DOI: 10.5061/dryad.vq83bk44j
title: Cranial modularity drives phenotypic diversification and adaptive radiation of Antarctic icefishes

description: Modularity among traits is thought to drive morphological evolution and
 diversification, with more modular species often showing greater disparity
 and faster evolutionary rates. However, recent studies suggest this
 pattern is not universal, as higher integration can sometimes be linked to
 faster rates of evolution. In adaptive radiation, modularity likely
 facilitates morphological divergence, but its specific role in trait
 diversification within these events remains uncertain. Antarctic icefishes
 (Perciformes: Notothenioidei) have undergone adaptive radiation in the
 frigid Southern Ocean, yet the role of modularity in their craniofacial
 evolution remains poorly understood. Emerging from a common ancestor 22
 million years ago, these fishes developed unique morpho-physiological
 adaptations, such as antifreeze glycoproteins, that contributed to their
 evolutionary success, but the contribution of cranial modularity to their
 diversification is still unexplored. Here, we analyze skull shape across
 172 perciform species using micro-CT scanning and geometric morphometrics
 to investigate the tempo and mode of skull evolution in 80 notothenioids
 versus 92 perciform relatives. Notothenioids exhibit considerable cranial
 shape diversity, with skull shapes ranging from short to long faces. Fast
 rates of skull shape evolution occurred in smaller subclades following the
 emergence of cranial elongation, a derived trait within notothenioids.
 They also exhibit elevated evolutionary modularity relative to their
 perciform relatives, with reduced covariation among skeletal elements over
 time, likely corresponding with Miocene cooling events and the formation
 of the Antarctic Circumpolar Current. We propose that greater phenotypic
 modularity in notothenioid skulls represents a pivotal innovation,
 facilitating their evolutionary response to new ecological opportunities
 in the Antarctic.
description: Morphological sampling and shape analyses. To investigate skull
 shape evolution in Notothenioidei, we quantified the skull shapes of 80
 Notothenioidei species (representing approximately 51% of total clade
 diversity, with all seven families included) and 92 species from other
 Perciformes (encompassing 30 different families). This dataset includes a
 total of 172 species belonging to 37 families, covering 66% of the
 Perciformes families (SI Appendix, Table S4 and Fig. S1). We analyzed the
 three-dimensional skull morphology of each species through micro-computed
 tomography (μCT) scans. The specimens were scanned at both Rice University
 and the Natural History Museum of Los Angeles County (LACM) under the
 oVert and ScanAllFishes initiatives. Additionally, we obtained further
 scans from Morphosource (http://morphosource.org). These scans were processed in Amira v2.0.0 (Thermo Fisher Scientific, Waltham, MA) to isolate the skull by removing scales and other debris. The resulting skulls were converted into 3D meshes and saved as .ply files. We then digitized the mesh files with 145 three-dimensional landmarks (comprising both 55 fixed and 90 semi-sliding types; SI Appendix, Table S4) using the Checkpoint software (Stratovan, Davis, CA). All landmarks were consistently placed on the left side of the skull. After digitization, to account for rotation and translation inherent in the highly kinetic articulating components of the fish skull, we first aligned each dataset using Generalized Procrustes Analysis (GPA) with the gpagen function in the <em>geomorph</em> package, version 4.0.8 (72) in R, version 4.4.1 (73). Next, a local superimposition was performed to standardize the positioning of the various skull elements. To analyze the shape evolution of individual bones, we subdivided our comprehensive skull shape dataset into smaller datasets for each bone, with the raw coordinates for each bone individually superimposed. Following the local superimposition, using the findmeanspecies function, we determined the mean species shape for each group (Notothenioidei and Other Perciformes). After performing the local superimposition, we conducted a principal components analysis (PCA) to explore the primary axes of skull shape variation. Additionally, we utilized the <em>plotRefToTarget</em> function in the <em>geomorph</em> package to illustrate the main axes of skull shape variation as ball-and-stick plots (SI Appendix, Fig. S3). Phylomorphospace and morphological disparity. To visualize the major axes of shape variation and evolution of Notothenioidei, we employed a phylomorphospace approach using the first two principal components (PCs) of shape variation, which accounted for 56% of the total shape variance, and the pruned, time-calibrated phylogeny from Rabosky et al. We pruned the phylogeny using the <em>drop.tip</em> function in the *ape *package version 5.0 to only include the taxa present in our study.
description: # Cranial modularity drives phenotypic diversification and adaptive
 radiation of Antarctic icefishes
 [https://doi.org/10.5061/dryad.vq83bk44j](https://doi.org/10.5061/dryad.vq83bk44j) ## Description of the data and file structure ### Files and variables #### Table S1: Species list used in our study (word format): species, sample number, and origins (Museum or Fieldwork) * *Species*: Currently valid scientific name * *Source/Museum*: Fieldwork Belize, museums (BMNH, ANSP, FMNH, KAUM, UF, UWFC, AM, USNM, AMNH, JFBM, UVI), or Uncat (species not deposited in a museum) * *Catalog number*: voucher number associated with the museum or Uncat (species not deposited in a museum) - **Shape dataset** (merged_icefishescoords_final.csv): the superimposed landmarks coordinates of 172 perciform species (80 notothenioids and 92 perciform relatives) * *Species* (categorical variable): Perciform specimens  * *V1-V435*(numerical variable): landmark coordinates in a 2d array formart - **Actinopterygii tree** (actinopt_12k_treePL.nwk): the phylogeny (TRE format) * Phylogenetic tree Rabosky et al. 2018 (Fish Tree of Life). - **Classifiers** (icefish_group_final2.csv): Table containing the name of the species ,indicating the classification according to two taxonomic groups * Specimen (categorical variable): individual species' name * Species (categorical variable): Currently valid scientific name * Group (categorical variable):  1 = Other Perciformes; 2 = Nototheniiodei species; * Group_family_1 (categorical variable): Bathydraconidae; Bovichtidae; Channichthyidae; Eleginopsidae; Harpagiferidae; Nototheniidae; Pseudaphritidae; Other Perciformes; * Group_family_2 (categorical variable):  1 = Bathydraconidae; 2 = Bovichtidae; 3 = Channichthyidae; 4 = Eleginopsidae; 5 = Harpagiferidae; 6 = Nototheniidae; 7 = Pseudaphritidae; 8 = Other Perciformes ; * Region (categorical variable): P_nA = Perciformes with non-Antarctica geographical distribution; PA = Perciformes with geographical distribution restricted to Antarctica; NA = Nototheniiodei with geographical distribution restricted to Antarctica; N_sA = Nototheniiodei with subAntarctica geographical distribution; N_nA = Nototheniiodei with non-Antarctica geographical distribution; * Group_region (categorical variable): 1 = NA = Nototheniiodei with geographical distribution restricted to Antarctica; 2 = N_nA = Nototheniiodei with non-Antarctica geographical distribution; 3 =  N_sA = Nototheniiodei with subAntarctica geographical distribution; 4 = PA = Perciformes with geographical distribution restricted to Antarctica; 5 = P_nA = Perciformes with non-Antarctica geographical distribution. #### Code/Software R is required to run *I*cefishes_script_final_2.R; the script was created using version 4.3.2. Annotations are provided throughout the script.