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Ecological determinants of clinal morphological variation in the cranium of theNorth American gray wolfAuthor(s): F. Robin O'Keefe , Julie Meachen , Elizabeth V. Fet , and Alexandria BrannickSource: Journal of Mammalogy, 94(6):1223-1236. 2013.Published By: American Society of MammalogistsDOI: http://dx.doi.org/10.1644/13-MAMM-A-069URL: http://www.bioone.org/doi/full/10.1644/13-MAMM-A-069

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Journal of Mammalogy, 94(6):1223–1236, 2013

Ecological determinants of clinal morphological variation in thecranium of the North American gray wolf

F. ROBIN O’KEEFE,* JULIE MEACHEN, ELIZABETH V. FET, AND ALEXANDRIA BRANNICK

Department of Biological Sciences, Marshall University, One John Marshall Drive, Huntington, WV 25755, USA (FRO,EVF, AB)Joan C. Edwards School of Medicine, Marshall University, 1600 Medical Center Drive, Huntington, WV 25755, USA(JM)

* Correspondent: [email protected]

The gray wolf (Canis lupus) exhibits both genetic and morphologic clinal variation across North America.

Although shape variation in wolf populations has been documented, no study has been made to exhaustively

quantify it, or to correlate morphologic variation with environmental variables. This study utilizes a large

historical database of wolf skull linear measurements to analyze shape, and attempts to correlate it with wolf

ecology. A variety of statistical tests are employed; size and shape are examined through a principal component

analysis and a calculation of allometry vectors. Multiple regression analysis (both global and stepwise) are then

used to test the resulting principal components against various biotic and abiotic factors. In addition, the effects

of sexual dimorphism and taxonomy on morphology are explored through 1-way analysis of variance and

canonical variates analysis, respectively. Several patterns are revealed, including size increase with latitude in

accord with Bergmann’s rule. Static allometry is significant, the fundamental pattern being a decrease in the

robusticity of the basicranium relative to the viscerocranium. Sexual dimorphism, allometry, and a correlation

with precipitation are other key factors driving morphological variation. Examination of these patterns has

allowed us to make conclusions about the direct and indirect ways the environment has affected clinal variation

in wolves.

Key words: allometry, biogeography, carnivory, morphometrics

� 2013 American Society of Mammalogists

DOI: 10.1644/13-MAMM-A-069

The gray wolf (Canis lupus) may be the most familiar of

large mammalian carnivores. Its relationship with humans goes

back millennia and includes the origin of the domestic dog

(Vila et al. 1997; Wang and Tedford 2008), as well as a long

history of uneasy cohabitation, persecution, and coevolution

with humans (Schleidt and Shalter 2003). The gray wolf once

possessed the widest geographic range of any land mammal,

consisting of the entire Northern Hemisphere north of 13–208N

latitude (Boitani 2003), and encompassing the vast range of

environments found therein. This historical range has varied

widely over the last several thousand years due to changes in

human persecution patterns (Okarma 1993); currently, the

range of C. lupus is restricted to a far northern distribution,

having been extirpated in southern Europe, and in most of

North America south of the Canadian border (Leonard et al.

2005). However, active recolonization efforts are again

expanding this range (Paquet and Carbyn 2003).

Concerted efforts to destroy the gray wolf in North America

over the last 150 years have produced 1 unintended benefit.

Large museum collections of gray wolf pelts and skeletal

material were amassed during this time, and this material is

now available for study. Goldman (1944) utilized the products

of ‘‘predatory animal control work’’ undertaken by the United

States Biological Survey in his analysis and classification of

North American wolves. This sample numbered 1,368

individuals. The pioneering biometrician Pierre Jolicoeur

analyzed a more recently collected sample of 499 wolf skulls

from northwestern Canada in one of the 1st applications of

canonical variates analysis (CVA) to vertebrate biology

(Jolicoeur 1959, 1975). Lastly, the work of Nowak (1979) on

subspecific taxonomy of C. lupus relied originally on a sample

of 379 wolves, whereas a later analysis (Nowak 1995) utilized

580 skulls. Bogan and Mehlhop (1983) analyzed 160 crania

from the southwestern United States in their discussion of

w w w . m a m m a l o g y . o r g

1223

http://www.mammalogy.org

subspecies taxonomy in this region. In summary, a wealth of

material is available for the study of geographic variation in the

gray wolf.

The studies listed above are concerned primarily or solely

with skulls, and all contain data in a ‘‘traditional’’ morpho-

metric format, comprising linear measurements of various

features rather than landmark coordinates (Marcus 1990). The

primary aim of most has been taxonomic, attempting to

identify combinations of skull characters sufficient to diagnose

various subspecies. C. lupus is a geographically variable

species and conservation efforts have put a premium on

subspecies taxonomy (Nowak 1979, 1995, 2003). This

taxonomy has changed markedly over time. Goldman (1944)

identified 23 subspecies of C. lupus in North America, whereas

Hall (1981) accepted 24 subspecies as valid. However, as early

as 1959 Jolicoeur believed that wolf populations lacked

discrete morphological division, and this impression was

confirmed by later authors (reviewed by Nowak [2003]).

Studies analyzing shape variability in C. lupus in a non-

taxonomic context are rare (Jolicoeur 1959, 1975; Skeel and

Carbyn 1977). In these studies variation was found to be clinal,

body size was found to increase with latitude in accord with

Bergmann’s rule, and there were few distinct size differences

among populations. Sexual dimorphism was significant and

largely size related.

More recently, a flood of genetic evidence has added to the

discussion on wolf taxonomy, significantly clarifying the

biological architecture of the Canis species complex in North

America (Rutledge et al. 2012) and furnishing a definitive

taxonomy (Koblmuller et al. 2009; vonHoldt et al. 2011;

Chambers et al. 2012). These genetic analyses have shown that

gene flow among populations is uniformly high, and that

geographic distance is the best predictor of genetic distance.

Variation is broadly clinal with hybrid zones of varying width,

and the use of typological subspecies is probably misleading

(Wayne and Vila 2003). In general, North American C. lupus is

a cosmopolitan and mobile species with ample gene flow, yet

with significant, fitness-related clinal variation in morphology

(Smith et al. 1997; Munoz-Fuentes et al. 2009).

The source and maintenance of clinal variation has been the

subject of several recent studies. Genetic differentiation in

European (Pilot et al. 2006, 2012) and North American

(Carmichael et al. 2007; Munoz-Fuentes et al. 2009) wolves

appears to be driven by ecological factors, probably through

the mechanism of natal habitat-biased dispersal (Haughland

and Larsen 2004a, 2004b). Munoz-Fuentes et al. (2009) further

demonstrated that genetic differentiation is correlated with

morphological differences. Wolves from coastal British

Columbia, Canada, differ genetically and morphologically

from those from the continental interior, even though dispersal

distances are relatively short. A recent study examined the

influence of geography and genetics on wolf hunting behaviors

in Europe and found a correlation between clinal variation,

genetic signal, and prey preference (Pilot et al. 2012). Also,

landscape variation has been shown to affect prey-hunting

strategies in wolves (McPhee et al. 2012). Fitness-related

morphology also is labile chronologically; for instance,

Leonard et al. (2007) document the presence and extinction

of a specialized, robust gray wolf ecomorph in Siberia. All

these studies implicitly or explicitly implicate environmental

factors as determinants of shape, and all demonstrate that

wolves of the genus Canis are a widespread yet locally adapted

species.

However, no study to date has quantified clinal variation in

wolf skull shape across the whole of North America, nor has

the purported link between shape variation and climate been

formally tested. Here we perform such a study. We 1st quantify

morphological variation in wolf skulls across a broad

geographic area, then explore how this variation correlates

with environmental variables such as climate and precipitation,

as well as biological variables such as taxonomy and sex. We

began by compiling a cranial morphometric data set from a

large sample of North American gray wolves. These data were

culled from a historical study and comprise linear measure-

ments, and the sampled skulls span the original geographic

range from Mexico to Alaska. The data set includes locality

data (in latitude and longitude) for each skull. We then

appended modern, high-resolution climate data to the data set

TABLE 1.—Measurement descriptions quoted from Goldman (1944:409); see Fig. 1.

