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    Succession of micro-food webs and their role in lignocellulose degradation during pepper stalk composting

    AbstractAbtract. Multitrophic interactions among bacteria, fungi, protists, and nematodes play vital roles in organic matter decomposition in soil ecosystems, yet their contributions during composting remain poorly understood. Improving our understanding of these cross-trophic dynamics is essential for optimizing microbial regulation in composting systems. In this study, we investigated the community dynamics and interactions of these trophic groups throughout the aerobic composting of pepper stalks and evaluated their collective influence on lignocellulose degradation. Bacteria expanded rapidly in the early phase, dominating organic matter decomposition, but declined during maturation. Fungi remained low in abundance, with Aspergillus transiently dominating the thermophilic stage. Phagotrophic protists shifted from Colpodella to stress-tolerant Oxytrichidae, while nematodes, which were absent at peak temperature, recovered later, shifting from Rhabditella to Panagrolaimus. Pronounced temporal shifts in community composition and diversity were observed. Co-occurrence network analysis showed increasing interaction complexity over time, with bacterial and fungal taxa dominating lignocellulose-associated modules. Protists and nematodes exerted top-down effects via trophic cascades, indirectly enhancing microbial activity. Structural equation modeling confirmed that food web complexity mediated the indirect effects of community structure on lignocellulose degradation. These findings provide novel insights into compost multitrophic dynamics and highlight the ecological importance of cross-trophic interactions in optimizing decomposition efficiency.

