Abstract
Agricultural antibiotic contamination poses increasing threats to crop productivity and ecosystem stability through disruption of the plant-associated microbiome. While antibiotic impacts on bulk soil and rhizosphere communities are documented, the extent to which spatial compartmentalization across the plant-soil continuum buffers these effects remains poorly understood. Here, we investigated how compartment-specific selective pressures influence bacterial community assembly, functional resilience, and interaction networks under antibiotic stress. Lettuce (Lactuca sativa) was grown under five treatments in a completely randomized greenhouse design: T1 (sulfamethoxazole [SMX], 3 mg kg⁻¹ + manure + plant), T2 (trimethoprim [TMP], 3 mg kg⁻¹ + manure + plant), T3 (manure + plant, antibiotic-free control), T4 (manure only, plant-free control), and T5 (soil only, negative control). Bacterial communities were profiled across bulk soil, rhizosphere, and endosphere compartments using full-length 16 S rRNA gene sequencing. Spatial compartmentalization emerged as the primary driver of bacteriome structure and functional potential, surpassing antibiotic treatment effects across all analytical approaches. PERMANOVA revealed significant compartment-driven community structuring (R² = 0.189, P = 0.001), while treatment effects were non-significant (R² = 0.145, P = 0.116). Endosphere communities exhibited substantially lower alpha diversity than bulk soil and rhizosphere (P = 0.0001), with significant treatment × compartment interactions (P = 0.007). Antibiotic treatments selectively enriched xenobiotic degradation (P = 0.042) and secondary metabolism functions, particularly in bulk soil, without systematically increasing pathogen-associated or resistance-related functions. Network analysis revealed reduced bacterial connectivity under antibiotic pressure, yet cooperative interactions dominated across all treatments. Compositional differential abundance testing (ALDEx2) detected no significantly altered taxa for primary antibiotic contrasts (T1 vs. T3, T2 vs. T3), indicating context-driven rather than antibiotic-driven compositional changes. Functional diversity was significantly structured by compartment (Shannon P = 0.0017; richness P = 0.0039), while core plant-beneficial functions remained stable across treatments, with large effect sizes (Cohen’s d ≥ 0.8) restricted to antibiotic degradation and secondary metabolism pathways. Our findings demonstrate that plant-microbe spatial structuring provides an ecological buffer that maintains core bacteriome functions against pharmaceutical disturbance, preserving plant-beneficial capabilities despite compositional shifts. The selective enrichment of antibiotic degradation pathways suggests potential for microbiome-assisted mitigation of pharmaceutical residues in agricultural systems. These results provide insights for developing compartment-specific microbiome management strategies that integrate with One Health approaches to enhance agricultural resilience under increasing pharmaceutical pressure in agroecosystems.
Similar content being viewed by others
Characteristics of rhizosphere and endogenous bacterial community of Ulleung-sanmaneul, an endemic plant in Korea: application for alleviating salt stress
Temporal dynamics of the tomato rhizosphere microbiome in response to synthetic communities of plant growth-promoting rhizobacteria
Antifungal properties of Eucalyptus endophytic Streptomyces strains
Data availability
All sequence data generated in this project is available at NCBI under BioProject ID PRJNA1302662.
References
Law, S. R. et al. Life at the borderlands: microbiomes of interfaces critical to One Health. FEMS Microbiol. Rev. 48, fuae008. https://doi.org/10.1093/femsre/fuae008 (2024).
Heuer, H., Schmitt, H. & Smalla, K. Antibiotic resistance gene spread due to manure application on agricultural fields. Curr. Opin. Microbiol. 14, 236–243. https://doi.org/10.1016/j.mib.2011.04.009 (2011).
Jechalke, S., Heuer, H., Siemens, J., Amelung, W. & Smalla, K. Fate and effects of veterinary antibiotics in soil. Trends Microbiol. 22, 536–545. https://doi.org/10.1016/j.tim.2014.05.005 (2014).
Olanrewaju, O. S., Glick, B. R. & Babalola, O. O. Metabolomics-guided utilization of beneficial microbes for climate-resilient crops. Curr. Opin. Chem. Biol. 79 https://doi.org/10.1016/j.cbpa.2024.102427 (2024).
Sillen, W. M. A. et al. Nanoparticle treatment of maize analyzed through the metatranscriptome: compromised nitrogen cycling, possible phytopathogen selection, and plant hormesis. Microbiome 8, 127. https://doi.org/10.1186/s40168-020-00904-y (2020).
