DOI: 10.5061/dryad.x95x69pmx
title: Disentangling mechanical and sensory modules in the radiation of Noctilionoid bats

description: The vertebrate cranium is a complex anatomical structure with diverse
 mechanical and sensory functions. Shifts between modularity and
 integration in both sets of functions, especially mechanical function,
 have been implicated in adaptive diversification. However, how mechanical
 and sensory systems and functions have coevolved and how their
 interrelationship contributes to phenotypic disparity remains largely
 unexplored. To examine the modularity, integration, and evolutionary rates
 of sensory and mechanical structures within the head, we analyzed hard and
 soft tissue scans from ecologically diverse bats from the superfamily
 Noctilionoidea, which range from generalized insectivores to derived
 frugivores and nectarivores. We identified eight cranial regions as
 distinct modules — five associated with bite force and three linked to the
 olfactory, visual, and auditory systems, respectively — whose
 interrelationships differ between Neotropical leaf-nosed bats (Family
 Phyllostomidae) and other noctilionoids. Our analyses suggest that the
 peak rates of sensory module evolution predate those of mechanical
 modules. This finding is consistent with transitions to new diets first
 involving changes in the detection of novel food items, followed by
 adaptations to process them. We propose the coevolution of structures
 influencing bite force, olfaction, vision, and hearing constituted a
 structural opportunity that allowed the phyllostomid ancestor to take
 advantage of existing ecological opportunity and the group to become a
 classic example of adaptive radiation.
description: To quantify the sizes and shapes of mechanical structures, the
 olfactory bulb, and cochlea, we visualized the heads of bats using a
 combination of standard computed tomography (CT) for hard tissues. Bat
 specimens were scanned using a Nikon Metrology (X-Tek) HMXST225 microCT
 system at the Center for Nanoscale Systems at Harvard University.
 Three-dimensional (3D) images were processed following (Hedrick et al.
 2020). We generated image stacks using proprietary software associated
 with the X-Tek scanner (CTPro, Nikon Metrology Inc., Japan), segmented
 image stacks using Mimics v. 16.0 (Materialise, Leuven, Belgium), created
 meshes using VGStudio Max 3.0 (Volume Graphics Inc., Germany) and exported
 them as PLY files. We used reconstructions of eyes
 previously published by (Hall et al. 2021), which include diffusible
 iodine-based contrast-enhanced computed tomography (DiceCT; (Gignac et al.
 2016; Hedrick and Dumont 2018)) from the same specimens (S1 Table).
  Briefly, we defined the orbital space surrounding the left eye by its
 muscular boundaries and eyelid. We used the volume and location of the
 orbital space as a proxy for eye location and volume because it is less
 subject to distortion than the globe itself in fluid-preserved museum
 specimens (Hedrick and Dumont 2018). Placing
 Landmarks Our data set included a total of 322
 landmarks: 43 fixed landmarks, 160 sliding semi-landmarks on curves, 55
 surface landmarks on the eye, and 64 surface landmarks on a patch placed
 on the palate (S2 Table, S1 & 2 Video) that were placed on 3D
 meshes using IDAV Landmark Version 3.6 (Wiley et al. 2005). Sliding semi-
 and surface landmarks are adjusted to reduce their weight in the analysis,
 thus reducing the potential effect of their representation by large
 numbers of semi- and surface landmarks. These methods involve sliding
 neighboring landmarks along curves and surfaces to minimize bending energy
 and ensure that the arbitrary spacing of semi-landmarks does not influence
 shape variation (Bookstein 1997; Gunz and Mitteroecker 2013). We placed
 external landmarks on the external surface of the cranium as per Hedrick
 et al. (2020). To landmark structures on internal surfaces of the bony
 skull, 3D models were digitally dissected in Geomagic Studio 2014
 (3DSystems, SC, USA) into three parts to reveal the cochlea, the
 impression left by the olfactory bulb on the internal surface of the skull
 (anterior cranial fossa), and the internal surface of the cranial
 base. We identified landmarks to represent the eye in
 three steps. First, we used Mimics to calculate the volume and centroid of
 the orbital space reconstructed by Hall et al (2021). We then generated a
 sphere of equal volume around the centroid and placed 55 landmarks on its
 surface. Reducing the eye landmarks to seven (one in the center and six
 across perpendicular poles) did not change the results of tests for
 modularity (S9 Table)), and so we proceeded with the data set of 55 eye
 landmarks. Because our eye data are based on idealized and identical
 shapes, the results of our analyses reflect variation in the orientation
 and overall size of the eye. Throughout the landmarking process, we kept
 all parts of each specimen in the same coordinate system so that landmarks
 could be concatenated into a single file using custom R code.