Measurement Description

Greatest length Length from anterior tip of premaxillae to the posterior point of inion in median line over

foramen magnum

Condylobasal length Length from anterior tip of premaxillae to posterior plane of occipital condyles

Zygomatic breadth Greatest distance across zygomata

Squamosal constriction Distance across squamosals at constriction behind zygomata

Width of rostrum Width of rostrum at constriction behind canines

Interorbital breadth Least distance between orbits

Postorbital constriction Least width of frontals at constriction behind postorbital process

Length of mandible Distance from anterior end of mandible to plane of posterior ends of angles, the right and

left sides measured together

Height of coronoid process Vertical height from lower border of angle

Maxillary toothrow, crown length Greatest distance from curved front of canine to back of cingulum of posterior upper molar

Upper carnassial, crown length (outer side), and crown width Anteroposterior diameter of crown on outer side, and transverse diameter at widest point

anteriorly

First upper molar, anteroposterior diameter, and transverse diameter Greatest anteroposterior diameter of crown on outer side, and greatest transverse diameter

Lower carnassial (crown length) Anteroposterior diameter of cingulum

1224 Vol. 94, No. 6JOURNAL OF MAMMALOGY

by referencing each location. This data set proves to be a rich

source of insight into the intrinsic and extrinsic factors driving

local wolf adaptation.

MATERIALS AND METHODS

The statistical strategy adopted here is to regress traditional

morphometric data against presumptive biotic and abiotic

forcing factors. Size and shape are investigated using standard

principal component analysis (PCA), along with multivariate

allometry. Because many of the abiotic factors of interest are

correlated, for example, latitude and temperature, we use

multiple regression modeling in an attempt to untangle the

influence of each factor on each morphological variable.

Stepwise regression is then used to construct a best-fit model

for each principal component (PC). Lastly, although modern

wolf taxonomy is no longer based solely on morphological

data, it is still pertinent to that question (Chambers et al. 2012).

We therefore performed a CVA on both the raw data and size-

adjusted data. Chambers et al. (2012) posit 4 valid subspecies

of C. lupus in North America: C. l. arctos, C. l. baileyi, C. l.nubilus, and C. l. occidentalis. They have raised the previous

subspecies C. l. lycaon to species status, now designated C.

lycaon (Chambers et al. 2012). However, this taxonomy is

contentious. In this paper we accept the most broadly

recognized taxonomy, where C. l. lycaon remains a subspecies

(Koblmuller et al. 2009; vonHoldt et al. 2011). In most taxon-

based analyses in this paper, C. l. arctos is excluded because of

low sample size (n ¼ 2).

Data Compilation

Morphometric data.—Acquisition of the raw data began

with manual entry of morphometric data from Goldman

(1944). Chapter 13 of that study gives measurement details

(pp. 408–410). As stated by Goldman (1944), all wolves

measured were adults, as determined by eruption of the

complete adult dentition. However, because adult dentition is

in place by a somatic age of about 6–7 months, whereas full

body stature is not reached until 12–14 months and mass can

continue to increase (Kreeger 2003), we assumed that

significant ontogenetic variation still exists within the data.

Although Goldman (1944) also recorded data for body mass

and coat color, these data were significantly more restricted in

scope, and so were excluded here in the interest of sample size.

A total of 15 cranial measurements were recorded by Goldman

FIG. 1.—Measurements used in this study, as described by Goldman (1944). Measurements are as follows: A) greatest length of cranium; B)

condylobasal length; C) zygomatic breadth; D) squamosal constriction; E) width of rostrum; F) interorbital breadth; G) postorbital constriction; H)

length of mandible; I) height of coronoid process; J) maxillary toothrow, crown length; K) upper carnassial, crown length (outer side); L) upper

carnassial, crown width; M) 1st upper molar, anteroposterior diameter; N) 1st upper molar, transverse diameter; and O) lower carnassial crown

length.

December 2013 1225O’KEEFE ET AL.—WOLF CRANIAL VARIATION

(1944; Table 1; Fig. 1). The data set constructed for this paper

contains data from 312 wolves. Two hundred eighty-nine of

these had no missing measurements. One hundred thirty-six of

the wolves were female; 176 were male. Seventeen of the

wolves had no recoverable locality data, and 3 of these also

were missing morphometric data. All of the multivariate results

reported here are derived from analysis of the 289 wolves with

complete data; the univariate results utilize all available

measurements. See Supporting Information S1 (DOI: 10.

1644/13-MAMM-A-069.S1) for complete data.

FIG. 2.—Geographical locations of all captures for wolves in this study, shown on a Mercator projection of North America. Latitude and

longitude are calculated from locality information in Goldman (1944) and Google Earth (https://maps.google.com/). The abiotic variable mean

annual temperature is plotted from Hijmans et al. (2005).

TABLE 2.—Summary of eigenanalysis of the craniometric data set for Canis lupus, n¼ 263. Ordination was performed on the correlation matrix

and principal component (PC) coefficients are standardized so that the sum of squared coefficients for each vector is 1. Note that PC 1 is

dominated by the size factor, with coefficients that are universally high and positive; however, there is significant shape change as well on this

axis. PC 2 clearly portrays tooth size against the remainder of the skull and is a clear signal of the continued hypertrophy of the skull with respect

to the teeth after dental maturity but before full somatic maturity. Interpretation of PC 3 is more difficult, but seems to indicate an increase in the

size of the frontal area of the brain, and modest tooth size increase, relative to the rest of the skull. This axis is correlated with precipitation and

probably driven by ecological factors. PC 4 is again correlated with sex and taxon, and records residual size-correlated shape variation.

PC 1 PC 2 PC 3 PC 4 PC 5

Eigenvalue 10.164 1.452 0.750 0.577 0.410

Percent 67.761 9.680 5.003 3.849 2.735

Cumulative percent 67.761 77.442 82.445 86.293 89.028

Greatest length 0.295 0.104 �0.148 �0.191 �0.120

Condylobasal length 0.295 0.053 �0.143 �0.289 �0.153

Zygomatic breadth 0.270 0.230 �0.159 0.134 0.094

Squamosal constriction 0.265 0.157 �0.234 0.146 �0.344

Width of rostrum 0.262 0.100 �0.162 0.307 0.324

Interorbital breadth 0.230 0.314 0.361 0.096 0.575

Postorbital constriction 0.168 0.379 0.747 �0.100 �0.326

Length of mandible 0.294 0.137 �0.118 �0.239 �0.120

Height of coronoid process 0.256 0.230 �0.235 0.183 0.170

Maxillary toothrow, crown length 0.287 �0.014 �0.105 �0.333 �0.114

Upper carnassial, crown length (outer side) 0.266 �0.277 0.085 0.169 0.030

Upper carnassial, crown width 0.223 �0.274 0.159 0.618 �0.379

First upper molar, anteroposterior diameter 0.219 �0.435 0.156 �0.289 0.297

First upper molar, transverse diameter 0.246 �0.368 0.125 �0.160 0.024

Lower carnassial crown length 0.264 �0.329 0.104 0.074 0.054


Locality data.—Locality data also were acquired from

Goldman (1944). This process was necessarily imprecise;

Goldman (1944) generally cites municipalities for his locality

of collection. However, working on the assumption that most

wolves registered at a given place came from the general area,

we used the municipalities as locations. These locations were

entered into Google Maps (https://maps.google.com/) and the

latitude and longitude of each location were recorded (Fig. 2).

The quality of climate data available from modern high-

resolution databases is high, on the scale of kilometers to tens

of kilometers (Hijmans et al. 2005), so the error in location of

collection is probably the more significant source of error in the

data set. The home-range size of wolves is highly variable, but

range radii of 50 miles (~80 kilometers) are known to occur,

and some wolves migrate with prey species (Mech and Boitani

2003); therefore, we believe our locality data are a reasonable

approximation of a given wolf’s area or origin.

Abiotic data.—Climatic data were taken from the database

compiled by Hijmans et al. (2005) and downloaded as surfaces

for all of North America in ArcGIS (Environmental Systems

Research Institute [ESRI] 2011). The latitude and longitude of

capture recordings were then queried to this database, and the

climate data for each point were recorded. These variables

included mean yearly temperature (Tavg), maximum and

minimum monthly mean temperature (Tmax C and Tmin C,

respectively), yearly mean precipitation (YMP), and yearly

precipitation variance (YPV).

