Glob Change Biol. 2024;30:e17344.  | 1 of 22 https://doi.org/10.1111/gcb.17344 wileyonlinelibrary.com/journal/gcb Received: 31 December 2023  | Revised: 20 April 2024  | Accepted: 29 April 2024 DOI: 10.1111/gcb.17344 R E S E A R C H A R T I C L E Southern Africa's Great Escarpment as an amphitheater of climate- driven diversification and a buffer against future climate change in bats Peter J. Taylor1  | Teresa C. Kearney2,3  | Vincent Ralph Clark4  | Alexandra Howard1  | Monday V. Mdluli1  | Wanda Markotter5  | Marike Geldenhuys5  | Leigh R. Richards6  | Andrinajoro R. Rakotoarivelo1  | Johan Watson7 | Julio Balona8 | Ara Monadjem9,10 1Afromontane Research Unit & Department of Zoology & Entomology, University of the Free State Qwaqwa Campus, Phuthaditjhaba, South Africa 2Ditsong National Museum of Natural History, Pretoria, South Africa 3School of Animal, Plant and Environmental Sciences, University of the Witwatersrand, Johannesburg, South Africa 4Afromontane Research Unit & Department of Geography, University of the Free State: Qwaqwa Campus, Phuthaditjhaba, South Africa 5Centre for Viral Zoonoses, Department of Medical Virology, University of Pretoria, Pretoria, South Africa 6Durban Natural Science Museum, Durban, South Africa 7Department of Economic Development, Tourism and Environmental Affairs, Biodiversity Research, Bloemfontein, South Africa 8Gauteng and Northern Regions Bat Interest Group, Johannesburg, South Africa 9Department of Biological Sciences, University of Eswatini, Kwaluseni, Eswatini 10Mammal Research Institute, Department of Zoology & Entomology, University of Pretoria, Hatfield, South Africa This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. © 2024 The Author(s). Global Change Biology published by John Wiley & Sons Ltd. Correspondence Peter J. Taylor, Afromontane Research Unit & Department of Zoology & Entomology, University of the Free State, Qwaqwa Campus, Phuthaditjhaba, South Africa. Email: taylorpj@ufs.ac.za Funding information National Research Foundation and Department of Science and Innovation of South Africa, Grant/Award Number: 128386; Afromontane Research Unit, University of the Free State; National Research Foundation Abstract Hosting 1460 plant and 126 vertebrate endemic species, the Great Escarpment (hereafter, Escarpment) forms a semi- circular “amphitheater” of mountains girdling southern Africa from arid west to temperate east. Since arid and temperate biota are usually studied separately, earlier studies overlooked the biogeographical impor- tance of the Escarpment as a whole. Bats disperse more widely than other mam- malian taxa, with related species and intraspecific lineages occupying both arid and temperate highlands of the Escarpment, providing an excellent model to address this knowledge gap. We investigated patterns of speciation and micro- endemism from modeled past, present, and future distributions in six clades of southern African bats from three families (Rhinolophidae, Cistugidae, and Vespertilionidae) having differ- ent crown ages (Pleistocene to Miocene) and biome affiliations (temperate to arid). We estimated mtDNA relaxed clock dates of key divergence events across the six clades in relation both to biogeographical features and patterns of phenotypic vari- ation in crania, bacula and echolocation calls. In horseshoe bats (Rhinolophidae), both the western and eastern “arms” of the Escarpment have facilitated dispersals from the Afrotropics into southern Africa. Pleistocene and pre- Pleistocene “species https://doi.org/10.1111/gcb.17344 www.wileyonlinelibrary.com/journal/gcb mailto: https://orcid.org/0000-0001-9048-7366 https://orcid.org/0000-0002-0050-4060 https://orcid.org/0000-0001-5058-0742 https://orcid.org/0000-0002-6121-4438 https://orcid.org/0000-0002-5830-0116 https://orcid.org/0000-0002-7550-0080 https://orcid.org/0000-0003-4005-118X https://orcid.org/0000-0003-2599-5262 https://orcid.org/0000-0001-9318-7465 https://orcid.org/0000-0003-1906-4023 http://creativecommons.org/licenses/by/4.0/ mailto:taylorpj@ufs.ac.za http://crossmark.crossref.org/dialog/?doi=10.1111%2Fgcb.17344&domain=pdf&date_stamp=2024-06-05 00400305 Highlight 00400305 Highlight 2 of 22  |     TAYLOR et al. 1  |  INTRODUC TION The uplift and easterly tilting of the southern African Escarpment ca. 23 million years ago (Ma), and the formation of the Benguela Current ca. 11 Ma, resulted in strong aridity and elevation gradi- ents, with arid and semi- arid (hereafter, “arid”) highlands in the west and temperate high mountains in the east, rising to 3482 m in the Maloti Drakensberg (Couvreur et al., 2021; Partridge & Maud, 1987). Including the Cape Fold Belt (CFB) on its southern edge, the 5000 km long Escarpment hosts over 8500 plant species, including 1460 en- demic plants, and at least 126 endemic vertebrate species (Clark, Barker, & Mucina, 2011a; Huntley, 2023; Mendelsohn et al., 2023). Both ecological and evolutionary “actors” explain the exception- ally high diversity and endemism of this semi- circular, subcontinental “amphitheater” of highlands. Ecologically distinct sections (“tiers”) of the Escarpment correspond with recognized biomes and ecoregions (Dinerstein et al., 2017; Table S1; Figure 1a). Spanning temperate (northern) to arid (southern) biomes, the Highlands and Escarpments of Angola and Namibia (HEAN; Mendelsohn et al., 2023) form the western rim. The southern Escarpment is formed by the Richtersveld, Hantam–Roggeveld and Nuweveldberge highlands in the Nama Karoo and Succulent Karoo biomes of South Africa (Mucina & Rutherford, 2006). South of the southern Escarpment, separated by the Little Karoo semi- desert, lie the geologically dis- tinct CFB mountains covering the Core of the Greater Cape Floristic Region (CFR) and Fynbos Biome (Mucina & Rutherford, 2006). The eastern rim of the Escarpment coincides with the Sneeuberg and Maloti Drakensberg (= Drakensberg Centre for Plant Endemism; Carbutt, 2019) mountains which continue northwards as the Greater Midlands (Carbutt, 2023), Limpopo–Mpumalanga–Eswatini Escarpment (LMEE; Clark et al., 2022) and Soutpansberg (Hahn, 2017; Van Wyk & Smith, 2001). The Manica Highlands of Zimbabwe and Mozambique (Chimanimani- Nyanga centers of plant endemism (CPE); Clark et al., 2017; Van Wyk & Smith, 2001) can be regarded as an extension of the Escarpment (Clark, Barker, & Mucina, 2011a), and also provide a bridge with the Eastern Afromontane Biodiversity Hotspot (Mittermeier et al., 2011), which extends north to Ethiopia and Arabia, encompassing the South- East African Montane Archipelago ecoregion (SEAMA; Bayliss et al., 2024), Eastern Arc Mountains (EAM) and the East African Rift System (EARS; Figure 1a). Tectonics, topography, and past climate change have shaped evolutionary processes like dispersal, vicariance, extinction, adap- tation, and speciation (Couvreur et al., 2021; Rangel et al., 2018; Voelker, 1999). Tectonic processes explain the evolution of the Maloti Drakensberg flora from the Cape flora, and subsequent dispersal and radiation into newly formed tropical African mountains over the past 17 Ma (Galley et al., 2007). The formation and extension of the East African Rift from the Miocene (23–5.3 Ma) to the Pleistocene (2.6–0.012 Ma) led to forest fragmentation and the vicariance of highland from ancestral lowland taxa, for example, in wood mice, Hylomyscus (Nicolas et al., 2020). The early Pleistocene (ca. 2.5 Ma) formation of the Ethiopian Rift Valley explained vicariance between ancestral western and eastern species in the Ethiopian- endemic ro- dent genus Stenocephalemys (Bryja et al., 2018). Past climate and vegetation change were critical drivers of Afromontane diversity, particularly during the Pleistocene (2.6 Ma–18 Ka) and Pliocene (5.3–2.6 Ma) periods. Pliocene forest contraction explains biogeographical patterns in Afromontane birds (Voelker et al., 2010; Outlaw et al., 2007) and chameleons (Tolley et al., 2011). Analyses of Antarctic marine sediments from dust particles from southern African deserts indicated a 5°C de- cline in global temperature in the early Pleistocene, and the pro- nounced ca. 100,000- year (100 Ka) periodicity of cold and warm cycles (Milankovitch cycles) after the mid- Pleistocene transition event (ca. 900 Ka), leading up to the last glacial maximum (LGM) at about 18 Ka (Couvreur et al., 2021; DeMenocal, 1995, 2004; Schefuss et al., 2003). Geomorphological and archeological evi- dence confirms the existence of cold cycles (5–10°C lower than pumps” and temperate refugia explained observed patterns of speciation, intraspe- cific divergence and, in two cases, mtDNA introgression. The Maloti- Drakensberg is a center of micro- endemism for bats, housing three newly described or undescribed species. Vicariance across biogeographic barriers gave rise to 29 micro- endemic spe- cies and intraspecific lineages whose distributions were congruent with those identi- fied in other phytogeographic and zoogeographic studies. Although Köppen–Geiger climate models predict a widespread replacement of current temperate ecosystems in southern Africa by tropical or arid ecosystems by 2070–2100, future climate Maxent models for 13 bat species (all but one of those analyzed above) showed minimal range changes in temperate species from the eastern Escarpment by 2070, possibly due to the buffering effect of mountains to climate change. K E Y W O R D S Afromontane, baculum, biodiversity evolution, Chiroptera, craniometric, cytochrome- b, echolocation frequency, geographical range, phenotype 13652486, 2024, 6, D ow nloaded from https://onlinelibrary.w iley.com /doi/10.1111/gcb.17344 by South A frican M edical R esearch, W iley O nline L ibrary on [09/07/2024]. See the T erm s and C onditions (https://onlinelibrary.w iley.com /term s-and-conditions) on W iley O nline L ibrary for rules of use; O A articles are governed by the applicable C reative C om m ons L icense     |  3 of 22TAYLOR et al. present) and small “niche glaciers” in the Drakensberg of South Africa and Lesotho during the late Pleistocene including the LGM (Fitchett et al., 2016; Grab, 1996; Harper, 1969). Pleistocene cli- matic fluctuations drove repeated expansion and contraction of Afromontane habitats on adjacent mountains, resulting in allopat- ric or peripatric vicariance and speciation during periods of iso- lation (montane refugia mechanism: Bryja et al., 2014; Couvreur et al., 2021; Taylor et al., 2014). During Holocene (0–18 Ka) and Pleistocene hypothermal periods, frost- dependent grasslands ex- tended more widely across southern Africa, possibly connecting species currently restricted either to the western, southern, or eastern Escarpment (Brain, 1985; Figure 1b). Many studies show the biogeographical connectivity of the southern and eastern sections of the Afromontane Archipelago (“Cape to Cairo Floristic Highway”; Clark, Barker, & Mucina, 2011a) and their importance as either dispersal corridors, “cradles” (high speciation rate) or “museums” (high species persistence) of mon- tane plants and animals (e.g., Clark, Barker, & Mucina, 2011b; Couvreur et al., 2021; Dianat et al., 2024; Fjeldså & Lovett, 1997; Fjeldså et al., 2012; Galley et al., 2007; Lawson et al., 2015; Nicolas et al., 2020; Onditi et al., 2021; Tolley et al., 2011; Taylor et al., 2012, 2014; Voelker et al., 2010, 2021). Fewer studies demonstrate the role of the western Escarpment (HEAN and Richtersveld Mountains of South Africa) in the speciation of arid species (Huntley, 2023; Huntley et al., 2019; Linder et al., 2012; Matthee & Flemming, 2002; Matthee & Robinson, 1996; Mendelsohn et al., 2023; Mills et al., 2011). Biogeographical connections are known to link the Angolan (HEAN) and East African highlands, for example, in rodents (Krásová et al., 2021) and birds (Mills et al., 2011; Vaz da Silva, 2015). However, the evolutionary and biogeographical