Table 1. Evolutionary questions that the study of developmental bias helps to answer.

Question	Answer with key reference
Why is the influence of genetic and environmental change on phenotypes not uniform?	The feedback, modular structure, and nonlinear interactions of regulatory networks allow developmental systems to exhibit both robustness (i.e., no or small phenotypic change even under large perturbation) and innovation (i.e., large yet functionally integrated phenotypic change even under small perturbation) (Wagner 2011).
How can regulatory networks facilitate the expression of functional phenotypes when populations are exposed to novel environments?	As regulatory interactions evolve, they discover underlying structural regularities of the environments to which they become adapted, including through modular structure, making it possible to reach new adaptive combinations of characters through a small number of mutations (Watson and Szathmary 2016).
Why did a great deal of morphological variation evolve early in the history of multicellular life?	Simple, low-dimensional ancestral regulatory networks will tend to produce greater disparity among the set of common phenotypes than derived high-dimensional networks because ancestral genotypes are less constrained by regulatory epistasis (Borenstein and Krakauer 2008).
Why do phenotypes occupy only a small region of possible phenotype space?	Chance and the adaptive demands of natural selection combine with regulatory epistasis in evolving networks to leave only a fraction of possible phenotypes reachable (Wagner 2011).
How can developmental processes influence the direction of phenotypic evolution?	Evolution of regulatory networks illustrate that the phenotypic variation available for natural selection will typically be biased, sometimes in a functional manner, even when mutations are randomly distributed (Watson and Szathmary 2016).
How does developmental bias contribute to evolvability?	Developmental bias increases the recurrence and fitness of new phenotypes, thereby reducing the amount of genetic change needed to convert them into adaptive phenotypes (Watson and Szathmary 2016). Developmental bias may thus increase evolvability by making it more likely that adaptive phenotypes arise.
How does developmental bias shape macro-evolutionary patterns?	Analyses of regulatory networks reveals that stabilizing selection will push evolving populations to regions of genotype space where changes in topology do not affect the phenotype (generating stasis), while disruptive selection shifts populations to regions in which rapid change can ensue (Wagner 2011).

This table provides only brief summary statements. Readers are referred to the main text for full explanations.