Soil Vegetation Research Articles

Structure and function role of soil nematode communities in different types of vegetation in Yogyakarta and Central Java, Indonesia

Abstract. Indarti S, Utami SNH, Putra NS, Maharani R. 2024. Structure and function role of soil nematode communities in different types of vegetation in Yogyakarta and Central Java, Indonesia. Biodiversitas 25: 2765-2772. Soil nematodes are crucial parts of the soil organism community and play a significant role in many ecological soil processes. It is necessary to pay greater attention to the lack of knowledge regarding the composition and role of the soil nematode community in different vegetations. Hence, the nematode structure community from minimal to soil stillages have been observed and compared from seven distinct types of vegetation. This study aimed to determine the soil nematode community structure, which includes the dominance, abundance, richness, diversity, and functional role of nematodes, as well as other chemical soil properties. Vegetation type represents soil management and has an impact on the life of soil organisms such as nematodes. A total of approximately 2328 soil nematodes were found, and 18 genera were identified. The Nematode Indicator Joint Analysis (NINJA) revealed that the vegetation type, land use, and soil chemical properties influence the nematode’s abundance, dominance, and structure differently. Through those results, the authors confirmed that soil nematodes in cultivated vegetation are dominated by the trophic groups of plant-parasitic nematodes and bacterivores. Conversely, predator, omnivores, and fungivores nematodes dominate in soil with minimal tillage. Overall, this study highlights the importance of soil nematode analysis in sustainable agricultural soil ecosystems.

Agricultural Drought Model Based on Machine Learning Cubist Algorithm and its Evaluation

Soil moisture is the most direct evaluation index for agricultural drought. It is not only directly affected by meteorological conditions such as precipitation and temperature but is also indirectly influenced by environmental factors such as climate zone, surface vegetation type, soil type, elevation, and irrigation conditions. These influencing factors have a complex, nonlinear relationship with soil moisture. It is difficult to accurately describe this non-linear relationship using a single indicator constructed from meteorological data, remote sensing data, and other data. It is also difficult to fully consider environmental factors using a single drought index on a large scale. Machine learning (ML) models provide new technology for nonlinear problems such as soil moisture retrieval. Based on the multi-source drought indexes calculated by meteorological, remote sensing, and land surface model data, and environmental factors, and using the Cubist algorithm based on a classification decision tree (CART), a comprehensive agricultural drought monitoring model at 10 cm, 20 cm, and 50 cm depth in Gansu Province is established. The influence of environmental factors and meteorological factors on the accuracy of the comprehensive model is discussed, and the accuracy of the comprehensive model is evaluated. The results show that the comprehensive model has a significant improvement in accuracy compared to the single variable model, which is a decrease of about 26% and 28% in RMSE and MAPE, respectively, compared to the best MCI model. Environmental factors such as season, DEM, and climate zone, especially the DEM, play a crucial role in improving the accuracy of the integrated model. These three environmental factors can comprehensively reduce the average RMSE of the comprehensive model by about 25%. Compared to environmental factors, meteorological factors have a slightly weaker effect on improving the accuracy of comprehensive models, which is a decrease of about 6.5% in RMSE. The fitting accuracy of the comprehensive model in humid and semi-humid areas, as well as semi-arid and semi-humid areas, is significantly higher than that in arid and semi-arid areas. These research results have important guiding significance for improving the accuracy of agricultural drought monitoring in Gansu Province.

