<i>spatialMaxent</i>: Adapting species distribution modeling to spatial data

Abstract

Conventional practices in species distribution modeling lack predictive power when the spatial structure of data is not taken into account. However, choosing a modeling approach that accounts for overfitting during model training can improve predictive performance on spatially separated test data, leading to more reliable models. This study introduces <i>spatialMaxent</i> (https://github.com/envima/spatialMaxent), a software that combines state-of-the-art spatial modeling techniques with the popular species distribution modeling software Maxent. It includes forward-variable-selection, forward-feature-selection, and regularization-multiplier tuning based on spatial cross-validation, which enables addressing overfitting during model training by considering the impact of spatial dependency in the training data. We assessed the performance of <i>spatialMaxent</i> using the National Center for Ecological Analysis and Synthesis dataset, which contains over 200 anonymized species across six regions worldwide. Our results show that <i>spatialMaxent</i> outperforms both conventional Maxent and models optimized according to literature recommendations without using a spatial tuning strategy in 80 percent of the cases. <i>spatialMaxent</i> is user-friendly and easily accessible to researchers, government authorities, and conservation practitioners. Therefore, it has the potential to play an important role in addressing pressing challenges of biodiversity conservation.