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On average across the species, model performance was mostly influenced by the choice of predictor set, followed by the choice of modeling technique. The number of the pseudo-absences did not have a strong effect on the model performance. Based on spatially independent testing, ensemble models based on species-specific nonredundant predictor sets revealed the highest predictive performance. In contrast, the Random Forest technique yielded the highest model performance in cross-validation but had the largest decrease in model performance
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