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    IntroductionPeppers (Capsicum spp.) are one of the most important crops, with China accounting for approximately one-third of the world’s pepper production1. In 2019, China’s pepper cultivation area reached 21,474 square kilometers, making it the largest vegetable crop by planting area in the country2. The large-scale cultivation of peppers generates a substantial amount of waste, including pepper stalks and other residues. Disposal methods such as discarding or burning in fields not only waste valuable organic material but also contribute to environmental pollution.Aerobic composting, a process of controlled biological decomposition under oxygen-rich conditions, offers a promising solution for managing pepper stalk waste3. This method exposes organic residues to microbial activity, enabling thermophilic decomposition and maturation4. Aerobic composting accelerates organic matter degradation, eliminates pathogens and parasite eggs, and converts organic materials into stable humus, making it a highly efficient and eco-friendly approach to waste management5,6. However, pepper stalks contain a large amount of lignocellulose (composed of lignin, cellulose, and hemicellulose), which is highly resistant to microbial degradation7. This recalcitrance extends the processing time during composting and hinders the production of humus8. To address these limitations, co-composting with other materials such as manure and wheat straw is commonly employed. These amendments provide easily degradable organic matter and low-lignification components, enhancing the decomposition efficiency of pepper stalks9.Bacteria and fungi are the primary drivers of organic matter decomposition during the composting process, and as such, they have garnered significant research interest. Bacteria, the most abundant microorganisms in compost, exhibit rapid growth and the ability to utilize easily degradable organic matter such as starch and proteins10,11. Fungi, on the other hand, are involved in breaking down the more recalcitrant components of organic waste. For example, some fungi within the Ascomycota and Basidiomycota phyla produce cellulase-like enzymes that effectively degrade lignin and cellulose12. Furthermore, fungi contribute to the physical disruption of materials through hyphal penetration, thereby enhancing the breakdown of lignocellulose13. In addition to bacteria and fungi, phagotrophic protists and nematodes, as key compost biota, also play crucial roles in regulating the composting process, yet they have often been overlooked in composting studies. In soil ecosystems, these organisms constitute integral components of the micro-food web, a multitrophic framework that describes the flow of energy and nutrients among microbes and their predators14. Although this concept has been extensively applied in soil ecology to elucidate decomposition dynamics and nutrient cycling15,16, its relevance to composting systems has received far less attention. Composting, however, represents a highly dynamic, microbially driven process analogous to soil systems, where similar trophic interactions occur under accelerated and thermophilic conditions. Integrating the micro-food web perspective into compost ecology may therefore offer a more comprehensive understanding of biotic interactions and their regulatory roles during organic matter decomposition. By preying on bacteria and fungi, phagotrophic protists help regulate microbial communities in compost. However, research on protists in composting remains limited. Liu et al. (2024) identified Ciliophora as key taxa associated with compost maturity during cow manure composting17. Similarly, Yin et al. (2022) reported that phagotrophic protists were critical predators regulating the structure of denitrifying bacterial communities during the co-composting of pig manure and wheat straw18. These studies underscore the significant roles of phagotrophic protists in compost systems. Nematodes, spanning all trophic levels from primary consumers to specialized predators, play a critical role in the micro-food web19. They are classified into functional groups such as bacterial-feeders, fungal-feeders, plant-feeders, and omnivores20. Based on their r and k life-strategy characteristics, nematodes are classified into cp-groups21. Throughout the composting process, significant changes occur in nematode species composition, life strategies, and feeding behaviors. Bacterial-feeding enrichment opportunists (cp-1) dominate the thermophilic phase, followed by bacterial- and fungal-feeding general opportunists (cp-2), while predator and fungal-feeding nematodes become more prominent in the cooling and maturation stages22. These shifts in nematode community structure can have profound effects on microbial populations and their activity23. Together with bacteria, fungi, and phagotrophic protists, nematodes form a dynamic micro-food web that drives organic matter decomposition and nutrient turnover in compost. However, current research has predominantly focused on changes in individual trophic levels during the composting process, with limited attention given to the overall response of the micro-food web and its regulatory role in the composting process.Recent advances in ecological network analysis have provided powerful tools to quantify the complex web of interactions among microorganisms and higher trophic groups24,25. In this framework, microbial or multitrophic networks are constructed based on correlations or co-occurrence patterns among taxa, revealing potential ecological interactions such as competition, mutualism, or predation26. Network complexity, typically expressed through metrics such as the number of nodes and edges and network connectivity, reflects the degree of interdependence and stability within biological communities27,28. In compost ecosystems, where microbial communities experience rapid turnover and strong environmental fluctuations, changes in network complexity can indicate shifts in system stability and functional coordination among decomposer guilds29.To fill this research gap, we focus on the composting of pepper stalks to investigate the dynamic changes of micro-food web attributes (multitrophic diversity, multitrophic composition and network complexity) during aerobic composting, as well as their impact on lignocellulose degradation. We hypothesized that: (i) multitrophic diversity, community composition, and network complexity exhibit significant dynamic changes during the composting process; (ii) the degradation of lignocellulose is regulated by the complexity of micro-food web interaction.Materials and methodsComposting materialsThe experimental site was located at the Key Laboratory of Fertilization from Agricultural Wastes, Hubei Academy of Agricultural Sciences, Wuhan. The composting materials included pepper straw, pig manure and wheat straw, with pig manure providing essential nitrogen and microbial inoculum, while wheat straw contributed to adjusting the C/N ratio and improving porosity. The pepper straw was sourced from the Industrial Crops Institute, Hubei Academy of Agricultural Sciences; the pig manure was obtained from the Jinshui Base of the Hubei Academy of Agricultural Sciences; the wheat straw was provided by Hubei Guangmei Technology Co., Ltd. The physicochemical properties of these materials are shown in Table S1 (Supplementary Material).Composting experiments and sample collectionThe composting system employed was a customized VTD-100 reactor manufactured by Qinhuangdao Nuoxin Environmental Protection Technology Co., Ltd. (China). Constructed from stainless steel, the reactor was insulated with a 3 cm-thick thermal layer and had an effective capacity of 100 L (Fig. S1, Supplementary Material). The experiment was set up with four independent replicates. The pepper straw, pig manure, and wheat straw were mixed at a ratio of 7:1:1.25 (w/w), with the carbon to nitrogen (C/N) ratio adjusted to 25 and the moisture content adjusted to 55%. According to the temperature profile, the composting process proceeded for a total of 26 days. Turning was conducted on composting days 4, 6, 8, 9, 13, and 20 to enhance oxygen content in the materials. The temperature of the core fermentation zone of the composting pile was measured at 9:00 and 16:00 daily using a stainless-steel compost thermometer, and the average value was taken as the daily pile temperature, while also recording the ambient temperature.Compost samples were collected at five representative time points (Days 0, 2, 7, 10, and 26) corresponding to distinct thermal phases (initial, mesophilic, thermophilic, cooling, and maturation). Samples were obtained from five positions (four corners and the center) at approximately 20 cm depth, targeting the core fermentation zone where microbial activity and temperature were most stable. Subsamples were pooled and homogenized to form a composite sample (~ 1000 g). All samples were divided into three subgroups: one portion was air-dried for physicochemical analyses; another portion was used fresh to determine moisture content and for nematode extraction; and a third portion was used for microbial analysis (stored at −80 °C).Analysis of composting physicochemical propertiesTotal organic carbon (TOC) was determined using the potassium dichromate external heat source method. H2SO4-H2O2 digestion was used for the determination of total nitrogen (TN), total phosphorus (TP) and total potassium (TK). TN was determined by the semi-micro Kjeldahl method, TP by the vanadium-molybdate yellow colorimetric method, and TK by flame photometry. The pH and electrical conductivity (EC) were determined using a 1:10 solid-liquid extraction ratio; The pH was measured with a pH meter (INESA, PHS-3E, China), and EC was measured with a conductivity meter (OHAUS, ST3100C, China). Ash content was measured using the muffle furnace incineration method (550 °C for 6 h). Lignocellulose content was determined by Van Soest detergent fiber analysis method, using the automatic cellulose analyzer (Fibertec™ 8000, Denmark) to measure neutral detergent fiber (NDF), acid detergent fiber (ADF), and acid detergent lignin (ADL). Hemicellulose content was calculated as the difference between NDF and ADF, cellulose content as the difference between ADF and ADL, and lignin content as the difference between ADL and ash content. The degradation rate of lignocellulose was calculated as follows:Degradation rate (%) = (C0 − Ct)/C0 × 100.Where C0 is the initial content and Ct represents the residual content at sampling time point.DNA extraction, quantitative PCR and high-throughput sequencingDNA was extracted using the MoBio PowerSoil DNA Isolation kit, and the concentration and quality of DNA were measured by the NanoDrop 2000 (Thermo Fisher Scientific). The abundance of bacteria and fungi was quantified using quantitative PCR (qPCR) on an ABI Prism 7500 cycler (Applied Biosystems, Germany), with primers 338 F/806R30, and ITS1F/ITS2R31, respectively. Reaction mixtures (20 µl) contained 10 µL of 2×ChamQ SYBR Color qPCR Master Mix, 0.4 µl 50×ROX Reference Dye 1, 0.8 µl of each primer (5 µM), 2 µl of template DNA (10 ng/µl) and 6 µl of ddH2O. PCR conditions were 3 min at 95 °C, followed by 40 cycles of 95 °C for 5 s, 58 °C for 30 s, and 72 °C for 1 min. Each plate included triplicate reactions per DNA sample, the appropriate set of standards and negative controls.The V3-V4 region of bacterial 16S rRNA gene, ITS2 region of fungal rRNA operon and V4 region of protist 18S rRNA gene were amplified using the primers 338F/806R (5’-ACTCCTACGGGAGGCAGCAG-3’, 5’-GGACTACHVGGGTWTCTAAT-3’), ITS1F/ITS2R (5’-CTTGGTCATTTAGAGGAAGTAA-3’, 5’-GCTGCGTTCTTCATCGATGC-3’), and TAReuk454F/TAReukR (5’-CCAGCASCYGCGGTAATTCC-3’, 5’-ACTTTCGTTCTTGATYRA-3’)32, respectively. The high-throughput sequencing of bacteria, fungi and phagotrophic protists were conducted on the Illumina MiSeq PE300 platform at Shanghai Majorbio Biotechnology Co. Ltd, (Shanghai, China). The raw sequences were processed using the QIIME1 pipeline. Briefly, the quality control of the raw sequences was examined by fastp33, and pair-end sequences were merged