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While the application of machine-learning algorithms has been highly simplified in the last years due to their well-documented integration in commonly used statistical programming languages (such as R or Python), there are several practical challenges in the field of ecological modeling related to unbiased performance estimation. One is the influence of spatial autocorrelation in both hyperparameter tuning and performance estimation. Grouped cross-validation strategies have been proposed in recent years in environmental as well as medical contexts to reduce bias in predictive performance. In this study we show the effects of spatial autocorrelation on hyperparameter tuning and performance estimation by comparing several widely used machine-learning algorithms such as boosted regression trees (BRT), k-nearest neighbor (KNN), random forest (RF) and support vector machine (SVM) with traditional …