Table 3.

Summary of the most frequently used methods to test the predictive accuracy of Resource Selection Functions (RSF). The number of bins and bin classification method is described in addition to the number of percentage of studies (out of 101 studies reviewed) that used the method.

Method	Description		Number of Studies	% studies reviewed
	Number of bins	Bin classification
Spearman rank correlation between bin rank and testing points in each bin following Boyce et al. (2002)	3 – 20	Undefined, equal area, quantiles	50	49.50%
No method for testing predictive accuracy reported	N/A	N/A	17	17.83%
Receiver Operating Characteristics (ROC) / Area Under Curve (AUC)	2	binary	15	14.85%
Regression between bin rank and expected number of points in each bin following Johnson et al. (2006)	5 – 20	Undefined, equal area, equal probability, quantile	14	13.86%
Frequency of testing data in RSF bins following Fortin et al. (2009)	5, 20	Equal area	2	2.97%
Alternative methods with a sample size of 1			11	10.89%

^**some studies used multiple accuracy metrics, and percentages do not sum to 100%.