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Biological Journal of the Linnean Society, 2011, 104, 207–216. With 3 figures
No relationship between canalization and
developmental stability of the skull in a natural
population of Mastomys natalensis (Rodentia: Muridae)
MATTEO BRENO1*, HERWIG LEIRS1,2 and STEFAN VAN DONGEN1
1
Evolutionary Ecology Group, Department of Biology, University of Antwerp, Groenenborgerlaan 171,
B-2020 Antwerp, Belgium
2
Danish Pest Infestation Laboratory, Department of Integrated Pest Management, Faculty of
Agricultural Sciences, University of Aarhus, Skovbrynet 14, DK-2800 Lyngby, Denmark
Received 1 February 2011; revised 25 March 2011; accepted for publication 25 March 2011
bij_1702
207..216
The aim of the present work was to investigate the relationship between canalization and developmental stability
under varying environmental conditions. Three different cohorts of Mastomys natalensis (Rodentia, Muridae),
displaying different growth trajectories, were analysed by means of geometric morphometrics. A set of 23
landmarks was digitalized on the dorsal skull of 292 specimens from Morogoro (Tanzania). Patterns of among- and
within-individual (measured as fluctuating asymmetry, FA) variation were assessed and compared among and
within the three groups to test for the presence of a common mechanism between canalization and developmental
stability. Results showed that there was no congruence between canalization and developmental stability: (1) levels
of FA and among-individual variation varied in a discordant fashion, (2) no correspondence between the variance–
covariance matrix of among- and within individual variation was found, and (3) environmental effects were able
to alter the covariance structure of among-individual variation leaving patterns associated with fluctuating
asymmetry unaffected. These findings support the view of multiple mechanisms underlying developmental
buffering of shape variation. © 2011 The Linnean Society of London, Biological Journal of the Linnean Society,
2011, 104, 207–216.
ADDITIONAL KEYWORDS: fluctuating asymmetry – geometric morphometrics – shape variation.
INTRODUCTION
Developmental homeostasis, the ability to maintain
phenotypic consistency under environmental and
genetic variation (Debat & David, 2001), consists of
two main components: canalization and developmental stability (DS). Canalization (Waddington, 1942)
reduces phenotypic variation under varying genetic
and/or environmental conditions, thereby reducing
between-individual variation. DS buffers against local
and small random developmental perturbations
(Willmore, Young & Richtsmeier, 2007), termed developmental noise (Waddington, 1957), reducing withinindividual variation. Canalization and DS are two
important determinants of phenotypic variability as
*Corresponding author. E-mail: [email protected]
Data deposited at Dryad: doi:10.5061/dryad.9054.
they modulate its expression, typically reducing it
(Hallgrimsson, Willmore & Hall, 2002). A distinction
exists between environmental and genetic canalization with the former buffering against environmental
stress, whereas the latter confers robustness against
mutation (Dworkin, 2005; Santos, Iriarte & Céspedes,
2005; Debat, Debelle & Dworkin, 2009). Environmental canalization is thus seen as the opposite of
phenotypic plasticity (Debat & David, 2001). The distinction between the two components of developmental homeostasis was first proposed by Waddington
(1957) who hypothesized that variation buffered by
canalization was different from DS. However, identifying environmental factors associated with either
canalization or DS is difficult and arbitrary. Therefore, canalization and DS are better identified in
relation to how they are measured (Willmore et al.,
2007). Lower among-individual variation for a given
© 2011 The Linnean Society of London, Biological Journal of the Linnean Society, 2011, 104, 207–216
207
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M. BRENO ET AL.
