## 4 Materials and Methods
### Ecological Niche Modelling in Geographical Space  
We obtained a total of 344 precise geographical coordinates of presence for 21 _Jaborosa_ species, 289 for the Andean clade and 55 for the Lowland clade in the field or from vouchers deposited at CORD herbarium (coordinates data available upon request to corresponding author). From this dataset, we retained 196 points for the 18 species of the Andean clade, and 43 for the three species of the Lowland clade to prevent any bias due to high point density derived mainly from oversampling of more accessible areas. The model was manually calibrated in QGIS v. 3.10.4 [116]; 70% of the occurrence dataset was used for calibration and 30 % for validation. We delimited the extent of the modelling area (\"M\" sensu [117]) following topographic criteria, under the hypothesis that each clade distribution was constrained by the altitude.  
To adjust ecological niche models (ENMs; [118]), we used 19 bioclimatic variables (2000s decade; 2.5 arcmin resolution) downloaded from the Dryad Digital Repository at [[http://datadryad.org/resource/doi:10.5061/dryad.s2v81](http://datadryad.org/resource/doi:10.5061/dryad.s2v81)]([http://datadryad.org/resource/doi:10.5061/dryad.s2v81](http://datadryad.org/resource/doi:10.5061/dryad.s2v81)) (accessed on 20 October 2020), which were obtained from a global set of satellite-based bioclimatic variables (MERRAclim; [119]), and the Hydro-1K (HYDRO1k Elevation Derivative Database, [[https://doi.org/10.5066/F77P8](https://doi.org/10.5066/F77P8) WN0]([https://doi.org/10.5066/F77P8](https://doi.org/10.5066/F77P8) WN0)) (accessed on 20 October 2020), a topographical database downloaded from the US Geological Survey (2001; [[http://lpdaac.usgs.gov](http://lpdaac.usgs.gov)]([http://lpdaac.usgs.gov](http://lpdaac.usgs.gov)) (accessed on 20 October 2020). In the calibration, we discarded highly correlated environmental layers (Pearson > 0.9) which also showed a lower gain, according to the permutation importance and jackknife test.  
We tested a total of six different modelling algorithms: BIOCLIM [120], Domain [121], and MaxEnt [122] using the R package _dismo_[123]; generalized linear model using _glm_ (see [124]); random forest [125] with _randomForest_[126]; and support vector machines [127] with _kernlab_[128]. In particular, MaxEnt ENM was evaluated with the R package _ENMeval_[129] to test a set of predictor variables (\"feature types\") together with different multiplier regularization values, totaling 48 models, of which the three with the lowest AICc values and lowest omission rates were chosen, and then modeled in MaxEnt v.3.3.3 [122]. We validated all algorithms using 30% of the presence points and ten absence points for each clade in NicheToolBox using partial receiver operating characteristic (ROC) and threshold-dependent tests [130]. Suitability and binary maps of all models were generated using the smallest suitability value from the species presence points as the threshold value.  
Considering the validation metrics, we selected for the Andean Clade: MaxEnt (feature types: Linear and Quadratic, regularization multiplier: 3) and Bioclim algorithms; and for the Subtropical Lowland Clade: Bioclim and support vector machines algorithms. In order to capture most of the variation in the environmental data among _Jaborosa_ species we conducted a principal component analysis (PCA) by using the _princomp()_ function in R software. We included the environmental variables that most contributed (adding up at least 90%) to the ENMs of both clades and extracted the first principal component (PC1) that captured environmental variation. These PC1 scores were used in subsequent analyses.