Effects of Ageratina adenophora Invasion on Soil Nutrients, Enzyme Activities, and Microbial Composition/Function in Karst Areas of Central Guizhou

Effects of Ageratina adenophora Invasion on Soil Nutrients, Enzyme Activities, and Microbial Composition/Function in Karst Areas of Central Guizhou | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Effects of Ageratina adenophora Invasion on Soil Nutrients, Enzyme Activities, and Microbial Composition/Function in Karst Areas of Central Guizhou Jiawei Wu, Jiaguo Wang, Weijie Li This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8826938/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This study elucidates the regulatory effects of Ageratina adenophora invasion on soil nutrient cycling, enzyme activities, and microbial composition/function in karst ecosystems of central Guizhou and reveals the plant–soil–microbe feedback-driven invasion loop. Four invasion gradients were established in Guanling County: CK (uninvaded), L (10–30% cover), M (30–60%), and H (60–90%). Soil nutrients, enzyme activities, metagenomic profiles, and nutrient contents in vegetative organs of A. adenophora and native Artemisia argyi were analysed. Key results: (1) Soil microbial communities included 7 domains, 198 phyla, 179 classes, 364 orders, 856 families, 3,531 genera, and 22,111 species. No significant differences were detected in soil nutrients, enzyme activities, or microbial composition/structure/diversity across invasion gradients. (2) Invaded soils presented significantly greater soil organic carbon (SOC), total nitrogen (TN), and pH than CK soils. Sucrase and urease activities were significantly elevated. Compared with A. argyi leaves, A. adenophora leaves contained significantly greater TN and total potassium (TK). (3) Microbial Shannon diversity ( H' ) and Pielou evenness ( J ) were significantly decreased. Verrucomicrobia and Candidatus Eisenbacteria were significantly enriched in invaded vs. CK soils. (4) Functional gene (K13038 and K00639) levels were significantly correlated with invasion intensity. Carbohydrate metabolism genes ( GT10 and CBM4) were upregulated by 1.5- and 1.7-fold, respectively; the stress resistance gene merG was upregulated 2.3-fold. These changes collectively demonstrate that A. adenophora invasion increases SOC/TN content and sucrase/urease activity, restructures microbial communities, and upregulates carbohydrate metabolism/stress resistance-related genes, establishing a self-reinforcing "invasive plant‒soil–microbe" feedback loop in karst ecosystems. Ageratina adenophora plant invasion soil microbial community ecological effect metagenomics Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 1. Introduction The objective of this template is to enable you in an easy way to style your article attractively in a style similar to that of Computer Physics Communications . It should be emphasized, however, that the final appearance of your paper in print and in electronic media will very likely vary to some extent from the presentation achieved in this Word® document. Ageratina adenophora , a globally invasive plant, severely impacts biodiversity, ecosystems, and economies through rapid allelochemical release and efficient nutrient acquisition (Sun et al. 2004; Wang et al. 2005; Wang and Wang 2006; Zhang et al. 2023). To date, it has invaded more than 30 countries worldwide (Changjun et al. 2021). Native to Mexico (Wang et al. 2005), this species was introduced to Yunnan, China, in the 1940s (Sun et al. 2004; Wang and Wang 2006; Zhu et al. 2007; Sang et al. 2010). Owing to its allelopathic potency and high ecological adaptability, A. Adenophora has rapidly colonized Guizhou, Sichuan, Guangxi, and Tibet (Wang et al. 2005; Zhang et al. 2022). Currently, large-scale infestations occur in Yunnan and Guizhou Provinces, causing significant ecological and economic damage (Sang et al. 2010). The biological traits of A. adenophora constitute key drivers of its rapid invasion success. As a perennial herb reaching 1–2 m in height, this species exhibits broad adaptability and light preference, primarily colonizing slopes, roadsides, wastelands, and forest edges (Wang et al. 2011). Its high photosynthetic rate enables rapid biomass accumulation. A. adenophora employs both sexual and asexual reproduction, with damaged rhizomes readily sprouting new shoots (Wang et al. 2006). A single plant produces tens of thousands of viable seeds dispersed via wind, water, animal, and anthropogenic activities (Wang et al. 2006). Critically, allelochemical release underpins invasion success by inhibiting native plants (Li et al. 2017). Allelopathy—defined as plant-mediated chemical interactions affecting neighbouring organisms (Arora et al. 2024; Kumar et al. 2024)—operates through multiple pathways: root exudation, leaf leaching, and litter decomposition (Arora et al. 2024). Key allelochemicals include terpenoids (Zhao et al. 2009) and phenolic compounds (Xie et al. 2010), which suppress the growth and seed germination of native flora, ultimately restructuring the plant community composition and reducing diversity (Rai et al. 2023; Wang et al. 2025). The karst region of central Guizhou features unique geological settings and fragile ecosystems characterized by nutrient-poor soils (Wang et al. 2019), severe water erosion, and rocky desertification (Chen et al. 2018a). Its distinctive geological structure—rugged terrain with thin soil layers—results in poor soil retention capacity, driving both surface soil loss and subsurface leakage (Sun et al. 2020). The soils in this region are predominantly calcareous, with low nutrient availability (organic matter, total nitrogen, total phosphorus, and available phosphorus) due to limestone weathering (Li et al. 2022a). A. adenophora exploits these conditions through exceptional environmental adaptability (Xia et al. 2020) and multimechanistic resource competition (Zhao et al. 2009; Zheng et al. 2012; Shen et al. 2020). The unique karst geomorphology and microclimates further facilitate its invasion (Li et al. 2022b). A. adenophora invasion has multifaceted effects on soil nutrients, enzymes, and microbial communities. It modifies soil physicochemical properties, typically increasing nitrogen (N), nitrate (NO₃⁻-N), ammonium (NH₄⁺-N), available potassium (AK), and available phosphorus (AP) contents, although reductions in total phosphorus (TP) and potassium (TK) may occur (Deng et al. 2015). Concurrently, soil pH shifts are frequently observed (Xia et al. 2024). The effects on soil enzyme activities are context dependent and vary by enzyme type and invasion intensity (Darji et al. 2024). Crucially, invasion restructures microbial composition, functionality, and diversity (Wan et al. 2010; Bajpai and Inderjit 2013; Xiao et al. 2014; Chen et al. 2015; Balami et al. 2017). This includes reduced microbial α diversity, decreased fungal abundance (Xiao et al. 2014; Balami et al. 2017), and altered community assemblages (Wan-Xue et al. 2010). Southwest China's karst region, characterized by impoverished soils and unique hydrology, constitutes an ecologically fragile zone highly vulnerable to A. adenophora invasion (Wang et al. 2019). Guanling County in Guizhou exemplifies typical karst mountainous terrain where nutrient limitation prevails. Crucially, how A. adenophora maintains invasion dominance by restructuring the soil microbial composition/function to drive a self-reinforcing allelopathy‒nutrient feedback loop remains unexplored. This study integrates soil physicochemical analyses, enzyme activity assays, and metagenomic sequencing to ( 1 ) determine the responses of karst soil nutrients, enzyme activities, and microbial communities to A. adenophora invasion; ( 2 ) decipher differentiation patterns in microbial community structure (diversity/composition) and functional genes; and ( 3 ) reveal plant‒soil‒microbe interaction networks, providing theoretical foundations for karst ecological barrier restoration. 2. Materials and Methods 2.1. Site Description The study area is located in Guanling County (25.908877°N, 105.605618°E), Anshun city, Guizhou Province (Fig. 1 ). Situated in the upper reaches of the Dabang River, this karst terrain features hilly and mountainous topography at 1,064 m in elevation. The regional climate data indicate that the mean annual temperature is 16.2°C, the average annual frost-free period is 308 days, the mean annual sunshine duration is 1,381.6 hours, and the mean annual precipitation is 1,370 mm. A. Adenophora predominantly invades forestlands, roadsides, ditches, and dry farmlands, resulting in contiguous infestations. This area represents one of the most severely invaded zones in Guizhou Province. 2.2. Experimental Design Genomic DNA was extracted via the TIANamp Soil DNA Kit (DP705, Tiangen Biotech) per the manufacturer's protocol. The concentration of the extracted DNA was assessed with a Qubit™ 3.0 fluorometer (Invitrogen) with a dsDNA HS Assay Kit . The integrity was determined via 1% agarose gel electrophoresis, and library preparation was performed with the VAHTS® Universal Plus DNA Library Prep Kit for Illumina (ND617). Library quality control included 2 factors: the size distribution was determined via a Qsep-400 Bio-Fragment Analyzer, and quantification was performed using a Qubit 3.0 fluorometer. The final libraries were sequenced on an Illumina NovaSeq 6000 platform with a paired-end 150 bp (PE150) strategy. 2.3. Soil Physicochemical Properties and Plant Nutrient Analyses Total carbon (TC) and total nitrogen (TN) were determined by elemental analysis (Thermo Fisher Scientific elemental analyser) via high-temperature catalytic combustion in oxygen, converting carbon to CO₂ and nitrogen to N₂. Total phosphorus (TP) was measured via molybdenum-antimony anti-spectrophotometry using a UV-1800PC spectrophotometer (MAPADA, Shanghai). Total potassium (TK) was quantified via flame photometry (FP6410 flame photometer, INESA, Shanghai). The soil organic matter (SOM) content was analysed via the potassium dichromate oxidation‒external heating method (DF-101S oil bath), followed by titration. The soil organic carbon (SOC) content was calculated as 58% SOM (based on the dichromate oxidation method). pH was measured potentiometrically (Sartorius PB-1 pH meter) in a 1:2.5 soil:water suspension. Dry matter (DM) was determined by oven drying at 105 ± 5°C to a constant weight. Plant nutrients (TC, TN, TP, and TK) in A. adenophora and A. argyi were analysed using identical methods as those used for the corresponding soil parameters. 2.4. Enzyme Activity Assays All enzymatic analyses were performed using a SpectraMax single-mode reader for absorbance measurements. The specific methods used included urease (UE), which is quantified by indophenol blue colorimetry on the basis of ammonia production from urea hydrolysis and reacts with phenol‒sodium hypochlorite to form indophenol blue. Phosphatase (PHO) was measured via disodium phenyl phosphate colorimetry, which detects phenol release through chromogenic reactions. Catalase (CAT) was assessed by the UV absorption method at 240 nm, H₂O₂ consumption was directly quantified after enzymatic decomposition. Sucrase (SUC) and amylase (AMS) were determined using 3,5-dinitrosalicylic acid (DNS) colorimetry at 540 nm, where reducing sugars from substrate hydrolysis react with DNS to form reddish-brown compounds. 2.5. Metagenomic Sequencing Procedures Genomic DNA was extracted via the TIANamp Soil DNA Kit (DP705, Tiangen Biotech) per the manufacturer's protocol. The concentration of the extracted DNA was assessed with a Qubit™ 3.0 fluorometer (Invitrogen) with a dsDNA HS Assay Kit . The integrity was determined via 1% agarose gel electrophoresis, and library preparation was performed with the VAHTS® Universal Plus DNA Library Prep Kit for Illumina (ND617). Library quality control included 2 factors: the size distribution was determined via a Qsep-400 Bio-Fragment Analyzer, and quantification was performed using a Qubit 3.0 fluorometer. The final libraries were sequenced on an Illumina NovaSeq 6000 platform with a paired-end 150 bp (PE150) strategy. 2.6. Sequence Processing and Assembly The raw sequencing reads underwent rigorous quality control to generate clean reads for downstream analysis. Quality trimming was performed with fastp (v0.23.4) (Chen et al. 2018b) to remove low-quality bases. For host decontamination, clean reads were aligned to host genomes using Bowtie2 (Langmead and Salzberg 2012), with subsequent removal of host-derived sequences. For de novo assembly, processed reads were assembled with MEGAHIT (Li et al. 2015), and contigs ≥ 300 bp were retained. For assembly evaluation, quality was assessed via the QUAST (Gurevich et al. 2013). For gene catalogue construction, contigs were clustered into nonredundant gene catalogues via MMseq2 (v12-113e3; Mirdita et al. 2019) at 90% sequence identity and 80% coverage thresholds. The predicted genes were functionally annotated against Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), evolutionary genealogy of genes: Nonsupervised Orthologous Groups (eggNOG), Carbohydrate-Active enZymes (CAZy), and the Comprehensive Antibiotic Resistance Database (CARD). 2.7. Data Analysis Statistical analyses were performed using Microsoft Excel 2019. Student's t tests were used to assess differences in alpha diversity indices, soil nutrients, enzyme activities, and species abundance between groups. Nonmetric multidimensional scaling (NMDS) based on Bray‒Curtis distance matrices was used to visualize community composition variations. All graphical outputs were generated with R software (v4.3.1) via the ggplot2 and vegan packages. 3. Results 3.1. Soil Nutrient Alterations A. adenophora invasion significantly altered the soil nutrient content and pH (Fig. 2 ). Compared with the uninvaded control (CK), the invaded soils presented greater pH (P < 0.05), elevated soil organic carbon and total nitrogen (P < 0.05), and nonsignificant increases in soil organic matter, total carbon, total potassium, total phosphorus, and dry matter. These results indicate that invasion establishes an allelopathy‒nutrient positive feedback loop by selectively enhancing key nutrients (SOC and TN) coupled with slight alkalinization, potentially favouring invasive growth. 