

Examination of VE calculated from the distance data again

reveals a similar pattern to VE calculated from Procrustes-

transformed data (Fig. 10).

DiscussionComparison of data collected for the same specimens using the

two- and three-dimensional procedures described here revealed

that parallax and out-of-plane rotation influence the numerical

value of the components of the estimated covariance matrices

(Fig. 3). It is important to note, however, that nearly identical ma-

trix repeatabilities were calculated for these datasets, and that the

matrices exhibit a significant and moderately high vector correla-

tion, indicating that the internal structures of these matrices share

significant similarity. In other words, the relationships among the

components of the covariance matrix, and the covariance structure

that these relationships describe, are similar. They are simply mea-

sured in a slightly different way, as a result of using two different

tools. The results presented here are intended to provide a founda-

tion for the study and comparison of the mechanisms responsible

for maintenance of phenotypic covariance structure over evolu-

tionary time. As such, the data collection techniques employed

herein are both justifiable and appropriate for the investigation

at hand. Future work will no doubt benefit from technological

Table 8. Wild muroid random skewers matrix correlations. All correlations are highly significant (P≤0.001).

Calomys Eliurus Mus Praomys Rattus Rhabdomys

Calomys 1.000Eliurus 0.653 1.000Mus 0.753 0.611 1.000Praomys 0.639 0.575 0.675 1.000Rattus 0.694 0.675 0.589 0.553 1.000Rhabdomys 0.827 0.545 0.716 0.575 0.599 1.000

advances that make three-dimensional data acquisition more effi-

cacious, and it will be important to compare the results of future

studies that employ a single data collection technique to those

presented here to further refine and improve our understanding of

phenotypic covariance structure.

The presence of common factors in morphometric data leads

to unreliable interpretation of such data (Mitteroecker and Book-

stein 2007). Shape-correlated size variation (allometry) is among

the most important common factors in morphometric data (e.g.,

Klingenberg 1998; Young and Hallgrımsson 2005; Zelditch et al.

2006; Mitteroecker and Bookstein 2007), and may swamp other

aspects of shape integration that are revealed in such data. It

is customary to remove the effects of allometry on geometric

morphometric data through the use of Procrustes superimposi-

tion followed by regression on centroid size. It could be argued,

however, that because this technique aligns all landmark config-

urations with a reference, it introduces a new common factor in

the process of removing size. The results of our analyses using

interlandmark distances exhibit broad similarity with the results

of the main analysis using Procrustes-transformed data. This sug-

gests that the Procrustes superimposition is unlikely to be exerting

a large effect on the results. There are, however, some quantita-

tive differences between the two analyses, and allometric size

is known to generate phenotypic integration (Olson and Miller

EVOLUTION JUNE 2009 1 5 4 9

H. A. JAMNICZKY AND B. HALLGRIMSSON

1 2 3 4

0.4

0.5

0.6

0.7

0.8

*

Comparison

Vec

tor

Cor

rela

tion

Figure 6. Boxplot of vector correlations from random skewers analysis for all comparisons (whiskers represent range of observed

correlations). Comparisons: 1 = among craniofacial mutant mice (mean correlation 0.498); 2 = between mutants and nonmutant mice

(mean correlation 0.506); 3 = among nonmutant mice (mean correlation 0.504); 4 = among wild muroidea (mean correlation 0.645). ∗: P =4.419 × 10−07.

0.02 0.04 0.06 0.08 0.10

0.6

0.8

1.0

1.2

1.4

1.6

Procrustes Distance

Cov

aria

nce

Dis

tanc

e

wild vs. wild

mutant vs. mutant

mutant vs. normal

normal vs. normal

Figure 7. Scatterplot of covariance distance between groups against Procrustes distance between group mean shapes for all possible

pairwise comparisons of covariance matrices. •, mutant vs. mutant phenotype (slope = 3.732, P = 0.01771); � , mutant vs. normal

phenotype (slope = 5.571, P = 0.003); ♦, normal vs. normal phenotype (slope = 10.371, P = 0.009); �, wild vs. wild (slope = 0.97023, P =0.505).

