david.jacobs@uct.ac.za
(DSJ)
Abstract
The relative contributions of adaptation and drift to morphological
diversification of the crania of echolocating mammals was investigated
using two horseshoe bat species, Rhinolophus simulator andR. cf. simulator as test
cases. We used 3D geometric morphometrics to compare the shapes of
skulls of the two lineages collected at various localities in southern
Africa. Shape variation was predominantly attributed to selective
forces; the between population variance (B ) was not proportional
to the within population variance (W ). Modularity was evident in
the crania of R. simulator but absent in the crania of R.
cf. simulator and the mandibles of both species. The skulls of the two
lineages thus appeared to be under different selection pressures,
despite the overlap in their distributions. Selection acted mainly on
the nasal dome region of R. cf. simulator whereas selection acted
more on the cranium and mandibles than on the nasal domes of R.
simulator . Probably the relatively higher echolocation frequencies used
by R. cf. simulator , the shape of the nasal dome, which acts as a
frequency dependent acoustic horn, is more crucial than in R.
simulator , allowing maximization of the intensity of the emitted calls
and resulting in comparable detection distances. In contrast, selection
pressure is probably more pronounced on the mandibles and cranium ofR. simulator to compensate for the loss in bite force because of
its elongated rostrum. The predominance of selection probably reflects
the stringent association between environment and the optimal
functioning of phenotypic characters associated with echolocation and
feeding in bats.
Introduction
Understanding the relative
contributions of drift and adaptation to organismal diversification is
fundamental to studies of evolutionary ecology. To avoid overestimation
of selection, drift should always be explicitly accounted for (Bettiet al. , 2010). However, quantifying the relative contributions of
these processes to phenotypic diversification is challenging because
distinguishing the two processes and identifying their impacts on
diversity is difficult (Brandon & Carson, 1996; Millstein, 2002, 2008;
Brandon, 2005). Fortunately, there has been some progress in this regard
(Millstein, 2008). Adaptation is deterministic and results in phenotypic
patterns correlated to environmental/climatic clines (Millstein, 2008).
In contrast drift is neutral and results from random processes affecting
the genetic composition of populations (Millstein, 2008). In many cases,
drift is assumed when evidence for selection is not found (Millstein
2008). However, mathematical approaches e.g. Lande’s model (Lande, 1976,
1979), that allow the quantification of the effects of drift on patterns
of phenotypic variation, has made it possible to directly determine the
relative importance of drift and selection to phenotypic variation.
Although the application of Lande’s model to phenotypic traits that vary
seasonally (e.g. body weight) or are flexible (e.g. behaviour) is
theoretically possible e.g. Mutumi et al . (Mutumi et al. ,
2017), application of the model to such data might lead to different
results depending on when the traits are sampled. In contrast, hard
tissue e.g., bony skeletons including skulls provide a more permanent
record of the evolutionary processes that a species has endured over its
history. Several studies have therefore suggested the use of skulls and
geometric morphometrics for enquiries into the relative roles of drift
and selection e.g., Evin et al . (Evin et al. , 2008).
Skulls serve functions crucial to the fitness of organisms and their
diversification is likely primarily through adaptation (Santana et
al. , 2012). The neurosensory system (brain), diet acquisition
structures, olfactory system, visual system, speech and sound systems
are integrated and housed in the skull. Skulls are therefore subject to
diverse selection pressures imposed by the environment on these systems
(Cheverud, 1982; Pedersen, 1998; Klingenberg, 2008). For example, the
evolution of increased head height, prominent temporal ridge and huge
jaw adductor muscles in Chamaeleonid lizards were associated with strong
bite force (Herrel & Holanova, 2008). The association between skull
morphology and bite force has also been demonstrated in many other
vertebrates (Cleuren et al. , 1995; Freeman & Lemen, 2008; Curtiset al. , 2010; Davis et al. , 2010). For example, elongated
snouts in some fish appear to be an adaptation which facilitates feeding
through suction (Westneat, 2005). Besides dietary adaptations, other
behaviours relevant to fitness have shaped the evolution of skull shape.
