bs_bs_banner
Biological Journal of the Linnean Society, 2013, 109, 434–440. With 2 figures
Relationship between canalization and developmental
stability of foetal rabbit limbs in two reproductive
toxicological experiments
MATTEO BRENO*, JESSICA BOTS and STEFAN VAN DONGEN
Evolutionary Ecology Group, Department of Biology, University of Antwerp, Groenenborgerlaan 171,
B-2020 Antwerp, Belgium
Received 19 November 2012; revised 10 January 2013; accepted for publication 10 January 2013
The mechanisms of developmental buffering and its relevance to the evolutionary process have recently attracted
a lot of attention in both developmental and evolutionary biology. Among other things, whether the two components
of developmental buffering [i.e. canalization and developmental stability (DS)] have a common basis has long been
the subject of debate. In the present study, we examine the association between fluctuating asymmetry (i.e. the
directionally random asymmetry of bilateral structures), a measure of DS, and between-individual variation of long
bones in over 1000 rabbit foetuses. The lack of correlations between fluctuating asymmetry and between-individual
variation at the individual, litter and treatment level, in combination with the absence of correspondence among
covariance matrices, supports distinct developmental mechanisms for DS and canalization. We discuss our results
in the context of recent insights into the mechanisms of developmental buffering. © 2013 The Linnean Society of
London, Biological Journal of the Linnean Society, 2013, 109, 434–440.
ADDITIONAL KEYWORDS: fluctuating asymmetry – limb development.
INTRODUCTION
Developmental buffering or homeostasis is the ability
to maintain a consistent phenotypic expression under
perturbations of various origins (Debat & David,
2001). It is an important factor in evolutionary
biology because it can restrict the variation upon
which natural selection acts (Gibson & Dworkin,
2004; Flatt, 2005) and influences the adaptive accuracy of a trait (Hansen, Carter & Pelabon, 2006). The
two main components of developmental buffering
[canalization and developmental stability (DS)] are
generally described to buffer variation of different
origins and are identified by the patterns of variation
that they represent (Willmore, Young & Richtsmeier,
2007). Canalization refers to the ability to modulate
the amount of phenotypic variation in the presence of
environmental or genetic perturbations, typically by
reducing between-individual variation. The process of
DS buffers against random perturbations arising
*Corresponding author. E-mail: [email protected]
434
during development (i.e. developmental noise), hence
reducing within-individual variation. Canalization is
thus inferred by comparing levels and patterns of
between-individual variation and DS by fluctuating
asymmetry (FA) (i.e. random deviations from symmetry in symmetrical structures). A central research
question is the relationship between canalization and
DS (Hallgrimsson, Willmore & Hall, 2002; Willmore,
Klingenberg & Hallgrimsson, 2005). Although canalization and DS represent different patterns of phenotypic variation (i.e. between and within individual
variation resp.), it is not clear whether they share
common developmental mechanisms. The covariation
between the two components of developmental buffering has been investigated in many species with
different approaches, although it has yielded heterogeneous results (Willmore et al., 2005, 2007; Breno,
Leirs & Van Dongen, 2011).
Generally, the link between DS and canalization
is investigated by comparing amounts and patterns
of FA and between-individual variation at various
levels. Studies in insects often reveal associations between canalization and DS (Clarke, 1998;
© 2013 The Linnean Society of London, Biological Journal of the Linnean Society, 2013, 109, 434–440
CANALIZATION AND DS IN FOETAL LIMB
Klingenberg & McIntyre, 1998; Klingenberg et al.,
2001; Reale & Roff, 2003; Dworkin, 2005; Santos,
Iriarte & Céspedes, 2005; Breuker, Patterson &
Klingenberg, 2006; Debat et al., 2006, 2008; Debat,
Debelle & Dworkin, 2009; Pélabon et al., 2010),
whereas those in mammals are rather mixed (Debat
et al., 2000; Hallgrimsson et al., 2002; Klingenberg,
Mebus & Auffray, 2003; Willmore et al., 2005; Breno
et al., 2011). Recent overviews of this literature are
provided by Willmore et al. (2005) and Breno, Leirs &
Van Dongen (2011).
