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Abstract Newly-developed methods for utilizing performance surfaces—multivariate representations of the relationship between phenotype and functional performance—allow researchers to test hypotheses about adaptive landscapes and evolutionary diversification with explicit attention to functional factors. Here, information from performance surfaces of three turtle shell functions—shell strength, hydrodynamics, and self-righting—is used to test the hypothesis that turtle lineages transitioning from aquatic to terrestrial habitats show patterns of shell shape evolution consistent with decreased importance of hydrodynamic performance. Turtle shells are excellent model systems for evolutionary functional analysis. The evolution of terrestriality is an interesting test case for the efficacy of these methods because terrestrial turtles do not show a straightforward pattern of morphological convergence in shell shape: many terrestrial lineages show increased shell height, typically assumed to decrease hydrodynamic performance, but there are also several lineages where the evolution of terrestriality was accompanied by shell flattening. Performance surface analyses allow exploration of these complex patterns and explicit quantitative analysis of the functional implications of changes in shell shape. Ten lineages were examined. Nearly all terrestrial lineages, including those which experienced decreased shell height, are associated with morphological changes consistent with a decrease in the importance of shell hydrodynamics. This implies a common selective pattern across lineages showing divergent morphological patterns. Performance studies such as these hold great potential for integrating adaptive and performance data in macroevolutionary studies. 

Abstract Linking morphology and function is critical to understanding the evolution of organismal shape. Performance landscapes, or performance surfaces, associate empirical functional performance data with a morphospace to assess how shape variation relates to functional variation. Performance surfaces for multiple functions also can be combined to understand the functional trade‐offs that affect the morphology of a particular structure across species. However, morphological performance surfaces usually require empirical determination of performance for a number of theoretical shapes that are evenly distributed throughout the morphospace. This process is time‐consuming, and is problematic for structures that are difficult to precisely manipulate.We sought to (a) understand the degree and pattern of sampling required to produce a reliable and nuanced performance surface and (b) investigate the possibility of building a surface using only naturally occurring morphologies. To do this, we subsampled a pre‐existing set of turtle shell performance surfaces in four different ways: first, uniform subsampling of theoretical morphologies across the surface; second, random subsampling of theoretical morphologies across the surface; third, a combination uniform/random subsampling method called close‐pairs sampling and fourth, subsampling only points on the surface known to correspond to a naturally occurring turtle shell morphology. Each subset was interpolated with ordinary Kriging to produce a new performance surface for comparison to the original.We found that using a fraction of the theoretical morphologies examined in the original study (half as many or fewer) was sufficient to produce a performance surface bearing close resemblance to the original (Pearson correlation ≥0.90); close‐pairs sampling dramatically increased the power of small sample sizes. We also discovered that only sampling points on the surface corresponding to naturally occurring morphologies produced an accurate surface, but results were better when individual specimens, rather than species averages, were used.Our findings demonstrate the viability of using performance surfaces to understand the evolution of complex morphologies for which theoretical shape modelling is difficult or computationally burdensome. Both lower levels of carefully configured sampling throughout the theoretical morphospace, and development of performance surfaces using only data from naturally occurring morphologies, are acceptable alternatives to the dense theoretical shape sampling employed in previous studies. ​