Craniofacial anatomy co-varies spatially within an individual and temporally across both
ontogenetic and evolutionary time (Olson and Miller, 1958). These patterns can be assessed to test hypotheses about the underlying functional, phylogenetic, or developmental relationships that structure these morphological patterns and contribute to our knowledge of how adaptive processes create and modify complex structures. Mammalian skulls are very complex in that they house anatomical systems designed to accomplish specific tasks: seeing, hearing,
masticating, tasting, swallowing, thinking, and smelling. In answering a number of evolutionary questions, such as how ecology contributes to form (e.g., hyenas incorporate crushed bone into their dietary repertoire so is this why they have large sagittal crests?) or how life history affects development (e.g., because newborn South American opossums, genus Monodelphis, are extremely altricial but must be able to acquire milk from their mothers, is that why their mouths
and forelimbs are more developed than their eyes and hindlimbs at birth?), it is tempting to reduce organisms into modules. A larger, more complex structure can be considered modular if its constituent parts are quasi-autonomous and internally unified to the (partial) exclusion of other such parts (Wagner et al., 2007). However, because the structure as a whole must remain adapted to an organism’s environment, it should also be considered in a holistic, integrated manner. The implications of these interrelated and complementary views of modularity and morphological integration for the study of craniofacial anatomy and evolution are discussed below.
2.5.1 Craniofacial Modularity
Modularity is concerned with whether a complex system can be subdivided into relatively independent smaller units, within which the component elements interact primarily with each other and, in doing so, contribute to the greater whole (Klingenberg, 2008). Modules can be formed for a variety of reasons. Developmental modules comprise tissues formed via the same molecular pathways, from the same embryonic germ layer, or because they are affected by the same developmental process (e.g., tissue interactions, hormone exposure). They interact as they develop during an individual’s ontogeny and affect each other’s final forms. The individual components of a functional module may not be related developmentally, and could even participate in different developmental modules, but they interact to perform a specific function during the lifespan of an individual. This reinforces the fact that modules are quasi-autonomous in that units of one such module are not precluded from also participating in other such modules (Winther, 2001).
Determining whether modules are congruent within an organism—for instance, if traits that develop together also function together or if traits affected by variation in the same gene(s)
level (Wagner et al., 2005; Villmoare et al., 2014). In rare cases, the pathways producing morphological variation are known. More often patterns of variational modularity (Wagner et al., 2007), or the degree to which morphological traits covary (Fig. 2.13), are measured, and from them we infer the unknown biochemical and gene regulatory interaction networks that produce these variational modules.
Understanding how genetic variation is translated into and modified by epigenetic factors to produce and structure phenotypic variation is known as genotype-phenotype (GP) mapping (Mezey et al., 2000; Wagner, 1996). The opposite is also true: examining the GP-map (Fig. 2.14) provides information on genetic modularity. Traits within a genetic module are ones that
Figure 2.13 Cartoon Diagram of Modularity. Traits are represented by grayscale circles, modules by speckled polygons with rounded corners, and significant correlations as dashed lines. Traits that are correlated are considered integrated. Panels: (top) Module boundaries have been correctly drawn around their traits because there are more correlations between traits within a single module than there are between two adjacent modules. (bottom) The traits and lines of integration are in the exact same locations but the module borders have been redrawn incorrectly because there are now just as many correlations among traits within a module as there are between modules.
are affected by variation in the same gene (i.e., pleiotropy), interactions among genes (i.e., epistasis) that affect multiple traits (Wolf et al., 2005, 2006), or variation in two linked genes, as measured by either genetic linkage within a pedigree or linkage disequilibrium within a
population (see 2.6.5 Methods for Conducting Quantitative Genetic Research). This is important because traits with a common genetic basis are inherited together and, therefore, evolve
together (Lande, 1979; Cheverud, 1996a): they are evolutionary modules.
Examination of modularity in the skulls of NWM (Marroig and Cheverud, 2001), OWM (Cheverud, 1989; de Oliveira et al., 2009), and hominoids (Ackermann, 2002) suggests that craniofacial variation patterns have remained relatively stable during anthropoid evolution. This is surprising given the diversity in cranial shape and size recognized across Anthropoidea and is likely explained by a conserved genetic basis for craniofacial form (de Oliveira et al., 2009). Factors having a global effect on (overall) craniofacial form are typically under stronger purifying selection while those having local effects, such as those operating solely within one or a few modules, are more likely free to vary (Mitteroecker and Bookstein, 2008). This observation has been used to explain why human heads look so different from those of most other primates: the strength of covariation among craniofacial modules in hominins has been reduced, allowing for the emergence of new patterns and novel factors with purely local effects (Wagner, 2007).