importance of the entire Escarpment has not been fully investigated previously. Because of their greater dispersal capacity compared with more sed- entary taxa, bats are widely distributed across both arid (western) and temperate (southern and eastern) mountains of the Escarpment and therefore provide an excellent model to address this gap. Because they depend on rocky crevices and caves for roost- ing, the distribution ranges of several bat species correspond with mountainous areas of the CFB, the Escarpment, and the broader Afromontane Archipelago (Table S2; Figure 1a,b; Monadjem et al., 2020; Schoeman et al., 2013). We define southern Africa to extend from the DRC south of 4° S to South Africa (Monadjem et al., 2020). We use the term “paramontane” to describe bats F I G U R E 1 Maps of southern, central, and eastern Africa showing (a) topographical features referred to in this study (see text for details), and (b) the extent of minimum monthly temperatures (bioclim6) <0°C from present and past (last glacial maximum [LGM]) models (from Worldclim; https:// www. world clim. com/ ; see Methods for more details). Gray or darker shading in both maps indicates mountains >1200 m in elevation. In (a), the acronym HEAN stands for the Highlands and Escarpments of Angola and Namibia (Mendelsohn et al., 2023); SEAMA stands for the South- East African Montane Archipelago (Bayliss et al., 2024); LMEE stands for the Limpopo–Mpumalanga– Eswatini Escarpment (Clark et al., 2022). The map in (b) shows distribution points of horseshoe bats, Rhinolophus (crosses), wing- gland bats, Cistugo (open triangles) and long- eared bats, Laephotis (open squares) based on morphological and molecular results from this study and from published a GenBank cyt- b sequences. In (b), minimum monthly temperatures <0°C indicated for the present (blue) and LGM (red), approximating the extent of frost (and hence temperate grasslands) currently and during the LGM (idea from Brain, 1985). Map lines delineate study areas and do not necessarily depict accepted national boundaries. 13652486, 2024, 6, D ow nloaded from https://onlinelibrary.w iley.com /doi/10.1111/gcb.17344 by South A frican M edical R esearch, W iley O nline L ibrary on [09/07/2024]. See the T erm s and C onditions (https://onlinelibrary.w iley.com /term s-and-conditions) on W iley O nline L ibrary for rules of use; O A articles are governed by the applicable C reative C om m ons L icense https://www.worldclim.com/version2 4 of 22  |     TAYLOR et al. whose distributions encompass either lower and/or higher moun- tain slopes and plateaux (Fahr & Ebigbo, 2003; Koopman, 1983). We selected six paramontane species groups or genera of bats compris- ing 14 species from three families of both recognized sub- orders (Pteropodiformes: Rhinolophidae; Vespertilioniformes: Cistugidae, and Vespertilionidae; Table S2). Their combined distribution ranges coincide with high (>1200 m) elevation and major topographic fea- tures (Figure 1a,b), and climate zones based on the Köppen–Geiger system (Table S1; Figure S1; Beck et al., 2018). Based on future cli- mate models, temperate conditions (green colors) are expected to contract by 2070–2100 at the expense of more tropical (blue) or arid (red) zones in southern Africa, raising a possible red flag for the conservation impacts of climate change on temperate- adapted fauna and flora in general (Figure S1). For species having an adequate number of occurrence records, we here test this general hypothe- sis by using Maxent niche models to predict future climate change projected ranges of 13 of the 14 paramontane bat species for 2070 (from IPCC6 models). Horseshoe bats (Rhinolophus) are a diverse group of >100 spe- cies, including a growing number of newly discovered cryptic spe- cies (Benda & Vallo, 2012; Benda et al., 2024; Curran et al., 2022; Dool et al., 2016, Demos et al., 2019; Monadjem et al., 2020; Taylor et al., 2012, 2018; Uvizl et al., 2024). Six Afrotropical groups are recognized, capensis, darlingi, ferrumequinum, fumigatus, landeri, and maclaudi, of which all but maclaudi are found in southern Africa (Csorba et al., 2003; Hutson et al., 2019). Due to the ambiguous position of R. darlingi either in the ferrumequinum (morphology) or fumigatus (molecular) group (Hutson et al., 2019), we here retain it as a distinct species group. Although having a tropical forest center of origin, four of the groups here studied have members occupy- ing both arid and mesic zones across the Escarpment, the capensis group, the ferrumequinum group, the darlingi group, and the fumig- atus group. Two other genera of bats (Cistugo, Laephotis) have arid and mesic relatives in southern Africa. Southern African- endemic wing- gland bats (Family Cistugidae) comprise two closely related species, Cistugo seabrae (arid, western Escarpment) and C. lesueuri (mesic, CFB and eastern Escarpment; Monadjem et al., 2020; Stadelmann et al., 2007). Long- eared bats within the genus Laephotis (Family Vespertilionidae), comprise central African Miombo savanna (L. angolensis including L. botswanae s.s), arid (L. namibensis s.s.), Mediterranean CFB (L. cf. namibensis), temperate- montane Maloti Drakensberg (L. cf. wintoni, L. cf. botswanae), and East African temperate- montane (L. wintoni s.s.) species and lineages. Some of these (L. cf. namibensis, L. cf. botswanae, and L. cf. wintoni) have not been formally described but represent distinct molecular (cyt- b) and/ or morphological entities (Monadjem et al., 2021; Taylor et al., 2022). Based on our combined biogeographical, molecular, and pheno- typic data, we predicted that Pleistocene climate fluctuations super- imposed on the geomorphology of the Escarpment would explain vicariance patterns, introgression events, and adaptive differences between lineages within species currently occurring widely on arid and temperate mountains, while Pliocene forest contraction would explain both dispersal pathways and vicariance patterns between older species pairs from arid and temperate mountains, especially in Rhinolophus clades known to have tropical origins and older crown diversification dates. Due to the scarcity of nuclear (nuc) DNA se- quences for our study taxa and region, our phylogeographic study was based on mitochondrial (mt) DNA (cyt- b) sequences. Recent studies in African horseshoe bats have shown that the two ap- proaches are complementary, with mtDNA being faster coalesc- ing, revealing finer- scale intraspecific phylogeographic patterns, and nucDNA being more reliable for species and higher taxa rec- ognition (Benda et al., 2024; Demos et al., 2019; Uvizl et al., 2024). Discordance between mtDNA and nucDNA arises due to mtDNA introgression between species, a relative frequency occurrence in the evolution of bats. We show how, in the absence of nucDNA se- quences, morphological characters in combination with mtDNA se- quence data can identify potential introgression events. We identify one such introgression event between R. damarensis and the newly described R. cervenyi, which has been independently corroborated by a comparison of mtDNA and nucDNA from the same taxa (Benda et al., 2024). 2  |  MATERIAL S AND METHODS 2.1  |  Data collection and acoustic recording For the six selected species groups, we compiled acoustic param- eters from echolocation calls from the literature and new acoustic recordings obtained during this study (211 new call sequences; see details below). We compiled cyt- b sequences from GenBank (https:// www. ncbi. nlm. nih. gov/ nuccore), and we sequenced tissue samples from six newly captured individuals or museum specimens, comprising four species from the KZN and Free State provinces of South Africa (see below). We conducted craniometric and bacular (penis bone) measurements from museum and private collections (54 bacula and 424 skulls; see details below; Table S3). For R. cervenyi and R. acrotis, we conducted passive acoustic monitoring of echolocation calls using Song Meter (SM) Mini- 4 bat detectors (Wildlife Acoustics, USA), deployed in the eastern Free State province at five apple farms between Warden and Fouriesburg, in the Golden Gate Highlands National Park and the Witsieshoek Community Conservation Area (99 call sequences). With Echo Meter- 3 (EM3) bat detectors (Wildlife Acoustics, USA), we also actively recorded handheld, flight cage- flown or release calls from individuals of R. cervenyi (48 call sequences) and C. lesueuri (n = 64 call sequences) captured at Schaapplaats Farm near Clarens using mistnets of 6, 9, and 12 m lengths (Avinet, USA) and a two- bank harp trap (Faunatech, Australia). Selected call sequences of high signal- to- noise ratio from both passive and active recordings were analyzed with Kaleidoscope Pro v. 4.5.5 (Wildlife Acoustics, USA). For each call sequence, we obtained the number of pulses, characteristic (Fc), minimum (Fmin), and maximum (Fmax) frequencies (kHz) and call du- ration (ms). 13652486, 2024, 6, D ow nloaded from https://onlinelibrary.w iley.com /doi/10.1111/gcb.17344 by South A frican M edical R esearch, W iley O nline L ibrary on [09/07/2024]. See the T erm s and C onditions (https://onlinelibrary.w iley.com /term s-and-conditions) on W iley O nline L ibrary for rules of use; O A articles are governed by the applicable C reative C om m ons L icense https://www.ncbi.nlm.nih.gov/nuccore     |  5 of 22TAYLOR et al. After obtaining acoustic recordings and standard external measurements, two specimens were then sacrificed using cer- vical dislocation, pectoral muscle samples were dissected and stored in DNA/RNA Shield and these specimens were prepared in formalin and donated to the mammal collections of the National Museum, Bloemfontein, and the Durban Natural Science Museum. Collection of bats for the project was allowed with the permis- sion of the Animal Ethics (Clearance no.: UFS- AED2021/0029/21) and Biosafety Ethics (Clearance no.: UFS- ESD2021/0233/22) Committees of the University of the Free State and also with permission to do research in terms of Section 20 of the animal diseases act, 1984 (Act No.: 35 OF 1984) from the Department of Agriculture, Land Reform, and Rural Development DALLRD (Reference no.: 12/11/1/4 (2038 RJ)). Geographical coordinates of relevant GenBank sequences were obtained from Demos et al. (2019), Dool et al. (2016), Taylor et al. (2018, 2022), Curran et al. (2022), and Howard et al. (2022), while coordinates of measured museum specimens were obtained by TCK and PJT. Distribution points of GenBank voucher specimens and museum specimens were plotted using QGIS v. 3.10 (QGIS Development Team, 2023) and NASA's Global Digital Elevation Model (NASA JPL, 2013). Shapefiles of species ranges were down- loaded, with permission, from the IUCN Redlist website (https:// www. iucnr edlist. org/ ). 