Ivan A. Smorkalov

The aim of this study was to investigate the patterns of plant diversity formation on extensive green roofs in a continental climate and the role of adjacent lawns as sources of colonizing species. In 2024, extensive green roofs were established in Moscow and Yekaterinburg on two substrate types (regional and universal) at two depths (15 and 25 cm), with 3 replicates per treatment. In May 2025, regional plant species (seedlings) were planted: in Moscow - Festuca glauca Vill., Thymus serpyllum L., Achillea millefolium L.; in Yekaterinburg - Aster alpinus L., Festuca glauca Vill., Festuca rubra L., Origanum vulgare L., Dianthus acicularis Fisch. ex Ledeb., Veronica spicata L. Weeds were not removed. In September 2025, species were recorded, and community similarity (Jaccard coefficient, Bray - Curtis index) was measured between lawns and roofs, as well as within roofs between different treatments, together with alpha diversity (Shannon index H’, Simpson index 1-D, and evenness). In parallel, the species composition of adjacent lawns was surveyed. In total, 50-60% of roof species were also present on lawns; the Jaccard coefficient ranged from 0.14 to 0.32; H’ for roofs ranged from 1.08 to 2.08 (13-15 species). Unique species of Moscow roofs were dominated by R/CR strategies ( Lactuca saligna L., Lactuca serriola L., Amaranthus retroflexus L., Portulaca oleracea L., Digitaria sanguinalis (L.) Scop., Gnaphalium uliginosum L., Iva xanthiifolia Nutt., Acalypha australis L., Euphorbia virgata Waldst. & Kit., Lipandra polysperma (L.) S. Fuentes, Uotila & Borsch, Epilobium obscurum Schreb., Myosotis sparsiflora J.C.Mikan ex Pohl, Veronica longifolia L., Festuca arundinacea Schreb.), whereas in Yekaterinburg C/CR species dominated ( Trifolium arvense L., Medicago falcata L., Lolium perenne L., Rumex obt usifolius L., Viola tricolor L., Galium spurium L., Arenaria serpyllifolia L., Setaria viridis (L.) P.Beauv., Rudbeckia hirta L.). High spatial heterogeneity in similarity and diversity indices was observed for Moscow roofs, whereas substrate type and depth had no significant effect. Thus, lawns were the main source of spontaneous colonizers of green roofs. Vegetation succession during the first year on green roofs went through an R/CR stage, probably under the dominant influence of climatic factors.

The annual flux of carbon dioxide from soils across different biomes plays a key role in global climate models and terrestrial carbon cycle analysis. However, there are significant gaps in such research at a regional scale. Due to the high labor intensity of obtaining daily soil respiration indicators, various modeling methods are used. In this work, based on 2760 measurements of soil respiration in spruce forests of the Ural-Carbon carbon supersite (Middle Urals), carried out in the fall of 2021 and from April to October 2022, using classical regression approaches and machine learning, annual soil respiration indicators were estimated. We also investigated the dependence of the results on the complexity of the model (number of predictors) and the methods used (random forest model extrapolation and combined approaches for estimating winter CO₂ fluxes). The “simplified” model with seven predictors showed only a slight decrease in accuracy compared to the full model with 21 predictors (R² = 0.89, MSE = 0.22 vs. R² = 0.92, MSE = 0.31). Remote sensing-based predictors contributed more to model accuracy than field data. While initial results varied across methods, incorporating literature-based winter respiration values into the random forest model and averaging combined-approach estimates yielded consistent annual soil respiration values: 830.3 ± 6.4 and 851.6 ± 8.0 g C/m² year, respectively.

A significant portion of the current knowledge regarding the use of iron nanoparticles for remediating metal-contaminated soils is derived from laboratory experiments, leaving several unanswered questions. This article presents a field experiment comparing the efficacy of magnetite nanoparticles and microparticles for the immobilization of metals and the growth of plants in metal-contaminated soils. This study aimed to investigate the effects of magnetite particle size on metal immobilization and plant growth in soils exposed to airborne pollution from the Middle-Urals Copper Smelter in the southern taiga subzone near Revda, Russia, 50 km from Ekaterinburg. Magnetite nano- and microparticles were added to forest litter at a 4 % w/w dose. The total metal contents in litter from the study plots were 1-2 orders of magnitude higher than background metal concentrations. The magnetite nanoparticle treatment was found to decrease the concentration of exchangeable copper in soil and improve the growth of red fescue (Festuca rubra L.) on polluted soil compared to the control. In contrast, magnetite microparticles did not show any statistically significant effects. These findings are in line with laboratory results that demonstrated the superior metal adsorption properties of magnetite nanoparticles compared to microparticles. However, this study was limited in duration (2 months), and longer field studies would be necessary to confirm the role of iron particle size in the rehabilitation of metal-contaminated soils.