1 5 5 0 EVOLUTION JUNE 2009

COVARIANCE STRUCTURE IN LABORATORY AND WILD MUROIDS

AWS BMO CRF LTL MCP NIP PTN C57 CBA DBA FVB

Eliuru

s

0.00

20

.00

60

.010

0.01

4

Calom

ys Mus

Praom

ys

Rattu

s

Rhabd

omys

VE

Figure 8. Boxplot of the variance of eigenvalues (VE) resampling distribution (whiskers represent resampling range). Observed VE

values: AWS = 0.0025, BMO = 0.0059, CRF = 0.0114, LTL = 0.0024, MCP = 0.0025, NIP = 0.0028, PTN = 0.0050, C57 = 0.0030, CBA = 0.0025,

DBA = 0.0030, FVB = 0.0022, Calomys callosus = 0.0027, Eliurus majori = 0.0015, Mus musculus = 0.0026, Praomys delectorum = 0.0025,

Rattus tanezumi = 0.0018, Rhabdomys pumilio = 0.0040. See Table 2 for abbreviations.

1958). The wild taxa in the present study exhibit substantial size

variation in the cranium, and it is generally considered conserva-

tive in such cases to remove size to avoid confounding the effects

of size and shape on overall integration (Ackermann 2005). We

therefore focus the remainder of the discussion on the results

obtained from Procrustes-transformed data, and stress that refer-

ences to morphological integration in what follows are concerned

with shape-based integration only.

Previous models of phenotypic evolution have largely as-

sumed, either implicitly or explicitly, that covariance structure

follows directly from the nature of the particular developmental

constraints responsible for the generation of observed phenotypes

(e.g., Riska 1986; Atchley and Hall 1991; Hallgrımsson et al.

2002; Klingenberg 2004). Taxa that have diverged morphologi-

cally due to an accumulation of changes in developmental pro-

Table 9. Wild muroid covariance matrix similarity, derived from interlandmark distances. Matrix repeatability (t) is given in bold along

the diagonal, with raw correlations below and adjusted correlations above. All correlations are significant (P≤0.05), except those marked

by an asterisk (∗).

Calomys Eliurus Mus Praomys Rattus Rhabdomys

Calomys 0.947 0.275∗ 0.629 0.441 0.524 0.907Eliurus 0.253 0.892 0.439 0.161∗ 0.586 0.500Mus 0.593 0.402 0.939 0.713 0.720 0.592Praomys 0.418 0.148 0.672 0.947 0.516 0.235Rattus 0.481 0.522 0.659 0.474 0.891 0.538Rhabdomys 0.871 0.466 0.566 0.226 0.501 0.974

cesses against a background of high, but stable, variance maintain

a much narrower range of covariance structures. We suggest that

conservation of covariance structure over time is the result of the

interplay of several different evolutionary mechanisms with dif-

ferential and potentially overlapping effects, and that the role of

mechanisms other than constraint may have been underappreci-

ated. Although we do not dispute that covariance is the result of

the action of developmental processes, as suggested by previous

authors (e.g., Atchley et al. 1981; Cheverud 1996a; Zelditch et al.

2006), we argue that stabilizing selection may have a relatively

greater effect on those processes to maintain variance levels of de-

velopmental processes relative to one another. Theoretical work

on the nature of genetic covariance and the genotype–phenotype

map (e.g., Lande 1980; Cheverud 1984, 1996a; Wagner and Al-

tenberg 1996; Jones et al. 2007) predicts that stabilizing selection

EVOLUTION JUNE 2009 1 5 5 1

H. A. JAMNICZKY AND B. HALLGRIMSSON

-0.6 -0.4 -0.2 0.0 0.2 0.4

-0.4

-0.2

0.0

0.2

PCo1

PC

o2

Calomys

Rhabdomys

Eliurus

Mus

PraomysRattus

Figure 9. Bivariate plot of PCO results for wild muroids, based on

interlandmark distances. PCo, principal coordinate.

should have a strong influence on covariance structure patterning.

By comparing natural populations to groups undergoing dramati-

cally lessened levels of natural selection, we sought to explore the

complex causal relationships between evolutionary mechanisms

and covariance structure patterning.

Both the standard correlation analysis and the random skew-

ers technique employed here showed that the covariance structures

of wild-caught samples from related species of rodents are sig-

nificantly more similar to each other than are those of different

strains and mutants of laboratory mice (Figs. 5 and 7; Tables 5–

8). Wild populations differ from laboratory mice in that genetic

variances in wild populations are higher than those of inbred lab-

oratory strains, where genetic variance theoretically approaches

zero. This trend holds for phenotypic variance as well in the

present study, where the total variance of the wild group was in-

deed larger than that of the laboratory group. Single mutations that

affect the variance of a covariance-generating process would be

unlikely to significantly alter covariance structure in natural pop-

ulations, because this structure is determined by large amounts of

Table 10. Wild muroid random skewers matrix correlations, based on interlandmark distances. All correlations are significant (P≤0.05),

except those marked by an asterisk (∗).