TABLE 3.—Multiple regression results for principal component (PC) scores regressed against abiotic variables and sex of Canis lupus. Abiotic

variables include yearly mean precipitation (YMP), yearly precipitation variance (YPV), latitude (Lat), longitude (Long), maximum monthly mean

temperature (Tmax C), minimum monthly mean temperature (Tmin C), mean yearly temperature (Tavg), and sex. All regressions have n¼ 263,

the number of wolves with complete shape and abiotic data. For details on the global multiple regressions and stepwise regression modeling see

‘‘Materials and Methods.’’ AIC¼Akaike information criterion. Significance at the P , 0.05 level is indicated by 1 asterisk. Significance at the P, 0.01 level is indicated by 2 asterisks.

Shape variable

global regression

Modeled

variables t-ratio Prob. . jtjStepwise regression

model F-ratio Prob. . F

PC 1 YMP �1.64 0.1032 PC 1 5.528 0.0195

R2 ¼ 0.7576 YPV 1.36 0.1743 P ¼ 5

F-ratio ¼ 91.6109 Lat 3.06 0.0025** R2 ¼ 0.7567 24.197 0

P , 0.0001** Long �2.54 0.0116* AIC ¼ 244.8258 20.185 0

Tmax C �2.48 0.0139*

Tmin C �2.53 0.0121*

Tavg 2.8 0.0055**

Sex 18.9 0.0001** 355.368 0

Taxon 7.32 0.0001** 55.76 0

PC 2 YMP �0.32 0.7487 PC 2

R2 ¼ 0.0643 YPV 0.07 0.9447 P ¼ 2

F-ratio ¼ 1.9231 Lat 2.2 0.0288* R2 ¼ 0.0404

P , 0.0492* Long �1.08 0.2797 AIC ¼ 94.2512 9.951 0.0018

Tmax C �0.81 0.4184

Tmin C �0.23 0.815

Tavg 1.53 0.1263

Sex 0.73 0.4684

Taxon �2.16 0.0319* 6.361 0.0123

PC 3 YMP �3.46 0.0006** PC 3 11.633 0.0008

R2 ¼ 0.1380 YPV 4.54 0.0001** P ¼ 4 22.722 0

F-ratio ¼ 4.4387 Lat 0.23 0.8148 R2 ¼ 0.1300

P , 0.0001** Long 3.02 0.0028** AIC ¼ �93.2149 13.773 0.0003

Tmax C �1.48 0.139 13.614 0.0003

Tmin C 0.74 0.46

Tavg �0.03 0.9726

Sex �0.96 0.3373

Taxon 0.15 0.8845

PC 4 YMP �1.98 0.0487* PC 4 6.349 0.0124

R2 ¼ 0.2872 YPV 3.2 0.0015** P ¼ 6 13.058 0.0004

F-ratio ¼ 11.2793 Lat �3.69 0.0003** R2 ¼ 0.2853 23.528 0

P , 0.0001** Long �0.38 0.7051 AIC ¼ �204.089

Tmax C 0.48 0.6299

Tmin C �0.40 0.6907 2.074 0.1511

Tavg �0.72 0.4737

Sex 3.6 0.0004** 13.248 0.0003

Taxon 2.52 0.0122* 7.25 0.0076


Data Analysis

Principal component analysis (PCA).—Given the number of

variables and their uniformly high covariance, eigenanalysis is

appropriate for reducing the dimensionality of the data,

allowing them to be summarized by a relatively small

number of composite variables. Because the variances of the

variables were heterogeneous, the correlation rather than

covariance matrix was used in the PCA (Reyment and

Joreskog 1996), except in the special case of calculation of

the allometry vector (see below). The results of the PCA are

reported in Table 2.

The PCA was performed on the raw data, without log

transformation. As pointed out by Jungers et al. (1995), the log

transform is overused in biological analysis and is often not

necessary, even when size variation is large. In this study,

exploratory analyses showed that ordinations (both PCA and

CVA) and multiple regressions run on log-transformed data

were essentially identical to those from the raw data. This

demonstrates that nonlinearities are not biasing the ordinations,

and that the log transform is not necessary.

Multiple regression.—Once the correlation structure of the

core morphometric matrix was determined, individual scores

for each PC deemed significant (PCs 1–4) were calculated and

appended to the data set. These PC scores are Mossiman shape

variables and are therefore permissible for use in regression

and other parametric statistical analysis techniques (Mosimann

and Malley 1979; Reyment 1991).

We explored the correlation of each PC with a range of

biotic and abiotic factors through the use of multiple

regression. The variables utilized in multiple regression were

yearly mean precipitation (YMP), yearly precipitation variance

(YPV), latitude (Lat), longitude (Long), maximum monthly

mean temperature (Tmax C), minimum monthly mean

temperature (Tmin C), mean yearly temperature (Tavg), sex

(male or female), and taxon (functionally this was 4 classes, the

valid subspecies of C. lupus save for C. l. arctos, whose n¼ 2).

These variables were entered into a multiple regression model,

and regressed on each of the 4 PCs of interest. Global

significance for each regression, and for significances for each

predictor variable, are reported in Table 3. We also

experimented with stepwise regression; the results of stepwise

modeling for PCs 1–4 were very similar to the global multiple

regression models. The best-fit stepwise regression model is

also reported in Table 3; the number of parameters participat-

ing in each model was determined by iterating the entry of

variables into the model and selecting the model with the

lowest Akaike information criterion (AIC).

Allometry and sexual dimorphism.—Given the amount of

size variation in the data set, and the documented existence of

sexual dimorphism within C. lupus (Goldman [1944] and more

recent references), we performed formal analyses to investigate

allometry and sexual dimorphism. To analyze allometry, we 1st

calculated the global (all taxa combined) multivariate allometry

vector introduced by Jolicoeur (1963). This vector is defined as

the 1st PC derived from eigenanalysis of the covariance matrix

of log-transformed variables (O’Keefe et al. 1999). The global

allometry vector, and its comparison with the vector of

isometry, is depicted in Fig. 3. Significance values for the

deviation of each coefficient from isometry were not calculated

by bootstrapping the eigenanalysis; significance was calculated

from regression of each univariate variable against the

geometric mean (GM) size estimator. Exploratory analysis

using other size estimators (PC 1 score, greatest length)

demonstrates that the results obtained are robust. In these

regressions and those discussed below, ordinary least squares

regression was used; however, because the R2 values were very

high, calculation of the reduced major axis slope was not

necessary.

Not surprisingly, the multivariate allometry vector differs

significantly from isometry (see ‘‘Results’’); however, we note

that because of the presence of significant late-stage somatic

growth (after the eruption of adult dentition—Kreeger 2003),

and the presence of different taxa, the global allometry vector

will contain phylogenetic, ontogenetic, and static components

(Klingenberg 1996). The presence of significant phylogenetic

allometry (that among taxa) is a possibility, and we tested for

this by regressing the allometry vector scores of each taxon

against the GM. This analysis is equivalent to a standard

FIG. 3.—Histogram depicting the multivariate allometry vector (the

1st eigenvector derived from the covariance matrix of ln-transformed

variables; see ‘‘Materials and Methods’’). Face, rostrum, and mandible

sizes increase with positive allometry, whereas tooth and brain sizes

increase with negative allometry. The coefficient of isometry for 15

variables is 0.258. Significance at the P , 0.01 level is indicated by 2

asterisks. Significance was determined by standard parametric reduced

major axis regression of bivariate allometry of each variable versus the

geometric mean.


bivariate allometry regression of a given variable against size,

but the dependent variable in this case is the synthetic

multivariate allometry variable rather than a univariate

measurement. Because the GM is an isometric size estimator,

a significant difference from a slope of 1 in this regression

indicates that the growth trajectory of the sample varies

significantly from isometry. The actual regression in this case,

and in those described below, was performed with lnGM on the

abscissa and ln(AV þ 10), where AV is the allometry vector,

on the ordinate. The addition of a scalar to the allometry vector

scores is necessary because the ordination used to calculate the

scores is mean-centered to 0; therefore, half of the scores will

be less than 1, and their logarithms will be imaginary. All

details of allometry vector comparisons are reported in Table 4.