Interaction Effects of Vegetation and Soil Factors on Microbial Communities in Alpine Steppe Under Degradation

To clarify the regulating effect of vegetation and soil factors on microbial communities in the alpine steppe under degradation on the Qinghai-Xizang Plateau, the alpine steppe in the Sanjiangyuan area of the Qinghai-Tibet Plateau was chosen. We analyzed the differences in vegetation and soil factors in different stages of degradation （non-degradation, moderate degradation, and severe degradation） and detected the variations in microbial community characteristics in the alpine steppe under different degradation stages using high-throughput sequencing technology. Eventually, redundancy analysis （RDA） and multiple regression matrixes （MRM） based on the similarity or dissimilarity matrix were used to identify key environmental factors regulating microbial （bacterial and fungal） community changes under degradation. The results showed that the degradation of the alpine steppe significantly changed the community coverage, height, biomass, and important value of graminae； significantly reduced the contents of soil organic matter, total nitrogen, total phosphorus, and silt； and increased the soil bulk density and sand content. Degradation did not change the composition of bacteria and fungi, but their composition proportions changed and also resulted in the loss of microbial richness （Chao1 index and Richness index） but did not significantly change the microbial diversity （Shannon index）. With the occurrence of degradation, the vegetation characteristics, soil physicochemical properties, and microbial diversity showed a consistent change trend. Combined with the characteristics of the network topology changes （the number of nodes and clustering coefficient significantly decreased）, it was found that degradation of the alpine steppe led to the decline of interspecies interactions, decentralization of network, and homogenization of microorganisms, but the cooperation relations among the species were maintained （positive correlation connections accounted for more than 90% in all degradation stages）. Under the alpine steppe degradation, the vegetation-soil interaction had the greatest effect on soil bacterial community, whereas soil physicochemical properties had the greatest influence on soil fungal community. Specifically, vegetation community height, biomass, and soil bulk density were the mutual factors regulating soil microorganisms, whereas the vegetation Simpson index, important value of graminae, soil total phosphorus, total potassium, and silt content were the unique factors affecting the soil bacterial community, and soil pH and total nitrogen content were the particular factors affecting the soil fungal community.

The role of near‐surface vegetation in modulating overland flow resistance in grasslands of the Chinese Loess Plateau

AbstractVegetation restoration significantly increases overland flow resistance and decreases soil erosion. The cause of soil erosion is unknown, though grain resistance and form resistance are frequently confused with the overall overland flow resistance. The zonal specie of Bothriochloa ischcemum (Linn.). Keng (BI) and the dominant specie of Artemisia vestita Wall. ex Bess (AG) were selected to investigate the effect of vegetation near soil surface characteristics derived by vegetation restoration on overland flow resistance of grain resistance and form resistance. In total, 24 steel tanks were planted at six planting densities of 5, 10, 15, 20, 25, and 30 plants m−2, and two tanks were used as bare soil controls. Their hydraulic characteristics were tested under a constant rainfall intensity (60 mm h−1) on a 26.2% slope. The results showed that the grain resistance (fg) and form resistance (ff) ranged from 0.06 to 0.35 and 18.01 to 95.77 under six planting densities. Vegetation increased overland flow resistance by more than 90%, which was mainly attributed to ff. Vegetation near soil surface factors of the aboveground part, biological soil crusts, and root collar increased ff by 18.71 to 82.13, 0.74 to 11.50, and 1.61 to 3.40, respectively. Compared with BI, the mean value of fg in AG was 48.87% lower, whereas the mean value of ff was 1.36 times greater. The fg decreased with soil organic matter as a power function. The ff decreased with the Reynolds number, Froude number, and flow velocity and increased with vegetation coverage, aboveground biomass, and root collar area as power functions. Soil properties explained the majority of the variation in fg, and near the vegetation soil surface factors explained the majority of the variation in ff. Finally, ff was simulated using the Reynolds number, vegetation coverage, and root collar area. In addition, a simulation equation for the total flow resistance was established by distinguishing the grain resistance and the form resistance, and the performance of the model developed in this study was satisfactory (NSE = 0.97).

