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The threespine stickleback (Gasterosteus aculeatus) has emerged as an important model organism in evolutionary ecology, largely due to the repeated, parallel evolution of divergent morphotypes found in populations having colonized freshwater habitats. However, morphological divergence following colonization is not a universal phenomenon. We explore this in a large-scale estuarine ecosystem inhabited by two parapatric stickleback demes, each physiologically adapted to divergent osmoregulatory environments (fresh vs. saline waters). Using geometric morphometric analyses of wild-caught individuals, we detected significant differences between demes, in addition to sexual dimorphism, in body shape. However, rearing full-sib families from each deme under controlled, reciprocal salinity conditions revealed no differences between genotypes and highly significant environmental effects. It is also noteworthy that fish from both demes were fully plated, whether found in the wild or reared under reciprocal salinity conditions. Although we found significant heritability for body shape, we also noted significant direct environmental effects for many latent shape variables. Moreover, we found little evidence for diversifying selection acting on body size and shape (QST). Nevertheless, uniform compressive variation did exceed neutral expectations, yet despite evidence of both allometry and genetic correlation with body length, we detected no correlated signatures of selection. Taken together, these results suggest that much of the morphological divergence observed in this system is the result of plastic responses to environmental variation rather than adaptive differentiation.
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The study of adaptive phenotypic variation has contributed significantly to our understanding of contemporary evolution and the rate at which natural selection can act. One area of active interest is the quantification of morphological variation across heterogeneous environments. Although studies of wild populations in relation to their natural ecology can be invaluable in understanding the role of selection in promoting biological diversity (Endler, 1986; Feder & Mitchell-Olds, 2003), interpreting patterns of variation in the wild is a challenge fraught with potential misinterpretations, not least of which is the confounding of evolutionarily neutral differences for divergence induced by natural selection. In this regard, environmental variation itself may be particularly problematic. Covariation between environmental and phenotypic gradients may reflect changing selective pressures, but local adaptation cannot be assumed: such patterns may also belie effectively neutral variation resulting from direct environmental inputs. Expression of all but the most canalized traits will be subject to some degree of direct influence due to myriad environmental effects, and environmental inputs/perturbations during developmental can have pronounced effects on phenotypic variation, often with little affect on fitness (Ghalambor et al., 2007; Georga & Koumoundouros, 2010). This is not to suggest that plasticity cannot be advantageous; indeed, it is well established that plasticity can be an adaptive response to environmental variation (Gotthard & Nylin, 1995; Ghalambor et al., 2007; Beldade et al., 2011). Moreover, developmental plasticity in particular has been hypothesized to be a significant source of evolutionary novelty (West-Eberhard, 2005; Moczek et al., 2011). Thus, dissecting phenotypic variation into its genetic and plastic components represents an endeavour not only promoting a greater understanding of the proximate mechanisms underlying morphological diversity, but one with potentially broader implications for evolutionary theory. To this end, model systems that are amenable to both experimental and observational approaches may be best-positioned to yield the greatest insights. Such common garden experiments have been used to demonstrate the interaction between genetic and environmental effects on morphology (Marcil et al., 2006; Parsons & Robinson, 2007). These studies are of considerable interest in that they reveal the plastic nature of the phenotype; however, the genetic component often remains implicit. Yet models in which formalized genetic analyses can be applied may be capable of producing even greater insights, permitting a more quantitative comparison of the relative contributions of environmental and genetic sources of variance. Moreover, without evidence for heritable variation underlying any focal trait, subsequent inference regarding its adaptive potential will remain suspect.
Heritability of form has been estimated based on a number of complex shape descriptors ranging from linear combinations of shape variables (Baumgartner, 1995), distances in multivariate shape space (Monteiro et al., 2002), scale-independent landmark coordinate vectors (Klingenberg & Leamy, 2001) and even integrative Fourier shape outlines (Currie et al., 2000). Moreover, analyses of the genetic architecture of shape have identified quantitative trait loci (QTL) for multivariate shape descriptors (Zimmerman et al., 2000; Klingenberg et al., 2001; Workman et al., 2002; Albert et al., 2008). However, although geometric morphometrics can be a powerful and efficient means of describing variation in shape, not all resultant latent variables may be appropriate for genetic analysis. This was highlighted by Berner et al. (2011), who suggest that relative warp scores are likely to yield problematic estimates arising through artificial covariance induced by PCA during the calculation of relative warps. Thus, although the central challenge towards a full understanding of whole-body-shape determinism lies in disentangling the relative role of all salient sources of variation, there may be a fundamental conflict between the most biologically/functionally meaningful representation of that variance and its mathematical tractability. The solution to this problem is not obvious, but the need to assess ‘shape’ within an evolutionary framework still remains. One practical ‘work-around’ would be to base estimates on partial warp scores – the analytical precursors of relative warps – as partial warp scores are amenable to analysis via traditional statistical tests (Zelditch et al., 2004). We reason that this should also extend to quantitative genetic analyses, which are essentially an exercise in linear mixed-effects modelling (Kruuk, 2004).
