## 4 Discussion
In Mongolia, the nomadic styles (i.e., seasonal-pasture selection and the range and timing of transient movements) are locally similar but regionally different depending on various factors, such as climate regime, steppe type, water resource availability, and topography [21; 54]. When considering the regional nomadic styles, the _soum_ is a more appropriate unit than the _aimag_, which is wide enough to mix several steppe types. Hence, our _soum_-scale studies contributed to achieving better spatial details in analyzing the local natural drivers of livestock population change across Mongolia, which has not been addressed in other studies so far. Nevertheless, in order to deal with the impact of livestock on vegetation and the resulting desertification issues, it is necessary to examine the relationship between climate-vegetation-livestock that occurs at the scale of nomadic landscapes [27; 55; 56; 57]. It would be nearly impossible to produce landscape-scale climate-nomadic pastoral data for all of Mongolia. Thus, it will be a challenging task to link the knowledge gained from detailed seasonal pasture-level analysis for a few nomadic landscapes intensively studied with the _soum_-scale nationwide research as presented in this study. Here, spatially fine-scale satellite data can contribute to developing the necessary datasets such as vegetation coverage rate, biomass, aridity, etc. This cross-scale research in Mongolian nomadic pastures would advance our understanding of the complex multi-hazard mechanisms such as drought, _Dzud_, and desertification. In conclusion, this study showed that the annual change in livestock numbers can be successfully estimated in county-level _soum_ administrative districts using climate and vegetation data produced from multi-sensor satellite data. Summer and winter variables appeared to be almost equally important in determining the livestock change across Mongolia. This study confirmed that the RF machine learning model estimates the livestock changes accurately with a high R\({}^{2}\) value of above 0.9 in most _soums_ (81%). In contrast, the SMR model provided more intuitive insights in interpreting relationships between the key factors and the livestock change and _Dzud_ outbreaks. The key regional factors discovered in this study generally well reflected the endemic climate characteristics of each region. However, the regionality of snowfall as the key factor was ambiguous except for some mountainous areas, which seem to be the result of the reflecting complexity of snowfall processes. These conclusions therefore suggest that ongoing climate change may have profound impacts on climate-vegetation-livestock interactions in nomadic pastures of Mongolia by causing changes in the characteristics and intensity of local endemic climate. However, as this study targeted only one _Dzud_-recovery-_Dzud_ period from 2003 to 2010, generalizing the above conclusions appears to be reserved, which calls for future efforts to develop various long-term time series datasets on local climate and vegetation using satellites and other sources. Finally, it is inferred that our methodology combining the conceptual nomadic pastoralism model, multi-satellite data, and machine learning can be further applied to explain the livestock change and _Dzud_ occurrence in places where nomadic pastoralism is still practiced, such as Kazakhstan and Tibet. Conceptualization, S.K. and N.C.; methodology, S.K., N.C. and K.J. ; software, N.C. and B.S. ; validation, S.K. and N.C.; formal Analysis, S.K. and N.C.; investigation, S.K. ; resources, A.N., B.-E.L., O.K. and T.T. ; writing--original draft preparation, S.K. ; writing--review and editing, N.C.; visualization, N.C.; supervision, S.K. ; funding acquisition, S.K. All authors have read and agreed to the published version of the manuscript. This work was supported by a research grant from the National Institute of Forest Science (FM0103-2021-02). The raw data supporting the conclusions of this article will be made available by the authors on request.