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In recent years, the fields of evolutionary biomechanics and morphology have developed into a deeply quantitative and integrative science, resulting in a much richer understanding of how structural relationships shape macroevolutionary patterns. This issue highlights new research at the conceptual and experimental cutting edge, with a special focus on applying big data approaches to classic questions in form–function evolution. As this issue illustrates, new technologies and analytical tools are facilitating the integration of biomechanics, functional morphology, and phylogenetic comparative methods to catalyze a new, more integrative discipline. Although we are at the cusp of the big data generation of organismal biology, the field is nonetheless still data-limited. This data bottleneck is primarily due to the rate-limiting steps of digitizing specimens, recording and tracking organismal movements, and extracting patterns from massive datasets. Automation and machine-learning approaches hold great promise to help data generation keep pace with ideas. As a final and important note, almost all the research presented in this issue relied on specimens— totaling the tens of thousands—provided by museum collections. Without collection, curation, and conservation of museum specimens, the future of the field is much less bright. 

Abstract Coral reef fishes constitute one of the most diverse assemblages of vertebrates on the planet. Color patterns are known to serve a number of functions including intra- and inter-specific signaling, camouflage, mimicry, and defense. However, the relative importance of these and other factors in shaping color pattern evolution is poorly understood. Here we conduct a comparative phylogenetic analysis of color pattern evolution in the butterflyfishes (Chaetodontidae). Using recently developed tools for quantifying color pattern geometry as well as machine learning approaches, we investigate the tempo of evolution of color pattern elements and test whether ecological variables relating to defense, depth, and social behavior predict color pattern evolution. Butterflyfishes exhibit high diversity in measures of chromatic conspicuousness and the degrees of fine versus gross scale color patterning. Surprisingly, most diversity in color pattern was not predicted by any of the measures of ecology in our study, although we did find a significant but weak relationship between the level of fine scale patterning and some aspects of defensive morphology. We find that the tempo of color pattern diversification in butterflyfishes has increased toward the present and suggest that rapid evolution, presumably in response to evolutionary pressures surrounding speciation and lineage divergence, has effectively decoupled color pattern geometry from some aspects of ecology. Machine learning classification of color pattern appears to rely on a set of features that are weakly correlated with current color pattern geometry descriptors, but that may be better suited for the detection of discrete components of color pattern. A key challenge for future studies lies in determining whether rapid evolution has generally decoupled color patterns from ecology, or whether convergence in function produces convergence in color pattern at phylogenetic scales. 

Abstract In recent years, the fields of evolutionary biomechanics and morphology have developed into a deeply quantitative and integrative science, resulting in a much richer understanding of how structural relationships shape macroevolutionary patterns. This issue highlights new research at the conceptual and experimental cutting edge, with a special focus on applying big data approaches to classic questions in form–function evolution. As this issue illustrates, new technologies and analytical tools are facilitating the integration of biomechanics, functional morphology, and phylogenetic comparative methods to catalyze a new, more integrative discipline. Although we are at the cusp of the big data generation of organismal biology, the field is nonetheless still data-limited. This data bottleneck is primarily due to the rate-limiting steps of digitizing specimens, recording and tracking organismal movements, and extracting patterns from massive datasets. Automation and machine-learning approaches hold great promise to help data generation keep pace with ideas. As a final and important note, almost all the research presented in this issue relied on specimens—totaling the tens of thousands—provided by museum collections. Without collection, curation, and conservation of museum specimens, the future of the field is much less bright. 