The threespine stickleback (Gasterosteus aculeatus) has emerged as a premiere model in the study of evolutionary ecology, largely due to the species’ multiple, parallel examples of adaptive morphological divergence (Bell & Foster, 1994; Schluter, 1996; McKinnon & Rundle, 2002). Marine populations are ancestral, and this body form has been highly conserved since the Miocene (Bell, 1994; Walker & Bell, 2000); however, colonization of freshwater habitats has resulted in myriad changes in body morphology (Reid & Peichel, 2010). Morphological divergence in freshwater has been shown to have a genetic basis (Schluter et al., 2004; Berner et al., 2011), although the degree of differentiation from the ancestral form appears to be dependent upon how different novel environments are from the marine milieu (Spoljaric & Reimchen, 2007). Numerous studies have used geometric morphometrics to capture the divergence among populations occupying unique habitats and trophic niches (Baumgartner, 1995; Spoljaric & Reimchen, 2007; Sharpe et al., 2008; Aguirre, 2009). Many have focused on variation in specific anatomical structures, from which adaptive significance is argued on functional grounds (Caldecutt & Adams, 1998; Kimmel et al., 2008; Arif et al., 2009), whereas others have associated whole-body-shape variation with adaptive functionality underlying differentiation (Walker, 1997; Sharpe et al., 2008; Hendry et al., 2011). However, striking morphological divergence between habitats is not ubiquitous (Berner et al., 2008; Kaeuffer et al., 2012), and exploring more subtle differences in form may be equally illuminating regarding the conditions favouring and/or impeding differentiation in body shape.
The St Lawrence River estuary represents an ideal system in which to explore the genetic and environmental components of phenotypic variation. One of the principal environmental features of the estuary is its gradation into three hydrological zones, each characterized by unique biological, physiochemical and tidal properties (Vincent & Dodson, 1999). The fluvial estuary (aka upper estuary), although tidal in nature, is a uniquely freshwater zone extending upstream approximately 160 km from the eastern end of Île d’Orléans. The middle estuary, located between the eastern tip of Île d’Orléans and the Saguenay Fjord, is characterized by significant current reversals and strong mixing associated with the diurnal tidal cycles. Consequently, this is a highly turbid and biologically productive section, with salinity ranging from 0.5 to 25 practical salinity units (psu). The maritime estuary (aka lower estuary) is a 230-km stretch ultimately discharging into the Gulf of St Lawrence. Hydraulic dynamics shift from tide-dominated to wave-dominated, and the biological and physiochemical properties more closely resemble those of the marine environment. Extant stickleback are partitioned into two demes whose geographical ranges correspond to the freshwater/saltwater division of the estuary (McCairns & Bernatchez, 2008). Genetic differentiation is weak (FST = 0.006; P < 0.001) yet temporally stable, and ecological factors independent of geographical distance, particularly salinity, explain the greatest proportion of genetic variance. Moreover, these salinity differences represent unique selective pressures that appear to be driving a nascent adaptive, physiological divergence between demes (McCairns & Bernatchez, 2010). Yet, in contrast to the diversity of forms seen in populations inhabiting similar environmental gradients, stickleback in the St Lawrence exhibit no strikingly obvious morphological differences across the range of environmental conditions encountered. Thus, any morphological divergence between demes – whether plastic or adaptive – must be subtle and as such will require more sophisticated analytical means for its detection.
