Philippa Kaur

AbstractThe Danjiangkou Reservoir (DR) serves as the primary water source for the Middle Route of China’s South-to-North Water Diversion Project (SNWDP), and its ecological security (ES) is critical to water supply safety in North China and to broader regional sustainability. However, systematic assessments of ES in the DR region remain limited. In this study, a health–risk–service framework was developed to evaluate the evolution of the ecological security in DR across three benchmark years (2003, 2013, and 2023). Furthermore, the XGBoost–SHAP model was employed to uncover the dominant natural, anthropogenic, and landscape influential factors behind ES variation. The results indicate that: (1) The proposed framework effectively captures the ES status of DR, with a strong correlation between ecological security index (ESI) and remote sensing ecological index (RSEI) (R² > 0.8, P < 0.001); (2) ESI exhibited a fluctuating upward trend over time, with over 95% of the area classified as Medium or above in terms of ecological security. The ESI hotspots were primarily distributed in the northern and southern regions, which are dominated by forest cover, whereas the cold spots were mainly concentrated in the central region, characterized by cropland and built-up land; (3) Results from the XGBoost–SHAP model revealed that ESI is influenced by multiple factors in a nonlinear fashion. NDVI and LPI were the primary positive contributors, whereas HDI and urbanization had negative impacts, with all these relationships exhibiting nonlinear threshold effects. Notably, threshold effects were identified within specific ranges of these variables. This framework provides a practical approach for evaluating ESI in reservoir regions and offers a scientific foundation for ecological protection and source-area ecological security management in cross-basin water diversion projects such as the DR.

Similar content being viewed by others

Integrating ecosystem service trade-offs and bundles for ecological zoning and management optimization: A case study of the Danjiangkou reservoir area, China

Article
Open access
04 December 2025

Dynamic monitoring of ecological security patterns in arid zone oases: a remote sensing-based ecological index evolution analysis

Article
Open access
10 December 2025

Ecological sensitivity assessment and driving force analysis of the Tarim river basin