Because environmental conditions may affect both
DS and canalization, their covariation should be
assessed across multiple environments that are able
to alter both (Hoffman & Woods, 2001). However,
this approach is used less often and has also showed
mixed results (Hoffman & Woods, 2001; Debat et al.,
2006, 2009; Breno et al., 2011; Jojić, Blagojević &
Vujošević, 2011). The present study aimed to investigate the relationship between canalization and DS
in long bones from a sample of the New Zealand
white rabbit (Oryctolagus cuniculus) foetuses bred
under different levels of stress. We obtained data
from two toxicological experiments aiming to assess
the effect of two compounds on foetal toxicity.
Because an increase in trait variation is expected in
the presence of environmental stress (Willmore
et al., 2007), we first investigated the effect of treatment on DS and canalization. In particular, if DS
and canalization share a common mechanism, an
increase in between-individual variation is expected
for those traits that display the sharpest increase in
FA. Therefore, we specifically addressed the relationship between FA and between-individual variation by correlating them across individuals and
litters, within and across treatments. Thus, we
aimed to assess not only the relationship between
DS and canalization, but also the effect of environmental stress on such relationship. This last aspect
is further investigated by comparing covariance
matrices for FA and individual variation for each
experiment and treatment.
MATERIAL AND METHODS
The animals were housed and maintained under
Belgian regulations for animal health. The work was
approved by the ethical committee for animal experimentation of Janssen Pharmaceutica NV in accordance with Belgian law.
We obtained data from two toxicological experiments aimed at assessing the effect of two compounds
(of which we cannot disclose the names as a result
of company policy) on foetal toxicity. Pregnant
females originated from a large outbreed population.
435
Compound A (experiment 1 below) is an antihyperglycaemic agent for the treatment of adults with
diabetes type II. Its development has been stopped
because of the lack of therapeutic effects in clinical
trials and the compound is no longer produced. Compound B (experiment 2 below) is used for the prevention and treatment of coccidiosis in broiler chickens
and growing turkeys. This anti-protozoal agent for
veterinary use works locally in the gastrointestinal
tract and has very limited systemic exposure. Each
experiment comprises three treatments (100, 500,
and 1500 mg kg-1 for the first experiment and 80, 320,
1280 mg kg-1 for the second one) and one control
(0 mg kg-1 for both experiments; vehicle). A complete
list of toxicological and teratological records regarding
mother and litter status will be provided elsewhere
(J. Bots, M. Breno, L. De Schaepdrijver & S. Van
Dongen, unpubl. data; M. Breno, J. Bots, L. De
Schaepdrijver & S. Van Dongen, unpubl. data). Doses
were chosen such that the low dose is not toxic (i.e. in
the pharmacological range), whereas, in the mediumand high-dose groups, toxicity is expected. Treatments were administrated daily during the period of
organogenesis and limb development from day 6 up to
day 18 of pregnancy. The females were killed on day
28 of pregnancy and a necropsy was performed. Foetuses were weighed and examined for external, visceral, and skeletal abnormalities. Compound A
resulted in maternal toxicity in the groups receiving
500 and 1500 mg kg-1, as indicated by a decrease in
body weight and food consumption (details not
shown). Related to the maternal toxicity, lower foetal
weights were recorded at term, as well as the incomplete ossification of several axial bones at skeletal
examination. No major developmental abnormalities
emerged. Compound B directly affected the development of the foetuses, with an increase in number of
malformed foetuses in the high-dose group receiving
1280 mg kg-1 (details not shown). The most common
developmental abnormalities involved nasal and
frontal bones for the skull, reduced ossification of
axial elements, the presence of a rudimentary or
complete 13th rib, fused or rudimentary sternum, and
reduced ossification of the pubis (M. Breno, J. Bots,
L. De Schaepdrijver & S. Van Dongen, unpubl. data).
None of the compounds directly affected limb development (details not shown), except for a reduced
ossification of the tarsal bone in a few cases (approximately 3%). This work had been approved by the
ethical committee for animal experimentation of
Janssen Pharmaceutica NV.
Foetuses were cleared and processed for bone staining with alizarine red. Digital images of limbs were
taken in a standardized manner (Nikon D300s
camera with a Sigma 105 mm macro lens), placing
the bone parallel to the camera lens. Each individual
© 2013 The Linnean Society of London, Biological Journal of the Linnean Society, 2013, 109, 434–440
436
M. BRENO ET AL.
Figure 1. Image depicting the fore- and hindlimbs of
rabbit foetuses (dorsal view). Black bars represent the
measured traits.
was photographed twice after independent positioning, and each image was measured once or twice
using IMAGEJ (Rasband, 1997–2011) (repeated
measures of the same picture were taken for approximately 40% of the specimens). Six traits were studied
for FA: humerus, ulna, radius, femur, tibia, and fibula
linear lengths (diaphysis) (Fig. 1). In total, 1126 foetuses from 133 litters were digitized. Two operators
(MB and JB) measured half of each dataset, and
a preliminary analysis showed no difference as a
result of operator (details not shown). As a result of
damaged specimens, the final sample size ranged
from 1018 for humerus to 1097 for radius.
