Spatial-Temporal Variability of Soil Respiration in a Temperate Continental Climate of the Southern Moscow Region

Soil respiration (SR) is characterized by high spatial and temporal variability, which entails a significant uncertainty in estimates of CO2 emission from soils at the levels of individual ecosystems, regions, and the world as a whole. The primary objective of this study was to quantify the contribution of temporal and spatial variability to SR at the annual and seasonal scales by analyzing year-round SR monitoring data from November 2024 to October 2025 in six different biotopes in the southern Moscow region, which belongs to the temperate continental climate zone. We measured SR three–four times per month, using the chamber method with parallel monitoring of soil hydrothermal characteristics. The biotopes differed in vegetation type (forest, meadow, and agrocenosis) and soil (Entic Podzol (Arenic) and Haplic Luvisols (Siltic). It was revealed that differences between the average SR values in paired comparisons of the studied biotopes were more often statistically significant in the warm (May–October) and summer (June–August) periods and were least pronounced in the cold season and spring. The use of the nonparametric analysis of variance (PERMANOVA) showed that the contribution of the time factor (seasonality) to the total SR variability over the annual measurement cycle was 85%, while the spatial factor, determined by the type of biotope, was responsible for only 4% of the total SR variance. The “Time” factor was decisive in forming the total SR variability in all seasons of the year except winter with the highest values of share participation (62–67%) in the warm, spring, and autumn periods, when the dynamics of air and soil temperature, which are the main predictors of SR intensity, are most pronounced. The contribution of the “Biotope” factor was greatest in the winter and summer seasons, amounting to 39 and 25%, respectively. These results should be considered, when planning field experiments, for identifying spatial and temporal relationships with other variables, and for predicting carbon emission losses from soils to reduce its uncertainty.