Using soil bacterial communities to predict physico-chemical variables and soil quality

Transitioning from extensive pastures to silvopastoral systems improves multiple soil ecosystem services in Colombian Amazon

Silvopastoral systems are gaining attention as a sustainable approach to address the environmental challenges caused by traditional livestock farming in the Amazon. This study examines the effects of transitioning from extensive pastures to silvopastoral systems on soil ecosystem services (SES) in the Colombian Amazon. We identified sensitive soil indicators to monitor SES changes in response to livestock management, and synergies and tradeoffs between them. Soil samples were collected from three locations to a depth of 30 cm across three land uses: traditional pastures, 15-years-old silvopastoral systems implemented on former pastures, and secondary vegetation. We assessed five critical soil-related ecosystem services: support for plant growth, carbon storage, nutrient cycling, erosion control, and water regulation and aeration. Our findings reveal significant enhancements in multiple soil ecosystem services in silvopastoral systems, with increases of 32 % in plant growth support, 34 % in nutrient cycling, and 16 % in erosion control compared to traditional pastures. Although soil carbon (C) storage showed no significant differences between pastures and silvopastoral systems, both systems exhibited values 16 % higher than secondary vegetation. A positive interconnection among carbon storage, nutrient cycling, and erosion control services was revealed, highlighting them as major drivers of synergies. Seven soil properties were identified as sensitive indicators of changes in land management, which may serve as key proxy for monitoring soil ecosystem services: i) soil organic carbon, ii) pH, iii) soil base saturation, iv) copper, v) magnesium, vi) zinc, and vii) aluminum. These results highlight the potential of silvopastoral systems to improve soil health and resilience, offering a sustainable alternative to traditional livestock management. This study underscores the importance of integrating trees into pastures to restore degraded lands and promote sustainable agricultural practices in tropical regions, contributing to global environmental conservation efforts.

Temporal Beta Diversity of Bacteria in Streams: Network Position Matters But Differently for Bacterioplankton and Biofilm Communities

Concern about biodiversity loss has yielded a surge of studies on temporal change in α-diversity, whereas temporal β-diversity has gained less interest. We sampled bacterioplankton, biofilm, and riparian soil bacteria repeatedly across the open-water season in a pristine stream network to determine the level of temporal β-diversity in relation to stream network position and environmental variability. We tested the hypothesis that aquatic bacterial communities in isolated and environmentally heterogenous headwaters exhibit high temporal β-diversity while the better-connected and environmentally more stable mainstem sections support more stable communities, and soil communities bear no relationship to network position. As expected, temporal β-diversity decreased from headwaters toward mainstems for bacterioplankton. Against expectations, an opposite pattern was observed for biofilm. For bacterioplankton, temporal β-diversity was positively related to temporal variability in water chemistry. For biofilm bacteria, temporal variability was negatively related to variability in temperature. Temporal β-diversity of soil communities did not show any response to stream network position, but was strongly related to variability in the soil environment. The two aquatic habitats and riparian soils supported distinctly different bacterial communities. The number of ASVs shared between the soil and the aquatic communities decreased along the network, and more so for bacterioplankton. The higher temporal variability of bacterial communities in the headwaters likely results from temporally variable input of propagules from riparian soil, emphasizing the role of land-water connection and network position to bacterioplankton community composition. Overall, bacterial communities exhibited high temporal variability, highlighting the importance of temporal replication to fully capture their network-scale biodiversity.

Spartina alterniflora invasion significantly alters the assembly and structure of soil bacterial communities in the Yellow River Delta

Soil microbial communities are integral to almost all terrestrial biogeochemical cycles, which are essential to coastal wetland functioning. However, how soil bacterial community assembly, composition, and structure respond to native and non-native plant invasions in coastal wetlands remains unclear. In this study of the coastal wetlands of the Yellow River Delta in China, the assembly, community composition, and diversity of soil bacterial communities associated with four wetland plant species (Phragmites australis, Spartina alterniflora, Suaeda salsa, and Tamarix chinensis) and four soil depths (0-10 cm, 10-20 cm, 20-30 cm, and 30-40 cm) were characterized using high-throughput sequencing. Plant species identity, as well as environmental factors, rather than soil depth, was found to play predominant roles in shaping the diversity and structure of wetland soil bacterial communities. S. alterniflora invasion altered bacterial community structure and increased bacterial diversity. Phragmites australis-associated bacterial communities were enriched with sulfate-reducing bacteria such as Desulfurivibrio and Desulfuromonas. In comparison, S. alterniflora-associated bacterial communities were enriched with both sulfate-reducing bacteria (SEEP-SRB1) and sulfate-oxidizing bacteria (Sulfurimonas), which maintained a dynamic balance in the local sulfur-cycle, and thereby enhanced S. alterniflora growth. In addition, stochastic processes dominated the assembly of soil bacterial communities associated with all four plant species, but were most important for the S. alterniflora community. The S. alterniflora-associated bacterial community also showed stronger interactions and more extensive connections among bacterial taxa; a co-occurrence network for this community had the greatest average clustering coefficient, average degree, modularity, and number of links and nodes, but the lowest average path length. Altogether, individual plant species had distinct effects on soil bacterial community assembly and structure, with the invasive species having the strongest impact. These results provide insights into microbial ecology and inform management strategies for coastal wetland restoration.

Urbanized lands degrade surrounding grasslands by deteriorating the interactions between plants and soil microbiome

To mitigate overgrazing on grasslands, towns were constructed in some pastoral regions of China to relocate pastoralists. Nevertheless, whether and how the urbanized lands impact the surrounding grassland ecosystem remains unclear. We assessed the impacts of urbanized lands on the plant and soil interactions within the surrounding grasslands in order to ensure an eco-sustainable pastoralist relocation. The town with 1 km radius was selected as urbanization sample and a grassland with 1 km radius was selected as nature grassland sample. Plants and soil were investigated in nature grassland (NG), and areas 1 km (T-1 km), 2 km (T-2 km), and 3 km (T-3 km) from the center of the town. In T-1 km and T-2 km, compared to the NG, plant diversity, the abundance of dominant plant species, the abundance of soil wood saprotroph fungi, soil water content (SWC), and total organic carbon (TOC) decreased, while soil plant pathogen fungi, soil pH, and total phosphatase (TP) increased. Conversely, no such changes were observed in T-3 km. The results of Mantel test and Partial least squares path model suggest that the decrease in soil TOC and SWC, along with the increase in pH and TP in T-1 km and T-2 km, lead to a decline in wood saprotroph fungi and an increase in plant pathogen fungi, ultimately resulting in reductions in plant diversity and the abundance of dominant plant species. These results indicate that towns in pastoral areas can lead to surrounding grassland degradation by deteriorating the plant-soil interactions.

Delineating the soil physicochemical and microbiological factors conferring disease suppression in organic farms

Organic farming utilizes farmyard manure, compost, and organic wastes as sources of nutrients and organic matter. Soil under organic farming exhibits increased microbial diversity, and thus, becomes naturally suppressive to the development of soil-borne pathogens due to the latter's competition with resident microbial communities. Such soils that exhibit resistance to soil-borne phytopathogens are called disease-suppressive soils. Based on the phytopathogen suppression range, soil disease suppressiveness is categorised as specific- or general- disease suppression. Disease suppressiveness can either occur naturally or can be induced by manipulating soil properties, including the microbiome responsible for conferring protection against soil-borne pathogens. While the induction of general disease suppression in agricultural soils is important for limiting pathogenic attacks on crops, the factors responsible for the phenomenon are yet to be identified. Limited efforts have been made to understand the systemic mechanisms involved in developing disease suppression in organically farmed soils. Identifying the critical factors could be useful for inducing disease suppressiveness in conducive soils as a cost-effective alternative to the application of pesticides and fungicides. Therefore, this review examines the soil properties, including microbiota, and assesses indicators related to disease suppression, for the process to be employed as a tactical option to reduce pesticide use in agriculture.

Using Sediment Bacterial Communities to Predict Trace Metal Pollution Risk in Coastal Environment Management: Feasibility, Reliability, and Practicability

The distribution of trace metals (TMs) in a continuous water body often exhibits watershed attributes, but the tidal gates of the coastal rivers may alter their transformation and accumulation patterns. Therefore, a tidal gate-controlled coastal river was selected to test the distribution and accumulation risks of Al, As, Cr, Cu, Fe, Mn, Ni, Sr, and Zn in the catchment area (CA), estuarine area (EA), and offshore area (OA). Associations between TMs and bacterial communities were analyzed to assess the feasibility of using bacterial parameters as ecological indicators. The results showed that As and Cr were the key pollutants due to the higher enrichment factor and geoaccumulation index, reaching slight to moderate pollution levels. The Nemero index was highest in EAs (14.93), indicating a higher pollution risk in sediments near tide gates. Although the TM dynamics can be explained by the metal-indicating effects of Fe and Mn, they have no linear relationships with toxic metals. Interestingly, the metabolic abundance of bacterial communities showed good correlations with different TMs in the sediment. These results highlight bacterial community characteristics as effective biomarkers for assessing TM pollution and practical tools for managing pollution control in coastal environment.

