Geographical Variation of Rhinolophus affinis (Chiroptera: Rhinolophidae) in the Sundaic Subregion of Southeast Asia, including the Malay Peninsula, Borneo and Sumatra

Ith, S. and Bumrungsri, S. and Thomas, N.M. and Bates, P.J.J and Willette, D.A. and Khan, F.A.A. and Wonglapsuwan, M. and Soisook, P. and Maryanto, I. and Huang, J.C.-C. and Furey, N.M. (2016) Geographical Variation of Rhinolophus affinis (Chiroptera: Rhinolophidae) in the Sundaic Subregion of Southeast Asia, including the Malay Peninsula, Borneo and Sumatra. Acta Chiropterologica, 18 (1). pp. 141-161. ISSN 1508-1109

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Abstract

Rhinolophus affinis sensu lato is a widespread bat species in South and Southeast Asia which shows considerable geographical variation in its morphology, echolocation call frequencies and genetics. The taxonomic status of the taxon in the Sundaic subregion remains uncertain however as the limited studies to date have been largely based on morphology. The aim of the present study was to determine the taxonomic status of subspecific forms recognized in the subregion and to evaluate phylogeographic distinctiveness between those occurring in Borneo and the Malay Peninsula using genetic, morphological and acoustic datasets. Two forms were confirmed: R. a. nesites from Borneo and R. a. superans from the peninsula. The previous recognition of a population from southernmost Sumatra as R. a. superans was not supported, however, as this form is likely R. a. affinis. Genetic divergence between these three forms is rather deep and is estimated to have occurred during the arid climatic period of the Pleistocene when suitable habitats were reduced to isolated pockets. Our results support the phylogeographic distinctiveness hypothesis as R. affinis sensu lato shows discrete affinities between Borneo and the Malay Peninsula. Discovery of new forms of R. affinis is likely with greater sampling effort throughout the region. Our study also demonstrates the importance of employing multiple datasets in taxonomic evaluations, as the use of morphological and/or acoustic datasets alone could lead to erroneous conclusions.

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