In this study, we quantify shape variation in sticklebacks originating from each deme inhabiting the St Lawrence estuary. Additionally, we compare differences in the number of lateral plates, an important and well-studied meristic trait frequently associated with freshwater adaptation (Colosimo et al., 2005; Barrett et al., 2008). We use geometric morphometric analyses to define latent variables for shape in samples of mature stickleback captured on their spawning grounds and for laboratory crosses from these same demes reared under controlled environmental conditions. Using artificial crosses of known pedigree raised under reciprocal environmental conditions, we are able to quantify both the additive genetic and environmental components of phenotypic variation in shape. Consequently, we are able to better interpret the evolutionary significance and potential of shape differences observed in the wild.
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The vast majority of individuals, both wild-caught and laboratory-reared, could be classified as fully plated. Although there was some variation in plate number among individuals, nearly all fish possessed between 27 and 33 lateral plates. Only 1/550 mature sticklebacks sampled from spawning sites in the St Lawrence estuary had a reduced number of lateral plates (n = 14). Overall, we detected no significant differences in plate number between sexes or between demes (Table 1). Similar results were also obtained under common environmental conditions, with no significant differences between crosses or experimental salinities (Table 1). However, laboratory-reared fish did exhibit somewhat more variation in plate number. Although the majority of these individuals also possessed between 27 and 33 plates, a greater proportion had reduced plate numbers (11–26 plates; 40/615 individuals). This variability was more prevalent in purebred families (FW–FW = 11/16; SW–SW = 16/16) than in hybrid crosses (13/32) and could not be attributable to specific progenitors (12/16 dams and 14/16 sires). Moreover, most of these families contained fewer than three individuals with reduced plate numbers; only three families had 5–6 offspring with this phenotype.
Table 1. Analyses of the conditional mean number of lateral plates (Cond. Mean). Estimates are based on the posterior distributions of GLMM models, conditional on random variation among sampling sites for mature individuals from the freshwater (FW) and maritime (SW) demes (Wild), or among full-sib families of purebred F1 crosses raised in reciprocal salinity conditions (Lab).
|Deme (D)||Sex (S)||Cond. Mean No. of Plates||Fixed effects||P-value|
| SW||Female||30.7||D × S||0.892|
|Male||30.9|| || |
|Cross (G)||Env. (E)||Cond. Mean No. of Plates||Fixed effects||P-value|
| SW–SW||Freshwater||30.4||G × E||0.143|
|Saltwater||29.7|| || |
Estimates of size frequencies of sticklebacks taken from spawning sites suggested that two age classes were likely sampled (Fig. 2). These data were also suggestive of size differences between sexes and demes: displacement of distributions suggested maritime fish are generally larger than freshwater sticklebacks and that females of the same age class are larger than males. Comparison of size-at-age data for fish reared under experimental conditions revealed no significant differences between demes (P = 0.360; Table 2), but a significant effect of salinity (P = 0.005). However, growth trajectories did differ significantly between demes (Fig. 2c): both parameters of the von Bertalanffy growth function differed significantly between crosses (i.e. deme genotypes) – parameter estimates were higher for the maritime demes – and the parameter describing initial growth rate (q) also exhibited significant salinity-related variation (Table 2).
Figure 2. Frequency distributions of standard length estimated from samples of male (a) and female (b) sticklebacks collected from spawning grounds of the St Lawrence River estuary. Samples from sites within the range of the freshwater deme are plotted as broken, black lines; solid grey lines denote samples corresponding to the maritime deme. (c) Mean growth trajectories for fish with a freshwater (black lines) or maritime (grey lines) genetic background. Individuals sampled randomly from full-sib families were reared in freshwater (< 1‰; broken lines) or saltwater (20‰; solid lines). Growth trajectories were determined by fitting size-at-age data to von Bertalanffy growth curves via nonlinear mixed-effects modelling (see Materials and Methods for model parameterization).
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Table 2. anova of mixed-effects models for standard length (SL) at 230 days, and for parameters of the von Bertalanffy growth model (q and k; eqn 1) of purebred F1 crosses raised under reciprocal salinities. Fixed factors describe the effects of cross (i.e. genotype, G), environmental salinity (E) and genotype–environment interaction (G × E). Random variation among full-sib families is reported in square brackets. For SL data, significance of F ratios (P-value) is evaluated against a distribution of F ratios simulated under a null model (10 000 simulations). For growth model parameters, F ratios are evaluated on the basis of Wald’s F-test. Note that sum of square variation for this model (SS*) is not provided by the ‘nlme’ package, so they have been approximated from the reported F ratio and within-group variance.