Endophytic Bacterial Community, Core Taxa, and Functional Variations Within the Fruiting Bodies of Laccaria

Macrofungi do not exist in isolation but establish symbiotic relationships with microorganisms, particularly bacteria, within their fruiting bodies. Herein, we examined the fruiting bodies' bacteriome of seven species of the genus Laccaria collected from four locations in Yunnan, China. By analyzing bacterial diversity, community structure, and function through 16S rRNA sequencing, we observed the following: (1) In total, 4,840,291 high-quality bacterial sequences obtained from the fruiting bodies were grouped into 16,577 amplicon sequence variants (ASVs), and all samples comprised 23 shared bacterial ASVs. (2) The Allorhizobium-Neorhizobium-Pararhizobium-Rhizobium complex was found to be the most abundant and presumably coexisting bacterium. (3) A network analysis revealed that endophytic bacteria formed functional groups, which were dominated by the genera Allorhizobium-Neorhizobium-Pararhizobium-Rhizobium, Novosphingobium, and Variovorax. (4) The diversity, community structure, and dominance of ecological functions (chemoheterotrophy and nitrogen cycling) among endophytic bacteria were significantly shaped by geographic location, habitat, and fungal genotype, rather than fruiting body type. (5) A large number of the endophytic bacteria within Laccaria are bacteria that promote plant growth; however, some pathogenic bacteria that pose a threat to human health might also be present. This research advances our understanding of the microbial ecology of Laccaria and the factors shaping its endophytic bacterial communities.

Strengthened plant-microorganism interaction after topsoil removal cause more deterministic microbial assembly processes and increased soil nitrogen mineralization

Topsoil removal, among other restoration measures, has been recognized as one of the most successful methods to restore biodiversity and ecosystem functioning in European grasslands. However, knowledge about how removal as well as other restoration methods influence interactions between plant and microbial communities is very limited. The aims of the current study were to understand the impact of topsoil removal on plant-microorganism interactions and on soil nitrogen (N) mineralization, as one example of ecosystem functioning. We examined how three different grassland restoration methods, namely 'Harvest only', 'Topsoil removal' and 'Topsoil removal + Propagules (plant seed addition)', affected i) the interactions between plants and soil microorganisms, ii) soil microbial community assembly processes, and iii) soil N mineralization. We compared the outcome of these three restoration methods to initial degraded and target semi-natural grasslands in the Canton of Zurich, Switzerland. We were able to show that 'Topsoil removal' and 'Topsoil removal + Propagules', but not 'Harvest only', reduced the soil total N pool and available N concentration, but increased soil N mineralization and strengthened the plant-microorganism interactions. Microbial community assembly processes shifted towards more deterministic after both topsoil removal treatments. These shifts could be attributed to an increase in dispersal limitation and selection due to stronger interactions between plants and soil microorganisms. The negative relationship between soil N mineralization and microbial community stochasticity indicated that microbial assembly processes, to some extent, can be incorporated into model predictions of soil functions. Overall, the results suggest that topsoil removal may change the microbial assembly processes and thus the functioning of grassland ecosystems by enhancing the interaction between plants and soil microorganisms.

Insights into effects of conventional and biodegradable microplastics on organic carbon decomposition in different soil aggregates

The impacts of microplastics on soil ecological functions such as carbon recycling and soil structure maintenance have been extensively focused. However, the mechanisms underlying the impacts of microplastics on soil carbon transformation and soil microbial community at soil aggregate scale have not been clarified yet. In this work, the effects and action mechanisms of traditional microplastic polypropylene (PP) and degradable microplastic polylactic acid (PLA) on carbon transformation in three sizes of soil aggregates were investigated. The results showed that both PP and PLA promoted CO2 emission, and the effect depended on the type and content of microplastics, and the size of soil aggregates. Changes in soil carbon stocks were mainly driven by changes in organic carbon associated with macroaggregates. For macroaggregates, PP microplastics decreased soil organic carbon (SOC) as well as dissolved organic carbon (DOC). These changes were reversed in microaggregates and silt and clay. Interestingly, PLA increased the SOC, DOC and CO2 emissions in bulk soil and all three aggregates with a dose-effect response. These changes were associated with soil microbes, functional genes and enzymes associated with the degradation of labile and recalcitrant carbon fractions. Furthermore, PP and PLA reduced bacterial community diversities and shifted bacterial community structures in both the three aggregates and in bulk soil. Alterations of functional genes induced by microplastics were the key driving factors of their impacts on carbon transformation in soil aggregates. This research opened up a new insight into the mechanisms underlying the impacts of microplastics on soil carbon transformation, and helped us make rational assessments of the risks and the disturbances of microplastics on soil carbon cycling.

Unveiling the Hidden Responses: Metagenomic Insights into Dwarf Bamboo (Fargesia denudata) Rhizosphere under Drought and Nitrogen Challenges

Dwarf bamboo (Fargesia denudata) is a crucial food source for the giant pandas. With its shallow root system and rapid growth, dwarf bamboo is highly sensitive to drought stress and nitrogen deposition, both major concerns of global climate change affecting plant growth and rhizosphere environments. However, few reports address the response mechanisms of the dwarf bamboo rhizosphere environment to these two factors. Therefore, this study investigated the effects of drought stress and nitrogen deposition on the physicochemical properties and microbial community composition of the arrow bamboo rhizosphere soil, using metagenomic sequencing to analyze functional genes involved in carbon and nitrogen cycles. Both drought stress and nitrogen deposition significantly altered the soil nutrient content, but their combination had no significant impact on these indicators. Nitrogen deposition increased the relative abundance of the microbial functional gene nrfA, while decreasing the abundances of nirK, nosZ, norB, and nifH. Drought stress inhibited the functional genes of key microbial enzymes involved in starch and sucrose metabolism, but promoted those involved in galactose metabolism, inositol phosphate metabolism, and hemicellulose degradation. NO3--N showed the highest correlation with N-cycling functional genes (p < 0.01). Total C and total N had the greatest impact on the relative abundance of key enzyme functional genes involved in carbon degradation. This research provides theoretical and technical references for the sustainable management and conservation of dwarf bamboo forests in giant panda habitats under global climate change.

Diversity and community distribution of soil bacterial in the Yellow River irrigation area of Ningxia, China

The bacterial community performs an essential ecological role in maintaining agriculture systems. The roles of bacteria in the forest, marine, and agricultural systems have been studied extensively and intensively. However, similar studies in the areas irrigated by the Yellow River remain limited. In this study, we used Illumina sequencing analysis with the 16S rRNA method to analyze the bacterial diversity, community structure, and influencing factors in soil samples from eight regions of the Yellow River irrigation area in northwestern China. The bacterial community structure and diversity varied among samples from the eight regions. The samples differed significantly in terms of the bacterial community composition. Proteobacteria (approximately 12.4%-55.7%) accounted for the largest proportion and was the dominant bacteria, followed by Actinobacteria (approximately 9.2%-39.7%), Bacteroidetes (approximately 1.8%-21.5%), and Chloroflexi (approximately 2.7%-12.6%). Among the physicochemical variables, the soil pH in the eight regions was mildly alkaline, and the total nitrogen, total phosphorus, and total potassium contents in the soils differed significantly. However, the trend in the variations of the above variables was essentially similar. Soil bacteria in Yongning county had greater Chao1, Shannon-Wiener, and Simpson indices than those in the other regions. Notably, soil moisture, organic matter, and total nitrogen were recognized as the primary factors influencing the bacterial community in the Yellow River irrigation area. Our results revealed the laws of variation in soil bacterial diversity and community composition in the Yellow River irrigation area. Our findings could be beneficial for maintaining sustainable ecological practices in the Yellow River irrigation area.

Sandy loam soil maintains better physicochemical parameters and more abundant beneficial microbiomes than clay soil in Stevia rebaudiana cultivation

Depending on the texture of soil, different physicochemical and microbiological parameters are characterized, and these characteristics are influenced by crop cultivation. Stevia, a popular zero-calorie sweetener crop, is widely cultivated around the world on various soil textures. Sandy loam and clay soil show great differences in physicochemical and biological parameters and are often used for Stevia cultivation. To understand the effects of Stevia cultivation on soil physicochemical and biological features, we investigated the changes of physicochemical and microbiological parameters in sandy loam and clay soil following Stevia cultivation. This study was carried out through different physiological and biochemical assays and microbiomic analysis. The results indicated that the sandy loam soil had significantly lower pH and higher nutrient content in the rhizosphere and bulk soils after the Stevia cultivation. The sandy loam soil maintained higher bacterial diversity and richness than the clay soil after Stevia harvest. Beneficial bacteria such as Dongia, SWB02, Chryseolinea, Bryobacter and Devosia were enriched in the sandy loam soil; however, bacteria such as RB41, Haliangium and Ramlibacter, which are unfavorable for nutrient accumulation, predominated in clay soil. Redundancy analysis indicated that the variation in the composition of bacterial community was mainly driven by soil pH, organic matter, total nitrogen, available phosphorus, and microbial biomass phosphorus. This study provides a deeper understanding of physicochemical and microbiological changes in different soil textures after Stevia cultivation and guidance on fertilizer management for Stevia rotational cultivation.

