A network of interactions is called modular if it is subdivided into relatively autonomous, internally highly connected components. Modularity has emerged as a rallying point for research in developmental and evolutionary biology (and specifically evo-devo), as well as in molecular systems biology. Here we review the evidence for modularity and models about its origin. Although there is an emerging agreement that organisms have a modular organization, the main open problem is the question of whether modules arise through the action of natural selection or because of biased mutational mechanisms.

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Abstract. Robustness is the invariance of phenotypes in the face of perturbation. The robustness of phenotypes appears at various levels of biological organization, including gene expression, protein folding, metabolic flux, physiological homeostasis, development, and even organismal fitness. The mechanisms underlying robustness are diverse, ranging from thermodynamic stability at the RNA and protein level to behavior at the organismal level. Phenotypes can be robust either against heritable perturbations (e.g., mutations) or nonheritable perturbations (e.g., the weather). Here we primarily focus on the first kind of robustness-genetic robustness-and survey three growing avenues of research: (1) measuring genetic robustness in nature and in the laboratory; (2) understanding the evolution of genetic robustness; and (3) exploring the implications of genetic robustness for future evolution.

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Similarity of genetic and phenotypic variation patterns among populations is important for making quantitative inferences about past evolutionary forces acting to differentiate populations and for evaluating the evolution of relationships among traits in response to new functional and developmental relationships. Here, phenotypic co‐variance and correlation structure is compared among Platyrrhine Neotropical primates. Comparisons range from among species within a genus to the superfamily level. Matrix correlation followed by Mantel's test and vector correlation among responses to random natural selection vectors (random skewers) were used to compare correlation and variance/covariance matrices of 39 skull traits. Sampling errors involved in matrix estimates were taken into account in comparisons using matrix repeatability to set upper limits for each pairwise comparison. Results indicate that covariance structure is not strictly constant but that the amount of variance pattern divergence observed among taxa is generally low and not associated with taxonomic distance. Specific instances of divergence are identified. There is no correlation between the amount of divergence in covariance patterns among the 16 genera and their phylogenetic distance derived from a conjoint analysis of four already published nuclear gene datasets. In contrast, there is a significant correlation between phylogenetic distance and morphological distance (Mahalanobis distance among genus centroids). This result indicates that while the phenotypic means were evolving during the last 30 millions years of New World monkey evolution, phenotypic covariance structures of Neotropical primate skulls have remained relatively consistent. Neotropical primates can be divided into four major groups based on their feeding habits (fruit‐leaves, seed‐fruits, insect‐fruits, and gum‐insect‐fruits). Differences in phenotypic covariance structure are correlated with differences in feeding habits, indicating that to some extent changes in interrelationships among skull traits are associated with changes in feeding habits. Finally, common patterns and levels of morphological integration are found among Platyrrhine primates, suggesting that functional/developmental integration could be one major factor keeping covariance structure relatively stable during evolutionary diversification of South American monkeys.

lutionary inference, even in the absence of genetic data. In this report, I compare the level and pattern of genetic and phenotypic correlation in order to determine the practical importance of obtaining genetic-correlation estimates for the purpose ofmaking inferences about the coordinated evolution of traits. If phenotypic values can be substituted for genetic ones in some instances, the growing body of evolutionary quantitative-genetic theory can be applied more widely than it is today.There are two reasons one might expect genetic and phenotypic correlations to be similar. First, under the usual additive model, phenotypic correlations are the sum of genetic and environmental components, the causal correlations being weighted by the relative importance of heritable and nonheritable effects, respectively: Evolution. 42(5)Abstract. -Genetic variances and correlations lie at the center of quantitative evolutionary theory. They are often difficult to estimate, however, due to the large samples of related individuals that are required. I investigated the relationship ofgenetic-and phenotypic-correlation magnitudes and patterns in 41 pairs of matrices drawn from the literature in order to determine their degree of similarity and whether phenotypic parameters could be used in place of their genetic counterparts in situations where genetic variances and correlations cannot be precisely estimated. The analysis indicates that squared genetic correlations were on average much higher than squared phenotypic correlations and that genetic and phenotypic correlations had only broadly similar patterns. These results could be due either to biological causes or to imprecision of genetic-correlation estimates due to sampling error. When only those studies based on the largest sample sizes (effective sample size of 40 or more) were included, squared genetic-correlation estimates were only slightly greater than their phenotypic counterparts and the patterns of correlation were strikingly similar. Thus, much of the dissimilarity between phenotypic-and genetic-correlation estimates seems to be due to imprecise estimates of genetic correlations. Phenotypic correlations are likely to be fair estimates of their genetic counterparts in many situations. These further results also indicate that genetic and environmental causes of phenotypic variation tend to act on growth and development in a similar manner.