Model parameterization of four species distribution models

Species Distribution Models (SDMs) are practical tools to assess the habitat suitability of species with numerous applications in environmental management and conservation planning. The manipulation of the input data to deal with their spatial bias is one of the advantageous methods to enhance the performance of SDMs. However, the development of a model parameterization approach covering different SDMs to achieve well-performing models has rarely been implemented. We integrated input data manipulation and model tuning for four commonly-used SDMs: generalized linear model (GLM), gradient boosted model (GBM), random forest (RF), and maximum entropy (MaxEnt), and compared their predictive performance to model geographically imbalanced biased data of a rare species complex of mountain vipers. Models were tuned up based on a range of model-specific parameters considering two background selection methods: random and background weighting schemes. The performance of the fine-tuned models was as..., ,