10.5061/DRYAD.95X69P8G9
Leiser-Miller, Leith
0000-0003-4886-1494
University of Washington
Santana, Sharlene
0000-0001-6463-3569
University of Washington
Morphological diversity in the sensory system of phyllostomid bats:
implications for acoustic and dietary ecology
Dryad
dataset
2020
Phyllostomidae
tropical bats
nose leaf
Pinna
Acoustics
National Science Foundation
https://ror.org/021nxhr62
1456375
2020-03-25T00:00:00Z
2020-03-25T00:00:00Z
en
640548 bytes
2
CC0 1.0 Universal (CC0 1.0) Public Domain Dedication
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.
3D Imaging and shape analyses 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., Glossophaga soricina; 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. Acoustics 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 (Chrotopterus auritus, Glyphonycteris sylvestris, Phyllostomus
hastatus, Sturnira lilium), 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 (Carollia castanea)
indicate that foraging call parameters fall well within the range of
values recorded for release calls (Leiser-Miller, in press). Statistical
analyses 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 between modules than within 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). 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.