Combined application of organic wastes and Trichoderma longibraciatum to promote vegetation restoration and soil quality on mining waste dump sites

Combined application of organic wastes and Trichoderma longibraciatum to promote vegetation restoration and soil quality on mining waste dump sites

Catalytic hairpin-assembled and DNAzyme-driven DNA walker integrated hybridization chain reaction to construct a label-free electrochemical aptasensor for the detection of acetamiprid

As a new type of nicotine pesticide, the excessive residue of acetamiprid (ACE) in food and the environment seriously poses human health. Hence, this article developed an electrochemical aptasensor for detecting ACE based on a cascaded amplification circuit composed of catalytic hairpin-assembled and DNAzyme-driven DNA walker and hybridization chain reaction. Through the target triggered catalytic hairpin assembly (CHA), the free travelling strand of DNA walker and intact catalytic core of DNAzyme were formed, and with the assistance of Mg2+, DNAzyme was activated, driving the travelling strand to walk autonomously, repeatedly cutting hairpin DNA substrates on the electrode surface, and obtaining abundant DNA fragments. These fragments contained the initiators of hybridization chain reaction (HCR) could trigger HCR and produce the long double-strand DNA (dsDNA) for loading methylene blue (MB). Owing to the fact that Ce(Ⅲ, Ⅳ)-MOF material with electrocatalytic activity, the electrochemical reduction of MB could be effectively catalyzed, obtain significantly enhanced current signals, and ultimately achieve label-free and sensitive detection of ACE, with a detection limit of 16.38 fM (S/N = 3). Moreover, the aptasensor demonstrated excellent selectivity, stability, and reproducibility, and exhibited good reliability for detecting ACE in the vegetable soil and tomato samples, providing a new idea for the detection of pesticide residues in the fields of food and environment.

Assessment of spatial cyclone surge susceptibility through GIS-based AHP multi-criteria analysis and frequency ratio: a case study from the Bangladesh coast

Tropical cyclones, including surge inundation, are a common event in the coastal regions of Bangladesh. The surge washes out the area within a very short period and remains in flooded condition for several days. Spatial analysis to understand the surge susceptibility level can assist the cyclone management system. Surge susceptibility analysis could be one of the most essential parts of disaster risk reduction through which cyclone vulnerability can be minimized. A Geographic Information Systems-based analytical hierarchy process (AHP) multi-criteria analysis and bivariate frequency ratio (FR) techniques were conducted to understand the surge susceptibility level of a cyclone-prone area on the Bangladesh coast. A total of 10 criteria were considered influential to surge flooding, i.e. Topographic Wetness Index, elevation, wind velocity, slope, distance from sea and rivers, drainage density, Land Use and Land Cover, Normalized Difference Vegetation Index, precipitation, and soil types. The final surge susceptibility maps were categorized into five classes, i.e. very low, low, moderate, high, and very high. Conferring to these susceptibility classes, policymakers can make decisions for future land use management and disaster risk reduction activities. According to this research, AHP showed better precision (Receiver Operating Characteristic) than FR for surge susceptibility prediction on the Bangladesh coast.

Research on ecological restoration and green reclamation technology of goaf in phosphorus mines

The subsidiary of Yunnan Phosphate Group Co., Ltd., Kunyang Phosphate Mine’s mining area is the subject of study. The mining of open-pit phosphate mines has caused significant damage to the ecological environment. Therefore, carrying out ecological restoration and green reclamation of the ecosystem in the mining area has become the top priority for current development. This article establishes an evaluation system for ecological restoration indicators, selecting four indicators including vegetation coverage, soil and water conservation, restoration of native plants, and Plant Species Diversity Index to assess the effects of ecological restoration and green reclamation of phosphate mines. The techniques of reconstructing soil ecological structure using phosphate tailings substrate and improving acidic soil with soil conditioner and calcium-magnesium phosphate compound fertilizer were applied. A series of other measures were taken, including: drafting scientific ecological restoration plans; employing physical, chemical, and biological methods for ecological restoration and green reclamation; selecting suitable plant species for planting; enhancing planting techniques; and strengthening post-restoration ecological monitoring and regulation. After ecological restoration and green reclamation, the Ecological Remediation Effect Index (EREI) for the years 2020, 2021, and 2022 were 48.40, 87.38, and 93.23 respectively, indicating significant improvement in the ecological environment. Furthermore, the difficulties encountered during the ecological system restoration process of the mine and the future development directions were summarized, providing practical and guiding significance for ecological restoration and green reclamation of abandoned mining areas both domestically and internationally.

