Linking Vegetation, Soil Carbon Stocks, and Earthworms in Upland Coniferous–Broadleaf Forests

Linking vegetation, soil biota, and soil carbon stocks in forests has a high predictive value. The specific aim of this study was to identify the relationships between vegetation, earthworms, and soil carbon stocks in nine types of forests dominating autonomous landscape positions in a coniferous–broadleaf forest zone of the European part of Russia. Mountain forests were selected in the Northwest Caucasus, while plain forests were selected in Bryansk Polesie and on the Moskva-Oka plain. One-way analysis of variance (ANOVA) and v-tests were used to assess the impact of different factors on soil C stocks. To assess the contribution of vegetation, litter quality, and earthworms to variation of carbon stocks in organic (FH-layer) and mineral layer (0–50 cm), the method of hierarchical partitioning was performed. The highest C stocks in the organic horizons were associated with the low-quality litter, i.e., with a low base saturation, high acidity, and wide C/N ratio. The highest soil C stocks in the mineral layers were found in mixed forests with the highest richness of plant species, producing litterfall of different quality. The С stock in the organic horizon was negatively related to the biomass of worms that process the litter, while the carbon stock in the mineral layers was positively related to the biomass of worms whose life activity is related to the mineral layers. These findings demonstrated the substantial influence of plants producing a litter of different quality, and of earthworms, belonging to different functional groups, on soil С stocks in coniferous–broadleaf forests.

Soil Biotic and Abiotic Traits as Driven Factors for Site Quality of Araucaria Angustifolia Plantations

Purpose: The role of soil biotic and abiotic factors in crucial ecosystem services such as primary production, organic matter dynamics, nutrient cycling, and soil biota community structure in the Araucaria ecosystem remains poorly quantified. We aimed to understand how the site quality affects the development of organic horizons, root growth, soil chemical properties, and the entire soil biota community in even aged and monospecific Araucaria angustifolia plantations. Methods: We collected soil monoliths to describe layers of organic matter and the complex soil food web into these layers. We determined soil pH, soil moisture, total nitrogen, available P, and total organic carbon into each layer (litter, F-layer, H-layer, and A horizon), the biomass of fine roots, the community structure of soil biota, arbuscular mycorrhizal fungi, and nematodes, as well as the microbial biomass carbon. Results: In the high-quality site, there was significantly higher organic matter formation, nutrient cycling (N and P), root growth, soil moisture, soil biota diversity, arbuscular mycorrhizal fungi, nematodes, and microbial activity evaluated by the microbial biomass carbon compared to the low-quality site. Conclusions: High-quality sites promote the development of organic horizons, root growth on superficial layers that provide plant nutrient release, the A horizon nutrient contents, and the entire soil biota community in monospecific Araucaria angustifolia plantations located on humid subtropical Cambisols. This creates a positive plant-soil feedback that maintains soil quality and increases primary production, nutrient cycling, and habitat and food for the soil food web.

Ectomycorrhizas accelerate decomposition to a greater extent than arbuscular mycorrhizas in a northern deciduous forest

It has been proposed that ectomycorrhizal (EcM) fungi slow down decomposition by competing with free-living saprotrophs for organic nutrients and other soil resources (known as the “Gadgil effect”), thereby increasing soil carbon sequestration. As such, this Gadgil effect should depend on soil organic matter age and quality, but this remains unstudied. In addition, the Gadgil effect is not expected to occur in arbuscular mycorrhizal (AM) forests since AM fungi cannot access directly nutrients from soil organic matter, yet few direct comparisons between EcM and AM forests have been made. We performed a two-year reciprocal decomposition experiment of soil organic horizons (litter - L, fragmented - F, humic - H) in adjacent temperate deciduous forests dominated by EcM or AM trees. Litterbags were made of different mesh sizes allowing or excluding ingrowth of external fungal hyphae, which are primarily mycorrhizal in these forests other than for the most-recent superficial litter horizon. As expected, organic matter originating from deeper horizons and from EcM forests was of lower quality (e.g. higher lignin to nitrogen ratios) and decomposed more slowly. However, contrary to the Gadgil effect, organic matter exposed to external fungal hyphae (i.e. primarily mycorrhizal) actually decomposed faster in both forest types, and this effect was strongest in EcM forests, particularly in the F horizon. Unexpectedly, organic matter decomposition was faster in EcM than in AM forests, regardless of organic matter origin. Overall, our study reinforces the view that temperate EcM forests store greater amounts of soil organic carbon than AM forests, but suggests that this is due to factors other than the Gadgil effect.

