When compared to other hominids-great apes including humans-the human pelvis reveals a fundamental reorganization of bony morphology comprised of multiple trait-level changes, many of which are associated with bipedal locomotion. Establishing how patterns of integration-correlations and covariances among traits-within the pelvis have evolved in concert with morphology is essential to explaining this evolutionary transition because integration may facilitate or constrain morphological evolution.Here, we show that the human hip bone has significantly lower levels of integration and constraint overall when compared to other hominids, that the focus of these changes is on traits hypothesized to play major functional roles in bipedalism, and we provide evidence that the human hip was reintegrated in a pattern distinct from other members of this group. Additionally, the evolutionary transition from a nonhuman great ape-like to human hip bone morphology was significantly easier to traverse using the human integration pattern in each comparison, which suggests hominin patterns may have evolved to facilitate this transition.Our results suggest natural selection for bipedalism broke down earlier hominid integration patterns, allowing relevant traits to respond to separate selection pressures to a greater extent than was previously possible, and reintegrated traits in a way that could have facilitated evolution along the vector specifying ancestral hominid and hominin morphological differences.
1. The variational properties of living organisms are an important component of current evolutionary theory. As a consequence, researchers working on the field of multivariate evolution have increasingly used integration and evolvability statistics as a way of capturing the potentially complex patterns of trait association and their effects over evolutionary trajectories. Little attention has been paid, however, to the cascading effects that inaccurate estimates of trait covariance have on these widely used evolutionary statistics.
2. Here, we analyze the relationship between sampling effort and inaccuracy in evolvability and integration statistics calculated from 10-trait matrices with varying patterns of covariation and magnitudes of integration. We then extrapolate our initial approach to different numbers of traits and different magnitudes of integration and estimate general equations relating the inaccuracy of the statistics of interest to sampling effort. We validate our equations using a dataset of cranial traits, and use them to make sample size recommendations.
3. Our results suggest that highly inaccurate estimates of evolvability and integration statistics resulting from small sample sizes are likely common in the literature, given the sampling effort necessary to properly estimate them. We also show that patterns of covariation have no effect on the sampling properties of these statistics, but overall magnitudes of integration interact with sample size and lead to varying degrees of bias, imprecision, and inaccuracy.
4. Finally, we provide R functions that can be used to calculate recommended sample sizes or to simply estimate the level of inaccuracy that should be expected in these statistics, given a sampling design.
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