Soil fungal and bacterial communities reflect differently tundra vegetation state transitions and soil physico-chemical properties.

Strong disturbances may induce ecosystem transitions into new alternative states that sustain through plant-soil interactions, such as the transition of dwarf shrub-dominated into graminoid-dominated vegetation by herbivory in tundra. Little evidence exists on soil microbial communities in alternative states, and along the slow process of ecosystem return into the predisturbance state. We analysed vegetation, soil microbial communities and activities as well as soil physico-chemical properties in historical reindeer enclosures in northernmost Finland in the following plot types: control heaths in the surrounding tundra; graminoid-dominated; 'shifting'; and recovered dwarf shrub-dominated vegetation inside enclosures. Soil fungal communities followed changes in vegetation, whereas bacterial communities were more affected by soil physico-chemical properties. Graminoid plots were characterized by moulds, pathotrophs and dark septate endophytes. Ericoid mycorrhizal and saprotrophic fungi were typical for control and recovered plots. Soil microbial communities inside the enclosures showed historical contingency, as their spatial variation was high in recovered plots despite the vegetation being more homogeneous. Self-maintaining feedback loops between plant functional types, soil microbial communities, and carbon and nutrient mineralization act effectively to stabilize alternative vegetation states, but once predisturbance vegetation reestablishes itself, soil microbial communities and physico-chemical properties return back towards their predisturbance state.

RUSLE‐based scenarios for sustainable soil management: Case studies from Romanian Subcarpathians

AbstractSoil erosion is one of the major threats to soil sustainability and a global environmental issue causing serious losses of the fertile upper layer of soil, affecting land productivity. Among natural processes and human activity factors, the highest sensitivity of soil loss rate is related to climate changes, as well as land cover/land use transformations. The aim of our paper is to assess the efficacy of various land cover and land use management practices under current climate conditions, as a decision‐making indicator in searching for sustainable soil‐use solutions. The approach is focused on two complementary case studies from the non‐arable hilly area of Romanian Subcarpathians and it is based on aggregating and processing Earth Observation (EO) techniques together with the Revised Universal Soil Loss Equation (RUSLE) equation. The workflow follows three stages: (1) the assessment of the present‐day status of soil erosion, as baseline scenario; (2) the analysis of historical soil erosion dynamics within the last 35 years; and (3) the prediction of soil loss rates in different scenarios of changed conditions related to land cover management and support practices against erosion. The results demonstrate the effectiveness of human interventions in soil erosion prevention, mitigation, or conservation. Soil‐improving management through vegetative measures and soil practices, like grazing management and mulching/manure application, together with forest recovery on eroded slopes may reduce soil loss rates by 50%–70%. However, abandoning the land and allowing the environment to change uncontrollably is a regional‐specific strategy that could accelerate soil erosion on the slopes that are already affected, while decelerating on the others by forest and shrubs regrowth. The significance of our research is related to the identification of the optimal soil use strategies that balance the local communities' economic interests with the effectiveness of sustainable soil management practices, thereby assisting in the achievement of the UN Sustainable Development Goals (SDGs) as indicators for a sustainable future.

Tree species explain only half of explained spatial variability in plant water sensitivity.

