Publications

Plant litter decomposition plays a vital role in soil carbon (C) cycling and nutrient release, significantly influencing agricultural resource utilization and soil fertility management. Litter quality—defined by its C, nitrogen (N), and phosphorus (P) contents, as well as C:N:P stoichiometry—is a key factor regulating its decomposition. However, the influence of litter C:P ratios on plant litter decomposition, particularly in relation to changes in soil C:N:P stoichiometry, microbial biomass, and extracellular enzyme activities, remains unclear, especially in agroecosystems. In this study, the effects of litter C:P ratios on its decomposition were investigated using 13C-labeled plant litter with naturally occurring gradients of C:P ratios (ranging from 377 to 1,288) in an 84-day incubation experiment. After 84 days, cumulative 13CO2 emissions derived from litter accounted for approximately 50 % of total CO2 emissions. Litter with higher P content increased 13CO2 emissions, whereas higher litter C:P ratios suppressed emissions, indicating that litter with lower C:P ratios decomposed more rapidly in the short-term (14 days). In contrast, elevated soil exoenzymatic C:P and N:P ratios stimulated 13CO2 emissions during long-term decomposition (84 days). These findings suggest that litter P content primarily regulates short-term decomposition, while soil enzyme activity plays a key role in long-term decomposition. Overall, this study highlights the pivotal role of P limitation in litter decomposition, particularly in the early stages, and underscores the potential benefits of P fertilization in enhancing decomposition rates, thereby improving resource efficiency and soil fertility in agroecosystems.

No abstract is provided for this article.

Microorganisms are vital in mediating the earth's biogeochemical cycles; yet, despite our rapidly increasing ability to explore complex environmental microbial communities, the relationship between microbial community structure and ecosystem processes remains poorly understood. Here, we address a fundamental and unanswered question in microbial ecology: 'When do we need to understand microbial community structure to accurately predict function?' We present a statistical analysis investigating the value of environmental data and microbial community structure independently and in combination for explaining rates of carbon and nitrogen cycling processes within 82 global datasets. Environmental variables were the strongest predictors of process rates but left 44% of variation unexplained on average, suggesting the potential for microbial data to increase model accuracy. Although only 29% of our datasets were significantly improved by adding information on microbial community structure, we observed improvement in models of processes mediated by narrow phylogenetic guilds via functional gene data, and conversely, improvement in models of facultative microbial processes via community diversity metrics. Our results also suggest that microbial diversity can strengthen predictions of respiration rates beyond microbial biomass parameters, as 53% of models were improved by incorporating both sets of predictors compared to 35% by microbial biomass alone. Our analysis represents the first comprehensive analysis of research examining links between microbial community structure and ecosystem function. Taken together, our results indicate that a greater understanding of microbial communities informed by ecological principles may enhance our ability to predict ecosystem process rates relative to assessments based on environmental variables and microbial physiology.

<p>Global peatlands store >600 Gt of Carbon (C) but are highly vulnerable to degradation following drainage for agriculture. The extensively drained East Anglian Fens include half of England’s most productive agricultural land, produce ~33% of England’s vegetables and support a food production industry worth approximately £3 billion GBP.  However under arable management, these fen peat soils produce ~37.5 t CO<sub>2</sub> eq ha<sup>-1</sup> of total greenhouse gas (GHG) emissions annually. This is likely to be the largest source of land use GHG emissions in the UK per unit area and there is interest in developing responsible management approaches to reduce emissions whilst maintaining economically productive systems. Lettuce (Lactuca sativa) is amongst the UK’s most valuable crops and a substantial proportion of UK production occurs in the Fens. We undertook a life cycle assessment to compare the carbon footprint of UK Fen lettuce with alternative sources of lettuce for the UK market. We also examined the potential for responsible peat management strategies and more efficient production to reduce the carbon footprint of Fen lettuce. It is hoped this study will help to inform land use decision making and encourage responsible management of UK lowland peat resources.</p>

Plastic mulch films (PMFs) enhance crop productivity but degrade into microplastics, raising concerns about their effects on soil health. This study compared the effect of conventional polyethylene (PE) and biodegradable poly(3-hydroxybutyrate-co-3-hydroxyvalerate) (PHBV) microplastics on soil organic matter (SOM) turnover through real (native SOM) and apparent (microbial biomass) priming effects. Using ¹⁴C-labelled assays, we assessed three microplastic loading rates (10, 100, 1000 kg ha^-1) with addition of nitrogen (N) (80 kg N ha^-1), and without. Comparing responses to inert sand and organic residues (clover, straw). Nitrogen addition significantly enhanced real priming in all treatments. PHBV caused dose-dependent (although non-significant) positive priming, greater than equivalent straw or clover loading. Generally, apparent priming was negative without N but positive with N, suggesting microbial responses depend on substrate stoichiometry and accessibility. Microbial (16S rRNA and ITS2) sequencing showed that N addition mainly drove bacterial community changes, while PHBV uniquely altered fungal communities, increasing Ascomycota dominance in a dose-dependent manner. PE behaved similarly to inert sand, indicating minimal impact on SOM turnover. While biodegradable plastics reduce conventional microplastic accumulation, PHBV may enhance SOM loss via priming (with no direct analogue found in this study), with potential long-term consequences for soil carbon stocks. These findings emphasise the importance of evaluating biodegradable plastics within broader soil nutrient and carbon cycling frameworks.