Unbiased FA estimates and trait size, corrected for
measurement error and directional asymmetry, were
extracted from a linear mixed regression model (Van
Dongen, Molenberghs & Matthysen, 1999). To obtain
measures of between-individual variation, trait size
was modelled with treatment (four treatment groups:
control, low, medium, and high) nested within experiment (two experiments: A and B) as fixed factors and
litter as random effect. Absolute values of the residuals from this linear model were used as input for the
canalization analysis (i.e. corrected size reflecting
between-individual variation). Differences in canalization were tested using Levene’s test by submitting
the absolute value of these residuals to a nested
analysis of variance (ANOVA) (treatment nested
within experiment) with litter as random intercept.
The same analysis was performed on the unsigned FA
to test for and compare the effect of treatment on FA
and canalization.
The relationship between canalization and DS was
investigated in several ways. First, correlations
between unsigned individual FA and absolute residuals were computed for each trait for the whole dataset.
Second, correlations at the litter level were computed
correcting for experiment and treatment effect. In this
analysis, the absolute distance between average litter
size and average treatment size was correlated with
the average litter FA computed on residuals from a
linear model where individual FAs were regressed on
experiment and treatment. Finally, correlations
between Levene statistics computed for each combination of experiment and treatment and average FA for
the same groups were computed. Spearman’s rank
correlations were preferred over Pearson’s productmoment coefficients because our analyses deal with
absolute values. Next, sixteen variance–covariance
matrices were computed for both FA and canalization
corresponding to each of the experiment/treatment
groups. The method of random skewers (Cheverud &
Marroig, 2007) was used to estimate the similarities
among the covariance matrices. In this approach, a
random selection vector of length one, generated from
a uniform distribution, was applied to both matrices.
The resulting vectors are then compared by measuring
the correlation (cosine) among them. The average
correlation in response to 1000 random vectors was
used to measure the similarity between two matrices.
The significance of these correlations was assessed by
the distribution of 10 000 correlations among random
vectors of six elements. The idea behind random
skewers methods is that, if two matrices are similar,
the average response to selection is expected to be very
close (i.e. the correlation between the two resulting
vectors is one if the two matrices are equal). On the
other hand, when two matrices are totally unrelated,
the correlation between the resulting vectors is
expected to be zero (Cheverud & Marroig, 2007).
Finally a principal coordinate analysis, also known as
multidimensional scaling, was applied to the pairwise
distances among the sixteen matrices of the previous
analysis. In this way, we constructed a graphical
depiction of the relationships/similarities among these
covariance matrices. The distance between two matrices was computed as the square root of the summed
squared logarithms of the relative eigenvalues. This
metric (Mitteroecker & Bookstein, 2008) represents
the shortest path between two matrices in the proper
space (Breno et al., 2011; Gonzalez, Hallgrimsson &
Oyhenart, 2011). No relationship between trait size
and FA was found, whereas analysis with residuals
corrected for trait size yielded the same results. For
this reason, we reported the results for sizeuncorrected analyses.
All analyses were performed in R (R Development
Core Team, 2011).
RESULTS
Table 1 shows the results of nested mixed ANOVA
on residuals and FAs. The effect of experiment and
treatment on variability was generally weak, with a
nonsignificant reduction of phenotypic variation in
© 2013 The Linnean Society of London, Biological Journal of the Linnean Society, 2013, 109, 434–440
437
CANALIZATION AND DS IN FOETAL LIMB
Table 1. Significance tests for the nested Levene’s test on between-individual variation in trait size and for the analysis
of variance on unsigned fluctuating asymmetry for each trait
Between-individual variation
Within-individual variation (FA)
Trait
Experiment
Treatment
Experiment
Treatment
Humerus
Ulna
Radius
Femur
Tibia
Fibula
F1,124 = 0.003
F1,125 = 0.88
F1,125 = 0.24
F1,125 = 0.04
F1,124 = 0.16
F1,124 = 0.06
F6,124 = 1.5
F6,125 = 1.47
F6,125 = 1.67
F6,125 = 1.14
F6,124 = 0.72
F6,124 = 1.32
F1,124 = 11.25
F1,125 = 21.4
F1,125 = 0.85
F1,125 = 10.46
F1,124 = 0.12
F1,124 = 0.11
F6,124 = 0.94
F6,125 = 0.82
F6,125 = 2
F6,125 = 1.77
F6,124 = 0.36
F6,124 = 2.8
Values shown in bold are significant at P < 0.05.