Article
Open access
03 October 2025

Data availability

The datasets used and/or analysed during the current study available from the corresponding author on reasonable request.
ReferencesLiu, C. L. et al. Global trends and characteristics of ecological security research in the early 21st century: A literature review and bibliometric analysis. Ecol. Indic. 137, 108734. https://doi.org/10.1016/j.ecolind.2022.108734 (2022).
Google Scholar 
Peng, C. C., Li, B. & Nan, B. An analysis framework for the ecological security of urban agglomeration: A case study of the Beijing-Tianjin-Hebei urban agglomeration. J. Clean. Prod. 315, 128111. https://doi.org/10.1016/j.jclepro.2021.128111 (2021).
Google Scholar 
Liu, C. X., Wu, X. L. & Wang, L. Analysis on land ecological security change and affect factors using RS and GWR in the Danjiangkou reservoir area, China. Appl. Geogr. 105, 1–14. https://doi.org/10.1016/j.apgeog.2019.02.009 (2019).
Google Scholar 
Xie, B., Jones, P., Dwivedi, R., Bao, L. & Liang, R. Evaluation, comparison, and unique features of ecological security in Southwest china: A case study of Yunnan Province. Ecol. Indic. 153, 110453. https://doi.org/10.1016/j.ecolind.2023.110453 (2023).
Google Scholar 
Wu, X. L. & Zhang, H. D. Evaluation of ecological environmental quality and factor explanatory power analysis in western Chongqing. China Ecol. Indic. https://doi.org/10.1016/j.ecolind.2021.108311 (2021).
Google Scholar 
Ding, M. M. et al. Construction and optimization strategy of ecological security pattern in a rapidly urbanizing region: A case study in central-south China. Ecol. Indic. https://doi.org/10.1016/j.ecolind.2019.106030 (2022).
Google Scholar 
Ghosh, S., Das Chatterjee, N. & Dinda, S. Urban ecological security assessment and forecasting using integrated DEMATEL-ANP and CA-Markov models: a case study on Kolkata metropolitan Area, India. Sustain. Cities Soc. 68, 102773. https://doi.org/10.1016/j.scs.2021.102773 (2021).
Google Scholar 
Pan, Z., Gao, G. & Fu, B. Spatiotemporal changes and driving forces of ecosystem vulnerability in the Yangtze river Basin, china: quantification using habitat-structure-function framework. Sci. Total Environ. 835, 155494. https://doi.org/10.1016/j.scitotenv.2022.155494 (2022).
Google Scholar 
Bi, M., Xie, G. & Yao, C. Ecological security assessment based on the renewable ecological footprint in the Guangdong-Hong Kong-Macao greater Bay Area, China. Ecol. Indicat. 116, 106432. https://doi.org/10.1016/j.ecolind.2020.106432 (2020).
Google Scholar 
Liu, W. et al. Spatio-temporal variations of ecosystem services and their drivers in the Pearl river Delta, China. J. Clean. Prod. 337, 130466. https://doi.org/10.1016/j.jclepro.2022.130466 (2022).
Google Scholar 
Zhu, Q. & Cai, Y. Integrating ecological risk, ecosystem health, and ecosystem services for assessing regional ecological security and its driving factors: insights from a large river basin in China. Ecol. Indicat. 155, 110954. https://doi.org/10.1016/j.ecolind.2023.110954 (2023).
Google Scholar 
Liu, C. et al. Temporal and Spatial variations of ecological security on the Northeastern Tibetan plateau integrating ecosystem health-risk-services framework. Ecol. Indicat. 158, 111365. https://doi.org/10.1016/j.ecolind.2023.111365 (2024).
Google Scholar 
Cheng, H. R., Zhu, L. K. & Meng, J. J. Fuzzy evaluation of the ecological security of land resources in mainland China based on the Pressure-State-Response framework. Sci. Total Environ. https://doi.org/10.1016/j.scitotenv.2021.150053 (2022).
Google Scholar 
Yang, Y. & Cai, Z. X. Ecological security assessment of the Guanzhong Plain urban agglomeration based on an adapted ecological footprint model. J. Clean. Prod. https://doi.org/10.1016/j.jclepro.2020.120973 (2020).
Google Scholar 
Sun, M. et al. Comprehensive partitions and different strategies based on ecological security and economic development in Guizhou Province, China. J. Clean. Prod. 274, 122794. https://doi.org/10.1016/j.jclepro.2020.122794 (2020).
Google Scholar 
Karimian, H., Zou, W., Chen, Y., Xia, J. & Wang, Z. Landscape ecological risk assessment and driving factor analysis in Dongjiang river watershed. Chemosphere 307, 135835. https://doi.org/10.1016/j.chemosphere.2022.135835 (2022).
Google Scholar 
Hasan, S. S., Zhen, L., Miah, M. G., Ahamed, T. & Samie, A. Impact of land use change on ecosystem services: a review. Environ. Dev. 34, 100527. https://doi.org/10.1016/j.envdev.2020.100527 (2020).
Google Scholar 
Lorilla, R. S., Poirazidis, K., Detsis, V., Kalogirou, S. & Chalkias, C. Socio-ecological determinants of multiple ecosystem services on the mediterranean landscapes of the ionian Islands (Greece). Ecol. Model. 422, 108994. https://doi.org/10.1016/j.ecolmodel.2020.108994 (2020).
Google Scholar 
Li, J., Bai, Y. & Alatalo, J. M. Impacts of rural tourism-driven land use change on ecosystems services provision in Erhai lake basin. China Ecosyst. Serv. 42, 101081. https://doi.org/10.1016/j.ecoser.2020.101081 (2020).
Google Scholar 
Sun, B. F., Zhao, H., Lu, F. & Wang, X. K. Spatial and Temporal patterns of carbon sequestration in the Northeastern forest regions and its impact factors analysis. Acta Geogr. Sin. 38 (14), 4975–4983. https://doi.org/10.5846/stxb201706201119 (2018).
Google Scholar 
Li, W., Kang, J. & Wang, Y. Spatiotemporal changes and driving forces of ecological security in the Chengdu-Chongqing urban agglomeration, china: quantification using health-services-risk framework. J. Clean. Prod. 389, 136135. https://doi.org/10.1016/j.jclepro.2023.136135 (2023).
Google Scholar 
Wang, Z., Liu, S. & Su, Y. Spatiotemporal evolution of habitat quality and its response to landscape patterns in karst mountainous cities: a case study of Guiyang City in China. Environ. Sci. Pollut R. 30, 114391–114405. https://doi.org/10.1007/s11356-023-30420-z (2023).
Google Scholar 
Zhao, Z. et al. Achieving the supply-demand balance of ecosystem services through zoning regulation based on land use thresholds. Land. Use Policy. 139, 107056. https://doi.org/10.1016/j.landusepol.2024.107056 (2024).
Google Scholar 
Shen, W., Li, Y. & Qin, Y. Research on the influencing factors and multi-scale regulatory pathway of ecosystem health: A case study in the middle reaches of the yellow river. China J. Clean. Prod. 406, 137038. https://doi.org/10.1016/j.jclepro.2023.137038 (2023).
Google Scholar 
Gorosito, I., Bermúdez, M. & Busch, M. Advantages of combining generalized linear models and occupancy models to find indicators of habitat selection: small mammals in agroecosystems as a case study. Ecol. Indic. 85, 1–10. https://doi.org/10.1016/j.ecolind.2017.10.003 (2018).
Google Scholar 
Kun, L., Junsan, Z., Yongping, L. & Yilin, L. Identifying trade-offs and synergies among land use functions using an XGBoost-SHAP model: A case study of Kunming, China. Ecol. Indicat. 172, 113330. https://doi.org/10.1016/j.ecolind.2025.113330 (2025).
Google Scholar 
Guo, S. et al. Threshold effect of ecosystem services in response to climate change, human activity and landscape pattern in the upper and middle yellow river of China. Ecol. Indic. 136, 108603. https://doi.org/10.1016/j.ecolind.2022.108603 (2022).
Google Scholar 
Yichao, T. et al. Use of interpretable machine learning for Understanding ecosystem service trade-offs and their driving mechanisms in karst peak-cluster depression basin, China. Ecol. Indic. 166, 112474. https://doi.org/10.1016/j.ecolind.2024.112474 (2024).
Google Scholar 
Li, K., Zhao, J. & Lin, Y. Debris-flow susceptibility assessment in Dongchuan using stacking ensemble learning including multiple heterogeneous learners with RFE for factor optimization. Nat. Hazard. 118, 2477–2511. https://doi.org/10.1007/s11069-023-06099-3 (2023).
Google Scholar 
Ma, M. et al. XGBoost-based method for flash flood risk assessment. J. Hydrol. 598, 126382. https://doi.org/10.1016/j.jhydrol.2021.126382 (2021).
Google Scholar 
Ouyang, N., Rui, X., Zhang, X., Tang, H. & Xie, Y. Spatiotemporal evolution of ecosystem health and its driving factors in the Southwestern karst regions of China. Ecol. Indic. 166, 112530. https://doi.org/10.1016/j.ecolind.2024.112530 (2024).
Google Scholar 
Peng, Y. & Unluer, C. Interpretable machine learning-based analysis of hydration and carbonation of carbonated reactive Magnesia cement mixes. J. Clean. Prod. 434, 140054. https://doi.org/10.1016/j.jclepro.2023.140054 (2024).
Google Scholar 
Tong, Z. et al. Unravel the spatio-temporal patterns and their nonlinear relationship with correlates of dockless shared bikes near metro stations. Geo-Spatial Inf. Sci. 26, 577–598. https://doi.org/10.1080/10095020.2022.2137857 (2022).
Google Scholar 
Kumar, S. & Kondaveeti, H. Towards transparency in AI: explainable bird species image classification for ecological research. Ecol. Indic. 169, 112886. https://doi.org/10.1016/j.ecolind.2024.112886 (2024).
Google Scholar 
Yin, H. et al. Analyzing economy-society-environment sustainability from the perspective of urban Spatial structure: A case study of the Yangtze river delta urban agglomeration. Sustainable Cities Soc. 96, 104691. https://doi.org/10.1016/j.scs.2023.104691 (2023).
Google Scholar 
Wang, X. J. et al. The new concept of water resources management in china: ensuring water security in changing environment. Environ. Dev. Sustain. 20, 897–909. https://doi.org/10.1007/s10668-017-9918-8 (2017).
Google Scholar 
Liu, H., Yin, J. & Feng, L. The dynamic changes in the storage of the Danjiangkou reservoir and the influence of the South-North water transfer project. Sci. Rep. 8, 8710. https://doi.org/10.1038/s41598-018-26788-5 (2018).
Google Scholar 
Ma, Y. J. et al. Water loss by evaporation from china’s South-North water transfer project. Ecol. Eng. 95, 206–215. https://doi.org/10.1016/j.ecoleng.2016.06.086 (2016).
Google Scholar 
Liu, L. et al. Land use and ecosystem services evolution in Danjiangkou reservoir Area, china: implications for sustainable management of National projects. Land 12, 788. https://doi.org/10.3390/land12040788 (2023).
Google Scholar 
Yang, J. & Huang, X. The 30 m annual land cover dataset and its dynamics in China from 1990 to 2019, Earth Syst. Sci. Data, 13, 3907–3925. https://doi.org/10.5194/essd-13-3907-2021. (2021).Xiao, R. et al. Ecosystem health assessment: A comprehensive and detailed analysis of the case study in coastal metropolitan region, Eastern China. Ecol. Ind. 98, 363–376. https://doi.org/10.1016/j.ecolind.2018.11.010 (2019).
Google Scholar 
Chen, W., Gu, T. & Zeng, J. Urbanisation and ecosystem health in the middle reaches of the Yangtze river urban agglomerations, china: A U-curve relationship. J. Environ. Manage. 318, 115565. https://doi.org/10.1016/j.jenvman.2022.115565 (2022).
Google Scholar 
Qiao, F. W., Bai, Y. P., Xie, L. X., Yang, X. D. & Sun, S. S. Spatio-temporal characteristics of landscape ecological risks in the ecological functional zone of the upper yellow River, China. Int. J. Environ. Res. Public. Health. 18, 12943. https://doi.org/10.3390/ijerph182412943 (2021).
Google Scholar 
Wang, H. et al. Spatial-temporal pattern analysis of landscape ecological risk assessment based on land use/land cover change in Baishuijiang National nature reserve in Gansu Province, China. Ecol. Indic. https://doi.org/10.1016/j.ecolind.2021.107454 (2021).
Google Scholar 
Mann, D., Anees, M. M., Rankavat, S. & Joshi, P. K. Spatio-temporal variations in landscape ecological risk related to road network in the Central Himalaya. Hum. Ecol. Risk Assess. https://doi.org/10.1080/10807039.2019.1710693 (2020).
Google Scholar 
Zhang, D. et al. Land use/cover predictions incorporating ecological security for the Yangtze river delta region, China. Ecol. Indicat. 119, 106841. https://doi.org/10.1016/j.ecolind.2020.106841 (2020).
Google Scholar 
Li, S. et al. Optimization of landscape pattern in China Luojiang Xiaoxi basin based on landscape ecological risk assessment. Ecol. Ind. 146, 109887. https://doi.org/10.1016/j.ecolind.2023.109887 (2023).
Google Scholar 
Xue, C., Zhang, H., Wu, S., Chen, J. & Chen, X. Spatial-temporal evolution of ecosystem services and its potential drivers: A Geospatial perspective from Bairin left banner. China Ecol. Indic. 137, 108760. https://doi.org/10.1016/j.ecolind.2022.108760 (2022).
Google Scholar 
Xu, H. Q. et al. Prediction of ecological effects of potential population and impervious surface increases using a remote sensing based ecological index (RSEI). Ecol. Indic. 93, 730–740. https://doi.org/10.1016/j.ecolind.2018.05.055 (2018).
Google Scholar 
Xu, H. Q., Wang, Y. F., Guan, H. D., Shi, T. T. & Hu, X. S. Detecting ecological changes with a remote sensing based ecological index (RSEI) produced time series and change vector analysis. Remote Sens. https://doi.org/10.3390/rs11202345 (2019).
Google Scholar 
Wang, H. et al. Remote sensing-based approach for the assessing of ecological environmental quality variations using Google Earth Engine: A case study in the Qilian Mountains, Northwest China. Remote Sens. https://doi.org/10.3390/rs15040960 (2023).
Google Scholar 
Xu, H. Q. & Deng, W. H. Rationality analysis of MRSEI and its difference with RSEI. Remote Sens. Technol. Application 37, 1–7. https://doi.org/10.11873/j.issn.1004-0323.2022.1.0001 (2022).
Google Scholar 
Getis, A. & Ord, J. K. The analysis of Spatial association by use of distance statistics. Geogr. Anal. 24 (3), 189–206. https://doi.org/10.1111/j.1538-4632.1992.tb00261.x (1992).
Google Scholar 
Hou, M. J. et al. Ecological risk assessment and impact factor analysis of alpine wetland ecosystem based on LUCC and boosted regression tree on the Zoige Plateau, China. Remote Sens. https://doi.org/10.3390/rs12030368 (2020).
Google Scholar 
Samat, A. et al. Meta-XGBoost for hyperspectral image classification using extended MSER-guided morphological profiles. Remote Sens-Basel 12(12), 1973. https://doi.org/10.3390/rs12121973 (2020).
Google Scholar 
Ho, L. S. & Tran, V. Q. Machine learning approach for predicting and evaluating California bearing ratio of stabilized soil containing industrial waste. J. Clean. Prod. 370, 133587. https://doi.org/10.1016/j.jclepro.2022.133587 (2022).
Google Scholar 
Jamei, M. et al. Application of an explainable glass-box machine learning approach for prognostic analysis of a biogas-powered small agriculture engine. Energy 288, 129862. https://doi.org/10.1016/j.energy.2023.129862 (2024).
Google Scholar 
Li, Z., Xia, J., Deng, X. & Yan, H. Multilevel modelling of impacts of human and natural factors on ecosystem services change in an oasis, Northwest China. Resour. Conserv. Recycl. 169, 105474. https://doi.org/10.1016/j.resconrec.2021.105474 (2021).
Google Scholar 
Ma, L., Bo, J., Li, X., Fang, F. & Cheng, W. Identifyingkey landscape pattern indices influencing the ecological security of inland river basin: the middle and lower reaches of Shule river basin as an example. Sci. Total Environ. 674, 424–438. https://doi.org/10.1016/j.scitotenv.2019.04.107 (2019).
Google Scholar 
Chen, W., Chi, G. & Li, J. The Spatial aspect of ecosystem services balance and its determinants. Land. Use Pol. 90, 104263. https://doi.org/10.1016/j.landusepol.2019.104263 (2020).
Google Scholar 
Peng, J., Pan, Y., Liu, Y., Zhao, H. & Wang, Y. Linking ecological degradation risk to identify ecological security patterns in a rapidly urbanizing landscape. Habitat Int. 71, 110–124. https://doi.org/10.1016/j.habitatint.2017.11.010 (2018).
Google Scholar 
Feng, Y. et al. Evaluating land ecological security and examining its relationships with driving factors using GIS and generalized additive model. Sci. Total Environ. 633, 1469–1479. https://doi.org/10.1016/j.jclepro.2022.133587 (2018).
Google Scholar 
Tang, C. C., Wu, X. F., Zheng, Q. Q. & Lyu, N. Ecological security evaluations of the tourism industry in ecological conservation development areas: A case study of beijing’s ECDA. J. Clean. Prod. 197, 999–1010. https://doi.org/10.1016/j.jclepro.2018.06.232 (2018).
Google Scholar 
Gao, J., Zuo, L. & Liu, W. Environmental determinants impacting the Spatial heterogeneity of karst ecosystem services in Southwest China. Land. Degrad. Dev. 32 (4), 1718–1731. https://doi.org/10.1002/ldr.3815 (2021).
Google Scholar 
Zhu, C. et al. Identifying the trade-offs and synergies among land use functions and their influencing factors from a Geospatial perspective: A case study in Hangzhou. China J. Clean. Prod. 314, 128026. https://doi.org/10.1016/j.jclepro.2021.128026 (2021).
Google Scholar 
Zhang, W., Chang, W. J., Zhu, Z. C. & Hui, Z. Landscape ecological risk assessment of Chinese coastal cities based on land use change. Appl. Geogr. 117, 102174. https://doi.org/10.1016/j.apgeog.2020.102174 (2020).
Google Scholar 
Download referencesFundingThis study was supported by the National Key Research and Development Program of China (Grant No. 2022YFF0711601); the Opening Fund of Key Laboratory of Geological Survey and Evaluation of Ministry of Education (Grant No. GLAB2022ZR01) and the Fundamental Research Funds for the Central Universities; the Programs national natural science foundation of China (42471475).Author informationAuthors and AffiliationsSchool of Geography and Information Engineering, China University of Geosciences, Wuhan, 430074, ChinaYinghui Chang & Liang WuSchool of Computer Science, China University of Geosciences, Wuhan, 430074, ChinaLiang Wu, Zhanlong Chen & Chuncheng YangAuthorsYinghui ChangView author publicationsSearch author on:PubMed Google ScholarLiang WuView author publicationsSearch author on:PubMed Google ScholarZhanlong ChenView author publicationsSearch author on:PubMed Google ScholarChuncheng YangView author publicationsSearch author on:PubMed Google ScholarContributionsY.C. (Yinghui Chang) contributed to the study design and wrote the manuscript; L.W. (Liang Wu) discussed the original idea, revised the manuscript; Z.C. (Zhanlong Chen) and C.Y. (Chuncheng Yang) were involved in drafting and checking of the manuscript. All authors contributed to the article and approved the submitted version.Corresponding authorCorrespondence to
Liang Wu.Ethics declarations