These are grooming (Rosenberger & Strasser, 1985), fighting with
conspecifics (Huyghe et al. , 2005), building shelters (Zuriet al. , 1999; Hansell, 2000; Santana & Dumont, 2011) and sensing
the environment (Oelschläger, 1990; Ross & Kirk, 2007).
The role of drift was demonstrated in the evolution of human skull form
and shape (Rogers Ackermann & Cheverud, 2002; Betti et al. ,
2010) using quantitative models. Smith (Smith, 2011) showed that some
parts (basicranium, temporal bone, and face) of the skull evolved
neutrally whereas the mandible evolved through selection. Quantitative
and population genetic methods have shown that isolation between
Neanderthal and modern human populations led to cranial diversification
through genetic drift rather than the commonly proposed adaptive
explanations (Weaver et al. , 2007). Similarly, Ackermann and
Cheverud (Rogers Ackermann & Cheverud, 2002; Ackermann & Cheverud,
2004) applied Lande’s model (Lande, 1976, 1979) to variation in the
shape and size of human and monkey skulls and found that drift played a
significant role. The role of selection may thus be exaggerated if drift
is not accounted for quantitatively (Smith, 2011). This is especially
important because studies within the same genus have yielded conflicting
results. For example, drift was proposed as the cause of phenotypic
convergence and divergence in two horseshoe bats, Rhinolophus
darlingi (Jacobs et al. , 2013) and Rhinolophus monoceros(Chen et al. , 2009), respectively. In contrast, selection was
implicated in the divergence within two other horseshoe bat species,Rhinolophus capensis (Odendaal et al. , 2014), and R.
ferrumequinum (Sun et al. , 2013). Thus, two of the four studies
on horseshoe bats (genus Rhinolophus) suggest that selection is the
predominant driver of diversification but the other two suggest that
drift is the main factor. A rigorous test of the processes behind
phenotypic diversification should therefore employ models that weigh the
relative contributions of adaptation and drift to determine which is the
more dominant process shaping phenotypic variation.
The evolution of skull morphology in animals that rely on acoustic
signals for communication or navigation (e.g. bats, dolphins, whales,
rodents and birds) is particularly interesting because it adds a whole
suite of selection pressures on the skull besides those associated with
diet and the other five senses (Santana & Lofgren, 2013). For example,
there are prominent resonant chambers (forming the nasal dome) in the
nasal region of the skulls of horseshoe bats (Rhinolophidae) which acts
as an acoustic horn (Hartley & Suthers, 1988; Pedersen, 1998) allowing
echolocation call frequencies to be filtered and emitted at high
intensity.
Using 3-D geometric morphometrics and Lande’s model we investigated the
relative roles of adaptation and drift in two African horseshoe bat
lineages, Rhinolophus simulator and R. cf. simulator (Doolet al. , 2016) that are of similar size but differ markedly in the
frequency of their echolocation calls. R. cf. simulator was
previously classified as R. swinnyi but genetic analyses, using
six nuclear markers and an mtDNA fragment (Dool et al. , 2016),
indicated that R. swinnyi to the northeast of South Africa was
indistinguishable from R. simulator , despite marked differences
in the frequency of their echolocation pulses. The frequency of
echolocation pulses have a direct impact on the operational range of
echolocation and is generally inversely correlated with body size in
bats (Jones, 1996, 1999; Jacobs et al. , 2007; Jacobs & Bastian,
2018) and with the volume of the nasal dome in the Rhinolophidae (Jacobset al. , 2014). R. cf. simulator uses higher frequency
echolocation calls which are more affected by atmospheric attenuation
and probably must emit its calls at greater intensity to achieve the
same operational range as R. simulator . We therefore hypothesised
that selection rather than drift should be the predominant process in
the evolution of skull shape because of the vital sensory and foraging
functions of the skull. We predicted: 1) significant deviation from
proportionality between the within and between population trait variance
in both species (Rogers Ackermann & Cheverud, 2002); 2) modularity
should be more prevalent in the crania of both species than in the
mandible because of the central role of echolocation to the survival and
reproduction of bats. Independence between the cranium and muzzle allows
for relatively more flexible response to sensory driven selection.
