Iranian Journal of Applied Ecology

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Khosravi R, Rahimi-Nezhad H, Nikaeen G, Fallah Shamsi S R. Comparison of Spatial Bias Correction Methods for Presence Data in Improving Species Distribution Model Predictions. Iranian Journal of Applied Ecology 2025; 14 (3) :21-36
URL: http://ijae.iut.ac.ir/article-1-1292-en.html

Spatial bias in mammal occurrence data due to uneven sampling represents a major challenge for species distribution models. Therefore, assessing bias in presence data is a prerequisite for improving the accuracy of models. At the present study, a range of commonly used and novel methods for correcting spatial bias was applied to the presence data of two herbivores, the wild goat (Capra aegagrus) and the wild sheep (Ovis gmelini / O. vignei) and by implementing different bias-correction approaches, the effect of heterogeneous sampling effort on model performance was evaluated. The effectiveness of each method was further assessed using simulated presence records generated for a set of virtual species. While all methods showed high performance in prediction the spatial range of the species (AUC > 0.75), similarity indices indicated that combination of target-group approach, used as a basis for selecting backgrounds, and filtering presence data within a geographic space performed better than the other methods. The findings demonstrated that correcting spatial bias in presence data plays a fundamental role in improving the accuracy of distribution models and effectively reduced the impact of uneven sampling effort. The proposed approaches provide a useful framework for improving distribution modelling of other species.