We studied elevational gradients of reptile richness in the southern Western Ghats of India. We tested different hypotheses explaining elevational diversity and examined the major drivers of species richness patterns. We collected data on reptiles using Visual Encounter Survey (VES) methods in each 100 m elvation zones in the Agasthyamalai Hills which has elevation ranging from 100 m to 1500 m a.s.l.. We used 10×10 m quadrats for estimating tree density data that we used in our final analysis. We also collected climatic data such as mean annual temperature and precipitation representing each sampling location as bioclimatic variables influencing species richness. Here we submit all these datasets concerning reptiles (richness and abundance) and bioclimatic variables (along with the R-script for analysis) that we used for  our manuscript titled "Elevational gradients of reptile richness in the southern Western Ghats of India: evaluating spatial and bioclimatic drivers."

We used Visual Encounter Survey (VES) method in each 100 m zone for sampling reptiles across an elevational gradient of 100 to 1500 m a.sl.. VES is a time-contrained method, and each sample consists of one-hour search by two persons as described in our MS (Jins et al. 2020). The number of samples in each elevation zone is provided in our dataset. We used 10×10 m quadrats for sampling tree density. We also collected data on mean annual temperature and precipitation representing each sampling locations from Chelsa climate (https://chelsa-climate.org/). Species abundances in each site is given in 'sample.data'. This is basically a reshaped dataset (of reptile richness/abundance along elevation) for the easy use in R-platform. The list of reptiles recorded in VES is given in 'sp.data'. The major dataset we used for regression analysis is given in 'sites'.

We are providing the complete R-script along with datasets if someone want to use the analytical approaches we followed in our paper.