Potential applications of microbial genomics in nuclear non-proliferation

As nuclear technology evolves in response to increased demand for diversification and decarbonization of the energy sector, new and innovative approaches are needed to effectively identify and deter the proliferation of nuclear arms, while ensuring safe development of global nuclear energy resources. Preventing the use of nuclear material and technology for unsanctioned development of nuclear weapons has been a long-standing challenge for the International Atomic Energy Agency and signatories of the Treaty on the Non-Proliferation of Nuclear Weapons. Environmental swipe sampling has proven to be an effective technique for characterizing clandestine proliferation activities within and around known locations of nuclear facilities and sites. However, limited tools and techniques exist for detecting nuclear proliferation in unknown locations beyond the boundaries of declared nuclear fuel cycle facilities, representing a critical gap in non-proliferation safeguards. Microbiomes, defined as "characteristic communities of microorganisms" found in specific habitats with distinct physical and chemical properties, can provide valuable information about the conditions and activities occurring in the surrounding environment. Microorganisms are known to inhabit radionuclide-contaminated sites, spent nuclear fuel storage pools, and cooling systems of water-cooled nuclear reactors, where they can cause radionuclide migration and corrosion of critical structures. Microbial transformation of radionuclides is a well-established process that has been documented in numerous field and laboratory studies. These studies helped to identify key bacterial taxa and microbially-mediated processes that directly and indirectly control the transformation, mobility, and fate of radionuclides in the environment. Expanding on this work, other studies have used microbial genomics integrated with machine learning models to successfully monitor and predict the occurrence of heavy metals, radionuclides, and other process wastes in the environment, indicating the potential role of nuclear activities in shaping microbial community structure and function. Results of this previous body of work suggest fundamental geochemical-microbial interactions occurring at nuclear fuel cycle facilities could give rise to microbiomes that are characteristic of nuclear activities. These microbiomes could provide valuable information for monitoring nuclear fuel cycle facilities, planning environmental sampling campaigns, and developing biosensor technology for the detection of undisclosed fuel cycle activities and proliferation concerns.

Characteristics of Bacterial Community Structure and Function in Artificial Soil Prepared Using Red Mud and Phosphogypsum

The preparation of artificial soil is a potential cooperative resource utilization scheme for red mud and phosphogypsum on a large scale, with a low cost and simple operation. The characteristics of the bacterial community structure and function in three artificial soils were systematically studied for the first time. Relatively rich bacterial communities were formed in the artificial soils, with relatively high abundances of bacterial phyla (e.g., Cyanobacteria, Proteobacteria, Actinobacteriota, and Chloroflexi) and bacterial genera (e.g., Microcoleus_PCC-7113, Rheinheimera, and Egicoccus), which can play key roles in various nutrient transformations, resistance to saline-alkali stress and pollutant toxicity, the enhancement of various soil enzyme activities, and the ecosystem construction of artificial soil. There were diverse bacterial functions (e.g., photoautotrophy, chemoheterotrophy, aromatic compound degradation, fermentation, nitrate reduction, cellulolysis, nitrogen fixation, etc.), indicating the possibility of various bacteria-dominated biochemical reactions in the artificial soil, which can significantly enrich the nutrient cycling and energy flow and enhance the fertility of the artificial soil and the activity of the soil life. The bacterial communities in the different artificial soils were generally correlated with major physicochemical factors (e.g., pH, OM, TN, AN, and AP), as well as enzyme activity factors (e.g., S-UE, S-SC, S-AKP, S-CAT, and S-AP), which comprehensively illustrates the complexity of the interaction between bacterial communities and environmental factors in artificial soils, and which may affect the succession direction of bacterial communities, the quality of the artificial soil environment, and the speed and direction of the development and maturity of the artificial soil. This study provides an important scientific basis for the synergistic soilization of two typical industrial solid wastes, red mud and phosphogypsum, specifically for the microbial mechanism, for the further evolution and development of artificial soil prepared using red mud and phosphogypsum.

Exogenous selenium mitigates cadmium uptake and accumulation in two rice (Oryza sativa L.) varieties in cadmium-contaminated soil

Rice grown in cadmium (Cd)-contaminated soil, is a potential threat to human health, but exogenous selenium (Se) application on rice can mitigate Cd toxicity. However, the mechanisms underlying Se mitigation of Cd stress in ratoon rice (RR) are still poorly understood. We conducted a pot experiment with moderate Cd-contaminated yellow-brown paddy soil on two rice varieties 'Taoyouxiangzhan' (TX) and 'Liangyou 6326'(LY). For all treatments, 1.0 mg kg-1 sodium selenite solution was added to soil. Treatment T1 was sodium selenite only, and in the other treatments 100 mg L-1 Se solution was sprayed on the leaves at seedling stage (T2), at tillering stage (T3), and in early anthesis stage (T4). Se treatments decreased Cd accumulation in rice grains and herbage. Under foliar spraying 100 mg L-1 Se at the seedling + 1.0 mg kg-1 Se in soil (T2), leaf Cd content decreased 16.95% in the current season and grains content decreased 46.67% in the subsequent season. Furthermore, grain Se content increased 0.94 mg kg-1 for the TX variety combined with the analysis of Cd bio-accumulation factor in grains, and Se treatments effectively decreased Cd grain concentrations due to reduced Cd translocation from roots to grains. TX variety rice showed a more pronounced response to Se treatments than LY.

A smartphone-based approach for comprehensive soil microbiome profiling

The soil microbiome is crucial for nutrient cycling, health, and plant growth. This study presents a smartphone-based approach as a low-cost and portable alternative to traditional methods for classifying bacterial species and characterizing microbial communities in soil samples. By harnessing bacterial autofluorescence detection and machine learning algorithms, the platform achieved an average accuracy of 88% in distinguishing common soil-related bacterial species despite the lack of biomarkers, nucleic acid amplification, or gene sequencing. Furthermore, it successfully identified dominant species within various bacterial mixtures with an accuracy of 76% and three-level soil health identification at an accuracy of 80%-82%, providing insights into microbial community dynamics. The influence of other soil conditions (pH and moisture) was relatively minor, showcasing the platform's robustness. Various field soil samples were also tested with this platform at 80% accuracy compared with the laboratory analyses, demonstrating the practicality and usability of this approach for on-site soil analysis. This study highlights the potential of the smartphone-based system as a valuable tool for soil assessment, microbial monitoring, and environmental management.

Engineering agricultural soil microbiomes and predicting plant phenotypes

Plant growth-promoting rhizobacteria (PGPR) can improve crop yields, nutrient use efficiency, plant tolerance to stressors, and confer benefits to future generations of crops grown in the same soil. Unlocking the potential of microbial communities in the rhizosphere and endosphere is therefore of great interest for sustainable agriculture advancements. Before plant microbiomes can be engineered to confer desirable phenotypic effects on their plant hosts, a deeper understanding of the interacting factors influencing rhizosphere community structure and function is needed. Dealing with this complexity is becoming more feasible using computational approaches. In this review, we discuss recent advances at the intersection of experimental and computational strategies for the investigation of plant-microbiome interactions and the engineering of desirable soil microbiomes.

Effects of forest age and season on soil microbial communities in Chinese fir plantations

Understanding changes in the distribution patterns and diversity of soil microbial communities from the perspectives of age-related changes, seasonal variations, and the interaction between the two factors can facilitate the management of plantations. In Chinese fir plantations, we collected soils from different depths in over-mature forests, mature forests, near-mature forests, middle-aged forests, and young forests in summer, autumn, and winter in China's subtropical regions. As the forests developed, bacterial and fungal communities' diversity changed, reached a minimum value at near-mature forests, and then increased in mature forests or over-mature forests. Near-mature forests had the lowest topological properties. The Shannon index of microbial communities varied with seasonal changes (P < 0.05). Bacterial and fungal community composition at genus level was more closely related to temperature indicators (including daily average temperature, daily maximum temperature, and daily minimum temperature) (P < 0.01, 0.5554 < R2 <0.8185) than daily average precipitation (P > 0.05, 0.0321 < R2 <0.6773). Bacteria were clustered by season and fungi were clustered by forest age. We suggested that extending the tree cultivation time of plantations could promote microbial community recovery. In addition, we found some species worthy of attention, including Bacteroidetes in autumn in over-mature forests, and Firmicutes in summer in young forests.IMPORTANCEChinese fir [Cunninghamia lanceolata (Lamb.) Hook] is an important fast-growing species with the largest artificial forest area in China, with the outstanding problems of low quality in soil. Soil microorganisms play a crucial role in soil fertility by decomposing organic matter, optimizing soil structure, and releasing essential nutrients for plant growth. In order to maintain healthy soil quality and prevent nutrient depletion and land degradation, it is crucial to understand the changes of soil microbial composition and diversity. Our study determined to reveal the change of soil microbial community from stand age, season, and the interaction between the two aspects, which is helpful to understand how interannual changes in different years and seasonal changes in one year affect soil fertility restoration and sustainable forest plantation management. It is a meaningful exploration of soil microbial communities and provides new information for further research.

