Species distribution model accuracy is strongly influenced by the choice of calibration area - Luna, Sergio; Peña-Peniche, Alexander; Mendoza-Alfaro, Roberto (Biodiversity Informatics, 2024)
Species distribution models (SDM) are widely used tools in ecology and conservation aimed at predicting the potential distribution of a species based on its environmental requirements and occurrence data. SDM face many challenges and uncertainties that influence their accuracy. Selecting the ideal calibration area is one of these difficulties. This study analyzes the influence of the extent of the calibration area on the accuracy of SDM through simulations with virtual species. Using bioclimatic variables, 100 virtual species were generated. Occurrence probabilities were determined based on environmental suitability, spatial sampling bias, and accessible areas. SDM were built using MaxEnt, varying size of calibration area, spatial filtering of occurrence records, predictor collinearity treatment, and regularization parameter. Model performance was assessed in terms of functional accuracy (true model accuracy) and discrimination accuracy (model ability to separate occurrence from random sites). Results show that the extent of the calibration area was the most influential factor (explaining 50% of the variance in functional accuracy), while regularization multiplier, predictor collinearity, and spatial thinning had minimal impact (about 4% of explained variance combined). Overall, larger calibration areas generally led to higher functional accuracy, although it varies across species. The correlation between functional and discrimination accuracy was relatively low, indicating that models performing well in one metric may not excel in the other. In conclusion, this research advances the discussion on calibration area selection, providing insights on its substantial effects on model accuracy. Our findings demonstrate that the size of the calibration area is one of the most critical factors affecting the accuracy of models, surpassing the influence of other factors. These insights highlight the importance of select appropriate calibration areas to improve model predictions and ensure more reliable applications of the models.