The effect of individual trees on soil and litter respiration in forests polluted with heavy metals from copper smelter emissions was investigated for the first time. We tested the hypothesis that the portion of spatial variability in soil respiration explained by the distance from the tree trunk decreases on polluted plots in comparison with the background area. The study was performed in the southern taiga spruce–fir and birch forests under long-term pollution from the Middle Ural Copper Smelter in the Revda City, Sverdlovsk region, Russia. Measurement points were located near spruce and birch trees at different distances from tree trunks (tree-base plot, the middle of the crown projection, and canopy gap). The total CO₂ emission, litter respiration, litter contribution to soil respiration, specific respiratory activity of litter, and litter stock were measured at each point. In the background area, soil respiration decreased from the tree trunk to the canopy gap. The hypothesis was only partially confirmed: the portion of respiration variance explained by the distance from tree trunks decreased in polluted areas in comparison with the background areas in spruce forests but did not change in birch forests. The observed change in spruce forests was related to a drop in specific respiratory activity of litter, though litter stock was considerably higher near the tree trunk than in the canopy gap. We propose to locate measurement points in the middle of the crown projection, i.e., at a sufficient distance from tree trunks and away from the canopy gaps, to reduce potential bias in soil respiration estimates.

The effect of individual trees on soil and litter respiration in forests polluted with heavy metals from copper smelter emissions was investigated for the first time. We tested the hypothesis that polluted sites exhibit a d-ecrease in the portion of spatial variance of soil respiration associated with the distance to the tree trunk compared to the background area. The study was conducted in the southern taiga spruce-fir and birch forests exposed to long-term pollution from the Middle Ural Copper Smelter in Revda City, Sverdlovsk region, Russia. Measurement points were placed near spruce and birch trees at different distances from the tree trunk (tree-base site, middle of the crown projection, and canopy gap), and total CO2 emission, litter respiration, litter contribution to soil respiration, litter-specific respiratory activity, and litter stock were measured at each point. In the background area, soil respiration decreased from the tree trunk to the canopy gap. The hypothesis was partially confirmed, as the variance portion associated with distance to tree trunks decreased in spruce forests with increasing pollution but did not change in birch forests. A change in spruce forests was due to a decline in litter-specific respiratory activity, while litter stock was considerably higher in the tree-base site than in the canopy gap. It is proposed to locate measurement points in the middle of the crown projection, at a sufficient distance from tree trunks and outside the canopy gaps, to minimize bias in soil respiration estimates.

Soil respiration modeling (i.e., simulation of carbon dioxide emissions from the soil surface) makes it possible to analyze and forecast changes in the carbon cycle in terrestrial ecosystems. Along with classical regression models, researchers currently use machine learning methods based on neural networks or regression tree ensembles. However, models produced using these methods are often used as ‘black boxes’, which hinders the analysis of process mechanisms. This paper demonstrates capabilities of the Random Forest algorithm that can be successfully used to estimate effects exercised by various factors on soil respiration based on features importance measurements. Using variance separation, predictors have been classified either as spatial (biotope type, soil type, vegetation type, and soil moisture content) or temporal (soil and air temperature, NDVI, LAI, FPAR, and SPEI). Several models were produced based on 5670 respiration measurements performed during five growing seasons (2012–2016) on 30 sampling plots in south-taiga pine forests and meadows that feature different vegetation and soil types but are confined to the same small area. Different models include different sets of predictors (all predictors, temporal predictors only, spatial predictors only, and temperature and humidity only), and their accuracy reaches R² = 0.88 (MSE = 0.47). It is established that soil respiration depends primarily on temporal factors whose importance ranges from 76 to 91%. In forests, the effect of spatial factors on respiration is stronger than in meadows.