Calomys Eliurus Mus Praomys Rattus Rhabdomys

Calomys 1.000Eliurus 0.737 1.000Mus 0.847 0.733 1.000Praomys 0.671 0.513 0.782 1.000Rattus 0.798 0.874 0.810 0.599 1.000Rhabdomys 0.880 0.748 0.747 0.458 0.763 1.000

genetic variance acting on many different processes. Any signal

produced by single mutations would be nearly invisible against

this background of high variance. We note, however, that muta-

tions of large effect are also likely to be severely deleterious and

are therefore unusual in natural populations.

Lower correlations among covariance matrices are to be ex-

pected in groups in which developmental regulation is perturbed,

given the ability of small genetic change to result in large alter-

ations to the variance of covariance-generating processes in the

development of organismal form when background variance is

low (Hallgrımsson et al. 2007). Perturbation of these processes

by mutation(s) or selection enlarges the range of allowable co-

variance structures, and thus results in covariance matrices that

are further apart within the overarching conserved phenotypic

covariance structure. The ease with which such changes to devel-

opmental processes can disrupt covariance structure relationships

when the effects of selection are minimized indicates that devel-

opmental constraints cannot be solely responsible for the main-

tenance, over evolutionary time, of natural covariance structure

patterning. Our observation that covariance structure in natural

populations is relatively stable despite phylogenetic and func-

tional divergence over substantial periods of evolutionary time,

consistent with other studies (e.g., Steppan 1997a,b; Ackermann

and Cheverud 2000; Marroig and Cheverud 2001; Young and

Hallgrımsson 2005; Goswami 2006), instead implies an impor-

tant role for other mechanisms, in particular natural selection, in

the maintenance of covariance structure.

Further exploration of the covariance structure space occu-

pied by each group, using PCO, revealed three clusters within both

groups, although these were more well defined in the laboratory

group (Fig. 6). The clusters in the laboratory group correspond

weakly to those represented in the CVA analysis (Fig. 4A), but this

trend is not evident in the wild group. Interestingly, the clusters

revealed in the laboratory data do not appear to represent groups

with mutations vs. those without, or to correspond to types of mu-

tational effects. We interpret this as evidence for the complexity

of interactions between covariance-generating processes over the

course of development, recently articulated using the metaphor of

a palimpsest (Hallgrımsson et al. 2007a).

1 5 5 2 EVOLUTION JUNE 2009

COVARIANCE STRUCTURE IN LABORATORY AND WILD MUROIDS

Calomys Eliurus Mus Praomys Rattus

0.01

0.02

0.03

0.04

0.05

0.06

Rhabdomys

VE

Figure 10. Boxplot of the variance of eigenvalues (VE) resam-

pling distribution (whiskers represent resampling range), based

on interlandmark distances. Observed VE values: Calomys callo-

sus = 0.0256, Eliurus majori = 0.0223, Mus musculus = 0.0202,

Praomys delectorum = 0.0195, Rattus tanezumi = 0.0225, Rhab-

domys pumilio = 0.0516.

Analysis of covariance distance allows us to further demon-

strate that the relationship between covariance structure and phe-

notypic outcome is neither linear nor easily predictable, and in-

stead suggests the action of multiple evolutionary mechanisms

with overlapping effects. A plot of covariance distance regressed

against Procrustes distance would be expected to show a lack of

structure in those comparisons involving laboratory mice, where

covariance structure space has been considerably expanded due

to mutation and artificially elevated directional selection. Such

a plot would be expected to show structure in comparisons in-

volving wild muroids if there were a direct relationship between

occupation of covariance structure space and phenotype space.

Interestingly, our results show only weak structure throughout the

data (Fig. 8). We interpret this result as further evidence that the

covariation structure space occupied by group of related organ-

isms is an emergent property resulting from the summed vari-

ances of many covariance-generating developmental processes

(Hallgrımsson et al. 2007a), and that direct perturbations of de-

velopmental processes in the near-absence of selection can have a

strong destabilizing effect on covariance structure. These results

thus strengthen our argument that developmental constraints are

not solely responsible for the maintenance of covariance structure,

given the ease with which such structures can be altered.