Upon environmental weathering, plastic materials form smaller sized microplastics, of which the contamination in agricultural fields is of significant importance and increasing social concern. Plastic mulch films are considered a major source of agricultural soil microplastic pollution. However, the mechanism and kinetics of microplastic formation from plastic mulch films were rarely understood. In this study, the rate of microplastic generation from typical mulch films, such as oxodegradable, biodegradable, and conventional non-degradable (polyethylene, PE) mulch films, were quantified in soil under simulated UV irradiation. Results showed that microplastic formation was more rapid from biodegradable mulch film, followed sequentially by oxodegradable mulch film, white PE mulch film, and black PE mulch film. The kinetics of microplastic generation strictly followed the Schwarzchild's law, with exponential growth at indexes between 1.6309 and 2.0502 in the microplastic generation model. At a cumulative UV irradiation of 2.1 MJ/m2, the average quantity of microplastics released from biodegradable, oxodegradable, and white and black non-degradable mulch films were 475, 266, 163, 147 particles/cm2, respectively; with particle sizes largely distributed within 0.02–0.10 mm range. Concurrent increase in crystallinity and surface erosion of the mulch films were observed upon UV irradiation, which further determined the accessibility and activity of the materials to photo-oxidation (reflected as HI indexes), therefore played a critical role on the quantity and size ranges of microplastic debris.

Wastewater-based epidemiology (WBE) has been widely used to track levels of SARS-CoV-2 infection in the community during the COVID-19 pandemic. Due to the rapid expansion of WBE, many methods have been used and developed for virus concentration and detection in wastewater. However, very little information is available on the relative performance of these approaches. In this study, we compared the performance of five commonly used wastewater concentration methods for the detection and quantification of pathogenic viruses (SARS-CoV-2, norovirus, rotavirus, influenza, and measles viruses), fecal indicator viruses (crAssphage, adenovirus, pepper mild mottle virus), and process control viruses (murine norovirus and bacteriophage Phi6) in laboratory spiking experiments. The methods evaluated included those based on either ultrafiltration (Amicon centrifugation units and InnovaPrep device) or precipitation (using polyethylene glycol [PEG], beef extract-enhanced PEG, and ammonium sulfate). The two best methods were further tested on 115 unspiked wastewater samples. We found that the volume and composition of the wastewater and the characteristics of the target viruses greatly affected virus recovery, regardless of the method used for concentration. All tested methods are suitable for routine virus concentration; however, the Amicon ultrafiltration method and the beef extract-enhanced PEG precipitation methods yielded the best recoveries. We recommend the use of ultrafiltration-based concentration for low sample volumes with high virus titers and ammonium levels and the use of precipitation-based concentration for rare pathogen detection in high-volume samples. IMPORTANCE As wastewater-based epidemiology is utilized for the surveillance of COVID-19 at the community level in many countries, it is crucial to develop and validate reliable methods for virus detection in sewage. The most important step in viral detection is the efficient concentration of the virus particles and/or their genome for subsequent analysis. In this study, we compared five different methods for the detection and quantification of different viruses in wastewater. We found that dead-end ultrafiltration and beef extract-enhanced polyethylene glycol precipitation were the most reliable approaches. We also discovered that sample volume and physico-chemical properties have a great effect on virus recovery. Hence, wastewater process methods and start volumes should be carefully selected in ongoing and future wastewater-based national surveillance programs for COVID-19 and beyond.

Metadata on TMEG-related sequences, used for diversity analysis of TMEG-related environmental clones. (XLS 40â kb)

The emergence of high-throughput DNA sequencing methods provides unprecedented opportunities to further unravel bacterial biodiversity and its worldwide role from human health to ecosystem functioning. However, despite the abundance of sequencing studies, combining data from multiple individual studies to address macroecological questions of bacterial diversity remains methodically challenging and plagued with biases. Here, using a machine-learning approach that accounts for differences among studies and complex interactions among taxa, we merge 30 independent bacterial data sets comprising 1,998 soil samples from 21 countries. Whereas previous meta-analysis efforts have focused on bacterial diversity measures or abundances of major taxa, we show that disparate amplicon sequence data can be combined at the taxonomy-based level to assess bacterial community structure. We find that rarer taxa are more important for structuring soil communities than abundant taxa, and that these rarer taxa are better predictors of community structure than environmental factors, which are often confounded across studies. We conclude that combining data from independent studies can be used to explore bacterial community dynamics, identify potential ‘indicator’ taxa with an important role in structuring communities, and propose hypotheses on the factors that shape bacterial biogeography that have been overlooked in the past. A machine-learning approach accounting for methodological differences in studies and complex interactions among taxa allows independent soil studies to be combined at the taxonomy-based level to assess bacterial community structure.