Meta-analysis of wastewater microbiome for antibiotic resistance profiling

The microbial composition and stress molecules are main drivers influencing the development and spread of antibiotic resistance bacteria (ARBs) and genes (ARGs) in the environment. A reliable and rapid method for identifying associations between microbiome composition and resistome remains challenging. In the present study, secondary metagenome data of sewage and hospital wastewaters were assessed for differential taxonomic and ARG profiling. Subsequently, Random Forest (RF)-based ML models were used to predict ARG profiles based on taxonomic composition and model validation on hospital wastewaters. Total ARG abundance was significantly higher in hospital wastewaters (15 ppm) than sewage (5 ppm), while the resistance towards methicillin, carbapenem, and fluoroquinolone were predominant. Although, Pseudomonas constituted major fraction, Streptomyces, Enterobacter, and Klebsiella were characteristic of hospital wastewaters. Prediction modeling showed that the relative abundance of pathogenic genera Escherichia, Vibrio, and Pseudomonas contributed most towards variations in total ARG count. Moreover, the model was able to identify host-specific patterns for contributing taxa and related ARGs with >90% accuracy in predicting the ARG subtype abundance. More than >80% accuracy was obtained for hospital wastewaters, demonstrating that the model can be validly extrapolated to different types of wastewater systems. Findings from the study showed that the ML approach could identify ARG profile based on bacterial composition including 16S rDNA amplicon data, and can serve as a viable alternative to metagenomic binning for identification of potential hosts of ARGs. Overall, this study demonstrates the promising application of ML techniques for predicting the spread of ARGs and provides guidance for early warning of ARBs emergence.

Dazomet fumigation modification of the soil microorganism community and promotion of Panax notoginseng growth

Introduction

Panax notoginseng, a medicinal herb in China, is attacked by several pathogens during its cultivation. Dazomet (DZ) is a soil fumigant that is effective in controlling soil-borne pathogens, but its long-term effects on P. notoginseng growth and soil properties are unknown.

Methods

We conducted field experiments over two consecutive years to assess the impact of three concentrations of DZ fumigation (35 kg/666.7 m2, 40 kg/666.7 m2, and 45 kg/666.7 m2) on soil physicochemical properties, microbial diversity, and P. notoginseng growth. Correlation analyses were performed between microbial community changes and soil properties, and functional predictions for soil microorganisms were conducted.

Results

DZ fumigation increased total nitrogen, total phosphorus, total potassium, available phosphorus, available potassium, and ammonia nitrogen levels in the soil. DZ fumigation promoted the nutrient accumulation and improvement of agronomic traits of P. notoginseng, resulted in a 2.83-3.81X yield increase, with the highest total saponin content increasing by 24.06%. And the 40 kg/666.7 m2 treatment had the most favorable impact on P. notoginseng growth and saponin accumulation. After DZ fumigation, there was a decrease in the relative abundance of pathogenic fungi such as Fusarium, Plectosphaerella, and Ilyonectria, while beneficial bacteria such as Ramlibacter, Burkholderia, and Rhodanobacteria increased. The effects of fumigation on soil microorganisms and soil physicochemical properties persisted for 18 months post-fumigation. DZ fumigation enhanced the relative abundance of bacteria involved in the biosynthesis of secondary metabolites and arbuscular mycorrhizal fungi, reduced the relative abundance of plant-animal pathogenic fungi, reduced the occurrence of soil-borne diseases.

Conclusion

In conclusion, DZ fumigation enhanced soil physicochemical properties, increased the proportion of beneficial bacteria in the soil, and rebalanced soil microorganism populations, consequently improving the growth environment of P. notoginseng and enhancing its growth, yield, and quality. This study offers a theoretical foundation for DZ fumigation as a potential solution to the continuous cropping issue in perennial medicinal plants such as P. notoginseng.

Spatio-temporal changes of small protist and free-living bacterial communities in a temperate dimictic lake: insights from metabarcoding and machine learning

Microbial communities, which include prokaryotes and protists, play an important role in aquatic ecosystems and influence ecological processes. To understand these communities, metabarcoding provides a powerful tool to assess their taxonomic composition and track spatio-temporal dynamics in both marine and freshwater environments. While marine ecosystems have been extensively studied, there is a notable research gap in understanding eukaryotic microbial communities in temperate lakes. Our study addresses this gap by investigating the free-living bacteria and small protist communities in Lake Roś (Poland), a dimictic temperate lake. Metabarcoding analysis revealed that both the bacterial and protist communities exhibit distinct seasonal patterns that are not necessarily shaped by dominant taxa. Furthermore, machine learning and statistical methods identified crucial amplicon sequence variants (ASVs) specific to each season. In addition, we identified a distinct community in the anoxic hypolimnion. We have also shown that the key factors shaping the composition of analysed community are temperature, oxygen, and silicon concentration. Understanding these community structures and the underlying factors is important in the context of climate change potentially impacting mixing patterns and leading to prolonged stratification.

Exploring freshwater stream bacterial communities as indicators of land use intensity

BACKGROUND

Stream ecosystems comprise complex interactions among biological communities and their physicochemical surroundings, contributing to their overall ecological health. Despite this, many monitoring programs ignore changes in the bacterial communities that are the base of food webs in streams, often focusing on stream physicochemical assessments or macroinvertebrate community diversity instead. We used 16S rRNA gene sequencing to assess bacterial community compositions within 600 New Zealand stream biofilm samples from 204 sites within a 6-week period (February-March 2010). Sites were either dominated by indigenous forests, exotic plantation forests, horticulture, or pastoral grasslands in the upstream catchment. We sought to predict each site's catchment land use and environmental conditions based on the composition of the stream bacterial communities.

RESULTS

Random forest modelling allowed us to use bacterial community composition to predict upstream catchment land use with 65% accuracy; urban sites were correctly assigned 90% of the time. Despite the variation inherent when sampling across a ~ 1000-km distance, bacterial community data could correctly differentiate undisturbed sites, grouped by their dominant environmental properties, with 75% accuracy. The positive correlations between actual values and those predicted by the models built using the stream biofilm bacterial data ranged from weak (average log N concentration in the stream water, R2 = 0.02) to strong (annual mean air temperature, R2 = 0.69).

CONCLUSIONS

Freshwater bacterial community data provide useful insights into land use impacts on stream ecosystems; they may be used as an additional measure to screen stream catchment attributes.

Preparation of bacterial fertilizer from biogas residue after anaerobic co-digestion of kitchen waste and residual sludge

Azotobacter chroococcum and Bacillus subtilis were selected as fermentation strains, and biogas residue after anaerobic digestion of kitchen waste and residual sludge was used as fermentation substrate. A single factor optimization test was used to optimize the solid-state fermentation parameters of biogas residue with the number of viable bacteria and the number of spores as indexes. The results showed that the optimum inoculation conditions involved the following: 55% initial moisture content, 15% initial inoculation amount, 30 ℃, and 1:1 initial inoculation ratio for 13 days. Pot experiment showed that the prepared three kinds of bacterial fertilizers could not only effectively promote the growth of white clover, improve the composition of soil nutrients, but also change the structure of soil bacterial community, which is of great significance to the health of soil ecosystem in white clover.

Soil conditions are a more important determinant of microbial community composition and functional potential than neighboring plant diversity

Replanting is an important tool for ecological recovery. Management strategies, such as planting areas with monocultures or species mixtures, have implications for restoration success. We used 16S and ITS rRNA gene amplicon sequencing and shotgun metagenomics to assess how the diversity of neighboring tree species impacted soil bacterial and fungal communities, and their functional potential, within the root zone of mānuka (Leptospermum scoparium) trees. We compared data from monoculture and mixed tree species plots and confirmed that soil microbial taxonomic and functional community profiles significantly differed (p < 0.001). Compared to the diversity of neighboring tree species within the plot, soil environmental conditions and geographic distance was more important for structuring the microbial communities. The bacterial communities appeared more impacted by soil conditions, while the fungal communities displayed stronger spatial structuring, possibly due to wider bacterial dispersal. The different mechanisms structuring bacterial and fungal communities could have implications for ecological restoration outcomes.

Enterobacteriaceae as a Key Indicator of Huanglongbing Infection in Diaphorina citri

Extensive microbial interactions occur within insect hosts. However, the interactions between the Huanglongbing (HLB) pathogen and endosymbiotic bacteria within the Asian citrus psyllid (ACP, Diaphorina citri Kuwayama) in wild populations remain elusive. Thus, this study aimed to detect the infection rates of HLB in the ACP across five localities in China, with a widespread prevalence in Ruijin (RJ, 58%), Huidong (HD, 28%), and Lingui (LG, 15%) populations. Next, microbial communities of RJ and LG populations collected from citrus were analyzed via 16S rRNA amplicon sequencing. The results revealed a markedly higher microbial diversity in the RJ population compared to the LG population. Moreover, the PCoA analysis identified significant differences in microbial communities between the two populations. Considering that the inter-population differences of Bray-Curtis dissimilarity in the RJ population exceeded those between populations, separate analyses were performed. Our findings indicated an increased abundance of Enterobacteriaceae in individuals infected with HLB in both populations. Random forest analysis also identified Enterobacteriaceae as a crucial indicator of HLB infection. Furthermore, the phylogenetic analysis suggested a potential regulatory role of ASV4017 in Enterobacteriaceae for ACP, suggesting its possible attractant activity. This research contributes to expanding the understanding of microbial communities associated with HLB infection, holding significant implications for HLB prevention and treatment.

