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To assess the predictive capacity of the MAXENT models, we split the data so that models were calibrated using 70% of the observed species data (training data) and evaluated for predictive accuracy using the remaining 30% of the data (test data).
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According to the normality of the distribution of the data (tested using a Lillefort's test) we used either parametric (e.g. t-test, ANOVA) or non-parametric (e.g. Wilcoxon, Kruskall-Wallis tests) statistical methods.
Given the normal distribution of the data, tested with Kolmogorov Smirnov tests, parametric tests were used.
Here, leaving out approximately 10% (n test = 7 to n test = 9) of the data as test set in the outer loop worked well.
Normal distribution of the data was tested using Kolmogorox-Smirnov test.
The normality of the data was tested by Kolmogorov Smirnov test.
The STHMM was run on 100 replicates of the data to test for consistency.
The normal distribution of the data was tested with Kolmogorov Smirnov normality test, before application of parametric tests.
The normality of the data was tested by the Kolmogorov Smirnov test.
The normal distribution of the data was tested by the D'Agostino Pearson test.
Normal distribution of the data was tested using the Kolmogorov Smirnov test.
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