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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