0.01
0.03
0.05
0.06
-0.02
-0.01
0.02
0.04
-0.17
-0.30
-0.16
-0.07
-0.19
0.12
0.12
0.31
-0.31
0.31
2
Humerus
Ulna
Radius
Femur
Tibia
Fibula
0
Treatment
PCO2
Litter
Values shown in bold are significant at P < 0.05.
−2
Individual
the high treatment of the second experiment (details
not shown). The same analysis on within-individual
variation (FA scores) revealed trait heterogeneity in
response to experiment and treatment (Table 1). This
heterogeneity appeared to be mainly a difference in
response between hind- and forelimbs, where FA was
higher in all treated groups of compound B but only
in the hind limbs (details not shown; M. Breno,
J. Bots, L. De Schaepdrijver & S. Van Dongen,
unpubl. data).
Table 2 shows the correlation coefficients at various
levels. Correlations between unsigned FA and
between-individual variation (i.e. absolute values of
residuals of individual trait size corrected for experiment and treatment effects) and between average FA
and Levene’s statistics at the litter level were all low
and not significant, except for femur. Furthermore,
correlations between FA and Levene statistics across
treatment were all not significant, yet statistical power
was low because correlations were based on eight data
points only. Nevertheless, no consistent positive or
negative correlation coefficients were found (Table 2).
The results of the random skewer analysis are
shown in Tables 3 and 4. The distribution of correlations among random vectors showed that coefficients
−4
Trait
4
Table 2. Correlation coefficients between fluctuating
asymmetry and phenotypic variation for each trait at
individual, litter and treatment level
control
low
medium
high
−4
exp1
exp2
CAN
FA
−2
0
2
4
PCO1
Figure 2. Principal coordinate analysis (PCO) on distances between covariance matrices of developmental
stability (measured by fluctuating asymmetry; FA) and
canalization (CAN). Open and filled symbol represent patterns of canalization and FA respectively. Experiments 1
and 2 are distinguished by black and grey symbols, respectively. The four treatments are: circle, control; square, low
dose; triangle, medium dose; diamond, high dose.
higher than 0.76 and 0.88 were significant at P < 0.05
and P < 0.01, respectively. All the covariance matrices
for canalization were highly similar, displaying correlations > 0.9. Covariance matrices for signed FA were
similar as well (except for one), although the correlations among them were lower than those for canalization (all but one > 0.80) (Table 3). Finally, none of
the comparisons between canalization and FA matrices showed significant similarity (Table 4).
The principal coordinate analysis yielded a highly
comparable result (Fig. 2). The first axis (95% of
© 2013 The Linnean Society of London, Biological Journal of the Linnean Society, 2013, 109, 434–440
438
M. BRENO ET AL.
Table 3. Random skewers analysis across covariance matrices associated with phenotypic variation (below the diagonal)
and fluctuating asymmetry (above the diagonal)
Control 1
Low 1
Medium 1
High 1
Control 2
Low 2
Medium 2
High 2
Control 1
Low 1
Medium 1
High 1
Control 2
Low 2
Medium 2
High 2
–
0.99
0.98
0.98
0.99
0.97
0.99
0.94
0.95
–
0.98
0.99
0.99
0.98
0.99
0.93
0.93
0.96
–
0.97
0.97
0.97
0.98
0.92
0.93
0.94
0.93
–
0.98
0.97
0.99
0.95
0.83
0.89
0.90
0.91
–
0.98
0.99
0.91
0.74
0.80
0.84
0.87
0.94
–
0.98
0.91
0.88
0.93
0.90
0.93
0.88
0.83
–
0.94
0.91
0.94
0.94
0.95
0.92
0.84
0.93
–
Values shown in bold are significant at P < 0.05. 1, experiment 1; 2, experiment 2.