using FLASH34. Operational taxonomic units (OTUs) of bacterial, fungal and phagotrophic protists sequences were identified using UCLUST at a 97% similarity cutoff. Taxonomic classification was conducted based on the SILVA v138 database for bacteria, the UNITE database 8.0 for fungi and the protist ribosomal reference database 4.5 for protist35. The protist OTU table was generated by removing OTUs classified as Rhodophyta, Streptophyta, Metazoa, Fungi, Opisthokonta_X, and unclassified taxa35. Protist functional groups were manually classified into phagotrophic and phototrophic categories based on their feeding modes. In this study, only phagotrophic protists were considered. Sequences were rarefied to the minimum sequencing depth at 26, 671 for bacteria, 32, 807 for fungi and 157 for phagotrophic protist.Nematode community analysisNematodes were extracted from 50 g of fresh compost using the Baermann funnel method36. Briefly, each sample was placed on a double layer of tissue paper inside a glass funnel filled with water, ensuring that the sample was fully submerged without direct contact with the bottom. The setup was left undisturbed at room temperature (approximately 20–25 °C) for 24–48 h to allow nematodes to migrate into the water and settle at the bottom of the stem. Afterward, the nematode suspension was collected from the bottom of the funnel, and the total number was counted under a microscope. Then, 100 individuals (or all if fewer than 100) were randomly selected and identified to the genus level using an optical microscope.Statistics analysisShannon index was calculated using the “vegan” package in R (version 4.0.2) to characterize the α-diversity of microbial communities. To represent multitrophic diversity, the shannon diversity indices of bacteria, fungi, phagotrophic protists and nematodes were first standardized (Z-score transformation) and then averaged37. Multitrophic composition was visualized through principal coordinates analysis (PCoA) based on genus-level Bray-Curtis dissimilarity matrices of bacterial, fungal, phagotrophic protist, and nematode communities using the “vegan” package, and the first axis of principal coordinates analysis (PCoA1) was used to quantify the multitrophic composition. The physicochemical parameters and α-diversity were analyzed using one-way analysis of variance (ANOVA) to evaluate differences across composting stages, with significant differences identified through Student-Newman-Keuls test (P < 0.05) using the SPSS v20.0. Co-occurrence networks were constructed based on Spearman correlation coefficients (r > 0.8, P < 0.01) to assess the interaction patterns among bacteria, fungi, phagotrophic protists and nematodes, and visualized by Gephi 0.10.1. The distribution patterns of the major modules were evaluated by standardizing and averaging the relative abundance of OTUs in each module38. Topological parameters (include node number, edge number and average weighted degree) of sub-networks were calculated to evaluate network complexity and its temporal dynamics during composting using the subgraph function of the “igraph” package in R39. Linear regression was employed to analyze the relationships between multitrophic diversity, composition, and complexity (average weighted degree) of micro-food web with degradation rates of lignocellulose using “ggplot2” package in R. The structural equation model (SEM) was constructed to evaluate the direct and indirect effects of composting pile properties (temperature, pH) on lignocellulose degradation through multitrophic diversity, multitrophic composition and micro-food web complexity. The model fit of the SEM was evaluated by a chi-square test (P > 0.05), the goodness-of-fit index (GFI > 0.9), and the root mean square error of approximation (RMSEA < 0.05).Results and discussionPhysicochemical process and lignocellulose degradation during compostingA typical composting process consists of mesophilic, thermophilic (> 45 °C), cooling and maturation phase40. In our study, the compost pile entered the thermophilic phase on Day 1, reached a maximum temperature of 64.1 °C on Day 2, and remained above 45.0 °C for 5 days (Fig. 1A). This sharp rise in temperature was attributed to the rapid biodegradation of easily degradable organic matter, which provided an abundant energy source for microbial metabolism during the initial phase of composting41. As the supply of organic matter diminished, microbial activity and heat generation gradually decreased, resulting in a steady decline in temperature on Day 6. By Day 26, the temperature had stabilized, approaching that of the ambient temperature, indicating the compost had reached the maturation phase. During the composting process, the pH value continuously increased due to ammonification42, reaching 9.6 by day 10, after which it remained stable (Fig. S2, Supplementary Material). The EC value initially declined but began rising after Day 2, continuing to increase due to the degradation of organic matter, which concentrated soluble salts43. In the early stages of composting (Day 2 to Day 7), TOC content significantly decreased due to the rapid decomposition of easily degradable organic matter by microorganisms. Afterward, the rate of TOC reduction slowed down. Meanwhile, TN, TP and total TK content levels increased rapidly between day 2 and day 10, followed by a slower rise. After composting, TN, TP, and TK contents increased from 1.38%, 0.71%, and 2.43% to 2.00%, 0.95%, and 3.18%, respectively. By day 26, the C/N ratio decreased to 20.8.As shown in Fig. 1B, the degradation rate of lignocellulosic components increased progressively throughout the composting process. During the thermophilic phase, the degradation rate of lignin and hemicellulose increased rapidly, while cellulose degradation remained relatively low. Notably, between Day 7 and Day 10, the degradation amount of cellulose exhibited a significant increase. In contrast, lignin degradation slowed considerably after Day 7. By the end of the composting, the degradation rate for cellulose, hemicellulose, and lignin were 5.75%, 7.47%, and 3.74%, respectively. Lignocellulose, being a high-molecular-weight organic compound, is relatively difficult for microorganisms to degrade8,44. During the composting process, readily degradable organic carbon sources such as starch, proteins, and sugars are preferentially metabolized by microorganisms45. As a result, the degradation rate of lignocellulose is initially low. The random, amorphous structure of hemicellulose makes it highly susceptible to microbial degradation, making it the most easily degradable substrate46,47. The slow degradation of cellulose in the early stages occurs because it is encapsulated by lignin, while hemicellulose forms a structural connection between lignin and cellulose fibers, further hindering cellulase binding44. From Day 7 to Day 10, the accelerated degradation of hemicellulose facilitated the degradation of cellulose. Compared to cellulose and hemicellulose, lignin has a more complex structure as an amorphous aromatic compound with a three-dimensional configuration, making it more resistant to microbial degradation48. Ligninolytic enzymes such as laccase and peroxidases are crucial for lignin degradation, and their activities are enhanced under thermophilic conditions49,50. After Day 7, the compost temperature decreased, resulting in a significantly reduced amount of lignin degradation.Fig. 1Temperature dynamics (A) and lignocellulose degradation (B) during the composting process.Full size imageSuccession of bacterial and fungal communities during compostingBacterial abundance increased rapidly in the early composting phase, indicating a dominant role in organic matter decomposition, but declined sharply by Day 26 (Fig. 2A). This pattern is consistent with previous findings that bacterial populations expand during active decomposition and contract during maturation due to substrate depletion51. Bacterial diversity was initially low on Day 0 but gradually increased throughout composting (Fig. 2B). At the genus level (Fig. 2C), Pantoea dominated the initial community (24.3%), while Bacillus became predominant during the thermophilic phase (21.8% on Day 2). Oceanobacillus also peaked at this stage (12.8%). The Bacillus genus is highly adaptable, with species that reproduce rapidly and can form spores under harsh conditions. Their ability to degrade complex organic matter, such as cellulose, and their heat tolerance make them effective functional inoculants in composting52,53. Similarly, due to its tolerance to high temperature and salinity stress, along with its nitrifying capacity, Oceanobacillus is well-suited for organic composting54. During the cooling phase, Saccharomonospora increased on Day 7 (15.9%) but declined by Day 26 (6.8%). As a thermotolerant Actinobacteria genus, it contributes to compost maturation by converting lignin and cellulose into humic substances and hydrolyzing phenolic compounds into less toxic forms, thereby reducing phytotoxicity and improving compost quality55,56.In contrast, fungal abundance was much lower than bacterial abundance, rapidly declining after Day 0 and remaining low throughout composting (Fig. 2D). This decline might be attributed to the faster metabolism of bacteria, which makes them more competitive than fungi. Additionally, bacterial higher surface-to-volume ratio, greater diversity, and shorter generation times enable them to better adapt to the swift fluctuations in substrate and environmental conditions throughout the composting process57. Fungal diversity remained relatively stable throughout the composting process (Fig. 2E), but notable shifts in community composition were observed (Fig. 2F). At the genus level, Aspergillus was predominant and exhibited marked temporal dynamics. It accounted for only 5.7% on Day 0 but rapidly increased to dominate the community between Day 2 and Day 10, with relative abundance ranging from 47.6% to 66.6%. By the end of composting, its abundance declined to 15.3%, while Hormographiella became dominant (42.5%). The transient dominance of Aspergillus during the thermophilic phase is consistent with its known role in lignocellulose degradation through the production of multi-enzymatic complexes capable of efficiently breaking down plant biomass58.Fig. 2The dynamic of bacterial and fungal communities during the composting process. (A) Quantification of bacterial abundance; (B) Changes in bacterial diversity (shannon index) during composting; (C) Relative abundance of dominant bacterial genus; (D) Quantification of fungal abundance; (E) Changes in fungal diversity (shannon index) during composting; (F) Relative abundance of dominant fungal genus. Different letters indicate significant differences (P < 0.05).Full size imageSuccession of higher trophic organisms and the multitrophic community during compostingHigher trophic organisms also exhibited pronounced temporal changes during composting. Phagotrophic protist diversity was high on Days 0 and 2 but declined markedly by Days 10 and 26 (Fig. 3A). Early in the process (Days 0–2), the community was primarily dominated by Colpodella (Fig. 3B), a genus of free-living, predatory flagellates that shares synapomorphic features with parasitic Apicomplexa59. As composting progressed, an unidentified genus from the family Oxytrichidae gradually increased in abundance and became dominant by Days 10 and 26. Members of Oxytrichidae, a family of bacterivorous ciliates in the order Sporadotrichida, are capable of forming resistant cysts, enabling survival under dry or otherwise unfavorable conditions60.Nematodes were also detected throughout the composting process. Although ubiquitous in compost ecosystems22,61, their abundance was highly temperature-dependent. Consistent with prior studies indicating a thermal threshold around 40 °C for nematode activity19, we observed a complete absence of nematodes at the peak thermophilic stage (Day 2), followed by a gradual recovery as temperatures declined, peaking at Day 26 (Fig. 3C). This pattern suggests that nematodes may have survived the thermophilic peak in the form of dormant stages or eggs, or found refuge in cooler outer