  Machine learning algorithms find frequent application in spatial prediction
of biotic and abiotic environmental variables. However, the characteristics of
spatial data, especially spatial autocorrelation, are widely ignored. We
hypothesize that this is problematic and results in models that can reproduce
training data but are unable to make spatial predictions beyond the locations
of the training samples. We assume that not only spatial validation strategies
but also spatial variable selection is essential for reliable spatial
predictions. We introduce two case studies that use remote sensing to predict
land cover and the leaf area index for the "Marburg Open Forest", an open
research and education site of Marburg University, Germany. We use the machine
learning algorithm Random Forests to train models using non-spatial and spatial
cross-validation strategies to understand how spatial variable selection
affects the predictions. Our findings confirm that spatial cross-validation is
essential in preventing overoptimistic model performance. We further show that
highly autocorrelated predictors (such as geolocation variables, e.g. latitude,
longitude) can lead to considerable overfitting and result in models that can
reproduce the training data but fail in making spatial predictions. The problem
becomes apparent in the visual assessment of the spatial predictions that show
clear artefacts that can be traced back to a misinterpretation of the spatially
autocorrelated predictors by the algorithm. Spatial variable selection could
automatically detect and remove such variables that lead to overfitting,
resulting in reliable spatial prediction patterns and improved statistical
spatial model performance. We conclude that in addition to spatial validation,
a spatial variable selection must be considered in spatial predictions of
ecological data to produce reliable predictions.
