Elevational patterns of non‐volant small mammal species richness in Gyirong Valley, Central Himalaya: Evaluating multiple spatial and environmental drivers

Abstract

AbstractAimDocumenting the elevational species richness patterns of non‐volant small mammals and assessing the roles of pure spatial factors and spatial structured environmental factors in shaping the elevational richness patterns.LocationGyirong Valley in the Mount Qomolangma National Nature Reserve, located in the southern Himalayas, China.MethodsField surveys were conducted at each of twelve 300‐m elevational bands along a gradient from 1,800 to 5,400 m above sea level (a.s.l). For the pure spatial variables, we calculated area and the spatial null model named MDE (cf. below). For spatial structured environmental variables, we calculated mean annual temperature, mean annual precipitation, mean annual temperature range, potential evapotranspiration (PET), the normalized difference vegetation index, plant species richness and habitat heterogeneity. Multivariate models of species richness against eight factors (excluding PET) for different species groups were used to test the explanatory power of both the spatial structured environmental variables and the pure spatial variables. In addition, mean annual precipitation and potential evapotranspiration were used to test the water–energy dynamics model for each species groups.ResultsSeven hundred and fifty‐five individuals of 22 species were documented over 21,600 trap nights. The elevational species richness pattern for all non‐volant small mammals was hump‐shaped with the highest richness occurring at 2,700–3,300 m a.s.l. Endemic and non‐endemic species as well as two elevational range size categories of small mammals also generally showed hump‐shaped species richness patterns. In most data sets, spatial structured environmental variables played more important roles than the pure spatial variables in shaping the elevational species richness patterns than the pure spatial factors, while the MDE contributed to richness patterns for large‐ranged species. The water–energy dynamics model explained 66% of the variation in all the non‐volant small mammals, 56% for endemic species, 88% for the non‐endemic species, 59% for the large‐ranged species, and 53% for the small‐ranged species.Main conclusionsAlthough no single key factor can explain all species richness patterns, we found that spatial structured environmental variables correlate well with the elevational species richness pattern of non‐volant small mammals. The water–energy dynamics model was found to explain non‐volant small mammal species richness along the Gyirong Valley.