3.2. Impact of A. adenophora Invasion on Soil Enzyme Activities A. adenophora invasion significantly altered soil enzyme activities (Fig. 3 ), with generally higher values in invaded soils than in the uninvaded control. The soil urease and phosphatase activities tended to increase with invasion intensity. Specifically, sucrase and urease activities were significantly elevated in invaded soils compared with those in CK soils ( P < 0.05). These changes suggest that invasion accelerates nutrient turnover through the selective activation of carbon/nitrogen-metabolizing enzymes (SUC and UE) to sustain competitive dominance, whereas the delayed response of phosphorus-cycling enzymes (nonsignificant PHO change) indicates a selective nutrient acquisition strategy. 3.3. Differences in Nutrient Contents between A. adenophora and A. argyi Significant differences in nutrient content were observed between the invasive A. adenophora and the native plant A. argyi (Fig. 4 ). Compared with A. argyi , A. adenophora generally presented higher total nitrogen and total potassium contents (p < 0.05), whereas A. argyi presented significantly greater total carbon contents in stem tissues. Both species displayed organ-specific nutrient allocation patterns, but A. adenophora presented more pronounced nutrient enrichment in leaf tissues. 3.4. Species Composition and Gene Counts A total of 12 soil samples were collected, including soil microorganisms spanning 7 domains, 198 phyla, 179 classes, 364 orders, 856 families, 3,531 genera, and 22,111 species. Analysis of gene count differences between A. adenophora -invaded and non-invaded conditions revealed minimal redundant gene numbers and the lowest proportion of unique redundant genes under invasion (Fig. 5 a and b). Compositional analysis revealed domain-level dominance by bacteria (83.8%), with minor components including Archaea, Fungi, Viruses, Metazoa, Eukaryota, and Viridiplantae (< 0.1%), alongside unclassified and unassigned taxa (Fig. 5 c). At the phylum level, the community was predominantly composed of Proteobacteria (31.2%), Acidobacteria , Actinobacteria , Chloroflexi , Gemmatimonadetes , Verrucomicrobia , Planctomycetes , and Nitrospirae (1.3%) (Fig. 5 d). The ten most abundant phyla were exclusively bacteria, with Proteobacteria , Acidobacteria and Gemmatimonadetes collectively constituting 56.7% of the community. Functionally, Proteobacteria —the dominant phylum—mediates carbon/nitrogen cycling, symbiosis, and pathogenesis; Acidobacteria represents oligotrophic specialists that decompose recalcitrant organics (cellulose) in acidic soils; and Gemmatimonadetes dominates arid soils with putative involvement in phosphate transport and metabolism. This microbial profile aligns with the oligotrophic, drought-prone karst environment of central Guizhou, indicating that A. adenophora invasion restructures soil microbiomes by selecting extremophile-adapted taxa. (a) Venn diagram. Ellipse colours denote distinct groups, with overlaps indicating shared non-redundant gene counts and non-overlapping areas representing group-specific non-redundant genes. (b) Non-redundant gene counts (box plot). The group categories are displayed on the x-axis, and the gene counts are displayed on the y-axis. Box plot elements: box tops/bottoms = upper/lower quartiles (interquartile range, IQR); central line = median; whiskers = minimum/maximum values within 1.5×IQR; outlying points beyond whiskers represent outliers. The numeric labels on the intergroup connectors indicate t test p values (p > 0.05 not displayed). (c) Domain-level taxonomic composition. The samples are arranged on the x-axis, with relative abundance percentages on the y-axis. The colour-coded bars represent taxa (one hue per taxon), and the bar length reflects the relative abundance proportions. For optimal visualization, only the top 10 most abundant taxa are shown; the remaining taxa are merged as "Others." "Unassigned" denotes taxonomically unannotated species. (d) Phylum-level composition showing the top 10 abundant taxa. 3.5. Divergence in Species Composition Analysis of the microbial communities in A. adenophora -invaded and non-invaded soils revealed significant compositional divergence (P < 0.05), with hierarchical clustering segregating the 12 soil samples into two distinct groups. Under heavy invasion (H), Candidatus Doudnabacteria and Candidatus Kerfeldbacteria presented the strongest positive correlations, whereas Candidatus Lindowbacteria and Candidatus Cloacimonetes presented minimal associations in control (CK) soils. Invasion conditions reduced the abundances of Actinobacteria and Candidatus Dormibacteraeota but increased those of most other taxa (Fig. 6 a). Among the 15 most abundant phyla, Verrucomicrobia , Actinobacteria , Candidatus Eisenbacteria, and Bacteroidetes dominated with marked intergroup differences. Actinobacteria peaked in CK, whereas Verrucomicrobia and Candidatus Eisenbacteria were significantly enriched in invaded soils (Fig. 6 b). These results collectively confirm profound invasion-induced microbial restructuring, identifying Candidatus Doudnabacteria and Candidatus Kerfeldbacteria as the taxa most responsive to A. adenophora invasion. (a) Phylum-level divergent taxa heatmap. Displayed taxa that passed differential abundance testing (p < 0.05). The left dendrogram clusters divergent taxa, the top dendrogram clusters samples, and the central heatmap visualizes abundance gradients. (b) Phylum-level divergent abundance bar plot. Left section: mean abundance bars (x-axis = mean proportion, y-axis = taxon names); centre: p value asterisks (*p < 0.05, **p ≤ 0.001); right: actual p values. The top 15 taxa were selected by a descending p value and sorted by descending abundance. 3.6. Species Diversity Composition To assess the impact of A. adenophora invasion on microbial diversity, alpha and beta diversity analyses were conducted. Alpha diversity was evaluated using the Chao1 richness index, Shannon diversity index, Simpson dominance index, and Pielou evenness index, whereas beta diversity was evaluated via principal component analysis (PCA) and nonmetric multidimensional scaling (NMDS) to resolve intersample compositional differences. Significant intergroup differences emerged in the alpha diversity indices (p < 0.05). Compared with the control soils, the invaded soils presented significantly lower Shannon diversity and Pielou evenness indices than the control soils (CK, p < 0.05), indicating microbial diversity loss and uneven species distribution following invasion. Conversely, the Simpson dominance and Chao1 richness indices did not significantly differ across groups (p > 0.05) (Fig. 7 a-d). These results collectively confirmed the greater microbial biodiversity and relative species richness in the CK soils than in the CK soils, indicating that Adenophora invasion profoundly restructured the microbial communities. The PCA results demonstrated a cumulative explanatory power of 75.25% (PC1 = 61.74%, PC2 = 13.51%), effectively capturing intersample compositional divergence. Visualization revealed clear segregation between the A. adenophora -invaded and control (CK) groups in ordination space, indicating distinct community structures. Conversely, the proximity among the different invasion-intensity groups suggested high compositional similarity (Fig. 7 e). NMDS analysis corroborated these findings, with a stress value of 0.0001 (reliability threshold: stress < 0.2; lower values indicate higher precision), confirming exceptional ordination reliability and strong concordance with the PCA results (Fig. 7 f). Overall, these results indicate that A. adenophora invasion significantly impacts microbial biodiversity and relative species richness, while minimal separation among invasion gradients reflects high community similarity. (a) Shannon index variation. X-axis: experimental groups; Y-axis: alpha diversity index. (b) Simpson index variation. X-axis: groups; Y-axis: alpha diversity index. (c) Chao1 index variation. X-axis: groups; Y-axis: alpha diversity index. (d) Pielou evenness index variation. X-axis: groups; Y-axis: alpha diversity index. (e) Principal component analysis (PCA) of species abundance. Points represent sample-specific compositions; colour coding denotes groups. X-axis: PC1 with explanatory power (%); Y-axis: PC2 with explanatory power (%). (f) Nonmetric multidimensional scaling (NMDS) ordination. Points correspond to samples; colours indicate groups. Stress values < 0.1 indicate acceptable ordination, whereas values < 0.05 demonstrate excellent representativeness. 3.7. Functional Gene Annotation eggNOG functional classification revealed that among the top 20 categories, [S]: Function unknown predominated, whereas [R]: General function prediction represented only the most abundant annotated function, and [N]: Cell motility was the least frequent (Fig. 8 a). The CAZy distribution was as follows: GH (32%), GT (38.9%), PL (1.7%), CE (5.1%), AA (1.8%), and CBM (20.5%), with GT being the most prevalent and PL the least abundant (Fig. 8 b). KEGG pathway analysis at level 2 identified four major categories: metabolism, genetic information processing, environmental information processing, and cellular processing. The global and overview maps revealed the primary metabolic pathways (highest relative abundance), followed by carbohydrate metabolism (sugar synthesis/degradation and monosaccharide/polysaccharide metabolism) and amino acid metabolism (third highest) (Fig. 8 c). Gene profiling revealed that profiles indicating the following 20 types of antibiotic resistance were the most enriched: multidrug, tetracycline, macrolide, peptide, glycopeptide, aminoglycoside, aminoglycoside, aminocoumarin, fluoroquinolone, mupirocin, nitroimidazole, pleuromutilin, rifamycin, lincosamide, fosfomycin, carbapenem, phenicol, disinfecting agents and antiseptics, sulfonamide and elfamycin resistance. Notably, multidrug, tetracycline and macrolide resistance-related genes exhibited the highest degree of enrichment (Fig. 8 d). (a) eggNOG functional classification. X-axis: eggNOG categories; Y-axis: relative abundance of functional genes. (b) CAZy distribution. The colour-coded sectors represent carbohydrate-active enzyme classes, with sector areas proportional to relative abundance. (c) KEGG pathway annotation at level 2. X-axis: relative abundance of functional genes; Y-axis: level-2 functional categories. (d) Antibiotic resistance gene abundance. X-axis: antibiotic resistance types; Y-axis: relative abundance of resistance genes. 3.8. Functional Gene Diversity Microbial functional gene diversity critically influences key biogeochemical processes and metabolic functions. In addition to the global and overview maps, carbohydrate metabolism, amino acid metabolism, and energy metabolism presented the highest gene abundances (Fig. 9 a). CAZy annotation revealed GT2 (glycosyltransferases, cellulose synthase), GT4 (glycosyltransferases), and CBM50 (carbohydrate-binding modules targeting pectin compounds, such as pectate and galacturonate) as predominantly enriched categories (Fig. 9 b), which drive microbial decomposition of plant cell walls, host‒microbe interactions, and sugar metabolism regulation. The CARD annotation encompassed 10 antibiotic resistance classes: nitroimidazole, mupirocin, fluoroquinolone, aminocoumarin, aminoglycoside, glycopeptide, peptide, macrolide, tetracycline, and multidrug (Fig. 9 c). Functional gene PCA demonstrated a cumulative explanatory power of 71.23% (PC1 = 52.45%, PC2 = 18.78%), indicating robust ordination efficacy (Fig. 9 d). (a) Functional composition scatter plot. X-axis: KEGG level-2 metabolic pathways; Y-axis: relative abundance of functional gene categories. (b) CAZy enzyme abundance bubble plot. X-axis: sample names; Y-axis: enzyme classes. The bubble size corresponds to the magnitude of the relative abundance. (c) Circos diagram of the CARD antibiotic resistome. Outer ring: right semicircle = samples/groups, left semicircle = resistance gene types (radial scale = abundance proportion). Inner ribbons connect resistance genes to samples/groups, revealing gene functional composition per sample and sample distribution per gene; ribbon width indicates proportional distribution. (d) Functional gene PCA ordination. Points represent samples; colour coding denotes groups. X-axis: PC1 with explanatory power (%); Y-axis: PC2 with explanatory power (%). Heatmap analysis revealed significant differences between A. adenophora -invaded and control (CK) soils (Fig. 10 a), with 26 functional genes segregating into two distinct clusters. Sixteen genes presented significant invasion-associated enrichment, whereas ten presented CK-specific associations. Pathways significantly linked to invasion included photosynthesis; linoleic acid metabolism; one carbon pool by folate; streptomycin biosynthesis; polyketide sugar unit biosynthesis; metabolic pathways; the sulphur relay system; lysine biosynthesis; folate biosynthesis; the phosphotransferase system (PTS); aminoacyl-tRNA biosynthesis; acarbose and validamycin biosynthesis; nicotinate and nicotinamide metabolism; riboflavin metabolism; terpenoid backbone biosynthesis; and lipopolysaccharide biosynthesis. Heatmap analysis at the KEGG Orthology (KO) level revealed significant compositional divergence between A. adenophora -invaded and control (CK) soils (P < 0.05, Fig. 10 b), with metabolic enzymes segregating into two distinct clusters. The invasion-associated KO terms included K00773, K03655, K11749, K01409, K02652, K02038, K07263, K01710, K02346, K07300, K03321, K04042, K14415, K03639, K01928, K02669, K06941, K02666, K01662, K03750, K06180, K06969, K00639, K03587, K03820, K03545, K00099, K08483, K02517, K01933, K01754, K03631, K00943, K02037, K03833, K01893, K02005, K02653, K00703, K02662, K13038, K04066, K00973, K01885, K02039, K01924, K00812, K01952, K07462, K01961, K03572, K00602, K07391, K00821, K01887, K00806, K00104, and K03564. CK-associated terms included K00135, K01869, K01902, K00252, K01939, K02314, K01434, K01692, K03799, K01687, K00265, K03496, K00525, K03046, K02358, K03544, K06994, K00915, K01621, K00164, K02112, K10112, K01251, K00162, K01426, K05343, K02274, K01915, K01652, K01214, K01897, K07045, K15371, K04043, K00666, K03694, K14162, K01113, K03553, K02600, K02519, and K03466. Notably, K13038 and K00639 encode flavonoid synthases and polyketide synthases that produce allelochemicals that inhibit seed germination in competing species, whereas K02038, K01933, and K01893 encode nutrient transporters and nitrogenases that monopolize nitrogen/phosphorus resources. Heatmap analysis of carbohydrate-active enzyme families