bioRxivZoology
Cryptic phylogeographic history sheds light on the generation of species diversity in sky-island mountains
K. He, T. Wan, K.-P. Koepfli, W. Jin, S.-Y. Liu, X.-L. Jiang
Abstract
Biodiversity hotspots should be given high priority for conservation under the situation of global climate change. The sky islands in southwestern China are characterized by extraordinarily high species diversity and are among one of the worlds top biodiversity hotspots. However, neither the actual species diversity in this region or mechanisms generating this diversity are well explored. Here, we report on the phylogeographic analysis of the long-tailed mole (Scaptonyx fusicaudus), a semi-fossorial mammal that inhabits the montane cool forests across the Chinese sky islands and is considered to represent one species divided into two subspecies. Analyses using DNA sequence data from one mitochondrial and six nuclear genes revealed that populations inhabiting different mountains exhibited exceptionally strong geographic structure. The lowlands and large rivers act as \"soft\" and \"hard\" barriers to dispersal, respectively, isolating evolutionary lineages for up to 11 million years. Our results suggest that the mountain ranges act as interglacial refugia buffering populations from climate fluctuations, further facilitating allopatric diversification. Strikingly, species delimitation analyses suggests that the long-tailed mole may comprise 18 operational taxonomic units and 17 putative species. Our results suggest that for low-vagility species, the complex topography of the Chinese sky islands has shaped genetic diversity and structure and promoted exceptional diversification through a combination of eco-environmental stability as well as geographic fragmentation. The patterns observed in S. fusicaudus may be representative for other cold- adapted species, reflecting the generation of mammalian faunal diversity in the sky-island mountains of southwestern China.
- organism: mouse
- assay: DNA methylation
- research focus: phylogenetics / evolution
- approach: statistical model
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