The global allometry vector also is correlated with sexual

dimorphism, because most (but not all) variation due to sex is

due to size and size-correlated shape variation. Sexual

dimorphism is an important part of the covariance structure

of the morphometric data, and is of obvious biological

significance. We therefore investigated the dimorphism in

each variable individually with simple 1-way analyses of

variance (ANOVAs) with sex as the independent variable;

results are presented in Table 5. An isometric size correction

was made by dividing each data row by its GM before the

ANOVA was performed.

Canonical variates analysis.—We employed the commonly

accepted taxonomy of the C. lupus species complex as a factor

in this study, including C. l. baileyi, C. l. nubilus, C. l.occidentalis, and C. l. lycaon. To investigate the impact of the

size factor, the CVA was run both on the raw data and on the

data with each value row normalized to its GM. Canonical

details are reported in Table 6.

RESULTS

Covariance Structure

Analysis of the resulting ordination via regression on

geographic location reveals north–south and east–west trends

in morphological variation. Regressions against temperature

and precipitation quantify morphological variation on a

continental scale. Table 2 reports the coefficients for the first

5 PCs; however, the 1st component carries 68% of the variance

in the correlation matrix. This, and the uniformly high and

positive coefficients on PC 1, indicate a large amount of size

variation in the sample, resulting in a robust size factor that is

TABLE 4.—A) Allometry vector calculations. All vectors were calculated as the 1st principal component of the covariance matrix derived from

ln-transformed data. Allometry vectors were then regressed on the geometric mean to yield an overall measure of allometry. The global vector

reflects positive allometry, being significantly different than 1. The vectors for each taxon also are significantly different, save for comparisons

with Canis lupus baileyi; this taxon has a small sample size and relatively small amount of size variation, and the vector is probably not

dependable. B) Allometry vectors for each taxon; the global allometry vector is identical to the one shown in Fig. 3.

A)

Taxon R2 P n Slope SE Lower 2 SE Upper 2 SE

Size

rank

C. l. baileyi 0.9965 0.0001 18 1.069 0.0158 1.0374 1.1006 3

C. l. lycaon 0.9978 0.0001 29 1.09 0.0099 1.0702 1.1098 3

C. l. nubilus 0.9969 0.0001 181 1.062 0.0044 1.0532 1.0708 2

C. l. occidentalis 0.9972 0.0001 59 1.033 0.0072 1.0186 1.0474 1

All 0.9978 0 289 1.053 0.0029 1.0472 1.0588

B)

Allometry vector Global

C. l.baileyi

C. l.lycaon

C. l.nubilus

C. l.occidentalis

Percent variance explained 64.4058 45.9529 64.5880 51.6576 60.0212

lnGreatest length 0.2625 0.1623 0.2485 0.2350 0.2175

lnCondylobasal length 0.2517 0.2162 0.2393 0.2285 0.1892

lnZygomatic breadth 0.2528 0.2346 0.2607 0.2917 0.2410

lnSquamosal constriction 0.2168 0.2007 0.1686 0.2427 0.1634

lnWidth of rostrum 0.3211 0.3027 0.3090 0.3275 0.3122

lnInterorbital breadth 0.3140 0.0456 0.3755 0.3643 0.4429

lnPostorbital constriction 0.2368 �0.2317 0.3430 0.1955 0.3103

lnLength of mandible 0.2811 0.1755 0.2683 0.2563 0.2196

lnHeight of coronoid process 0.3191 0.2952 0.2726 0.3143 0.3495

lnMaxillary toothrow, crown length 0.2327 0.1824 0.2508 0.2095 0.1805

lnUpper carnassial, crown length (outer side) 0.2367 0.3006 0.2077 0.2378 0.2467

lnUpper carnassial, crown width 0.2499 0.4472 0.2946 0.2533 0.2417

lnFirst upper molar, anteroposterior diameter 0.1946 0.1972 0.1377 0.2034 0.2040

lnFirst upper molar, transverse diameter 0.2282 0.3482 0.2041 0.2118 0.1629

lnLower carnassial crown length 0.2358 0.2873 0.1753 0.2346 0.2276


the strongest single driver behind the ordination. The 2nd PC

accounts for just under 10% of the variation, whereas the 3rd

accounts for 5%, PC 4 for less than 4%, and PC 5 for less than

3%. In a matrix of 15 completely uncorrelated variables, each

variable would account for about 7% of the variance in the

correlation matrix. Therefore only PCs 1 and 2 are greater than

the ‘‘strength’’ of 1 variable, whereas PC 3 is probably also of

interest, with PC 4 marginally so. However, in covariance

matrices with a very strong underlying factor driving

covariance (such as size), the eigenvalue of the 1st PC will

be large, and all others relatively small. In these cases, using

the criterion of the ‘‘strength’’ of 1 variable to determine the

significance of PCs may be misleading. (This criterion is

arbitrary in any case [Reyment 1991]; the true limiting

consideration in the interpretation of successively smaller

PCs is the magnitude of the measurement error in the original

data, a quantity that we cannot determine given the historical

nature of the data.) We further explored the wolf data by

running a PCA after dividing each row by its GM. This is an

isometric, approximate size correction, and removes much of

the size factor from the resulting ordination. The eigenvalues

from this PCA run 27.2%, 19.3%, 10.6%, 7.8%, and 5.56% for

PCs 1–5, respectively. In this size-corrected analysis, PCs 1–4

are all greater than the ‘‘strength’’ of 1 variable, whereas PC 5

and lower are not. Based on this consideration we offer

interpretations of the first 4 PCs below, although we note that

PCs 3 and 4 carry a relatively small proportion of the variance.

Principal component 1 size axis.—The results of the PCA

yielded 4 axes of possible interest. PC 1 (67% of variance

explained) is dominated by the size factor (Table 2) given that

all coefficients are large and positive. However, this vector is

not isometric, with significant size-related shape variation. In

general this shape variation is a less extreme version of the

variation carried on the allometry vector; however, much of the

ontogenetic negative allometry captured by the allometry

vector is captured by PC 2 in this ordination (see below). PC 1

is therefore a measure of general size, and also carries static

and phylogenetic allometry in the form of increased snout

length and decreased relative brain size as scores increase. PC

1 is highly correlated with GM and accounts for 67% of the

variance in the data set, indicating that the size factor is the

dominant contributor to the correlation structure. We would

anticipate PC 1 to correlate with any factor that might affect

body size.

Principal component 2 ontogeny axis.—Principal

component 2 accounts for 9.7% of the variance in the wolf

data set. For this axis, all measurements taken directly from the

teeth load against all other measurements. This axis therefore

contrasts the size of the teeth relative to the bony parts of the

skull. This signal is expected, because canids reach dental

maturity at about 6 or 7 months, whereas full somatic growth

can take a year or more (Kreeger 2003). An axis that contrasts

modest growth of the skull around static teeth should therefore

occur in all populations, and this signal is conserved among all

TABLE 5.—Summary of t-test on means of 1-way ANOVA with sex

as the independent variable for the variables shown. Significance at P, 0.01 is denoted with two asterisks. GM ¼ geometric mean; F ¼female, M ¼ male.

Variable Sex ANOVA

Greatest length/GM 0.593

Condylobasal length/GM 0.046* F . M

Zygomatic breadth/GM 0.004** M . F

Squamosal constriction/GM 0.346

Rostrum width/GM 0.0002** M . F

Interorbital breadth/GM 0.0009** M . F

Postorbital constriction/GM 0.008** F . M

Mandible length/GM 0.887

Coronoid height/GM 0.0001** M . F

Maxillary toothrow crown length/GM 0.0001** F . M

P4 crown length/GM 0.430

P4 crown width/GM 0.983

M1 anteroposterior diameter/GM 0.012* F . M

MI transverse diameter/GM 0.004** F . M

m1 crown length/GM 0.524

TABLE 6.—Results of canonical variates analysis (CVA) of

taxonomic units of Canis in North America. C. lupus arctos is not

included because of small sample size in the data set. Two analyses

were done, the 1st on the raw data (CVAR), the 2nd on size-

standardized data (CVAS). The results of both analyses are highly

significant. Part A of the table shows canonical details for both

analyses, and analytical details are: Raw canonical: Wilks’ k P-value ,

0.0001**, approximate F¼12.6269; size-adjusted canonical: Wilks’ k

P-value , 0.0001**, approximate F¼ 9.1110. C. l. baileyi differs the

most from all other units based on the measurements in this study,

with perfect classification in both analyses. C. l. lycaon is classified

successfully in 76% of cases with size included, and in 69% with size

removed. Classifications are shown in part B of the table. C. l. nubilusand C. l. occidentalis are relatively similar and segregate rather poorly.