Competing interests
The authors declare no competing interests.

Additional informationPublisher’s noteSpringer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.Rights and permissions
Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
Reprints and permissionsAbout this articleCite this articleChang, Y., Wu, L., Chen, Z. et al. Quantitative assessment of ecological security and its influencing factors in the Danjiangkou Reservoir based on a health–risk–service framework.
Sci Rep (2026). https://doi.org/10.1038/s41598-025-34039-7Download citationReceived: 19 October 2025Accepted: 24 December 2025Published: 05 January 2026DOI: https://doi.org/10.1038/s41598-025-34039-7Share this articleAnyone you share the following link with will be able to read this content:Get shareable linkSorry, a shareable link is not currently available for this article.Copy shareable link to clipboard
Provided by the Springer Nature SharedIt content-sharing initiative
KeywordsEcosystem securityAssessment systemDriving factorsXGBoost-SHAPDanjiangkou Reservoir More

AbstractWhile mobile genetic elements (MGEs) critically influence antibiotic resistance gene (ARG) dissemination, the regulatory role of bacteriophages as unique MGEs remains enigmatic in natural ecosystems. Through a global-scale phage-resistome interrogation spanning 840 groundwater metagenomes, we established a large aquifer resistome repository and uncovered three paradigm-shifting discoveries. First, phages harboured markedly fewer ARGs compared to plasmids and integrative elements, but their bacterial hosts paradoxically maintained the highest anti-phage defence gene inventories, showing an evolutionary equilibrium where investment in phage defence constrains ARG acquisition. Second, lytic phages demonstrated dual functionality characterized with directly suppressing ARG transmission through host lysis while indirectly enriching defence genes that inhibit horizontal gene transfer. Third, vertical inheritance sustained ARGs in 11.2% of MGE-free groundwater microbes. We further extended linkages between ARG profiles, phage defences and biogeochemical genes, revealing phage-mediated co-occurrence of ARGs and denitrification genes in shared hosts. These findings pioneer a phage-centric framework for resistome evolution, guiding phage-based ARG mitigation in groundwater ecosystems.

Access through your institution

Buy or subscribe

This is a preview of subscription content, access via your institution

Access options

Access through your institution

Subscribe to this journal

Receive 12 digital issues and online access to articles

$119.00 per year
only $9.92 per issue

Learn more

Buy this articlePurchase on SpringerLinkInstant access to the full article PDF.USD 39.95Prices may be subject to local taxes which are calculated during checkout

Additional access options:

Log in

Learn about institutional subscriptions

Read our FAQs

Contact customer support

Fig. 1: A global atlas of groundwater resistomes reveals expansive ARG landscapes.Fig. 2: MGE-mediated ARG mobilization patterns in global groundwater.Fig. 3: Interplay between phage infection, host defence systems and ARG dissemination.Fig. 4: Vertical gene transfer sustains ARG persistence in aquifer microbiomes.Fig. 5: Phage–host dynamics link microbial resistance, defence and aquifer ecosystem functions.

Similar content being viewed by others

Genetic compatibility and ecological connectivity drive the dissemination of antibiotic resistance genes

Article
Open access
16 March 2025

Prophage-encoded antibiotic resistance genes are enriched in human-impacted environments

Article
Open access
27 September 2024

Evidence for wastewaters as environments where mobile antibiotic resistance genes emerge

Article
Open access
25 March 2023

Data availability

Domestic groundwater data generated for this study have been deposited in the NCBI Sequence Read Archive under accession code PRJNA858913. Publicly available groundwater metagenomes are listed with their BioProject accession numbers in Supplementary Table 2. Source data are provided with the paper.
Code availability

The R scripts used are publicly available via Zenodo at https://doi.org/10.5281/zenodo.17540538 (ref. 76).
ReferencesXue, S. et al. Pollution prediction for heavy metals in soil-groundwater systems at smelting sites. Chem. Eng J. 473, 145499 (2023).Article 
CAS 

Google Scholar 
Evich, M. G. et al. Mineralogical controls on PFAS and anthropogenic anions in subsurface soils and aquifers. Nat. Commun. 16, 3118 (2025).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Larsson, D. G. J. & Flach, C.-F. Antibiotic resistance in the environment. Nat. Rev. Microbiol. 20, 257–269 (2022).Article 
CAS 
PubMed 

Google Scholar 
Wang, J. et al. Supercarriers of antibiotic resistome in a world’s large river. Microbiome 10, 111 (2022).Article 
PubMed 
PubMed Central 

Google Scholar 
Knapp, C. W., Dolfing, J., Ehlert, P. A. I. & Graham, D. W. Evidence of increasing antibiotic resistance gene abundances in archived soils since 1940. Environ. Sci. Technol. 44, 580–587 (2010).Article 
CAS 
PubMed 

Google Scholar 
Zhu, Y.-G. et al. Continental-scale pollution of estuaries with antibiotic resistance genes. Nat. Microbiol. 2, 16270 (2017).Article 
CAS 
PubMed 

Google Scholar 
Li, Y. et al. Engineered DNA scavenger for mitigating antibiotic resistance proliferation in wastewater treatment. Nat. Water 2, 758–769 (2024).Article 
CAS 

Google Scholar 
Gao, Y. et al. The mystery of rich human gut antibiotic resistome in the Yellow River with hyper-concentrated sediment-laden flow. Water Res. 258, 121763 (2024).Article 
CAS 
PubMed 

Google Scholar 
Zhu, C. et al. Global diversity and distribution of antibiotic resistance genes in human wastewater treatment systems. Nat. Commun. 16, 4006 (2025).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Szekeres, E. et al. Investigating antibiotics, antibiotic resistance genes, and microbial contaminants in groundwater in relation to the proximity of urban areas. Environ. Pollut. 236, 734–744 (2018).Article 
CAS 
PubMed 

Google Scholar 
Herrmann, M. et al. Predominance of Cand. Patescibacteria in groundwater is caused by their preferential mobilization from soils and flourishing under oligotrophic conditions. Front. Microbiol. 10, 1407 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
Griebler, C. & Avramov, M. Groundwater ecosystem services: a review. Freshwater Sci. 34, 355–367 (2015).Article 

Google Scholar 
Retter, A., Karwautz, C. & Griebler, C. Groundwater microbial communities in times of climate change. Curr. Issues Mol. Biol. 41, 509–538 (2021).Article 
PubMed 

Google Scholar 
Sonthiphand, P. et al. Metagenomic insights into microbial diversity in a groundwater basin impacted by a variety of anthropogenic activities. Environ. Sci. Pollut. Res. 26, 26765–26781 (2019).Article 
CAS 

Google Scholar 
Vaz-Moreira, I., Nunes, O. C. & Manaia, C. M. Bacterial diversity and antibiotic resistance in water habitats: searching the links with the human microbiome. FEMS Microbiol. Rev. 38, 761–778 (2014).Article 
CAS 
PubMed 

