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

Abstract

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

Geographic Variation in the Acoustic Traits of Greater Horseshoe Bats: Testing the Importance of Drift and Ecological Selection in Evolutionary Processes

“…However, we did not detect the effect of MAPW and MARH on RF of echolocation calls within R. ferrumequinum region (Table 5). Studies in other bat species have shown varying roles of precipitation and humidity, with some studies agreeing with ours in finding no relationship [31], while others have found a positive or negative relationship [12], [13], [29]. Overall, our data suggested that ecological selection (to temperature, but not moisture) contributed, or is correlated with factors that contributed to the observed geographic variation in the RF of R. ferrumequinum , though we recognize there may be other ecological variables, such as habitat structure, that are important as well.…”

“…Currently, few studies have reported patterns of intraspecific geographic variation in echolocation calls. Although there have been investigations of echolocation calls in R. monoceros [2], R. cornutus [6], R. pusillus [13], R. clivosus [29], Hipposideros ruber [12], H. larvatus [22], Craseonycteris thonglongyai [11], and Rhinonicteris aurantia [30], [31], not all of these have incorporated genetic data. Thus the evolutionary forces and the meaning of intraspecific acoustic divergence are largely unexplored compared with the rich biodiversity of bats.…”

Molecular phylogeny of Panaspis and Afroablepharus skinks (Squamata: Scincidae) in the savannas of sub-Saharan Africa

“…Although most of our recovered lineages are not considered to be Afromontane, their divergences might be explained by the irregular physiography seen along the areas where these populations occur. This pattern of micro-endemism has been documented in other skinks (Parham and Papenfuss, 2009), geckos (Travers et al, 2014), chameleons (Glaw et al, 2012), chelonians (Daniels et al, 2007; Petzold et al, 2014), birds (Huseman et al, 2013), and mammals (Taylor et al, 2011; Stoffberg et al, 2012). For example, Tanzania has two populations that are separated by the Great Rift Valley: P .…”

Phylogenetic conservatism of climatic niche in bats

“…To characterize the climatic niches of bat species, we used a multivariate representation based on nine climatic variables that are highly related to the ecological and physiological tolerances of bats and have been suggested as effective predictors of their geographical distributions (e.g., Dixon, 2011;Flanders, Wei, Rossiter, & Zhang, 2011;Monadjem, Taylor, Cotterill, & Schoeman, 2010;Ratrimomanarivo et al, 2009;Schoeman, Cotterill, Taylor, & Monadjem, 2013;Stoffberg, Schoeman, & Matthee, 2012). These variables, obtained from the Worldclim online database (Hijmans, Cameron, Parra, Jones, & Jarvis, 2005), were as follows: Annual mean temperature (BIO1), isothermality (BIO3), temperature seasonality (BIO4), maximal temperature of the warmest month (BIO5), minimal temperature of the coldest month (BIO6), annual precipitation (BIO12), precipitation of the wettest month (BIO13), precipitation of the driest month (BIO14) and precipitation seasonality (BIO15).…”