Raw canonical: Wilks’ k P-value , 0.0001**, approximate F ¼12.6269; size-adjusted canonical: Wilks’ k P-value , 0.0001**,

approximate F ¼ 9.1110.

A)

Eigenvalue

Percent variance

explained

Canonical

correlation

CVAR 1 1.4091 61.7180 0.7648

CVAR 2 0.5802 25.4131 0.6059

CVAR 3 0.2938 12.8689 0.4765

CVAS 1 0.7625 49.0786 0.6577

CVAS 2 0.4838 31.1437 0.5710

CVAS 3 0.3073 19.7778 0.4848

B)

Actual/class

C. l.

baileyi

C. l.

lycaon

C. l.

nubilus

C. l.

occidentalis

Raw canonical

C. l. baileyi 18 0 0 0

C. l. lycaon 1 22 3 3

C. l. nubilus 10 21 137 13

C. l. occidentalis 0 3 7 49

Size-adjusted canonical

C. l. baileyi 18 0 0 0

C. l. lycaon 1 21 3 4

C. l. nubilus 12 17 118 34

C. l. occidentalis 1 4 9 45


subspecies. Significantly, if this interpretation is correct, it

predicts that PC 2 should not be significantly correlated with

any outside variable, because it is an intrinsic pattern within the

growth of all wolves. This lack of correlation is exactly as

predicted (see below).

Principal component 3 ecological axis.—Interpretation of

PC 3 (5%) is more difficult. We know that it is ecologically

significant (see below), so it carries a coherent signal. Scoring

positively on this axis are interorbital breadth and postorbital

constriction; the tooth variables are only slightly positive,

whereas the others are negative. This axis seems to indicate a

broad forehead and large frontal part of the cranial cavity,

along with modest tooth size increase, against the rest of the

skull. There are no obvious a priori predications about the

correlation of extrinsic factors given the structure of this vector.

Principal component 4 axis.—This axis accounts for 3.9%

of the variance in the data set and is therefore of marginal

importance. It also is difficult to interpret; it seems to capture

residual shape variation due to size, given its regression results

(below). The coefficients reflect this, with high positive loading

for rostrum width and interorbital breadth, and large negative

loading for squamosal constriction and postorbital constriction.

This axis is therefore an index of basicranial size relative to

viscerocranial size; however, maxillary toothrow and mandible

length both load with the basicranial measures, whereas the

only tooth measurement with a strong loading is the width of

P4, again loading with the basicranium. Skulls measuring high

on this axis will therefore have a relatively small brain with

relatively wide forehead and snout, but the snout also is

relatively short.

Multiple Regression

Principal component 1 size axis.—As predicted, this vector

is highly correlated with intrinsic and extrinsic factors

influencing size (Table 3). One advantage of the PC

ordination is that, to a large degree, it segregates ontogenetic

allometry to PC 2. Therefore, the nonisometric aspects of PC 1

will reflect static allometry, phylogenetic allometry, and sexual

dimorphism. The global multiple regression is highly

significant; the 4 factors with the highest correlations are sex,

taxon, latitude, and Tavg, all of which have highly significantFIG. 4.—A) Principal component (PC) 1 calculated from the 15 raw

measurements. Complete data (shape plus locality) is available for 269

wolves; 284 wolves had complete shape data. Analysis was performed

on the correlation matrix; for eigenanalysis details see Table 2. All

coefficients on PC 1 are high and positive, indicating that PC 1 has a

large size component. The abscissa is latitude, a variable highly

correlated with PC 1 (see multivariate regression, Table 3). Body size

increases significantly with latitude, in accordance with Bergmann’s

rule. Sexual dimorphism also increases with latitude, as shown by the

significantly different slopes of male only (blue) and female only (red)

linear regression. Wolves are split by sex in the inset graph; in the

main graph they are split by taxonomy following Chambers et al.

(2012). Confidence intervals on slopes are P , 0.05, and are male-

only, female-only, and combined as indicated. Regression details are

as follows: male-only: R2¼0.423, PC 1¼�5.60þ0.153 Lat, slope SE

�6 0.015, P , 0.0001**; female-only: R2 ¼ 0.334, PC 1 ¼�8.17 þ0.129 Lat, slope SE 6 0.017, P , 0.0001**; combined: R2¼ 0.261,

PC 1¼�6.95þ 0.147 Lat, slope SE 6 0.015, P , 0.0001** (where

Lat is latitude). B) Principal component 3 plotted against yearly mean

precipitation (YMP), with which it is correlated. PC 3 records shape

change in response to aridity, and also is highly correlated with annual

precipitation variance and with longitude. Individuals that are more

positive on PC 3 have relatively large anterior brain size and a modest

increase in relative tooth size. Confidence interval on slope is P ,

0.05. Regression details are as follows: R2¼ 0.036, PC 3¼�0.924þ0.241 YMP, P , 0.0020**. Significance at the P , 0.05 level is

indicated by 2 asterisks.


P-values. We illustrate PC 1 by its bivariate regression on

latitude in Fig. 4. The wolves are split with respect to sex, and

it is clear that male mean general size is always larger than

female mean size. Also, there is a clear increase in mean body

size with latitude. This pattern has been identified by most

previous studies on wolf morphometrics that sample a

sufficiently wide latitude range, including Goldman (1944),

using these data. Goldman (1944) noted sexual dimorphism

and intertaxon size variability, and we found these correlations

as well, particularly the hypertrophy of the snout relative to the

basicranium. There was no correlation of PC 1 with

precipitation factors. The stepwise regression analysis returns

a strongly supported best-fit model with 5 parameters; sexual

dimorphism is by far the most important component of this

model, followed by taxonomic membership, and then

geographic location.

Principal component 2 ontogeny axis.—As predicted, the

axis representing ontogenetic increase in skull size relative to

the teeth shows almost no significant correlation with any

factor. The global regression is marginally significant at the

0.0492 level, and the R2 on the regression is weak (0.064). The

2 factors driving this weak correlation are latitude and

taxonomy. The stepwise regression model also is very weak,

giving a 2-parameter model comprising longitude and

taxonomy.

Principal component 3 ecological axis.—The multiple

regression for PC 3 is strong (P , 0.001, R2 ¼ 0.138). The

factors driving this correlation are yearly mean precipitation,

yearly precipitation, and longitude. Longitude is correlated

with the precipitation variables, and PC 3 is documenting

variation in response to differences in aridity. To demonstrate

this we plotted PC 3 against yearly mean precipitation in Fig. 4.

Significantly, the variation captured by PC 3 is not correlated

with taxonomy, and this trend is one that is common to all taxa

whose ranges encompass adequate variation in aridity. The

stepwise regression model is in complete accord, returning a

well-supported 4-parameter model comprising precipitation

mean, precipitation variance, longitude, and maximum

temperature.

Principal component 4 axis.—Principal component 4 has a

highly significant multiple regression (P , 0.001, R2¼ 0.287).

The variables driving this regression are sex, latitude, and the

precipitation variables, with a marginal contribution from

taxonomy. The stepwise regression analysis returns a well-

supported model of 6 parameters, with contributions from sex and

taxonomy, but also both precipitation variables as well as latitude

and minimum temperature. This axis appears to be a hybrid,

recording residual size-related variation but also carrying some

ecological signal. It is therefore difficult to interpret because the

underlying factors are confounded.

Allometry

Allometry was 1st analyzed as a pooled vector, and then

split out by taxon for comparison of allometry vectors (Table

4). Although there is significant variance in direction of the

allometry vector among taxa, the overall pattern in all taxa is

similar, and is well summarized by the pooled vector (Fig. 3).

The multivariate allometry vector is positive in aggregate,

differing significantly from isometry; however, there is a mix

of positively and negatively allometric variables obscured by

this general trend. Variables changing with significant negative

allometry include squamosal constriction, maxillary crown

length, and both measures of M1. The other tooth variables

also are negatively allometric, but not significantly so.

Positively allometric variables include rostrum width, interor-

bital breadth, mandible length, and coronoid process height.

