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For each observed variable, data are distributed with normality, which meet the requirements of the model.
Data pre-processing and fuzzification : The predictor variable data are pre-processed to convert them into the desired spatio-temporal resolution, as described in detail in Buczak et al. [ 21 ].
Predicted Y -factors would have to be used in the clustering instead of the Y -factors directly calculated from the state variable data in the classical metamodelling, since otherwise the classification of new observations (for which state variable data are not available) would not be possible on variables equivalent to those used to cluster the calibration set observations.
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The records of 1699 specimens collected from 24 climates of Iran besides the altitude and climate variable data were used for modeling analysis.
Comparisons of two variable data were performed using two-way ANOVA with Bonferroni post test.
Categorical variable data were reported as frequencies with percentages.
All variable data were tested for normality using the Kolmogorov-Smirnov test.
Variable data were analyzed through Kaplan Meier methods to estimate the cumulative probabilities of overall survival.
Dichotomous variable data were analyzed by the Fisher exact test and χ2 analysis.
All of the continuous variable data were reported as mean ± standard deviation.
Current and past (at LGM) climate variable data was obtained from WorldClim version 1.4 [ 65].
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