## 4 Discussion
Thus, it implies that _Dzud_ severity can be exaggerated when harsh winter climates overlap poor summer conditions, especially in areas where livestock gains and losses are controlled by endemic summer variables. The _soums_ with such conditions are widely distributed in the desert steppe and typical steppe regions in southern and central Mongolia. On the other hand, by comparing both the RF and SMR models, respective advantages and limitations of each can be addressed with respect to reproduction accuracy and factor analysis for the annual livestock change. First of all, the fact that the RF model successfully reproduced the annual livestock change in each _soum_ with high accuracy (R\({}^{2}\) above 0.9 in most _soums_) (Figure 7) suggests a high potential of the machine learning method in estimating the temporal livestock change across Mongolia. In contrast, the SMR model had relatively low reproducibility for the annual livestock changes and even failed to develop the regression models in about 30% of _soums_. Despite its shortcomings, the SMR model yields several interesting results helpful for improving our intuitive understanding of the factors causing annual livestock change as well as the issues necessary for further enhancement of our schematic landscape model (Figure 2). The model resulted in better clustered patterns of the key factors on a geographic map (Figure 7) and on the temperature-precipitation scatterplots (Figure 8); marked changes in the compositions of key factors for different _Dzud_ strength class (Figure 9); showed a certain relationship between the _Dzud_ strength and model success rate; and identified areas where the model was not created. Because the SMR model emphasizes the independent linear relationship of each variable, there is a risk of excessively removing variables that contribute little to the explanatory power of the model [47]. In contrast, the RF model develops diverse nonlinear relationships between multiple variables through the number of nodes at multiple layers to fit the target variable as best as possible [49]. Likewise, in our study, the RF model explained the annual livestock change, with high R\({}^{2}\) values (mostly above 0.8) in _soums_ where the SMR model failed. Hence, for those _soums_, nonlinear relationships between natural factors were necessary to explain the livestock change. Interestingly, such _soums_ were distributed regionally, and they were adjacent to each other rather than randomly scattered (Figure 7). For example, 90% of the _soums_ where the SMR failed were adjacent to each other, usually near cities or along railroads or paved roads. Other failed _soums_ are where livestock numbers increased despite the nationwide _Dzud_ in 2010. The above discussion implies that the low explanatory power and significant failure rate of the SMR model could be caused by non-natural social and economic drivers that were not considered in this study (e.g., high slaughter near cities and frequent livestock trade through transportation, etc.). In addition, this study did not consider recognition adaptation frameworks for lessening the _Dzud_, such as traditional ecological knowledge [2] or community-based rangeland management [18]. All the additional social and economic drivers suggest the need for model improvement and new data production to gain further perspectives on the natural control mechanism and effects of social drivers on the livestock change and _Dzud_ occurrence. This study implicitly assumed Mongolian pasturelands as a huge mosaic network composed of unit nomadic landscape pastures that are locally similar in structure and function and have similar natural conditions at the _soum_ scale [21; 51]. The concept of similarity has been widely adopted in ecosystem models for up-scaling processes (e.g.,stand-level 'big-leaf' vegetation models) [52; 53].