## 1 Introduction  
The CO\({}_{2}\) efflux from soils, frequently termed as soil respiration (SR) [1; 2], is the second-largest flux between the Earth's surface and atmosphere after photosynthesis [3; 4]. SR reflects mainly biological processes, but it includes two distinct components: respiration of plant roots (RR) and heterotrophic microorganisms in soil (HR). The first, with above-ground plant biomass respiration, constitutes losses of gross primary production (GPP), and the second reflects the decomposition of organic matter entering and available in soil [5]. Therefore, only part of SR is linked with a pathway of carbon loss from soil. The specific functional role of these CO\({}_{2}\) sources in the ecosystem carbon cycle makes it necessary to separate the SR into root and heterotrophic components to relate soil carbon turnover with global warming. There are concerns that an increase in global temperature may increase the ratio of HR to net primary production (NPP = GPP \(-\) plant respiration). Hence, increasing HR vs. NPP due to rising temperatures may reduce the ability of ecosystems to absorb CO\({}_{2}\) released through anthropogenic activities due to the fertilization effect [6]. The proximity of global estimates of NPP in terrestrial ecosystems (56 Gt C/year) [7] and HR (51 Gt C/year) [8; 9; 10; 11] imposes special requirements to reduce their uncertainty. However, all methods are characterized by a high degree of uncertainty in the estimates, their spatial distribution and variability, as well as temporal dynamics, whether they involve extrapolating SR and its components from local measurements to regional and global scales (bottom-up approach) or estimating them using GPP and NPP data (top-down approach) [4; 8; 10; 12; 13; 14]. Despite the growth of observational data and the application of machine learning techniques for their analysis, there has not been a convergence in global estimates of SR. Instead, there has been an increase in the variability of these estimates [13]. Since HR is often calculated based on an empirical relationship with SR or obtained as a product of Earth system models [10], or scaled based on a smaller HR dataset compared to SR [15], it is clear that there is a problem with the scaling of HR, similar to the scaling of SR. The type of ecosystem and vegetation cover have a strong effect on SR because they are responsible for the input of organic compounds into soil, microclimate, biodiversity, and food webs, as well as soil properties. Reflecting the total biological activity of an ecosystem, SR and, particularly, HR are controlled both by the climatic conditions and vegetation type [16; 17; 18]. The average contribution of root respiration to the total SR flux from the soils varies from 38% in agrocones to 63-72% in the tundra and northern taiga ecosystems [1]. However, the contribution of RR to SR can vary significantly depending on the time, place, and method of CO\({}_{2}\) flow separation, with values ranging from less than 10% to more than 90% [19; 20]. The seasonal and interannual soil CO\({}_{2}\) fluxes and their structure are linked with the ecosystem type [21; 22; 23], whereas the reaction of the SR to the climatic changes and extremal events [24] may be associated with the individual soil carbon pools with different turnover times [25]. The importance of regularly monitoring the SR structure is highlighted by the presence of seasonal dynamics specific to different ecosystems [22; 26; 27]. The heterogeneous response of separate SOM components to changing climatic conditions deepens our understanding of the relationship between global warming and the terrestrial carbon cycle. We estimated the seasonal dynamics of SOM-derived and root-derived components of SR in forest and grassland ecosystems for two consecutive years. Our objectives were (i) to compare ecosystem-driving seasonal dynamics of HR and RR; and (ii) to evaluate the response of these components to changing weather conditions.