Wen, T. et al. Specific metabolites drive the deterministic assembly of diseased rhizosphere microbiome through weakening microbial degradation of autotoxin. Microbiome 10, 177. https://doi.org/10.1186/s40168-022-01375-z (2022).
Zhao, F. et al. Reducing risks of antibiotics to crop production requires land system intensification within thresholds. Nat. Commun. 14, 6094. https://doi.org/10.1038/s41467-023-41258-x (2023).
Delgado-Baquerizo, M. et al. Global homogenization of the structure and function in the soil microbiome of urban greenspaces. Sci. Adv. 7, eabg5809. https://doi.org/10.1126/sciadv.abg5809 (2021).
Pärnänen, K. M. M. et al. Antibiotic resistance in European wastewater treatment plants mirrors the pattern of clinical antibiotic resistance prevalence. Sci. Adv. 5, eaau9124. https://doi.org/10.1126/sciadv.aau9124 (2019).
Li, S., Ondon, B. S., Ho, S. H. & Li, F. Emerging soil contamination of antibiotics resistance bacteria (ARB) carrying genes (ARGs): New challenges for soil remediation and conservation. Environ. Res. 219, 115132. https://doi.org/10.1016/j.envres.2022.115132 (2023).
Wang, F. et al. Antibiotic resistance in the soil ecosystem: A One Health perspective. Curr. Opin. Environ. Sci. Health. 20 https://doi.org/10.1016/j.coesh.2021.100230 (2021).
Zhao, F. et al. Veterinary antibiotics can reduce crop yields by modifying soil bacterial community and earthworm population in agro-ecosystems. Sci. Total Environ. 808 https://doi.org/10.1016/j.scitotenv.2021.152056 (2022).
Muhammad, J. et al. Antibiotics in poultry manure and their associated health issues: a systematic review. J. Soils Sediments. 20, 486–497. https://doi.org/10.1007/s11368-019-02360-0 (2020).
Wu, R. et al. Transmission pathways and intrinsic mechanisms of antibiotic resistance genes in soil-plant systems: A review. Environ. Technol. Innov. 37 https://doi.org/10.1016/j.eti.2024.103985 (2025).
Zhang, Z. et al. Effects of environmentally relevant concentrations of oxytetracycline and sulfadiazine on the bacterial communities, antibiotic resistance genes, and functional genes are different between maize rhizosphere and bulk soil. Environ. Sci. Pollut. Res. 31, 22663–22678. https://doi.org/10.1007/s11356-024-32578-6 (2024).
Shen, Y. et al. Dominant microbiome iteration and antibiotic resistance genes propagation way dictate the antibiotic resistance genes contamination degree in soil-plant system. J. Clean. Prod. 464 https://doi.org/10.1016/j.jclepro.2024.142786 (2024).
Hernando-Amado, S., Coque, T. M., Baquero, F. & Martínez, J. L. Defining and combating antibiotic resistance from One Health and Global Health perspectives. Nat. Microbiol. 4, 1432–1442. https://doi.org/10.1038/s41564-019-0503-9 (2019).
Zhao, Y. et al. Influence of Legacy Mercury on Antibiotic Resistomes: Evidence from Agricultural Soils with Different Cropping Systems. Environ. Sci. Technol. 55, 13913–13922. https://doi.org/10.1021/acs.est.1c04030 (2021).
Zheng, D. et al. Global biogeography and projection of soil antibiotic resistance genes. Sci. Adv. 8, eabq8015. https://doi.org/10.1126/sciadv.abq8015 (2022).
Liu, Z. T. et al. Organic fertilization co-selects genetically linked antibiotic and metal(loid) resistance genes in global soil microbiome. Nat. Commun. 15, 5168. https://doi.org/10.1038/s41467-024-49165-5 (2024).
Che, Y. et al. High-resolution genomic surveillance elucidates a multilayered hierarchical transfer of resistance between WWTP- and human/animal-associated bacteria. Microbiome 10, 16. https://doi.org/10.1186/s40168-021-01192-w (2022).
Zhu, C. et al. Global diversity and distribution of antibiotic resistance genes in human wastewater treatment systems. Nat. Commun. 16, 4006. https://doi.org/10.1038/s41467-025-59019-3 (2025).
Rashidi, A. et al. Gut microbiota response to antibiotics is personalized and depends on baseline microbiota. Microbiome 9, 211. https://doi.org/10.1186/s40168-021-01170-2 (2021).
