Data for "Symmetries in Montane Species Ranges Elucidate the Mechanistic Link between Environment and Abundance"

Description of the data and file structure Files and variables File: eap_geb_data.xlsx Description: The datafile contains 9 sheets. Details of specific column names are listed at the top of each sheet. 1\. [A.SD] [Asymmetry derived using standard deviation] 2\. [A.No] [Asymmetry derived using number of records] 3\. [A.Pk] [Asymmetry derived using the shape near the peak] Each of the above sheets contain columns for species names, observed species values, and 400 simulated species values. 4\. [Spearman] [Spearman Rank Correlation between Asymmetry and Modal Elevation for observed species values, and 400 simulated species values.] 5\. [OrthoRegress] [Orthogonal Linear Regression between Asymmetry and Modal Elevation for observed species values, and 400 simulated species values.] 6\. [UnweightRegress] [Unweighted Linear Regression between Asymmetry and Modal Elevation for observed species values, and 400 simulated species values.] 7\. [CumulativeProfile] [Community mean (species-averaged) Abundance profile for observed species values, and 400 simulated species values.] 8\. [K-SD-eta-alpha] [Simulated look-up table for the dependence of kurtosis on the trait-fitness power index] 9\. [Table-manuscript] [Appears as Table 2 in the manuscript and references quoted values to how they were derived from the previous sheets.] Code/software SOFTWARE VERSIONS 400 profiles were simulated for each species using the negative binomial random number generator (Lindén and Mäntyniemi 2011; rnbinom in R; R Core Team 2021). All hypotheses were tested using two-tailed tests and errors from Monte-Carlo simulations. All analyses were performed using custom scripts written in the R computing platform. REFERENCES 1. Lindén, Andreas, and Samu Mäntyniemi. 2011. “Using the Negative Binomial Distribution to Model Overdispersion in Ecological Count Data.” Ecology 92 (7): 1414–21. https://doi.org/10.1890/10-1831.1. 2. R Core Team. 2021. “R: A Language and Environment for Statistical Computing.” Vienna, Austria: R Foundation for Statistical Computing. https://www.r-project.org/.