Deciphering and predicting changes in antibiotic resistance genes during pig manure aerobic composting via machine learning model

Livestock manure is one of the most important pools of antibiotic resistance genes (ARGs) in the environment. Aerobic composting can effectively reduce the spread of antibiotic resistance risk in livestock manure. Understanding the effect of aerobic composting process parameters on manure-sourced ARGs is important to control their spreading risk. In this study, the effects of process parameters on ARGs during aerobic composting of pig manure were explored through data mining based on 191 valid data collected from literature. Machine learning (ML) models (XGBoost and Random Forest) were utilized to predict the rate of ARGs changes during pig manure composting. The model evaluation index of the XGBoost model (R2 = 0.651) was higher than that of the Random Forest (R2 = 0.490), indicating that XGBoost had better prediction performance. Feature importance was further calculated for the XGBoost model, and the XGBoost black box model was interpreted by Shapley additive explanations analysis. Results indicated that the influencing factors on the ARGs variation in pig manure were sequentially divided into thermophilic period, total composting period, composting real time, and thermophilic stage average temperature. The findings gave an insight into the application of ML models to predict and decipher the ARG changes during manure composting and provided suggestions for better composting manipulation and optimization of process parameters.

Rhizosphere microbial markers (micro-markers): A new physical examination indicator for traditional Chinese medicines

Rhizosphere microorganisms, as one of the most important components of the soil microbiota and plant holobiont, play a key role in the medicinal plant-soil ecosystem, which are closely related to the growth, adaptability, nutrient absorption, stress tolerance and pathogen resistance of host plants. In recent years, with the wide application of molecular biology and omics technologies, the outcomes of rhizosphere microorganisms on the health, biomass production and secondary metabolite biosynthesis of medicinal plants have received extensive attention. However, whether or to what extent rhizosphere microorganisms can contribute to the construction of the quality evaluation system of Chinese medicinal materials is still elusive. Based on the significant role of rhizosphere microbes in the survival and quality formation of medicinal plants, this paper proposed a new concept of rhizosphere microbial markers (micro-markers), expounded the relevant research methods and ideas of applying the new concept, highlighted the importance of micro-markers in the quality evaluation and control system of traditional Chinese medicines (TCMs), and introduced the potential value in soil environmental assessment, plant pest control and quality assessment of TCMs. It provides reference for developing ecological planting of TCMs and ensuring the production of high quality TCMs by regulating rhizosphere microbial communities.

Machine learning-based soil quality assessment for enhancing environmental monitoring in iron ore mining-impacted ecosystems

In November 2015, a catastrophic rupture of the Fundão dam in Mariana (Brazil), resulted in extensive socio-economic and environmental repercussions that persist to this day. In response, several reforestation programs were initiated to remediate the impacted regions. However, accurately assessing soil health in these areas is a complex endeavor. This study employs machine learning techniques to predict soil quality indicators that effectively differentiate between the stages of recovery in these areas. For this, a comprehensive set of soil parameters, encompassing 3 biological, 16 chemical, and 3 physical parameters, were evaluated for samples exposed to mining tailings and those unaffected, totaling 81 and 6 samples, respectively, which were evaluated over 2 years. The most robust model was the decision tree with a restriction of fewer levels to simplify the tree structure. In this model, Cation Exchange Capacity (CEC), Microbial Biomass Carbon (MBC), Base Saturation (BS), and Effective Cation Exchange Capacity (eCEC) emerged as the most pivotal factors influencing model fitting. This model achieved an accuracy score of 92% during training and 93% during testing for determining stages of recovery. The model developed in this study has the potential to revolutionize the monitoring efforts conducted by regulatory agencies in these regions. By reducing the number of parameters that necessitate evaluation, this enhanced efficiency promises to expedite recovery monitoring, simultaneously enhancing cost-effectiveness while upholding the analytical rigor of assessments.

Keystone taxa enhance the stability of soil bacterial communities and multifunctionality under steelworks disturbance

Continuous discharge of wastewater, emissions, and solid wastes from steelworks poses environmental risks to ecosystems. However, the role of keystone taxa in maintaining multifunctional stability during environmental disturbances remains poorly understood. To address this, we investigated the community diversity, assembly mechanisms, and soil multifunctionality of soils collected from within the steelworks (I), within 2.5 km radius from the steelworks (E), and from an undisturbed area (CK) in Jiangsu Province, China, via 16 S rRNA sequencing. Significant differences were found in the Chao1 and the richness indexes of the total taxa (p < 0.05), while the diversity of keystone taxa was not significant at each site (p > 0.05). The deterministic processes for total taxa were 42.9%, 61.9% and 47.7% in CK, E, and I, respectively. Steelworks stress increased the deterministicity of keystone taxa from 52.3% in CK to 61.9% in E and I soils. The average multifunctionality indices were 0.518, 0.506 and 0.513 for CK, E and I, respectively. Although the soil multifunctionality was positive correlated with α diversity of both the total and keystone taxa, the average degree of keystone taxa in functional network increased significantly (79.96 and 65.58, respectively), while the average degree of total taxa decreased (44.59 and 51.25, respectively) in the E and I. This suggests keystone taxa contribute to promoting the stability of ecosystems. With increasing disturbance, keystone taxa shift their function from basic metabolism (ribosome biogenesis) to detoxification (xenobiotics biodegradation, metabolism, and benzoate degradation). Here we show that keystone taxa are the most important factor in maintaining stable microbial communities and functions, providing new insights for mitigating pollution stress and soil health protection.

Preliminary construction of a microecological evaluation model for uranium-contaminated soil

With the extensive development of nuclear energy, soil uranium contamination has become an increasingly prominent problem. The development of evaluation systems for various uranium contamination levels and soil microhabitats is critical. In this study, the effects of uranium contamination on the carbon source metabolic capacity and microbial community structure of soil microbial communities were investigated using Biolog microplate technology and high-throughput sequencing, and the responses of soil biochemical properties to uranium were also analyzed. Then, ten key biological indicators as reliable input variables, including arylsulfatase, biomass nitrogen, metabolic entropy, microbial entropy, Simpson, Shannon, McIntosh, Nocardioides, Lysobacter, and Mycoleptodisus, were screened by random forest (RF), Boruta, and grey relational analysis (GRA). The optimal uranium-contaminated soil microbiological evaluation model was obtained by comparing the performance of three evaluation methods: partial least squares regression (PLS), support vector regression (SVR), and improved particle algorithm (IPSO-SVR). Consequently, partial least squares regression (PLS) has a higher R2 (0.932) and a lower RMSE value (0.214) compared to the other. This research provides a new evaluation method to describe the relationship between soil ecological effects and biological indicators under nuclear contamination.

Bacterial community shifts occur primarily through rhizosphere expansion in response to subsoil amendments

To comprehensively evaluate the impact of agricultural management practices on soil productivity, it is imperative to conduct a thorough analysis of soil bacterial ecology. Deep-banding nutrient-rich amendments is a soil management practice that aims to improve plant growth and soil structure by addressing the plant-growth constraints posed by dense-clay subsoils. However, the response of bacterial communities to deep-banded amendments has not been thoroughly studied. To address this knowledge gap, we conducted a controlled-environment column experiment to examine the effects of different types of soil amendments (poultry litter, wheat straw + chemical fertiliser and chemical fertiliser alone) on bacterial taxonomic composition in simulated dense-clay subsoils. We evaluated the bacterial taxonomic and ecological group composition in soils beside and below the amendment using 16S rRNA amplicon sequencing and robust statistical methods. Our results indicate that deep-banded amendments alter bacterial communities through direct and indirect mechanisms. All amendments directly facilitated a shift in bacterial communities in the absence of growing wheat. However, a combination of amendments with growing wheat led to a more pronounced bacterial community shift which was distinct from and eclipsed the direct impact of the amendments and plants alone. This indirect mechanism was evidenced to be mediated primarily by plant growth and hypothesised to result from an enhancement in wheat root distribution, density and rhizodeposition changes. Therefore, we propose that subsoil amendments regardless of type facilitated an expansion in the rhizosphere which engineered a substantial plant-mediated bacterial community response within the simulated dense-clay subsoils. Overall, our findings highlight the importance of considering the complex and synergistic interactions between soil physicochemical properties, plant growth and bacterial communities when assessing agricultural management strategies for improving soil and plant productivity.