Lavelle, A. et al. Baseline microbiota composition modulates antibiotic-mediated effects on the gut microbiota and host. Microbiome 7, 111. https://doi.org/10.1186/s40168-019-0725-3 (2019).
Philippot, L., Raaijmakers, J. M., Lemanceau, P. & van der Putten, W. H. Going back to the roots: the microbial ecology of the rhizosphere. Nat. Rev. Microbiol. 11, 789–799. https://doi.org/10.1038/nrmicro3109 (2013).
Bulgarelli, D., Schlaeppi, K., Spaepen, S., van Themaat, E. V. L. & Schulze-Lefert, P. Structure and Functions of the Bacterial Microbiota of Plants. Annu. Rev. Plant Biol. 64, 807–838. https://doi.org/10.1146/annurev-arplant-050312-120106 (2013).
Hardoim Pablo, R. et al. The Hidden World within Plants: Ecological and Evolutionary Considerations for Defining Functioning of Microbial Endophytes. Microbiol. Mol. Biol. Rev. 79, 293–320. https://doi.org/10.1128/mmbr.00050-14 (2015).
Compant, S., Clément, C. & Sessitsch, A. Plant growth-promoting bacteria in the rhizo- and endosphere of plants: Their role, colonization, mechanisms involved and prospects for utilization. Soil Biol. Biochem. 42, 669–678. https://doi.org/10.1016/j.soilbio.2009.11.024 (2010).
Bik Holly, M. et al. Microbial Community Succession and Nutrient Cycling Responses following Perturbations of Experimental Saltwater Aquaria. mSphere, (2019).
Zhou, H. et al. Changes in the soil microbial communities of alpine steppe at Qinghai-Tibetan Plateau under different degradation levels. Sci. Total Environ. 651, 2281–2291. https://doi.org/10.1016/j.scitotenv.2018.09.336 (2019).
Zeng, Q. et al. Effects of decabromodiphenyl ethane (DBDPE) exposure on soil microbial community: Nitrogen cycle, microbial defense and repair and antibiotic resistance genes transfer. J. Environ. Manage. 376 https://doi.org/10.1016/j.jenvman.2025.124503 (2025).
Zhang, Z., Zhao, L., Jin, Q., Luo, Q. & He, H. Combined contamination of microplastic and antibiotic alters the composition of microbial community and metabolism in wheat and maize rhizosphere soil. J. Hazard. Mater. 473, 134618. https://doi.org/10.1016/j.jhazmat.2024.134618 (2024).
Li, Y. et al. Rhizosphere Bacteria Help to Compensate for Pesticide-Induced Stress in Plants. Environ. Sci. Technol. 58, 12542–12553. https://doi.org/10.1021/acs.est.4c04196 (2024).
Baker, N. R. et al. Nutrient and moisture limitations reveal keystone metabolites linking rhizosphere metabolomes and microbiomes. Proceedings of the National Academy of Sciences https://doi.org/10.1073/pnas.2303439121 (2024).
Liu, F. et al. Effects of six selected antibiotics on plant growth and soil microbial and enzymatic activities. Environ. Pollut. 157, 1636–1642. https://doi.org/10.1016/j.envpol.2008.12.021 (2009).
Revellin, C., Hartmann, A., Solanas, S. & Topp, E. Long-Term Exposure of Agricultural Soil to Veterinary Antibiotics Changes the Population Structure of Symbiotic Nitrogen-Fixing Rhizobacteria Occupying Nodules of Soybeans (Glycine max). Appl. Environ. Microbiol. 84, e00109–00118. https://doi.org/10.1128/AEM.00109-18 (2018).
Weisburg, W. G., Barns, S. M., Pelletier, D. A. & Lane, D. J. 16S ribosomal DNA amplification for phylogenetic study. J. Bacteriol. 173, 697–703. https://doi.org/10.1128/jb.173.2.697-703.1991 (1991).
Cousson, A., Mahé, F., Guyet, U., Razafimahafaly, D. & Bernard, L. NanoASV: a snakemake workflow for reproducible field-based Nanopore full-length 16S metabarcoding amplicon data analysis. Bioinformatics 41 https://doi.org/10.1093/bioinformatics/btaf089 (2025).
De Coster, W. & Rademakers, R. NanoPack2: population-scale evaluation of long-read sequencing data. Bioinformatics 39 https://doi.org/10.1093/bioinformatics/btad311 (2023).
