Side-necked Versus Hidden-necked: A Comparison of Shell Morphology Between Pleurodiran and Cryptodiran Turtles

2020

Current Zoology

Contemporary methods for visualizing phenotypic evolution, such as phylomorphospaces, often reveal patterns which depart strongly from a naïve expectation of consistently divergent branching and expansion. Instead, branches regularly crisscross as convergence, reversals, or other forms of homoplasy occur, forming patterns described as “birds’ nests”, “flies in vials”, or, less elegantly, “a mess”. In other words, the phenotypic tree of life often appears highly tangled. Various explanations are given for this, such as differential degrees of developmental constraint, adaptation, or lack of adaptation. However, null expectations for the magnitude of disorder or “tangling” have never been established, so it is unclear which or even whether various evolutionary factors are required to explain messy patterns of evolution. I simulated evolution along phylogenies under a number of varying parameters (number of taxa, number of traits) and models (Brownian motion, Ornstein-Uhlenbeck (OU)-based, early burst, and character displacement) and quantified disorder using two measures. All models produce substantial amounts of disorder. Disorder increases with tree size and the number of phenotypic traits. OU models produced the largest amounts of disorder – adaptive peaks influence lineages to evolve within restricted areas, with concomitant increases in crossing of branches and density of evolution. Large early changes in trait values can be important in minimizing disorder. Character displacement consistently produced trees with low (but not absent) disorder. Overall, neither constraints nor a lack of adaptation are required to explain messy phylomorphospaces – both stochastic and deterministic processes can act to produce the tantalizingly tangled phenotypic tree of life.

Section: Discussionmentioning

confidence: 99%

“…( B ) A relatively orderly and well-known phylomorphospace visualization of Anolis lizard ecomorph evolution; data from Revell (2012) . ( C ) A relatively orderly phylomorphospace plot of pleurodire turtle shell shape; from Wise and Stayton (2017) .…”

mentioning

confidence: 99%

Are our phylomorphospace plots so terribly tangled? An investigation of disorder in data simulated under adaptive and nonadaptive models

2020

Current Zoology

“…Performance surfaces were built for three functions affected by shell morphology: mechanical strength or load resistance, hydrodynamic efficiency, and self‐righting ability when overturned. This list is not exhaustive–turtle shells perform physiological and metabolic functions (Jackson ), influence limb movement (Wise and Stayton ), and exchange heat with the environment (Boyer ). However, the three functions chosen here are the only ones with known validated relationships between morphology and performance (see below).…”

Section: Methodsmentioning

confidence: 99%

Performance in three shell functions predicts the phenotypic distribution of hard‐shelled turtles

2019

Evolution

Self Cite

Adaptive landscapes have served as fruitful guides to evolutionary research for nearly a century. Current methods guided by landscape frameworks mostly utilize evolutionary modeling (e.g., fitting data to Ornstein–Uhlenbeck models) to make inferences about adaptive peaks. Recent alternative methods utilize known relationships between phenotypes and functional performance to derive information about adaptive landscapes; this information can then help explain the distribution of species in phenotypic space and help infer the relative importance of various functions for guiding diversification. Here, data on performance for three turtle shell functions–strength, hydrodynamic efficiency, and self‐righting ability–are used to develop a set of predicted performance optima in shell shape space. The distribution of performance optima shows significant similarity to the distribution of existing turtle species and helps explain the absence of shells in otherwise anomalously empty regions of morphospace. The method outperforms a modeling‐based approach in inferring the location of reasonable adaptive peaks and in explaining the shape of the phenotypic distributions of turtle shells. Performance surface‐based methods allow researchers to more directly connect functional performance with macroevolutionary diversification, and to explain the distribution of species (including presences and absences) across phenotypic space.

“…A female Pelomedusa subrufa (Carnegie Museum of Natural History specimen 62245, 152 mm SCL) was scanned at the University of Texas High‐Resolution X‐ray Computed Tomography Facility along its coronal axis. Pelomedusa subrufa was chosen as the ‘base’ pleurodire as it lies close to the average shape for pleurodires (Wise & Stayton, ) and does not possess any kinetic elements such as plastral hinges (as are found in other pleurodire species – Pritchard, ). This particular specimen was collected in Garamba National Park, Democratic Republic of the Congo, and again showed no obvious external or internal shell abnormalities.…”

Section: Methodsmentioning

confidence: 99%

Warped finite element models predict whole shell failure in turtle shells

2018

Journal of Anatomy

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Finite element (FE) models have become increasingly popular in comparative biomechanical studies, with researchers continually developing methods such as 'warping' preexisting models to facilitate analyses. However, few studies have investigated how well FE models can predict biologically crucial whole-structure performance or whether 'warped' models can provide useful information about the mechanical behavior of actual specimens. This study addresses both of these issues through a validation of warped FE models of turtle shells. FE models for 40 turtle specimens were built using 3D landmark coordinates and thin-plate spline interpolations to warp preexisting turtle shell models. Each actual turtle specimen was loaded to failure, and the load at failure and mode of fracture were then compared with the behavior predicted by the models. Overall, the models performed very well, despite the fact that many simplifying assumptions were made for analysis. Regressions of observed on predicted loads were significant for the dataset as a whole, as well as in separate analyses within two turtle species, and the direction of fracture was generally consistent with the patterns of stresses observed in the models. This was true even when size (an important factor in determining strength) was removed from analyses - the models were also able to predict which shells would be relatively stronger or weaker. Although some residual variation remains unexplained, this study supports the idea that warped FE models run with simplifying assumptions at least can provide useful information for comparative biomechanical studies.