2.2  |  Biogeographical classification and Maxent distribution modeling We divided paramontane lineages of 14 recognized taxa into five biome categories (i–v) that partially coincided with both the re- vised Köppen–Geiger climate classification map (Beck et al., 2018; https:// www. gloh2o. org/ koppen/ ) and biomes and ecoregions de- fined by Dinerstein et al. (2017). Two of these biomes were further subdivided into ecoregions and geographic units (Tables S1 and S2). For the mapping of molecular clades, we used only DNA voucher records or specimen records from which diagnostic characteristics were available. To obtain projections of climate- change- predicted past and fu- ture ranges of species, we used the Maxent program (Phillips et al., 2006), implemented in R version 4.3.3 (R Core Team, 2024) and the packages, “usdm,” “dismo,” “sp,” “raster,” “spatialEco,” “ncdf4,” “tidyr,” and “dplyr.” A total of 821 occurrence records from 13 of our 14 study taxa (with n > 5 occurrence records: two of Cistugo, two of Laephotis, and nine of Rhinolophus) were obtained from Monadjem et al. (2020), supplemented with recent records obtained by AM (un- published data) and from this study for R. cervenyi. The taxonomy followed Monadjem et al. (2020), as updated by Benda et al. (2024), Uvizl et al. (2024) and the results of this study. A total of 19 biocli- matic (Bioclim) variables (downloaded from Worldclim v. 2; Fick & Hijmans, 2017; https:// www. world clim. org/ data/ cmip6/ at 2.5° res- olution) were clipped to a map of southern Africa following the bor- ders in Monadjem et al. (2020). We used the “vif” (variance inflation factor) in package “usdm” to remove highly correlated Bioclim vari- ables, resulting in a final subset of 10 variables used for modeling (bio2, bio3, bio5, bio8, bio9, bio13, bio14, bio15, bio18, bio19). Maps of habitat suitability probabilities were converted into binary maps using the maximum specificity and sensitivity threshold. To model predicted future climate effects, we downloaded at 2.5° resolution, 19 Bioclim variables for two general circulation models (GCMs), Had- GEM3 and MPI- ESM2, for the worst- case Socioeconomic Pathway (SSP), SSP5- 8.5 (ca. RPC 8.5 of IPCC5) from the sixth phase of the Coupled Model Intercomparison Project (CMIP6), reported by the sixth assessment report of Intergovernmental Panel on Climate Change (IPCC6). Since the re- sults were very similar for both GCM's, we present here only the results from Had- GEM3. To model past distributions from the LGM, we downloaded Bioclim variables from the MIROC- ESM GCM of the CPIM5, which were analyzed as described above. 2.3  |  Molecular sequences Cyt- b sequences were compiled from GenBank and obtained for newly collected samples of pectoral muscle stored in DNA/RNA Shield of R. cervenyi and C. lesueuri from the eastern Free State Province, as well as from skull scrapings of one museum specimen (DM 7504) of C. lesueuri from the KwaZulu- Natal Drakensberg. We followed Taylor et al. (2022) for DNA extraction and cytochrome- b sequencing. DNA was extracted from pectoral muscle tissues using the Zymogen Quick- DNA Miniprep Plus Kit (Irvine, CA, USA). For the cyt- b gene amplification, we prepared 1 × DreamTaq buffer (10×, Thermo Fisher Scientific, Waltham, MA, USA), 2 μL cyt- b- LGL 765 forward primer (10 mM, Metabion International AG, Planegg, Germany) (5′- GAA AAA CCA YCG TTG TWA TTC AAC T- 3′), 2 μL cyt- b- LGL 766 reverse primer (5′- GTT TAA TTA GAA TYT YAG CTT TGG G- 3′) (10 mM, Metabion International) (Bickham et al., 1995), 1 μL dNTPs mix (10 mM, Thermo Scientific), 2 μL MgCl2 (25 mM, Thermo Scientific), 0.25 μL DreamTaq polymerase (5 U/μL, Thermo Scientific), and nuclease- free water (Ambion, Foster City, CA, USA) to a final volume of 45 μL. A volume of 5 μL extracted DNA was added to the reaction and incubated in a SimpliAmp automated ther- mal cycler (Thermo Fisher Scientific) at 94°C for 2 min, followed by 40 cycles of 94°C for 30 s, 42°C for 30 s, and 72°C for 90 s, with a final extension step of 72°C for 10 min. New cyt- b sequences from this study were added to the GenBank database under the following GenBank accession numbers: OR594340 (Field no.: “PT2021- 18,” Rhinolophus cervenyi) and OR594341 (Field no.: “PT2021- 19,” Cistugo le- sueuri) from Schaapplaats Farm in the Free State Province, from R. acrotis (GenBank no.: PP706136 = “Bat53”), R. swinnyi (GenBank no.: PP706135 = “Bat52”) and R. cervenyi (GenBank no.: PP706137 = “Bat6”) from Wakefield Farm in KwaZulu- Natal Province (Howard et al., 2022), and PP101597 from C. lesueuri from Kamberg in KwaZulu- Natal Province (Durban Natural Science Museum accession no.: “DM 7504”). 13652486, 2024, 6, D ow nloaded from https://onlinelibrary.w iley.com /doi/10.1111/gcb.17344 by South A frican M edical R esearch, W iley O nline L ibrary on [09/07/2024]. See the T erm s and C onditions (https://onlinelibrary.w iley.com /term s-and-conditions) on W iley O nline L ibrary for rules of use; O A articles are governed by the applicable C reative C om m ons L icense https://www.iucnredlist.org/ https://www.iucnredlist.org/ https://www.gloh2o.org/koppen/ https://www.worldclim.org/data/cmip6/ 6 of 22  |     TAYLOR et al. 2.4  |  Phylogenetic analyses Separate analyses were performed on three cyt- b datasets, each focused on southern African taxa with east and west African se- quences and outgroups added for reference: (i) Rhinolophidae (horseshoe bats) from the R. capensis, R. damarensis, R. ferrumequi- num, and R. fumigatus groups, with Hipposideridae (Hipposideros ruber) as outgroup; (ii) Cistugidae (wing- gland bats), with Vespertilionidae (Myotis myotis) as outgroup, and (iii) long- eared bats of the genus Laephotis of the family Vespertilionidae from southern and eastern Africa, with Miniopteridae (Miniopterus fra- terculus) as outgroup. We analyzed 54 sequences and 773 base pairs in total for Rhinolophidae, 10 sequences and 646 base pairs for Cistugidae (except for DM 7504 for which only 291 base pairs were available) and 29 sequences and 646 base pairs for Vespertilionidae. FASTA files of all sequence data files for Rhinolophus, Cistugo, and Laephotis are provided in Datasets S1, S2, and S3, respectively. Sequences were aligned using MAFFT v.7 (Katoh & Standley, 2013) to generate a multiple sequence alignment (MSA) using default parameters. The MSA ensures proper alignment of homologous positions across sequences. We used the Basic Local Alignment Search Tool (BLAST) of NCBI GenBank (https:// blast. ncbi. nlm. nih. gov/ Blast. cgi) to perform searches of nucleo- tide sequences on NCBI GenBank mostly closely matching our new cyt- b sequences. To determine the best- fit substitution model for the aligned sequences, ModelFinder (−m MFP) option (Kalyaanamoorthy et al., 2017) within IQ- TREE v. 2.0.3 (Minh et al., 2020; Nguyen et al., 2015) was employed. ModelFinder uti- lizes the Bayesian information criterion (BIC) to select the most appropriate substitution model that best describes the evolu- tionary process of the sequences. For Cistugo, the best- fit model according to BIC was TIM2 + F + I + G4. For Laephotis the best- fit model according to BIC was TN + F + I + G4. For Rhinolophus, the best- fit model according to BIC was TPM2u + F + G4. The phylogenetic tree was inferred using the maximum likelihood (ML) method implemented in IQ- TREE v. 2.0.3 (Minh et al., 2020; Nguyen et al., 2015). The previously selected substitution model was applied during tree construction. Branch support for the ML analysis was assessed with 1000 SH- like approximate like- lihood ratio test (SH- aLRT) replicates (Guindon et al., 2010), aBayes posterior probability (Anisimova et al., 2011), and 1000 ultra- fast bootstrap (UFBS) replicates (Hoang et al., 2018; Quang et al., 2014). Node support values, SH- aLRT, aBayes, and UFBS were considered strong when higher than 80%, 0.7, and 95%, re- spectively (Anisimova et al., 2011; Guindon et al., 2010; Hoang et al., 2018). The resulting phylogenetic tree, along with the associated support values, was visualized using FigTree v 1.4.4 (Rambaut, 2018) to inspect the inferred relationships among the taxa. For each dataset, MEGA program v. 11 (Tamura et al., 2021) was used to calculate uncorrected P- divergences between and within species. For dating purposes, phylogenetic trees were also inferred using Bayesian analysis as implemented in BEAST v.1.10.4 (Drummond et al., 2018). We used subsets of each of the above datasets with representatives of the key well- supported clades identified by ML (30 sequences for Rhinolophidae, 16 for Cistigudae, and 19 for Vespertilionidae). We used an uncorrelated relaxed molecular clock and the tree prior was set to “Speciation: calibrated Yule.” Bayesian Markov Chain Monte Carlo (MCMC) chains were set to 25 million iterations, sampling every 1000 steps for optimal ESS scores and four independent runs were performed. Output log files were visu- ally inspected to check for convergence in parameters using Tracer v.1.7.2 software (Rambaut et al., 2021). The effective sample size (ESS) values were always >500 for all parameters estimated, sug- gesting that the MCMC runs had reached stationarity. LogCombiner v.1.10 (Rambaut & Drummond, 2018b), was used to combine log and tree files for all four runs for each dataset. The final phyloge- nies were then constructed in TreeAnnotator v.1.10.4 (Rambaut & Drummond, 2018a) with a burn- in value of 10%. For visualization and manipulation of the phylogenetic trees, FigTree v.1.4.4 software was used. 2.5  |  Molecular dating We estimated the time to the most recent common ancestor (TMRCA) for clades of interest using BEAST v1.10.4 (Drummond et al., 2012, 2018) as described above. We used different fos- sil calibration points for the three different analyses. As shown clearly by Morales et al. (2019), the choice of fossil calibration dates can result in widely varying date estimates. Taxonomic con- troversy about the generic affiliation of fossil taxa, for example, in the genus Myotis, can also result in widely varying calibration points. We used a definition of “true” Myotis provided by Morales et al. (2019), which defines the mystacinus clade as sister to the group, and we therefore included a sequence of M. mystacinus in our sample of Myotis species for the separate analyses of Cistugo and Laephotis. Following Jacobs et al. (2013), Foley et al. (2015), Amador et al. (2018), and Dool et al. (2016), the BEAST trees for Rhinolophidae were calibrated using normal tree priors with (i) an upper limit of 5.3 Ma and a lower limit of 1.8 Ma for the sister spe- cies split between R. ferrumequinum and R. acrotis based on fos- sil dates for R. ferrumequinum, described from deposits in Poland (Woloszyn, 1987), (ii) an upper limit of 18.74 Ma and lower limit 15.07 Ma for the crown age of Rhinolophidae (Amador et al., 2018; Dool et al., 2016; Foley et al., 2015), and (iii) an upper limit of 55 Ma and a lower limit of 37 Ma for the split between the Rhinolophidae and the Hipposideridae (Amador et al., 2018; Eick et al., 2005; Teeling et al., 2005). Following Stadelmann et al. (2007), Ruedi et al. (2013), Amador et al. (2018) and Morales et al. (2019), BEAST trees of southern African Cistugo species as well as southern African Laephotis used the same calibration points with normal priors for (i) 28–37 Ma for Myotis- Murina- Kerivoula (Morales et al., 2019; Ruedi 13652486, 2024, 6, D ow nloaded from https://onlinelibrary.w iley.com /doi/10.1111/gcb.17344 by South A frican M edical R esearch, W iley O nline L ibrary on [09/07/2024]. See the T erm s and C onditions (https://onlinelibrary.w iley.com /term s-and-conditions) on W iley O nline L ibrary for rules of use; O A articles are governed by the applicable C reative C om m ons L icense https://blast.ncbi.nlm.nih.gov/Blast.cgi https://blast.ncbi.nlm.nih.gov/Blast.cgi     |  7 of 22TAYLOR et al. et al., 2013), (ii) 20–34 Ma for the crown date of Myotis assum- ing a conservative Oligocene radiation for “true” Myotis (Morales et al., 2019), and (iii) 3.6–11.2 Ma for the node M. bechsteinii- daubentonii (Morales et al., 2019; Stadelmann et al., 2007). In addition to these dates, following the recent dated phylogeny of Amador et al. (2018), we constrained trees to the estimated di- vergence times for Cistugidae- Vespertilionidae (43.2 Ma), for the Cistugidae analysis, and Miniopteridae- Vespertilionidae (47.8 Ma) for the Laephotis analysis. In both cases, we used normal priors with varying SDs from 1 to 2 in different runs. Estimated divergence times did not vary substantially with varying SDs for nodes of in- terest in this study. We similarly found that nodes of interest were robust when using alternative priors such as the lognormal prior, al- though the latter often produced high intervals or extreme median values of node dates. Since the fossil calibrations we used did not assume a