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

Abstract

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.

The Paradox Behind the Pattern of Rapid Adaptive Radiation: How Can the Speciation Process Sustain Itself Through an Early Burst?

“…Challenging work remains in combining complex empirical fitness or performance landscapes with complex genetic architectures for ecological and mating loci originating within diverse spatial and temporal contexts. This complexity may be needed to actually make predictions about the extent of diversification in natural case studies of rapid radiation (e.g., Bolnick 2011, Gavrilets 2014, Gavrilets et al 2007, Martin 2013, Recknagel et al 2014, Stayton 2019, Wagner et al 2012. Furthermore, most speciation models have yet to confront the realworld problems of a distribution of allelic effect sizes for each complex trait (Kopp & Matuszewski 2014, Matuszewski et al 2015, Rockman 2012, each with its own distinct spatiotemporal origins (e.g., Marques et al 2019, Richards & Martin 2017.…”

“…However, it remains an open question how competition among phenotypes scales with phenotypic distance on fitness landscapes, particularly between distinct ecological niches. Some studies find no evidence for negative frequency-dependent competition in experiments spanning hybrid phenotypes and multiple species (Keagy et al 2016, Martin 2016b of an individual phenotype appears to matter far more than competitor frequency at these broader phenotypic scales (Higham et al 2016, Holzman et al 2012, Stayton 2019. Similarly, stable fitness peaks may also arise from heterogeneous resource distributions within an environment, most notably the complex adaptive landscape inferred from the abundance of seed sizes for Galápagos finches (Schluter & Grant 1984a) or the diversity of cone types used by crossbills (Benkman 2003).…”

Rapid adaptive evolution of scale-eating kinematics to a novel ecological niche

“…Future work should estimate performance across multiple performance axes (e.g. Stayton, 2019;Keren et al, 2018 preprint, Dickson andPierce, 2019), ideally using F2 hybrids. F2 hybrids are a useful tool for this type of experiment, as they are the first generation of offspring in which recombination among parental alleles can produce new combinations of kinematic, morphological and behavioral traits not observed in the F0 or F1 generations.…”

Exploring the functional meaning of head shape disparity in aquatic snakes

“…Our results are also consistent with the drag and added mass coefficients found in the literature for prolate spheroids (Vogel, 1994). Drag coefficient have been calculated for a variety of other aquatic animals such as invertebrates (Alexander, 1990; Chamberlain & Westermann, 1976), fish (Webb, 1975), amphibians (Gal & Blake, 1988), turtles (Stayton, 2019), birds (Nachtigall & Bilo, 1980), mammals (Fish, 1993, 2000)). Yet, to be comparable, the drag coefficient must be calculated with the same reference area (i.e.…”

“…Future work should include the development of feeding models in order to measure performance related to food acquisition and swallowing in snakes, as it is probably the more constrained and fitness relevant activity of a snakes’ head. Developing such a model, combined with Computational Fluid Dynamic models could allow the use of performance surfaces (Stayton, 2019) and may thus offer a more thorough understanding of the phenotypic disparity of the head in snakes and its relationship with functional demands. Ultimately, such an approach should help in untangling the interplay between different selective pressures and phenotypic responses and the mechanisms that are at the origin of evolutionary processes such as invasion of new media, adaptation to new niches through phenotypic plasticity.…”