Additional file 1 of Determinants of plant species richness along elevational gradients: insights with climate, energy and water–energy dynamics
Creators: name: Kumar, Abhishek
name: Patil, Meenu
name: Kumar, Pardeep
name: Singh, Anand Narain
Publisher: name: figshare
Year: 2024
Subjects: subject: Ecology
subject: FOS: Biological sciences

description: Additional file 1: Fig. S1. Final set of uncorrelated explanatory variables for (a) Morni Hills, (b) Chail WLS, (c) Churdhar WLS, and (d) All Sites. The values represent Spearman’s correlation coefficient and the transparency of background is scaled to the magnitude of correlation coefficient. Fig. S2. Quantile–quantile (Q-Q) plot demonstrating model diagnostics for elevational patterns of species richness in (a) Morni Hills, (b) Chail WLS, (c) Churdhar WLS, and (d) All Sites. The plot shows the overall deviations from the simulation-based expected distribution with added tests for correct distribution (KS test), dispersion and outliers. The outliers are defined as values outside the simulation envelope. The plot assesses the goodness-of-fit between observed and expected quantiles, aiding in evaluating model assumptions and performance. Fig. S3. Residual plots showing the deviations in model residuals against the model predictions for elevational patterns of species richness in (a) Morni Hills, (b) Chail WLS, (c) Churdhar WLS, and (d) All Sites. The deviation from the uniformity (in the y-direction) was estimated by comparing the empirical 0.25, 0.5 and 0.75 quantiles in the y-direction (red solid lines) with the theoretical 0.25, 0.5 and 0.75 quantiles (dashed black line). The simulation-based outliers are highlighted as red stars. Fig. S4. Boxplots showing the distribution of environmental variables across the study sites. The predictor variables included Mean Annual Temperature (MAT) and Temperature Seasonality (TSE) as temperature variables; Mean Total Annual Precipitation (MAP) and Precipitation Seasonality (PSE) as precipitation variables; Net Primary Productivity (NPP) and Soil Organic Carbon (SOC) as energy variables; Enhanced Vegetation Index Homogeneity Index (EHM) and Terrain Ruggedness Index (TRI) as heterogeneity variables; Actual Evapotranspiration (AET), Potential Evapotranspiration (PET), Water Deficit (WDF) as water availability variables; and elevation (ELE) to represent the elevational gradients. Fig. S5. Distribution of predictors variables and species richness (S) along the entire elevational gradient. The colours represent the category of environmental variables. The predictor variables included Mean Annual Temperature (MAT) and Temperature Seasonality (TSE) as temperature variables; Mean Total Annual Precipitation (MAP) and Precipitation Seasonality (PSE) as precipitation variables; Net Primary Productivity (NPP) and Soil Organic Carbon (SOC) as energy variables; Enhanced Vegetation Index Homogeneity Index (EHM) and Terrain Ruggedness Index (TRI) as heterogeneity variables; Actual Evapotranspiration (AET), Potential Evapotranspiration (PET), and Water Deficit (WDF) as water availability variables. Table S1. Summary of primary variable selection for explaining species richness at lower (Morni), intermediate (Chail), higher (Churdhar) and full (All) elevational gradient. Table S2. Results of model selection for exploring determinants of species richness in selected sites. Table S3. Explanatory power of species richness models for lower (Morni), intermediate (Chail), higher (Churdhar) and full (All) elevational gradient. Table S4. Summary of model averaging of competitive models (ΔAICc &lt; 2) for Morni Hills. Table S5. Results of model selection to identify the best SEM model for exploring determinants of species richness along elevational gradients