Figure 1.
The location of the Himalaya and study area.
Upper left: The Indian Himalaya (in grey shade), and Nepal and Bhutan Himalaya. Lower left: Geographical coordinates of the Himalaya (26°30′ –37° N latitude and 72°–97°30′ E longitude). Right: Geographical coordinates of Sikkim Himalaya and location of sampling sites along Teesta river that constituted our study area.
Table 1.
Environmental variables considered for building of regression models of fish species richness in Teesta river.
Figure 2.
Fish species richness plots along the elevational gradient in the Himalaya.
(A) total species richness (n = 179), non endemic species richness (n = 150) and endemic species richness (n = 29). (B) Species richness plots along the elevational gradient in the Teesta river. The fitted lines for total richness, non endemic richness in the Himalayan rivers and total fish species richness in the Teesta river represent a GAM model. However, for the endemic species of the Himalayan rivers the fitted line represents GLM model.
Table 2.
Summary of the regression models between fish species richness and the Himalayan elevational gradient.
Table 3.
Ranges and Pearson’s correlation coefficient value of different environmental model variables against elevation in Teesta river.
Table 4.
Summary statistics for the selection of model variables.
Figure 3.
The significant variables, which effected maximum changes in percentage D2 values.
(A) Water temperature represents physico-chemical model. (B) Phytoplankton density represents biological model. (C) Water discharge represents the physiographic model. (D) Basin area represents the topographic model. Discharge was the most important determining factor of fish species richness pattern followed by basin area and water temperature in decreasing order.
Table 5.
Summary statistics of linear (l) and quadratic (q) parameters of variables for selected models and resultant effects of parameter removal on model performance.
Table 6.
Test of model robustness by cross-validation: D2 values represent model fits while MAE represent mean absolute errors in number of species for the five proposed models.