Li, H. Minimap2: pairwise alignment for nucleotide sequences. Bioinformatics 34, 3094–3100. https://doi.org/10.1093/bioinformatics/bty191 (2018).
Quast, C. et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 41, D590–D596. https://doi.org/10.1093/nar/gks1219 (2012).
Danecek, P. et al. Twelve years of SAMtools and BCFtools. GigaScience 10 https://doi.org/10.1093/gigascience/giab008 (2021).
Rognes, T., Flouri, T., Nichols, B., Quince, C. & Mahé, F. VSEARCH: a versatile open source tool for metagenomics. PeerJ 4, e2584. https://doi.org/10.7717/peerj.2584 (2016).
Katoh, K. & Standley, D. M. MAFFT Multiple Sequence Alignment Software Version 7: Improvements in Performance and Usability. Mol. Biol. Evol. 30, 772–780. https://doi.org/10.1093/molbev/mst010 (2013).
Price, M. N., Dehal, P. S. & Arkin, A. P. FastTree: Computing Large Minimum Evolution Trees with Profiles instead of a Distance Matrix. Mol. Biol. Evol. 26, 1641–1650. https://doi.org/10.1093/molbev/msp077 (2009).
Davis, N. M., Proctor, D. M., Holmes, S. P., Relman, D. A. & Callahan, B. J. Simple statistical identification and removal of contaminant sequences in marker-gene and metagenomics data. Microbiome 6, 226. https://doi.org/10.1186/s40168-018-0605-2 (2018).
Salter, S. J. et al. Reagent and laboratory contamination can critically impact sequence-based microbiome analyses. BMC Biol. 12, 87. https://doi.org/10.1186/s12915-014-0087-z (2014).
Louca, S., Parfrey, L. W. & Doebeli, M. Decoupling function and taxonomy in the global ocean microbiome. Science 353, 1272–1277. https://doi.org/10.1126/science.aaf4507 (2016).
McMurdie, P. J. & Holmes, S. phyloseq: An R Package for Reproducible Interactive Analysis and Graphics of Microbiome Census Data. PLOS ONE. 8, e61217. https://doi.org/10.1371/journal.pone.0061217 (2013).
Oksanen, J. et al. The vegan package. Community Ecol. package. 10, 719 (2007).
Wickham, H. ggplot2. WIRE Comput. Stat. 3, 180–185. https://doi.org/10.1002/wics.147 (2011).
McMurdie, P. J., Holmes, S. & Waste Not Want Not: Why Rarefying Microbiome Data Is Inadmissible. PLoS Comput. Biol. 10, e1003531. https://doi.org/10.1371/journal.pcbi.1003531 (2014).
Bray, J. R. & Curtis, J. T. An Ordination of the Upland Forest Communities of Southern Wisconsin. Ecol. Monogr. 27, 325–349. https://doi.org/10.2307/1942268 (1957).
Jaccard, P. Distribution de la flore alpine dans le bassin des Dranses et dans quelques régions voisines. Bull. Soc. Vaudoise Sci. Nat. 37, 241–272 (1901).
Lozupone, C. & Knight, R. UniFrac: a New Phylogenetic Method for Comparing Microbial Communities. Appl. Environ. Microbiol. 71, 8228–8235. https://doi.org/10.1128/AEM.71.12.8228-8235.2005 (2005).
Lozupone Catherine, A., Hamady, M., Kelley Scott, T. & Knight, R. Quantitative and Qualitative β Diversity Measures Lead to Different Insights into Factors That Structure Microbial Communities. Appl. Environ. Microbiol. 73, 1576–1585. https://doi.org/10.1128/AEM.01996-06 (2007).
Legendre, P. & Gallagher, E. D. Ecologically meaningful transformations for ordination of species data. Oecologia 129, 271–280. https://doi.org/10.1007/s004420100716 (2001).
Anderson, M. J. A new method for non-parametric multivariate analysis of variance. Austral Ecol. 26, 32–46. https://doi.org/10.1111/j.1442-9993.2001.01070.pp.x (2001).
Anderson, M. J. Distance-Based Tests for Homogeneity of Multivariate Dispersions. Biometrics 62, 245–253. https://doi.org/10.1111/j.1541-0420.2005.00440.x (2005).
Stegen, J. C., Lin, X., Konopka, A. E. & Fredrickson, J. K. Stochastic and deterministic assembly processes in subsurface microbial communities. ISME J. 6, 1653–1664. https://doi.org/10.1038/ismej.2012.22 (2012).