Google Scholar 
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 (2019).Article 
CAS 
PubMed 

Google Scholar 
Zhang, Y. et al. Comparison of microbiomes and resistomes in two karst groundwater sites in Chongqing, China. Groundwater 57, 807–818 (2019).Article 
CAS 

Google Scholar 
Zaouri, N., Jumat, M. R., Cheema, T. & Hong, P.-Y. Metagenomics-based evaluation of groundwater microbial profiles in response to treated wastewater discharge. Environ. Res. 180, 108835 (2020).Article 
CAS 
PubMed 

Google Scholar 
Sommer, M. O. A., Munck, C., Toft-Kehler, R. V. & Andersson, D. I. Prediction of antibiotic resistance: time for a new preclinical paradigm?. Nat. Rev. Microbiol. 15, 689–696 (2017).Article 
CAS 
PubMed 

Google Scholar 
Patangia, D. V., Ryan, C. A., Dempsey, E., Stanton, C. & Ross, R. P. Vertical transfer of antibiotics and antibiotic resistant strains across the mother/baby axis. Trends Microbiol. 30, 47–56 (2022).Article 
CAS 
PubMed 

Google Scholar 
Diebold, P. J. et al. Clinically relevant antibiotic resistance genes are linked to a limited set of taxa within gut microbiome worldwide. Nat. Commun. 14, 7366 (2023).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Botelho, J. Defense systems are pervasive across chromosomally integrated mobile genetic elements and are inversely correlated to virulence and antimicrobial resistance. Nucleic Acids Res. 51, 4385–4397 (2023).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Ellabaan, M. M. H., Munck, C., Porse, A., Imamovic, L. & Sommer, M. O. A. Forecasting the dissemination of antibiotic resistance genes across bacterial genomes. Nat. Commun. 12, 2435 (2021).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Blanco-Picazo, P. et al. Dominance of phage particles carrying antibiotic resistance genes in the viromes of retail food sources. ISME J. 17, 195–203 (2023).Article 
CAS 
PubMed 

Google Scholar 
Enault, F. et al. Phages rarely encode antibiotic resistance genes: a cautionary tale for virome analyses. ISME J. 11, 237–247 (2017).Article 
CAS 
PubMed 

Google Scholar 
Shuai, X. et al. Bacteriophages: vectors of or weapons against the transmission of antibiotic resistance genes in hospital wastewater systems?. Water Res. 248, 120833 (2024).Article 
CAS 
PubMed 

Google Scholar 
LeGault, K., Hays, S., Angermeyer, A. & McKitterick, A. Temporal shifts in antibiotic resistance elements govern phage-pathogen conflicts. Science 373, eabg2166 (2021).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Borodovich, T., Shkoporov, A. N., Ross, R. P. & Hill, C. Phage-mediated horizontal gene transfer and its implications for the human gut microbiome. Gastroenterol. Rep. 10, goac012 (2022).Article 

Google Scholar 
Moura De Sousa, J., Lourenço, M. & Gordo, I. Horizontal gene transfer among host-associated microbes. Cell Host Microbe 31, 513–527 (2023).Article 
CAS 
PubMed 

Google Scholar 
Xia, R. et al. Bacterium-phage symbiosis facilitates the enrichment of bacterial pathogens and antibiotic-resistant bacteria in the plastisphere. Environ. Sci. Technol. 59, 2948–2960 (2025).Article 
CAS 
PubMed 

Google Scholar 
Liao, H. et al. Prophage-encoded antibiotic resistance genes are enriched in human-impacted environments. Nat. Commun. 15, 8315 (2024).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Pfeifer, E., Sousa, J. M., Touchon, M. & Rocha, E. P. When bacteria are phage playgrounds: interactions between viruses, cells, and mobile genetic elements. Curr. Opin. Microbiol. 70, 102230 (2022).Article 
CAS 
PubMed 

Google Scholar 
Correa, A. M. S. et al. Revisiting the rules of life for viruses of microorganisms. Nat. Rev. Microbiol. 19, 501–513 (2021).Article 
CAS 
PubMed 

Google Scholar 
Wang, D. et al. Identifying ARG-carrying bacteriophages in a lake replenished by reclaimed water using deep learning techniques. Water Res. 248, 120859 (2024).Article 
CAS 
PubMed 

Google Scholar 
Li, Z. et al. Metagenome sequencing reveals shifts in phage-associated antibiotic resistance genes from influent to effluent in wastewater treatment plants. Water Res. 253, 121289 (2024).Article 
CAS 
PubMed 

Google Scholar 
Beavogui, A. et al. The defensome of complex bacterial communities. Nat. Commun. 15, 2146 (2024).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Wei, Z. et al. High-throughput single-cell technology reveals the contribution of horizontal gene transfer to typical antibiotic resistance gene dissemination in wastewater treatment plants. Environ. Sci. Technol. 55, 11824–11834 (2021).Article 
CAS 
PubMed 

Google Scholar 
Pfeifer, E., Bonnin, R. A. & Rocha, E. P. C. Phage-plasmids spread antibiotic resistance genes through infection and lysogenic conversion. mBio 13, e0185122 (2022).Article 
PubMed 

Google Scholar 
Shaw, L. P., Rocha, E. P. C. & MacLean, R. C. Restriction-modification systems have shaped the evolution and distribution of plasmids across bacteria. Nucleic Acids Res. 51, 6806–6818 (2023).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Upreti, C., Kumar, P., Durso, L. M. & Palmer, K. L. CRISPR-Cas inhibits plasmid transfer and immunizes bacteria against antibiotic resistance acquisition in manure. Appl. Environ. Microbiol. 90, e0087624 (2024).Article 
PubMed 

Google Scholar 
Xu, N. et al. A global atlas of marine antibiotic resistance genes and their expression. Water Res. 244, 120488 (2023).Article 
CAS 
PubMed 

Google Scholar 
Zhao, Y. et al. Global soil antibiotic resistance genes are associated with increasing risk and connectivity to human resistome. Nat. Commun. 16, 7141 (2025).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Feiner, R. et al. A new perspective on lysogeny: prophages as active regulatory switches of bacteria. Nat. Rev. Microbiol. 13, 641–650 (2015).Article 
CAS 
PubMed 

Google Scholar 
Debroas, D. & Siguret, C. Viruses as key reservoirs of antibiotic resistance genes in the environment. ISME J. 13, 2856–2867 (2019).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Yi, Y. et al. A systematic analysis of marine lysogens and proviruses. Nat. Commun. 14, 6013 (2023).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Danko, D. et al. A global metagenomic map of urban microbiomes and antimicrobial resistance. Cell 184, 3376–3393.e17 (2021).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Wang, F. et al. Antibiotic resistance genes link to nitrogen removal potential via co-hosting preference for denitrification genes in a subtropical estuary. J. Hazard. Mater. 498, 139801 (2025).Article 
CAS 
PubMed 

Google Scholar 
Bernheim, A. & Sorek, R. The pan-immune system of bacteria: antiviral defence as a community resource. Nat. Rev. Microbiol. 18, 113–119 (2020).Article 
CAS 
PubMed 

Google Scholar 
Langmüller, A. M. et al. Fitness effects of CRISPR endonucleases in Drosophila melanogaster populations. eLife 11, e71809 (2022).Article 
PubMed 
PubMed Central 

Google Scholar 
Dimitriu, T., Szczelkun, M. D. & Westra, E. R. Evolutionary ecology and interplay of prokaryotic innate and adaptive immune systems. Curr. Biol. 30, R1189–R1202 (2020).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Wu, Z. et al. Unveiling the unknown viral world in groundwater. Nat. Commun. 15, 6788 (2024).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Li, D., Liu, C.-M., Luo, R., Sadakane, K. & Lam, T.-W. MEGAHIT: an ultra-fast single-node solution for large and complex metagenomics assembly via succinct de Bruijn graph. Bioinformatics 31, 1674–1676 (2015).Article 
CAS 
PubMed 

Google Scholar 
Uritskiy, G. V., DiRuggiero, J. & Taylor, J. MetaWRAP—a flexible pipeline for genome-resolved metagenomic data analysis. Microbiome 6, 158 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
Parks, D. H., Imelfort, M., Skennerton, C. T., Hugenholtz, P. & Tyson, G. W. CheckM: assessing the quality of microbial genomes recovered from isolates, single cells, and metagenomes. Genome Res. 25, 1043–1055 (2015).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Chaumeil, P.-A., Mussig, A. J., Hugenholtz, P. & Parks, D. H. GTDB-Tk: a toolkit to classify genomes with the Genome Taxonomy Database. Bioinformatics 36, 1925–1927 (2020).Article 
CAS 

Google Scholar 
Camargo, A. P. et al. Identification of mobile genetic elements with geNomad. Nat. Biotechnol. 42, 1303–1312 (2024).Article 
CAS 
PubMed 

Google Scholar 
Nayfach, S. et al. CheckV assesses the quality and completeness of metagenome-assembled viral genomes. Nat. Biotechnol. 39, 578–585 (2021).Article 
CAS 
PubMed 

Google Scholar 
Kieft, K., Zhou, Z. & Anantharaman, K. VIBRANT: automated recovery, annotation and curation of microbial viruses, and evaluation of viral community function from genomic sequences. Microbiome 8, 90 (2020).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Hockenberry, A. J. & Wilke, C. O. BACPHLIP: predicting bacteriophage lifestyle from conserved protein domains. PeerJ 9, e11396 (2021).Article 
PubMed 
PubMed Central 