zones of the compost pile22. Only two genera, Rhabditella and Panagrolaimus, were detected using the shallow plate method (Fig. 3D), likely due to the biofumigation effect of Capsicum annuum residues, which are known to suppress plant-parasitic nematodes62. Both Rhabditella and Panagrolaimus are bacterial-feeding enrichment opportunists (cp-1), well-adapted to early compost environments19. Rhabditella was dominant at Day 0, while Panagrolaimus became predominant from Day 7 onward.Fig. 3The dynamic of phagotrophic protist and nematode community during the composting process. (A) Changes in phagotrophic protist diversity (shannon index) during composting; (B) Relative abundance of dominant phagotrophic protist genus; (C) Changes in nematode abundance (individuals per 100 g dry compost) during composting; (D) Relative abundance of nematode genus. Different letters indicate significant differences (P < 0.05).Full size imageThe successional shifts in both phagotrophic protists and nematodes suggest dynamic changes in predator–prey interactions and energy transfer within the compost micro-food web. When integrating all trophic groups, significant temporal shifts in overall multitrophic diversity and community composition were observed. As shown in Fig. 4A, multitrophic diversity was lowest at Day 0, increasing sharply by Day 2 and then remaining relatively stable through Day 26. PCoA revealed clear compositional divergence across time points, with distinct clusters corresponding to Days 0, 2, 7, 10, and 26 (Fig. 4B). Axis 1 explained 51.91% of the variance, and Axis 2 explained 24.77%, indicating that composting had a strong effect on community succession across trophic levels. These temporal shifts in multitrophic diversity and composition reflect not only species turnover but also changes in ecological interactions such as competition, predation, and facilitation. The rapid increase in diversity by Day 2 suggests a swift community reassembly following initial disturbance, likely driven by temperature, substrate availability, and oxygen gradients63. The stabilization of diversity from Day 2 onward may indicate the formation of a relatively resilient and functionally integrated micro-food web. Moreover, the clear separation of communities across time points in PCoA underscores the stage-specific structuring of trophic interactions, highlighting the importance of temporal niche differentiation in shaping food web complexity during composting64.Fig. 4The dynamic of multitrophic diversity (A) and composition (B) during the composting process.Full size imageStructural development of micro-food web complexity during compostingNetwork analysis has emerged as a powerful tool for unraveling complex and dynamic interactions among microbial taxa in environmental systems26,65. To characterize the temporal dynamics of trophic interactions within the compost micro-food web, a multi-kingdom co-occurrence network was constructed based on bacteria, fungi, phagotrophic protists, and nematodes (Fig. 5A). The results revealed that bacteria dominated the network in terms of node abundance, underscoring their central role in organic matter decomposition. Despite the detection of only two nematode genera in the compost, Panagrolaimus remained within the filtered network (Spearman r > 0.8, P < 0.01) and was significantly associated with 38 nodes, including 33 bacterial taxa. This suggests that, even with limited diversity, nematodes can exert notable ecological influence by mediating trophic interactions and modulating microbial activity66.Modules—clusters of tightly connected nodes—can represent ecological niches or functional microbial units67. Three major modules (Modules 1–3) were identified in the multi-trophic network (Fig. 5A). Module 1 was the largest and most complex, containing 138 nodes and representing a densely interconnected multi-trophic structure (Table S2, Supplementary Material). It was dominated by Proteobacteria, Firmicutes, Bacteroidota, and Actinobacteriota, along with phagotrophic protists and Panagrolaimus. The normalized abundance of Module 1 decreased during the thermophilic phase (Day 2) but recovered and peaked by Day 10 (Fig. 5B), suggesting that these taxa are sensitive to extreme heat but play critical roles in restoring ecosystem stability during the cooling phase. Module 2, characterized by high taxonomic diversity and trophic interactions, included Proteobacteria, Bacteroidota, Actinobacteriota, Ascomycota, and several phagotrophic protists (Cercozoa and Ciliophora) (Table S2, Supplementary Material). Its normalized abundance was low in early composting stages but increased significantly after Day 10, peaking at Day 26 (Fig. 5B), indicating its key role in driving organic matter degradation during the mid-to-late phases of composting. Module 3 mainly consisted of Firmicutes and Ascomycota, with consistently positive associations among its members (Table S2, Supplementary Material). This module likely represents cooperative interactions between bacterial and fungal taxa known to initiate the breakdown of simple organic substrates40. The peak abundance of Module 3 on Day 2 emphasizes its importance in the rapid degradation of labile materials during the thermophilic stage. However, its gradual decline over time reflects a reduced contribution as the compost matures and substrates become more recalcitrant.Subnetwork topological analysis revealed pronounced temporal changes in food web complexity68. Subnetwork topological parameters, such as the number of nodes, number of edges, and average degree, steadily increased and peaked during the maturation phase (Fig. 5C), indicating intensified biotic interactions among bacteria, fungi, protists, and nematodes. This increase likely resulted from a drop in compost temperature, which enabled the proliferation of low-trophic-level taxa (e.g., bacteria) and the re-emergence of high-trophic-level organisms such as nematodes. These shifts enhanced both within-group interactions (e.g., bacteria-bacteria) and cross-trophic linkages (e.g., bacteria-protist or bacteria-nematode), contributing to a more integrated and functionally complex food web toward the end of composting.Fig. 5Co-occurrence network of the micro-food web during the composting. (A) Co-occurrence network analysis of the micro-food web. Left: Nodes represent different taxa, color-coded by group (bacteria, fungi, protists, and nematodes). Right: Nodes are grouped by modules (Module 1, Module 2, Module 3, and others), with edges representing positive (red) and negative (green) interactions between taxa. Node size reflects the degree of connectivity. Changes in the normalized averaged abundance of three modules during composting (B). Changes in network topological parameters during composting (C).Full size imageRelationship between micro-food web community attributes and lignocellulose degradationOur results revealed a significant relationship between micro-food web attributes and lignocellulose degradation dynamics during composting. As shown in Fig. 6A, the multitrophic composition showed a significant negative correlation with the degradation rates of hemicellulose (R2 = 0.69, P = 0.000), cellulose (R2 = 0.61, P = 0.000), and lignin (R2 = 0.65, P = 0.000). In contrast, the micro-food web complexity exhibited strong positive correlations with the degradation rates of hemicellulose (R2 = 0.69, P = 0.000), cellulose (R2 = 0.59, P = 0.000), and lignin (R2 = 0.67, P = 0.000). These findings suggest that interactions within the micro-food web facilitate the degradation of lignocellulose. In comparison, multitrophic diversity showed no significant correlations with lignocellulose degradation. This weak association may reflect a decoupling between diversity and functional efficiency under strong environmental filtering, where only a subset of heat-tolerant taxa remain metabolically active during the thermophilic phase69,70,71. During this phase, extreme temperature and limited oxygen further constrain the interconnectivity of micro-food webs. In contrast, as physicochemical conditions become more stable during the cooling and maturation stages, trophic interactions among bacteria, fungi, protists, and nematodes strengthen and play a more pronounced role in regulating substrate decomposition19. These intensified cross-kingdom interactions likely enhance substrate accessibility and enzymatic efficiency through selective grazing and stimulation of microbial turnover.The structural equation model (SEM) (Fig. 6B) further underscored the critical role of interaction complexity in lignocellulose degradation. It showed that compost temperature directly influenced both multitrophic diversity and interaction complexity, while pH exerted a significant impact on community diversity, composition, and network complexity. Critically, neither multitrophic diversity nor composition directly affected lignocellulose degradation—instead, their influence was mediated through micro-food web complexity. This pathway underscores the regulatory importance of ecological interactions in driving decomposition processes. Previous studies have demonstrated that microbial co-occurrence networks—particularly involving bacterial and fungal communities—can shape decomposition rates by modulating functional potential and substrate targeting40. Our findings expand upon this by incorporating higher trophic levels (e.g., protists and nematodes), suggesting that food web complexity at the ecosystem scale is a more comprehensive and sensitive predictor of lignocellulose degradation efficiency. In the compost environment, cross-kingdom interactions (e.g., bacteria-nematodes) may promote substrate accessibility through selective grazing, enzymatic stimulation, and spatial structuring of microbial activity72. Therefore, fostering a complex and well-connected micro-food web could be an effective strategy to accelerate compost maturation and improve lignocellulose decomposition. These findings also have practical implications for composting management. Enhancing the diversity and activity of microfaunal communities through moisture control, proper aeration, or targeted inoculation may improve decomposition efficiency and reduce composting time.Fig. 6Relationship between micro-food web community attributes and lignocellulose degradation. (A) Linear regression of multitrophic diversity, multitrophic composition, and complexity of micro-food web with degradation rates of lignocellulose; (B) Structural equation model (SEM) of key driving factors. The red line indicates a positive correlation, the blue line indicates a negative correlation, and the gray dashed arrows indicate non-significant relationships. Numbers next to the arrows indicate standardized path coefficients. Significance levels: * P < 0.05, ** P < 0.01, and *** P < 0.001.Full size imageConclusionThis study investigated the dynamic changes of the micro-food web during pepper straw composting and its impact on lignocellulose degradation. The results revealed distinct temporal patterns in the abundance, diversity, and species composition of bacterial, fungal, phagotrophic protist, and nematode communities, reflecting the unique contributions of different trophic groups to the composting process. Bacteria and fungi were identified as key drivers of organic matter decomposition, while protists and nematodes indirectly influenced lignocellulose degradation by regulating microbial communities through predation. The significant association between micro-food web interactions and lignocellulose content underscores the critical role of cross-trophic interactions in facilitating lignocellulose breakdown. Notably, the regulatory effects of the micro-food web varied across composting stages, with stronger trophic interactions emerging during the cooling and maturation phases. These findings highlight the importance of multitrophic interactions within the micro-food web during compost maturation and suggest the potential for optimizing composting strategies by modulating these interactions. However, further research is needed to deepen our understanding of the specific functional roles and mechanisms within the micro-food web.

