SDMtune: An R package to tune and evaluate species distribution models

• Main Authors: Sergio Vignali, Arnaud G. Barras, Raphaël Arlettaz, Veronika Braunisch
• Format: Article
• Language: English
• Published: Wiley 2020-10-01
• Series: Ecology and Evolution
• Subjects: ecological niche model; fine‐tuning; genetic algorithm; machine learning; model complexity; variable selection
• Online Access: https://doi.org/10.1002/ece3.6786

Abstract Balancing model complexity is a key challenge of modern computational ecology, particularly so since the spread of machine learning algorithms. Species distribution models are often implemented using a wide variety of machine learning algorithms that can be fine‐tuned to achieve the best model prediction while avoiding overfitting. We have released SDMtune, a new R package that aims to facilitate training, tuning, and evaluation of species distribution models in a unified framework. The main innovations of this package are its functions to perform data‐driven variable selection, and a novel genetic algorithm to tune model hyperparameters. Real‐time and interactive charts are displayed during the execution of several functions to help users understand the effect of removing a variable or varying model hyperparameters on model performance. SDMtune supports three different metrics to evaluate model performance: the area under the receiver operating characteristic curve, the true skill statistic, and Akaike's information criterion corrected for small sample sizes. It implements four statistical methods: artificial neural networks, boosted regression trees, maximum entropy modeling, and random forest. Moreover, it includes functions to display the outputs and create a final report. SDMtune therefore represents a new, unified and user‐friendly framework for the still‐growing field of species distribution modeling.