Google Scholar 
Camacho, C. et al. BLAST+: architecture and applications. BMC Bioinformatics 10, 421 (2009).Article 
PubMed 
PubMed Central 

Google Scholar 
Lu, C. et al. Prokaryotic virus host predictor: a Gaussian model for host prediction of prokaryotic viruses in metagenomics. BMC Biol. 19, 5 (2021).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Tesson, F. et al. Systematic and quantitative view of the antiviral arsenal of prokaryotes. Nat. Commun. 13, 2561 (2022).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Yin, X. et al. ARGs-OAP v2.0 with an expanded SARG database and hidden Markov models for enhancement characterization and quantification of antibiotic resistance genes in environmental metagenomes. Bioinformatics 1, 2263–2270 (2018).Article 

Google Scholar 
Seemann, T. Prokka: rapid prokaryotic genome annotation. Bioinformatics 30, 2068–2069 (2014).Article 
CAS 
PubMed 

Google Scholar 
Brown, C. L. et al. mobileOG-db: a manually curated database of protein families mediating the life cycle of bacterial mobile genetic elements. Appl. Environ. Microbiol. 88, e00991-22 (2022).Article 
PubMed 
PubMed Central 

Google Scholar 
Nayfach, S. et al. A genomic catalog of Earth’s microbiomes. Nat. Biotechnol. 39, 499–509 (2021).Article 
CAS 
PubMed 

Google Scholar 
Katoh, K. & Standley, D. M. MAFFT multiple sequence alignment software version 7: improvements in performance and usability. Mol. Biol. Evol. 30, 772–780 (2013).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Capella-Gutiérrez, S., Silla-Martínez, J. M. & Gabaldón, T. trimAl: a tool for automated alignment trimming in large-scale phylogenetic analyses. Bioinformatics 25, 1972–1973 (2009).Article 
PubMed 
PubMed Central 

Google Scholar 
Minh, B. Q. et al. IQ-TREE 2: new models and efficient methods for phylogenetic inference in the genomic era. Mol. Biol. Evol. 37, 1530–1534 (2020).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Adékambi, T., Drancourt, M. & Raoult, D. The rpoB gene as a tool for clinical microbiologists. Trends Microbiol. 17, 37–45 (2009).Article 
PubMed 

Google Scholar 
Nagies, F. S. P., Brueckner, J., Tria, F. D. K. & Martin, W. F. A spectrum of verticality across genes. PLoS Genet. 16, e1009200 (2020).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Ribeiro, G. M. & Lahr, D. J. G. A comparative study indicates vertical inheritance and horizontal gene transfer of arsenic resistance-related genes in eukaryotes. Mol. Phylogenet. Evol. 173, 107479 (2022).Article 
CAS 
PubMed 

Google Scholar 
Holt, C. C. et al. Multiple parallel origins of parasitic Marine Alveolates. Nat. Commun. 14, 7049 (2023).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Goluch, T., Bogdanowicz, D. & Giaro, K. Visual TreeCmp: comprehensive comparison of phylogenetic trees on the web. Methods Ecol. Evol. 11, 494–499 (2020).Article 

Google Scholar 
Zhao, R. et al. Deciphering of microbial community and antibiotic resistance genes in activated sludge reactors under high selective pressure of different antibiotics. Water Res. 151, 388–402 (2019).Article 
CAS 
PubMed 

Google Scholar 
Cao, H. Phage-mediated resistome dynamics in global aquifers. Zenodo https://doi.org/10.5281/zenodo.17540538 (2025).Download referencesAcknowledgementsThis work was supported by the National Natural Science Foundation of China (grant numbers U2240205 and 51721006 to J.R.N.).Author informationAuthor notesJinren NiPresent address: College of Environmental Sciences and Engineering, Peking University, Beijing, P. R. ChinaAuthors and AffiliationsEco-environment and Resource Efficiency Research Laboratory, School of Environment and Energy, Peking University Shenzhen Graduate School, Shenzhen, People’s Republic of ChinaHuaiyu Cao, Songfeng Liu, Jiawen Wang & Jinren NiEnvironmental Microbiome and Innovative Genomics Laboratory, College of Environmental Sciences and Engineering, Peking University, Beijing, People’s Republic of ChinaHuaiyu Cao, Pinggui Cai & Pengwei LiEnvironmental Protection Key Laboratory of All Material Fluxes in River Ecosystems, Ministry of Ecology and Environment, Beijing, People’s Republic of ChinaPengwei Li & Jinren NiAuthorsHuaiyu CaoView author publicationsSearch author on:PubMed Google ScholarSongfeng LiuView author publicationsSearch author on:PubMed Google ScholarPinggui CaiView author publicationsSearch author on:PubMed Google ScholarPengwei LiView author publicationsSearch author on:PubMed Google ScholarJiawen WangView author publicationsSearch author on:PubMed Google ScholarJinren NiView author publicationsSearch author on:PubMed Google ScholarContributionsJ.R.N. designed the research. H.Y.C., S.F.L. and P.G.C. conducted the statistical analysis with help of P.W.L. and J.W.W. H.Y.C. and J.R.N. wrote the paper. All the authors read and approved the final paper.Corresponding authorCorrespondence to
Jinren Ni.Ethics declarations

Competing interests
The authors declare no competing interests.

Peer review

Peer review information
Nature Water thanks Liping Ma, Yanni Sun and Pingfeng Yu for their contribution to the peer review of this work.

Additional informationPublisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.Extended dataExtended Data Fig. 1 Composition and geographic distribution of antibiotic resistance genes (ARGs) across global aquifer metagenomes.a. Total abundance and prevalence of ARGs detected in all metagenomes. The majority of ARGs (6,935 of 9,681) are sparsely distributed, appearing in < 10% of samples (peripheral ARGs); whereas a core set of 392 ARGs occurs in > 75% of samples (core ARGs). b. Relative abundance of ARG types based on read-level profiling; MLS: macrolide-lincosamide-streptogramin. c. Upset plot showing ARG subtypes uniquely detected in specific continental combinations. d. Composition of ARGs by resistance mechanisms based on MAG-level analysis, reflecting host-associated ARG preferences.Source dataExtended Data Fig. 2 Characterization of mobile genetic elements (MGEs) associated with transferable ARGs in groundwater.a. Functional composition of annotated MGEs linked to ARGs in groundwater metagenomes. b. Distribution of transferable ARGs across different MGE types. Transferable ARGs were categorized as MGE-single if they were associated with only one type of MGE, and as MGE-multi if detected in association with more than one type of MGE. c. MGE repertoire associated with the most pervasive transferable ARGs (detected in > 100 MGE sequences). For each ARG, both the number and diversity of linked MGEs are shown.Source dataExtended Data Fig. 3 Distribution and characteristics of bacterial defence systems in groundwater microbiomes.a. Composition of defence gene families identified across all groundwater MAGs. b. Distribution of defence-encoding genomes across the ten most represented bacterial phyla. Bars indicate the proportion of genomes carrying defence systems, with the total number of unique DGs per phylum shown alongside. c. Defence gene (DG) counts in MAGs predicted to be phage-susceptible (P-phage, n = 1,458) versus those without phage-linked contigs (NP-phage, n = 1,452). Each point represents the number of DGs in an individual host genome. Statistical significance was evaluated using a two-sided Wilcoxon rank-sum test (p < 2 × 10−16). d. DG counts in L-phage (n = 908) versus NL-phage (n = 376) hosts. Each point represents an individual host genome. Box plots indicate the median (center line and red point), interquartile range (box), and 1.5 × the interquartile range (whiskers). Statistical significance was determined using a two-sided Wilcoxon rank-sum test. e. Number of ARG types associated with NP-phage, NL-phage, and L-phage hosts.Source dataExtended Data Fig. 4 Defence system distribution between P-phage and NP-phage MAGs across aquatic ecosystems (freshwater, marine, wastewater).P-phage MAGs consistently encoded more defence systems (DSs) than NP-phage MAGs, with significant differences detected across all ecosystems (two-sided Wilcoxon rank-sum test; **, p < 0.01; ***, p < 0.001; ****, p < 0.0001). The magnitude of phage–host antagonism decreased along the gradient from freshwater to marine and wastewater environments. Each point represents the number of DSs in an individual host genome. Box plots indicate the median (center line and red point), interquartile range (box), and 1.5 × the interquartile range (whiskers). Sample sizes and exact p values were as follows: Freshwater: NP-phage (n = 432) and P-phage (n = 249), p = 3.12 × 10⁻11; Marine: NP-phage (n = 252) and P-phage (n = 379), p = 6.82 × 10⁻4; Wastewater: NP-phage (n = 203) and P-phage (n = 210), p = 8.26 × 10⁻3.Source dataExtended Data Fig. 5 Vertical gene transfer sustains ARG persistence in aquifer microbiomes.Comparison between the MAG-based phylogenetic tree and the phylogeny of the rsmA resistance gene within two bacterial genera: 202FULL6113 (a) and JADFDG01 (b). Topological similarity between the two phylogenies was assessed using Robinson–Foulds (RF) distance.Source dataExtended Data Fig. 6 Assessment of vertical and horizontal contributions to ARG dissemination in groundwater microbial communities.Procrustes analysis at the phylum (a) and genus (b) levels compares microbial community composition (read-based) with ARG subtype profiles. Higher Procrustes m2 values indicate greater deviation from vertical inheritance, suggesting stronger influence of HGT on ARG distribution. Statistical significance was evaluated using a two-sided Procrustes permutation test (999 permutations; p < 0.001).Source dataExtended Data Fig. 7 Phage–host interactions shape microbial nitrogen cycling potential in aquifer ecosystems.a. Module completeness of functional gene markers associated with eight nitrogen cycling processes in P-phage (n = 1,458) and NP-phage (n = 1,452) genomes. Each bar represents the mean metabolic completeness for the corresponding nitrogen transformation process within each group, and error bars indicate the standard error of the mean (SEM). Statistical differences between groups were evaluated using a two-sided Wilcoxon rank-sum test; ns, not significant; *, p < 0.05; **, p < 0.01; ***, p < 0.001. Exact p-values for A–H are: A, p = 1.36 × 10−2; B, p = 0.19; C, p = 8.37 × 10−7; D, p = 5.24 × 10−3; E, p = 2.39 × 10−3; F, p = 0.30; G, p = 2.73 × 10−12; H, p = 1.97 × 10−6. b. Contribution of lytic phages to nitrogen cycling in all phage-infected microbial hosts, illustrated by a metabolic pathway map highlighting their role in aquifer nitrogen transformations.Source dataSupplementary informationReporting SummarySupplementary TablesSupplementary Tables 1–14.Source dataSource Data Fig. 1Statistical source data.Source Data Fig. 2Statistical source data.Source Data Fig. 3Statistical source data.Source Data Fig. 4Statistical source dataSource Data Fig. 5Statistical source data.Source Data Extended Data Fig. 1Statistical source data.Source Data Extended Data Fig. 2Statistical source data.Source Data Extended Data Fig. 3Statistical source data.Source Data Extended Data Fig. 4Statistical source data.Source Data Extended Data Fig. 5Statistical source data.Source Data Extended Data Fig. 6Statistical source data.Source Data Extended Data Fig. 7Statistical source data.Rights and permissionsSpringer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.Reprints and permissionsAbout this articleCite this articleCao, H., Liu, S., Cai, P. et al. Phage-mediated resistome dynamics in global aquifers.
Nat Water (2026). https://doi.org/10.1038/s44221-025-00558-wDownload citationReceived: 01 July 2025Accepted: 11 November 2025Published: 05 January 2026Version of record: 05 January 2026DOI: https://doi.org/10.1038/s44221-025-00558-wShare this articleAnyone you share the following link with will be able to read this content:Get shareable linkSorry, a shareable link is not currently available for this article.Copy shareable link to clipboard
Provided by the Springer Nature SharedIt content-sharing initiative More

