Journal
ECOLOGY AND EVOLUTION
Volume 10, Issue 20, Pages 11488-11506Publisher
WILEY
DOI: 10.1002/ece3.6786
Keywords
ecological niche model; fine-tuning; genetic algorithm; machine learning; model complexity; variable selection
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Funding
- Samy Harshallanos
- Alfons und Mathilde Suter-Caduff Stiftung
- Beat und Dieter Jutzler Stiftung
- WWF Switzerland
- Ernst Gohner Stiftung
- Swiss Federal Office for the Environment
- Swiss Federal Office for Energy
- Stiftung Temperatio
- Stiftung Dreiklang fur okologische Forschung und Bildung
- UniBern Forschungsstiftung
- Steffen Gysel Stiftung fur Natur und Vogelschutz
- Parrotia Stiftung
- Sophie und Karl Binding Stiftung
- Margarethe und Rudolf Gsell-Stiftung
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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 releasedSDMtune, 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.SDMtunesupports 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.SDMtunetherefore represents a new, unified and user-friendly framework for the still-growing field of species distribution modeling.
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