Spatiotemporal patterns of plant water uptake, loss, and storage exert a first-order control on photosynthesis and evapotranspiration. Many studies of plant responses to water stress have focused on differences between species because of their different stomatal closure, xylem conductance, and root traits. However, several other ecohydrological factors are also relevant, including soil hydraulics, topographically driven redistribution of water, plant adaptation to local climatic variations, and changes in vegetation density. Here, we seek to understand the relative importance of the dominant species for regional-scale variations in woody plant responses to water stress. We map plant water sensitivity (PWS) based on the response of remotely sensed live fuel moisture content to variations in hydrometeorology using an auto-regressive model. Live fuel moisture content dynamics are informative of PWS because they directly reflect vegetation water content and therefore patterns of plant water uptake and evapotranspiration. The PWS is studied using 21,455 wooded locations containing U.S. Forest Service Forest Inventory and Analysis plots across the western United States, where species cover is known and where a single species is locally dominant. Using a species-specific mean PWS value explains 23% of observed PWS variability. By contrast, a random forest driven by mean vegetation density, mean climate, soil properties, and topographic descriptors explains 43% of observed PWS variability. Thus, the dominant species explains only 53% (23% compared to 43%) of explainable variations in PWS. Mean climate and mean NDVI also exert significant influence on PWS. Our results suggest that studies of differences between species should explicitly consider the environments (climate, soil, topography) in which observations for each species are made, and whether those environments are representative of the entire species range.

Barren to green in a single application: Revitalizing brownfield soil with simulated root exudates

Barren, metal-contaminated soils lack plants and root exudate inputs, exhibit low microbial abundance and functioning, and often require soil revitalization to revegetate. While the effects of simulated root exudates (SRE) have been investigated in uncontaminated, vegetated soils, their potential for remediating post-industrial barren, contaminated soils has not been examined or leveraged. We asked whether priming brownfield soils with a laboratory-prepared SRE solution stimulates native soil microbial metabolism and functioning and how long the effects last. Moreover, we compared a cost-effective single SRE addition to repeated SRE additions. We collected soils from a metal-contaminated, abandoned industrial rail yard (barren and vegetated sites) and a vegetated agricultural reference site, established microcosms, and treated the soils with either a single or repeated SRE addition/s. By day 30, SRE-enriched barren, brownfield soils showed significantly higher soil respiration rates than the untreated control soils. Phosphatase activities were significantly higher even 210 days after a single SRE addition. Plants were introduced 282 days after the single SRE addition. The average shoot height (16 ± 0.3 cm) and total plant biomass (0.5 ± 0.02 g) of plants grown in single addition SRE enriched barren soil were significantly higher than the controls (9 ± 0.9 cm and 0.3 ± 0.02 g, respectively). The increased soil microbial functioning and productivity indicate that a single SRE application holds promise as a field-ready technology to revitalize barren, poorly functioning brownfield soils. SRE application may also be a pragmatic and innovative approach to enable successful phytoremediation and re-greening of industrial barrens.

Effect of Gastrodia elata Bl Cultivation under Forest Stands on Runoff, Erosion, and Nutrient Loss

(1) Background: The understory planting of Chinese herbal medicine is a common soil and water conservation farming measure, and this approach makes full use of the natural conditions of the understory. However, a large number of studies on soil erosion have focused on the simulation of natural indoor conditions, and there are very few investigations on soil erosion caused by understory planting in the field. This study aims to investigate the effects of different slopes on soil and water and nitrogen–phosphorus nutrient loss from understory planting of Gastrodia elata Bl by changing the vegetation structure and soil structure of forest land. (2) Methods: To reveal the nitrogen and phosphorus loss and flow and sediment characteristics of the understory planting of Gastrodia elata Bl, runoff plots were set up in a field, and three surface slopes (5°, 15°, and 20°) were designed to collect runoff sediments and compare the soil and water loss between the natural slopes and those with Gastrodia elata Bl. This provides a basis for the restoration of vegetation cover and the enhancement of soil fertility. (3) Results: The total loss of soil, water, nitrogen, and phosphorus in the forested land with Gastrodia elata Bl increased significantly compared with that in the natural forested land, and the greater the slope was, the greater the loss was. (4) Conclusions: Planting Gastrodia elata Bl should be avoided in areas with steep slopes and serious soil erosion. However, some soil and water conservation engineering measures can be taken, such as the construction of retaining walls, drainage ditches, etc., to minimize the scouring and erosion of soil by rainwater.