AbstractAccurate assessment of soil organic carbon (SOC) is essential for sustainable cropland management and carbon sequestration monitoring. However, high-resolution SOC mapping remains challenging due to two persistent limitations: (1) the difficulty of extracting true bare-soil reflectance—especially when single-date imagery is used and spectral signals remain influenced by vegetation, residue, and soil moisture; and (2) reliance on models that require large training datasets and may underperform in typical small-sample soil survey settings. To address these challenges, we developed an approach that integrates multi-temporal Sentinel-2 bare-soil composites with a transformer-based foundation model—Tabular Prior-data Fitted Network (TabPFN)—for SOC prediction in the black soil region of Northeast China. Bare soil pixels were extracted using a Normalized Difference Vegetation Index threshold (0.1–0.4), and two compositing strategies—the 50th percentile (P50) and 90th percentile (P90)—were compared. We systematically evaluated three advanced algorithms: TabPFN, convolutional neural network (CNN), and Extreme Gradient Boosting (XGBoost). Results demonstrated that the TabPFN model coupled with P50 composites achieved the highest prediction accuracy (R2 = 0.78, RMSE = 1.90 g kg⁻1), outperforming CNN and XGBoost by 4–6%. TabPFN’s distinct advantage lies in its design as a prior-data fitted transformer, which enables robust generalization from limited samples (N = 174) without extensive hyperparameter tuning, effectively addressing the “small data” challenge pervasive in digital soil mapping. SHapley Additive exPlanations analysis indicated that shortwave infrared band (B12) and precipitation have the greatest effect on model output, indicating joint role of soil spectral response and climate variability. This is one of the first studies to apply the TabPFN architecture to SOC estimation, offering a novel, interpretable, and scalable workflow that bridges the gap between data scarcity and model complexity. The proposed framework provides a reliable tool for high-resolution SOC mapping in heterogeneous croplands, supporting precision agriculture and long-term carbon accounting initiatives.

Similar content being viewed by others

A national soil organic carbon density dataset (2010–2024) in China

Article
Open access
25 August 2025

Innovative approaches in soil carbon sequestration modelling for better prediction with limited data

Article
Open access
08 February 2024

Predicting soil organic carbon with ensemble learning techniques by using satellite images for precision farming