Peat deposits store more carbon than trees in forested peatlands of the boreal biome

Peatlands are significant carbon (C) stores, playing a key role in nature-based climate change mitigation. While the effectiveness of non-forested peatlands as C reservoirs is increasingly recognized, the C sequestration function of forested peatlands remains poorly documented, despite their widespread distribution. Here, we evaluate the C sequestration potential of pristine boreal forested peatlands over both recent and millennial timescales. C stock estimates reveal that most of the carbon stored in these ecosystems is found in organic horizons (22.6–66.0 kg m−2), whereas tree C mass (2.8–5.7 kg m−2) decreases with thickening peat. For the first time, we compare the boreal C storage capacities of peat layers and tree biomass on the same timescale, showing that organic horizons (11.0–12.6 kg m−2) can store more carbon than tree aboveground and belowground biomass (2.8–5.7 kg m−2) even over a short time period (last 200 years). We also show that forested peatlands have similar recent rates of C accumulation to boreal non-forested peatlands but lower long-term rates, suggesting higher decay and more important peat layer combustion during fire events. Our findings highlight the significance of forested peatlands for C sequestration and suggest that greater consideration should be given to peat C stores in national greenhouse gas inventories and conservation policies.

Phosphorus deficiencies invoke optimal allocation of exoenzymes by ectomycorrhizas

Ectomycorrhizal (EM) fungi can acquire phosphorus (P) through the production of extracellular hydrolytic enzymes (exoenzymes), but it is unclear as to the manner and extent native EM fungal communities respond to declining soil P availability. We examined the activity of six exoenzymes (xylosidase, N-acetyl glucosaminidase, β-glucosidase, acid phosphomonoesterase, acid phosphodiesterase [APD], laccase) from EM roots of Pseudotsuga menzesii across a soil podzolization gradient of coastal British Columbia. We found that APD activity increased fourfold in a curvilinear association with declining inorganic P. Exoenzyme activity was not related to organic P content, but at a finer resolution using 31P-NMR, there was a strong positive relationship between APD activity and the ratio of phosphodiesters to orthophosphate of surface organic horizons (forest floors). Substantial increases (two- to fivefold) in most exoenzymes were aligned with declining foliar P concentrations of P. menzesii, but responses were statistically better in relation to foliar nitrogen (N):P ratios. EM fungal species with consistently high production of key exoenzymes were exclusive to Podzol plots. Phosphorus deficiencies in relation to N limitations may provide the best predictor of exoenzyme investment, reflecting an optimal allocation strategy for EM fungi. Resource constraints contribute to species turnover and the assembly of distinct, well-adapted EM fungal communities.

ECOLOGICAL AND GEOCHEMICAL ESTIMATION OF THE SPREADING OF TECHNOGENIC ELEMENTS IN THE TERRITORY OF KIVERTSIV NATIONAL NATURAL PARK "TSUMANSKA PUSHCHA"

Analysis of heavy metals content in the soils of the territory showed its heterogeneous distribution and dependence on available sources of technogenic impact. Geochemical accumulation coefficients were calculated and analyzed. The highest concentrations of manganese and chromium are in soils developed on forest deposits. The highest concentrations of nickel and copper are in soils developed on water-glacial deposits. Most of the studied heavy metals exceed the regional geochemical background. In terms of gross content in soils, trace elements form the following geochemical series: Zn> Cu> Pb> Ni> Mn> Cr. Accumulation of lead in forest deposits up to 2-3 MPC was defined. Most heavy metals in the soil are nonuniformity distributed. High values of the variation coefficient (V) – more than 34 % – are typical for inhomogeneous set of data on the concentrations of all investigated heavy metals. The highest index of variation was defined for lead concentration and equal to 170%, the lowest values were obtained for chromium content and equal to 34 % and zinc – 36 %. In the investigated soils there are two types of micronutrient distribution: elements accumulation in organic horizons with concentration decrease in the lower part of the profile and distribution by sedentary – illuvial type. The first type is characterized by increasing distribution of the following elements Zn, Pb; the second one is characterized Co, Ni, Cu, Mn. The ecological and geochemical assessment of technogenic elements distribution of the NNP Tsumanska Pushcha territory is made on the basis of the comparison of technogenic geochemical specialization of different environments (water, soils, etc.). For assessing the role of landscape components in the general geochemical anomaly of the territory, it is proposed to create total ecological and geochemical estimations. It represents the total values of pollutant concentration coefficients in individual components. Further researchers should focus on the analysis of macronutrients and genetic types of soils; determination of the reference content of elements in geochemical landscapes for defining intensity migration and characteristics of elements distribution; conducting biogeochemical zoning.

