DOI: 10.5061/dryad.95x69p8g9
title: Morphological diversity in the sensory system of phyllostomid bats: implications for acoustic and dietary ecology

description: 1. Sensory systems perform fitness-relevant functions, and specialized
 sensory structures allow organisms to accomplish challenging tasks.
 However, broad comparative analyses of sensory morphologies and their
 performance are lacking for diverse mammalian radiations. 2. Neotropical
 leaf-nosed bats (Phyllostomidae) are one of the most ecologically diverse
 mammal groups; including a wide range of diets and foraging behaviors, and
 extreme morphological variation in external sensory structures used in
 echolocation (nose leaf and pinnae). 3. We coupled 3D geometric
 morphometrics and acoustic field recordings under a phylogenetic framework
 to investigate the mechanisms underlying the diversification of external
 sensory morphologies in phyllostomids, and explored the potential
 implications of sensory morphological diversity to functional outputs and
 dietary ecology. 4. We found that the nose leaf consists of two
 evolutionary modules, spear and horseshoe, suggesting that modularity
 enabled morphological and functional diversification of this structure. 5.
 We found a significant association between some aspects of nose leaf shape
 and maximum frequency and bandwidth of echolocation calls, but not between
 pinnae shape and echolocation call parameters. This may be explained by
 the use of multiple sensory modes across phyllostomids and plasticity of
 some echolocation call parameters. 6. Species with different diets
 significantly differed in nose leaf shape, specifically in spear breadth,
 presence of a midrib, and cupping and anterior rotation of the horseshoe.
 This may relate to different levels of prey type specificity within each
 diet. Pinnae shape significantly differed between species that consume
 non-mobile, non-evasive prey (broad rounded, cupped pinnae) and mobile,
 evasive prey (flattened pinnae with a sharp tapering apex). This may
 reflect the use of different sound cues to detect prey. 7. Our results
 give insight into the morphological evolution of external sensory
 structures in bats, and highlight new links between morphological
 diversity and ecology.
description: <i>3D Imaging and shape analyses</i> We quantified the three-dimensional
 morphology of nose leaves and pinnae for 46 adult bats from 33
 phyllostomids species that span the diversity in body size, nose leaf and
 pinnae morphologies, and dietary ecology within the family. The majority
 of specimens used (28 species) were collected by us in the field following
 approved methods (University of Washington IACUC protocol 4307-01), and the
 remainder (5 species) were fluid-preserved museum specimens in which the
 nose leaf and pinnae were preserved in their natural position
 (supplemental Table 1). Based on information and classifications from the
 literature, we grouped species into six taxonomic dietary categories
 (animalivores, insectivores, nectarivores, frugivores, omnivores, and
 sanguinivores; Giannini & Kalko, 2004), and two functional dietary
 categories: predators of non-mobile/non-evasive prey (nectarivores,
 frugivores, omnivores and sanguinivores), and predators of mobile, evasive
 prey (insectivores, animalivores). While assignment of species to these
 broadly defined dietary categories may be an over simplification of the
 breadth of their ecological roles (e.g., <i>Glossophaga
 soricina</i>; Clare et al., 2014), these classifications were
 necessary to overcome limitations due to sample sizes and the lack of
 quantitative dietary data that could inform more detailed
 analyses. Unless the nose leaf
 and pinnae are adequately fixed during specimen preservation, this process
 can alter their shape (e.g., resulting in bent nose leaves). Furthermore,
 high-resolution imaging (such as µCT scanning, below) of these structures
 yields better results if they are scanned in isolation from denser
 structures like the skull. Thus, we captured pinnae and nose leaf
 morphology by taking casts from freshly euthanized animals. To do so, we
 used a President Jet dispenser gun to apply President dental molding epoxy
 (Epo-tek 301) to the pinnae and nose leaf (Fig. 2A). We allowed casts to
 dry on the specimen for a minimum of five minutes before carefully
 removing them. Due to limitations of field conditions and primarily using
 freshly collected specimens, we were not able to assess the repeatability
 of this technique. However, individuals of a species cluster closely
 together in morphospace, which indicates that this casting method is
 adequate for capturing interspecific variation. To increase the size
 and taxonomic scope of our dataset, we were also able to use several
 fluid-preserved specimens that were specifically preserved to avoid
 deformation of soft tissues and could be destructively sampled (i.e., nose
 leaf could be dissected out for µCT scanning). This additional source of
 specimens did not seem to introduce errors in our quantification of
 morphology. We created 3D digital models of the nose leaf and pinnae by
 scanning either specimens or epoxy casts on a Skyscan 1174 µCT scanner
 (Bruker MicroCT, Kontich, Belgium) at a 17- 30.1 μm resolution, depending
 on the size of the cast or specimen.  We used NRecon (Microphotonics,
 Allentown, PA) to convert CT shadow images into image stacks (“slices”),
 and imported these into Mimics 17.0 (Materialise NV, Leuven, Belgium,
 2014) to segment nose leaf and pinnae and produce 3D surface (*.stl) files
 (Fig. 2B). We imported raw stl files into Geomagic Studio 2014.1.0 (3D
 Systems, SC, USA, 2014) to remove scanning artifacts (e.g., debris in
 molds) from the models. To quantify nose leaf and pinnae
 shape, we used 3D geometric morphometric analyses (Bookstein, 1997;
 Zelditch, Swiderski, Sheets, & Fink, 2004). These were based on
 single point landmarks and surface patches, all placed on 3D models using
 Stratovan Checkpoint© (Stratovan Corporation, Davis, CA). For the nose
 leaf, we placed: (1) single-point landmarks at the base of each nostril
 and the apex of the spear, (2) evenly-spaced semi-landmarks around the
 nose leaf perimeter, and (3) two “patches” of semi-landmarks in a grid
 across the surface of the spear and the surface of the horseshoe,
 respectively (Fig. 2D). To analyze shape changes of subcomponents of the
 nose leaf separately (i.e., spear and horseshoe), we added landmarks to
 ensure each subcomponent had a sufficient number of true landmarks. For
 the spear, we placed a single-point landmark at the apex of the spear, two
 landmarks at the point where the spear meets the horseshoe, and a patch of
 semi-landmarks over the anterior surface of the spear (Fig. 2D). Some
 species lack a spear, and therefore were not included in analyses of that
 structure. For the horseshoe, we placed a single-point landmark on each
 nostril and one patch over the surface of horseshoe.  For pinnae, we placed
 two landmarks at the points where the pinna attaches to the head, and a
 patch of semi-landmarks across its surface (Fig. 2C).  We exported
 landmark coordinates for each specimen as .csv files and computed species
 means for landmark coordinates in Excel. We then performed Procrustes
 superimposition analyses to scale, align and rotate landmark
 configurations (Rohlf, 1990), and obtain a set of variables describing the
 shape of the entire nose leaf, spear, horseshoe and pinnae across species.
