Computational protocol: Relation between Water Balance and Climatic Variables Associated with the Geographical Distribution of Anurans

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Protocol publication

[…] Geographical coordinates of occurrence for each species were compiled from the speciesLink Project (– Figs) []. For each coordinate, mean data from 1950 to 2000 on eight climatic variables (annual mean temperature, maximum temperature of the warmest month, minimum temperature of the coldest month, temperature seasonality, annual precipitation, precipitation of the wettest month, precipitation of the driest month and precipitation seasonality) were extracted from Worldclim with 30 arc-seconds resolution [,] using DIVA-GIS [] version 7.5.0.0. [...] Descriptive statistics were performed for all physiological and climatic data per species, and data were posteriorly transformed to Log10 for subsequent analyses. For each species, means of the eight climatic variables extracted from each locality were implemented in principal component analyses (PCA), and the scores from the components with eigenvalues greater than 1.0 were saved for a posteriori analyses. We considered any absolute values higher than 0.65 as a high load. Again, given that the number of species included in the study differed for some physiological measurements, two PCAs were conducted: one containing climatic data for the 16 species from which there were data on REWL, and another containing climatic data for the 13 species from which data on RWU and SLPD were available.Phylogenetic regressions were used to investigate the relationship between the physiological variables and body mass [], and the residuals of data phylogenetic corrected by size were saved to be implemented in a posteriori analyses. Phylogenetic regressions [] were employed to investigate the relationships between physiological variables and climatic data. Physiological variables corrected by size (SLPD, REWL and RWU) were entered into the regression models as dependent variables, and two components from the PCA of climatic variables with eigenvalues higher than 1.0 were entered as predictors. Additionally, a phylogenetic ANOVA was implemented using the biome where individuals from the different species were collected for physiological measurements (Atlantic Forest and the Cerrado) as a categorical factor.Descriptive statistics and principal component analyses of the climatic variables were performed using the software SPSS for Windows version 13.0. Phylogenetic trees were built using Mesquite version 2.75 (build 564). Procedures for phylogenetic size-correction, phylogenetic regressions and phylogenetic ANOVA were conducted with the software R version 3.0.2 (2013-09-25). The phylogenetic regressions were performed using the function gls, from the package nlme to fit a linear model using generalized least squares. The function corPagel from the package ape, was used to determine the structure of Pagel's “lambda” correlation. The comparison between sites of collection in the Atlantic Forest and the Cerrado was performed using the function phylANOVA, from the package phytools, with 1000 simulations and Bonferroni correction. […]

Pipeline specifications

Software tools DIVA-GIS, Phytools
Databases speciesLink
Application Phylogenetics