revealed significant compositional divergence between A. adenophora -invaded and control (CK) soils (P < 0.05, Fig. 10 c), with enzymatic profiles segregating into two distinct clusters. The invasion-associated CAZy families included GT105, GT89, PL39, GH92, CBM42, GT9, GH85, CBM16, CBM67, CBM4, CBM6, PL0, GH50, AA5, GH120, GH81, GH144, GH64, CBM64, GH160, CBM0, CBM26, CBM57, PL9, CBM21, GH142, PL2, GT3, GH128, CBM60, GH141, GH2, and GH30. The CK-associated families included GH3, GT28, GH100, GT1, PL7, GH77, GH1, GT58, AA3, AA12, GH76, GH4, GH101, and GH15. The number of invasion-enriched families was 1.2× (GT105), 1.7× (CBM42), 1.7× (PL9), and 1.5× (GH144) greater than that in the CK. A. Adenophora invasion leverages GT105 and CBM42 for complex exopolysaccharide synthesis, PL9 and GH144 for plant polysaccharide degradation, AA5 for phenolic detoxification, and CBM60 for stress resistance, whereas CK communities primarily utilize GH3/GH15 for carbon acquisition. Heatmap analysis at KEGG level 3 revealed significant compositional divergence between A. adenophora -invaded and control (CK) soils (P < 0.05, Fig. 10 d), with differentially abundant genes clustering into two distinct groups. The invasion-associated genes included cutA , ALU1-P, eefA , zwS/hydG , pmpM , galE , actA , copR , crdA , smdA , vmeF , mepA , norM/pmpM , merG , recG , nczA , smdB , G2alt , and troD . The CK-associated genes included ctpD , actR , pgpA/ItpgpA , emrB , adeE , bepC , yfmP , fetB/ybbM , adeG , srpC , and silR . The number of invasion-enriched genes was 2.3-fold ( merG ), 1.4-fold ( norM ), 1.1-fold ( recG ), 1.3-fold ( smdB ), 1.1-fold ( zraS ), and 1.1-fold ( copR ) greater than that in the CK soils. Mechanistically, invasion-associated merG and norm confer heavy metal resistance, recG and smdB facilitate oxidative damage repair, while zraS and copR encode two-component regulatory systems, collectively enhancing adaptive advantages during A. adenophora invasion. KEGG level-3 bar plot analysis (Fig. 10 e) revealed significant intergroup differences (P < 0.05), with aminoacyl-tRNA biosynthesis exhibiting the highest abundance among the top 15 pathways, whereas biosynthesis of ansamycins was the lowest. The control (CK) values were significantly lower than those of the invaded groups. Concurrently, genus-level functional gene profiling (Fig. 10 d) revealed the following order of abundance: copR > gale > vmeF > smdB > actA > adeG > actR > norM/pmpM > ALU1-P> crdA > emrB > trod > pgpA/ItpgpA > yfmP > fetB/ybbM . The invasion-enriched genes copR , galE , vmeF , smdB , and actA presented 1.1-fold, 1.23-fold, 1.19-fold, 1.21-fold, and 1.45-fold greater abundances than CK, respectively. (a) Differential functional gene abundance heatmap. (b) KEGG Orthology (KO) level differential abundance heatmap. (c) CAZy family-level differential abundance heatmap. (d) Differential functional gene abundance heatmap. (e) KEGG level-3 differential gene abundance bar plot. (f) Genus-level differential gene abundance bar plot. Displayed genes that passed differential abundance testing (p < 0.05). The left dendrogram clusters the differentially abundant genes; the top dendrogram clusters the samples; the central heatmap visualizes the abundance gradients. Correlation network analysis (Fig. 11 a) identified metabolic pathways , biosynthesis of secondary metabolites , and microbial metabolism in diverse environments as the most abundant pathways, exhibiting strong positive/negative correlations with the top 20 functional genes across multiple metabolic processes. The RDA ordination (Fig. 11 b) revealed a cumulative explanatory power of 57.65% (Axis 1: 34.98%; Axis 2: 22.67%), indicating moderate functional gene interpretability. Significant positive correlations included Biosynthesis of antibiotics , purine metabolism , biosynthesis of secondary metabolites , and metabolic pathways with sucrase (SUC) and phosphatase (PHO); biosynthesis of amino acids and two-component system with total potassium (TK), total phosphorus (TP), and catalase (CAT); and carbon metabolism and microbial metabolism in diverse environments with urease (UE), total carbon (TC), soil organic matter (SOM), soil organic carbon (SOC), total nitrogen (TN), and ammonium nitrogen (AMS) contents. A functional gene‒environment heatmap analysis (Fig. 11 c) segregated the soil parameters into two clusters: Cluster 1 (positively correlated with TK, TP, SUC, PHO, and CAT): fructose and mannose metabolism; one carbon pool by folate; streptomycin biosynthesis; biosynthesis of secondary metabolites; base excision repair; protein export; glycerophospholipid metabolism; biosynthesis of amino acids; homologous recombination; mismatch repair; porphyrin and chlorophyll metabolism; ribosome; bacterial secretion system; two-component system; vitamin B6 metabolism; chlorocyclohexane and chlorobenzene degradation; nicotinate and nicotinamide metabolism; glycerolipid metabolism; polyketide sugar unit biosynthesis; terpenoid backbone biosynthesis; lipopolysaccharide biosynthesis; riboflavin metabolism; metabolic pathways; thiamine metabolism; lysine biosynthesis; folate biosynthesis; sulphur relay system; biotin metabolism; phenylalanine, tyrosine and tryptophan biosynthesis; ubiquinone and other terpenoid-quinone biosynthesis; fatty acid biosynthesis; fatty acid metabolism; biosynthesis of unsaturated fatty acids; carbon fixation in prokaryotes; aminoacyl-tRNA biosynthesis; inositol phosphate metabolism; peptidoglycan biosynthesis; amino sugar and nucleotide sugar metabolism; and D-glutamine and D-glutamate metabolism. Cluster 2 (positively correlated with SOC, TC, SOM, UE, AMS, and TN): residual functional categories. (a) Correlation network. Circles represent functional genes, with size denoting abundance; connecting lines indicate intergene correlations, where line thickness corresponds to correlation strength, and colour (red = positive, green = negative) signifies directionality. (b) RDA ordination of functional genes: ( 1 ) blue arrows denote environmental factors; arrow length indicates influence strength on community variation; ( 2 ) arrow angle relative to axes reflects factor‒axis correlation (smaller angles = higher correlation); ( 3 ) black points represent functions; proximity to an arrow indicates stronger factor‒function interaction; ( 4 ) points aligned with arrow direction indicate positive factor‒function covariation, whereas opposite alignment indicates negative covariation; ( 5 ) axis scales derived from regression values of samples against environmental factors; ( 6 ) RDA1/RDA2 axes represent primary ordination vectors, with labelled percentages indicating cumulative explanatory power for community structure. (c) Function-environment heatmap. X-axis: environmental factors; Y-axis: functional genes. 3.9. Soil Nutrients and Enzymes as Determinants of Microbial Diversity Soil nutrients and enzymes critically influence microbial diversity. The RDA ordination revealed a cumulative explanatory power of 69.96% (RDA1 = 47.9%, RDA2 = 22.06%), demonstrating robust interpretability. Significant positive correlations emerged between Viridiplantae , Archaea , Fungi , Eukaryota and catalase (CAT), total phosphorus (TP), phosphatase (PHO), and total potassium (TK), while Bacteria were positively correlated with TK, soil organic carbon (SOC), total carbon (TC), soil organic matter (SOM), urease (UE), and sucrase (SUC) (Fig. 12 a). Species‒environment heatmap analysis segregated the taxa into two clusters: Cluster 1 (positively correlated with PHO, TP, TK, CAT, and SUC): candidate division NC10 , Candidatus Rokubacteria , Crenarchaeota , Nitrospinae , Candidatus Aenigmarchaeota , einococcus Thermus , Abditibacteriota , Thaumarchaeota , Chloroflexi , Candidatus Roizmanbacteria , Acidobacteria , Candidatus Tectomicrobia , Actinobacteria , ndidatus Dormibacteraeota , andidatus Giovannonibacteria , andidatus Microgenomates , andidate division WWE3 , yanobacteria , tribacterota , andidatus Blackallbacteria , alneolaeota , andidatus Magasanikbacteria , roviricota , andidatus Saccharibacteria , and andidatus_Kaiserbacteria . Cluster 2 was positively correlated with SOC, TC, SOM, UE, ammonium nitrogen (AMS), and TN: residual taxa (Fig. 12 b). (a) RDA ordination diagram (displaying species). ( 1 ) Blue arrows represent environmental factors; arrow length indicates the strength of the factor’s influence on community variation, with longer arrows denoting greater influence. ( 2 ) The angle between an arrow and an ordination axis reflects the factor‒axis correlation, where smaller angles indicate stronger correlations. ( 3 ) Black dots denote species; closer proximity between a species point and an arrow signifies a stronger species‒environmental factor interaction. ( 4 ) Species points aligned with an arrow indicate a positive correlation between the factor and species variation, whereas those in the opposite direction indicate a negative correlation. ( 5 ) Scale ticks on the horizontal and vertical axes represent values generated for samples during regression analysis with environmental factors. ( 6 ) The RDA1 and RDA2 axes represent the two variables with the highest explanatory power; their axis labels indicate the proportion of environmental variation explained by each axis, and a larger combined sum corresponds to greater explanatory capacity for overall community structure and species distribution patterns. (b) Species‒environmental factor correlation heatmap: Environmental factors are displayed on the horizontal axis (showing the top 20 species with the smallest p values), with species on the vertical axis. 4. Discussion 4.1. Synergistic Drive by Soil Nutrients and Allelopathy A. adenophora synergistically drives its invasion by altering soil nutrients and releasing allelochemicals. Compared with native species, invasive plants often exhibit increased biomass, increased nitrogen availability, altered nitrogen fixation rates, and the production of litter with higher decomposition rates in invaded habitats (Ehrenfeld 2003). During the initial invasion phase, A. adenophora likely promotes its own growth while inhibiting native plants by increasing the soil nutrient content, particularly the nitrogen content (Deng et al. 2015; Jiao and Huang 2024). This study demonstrated that A. adenophora invasion led to significant accumulation of soil SOC and TN (p < 0.05) and weak alkalization of the soil pH (Fig. 2 ). This may stem from the regulation of soil mineralization processes by allelochemicals, which aligns with the significant correlations observed with the functional genes K00639 and K13038. In areas invaded by exotic species, litter decomposition rates and nutrient availability typically increase (Standish et al. 2004; Martin et al. 2010), which could also contribute to elevated soil nutrient levels. The allelochemicals released by A. adenophora can directly suppress the growth of native plants, reducing their ability to compete for resources (Inderjit et al. 2011; Jiao and Huang 2024; Wu et al. 2025). Furthermore, the TN and TK contents in A. adenophora leaves were greater than those in A. argyi leaves (Fig. 4 ), suggesting that its leaf litter may also be an important supplementary source of soil nitrogen. 4.2. Selective Activation of Enzyme Activities The invasion process of A. adenophora is closely linked to soil nutrients, microbial communities, and enzyme activities (Li et al. 2017). Its invasion leads to significant changes in soil nutrients and enzyme activities (Sun et al. 2013; Deng et al. 2015). A. adenophora invasion alters the structure and diversity of soil microbial communities, which in turn affects the activities of enzymes involved in nutrient transformation (Wan-Xue et al. 2010; Balami et al. 2017). Microorganisms acquire nutrients by secreting specific extracellular enzymes to degrade soil organic matter (Sinsabaugh et al. 2009). This study revealed that under A. adenophora invasion, SUC (sucrase) and UE (urease) activities were significantly greater than those in the control group (CK) (p < 0.05) (Fig. 3 ), indicating that invasion preferentially stimulates the carbon and nitrogen metabolic enzyme systems, accelerating the decomposition of carbohydrates and urea to support rapid growth. In contrast, PHO (phosphatase) activity did not change significantly, suggesting that phosphorus acquisition is not the core of its competitive advantage. This finding, combined with the finding that extracellular polysaccharide synthesis is dominated by certain genes, such as GT105 and CBM42 (Fig. 10 c), collectively indicates efficient monopolization of carbon resources. 4.3. Functional Remodelling of Microbial Communities A. adenophora invasion remodels microbial communities through multiple pathways, thereby affecting soil nutrient cycling and ecosystem functions (Wan-Xue et al. 2010; Zhao et al. 2019; Li et al. 2022a; Wu et al. 2025). During plant invasion, bacterial diversity is a primary response variable, with invasive plants often leading to an increase in bacterial diversity (Torres et al. 2021). A. adenophora invasion shifts the dominant soil microbial composition from Aeromicrobium and Marmoricola to Reyranella and Bradyrhizobium (Li et al. 2022c). Post-invasion, A. adenophora selectively enriches less abundant genera, such as Clostridium and Enterobacter . At the phylum level, the community shifts from Bacteroidetes -dominated to Proteobacteria -dominated (Chen et al. 2019). This study demonstrated that under A. adenophora invasion, the soil microbial Shannon index was lower (p < 0.05) (Fig. 7 a), and the species composition significantly differed (p < 0.05) (Fig. 7 e), indicating that invasion resulted in the selection of a reduced number of dominant bacterial groups, such as Verrucomicrobia , Proteobacteria , and Acidobacteria . 4.4. Interactive Feedback in the A. adenophora–Soil–Microbe System A. adenophora establishes an invasive plant‒soil feedback mechanism by altering soil properties and microbial communities. Its post-invasion secretions and allelochemicals can inhibit the growth of other plants, thereby reducing competition (Li et al. 2017; Kumar et al. 2024). Simultaneously, by modifying soil nutrient availability and enzyme activities, A. adenophora affects the nutrient uptake of other plants, resulting in a competitive advantage (Deng et al. 2015). Furthermore, invasion by A. adenophora increases the number of microbes beneficial to its invasion (Chen et al. 2019), increasing its invasive capacity and forming a positive invasive plant‒soil–microbe feedback loop (Fang et al. 2019). As litter serves as a source of nutrient input, invasion by exotic species increases litter decomposition rates and nutrient availability (Standish et al. 2004; Martin et al. 2010). This further establishes a positive feedback loop of "high-quality litter input → stimulation of enzyme activity → enrichment of functional microbes → nutrient recycling," ultimately leading to intensified invasion. 