Table 4. Random skewers analysis between phenotypic variation and fluctuating asymmetry
Control 1
Low 1
Medium 1
High 1
Control 2
Low 2
Medium 2
High 2
Control 1
Low 1
Medium 1
High 1
Control 2
Low 2
Medium 2
High 2
0.52
0.54
0.53
0.49
0.54
0.44
0.55
0.53
0.49
0.51
0.52
0.48
0.53
0.45
0.55
0.50
0.49
0.50
0.50
0.45
0.49
0.40
0.53
0.50
0.53
0.55
0.57
0.51
0.54
0.45
0.58
0.54
0.48
0.51
0.50
0.46
0.50
0.42
0.52
0.49
0.48
0.50
0.49
0.43
0.48
0.39
0.50
0.46
0.51
0.55
0.54
0.48
0.53
0.44
0.55
0.52
0.64
0.70
0.72
0.66
0.71
0.62
0.72
0.69
1, experiment 1; 2, experiment 2.
variation) showed a clear separation between
the covariance matrixes for FA and canalization.
Although the covariance matrices for canalization
clustered together tightly (with a small exception for
high treatment of the second experiment), the FA
matrices tended to scatter along the second axis,
which, however, contained a small amount of variation only (1%) (Fig. 2).
DISCUSSION
Investigating the link between developmental stability
and canalization of rabbit foetal limbs yielded generally low and insignificant correlations between FA and
individual variation at different levels. Not only the
magnitude, but also the covariance matrices reflecting
either canalization or developmental stability showed
no correspondence. Because only two other studies
investigated the relationship between DS and canalization in mammalian limbs (Hallgrimsson et al., 2002,
2003), drawing general conclusions is premature, especially considering the different methods used.
In general, comparing patterns of variation may
help to determine the mechanism involved in developmental buffering, in the sense that congruence
between levels/patterns of FA and phenotypic variance leads to the conclusion of a common developmental basis (i.e. the robustness of the developmental
system itself; Willmore et al., 2005; Breuker et al.,
2006). A lack of such congruence implies distinct
mechanisms and suggests, at least theoretically, that
FA and phenotypic variance may evolve separately
(Pélabon et al., 2004).
Studies targeting specific molecular pathways have
yielded a mix of results, often showing trait-specific
effects. A common approach is to assess different
patterns of variation after the alteration of the heat
shock protein 90 (Hsp90), which is considered to
represent a specific mechanism for canalization
(Rutherford & Lindquist, 1998; Rutherford, 2000) An
increase in phenotypic variation but not in FA was
shown by Milton et al. (2003). Elevated FA and individual variation was observed after introgression of a
mutated Hsp83 but not after pharmacological inhibition of hsp90 or two hsp83 mutations (Debat et al.,
2006). Takahashi et al. (2010) found that four out of
nine genes for Hsp played a role in developmental
buffering with no significant correlations between FA
and between-individual variation (except for one trait
in males). Takahashi et al. (2011a) found that
© 2013 The Linnean Society of London, Biological Journal of the Linnean Society, 2013, 109, 434–440
CANALIZATION AND DS IN FOETAL LIMB
Hsp70Ba may play a trait-specific role in canalization
but not in DS in bristle number and wing size in
females.
Although mechanisms behind developmental buffering are still poorly understood, recent findings
may provide new insights into the genetic control
of buffering components. Takahashi et al. (2011b)
identified over 90 genomic regions affecting FA in
Drosphila melanogaster, suggesting the possibility for
existence of trait-specific and trait-aspecific mechanisms. Debat et al. (2011) found that overexpression
of the gene Cyclin G (Cyc G) in D. melanogaster led to
a 40-fold increase in FA, showing that Cyc G is a good
candidate for the genetic control of DS. Although the
results from quantitative genetics analyses have
reported some genetic control for the environmental
components of phenotypic variation (Dworkin, 2005;
Hill & Mulder, 2010), heritability estimates for FA are
generally low and epistasis is assumed to play a
major role in controlling FA (Leamy & Klingenberg,
2005; Pélabon et al., 2010; but see also Carter &
Houle, 2011). Thus, in conclusion, in combination
with the evidence reviewed above showing that, at
least to some extent, DS and canalization have distinct molecular and genetic backgrounds, the lack of
congruence between FA and between-individual variation in foetal rabbit limb bones adds to the growing
evidence indicating that the two components of developmental homeostasis can be expected to evolve in
different directions.
