Deciphering the Palimpsest: Studying the Relationship Between Morphological Integration and Phenotypic Covariation

Abstract

Organisms represent a complex arrangement of anatomical structures and individuated parts that must maintain functional associations through development. This integration of variation between functionally related body parts and the modular organization of development are fundamental determinants of their evolvability. This is because integration results in the expression of coordinated variation that can create preferred directions for evolutionary change, while modularity enables variation in a group of traits or regions to accumulate without deleterious effects on other aspects of the organism. Using our own work on both model systems (e.g., lab mice, avians) and natural populations of rodents and primates, we explore in this paper the relationship between patterns of phenotypic covariation and the developmental determinants of integration that those patterns are assumed to reflect. We show that integration cannot be reliably studied through phenotypic covariance patterns alone and argue that the relationship between phenotypic covariation and integration is obscured in two ways. One is the superimposition of multiple determinants of covariance in complex systems and the other is the dependence of covariation structure on variances in covariance-generating processes. As a consequence, we argue that the direct study of the developmental determinants of integration in model systems is necessary to fully interpret patterns of covariation in natural populations, to link covariation patterns to the processes that generate them, and to understand their significance for evolutionary explanation.

Fig. 1

Schematic showing the evolution of integration and modularity based on Cheverud (1996) and Wagner (1996). The example illustrated is shared versus divergent limb function. The embryo shown is modified from Blechschmidt (1961)

Fig. 2

Hypothetical example showing how differences in phenotypic variance could be generated by nonlinear relationships between developmental determinants and phenotypic outcomes. The grey areas represent the variances of the developmental factor and the phenotypic result. In both cases, the variance of the developmental factor is the same. The phenotypic variance, however depends on the mean because of the underlying nonlinearity

Fig. 3

Analysis of Brachymorph and Trsp^fl/flCol2a1-Cre Mice. a PCA plot for samples of both mutants with controls as well as the collagen specific knockout of the Pten gene. PC1 captures the variation in chondrocranial growth among the samples. b Average shapes obtained by superimposition and averaging of the individuals included in the analysis shown in A. c 3D morphing of PC1 showing the pattern of covariation in overall craniofacial shape that corresponds to chondrocranial growth. d Volumetric shape comparisons using the method of Kristensen et al. (2008). e The scaled variances of eigenvalues and multivariate variances for the Brachymorph samples and C5B7BL/6J wildtype mice. The error bars shown are standard deviations for both variables obtained through resampling the original datasets with replacement 1000 times. This also reveals that the differences are highly significant (P < 0.001)

Fig. 4

Morphometric analysis of the megencephaly mutant. a PCA plot of mceph homozyotes, heterozygote littermate controls and C57BL/6J mice. b Average shapes obtained by superimposition and averaging of the individuals included in the analysis shown in A. c Wireframe deformation showing the shape variation along PC1. This variation is distributed throughout the skull, particularly the basicranium. d Volumetric shape comparisons showing that the largest differences are in the neurocranium. e The scaled variances of eigenvalues and multivariate variances. The error bars shown are standard deviations for both variables obtained through resampling the original datasets with replacement 1000 times. This also reveals that the differences are highly significant (P < 0.001)

Fig. 5

Morphometric analysis of the Nipbl mutant. a PCA plot for Nipbl heterozygotes and wildtype controls. b Average shapes obtained by superimposition and averaging of the individuals included in the analysis shown in a. c Wireframe deformation showing the shape variation along PC1. d Volumetric shape comparisons showing that the largest differences are in the face and basicranium. e The scaled variances of eigenvalues and multivariate variances. The error bars shown are standard deviations for both variables obtained through resampling the original datasets with replacement 1000 times. This reveals that the differences are significant (P < 0.001) for variance but not for SVE

Fig. 6

Morphometric analysis of the A/WySn mutant. a Shows a PCA of embryonic craniofacial morphology. These data were standardized to TS 16 and to centroid size so as to remove shape variation associated with developmental stage and size. The reason for the double standardization is that A/WySn embryos tend to be smaller and delayed relative to stage. b Shows the mean shape at TS16. In this case as well as a gradient map showing the shape comparison of these two averages. The largest difference is in the magnitude of maxillary prominence outgrowth. c Wireframes depicting the mean shapes in data standardized to centroid size and TS at TS16. d The scaled variances of eigenvalues and multivariate variances for the embryonic sample. The error bars shown are standard deviations for both variables obtained through resampling the original datasets with replacement 1000 times. The SVE difference between A/WySn and the other strains is significant using both the Procrustes Distance based ANOVA (Zelditch et al. 2004) and resampling of SVE (P < 0.001). The variances are also significantly different between A/WySn and the other groups based on resampling (P < 0.001). e PCA plot based on Procrustes superimposed landmark data from adult (90 day) samples. f The scaled variances of eigenvalues and multivariate variances. The error bars shown are standard deviations for both variables obtained through resampling the original datasets with replacement 1000 times. Adult A/WySn mice do not differ significantly from the others in either measure. g 3D wireframe showing the variation in shape among A/WySn, AJ and A/WySn backcross mice that have C57BL/6J alleles for clf1 and clf2 Facial length varies along this axis with the longest faces in A/WySn mice

