Geometric morphometric analyses define riverine and lacustrine species flocks of Himalayan snowtrout (Cyprinidae: Schizothorax) in Nepal

Abstract

Freshwater fishes in the river and lake systems in the Himalayas and Tibetan Plateau are morphologically diverged but the evolutionary relationship of putative subspecies separated in these freshwater systems has not been explored. Snowtrout (Schizothorax spp.) are minnows (Cyprinidae) broadly distributed in Asia. Body shapes of 3 Lake Rara (northwest Nepal) endemics (S. macrophthalmus, S. nepalensis, S. raraensis) and 2 widely distributed riverine species (S. progastus, S. richardsonii) across 3 drainages in Nepal (i.e. Karnali, Gandaki, and Koshi Rivers) were studied using geometric morphometry. Data were derived from museum voucher specimens/ tissues collected in 1984-1986 and 1996 (Lake Rara). Cartesian coordinates of 18 anatomical points (Type I landmarks) from 528 individuals were digitized; shape variation was then quantified with principal component analysis and visualized with thin-plate splines derived from a Procrustes analysis. Models of shape variation (i.e. taxonomy versus geography) were tested with a multivariate analysis of variance and a morphological distance matrix. Phylogeographic relationships were examined with a haplotype network (N = 115) derived from 1140 base pairs of the mitochondrial DNA cytochrome b gene, and selected GenBank sequences (N = 5). Koshi River snowtrout diverged morphologically from conspecifics, consistent with the phylogeographic data. In contrast, Gandaki and Karnali River snowtrout grouped by morphotype (up- versus downstream) irrespective of geographic origin, yet clustered separately within the haplotype network. Lake Rara snowtrout were morphologically but not genetically distinct, due to incomplete lineage sorting. Morphological and genetic variability in Schizothorax from Nepal represent a mosaic driven by isolation (= vicariance) and specialization (= adaptation), with taxonomy insufficiently reflecting diversity. Additional data are required to appropriately derive management and effective conservation plans.