Variable preparation
Reduction of multicollinearity for all variables was performed by
constructing a correlation matrix and performing hierarchical cluster
analysis, which groups variables according to their mutually related
correlations (Benito, Cayuela, & Albuquerque, 2013; Sarstedt & Mooi,
2014; Albuquerque et al., 2018). A cutoff of 0.5 Pearson’s correlation
index was used; all variables correlated higher than 0.5 were discarded
(Albuquerque et al.). Biserial correlation analysis, with variables
correlated to presence/absence data for Cochemiea halei , was
performed for all variables below 0.5 (Kraemer, 2006; Stolar & Nielsen,
2015, Albuquerque et al.). From each cluster of correlated variables as
derived from the hierarchical cluster analysis, the variable with the
highest correlation to the distribution of C. halei was chosen
for use in modeling.