Stegen, J. C. et al. Quantifying community assembly processes and identifying features that impose them. ISME J. 7, 2069–2079. https://doi.org/10.1038/ismej.2013.93 (2013).
Ning, D. et al. A quantitative framework reveals ecological drivers of grassland microbial community assembly in response to warming. Nat. Commun. 11, 4717. https://doi.org/10.1038/s41467-020-18560-z (2020).
Lundberg, D. S. et al. Defining the core Arabidopsis thaliana root microbiome. Nature 488, 86–90. https://doi.org/10.1038/nature11237 (2012).
Friedman, J. & Alm, E. J. Inferring Correlation Networks from Genomic Survey Data. PLoS Comput. Biol. 8, e1002687. https://doi.org/10.1371/journal.pcbi.1002687 (2012).
Csárdi, G. & Nepusz, T. The igraph software. Int. J. Complex. Syst. 1695, 1–9 (2006).
Montoya, J. M., Pimm, S. L. & Solé, R. V. Ecological networks and their fragility. Nature 442, 259–264. https://doi.org/10.1038/nature04927 (2006).
de Vries, F. T. et al. Soil bacterial networks are less stable under drought than fungal networks. Nat. Commun. 9, 3033. https://doi.org/10.1038/s41467-018-05516-7 (2018).
Lin, H. & Peddada, S. D. Analysis of compositions of microbiomes with bias correction. Nat. Commun. 11, 3514. https://doi.org/10.1038/s41467-020-17041-7 (2020).
Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550. https://doi.org/10.1186/s13059-014-0550-8 (2014).
Li, H. et al. Root chemistry and microbe interactions contribute to metal(loid) tolerance of an aromatic plant – Vetiver grass. J. Hazard. Mater. 461 https://doi.org/10.1016/j.jhazmat.2023.132648 (2024).
Yin, L. et al. Uptake of the Plant Agriculture-Used Antibiotics Oxytetracycline and Streptomycin by Cherry RadishEffect on Plant Microbiome and the Potential Health Risk. J. Agric. Food Chem. 71, 4561–4570. https://doi.org/10.1021/acs.jafc.3c01052 (2023).
Lucas, J. M., Sone, B. M., Whitmore, D. & Strickland, M. S. Antibiotics and temperature interact to disrupt soil communities and nutrient cycling. Soil Biol. Biochem. 163 https://doi.org/10.1016/j.soilbio.2021.108437 (2021).
Huang, Y. H. et al. Root-associated bacteria strengthen their community stability against disturbance of antibiotics on structure and functions. J. Hazard. Mater. 465 https://doi.org/10.1016/j.jhazmat.2023.133317 (2024).
Santos-Medellín, C., Edwards, J., Liechty, Z., Nguyen, B. & Sundaresan, V. Drought Stress Results in a Compartment-Specific Restructuring of the Rice Root-Associated Microbiomes. mBio 8, 101128mbio00764–101128mbio00717. https://doi.org/10.1128/mbio.00764-17 (2017).
Agoussar, A., Azarbad, H., Tremblay, J. & Yergeau, É. The resistance of the wheat microbial community to water stress is more influenced by plant compartment than reduced water availability. FEMS Microbiol. Ecol. 97 https://doi.org/10.1093/femsec/fiab149 (2021).
Li, Y. et al. Divergent Assembly Processes of Phyllosphere and Rhizosphere Microbial Communities Along Environmental Gradient. Plant. Cell. Environ. 48, 1380–1392. https://doi.org/10.1111/pce.15224 (2025).
Bell Jennifer, K., Mamet Steven, D. & Helgason, B. Siciliano Steven, D. Brassica napus Bacterial Assembly Processes Vary with Plant Compartment and Growth Stage but Not between Lines. Appl. Environ. Microbiol. 88, e00273–e00222. https://doi.org/10.1128/aem.00273-22 (2022).
Xie, H., Chai, Y., Liu, Z., Hao, W. & Gai, J. Community assembly of endophytic bacteria and fungi differs in soil-root continuum of Carex cepillacea. Appl. Soil. Ecol. 194 https://doi.org/10.1016/j.apsoil.2023.105206 (2024).
Liang, J. et al. Bacterial wilt affects the structure and assembly of microbial communities along the soil-root continuum. Environ. Microbiome. 19, 6. https://doi.org/10.1186/s40793-024-00548-7 (2024).
