Morphological integration and modularity in the hyperkinetic feeding system of aquatic‐foraging snakes

Abstract

The kinetic skull is a key innovation that allowed snakes to capture, manipulate, and swallow prey exclusively using their heads using the coordinated movement of eight bones. Despite these unique feeding behaviors, patterns of evolutionary integration and modularity within the feeding bones of snakes in a phylogenetic framework have yet to be addressed. Here, we use a dataset of 60 μCT‐scanned skulls and high‐density geometric morphometric methods to address the origin and patterns of variation and integration in the feeding bones of aquatic‐foraging snakes. By comparing alternate superimposition protocols allowing us to analyze the entire kinetic feeding system simultaneously, we find that the feeding bones are highly integrated, driven predominantly by functional selective pressures. The most supported pattern of modularity contains four modules, each associated with distinct functional roles: the mandible, the palatopterygoid arch, the maxilla, and the suspensorium. Further, the morphological disparity of each bone is not linked to its magnitude of integration, indicating that integration within the feeding system does not strongly constrain morphological evolution, and that adequate biomechanical solutions to a wide range of feeding ecologies and behaviors are readily evolvable within the constraint due to integration in the snake feeding system.

Local Superimpositions Facilitate Morphometric Analysis of Complex Articulating Structures

“…Unlike rotation and position, relative size variation of landmark subsets between observations is independent of preservational variation. Development of local superimposition procedures that maintain this size variation (as fixed angle methods do) while also fixing angles between component parts of a complex articulating structure (e.g., “iterative rearticulations” and “superimposed centroids” approaches in Rhoda et al 2021 ) should be an avenue for future research.…”

“…The matched local superimpositions procedure incorporates information about the relative orientation, position, and scale of an anatomically realistic reference landmark configuration ( Fig. 1C ) ( Rhoda et al 2021 ). This procedure is a direct extension of that advocated by Collyer et al (2020) with relative orientation and position of each subset included, permitting clearer biological interpretation of patterns of shape variation.…”

“…We present 2 empirical examples to demonstrate the promise of local superimpositions in analyzing complex articulating structures, the jaws of aquatic-foraging snakes (colubriform species from Rhoda et al 2021 ; non-colubriforms were excluded because of significant structural changes that are beyond the scope of this study; Cundall and Irish 2008 ), and the skulls of wrasses ( Evans et al 2019 ). The snake dataset contains 1335 total landmarks and 29 species, and the wrasse dataset contains 178 landmarks and 30 species.…”

“…Local superimpositions —Any superimposition procedure that involves independently superimposing landmark subsets and concatenating the independently-superimposed coordinates into a common coordinate space. The combined subsets approach ( Collyer et al 2020 ), Hellert’s (2019) and the matched local superimpositions approach ( Rhoda et al 2021 ) are each local superimposition procedures that differ in the relative sizes, positions, and orientations of the locally-superimposed landmark subsets. Local superimpositions are desirable because they completely eliminate variation due to mobility in an articulating structure.…”

“…This enabled within- and between-module landmark covariance to be considered at once while also giving clarity during the visualization of results from exploratory analyses of modularity. Rhoda et al (2021) presented another local superimposition approach, where an anatomically accurate reference landmark configuration was used as an anchor to place locally-superimposed landmark subsets onto their corresponding subset in the reference configuration ( Fig. 1c ).…”