Additionally, the existence of modularity would indicate that the skull
is under directional selection because drift and stabilizing selection
are inefficient at creating modularity (Melo & Marroig, 2015).
Materials and Methods
Study sites and animals
Skulls were extracted from voucher specimens of both lineages collected
in support of two other studies, Mutumi et al . (Mutumi et
al. , 2016) and Dool et al . (Dool et al. , 2016). These
skulls were supplemented with museum specimens of both lineages (S1
Table). The distributional ranges of the two focal speciesRhinolophus simulator (four localities) and R. cf.
simulator (four localities) follow a latitudinal gradient ranging from
16°S to 32°S in south eastern Africa (fig. 1 in Mutumi et al .
(Mutumi et al. , 2016)). Both R. simulator and R. cf.
simulator lineages have pulses dominated by a constant frequency but at
different frequencies with means of 80 and 107 kHz, respectively, when
at rest (see fig. S1 in Mutumi et al. (Mutumi et al. ,
2016)). The two lineages occur in seven woodland types; eastern half of
southern Africa, ranging from DRC in the north, through Zimbabwe and
Botswana into South Africa in the south. Woodland types include the
Central Zambezian Miombo woodland in DRC and Zambia, the Zambezian and
Mopane woodlands, Southern Miombo woodlands, and the Eastern Zimbabwe
Montane Forest-grassland Mosaic (Olson et al. , 2001). The
southern-most populations occur within Highveld grasslands. In Botswana
the sampling site occurred in an ecotone of three woodlands: Kalahari
Acacia-Baekiaea, Kalahari Xeric Savannah, and Southern Africa bushveld.
Botswana sites experience the driest climate and the Eastern Zimbabwe
Montane Forest-grassland Mosaic, the wettest (Olson et al. ,
2001).
The specimens were grouped according to the geographic location where
they were captured (S1 Fig a and b; Table A1). These locations included
north-eastern South Africa (NE), northern Zimbabwe and combined southern
Zambia (NZ), Democratic Republic of Congo (DR), south-eastern South
Africa (SE), southern Zimbabwe and northern South Africa combined (SZ;
S1 Fig a and b; Table A1).
3-D images of each skull were captured through micro-focus X-ray
tomography at the South African Nuclear Energy Corporation (NECSA,
Pretoria, South Africa; (Hoffman & De Beer, 2012)) following the same
procedures as in Jacobs et al . (Jacobs et al. , 2014). All
images were imported into the 3-D imaging software, Avizo (version 8.0;
Visualization Sciences Working Group, Merignac, France) as volume files.
After creating iso-surfaces from the volume files in Avizo, files were
saved in ‘Stanford ply’ format and opened in Meshlab (version 1.3.3,
Visual Computing Lab of
ISTI - CNR, Italy) for placing
landmarks. Landmarks were chosen depending on their homology (common and
repeatable points on all skulls for each lineage). One skull and one
mandible were first tested by repeating the land-marking process 10
times to determine the precision at which landmarks could be placed.
Among the tested landmarks the most precise were selected, i.e., 24
landmarks for the skull and 15 for the mandible (S2 Table). Landmarks
were placed on only the right half of the skull and the right mandible
to control for possible asymmetry (Jacobs et al. , 2014). Each
landmark in the 3D space had three coordinates (x, y and z). These sets
of three coordinates were used in MorphoJ (version 1.7.0_45;
(Klingenberg, 2011)) to analyse shape variation in skulls and mandibles
of the two lineages across different localities.