ACKNOWLEDGEMENTS
This work benefited from a research grant from the
University of Antwerp (BOF-KP 4382). MB holds a
PhD Fellowship from the Research Foundation –
Flanders (FWO). JB is a postdoctoral fellow with the
Research Foundation – Flanders (FWO). We thank
Steven Gangestad for providing us with theoretical
considerations. Luc De Bruyn provided valuable
advice on taking photographs of the rabbit foetuses.
We are indebted to Peter Delille and Luc De Schaepdrijver for providing us access to their samples, data,
and facilities, as well as for technical help. We thank
the editor and the reviewers for their comments on an
early version of the manuscript.
REFERENCES
Breno M, Leirs H, Van Dongen S. 2011. No relationship
between canalization and developmental stability of
the skull in a natural population of Mastomys natalensis
(Rodentia: Muridae). Biological Journal of The Linnean
Society 104: 207–216.
Breuker CJ, Patterson JS, Klingenberg CP. 2006. A
439
single basis for developmental buffering of Drosophila wing
shape. PLoS ONE 1: e7.
Carter AJR, Houle D. 2011. Artificial selection reveals heritable variation for developmental instability. Evolution 65:
3558–3564.
Cheverud JM, Marroig G. 2007. Comparing covariance
matrices: random skewers method compared to the common
principal components model. Genetics and Molecular
Biology 30: 461–469.
Clarke G. 1998. The genetic basis of developmental stability.
V. Inter- and intra-individual character variation. Heredity
80: 562–567.
Debat V, Alibert P, David P, Paradis E, Auffray JC. 2000.
Independence between developmental stability and canalization in the skull of the house mouse. Proceedings of the
Royal Society of London Series B, Biological Sciences 267:
423–430.
Debat V, Bloyer S, Faradji F, Gidaszewski N, Navarro
N, Orozco-Terwengel P, Ribeiro V, Schlötterer C,
Deutsch JS, Peronnet F. 2011. Developmental stability: a
major role for cyclin G in Drosophila melanogaster. PLoS
Genetics 7: e1002314.
Debat V, Cornette R, Korol AB, Nevo E, Soulet D, David
JR. 2008. Multidimensional analysis of Drosophila wing
variation in Evolution Canyon. Journal of Genetics 87:
407–419.
Debat V, David P. 2001. Mapping phenotypes: canalization,
plasticity and developmental stability. Trends in Ecology &
Evolution 16: 555–561.
Debat V, Debelle A, Dworkin I. 2009. Plasticity, canalization, and developmental stability of the drosophila wing:
joint effects of mutations and developmental temperature.
Evolution 63: 2864–2876.
Debat V, Milton CC, Rutherford S, Klingenberg CP,
Hoffmann AA. 2006. Hsp90 and the quantitative variation
of wing shape in Drosophila melanogaster. Evolution 60:
2529–2538.
Dworkin I. 2005. A study of canalization and developmental
stability in the sternopleural bristle system of Drosophila
melanogaster. Evolution 59: 1500–1509.
Flatt T. 2005. The evolutionary genetics of canalization.
Quarterly Review of Biology 80: 287–316.
Gibson G, Dworkin I. 2004. Uncovering cryptic genetic
variation. Nature Reviews Genetics 5: 681–690.
Gonzalez PN, Hallgrimsson B, Oyhenart EE. 2011. Developmental plasticity in covariance structure of the skull:
effects of prenatal stress. Journal of Anatomy 218: 243–257.
Hallgrimsson B, Miyake T, Willmore KE, Hall BK. 2003.
Embryological origins of developmental stability: size, shape
and fluctuating asymmetry in prenatal random bred mice.
Journal of Experimental Zoology Part B 296: 40–57.
Hallgrimsson B, Willmore KE, Hall BK. 2002. Canalization, developmental stability, and morphological integration
in primate limbs. American Journal of Physical Anthropology 35: 131–158.
Hansen TF, Carter AJR, Pelabon C. 2006. On adaptive
accuracy and precision in natural populations. American
Naturalist 168: 168–181.
© 2013 The Linnean Society of London, Biological Journal of the Linnean Society, 2013, 109, 434–440
440
M. BRENO ET AL.
Hill WG, Mulder HA. 2010. Genetic analysis of environmental variation. Genetical Research 92: 381–395.
Hoffmann A, Woods R. 2001. Trait variability and stress:
canalization, developmental stability and the need for a
broad approach. Ecology Letters 4: 97–101.