Modularity promotes evolvability—the ability to respond to selection (Houle, 1992; see 2.2 Craniofacial Diversity). Organizing traits into modules maintains organismal cohesion by constraining traits to covary while allowing evolutionary forces to act simultaneously within modules without causing correlated responses in other distinct modules (Lande, 1979; Klingenberg, 2008). In other words, by definition, in the short run, the effect of interactions among parts within a module will be greater on components that share a module. However, in
Figure 2.14 Cartoon Diagram of Genotype-Phenotype Map Characteristics. Individuals are represented by circles and their phenotype is a grayscale color, gene effects by dashed lines,
chromosomes by horizontal solid lines, and genes by filled rectangles (stacked rectangles in panels C and D are alleles of the same gene so, if each rectangle of the pair has a different fill they demonstrate a heterozygous condition whereas a pair with the same fill demonstrates homozygosity). Panels:
A) What is the relationship between traits and genes? A1: Mendelian, A2: pleiotropy, A3: polygeny. B) How many genes are involved? B1: monogeny, the gene contributes 100% to the phenotype. B2:
oligogeny, each gene contributes 25%. B3: polygeny, each gene contributes 10%.
C) Dominance. C1: white homozygote, C2: heterozygote with no dominance deviation because the phenotype (50% gray) is exactly halfway between the two homozygotes, C3: heterozygote with dominance deviation because of an interaction between the two different alleles causing the trait (75% gray) to be more similar to one of the two homozygotes, C4: black homozygote.
D) Epistasis. D1: Although the genotypes of the two individuals are different at the primary locus (one is homozygous dominant and the other homozygous recessive), the genotype at a linked locus (the hashed-rectangles) masks the effects of the primary locus alleles. D2: Two individuals with the same genotypes at the primary locus as in D1, but being a heterozygote at the linked locus allows the primary locus genotype to dictate the individuals’ phenotypes.
result is that, paradoxically, evolvability of a system is proportional to its modularity (Wagner and Altenberg, 1996)
For example, Drake and Klingenberg (2010) determined that, rather than being an integrated whole, dog skulls comprise separate neurocranial and rostral modules. The tissues that comprise these modules are primarily derived from paraxial mesoderm and the neural crest, respectively (Noden and Trainor, 2005; see 2.4.1 Craniofacial Embryogenesis).
Consequently, one possible explanation for the astounding diversity of dog skull shapes is that modularity has permitted genetic modifications to developmental processes differentially affecting these two cell lineages, leading to diversification in how the two modules relate to one another in different breeds.
However, Klingenberg et al. (2004) found that genetic variation affecting multiple dimensions of the mouse mandible was not constrained to affect dimensions contained within the same module. In other words, a single genetic variant was just as likely to affect portions of both the alveolus and the ascending ramus—the two separate modules of the mouse
complicates the picture but makes sense in light of the fact that, despite a complex structure being modular in design, the individual modules must remain integrated enough with one another to still interact synergistically.13
2.5.2 Morphological Integration in the Cranium
Interactions among processes that produce subunits within a system contribute to the degree of integration, or covariation, among the different elements. Therefore, an alternative concept of modularity is that it is the pattern of differential intensity of integration across a complex system. As many modules share components (e.g., in primates the roof of the orbit is the floor of the anterior brain case and the floor of the nasal cavity is the roof of the oral cavity), change in one module that may individually increase fitness has the potential of producing a net decrease in fitness through its effect on the function or development of a related module. In a similar manner, systems that are tightly integrated are typically free only to vary along a few morphological axes because change to any portion of the structure results in change to all portions. As a result, constraints are imposed on both the degree of modularity a system can maintain and the extent of its integration to strike a balance between the two.