minimum age having a higher probability than older dates (i.e., a right- tailed lognormal distribution) we preferred the normal distribution which also resulted in higher ESS values in general and better convergence of parameters between runs. 2.6  |  Craniometric and bacular morphological variation TCK measured 13 craniometric variables in 273 skulls of Rhinolophus (following Csorba et al., 2003; Jacobs et al., 2013 and Curran et al., 2022) and 17 variables in 65 skulls of Laephotis (fol- lowing Kearney & Seamark, 2005). JW measured 17 variables in 86 skulls of Cistugo. Skulls were measured from 12 museum col- lections, as well as privately collected specimens (Table S3). TCK measured nine variables in 54 prepared bacula (penis bones) of 11 Rhinolophus taxa recognized by this study. Lateral, dorsal, and ven- tral photographs were obtained for representative bacula of each of the 11 species. Because previous studies found no evidence for sexual dimorphism in craniometric variables, in Rhinolophus (Taylor et al., 2018) and African pipistrelloid bats (Taylor et al., 2022), we combined data for the different sexes. The degree of molar tooth wear (“age class”) was noted in each skull. To minimize variation due to age differences, juveniles, having unfused phalangeal epi- physes, and/or unworn molars, were excluded. Landmarks for dif- ferent craniometric measurements are illustrated and described in Figure S2. Principal component analysis (PCA) was performed on six datasets comprising nine to 17 log- transformed cranial vari- ables for the six selected species- complexes, four within Rhinolophus and one each for Laephotis and Cistugo. In all cases, PCA was performed on the variable- covariance matrix and no rotation was applied. Since the first two components explained >70% of total variance (except for R. acrotis), we plotted biplots of scores from PC1 and 2, as well as tables of variable eigen- vector loadings for the first three PCs, using the PAST package (Hammer et al., 2001). Input datafiles for all the above analyses are provided in Datasets S4–S6. 3  |  RESULTS 3.1  |  Phylogeny and dating Maximum likelihood trees from cyt- b sequences are shown in Figure 2a–f (selected clades shown in detail for clarity), and Figures S3–S5 (complete ML trees with outgroups), while dated Bayesian trees for the three families are shown in Figures S6–S8. Uncorrected cyt- b P- distances between species and some intraspe- cific lineages are shown in Tables S4–S6. Node support values and median dates are shown for the major nodes in the ML trees. While the stem dates of all the taxa depicted divergences of the ances- tors of Rhinolophidae, Cistugidae, and Vespertilionidae clades in the Eocene (>40 Ma), the crown dates for diversification within the three selected families varied considerably from 16.8 (95% HPD 14.9–18.69) Ma for Rhinolophidae (Figure S6), 1.42 (0.72–2.48) Ma for Cistugo (Figure S7), 3.46 (0.6–11.9) Ma for Laephotis, and 1.46 (0.19–4.43) Ma for the long- eared Laephotis clade (Figure S8). The genera Rhinolophus, Cistugo, and Laephotis were all well supported (Figures S2–S4). 3.2  |  Phylogeography Lineages within each of the six bat species groups have distributions that are highly concordant with the boundaries of biomes across dif- ferent sections of the Escarpment (Figure 2a–f), as detailed below. Within the R. capensis group, two major clades diverged at 9.27 (6.35–12.78) Ma in the Miocene. Further divergence in each of these clades occurred in the late Pliocene. Firstly, arid- adapted R. denti from the western Escarpment diverged from R. cf. denti from central Africa (W DRC and Gabon) at 3.13 (1.44–4.96) Ma and this combined clade formed an unresolved polychotomy (at Pliocene/ Miocene boundary, 5.51 (3.48–8.07) Ma) with R. rhodesiae and R. namuli (temperate forests in eastern Escarpment, SEAMA and EARS) and R. simulator (savannas from northern South Africa to Ethiopia). Secondly, R. swinnyi from the eastern slopes of the Maloti- Drakensberg diverged at 5.57 (2.87–8.7) Ma from R. capensis from the Fynbos and Succulent Karoo biomes of the CFB and southern Escarpment (Figure 2a). Within the R. darlingi group, R. cervenyi from the Maloti- Drakensberg formed a surprising but well- supported monophyletic clade with R. damarensis from the semi- arid Nama Karoo biome of the Northern Cape province, separated by a geographic distance of >300 km and an uncorrected P- distance of 0.7% (Figure 2b; Table S4). In turn, this combined Drakensberg + Nama Karoo clade diverged 4.02 (2.23–6.49) Ma from a northern arid clade comprising R. damarensis from N Namibia and a sequence from central African tropical forest (W DRC), the latter two sub- clades diverging from each other at 2.4 (1.1–4.14) Ma (Figure 2b; Figures S3 and S6). As newly recognized by Uvizl et al. (2024), R. acrotis (formerly R. clivosus) is broadly distributed throughout southern, central eastern, and northeastern Africa as far north as Ethiopia, comprising, in our 13652486, 2024, 6, D ow nloaded from https://onlinelibrary.w iley.com /doi/10.1111/gcb.17344 by South A frican M edical R esearch, W iley O nline L ibrary on [09/07/2024]. See the T erm s and C onditions (https://onlinelibrary.w iley.com /term s-and-conditions) on W iley O nline L ibrary for rules of use; O A articles are governed by the applicable C reative C om m ons L icense 8 of 22  |     TAYLOR et al. study, three distinct, latitudinally structured mtDNA lineages sepa- rated by cyt- b distances of 2.2–3.4% (Figure 2c, Table S4). Its sister species, R. ferrumequinum from the Middle East, North Africa and Europe diverged from R. acrotis in the Pliocene, about 4.26 (3.03– 5.59) Ma (Figure S5). Based on Demos et al. (2019), and our study, two lineages occur in southern Africa, Lineage 3 from the southern and eastern Escarpment and Manica Highlands and Lineage 1 from the SEAMA and EAM. Lineages 2 and 4–6 occur further north in the EARS (Figure 2c; Tables S1 and S2). Based on D- loop sequences, Stoffberg et al. (2012) recognized five lineages in South Africa (a–e) and one in Zimbabwe (e) (Table S1). Our ML (Figure 2c) and BEAST (Figure S5) trees confirmed the geographical patterns above, with Lineages 1 and 3 diverging in the late Pliocene at 2.74 (1.69–3.92) Ma. Within Lineage 3, Lineages (a–d) from arid, Mediterranean, and temperate biomes (southern, central, and western South Africa) and Lineage (e) from highlands of northern South Africa and Zimbabwe formed two well- supported cyt- b clades which diverged 1.83 (1.04– 2.79) Ma. 13652486, 2024, 6, D ow nloaded from https://onlinelibrary.w iley.com /doi/10.1111/gcb.17344 by South A frican M edical R esearch, W iley O nline L ibrary on [09/07/2024]. See the T erm s and C onditions (https://onlinelibrary.w iley.com /term s-and-conditions) on W iley O nline L ibrary for rules of use; O A articles are governed by the applicable C reative C om m ons L icense     |  9 of 22TAYLOR et al. Rhinolophus fumigatus is broadly distributed in Africa, with dis- junct recognized subspecies from the western Escarpment (arid savanna; R. f. aethiops) and eastern Escarpment (mesic savanna woodland; R. f. fumigatus; Figure 2d). Following the nomenclature of Demos et al. (2019), three distinct genetic clades occur in southern Africa, “R. fumigatus- eloquens” Lineages 4, 6, and 7, which diverged from each other in the late Pliocene 3.27 (1.83–5.07) Ma (Figure 2d; Figures S4 and S7). Lineage 6 corresponds with the arid R. f. aethiops, while Lineages 4 and 7 occur sympatrically in Zimbabwe, Mozambique, and Malawi. Although the stem divergence date of wing- gland bats (Cistugidae) was 42.7 (38.91–46.45) Ma, crown diversification oc- curred much later in the early Pleistocene, 1.42 (0. 72–2.48) Ma (Figure 2d; Figures S3 and S6). Arid (C. seabrae) and Mediterranean (C. lesueuri in part) clades formed well- supported (87%/1.0/70%) sis- ter clades, with two new sequences of C. lesueuri from the Maloti Drakensberg appearing sister to this clade. A relatively deep diver- gence (p = 2.4%; 1.11 (0.52–1.95) Ma) within the arid C. seabrae clade separated Northern Cape and Namibian sequences. (Figure 2d; Table S5). We follow the taxonomy of Taylor et al. (2022) who showed that L. botswanae sensu stricto (from Botswana) was synonymous with L. angolensis, while specimens of L. botswanae senso lato from temperate- paramontane localities in South Africa comprised a uniquely distinct clade, here termed L. cf. botswanae. From the pres- ent ML analysis of cyt- b sequences, long- eared bats formed a well- supported clade comprising sub- clades from temperate- montane (L. wintoni, L. cf. botswanae), savanna (L. angolensis), Mediterranean (L. cf. namibensis), and arid (L. namibensis) lineages which diverged from each other 1.46 (0.19–4.43) Ma (p = 3.0–4.7%; Table S6; Figure 2f). While savanna, arid, Mediterranean, and one temperate- montane (L. wintoni) clade were all well- supported, two L. cf. botswanae sequences from the Eastern Cape, and Limpopo Provinces of South Africa were sister to this combined clade despite their close genetic similarity to each other (p = 0.5%; Table S6). 3.3  |  Phenotypic patterns Craniometric relationships are summarized by bivariate plots of PC1 and PC2 scores (Figure 2a–f, right panels) and PC eigenvalues and variable loadings (Tables S7 and S8). Echolocation call parameters for R. acrotis, R. cervenyi, and C. lesueuri from this study are shown in Table S9. Bacular measurements and representative photographs of the baculum of all species in the study are shown in Table S10 and Figure S9, respectively. In the R. capensis, R. darlingi, and R. fumigatus groups, species from different biomes are distinguished from each other in cranial size (PC1 scores; Figure 2a,b,d; Table S7a,b,d). In the R. capensis group, cranial size (PC1 scores) is inversely correlated with peak call frequency: the smallest sized taxa (low PC1 scores), R. denti, R. rhodesiae and R. swinnyi have high peak frequencies (100–111 kHz) while the larger sized R. capensis and R. namuli have lower frequencies of 76–86 kHz and the intermediate- sized R. simu- lator has an intermediate frequency of 80–86 kHz (Figure 2a, right panel). We present new echolocation and baculum size and shape data for the newly described R. cervenyi; it has a maximum frequency of 82 kHz from passive recordings, 84 kHz from handheld recordings, and 85 kHz from release calls (Table S9). Its baculum is greatly en- larged and uniquely shaped compared with all other species in our study (Figure S9 (a: f) and S9 (b: a)). In contrast to the other three older species groups mentioned above, the widespread R. acrotis diversified more recently in the late Pliocene/early Pleistocene (after 2.7 Ma) and shows very high intra- specific variability in cranial size and shape (Figure 2c; Table S7c) and F I G U R E 2 Distributions (left), maximum likelihood (ML) phylogenetic trees (middle), principal component analysis (PCA) ordination plots from cranial measurements, photographs or drawings of the baculum and sonograms of echolocation calls (right) of selected groups of paramontane southern African bats having ranges categorized as arid (red symbols), Mediterranean (turquoise symbols), temperate- montane (blue), savanna- montane (orange), and tropical rain forest (green; see Table S1 for classification): horseshoe bats (Rhinolophus) of the R. capensis (a), R. darlingi (b), R. ferrumequinum (c), R. fumigatus (d) groups, wing- gland bats (Family Cistugidae, genus Cistugo (e), and long- eared serotine bats of the genus Laephotis (f)). Distribution maps were based on IUCN Redlist maps (open polygons), correctly identified vouchers from molecular studies (colored symbols; this study; GenBank; Curran et al., 2022; Demos et al., 2019; Dool et al., 2016; Taylor et al., 2018) and skulls measured in this study (crosses). In a few cases (see legends), GBIF records were indicated for the Angolan range of species. Gray shading indicates elevations over 1200 m a.s.l. Phylogenetic trees are shown for sub- clades (i.e., excluding outgroups) of three separate ML analyses undertaken with IQTREE of Rhinolophus, Cistugo, and Laephotis (Figures S2–S4). Values above nodes (in bold) represent median dates obtained for corresponding nodes from separate BEAST analyses in Figures S5–S7 (see text for details). Node support values for ML trees, obtained by the IQTREE program, are given below the nodes for SH- like approximate likelihood ratio tests (SH- aLRT), aBayes posterior probabilities, and ultra- fast bootstrap values (UFBS) respectively (see text for details). Tip labels marked in bold represent new sequences from this study. Underlined tip labels represent two instances of mtDNA introgression where morphologically distinct taxa from different biomes have near- identical cyt- b sequences. Species ranges of echolocation call peak frequencies were obtained from the literature for Rhinolophidae (Adams & Kwiecinski, 2018; Curran et al., 2022; Jacobs et al., 2013; Jacobs et al., 2017; Laverty & Berger, 2020; Monadjem et al., 2020; Mutumi et al., 2016; Odendaal & Jacobs, 2011; Odendaal et al., 2014; Schoeman & Jacobs, 2008), Cistugo (Monadjem et al., 2020; Schoeman & Jacobs, 2003, 2008), and long- eared Laephotis (Adams & Kwiecinski, 2018; Jacobs et al., 2005; Monadjem et al., 2020; Pierce et al., 2011). Bacula photographs and drawings were obtained from this study as well as Benda and Vallo (2012), Taylor et al. (2018), Curran et al. (2022). Abbreviation of South African province names: EC, Eastern Cape; FS, Free State; GP, Gauteng; KZN, KwaZulu- Natal; LP, Limpopo; MP, Mpumalanga; NC, Northern Cape; WC, Western Cape. Map lines delineate study areas and do not necessarily depict accepted national boundaries. 13652486, 2024, 6, D ow nloaded from https://onlinelibrary.w iley.com /doi/10.1111/gcb.17344 by South A frican M edical R esearch, W iley O nline L ibrary on [09/07/2024]. See the T erm s and C onditions (https://onlinelibrary.w iley.com /term s-and-conditions) on W iley O nline L ibrary for rules of use; O A articles are governed by the applicable C reative C om m ons L icense 10 of 22  |     TAYLOR et al. echolocation frequency (88–92 kHz in South Africa and Zimbabwe; 81–82 kHz in SEAMA and 100 kHz in Kenya; Figure 2c). However, baculum size and shape is highly uniform within the species, at least for specimens here studied (Figure 2c; Figure S9). Despite relatively recent diversification in the Pleistocene (1.42 Ma), arid (C. seabrae) wing- gland bats are distinctly smaller in skull size (lower PC1 scores) than those from temperate and Mediterranean biomes (C. lesueuri) (Figure 2e; Table S8a). With the same crown diversification date (1.46 Ma), two cranial size groups were defined within the long- eared Laephotis group, a small- sized group containing savanna and temperate (southern African) taxa and a larger sized group containing arid, temper- ate (East African) and Mediterranean taxa. Within the former group, temperate L. cf. botswanae are slightly larger than savanna L. angolensis, but with some overlap between them (Figure 2f; Table S8b). F I G U R E 2  (Continued) 13652486, 2024, 6, D ow nloaded from https://onlinelibrary.w iley.com /doi/10.1111/gcb.17344 by South A frican M edical R esearch, W iley O nline L ibrary on [09/07/2024]. See the T erm s and C onditions (https://onlinelibrary.w iley.com /term s-and-conditions) on W iley O nline L ibrary for rules of use; O A articles are governed by the applicable C reative C om m ons L icense     |  11 of 22TAYLOR et al. 3.4  |  Distribution models Model fit for all models was high (Table 1: AUC 0.890–0.997). As determined by percentage contributions and the jackknife test, dif- ferent combinations of precipitation and temperature variables were important for different species and biomes. Precipitation of the wet- test month (Bio13; lowest values more suitable) was the most im- portant environmental variable in the three arid- adapted species, R. denti, R. damarensis and C. seabrae, while mean temperature of the driest quarter (Bio9; intermediate values optimal) was the most important variable in mesic savanna species, R. darlingi, R. simulator, and L. angolensis, as well as in the high- elevation, montane- adapted R. cervenyi (lowest values optimal), and precipitation of the driest month (Bio14; intermediate to higher values most suitable) was the most important variable for temperate species like R. acrotis, R. swin- nyi, R. cf. botswanae. Maxent past (LGM) models showed that the ranges of temper- ate, savanna, and Mediterranean species were likely very similar during the LGM compared with present ranges, except in the case of L. cf. botswanae where populations were more connected across the Drakensberg than is currently the case (Figure 4). On the other hand, species from arid environments had more extensive ranges in the LGM, in R. denti which probably occurred much more extensively throughout Botswana and the central regions of South Africa; R. damarensis was distributed more extensively in the central highveld of South Africa in closer proximity to the Drakensberg than currently recorded. Compared with present models, Maxent future (2070) distribu- tion models projected (i) decreases in the ranges of species asso- ciated with cold arid, winter- rainfall and temperate climates from higher- lying central African miombo savannas, (ii) increases in spe- cies from hot arid climates, but (iii) minimal changes in temperate species associated with the eastern Great Escarpment, possibly due to the buffering effect of mountains to climate change (Figure 4). 4  |  DISCUSSION 4.1  |  Taxonomic implications Many new and often cryptic species of Afromontane small mammals have been described in the last three decades (Benda et al., 2024; Curran et al., 2022; Lavrenchenko et al., 2016; Monadjem et al., 2013, 2021; Taylor, Denys, & Cotterill, 2019; Taylor, Kearney, et al., 2019; Uvizl et al., 2024; Voelker et al., 2021). Our analysis iden- tified at least four cryptic species that probably merit formal taxo- nomic recognition (denoted as “c.f.” in Table S2). In fact, one of these was recently formally described as a new species based on speci- mens and sequences from Lesotho, R. cervenyi (Benda et al., 2024). Although Benda et al. (2024) described this mountain species as occurring almost exclusively in Lesotho, based on our genetic, morphometric, and acoustic data (Figure 2b), it is also distributed from the Maloti Drakensberg in the Free State and KwaZulu- Natal provinces and from the Mpumalanga Escarpment, extending also into the KwaZulu- Natal midlands. Specimens of this species were previously misidentified as R. acrotis (Benda et al., 2024); however, in our study in the Maloti Drakensberg, R. acrotis and R. cervenyi had divergent peak frequencies (mean = 92 kHz, 86–94 kHz, n = 50; mean = 82.4 kHz, 78–86 kHz, n = 49, respectively), and occupied lower (<1900 m) and higher (1700–3060 m) elevations respectively, with limited sympatry at two localities in the Golden Gate Highlands National Park and nearby apple farms (Table S1). As also deduced by Benda et al. (2024), the near- identity of the cyt- b sequences of R. cervenyi and the southern clade of R. damarensis may be explained by recent mtDNA introgression (see Section 4.2.2). Similarly, mtDNA introgression explains the genetic proximity of R. simulator and R. rhodesiae where the ranges of the two spe- cies overlap, despite clear- cut species differences in echolocation call frequency (81 and 101 kHz respectively) and external (forearm length), cranial and bacular size and shape which are maintained in sympatry at several localities in South Africa, Zimbabwe and Zambia (Figure 2a; Figure S9; Taylor et al., 2018). Instead, R. rhodesiae is indistinguishable in cranial or external morphology from R. swinnyi which occurs in the Eastern Cape and southern KwaZulu- Natal, de- spite deep genetic divergence between them presumably resulting from introgression of the R. simulator maternal genome into the for- mer but not the latter. While this situation might argue for the con- specificity of R. swinnyi and R. rhodesiae, they are easily distinguished on call frequency and baculum size and shape (Figure 2a; Table S3; gracile in swinnyi and robust in rhodesiae), suggesting that speciation of the two small- sized species preceded introgression of R. rhodesiae from the larger sized R. simulator. This example underlines the impor- tance of an integrative taxonomic approach (Goodman et al., 2015); considering only molecular evidence without morphological or bio- geographical context would erroneously conclude that two (not three) species occur in this group. A precise taxonomic arrangement also allows meaningful analysis of biogeographic affinities between species (see Section 4.2). Our combined data argue for specific recognition of R. f. aethiops from the semi- arid HEAN which is geographically and ecologi- cally disjunct from R. f. fumigatus from the eastern Escarpment (Soutpansberg and Manica highlands), EAM, and EARS. The former is significantly larger in cranial size (mean skull length 24.0 mm, 23.7– 24.4 mm, n = 4) and forearm length (mean 55.2 mm, 51.0–58.0 mm, n = 16) than R. f. fumigatus from southern and central Africa (mean skull length 22.3 mm, 21.6–23.1 mm, n = 11; mean forearm length 51.5 mm, 46.5–58.0 mm, n = 17). Given genetic (p = 3%; Table S6) and biome differences, L. cf. bo- tswanae from the eastern Maloti Drakensberg and Greater Midlands, LMME, Soutpansberg, and Mozambique may prove to be a species distinct from L. angolensis (including L. botswanae s.s.) from Miombo highland plateaux of central- south Africa. Similarly, L cf. namibensis (Mediterranean- temperate) may prove to be species distinct from L. namibensis s.s. (arid); their genetic divergence (p = 3.7%) exceeds that found between “good” species such as L. wintoni and L. angolensis 3.0%; Table S6. 13652486, 2024, 6, D ow nloaded from https://onlinelibrary.w iley.com /doi/10.1111/gcb.17344 by South A frican M edical R esearch, W iley O nline L ibrary on [09/07/2024]. See the T erm s and C onditions (https://onlinelibrary.w iley.com /term s-and-conditions) on W iley O nline L ibrary for rules of use; O A articles are governed by the applicable C reative C om m ons L icense 12 of 22  |     TAYLOR et al. TA B L E 1 Summary of model fit, based on the area under the curve (AUC) of the receiver operating characteristic (ROC) for training data, and the most important bioclimatic variables in past, present, and future (2070) Maxent models of 13 bat species included in this study. Species N Model AUC Top three variables contributing most to model Rank 1 % contr. Rank 2 % contr. Rank 3 % contr. R. capensis 29 LGM 0.991 Bio19 39.4 Bio8* 26.8 Bio15 13.4 29 Present 0.95 Bio19** 41.7 Bio8* 27.1 Bio15 17.5 29 2070 0.991 Bio19 35.6 Bio8* 33.2 Bio13 13.3 R. cervenyi 16 LGM 0.997 Bio9*** 57.9 Bio14 23.2 Bio8 7.4 16 Present 0.997 Bio5* 48.6 Bio9** 33.2 Bio14 13.3 16 2070 0.997 Bio9*** 43.5 Bio14 18.5 Bio18 13.4 R. acrotis 201 LGM 0.905 Bio14* 36.7 Bio8 28.5 Bio3** 18.7 201 Present 0.90 Bio14 38.0 Bio8 24.2 Bio3** 17.6 201 2070 0.904 Bio14 36.1 Bio8 28.4 Bio3** 20.2 R. damarensis 40 LGM 0.945 Bio13* 37.9 Bio3** 18.3 Bio2 16.6 40 Present 0.925 Bio13*** 53.6 Bio9 12.9 Bio3 12.3 40 2070 0.940 Bio13*** 59.3 Bio9 17.7 Bio15 6.4 R. darlingi 105 LGM 0.945 Bio3* 28.3 Bio9** 23.9 Bio18 17.2 105 Present 0.952 Bio9 31.6 Bio14 16.5 Bio2** 16.3 105 2070 0.953 Bio9*** 33.8 Bio14 15.3 Bio2 11.3 R. denti 20 LGM 0.941 Bio13*** 59.5 Bio2 19.9 Bio18 11.2 20 Present 0.941 Bio13*** 41.6 Bio2 34.0 Bio18 7.0 20 2070 0.939 Bio13*** 46.5 Bio2 42.0 Bio8 5.2 R. rhodesiae 53 LGM 0.908 Bio13 33.2 Bio3 23.2 Bio2*** 22.9 53 Present 0.899 Bio3 23.8 Bio13 21.8 Bio2 20.6 53 2070 0.890 Bio13 27.8 Bio3* 22.8 Bio18** 14.2 R. simulator 109 LGM 0.918 Bio9** 31.7 Bio14 22.3 Bio3* 19.1 109 Present 0.918 Bio9** 34.1 Bio14 17.5 Bio18 16.2 109 2070 0.919 Bio9*** 41.1 Bio14 17.6 Bio18 13.8 R. swinnyi 8 LGM 0.988 Bio14** 76.5 Bio9 17.9 Bio5 4.1 8 Present 0.995 Bio14* 71.4 Bio5** 21.8 Bio18 3.9 8 2070 0.996 Bio14* 64.8 Bio5** 29 Bio18 3.6 C. lesueuri 20 LGM 0.972 Bio14 45.1 Bio8** 28.8 Bio9* 15.9 20 Present 0.97 Bio8*** 43.8 Bio14 40.3 Bio9 14.4 20 2070 0.982 Bio8** 39.7 Bio14 39.6 Bio9 14.9 C. seabrae 13 LGM 0.989 Bio18* 43.7 Bio13 34.8 Bio8** 18.7 13 Present 0.979 Bio13*** 84.9 Bio8 6.9 Bio18 2.3 13 2070 0.979 Bio13*** 89.3 Bio8 6.0 Bio2 2.5 L. angolensis 42 LGM 0.912 Bio9* 32 Bio18 23.4 Bio15** 16.1 42 Present 0.905 Bio9* 34.7 Bio13 25.6 Bio15** 24.8 42 2070 0.899 Bio9* 26.9 Bio13 21.0 Bio14 16.7 L. cf. botswanae 10 LGM 0.982 Bio14*** 61.3 Bio9 22.7 Bio18 11.7 10 Present 0.98 Bio14*** 49.0 Bio9 31.2 Bio18 17.1 10 2070 0.976 Bio14* 35 Bio9** 27.5 Bio5 18.3 Note: N refers to the number of occurrence records, and % contr. to the relative percentage contribution of Worldclim2 Bioclimate (Bioclim) variables. Based on the jackknife test of variable importance, the variable marked with a single asterisk is that having the most information on its own while two asterisks indicates the variable having the most information that is not present in other variables and three asterisks indicate when both the above apply. 