Inside or Outside: Quantifying Extrapolation Across River Networks

Abstract

Regression techniques are often used to predict responses across landscapes or under scenarios describing changes in climate, management, or land cover. The ability of random forests (RFs) and multivariate adaptive regression splines (MARS) to predict flow variability, low flow, Escherichia coli, and a macroinvertebrate community index was compared. Cross validation was applied to test predictive performance across an induced spectrum of interpolation to extrapolation by splitting each data set into two geographical, environmental, and random groups. RF and MARS both represent nonlinear and interacting patterns but showed contrasting ability to interpolate and extrapolate. RF always performed better than MARS when interpolating within environmental space or extrapolating in geographical space. RF models for all four responses were transferable in geographic space but not to environmental conditions outside the training data. Neither technique was successful when extrapolating across environmental gradients, although RF out-performed MARS, despite RF predictions being constrained by the training data. New methods to quantify interpolation versus extrapolation for predictions are demonstrated. Degree of extrapolation is calculated by transforming both the training data and new predictors in response turnover space. A decline in cross-validation performance was related to an increase in degree of extrapolation regardless of whether extrapolating in geographical or environmental space. Degree of extrapolation is valuable. It identifies those predictions that are more reliable because they represent interpolation versus those that are more uncertain that represent extrapolation. For example, high degree of extrapolation under climate or land cover change indicates increased risk of producing misleading predictions from both RF and MARS.