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

Vignali, Sergio; Barras, Arnaud Gian; Arlettaz, Raphaël; Braunisch, Veronika (2020). SDMtune: An R package to tune and evaluate species distribution models. Ecology and evolution, 10(20), pp. 11488-11506. John Wiley & Sons, Inc. 10.1002/ece3.6786

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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.

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