Together these variables document an increase in viscerocra-

nium size at the expense of the basicranium and teeth; negative

allometry of brain size relative to the rest of the skull is a well

known feature of tetrapod ontogeny in general (Goodrich

1930). The negative allometry in the teeth is similar to that

reflected in PC 2, and is a consequence of the rest of the skull

‘‘growing up’’ around the teeth. Lastly, negative allometry in

M1 and positive allometry in coronoid height reflects sexual

dimorphism. General allometry in wolf skulls can therefore be

characterized as an increase in the size of the viscerocranium

relative to the brain and dentition.

Further analysis demonstrates significant differences in the

allometry vectors among taxa (Table 4). These differences are

relatively minor except for the vector of C. l. baileyi. This

vector has 1 negative eigenvalue, probably resulting from the

impact of an outlier on the small sample size (n¼ 18). Further

inspection of the data for C. l. baileyi shows that there is

relatively less size variance in this sample when compared to

the other 3 taxa. The standard deviation on its slope also is very

wide, making statistical comparisons fruitless. We therefore do

not consider C. l. baileyi further.

Sexual Dimorphism

Most of the sexual dimorphism among wolves is linked

directly to body size; the overall trend of relative increase in the

viscerocranium versus the basicranium (PC 1) is correlated

with sex more highly than with any other factor (Table 3).

Goldman recognized this in his 1944 study. However, there are

more subtle differences between males and females in the data

set, as revealed by the univariate ANOVAs (Table 5) and the

allometry vectors. Measures of the skull that vary between the

sexes clearly reflect the relative size of the viscerocranium

versus the basicranium. However, the coronoid process is

significantly taller in males, whereas the maxillary toothrow is

longer and M1 is larger in females. The magnitude of sexual

dimorphism also varies with latitude (Fig. 4); the slope of male

size increase with latitude is 0.1532 (6 0.015 SE, n ¼ 151),

whereas that of females is 0.1287 (6 0.017, n ¼ 118).

However, the error on the slopes is such that this trend is not

statistically significant.

Canonical Variates

Results of the CVA are highly significant, with the 15

measurements reported by Goldman (1944) forming a good

basis for taxonomic segregation in aggregate. Both the CVA on


the raw data (CVAR) and that on size-adjusted data (CVAS)

give 3 significant discriminant axes. The CVAR 1 (for

ordination containing size) explains about 62% of the

taxonomic variation in the data set. This is similar to the

coefficient in PC 1, and as we know from the regression

analysis and from Goldman (1944), size is a major source of

variation among taxa. As expected, C. l. occidentalis, the taxon

with the largest body size, is segregated from the other taxa on

this axis, with the other taxa descending in size rank at lower

scores on this axis. Inspection of the bivariate plot shows that

the variables greatest length and mandible length are important

on this axis, demonstrating that the previously encountered

allometry is here as well. The 2nd discriminant function of this

ordination differentiates C. l. nubilus from C. l. baileyi and C. l.lycaon, with C. l. occidentalis in an intermediate position. The

bivariate plot for this axis indicates that rostrum width and the

M1 transverse diameter are important in the positive direction,

whereas P4 crown length and condylobasal length are

important in the negative direction. This implies that C. l.nubilus, and to a lesser extent C. l. occidentalis, has a wider

snout, a broader M1, and a smaller P4 relative to the smaller

taxa (C. l. lycaon and C. l. baileyi).The 2nd ordination, with isometric size removed via division

by the GM, is very similar to the 1st. Because the size

correction was isometric, allometric shape variance is still

found in this ordination, but because isometric size variation

has been removed the size factor no longer dominates the

ordination. The percent variance explained is therefore less for

the 1st discriminate function, and greater in the 2nd, when

compared to those of the raw CVA. However, a rigid rotation

of the ordination (Fig. 5) shows that the overall ordination and

segregation are quite similar.

DISCUSSION

The analysis of the covariation among measured variables of

the skull presented here reproduces several patterns that have

already been documented for wolves, such as sexual

dimorphism and change in size with latitude. However, we

present quantitative treatments of these phenomena across a

broad geographic area for the 1st time, allowing more precise

statements about these patterns, particularly in the multiple and

stepwise regressions with PC 1, analysis of the allometry

vectors, and examination of sexual dimorphism. We also

identify novel axes of variation that are of interest, particularly

an ontogenetic allometry axis (PC 2) and an axis that is linked

with precipitation (PC 3). Lastly, discriminant function

analysis yields a picture of significant but clinal variation,

with axes that are highly significant yet fail to achieve complete

segregation. The variables reported here are good segregators

of the 4 taxa considered. However, the taxa do not form

discreet clusters in discriminant space, and there are significant

FIG. 5.—Canonical variates analysis of raw (CVAR, plot A) and size-adjusted (CVAS, plot B) wolf data. Both discriminant functions are

highly significant even though size is a good segregator; for univariate taxonomic principal component analysis (PCA) ANOVAs see Table 4. The

15 variables reported here are fairly good estimators of species membership, although only Canis lupus baileyi is classified correctly in all cases (n¼ 18); see Table 5 for canonical details. Note that CVAR 1 is correlated with sex (Kruskal–Wallis): nonparametric rank sums test, P , 0.0001.

Sexual variation is relegated to correlation with only CVAS 3 in the size-adjusted analysis (not shown); Kruskal–Wallis P , 0.0001.


numbers of failures of discrimination for both the CVAR and

CVAS (Table 6). Failure to discriminate cannot be attributed to

lack of statistical power, because the samples here are

relatively large. Also, the taxon with the smallest sample size

(C. l. baileyi, n ¼ 18) is in fact the only taxon with perfect

discrimination. The pattern seen in the discrimination is

probably real, with significant size and shape overlap among

taxa. One would expect this in a species complex with broadly

clinal variation and significant zones of hybridization among

taxa, as is the case for the Canis complex (Chambers et al.

2012).

One serendipitous property of the PCA is that it yields a

clean ontogenetic allometry axis as PC 2. The signal of skull

growth relative to the teeth on this axis is unambiguous, and

the lack of significant correlation in the multiple regression

demonstrates that this variation is common to all wolves

independent of size, taxon, or sex. We can say more about PC

1, because we can assume that ontogeny is a minor component

of its variance. Therefore, allometric change in PC 1 is

attributable to static allometry (difference in adult size), sexual

dimorphism, and phylogenetic allometry. All of these factors

are highly correlated with PC 1, making this axis a good

representation of how shape changes with body size increase

across latitude, taxon, and sex. The pattern on PC 1 is clear,

with relative decrease in the basicranium and increasing

robusticity in the viscerocranium. This trend is common

among taxa and clinal with respect to taxon and sex; this is in

accord with Goldman’s (1944) original findings and those of

later authors, up to and including Chambers et al. (2012).

Unlike many other taxa (Geist 1987), the members of the

genus Canis reported here conform strongly to Bergmann’s

rule, with increase in size at higher latitudes and lower

temperatures (Blanckenhorn et al. 2006, and references

therein). Geist (1987) found that Bergmann’s rule in wolves

has less to do with temperature than with food availability;

north of 658N latitude, wolves show a decreasing size trend

correlated with smaller prey populations. Wolves also display

Rensch’s rule, with sexual dimorphism increasing with

latitude. However, given the amount of scatter within males

and females, this trend was not statistically significant. The

amount of scatter in Fig. 4 (PC 1 versus latitude) is itself

significant; wolves in general show much size variation and

overlap between large females and small males. Some of this

scatter may be due to a lack of precision in our locality

estimates; however, we doubt that this is large enough to

account for a significant portion of the variation seen. The

members of the genus Canis reported here are variable, and this

appears to be a trait of their population biology. The variation

is broadly clinal and strongly linked to sex, and is probably

maintained by extensive hybridization in hybrid zones between

taxa (Chambers et al. 2012).

MacNulty et al. (2009) also noted that male wolves continue

to increase in body size until age 4.75 years, thereafter showing

a decline in body size. However, female wolves did not show

this decline, but continue to increase in size throughout their

lives. Although male skull size would likely not decrease,

female wolves may continue to show minor but measureable

skeletal growth far into adulthood, as has been demonstrated in

some other carnivorans (Binder and Van Valkenburgh 2000;

Meachen-Samuels and Binder 2010). This may contribute to

the overlap in skull size between males and females.