5. Conclusion This study reveals the response mechanism of the "invasive plant–soil physicochemical properties–microorganism" system during A. adenophora invasion into the karst ecosystem of central Guizhou. At the biochemical level, A. adenophora invasion significantly increased the SOC and TN contents and pH (p < 0.05), creating a weakly alkaline, nitrogen-enriched environment. This reflects a "carbon‒nitrogen prioritization" strategy under karst regional conditions while simultaneously forming a weakly alkaline soil environment favourable for A. adenophora survival. Under invasion conditions, sucrase (SUC) and urease (UE) activities were significantly greater than those in the control group (CK) and were positively correlated with the abundances of Proteobacteria and Gemmatimonadetes . At the microbial level, A. adenophora invasion reduced the microbial Shannon index (p < 0.05) but enriched bacteria, such as Verrucomicrobia and Candidatus Eisenbacteria . It upregulated genes involved in nutrient acquisition (GT105/CBM42), allelochemical synthesis (K00639/K13038), and stress resistance (merG/recG), enhancing niche competitive advantages. A. adenophora invasion establishes a self-reinforcing feedback loop of " A. adenophora invasion → soil nutrient enrichment and enzyme stimulation → microbial functional adaptation," providing a favourable foundation for its invasion success. Declarations Declaration of Competing Interest We affirm that no competing financial interests or personal relationships exist which could be reasonably construed as influencing the research presented in this work. Funding This study was supported by the Youth Science and Technology Development Project of Guizhou Academy of Sciences (Grant No. 52000024P00280H10021H) ,Guizhou Provincial Key Laboratory of Agricultural Biosafety (Grant No. QKHZSYS[2026]024) and the earmarked fund for Guizhou Modern Agriculture Research System (Grant No. GZSTCYJSTX[2025]—01). Author Contribution L is responsible for providing the ideas and funding sources, W is mainly in charge of the methods, and W is responsible for the overall conception of the text and the experiments. Acknowledgments We extend our sincere gratitude to Dr. Weijie Li for his support and Dr. Jiaguo Wang for his assistance. We acknowledge AJE ( www.aje.cn ) for professional language editing services. Data availability Data will be made available on request. References Arora S, Husain T, Prasad SM (2024) Allelochemicals as biocontrol agents: Promising aspects, challenges and opportunities. South Afr J Bot 166:503–511. https://doi:10.1016/j.sajb.2024.01.029 Bajpai D, Inderjit (2013) Impact of nitrogen availability and soil communities on biomass accumulation of an invasive species. AoB PLANTS 5:plt045. https://doi:10.1093/aobpla/plt045 Balami S, Thapa LB, Jha SK (2017) Effect of invasive Ageratina adenophora on species richness and composition of saprotrophic and pathogenic soil fungi. 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07:08:59","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8826938/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8826938/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":102753952,"identity":"c3c1c45c-3dbc-4712-a747-739316764ef7","added_by":"auto","created_at":"2026-02-16 09:36:40","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":414417,"visible":true,"origin":"","legend":"\u003cp\u003eLocation map of the study area.\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8826938/v1/8c51f97fb3702b6219edfca9.jpeg"},{"id":102753735,"identity":"18ea55b9-442f-473b-a887-36c0c74187e3","added_by":"auto","created_at":"2026-02-16 09:36:21","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":138290,"visible":true,"origin":"","legend":"\u003cp\u003eEffects of \u003cem\u003eA. adenophora\u003c/em\u003einvasion on the soil nutrient content.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8826938/v1/3dd8eb1225016332f8fa2862.png"},{"id":102753855,"identity":"e6eb3fa7-2ca4-4174-abb0-3fae072ef6c2","added_by":"auto","created_at":"2026-02-16 09:36:28","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":107819,"visible":true,"origin":"","legend":"\u003cp\u003eEffects of \u003cem\u003eA. adenophora \u003c/em\u003einvasion on soil enzyme activities.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8826938/v1/3d286c25a662b2822615a572.png"},{"id":102753984,"identity":"75cbc4cd-f036-4e38-86d7-c7399c201f9f","added_by":"auto","created_at":"2026-02-16 09:36:48","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":68754,"visible":true,"origin":"","legend":"\u003cp\u003eNutrient contents of\u003cem\u003e A. adenophora\u003c/em\u003e and\u003cem\u003e A. argyi.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-8826938/v1/6b7ac2cce949dc398d544137.png"},{"id":102753783,"identity":"8c080042-22a2-49ea-ac5e-c11716c1c8f1","added_by":"auto","created_at":"2026-02-16 09:36:26","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":226991,"visible":true,"origin":"","legend":"\u003cp\u003eNon-redundant genes and species composition.\u003c/p\u003e","description":"","filename":"floatimage5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8826938/v1/d9cb647768ce8d7556aa1a61.jpeg"},{"id":102753983,"identity":"3e10db79-7b2e-493c-8d34-f8551abc0a98","added_by":"auto","created_at":"2026-02-16 09:36:46","extension":"jpeg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":93148,"visible":true,"origin":"","legend":"\u003cp\u003eSpecies composition divergence.\u003c/p\u003e","description":"","filename":"floatimage6.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8826938/v1/6c95ae997c2dabe0bacfa1d8.jpeg"},{"id":102753779,"identity":"f74052d8-04c1-444f-b0cf-57c29525fe6f","added_by":"auto","created_at":"2026-02-16 09:36:26","extension":"jpeg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":139834,"visible":true,"origin":"","legend":"\u003cp\u003eVariations in soil microbial alpha and beta diversity.\u003c/p\u003e","description":"","filename":"floatimage7.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8826938/v1/f577d1b1f1b1457706a433b2.jpeg"},{"id":102753870,"identity":"303856d8-6869-4bed-87bb-b0aeb49a77a0","added_by":"auto","created_at":"2026-02-16 09:36:32","extension":"jpeg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":96901,"visible":true,"origin":"","legend":"\u003cp\u003eFunctional gene annotation.\u003c/p\u003e","description":"","filename":"floatimage8.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8826938/v1/240dfa3151f207f65961eff7.jpeg"},{"id":102753723,"identity":"29326cef-d190-4452-be9c-ad2a0b29f0fd","added_by":"auto","created_at":"2026-02-16 09:36:11","extension":"jpeg","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":122017,"visible":true,"origin":"","legend":"\u003cp\u003eFunctional gene composition and diversity analysis.\u003c/p\u003e","description":"","filename":"floatimage9.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8826938/v1/5fee8bc81b606bb3c5280666.jpeg"},{"id":102753739,"identity":"a6e2fbb5-f2a3-431e-ae99-212abe167e4b","added_by":"auto","created_at":"2026-02-16 09:36:22","extension":"jpeg","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":503781,"visible":true,"origin":"","legend":"\u003cp\u003eDifferential functional genes.\u003c/p\u003e","description":"","filename":"floatimage10.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8826938/v1/c9ac34dfb9c5d307896522cf.jpeg"},{"id":102753951,"identity":"7afe9167-6d4b-4335-9ff5-e1954a6254a3","added_by":"auto","created_at":"2026-02-16 09:36:40","extension":"jpeg","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":217853,"visible":true,"origin":"","legend":"\u003cp\u003eFunctional gene correlation analysis.\u003c/p\u003e","description":"","filename":"floatimage11.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8826938/v1/107f30675c68430385869539.jpeg"},{"id":102753862,"identity":"461028a3-3327-4070-a6df-cc7eb67ed6ec","added_by":"auto","created_at":"2026-02-16 09:36:29","extension":"jpeg","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":88117,"visible":true,"origin":"","legend":"\u003cp\u003eSpecies‒environmental factor correlation analysis.\u003c/p\u003e","description":"","filename":"floatimage12.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8826938/v1/f5a621d283afe4bf10558d34.jpeg"},{"id":106197436,"identity":"bfde0ff5-4498-4150-98f2-21597a0d68e3","added_by":"auto","created_at":"2026-04-06 00:54:06","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3087058,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8826938/v1/ca69252a-59d0-4d88-8bab-2a030dceaea1.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eEffects of \u003cem\u003eAgeratina adenophora\u003c/em\u003e Invasion on Soil Nutrients, Enzyme Activities, and Microbial Composition/Function in Karst Areas of Central Guizhou\u003c/p\u003e","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe objective of this template is to enable you in an easy way to style your article attractively in a style similar to that of \u003cem\u003eComputer Physics Communications\u003c/em\u003e. It should be emphasized, however, that the final appearance of your paper in print and in electronic media will very likely \u003cem\u003evary to some extent\u003c/em\u003e from the presentation achieved in this Word\u0026reg; document. \u003cem\u003eAgeratina adenophora\u003c/em\u003e, a globally invasive plant, severely impacts biodiversity, ecosystems, and economies through rapid allelochemical release and efficient nutrient acquisition (Sun \u003cem\u003eet al.\u003c/em\u003e 2004; Wang \u003cem\u003eet al.\u003c/em\u003e 2005; Wang and Wang 2006; Zhang \u003cem\u003eet al.\u003c/em\u003e 2023). To date, it has invaded more than 30 countries worldwide (Changjun \u003cem\u003eet al.\u003c/em\u003e 2021). Native to Mexico (Wang \u003cem\u003eet al.\u003c/em\u003e 2005), this species was introduced to Yunnan, China, in the 1940s (Sun \u003cem\u003eet al.\u003c/em\u003e 2004; Wang and Wang 2006; Zhu \u003cem\u003eet al.\u003c/em\u003e 2007; Sang \u003cem\u003eet al.\u003c/em\u003e 2010). Owing to its allelopathic potency and high ecological adaptability, \u003cem\u003eA. Adenophora\u003c/em\u003e has rapidly colonized Guizhou, Sichuan, Guangxi, and Tibet (Wang \u003cem\u003eet al.\u003c/em\u003e 2005; Zhang \u003cem\u003eet al.\u003c/em\u003e 2022). Currently, large-scale infestations occur in Yunnan and Guizhou Provinces, causing significant ecological and economic damage (Sang \u003cem\u003eet al.\u003c/em\u003e 2010).\u003c/p\u003e \u003cp\u003eThe biological traits of \u003cem\u003eA. adenophora\u003c/em\u003e constitute key drivers of its rapid invasion success. As a perennial herb reaching 1\u0026ndash;2 m in height, this species exhibits broad adaptability and light preference, primarily colonizing slopes, roadsides, wastelands, and forest edges (Wang \u003cem\u003eet al.\u003c/em\u003e 2011). Its high photosynthetic rate enables rapid biomass accumulation. \u003cem\u003eA. adenophora\u003c/em\u003e employs both sexual and asexual reproduction, with damaged rhizomes readily sprouting new shoots (Wang \u003cem\u003eet al.\u003c/em\u003e 2006). A single plant produces tens of thousands of viable seeds dispersed via wind, water, animal, and anthropogenic activities (Wang \u003cem\u003eet al.\u003c/em\u003e 2006). Critically, allelochemical release underpins invasion success by inhibiting native plants (Li \u003cem\u003eet al.\u003c/em\u003e 2017). Allelopathy\u0026mdash;defined as plant-mediated chemical interactions affecting neighbouring organisms (Arora \u003cem\u003eet al.\u003c/em\u003e 2024; Kumar \u003cem\u003eet al.\u003c/em\u003e 2024)\u0026mdash;operates through multiple pathways: root exudation, leaf leaching, and litter decomposition (Arora \u003cem\u003eet al.\u003c/em\u003e 2024). Key allelochemicals include terpenoids (Zhao \u003cem\u003eet al.\u003c/em\u003e 2009) and phenolic compounds (Xie \u003cem\u003eet al.\u003c/em\u003e 2010), which suppress the growth and seed germination of native flora, ultimately restructuring the plant community composition and reducing diversity (Rai \u003cem\u003eet al.\u003c/em\u003e 2023; Wang \u003cem\u003eet al.\u003c/em\u003e 2025).\u003c/p\u003e \u003cp\u003eThe karst region of central Guizhou features unique geological settings and fragile ecosystems characterized by nutrient-poor soils (Wang \u003cem\u003eet al.\u003c/em\u003e 2019), severe water erosion, and rocky desertification (Chen \u003cem\u003eet al.\u003c/em\u003e 2018a). Its distinctive geological structure\u0026mdash;rugged terrain with thin soil layers\u0026mdash;results in poor soil retention capacity, driving both surface soil loss and subsurface leakage (Sun \u003cem\u003eet al.\u003c/em\u003e 2020). The soils in this region are predominantly calcareous, with low nutrient availability (organic matter, total nitrogen, total phosphorus, and available phosphorus) due to limestone weathering (Li \u003cem\u003eet al.\u003c/em\u003e 2022a). \u003cem\u003eA. adenophora\u003c/em\u003e exploits these conditions through exceptional environmental adaptability (Xia \u003cem\u003eet al.\u003c/em\u003e 2020) and multimechanistic resource competition (Zhao \u003cem\u003eet al.\u003c/em\u003e 2009; Zheng \u003cem\u003eet al.\u003c/em\u003e 2012; Shen \u003cem\u003eet al.\u003c/em\u003e 2020). The unique karst geomorphology and microclimates further facilitate its invasion (Li \u003cem\u003eet al.\u003c/em\u003e 2022b).\u003c/p\u003e \u003cp\u003e \u003cem\u003eA. adenophora\u003c/em\u003e invasion has multifaceted effects on soil nutrients, enzymes, and microbial communities. It modifies soil physicochemical properties, typically increasing nitrogen (N), nitrate (NO₃⁻-N), ammonium (NH₄⁺-N), available potassium (AK), and available phosphorus (AP) contents, although reductions in total phosphorus (TP) and potassium (TK) may occur (Deng \u003cem\u003eet al.\u003c/em\u003e 2015). Concurrently, soil pH shifts are frequently observed (Xia \u003cem\u003eet al.