Fig. 7

a Comparison of the variance scaled variances of eigenvalues (SVE) across inbred mouse strains and wild muroid rodents. This graphs shows the greater variation in SVE among inbred mutant and wildtype strains. b Shows the mean matrix correlations among these groups. This shows the significantly greater stability of covariance structure in the wild muroid sample

Fig. 8

The relationship between variance and integratedness in inbred mice. a Shape variances for wildtype strains and mutants showing the higher variances in mutant strains. All strains are inbred with the exception of BlabCQTL and HSD. b Regression of SVE on variance (r = 0.85, P < 0.01). Error bars are standard deviations obtained by resampling the original data with replacement. c Plot of the resampled data. This plot shows that while there are some artifactual correlations between SVE and variance within samples, the pattern across samples is much more distinct. d Typical eigenvalue distributions for high and low variance samples. SVE is increasing with variance because of how that variance is distributed across principal components and not simply as an artifact of variance

Fig. 9

Longitudinal section through the proximal tibial growth plate of a nine-week old Mongolian gerbil (Meriones unguiculatus), illustrating the process of endochondral bone growth. In the growth plate, chondrocytes are organized into columns oriented parallel to the direction of longitudinal growth. Initially dormant chondrocytes in the resting zone (RZ), adjacent to the epiphysis (Epi), eventually undergo a highly orchestrated life cycle of cell proliferation (proliferating zone, PZ), hypertrophy (hypertrophic zone, HZ), and apoptosis near the metaphysis (Meta). During the latter phase the cells are resorbed by chondroclasts, leaving behind a cartilaginous “scaffold” for osteoid deposition. This life cycle is the same in the growth plates of all vertebrate long bones, providing a limited number of developmental mechanisms to generate variation in limb bone length within/among species. Scale bar = 200 μm

Fig. 10

Diagram showing the relationship between genotype, organismal development and adult covariation structure in vertebrate limbs. Vertebrate limb development consists of two major phases, an embryonic patterning phase during which limb identity is specified and the mesenchymal precursors of the future limb bones are established with the proper three-dimensional (e.g., proximodistal (PD), anteroposterior (AP)) patterning (1), and a fetal and postnatal growth phase in which growth occurs chiefly via endochondral bone growth (2). Fore-(FL) and hindlimbs (HL) share a common genetic architecture. Accordingly, variance in shared developmental processes (upper solid arrow) is expected to increase covariation between limbs overall and between homologous elements. Conversely, a few early and late limb patterning and developmental processes are known to be uniquely expressed in the fore- and hindlimb, and in individual elements (lower dashed arrow) (Ruvinsky and Gibson-Brown 2000; Capdevila and Izpisua Belmonte 2001). Variance in these processes may reduce covariation between the limbs, and/or between neighboring elements within a limb, and thus increase independently selectable variation among individual limb bones. The adult limb phenotype represents the cumulative and superimposed effects of these developmental processes on covariance structure. Note that processes specific to homologous elements may increase covariation between them (dashed horizontal arrows) even though they may reduce covariation with neighboring elements within a limb (solid vertical arrows). Other abbreviations:, AER apical ectodermal ridge, ZPA zone of polarizing activity, S stylopod, Z zeugopod, A autopod. Growth plate abbreviations as in Fig. 9

Fig. 11

Schematic showing the spatial relationship of the FEZ and regions of the brain that express Shh during midfacial formation and outgrowth. Shh expression in the brain (DC: Diencephalon; TE: Telencephalon) initiates mesenchymal proliferation and the formation of the FEZ. Shh expression in the FEZ then continues to regulate outgrowth through proliferation within the frontonasal prominence (FNP). LN Lateral nasal process; MX Maxillary process; MD Mandibular process)

Fig. 12

Schematic illustration of the Palimpsest Model as applied to the mouse skull. Multiple developmental processes acting at different times and influencing overlapping anatomical regions each leave a covariation imprint that adds up to a very complex covariation structure

Fig. 13

Tests of modularity using the method of Klingenberg (2009) based on 3D landmark data for 300 mice from 12 wildtype inbred strains. The anatomical regions tested are shown both as outlines and in terms of the division of landmarks into hypothetical modules. The histograms show the distribution of RV coefficients obtained from permutation of all possible combinations of contiguous landmarks. The RV coefficient is a measure of the strength of internal (within-module) covariance relative to external covariance. These results show that cranial covariation structure tends not to conform to simple hypotheses about modularity in this sample