trait reflects canalization, while the minimization of
phenotypic variation that arises during development
reflects DS (Willmore & Hallgrimsson, 2005). DS is
inferred by the degree of directionally random asymmetry of bilateral structures [i.e. fluctuating asymmetry (FA)]. Although most research has focused on the
effects of stress on developmental homeostasis, developmental buffering is also thought to influence the
evolutionary potential of traits. Through favouring
a coherent phenotypic expression despite genetic and
environmental variation, canalization can conceal
genetic variation from selection, leading to the accumulation of genetic variation. Such a build-up of
genetic variation can be exposed to natural selection
if the buffering mechanism is challenged (Gibson &
Dworkin, 2004; Flatt, 2005; Swindell & Bouzat, 2006;
Schlichting, 2008; Talloen et al., 2009). Although
canalization and DS can be seen as distinct modulators of phenotypic variability (Hendrikse, Parsons
& Hallgrimsson, 2007), it remains unclear to what
extent they share a common mechanism. While theoretical models have suggested the presence of a
common biological pattern (Klingenberg & Nijhout,
1999; Meiklejohn & Hartl, 2002; Siegal & Bergman,
2002), empirical results have demonstrated both the
existence of independent mechanisms and a common
basis for the two processes. Moreover, it has been
suggested that this relationship may vary according
to traits and populations under investigation (Debat
et al., 2008; Vishala & Singh, 2008a, b). Debat et al.
(2000) studied the relationship between canalization
and DS in two laboratory strains of house mouse Mus
musculus and their F1 hybrids, and found no correlations. Concordance among patterns of FA and
among-individual variation was reported for mice
limb bones (Hallgrimsson et al., 2002) and mandibles
(Klingenberg, Mebus & Auffray, 2003). Willmore,
Klingenberg & Hallgrimsson (2005) analysed the
relationship between FA and environmental variance
in the skull of rhesus macaque Macaca mulatta,
reporting significant but low correlations. Studies on
wing shape reported high correlations in Drosophila
melanogaster, tsetse fly (Glossina palpalis gambiensis) and bumblebee (Bombus empatiens) (Klingenberg
& McIntyre, 1998; Klingenberg et al., 2001; Santos,
Iriarte & Céspedes, 2005; Breuker, Patterson & Klingenberg, 2006) as well as for other traits in 11 invertebrate species (Clarke, 1998); whereas the opposite
result was found for cricket limbs (Reale & Roff,
2003). Studies involving the alteration of the heat
shock protein 90 (Hsp90) considered a specific mechanism for canalization (Rutherford & Lindquist, 1998;
Rutherford, 2000), by either a mutational or pharmacological approach, resulted in an increase of phenotypic variance but did not show any increase of FA,
leading to the conclusion that two distinct mecha-
nisms for DS and canalization exist (Milton et al.,
2003). However, a study involving the role of Hsp90 in
quantitative genetic variation for D. melanogaster
wing shape (Debat et al., 2006) showed that introgression of a mutation of Hsp83 (the Drosophila Hsp90
gene) led to an increase in both FA and amongindividual variation, whereas neither pharmacological inhibition of hsp90 nor two hsp83 mutations led to
an increase in phenotypic variation.
There are several ways to test for a common basis
of canalization and DS. Correlations in the population
levels of FA and among-individual variation across
groups experiencing different levels of stress are commonly used (Hoffmann & Woods, 2001; Reale & Roff,
2003; Vishala & Singh, 2008a). Alternatively, positive
correlations in the variance–covariance matrices of
variation in asymmetry in shape (within-individual
variability) and in shape variation among individuals
within a population may suggest a common basis
of canalization and DS (Klingenberg & McIntyre,
1998). Finally, if canalization and DS share a common
mechanism, changes in the variance–covariance
matrix of either one of them due to changes in the
environmental conditions can be expected to correspond to a similar change in the other. Here we apply
all three approaches to study the common basis of
canalization and DS.
Because a decrease in developmental buffering can
be expected in the presence of environmental perturbations, we study environmental canalization and DS
in different cohorts of a single population of Mastomys
natalensis (Rodentia: Muridae). Abundance and the
distribution of rain have been linked to the presence of
different growth pathways across different cohorts
(generation types) within a single population of M. natalensis in Morogoro (Tanzania) (Leirs et al., 1990).
Leirs, Verhagen & Verheyen (1993) identified three
distinct growth patterns defining three different generation types. Generation a is born in the middle of the
year (main breeding season) and displays the longest
period of reduced growth, reaching maximum size in
the following year. The so-called b generation is born in
the main breeding season as well, but due to abundant
off-season rain (end of the year), it has a shorter period
of growth stop and it is able to mature early in the
following year and reproduce earlier. Their offspring,
generation g, appears in the off-season period, only if
rains are particularly abundant, and is already fully
grown in the main breeding season of the same year.