\u003c/em\u003e 2024). The effects on soil enzyme activities are context dependent and vary by enzyme type and invasion intensity (Darji \u003cem\u003eet al.\u003c/em\u003e 2024). Crucially, invasion restructures microbial composition, functionality, and diversity (Wan \u003cem\u003eet al.\u003c/em\u003e 2010; Bajpai and Inderjit 2013; Xiao \u003cem\u003eet al.\u003c/em\u003e 2014; Chen \u003cem\u003eet al.\u003c/em\u003e 2015; Balami \u003cem\u003eet al.\u003c/em\u003e 2017). This includes reduced microbial α diversity, decreased fungal abundance (Xiao \u003cem\u003eet al.\u003c/em\u003e 2014; Balami \u003cem\u003eet al.\u003c/em\u003e 2017), and altered community assemblages (Wan-Xue \u003cem\u003eet al.\u003c/em\u003e 2010).\u003c/p\u003e \u003cp\u003eSouthwest China's karst region, characterized by impoverished soils and unique hydrology, constitutes an ecologically fragile zone highly vulnerable to \u003cem\u003eA. adenophora\u003c/em\u003e invasion (Wang \u003cem\u003eet al.\u003c/em\u003e 2019). Guanling County in Guizhou exemplifies typical karst mountainous terrain where nutrient limitation prevails. Crucially, how \u003cem\u003eA. adenophora\u003c/em\u003e maintains invasion dominance by restructuring the soil microbial composition/function to drive a self-reinforcing \u003cem\u003eallelopathy‒nutrient feedback loop\u003c/em\u003e remains unexplored. This study integrates soil physicochemical analyses, enzyme activity assays, and metagenomic sequencing to (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) determine the responses of karst soil nutrients, enzyme activities, and microbial communities to \u003cem\u003eA. adenophora\u003c/em\u003e invasion; (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) decipher differentiation patterns in microbial community structure (diversity/composition) and functional genes; and (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) reveal plant‒soil‒microbe interaction networks, providing theoretical foundations for karst ecological barrier restoration.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Site Description\u003c/h2\u003e \u003cp\u003eThe study area is located in Guanling County (25.908877\u0026deg;N, 105.605618\u0026deg;E), Anshun city, Guizhou Province (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Situated in the upper reaches of the Dabang River, this karst terrain features hilly and mountainous topography at 1,064 m in elevation. The regional climate data indicate that the mean annual temperature is 16.2\u0026deg;C, the average annual frost-free period is 308 days, the mean annual sunshine duration is 1,381.6 hours, and the mean annual precipitation is 1,370 mm. \u003cem\u003eA. Adenophora\u003c/em\u003e predominantly invades forestlands, roadsides, ditches, and dry farmlands, resulting in contiguous infestations. This area represents one of the most severely invaded zones in Guizhou Province.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Experimental Design\u003c/h2\u003e \u003cp\u003eGenomic DNA was extracted via the TIANamp Soil DNA Kit (DP705, Tiangen Biotech) per the manufacturer's protocol. The concentration of the extracted DNA was assessed with a \u003cem\u003eQubit\u0026trade; 3.0\u003c/em\u003e fluorometer (Invitrogen) with a \u003cem\u003edsDNA HS Assay Kit\u003c/em\u003e. The integrity was determined via 1% agarose gel electrophoresis, and library preparation was performed with the VAHTS\u0026reg; Universal Plus DNA Library Prep Kit for Illumina (ND617). Library quality control included 2 factors: the size distribution was determined via a Qsep-400 Bio-Fragment Analyzer, and quantification was performed using a \u003cem\u003eQubit 3.0\u003c/em\u003e fluorometer. The final libraries were sequenced on an Illumina NovaSeq 6000 platform with a paired-end 150 bp (PE150) strategy.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Soil Physicochemical Properties and Plant Nutrient Analyses\u003c/h2\u003e \u003cp\u003eTotal carbon (TC) and total nitrogen (TN) were determined by elemental analysis (Thermo Fisher Scientific elemental analyser) via high-temperature catalytic combustion in oxygen, converting carbon to CO₂ and nitrogen to N₂. Total phosphorus (TP) was measured via molybdenum-antimony anti-spectrophotometry using a UV-1800PC spectrophotometer (MAPADA, Shanghai). Total potassium (TK) was quantified via flame photometry (FP6410 flame photometer, INESA, Shanghai). The soil organic matter (SOM) content was analysed via the potassium dichromate oxidation‒external heating method (DF-101S oil bath), followed by titration. The soil organic carbon (SOC) content was calculated as 58% SOM (based on the dichromate oxidation method). pH was measured potentiometrically (Sartorius PB-1 pH meter) in a 1:2.5 soil:water suspension. Dry matter (DM) was determined by oven drying at 105\u0026thinsp;\u0026plusmn;\u0026thinsp;5\u0026deg;C to a constant weight. Plant nutrients (TC, TN, TP, and TK) in \u003cem\u003eA. adenophora\u003c/em\u003e and \u003cem\u003eA. argyi\u003c/em\u003e were analysed using identical methods as those used for the corresponding soil parameters.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Enzyme Activity Assays\u003c/h2\u003e \u003cp\u003eAll enzymatic analyses were performed using a SpectraMax single-mode reader for absorbance measurements. The specific methods used included urease (UE), which is quantified by indophenol blue colorimetry on the basis of ammonia production from urea hydrolysis and reacts with phenol‒sodium hypochlorite to form indophenol blue. Phosphatase (PHO) was measured via disodium phenyl phosphate colorimetry, which detects phenol release through chromogenic reactions. Catalase (CAT) was assessed by the UV absorption method at 240 nm, H₂O₂ consumption was directly quantified after enzymatic decomposition. Sucrase (SUC) and amylase (AMS) were determined using 3,5-dinitrosalicylic acid (DNS) colorimetry at 540 nm, where reducing sugars from substrate hydrolysis react with DNS to form reddish-brown compounds.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5. Metagenomic Sequencing Procedures\u003c/h2\u003e \u003cp\u003eGenomic DNA was extracted via the TIANamp Soil DNA Kit (DP705, Tiangen Biotech) per the manufacturer's protocol. The concentration of the extracted DNA was assessed with a \u003cem\u003eQubit\u0026trade; 3.0\u003c/em\u003e fluorometer (Invitrogen) with a \u003cem\u003edsDNA HS Assay Kit\u003c/em\u003e. The integrity was determined via 1% agarose gel electrophoresis, and library preparation was performed with the VAHTS\u0026reg; Universal Plus DNA Library Prep Kit for Illumina (ND617). Library quality control included 2 factors: the size distribution was determined via a Qsep-400 Bio-Fragment Analyzer, and quantification was performed using a \u003cem\u003eQubit 3.0\u003c/em\u003e fluorometer. The final libraries were sequenced on an Illumina NovaSeq 6000 platform with a paired-end 150 bp (PE150) strategy.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6. Sequence Processing and Assembly\u003c/h2\u003e \u003cp\u003eThe raw sequencing reads underwent rigorous quality control to generate clean reads for downstream analysis. Quality trimming was performed with fastp (v0.23.4) (Chen \u003cem\u003eet al.\u003c/em\u003e 2018b) to remove low-quality bases. For host decontamination, clean reads were aligned to host genomes using Bowtie2 (Langmead and Salzberg 2012), with subsequent removal of host-derived sequences. For \u003cem\u003ede novo\u003c/em\u003e assembly, processed reads were assembled with MEGAHIT (Li \u003cem\u003eet al.\u003c/em\u003e 2015), and contigs\u0026thinsp;\u0026ge;\u0026thinsp;300 bp were retained. For assembly evaluation, quality was assessed via the QUAST (Gurevich \u003cem\u003eet al.\u003c/em\u003e 2013). For gene catalogue construction, contigs were clustered into nonredundant gene catalogues via MMseq2 (v12-113e3; Mirdita \u003cem\u003eet al.\u003c/em\u003e 2019) at 90% sequence identity and 80% coverage thresholds. The predicted genes were functionally annotated against Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), evolutionary genealogy of genes: Nonsupervised Orthologous Groups (eggNOG), Carbohydrate-Active enZymes (CAZy), and the Comprehensive Antibiotic Resistance Database (CARD).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.7. Data Analysis\u003c/h2\u003e \u003cp\u003eStatistical analyses were performed using Microsoft Excel 2019. Student's t tests were used to assess differences in alpha diversity indices, soil nutrients, enzyme activities, and species abundance between groups. Nonmetric multidimensional scaling (NMDS) based on Bray‒Curtis distance matrices was used to visualize community composition variations. All graphical outputs were generated with R software (v4.3.1) via the ggplot2 and vegan packages.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cp\u003e3.1. \u003cem\u003eSoil Nutrient Alterations\u003c/em\u003e\u003c/p\u003e \u003cp\u003e \u003cem\u003eA. adenophora\u003c/em\u003e invasion significantly altered the soil nutrient content and pH (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Compared with the uninvaded control (CK), the invaded soils presented greater pH (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), elevated soil organic carbon and total nitrogen (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), and nonsignificant increases in soil organic matter, total carbon, total potassium, total phosphorus, and dry matter. These results indicate that invasion establishes an allelopathy‒nutrient positive feedback loop by selectively enhancing key nutrients (SOC and TN) coupled with slight alkalinization, potentially favouring invasive growth.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e3.2. \u003cem\u003eImpact of A. adenophora Invasion on Soil Enzyme Activities\u003c/em\u003e\u003c/p\u003e \u003cp\u003e \u003cem\u003eA. adenophora\u003c/em\u003e invasion significantly altered soil enzyme activities (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), with generally higher values in invaded soils than in the uninvaded control. The soil urease and phosphatase activities tended to increase with invasion intensity. Specifically, sucrase and urease activities were significantly elevated in invaded soils compared with those in CK soils (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). These changes suggest that invasion accelerates nutrient turnover through the selective activation of carbon/nitrogen-metabolizing enzymes (SUC and UE) to sustain competitive dominance, whereas the delayed response of phosphorus-cycling enzymes (nonsignificant PHO change) indicates a selective nutrient acquisition strategy.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.3. Differences in Nutrient Contents between A. adenophora and A. argyi\u003c/h2\u003e \u003cp\u003eSignificant differences in nutrient content were observed between the invasive \u003cem\u003eA. adenophora\u003c/em\u003e and the native plant \u003cem\u003eA. argyi\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Compared with \u003cem\u003eA. argyi\u003c/em\u003e, \u003cem\u003eA. adenophora\u003c/em\u003e generally presented higher total nitrogen and total potassium contents (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), whereas \u003cem\u003eA. argyi\u003c/em\u003e presented significantly greater total carbon contents in stem tissues. Both species displayed organ-specific nutrient allocation patterns, but \u003cem\u003eA. adenophora\u003c/em\u003e presented more pronounced nutrient enrichment in leaf tissues.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.4. Species Composition and Gene Counts\u003c/h2\u003e \u003cp\u003eA total of 12 soil samples were collected, including soil microorganisms spanning 7 domains, 198 phyla, 179 classes, 364 orders, 856 families, 3,531 genera, and 22,111 species. Analysis of gene count differences between \u003cem\u003eA. adenophora\u003c/em\u003e-invaded and non-invaded conditions revealed minimal redundant gene numbers and the lowest proportion of unique redundant genes under invasion (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea and b).\u003c/p\u003e \u003cp\u003eCompositional analysis revealed domain-level dominance by bacteria (83.8%), with minor components including Archaea, Fungi, Viruses, Metazoa, Eukaryota, and Viridiplantae (\u0026lt;\u0026thinsp;0.1%), alongside unclassified and unassigned taxa (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ec). At the phylum level, the community was predominantly composed of \u003cem\u003eProteobacteria\u003c/em\u003e (31.2%), \u003cem\u003eAcidobacteria\u003c/em\u003e, \u003cem\u003eActinobacteria\u003c/em\u003e, \u003cem\u003eChloroflexi\u003c/em\u003e, \u003cem\u003eGemmatimonadetes\u003c/em\u003e, \u003cem\u003eVerrucomicrobia\u003c/em\u003e, \u003cem\u003ePlanctomycetes\u003c/em\u003e, and \u003cem\u003eNitrospirae\u003c/em\u003e (1.3%) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ed). The ten most abundant phyla were exclusively bacteria, with \u003cem\u003eProteobacteria\u003c/em\u003e, \u003cem\u003eAcidobacteria\u003c/em\u003e and \u003cem\u003eGemmatimonadetes\u003c/em\u003e collectively constituting 56.7% of the community. Functionally, \u003cem\u003eProteobacteria\u003c/em\u003e\u0026mdash;the dominant phylum\u0026mdash;mediates carbon/nitrogen cycling, symbiosis, and pathogenesis; \u003cem\u003eAcidobacteria\u003c/em\u003e represents oligotrophic specialists that decompose recalcitrant organics (cellulose) in acidic soils; and \u003cem\u003eGemmatimonadetes\u003c/em\u003e dominates arid soils with putative involvement in phosphate transport and metabolism. This microbial profile aligns with the oligotrophic, drought-prone karst environment of central Guizhou, indicating that \u003cem\u003eA. adenophora\u003c/em\u003e invasion restructures soil microbiomes by selecting extremophile-adapted taxa.