| Cross (G)||1||24.818||0.922||0.360|
| Env. (E)||1||216.830||8.055||0.005|
| G × E||1||5.766||0.214||0.645|
| [Family]|| ||13.381|| || |
| Residual|| ||26.919|| || |
| Cross (G)||2||2.959||845.225||< 0.001|
| Env. (E)||1||0.041||23.533||< 0.001|
| G × E||1||4.8 × 10−4||0.272||0.602|
| [Family]|| ||1.8 × 10−3|| || |
| Cross (G)||2||2.3 × 10−3||210.754||< 0.001|
| Env. (E)||1||6.5 × 10−7||0.118||0.731|
| G × E||1||1.3 × 10−8||0.002||0.961|
| [Family]|| ||5.5 × 10−6|| || |
| Residual|| ||9.71|| || |
Affine (i.e. uniform) deformations accounted for only 11% of global shape variation. Nonuniform deformations accounted for the remaining 89% of shape variation; however, only the first five partial warps explained at least 5% of total shape variation, respectively. Cumulatively, the combination of these scores captured 81.9% of total shape variation in wild-caught and laboratory-reared sticklebacks (Fig. S1). Significant correlations between centroid size and most latent shape variables, partial warps 4 and 5 excluded, were indicative of potential allometry effects (Fig. S2).
Overall shape differed significantly between demes and sexes, irrespective of random variation among sites and any potential effects of allometry (Table 3). Multivariate discrimination on the first two CVA axes captured 94% of variation in partial warp scores (ca. 77% total shape variation) and suggested a greater degree of differentiation between sexes (Fig. 3a). Interdeme differences were captured by the secondary CVA axis; moreover, these exhibited a significant degree of overlap (Fig. 3a,b). Compression and partial warp 1 contributed most to this axis, with lesser contributions from eigenvectors for shear and warps 2 and 4 (Fig. 3b). The dominant CVA axis (CV1) defined a region of shape space with no significant overlap between sexes, driven largely by a contribution from partial warp 2 and to lesser extent by partial warps 1, 3 and 4 (Fig. 3c). Procrustes superimpositions also reflected greater sexual dimorphism than differences between demes, revealing smaller interdeme differences between females (ρ = 0.026) and males (ρ = 0.019), compared to distances between sexes within each deme (ρFW = 0.041; ρSW = 0.042). Jackknife classifications were also informative in this regard, with 100% of specimens correctly separated by sex. Deme classification was less robust, with only 76% of freshwater individuals correctly assigned to their deme of origin and 75% of maritime individuals classified to their proper deme. Interestingly, females showed a greater tendency to be correctly classified by deme of origin (freshwater = 84%; maritime = 78%), compared to males (freshwater = 68%; maritime = 72%).
Table 3. Model selection criteria for mixed-effects multiresponse models of total shape variation among mature individuals sampled from the freshwater and maritime demes (Wild) and for purebred F1 crosses (Lab). Simple models testing for significant effects of deme (D) and sex (S), or cross (i.e. genotype, G) and environmental salinity (E), are contrasted with more complex models including centroid size as a covariate to control for potential allometric effects. Random effects includes variation among sampling sites within each deme or variation among full-sib families. Model selection is based on the deviance information criterion (DIC, parsimony model in bold) and P-values of effects determined from the posterior distribution of the parsimony models.
| Deme (D) + Sex (S)||−11911.00||S||< 0.001|
| D × S||−11911.23||D × S||0.106|
| Centroid Size (C) + D + S||−11908.65|| || |
| C + D × S||−11908.87|| || |
| C × D × S||−11906.33|| || |
| Cross (G) + Env. (E)||−7219.80||E||0.008|
| G × E||−7218.20||G × E||0.192|
| Centroid Size (C) + G + E||−7219.79|| || |
| C + G × E||−7215.36|| || |
| C × G × E||−7217.51|| || |
Figure 3. Multivariate discriminant analysis (CVA) of shape vectors defining affine and partial warp scores of mature sticklebacks sampled from spawning grounds (a). Solid grey symbols denote individuals of the maritime deme, whereas open black symbols denote freshwater individuals. Females are indicated by circles and males by triangles. Respective groupings are bound by approximate 90% confidence ellipses. Mean differentiation between demes (b) and sexes (c) is also shown, with relative contributions (loadings; arrow lengths) of each shape score’s contribution to group discrimination. CVA of purebred, laboratory-reared fish is plotted in (d). Grey symbols denote individuals with a maritime genetic background (SW–SW), whereas black symbols denote freshwater crosses (FW–FW). Individuals reared in FW (< 1‰) are plotted as open symbols, with approximate confidence ellipses as broken lines; individuals reared in SW (20‰) are denoted with solid symbols and lines. Discrimination between crosses (e) and rearing environments (f) is as above.
