Discussion
The relationship between the within and between group variance did not
comply with the predictions of the model for drift. As predicted,
geographic variation in the skulls and mandibles of both lineages was
thus likely the result of selection, in accordance with our first
prediction. Modularity was only supported in R. simulator skulls,
the cranium and muzzle evolving as separate and integrated units.
Contrary to our second prediction, the mandible of R. simulatorand both the skull and mandible of R. cf. simulator , did not show
modularity. Thus the two closely related lineages (Dool et al. ,
2016) showed contrasting results with respect to modularity. We also
found evidence consistent with our hypothesis that more prominent
variation in shape were seen in the nasal dome in R. cf.
simulator than in R. simulator . Thus Lande’s model indicates
that selection rather than drift has shaped the skulls of these two
lineages. The results on modularity (see reference 43) suggest that the
selection responsible for the diversification of R. simulator is
predominantly directional (in the skull) and stabilising in the
mandibles, whereas in R. cf. simulator , is mainly stabilising for
both the skull and the mandible.
To some extent, these results contrast with previous findings on the
relative contributions of drift and adaptation based on Lande’s model
which identified signals of drift in some instances (Marroig &
Cheverud, 2004; Weaver et al. , 2007). Mutumi et al .
(Mutumi et al. , 2017) report signals for drift but their study
was based on a broader range of phenotypic features including flight,
size and echolocation parameters. Perhaps the fact that the skull
incorporates several functions (e.g., feeding and echolocation) crucial
to fitness causes it to be under severe selection pressure that could
eliminate or obscure any drift that might have occurred. The head is
under the influence of multiple selective pressures because it houses
the structures used for a variety of crucial survival and reproduction
functions, particularly echolocation. Both lineages appear to have
experienced selection pressure associated with echolocation, a key
survival trait. Echolocation is a sophisticated sense that varies
strongly with the task at hand and environmental conditions (Schnitzleret al. , 2003; Jakobsen et al. , 2013; Luo et al. ,
2014).
It surprising that modularity was present only R. simulator and
not in R. cf. simulator because modularity has been reported
across 22 African and Asian species of rhinolophids (Santana & Lofgren,
2013). However, stabilizing selection is thought to mitigate against the
evolution of modularity (Melo & Marroig, 2015). The absence of
modularity in R. cf. simulator may be a consequence of
stabilizing selection to retain the adaptive complex between flight,
body size and echolocation. In this respect the evolution of R.
cf. simulator is similar to Phyllostomidae which is tightly integrated
and probably evolved under the constraint of preserving adaptive
complexes (Hedrick et al. , 2019). Body size, wing loading and
echolocation frequency in bats are associated allometrically and are
indicative of an adaptive complex (Jones, 1999; Jacobs et al. ,
2007; Jacobs & Bastian, 2018). With respect to these allometric
relationships R. cf. simulator is an average rhinolophid. Its
echolocation frequency and wing loading fall within the allometric
relationships of the genus (Jacobs et al. , 2007; Jacobs &
Bastian, 2018).
In contrast to R. cf. simulator there was evidence of modularity
in R. simulator suggesting that its skull was under directional
selection (48). Unlike R. cf. simulator , the adaptive complex
between echolocation frequency and body size is absent. Although its
wing loading scaled allometrically with body size, R. simulatorecholocated at a lower frequency for its body size (Jacobs et
al. , 2007; Jacobs & Bastian, 2018). Furthermore, it also had lower
echolocation frequencies than would be predicted by the volume of its
nasal capsules (Jacobs et al. , 2014). This suggests directional
selection for lower frequency echolocation, possibly to increase the
operational range of its echolocation, reflected in the phenotype of the
skull associated with echolocation. Lower frequency sound undergoes less
atmospheric attenuation than high frequency sound (Lawrence & Simmons,
1982) and, all else being, the echolocation of R. simulatorshould therefore have longer operational ranges than R. cf.
simulator , unless it emits echolocation pulse at lower intensities.
Currently the intensities at which these two lineages emit their
echolocation pulses are unknown. If the same, the fact that R.
simulator and R. cf. simulator were sometimes caught at the same
locality and from the same cave, suggests that their use of different
echolocation frequency with consequent differences in the operational
range of their echolocation pulses, may be a means of partitioning their
foraging habitat, if not their diet. In both lineages, the mandible
evolved as one complete module (ascending ramus and alveolar bone)
contrary to the mandibular modularity found in R. ferrumequinum(Jojić et al. , 2015). The mandible has therefore possibly evolved
under constraint and might be following a line of least evolutionary
resistance as in the phyllostomids (Hedrick et al. , 2019). The
mandible variations across localities did not show any difference
between the two species except the variations on the position of the
incisors that were seen in R. cf. simulator but not in R.
simulator . The similarities between the mandibles signifying close
similarities in diet between the two species.
The marked influence of echolocation on the skull of both R.
simulator and R. cf. simulator is also reflected in variations
in the shapes of cochlea in both species. This suggests that selection
has acted strongly on both sound production and perception functions in
the two lineages. Variations in the morphology of the cochlea are
related to variations in perceptions of sound, particularly in
rhinolophids (Davies et al. , 2013). For example, in rhinolophids
the cochlear basal turn is expanded, more so than in other bats (Davieset al. , 2013), probably because of the well-developed auditory
fovea in this taxon allowing the Doppler shift compensation upon which
high duty cycle echolocation is based (Neuweiler, 2003). The frequency
of echolocation pulses in rhinolophids are also negatively associated
with the length of the basilar membrane length and positively associated
with the number of cochlear turns (Davies et al. , 2013). These
relationships suggest that the cochlea of these bats probably track the
acoustic properties of the habitats they occupy, hence the geographic
variation reported here in both echolocation frequency and cochlea
morphology. The finer details of the mechanistic association between
cochlea morphology and echolocation parameters still need to be
elucidated (Davies et al. , 2013).
The differences in the selection
pressures experienced by the two lineages are remarkable given the
genetic similarity of the two lineages at least in the genetic markers
considered by Dool et al. (Dool et al. , 2016). The two lineages
were indistinguishable across 6 nuclear introns and one mitochondrial
fragment. It has been suggested that R. cf. simulator is possibly
a cryptic lineage, sister to R. simulator (Dool et al. ,
2016), a view supported by the differences in the evolution of skulls
reported here. However, the two lineages occur at the same sites and
sometimes in the same caves and there is some evidence of hybridization
between the two lineages. This raises the question of how they can
maintain such divergent and non-overlapping echolocation frequencies.
The answer to this question requires evolutionary development studies to
identify the loci which code for echolocation frequency (e.g., Sun et al
(Sun et al. , 2020)) and how these loci are assorted during gamete
formation.