## 6 Discussion
The achieved results showed that the effects of the different calibration/validation sets on the predictive capability of the prediction model are insignificant because they exhibit slight variations in the standard deviation values. Consequently, the susceptibility map showed low uncertainty values for each map pixel (Figure 4b). Overall, these findings corroborated the good quality of the dataset used in the susceptibility analysis and the robustness of the method applied [73; 86]. The susceptibility model allowed the assessment of the sensitivity of each predisposing factor providing valuable information for knowledge improving the spatial distribution of gully erosion. Therefore, in the current study, we evaluated the influence of the selected predisposing factors by calculating the PRI index. The outcomes showed that topographic factors like SPI, TWI, slope gradient, plan curvature, and LS mainly controlled the spatial pattern of gully erosion. The high influence of these factors on the susceptibility model agrees with previous studies affirming that the slope gradient, SPI, and LS show a high relation with gully development [46; 47; 80]. Indeed, these factors affect the runoff processes and consequently control the linear erosion intensity along the slopes [2; 45; 89]. For example, a high LS value increases the runoff velocity and, thus, raises the probability of gully erosion formation [90]. In this regard, several researchers in southern Italy identified topographic variables as the main factors in the occurrence of gully erosion [13; 91; 92]. Additionally, the results of the PRI model corroborate that other predisposing factors, such as lithology and drainage density, significantly impact the occurrence and further gully development in the study area (Figure 6). The lithological features in combination with soil texture distribution control the water infiltration rate and consequently influence gully formation and its evolution [45; 93]. During field investigations, it was observed that, in several cases, the gully landforms fall within the first- or second-order drainage network because concentrated runoff promotes the gully erosion formation [13; 32], whereas factors such as soil bulk density, TPI, and aspect do not contribute much to the susceptibility model (Figure 6). The goals obtained in this study highlighted that applying the MaxEnt method can provide a very good assessment of the spatial pattern of gully erosion-prone areas and identify zones that need urgent interventions for mitigating the gully erosion risk. In addition, the method could be used for mapping gully erosion susceptibility in other areas of the Calabria region, particularly where similar geological and topographical features prevail. Finally, although this study shows satisfactory results, future research is recommended to enhance gully erosion susceptibility mapping using site-specific variables, such as soil types, geotechnical parameters of the soil, and rainfall characteristics, which are not included in this study. In addition, possible future developments may involve analyses of the uncertainty of the input data, in combination with the detection of the raster resolution effect on prediction models.