Shao, L. et al. Variations in microbial assemblage between rhizosphere and root endosphere microbiomes contribute to host plant growth under cadmium stress. Appl. Environ. Microbiol. 89, e00960–e00923. https://doi.org/10.1128/aem.00960-23 (2023).
Compant, S. et al. The plant endosphere world – bacterial life within plants. Environ. Microbiol. 23, 1812–1829. https://doi.org/10.1111/1462-2920.15240 (2021).
Xing, Y. et al. The effect of plant compartment and geographical location on shaping microbiome of Pulsatilla chinensis. Appl. Microbiol. Biotechnol. 107, 5555–5567. https://doi.org/10.1007/s00253-023-12641-x (2023).
Li, Y., Qu, N., Li, S., Zhou, H. & Yue, M. Ecological mechanisms of microbial assembly in clonal plant Glechoma longituba: from soil to endosphere. Appl. Environ. Microbiol. 0, e00336–e00325. https://doi.org/10.1128/aem.00336-25 (2025).
Hassani, M. A., Durán, P. & Hacquard, S. Microbial interactions within the plant holobiont. Microbiome 6, 58. https://doi.org/10.1186/s40168-018-0445-0 (2018).
Liu, X. et al. Positive associations fuel soil biodiversity and ecological networks worldwide. Proceedings of the National Academy of Sciences 121, https://doi.org/10.1073/pnas.2308769121 (2024).
Delgado-Baquerizo, M. et al. Multiple elements of soil biodiversity drive ecosystem functions across biomes. Nat. Ecol. Evol. 4, 210–220. https://doi.org/10.1038/s41559-019-1084-y (2020).
Labouyrie, M. et al. Patterns in soil microbial diversity across Europe. Nat. Commun. 14, 3311. https://doi.org/10.1038/s41467-023-37937-4 (2023).
Bestion, E. et al. Phytoplankton biodiversity is more important for ecosystem functioning in highly variable thermal environments. Proc. Natl. Acad. Sci. 118 (e2019591118). https://doi.org/10.1073/pnas.2019591118 (2021).
Louca, S. et al. Function and functional redundancy in microbial systems. Nat. Ecol. Evol. 2, 936–943. https://doi.org/10.1038/s41559-018-0519-1 (2018).
Langille, M. G. I. et al. Predictive functional profiling of microbial communities using 16S rRNA marker gene sequences. Nat. Biotechnol. 31, 814–821. https://doi.org/10.1038/nbt.2676 (2013).
Acknowledgements
O.S.O. acknowledges fellowship support from the Oppenheimer Memorial Trust. Views expressed are those of the authors and not of the funding agencies. The authors would also like to acknowledge the UESM Sequence facility for the sequencing of the microbiomes.
Funding
This work is based on research supported by the International Atomic Energy Agency (D15022[CRP 2308]).
Author information
Authors and Affiliations
Contributions
C.C.B. and O.S.O. conceived the project and were responsible for the overall direction and planning. C.K.L., K.T., and O.S.O. were involved in conducting the majority of experiments, encompassing greenhouse experiments, sample collection, and DNA extraction. O.S.O. analyzed the sequence data. C.C.B. provided guidance throughout the manuscript writing process. O.S.O., C.C.B., and L.M.T. supervised the study. O.S.O. and K.T. wrote the paper. All authors reviewed the manuscript.
Corresponding authors
Ethics declarations
Competing interests
The authors declare no competing interests.
Additional information
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary Information
Below is the link to the electronic supplementary material.
Supplementary Material 1 (download JPG )
Supplementary Material 2 (download JPG )
Supplementary Material 3 (download JPG )
Supplementary Material 4 (download JPG )
Supplementary Material 5 (download JPG )
Supplementary Material 6 (download JPG )
Supplementary Material 7 (download JPG )
Supplementary Material 8 (download JPG )
Supplementary Material 9 (download JPG )
Supplementary Material 10 (download XLSX )
Rights and permissions
Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
Reprints and permissions
About this article
Cite this article
Lenonyane, C.K., Tsholo, K., Molale-Tom, L.G. et al. Plant spatial compartmentalization buffers bacteriome structure and function under antibiotic stress.
Sci Rep (2026). https://doi.org/10.1038/s41598-026-46797-z
Received:
Accepted:
Published:
DOI: https://doi.org/10.1038/s41598-026-46797-z
Keywords
- Plant compartments
- Antibiotic resistance
- Functional ecology
- Plant community structure
- One health
Source: Ecology - nature.com