Principal component 3 demonstrates that skull morphology

is correlated with precipitation. Wolves in the wettest areas

show larger frontal bones and larger (longer and wider) teeth.

This finding may be correlated with increased prey acquisition

in areas with higher primary productivity associated with high

precipitation. A recent study on Japanese deer found that body

size was positively correlated with precipitation, and that these

areas also had the highest productivity (Terada et al. 2012).

Similar results also were found in woodrats (Cordero and Epps

2012) and ground squirrels (Gur 2010). This indicates that

precipitation is an important factor impacting prey size, and

that its effects are visible in wolves. However, the exact

mechanics driving this correlation are unclear, and a topic of

further research.

We did identify several single measures that varied

significantly between the sexes (Table 5). Most of these are

attributable to static allometry as described above; however,

males have significantly taller coronoid processes, whereas

females have significantly larger M1s. However, Gittleman and

Van Valkenburgh (1997) found that the carnassial teeth (lower

m1 and upper P4; the primary meat-shearing teeth in

carnivorans) showed less sexual dimorphism than the canines

in many carnivorans, indicating that females and males use

their shearing teeth roughly equally for food processing. We

replicated this finding, but the difference in molar size implies

a sex-based difference in use of the more-posterior grinding

teeth.

Large molars and a relatively short coronoid process may

indicate more masseter use in females than in males, involving

more food processing and less prey acquisition. Subsidiary

analyses demonstrate that this is not a pure size pattern;

relatively small males still have tall coronoids and small molars

when compared to females of equivalent size. MacNulty et al.

(2009) found that prey acquisition and body size were

positively correlated; larger males generally outperform

females when handling prey. This may influence the feeding

behavior of wolves, causing females to consume prey quickly

to assure they can get their fill. Additionally, females may have

larger teeth overall to put them on par or ahead of males while

feeding if they fall short in pursuit and prey killing. Finally, we

speculate that relatively large female grinding dentition also

may be due to more complete carcass processing due to

increased nutrient needs during lactation. Sexual dimorphism is

thus a vector carrying size variation, with the basicranial–

viscerocranial trade-off implicit in all size change in Canis, but

also carrying positive allometry in coronoid height and

negative allometry in the size of M1.

Assuming that fossil wolf biology was similar to that of

extant wolves, these metrics also may be useful for establishing

the sex of fossil wolves. Given the amount of overlap between

the sexes, perfect discrimination is not possible, but samples of


wolves with significant statistical power should show a

negative correlation between body size and molar size and a

positive correlation between coronoid height and body size.

Analysis of the multivariate allometry vectors is more

complex than that of the PC vectors. The reason for this is that

the coefficients for the allometry vector are similar to those of

PC 1, but also carry an overprint of ontogenetic allometry as

well. This signal resides in PC 2 in the PCA. Study of the

allometry vectors is hampered by the small sample size for C. l.baileyi. However, the allometry vectors of the other 3 taxa

show an interesting trend, with C. l. lycaon having the steepest

slope relative to isometry, followed by C. l. nubilus, then C. l.occidentalis. This also is the rank order of mean body size in

reverse; C. l. lycaon is the smallest in size, whereas C. l.occidentalis is the largest. This pattern might be explainable by

the populations growing toward a similar adult shape, but those

with smaller size having to adopt more extreme allometry to do

so. In terms of Gould’s clock model (Gould 1977) this would

be an example of ‘‘proportioned dwarfism.’’ If one envisions

what proportioned dwarfism would mean in vector space, it

entails greater shape change over a smaller range of body size.

This requires a vector with a more positively allometric slope,

whereas proportioned gigantism would require an isometric

vector. One also may predict that the coefficients of the

allometry vector would change in a regular way, with

allometric measures approaching isometry as body size

increases. However, this is not the pattern seen when

comparing the allometry vectors among taxa, so a simple

Gouldian interpretation of this heterochrony breaks down. In

fact the adult shapes of the different taxa do differ, and the

ontogenetic trajectories used to get to them vary in a complex

manner; this type of complex heterochrony has been observed

in reptiles as well (O’Keefe et al. 1999).

Using a large data set, we have shown that wolf skull

morphology is indirectly influenced by both temperature

(Bergmann’s rule) and precipitation, and directly influenced

by prey availability and primary productivity. Wolf subspecies

show clinal variation across subspecies and static and

phylogenetic allometry play a large role in the differences

among these groups. Our analyses showed that the differences

between the sexes have a large allometric component and we

were able to tease this out of our data using PCA and

multivariate allometry vectors. Static allometry seems to be the

main morphological component that is changing in wolves,

allowing for small concurrent changes in ecology, such as

differences in feeding ecology found in disparate populations

of grey wolves in Europe (Pilot et al. 2012). In addition,

landscape heterogeneity plays a role in how wolves choose to

hunt and kill prey as well (McPhee et al. 2012), which can

thereby shape morphological changes via adaptation, as we

observed in the clinal variation related to precipitation. Lastly,

sexual dimorphism is considerable, but males and females

overlap widely, and most of the intersex variation is confined

to the static allometry axis. However, sexual differences in

coronoid height and molar size hint at difference in hunting

success and carcass utilization between males and females.

ACKNOWLEDGMENTS

This study was supported by funds from the Marshall Foundation

and a grant from the National Aeronautics and Space Administration

West Virginia Space Grant Consortium to FRO. B. Van Valkenburgh

provided valuable input during the gestation of this paper.

SUPPORTING INFORMATION

SUPPORTING INFORMATION S1.—Data analyzed in this paper.

Found at DOI: 10.1644/13-MAMM-A-069.S1

LITERATURE CITED

BINDER, W. J., AND B. VAN VALKENBURGH. 2000. Development of bite

strength and feeding behaviour in juvenile spotted hyenas (Crocutacrocuta). Zoological Journal of the Linnean Society (London)

252:273–283.

BLANCKENHORN, W. U., R. C. STILLWELL, K. A. YOUNG, C. W. FOX, AND

K. G. ASHTON. 2006. When Rensch meets Bergmann: does sexual

size dimorphism change systematically with latitude? Evolution

60:2004–2011.

BOGAN, M. A., AND P. MEHLHOP. 1983. Systematic relationships of

gray wolves (Canis lupus) in southwestern North America.

Occasional Papers of the Museum of Southwestern Biology 1:1–21.

BOITANI, L. 2003. Wolf conservation and recovery. Pp. 317–340 in

Wolves: behavior, ecology, and conservation (L. D. Mech and L.

Boitani, eds.). University of Chicago Press, Chicago, Illinois.

CARMICHAEL, L. E., ET AL. 2007. Historical and ecological determinants

of genetic structure in arctic canids. Molecular Ecology 16:3466–

3483.

CHAMBERS, S. M., S. R. FAIN, B. FAZIO, AND M. AMARAL. 2012. An

account of the taxonomy of North American wolves from

morphological and genetic analyses. North American Fauna 77:1–

67.

CORDERO, G. A., AND C. W. EPPS. 2012. From desert to rainforest:

phenotypic variation in functionally important traits of bushy-tailed

woodrats (Neotoma cinerea) across two climatic extremes. Journal

of Mammalian Evolution 19:135–153.

ENVIRONMENTAL SYSTEMS RESEARCH INSTITUTE (ESRI). 2011. ArcGIS

Desktop: release 10. Environmental Systems Research Institute,

Redlands, California.

GEIST, V. 1987. Bergmann’s rule is invalid. Canadian Journal of

Zoology 65:1035–1038.

GITTLEMAN, J. L., AND B. VAN VALKENBURGH. 1997. Sexual

dimorphism in the canines and skulls of carnivores: effects of size,

phylogency, and behavioural ecology. Journal of Zoology 242:97–

117.

GOLDMAN, E. A. 1944. Classification of wolves. Pp. 389–636 in The

wolves of North America, part 2 (S. P. Young and E. A. Goldman,

eds.). Dover Publications, Inc., New York.

GOODRICH, E. S. 1930. Studies on the structure and development of

vertebrates. Macmillan and Co., London, United Kingdom.

GOULD, S. J. 1977. Ontogeny and phylogeny. Belknap Press of

Harvard University Press, Cambridge, Massachusetts.

