We used the four environmental variables of the GEnS classification (Growing Degree-Days on a 0 °C base, Temperature Seasonality, Aridity Index and Potential Evapotranspiration Seasonality) in our ecological niche model analysis to represent dominant bioclimatic trends. These four variables show the lowest correlation with each other and determine 99.9% of the total variation of 36 available bioclimatic variables (Metzger et al., 2013). The modelling extent (−19°W, 94°E, −36°S, 50°N) is defined by the area accessible to the lion over historical times.

The MaxEnt modelling approach was applied as outlined in Cooper et al. (2016) to create a habitat suitability model of the lion for the present day. The model was run to fit a Poisson point-process model by displaying raw output under the following settings: ‘noremoveduplicatepresencerecords’, ‘noaddsamplestobackground’. Regularization multipliers of 2 and 100,000 background points were chosen (see Appendix S3 for full parameters). Model performance was measured using the mean area under the receiver operator curve (AUC) (Phillips & Dudík, 2008) from k-fold cross-validation and spatially independent cross-validation using the ENMeval package (Muscarella et al., 2014) in R (R Core Team, 2015). Spatially independent cross validation is important given the potential for spatial autocorrelation of our localities. Threshold values of suitable/unsuitable areas were derived from the MaxEnt model for comparison with global environmental strata. A modified lowest-presence threshold (Costa et al., 2010) was used to determine a binary output of suitable lion habitat. We allowed an omission error of 10% (e = 10%) to determine this threshold, which accounts for a level of uncertainty in the quality of our locality records (Peterson, Papeş & Soberón, 2008).

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