Different pressures are thus expected to act upon
different generation types, allowing the effect of a
range of environmental disturbances within a genetically homogeneous population to be tested (Van Hooft
et al., 2008) in the field. Fadda & Leirs (2009) and
Breno, Leirs & Van Dongen. (in press) analysed postnatal ontogeny of the skull in the three generation
© 2011 The Linnean Society of London, Biological Journal of the Linnean Society, 2011, 104, 207–216
DEVELOPMENTAL BUFFERING IN M. NATALENSIS
types, and showed that growth trajectories were deeply
affected by environmental conditions. Variation in
growth is reflected in life histories, where generation g
reproduces at the youngest age and has a better
survival until reproduction and generation a has the
lowest survival and reproduces latest (Leirs, 1995).
Geometric morphometrics on landmarks of the
dorsal surface of the skull was applied in this study to
test three hypotheses as detailed above. First, levels
of FA and among-individual variation were compared
among generation types. However, the sensitivity of
this test will be limited because there are only three
generation types. Second, the presence of a link
between DS and canalization was investigated by
testing for correlations in the variance–covariance
matrices of FA and among-individual variation within
the three generation types. And finally, we compare
the effect of growth trajectory (i.e. generation type)
on the variance–covariance matrices of FA and
among-individual variation.
MATERIALS AND METHODS
Mastomys natalensis used in this study were collected
in Morogoro (Tanzania) in 1987 and 1988, a period
when the three generation types were found (Leirs,
1995). A total of 292 skulls for which precise age
estimates, by means of eye lens examinations (Leirs,
1995), were available were photographed and measured twice. A set of 23 landmarks covering the whole
surface of the dorsal skulls was digitized in two
dimensions using ImageJ (Rasband, 1997–2011).
Based on age and date of capture, the specimens were
subdivided: 61 of generation alpha (a, long growth
stop), 125 of generation beta (b, short growth stop), and
105 of generation gamma (g, fast growing). We applied
geometric morphometrics based on Procrustes superimposition (Klingenberg, Barluenga & Meyer, 2002) to
quantify asymmetry and among-individual variation.
We first explored the need to use size correction.
Procrustes fit on the entire dataset was followed by a
separate Procrustes ANOVA for each generation type
to assess measurement error and to test for directional asymmetry, among-individual variation and
FA. Mean squares obtained from the Procrustes
ANOVA were used to compute a group index for FA
and among-individual variation. Because the skull is
a structure with object symmetry (i.e. structures with
an internal plane of symmetry), the symmetric and
asymmetric component of shape variation occupy different subspaces and mean squares for FA and
among-individual variation were corrected for asymmetric and symmetric components of error, respectively. These indices were then compared among
generation types by means of a parametric F-test.
Individual FA scores were obtained using the Pro-
209
crustes distance (square root of the sum of squared
distances between corresponding landmarks of two
configurations) between the original configuration
and the corresponding reflected and relabelled one
after correcting for directional asymmetry. Individual
FA scores were then compared between generation
types with an ANOVA and P-values were obtained by
permutation (10 000 random permutations). A modified version of Levene’s test was used to compare
among-individual variation between generation types.
This test makes use of Procrustes distances from the
mean as deviations from the average configuration of
the group. Differences among groups were tested by
permutation (10 000 permutations).
Variance–covariance matrices corresponding to FA
and among-individual variation were computed and
corrected for the respective error. These matrices
were then used to compare patterns of amongindividual variation and FA among generation types
and to explore the relationship between FA and
among-individual variation within generation types.