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We detected no significant differences between genotypes of purebred F1 crosses. (P = 0.336; Table 3). Two CVA axes captured 91% of variation in partial warp scores (ca. 74% of total shape variation) and suggested that cross-specific shape differences (Fig. 3d, CV1) were driven almost exclusively by compressive deformations (Fig. 3e). The second axis captured shape variation principally associated with salinity differences and was largely defined by variation in partial warp 1 scores, with lesser contributions from shear deformation and warps 3 and 5. Trends in the degree of overlap of confidence ellipses suggested that shape differences were more pronounced when fish were reared in freshwater compared to saltwater (Fig. 3d). Group classification based on morphological score also reflected this overlap, with only freshwater crosses reared in their native salinity (< 1‰) exhibiting a reasonably high degree of correct identification (78.7%); for the remaining groups, individuals were correctly classified in < 60% of cases (range 52.2–59.3%). Classification by cross type, irrespective of rearing environment (freshwater = 77.4%; maritime = 79.2%), and classification by rearing environment, irrespective of cross (freshwater = 78.7%; saltwater = 79.0%), suggested similar levels of morphological overlap due to genotypic and salinity effects. This was supported by analyses of partial warp scores, which revealed no significant genotype–environment interaction in overall morphological variation (P = 0.192; Table 3). Yet, despite considerable overlap among groups in multivariate space (Fig. 3d), shape vectors differed significantly between rearing environments (P = 0.008; Table 3).
Individuals from sites with salinities atypical of their respective demes also exhibited significant shape differences from both deme means, and trends in distance data suggested most samples were closer in Procrustes shape space to that of the alternate deme with similar native salinities (Table 4; see also Fig. S4). In contrast, all laboratory-reared crosses appeared closer to the FW deme average, irrespective of their genetic background (Table 4). For wild fish, Riemannian shape distances between the focal groups (BP and CHAT) and the reference samples were relatively uniform, suggesting that these individuals were not significantly closer in shape space to that of their proper demes. This same pattern was observed in classification results. Globally, 31.3% of BP fish were correctly grouped with the freshwater deme, and 25.6% of CHAT samples were correctly classified as maritime in shape space. Only 58.3% of BP females were classified as freshwater females, and the others were misidentified as maritime females. Results were far worse for BP males wherein 95.8% were improperly classified as maritime males; only 4.2% were correctly classified. Aberrant classifications were also typical for CHAT fish in which only 26.7% of females and 25.0% of males were correctly classified. Nevertheless, sexual dimorphism was largely evident, with 100% of BP individuals grouped by their correct sex. All CHAT females were also correctly identified as females, and 91.7% this site’s males were correctly classified.
Table 4. Differences in Procrustes superimpositions of landmark data. Focal groups are contrasted with mean shape data corresponding to mature individuals sampled from the freshwater (FW) and maritime (SW) demes inhabiting sites with salinities typical of the respective environments (FW: CR and LEV; SW: BAC and RIKI). Fish sampled from sites with atypical environmental conditions (Wild) were contrasted only with individuals of the same sex. Purebred F1 crosses (Lab) from reciprocal salinity conditions (indicated in parentheses) were contrasted with mean shape data averaged over sexes. ρ is the Riemannian shape distance between Procrustes superimpositions, and λmin represents an unbiased difference statistic, evaluated via bootstrapping (see Materials and Methods for details). Visualizations of superimpositions are available as online supplementary material (Figs S4 and S5).
