Relative frequency function models for species distribution modeling

Abstract

A new approach to habitat distribution modeling is presented and tested with data on North American plants. The relative frequency function (RFF) algorithm compares the relative frequencies of a species’ sample points to that of random points or absence points on the landscape to compute a frequency ratio. The relative frequency ratio r is smoothed across the range of values using moving, overlapping windows. The ratio of frequencies at a sample point for each variable is used to compute the geometric mean score for all data with non‐missing values. Variables are added using a forward stepwise method. Confidence intervals are computed with bootstrap resampling. The method was tested with artificial and species habitat and geographic range data. The RFF method in all cases gave results comparable to other methods tested. For the species with good geographic range maps, the results were consistent with known biogeography.The RFF method is particularly well‐suited to irregularly shaped distributions and can classify sample points even when the data contain missing values. The method is extremely simple to use and comes with a free software tool, does not require a large sample size, does not require absence data, and is more interpretable and portable than certain other methods.