Reduced overall shape-related integration in the nonmutant

laboratory mice (Fig. 9) most likely occurs because some devel-

opmental factor that has little effect on covariance structure in

the wild-type, varies among individuals producing an integrated

set of phenotypic responses that increases integration. In other

words, the mutation responsible for the major phenotypic effect

introduces or elevates a developmental factor that previously had

little effect on overall covariance structure, such that it has become

relatively more important, resulting in the distribution of variance

across a larger number of eigenvalues of the covariance matrix

and a correspondingly relaxed phenotypic covariance structure

(Hallgrımsson et al. 2007a). When the effect of the mutation on

the variance of a developmental factor is very large, the new fac-

tor can come to dominate the covariation structure, producing an

increase in integration. Three mouse mutants investigated in this

study, exhibiting particular mutations that directly affect craniofa-

cial phenotype (BMO, CRF, and PTN), demonstrate significantly

higher integration than wild muroids (Fig. 9). This suggests that

the mutations in these mice have large pleiotropic effects across

multiple processes, an argument made previously for the BMO

mutation (Hallgrımsson et al. 2006). Richtsmeier et al. (2006)

report a similar phenomenon in human crania. In contrast, the

other four mutant strains exhibit similar integration with respect

to the wild muroids (Fig. 9). This result indicates that the epige-

netic effects of these mutations are less pronounced, and that the

effects of selection on these strains may be more strongly evident.

Mutations can thus either increase or decrease morphological in-

tegration depending on the process(es) upon which they act and

the magnitude of their effect on those processes.

There are several factors that may be confounding these re-

sults, and future studies will be required to quantify the effects

of these factors. The use of a single data collection technique for

all samples will remove a potential source of experimental error.

Also, sample sizes for the laboratory group are smaller than those

for the wild group. It is possible that greater sampling error in

these small samples may be contributing to variation in covari-

ance structure (Cheverud 1996), although we have attempted to

compensate for this phenomenon by using adjusted matrix corre-

lations. Larger sample sizes will help to minimize this problem.

Third, the two groups studied here exhibit substantial differences

in heterozygosity. The laboratory mice are inbred and therefore

homozygous for alleles that show partial loss of function, and thus

express phenotypes that are normally recessive. It has been shown

that the developmental mapping function for traits exhibiting par-

tial dominance is a “diminishing returns” curve, whose slope is

steepest when the genotype is homozygous for the recessive al-

lele. It has been further shown that the magnitude of phenotypic

response to developmental perturbation is greatest in regions of

the developmental mapping function where the slope is steep-

est (Klingenberg 2006; Klingenberg and Nijhout 1999). Thus the

laboratory mice studied here may be exhibiting relatively greater

variation resulting from developmental perturbations such as mi-

croenvironmental changes or random noise. In contrast, the wild

group may be more strongly buffered against such perturbation

EVOLUTION JUNE 2009 1 5 5 3

H. A. JAMNICZKY AND B. HALLGRIMSSON

by virtue of substantially higher levels of heterozygosity. Thus

differences in heterozygosity between the two groups sampled

may be partly responsible for the observed differences in covari-

ance structure reported here. Finally, we have hypothesized that of

the several evolutionary mechanisms at work in the maintenance

of covariance structure over time, stabilizing selection is likely

to be of primary importance. It is also possible that gene flow

among related populations helps to stabilize this structure (Riska

1985; Steppan 1997b). As discussed above, there are likely many

mechanisms working together to produce observed covariation

structure, and further experimental work will be required to tease

apart and quantify the relative contributions of each. The criti-

cal experimental evidence we demonstrate here is the ease with

which covariance structure can be perturbed when the intensity

of selection is dramatically reduced. These results, although not

conclusive, hint at a relatively important role for stabilizing se-

lection among the evolutionary mechanisms acting to stabilize

covariance structure.

The fundamental observation from this study is that covari-

ance structures of inbred and/or mutant strains of mice can be

more divergent from each other than the covariance structures of

different but related species of rodents. In other words, covariance

structure is sufficiently plastic that it can be altered significantly

by single mutations or the small genetic differences that separate

inbred strains of mice and yet, it is sufficiently conservative that it

is conserved among related species of rodents over tens of millions

of years of evolutionary time. The most reasonable explanation for

this observation is that stabilizing selection is a relatively more im-

portant force among the several evolutionary mechanisms acting

to maintain covariance structure over evolutionary time, despite

the developmental propensity for significant variation. Our results

further suggest that developmental constraints are not the primary

mechanism responsible for the maintenance of covariance struc-

ture over evolutionary time, as is a common view.