|Focal group||FW Deme||SW Deme|
| BP (females)||0.035||617.4||0.001||0.035||592.6||0.001|
| BP (males)||0.035||1122.0||0.001||0.030||716.1||0.001|
| CHAT (females)||0.032||414.3||0.001||0.030||325.9||0.001|
| CHAT (males)||0.029||307.8||0.001||0.030||394.7||0.001|
| FW–FW (< 1‰)||0.049||1121.9||0.001||0.052||1692.2||0.001|
| FW–FW (20‰)||0.025||440.9||0.001||0.035||646.8||0.001|
| SW–SW (< 1‰)||0.041||555.4||0.001||0.041||695.2||0.001|
| SW–SW (20‰)||0.026||687.6||0.001||0.034||830.2||0.001|
Quantitative genetics and differentiation
In general, differentiation in partial warp scores did not exceed neutral expectations, except for uniform compressive deformation (Compr), which was significantly > FST (Fig. S6). PST estimates largely mirrored those of FST, except for that of partial warp 1 scores that exceed neutral expectation in wild-caught fish, but not in the laboratory. With the exception of lateral plate number and two latent shape variables (warp 2 and warp 5), all traits exhibited significant additive genetic variance (Table 5). Phenotypic variation in plate number and body size exhibited significant effects of environmental salinity; however, for latent shape variables, significant VE was detected only in uniform shape scores and the first partial warp (Table 5). We detected significant genetic correlations between SL and all shape variables, excluding partial warp 3 scores (Table 6). Components of uniform variation were uncorrelated, but both shear (warp 1 and warp 4) and Compr (warp 3) were significantly correlated with at least one nonuniform component of variation. Most partial warp scores exhibited no significant genetic correlations, with the exception of warp 3 and warp 4 (Table 6).
Table 5. Partitioning of phenotypic variance into additive genetic effects (VA), direct effects of environmental salinity (VE) and residual error (Vresidual). Parameter estimates are based on the posterior mode of 1000 MCMC samples; 95% posterior density interval estimates are in parentheses. Nonsignificant parameter estimates are italicized and are assumed to include zero (lower PDI).
|Trait||DIC null||DIC VA||VA (95% PDI)||VE (95% PDI)||Vresidual (95% PDI)|
|No. plates||3408.0||3945.5||0.033 (0–0.037)||0.002 (1.4 × 10−4–0.005)||0.021 (0.014–0.027)|
|SL (45 days)||8012.9||6145.5||5.623 (3.456–6.812)||1.404 (1.013–1.631)||0.217 (0.001–1.032)|
|SL (230 days)||2345.6||2266.9||14.552 (4.545–35.736)||6.94 (1.845–10.159)||18.097 (1.925–31.153)|
|Shear||−2126.5||−2153.2||3.4 × 10−5 (1.2 × 10−5–8.8 × 10−5)||1.9 × 10−5 (1.3 × 10−7–4.2 × 10−5)||1.2 × 10−4 (5.5 × 10−5–1.7 × 10−4)|
|Compr||−2024.0||−2071.8||6.4 × 10−5 (2.4 × 10−5–1.5 × 10−4)||1.0 × 10−5 (6.5 × 10−9–5.0 × 10−5)||1.5 × 10−4 (6.0 × 10−5–2.1 × 10−4)|
|Warp 1||−1587.0||−1604.9||1.4 × 10−4 (4.3 × 10−5–3.7 × 10−4)||1.1 × 10−4 (4.8 × 10−6–1.8 × 10−4)||5.0 × 10−4 (2.6 × 10−4–7.4 × 10−4)|
|Warp 2||−1790.2||−1786.6||4.3 × 10−5 (0–1.0 × 10−4)||4.2 × 10−5 (0–1.1 × 10−4)||3.6 × 10−4 (2.2 × 10−4–4.6 × 10−4)|
|Warp 3||−1898.4||−1956.6||1.1 × 10−4 (3.2 × 10−5–2.3 × 10−4)||1.5 × 10−5 (0–6.0 × 10−5)||2.0 × 10−4 (8.1 × 10−5–2.8 × 10−4)|
|Warp 4||−1951.6||−1972.5||4.8 × 10−4 (1.2 × 10−5–1.4 × 10−4)||1.3 × 10−5 (0–6.0 × 10−5)||2.0 × 10−4 (1.0 × 10−4–2.7 × 10−4)|
|Warp 5||−2051.0||−2048.2||2.1 × 10−5 (0–4.9 × 10−5)||5.9 × 10−6 (0–4.4 × 10−5)||1.8 × 10−4 (1.2 × 10−4–2.2 × 10−4)|
Table 6. G matrix of size and shape variables. Narrow-sense heritability estimates (h2) and their 95% PDIs are boldfaced on the diagonal. Point estimates of the genetic correlation (G) between traits are in the lower triangle. Nonsignificant estimates are presumed to be zero; however, point estimates are presented in italics. 95% PDI estimates are in the parentheses in the upper triangle, with nonsignificant estimates italicized.