Anthropogenic forest degradation and its impact on soil erosion in the mountainous region of northern China

Soil erosion has significant negative effects on the eco-environment, and it also threatens the sustainability of agricultural production. However, studies on soil erosion in northern China during the historical period, and its relationship with climate change and human activities, are rare. Here we present a 2900-year pollen and geochemical record from a sediment core from Lingganhai Lake, an alpine freshwater lake on the Chinese Loess Plateau in northern China. We observed an abrupt increase in the influx of detrital elements at ∼ 750 CE, accompanied by decreased tree pollen and increased cereal pollen percentages, which suggest severe soil erosion induced by intensified human activities. Coeval pollen and fungal spore records from other sites in the vicinity of Lingganhai Lake also indicate reduced forest cover, intensified agricultural and grazing activity, and increased dust storm frequency. During 1250–1380 CE, decreased soil erosion at Lingganhai Lake was accompanied by forest recovery and a reduction in the intensity of human activities. However, after ∼ 1380 CE, intensive soil erosion was renewed, likely caused by enhanced human activity, which partially overlapped with intensified dust activity in northern China during the Little Ice Age. Overall, our results demonstrate the close link between vegetation cover and soil erosion, and they highlight the importance of the maintenance of montane forest in northern China for soil preservation.

A geospatial analysis of local intermediate snail host distributions provides insight into schistosomiasis risk within under-sampled areas of southern Lake Malawi

BackgroundAlong the southern shoreline of Lake Malawi, the incidence of schistosomiasis is increasing with snails of the genera Bulinus and Biomphalaria transmitting urogenital and intestinal schistosomiasis, respectively. Since the underlying distribution of snails is partially known, often being focal, developing pragmatic spatial models that interpolate snail information across under-sampled regions is required to understand and assess current and future risk of schistosomiasis.MethodsA secondary geospatial analysis of recently collected malacological and environmental survey data was undertaken. Using a Bayesian Poisson latent Gaussian process model, abundance data were fitted for Bulinus and Biomphalaria. Interpolating the abundance of snails along the shoreline (given their relative distance along the shoreline) was achieved by smoothing, using extracted environmental rainfall, land surface temperature (LST), evapotranspiration, normalised difference vegetation index (NDVI) and soil type covariate data for all predicted locations. Our adopted model used a combination of two-dimensional (2D) and one dimensional (1D) mapping.ResultsA significant association between normalised difference vegetation index (NDVI) and abundance of Bulinus spp. was detected (log risk ratio − 0.83, 95% CrI − 1.57, − 0.09). A qualitatively similar association was found between NDVI and Biomphalaria sp. but was not statistically significant (log risk ratio − 1.42, 95% CrI − 3.09, 0.10). Analyses of all other environmental data were considered non-significant.ConclusionsThe spatial range in which interpolation of snail distributions is possible appears < 10km owing to fine-scale biotic and abiotic heterogeneities. The forthcoming challenge is to refine geospatial sampling frameworks with future opportunities to map schistosomiasis within actual or predicted snail distributions. In so doing, this would better reveal local environmental transmission possibilities.Graphical

Seasonal Maize yield forecasting in South and East African Countries using hybrid Earth observation models

