5. Conclusion
Spatial bias of the input data is one of the main sources of uncertainty in the species distribution modeling approaches. This issue is particularly important for scarce species with geographically imbalanced biased data of their distribution ranges. Despite the great emphasis on the importance of model tuning and input data manipulation in improving SDMs, the performance of different models in using such an approach has not received much attention. In this research we evaluated the performance of four commonly-used SDMs to predict imbalanced biased occurrence points based on two methods of background data selection including random and background weighting. Our result reveals that different models produced dissimilar results for two background selection schemes. Complex GBM and RF models, due to their interpolative conception, showed inefficiency in predicting test points, especially for the background weighting mode. The GLM over-predicted presence areas due to its extrapolative nature. In spite of being a machine learning method, MaxEnt show a comparable performance in predicting test points in two background selection schemes. The results of the present study emphasize the proficiency of MaxEnt model in generating reproducible comparisons particularly when the input data is being completed.