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e(a) Venn diagram. Ellipse colours denote distinct groups, with overlaps indicating shared non-redundant gene counts and non-overlapping areas representing group-specific non-redundant genes. (b) Non-redundant gene counts (box plot). The group categories are displayed on the x-axis, and the gene counts are displayed on the y-axis. Box plot elements: box tops/bottoms\u0026thinsp;=\u0026thinsp;upper/lower quartiles (interquartile range, IQR); central line\u0026thinsp;=\u0026thinsp;median; whiskers\u0026thinsp;=\u0026thinsp;minimum/maximum values within 1.5\u0026times;IQR; outlying points beyond whiskers represent outliers. The numeric labels on the intergroup connectors indicate t test p values (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05 not displayed). (c) Domain-level taxonomic composition. The samples are arranged on the x-axis, with relative abundance percentages on the y-axis. The colour-coded bars represent taxa (one hue per taxon), and the bar length reflects the relative abundance proportions. For optimal visualization, only the top 10 most abundant taxa are shown; the remaining taxa are merged as \"Others.\" \"Unassigned\" denotes taxonomically unannotated species. (d) Phylum-level composition showing the top 10 abundant taxa.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.5. Divergence in Species Composition\u003c/h2\u003e \u003cp\u003eAnalysis of the microbial communities in \u003cem\u003eA. adenophora\u003c/em\u003e-invaded and non-invaded soils revealed significant compositional divergence (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), with hierarchical clustering segregating the 12 soil samples into two distinct groups. Under heavy invasion (H), \u003cem\u003eCandidatus\u003c/em\u003e Doudnabacteria and \u003cem\u003eCandidatus\u003c/em\u003e Kerfeldbacteria presented the strongest positive correlations, whereas \u003cem\u003eCandidatus\u003c/em\u003e Lindowbacteria and \u003cem\u003eCandidatus\u003c/em\u003e Cloacimonetes presented minimal associations in control (CK) soils. Invasion conditions reduced the abundances of \u003cem\u003eActinobacteria\u003c/em\u003e and \u003cem\u003eCandidatus\u003c/em\u003e Dormibacteraeota but increased those of most other taxa (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ea). Among the 15 most abundant phyla, \u003cem\u003eVerrucomicrobia\u003c/em\u003e, \u003cem\u003eActinobacteria\u003c/em\u003e, \u003cem\u003eCandidatus\u003c/em\u003e Eisenbacteria, and \u003cem\u003eBacteroidetes\u003c/em\u003e dominated with marked intergroup differences. \u003cem\u003eActinobacteria\u003c/em\u003e peaked in CK, whereas \u003cem\u003eVerrucomicrobia\u003c/em\u003e and \u003cem\u003eCandidatus\u003c/em\u003e Eisenbacteria were significantly enriched in invaded soils (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eb). These results collectively confirm profound invasion-induced microbial restructuring, identifying \u003cem\u003eCandidatus\u003c/em\u003e Doudnabacteria and \u003cem\u003eCandidatus\u003c/em\u003e Kerfeldbacteria as the taxa most responsive to \u003cem\u003eA. adenophora\u003c/em\u003e invasion.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e(a) Phylum-level divergent taxa heatmap. Displayed taxa that passed differential abundance testing (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The left dendrogram clusters divergent taxa, the top dendrogram clusters samples, and the central heatmap visualizes abundance gradients. (b) Phylum-level divergent abundance bar plot. Left section: mean abundance bars (x-axis\u0026thinsp;=\u0026thinsp;mean proportion, y-axis\u0026thinsp;=\u0026thinsp;taxon names); centre: p value asterisks (*p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, **p\u0026thinsp;\u0026le;\u0026thinsp;0.001); right: actual p values. The top 15 taxa were selected by a descending p value and sorted by descending abundance.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.6. Species Diversity Composition\u003c/h2\u003e \u003cp\u003eTo assess the impact of \u003cem\u003eA. adenophora\u003c/em\u003e invasion on microbial diversity, alpha and beta diversity analyses were conducted. Alpha diversity was evaluated using the Chao1 richness index, Shannon diversity index, Simpson dominance index, and Pielou evenness index, whereas beta diversity was evaluated via principal component analysis (PCA) and nonmetric multidimensional scaling (NMDS) to resolve intersample compositional differences.\u003c/p\u003e \u003cp\u003eSignificant intergroup differences emerged in the alpha diversity indices (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Compared with the control soils, the invaded soils presented significantly lower Shannon diversity and Pielou evenness indices than the control soils (CK, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), indicating microbial diversity loss and uneven species distribution following invasion. Conversely, the Simpson dominance and Chao1 richness indices did not significantly differ across groups (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ea-d). These results collectively confirmed the greater microbial biodiversity and relative species richness in the CK soils than in the CK soils, indicating that \u003cem\u003eAdenophora\u003c/em\u003e invasion profoundly restructured the microbial communities.\u003c/p\u003e \u003cp\u003eThe PCA results demonstrated a cumulative explanatory power of 75.25% (PC1\u0026thinsp;=\u0026thinsp;61.74%, PC2\u0026thinsp;=\u0026thinsp;13.51%), effectively capturing intersample compositional divergence. Visualization revealed clear segregation between the \u003cem\u003eA. adenophora\u003c/em\u003e-invaded and control (CK) groups in ordination space, indicating distinct community structures. Conversely, the proximity among the different invasion-intensity groups suggested high compositional similarity (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ee). NMDS analysis corroborated these findings, with a stress value of 0.0001 (reliability threshold: stress\u0026thinsp;\u0026lt;\u0026thinsp;0.2; lower values indicate higher precision), confirming exceptional ordination reliability and strong concordance with the PCA results (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ef). Overall, these results indicate that \u003cem\u003eA. adenophora\u003c/em\u003e invasion significantly impacts microbial biodiversity and relative species richness, while minimal separation among invasion gradients reflects high community similarity.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e(a) Shannon index variation. X-axis: experimental groups; Y-axis: alpha diversity index. (b) Simpson index variation. X-axis: groups; Y-axis: alpha diversity index. (c) Chao1 index variation. X-axis: groups; Y-axis: alpha diversity index. (d) Pielou evenness index variation. X-axis: groups; Y-axis: alpha diversity index. (e) Principal component analysis (PCA) of species abundance. Points represent sample-specific compositions; colour coding denotes groups. X-axis: PC1 with explanatory power (%); Y-axis: PC2 with explanatory power (%). (f) Nonmetric multidimensional scaling (NMDS) ordination. Points correspond to samples; colours indicate groups. Stress values\u0026thinsp;\u0026lt;\u0026thinsp;0.1 indicate acceptable ordination, whereas values\u0026thinsp;\u0026lt;\u0026thinsp;0.05 demonstrate excellent representativeness.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.7. Functional Gene Annotation\u003c/h2\u003e \u003cp\u003eeggNOG functional classification revealed that among the top 20 categories, [S]: Function unknown predominated, whereas [R]: General function prediction represented only the most abundant annotated function, and [N]: Cell motility was the least frequent (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003ea). The CAZy distribution was as follows: GH (32%), GT (38.9%), PL (1.7%), CE (5.1%), AA (1.8%), and CBM (20.5%), with GT being the most prevalent and PL the least abundant (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eb). KEGG pathway analysis at level 2 identified four major categories: metabolism, genetic information processing, environmental information processing, and cellular processing. The global and overview maps revealed the primary metabolic pathways (highest relative abundance), followed by carbohydrate metabolism (sugar synthesis/degradation and monosaccharide/polysaccharide metabolism) and amino acid metabolism (third highest) (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003ec). Gene profiling revealed that profiles indicating the following 20 types of antibiotic resistance were the most enriched: multidrug, tetracycline, macrolide, peptide, glycopeptide, aminoglycoside, aminoglycoside, aminocoumarin, fluoroquinolone, mupirocin, nitroimidazole, pleuromutilin, rifamycin, lincosamide, fosfomycin, carbapenem, phenicol, disinfecting agents and antiseptics, sulfonamide and elfamycin resistance. Notably, multidrug, tetracycline and macrolide resistance-related genes exhibited the highest degree of enrichment (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003ed).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e(a) eggNOG functional classification. X-axis: eggNOG categories; Y-axis: relative abundance of functional genes. (b) CAZy distribution. The colour-coded sectors represent carbohydrate-active enzyme classes, with sector areas proportional to relative abundance. (c) KEGG pathway annotation at level 2. X-axis: relative abundance of functional genes; Y-axis: level-2 functional categories. (d) Antibiotic resistance gene abundance. X-axis: antibiotic resistance types; Y-axis: relative abundance of resistance genes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.8. Functional Gene Diversity\u003c/h2\u003e \u003cp\u003eMicrobial functional gene diversity critically influences key biogeochemical processes and metabolic functions. In addition to the global and overview maps, carbohydrate metabolism, amino acid metabolism, and energy metabolism presented the highest gene abundances (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003ea). CAZy annotation revealed GT2 (glycosyltransferases, cellulose synthase), GT4 (glycosyltransferases), and CBM50 (carbohydrate-binding modules targeting pectin compounds, such as pectate and galacturonate) as predominantly enriched categories (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eb), which drive microbial decomposition of plant cell walls, host‒microbe interactions, and sugar metabolism regulation. The CARD annotation encompassed 10 antibiotic resistance classes: nitroimidazole, mupirocin, fluoroquinolone, aminocoumarin, aminoglycoside, glycopeptide, peptide, macrolide, tetracycline, and multidrug (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003ec). Functional gene PCA demonstrated a cumulative explanatory power of 71.23% (PC1\u0026thinsp;=\u0026thinsp;52.45%, PC2\u0026thinsp;=\u0026thinsp;18.78%), indicating robust ordination efficacy (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003ed).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e(a) Functional composition scatter plot. X-axis: KEGG level-2 metabolic pathways; Y-axis: relative abundance of functional gene categories. (b) CAZy enzyme abundance bubble plot. X-axis: sample names; Y-axis: enzyme classes. The bubble size corresponds to the magnitude of the relative abundance. (c) Circos diagram of the CARD antibiotic resistome. Outer ring: right semicircle\u0026thinsp;=\u0026thinsp;samples/groups, left semicircle\u0026thinsp;=\u0026thinsp;resistance gene types (radial scale\u0026thinsp;=\u0026thinsp;abundance proportion). Inner ribbons connect resistance genes to samples/groups, revealing gene functional composition per sample and sample distribution per gene; ribbon width indicates proportional distribution. (d) Functional gene PCA ordination. Points represent samples; colour coding denotes groups. X-axis: PC1 with explanatory power (%); Y-axis: PC2 with explanatory power (%).\u003c/p\u003e \u003cp\u003eHeatmap analysis revealed significant differences between \u003cem\u003eA. adenophora\u003c/em\u003e-invaded and control (CK) soils (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003ea), with 26 functional genes segregating into two distinct clusters. Sixteen genes presented significant invasion-associated enrichment, whereas ten presented CK-specific associations. Pathways significantly linked to invasion included photosynthesis; linoleic acid metabolism; one carbon pool by folate; streptomycin biosynthesis; polyketide sugar unit biosynthesis; metabolic pathways; the sulphur relay system; lysine biosynthesis; folate biosynthesis; the phosphotransferase system (PTS); aminoacyl-tRNA biosynthesis; acarbose and validamycin biosynthesis; nicotinate and nicotinamide metabolism; riboflavin metabolism; terpenoid backbone biosynthesis; and lipopolysaccharide biosynthesis.\u003c/p\u003e \u003cp\u003eHeatmap analysis at the KEGG Orthology (KO) level revealed significant compositional divergence between \u003cem\u003eA. adenophora\u003c/em\u003e-invaded and control (CK) soils (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05, Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003eb), with metabolic enzymes segregating into two distinct clusters. The invasion-associated KO terms included K00773, K03655, K11749, K01409, K02652, K02038, K07263, K01710, K02346, K07300, K03321, K04042, K14415, K03639, K01928, K02669, K06941, K02666, K01662, K03750, K06180, K06969, K00639, K03587, K03820, K03545, K00099, K08483, K02517, K01933, K01754, K03631, K00943, K02037, K03833, K01893, K02005, K02653, K00703, K02662, K13038, K04066, K00973, K01885, K02039, K01924, K00812, K01952, K07462, K01961, K03572, K00602, K07391, K00821, K01887, K00806, K00104, and K03564. CK-associated terms included K00135, K01869, K01902, K00252, K01939, K02314, K01434, K01692, K03799, K01687, K00265, K03496, K00525, K03046, K02358, K03544, K06994, K00915, K01621, K00164, K02112, K10112, K01251, K00162, K01426, K05343, K02274, K01915, K01652, K01214, K01897, K07045, K15371, K04043, K00666, K03694, K14162, K01113, K03553, K02600, K02519, and K03466. Notably, K13038 and K00639 encode flavonoid synthases and polyketide synthases that produce allelochemicals that inhibit seed germination in competing species, whereas K02038, K01933, and K01893 encode nutrient transporters and nitrogenases that monopolize nitrogen/phosphorus resources.