Lion locality records were collected from across the known historical range, and from a range of independent sources (Table 1), so as to reduce the influence of sampling bias in our data (Fei & Yu, 2016). The temporal range of these records captures the climatic conditions experienced by modern lions and allows the inclusion of records from now extinct populations. The recognition of spatial error in locality data is an important consideration (Raxworthy et al., 2007). The maximum locality error of data used here is 50 km, which is acceptable given a ∼20 km diameter of lion home ranges, and much greater dispersal distance of male lions (Funston et al., 2003).

GEnS describes relatively similar biophysical environments, which are derived through statistical clustering of the principal components of four bioclimatic variables: Growing Degree-Days on a 0 °C base (Metzger et al., 2013a), Temperature Seasonality (Hijmans et al., 2005), Aridity Index and Potential Evapotranspiration Seasonality (Zomer et al., 2007; Zomer et al., 2008). Using the modelling approach of Soteriades et al. (2017), the strata were created on a global scale for present day conditions, and for the mid-Holocene and the Last Glacial Maximum using coupled General Circulation Models (GCMs). We used a Random Forest classifier developed by (Soteriades et al., 2017) on each GCM to create 125 multivariate strata characterised by similar climatic conditions. This was performed using the data-mining and machine-learning software Weka 3.6.4 (Frank, Hall & Witten, 2016) at a resolution. The strata were further aggregated into 18 easily interpretable, structured bioclimatic zones (Metzger et al., 2013). Global Environmental Zones are an established aggregation of Global Environmental Strata, created to provide consistent nomenclature and to support the summarising and reporting of results (Metzger et al., 2013). Global Environmental Zones (GEnZ) and Strata (GEnS) have been made available for present day, mid-Holocene and Last Glacial Maximum conditions at http://hdl.handle.net/10283/3274 (Cooper et al., 2020)—see Appendix S1.

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