Evaluation of transfer functions in spatially structured environments

Abstract

Transfer function training set sites close to each other tend to have similar species assemblages and environmental conditions in both oceanic and terrestrial data sets. This is unremarkable, but as this lack of independence between sites violates the assumptions of many statistical tests, it has severe consequences for transfer function evaluation, possibly resulting in inappropriate model choice and misleading and over-optimistic estimates of a transfer function's performance. In this paper, we develop a simple graphical method to test if spatial autocorrelation affects a training set, develop a Monte Carlo geostatistical simulation as a null model to test the significance of transfer functions in autocorrelated environments, and introduce a cross-validation scheme that is more robust to autocorrelation. We use these tests to show that some recently-published transfer functions have no predictive power, and strongly recommend the use of these tests to make transfer functions more robust to autocorrelation.