Jojić V, Blagojević J, Vujošević M. 2011. B chromosomes
and cranial variability in yellow-necked field mice (Apodemus flavicollis). Journal of Mammalogy 92: 396–406.
Klingenberg CP, Badyaev AV, Sowry SM, Beckwith NJ.
2001. Inferring developmental modularity from morphological integration: analysis of individual variation and asymmetry in bumblebee wings. American Naturalist 157: 11–23.
Klingenberg CP, McIntyre G. 1998. Geometric morphometrics of developmental instability: analyzing patterns of fluctuating asymmetry with procrustes methods. Evolution 52:
1363–1375.
Klingenberg CP, Mebus K, Auffray J. 2003. Developmental integration in a complex morphological structure: how
distinct are the modules in the mouse mandible? Evolution
and Development 5: 522–531.
Leamy LJ, Klingenberg CP. 2005. The genetics and evolution of fluctuating asymmetry. Annual Review of Ecology,
Evolution, and Systematics 36: 1–21.
Milton CC, Huynh B, Batterham P, Rutherford SL, Hoffmann AA. 2003. Quantitative trait symmetry independent
of Hsp90 buffering: distinct modes of genetic canalization
and developmental stability. Proceedings of the National
Academy of Sciences of the United States of America 100:
13396–13401.
Mitteroecker P, Bookstein FL. 2008. The ontogenetic trajectory of the phenotypic covariance matrix, with examples
from craniofacial shape in rats and humans. Evolution 63:
727–757.
Pélabon C, Carlson ML, Hansen TF, Yoccoz NG, Armbruster WS. 2004. Consequences of inter-population
crosses on developmental stability and canalization of floral
traits in Dalechampia scandens (Euphorbiaceae). Journal of
Evolutionary Biology 17: 19–32.
Pélabon C, Hansen TF, Carter AJR, Houle D. 2010.
Evolution of variation and variability under fluctuating,
stabilizing, and disruptive selection. Evolution 64: 1912–
1925.
R Development Core Team. 2011. R: a language and environment for statistical computing. Vienna: R Foundation for
Statistical Computing. Available at: http://www.R-project.org
Rasband WS. 1997–2011. ImageJ. Bethesda, MD: US
National Institutes of Health. Available at: http://rsb.info.
nih.gov/ij/
Reale D, Roff D. 2003. Inbreeding, developmental stability,
and canalization in the sand cricket Gryllus firmus. Evolution 57: 597–605.
Rutherford S. 2000. From genotype to phenotype: buffering
mechanisms and the storage of genetic information. BioEssays 22: 1095–1105.
Rutherford S, Lindquist S. 1998. Hsp90 as a capacitor for
morphological evolution. Nature 396: 336–342.
Santos M, Iriarte P, Céspedes W. 2005. Genetics and
geometry of canalization and developmental stability in
Drosophila subobscura. BMC Evolutionary Biology 5: 7.
Takahashi KH, Daborn PJ, Hoffmann AA, TakanoShimizu T. 2011a. Environmental stress-dependent effects
of deletions encompassing Hsp70Ba on canalization and
quantitative trait asymmetry in Drosophila melanogaster.
PLoS ONE 6: e17295.
Takahashi KH, Okada Y, Teramura K, Tsujino M. 2011b.
Deficiency mapping of the genomic regions associated with
effects on developmental stability in Drosophila melanogaster. Evolution 65: 3565–3577.
Takahashi KH, Rako L, Takano-Shimizu T, Hoffmann
AA, Lee SF. 2010. Effects of small Hsp genes on developmental stability and microenvironmental canalization. BMC
Evolutionary Biology 10: 284.
Van Dongen S, Molenberghs G, Matthysen E. 1999. The
statistical analysis of fluctuating asymmetry: REML estimation of a mixed regression model. Journal of Evolutionary Biology 12: 94–102.
Willmore KE, Klingenberg CP, Hallgrimsson B. 2005.
The relationship between fluctuating asymmetry and environmental variance in rhesus macaque skulls. Evolution 59:
898–909.
Willmore KE, Young NM, Richtsmeier JT. 2007. Phenotypic variability: its components, measurement and underlying developmental processes. Evolutionary Biology 34:
99–120.
© 2013 The Linnean Society of London, Biological Journal of the Linnean Society, 2013, 109, 434–440