Environmental variables
To investigate correlations between the distribution of Cochemiea halei and its environment, we chose 19 energy and precipitation variables from WorldClim V. 2.0, averages from 1970 to 2000, at 30 arcsec resolution (Fick & Hijmans, 2017). Soil type was determined during field surveys using a Munsell soil identification color scale (Munsell Color, Grand Rapids, MI), categorizing soils into ultramafic (2.5Y hue with various color values and chroma) versus either “non-serpentine,” (approximately 7.5YR to 10YR), or sand (Roberts, 1980). Dense sampling of occurrences of C. halei with soil type data was performed in order to reduce error when interpolating for missing values (Carl & Kühn, 2007; Dormann & McPherson, 2007; Dormann et al. 2013). The soil type data from the field was mapped onto zones of ultramafic versus non-ultramafic substrate, as indicated in the geological map of Isla Magdalena and Isla Margarita by Rangin (1978). The soil type raster was generated using inverse distance weighted interpolation (Gonçalves, 2006; Grunwald, 2009) and improved using root mean squared error and 5-fold cross validation (Gonçalves, 2006).
Four representative concentration pathways (RCPs) were used in climate change projections: 2.6, representing the best case future concentration of carbon in the atmosphere, through intermediate levels 4.5 and 6.0, to the worst case scenario of 8.5, as outlined in the Intergovernmental Panel on Climate Change’s Fifth Assessment Report (IPCC 2013; Liddicoat, Jones, & Robertson, 2013). The climate data itself was derived from two general circulation models (GCMs). The GCMs used were the Hadley Center Global Environmental Model version 2-ES (HadGEM2-ES) and the Community Climate System Model v. 4 (CCSM4), both of which are frequently used in studies of climate change effects on habitat suitability (e.g., Bellouin et al., 2011; Leclère et al. 2014; McQuillan & Rice, 2015; Albuquerque et al., 2018). The HadGEM2-ES model scenarios include projections of changes in ocean temperature and sea ice, and are especially recommended for use in predicting changes in coastal habitat (Collins et al. 2008, Caesar et al., 2013).