    Data availability

    The datasets generated and/or analyzed during the current study are available in the NCBI SRA database (www.ncbi.nlm.nih.gov/sra) under accession numbers PRJNA1236533 for bacteria, PRJNA1236543 for fungi, and PRJNA1236582 for protist.
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    Reprints and permissionsAbout this articleCite this articleXu, M., Zhan, Y., Xu, J. et al. Succession of micro-food webs and their role in lignocellulose degradation during pepper stalk composting.
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    Assessing eco-environmental quality and its drivers in the Shandong section of the Yellow River Basin with an improved remote sensing ecological index

    AbstractThe Shandong section of the Yellow River Basin (SDYRB), a critical zone for ecological security in the lower reaches of the Yellow River, faces multiple ecological challenges including salinization, soil erosion, water scarcity, and anthropogenic pollution. These issues significantly hinder regional sustainable development. To assess eco-environmental quality in the SDYRB accurately, an Improved Remote Sensing Ecological Index (IRSEI) was developed by integrating the Composite Salinity Index (CSI) and Soil–Water Conservation Function Index (SWCFI). Utilizing multi-temporal imagery (2009–2023), this study analyzed spatio-temporal patterns of eco-environmental quality and their driving mechanisms. The results show that: (1) The overall eco-environmental quality exhibits a declining trend, with a spatial distribution pattern characterized as “superior in the west and poorer in the east”. High-quality areas were concentrated in western plains and Yellow River riparian zones, versus low-quality areas in eastern/northern coasts. (2) The global Moran’s I approached 1 and exhibited a gradual year-by-year decline, indicating persistent spatial agglomeration of ecological quality. Local spatial autocorrelation was predominantly characterized by High-High (H–H) and Low-Low (L–L) agglomerations, with low-value areas exhibiting an outward spread tendency. (3) Ecological quality fluctuated, declining significantly (2009–2014) before recovering (2019–2023). Degradation hotspots were identified in the northeast and southwest, whereas the improved areas were concentrated in the central region. (4) Ordinary Least Squares (OLS) regression and GeoDetector (GD) identified synergistic natural and anthropogenic driving factors: mean annual temperature, evapotranspiration, nighttime light intensity, and land use were dominant. This study improves the applicability and interpretability of IRSEI in salinized and soil-eroded regions by integrating CSI and SWCFI, offering a scientific foundation for ecological conservation and high-quality development in the SDYRB. The approach can also be extended to dynamic monitoring and evaluation of other similarly vulnerable ecological zones.

    Data availability

    The authors confirm that the data supporting the findings of this study are available within the article.
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    Download referencesFundingThe research was funded by the Jinan Municipal School-Integration Development Strategy Program (Phase II) (Grant No. JNSX2023107), Science and Technology Program of Shandong Provincial Department of Housing and Urban–Rural Development (Grant No. 2025KYKF-CSAQ182), Research Program of Qilu Institute of Technology (Grant No. QIT24TP006 and QIT24NN085) and Shandong Provincial Natural Science Foundation (Grant No. ZR2024QE385).Author informationAuthors and AffiliationsCivil Engineering Department, Qilu Institute of Technology, Jinan, 250200, ChinaPeipei Wang, Chun-Pin Tseng, Qinghao Wei, Min Qiao, Xiaoshuang Li & Ran AnMathematics Department, Brandeis University, Waltham, MA, USAYiyou FanAuthorsPeipei WangView author publicationsSearch author on:PubMed Google ScholarChun-Pin TsengView author publicationsSearch author on:PubMed Google ScholarYiyou FanView author publicationsSearch author on:PubMed Google ScholarQinghao WeiView author publicationsSearch author on:PubMed Google ScholarMin QiaoView author publicationsSearch author on:PubMed Google ScholarXiaoshuang LiView author publicationsSearch author on:PubMed Google ScholarRan AnView author publicationsSearch author on:PubMed Google ScholarContributionsConceptualization, P.W. and Q.W.; methodology, P.W. and C.T.; software, P.W.; validation, P.W., C.T. and P.W.; formal analysis, Q.W. and M.Q.; investigation, Y.F.; resources, P.W.; data curation, P.W. and Q.W.; writing—original draft preparation, P.W. and Q.W.; writing—review and editing, P.W. and C.T.; visualization, C.T., and M.Q.; supervision, X.L., and R.A.; project administration, R.A.; funding acquisition, C.T. All authors have read and agreed to the published version of the manuscript.Corresponding authorCorrespondence to
    Chun-Pin Tseng.Ethics declarations

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    Reprints and permissionsAbout this articleCite this articleWang, P., Tseng, CP., Fan, Y. et al. Assessing eco-environmental quality and its drivers in the Shandong section of the Yellow River Basin with an improved remote sensing ecological index.
    Sci Rep (2025). https://doi.org/10.1038/s41598-025-31580-3Download citationReceived: 16 July 2025Accepted: 03 December 2025Published: 28 December 2025DOI: https://doi.org/10.1038/s41598-025-31580-3Share this articleAnyone you share the following link with will be able to read this content:Get shareable linkSorry, a shareable link is not currently available for this article.Copy shareable link to clipboard
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    KeywordsThe Shandong section of the Yellow River Basin (SDYRB)Eco-environmental qualityImproved remote sensing ecological index (IRSEI)Spatial autocorrelationDriving factors More

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    Occurrence, environmental correlates, and risk assessment of Vibrio parahaemolyticus in Caspian sea coastal waters

    Abstract

    Vibrio parahaemolyticus (Vp) poses a significant public health concern in marine environments. This study evaluated the occurrence and health risk of Vp in coastal waters of the southern Caspian Sea during summer 2022 using the WHO-recommended QMRA framework. Forty-eight seawater samples collected from two beaches in Guilan Province revealed Vp concentrations ranging from 1.9 × 10⁵ to 5.0 × 10⁵ CFU L⁻¹. Monte Carlo simulation was applied to quantify uncertainty, showing higher median probabilities of illness (Pill) at Beach B (children: 9.0 × 10⁻³; adults: 3.7–5.8 × 10⁻³) than at Beach A (children: 5.0 × 10⁻³; adults: 2.0–3.4 × 10⁻³), all below the US. EPA threshold (0.036). The estimated disability-adjusted life years (DALYs) exceeded the WHO reference level (10⁻⁶ pppy) but remained below the US. EPA benchmark (10⁻⁴ pppy), indicating a low but non-negligible health burden, particularly among children. In addition, statistical analysis revealed positive correlations between Vp and salinity, temperature, and turbidity, and a negative correlation with pH. Sensitivity analysis revealed that Vp concentration was the dominant factor at Beach A, while ingested water volume had the greatest influence at Beach B. These results support targeted management measures to mitigate microbial risks in recreational waters.

