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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, 207–216 207 208 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. 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Variation: a central concept in biology. New York: Elsevier Academic Press, 191–218. Willmore K, Klingenberg CP, Hallgrimsson B. 2005. The relationship between fluctuating asymmetry and environmental variance in rhesus macaque skulls. Evolution 59: 898–909. Willmore K, Young NM, Richtsmeier JT. 2007. Phenotypic variability: its components, measurement and underlying developmental processes. Evolutionary Biology 34: 99–120. 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