GUR, H. 2010. Why do Anatolian ground squirrels exhibit a

Bergmannian size pattern? A phylogenetic comparative analysis

of geographic variation in body size. Biological Journal of the

Linnean Society 100:695–710.

HALL, E. R. 1981. The mammals of North America. John Wiley &

Sons, Inc., New York.

HAUGHLAND, D. L., AND K. W. LARSEN. 2004a. Ecology of NorthAmerican red squirrels across contrasting habitats: relating nataldispersal to habitat. Journal of Mammalogy 85:225–236.


HAUGHLAND, D. L., AND K. W. LARSEN. 2004b. Exploration correlateswith settlement: red squirrel dispersal in contrasting habitats.Journal of Animal Ecology 73:1024–1034.

HIJMANS, R. J., S. E. CAMERON, J. L. PARRA, P. G. JONES, AND A. JARVIS.2005. Very high resolution interpolated climate surfaces for globalland areas. International Journal of Climatology 25:1965–1978.

JOLICOEUR, P. 1959. Multivariate geographical variation in the wolfCanis lupus L. Evolution 13:283–299.

JOLICOEUR, P. 1963. The multivariate generalization of the allometryequation. Biometrics 19:497–499.

JOLICOEUR, P. 1975. Sexual dimorphism and geographical distance asfactors of skull variation in the wolf Canis lupus L. Pp. 54–61 inThe wild canids: their systematics, behavioral ecology andevolution (M. W. Fox, ed.). Van Nostrand Reinhold Co., NewYork.

JUNGERS, W. L., A. B. FALSETTI, AND C. E. WALL. 1995. Shape, relativesize, and size-adjustment in morphometrics. Yearbook of PhysicalAnthropology 38:137–161.

KLINGENBERG, P. C. 1996. Multivariate allometry. Pp. 23–49 inAdvances in morphometrics 284 (L. F. Marcus, M. Corti, A. Loy,G. J. Naylor, and D. E. Slice, eds.). Plenum Press, New York.

KOBLMULLER, S., M. NORD, R. K. WAYNE, AND J. A. LEONARD. 2009.Origin and status of the Great Lakes wolf. Molecular Ecology18:2313–2326.

KREEGER, T. J. 2003. The internal wolf: physiology, pathology, andpharmacology. Pp. 239–258 in Wolves: behavior, ecology, andconservation (L. D. Mech and L. Boitani, eds.). University ofChicago Press, Chicago, Illinois.

LEONARD, J. A., C. VILA, K. FOX-DOBBS, P. L. KOCH, R. WAYNE, AND

VAN VALKENBURGH. B. 2007. Megafaunal extinctions and thedisappearance of a specialized wolf ecomorph. Current Biology17:1146–1150.

LEONARD, J. A., C. VILA, AND R. K. WAYNE. 2005. Legacy lost: geneticvariability and population size of extirpated US grey wolves (Canislupus). Molecular Ecology 14:9–17.

MACNULTY, D. W., L. SMITH, D. MECH, AND L. E. EBERLY. 2009. Bodysize and predatory performance in wolves: is bigger better? Journalof Animal Ecology 78:532–539.

MARCUS, L. 1990. Traditional morphometrics. Pp. 77–122 inProceedings of the Michigan Morphometrics Workshop (F. J.Rohlf and F. L. Bookstein, eds.). Special Publication 2. Universityof Michigan Museum of Zoology, Ann Arbor.

MCPHEE, H. M., N. F. WEBB, AND E. H. MERRILL. 2012. Time-to-kill:measuring attack rates in a heterogeneous landscape with multipleprey types. Oikos 121:711–720.

MEACHEN-SAMUELS, J., AND W. J. BINDER. 2010. Sexual dimorphismand ontogenetic growth in the American lion and sabertoothed catfrom Rancho La Brea. Journal of Zoology (London) 280:271–279.

MECH, L. D., AND L. BOITANI. 2003. Wolf social ecology. Pp. 1–34 inWolves: behavior, ecology, and conservation (L. D. Mech and L.Boitani, eds.). University of Chicago Press, Chicago, Illinois.

MOSIMANN, J. E., AND J. D. MALLEY. 1979. Size and shape variables.Pp. 175–189 in Multivariate methods in ecological work (L. Orioci,C. R. Rao, and W. M. Stiteler, eds.). International CooperativePublishing House, Fairland, Maryland.

MUNOZ-FUENTES, V., C. T. DARIMONT, R. K. WAYNE, P. C. PAQUET, AND

J. A. LEONARD. 2009. Ecological factors drive differentiation inwolves from British Columbia. Journal of Biogeography 36:1516–1531.

NOWAK, R. M. 1979. North American Quaternary Canis. Monograph6. Museum of Natural History, University of Kansas, Lawrence.

NOWAK, R. M. 1995. Another look at wolf taxonomy. Pp. 375–397 inEcology and conservation of wolves in a changing world (L. N.Carbyn, S. H. Fritts, and D. R. Seip, eds.). Canadian CircumpolarInstitute, Edmonton, Alberta, Canada.

NOWAK, R. M. 2003. Wolf evolution and taxonomy. Pp. 239–258 inWolves: behavior, ecology, and conservation (L. D. Mech and L.Boitani, eds.). University of Chicago Press, Chicago, Illinois.

OKARMA, H. 1993. Status and management of the wolf in Poland.Biological Conservation 66:153–158.

O’KEEFE, F. R., O. RIEPPEL, AND P. M. SANDER. 1999. Shapedisassociation and inferred heterochrony in a clade of pachypleur-osaurs (Reptilia, Sauropterygia). Paleobiology 25:504–517.

PAQUET, P. C., AND L. N. CARBYN. 2003. Wolf, Canis lupus. Pp. 482–510 in Wild mammals of North America: biology, management,and conservation (G. A. Feldhamer, B. C. Thompson, and J. A.Chapman, eds.). Johns Hopkins University Press, Baltimore,Maryland.

PILOT, M., ET AL. 2006. Ecological factors influence population geneticstructure of European grey wolves. Molecular Ecology 15:4533–4553.

PILOT, M., W. JEDRZEJEWSKI, V. E. SIDOROVICH, W. MEIER-AUGENSTEIN,AND A. R. HOELZEL. 2012. Dietary differentiation and the evolutionof population genetic structure in a highly mobile carnivore. PLoSONE 7:e39341.

REYMENT, R. A. 1991. Multidimensional paleobiology. Pergamon,Oxford, United Kingdom.

REYMENT, R., AND K. G. JORESKOG. 1996. Applied factor analysis in thenatural sciences. Cambridge University Press, Cambridge, UnitedKingdom.

RUTLEDGE, L. Y., P. J. WILSON, C. F. KLUTSCH, B. R. PATTERSON, AND B.N. WHITE. 2012. Conservation genomics in perspective: a holisticapproach to understanding Canis evolution in North America.Biological Conservation 155:186–192.

SCHLEIDT, W. M., AND M. D. SHALTER. 2003. Co-evolution of humansand canids: an alternative view of dog domestication: homo hominilupus? Evolution and Cognition 9:57–72.

SKEEL, M. A., AND L. N. CARBYN. 1977. The morphologicalrelationship of gray wolves (Canis lupus) in national parks ofcentral Canada. Canadian Journal of Zoology 55:737–747.

SMITH, D., ET AL. 1997. Is incest common in gray wolf packs?Behavioral Ecology 8:384–391.

TERADA, C., S. TATSUZAWA, AND S. TAKASHI. 2012. Ecologicalcorrelates and determinants of deer morphology. Oecologia169:981–994.

VILA, C., ET AL. 1997. Multiple and ancient origins of the domesticdog. Science 276:1687–1689.

VONHOLDT, B. M., ET AL. 2011. A genome-wide perspective on theevolutionary history of enigmatic wolf-like canids. GenomeResearch 21:1294–1305.

WANG, X., AND R. H. TEDFORD. 2008. Dogs: their fossil relatives andevolutionary history. Columbia University Press, New York.

WAYNE, R. K., AND C. VILA. 2003. Molecular and genetic studies ofwolves. Pp. 218–238 in Wolves: behavior, ecology, and conserva-tion (L. D. Mech and L. Boitani, eds.). University of Chicago Press,Chicago, Illinois.

Submitted 14 March 2013. Accepted 22 July 2013.

Associate Editor was Ryan W. Norris.


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