    Data availability

    All data generated or analyzed during this study are included in this published article.
    Abbreviations
    Vp
    :
    Vibrio parahaemolyticus
    qPCR:
    quantitative Polymerase Chain Reaction

    tlh
    :
    thermolabile hemolysin
    QMRA:
    Quantitative Microbial Risk Assessment
    FIB:
    Fecal Indicator Bacteria
    GI:
    Gastrointestinal Illness
    CDC:
    Control Diseases Center
    LOD:
    Limit of Detection
    DALY:
    Disability-Adjusted Life Year
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    Download referencesAcknowledgementsThe authors express their gratitude to the Iran University of Medical Sciences, Tehran, Iran, for their support and cooperation in conducting this study.FundingThis research was financially supported by the Iran University of Medical Sciences under grant number 20016.Author informationAuthors and AffiliationsDepartment of Environmental Health Engineering, School of Public Health, Tehran University of Medical Sciences, Tehran, IranMohammad AhmadiStudent’s Scientific Research Center, Tehran University of Medical Sciences, Tehran, IranMohammad AhmadiDepartment of Environmental Health Engineering, School of Public Health, Iran University of Medical Sciences, Tehran, IranAli Esrafili & Roshanak Rezaei KalantaryPhysiology Research Center, Iran University of Medical Sciences, Tehran, IranHamidreza Pazoki-Toroudi & Fazel GorjipourDepartment of Physiology, Faculty of Medicine, Iran University of Medical Sciences, Tehran, IranHamidreza Pazoki-ToroudiAuthorsMohammad AhmadiView author publicationsSearch author on:PubMed Google ScholarAli EsrafiliView author publicationsSearch author on:PubMed Google ScholarHamidreza Pazoki-ToroudiView author publicationsSearch author on:PubMed Google ScholarFazel GorjipourView author publicationsSearch author on:PubMed Google ScholarRoshanak Rezaei KalantaryView author publicationsSearch author on:PubMed Google ScholarContributionsMohammad Ahmadi: Methodology, Validation, Writing – original draft, Writing – review & editing, Visualization. Ali Esrafili: Methodology, Validation, Writing – review & editing, Visualization. Hamidreza Pazoki-Toroudi: Methodology, Validation. Fazel Gorjipour : Methodology, Validation, Writing – review & editing. Roshanak Rezaei Kalantary : Data curation, Supervision, Project administration, Conceptualization, Validation, Resources, Writing – review & editing.Corresponding authorCorrespondence to
    Roshanak Rezaei Kalantary.Ethics declarations

    Competing interests
    The authors declare no competing interests.

    Additional informationPublisher’s noteSpringer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.Supplementary InformationBelow is the link to the electronic supplementary material.Supplementary Material 1Rights and permissions
    Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
    Reprints and permissionsAbout this articleCite this articleAhmadi, M., Esrafili, A., Pazoki-Toroudi, H. et al. Occurrence, environmental correlates, and risk assessment of Vibrio parahaemolyticus in Caspian sea coastal waters.
    Sci Rep (2025). https://doi.org/10.1038/s41598-025-28883-wDownload citationReceived: 10 June 2025Accepted: 13 November 2025Published: 28 December 2025DOI: https://doi.org/10.1038/s41598-025-28883-wShare this articleAnyone you share the following link with will be able to read this content:Get shareable linkSorry, a shareable link is not currently available for this article.Copy shareable link to clipboard
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    KeywordsCaspian seaCoastal waterProbability of illnessQMRA
    V. parahaemolyticus. DALY More

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    Integrating feature selection and explainable CNN for identification and classification of pests and beneficial insects

    AbstractReliable identification of agricultural pests and beneficial insects is crucial for sustainable crop protection and ecological balance, yet most vision-based models remain black boxes and require high-dimensional features. This paper proposes an explainable hybrid insect-classification framework that combines convolutional neural network (CNN) feature extraction with a dual–XAI feature selection strategy. SHapley Additive exPlanations (SHAP) and Permutation Feature Importance (PFI) are applied in parallel to rank handcrafted and CNN-derived features, and their intersection yields a compact, biologically meaningful subset for final classification. The selected features are evaluated using lightweight classifiers and a hybrid ensemble, enabling accurate inference under field variability. Experiments on a curated, balanced dataset of four classes (Colorado potato beetle, green peach aphid, seven-spot ladybird, and healthy leaves) collected under diverse lighting and background conditions achieve 96.7% overall accuracy, with precision, recall, and F1-scores all above 96%. Importantly, performance remains stable when reducing dimensionality, retaining (ge)90% accuracy using only the top 11 hybrid-selected features. These results demonstrate that integrating SHAP and PFI improves both robustness and interpretability, supporting practical deployment for automated pest monitoring and precision agriculture.

    Data availability

    The selected data sets are available from free and open access sources using the following link:https://doi.org/10. 34740/kaggle/dsv/12745007
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    Download referencesAcknowledgementsThe authors extend their appreciation to the Deanship of Scientific Research and Libraries in Multimedia University for funding this research work through the Program for Supporting Publication in Top-Impact Journals.FundingNot applicableAuthor informationAuthors and AffiliationsDepartment of Biotechnology Engineering, Bioenvironmental Engineering Research Center (BERC), International Islamic University Malaysia, 50728, Kuala Lumpur, MalaysiaNibedita DebDepartment of Electrical and Electronic Engineering, International University of Business Agriculture and Technology, Uttara, Dhaka, 1230, BangladeshTawfikur Rahman & Md. MoniruzzamanThe Saudi Technology Development and Investment Company (Taqnia), 12211, Riyadh, Saudi ArabiaAmeen Salem Bin ObadiFaculty of Artificial Intelligence and Engineering, Multimedia University, 63100, Cyberjaya, MalaysiaNoorlindawaty Md. JizatSpace Science Centre, Institute of Climate Change, Universiti Kebangsaan Malaysia (UKM), 43600, Bangi, MalaysiaSamir Salem Al-BawriDepartment of Electrical and Electronic Engineering, Southeast University, Dhaka, 1208, BangladeshAbdullah Al Mahfazur RahmanDepartment of Electronics & Communication Engineering, Faculty of Engineering & Petroleum, Hadhramout University, Mukalla, Hadhramout, YemenSamir Salem Al-BawriAuthorsNibedita DebView author publicationsSearch author on:PubMed Google ScholarTawfikur RahmanView author publicationsSearch author on:PubMed Google ScholarMd. MoniruzzamanView author publicationsSearch author on:PubMed Google ScholarAmeen Salem Bin ObadiView author publicationsSearch author on:PubMed Google ScholarNoorlindawaty Md. JizatView author publicationsSearch author on:PubMed Google ScholarSamir Salem Al-BawriView author publicationsSearch author on:PubMed Google ScholarAbdullah Al Mahfazur RahmanView author publicationsSearch author on:PubMed Google ScholarContributionsN. D. and T. R. Conceptualization, Methodology, Software, Visualization, Formal Analysis, Writing-original draft and review & editing. M.M. and A.S.B.O. Conceptualization, Methodology, Resource, Supervision, Visualization, Writing- review & editing. N.M.J. Data curation, Software, Resource, Formal Analysis. S. S. A. and A. A. M. R. Methodology, Data Curation, Visualization, Validation and Writing (Review & Editing)Corresponding authorsCorrespondence to
    Md. Moniruzzaman, Noorlindawaty Md. Jizat or Samir Salem Al-Bawri.Ethics declarations

    Competing interests
    The authors declare no competing interests.