A matrix correlation followed by a Mantel’s test was
used to assess the correlation among covariance
matrices; diagonal blocks corresponding to the variance and covariance of the coordinates at each landmark were excluded to study only the covariance
between different landmarks. Landmarks lying along
the midline were also excluded because, due to
Procrustes superimposition, they are forced to vary
only along one dimension. To provide a graphical
representation of similarities among the variance–
covariance matrices, a principal coordinate analysis
(PCO) was carried out. This analysis, also known as
multidimensional scaling, was applied to two different types of distances. We first used a Euclidean
distance, computed as one minus the squared correlation between two matrices. The second distance,
proposed by Mitteroecker & Bookstein (2009), represents the shortest path between two matrices in the
proper non-Euclidean space and is computed as
the square root of the summed squared logarithms of
the relative eigenvalues between two matrices. Covariance matrices used for this metric were computed
from the first ten PCs of the symmetric and asymmetric component of shape variation. This number
of PCs ensured covering 85% of the total variation
excluding measurement error often contained in the
last PCs. The asymmetric component was corrected
for directional asymmetry before performing the principal component analyses (PCA). Pairwise distances
were then computed among all variance–covariance
matrices of FA and among-individual variation across
all generation types. The principal coordinate
axes can be extracted and represented in a twodimensional graph, where the more closely located
matrices are more similar in their patterns of
© 2011 The Linnean Society of London, Biological Journal of the Linnean Society, 2011, 104, 207–216
210
M. BRENO ET AL.
Table 1. Procrustes ANOVA of the three generation types
Generation
type
Effect
d.f.
MS
F
Alpha
Side
Individual
FA
Error
21
1260
1260
2562
0.0004397343
0.0000550853
0.0000110531
0.0000029596
39.78
4.98
3.73
Beta
Side
Individual
FA
Error
21
2604
2604
5250
0.0009794790
0.0000774579
0.0000086972
0.0000019831
112.62
8.91
4.39
Gamma
Side
Individual
FA
Error
21
2184
2184
4410
0.0007724647
0.0000902981
0.0000115827
0.0000022475
66.69
7.80
5.15
landmark variation. A second way to visualize the
similarities and discrepancies in patterns of canalization and FA among generation types was based on
PCA of among-individual and FA covariance matrices.
Corresponding eigenvectors were compared to find
which components of variation are shared among
groups. Correlations between vectors were assessed
by computing the angle (q) between them. The significance of the angles was assessed with a bootstrap
procedure: if the confidence intervals of a given angle
surpassed the value of 90 (perpendicularity) the
vectors were considered not related.
The Procrustes ANOVA showed that all effects in all
generation types were highly significant (Table 1).
Levene’s test comparing centroid size (CS) among
generation types was not significant (P = 0.055). Multivariate regression of both symmetric and asymmetric components of shape variation on log-transformed
CS was performed to detect any allometric effect and
was tested with a permutation test (10 000 permutations). Size significantly affected the symmetric component of shape (all P < 0.001), where shape variation
attributable to size variation accounted for 4% for a,
16% for b, and 10.7% for g generation types. Regression for the asymmetric component of shape variation
was significant for b (P = 0.001) and g (P = 0.007) but
not for a (P = 0.42). The variation explained was less
than 1.5% in all generation types. All analyses were
conducted on both uncorrected data after Procrustes
superimposition and on data after correcting for size
by means of multivariate regression. Reported results
refer to non-corrected data because no substantial
differences were found, but eventual discrepancies
are described in the text. All the analyses were performed by using MorphoJ (Klingenberg, 2011) and R
(R Development Core Team, 2009); the R code used is
available upon request.
RESULTS
There was no indication of an association between
the level of FA and that of among-individual variation for the three generation types (Fig. 1). After
Bonferonni correction the g generation displayed
higher levels of among-individual variation than
both a (F2184,1260 = 1.71, P < 0.0001) and b (F2184,2604 =
1.17, P < 0.001) with the latter being more variable
than a (F2604,1260 = 1.465, P < 0.001). Pairwise comparisons of FA showed that both a and g had higher
levels of FA than b (F1260,2604 = 1.27, P < 0.001 for a;
F2184,2604 = 1.39, P < 0.001 for g) while no difference
was found between a and g (F2184,1260 = 1.09, P = 0.09).
