Molecular phylogenetics and the evolution of high-frequency echolocation in horseshoe bats (Genus Rhinolophus)

Includes bibliographical references (leaves 184-210).Horseshoe bats (genus Rhinolophus) belong to the Old World family Rhinolophidae. They are high-duty cycle bats and many species use echolocation calls dominated by high frequencies (above 60 kHz). Much is known about how they use their echolocation calls, but very little is known about why these bats use echolocation calls of such high frequencies, or what has caused the divergence in echolocation call frequency between rhinolophid species. I test five hypotheses that may explain the evolution and divergence of high frequencies in the horseshoe bats: (1) The Allotonic Frequency Hypothesis - echolocation frequencies outside of moth hearing range (allotonic frequencies) have evolved in response to moth hearing; (2) The Allometry Hypothesis - highfrequency echolocation calls are simply a function of body size; (3) The Acoustic Adaptation Hypothesis - selection pressures linked to habitat structure have shaped the evolution of high-frequency echolocation calls; (4) The Foraging Habitat Hypothesis - foraging style and habitat of a bat should correspond to echolocation call frequency and wing design; and (5) The Acoustic Communication Hypothesis - echolocation frequencies evolved under selection pressure which eliminated overlap among sympatric species of rhinolophids, within the context of effective communication

Correlated Genetic and Ecological Diversification in a Widespread Southern African Horseshoe Bat

The analysis of molecular data within a historical biogeographical framework, coupled with ecological characteristics can provide insight into the processes driving diversification. Here we assess the genetic and ecological diversity within a widespread horseshoe bat Rhinolophus clivosus sensu lato with specific emphasis on the southern African representatives which, although not currently recognized, were previously described as a separate species R. geoffroyi comprising four subspecies. Sequence divergence estimates of the mtDNA control region show that the southern African representatives of R. clivosus s.l. are as distinct from samples further north in Africa than they are from R. ferrumequinum, the sister-species to R. clivosus. Within South Africa, five genetically supported geographic groups exist and these groups are corroborated by echolocation and wing morphology data. The groups loosely correspond to the distributions of the previously defined subspecies and Maxent modelling shows a strong correlation between the detected groups and ecoregions. Based on molecular clock calibrations, it is evident that climatic cycling and related vegetation changes during the Quaternary may have facilitated diversification both genetically and ecologically

Field identification of two morphologically similar bats, Miniopterus schreibersii natalensis and Miniopterus fraterculus (Chiroptera: Vespertilionidae)

Miniopterus schreibersii natalensis and Miniopterus fraterculus are two morphologically similar, but genetically distinct, species of insectivorous bat that, more often than not, share roosts. Identifying these two species in the field is difficult because of an overlap in the ranges of both forearm and mass. We thus attempted to find morphological features that could be used to distinguish between these two species in the field. We compared cranial and external morphological measurements from museum specimens of the two species, using principal component analysis and discriminant function analysis, to determine which variables could be used to discriminate between them. Length of the hind foot and total body length were identified as the variables responsible for most of the variation between these two species. Miniopterus s. natalensis has a longer total body length (113.6 ± 3.5 mm) than M. fraterculus (102.2 ± 4.8mm) but a relatively shorter hind foot (9.1 ± 0.6 mm, 9.8 ± 0.8 mm, respectively). A function generated from standardized canonical variables, (HF × 0.279417) – (TL × 0.989306) + 100, and based on length of hind foot (HF) and total body length (TL) generated function scores <0 for M. s. natalensis and >0 for M. fraterculus. On the basis that positive values (above zero) indicated M. fraterculus, and negative values (below zero) indicated M. s. natalensis, we were able to correctly assign 20 individuals to their respective species using the above function. These individuals were previously identified as M. fraterculus or M. s. natalensis from their mtDNA sequences. The function thus provides a useful tool for discriminating between the two species in the field.Keywords: cryptic species, field identification, morpholog

The number of samples (N) and haplotypes (n haplotypes) for each sampling locality.

<p>Geographical coordinates are not available for Egypt, Kenya, Mozambique, Namibia and Tanzania and these samples were not used in the population-level analyses.</p

Uncorrected pairwise distances (%) between <i>R. ferrumequinum</i> (RF) and <i>R. clivosus</i> s.l. from Egypt (EGY), Kenya (KEN), Tanzania (TAN), Mozambique (MOZ), Namibia (NAM), and the five South African groups (see Fig. 2. Bold, italic values in the diagonal indicate within group distances and hyphens indicate n = 1).

<p>Uncorrected pairwise distances (%) between <i>R. ferrumequinum</i> (RF) and <i>R. clivosus</i> s.l. from Egypt (EGY), Kenya (KEN), Tanzania (TAN), Mozambique (MOZ), Namibia (NAM), and the five South African groups (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0031946#pone-0031946-g002" target="_blank">Fig. 2</a>. Bold, italic values in the diagonal indicate within group distances and hyphens indicate n = 1).</p

Minimum spanning network (95% threshold) for South African bats (including two individuals from Swaziland).

<p>Circle size represents haplotype frequency and patterns indicate the sampling locality for the proportion of individuals with that haplotype. Sampling locality codes are the same as in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0031946#pone-0031946-g002" target="_blank">Figure 2</a> and <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0031946#pone-0031946-t001" target="_blank">Table 1</a>. The inset shows the graphical interpolation-based representation of the genetic structure obtained over a South African landscape. Darker grey is indicative of high genetic diversity and lighter grey, of lower genetic diversity.</p

Matrix of pairwise Φ_ST values between the five groups.

<p>Φ_ST values are given below the diagonal and p values above the diagonal.</p

Plot of canonical scores from discriminant function analysis on echolocation and wing parameters.

<p>The five groups correspond to <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0031946#pone-0031946-g002" target="_blank">Figure 2</a> and squares = Group 3, open triangles = Group 1, closed triangles = Group 2, diamonds = Group 5 and circles = Group 4.</p

Size, wing and echolocation parameters (mean ± SD; minimum–maximum) for the total number of bats in each of the five groups (see Fig. 2 tree).

<p>WS: wingspan, WA: wing area, WL: wing loading, AR: aspect ratio, RF: resting frequency. N  =  number of bats, where sample sizes for each sex (m =  males; f  =  females) are provided in parentheses below the total number of bats.</p

Rough distributions of the four <i>R. geoffroyi</i> subspecies as described in Roberts [<b>28</b>].

<p>Type localities for three subspecies and the sampling sites used in this study are indicated where 1) Barberton, BT; 2) De Hoop, DHC; 3) Ferncliffe, FCC; 4) Winburg, FS; 5) Greyton, GREY; 6) Knysna, HKV; 7) Hopewell Farm, HWF; 8) Koegelbeen, KGB; 9) Kokstad, KSM; 10) Lajuma, LAJ; 11) Melmoth, MEL; 12) Maitland Mines, MM; 13) Postmasburg, POST; 14) Stellenbosch, STEL; 15) Sudwala, SUD; 16) Swaziland, SWZ; and 17) Yolland, YOL. The type locality for <i>R. g. zambesiensis</i> is only given as South Rhodesia, corresponding to present-day Zimbabwe.</p