Microorganism community structure: A characterisation of agrosystems from Madeira Archipelago

Microbial diversity profoundly influences soil ecosystem functions, making it vital to monitor community dynamics to comprehend its structure. Our study focused on six agrosystems in Madeira Archipelago, analysing bacteria, archaea, fungi and AMF through classical microbiology and molecular techniques. Despite distinct edaphoclimatic conditions and management practices, bacterial structures exhibited similarities, with Alphaproteobacteria at 18%-20%, Bacilli at 11%-18% and Clostridia at 9%-14%. The predominance of copiothrophic groups suggested that soil nutrient content was the driver of these communities. Regarding archaea, the communities changed among sites, and it was evident that agrosystems provided niches for methanogens. The Crenarchaeota varied between 15% and 29%, followed by two classes of Euryarchaeota, Methanomicrobia (17%-25%) and Methanococci (4%-32%). Fungal communities showed consistent composition at the class level but had differing diversity indices due to management practices and soil texture. Sordaryomycetes (21%-28%) and Agaricomycetes (15%-23%) were predominant. Conversely, AMF communities appeared to be also influenced by the agrosystem, with Glomus representing over 50% of the community in all agrosystems. These insights into microbial groups' susceptibilities to environmental conditions are crucial for maintaining healthy soil and predicting climate change effects on agrosystems' productivity, resilience and sustainability. Additionally, our findings enable the development of more robust prediction models for agricultural practices.

Continuous cropping disorders of eggplants (Solanum melongena L.) and tomatoes (Solanum lycopersicum L.) in suburban agriculture: Microbial structure and assembly processes

Deciphering the intricate relationships between microorganisms and plants remains a formidable challenge in plant microbial ecology, an area that holds promise for optimizing microbial interventions to enhance stress resilience and agricultural yields. In our investigation, we procured samples during 2019 and 2022 from a suburban agricultural greenhouse. Our study delineated the composition of bacterial and fungal communities across various ecological niches-namely, the rhizosphere soil, bulk soil, and phyllosphere of healthy, Ralstonia solanacearum-infected, and dead eggplants and tomatoes. The structure and composition of both fungal and bacterial communities change significantly under the influence of the host genotype across all samples. In the tomato or eggplant groups, bacterial wilt exerts a more pronounced impact on the bacterial community than on the fungal community. We speculate that the rhizosphere of healthy eggplants and tomatoes harbored more antibiotic-producing (e.g., Amycolatopsis and Penicillium) and biocontrol (e.g., Bacillus) strains, which can lead to have lower absolute abundance of R. solanacearum. In the context of R. solanacearum invasion, deterministic processes were responsible for shaping 70.67 % and 80.63 % of the bacterial community assembly in the rhizosphere of eggplants and tomatoes, respectively. Deterministic processes dominated the assembly of fungal communities in the rhizosphere of R. solanacearum-infected eggplants, whereas the opposite was true in the tomatoes. Homogeneous selection emerged as the predominant force governing the bacterial community assembly in the rhizospheres of R. solanacearum-infected eggplants and tomatoes. The bacterial co-occurrence networks in healthy rhizosphere soil were characterized by reduced vulnerability and enhanced stability (i.e., robustness index) and complexity (i.e., cohesion index), compared to their infected counterparts. In summary, complex microbial networks in rhizosphere soils are more resistant to invasion by soil-borne pathogens. The dynamics of bacterial interactions and community assembly processes are pivotal for effective microbiome management and offer predictive insights into the ecological ramifications of R. solanacearum invasions.

Differences in background environment and fertilization method mediate plant response to nitrogen fertilization in alpine grasslands on the Qinghai-Tibetan Plateau

Grassland degradation threatens ecosystem function and livestock production, partly induced by soil nutrient deficiency due to the lack of nutrient return to soils, which is largely ascribed to the intense grazing activities. Therefore, nitrogen (N) fertilization has been widely adopted to restore degraded Qinghai-Tibetan Plateau (QTP) grasslands. Despite numerous field manipulation studies investigating its effects on alpine grasslands, the patterns and thresholds of plant response to N fertilization remain unclear, thus hindering the prediction of its influences on the regional scale. Here, we established a random forest model to predict N fertilization effects on plant productivity based on a meta-analysis synthesizing 88 publications in QTP grasslands. Our results showed that N fertilization increased the aboveground biomass (AGB) by 46.51 %, varying wildly among plant functional groups. The positive fertilization effects intensified when the N fertilization rate increased to 272 kg ha-1 yr-1, and decreased after three years of continuous fertilization. These effects were more substantial when applying ammonium nitrate compared to urea. Further, a machine learning model was used to predict plant productivity response to N fertilization. The total explained variance and mean squared residuals ranged from 49.41 to 75.13 % and 0.011-0.058, respectively, both being the highest for grasses. The crucial predictors were identified as climatic and geographic factors, background AGB without N fertilization, and fertilization methods (i.e., rate, form, and duration). These predictors with easy access contributed 62.47 % of the prediction power of grasses' response, thus enhancing the generalizability and replicability of our model. Notably, if 30 % of yak dung is returned to soils on the QTP, the grassland productivity and plant carbon pool are predicted to increase by 5.90-6.51 % and 9.35-10.31 g C m-2 yr -1, respectively. Overall, the predictions of this study based on literature synthesis enhance our understanding of plant responses to N fertilization in QTP grasslands, thereby providing helpful information for grassland management policies. Conflict of interest: The authors declare no conflict of interest.

The mechanism of microbial community succession and microbial co-occurrence network in soil with compost application

The application of organic and chemical fertilizer into soil can regulate microbial communities. However, the response mechanism of microbial communities in soil to compost and chemical fertilizer application remain unclear. In this study, compost made of tobacco leaves individually and combined with chemical fertilizer was applied, respectively, to investigate their effect on soil microorganisms during the pot-culture process. High-throughput sequence, neutral community model and null model were employed to clarify how soil microbial community respond to the application of compost and chemical fertilizer. Furthermore, random forest model was applied to predict the relationships between the plant agronomical traits and the soil microorganism during the pot-culture process. The results demonstrated that the simultaneous application of compost and chemical fertilizer increased significantly the richness and diversity of the microorganisms in soil (p < 0.05), groups C and D led to a significant reduction in the number of nodes and edges in the microbial network (77.78 %-96.57 %). The dominant bacteria in the application of 50 g fertilizer accounted for the highest proportion (40 %) and organic matter was the main factors driving the change in bacterial communities. Compared to the tilled soil, the microbial communities of the soil with the simultaneous application of compost and chemical fertilizer were more susceptible to stochastic processes, and soil microorganisms had less influence on the growth of crops during pot-culture. In conclusion, the simultaneous application of compost and fertilizer altered the ecological functions of soil microbial communities, leading to an enhanced stochastic process of community formation.

Earthworms mediate the influence of polyethylene (PE) and polylactic acid (PLA) microplastics on soil bacterial communities

There is a growing body of evidence that suggests that both biodegradable and conventional (non-degradable) microplastics (MP) are hazardous to soil health by affecting the delivery of key ecological functions such as litter decomposition, nutrient cycling and water retention. Specifically, soil fauna may be harmed by the presence of MPs while also being involved in their disintegration, degradation, migration and transfer in soil. Therefore, a comprehensive understanding of the interactions between MPs and soil fauna is essential. Here, we conducted a 120-day soil microcosm experiment applying polyethylene (PE) and polylactic acid (PLA), in the absence/presence of the earthworm Eisenia nordenskioldi to estimate the relative singular and combined impact of MPs and earthworms on the soil bacterial community. Our findings revealed contrasting effects of PE and PLA on the composition and diversity of soil bacteria. All treatments affected the community and network structure of the soil bacterial community. Compared to the control (no MPs or earthworms), PE decreased bacterial alpha diversity, while PLA increased it. Patescibacteria were found to be significantly abundant in the PE group whereas Actinobacteria and Gemmatimonadetes were more abundant in PE, and PLA and earthworms groups. The presence of earthworms appeared to mediate the impact of PE/PLA on soil bacteria, potentially through bacterial consumption or by altering soil properties (e.g., pH, aeration, C availability). Earthworm presence also appeared to promote the chemical aging of PLA. Collectively, our results provide novel insights into the soil-fauna-driven impact of degradable/nondegradable MPs exposure on the long-term environmental risks associated with soil microorganisms.

pH-Related Changes in Soil Bacterial Communities in the Sanjiang Plain, Northeast China

Soil bacteria are crucial components of terrestrial ecosystems, playing an important role in soil biogeochemical cycles. Although bacterial community diversity and composition are regulated by many abiotic and biotic factors, how soil physiochemical properties impact the soil bacteria community diversity and composition in wetland ecosystems remains largely unknown. In this study, we used high-throughput sequencing technology to investigate the diversity and composition of a soil bacterial community, as well as used the structural equation modeling (SEM) method to investigate the relationships of the soil's physicochemical properties (i.e., soil pH, soil organic carbon (SOC), total nitrogen (TN), ammonium nitrogen (NH4+N), electrical conductivity (EC) and nitrate nitrogen (NO3-N)), and soil bacterial community structures in three typical wetland sites in the Sanjiang Plain wetland. Our results showed that the soil physicochemical properties significantly changed the α and β-diversity of the soil bacteria communities, e.g., soil TN, NH4+N, NO3-N, and SOC were the main soil factors affecting the soil bacterial α-diversity. The soil TN and pH were the key soil factors affecting the soil bacterial community. Our results suggest that changes in soil pH indirectly affect soil bacterial communities by altering the soil nitrogenous nutrient content.

