RESULTS
Morphological differences between populations
The Principal Component Analysis reveals no visually obvious shape
differentiation among populations along the two main axes of variation.
The first two principal components together account for 35% of the
total shape variation (PC1 = 24.58%, PC2 = 11.58%) (Supplementary
Figure 3). An example of shape
changes along the first principal component axis with allometric shape
changes put together with other unrelated shape changes is illustrated
in Figure 1.
Figure 1 : Shape changes
associated with the First Principal Component (above) and with allometry
(below). Spheres are the landmarks used in this study. Warmer colours
represent higher landmark variation between mean shapes. Vectors show
direction and magnitude of shape variation.
Despite the low differentiation of populations within a PCA, the
Procrustes ANOVA demonstrates that at least one of the five populations
differ in shape (F(4,91) = 3.125, R-squared = 0.121, p
< 0.001) (Table 1). The post-hoc pairwise comparisons between
the shape means of each population reveal significant differences in
shape among all populations. Intriguingly, the only sex-biased
population (Kimberley, which consisted mostly of males) shows no clear
difference with the remaining four populations (Figure 2). Groote
Eylandt specimens show a generally narrower skull as revealed by the
greater interlandmark distances in the zygomatic arches. The four
mainland populations have shorter muzzles than the Groote specimens, as
revealed by the shortening of the nasal and frontal areas. Northern
Territory specimens display elongated frontal bones. Pilbara specimens
exhibit an expansion of the braincase size and shortest muzzles relative
to the rest of the skull. Shape disparity (shape variance) differences
between island and mainland specimens were not significant (p =
0.604).
Figure 2 : Pairwise comparisons between means of each population
and visualization of interlandmark variation between populations mean
shapes. Warmer colours represent higher landmark variation between mean
shapes. Top left, comparisons of absolute mean shape of each population;
bottom right, comparisons of size-corrected mean shapes of each
population. Map on bottom left shows all specimen locations used in this
study.
Sexual dimorphism and allometry
We first confirmed that known sexual dimorphism in animal weight and
skeletal measurements (Oakwood, 1997; Schmitt et al., 1989) are
reflected in cranial size (Supplementary Figure 4) and shape (Table 1
and Supplementary Figure 5). Size differences are significant between
males and females (F(1,90) = 23.9, R-squared = 0.21, p
< 0.001), island and mainland populations
(F(1,99) = 14.15, R-squared = 0.125, p <
0.001) and populations (F(4,91) = 8.361, R-squared =
0.269, p < 0.001).
Females and males show the same homogenous allometric relationship with
no significant difference between slopes (p = 0.087), such that small
males and large females overlap on the allometric slope (see
Supplementary Figure 6). Just over half of sexual shape differences are
due to size (R-squared for shape between sexes = 0.102), with a
component of shape changes not due to size (R-squared of
allometry-adjusted shape differences between sexes = 0.043) (Table 1).
In other words, although there is some non-size-related variation
between sexes, small males and large females are similarly shaped
according to their common size. We therefore included individuals of
both sexes in our analyses of population differences and other pertinent
analyses.
In the full dataset, and among all variables tested, size manifests as
the strongest determinant of shape variation in northern quolls (p
< 0.001), accounting for 15.1 % of the total shape variation.
A Homogeneity of Slopes Test suggests no significant differences among
allometric slopes of each population (p = 0.203) (Table 1 and Figure 3),
meaning that the hypothesis of populations following the same allometric
slope is not rejected. Allometry-corrected shape analysis also reveals
the shape differences between populations; Procrustes ANOVA performed on
the residuals of allometry revealed significant differences between
populations (F(4,91) = 3.419, R-squared = 0.131, p
< 0.001). Pairwise comparisons between these size-corrected
shapes show similar significant differences between populations. Thus
allometry does not play a role in differentiating the shape of
populations.
Figure 3 : Allometry plot,
centroid sizes (proxy for body size) versus shape scores obtained from
the regression of shape on size (Drake & Klingenberg, 2008). Results of
Homogeneity of Slopes Test for allometric slopes of populations are
shown on Table 1.
Association of shape variation with geography, climate and size
Shape differences show significant differences along both latitudinal
(F(1,87) = 4.051, R-squared = 0.044, p = 0.002) and
longitudinal (F(1,87) = 3.023, R-squared = 0.034, p =
0.003) gradients on mainland specimens. Size is significantly different
along the latitudinal gradient (F(1,87) = 8.833,
R-squared = 0.092, p = 0.004; but not along the longitudinal gradient, p
= 0.117). Temperature and precipitation contribute small, but
significant, effects to shape (Temperature: F(1,87) =
2.006, R-squared = 0.023, p = 0.029; Precipitation:
F(1,87) = 3.411, R-squared = 0.038, p = 0.002). Size
differences are only significantly predicted by the effect of
precipitation (F(1,87) = 8.236, R-squared = 0.086, p =
0.005), but not by the effect of temperature (p = 0.794).
We dissected the influence of size, geography and climate (precipitation
+ temperature) with a variation partitioning analysis (Figure 4). The
full model [a + b + c + d + e + f + g] shows a significant effect of
these three factors on cranial shape variation (F(13,75)= 3.136, adjusted R-squared = 0.24, p < 0.001). Climatic
variables alone [c] do not explain any of the variation (p = 0.224),
however, they contribute to the model when geography is considered
jointly [f] (adjusted R-squared = 0.05). Pure geographical distances
[a] explain 3 % (adjusted R-squared = 0.033) of the shape variation
(F(10,75) = 1.371, p = 0.004). Finally, in accordance
with our predictions, size alone [b] contributes mostly to the model
by accounting for 17 % of the total cranial shape variation
(F(1,75) = 17.539, adjusted R-squared = 0.165, p
< 0.001).
Figure 4 : Schematic
representation of the variation partitioning analysis (VARPART), which
included effect of geography, size and combined climatic variables
(precipitation and temperature) on cranial shape. The values shown in
the diagram represent the individual fractions for each set. The outer
numbers are the adjusted R-squared values of pure geography [a],
pure size [b] and pure climate [c] and the inner values are the
adjusted R-squared values of the interaction of the corresponding
explanatory variables. The individual fraction for the interaction of
all three variables [g] is negligible and not shown. The amount of
unexplained shape by this model is depicted by the residuals (76%).
Circle sizes are schematic and do not represent the amount of shape
explained by the model.