Matrix correlations of the variance–covariance matrices of among-individual variation and FA were not
significant for any generation type. Neither a nor g or
b showed a significant correlation (-0.05, -0.11, and
0.06 for a, b, and g, respectively) (Fig. 2). Thus, patterns of within- and among-individual variation were
not correlated. In the principal coordinate analysis,
the first axis (PCO1) contained 83 and 93% of variation for the Euclidian and Mitteroecker distances,
respectively. This axis clearly separated between the
FA and the among-individual variance–covariance
matrices (Fig. 3). The second axis (PCO2) showed a
separation between a and b + g for the variance–
covariance matrices of the among-individual variation but not any difference in those of FA (Fig. 3). The
differences among generation types depicted by the
PCO were further explored by means of matrix correlation and by calculating the angles between corresponding principal components. As expected from
the PCO (Fig. 3), patterns of the variance–covariance
matrices of FA were very similar in the three generation types. All the matrix correlations were statistically significant (P < 0.001) and high (r = 0.78
between a and b; r = 0.75 between a and g; r = 0.80
between b and g). Moreover, the first principal
components associated with variance–covariance
matrices of FA were positively correlated among generation types in all comparisons (95% confidence
intervals from resampled distribution gave the
following results: 16 < q < 75 between a and b;
18 < q < 78 between a and g; 16 < q < 76 for b and g).
Variance–covariance matrices of among-individual
variation were correlated among generation types
(P < 0.001), but comparisons involving a displayed
some discrepancies with the other two generation
types (r = 0.61 between a and b, r = 0.59 between a
and g, and r = 0.93 between b and g). The first principal components were only significantly correlated
between b and g (95% confidence intervals from resampled distribution: 15 < q < 128 between a and b,
16 < q < 129 between a and g, and 11 < q < 23 between
b and g). Thus, these analyses statistically confirm
© 2011 The Linnean Society of London, Biological Journal of the Linnean Society, 2011, 104, 207–216
DEVELOPMENTAL BUFFERING IN M. NATALENSIS
211
8.5e−06
α
7.5e−06
8.0e−06
FA
9.0e−06
9.5e−06
γ
7.0e−06
β
5e−05
6e−05
7e−05
8e−05
Individual Variation
Figure 1. Relationship between FA and among-individual variation in the three generation types. Values are computed
as the trace of the relative variance–covariance matrix.
Figure 2. Graphical representation of matrix correlations between FA and among-individual variation for the three
generation types (squares represent a, circles b, and triangles g).
the pattern depicted by the PCO (Fig. 3), that the
variance–covariance matrices of within-individual
variation (i.e. FA) are very similar for the three generation types, while those for the among-individual
variation differ from those in FA. In addition, for
the variance–covariance matrices of the amongindividual variation, the a generation shows a different pattern compared with the other two. Shape
changes depicted by PCA are shown in the Appendix,
Figs A1 and A2.
© 2011 The Linnean Society of London, Biological Journal of the Linnean Society, 2011, 104, 207–216
212
M. BRENO ET AL.
Mitteroecker's
−2
0
PCO2
0.0
ALPHA
BETA
GAMMA
FA
SHAPE
−4
−0.4
−0.2
PCO2
0.2
2
0.4
4
Correlation
−0.4
−0.2
0.0
0.2
0.4
PCO1
−4
−2
0
2
4
PCO1
Figure 3. Principal coordinate analysis on both the Euclidian and Mitteroecker’s distance between variance–covariance
matrices of asymmetry (filled symbols) and among-individual variation (open symbols) in the three generation types
(squares represent a, circles b, and triangles g).
DISCUSSION
To study the association between FA and trait canalization across environments, data should be derived
from several environments that are able to alter FA
and /or canalization Hoffmann & Woods (2001). The
three generation types experienced different environmental conditions affecting post-natal ontogeny and
life-history traits. Although we tested for a common
basis of canalization and DS by comparing amongindividual variation and asymmetry across these
three generation types, and by correlating variance–
covariance matrices, we found no such congruence
between canalization and DS. FA and phenotypic
variation varied across generation types, but not in a
consistent fashion. Nevertheless, as there are only
three generation types, these comparisons were not
very sensitive to detecting associations. The absence
of a common mechanism, however, was further confirmed by the lack of significant correlations between
the variance–covariance matrices corresponding to
FA and among-individual variation within each generation type. Thus, the structure of the variance–
covariance matrix of FA and among-individual
variation was different (Fig. 3). Finally, a difference
in ontogeny affected the structure of the variance–
covariance matrices of the among-individual variation, but not of FA. Debat et al. (2009) reported a
similar result, and showed that the effect of environmental factors (temperature) affected amongindividual variation but not FA in D. melanogaster.