13652486, 2024, 6, D ow nloaded from https://onlinelibrary.w iley.com /doi/10.1111/gcb.17344 by South A frican M edical R esearch, W iley O nline L ibrary on [09/07/2024]. See the T erm s and C onditions (https://onlinelibrary.w iley.com /term s-and-conditions) on W iley O nline L ibrary for rules of use; O A articles are governed by the applicable C reative C om m ons L icense     |  13 of 22TAYLOR et al. 4.2  |  Biogeographic implications 4.2.1  |  Dispersal Our results suggest that the Escarpment has played an important role in promoting both vicariance and dispersal in bats in differ- ent geological periods. Both arms (western and eastern) of the Escarpment have acted as past dispersal routes from the tropics into temperate southern Africa. The western Escarpment (HEAN) was a corridor providing genetic connectivity between two arid- adapted horseshoe bats (R. denti and R. damarensis) and their cen- tral African sister taxa (R. cf. denti and R. cf damarensis respectively from western DRC and Gabon). Dispersal from tropical central Africa to southern Africa in these two species pairs predated the isolation and divergence of their most recent common ances- tors in the late Pliocene (3.13 Ma: R. denti- R. cf denti) and early Pleistocene (2.4 ma: R. damarensis- R. cf. damarensis). A more re- cent north–south dispersal along the HEAN is inferred from the hiatus between L. namibensis from the Namib Desert and L. cf. na- mibensis from the Cedarberg Range of the CFB, more than 600 km apart and 3.7% divergent in cyt- b sequences, indicating a late Pleistocene divergence of 0.34 (HPD 0.01–1.61) Ma. Supporting this central African biogeographic connection, at least five forest- endemic birds from the Angolan highlands have relatives from the Cameroun Volcanic Line in central Africa (Vaz da Silva, 2015), and some Angolan highland rodents have central African affiliations (Krásová et al., 2021). The eastern Escarpment was an important Pliocene (2.7–4.2 Ma) dispersal route from northeastern Africa into southern Africa for widespread paramontane species such as R. acrotis and R. fumigatus. In R. acrotis, this direction of dispersal is supported by the ML tree showing that the northern lineage from EAMS is sister relative to the successively more derived central and southern African lineages (Figure 2c). This pattern is suggestive of a step- like single radiation of the species from north to south during the early Pleistocene be- tween 2.4 and 1 Ma, with in situ differentiation in successive “steps,” as also proposed for samango monkeys, Cercopithecus albogularis (Guschanski et al., 2013; Linden et al., 2020), Afromontane musk shrews, Crocidura (Dianat et al., 2024), and rodents of the Lophuromys flavopunctatus group (Onditi et al., 2021). Repeated south–north ra- diations along this montane corridor was proposed for the rodent genus Otomys (Montgelard et al., 2023). 4.2.2  |  Vicariance and historical introgression Our results identified seven phylogeographic discontinuities, result- ing in 29 micro- endemic, mountain- associated lineages from tropi- cal, subtropical, arid, Mediterranean, or temperate biomes (Table S2; Figure 3). The distribution patterns outlined below for bats, from west to east, are supported by those of other Escarpment plant and animal taxa and recognized CPE, of which a few examples are men- tioned below. (i) HEAN (N- S). In spite being morphologically similar, a deep di- vergence (p = 4.0%) separates northern (N Namibia) and southern (S Namibia, N Cape) mtDNA clades of R. damarensis. The northern limit of the southern clade is at Maercker's Caves just east of the widest point of the Namib Desert. Since the Namib Desert has re- mained unchanged since 18 Ma, it is unlikely that vicariance could account for the deep divergence between the two arid clades. A more probable cause for this unusual pattern could be a history of range expansion and introgression between the southern part of R. damarensis and the morphologically distinct (but genetically similar) Maloti- Drakensberg species, R. cervenyi, as recently confirmed by Benda et al. (2024). The ranges of the two species probably over- lapped or at least occurred in closer parapatry (with mtDNA intro- gression between them) until recently, possibly as recently as the LGM (see Figure 4f). The range of the southern arid clade coincides remarkably with the simulated extent of frost conditions in the LGM compared with the present (Figure 1b), suggesting that this marks the extent of Pleistocene geographical range expansion of the temperate- montane R. cervenyi and therefore the overlap (and hence introgression) between the two species, R. cervenyi and R. damarensis (southern sub- clade). Following this reasoning, if there was no (or limited) historical range overlap or introgression between the two species in N Namibia, the northern clade would then rep- resent the un- introgressed or “true” R. damarensis mitochondrial genotype, while the southern arid clade may have been “captured” by R. cervenyi. The Pliocene divergence date would then indicate the date of speciation between arid R. damarensis and temperate R. cervenyi, thus explaining the considerable phenotypic divergence between the two species. The comparatively elongated and much broader baculum of R. cervenyi relative to R. damarensis suggests a pre- mating reproductive barrier that may have caused asymmetri- cal mating between the two species during sympatry to promote the spread of the cervenyi maternal lineage throughout the range of southern R. damarensis, for example, successful pairings between male damarensis and female cervenyi but not vice versa. The inci- dence of mtDNA introgression has been shown to be widespread in bats (e.g., Benda et al., 2024; Dool et al., 2016; Taylor et al., 2018) and other small mammals such as rodents (Bryja et al., 2018). (ii) HEAN—Karoo (Orange River gap). Within C. seabrae, northern and southern clades diverged in the mid- Pleistocene (ca. 1.43 Ma). The range of the northern clade coincides with the extent of the HEAN from southern Angola to at least as far south in Namibia as Aus, with the southern clade restricted to a small area of the Namaqualand- Richtersveld Ecoregion south of the Orange River in the Northern Cape. The break therefore coincides closely with the lower Orange River valley. The Orange River also marks the bound- ary between the historical ranges of the Cape Mountain Zebra (Equus z. zebra), from the CFB and southern Escarpment (from E Cape to the Karoo) and Hartmann's Mountain Zebra (E. z. hartmannae), from the HEAN (Skinner & Chimimba, 2005). (iii) Cape Fold Belt (CFB)—Escarpment (“Bedford Gap”). The Bedford Gap in the Eastern Cape province separates the CFB from the south- ern Escarpment (Sneeuberg and Great Winterberg- Amatola ranges). 13652486, 2024, 6, D ow nloaded from https://onlinelibrary.w iley.com /doi/10.1111/gcb.17344 by South A frican M edical R esearch, W iley O nline L ibrary on [09/07/2024]. See the T erm s and C onditions (https://onlinelibrary.w iley.com /term s-and-conditions) on W iley O nline L ibrary for rules of use; O A articles are governed by the applicable C reative C om m ons L icense 14 of 22  |     TAYLOR et al. This gap explains vicariance patterns between sister clades of rodents (Otomys, Rhabdomys) (Ganem et al., 2020; Taylor, Kearney, et al., 2019) and shrews (Myosorex varius) (Willows- Munro & Matthee, 2011). It was recognized as one of three “floristic connections” between the CFB and the southern Escarpment by Clark, Barker, and Mucina (2011a), Clark, Barker, McMaster, and Mucina (2011). In our study, the Bedford Gap explains one Pliocene and two Pleistocene vicariance events in bats. It coincides with the late Pliocene (5.7 Ma) split between the sister species, R. capensis, a predominantly CFB species, and R. swinnyi, a tem- perate forest species from the southeastern and eastern Escarpment (Moir et al., 2020, Monadjem et al., 2020; Figure 2a). Forests were continuously present in the Western Cape and southern Namaqualand up to 5.3 Ma (late Miocene), after which they retreated to major river valleys due to the onset of more arid conditions in central Africa (Scott et al., 1997). This may explain the late Miocene speciation of R. capensis from R. swinnyi. The same scenario was used to explain the presence of a relic species of Clivia (Clivia mirabilis) in the Hantam–Roggeveld mountains of the southwest Escarpment, >800 km from its closest relative, C. nobilis from coastal and scarp forests in the Eastern Cape (Dixon, 2011; Rourke, 2002). A more recent mid- Pleistocene vicariance event (0.99 Ma) sepa- rated R. acrotis from the CFB (Lineages 3b- c) from Lineage 3d from the southeastern and eastern Escarpment (Stoffberg et al., 2012; Table S2; Figure 2d; Figure S5). Similarly, based on our BEAST tree (though not ML tree), lineages of C. lesueuri from the CFB and east- ern Escarpment (western slopes of Maloti Drakensberg) diverged in the mid- Pleistocene at 0.52 Ma (Figure S6). Many botanical examples document biogeographical links between the CFB and different sections of the southern Escarpment (Clark et al., 2011a, 2011b, 2011c; Clark, Barker, McMaster, and Mucina (2011); Galley et al., 2007; Weimarck, 1941). For example, although similar floris- tically, the Hantam–Roggeveld Centre of Plant Endemism (southwestern Escarpment) differs in composition from the CFB located 80 km to the south (across the Tanqua Karoo semi- desert) in the absence of classical restioid, proteoid, and ericoid Fynbos elements from the former (Clark, Barker, & Mucina, 2011a, 2011b). Based on known sister- group relation- ships, Clark, Barker, and Mucina (2011a) reviewed several additional ex- amples which support biogeographic links between plant taxa from the CFB, the southern Escarpment, and the eastern Escarpment (Drakensberg CPE ). Notably, eight CFR- endemic species or subspecies occur not only in the CFB, but also from the adjacent, semi- arid, Namaqualand and Richtersveld Ecoregion of Dinerstein et al. (2017). These taxa have sis- ter groups from either the southwestern or southeastern sections of the southern Escarpment and/or the eastern Escarpment (Table S11). The close association between the CFB and arid Namaqualand elements in plants is echoed by the distribution of R. capensis which occurs throughout the CFB and Namaqualand (Figure 2a). By contrast, R. acrotis occurs throughout the CFB and eastern Escarpment, but is not found on the southern or western Escarpment or Namaqualand. In this case, isolated populations of the species from arid inselbergs of the Nama Karoo (north of the Escarpment) are likely to be Pleistocene re- fugia that were connected to the central plateau of South Africa during a previous hypothermal period (see (iv) below). (iv) Karoo- Drakensberg. In the absence of any obvious physical barriers between the limits of the western Maloti Drakensberg and Nama Karoo, vegetation barriers mediated by Pleistocene cyclical climatic and habitat changes (“species pumps”) have been advanced to explain the origin of arid South African vertebrate spe- cies (e.g., Matthee & Flemming, 2002; Outlaw et al., 2007; Swart et al., 2009). As argued above, such a late Pleistocene or Holocene species pump can explain repeated range expansion- contraction of temperate R. cervenyi and its mtDNA introgression with arid R. damarensis. Similarly, the outlying Nama Karoo Lineage (3c) of R. acrotis argues for historical connections between Nama Karoo refugia, the Maloti Drakensberg and CFB lineages (at ca. 0.99 Ma) associated with the range expansion and contraction during glacial and inter- glacial peaks, respectively (Figures 1b and 2c). A sim- ilar pattern is found in striped mice (Rhabdomys) where species associated with the Nama Karoo (R. bechuanae) and Grassland (R. dilectus) biomes form contact zones in the central Free State and Gauteng provinces (Ganem et al., 2020). Several bird species pairs follow this biogeographical pattern, for example, the western Karoo (Turdus smithi) and eastern Olive (T. olivaceous) Thrushes, (Bowie et al., 2004), the Yellow (Crithagra flaviventris) and Forest (C. scotops) Canaries, the Karoo (Prinia maculosa) and Drakensberg (P. hypoxantha) Prinias and Ludwig's (Neotis ludwigi) and Stanley's (N. denhami stanleyi) Bustards (Sinclair et al., 2020). Partly sympat- ric sister species of red rock hares are associated with the eastern and southern Escarpment (Pronolagus saundersiae) and the north- ern Karoo (P. rupestris) (Matthee & Robinson, 1996). (v) W- E Drakensberg. Although the highest Maloti Drakensberg peaks do not appear to separate any sister species of paramontane bats, they nevertheless appear to be an important barrier limiting the eastward or westward range of species found only on the western (L. cf. wintoni, C. lesueuri) or eastern (R. cf. swinnyi, L. cf. botswanae) aspects of this mountain range respectively. Those species from the moister eastern escarpment of the Drakensberg are forest- associated, while those from the drier (rain shadow) western slopes are typically grassland- associated. Since R. cervenyi was recorded from alpine el- evations (>3000 m), the high Maloti Drakensberg are not a barrier to this species, and it has been recorded from both the western and eastern aspects. The high Drakensberg is however a barrier to disper- sal separating two mtDNA clades of the vlei rat (Otomys auratus), one occurring on the South African highveld and western lower slopes of the Drakensberg, and the other on the eastern lower slopes of the Drakensberg. These two clades were estimated to have diverged just after the mid- Pleistocene transition at 0.68 Ma (Taylor et al., 2009). (vi) Southeastern- northeastern Escarpment (Greater Midlands- LMEE). One species pair exemplifies this pattern; R. swinnyi from the eastern Maloti Drakensberg foothills (the southern portion of the Greater Midlands CPE) and R. rhodesiae from the central and northern portions of the Greater Midlands CPE, the LMEE and Soutpansberg CPE, Zimbabwe, Zambia, Mozambique, Malawi and Tanzania (Figure 2a; Moir et al., 2020; Taylor et al., 2018). As pointed out in Section 4.1, the two species are most clearly distinguished on their baculum shape (gracile in swinnyi and robust in rhodesiae), 13652486, 2024, 6, D ow nloaded from https://onlinelibrary.w iley.com /doi/10.1111/gcb.17344 by South A frican M edical R esearch, W iley O nline L ibrary on [09/07/2024]. See the T erm s and C onditions (https://onlinelibrary.w iley.com /term s-and-conditions) on W iley O nline L ibrary for rules of use; O A articles are governed by the applicable C reative C om m ons L icense     |  15 of 22TAYLOR et al. and based on this diagnostic character, the two species overlap in the central Greater Midlands (KwaZulu- Natal province) where they are sympatric at one locality. Due to mtDNA introgression from R. simulator, it is not possible to date the divergence between the sibling species, or the likely cause of their vicariance. However, it is plausible to speculate that, during Pleistocene glacial conditions, the range of the ancestral temperate- montane species extended from Tanzania in the north to the Eastern Cape province in the south. A subsequent drier and warmer inter- glacial period would have led to local extinction in unsuitable habitats at mid- elevations (e.g., the Greater Midlands which range from 1400 to 1700 m; Carbutt, 2023). Higher mountains such as the Maloti Drakensberg in the south, and the LMEE and other ranges in the north, would have served as re- fugia within which occurred phenotypic divergence, most notable in baculum shape and echolocation call peak frequency. The ranges of the northern and southern clades would have expanded during a subsequent glacial period, resulting in the current contact zone in the central Greater Midlands. Congruent biogeographic patterns (Greater Midlands- LMME sibling lineage pairs) are known for for- est shrews (Myosorex varius and M. cf. tenuis; Taylor et al., 2013), rock hyraxes (Maswanganye et al., 2017), and samango monkey subspecies (Cercopithecus albogularis shwarzi and C. a. erythrarchus; Linden et al., 2020) as well as within the amaryllid plant genus Clivia, with the range of C. caulescens corresponding with the LMEE and Soutpansberg, and those of C. gardeni, C. robusta, and C. nobi- lis corresponding with scarp and coastal forests along the eastern Escarpment. (vii) Manica Highlands—SEAMA (Zambezi Valley gap). R. acrotis Lineages 3 and 1 correspond with this potential barrier (Figure 3). The ranges of R. rhodesiae and R. acrotis Lineage 3 link the LMEE and Soutpansberg of South F I G U R E 3 Map of southern, central, and eastern Africa showing major geographic features (as in Figure 1a) but with biogeographical barriers elucidated by this study indicated as red dashed lines, labelled as (i) to (vii) (see Discussion), and taxa specific to different ranges indicated according to the predominant biomes (green = tropical; red = arid, turquoise = Mediterranean, blue = temperate, orange = savanna). Note that only one savanna lineage is here indicated for ease of visualization. Map lines delineate study areas and do not necessarily depict accepted national boundaries. 13652486, 2024, 6, D ow nloaded from https://onlinelibrary.w iley.com /doi/10.1111/gcb.17344 by South A frican M edical R esearch, W iley O nline L ibrary on [09/07/2024]. See the T erm s and C onditions (https://onlinelibrary.w iley.com /term s-and-conditions) on W iley O nline L ibrary for rules of use; O A articles are governed by the applicable C reative C om m ons L icense 16 of 22  |     TAYLOR et al. 13652486, 2024, 6, D ow nloaded from https://onlinelibrary.w iley.com /doi/10.1111/gcb.17344 by South A frican M edical R esearch, W iley O nline L ibrary on [09/07/2024]. See the T erm s and C onditions (https://onlinelibrary.w iley.com /term s-and-conditions) on W iley O nline L ibrary for rules of use; O A articles are governed by the applicable C reative C om m ons L icense     |  17 of 22TAYLOR et al. Africa with the central and eastern highlands of Zimbabwe, across the arid Limpopo Valley which does not seem to be a barrier to dispersal in bats. Although not considered in this study, which focuses on the Escarpment, the distribution ranges of R. acrotis Lineages 1, 2, and 4–6 (Figure 3) demonstrate additional barriers that can be identified between the E Arc and western (Albertine) and eastern (Kenyan) Rift mountains (see also Nicolas et al., 2020; Taylor et al., 2009) and be- tween the Kenyan Rift and the Ethiopian mountains (see also Dianat et al., 2024 for shrews, and Onditi et al., 2021, Krásová et al., 2022 and Šumbera et al., 2018, for rodents). 4.3  |  Conservation implications of climate change Comparison of current (1980–2016) and future (2071–2100) Köppen– Geiger climate maps (Beck et al., 2018; Figure S1) suggest a reduction in the extent of temperate zones in southern Africa by 2070 at the expense of arid steppes and tropical savanna zones, particularly in medium elevation or isolated highlands such as the central plateaux and northern highlands of South Africa, central highlands of Angola, much of Zambia and central Zimbabwe and isolated inselbergs of the SEAMA. We predicted therefore that species reviewed here hav- ing temperate- montane ranges may be severely affected by climate change, particularly in the case of hibernating species of bats where warmer winters could mean a delayed or reduced period of hiberna- tion and/or premature arousal and birth of young during periods of low insect availability. However, data on the physiological capacity of bat hibernators to adapt to seasonal changes due to climate change are currently lacking, especially in the Global South (Festa et al., 2023). Our Maxent models of 13 paramontane bat species allowed us to test the above prediction. Contrary to our expectations, models did not predict a future reduction by 2070 in the ranges of temperate- montane F I G U R E 4  (Continued) F I G U R E 4 Maps of south- central Africa showing the distribution of Köppen–Geiger climate zones for the present (a) and projected future (2070) (b), as well as past (last glacial maximum: left panel), present (right panel), and projected future (2070; right panel) Maxent distribution models for five species groups of bats; Rhinolophus capensis group (c–e: green = R. swinnyi; blue = R. rhodesiae; orange = R. simulator; turquoise = R. capensis; red = R. denti); R. darlingi group (f–h: blue = R. cervenyi; orange = R. darlingi; red = R. damarensis), R. ferruquinum group, in part (i–k: blue = R. acrotis), Laephotis spp (l–n: blue = L. cf. botswanae; orange = L. angolensis), Cistugo spp (o–q: blue = C. lesueuri; red = C. seabrae). Details of Maxent models given in text. Ranges of species above indicated by colors corresponding to biomes recognized in this study (Tables S1 and S2) as follows: blue or green = temperate; orange = savanna; turquoise = Mediterranean; red = arid. Map lines delineate study areas and do not necessarily depict accepted national boundaries. 13652486, 2024, 6, D ow nloaded from https://onlinelibrary.w iley.com /doi/10.1111/gcb.17344 by South A frican M edical R esearch, W iley O nline L ibrary on [09/07/2024]. See the T erm s and C onditions (https://onlinelibrary.w iley.com /term s-and-conditions) on W iley O nline L ibrary for rules of use; O A articles are governed by the applicable C reative C om m ons L icense 18 of 22  |     TAYLOR et al. species associated with the higher- elevation Maloti Drakensberg (R. swinnyi, R. cervenyi, R. acrotis, and L. cf botswanae); instead, their ranges appear remarkably stable over time (Figure 4). We propose that this sta- bility may be at least partly due to the buffering effect of topographic ruggedness and elevational and habitat heterogeneity in high moun- tains, providing multiple potential refugia in the face of increasingly ad- verse climate conditions (Pauli & Halloy, 2019). Schoeman et al. (2013) found topographic ruggedness to be the primary driver of species richness in Southern African bats. As expected, bat species with mesic savanna distributions on lower- elevation plateaux and highlands (L. an- golensis, R. simulator, and R. darlingi) showed some contraction of their ranges in central Africa by 2070, but not as severe as predicted by the Köppen–Geiger climate models. 