    Ethical approval
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    Clinical trial number
    Not applicable

    Additional informationPublisher’s noteSpringer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.Rights and permissions
    Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
    Reprints and permissionsAbout this articleCite this articleDeb, N., Rahman, T., Moniruzzaman, M. et al. Integrating feature selection and explainable CNN for identification and classification of pests and beneficial insects.
    Sci Rep (2025). https://doi.org/10.1038/s41598-025-32520-xDownload citationReceived: 16 October 2025Accepted: 10 December 2025Published: 27 December 2025DOI: https://doi.org/10.1038/s41598-025-32520-xShare this articleAnyone you share the following link with will be able to read this content:Get shareable linkSorry, a shareable link is not currently available for this article.Copy shareable link to clipboard
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    KeywordsHybrid modelsFeature selectionPest detectionBeneficial insectsMachine learningAgricultural informatics. More

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    Radiological and radioecological risk assessment around the West Delta fossil-fuel power station in Egypt

    AbstractThere are serious ecological and radiological risks associated with the release and buildup of man-made and natural radionuclides. These risks are particularly relevant for fossil fuel power plants located in residential and agricultural areas. High-purity germanium (HPGe) detectors were employed to analyze environmental samples, including soil, water, and plants collected around the West Delta fossil fuel power station in Egypt. The activity levels of both man-made and naturally occurring radionuclides, such as 226Ra, 228Ra, and40K, were measured, and the corresponding ecological and radiological hazards were assessed using several radiological hazard indices. The findings showed elevated concentrations of 226Ra, 228Ra, and40K specifically in agricultural areas near the power station, with some values exceeding internationally recommended guideline values. The calculated radioecological indicators highlight potential long-term exposure risks for nearby populations and ecosystems. These results indicate the need for targeted monitoring and site-specific mitigation measures in the most impacted areas. while providing essential baseline data for future environmental monitoring. This study provides the first comprehensive radiological and radioecological assessment around the West Delta power station, offering new baseline data for environmental monitoring and risk management.

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    Data availability

    The datasets used and/or analysed during the current study available from the corresponding author on reasonable request.
    Abbreviations
    238U:
    Uranium-238

    235U:
    Uranium-235

    232Th:
    Thorium-232

    40K:
    Potassium-40

    137Cs:
    Cesium-137

    226Ra:
    Radium-226

    228Ra:
    Radium-228 (representative of the Th-232 decay series)

    214Pb:
    Lead-214
    Raeq
    :
    Radium equivalent
    Hex
    :
    External hazard index
    Hin
    :
    Internal hazard index
    D:
    Absorbed dose rate in air
    AED:
    Annual effective dose rate
    ELCR:
    Excess lifetime cancer risk
    ICP-OES:
    Inductively coupled plasma-optical emission spectroscopy
    HPGe:
    High-purity germanium detector
    EPA:
    Environmental Protection Agency
    IAEA:
    International Atomic Energy Agency
    ICRP:
    International commission on radiological protection
    NORMs:
    Naturally occurring radioactive materials
    UNSCEAR:
    United nation scientific committee on the effects of atomic radiation
    WHO:
    World Health Organization
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    Reprints and permissionsAbout this articleCite this articleElgingihy, S.M., Abdelsalam, A.A. & Saleh, I.H. Radiological and radioecological risk assessment around the West Delta fossil-fuel power station in Egypt.
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    No significant projected climate change effects on the geographic ranges of marine aquaculture species under the sustainable scenario (SSP 1-1.9, 1.5°C warming)

    AbstractAquaculture is increasingly relied upon for global seafood production, projected to be the leading supplier by 2030. Climate change impacts on species health and industry productivity are already evident, creating uncertainties around long-term aquaculture development. While these impacts have been projected for some species, around 62% of aquaculture production remains unassessed. We utilized climate dissimilarity to assess the exposure of 327 species—including those previously unassessed—in their native ranges to changing climates under three climate scenarios: SSP1-1.9, SSP3-7.0, and SSP5-8.5. We projected that under a sustainability scenario (SSP1-1.9), 41% of Exclusive Economic Zones (EEZ) remained unexposed, including high-value aquaculture regions. However, under increased emissions scenarios (SSP3-7.0 and SSP5-8.5) all current aquaculture EEZ are projected to be exposed. Semi-enclosed seas, like the Baltic, Black, and Red Seas, experience the largest dissimilarity, alongside equatorial regions. Our findings suggest widespread mitigation efforts are necessary to ensure the long-term resilience of marine aquaculture.

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    Data availability

    Climate data were retrieved from the Copernicus Marine Service at [http://resources.marine.copernicus.eu/products] and the Coupled Model Intercomparison Project (Phase 6) at [https://esgf-node.llnl.gov/projects/cmip6/] in May 2023. Species’ range maps were retrieved from AquaMaps at [https://www.aquamaps.org] in May 2023.
    Code availability

    Our manually-derived range maps are available on figshare89 and our code on GitHub (https://github.com/jorgeassis/climateAnalogs).
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    Download referencesAcknowledgementsThis study was supported by the Foundation for Science and Technology (FCT) of Portugal through projects UIDB/04326/2020 (https://doi.org/10.54499/UIDB/04326/2020), UIDP/04326/2020 (https://doi.org/10.54499/UIDP/04326/2020), LA/P/0101/2020 (https://doi.org/10.54499/LA/P/0101/2020), and the Individual Call to Scientific Employment Stimulus 2022.00861.CEECIND/CP1729/CT0003 (https://doi.org/10.54499/2022.00861.CEECIND/CP1729/CT0003). The authors thank the anonymous reviewers for their helpful comments on the manuscript.FundingOpen access funding provided by Nord University.Author informationAuthors and AffiliationsFaculty of Biosciences and Aquaculture, Nord University, Bodø, NorwayAmy Leigh Mackintosh, Griffin Goldstein Hill, Mark John Costello & Jorge AssisCentre of Marine Sciences, CCMAR, University of Algarve, Faro, PortugalJorge AssisAuthorsAmy Leigh MackintoshView author publicationsSearch author on:PubMed Google ScholarGriffin Goldstein HillView author publicationsSearch author on:PubMed Google ScholarMark John CostelloView author publicationsSearch author on:PubMed Google ScholarJorge AssisView author publicationsSearch author on:PubMed Google ScholarContributionsA.L.M., G.G.H.: Conceptualization, Methodology, Interpretive Analysis, Investigation, Data Curation, Writing—Original Draft, Writing—Review & Editing, and Visualization. M.J.C.: Conceptualization, Investigation, Writing—Review & Editing, and Funding Acquisition. J.A.: Conceptualization, Formal Analysis, Writing—Review & Editing, and Funding Acquisition. A.L.M. and G.G.H. contributed equally to this manuscript.Corresponding authorCorrespondence to
    Amy Leigh Mackintosh.Ethics declarations

    Competing interests
    The authors declare no competing interests.

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    Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
    Reprints and permissionsAbout this articleCite this articleMackintosh, A.L., Hill, G.G., Costello, M.J. et al. No significant projected climate change effects on the geographic ranges of marine aquaculture species under the sustainable scenario (SSP 1-1.9, 1.5°C warming).
    npj Ocean Sustain (2025). https://doi.org/10.1038/s44183-025-00178-7Download citationReceived: 20 March 2025Accepted: 03 December 2025Published: 27 December 2025DOI: https://doi.org/10.1038/s44183-025-00178-7Share this articleAnyone you share the following link with will be able to read this content:Get shareable linkSorry, a shareable link is not currently available for this article.Copy shareable link to clipboard
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    Synthesizing selection mosaic theory and host-pathogen theory to explain large-scale pathogen coexistence

    AbstractSelection mosaic theory explains observations of polymorphism in host-pathogen interactions in terms of spatially variable natural selection but does not account for population dynamics. In contrast, classical host-pathogen theory easily explains observations of population cycles, but does not explain the persistence of pathogen polymorphism. Here, we synthesize these two frameworks to understand the effects of population cycles on pathogen polymorphism. We show that geographic variation in the frequency of two morphotypes of a baculovirus that infects the Douglas-fir tussock moth (Orgyia pseudotsugata) depends on the frequency of Douglas-fir (Pseudotsuga menziesii), an important tussock moth host tree. The morphotype frequency data are best explained by host-pathogen models that combine a selection mosaic with population cycles. In our model, population cycles intensify pathogen competition across a selection mosaic, leading to a strong effect of Douglas-fir frequency on morphotype frequency that matches the data. Models without host-pathogen cycles or a selection mosaic project only weak effects of varying Douglas-fir frequency. Our model further projects that a biopesticide made up of both viral morphotypes would be more effective than the current single-morphotype biopesticide, demonstrating that our synthesis of selection mosaic theory and host-pathogen theory provides useful insights into pest management.