| ||SL||Shear||Compr||Warp 1||Warp 2||Warp 3||Warp 4||Warp 5|
|SL||0.404 (0.149 to 0.760)||(0.419 to 0.954)||(0.314 to 0.853)||(−0.986 to −0.867)||(−0.983 to −0.832)||(−0.776 to 0.071)||(0.210 to 0.911)||(−0.936 to −0.353)|
|Shear||0.791||0.195 (0.066 to 0.444)||(−0.575 to 0.574)||(−0.930 to −0.236)||(−0.807 to 0.209)||(−0.844 to 0.209)||(0.144 to 0.935)||(−0.428 to 0.782)|
|Compr||0.638||−0.159||0.245 (0.116 to 0.571)||(−0.877 to 0.189)||(−0.833 to 0.280)||(−0.952 to −0.593)||(−0.642 to 0.547)||(−0.914 to 0.128)|
|Warp 1||−0.958||−0.764||−0.509||0.179 (0.059 to 0.439)||(−0.362 to 0.815)||(−0.649 to 0.550)||(−0.888 to 0.101)||(−0.595 to 0.662)|
|Warp 2||−0.969||−0.664||−0.460||0.265||0.108 (0.041to0.229)||(−0.779 to 0.342)||(−0.769 to 0.413)||(−0.751 to 0.379)|
|Warp 3||−0.301||−0.531||−0.845||−0.014||−0.494||0.272 (0.135 to 0.619)||(−0.895 to −0.181)||(−0.732 to 0.521)|
|Warp 4||0.721||0.723||−0.113||−0.728||−0.147||−0.749||0.172 (0.065 to 0.461)||(−0.374 to 0.795)|
|Warp 5||−0.806||0.133||−0.564||0.126||−0.485||−0.055||−0.494||0.105 (0.035 to 0.220)|
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Geometric morphometric analyses of wild-caught individuals suggest that stickleback demes endemic to the St Lawrence River estuary differ in body form; however, purebred crosses reared under controlled environmental conditions indicate that variation within this system is most likely plastic. Although multivariate descriptors of shape are heritable, many are equally influenced by direct environmental contributions. Additionally, we show evidence of both allometry and genetic correlations, but no concomitant correlated signatures of selection on morphological variation, despite evidence of adaptive divergence between demes – previous work in this system has demonstrated weak population structure linked to environmental (i.e. salinity) heterogeneity, and adaptive differences in osmoregulatory physiology. Taken in their entirety, observations on stickleback demes of the St Lawrence estuary would seem to validate the perspective that inherently ‘plastic’ traits (e.g. behaviour and physiology) may be among the first to diverge among populations. Yet, one must also consider that the most divergent trait within this relatively young system, differential osmoregulatory capacity, retains a pattern of environmental sensitivity found within the ancestral phenotype. Thus, a predominant role of plasticity reiterated for morphology serves also as an important reminder for the unique capacity of the threespine stickleback to cope with heterogeneous environments over the species’ broad geographical range. In more general terms, these observations may speak to the fundamental importance of trait plasticity in modulating the evolutionary process, given that a species contemporary distribution may rely as much and perhaps more on plasticity than local adaptation. This may be particularly the case for G. aculeatus in many of its freshwater forms and localities.
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Figure S1 Screen plot of eigenvalues, expressed as a proportion of the sum of all eigenvalues, for partial warp scores.
Figure S2 Correlations between partial warp scores and centroid size.
Figure S3 Thin-plate spline (TPS) deformation grids for freshwater (a) and maritime (b) females, and for freshwater (c) and maritime (d) males; symbols denoting locations of landmarks are as per Fig. 3a.
Figure S4 Procrustes superimpositions of mean shape for females (a) and males (b) originating from the Baie St-Paul (BP) site, and for females (c) and males (d) from Cap Chat (CHAT).
Figure S5 Procrustes superimpositions of mean shape for purebred F1 crosses contrasted with those of wild-caught sticklebacks from two demes of the St Lawrence estuary.
Figure S6 Box-percentile plots (description inset; lower left panel) of the distribution of bootstrapped QST and PST values for partial warp scores and total number of lateral plates (No. Plates; data square root transformed).
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