Due to the global increase in CO₂ in the atmosphere, studies focusing on the carbon balance in forest ecosystems are currently particularly relevant. Abandoned agricultural lands could provide an important contribution to carbon sequestration in many parts of the world. In the broad-leaved forest zone of the Cis-Ural (Southern Ural region, Russia), the carbon sequestration dynamics in the biomass of woody and herbaceous plants, as well as in the litter and soil on abandoned arable lands repopulated with silver birch (Betula pendula), was studied. The data were collected on 35 round (with diameter of 30 m) sample plots located within communities representing the different stages of reforestation with tree stands aged 3 to 30 years. It was found that the carbon content of the stem wood and herbaceous understory did not depend on the succession stages, which largely corresponds to the literature data. The carbon content in root biomass and soil organic matter increased along with the growth of tree stands. While the forest stand grew, the carbon content in the grey forest soil increased from 2.5 to 4.4%, and in the more fertile dark grey forest soil it changed only slightly. The carbon deposition by the forest stands on the sample plots located on the dark grey forest soils was higher than on grey forest soils. The average rate of carbon sequestration in the tree stand was 2.7 t/ha/year. Most mature, 25–30-years-old silver birch tree stands provided the highest average annual increase in tree biomass and the rate of carbon sequestration evaluated was 9 t/ha/year. Also, the carbon pool in the 30 cm soil layer was 2.7 times greater than in the tree stand. It was concluded that abandoned agricultural lands overgrowing by forest in the Cis-Ural are promising for carbon sequestration.

This article considers the influence that suspensions of nanoparticles (sized from 10 to 60 nm) of iron oxo-compounds in different oxidation states have on biological objects. The suspension...

An analysis of the temperature regime and air humidity was carried out in ecotonal pine-broadleaf forestsof the suballianceTilio cordatae–Pinenion sylvestris on the Ufa Plateau (UP) and in the central part of the Southern Urals (MFZ – mountain forest zone of the Republic of Bashkortostan (RB)) based on data from temperature and humidity sensors (2022–2023) and the ERA5-Land reanalysis. The mean daily air temperature on the UP was on average 1,24°C higher than in the MFZ according to logger data and 2,3°C higher according to the ERA5-Land reanalysis. A high correlation between logger observations and reanalysis data was detected (R = 0,97–0,98). However, the air temperature measured by sensors was on average higher than the ERA5-Land estimates by 1,7°C on the UP and by 0,67°C in the MFZ. During extreme periods, discrepancies reached up to 13°C, which is associated with the buffering role of the forest canopy, the influence of the snow cover, and microlandscape features such as slope exposure and steepness. Relative air humidity at both sites showed very similar average values (74,3% on the UP and 74,0% in the MFZ) and a high synchrony in seasonal dynamics. The highest values were observed in November (up to 90%), while the lowest values occurred in May (about 50%). During the cold season, humidity remained high due to low temperatures and stable snow cover, whereas in spring its variability increased. Soil temperature in winter remained close to 0°C, indicating the strong insulating effect of the snow cover. In spring and summer, soil on the UP warmed more rapidly and reached higher values (up to +17…+18°C) than in the MFZ (up to +15…+16°C). These differences are explained by variations in slope exposure, relief, and conditions of solar radiation input. The obtained results demonstrate the important role of microlandscape factors and forest canopy in shaping the microclimatic regime of forest ecosystems. The obtained results highlight the key role of local factors (microrelief, forest canopy) in the formation of microclimate, which must be taken into account when predicting the response of forest ecosystems to global climate change, especially in ecotone communities sensitive to climate fluctuations.