ACKNOWLEDGMENTSWe thank B. Patterson and W. Stanley (FMNH) for access to specimensunder their care, N. Jones for software and statistical advice, W. Liu forlandmarking the laboratory mouse data set, N. Young for assistance withR, and C. Klingenberg, G. Marroig, P. D. Polly, S. Steppan, M. Zelditch,and an anonymous reviewer for insightful and constructive criticism ofearlier drafts. Funding for this research was provided by the AlbertaHeritage Foundation for Medical Research (HAJ), and by the NationalScience and Engineering Research Council of Canada, the CanadianFoundation for Innovation, Alberta Innovation and Science, the Cana-dian Institutes of Health Research, Genome Canada and Genome Alberta(BH).

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Associate Editor: Dr. S. Steppan

AppendixSPECIMENS EXAMINED

The skulls of adult individuals from six wild-caught muroid

species housed in the collections of the Field Museum of Nat-

ural History (FMNH) were imaged for this study. Taxa studied

include:

Calomys callosus (n = 57). Bolivia. Beni: San Joaquin

(FMNH 117965, 117968, 117969, 117970, 117971, 117972,

117974, 117976, 117977, 117980, 117981, 117983, 117987,

117991, 117992, 117997, 117998, 117999, 118014, 118018,

118019, 118027, 118029, 118030, 118037, 118038, 118110,

118111, 118114, 118116, 118117, 118119, 118120, 118121,

118122, 118123, 118124, 118125, 118126, 118127, 118128,

118130, 118131, 118132, 118133, 118134, 118135, 118136,

118137, 118139, 118140, 118141, 118142, 118143, 118145,

118146, 118150).

Eliurus majori (n = 47). Madagascar. Antsiranana Province

(FMNH 154052, 154053, 154054, 154242, 154243, 154245,

154289, 154535, 154536, 154537, 154538, 154539, 154603,

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H. A. JAMNICZKY AND B. HALLGRIMSSON

154604, 154605, 154606, 154607, 154608, 154609, 154610,

154611, 154612, 154613, 154614x.gif, 154615, 154616, 156341,

156342, 156343, 156344, 156345, 159624, 159625, 159626,

159627, 159628, 159629, 159630, 159637, 159709, 159710,

159711, 159712, 159714, 159715, 166211, 166212).

Mus musculus praetextus (n = 48). Egypt. Matruh (FMNH

100147, 100149, 100150, 100158, 100159, 100161, 100162,

100164, 100165, 100166, 100167, 100616, 100617, 100620,

100621, 100622, 100638, 100639, 100640, 101246, 101247,

101248, 101249, 101250, 101325, 101326, 101337, 101338,

101339, 101340, 101341, 101342, 101343, 101344, 101352,

101353, 101354, 101355, 101356, 101357, 101359, 101361,

101456, 101459, 101461, 101462, 106467, 107056).

Praomys delectorum (n = 60). Tanzania. Kilimanjaro: North

Pare Mts. (FMNH 192692, 192693, 192694, 192695, 192696,

192697, 192698, 192699, 192700, 192701, 192702, 192703,

192704, 192705, 192706, 192707, 192708, 192709, 192710,

192711, 192712, 192713, 192714, 192715, 192716, 192717,

192718, 192719, 192720, 192721, 192722, 192723, 192724,

192725, 192726, 192727, 192728, 192729, 192730, 192731,

192732, 192733, 192734, 192735, 192736, 192737, 192738,

192739, 192740, 192741, 192742, 192743, 192744, 192745,

192746, 192747, 192748, 192749, 192750, 192751).

Rattus tanezumi (n = 49). Philippine IS. Negros Is.: Bais

(FMNH 78248, 78249, 78252, 78253, 78254, 78257, 78258,

78259, 78260, 78262, 78266, 78267, 78269, 78271, 78272,

78276, 78277, 78278, 78280, 78281, 78282, 78284, 78287,

78288, 78289, 78290, 78296, 78297, 78298, 78300, 78301,

78302, 78303, 78306, 78307, 78308, 78309, 78310, 78311,

78312, 78314, 78315, 78316, 78318, 78319, 78320, 78321,

78322, 78324).

Rhabdomys pumilio (n = 49). Tanzania. Kilimanjaro: Moshi

(FMNH 174310, 174311, 174312, 174313, 174314, 174315,

174316, 174317, 174318, 174320, 174321, 174322, 174323,

174324, 174325, 174326, 174328, 174329, 174330, 174331,

174333, 174334, 174335, 174337, 174338, 174339, 174340,

174341, 174342, 174344, 174345, 174346, 174347, 174348,

174351, 174352, 174353, 174354, 174356, 174357, 174358,

174359, 174360, 174361, 174362, 174363, 174364, 174365,

174366).

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