Article
Open access
06 August 2025

Data availability

The datasets analyzed during the current study are not publicly available due to existing agreements and data-use restrictions but are available from the corresponding author on reasonable request.
ReferencesLal, R. Soil carbon sequestration impacts on global climate change and food security. Science 304, 1623–1627. https://doi.org/10.1126/science.1097396 (2004).
Google Scholar 
Leifeld, J. & Menichetti, L. The underappreciated potential of peatlands in global climate change mitigation strategies. Nat. Commun. 9, 1071. https://doi.org/10.1038/s41467-018-03406-6 (2018).
Google Scholar 
Six, J., Elliott, E. T. & Paustian, K. Soil Structure and Soil Organic Matter II. A Normalized Stability Index and the effect of mineralogy. Soil Sci. Soc. Am. J. 64, 1042–1049. https://doi.org/10.2136/sssaj2000.6431042x (2000).
Google Scholar 
Minasny, B., McBratney, A. B., Malone, B. P. & Wheeler, I. in Advances in Agronomy Vol. 118 (ed Donald L. Sparks) 1–47 (Academic Press, 2013).Qi, L., Ma, J., Sun, Q. & Shi, P. Mapping soil organic carbon sequestration potential in croplands using a combined proximal and remote sensing approach. Soil Tillage Res. 254, 106733. https://doi.org/10.1016/j.still.2025.106733 (2025).
Google Scholar 
Minasny, B. et al. Soil carbon 4 per mille. Geoderma 292, 59–86. https://doi.org/10.1016/j.geoderma.2017.01.002 (2017).
Google Scholar 
Chen, S. et al. Digital mapping of GlobalSoilMap soil properties at a broad scale: A review. Geoderma 409, 115567. https://doi.org/10.1016/j.geoderma.2021.115567 (2022).
Google Scholar 
Zhou, F. et al. Integrating historical crop rotation changes into soil organic matter mapping in the Cropland of Southeastern China. Earth’s Future 13, e2025EF006117, https://doi.org/10.1029/2025EF006117 (2025).Shi, P., Six, J., Sila, A., Vanlauwe, B. & Van Oost, K. Towards spatially continuous mapping of soil organic carbon in croplands using multitemporal Sentinel-2 remote sensing. ISPRS J. Photogramm. Remote. Sens. 193, 187–199. https://doi.org/10.1016/j.isprsjprs.2022.09.013 (2022).
Google Scholar 
McBratney, A. B., Mendonça Santos, M. L. & Minasny, B. On digital soil mapping. Geoderma 117, 3–52. https://doi.org/10.1016/S0016-7061(03)00223-4 (2003).
Google Scholar 
Poppiel, R. R., Paiva, AFd. S. & Demattê, J. A. M. Bridging the gap between soil spectroscopy and traditional laboratory: Insights for routine implementation. Geoderma 425, 116029. https://doi.org/10.1016/j.geoderma.2022.116029 (2022).
Google Scholar 
Castaldi, F., Chabrillat, S. & van Wesemael, B. Sampling strategies for soil property mapping using multispectral Sentinel-2 and Hyperspectral EnMAP Satellite Data. Remote Sensing 11, 309 (2019).
Google Scholar 
Gholizadeh, A., Žižala, D., Saberioon, M. & Borůvka, L. Soil organic carbon and texture retrieving and mapping using proximal, airborne and Sentinel-2 spectral imaging. Remote Sens. Environ. 218, 89–103. https://doi.org/10.1016/j.rse.2018.09.015 (2018).
Google Scholar 
Wadoux, A. M. J. C., Minasny, B. & McBratney, A. B. Machine learning for digital soil mapping: Applications, challenges and suggested solutions. Earth Sci. Rev. 210, 103359. https://doi.org/10.1016/j.earscirev.2020.103359 (2020).
Google Scholar 
Dvorakova, K., Shi, P., Limbourg, Q. & van Wesemael, B. Soil Organic carbon mapping from remote sensing: The effect of crop residues. Remote Sens. https://doi.org/10.3390/rs12121913 (2020).
Google Scholar 
Rogge, D. et al. Building an exposed soil composite processor (SCMaP) for mapping spatial and temporal characteristics of soils with Landsat imagery (1984–2014). Remote Sens. Environ. 205, 1–17. https://doi.org/10.1016/j.rse.2017.11.004 (2018).
Google Scholar 
Melo Dematte, J. A., Fongaro, C. T., Rizzo, R. & Safanelli, J. L. Geospatial Soil Sensing System (GEOS3): A powerful data mining procedure to retrieve soil spectral reflectance from satellite images. Remote Sens. Environ. 212, 161–217. https://doi.org/10.1016/j.rse.2018.04.047 (2018).
Google Scholar 
Castaldi, F. et al. Evaluating the capability of the Sentinel 2 data for soil organic carbon prediction in croplands. ISPRS J. Photogramm. Remote. Sens. 147, 267–282. https://doi.org/10.1016/j.isprsjprs.2018.11.026 (2019).
Google Scholar 
Vaudour, E. et al. Temporal mosaicking approaches of Sentinel-2 images for extending topsoil organic carbon content mapping in croplands. Int. J. Appl. Earth Obs. Geoinf. 96, 102277. https://doi.org/10.1016/j.jag.2020.102277 (2021).
Google Scholar 
Xue, J. et al. National-scale mapping topsoil organic carbon of cropland in China using multitemporal Sentinel-2 images. Geoderma 456, 117272. https://doi.org/10.1016/j.geoderma.2025.117272 (2025).
Google Scholar 
Zhu, Y., Qi, L., Wu, Z. & Shi, P. Spectra-based predictive mapping of soil organic carbon in croplands: Single-date versus multitemporal bare soil compositing approaches. Geoderma 449, 116987. https://doi.org/10.1016/j.geoderma.2024.116987 (2024).
Google Scholar 
Castaldi, F. et al. Assessing the capability of Sentinel-2 time-series to estimate soil organic carbon and clay content at local scale in croplands. ISPRS J. Photogramm. Remote. Sens. 199, 40–60. https://doi.org/10.1016/j.isprsjprs.2023.03.016 (2023).
Google Scholar 
Hong, Y. et al. Bridging the gap between laboratory VNIR-SWIR spectra and Landsat-8 bare soil composite image for soil organic carbon prediction. Remote Sens. Environ. 328, 114874. https://doi.org/10.1016/j.rse.2025.114874 (2025).
Google Scholar 
Zhang, M.-W. et al. Predicting spatial–temporal soil organic matter dynamics in a Mollisols region of the northern Songnen Plain, China, during 2009–2018 using a spectral-temporal feature set. Geoderma 461, 117461. https://doi.org/10.1016/j.geoderma.2025.117461 (2025).
Google Scholar 
Minasny, B. & McBratney, A. B. Digital soil mapping: A brief history and some lessons. Geoderma 264, 301–311. https://doi.org/10.1016/j.geoderma.2015.07.017 (2016).
Google Scholar 
Vaudour, E. et al. Satellite imagery to map topsoil organic carbon content over cultivated areas: An overview. Remote Sens. https://doi.org/10.3390/rs14122917 (2022).
Google Scholar 
Padarian, J., Minasny, B. & McBratney, A. B. Using deep learning for digital soil mapping. SOIL 5, 79–89. https://doi.org/10.5194/soil-5-79-2019 (2019).
Google Scholar 
Meng, X., Bao, Y., Wang, Y., Zhang, X. & Liu, H. An advanced soil organic carbon content prediction model via fused temporal-spatial-spectral (TSS) information based on machine learning and deep learning algorithms. Remote Sens. Environ. https://doi.org/10.1016/j.rse.2022.113166 (2022).
Google Scholar 
Meng, X., Bao, Y., Luo, C., Zhang, X. & Liu, H. SOC content of global Mollisols at a 30 m spatial resolution from 1984 to 2021 generated by the novel ML-CNN prediction model. Remote Sens. Environ. 300, 113911. https://doi.org/10.1016/j.rse.2023.113911 (2024).
Google Scholar 
Žížala, D. et al. Soil sampling design matters – Enhancing the efficiency of digital soil mapping at the field scale. Geoderma Reg. 39, e00874. https://doi.org/10.1016/j.geodrs.2024.e00874 (2024).
Google Scholar 
Hollmann, N. et al. Accurate predictions on small data with a tabular foundation model. Nature 637, 319–326. https://doi.org/10.1038/s41586-024-08328-6 (2025).
Google Scholar 
Heiden, U. et al. Soil reflectance composites-improved thresholding and performance evaluation. Remote Sens. https://doi.org/10.3390/rs14184526 (2022).
Google Scholar 
Kalopesa, E. et al. Large-scale soil organic carbon estimation via a multisource data fusion approach. Remote Sens. 17, 771 (2025).
Google Scholar 
Zhang, Y. et al. Estimation of coastal wetland soil organic carbon content in Western Bohai Bay Using Remote Sensing, Climate, and Topographic Data. Remote Sens. https://doi.org/10.3390/rs15174241 (2023).
Google Scholar 
Song, J. et al. Mapping soil organic matter in cultivated land based on multi-year composite images on monthly time scales. J. Integr. Agric. 23, 1393–1408. https://doi.org/10.1016/j.jia.2023.09.017 (2024).
Google Scholar 
Ben-Dor, E., Inbar, Y. & Chen, Y. The reflectance spectra of organic matter in the visible near-infrared and short wave infrared region (400–2500 nm) during a controlled decomposition process. Remote Sens. Environ. 61, 1–15. https://doi.org/10.1016/S0034-4257(96)00120-4 (1997).
Google Scholar 
Angelopoulou, T., Tziolas, N., Balafoutis, A., Zalidis, G. & Bochtis, D. Remote sensing techniques for soil organic carbon estimation: A review. Remote Sens. 11, 676 (2019).
Google Scholar 
Chen, X. et al. Effects of precipitation on soil organic carbon fractions in three subtropical forests in southern China. J. of Plant Ecol. 9, 10–19. https://doi.org/10.1093/jpe/rtv027 (2015).
Google Scholar 
Pallandt, M. et al. Modelling the effect of climate–substrate interactions on soil organic matter decomposition with the Jena Soil Model. Biogeosciences 22, 1907–1928. https://doi.org/10.5194/bg-22-1907-2025 (2025).
Google Scholar 
Wu, Y. et al. Mechanisms behind the soil organic carbon response to temperature elevations. Agriculture 15, 1118 (2025).
Google Scholar 
Zhao, M. et al. Soil mineral-associated organic carbon and its relationship to clay minerals across grassland transects in China. Appl. Sci. 14, 2061 (2024).
Google Scholar 
Xu, Z. & Tsang, D. C. W. Mineral-mediated stability of organic carbon in soil and relevant interaction mechanisms. Eco-Environ. Health 3, 59–76. https://doi.org/10.1016/j.eehl.2023.12.003 (2024).
Google Scholar 
Xue, B. et al. Effect of clay mineralogy and soil organic carbon in aggregates under straw incorporation. Agronomy 12, 534 (2022).
Google Scholar 
Minasny, B. et al. Soil science-informed machine learning. Geoderma 452, 117094. https://doi.org/10.1016/j.geoderma.2024.117094 (2024).
Google Scholar 
Ge, H. et al. Enhancing yield prediction in maize breeding using UAV-derived RGB imagery: a novel classification-integrated regression approach. Front. Plant Sci. https://doi.org/10.3389/fpls.2025.1511871 (2025).
Google Scholar 
Dong, Y. et al. A 30-m annual corn residue coverage dataset from 2013 to 2021 in Northeast China. Sci. Data 11, 216. https://doi.org/10.1038/s41597-024-02998-7 (2024).
Google Scholar 
Ma, J. & Shi, P. Remotely sensed inter-field variation in soil organic carbon content as influenced by the cumulative effect of conservation tillage in northeast China. Soil Tillage Res. 243, 106170. https://doi.org/10.1016/j.still.2024.106170 (2024).
Google Scholar 
Nelson, D. W. & Sommers, L. E. in Methods of Soil Analysis 539–579 (1982).Broeg, T., Don, A., Wiesmeier, M., Scholten, T. & Erasmi, S. Spatiotemporal Monitoring of Cropland Soil Organic Carbon Changes From Space. Glob. Change Biol. 30, e17608. https://doi.org/10.1111/gcb.17608 (2024).
Google Scholar 
Broeg, T. et al. Using local ensemble models and Landsat bare soil composites for large-scale soil organic carbon maps in cropland. Geoderma 444, 116850. https://doi.org/10.1016/j.geoderma.2024.116850 (2024).
Google Scholar 
Zayani, H. et al. Using machine-learning algorithms to predict soil organic carbon content from combined remote sensing imagery and laboratory vis-NIR spectral datasets. Remote Sens. https://doi.org/10.3390/rs15174264 (2023).
Google Scholar 
Wang, Q. et al. Incorporating agricultural practices in digital mapping improves prediction of cropland soil organic carbon content: The case of the Tuojiang River Basin. J. Environ. Manage. 330, 117203. https://doi.org/10.1016/j.jenvman.2022.117203 (2023).
Google Scholar 
Lundberg, S. M. & Lee, S.-I. in Proceedings of the 31st International Conference on Neural Information Processing Systems 4768–4777 (Curran Associates Inc., Long Beach, California, USA, 2017).Thaler, E. A., Larsen, I. J. & Yu, Q. A New Index for remote sensing of soil organic carbon based solely on visible wavelengths. Soil Sci. Soc. Am. J. 83, 1443–1450. https://doi.org/10.2136/sssaj2018.09.0318 (2019).
Google Scholar 
Marques, M. J., Alvarez, A., Carral, P., Sastre, B. & Bienes, R. The use of remote sensing to detect the consequences of erosion in gypsiferous soils. Int. Soil Water Conserv. Res. 8, 383–392. https://doi.org/10.1016/j.iswcr.2020.10.001 (2020).
Google Scholar 
Castaldi, F., Chabrillat, S., Don, A. & van Wesemael, B. Soil organic carbon mapping using LUCAS topsoil database and sentinel-2 data: An approach to reduce soil moisture and crop residue effects. Remote Sens. 11, 2121 (2019).
Google Scholar 
Download referencesFundingThis research was funded by the “Study on the Retrogressive Erosion Mechanism of Gully Heads with Different Parent Materials in Black Soil Regions of Low Mountains and Hills” project of Natural Science Foundation of Jilin Province, China (20250102200JC).Author informationAuthors and AffiliationsCollege of Economics and Management, Jilin Agricultural University, Changchun, 130118, ChinaNa Chen, Zhikang Wei, Ling Zhao & Song WuCollege of Earth Sciences, Jilin University, Changchun, 130061, ChinaXuancheng JinModern Industry College, Jilin Jianzhu University, Changchun, 130118, ChinaNan LinCollege of Resources and Environment, Jilin Agricultural University, Changchun, 130118, ChinaFan YangAuthorsNa ChenView author publicationsSearch author on:PubMed Google ScholarZhikang WeiView author publicationsSearch author on:PubMed Google ScholarXuancheng JinView author publicationsSearch author on:PubMed Google ScholarNan LinView author publicationsSearch author on:PubMed Google ScholarFan YangView author publicationsSearch author on:PubMed Google ScholarLing ZhaoView author publicationsSearch author on:PubMed Google ScholarSong WuView author publicationsSearch author on:PubMed Google ScholarContributionsConceptualization, Song Wu, Na Chen, and Zhikang Wei; methodology, Na Chen and Xuancheng Jin; software, Nan Lin; validation, Nan Lin; formal analysis, Zhikang Wei; resources, Ling Zhao and Na Chen; data curation, Xuancheng Jin; writing—original draft preparation, Song Wu, Zhikang Wei and Na Chen; writing—review and editing, Xuancheng Jin, Na Chen and Song Wu; visualization, Song Wu and Xuancheng Jin; supervision, Nan Lin; project administration, Song Wu; funding acquisition, Fan Yang. All authors have read and agreed to the published version of the manuscript.Corresponding authorCorrespondence to
Song Wu.Ethics declarations