Abstract The field of comparative morphology has entered a new phase with the rapid generation of high-resolution three-dimensional (3D) data. With freely available 3D data of thousands of species, methods for quantifying morphology that harness this rich phenotypic information are quickly emerging. Among these techniques, high-density geometric morphometric approaches provide a powerful and versatile framework to robustly characterize shape and phenotypic integration, the covariances among morphological traits. These methods are particularly useful for analyses of complex structures and across disparate taxa, which may share few landmarks of unambiguous homology. However, high-density geometric morphometrics also brings challenges, for example, with statistical, but not biological, covariances imposed by placement and sliding of semilandmarks and registration methods such as Procrustes superimposition. Here, we present simulations and case studies of high-density datasets for squamates, birds, and caecilians that exemplify the promise and challenges of high-dimensional analyses of phenotypic integration and modularity. We assess: (1) the relative merits of “big” high-density geometric morphometrics data over traditional shape data; (2) the impact of Procrustes superimposition on analyses of integration and modularity; and (3) differences in patterns of integration between analyses using high-density geometric morphometrics and those using discrete landmarks. We demonstrate that for many skull regions, 20–30 landmarks and/or semilandmarks are needed to accurately characterize their shape variation, and landmark-only analyses do a particularly poor job of capturing shape variation in vault and rostrum bones. Procrustes superimposition can mask modularity, especially when landmarks covary in parallel directions, but this effect decreases with more biologically complex covariance patterns. The directional effect of landmark variation on the position of the centroid affects recovery of covariance patterns more than landmark number does. Landmark-only and landmark-plus-sliding-semilandmark analyses of integration are generally congruent in overall pattern of integration, but landmark-only analyses tend to show higher integration between adjacent bones, especially when landmarks placed on the sutures between bones introduces a boundary bias. Allometry may be a stronger influence on patterns of integration in landmark-only analyses, which show stronger integration prior to removal of allometric effects compared to analyses including semilandmarks. High-density geometric morphometrics has its challenges and drawbacks, but our analyses of simulated and empirical datasets demonstrate that these potential issues are unlikely to obscure genuine biological signal. Rather, high-density geometric morphometric data exceed traditional landmark-based methods in characterization of morphology and allow more nuanced comparisons across disparate taxa. Combined with the rapid increases in 3D data availability, high-density morphometric approaches have immense potential to propel a new class of studies of comparative morphology and phenotypic integration. 

Abstract We present a dataset that quantifies body shape in three dimensions across the teleost phylogeny. Built by a team of researchers measuring easy-to-identify, functionally relevant traits on specimens at the Smithsonian National Museum of Natural History it contains data on 16,609 specimens from 6144 species across 394 families. Using phylogenetic comparative methods to analyze the dataset we describe the teleostean body shape morphospace and identify families with extraordinary rates of morphological evolution. Using log shape ratios, our preferred method of body-size correction, revealed that fish width is the primary axis of morphological evolution across teleosts, describing a continuum from narrow-bodied laterally compressed flatfishes to wide-bodied dorsoventrally flattened anglerfishes. Elongation is the secondary axis of morphological variation and occurs within the more narrow-bodied forms. This result highlights the importance of collecting shape on three dimensions when working across teleosts. Our analyses also uncovered the fastest rates of shape evolution within a clade formed by notothenioids and scorpaeniforms, which primarily thrive in cold waters and/or have benthic habits, along with freshwater elephantfishes, which as their name suggests, have a novel head and body shape. This unprecedented dataset of teleostean body shapes will enable the investigation of the factors that regulate shape diversification. Biomechanical principles, which relate body shape to performance and ecology, are one promising avenue for future research. 

Abstract Coral reefs are complex marine habitats that have been hypothesized to facilitate functional specialization and increased rates of functional and morphological evolution. Wrasses (Labridae: Percomorpha) in particular, have diversified extensively in these coral reef environments and have evolved adaptations to further exploit reef-specific resources. Prior studies have found that reef-dwelling wrasses exhibit higher rates of functional evolution, leading to higher functional variation than in non-reef dwelling wrasses. Here, we examine this hypothesis in the lower pharyngeal tooth plate of 134 species of reef and non-reef-associated labrid fishes using high-resolution morphological data in the form of micro-computed tomography scans and employing three-dimensional geometric morphometrics to quantify shape differences. We find that reef-dwelling wrasses do not differ from non-reef-associated wrasses in morphological disparity or rates of shape evolution. However, we find that some reef-associated species (e.g., parrotfishes and tubelips) exhibit elevated rates of pharyngeal jaw shape evolution and have colonized unique regions of morphospace. These results suggest that while coral reef association may provide the opportunity for specialization and morphological diversification, species must still be able to capitalize on the ecological opportunities to invade novel niche space, and that these novel invasions may prompt rapid rates of morphological evolution in the associated traits that allow them to capitalize on new resources. 