Responses of soil nutrients and rhizosphere microbial communities of a medicinal plant Pinelliaternata to vermicompost

Vermicomposting is an important strategy for restoring soil function and fertility. However, information on the effects of vermicompost application in intensive Pinellia ternata planting systems has rarely been reported. Here, we focus on the effects of different vermicompost levels and chemical fertilizer (CF) strategies on soil chemical properties, soil enzymes, and soil rhizosphere microbial communities (bacteria and fungi) in a field experiment. Compared to no added fertilizers (CK), vermicompost was more effective than the CF treatment in increasing P. ternata yield. We found that the 5 t ha-1 vermicompost treatment (VC2) significantly increased the tuber yield by 44.43% and 6.55% compared to the CK and CF treatment, respectively, and water-soluble exudates by 6.56% and 9.63% (P < 0.05). The vermicompost and CF treatments significantly increased the total phosphorus (TP), urease (Ure), and soil catalase (Cat) contents (P < 0.05). Compared to the vermicompost and CK treatments, the CF treatment significantly decreased soil organic carbon (SOC), C/N ratio, and soil acid phosphatase (Pac) (P < 0.05). Redundancy analysis (RDA) showed that Ure and total potassium (TK) were the major drivers in the bacterial community, whereas TP, total nitrogen (TN), Pac, and TK were the major drivers in the fungal community. We also found a positive correlation between soil enzyme activities, including between Ure and bacterial genera (Clostridium, Pseudoclavibacter, Stella, Hyphomicrobium, Mesorhizobium, and Adlercreutzia). In summary, vermicompost application promotes P. ternata soil microecosystems and improves soil fertility, soil enzyme activities, and rhizosphere microbial structure and function. Vermicomposting is a novel and promising approach to sustainable ecological cultivation of Chinese herbs via the promotion of soil properties and beneficial organisms.

Fine root decomposition in forest ecosystems: an ecological perspective

Fine root decomposition is a physio-biochemical activity that is critical to the global carbon cycle (C) in forest ecosystems. It is crucial to investigate the mechanisms and factors that control fine root decomposition in forest ecosystems to understand their system-level carbon balance. This process can be influenced by several abiotic (e.g., mean annual temperature, mean annual precipitation, site elevation, stand age, salinity, soil pH) and biotic (e.g., microorganism, substrate quality) variables. Comparing decomposition rates within sites reveals positive impacts of nitrogen and phosphorus concentrations and negative effects of lignin concentration. Nevertheless, estimating the actual fine root breakdown is difficult due to inadequate methods, anthropogenic activities, and the impact of climate change. Herein, we propose that how fine root substrate and soil physiochemical characteristics interact with soil microorganisms to influence fine root decomposition. This review summarized the elements that influence this process, as well as the research methods used to investigate it. There is also need to study the influence of annual and seasonal changes affecting fine root decomposition. This cumulative evidence will provide information on temporal and spatial dynamics of forest ecosystems, and will determine how logging and reforestation affect fine root decomposition.

Predicting soil fungal communities from chemical and physical properties

Introduction

Biogeography describes spatial patterns of diversity and explains why organisms occur in given conditions. While it is well established that the diversity of soil microbes is largely controlled by edaphic environmental variables, microbiome community prediction from soil properties has received less attention. In this study, we specifically investigated whether it is possible to predict the composition of soil fungal communities based on physicochemical soil data using multivariate ordination.

Materials and Methods

We sampled soil from 59 arable fields in Switzerland and assembled paired data of physicochemical soil properties as well as profiles of soil fungal communities. Fungal communities were characterized using long‐read sequencing of the entire ribosomal internal transcribed spacer. We used redundancy analysis to combine the physical and chemical soil measurements with the fungal community data.

Results

We identified a reduced set of 10 soil properties that explained fungal community composition. Soil properties with the strongest impact on the fungal community included pH, potassium and sand content. Finally, we evaluated the model for its suitability for prediction using leave‐one‐out validation. The prediction of community composition was successful for most soils, and only 3/59 soils could not be well predicted (Pearson correlation coefficients between observed and predicted communities of <0.5). Further, we successfully validated our prediction approach with a publicly available data set. With both data sets, prediction was less successful for soils characterized by very unique properties or diverging fungal communities, while it was successful for soils with similar characteristics and microbiome.

Conclusions

Reliable prediction of microbial communities from chemical soil properties could bypass the complex and laborious sequencing‐based generation of microbiota data, thereby making soil microbiome information available for agricultural purposes such as pathogen monitoring, field inoculation or yield projections.

Effects of different grassland use patterns on soil bacterial communities in the karst desertification areas

Soil bacteria are closely related to soil environmental factors, and their community structure is an important indicator of ecosystem health and sustainability. A large number of artificial grasslands have been established to control rocky desertification in the karst areas of southern China, but the influence of different use patterns on the soil bacterial community in artificial grasslands is not clear. In this study, three grassland use patterns [i.e., grazing (GG), mowing (MG), and enclosure (EG)] were used to investigate the effects of different use patterns on the soil bacterial community in artificial grassland by using 16S rDNA Illumina sequencing and 12 soil environmental indicators. It was found that, compared with EG, GG significantly changed soil pH, increased alkaline hydrolyzable nitrogen (AN) content (P < 0.05), and decreased soil total phosphorus (TP) content (P < 0.05). However, MG significantly decreased the contents of soil organic carbon (SOC), total phosphorus (TP), available nitrogen (AN), ammonium nitrogen (NH4+-N), β-1,4-glucosidase (BG), and N-acetyl-β-D-glucamosonidase (NAG) (P < 0.05). The relative abundance of chemoheterotrophy was significantly decreased by GG and MG (P < 0.05). GG significantly increased the relative abundance of Acidobacteria and Gemmatimonadota (P < 0.05) and significantly decreased the relative abundance of Proteobacteria (P < 0.05), but the richness index (Chao 1) and diversity index (Shannon) of the bacterial community in GG, MG, and EG were not significantly different (P > 0.05). The pH (R2 = 0.79, P = 0.029) was the main factor affecting the bacterial community structure. This finding can provide a scientific reference for ecological restoration and sustainable utilization of grasslands in the karst desertification areas.

Soil bacterial community composition is more stable in kiwifruit orchards relative to phyllosphere communities over time

BACKGROUND

Soil and phyllosphere (leaves and fruit) microbes play critical roles in the productivity and health of crops. However, microbial community dynamics are currently understudied in orchards, with a limited number incorporating temporal monitoring. We used 16S rRNA gene amplicon sequencing to investigate bacterial community temporal dynamics and community assembly processes on the leaves and fruit, and in the soil of 12 kiwifruit orchards across a cropping season in New Zealand.

RESULTS

Community composition significantly differed (P < 0.001) among the three sample types. However, the communities in the phyllosphere substrates more closely resembled each other, relative to the communities in the soil. There was more temporal stability in the soil bacterial community composition, relative to the communities residing on the leaves and fruit, and low similarity between the belowground and aboveground communities. Bacteria in the soil were more influenced by deterministic processes, while stochastic processes were more important for community assembly in the phyllosphere.

CONCLUSIONS

The higher temporal variability and the stochastic nature of the community assembly processes observed in the phyllosphere communities highlights why predicting the responsiveness of phyllosphere communities to environmental change, or the likelihood of pathogen invasion, can be challenging. The relative temporal stability and the influence of deterministic selection on soil microbial communities suggests a greater potential for their prediction and reliable manipulation.

Integrating microbial community properties, biomass and necromass to predict cropland soil organic carbon

Manipulating microorganisms to increase soil organic carbon (SOC) in croplands remains a challenge. Soil microbes are important drivers of SOC sequestration, especially via their necromass accumulation. However, microbial parameters are rarely used to predict cropland SOC stocks, possibly due to uncertainties regarding the relationships between microbial carbon pools, community properties and SOC. Herein we evaluated the microbial community properties (diversity and network complexity), microbial carbon pools (biomass and necromass carbon) and SOC in 468 cropland soils across northeast China. We found that not only microbial necromass carbon but also microbial community properties (diversity and network complexity) and biomass carbon were correlated with SOC. Microbial biomass carbon and diversity played more important role in predicting SOC for maize, while microbial network complexity was more important for rice. Models to predict SOC performed better when the microbial community and microbial carbon pools were included simultaneously. Taken together our results suggest that microbial carbon pools and community properties influence SOC accumulation in croplands, and management practices that improve these microbial parameters may increase cropland SOC levels.

