Data from: 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 assessed based on a recently identified localities of the
species. The results indicated that although the fine-tuned version of all
models shows great performance in predicting training data (AUC >
0.9 and TSS > 0.5), they produce different results in classifying
out-of-bag data. The GBM and RF with higher sensitivity of training data
showed more different performances. The GLM, despite having high
predictive performance for test data, showed lower specificity. It was
only the MaxEnt model that showed high predictive performance and
comparable results for identifying test data in both random and background
weighting procedures. Our results highlight that while GBM and RF are
prone to overfitting training data and GLM over-predict non-sampled areas
MaxEnt is capable of producing results that are both predictable
(extrapolative) and complex (interpolative). We discuss the assumptions of
each model and conclude that MaxEnt could be considered as a practical
method to cope with imbalanced-biased data in species distribution
modeling approaches.