Abstract Whether it is swimming, walking, eating, or jumping, motions are a fundamental way in which organisms interact with their environment. Understanding how morphology contributes to motion is a primary focus of kinematic research and is necessary for gaining insights into the evolution of functional systems. However, an element that is largely missing from traditional analyses of motion is the spatial context in which they occur. We explore an application of geometric morphometrics (GM) for analyzing and comparing motions to evaluate the outputs of biomechanical linkage models. We focus on a common model for oral jaw mechanics of perciform fishes, the fourbar linkage, using GM to summarize motion as a trajectory of shape change. Two traits derived from trajectories capture the total kinesis generated by a linkage (trajectory length) and the kinematic asynchrony (KA) of its mobile components (trajectory nonlinearity). Oral jaw fourbar data from two subfamilies of Malagasy cichlids were used to generate form–function landscapes, describing broad features of kinematic diversity. Our results suggest that kinesis and KA have complex relationships with fourbar morphology, each displaying a pattern in which different shapes possess equivalent kinematic trait values, known as many-to-one mapping of form-to-function. Additionally, we highlight the observation that KA captures temporal differences in the activation of motion components, a feature of kinesis that has long been appreciated but was difficult to measure. The methods used here to study fourbar linkages can also be applied to more complex biomechanical models and broadly to motions of live organisms. We suggest that they provide a suitable alternative to traditional approaches for evaluating linkage function and kinematics. 

Abstract Modern computational and imaging methods are revolutionizing the fields of comparative morphology, biomechanics, and ecomorphology. In particular, imaging tools such as X-ray micro computed tomography (µCT) and diffusible iodine-based contrast enhanced CT allow observing and measuring small and/or otherwise inaccessible anatomical structures, and creating highly accurate three-dimensional (3D) renditions that can be used in biomechanical modeling and tests of functional or evolutionary hypotheses. But, do the larger datasets generated through 3D digitization always confer greater power to uncover functional or evolutionary patterns, when compared with more traditional methodologies? And, if so, why? Here, we contrast the advantages and challenges of using data generated via (3D) CT methods versus more traditional (2D) approaches in the study of skull macroevolution and feeding functional morphology in bats. First, we test for the effect of dimensionality and landmark number on inferences of adaptive shifts during cranial evolution by contrasting results from 3D versus 2D geometric morphometric datasets of bat crania. We find sharp differences between results generated from the 3D versus some of the 2D datasets (xy, yz, ventral, and frontal), which appear to be primarily driven by the loss of critical dimensions of morphological variation rather than number of landmarks. Second, we examine differences in accuracy and precision among 2D and 3D predictive models of bite force by comparing three skull lever models that differ in the sources of skull and muscle anatomical data. We find that a 3D model that relies on skull µCT scans and muscle data partly derived from diceCT is slightly more accurate than models based on skull photographs or skull µCT and muscle data fully derived from dissections. However, the benefit of using the diceCT-informed model is modest given the effort it currently takes to virtually dissect muscles from CT scans. By contrasting traditional and modern tools, we illustrate when and why 3D datasets may be preferable over 2D data, and vice versa, and how different methodologies can complement each other in comparative analyses of morphological function and evolution. 

Abstract Animals use a diverse array of motion to feed, escape predators, and reproduce. Linking morphology, performance, and fitness is a foundational paradigm in organismal biology and evolution. Yet, the influence of mechanical relationships on evolutionary diversity remains unresolved. Here, I focus on the many-to-one mapping of form to function, a widespread, emergent property of many mechanical systems in nature, and discuss how mechanical redundancy influences the tempo and mode of phenotypic evolution. By supplying many possible morphological pathways for functional adaptation, many-to-one mapping can release morphology from selection on performance. Consequently, many-to-one mapping decouples morphological and functional diversification. In fish, for example, parallel morphological evolution is weaker for traits that contribute to mechanically redundant motions, like suction feeding performance, than for systems with one-to-one form–function relationships, like lower jaw lever ratios. As mechanical complexity increases, historical factors play a stronger role in shaping evolutionary trajectories. Many-to-one mapping, however, does not always result in equal freedom of morphological evolution. The kinematics of complex systems can often be reduced to variation in a few traits of high mechanical effect. In various different four-bar linkage systems, for example, mechanical output (kinematic transmission) is highly sensitive to size variation in one or two links, and insensitive to variation in the others. In four-bar linkage systems, faster rates of evolution are biased to traits of high mechanical effect. Mechanical sensitivity also results in stronger parallel evolution—evolutionary transitions in mechanical output are coupled with transition in linkages of high mechanical effect. In other words, the evolutionary dynamics of complex systems can actually approximate that of simpler, one-to-one systems when mechanical sensitivity is strong. When examined in a macroevolutionary framework, the same mechanical system may experience distinct selective pressures in different groups of organisms. For example, performance tradeoffs are stronger for organisms that use the same mechanical structure for more functions. In general, stronger performance tradeoffs result in less phenotypic diversity in the system and, sometimes, a slower rate of evolution. These macroevolutionary trends can contribute to unevenness in functional and lineage diversity across the tree of life. Finally, I discuss how the evolution of mechanical systems informs our understanding of the relative roles of determinism and contingency in evolution.