Assessing the Effectiveness of Eco-Friendly Management Approaches for Controlling Wheat Yellow Rust and Their Impact on Antioxidant Enzymes

Wheat stripe rust, caused by Puccinia striiformis f. sp. tritici (Pst), is a destructive disease that causes significant yield losses in wheat production worldwide, including in Egypt. The use of biocontrol agents is among the best eco-friendly management strategies to control this disease, as they are more sustainable and environmentally friendly than traditional chemical control methods. In a comparative analysis, antioxidant enzyme activity and various management approaches were compared with two bacterial biocontrol agents, Bacillus subtilis and Pseudomonas putida. This study showed the remarkable efficacy of endophytic bacteria, B. subtilis and P. putida, in mitigating wheat stripe rust infection across three wheat varieties, namely Misr1, Gimmeiza11, and Sids12. B. subtilis exhibited superior performance compared to P. putida, resulting in infection types of 1 and 2.66, respectively, following inoculation. The highest reduction rate was observed with Tilit fungicide (500 ppm), followed by B. subtilis and Salicylic acid (1000 ppm), respectively. Variations in wheat varieties' response to Pst infection were observed, with Misr1 exhibiting the lowest infection and Sids12 showing high susceptibility. Among the tested inducers, Salicylic acid demonstrated the greatest reduction in disease infection, followed by Indole acetic acid, while Oxalic acid exhibited the lowest decrease. Additionally, the study evaluated the activities of five antioxidant enzymes, including Catalase, Ascorbate peroxidase (APX), glutathione reductase (GR), Superoxide dismutase (SOD), and peroxidase (POX), in the wheat-stripe rust interaction under different integrated management approaches. The wheat variety Misr1 treated with Tilit (500 ppm), B. subtilis, Salicylic acid, Montoro (500 ppm), and P. putida exhibited the highest increase in all enzymatic activities. These findings provide valuable insights into the effectiveness of B. subtilis and P. putida as biocontrol agents for wheat stripe rust control in Egypt, emphasizing their potential role in sustainable, integrated, and environmentally friendly management practices.

Exploring the Frequency and Distribution of Ecological Non-monotonicity in Associations among Ecosystem Constituents

Complex links between biotic and abiotic constituents are fundamental for the functioning of ecosystems. Although non-monotonic interactions and associations are known to increase the stability, diversity, and productivity of ecosystems, they are frequently ignored by community-level standard statistical approaches. Using the copula-based dependence measure qad, capable of quantifying the directed and asymmetric dependence between variables for all forms of (functional) relationships, we determined the proportion of non-monotonic associations between different constituents of an ecosystem (plants, bacteria, fungi, and environmental parameters). Here, we show that up to 59% of all statistically significant associations are non-monotonic. Further, we show that pairwise associations between plants, bacteria, fungi, and environmental parameters are specifically characterized by their strength and degree of monotonicity, for example, microbe-microbe associations are on average stronger than and differ in degree of non-monotonicity from plant-microbe associations. Considering directed and non-monotonic associations, we extended the concept of ecosystem coupling providing more complete insights into the internal order of ecosystems. Our results emphasize the importance of ecological non-monotonicity in characterizing and understanding ecosystem patterns and processes.

Supplementary Information

The online version contains supplementary material available at 10.1007/s10021-023-00867-9.

Microbial source tracking to elucidate the impact of land-use and physiochemical water quality on fecal contamination in a mixed land-use watershed

Escherichia coli has been widely used as a fecal indicator bacterium (FIB) for monitoring water quality in drinking water sources and recreational water. However, fecal contamination sources remain difficult to identify and mitigate, as millions of cases of infectious diseases are reported yearly due to swimming and bathing in recreational water. The objective of this study was to apply molecular techniques for microbial source tracking (MST) to identify sources of fecal contamination in a representative mixed land-use watershed located in the Appalachian Mountains of the United States of America (USA). Monthly samples were collected over one year at 11 sites, including the confluence of key first-order streams in the study watershed representing distinct land-use types and anticipated fecal sources. Results indicated that coupled monitoring of host-specific MST markers with the FIB E. coli effectively identified sources and quantified fecal contamination in the study watershed. Human-associated MST markers were abundant primarily at developed sites, suggesting septic or sewer failure is a key source of fecal input to the watershed. Across the dataset, samples positive for E. coli and human MST markers were associated with a higher pH than those samples from which each target was not detected, thereby suggesting that acid mine drainage in the watershed likely contributed to inactivation or loss of culturability in E. coli. In addition, this research provides the first evidence that the BacCan-UCD marker is present in fox feces and can influence MST results in areas where substantial wildlife activity is present. Identifying the sources of fecal contamination and better understanding the impact of in-stream physiochemistry throughout this study will help to develop sustainable and effective watershed management plans to control fecal contamination to protect drinking water sources and recreational water.

Artificial Intelligence: A Promising Tool in Exploring the Phytomicrobiome in Managing Disease and Promoting Plant Health

There is increasing interest in harnessing the microbiome to improve cropping systems. With the availability of high-throughput and low-cost sequencing technologies, gathering microbiome data is becoming more routine. However, the analysis of microbiome data is challenged by the size and complexity of the data, and the incomplete nature of many microbiome databases. Further, to bring microbiome data value, it often needs to be analyzed in conjunction with other complex data that impact on crop health and disease management, such as plant genotype and environmental factors. Artificial intelligence (AI), boosted through deep learning (DL), has achieved significant breakthroughs and is a powerful tool for managing large complex datasets such as the interplay between the microbiome, crop plants, and their environment. In this review, we aim to provide readers with a brief introduction to AI techniques, and we introduce how AI has been applied to areas of microbiome sequencing taxonomy, the functional annotation for microbiome sequences, associating the microbiome community with host traits, designing synthetic communities, genomic selection, field phenotyping, and disease forecasting. At the end of this review, we proposed further efforts that are required to fully exploit the power of AI in studying phytomicrobiomes.

Dynamics of microbial community composition during degradation of silks in burial environment

The silk residues in the soil formed the unique niche, termed "silksphere." Here, we proposed a hypothesis that silksphere microbiota have great potential as a biomarker for unraveling the degradation of the ancient silk textiles with great archaeological and conservation values. To test our hypothesis, in this study, we monitored the dynamics of microbial community composition during silk degradation via both indoor soil microcosmos model and outdoor environment with amplicon sequencing against 16S and ITS gene. Microbial community divergence was evaluated with Welch two sample t-test, PCoA, negative binomial generalized log-linear model and clustering, etc. Community assembly mechanisms differences between silksphere and bulk soil microbiota were compared with dissimilarity-overlap curve (DOC) model, Neutral model and Null model. A well-established machine learning algorithm, random forest, was also applied to the screening of potential biomarkers of silk degradation. The results illustrated the ecological and microbial variability during the microbial degradation of silk. Vast majority of microbes populating the silksphere microbiota strongly diverged from those in bulk soil. Certain microbial flora can serve as an indicator of silk degradation, which would lead to a novel perspective to perform identification of archaeological silk residues in the field. To sum up, this study provides a new perspective to perform the identification of archaeological silk residue through the dynamics of microbial communities.

Co-Selection of Bacterial Metal and Antibiotic Resistance in Soil Laboratory Microcosms

Accumulation of heavy metals (HMs) in agricultural soil following the application of superphosphate fertilisers seems to induce resistance of soil bacteria to HMs and appears to co-select for resistance to antibiotics (Ab). This study aimed to investigate the selection of co-resistance of soil bacteria to HMs and Ab in uncontaminated soil incubated for 6 weeks at 25 °C in laboratory microcosms spiked with ranges of concentrations of cadmium (Cd), zinc (Zn) and mercury (Hg). Co-selection of HM and Ab resistance was assessed using plate culture on media with a range of HM and Ab concentrations, and pollution-induced community tolerance (PICT) assays. Bacterial diversity was profiled via terminal restriction fragment length polymorphism (TRFLP) assay and 16S rDNA sequencing of genomic DNA isolated from selected microcosms. Based on sequence data, the microbial communities exposed to HMs were found to differ significantly compared to control microcosms with no added HM across a range of taxonomic levels.

Contrasting responses of cuticular bacteria of Pardosa pseudoannulata under cadmium stress

Although research into how spiders respond to cadmium (Cd)-induced toxicity is ongoing, little is known about the effects of Cd contamination on the exogenous microorganisms of spiders. The current study used 16 S rRNA gene sequencing to evaluate the richness and structure of external bacterial communities in the wolf spider Pardosa pseudoannulata under long- and short-term Cd stress. Our results showed that Proteobacteria and Acidibacter were the dominating bacterial phylum and genus. The alpha diversity analysis showed that the high background of Cd concentration (Cd) reduced bacterial alpha diversity, and short-term Cd (SCd) stress elevated bacterial richness and diversity. Hub bacterial genera, including Stenotrophobacter, Hymenobacter, Chitinophaga, and Bryobacter, were identified by co-occurrence network analysis and showed high connectance with other bacterial genera. Further investigation revealed 15 and 14 bacterial taxa that were classified distinctively under SCd and Cd stresses. Interestingly, functional prediction analysis showed that Cd stress enhanced some crucial pathways involved in specialized functions, including manganese oxidation and aromatic compound degradation. Random forest and correlation analyses found that the spider's molting time was the dominant driver affecting bacterial phyla (i.e., Proteobacteria and Planctomycetes) and genera (e.g., Acidibacter, Reyranella, and Haliangium). Collectively, this comprehensive analysis creates new perspectives to investigate the divergent responses of microbial communities in the spiders' external habitat under Cd stress, and provides valuable viewpoints for Cd pollution evaluation and control.