Our results strongly contrast with the idea that
canalization and DS share a common mechanism.
Molecular research conducted on heat shock proteins
(HSPs) supports this view, proposing the existence of
multiple mechanisms. Experimental evidence corroborating this idea has been reported mainly for
Drosophila sp. For example, Vishala & Singh (2008a,
b) found that the effect of mutations and temperature
on FA in Drosophila ananasse was sex- and traitspecific. Debat et al. (2006) used different approaches
to alter Hsp90 in D. melanogaster; they found a mix of
positive and negative results concluding that Hsp90
is one of the many players involved in developmental
buffering of wing shape in D. melanogaster. Takahashi et al. (2010) investigated the role of nine genes
belonging to the HSP family on phenotypic variation
in several qualitative and quantitative morphological
traits in D. melanogaster. Of nine genes, four of them
were shown to be involved in developmental buffering
of specific traits; moreover, correlations between FA
and among-individual variation were low and not
significant, except for wing shape in males. Overall,
these results suggest the existence of different
molecular mechanisms acting on different traits and
influenced by different stresses.
© 2011 The Linnean Society of London, Biological Journal of the Linnean Society, 2011, 104, 207–216
DEVELOPMENTAL BUFFERING IN M. NATALENSIS
The importance of developmental buffering to evolutionary biology resides in its ability to reveal, if
altered, cryptic genetic variation on which natural
selection could eventually act upon. An important
question to address is how often this occurs and
which kinds of stress are able to alter the expression
of such variation in natural populations (Braendle &
Flatt, 2006). Although the available information did
not allow for any distinction among different components of variance, our results demonstrate that a
recurrent environmental factor such as the amount
and distribution of rain is able to change levels and
patterns of expressed among-individual variation,
while DS remained largely unaffected.
ACKNOWLEDGEMENTS
Fieldwork was carried out by different Tanzanian and
visiting staff at the SUA Pest Management Center,
Sokoine University of Agriculture, Morogoro, Tanzania. Financial support was provided by the Belgian
Directorate-General for Development Cooperation
and the Flemish Interuniversity Council-University
Development Cooperation. Chantal Bogaerts and
Wim Wendelen cleaned all the skull material for this
work. M. Breno holds a PhD Fellowship from the
Research Foundation – Flanders (FWO). We thank
Philipp Mitteroecker for insightful suggestions about
the metric he proposed.
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APPENDIX
The symmetric variation in a was more evenly distributed between the first two PCs, as explained by
the amount of variation contained in each component.
Shape change associated with PC1 involved mainly
length and width of the parietal region in a, and
width of the cranial region and length of nasals in b
and g. This pattern is very similar to the one depicted
by PC2 in a, although correlation between this component and PC1 in b and g were not significant.
Landmark displacement accounted for by PC2 in b
and g showed principally a shift in parietal and
frontal region. PC3 involved mainly changes in the
cranial region of all three generation types, but with
no similarities among the three groups. A shift in
landmarks of the facial region was also displayed in b.
Looking at the variance retained by each component it appears that variation of the asymmetric
component was more evenly spread among components. PC1 was similar across the three generation
types, involving shifts in the parietal, frontal, and
nasal region. PC2 and PC3, although not correlated
among generation types, involved principally landmark displacements in the cranial region.
© 2011 The Linnean Society of London, Biological Journal of the Linnean Society, 2011, 104, 207–216
Figure A1. Shape changes associated with the three first principal components depicted as landmark displacement for among-individual variation. Variation
explained by each component respectively was: 20, 19, and 13% for a, 38, 10, and 9% for b, and 42, 11 and 8% for g.
DEVELOPMENTAL BUFFERING IN M. NATALENSIS
© 2011 The Linnean Society of London, Biological Journal of the Linnean Society, 2011, 104, 207–216
215
Figure A2. Shape changes associated with the three first principal components depicted as landmark displacement for FA. Variation explained by each
component respectively was: 26, 16, and 14% for a, 23, 14, and 11% for b, and 25, 19, and 9% for g.
216
M. BRENO ET AL.
© 2011 The Linnean Society of London, Biological Journal of the Linnean Society, 2011, 104, 207–216