Species Distribution Modeling
As a preliminary estimate of habitat suitability given present-day climatic conditions, we estimated species distribution models using the ‘maxent’ algorithm in the ‘dismo’ package (Hijmans et al. 2017). We included the 31 GBS localities from our dataset and the 59 AHE localities from Pyron et al. (2022c). As an initial set of predictors, we included 37 variables: WorldClim (Fick and Hijmans 2017), ENVIREM (Title and Bemmels 2018), and Level IV ecoregions (Omernik and Griffith 2014). These were sampled at 30s resolution and re-projected to North America Albers Equal Area Conic (ESRI:102008). We used the ‘corSelect’ function in the ‘fuzzySim’ package (Barbosa 2015) to remove multicollinear predictors based on variance inflation factor (VIF). The retained set 6 BIOCLIM and 3 ENVIREM variables and the Level IV Ecoregions; the full list is given in the SI. We optimized a maxent model using the 90 localities and 1,000 background pseudo-absence points from a 250km radius. We then projected this model as a three-level binary prediction under the equate entropy of thresholded and original distributions, maximum training sensitivity plus specificity, and equal training sensitivity and specificity to evaluate varying degrees of potential occupancy across the range (Liu et al. 2015).