The state dynamics of Scots pine communities under conditions of a decrease in airborne industrial pollution

This work examines the changes in the life state of Scots pine communities located in the immediate vicinity of the source of aerial anthropogenic emission with a clear decrease in its level in the time interval 2015-2018. It is shown that due to the physiological and ontogenetic characteristics of woody forms of plants, the response to changes in the level of pollution by the tree layer will be insignificant in a relatively narrow time interval (within the limit of statistical error). However, this response to the reduction of pollution will still manifest itself. Also, one of the reasons for such a slow response can be considered the accumulation of heavy metals in the organic horizons of the soil, where heavy metals form complex compounds (chylates) that are inactive for eluvial processes. The most probable way of development of these communities, while maintaining the current level of emission, will be a gradual increase in the life state of the tree layer to a certain value.

Elemental composition of soils of the Pur-Taz interfluve

In order to evaluate the chemical composition of natural background environments of Pur-Taz interfluve (Western Siberia), the mineral components of soils, peats, lichens, and sphagnum mosses have been analyzed. The samples were tested using X-ray fluorescence technology. The average contents of hazardous metals in the soils of the Pur-Taz interfluve are either lower (for Cu, Pb, Zn, Ni, Sr) or equal to (for Hg, Co) the average values of these elements in the Earth's crust. This finding corresponds to the commonly held view that the contents of elements in the soils located in the north of Western Siberia are lower than the world averages. Additionally, in our samples low concentrations of copper and zinc have been observed. Since these microelements are important for soil physiology, this finding indicates unfavorable biochemical conditions in the research area. On the other hand, high concentrations are observed for inactive elements such as Mo, Sn and Zr. The significant differences have been identified in the composition of mineral and organic soil horizons. For instance, the average concentrations of P, Zn and S in organic horizons are 7.1, 8.1 and 18 times greater than in the illuvial mineral horizons, respectively. The intense accumulation of Zn, Cu, Cd, Hg has been recorded, all of them are chalcophiles in the ombrotrophic peat. This means that the chemical composition of soil is largely determined by biological accumulation of chalcophile elements. The content of lithophilic Al, Si, Ti and Zr, coming with dust precipitation from the atmosphere increases in the peat of dwarf shrub-moss-lichen tundras and larch woodlands. The revealed values of the elemental composition of soils can be recommended as background in the course of the environmental monitoring.

National spectral data and learning algorithms for potentially toxic elements modelling in forest soil horizons

Forest ecosystems are among the main parts of the biosphere; however, they have been endangered from the significant elevation and harmful effects of air and soil pollutants, including potentially toxic elements (PTEs). The concentration of PTEs in forest soils varies not only laterally but also vertically with depth. Forest surface organic horizons are of particular interest in forest ecosystem monitoring due to their role as stable adsorbents of the deposited atmospheric substances. Therefore, the main purpose of this study was to conduct rapid examinations of forest soils PTEs (Cr, Cu, Pb, Zn, and Al), testing the capability of VIS--NIR spectroscopy coupled with machine learning (ML) techniques (partial least square regression (PLSR), support vector machine regression (SVMR), and random forest (RF)) and fully connected neural network (FNN), a deep learning (DL) approach, in forest organic horizons. One-thousand-and-eighty forested sites across the Czech Republic at two soil layers, defining the fragmented (F) and humus (H) organic horizons, were investigated (total 2160 samples). PTEs as well as total Fe and SOC, as auxiliary data, were conventionally and spectrally determined and modelled in the combined organic horizons (F + H) and in each individual horizon using the ML and DL algorithms. Results indicated that the concentration of all PTEs was higher in the horizon H compared to the F horizon. Although the spectral reflectance of samples tended to decrease with increased PTEs concentration. Strongly significant positive correlations between all PTEs and total Fe in all horizons were obtained, which were higher in the H and F + H horizons than the F horizon. The highest correlations of PTEs with the spectra were at 460--590~nm, which is mostly linked to the presence of Fe-oxide. These results show the importance of Fe for spectral prediction of PTEs. Cr and Al were the most accurately predicted elements, regardless of the applied learning technique. SVMR provided the best results in assessing the H horizon (e.g., R\(^2\) = 0.88 and root mean square error (RMSE) = 3.01~mg/kg, and R\(^2\) = 0.82 and RMSE = 1682.25~mg/kg for Cr and Al, respectively); however, FNN predicted the combined F + H horizons the best (R\(^2\) = 0.89 and RMSE = 2.95~mg/kg, and R\(^2\) = 0.86 and RMSE = 1593.64~mg/kg for Cr and Al, respectively) due to the larger number of samples. In the F horizon, almost no parameters were predicted adequately. This study shows that given the availability of larger sample sizes, FNN can be a more promising technique compared to ML methods for assessment of Cr and Al concentration based on national spectral data in the forests of the Czech Republic.