Determining whether integration patterns are congruent across different organisms can elucidate the selective pressures that were unique to individual taxa, as these patterns affect the evolvability of trait means. Cheverud (1996b) compared patterns of developmental, functional, and genetic integration within cotton-top tamarins (Saguinus oedipus) to those within saddle- back tamarins (S. fuscicollis) and used the observed pattern of genetic covariance in tamarin crania to reconstruct the differential selection gradients that contributed to changes in trait
13
It is also possible this result is an artifact of the methods employed by Klingenberg and colleagues (2004). They used Procrustes analysis to quantify morphological variation. However, Garcia et al. (2015) have demonstrated that this practice is problematic because it lacks power to detect modularity patterns. Essentially, local shape variation is redistributed throughout the cranium during the registration process in which a mean landmark configuration is used as the baseline for calculating shape variables such as Procrustes distances and/or residuals.
means. He determined that the gradients were grossly similar, but the minor differential pattern did suggest that S. oedipus alone had experienced selection for an increased gape and an emphasis on the anterior dentition.14
2.5.3 Morphological Relationships in Baboon Skulls
Although the history of examining morphological relationships among baboon skull parts is not as deep as that of similar research for humans, we have learned a good deal about baboon craniofacial morphology. In comparing patterns of craniofacial variation between papionins, Joganic and colleagues (2012a) determined that the neurocranium of baboons is less globular than that of macaques, being more restricted mediolaterally in the base, and that their orbits are oriented more horizontally, perhaps as a result of the extreme degree of midfacial prognathism. Again, as with the masticatory morphology of S. oedipus, polarity for the differences (see Footnote 14) between baboon and macaque crania cannot be established without an outgroup, so it is possible macaques experienced selection for a more globular neurocranium and more vertically oriented orbits. However, because macaques likely retain more ancestral features, the increased body and facial size of baboons being an autapomorphy (see 2.3.2 Baboon
Systematics), and we know that allometric variation is significant in baboon crania (see below), the first scenario is the more likely of the two, at least for the trends in facial morphology.
Most craniofacial variation in baboons is related to differences in scaling, or allometry. Leigh (2006) determined that 76% of baboon craniofacial variation is attributable to ontogenetic allometry, or size-related shape change that occurs as individuals grow and develop. He also compared craniofacial growth trajectories among subspecies and determined that subspecies shared a common growth pattern; it was largely heterochronic changes in developmental timing
14
The actual polarity of these changes cannot be established and it may well have been S. fuscicollis that experienced selective pressure in the opposite direction (see 2.2.2 The Marriage of Morphological and Genetic
that produced interspecific craniofacial differences. Because subspecies share growth
trajectories—and baboon growth trajectories are dominated by size-related change—it is likely that selection on body size during growth was the most salient aspect of baboon evolution. Despite common growth patterns, some (5%) interspecific differences in form are independent of size, including bizygomatic breath, cranial flexion, and rostral dimensions (Leigh, 2006). One of the most important implications of Leigh’s (2006) results is that ontogenetic trajectories are non-linear and differ by sex, perhaps suggesting that craniofacial growth spurts occur at different times in different types of individuals, which influences inferences made from samples of individuals of unknown age.
Leigh (2006) also determined that sexual dimorphism is a major factor explaining craniofacial variation: female crania are relatively wider and they have larger neurocrania and shorter rostra than males. In examining the relationship between genetic and phenotypic variance in craniofacial parameters of demonstrated sexual dimorphism, Willmore et al. (2009) found little evidence to support the theory that sexually dimorphic traits (such as those of the baboon face) should show gene-by-sex interactions or X-linked inheritance patterns relative to traits in the rest of the cranium. The average intersex genetic correlation coefficient was 0.97, indicating that both sexes respond largely the same to selection. Instead, they determined that male genetic variance estimates for craniofacial traits are generally higher than those of females, resulting in increased sexual dimorphism, meaning they respond more readily to selection even if selection is the same across sexes.
Finally, Roseman et al. (2010) examined patterns of genetic covariance in traits from different regions of the baboon skull to determine if any might be better for use in phylogenetic reconstructions and to compare patterns of phenotypic and genetic variation. Prior to the work of Roseman and colleagues (2010), it was assumed that endochondrally ossifying components
of the skull would be better for phylogenetic analysis because they finish growing sooner and, therefore, might be less inclined to deform or remodel in response to masticatory stress (e.g., Corruccini and Beecher, 1984; de Beer, 1985). The logic was that this would make such traits more resistant to evolving in a homoplastic manner as a response to environmental effects. Contrary to expectation, the authors found no support for this assumption, the distribution of genetic effects being randomly distributed throughout the entire cranium. They also determined that patterns of phenotypic and genetic variation are largely similar, a result that echoes similar findings in the crania of macaques (Cheverud, 1982), tamarins (Cheverud, 1995), and a large sample of NWMs (19 species representing 7 genera; Marroig and Cheverud, 2001), suggesting that such patterns are conserved across Primates.