Fig 1 . Location of the study area (a), presence localities ofZ. spina-christi (b), andZ. nummularia (c).
2.3. Predictor variables
A comprehensive dataset comprising 19 bioclimatic variables, with a spatial resolution of 30” (approximately 1 km) was obtained from CHELSA ver. 2.1 (http://chelsa-climate.org) (Karger et al., 2017) to delineate the existing climatic niche of various species. Additionally, future climate variables were obtained from the 6th assessment report of the Intergovernmental Panel on Climate Change (IPCC AR6) for two distinct Shared Socioeconomic Pathways scenarios (SSP-126 and SSP-585). These climate projections were derived using the Global Circulation Model (GCM) of GFDL-ESM4 (Shaban et al., 2023; Mathias et al., 2023) and span two temporal scales: 2041-2070 and 2071-2100. To address collinearity issues among these variables, hierarchical cluster analysis was employed with Pearson’s correlation coefficient (with a cutoff set at 0.7) (Gallego‐Narbón et al., 2023). This approach was executed using the ’remove collinearity’ function in the R package ’virtualspecies’ (Leroy et al., 2016; Louppe et al., 2020; Almeida et al., 2023). Ultimately, a total of seven predictors were retained in the model, encompassing isothermality (bio3), maximum temperature of warmest month (bio5), temperature annual range (bio7), mean temperature of wettest quarter (bio8), precipitation seasonality (bio15), precipitation of wettest quarter (bio17), and precipitation of coldest quarter (bio19).
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