Climate change still adversely affects agriculture in the sub-Saharan Africa. There is need to strengthen early action to bolster livelihoods and food security. Most governments use pre- and post-harvest field surveys to capture statistics for National Food Balance Sheets (NFBS) key in food policy and economic planning. These surveys, though accurate, are costly, time consuming, and may not offer rapid yield estimates to support governments, emergency organizations, and related stakeholders to take advanced strategic decisions in the face of climate change. To help governments in Kenya (KEN), Zambia (ZMB), and Malawi (MWI) adopt digitally advanced maize yield forecasts, we developed a hybrid model based on the Regional Hydrologic Extremes Assessment System (RHEAS) and machine learning. The framework is set-up to use weather data (precipitation, temperature, and wind), simulations from RHEAS model (soil total moisture, soil temperature, solar radiation, surface temperature, net transpiration from vegetation, net evapotranspiration, and root zone soil moisture), simulations from DSSAT (leaf area index and water stress), and MODIS vegetation indices. Random Forest (RF) machine learning model emerged as the best hybrid setup for unit maize yield forecasts per administrative boundary scoring the lowest unbiased Root Mean Square Error (RMSE) of 0.16 MT/ha, 0.18 MT/ha, and 0.20 MT/ha in Malawi's Karonga district, Kenya's Homa Bay county, and Zambia's Senanga district respectively. According to relative RMSE, RF outperformed other hybrid models attaining the lowest score in all countries (ZMB: 25.96%, MWI: 28.97%, and KEN: 27.54%) followed by support vector machines (ZMB: 26.92%, MWI: 31.14%, and KEN: 29.50%), and linear regression (ZMB: 29.44%, MWI: 31.76%, and KEN: 47.00%). Lastly, the integration of VI and RHEAS information using hybrid models improved yield prediction. This information is useful for NFBS bulletins forecasts, design and certification of maize insurance contracts, and estimation of loss and damage in the advent of climate justice.

Metabolomics reveals changes in soil metabolic profiles during vegetation succession in karst area.

Soil metabolites are critical in regulating the dynamics of ecosystem structure and function, particularly in fragile karst ecosystems. Clarification of response of soil metabolism to vegetation succession in karst areas will contribute to the overall understanding and management of karst soils. Here, we investigated the metabolite characteristics of karst soils with different vegetation stages (grassland, brushwood, secondary forest and primary forest) based on untargeted metabolomics. We confirmed that the abundance and composition of soil metabolites altered with vegetation succession. Of the 403 metabolites we found, 157 had significantly varied expression levels across vegetation soils, including mainly lipids and lipid-like molecules, phenylpropanoids and polyketides, organic acids and derivatives. Certain soil metabolites, such as maltotetraose and bifurcose, were sensitive to vegetation succession, increasing significantly from grassland to brushwood and then decreasing dramatically in secondary and primary forests, making them possible indicators of karst vegetation succession. In addition, soil metabolic pathways, such as galactose metabolism and biosynthesis of unsaturated fatty acids, also changed with vegetation succession. This study characterized the soil metabolic profile in different vegetation stages during karst secondary succession, which would provide new insights for the management of karst soils.

Effect of Nitrogen and Phosphorus on Soil Enzyme Activities and Organic Carbon Stability in Qinghai–Tibet Plateau

Alpine grassland ecosystems are the most important ecosystem type, exhibiting a high sensitivity to anthropogenic nitrogen (N) and phosphorus (P) inputs into terrestrial ecosystems and significantly affecting the carbon (C) cycle within these ecosystems. However, the effects of N and P additions on soil C stability and the processes of organic C transformation remain unclear. This study measured the soil enzyme activities and oxidizable C fractions over a 9-year period following N and P additions to an alpine meadow in the Qinghai–Tibet Plateau. It included four treatments: control (CK), N addition, P addition, and combined N and P addition (NP), utilizing statistical methods such as analysis of variance (ANOVA), correlation analysis, and redundancy analysis. The findings indicated that NP addition significantly increased the non-labile soil oxidizable C fraction in the topsoil layer. Redundancy and correlation analyses revealed strong associations between the vegetation characteristics, C-cycling enzyme activities, soil-oxidized C fractions, and SOC stability index. These results underscore the role of NP addition in enhancing SOC accumulation and stability in grassland ecosystems, with the soil vegetation properties and C source enzyme activities serving as key regulators of the SOC stability. This study offers valuable insights into predicting the SOC dynamics amid rising N and P availability, thereby elucidating the effect of nutrient addition on soil C-cycling mechanisms.