5  |  CONCLUSIONS Under past climate change, the Great Escarpment of southern Africa has been an important evolutionary “amphitheater” of bat diversifi- cation, acting as both “cradle” (fostering speciation) and “museum” (fostering lineage persistence), and promoting dispersal and vicari- ance, particularly since the end of the Miocene, 5 Ma, resulting in 29 micro- endemic bat lineages in 14 species from six taxonomically diverse species groups. This high level of endemism and diversity associated with southern African mountains is reflected more gener- ally in plants and animals, providing testable hypotheses for future research. Not only do they house a rich legacy of endemic fauna and flora, but southern African mountains may be important buffers to future climate change, underlining the critical importance of con- serving mountain habitats, particularly grasslands which are under increasing anthropogenic threats due to new settlements, conver- sion to agriculture, increasing fire frequency, overgrazing and range- land degradation and poor protection status (Carbutt et al., 2017). AUTHOR CONTRIBUTIONS Peter J. Taylor: Conceptualization; data curation; formal analy- sis; methodology; visualization; writing – original draft; writing – review and editing. Teresa C. Kearney: Data curation; methodol- ogy; validation; writing – review and editing. Vincent Ralph Clark: Conceptualization; validation; writing – review and editing. Monday V. Mdluli: Data curation; methodology; writing – review and edit- ing. Alexandra Howard: Data curation; methodology; writing – review and editing. Wanda Markotter: Data curation; funding acquisition; project administration; writing – review and edit- ing. Marike Geldenhuys: Data curation; methodology; writing – review and editing. Leigh R. Richards: Data curation; writing – review and editing. Andrinajoro R. Rakotoarivelo: Methodology; writing – review and editing. Johan Watson: Data curation; formal analy- sis; methodology; writing – review and editing. Julio Balona: Data curation; validation; writing – review and editing. Ara Monadjem: Conceptualization; validation; writing – original draft; writing – review and editing. ACKNOWLEDG MENTS Dr Nico Avenant (Curator of Mammals) is thanked for loaning speci- mens of Laephotis wintoni from the National Museum in Bloemfontein for DNA extraction. Erika Strydom is thanked for laboratory techni- cal assistance with DNA sequencing in the laboratory of WM. PJT ac- knowledges the financial support of the University of the Free State (UFS) through its Inter- Disciplinary Grant, as well as the National Research Foundation (NRF). The Afromontane Research Unit of the UFS provided student bursary and field logistic support. This work was partly supported by the UFS- QQ Risk & Vulnerability Science Centre, through a DSI- NRF grant to VRC (grant no.: 128386). CONFLIC T OF INTERE S T S TATEMENT The authors declare no conflict of interest. DATA AVAIL ABILIT Y S TATEMENT Raw cyt- b sequence data (FASTA files; Datasets S1–S3) and cranio- metric and specimen data (Excel files; Datasets S4–S7) are openly available on Dryad at https:// doi. org/ 10. 5061/ dryad. 3bk3j 9ksc. ORCID Peter J. Taylor https://orcid.org/0000-0001-9048-7366 Teresa C. Kearney https://orcid.org/0000-0002-0050-4060 Vincent Ralph Clark https://orcid.org/0000-0001-5058-0742 Alexandra Howard https://orcid.org/0000-0002-6121-4438 Monday V. Mdluli https://orcid.org/0000-0002-5830-0116 Wanda Markotter https://orcid.org/0000-0002-7550-0080 Marike Geldenhuys https://orcid.org/0000-0003-4005-118X Leigh R. Richards https://orcid.org/0000-0003-2599-5262 Andrinajoro R. Rakotoarivelo https://orcid. org/0000-0001-9318-7465 Ara Monadjem https://orcid.org/0000-0003-1906-4023 R E FE R E N C E S Adams, R. A., & Kwiecinski, G. (2018). Sonar surveys for bat species richness and activity in the southern Kalahari Desert, Kgalagadi Transfrontier Park, South Africa. Diversity, 10, 103. https:// doi. org/ 10. 3390/ d1003 0103 Amador, L. I., Moyers Arévalo, R. L., Almeida, F. C., Catalano, S. A., & Giannini, N. P. (2018). Bat systematics in the light of unconstrained analyses of a comprehensive molecular supermatrix. Journal of Mammalian Evolution, 25, 37–70. https:// doi. org/ 10. 1007/ s1091 4- 016- 9363- 8 Anisimova, M., Gil, M., Dufayard, J.- F., Dessimoz, C., & Gascuel, O. (2011). Survey of branch support methods demonstrates accuracy, power, and robustness of fast likelihood- based approximation schemes. Systematic Biology, 60, 685–699. https:// doi. org/ 10. 1093/ sysbio/ syr041 Bayliss, J., Bittencourt- Silva, G. B., Branch, W. R., Bruessow, C., Collins, S., Congdon, T. C. E., Conradie, W., Curran, M., Daniels, S., Darbyshire, I., Farooq, H., Fishpool, L., Grantham, G., Magombo, Z., Matimele, H., Monadjem, A., Monteiro, J., Osborne, J., Saunders, J., … Platts, P. J. (2024). The South East Africa montane archipelago (SEAMA)—a biogeographical appraisal of a threatened ecoregion. Scientific Reports, 14, 5971. https:// doi. org/ 10. 1038/ s4159 8- 024- 54671 - z Beck, H. E., Zimmermann, N. E., McVicar, T. R., Vergopolan, N., Berg, A., & Wood, E. F. (2018). Present and future Köppen- Geiger climate 13652486, 2024, 6, D ow nloaded from https://onlinelibrary.w iley.com /doi/10.1111/gcb.17344 by South A frican M edical R esearch, W iley O nline L ibrary on [09/07/2024]. See the T erm s and C onditions (https://onlinelibrary.w iley.com /term s-and-conditions) on W iley O nline L ibrary for rules of use; O A articles are governed by the applicable C reative C om m ons L icense https://doi.org/10.5061/dryad.3bk3j9ksc https://orcid.org/0000-0001-9048-7366 https://orcid.org/0000-0001-9048-7366 https://orcid.org/0000-0002-0050-4060 https://orcid.org/0000-0002-0050-4060 https://orcid.org/0000-0001-5058-0742 https://orcid.org/0000-0001-5058-0742 https://orcid.org/0000-0002-6121-4438 https://orcid.org/0000-0002-6121-4438 https://orcid.org/0000-0002-5830-0116 https://orcid.org/0000-0002-5830-0116 https://orcid.org/0000-0002-7550-0080 https://orcid.org/0000-0002-7550-0080 https://orcid.org/0000-0003-4005-118X https://orcid.org/0000-0003-4005-118X https://orcid.org/0000-0003-2599-5262 https://orcid.org/0000-0003-2599-5262 https://orcid.org/0000-0001-9318-7465 https://orcid.org/0000-0001-9318-7465 https://orcid.org/0000-0001-9318-7465 https://orcid.org/0000-0003-1906-4023 https://orcid.org/0000-0003-1906-4023 https://doi.org/10.3390/d10030103 https://doi.org/10.3390/d10030103 https://doi.org/10.1007/s10914-016-9363-8 https://doi.org/10.1093/sysbio/syr041 https://doi.org/10.1093/sysbio/syr041 https://doi.org/10.1038/s41598-024-54671-z     |  19 of 22TAYLOR et al. classification maps at 1- km resolution. Scientific Data, 5, 180214. https:// doi. org/ 10. 1038/ sdata. 2018. 214 Benda, P., Uvizl, M., Eiseb, S. J., & Avenant, N. L. (2024). On the system- atic position of the horseshoe bats (Mammalia: Chiroptera) from Lesotho. Mammalia, 88, 239–258. https:// doi. org/ 10. 1515/ mamma lia- 2023- 0119 Benda, P., & Vallo, P. (2012). New look on the geographical variation in Rhinolophus clivosus with description of a new horseshoe bat spe- cies from Cyrenaica, Libya. Vespertilio, 16, 69–96. Bowie, R. C. K., Fjeldså, J., & Hackett, S. J. (2004). Biological and bio- geographical patterns in the African thrushes (Turdidae). African Zoology, 39(1), 53–62. Brain, C. K. (1985). Temperature- induced environmental changes as bio- logical isolating mechanisms in southern Africa. In E. S. Vrba (Ed.), Species and speciation (pp. 45–52). Transvaal Museum. Bryja, J., Kostin, D., Meheretu, Y., Šumbera, R., Bryjová, A., Kasso, M., Mikula, O., & Lavrenchenko, L. A. (2018). Reticulate Pleistocene evolution of Ethiopian rodent genus along remarkable altitudinal gradient. Molecular Phylogenetics and Evolution, 118, 75–87. https:// doi. org/ 10. 1016/j. ympev. 2017. 09. 020 Bryja, J., Mikula, O., Patzenhauerová, H., Oguge, N. O., Šumbera, R., & Verheyen, E. (2014). The role of dispersal and vicariance in the Pleistocene history of an east African mountain rodent, Praomys delectorum. Journal of Biogeography, 41(1), 196–208. https:// doi. org/ 10. 1111/ jbi. 12195 Carbutt, C. (2019). The Drakensberg Mountain Centre: A necessary revi- sion of southern Africa's high- elevation centre of plant endemism. South African Journal of Botany, 124, 508–529. Carbutt, C. (2023). The greater midlands—a mid- elevation centre of flo- ristic endemism in summer- rainfall eastern South Africa. Diversity, 15, 1137. https:// doi. org/ 10. 3390/ d1511 1137 Carbutt, C., Henwood, W. D., & Gilfedder, L. A. (2017). Global plight of native temperate grasslands: going, going, gone? Biodiversity and Conservation, 26, 2911–2932. https:// doi. org/ 10. 1007/ s1053 1- 017- 1398- 5 Clark, V. R., Barker, N., & Mucina, L. (2011c). Taking the scenic route – The southern great escarpment (South Africa) as part of the cape to Cairo floristic highway. Plant Ecology and Diversity, 4, 313–328. Clark, V. R., Barker, N. P., McMaster, C., & Mucina, L. (2011). The Boschberg (Somerset East, eastern cape)—A floristic cross- roads of the southern great escarpment. South African Journal of Botany, 77, 94–104. Clark, V. R., Barker, N. P., & Mucina, L. (2011a). The great escarpment of southern Africa—a new frontier for biodiversity exploration. Biodiversity and Conservation, 20, 2543–2561. Clark, V. R., Barker, N. P., & Mucina, L. (2011b). The Roggeveldberge – Notes on a botanically hot area on a cold corner of the southern great escarpment. South African Journal of Botany, 77, 112–126. Clark, V. R., Burrows, J. E., Turpin, B. C., Balkwill, K., Lötter, M., & Siebert, S. J. (2022). The Limpopo–Mpumalanga–Eswatini escarpment—Extra- ordinary endemic plant richness and extinction risk in a summer rainfall montane region of southern Africa. Frontiers in Ecology and Evolution, 10, 765854. https:// doi. org/ 10. 3389/ fevo. 2022. 765854 Clark, V. R., Timberlake, J. R., Hyde, M. A., Mapaura, A., Chapano, C., Coates Palgrave, M., Wursten, B. T., Ballings, P., Plowes, D. C. H., Muller, T., Childes, S. L., Dondeyne, S., Burrows, J. E., Burrows, S. M., Barker, N. P., Linder, H. P., & McGregor, G. K. (2017). A first comprehensive account of floristic diversity and endemism on the Nyanga massif, MH (Zimbabwe–Mozambique). Kirkia, 19, 1–53. Couvreur, T. L. P., Dauby, G., Blach- Overgaard, A., Deblauwe, V., Dessein, S., Droissart, V., Hardy, O. J., Harris, D. J., Janssens, S. B., Ley, A. C., Mackinder, B. A., Sonké, B., Sosef, M. S. M., Stévart, T., Svenning, J.- C., Wieringa, J. J., Faye, A., Missoup, A. D., Tolley, K. A., … Sepulchre, P. (2021). Tectonics, climate and the diversification of the tropical African terrestrial flora and fauna. Biological Reviews, 96, 16–51. https:// doi. org/ 10. 1111/ brv. 12644 Csorba, G., Ujhelyi, P., & Thomas, N. (2003). Horseshoe bats of the world (Chiroptera: Rhinolophidae). Alana Books. Curran, M., Kopp, M., Ruedi, M., & Bayliss, J. A. (2022). A new species of horseshoe bat (Chiroptera: Rhinolophidae) from mount Namuli, Mozambique. Acta Chiropterologica, 24, 19–40. DeMenocal, P. B. (1995). Plio- Pleistocene African Climate. Science, 270, 53–59. DeMenocal, P. B. (2004). African climate change and faunal evolu- tion during the Pliocene–Pleistocene. Earth and Planetary Science Letters, 220, 3–24. Demos, T. C., Webala, P. W., Goodman, S. M., Kerbis Peterhans, J. C., Bartonjo, M., & Patterson, B. D. (2019). Molecular phylogenet- ics of the African horseshoe bats (Chiroptera: Rhinolophidae): Expanded geographic and taxonomic sampling of the Afrotropics. BMC Evolutionary Biology, 19, 166. https:// doi. org/ 10. 1186/ s1286 2- 019- 1485- 1 Dianat, M., Konečný, A., Lavrenchenko, L. A., Kerbis Peterhans, J. C., Demos,