Wastewater treatment plants (WWTPs) are hot spots for the acquisition and spread of antimicrobial resistance (AMR). This regional-based study quantified antibiotic resistance genes (ARGs), mobile genetic elements (MGEs), and bacteria in hospital and community-derived wastewater and receiving environments, using high-throughput qPCR (HT-qPCR). This is the first study to apply Resistomap's Antibiotic Resistance Gene Index (ARGI) to find the overall ARG load in wastewater for AMR risk assessment. ARGI of WWTPs ranged from 2.0 to 2.3, indicating higher relative ARG levels than the mean European ARGI of 2.0, but lower than the global mean of 2.4. The highest diversity and abundance of ARGs were observed in untreated hospital and community wastewater. The reduction of total ARGs during wastewater treatment (0.2- 2 logs) and bacteria (0.3-1.5 logs) varied spatio-temporally across the WWTPs. Despite a decrease in ARG and bacterial abundance in treated effluents, substantial loads were still released into receiving environments. Notably, ARG levels in coastal sediments were comparable to those in untreated wastewater, and most ARGs were shared between wastewater and receiving environments, highlighting the impact of wastewater discharge on these ecosystems. Sewage outfall exposure increased ARGs in shellfish, emphasising risks to shellfish hygiene. This study provides evidence to inform policymaking, emphasising advanced wastewater treatment methods and combined storm overflow (CSO) management to mitigate ARG release, protecting water users and the food chain.

Genomic surveillance of SARS-CoV-2 has provided a critical evidence base for public health decisions throughout the pandemic. Sequencing data from clinical cases has helped to understand disease transmission and the spread of novel variants. Genomic wastewater surveillance can offer important, complementary information by providing frequency estimates of all variants circulating in a population without sampling biases. Here we show that genomic SARS-CoV-2 wastewater surveillance can detect fine-scale differences within urban centres, specifically within the city of Liverpool, UK, during the emergence of Alpha and Delta variants between November 2020 and June 2021. Furthermore, wastewater and clinical sequencing match well in the estimated timing of new variant rises and the first detection of a new variant in a given area may occur in either clinical or wastewater samples. The study's main limitation was sample quality when infection prevalence was low in spring 2021, resulting in a lower resolution of the rise of the Delta variant compared to the rise of the Alpha variant in the previous winter. The correspondence between wastewater and clinical variant frequencies demonstrates the reliability of wastewater surveillance. However, discrepancies in the first detection of the Alpha variant between the two approaches highlight that wastewater monitoring can also capture missing information, possibly resulting from asymptomatic cases or communities less engaged with testing programmes, as found by a simultaneous surge testing effort across the city.

Organic manure is widely applied in agricultural systems to improve soil nutrient cycling and other physicochemical properties. However, the biotic and abiotic mechanisms that drive C, N, P, and S cycling following manure application are not completely understood. In this study, soil samples were collected from long-term experimental plots that had been amended with farmyard manure or mineral fertilisers since 1964. Isotope labelling with 15N, 33P, and 35S; metagenomics; and high-throughput sequencing were used to reveal the relationships between C, N, P, and S dynamics and microbial community composition and functions depending on fertilisation. A clear niche differentiation was observed between bacteria and fungi under mineral and manure regimes. A network analysis showed that long-term manure application reduced the complexity and stability of soil microbial network. Furthermore, a variation partitioning analysis based on redundancy analysis indicated that microbial community variation was mainly driven by soil Cand N contents. Dissolved organic C was the most important factor regulating microbial community structure. Soil C and N contents explained 43.5% of bacterial and 37.9% of fungal community variations. In contrast, soil P and S contents explained 29.9% of bacterial and 20.3% of fungal community variations. Long-term manure application increased the abundance of most functional genes related to C, N, P, and S cycling. This led to increased C and N cycling rates under manure application, which provided sufficient substrates for microbial growth. Partial least squares path modelling indicated that soil physicochemical properties, especially dissolved organic carbon, directly influenced C and S cycling, whereas the N and P cycles were indirectly affected by the changes in microbial community composition. These results provide a new perspective on both direct and indirect effects of organic manure and inorganic fertilisers on the soil nutrient cycling processes mediated by soil microbial community.