    Data availability

    The raw data for the morphotype frequency dataset, field experiments, and line search results supporting the findings of this study are openly available in the GitHub repository at https://github.com/kpd19/Two_Pathogen_Evolution/, with a persistent identifier assigned to version 1.0.0 via Zenodo: https://doi.org/10.5281/zenodo.17574036. The Bayesian model outputs from Stan are very large and are available from the first author upon request. The data from the increased realizations from the line search results are also very large and are available from the first author upon request. The previously published data included as part of our morphotype frequency dataset can be found in Fig. 1 from Hughes37, Table 1 from Williams and Otvos65, and Table 1 from Williams et al.40. The National Forest Type Dataset for the continental United States was previously publicly available from the USDA Forest Service. The dataset is no longer hosted by the USDA and is available from the first author upon request. State and Province administrative boundaries for the United States and Canada used in the maps for this paper are publicly available for download from GADM v4.1 via https://geodata.ucdavis.edu/gadm/gadm4.1/shp/. Source data are provided with this paper.
    Code availability

    All code to perform the simulations and statistical analysis, as well as for plotting Figs. 1–6 and the supplementary data figures, is openly available in the GitHub repository at https://github.com/kpd19/Two_Pathogen_Evolution/, with a persistent identifier assigned to version 1.0.0 via Zenodo: https://doi.org/10.5281/zenodo.17574036.
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    Download referencesAcknowledgementsWe are extremely grateful for the support of many dedicated and talented field technicians: Rhiannon Archerelle, Ari Freedman, Amy Gannon, Laurel Haavik, Sophia Horigan, Amy Huang, Alison Hunter, Jessica Johnson, August Kramer, Allie Kreitman, Kate-Lynne Logan, and Chelsea Miller. Special thanks to the field technicians who made the work possible in the Summer of 2020, as well as Mary Johnson and Luis Marmolejo, who managed shipping. We thank Roy Magelssen and Connie Mehmel at the Forestry Sciences Laboratory in Wenatchee, Washington, for providing important institutional and biological knowledge of the system, and Joe Mihaljevic for important guidance in doing transmission experiments. We would like to thank Cara Brook, Sarah Cobey, and Tim Wootton for providing important feedback on earlier drafts of this work. Computational Resources were provided by the Research Computing Center at the University of Chicago. Help in morphotyping isolates was provided by the Electron Microscopy Center at the University of Chicago. Our work was supported by EEID NSF grant DEB-2109774 to G.D. and V.D. K.P.D. was supported by the University of Chicago Data Science for Energy and Environmental Research (DSEER) training grant as part of an NSF Research Traineeship program (1735359) and the U.S. Department of Education Quantitative Ecology GAANN training grant (P200A150101). W.T.K. and K.P.D. received separate awards from the University of Chicago Hinds Fund for Student Research. Our work was further supported by a grant from the Western Wildlands Environmental Threat Assessment Center to C.M.P., by NIFA Biological Sciences grant 2019-67014-29919 to V.D., by an ARCS Foundation Fellowship to W.T.K., and by a Theodore Roosevelt Memorial Grant through the American Museum of Natural History to W.T.K.Author informationAuthors and AffiliationsDepartment of Ecology and Evolution, University of Chicago, Chicago, IL, USAKatherine P. Dixon, William T. Koval & Greg DwyerPacific Northwest Research Station, USDA Forest Service, Wenatchee, WA, USACarlos M. PolivkaDepartment of Biology, Lewis & Clark College, Portland, OR, USAGrace BirdDepartment of Applied Mathematics, University of Colorado, Boulder, CO, USAVanja DukicAuthorsKatherine P. DixonView author publicationsSearch author on:PubMed Google ScholarWilliam T. KovalView author publicationsSearch author on:PubMed Google ScholarCarlos M. PolivkaView author publicationsSearch author on:PubMed Google ScholarGrace BirdView author publicationsSearch author on:PubMed Google ScholarVanja DukicView author publicationsSearch author on:PubMed Google ScholarGreg DwyerView author publicationsSearch author on:PubMed Google ScholarContributionsG.D. and K.P.D. planned and designed the research. W.T.K., C.M.P., G.B., G.D., and K.P.D. collected the data. K.P.D. analyzed the data. K.P.D., G.D., V.D., and C.M.P. contributed substantially to the discussion of the results and the writing of the manuscript.Corresponding authorCorrespondence to
    Greg Dwyer.Ethics declarations

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    Reprints and permissionsAbout this articleCite this articleDixon, K.P., Koval, W.T., Polivka, C.M. et al. Synthesizing selection mosaic theory and host-pathogen theory to explain large-scale pathogen coexistence.
    Nat Commun (2025). https://doi.org/10.1038/s41467-025-67952-6Download citationReceived: 14 November 2024Accepted: 12 December 2025Published: 26 December 2025DOI: https://doi.org/10.1038/s41467-025-67952-6Share this articleAnyone you share the following link with will be able to read this content:Get shareable linkSorry, a shareable link is not currently available for this article.Copy shareable link to clipboard
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    A comprehensive approach to enhancing irrigation network management through the water accounting plus framework

    Abstract

    Water management in irrigation networks is crucial for sustainable agriculture under conditions of water scarcity and climate variability. This study applies the water accounting plus (WA+) framework, integrating meteorological and remote sensing data (WaPOR), to analyze water fluxes, productivity, and spatial heterogeneity in the Qazvin Plain irrigation network from 2009 to 2021. The total net inflow during this period was approximately 10,582 MCM, with contributions from precipitation (≈ 20%), surface inflow (≈ 27%), and storage changes (≈ 53%). Analysis of evapotranspiration revealed that transpiration accounted for 80% of total ET, with 72% classified as beneficial (transpiration plus interception) and 28% as non-beneficial (soil evaporation and canopy interception). Spatial patterns indicate higher water availability in the eastern part of the network and deficits in the western region, highlighting the potential for improving water productivity through targeted interventions such as soil moisture conservation and optimized irrigation scheduling. These findings demonstrate the applicability of the WA + framework for enhancing water use efficiency and informing sustainable irrigation management in semi-arid regions.

    Data availability

    The datasets generated and/or analyzed during the current study are not publicly available due to privacy concerns and proprietary constraints, but they are available from the corresponding author on reasonable request.
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    Download referencesFundingThe authors received support to conduct the study but the support did not include support for the submitted work.Author informationAuthors and AffiliationsDepartment of Water Sciences and Engineering, Imam Khomeini International University, P.O. Box 3414896818, Qazvin, IranMahkameh Sadat NaeiniDepartment of Irrigation and Reclamation Engineering, Faculty of Agricultural Engineering and Technology, College of Agriculture and Natural Resources, University of Tehran, P. O. Box 4111, Karaj, 31587-77871, IranBijan NazariWater Engineering Department, Imam Khomeini International University, Qazvin, IranBijan NazariDepartment of Water Sciences and Engineering, Imam Khomeini International University, P.O. Box 3414896818, Qazvin, IranAbbas SotoodehniaAuthorsMahkameh Sadat NaeiniView author publicationsSearch author on:PubMed Google ScholarBijan NazariView author publicationsSearch author on:PubMed Google ScholarAbbas SotoodehniaView author publicationsSearch author on:PubMed Google ScholarContributionsMahkameh Sadat Naeini: Conceptualization; Data downloading and processing; Writing the original draft; Finalization. Bijan Nazari and Abbas Sotoodehnia: Supervision; Editing drafts; Providing suggestions and additions to improve the findings and their practical applicability.Corresponding authorCorrespondence to
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    Reprints and permissionsAbout this articleCite this articleNaeini, M.S., Nazari, B. & Sotoodehnia, A. A comprehensive approach to enhancing irrigation network management through the water accounting plus framework.
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    KeywordsClimate changeGroundwaterWaPORWater fluxesWater scarcity More