 We used the package “geomorph” (Adams & Otárola-Castillo, 2013) in
 R v 99.903 (R Core Team, 2017) for geometric morphometric
 analyses.  
 <i>Acoustics</i> Phyllostomid bats
 produce low-intensity calls (Brinkløv, Kalko, & Surlykke, 2009;
 Griffin, 1958) that are difficult to capture on passive recording devices.
 Consequently, call parameter data are sparse for most phyllostomid
 species. For this study, we collected 16-bit recordings of release calls
 using a microphone condenser (UltraSoundGate 116). Our sample included 101
 individuals spanning 33 species. We held each bat in hand, placed a
 microphone approximately six inches from its face, and then released the
 bat away from environmental clutter while recording the calls it emitted
 as it flew away. Since bats had to be released to document their natural
 calls, we did not use these same individuals in morphological analyses. We
 measured call parameters for 3–7 individuals per species, with the
 exception of species that were rare or difficult to capture at our study
 localities (<i>Chrotopterus auritus, Glyphonycteris sylvestris,
 Phyllostomus hastatus, Sturnira lilium</i>), for which we were able
 to record 1 individual per species. We analyzed release calls using
 Avisoft SASLabPro v. 5.2.12 (Avisoft Bioacoustics, Berlin, Germany) to
 extract the following echolocation call parameters: minimum frequency
 (kHz), maximum frequency (kHz), peak frequency (kHz) (i.e., frequency with
 the highest amplitude), and total bandwidth (kHz) across the call. We
 averaged call sequences per individual (a minimum of 5) and calculated
 means and standard deviation of each parameter (supplemental Table 2).
 While release calls may not fully reflect the echolocation capabilities of
 the species, our own comparisons of release calls with foraging calls for
 one species (<i>Carollia castanea</i>) indicate that foraging
 call parameters fall well within the range of values recorded for release
 calls (Leiser-Miller, in press).  
 <i>Statistical analyses</i> To
 test whether the nose leaf consists of two modules (spear and horseshoe;
 Fig. 2D), we used the function phylo.modularity (Geomorph package, Adams
 & Otárola-Castillo, 2013) to compute Covariance Ratio (CR) values
 for a two-module hypothesis based on the nose leaf landmark data, and
 estimate the p-value for this relationship over 1,000 random permutations.
 The CR ratio indicates the degree of covariation among landmarks within
 possible modules; values from 0 to 1 indicate less covariation
 <i>between</i> modules than <i>within</i> each
 module, supporting the modularity hypothesis, CR values greater than 1
 describe greater covariation between modules than within modules,
 supporting the null hypothesis of no modules (Adams, 2016).
 <i>            </i>To identify major axes of shape
 variation across sensory structures, we conducted phylogenetic Principal
 Component Analyses (pPCA), using the Rojas, Warsi, & Dávalos
 (2016) phylogeny, on the Procrustes (shape) coordinates for each
 structure/substructure using the R package “phytools” (Revell, 2012). We
 assessed the significance of pPCA axes via Horn’s parallel analysis from
 the ‘paran’ function in R (Dinno, 2015). Nose leaf and pinna shape axes
 were not correlated with size (forearm length; Supplementary Table 5), and
 therefore size was not considered in subsequent analyses. To identify if
 shapes of external sensory structures are correlated with call parameters,
 we ran separate phylogenetic generalized least squares (PGLS) regressions
 under Brownian motion of acoustic parameters across the call (minimum
 frequency, maximum frequency, peak frequency, and total bandwidth) against
 significant pPCs shape scores (see Results; nose leaf pPC 1-5; pinnae pPC
 1-4). Finally, we ran phylogenetic ANOVAs and post-hoc analyses to test
 for an association between diet category and nose leaf and pinnae shape,
 respectively. We used significant pPCs axes as response variables, and
 dietary category as the predicting factor.