Tidal restriction does not always supplant local conditions shaping estuarine plant communities

Salt marshes are a common feature of temperate estuaries and represent the world's most abundant wetland type. They also harbour very productive plant communities and provide numerous ecosystem services. In an anthropized setting, we aimed at describing the effect of tidal restriction on the diversity patterns of vegetation communities, and to link them to the physical and chemical parameters of the estuary's oligohaline marshes.The tidal marshes of the Seine Estuary in northern France display long-lasting human activities that have shaped the ecological processes of the river Seine and its associated marshes. One of the estuary's distinctive features is a dyke on the north shore, isolating the wetlands behind it from direct tidal regimes. These areas are submitted to flooding through water gates, but tidal restriction is known to alter ecosystem processes and to possibly modify vegetation communities. Over the course of three years, we have surveyed the vegetation communities and soil parameters of three levels along the topographical gradients of two sites under tidal restriction and one site under tidal influence, with cases of mowing and grazing.Overall species richness increased along the elevation gradient, as abiotic constraints are lifted. Decomposition of species turnover (beta diversity) revealed different replacement dynamics, with tidal restriction being associated with species replacement rather than abundance difference. Tidal restriction was also associated with higher temporal stability. Canonical Correspondence Analysis between vegetation and soil surveys revealed that topography, soil pH, conductivity and soil organic carbon explained most of the differences between vegetation communities. Higher elevation vegetation plots converged, regardless of tidal restriction or land-use, while lower elevation plots diverged between study sites.We found that tidal restriction attenuated the abiotic constraints on one site, while creating new constraints linked to freshwater retention on the other site. In addition, tidal restriction was associated with longer elevational gradients compared to the connected site. Tidal restriction might therefore be a valuable management tool for estuarine marshes, as well for restoration purposes as for the mitigation of future effects related to climate change induced sea level rise.

Chemical Speciation, Contamination, Human Health Risk Assessment, and Source Identification of Heavy Metals in Soils Composted with Swine Manure

ABSTRACT Heavy metal accumulation in the swine manure composting vegetable soil continues to attract attention for the reason that heavy metals are often added to animal feeds as growth promoters. The seven heavy metals (Cr, Ni, Cu, Zn, As, Cd, and Pb) concentration and chemical speciation in soil samples from 15 swine manure composting vegetable fields of Jiangxi Province were analyzed. The results showed that the average concentration of Cr, Ni Cu, Zn, As, Cd, and Pb was 48.46 ± 5.58 mg/kg, 29.06 ± 1.80 mg/kg, 48.16 ± 9.69 mg/kg, 142.32 ± 38.71 mg/kg, 10.62 ± 2.76 mg/kg, 0.13 ± 0.04 mg/kg, and 15.75 ± 2.36 mg/kg, respectively. The average concentration of Ni, Cu, Zn, and Cd was 1.12, 1.89, 1.77, and 1.17 times greater than their background values, whereas the average concentration of Cr, As, and Pb was lower than their background values. The chemical speciation analysis indicated that these heavy metals were dominated by residual fractions and have low mobility and bioavailability. The Geo-accumulation index (I geo) indicated that the soils were unpolluted with Cr, Ni, As, Cd, and Pb, while unpolluted to moderately polluted with Cu and Zn. The Nemerow comprehensive pollution index suggested that the soils were lightly polluted with heavy metals. They posed a considerable potential ecological risk to the soils. Based on the risk assessment code (RAC), these heavy metals exhibited no to medium risks. There was little non-carcinogenic risk and acceptable carcinogenic risk to humans from the heavy metals. Principal component analysis (PCA) indicated that Ni, Cu, Zn, and Cd originated from the use of a large quantity of swine manure; Pb and As mainly derived from atmospheric deposition; Cr was mainly from natural sources.