Landmark co-ordinates were analysed as follows. Firstly, a Procrustes
superimposition was done on the coordinates to remove variation because
of differences in orientation and scale and to standardise the landmarks
in a common coordinate system (Adams et al. , 2004). Outliers were
checked and extreme cases were double-checked against the original
volume files. Where necessary the landmarks were re-inserted on the
skull images. A covariance matrix was generated from the Procrustes
coordinates, on which a principal components analysis was performed to
explore variation in skull shape amongst the different localities for
each species. A Procrustes ANOVA (provided in MorphoJ software) was used
to test the significance of the differences in skull shapes across
localities and between sexes. To visualise the shape differences, a
Canonical Variate analysis (CVA) was used. Shape changes in the skulls
and mandibles were visualised using the wireframe outlines in MorphoJ
which compares shape variations against the average skull shape along
each Canonical Variate (CV) with the outlines at the extremes of each
CV.
Modularity was also investigated using a priori hypotheses
according to Klingenberg (Klingenberg, 2009). Modularity is the
differential evolution of different complexes, each complex consisting
of groups of traits that evolve together but relatively autonomously
from other such complexes (Cheverud, 1996; Wagner, 1996; Klingenberg,
2005). Processes contributing to modularity can be genetic,
developmental, functional, or environmental (Klingenberg, 2005). The
mandible was divided into subsets of seven (ascending ramus) and eight
(alveolar region) landmarks and the cranium was divided into subsets of
10 (basicranium) and eight (rostrum) landmarks as in Jojic et al .
(Jojić et al. , 2015). The strength of association between
hypothesized modules and all alternative partitions were tested by the
CR – covariance ratio in R statistics according to Adams (Adams &
Otárola-Castillo, 2013). The CR measures the strength of association
between two blocks, i.e., the two modules identified by the covariance
matrices of their landmark coordinates compared with the two
hypothesised modules (Adams & Otárola-Castillo, 2013). The CR varies
from 0 (completely uncorrelated data) to 1.0 (correlated). The strength
of the modularity was also measured by the Zcrcoefficient which measures the strength of modularity in each structure
– the more negative the coefficient the higher the strength of
modularity.
Lande’s Model
The relative contributions of drift and adaptation to the variation in
skull and mandible shape was tested by applying the principles of
Lande’s model (Lande, 1976, 1979) in the form of the β -test
(Rogers Ackermann & Cheverud, 2002), which is described in detail in
Mutumi et al . (Mutumi et al. , 2017). The β -test is
based on the hypothesis of a log-linear relationship between the
variation of phenotypic characteristics between (B) and within (W)
populations. If the slope of this relationship is not significantly
different from one the null hypothesis is accepted and the observed
variations in phenotypic traits can be attributed to neutral
evolutionary processes (mutation and drift). Otherwise, the null
hypothesis is rejected, which implies that non-neutral evolutionary
processes, such as natural selection, can be inferred as the dominant
driver of diversification.
Successive landmark coordinates were used to generate
Euclidean
distances (D ) for successive pairs of landmarks using the
following formula:
Where x, y and z are the 3-D landmark coordinates, the subscripts 1 and
2 denote successive positions, and Di is the
Euclidean distance for landmark i.
The resulting multivariate response matrix comprisingDi was used to derive the within locality
(W ) and between locality (B) variances following the
procedure outlined in Mutumi et al . (Mutumi et al. , 2017).
Briefly, the Di response matrix was fitted using
MANOVA with localities and sex as the categorical predictors to generate
a variance/covariance (V/CV) matrix for each species. A measure of the
within- population variance W was then obtained in the
form of eigenvalues derived from principal component analysis (PCA) on
the V/CV matrix. The between population variation B was estimated
through multiplication of the matrix of PCA-derived eigenvectors with
the matrix of Di means of each locality. We then
regressed the log-transformed within variance against the
log-transformed between variance and carried out regression t-tests to
test the hypothesis that there was no significant difference between the
regression slope and one as a function of:
Where β0 is the intercept term and ε is
the error (see Mutumi et al . (Mutumi et al. , 2017)).