\u003c/p\u003e \u003cp\u003eHeatmap analysis of carbohydrate-active enzyme families revealed significant compositional divergence between \u003cem\u003eA. adenophora\u003c/em\u003e-invaded and control (CK) soils (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05, Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003ec), with enzymatic profiles segregating into two distinct clusters. The invasion-associated CAZy families included GT105, GT89, PL39, GH92, CBM42, GT9, GH85, CBM16, CBM67, CBM4, CBM6, PL0, GH50, AA5, GH120, GH81, GH144, GH64, CBM64, GH160, CBM0, CBM26, CBM57, PL9, CBM21, GH142, PL2, GT3, GH128, CBM60, GH141, GH2, and GH30. The CK-associated families included GH3, GT28, GH100, GT1, PL7, GH77, GH1, GT58, AA3, AA12, GH76, GH4, GH101, and GH15. The number of invasion-enriched families was 1.2\u0026times; (GT105), 1.7\u0026times; (CBM42), 1.7\u0026times; (PL9), and 1.5\u0026times; (GH144) greater than that in the CK. \u003cem\u003eA. Adenophora\u003c/em\u003e invasion leverages GT105 and CBM42 for complex exopolysaccharide synthesis, PL9 and GH144 for plant polysaccharide degradation, AA5 for phenolic detoxification, and CBM60 for stress resistance, whereas CK communities primarily utilize GH3/GH15 for carbon acquisition.\u003c/p\u003e \u003cp\u003eHeatmap analysis at KEGG level 3 revealed significant compositional divergence between \u003cem\u003eA. adenophora\u003c/em\u003e-invaded and control (CK) soils (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05, Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003ed), with differentially abundant genes clustering into two distinct groups. The invasion-associated genes included \u003cem\u003ecutA\u003c/em\u003e, ALU1-P, \u003cem\u003eeefA\u003c/em\u003e, \u003cem\u003ezwS/hydG\u003c/em\u003e, \u003cem\u003epmpM\u003c/em\u003e, \u003cem\u003egalE\u003c/em\u003e, \u003cem\u003eactA\u003c/em\u003e, \u003cem\u003ecopR\u003c/em\u003e, \u003cem\u003ecrdA\u003c/em\u003e, \u003cem\u003esmdA\u003c/em\u003e, \u003cem\u003evmeF\u003c/em\u003e, \u003cem\u003emepA\u003c/em\u003e, \u003cem\u003enorM/pmpM\u003c/em\u003e, \u003cem\u003emerG\u003c/em\u003e, \u003cem\u003erecG\u003c/em\u003e, \u003cem\u003enczA\u003c/em\u003e, \u003cem\u003esmdB\u003c/em\u003e, \u003cem\u003eG2alt\u003c/em\u003e, and \u003cem\u003etroD\u003c/em\u003e. The CK-associated genes included \u003cem\u003ectpD\u003c/em\u003e, \u003cem\u003eactR\u003c/em\u003e, \u003cem\u003epgpA/ItpgpA\u003c/em\u003e, \u003cem\u003eemrB\u003c/em\u003e, \u003cem\u003eadeE\u003c/em\u003e, \u003cem\u003ebepC\u003c/em\u003e, \u003cem\u003eyfmP\u003c/em\u003e, \u003cem\u003efetB/ybbM\u003c/em\u003e, \u003cem\u003eadeG\u003c/em\u003e, \u003cem\u003esrpC\u003c/em\u003e, and \u003cem\u003esilR\u003c/em\u003e. The number of invasion-enriched genes was 2.3-fold (\u003cem\u003emerG\u003c/em\u003e), 1.4-fold (\u003cem\u003enorM\u003c/em\u003e), 1.1-fold (\u003cem\u003erecG\u003c/em\u003e), 1.3-fold (\u003cem\u003esmdB\u003c/em\u003e), 1.1-fold (\u003cem\u003ezraS\u003c/em\u003e), and 1.1-fold (\u003cem\u003ecopR\u003c/em\u003e) greater than that in the CK soils. Mechanistically, invasion-associated \u003cem\u003emerG\u003c/em\u003e and \u003cem\u003enorm\u003c/em\u003e confer heavy metal resistance, \u003cem\u003erecG\u003c/em\u003e and \u003cem\u003esmdB\u003c/em\u003e facilitate oxidative damage repair, while \u003cem\u003ezraS\u003c/em\u003e and \u003cem\u003ecopR\u003c/em\u003e encode two-component regulatory systems, collectively enhancing adaptive advantages during \u003cem\u003eA. adenophora\u003c/em\u003e invasion.\u003c/p\u003e \u003cp\u003eKEGG level-3 bar plot analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003ee) revealed significant intergroup differences (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), with aminoacyl-tRNA biosynthesis exhibiting the highest abundance among the top 15 pathways, whereas biosynthesis of ansamycins was the lowest. The control (CK) values were significantly lower than those of the invaded groups. Concurrently, genus-level functional gene profiling (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003ed) revealed the following order of abundance: \u003cem\u003ecopR\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;\u003cem\u003egale\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;\u003cem\u003evmeF\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;\u003cem\u003esmdB\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;\u003cem\u003eactA\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;\u003cem\u003eadeG\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;\u003cem\u003eactR\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;\u003cem\u003enorM/pmpM\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;ALU1-P\u0026gt; \u003cem\u003ecrdA\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;\u003cem\u003eemrB\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;\u003cem\u003etrod\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;\u003cem\u003epgpA/ItpgpA\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;\u003cem\u003eyfmP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;\u003cem\u003efetB/ybbM\u003c/em\u003e. The invasion-enriched genes \u003cem\u003ecopR\u003c/em\u003e, \u003cem\u003egalE\u003c/em\u003e, \u003cem\u003evmeF\u003c/em\u003e, \u003cem\u003esmdB\u003c/em\u003e, and \u003cem\u003eactA\u003c/em\u003e presented 1.1-fold, 1.23-fold, 1.19-fold, 1.21-fold, and 1.45-fold greater abundances than CK, respectively.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e(a) Differential functional gene abundance heatmap. (b) KEGG Orthology (KO) level differential abundance heatmap. (c) CAZy family-level differential abundance heatmap. (d) Differential functional gene abundance heatmap. (e) KEGG level-3 differential gene abundance bar plot. (f) Genus-level differential gene abundance bar plot. Displayed genes that passed differential abundance testing (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The left dendrogram clusters the differentially abundant genes; the top dendrogram clusters the samples; the central heatmap visualizes the abundance gradients.\u003c/p\u003e \u003cp\u003eCorrelation network analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003ea) identified \u003cem\u003emetabolic pathways\u003c/em\u003e, \u003cem\u003ebiosynthesis of secondary metabolites\u003c/em\u003e, and \u003cem\u003emicrobial metabolism in diverse environments\u003c/em\u003e as the most abundant pathways, exhibiting strong positive/negative correlations with the top 20 functional genes across multiple metabolic processes. The RDA ordination (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003eb) revealed a cumulative explanatory power of 57.65% (Axis 1: 34.98%; Axis 2: 22.67%), indicating moderate functional gene interpretability. Significant positive correlations included \u003cem\u003eBiosynthesis of antibiotics\u003c/em\u003e, \u003cem\u003epurine metabolism\u003c/em\u003e, \u003cem\u003ebiosynthesis of secondary metabolites\u003c/em\u003e, and \u003cem\u003emetabolic pathways\u003c/em\u003e with sucrase (SUC) and phosphatase (PHO); \u003cem\u003ebiosynthesis of amino acids\u003c/em\u003e and \u003cem\u003etwo-component system\u003c/em\u003e with total potassium (TK), total phosphorus (TP), and catalase (CAT); and \u003cem\u003ecarbon metabolism\u003c/em\u003e and \u003cem\u003emicrobial metabolism in diverse environments\u003c/em\u003e with urease (UE), total carbon (TC), soil organic matter (SOM), soil organic carbon (SOC), total nitrogen (TN), and ammonium nitrogen (AMS) contents. A functional gene‒environment heatmap analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003ec) segregated the soil parameters into two clusters: \u003cb\u003eCluster 1\u003c/b\u003e (positively correlated with TK, TP, SUC, PHO, and CAT): fructose and mannose metabolism; one carbon pool by folate; streptomycin biosynthesis; biosynthesis of secondary metabolites; base excision repair; protein export; glycerophospholipid metabolism; biosynthesis of amino acids; homologous recombination; mismatch repair; porphyrin and chlorophyll metabolism; ribosome; bacterial secretion system; two-component system; vitamin B6 metabolism; chlorocyclohexane and chlorobenzene degradation; nicotinate and nicotinamide metabolism; glycerolipid metabolism; polyketide sugar unit biosynthesis; terpenoid backbone biosynthesis; lipopolysaccharide biosynthesis; riboflavin metabolism; metabolic pathways; thiamine metabolism; lysine biosynthesis; folate biosynthesis; sulphur relay system; biotin metabolism; phenylalanine, tyrosine and tryptophan biosynthesis; ubiquinone and other terpenoid-quinone biosynthesis; fatty acid biosynthesis; fatty acid metabolism; biosynthesis of unsaturated fatty acids; carbon fixation in prokaryotes; aminoacyl-tRNA biosynthesis; inositol phosphate metabolism; peptidoglycan biosynthesis; amino sugar and nucleotide sugar metabolism; and D-glutamine and D-glutamate metabolism. \u003cb\u003eCluster 2\u003c/b\u003e (positively correlated with SOC, TC, SOM, UE, AMS, and TN): residual functional categories.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e(a) Correlation network. Circles represent functional genes, with size denoting abundance; connecting lines indicate intergene correlations, where line thickness corresponds to correlation strength, and colour (red\u0026thinsp;=\u0026thinsp;positive, green\u0026thinsp;=\u0026thinsp;negative) signifies directionality. (b) RDA ordination of functional genes: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) blue arrows denote environmental factors; arrow length indicates influence strength on community variation; (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) arrow angle relative to axes reflects factor‒axis correlation (smaller angles\u0026thinsp;=\u0026thinsp;higher correlation); (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) black points represent functions; proximity to an arrow indicates stronger factor‒function interaction; (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) points aligned with arrow direction indicate positive factor‒function covariation, whereas opposite alignment indicates negative covariation; (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e) axis scales derived from regression values of samples against environmental factors; (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e) RDA1/RDA2 axes represent primary ordination vectors, with labelled percentages indicating cumulative explanatory power for community structure. (c) Function-environment heatmap. X-axis: environmental factors; Y-axis: functional genes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e3.9. Soil Nutrients and Enzymes as Determinants of Microbial Diversity\u003c/h2\u003e \u003cp\u003eSoil nutrients and enzymes critically influence microbial diversity. The RDA ordination revealed a cumulative explanatory power of 69.96% (RDA1\u0026thinsp;=\u0026thinsp;47.9%, RDA2\u0026thinsp;=\u0026thinsp;22.06%), demonstrating robust interpretability. Significant positive correlations emerged between \u003cem\u003eViridiplantae\u003c/em\u003e, \u003cem\u003eArchaea\u003c/em\u003e, \u003cem\u003eFungi\u003c/em\u003e, \u003cem\u003eEukaryota\u003c/em\u003e and catalase (CAT), total phosphorus (TP), phosphatase (PHO), and total potassium (TK), while \u003cem\u003eBacteria\u003c/em\u003e were positively correlated with TK, soil organic carbon (SOC), total carbon (TC), soil organic matter (SOM), urease (UE), and sucrase (SUC) (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e12\u003c/span\u003ea). Species‒environment heatmap analysis segregated the taxa into two clusters: Cluster 1 (positively correlated with PHO, TP, TK, CAT, and SUC): \u003cem\u003ecandidate division NC10\u003c/em\u003e, \u003cem\u003eCandidatus Rokubacteria\u003c/em\u003e, \u003cem\u003eCrenarchaeota\u003c/em\u003e, \u003cem\u003eNitrospinae\u003c/em\u003e, \u003cem\u003eCandidatus Aenigmarchaeota\u003c/em\u003e, \u003cem\u003eeinococcus Thermus\u003c/em\u003e, \u003cem\u003eAbditibacteriota\u003c/em\u003e, \u003cem\u003eThaumarchaeota\u003c/em\u003e, \u003cem\u003eChloroflexi\u003c/em\u003e, \u003cem\u003eCandidatus Roizmanbacteria\u003c/em\u003e, \u003cem\u003eAcidobacteria\u003c/em\u003e, \u003cem\u003eCandidatus Tectomicrobia\u003c/em\u003e, \u003cem\u003eActinobacteria\u003c/em\u003e, \u003cem\u003endidatus Dormibacteraeota\u003c/em\u003e, \u003cem\u003eandidatus Giovannonibacteria\u003c/em\u003e, \u003cem\u003eandidatus Microgenomates\u003c/em\u003e, \u003cem\u003eandidate division WWE3\u003c/em\u003e, \u003cem\u003eyanobacteria\u003c/em\u003e, \u003cem\u003etribacterota\u003c/em\u003e, \u003cem\u003eandidatus Blackallbacteria\u003c/em\u003e, \u003cem\u003ealneolaeota\u003c/em\u003e, \u003cem\u003eandidatus Magasanikbacteria\u003c/em\u003e, \u003cem\u003eroviricota\u003c/em\u003e, \u003cem\u003eandidatus Saccharibacteria\u003c/em\u003e, and \u003cem\u003eandidatus_Kaiserbacteria\u003c/em\u003e. Cluster 2 was positively correlated with SOC, TC, SOM, UE, ammonium nitrogen (AMS), and TN: residual taxa (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e12\u003c/span\u003eb).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e(a) RDA ordination diagram (displaying species). (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) Blue arrows represent environmental factors; arrow length indicates the strength of the factor\u0026rsquo;s influence on community variation, with longer arrows denoting greater influence. (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) The angle between an arrow and an ordination axis reflects the factor‒axis correlation, where smaller angles indicate stronger correlations. (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) Black dots denote species; closer proximity between a species point and an arrow signifies a stronger species‒environmental factor interaction. (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) Species points aligned with an arrow indicate a positive correlation between the factor and species variation, whereas those in the opposite direction indicate a negative correlation. (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e) Scale ticks on the horizontal and vertical axes represent values generated for samples during regression analysis with environmental factors. (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e) The RDA1 and RDA2 axes represent the two variables with the highest explanatory power; their axis labels indicate the proportion of environmental variation explained by each axis, and a larger combined sum corresponds to greater explanatory capacity for overall community structure and species distribution patterns. (b) Species‒environmental factor correlation heatmap: Environmental factors are displayed on the horizontal axis (showing the top 20 species with the smallest p values), with species on the vertical axis.