 To ensure that the number of landmarks selected did not affect
 the number of modules recovered, we conducted a sensitivity analysis by
 downsampling the number of landmarks by 25%, 50%, and 75% and testing
 whether this changed the number of modules detected. Custom code was
 written in R to subsample within each module systematically; every second
 landmark (50%), one in every four landmarks (25%), and the first three of
 every four landmarks (75%). The same number of modules were recovered
 using EMMLi from the complete set of noctilionoid landmarks and all
 down-sampled datasets, and vector congruence correlation matrices of the
 data sets were strongly correlated (R2 = 0.73 – 0.95; S3 Table). We
 further tested the effect of landmark number on modularity by regressing
 the number of landmarks against the ρ coefficient of each module (a
 measure of integration), first for the whole noctilionoid group and then
 for the sub-groups of phyllostomids and other noctilionoids. We found no
 evidence that the number of landmarks in each module influenced modularity
 (S8 Table). Based on these analyses, we used the full set of 322 landmarks
 for further analyses. To employ phylogenetic comparative methods and
 adjust for unequal sample sizes in our analyses, we used species means for
 taxa represented by multiple individuals (7 species, S1 Table).
 Defining Modules To test hypotheses about
 modularity of sensory and mechanical systems we grouped landmarks into
 eight hypothetical modules that encompass anatomical structures associated
 with specific functions (Fig 1; S1 & 2 Video. Among mammals, the
 size of sensory structures is often directly related to function. To
 conserve the size component in our analyses of shape variation, we did not
 adjust data for allometry. Therefore, our landmark data still carry shape
 variation that is associated with size. The shapes of some mechanical
 regions are clearly linked to functional variation (e.g., Dumont 2004;
 Dumont et al. 2009, 2012; Santana et al. 2012; Neaux et al. 2021). Here we
 make inferences based on those linkages but do not directly measure
 mechanical variables. The shapes of sensory structures (e.g., olfactory
 bulb, cochlear region, and eye) are unlikely to perfectly reflect sensory
 ability. Likewise, it is likely that some shape changes don’t reflect
 functional change at all (Gould and Lewontin 1979). Nevertheless, we
 propose that there is at least circumstantial evidence to suggest that
 each of our eight modules carries some functional signal. 
 We defined three sensory modules that encompass the olfactory
 bulb (smell), the cochlea (sound), and the eye (vision) (Figure 1; S1
 & 2 Videos). Enlarged eyes are associated with increased visual
 acuity  (Müller and Peichl 2005; Müller et al. 2007; Land and Nilsson
 2012; Eklöf et al. 2014; Veilleux and Kirk 2014; Sadier et al. 2018) and
 eye orientation is related to activity pattern in primates (Heesy 2008).
 Larger olfactory bulbs support more expansive epithelia and therefore
 larger surface areas for odor detection (Barton et al. 1995; Buschhüter et
 al. 2008; Corfield et al. 2015). Enlarged olfactory epithelia are
 associated with frugivory among noctilionoid bats (Yohe et al. 2021).
 Finally, cochlear volume and shape are correlated with aspects of cochlear
 morphology that influence hearing performance (Kössl and Vater 1995; Kirk
 and Gosselin-Ildari 2009; Vater and Kössl 2011; Davies et al. 2013a,
 2013b). Among bats, variation in the relative volumes of sensory
 structures often tracks variation in foraging strategy and diet (Barton et
 al. 1995). For example, the ancestral phyllostomid diverged from its
 sister taxa in having relatively larger olfactory bulbs and eyes, which is
 characteristic of frugivory (Hall et al. 2021). Similarly, the shift to
 plant-based diet in palaeotropical fruit bats (Pteropodidae) allowed
 larger skulls and brain regions associated with vision, olfaction, and
 spatial memory (Thiagavel et al. 2018). We defined olfactory, cochlear,
 and eye modules with landmarks that encompass the three-dimensional volume
 as well as shape of those structures (Figure 1, S1 & 2 Videos).
 Olfactory module landmarks were placed on the impression of the olfactory
 bulb on the internal surface of the skull, cochlea module landmarks were
 placed on the region of the cranial base that encompasses the cochlea, and
 the eye module was defined by landmarks placed on the surface of the
 reconstructed orbital sphere. 
description: We used predominantly R statistics https://www.r-project.org/. For rates through time we used BayesTraits http://www.evolution.reading.ac.uk/BayesTraitsV4.0.0/BayesTraitsV4.0.0.html