Assessing angiosperm phylodiversity patterns in Argentinean phytogeographic provinces using presence-only data

<b>Appendix S1</b> Description of models for the different metrics (SR, PDI, NRI, and NTI) using a bayesian approach with Stan and the package brms v2.21.0.<b>ESM1.zip </b>Phylogenetic tree and absence-presence matrix used in phylogenetic diversity analyses.<b>Figure S1.</b> Boxplots showing marginal means (dots) and 95% confidence intervals (boxes) for PDI, NRI and NTI, with phytogeographic provinces as predictors, and obtained using generalized least squares with spatial correlation structure for errors. ALT: Altoandina, CHA: Chaco, EMP: Monte-Patagonia ecotone, ESP: Espinal, MON: Monte, PAM: Pampa, PAR: Paranaense, PAT: Patagonia, PRE: Prepuna, PUN: Puna, SUB: Subantartica, YUN: Yungas. Dashed line in PDI, NRI and NTI metrics indicated the lower 2.5% percentile of the z-distribution (values &lt; -1.96 indicate significant phylogenetic clustering at p &lt;0.05). <b>Figure S2.</b> Results of the Tukey test for pairwise mean comparisons between phytogeographic provinces (predictor) for SR, PDI, NRI, NTI (responses), and obtained using generalized least squares with spatial correlation structure for errors. Arrows indicate significant differences (dashed line p &lt;0.05, solid line p &lt;0.01) in the means of the connecting pair of provinces, with the tip of the arrow pointing to the one with the lower mean. ALT: Altoandina, CHA: Chaco, EMP: Monte-Patagonia ecotone, ESP: Espinal, MON: Monte, PAM: Pampa, PAR: Paranaense, PAT: Patagonia, PRE: Prepuna, PUN: Puna, SUB: Subantartica, YUN: Yungas.<b>Table S1. </b>Comparison of models with annual mean temperature (<b>bio1</b>), annual precipitation (<b>bio12</b>), and topographic heterogeneity (<b>topo</b>) as predictors of <b>PDI</b>, <b>NRI</b>, and <b>NTI </b>(responses), and including or not phytogeographic provinces (<b>prov</b>) as an additional predictor. All models correspond to mixed models including random effects (random-intercept variance) for vegetation units (<b>units</b>). AICc: Second-order Akaike Information Criterion, p LTR: likelihood ratio test bettwen upper and lower model, p Wald test: Wald Chi Square test for province effect.<b>Table S2. </b>Comparison of models with annual mean temperature (<b>bio1</b>), annual precipitation (<b>bio12</b>), and topographic heterogeneity (<b>topo</b>) as predictors of <b>PDI</b>, <b>NRI</b>, and <b>NTI </b>(responses), and including or not phytogeographic provinces (<b>prov</b>) as an additional predictor. All models were conducted using generalized least squares method with spatial correlation structure (spherical for PDI and gaussian form NRI and NTI). AICc: Second-order Akaike Information Criterion, p LTR: likelihood ratio test bettwen upper and lower model, p Wald test: Wald Chi Square test for province effect.