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003e4.1. \u003cem\u003eSynergistic Drive by Soil Nutrients and Allelopathy\u003c/em\u003e\u003c/p\u003e \u003cp\u003e \u003cem\u003eA. adenophora\u003c/em\u003e synergistically drives its invasion by altering soil nutrients and releasing allelochemicals. Compared with native species, invasive plants often exhibit increased biomass, increased nitrogen availability, altered nitrogen fixation rates, and the production of litter with higher decomposition rates in invaded habitats (Ehrenfeld 2003). During the initial invasion phase, \u003cem\u003eA. adenophora\u003c/em\u003e likely promotes its own growth while inhibiting native plants by increasing the soil nutrient content, particularly the nitrogen content (Deng \u003cem\u003eet al.\u003c/em\u003e 2015; Jiao and Huang 2024). This study demonstrated that \u003cem\u003eA. adenophora\u003c/em\u003e invasion led to significant accumulation of soil SOC and TN (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and weak alkalization of the soil pH (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). This may stem from the regulation of soil mineralization processes by allelochemicals, which aligns with the significant correlations observed with the functional genes K00639 and K13038. In areas invaded by exotic species, litter decomposition rates and nutrient availability typically increase (Standish \u003cem\u003eet al.\u003c/em\u003e 2004; Martin \u003cem\u003eet al.\u003c/em\u003e 2010), which could also contribute to elevated soil nutrient levels. The allelochemicals released by \u003cem\u003eA. adenophora\u003c/em\u003e can directly suppress the growth of native plants, reducing their ability to compete for resources (Inderjit \u003cem\u003eet al.\u003c/em\u003e 2011; Jiao and Huang 2024; Wu \u003cem\u003eet al.\u003c/em\u003e 2025). Furthermore, the TN and TK contents in \u003cem\u003eA. adenophora\u003c/em\u003e leaves were greater than those in \u003cem\u003eA. argyi\u003c/em\u003e leaves (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e), suggesting that its leaf litter may also be an important supplementary source of soil nitrogen.\u003c/p\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e4.2. Selective Activation of Enzyme Activities\u003c/h2\u003e \u003cp\u003eThe invasion process of \u003cem\u003eA. adenophora\u003c/em\u003e is closely linked to soil nutrients, microbial communities, and enzyme activities (Li \u003cem\u003eet al.\u003c/em\u003e 2017). Its invasion leads to significant changes in soil nutrients and enzyme activities (Sun \u003cem\u003eet al.\u003c/em\u003e 2013; Deng \u003cem\u003eet al.\u003c/em\u003e 2015). \u003cem\u003eA. adenophora\u003c/em\u003e invasion alters the structure and diversity of soil microbial communities, which in turn affects the activities of enzymes involved in nutrient transformation (Wan-Xue \u003cem\u003eet al.\u003c/em\u003e 2010; Balami \u003cem\u003eet al.\u003c/em\u003e 2017). Microorganisms acquire nutrients by secreting specific extracellular enzymes to degrade soil organic matter (Sinsabaugh \u003cem\u003eet al.\u003c/em\u003e 2009). This study revealed that under \u003cem\u003eA. adenophora\u003c/em\u003e invasion, SUC (sucrase) and UE (urease) activities were significantly greater than those in the control group (CK) (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), indicating that invasion preferentially stimulates the carbon and nitrogen metabolic enzyme systems, accelerating the decomposition of carbohydrates and urea to support rapid growth. In contrast, PHO (phosphatase) activity did not change significantly, suggesting that phosphorus acquisition is not the core of its competitive advantage. This finding, combined with the finding that extracellular polysaccharide synthesis is dominated by certain genes, such as GT105 and CBM42 (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003ec), collectively indicates efficient monopolization of carbon resources.\u003c/p\u003e \u003cp\u003e4.3. \u003cem\u003eFunctional Remodelling of Microbial Communities\u003c/em\u003e\u003c/p\u003e \u003cp\u003e \u003cem\u003eA. adenophora\u003c/em\u003e invasion remodels microbial communities through multiple pathways, thereby affecting soil nutrient cycling and ecosystem functions (Wan-Xue \u003cem\u003eet al.\u003c/em\u003e 2010; Zhao \u003cem\u003eet al.\u003c/em\u003e 2019; Li \u003cem\u003eet al.\u003c/em\u003e 2022a; Wu \u003cem\u003eet al.\u003c/em\u003e 2025). During plant invasion, bacterial diversity is a primary response variable, with invasive plants often leading to an increase in bacterial diversity (Torres \u003cem\u003eet al.\u003c/em\u003e 2021). \u003cem\u003eA. adenophora\u003c/em\u003e invasion shifts the dominant soil microbial composition from \u003cem\u003eAeromicrobium\u003c/em\u003e and \u003cem\u003eMarmoricola\u003c/em\u003e to \u003cem\u003eReyranella\u003c/em\u003e and \u003cem\u003eBradyrhizobium\u003c/em\u003e (Li \u003cem\u003eet al.\u003c/em\u003e 2022c). Post-invasion, \u003cem\u003eA. adenophora\u003c/em\u003e selectively enriches less abundant genera, such as \u003cem\u003eClostridium\u003c/em\u003e and \u003cem\u003eEnterobacter\u003c/em\u003e. At the phylum level, the community shifts from \u003cem\u003eBacteroidetes\u003c/em\u003e-dominated to \u003cem\u003eProteobacteria\u003c/em\u003e-dominated (Chen \u003cem\u003eet al.\u003c/em\u003e 2019). This study demonstrated that under \u003cem\u003eA. adenophora\u003c/em\u003e invasion, the soil microbial Shannon index was lower (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ea), and the species composition significantly differed (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ee), indicating that invasion resulted in the selection of a reduced number of dominant bacterial groups, such as \u003cem\u003eVerrucomicrobia\u003c/em\u003e, \u003cem\u003eProteobacteria\u003c/em\u003e, and \u003cem\u003eAcidobacteria\u003c/em\u003e.\u003c/p\u003e \u003cp\u003e4.4. \u003cem\u003eInteractive Feedback in the A. adenophora\u0026ndash;Soil\u0026ndash;Microbe System\u003c/em\u003e\u003c/p\u003e \u003cp\u003e \u003cem\u003eA. adenophora\u003c/em\u003e establishes an invasive plant‒soil feedback mechanism by altering soil properties and microbial communities. Its post-invasion secretions and allelochemicals can inhibit the growth of other plants, thereby reducing competition (Li \u003cem\u003eet al.\u003c/em\u003e 2017; Kumar \u003cem\u003eet al.\u003c/em\u003e 2024). Simultaneously, by modifying soil nutrient availability and enzyme activities, \u003cem\u003eA. adenophora\u003c/em\u003e affects the nutrient uptake of other plants, resulting in a competitive advantage (Deng \u003cem\u003eet al.\u003c/em\u003e 2015). Furthermore, invasion by \u003cem\u003eA. adenophora\u003c/em\u003e increases the number of microbes beneficial to its invasion (Chen \u003cem\u003eet al.\u003c/em\u003e 2019), increasing its invasive capacity and forming a positive invasive plant‒soil\u0026ndash;microbe feedback loop (Fang \u003cem\u003eet al.\u003c/em\u003e 2019). As litter serves as a source of nutrient input, invasion by exotic species increases litter decomposition rates and nutrient availability (Standish \u003cem\u003eet al.\u003c/em\u003e 2004; Martin \u003cem\u003eet al.\u003c/em\u003e 2010). This further establishes a positive feedback loop of \"high-quality litter input \u0026rarr; stimulation of enzyme activity \u0026rarr; enrichment of functional microbes \u0026rarr; nutrient recycling,\" ultimately leading to intensified invasion.\u003c/p\u003e \u003c/div\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThis study reveals the response mechanism of the \"invasive plant\u0026ndash;soil physicochemical properties\u0026ndash;microorganism\" system during \u003cem\u003eA. adenophora\u003c/em\u003e invasion into the karst ecosystem of central Guizhou. At the biochemical level, \u003cem\u003eA. adenophora\u003c/em\u003e invasion significantly increased the SOC and TN contents and pH (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), creating a weakly alkaline, nitrogen-enriched environment. This reflects a \"carbon‒nitrogen prioritization\" strategy under karst regional conditions while simultaneously forming a weakly alkaline soil environment favourable for \u003cem\u003eA. adenophora\u003c/em\u003e survival. Under invasion conditions, sucrase (SUC) and urease (UE) activities were significantly greater than those in the control group (CK) and were positively correlated with the abundances of \u003cem\u003eProteobacteria\u003c/em\u003e and \u003cem\u003eGemmatimonadetes\u003c/em\u003e. At the microbial level, \u003cem\u003eA. adenophora\u003c/em\u003e invasion reduced the microbial Shannon index (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) but enriched bacteria, such as \u003cem\u003eVerrucomicrobia\u003c/em\u003e and \u003cem\u003eCandidatus Eisenbacteria\u003c/em\u003e. It upregulated genes involved in nutrient acquisition (GT105/CBM42), allelochemical synthesis (K00639/K13038), and stress resistance (merG/recG), enhancing niche competitive advantages. \u003cem\u003eA. adenophora\u003c/em\u003e invasion establishes a self-reinforcing feedback loop of \"\u003cem\u003eA. adenophora\u003c/em\u003e invasion \u0026rarr; soil nutrient enrichment and enzyme stimulation \u0026rarr; microbial functional adaptation,\" providing a favourable foundation for its invasion success.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eDeclaration of Competing Interest\u003c/h2\u003e \u003cp\u003eWe affirm that no competing financial interests or personal relationships exist which could be reasonably construed as influencing the research presented in this work.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis study was supported by the Youth Science and Technology Development Project of Guizhou Academy of Sciences (Grant No. 52000024P00280H10021H) ,Guizhou Provincial Key Laboratory of Agricultural Biosafety (Grant No. QKHZSYS[2026]024) and the earmarked fund for Guizhou Modern Agriculture Research System (Grant No. GZSTCYJSTX[2025]\u0026mdash;01).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eL is responsible for providing the ideas and funding sources, W is mainly in charge of the methods, and W is responsible for the overall conception of the text and the experiments.\u003c/p\u003e\u003ch2\u003eAcknowledgments\u003c/h2\u003e \u003cp\u003eWe extend our sincere gratitude to Dr. Weijie Li for his support and Dr. Jiaguo Wang for his assistance. We acknowledge AJE (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ewww.aje.cn\u003c/span\u003e\u003cspan address=\"http://www.aje.cn\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) for professional language editing services.\u003c/p\u003e\u003ch2\u003eData availability\u003c/h2\u003e \u003cp\u003eData will be made available on request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eArora S, Husain T, Prasad SM (2024) Allelochemicals as biocontrol agents: Promising aspects, challenges and opportunities. 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Landscape Ecol 22:1143\u0026ndash;1154. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi:10.1007/s10980-007-9096-4\u003c/span\u003e\u003cspan address=\"https://doi:10.1007/s10980-007-9096-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Ageratina adenophora, plant invasion, soil microbial community, ecological effect, metagenomics","lastPublishedDoi":"10.21203/rs.3.rs-8826938/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8826938/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study elucidates the regulatory effects of \u003cem\u003eAgeratina adenophora\u003c/em\u003e invasion on soil nutrient cycling, enzyme activities, and microbial composition/function in karst ecosystems of central Guizhou and reveals the plant–soil–microbe feedback-driven invasion loop. Four invasion gradients were established in Guanling County: CK (uninvaded), L (10–30% cover), M (30–60%), and H (60–90%). Soil nutrients, enzyme activities, metagenomic profiles, and nutrient contents in vegetative organs of \u003cem\u003eA. adenophora\u003c/em\u003e and native \u003cem\u003eArtemisia argyi\u003c/em\u003e were analysed. Key results: (1) Soil microbial communities included 7 domains, 198 phyla, 179 classes, 364 orders, 856 families, 3,531 genera, and 22,111 species. No significant differences were detected in soil nutrients, enzyme activities, or microbial composition/structure/diversity across invasion gradients. (2) Invaded soils presented significantly greater soil organic carbon (SOC), total nitrogen (TN), and pH than CK soils. Sucrase and urease activities were significantly elevated.\u003cem\u003e \u003c/em\u003eCompared with\u003cem\u003e A. argyi\u003c/em\u003e leaves, \u003cem\u003eA. adenophora\u003c/em\u003e leaves contained significantly greater TN and total potassium (TK). (3) Microbial Shannon diversity (\u003cem\u003eH'\u003c/em\u003e) and Pielou evenness (\u003cem\u003eJ\u003c/em\u003e) were significantly decreased. \u003cem\u003eVerrucomicrobia\u003c/em\u003e and \u003cem\u003eCandidatus Eisenbacteria\u003c/em\u003e were significantly enriched in invaded vs. CK soils. (4) Functional gene (K13038 and K00639) levels were significantly correlated with invasion intensity. Carbohydrate metabolism genes (\u003cem\u003eGT10 \u003c/em\u003eand \u003cem\u003eCBM4) \u003c/em\u003ewere upregulated by 1.5- and 1.7-fold, respectively; the stress resistance gene \u003cem\u003emerG\u003c/em\u003e was upregulated 2.3-fold. These changes collectively demonstrate that \u003cem\u003eA. adenophora\u003c/em\u003e invasion increases SOC/TN content and sucrase/urease activity, restructures microbial communities, and upregulates carbohydrate metabolism/stress resistance-related genes, establishing a self-reinforcing \"invasive plant‒soil–microbe\" feedback loop in karst ecosystems.\u003c/p\u003e","manuscriptTitle":"Effects of Ageratina adenophora Invasion on Soil Nutrients, Enzyme Activities, and Microbial Composition/Function in Karst Areas of Central Guizhou","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-16 09:16:40","doi":"10.21203/rs.3.rs-8826938/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"f0c85ac6-9911-408b-91d7-cd020b8312b5","owner":[],"postedDate":"February 16th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-04-06T00:53:49+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-16 09:16:40","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8826938","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8826938","identity":"rs-8826938","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}