Competing interests
The authors declare no competing interests.

Additional informationPublisher’s noteSpringer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.Supplementary InformationSupplementary Information.Rights and permissions
Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
Reprints and permissionsAbout this articleCite this articleChen, N., Wei, Z., Jin, X. et al. Integrating transformer-based learning and Sentinel-2 bare soil composites for soil organic carbon mapping in the black soil region of Northeast China.
Sci Rep (2026). https://doi.org/10.1038/s41598-025-33682-4Download citationReceived: 14 November 2025Accepted: 22 December 2025Published: 05 January 2026DOI: https://doi.org/10.1038/s41598-025-33682-4Share this articleAnyone you share the following link with will be able to read this content:Get shareable linkSorry, a shareable link is not currently available for this article.Copy shareable link to clipboard
Provided by the Springer Nature SharedIt content-sharing initiative
KeywordsSoil organic carbonSentinel-2Digital soil mappingBare soil compositeTabPFNSHAP More

New studies that document the effect of polymetallic nodule mining vehicles on deep-sea biodiversity suggest that keeping up with technological innovations will be key to more realistic impact assessments of deep-sea mining.

Access through your institution

Buy or subscribe

This is a preview of subscription content, access via your institution

Access options

Access through your institution

Access Nature and 54 other Nature Portfolio journals

Get Nature+, our best-value online-access subscription

$32.99 / 30 days

cancel any time

Learn more

Subscribe to this journal

Receive 12 digital issues and online access to articles

$119.00 per year
only $9.92 per issue

Learn more

Rent or buy this article
Prices vary by article type
from$1.95
to$39.95

Learn more

Prices may be subject to local taxes which are calculated during checkout

Additional access options:

Log in

Learn about institutional subscriptions

Read our FAQs

Contact customer support

Fig. 1: Comparison of tracks from abyssal polymetallic nodule mining experiments and their respective disturbers or prototype collectors.

ReferencesLaTourrette, T., Villalobos, F., Yoshiara, E. & Tariq, Z. H. The Potential Impact of Seabed Mining on Critical Mineral Supply Chains and Global Geopolitics (RAND, 2025).Cato, A. & Evoy, P. Earth Syst. Govern. 24, 100249 (2025).Article 

Google Scholar 
Hallgren, A. & Hansson, A. Sustainability 13, 5261 (2021).Article 

Google Scholar 
Jones, D. O. B. et al. Nature 642, 112–118 (2025).Article 
PubMed 
PubMed Central 

Google Scholar 
Stewart, E. C. D. et al. Nat. Ecol. Evol. https://doi.org/10.1038/s41559-025-02911-4 (2025).Article 
PubMed 
PubMed Central 

Google Scholar 
Gazis, I.-Z. et al. Nat. Commun. 16, 1229 (2025).Article 
PubMed 
PubMed Central 

Google Scholar 
Muñoz-Royo, C. et al. Commun. Earth Environ. 2, 148 (2021).Article 

Google Scholar 
Lefaible, N. et al. Front. Mar. Sci. 11, 1380530 (2024).Article 

Google Scholar 
Jones, D. O. B. et al. PLoS ONE 12, e0171750 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
Zhang, X. et al. J. Mar. Sci. Eng. 12, 788 (2024).Article 

Google Scholar 
AMC/TMC. Technical Report Summary of Prefeasibility Study of NORI Area D, Clarion Clipperton Zone, TMC The Metals Company Inc., 447 (AMC/TMC, 2025).Cheng, Y., Dai, Y., Zhang, Y., Yang, C. & Liu, C. Sustainability 15, 4572 (2023).Article 

Google Scholar 
Vonnahme, T. R. et al. Sci. Adv. 6, eaaz5922 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Woolley, S. N. C. et al. Assessing the Quantitative Risk of Seafloor Polymetallic Nodule Mining on Ecosystem Indicators (CSIRO Marine Laboratories, 2025).Dambacher, J. M. et al. DPSIR Components and Candidate Indicators for Polymetalic Nodule Mining within the NORI-D Lease of the Clarion-Clipperton Zone (CSIRO Marine Laboratories, 2025).Download referencesAuthor informationAuthors and AffiliationsEarth Sciences New Zealand, Wellington, New ZealandJeroen Ingels, Daniel Leduc, Ailish Ullmann & Ashley RowdenTe Herenga Waka — Victoria University of Wellington, Wellington, New ZealandAilish Ullmann & Ashley RowdenAuthorsJeroen IngelsView author publicationsSearch author on:PubMed Google ScholarDaniel LeducView author publicationsSearch author on:PubMed Google ScholarAilish UllmannView author publicationsSearch author on:PubMed Google ScholarAshley RowdenView author publicationsSearch author on:PubMed Google ScholarCorresponding authorCorrespondence to
Jeroen Ingels.Ethics declarations

Competing interests
The authors are involved in scientific research projects that are funded in part by prospective deep-sea mining companies, which has provided valuable insights into technical and operational aspects of test mining operations. These companies did not fund this publication, nor did they contribute to or have any influence on the findings or opinions expressed by the authors.

Rights and permissionsReprints and permissionsAbout this articleCite this articleIngels, J., Leduc, D., Ullmann, A. et al. Assessing mining impacts in the deep sea.
Nat Ecol Evol (2026). https://doi.org/10.1038/s41559-025-02965-4Download citationPublished: 05 January 2026Version of record: 05 January 2026DOI: https://doi.org/10.1038/s41559-025-02965-4Share this articleAnyone you share the following link with will be able to read this content:Get shareable linkSorry, a shareable link is not currently available for this article.Copy shareable link to clipboard
Provided by the Springer Nature SharedIt content-sharing initiative More

AbstractThe black soldier fly (BSF) larvae is a rich and promising source of alternative protein that continues to increasingly gain global traction as a functional ingredient for sustainable livestock and fish production. The key setback to postharvest processing of stored BSF larvae (BSFL) products is the significant damage caused by two notable storage pests (Tribolium castaneum and Necrobia rufipes). Here, we present a comparative analysis of the complete mitochondrial genomes and gut microbiome profiles of T. castaneum and N. rufipes. The study mitogenomes were similar in size and structure to other coleopteran mitogenomes. The gut microbiome profiles of the two pests showed a high abundance of bacteria in the Proteobacteria and Firmicutes phyla. However, T. castaneum had 78% more phyla represented within its microbiome than N. rufipes. The most abundant genera in T. castaneum were Staphylococcus and Streptococcus, while in N. rufipes, the dominant genera were Klebsiella and Synechococcus. We also identified the presence of potentially clinically harmful microbial genera (Stenotrophomonas maltophilia) in the gut of T. castaneum and N. rufipes in relatively high abundance. These results provide insight into potential harmful associations in the gut of the storage pest, picked from contaminated, poorly processed BSFL products.

Similar content being viewed by others

Absence of microbiome triggers extensive changes in the transcriptional profile of Hermetia illucens during larval ontogeny

Article
Open access
10 February 2023

Mitogenome-wise codon usage pattern from comparative analysis of the first mitogenome of Blepharipa sp. (Muga uzifly) with other Oestroid flies

Article
Open access
29 April 2022

Chromosome-level genome assembly of cotton thrips Thrips tabaci (Thysanoptera: Thripidae)

Article
Open access
16 September 2024

Data availability

All sequences generated in this study were deposited in the GenBank database ( [www.ncbi.nlm.nih.gov/genbank](http:/www.ncbi.nlm.nih.gov/genbank) ) under the BioProject number: PRJNA995429 and accession number: OR450807.1.
ReferencesRumpold, B. A. & Schlüter, O. Insect-based protein sources and their potential for human consumption: Nutritional composition and processing. Anim. Front. 5, 20–24 (2015).
Google Scholar 
Chia, S. Y. et al. Nutritional composition of black soldier fly larvae feeding on agro-industrial by‐products. Entomol. Exp. Appl. 168, 472–481 (2020).
Google Scholar 
Khamis, F. M. et al. Insights in the global genetics and gut microbiome of black soldier fly, Hermetia illucens: Implications for animal feed safety control. Front. Microbiol. 11, 1538 (2020).
Google Scholar 
Zamri, M. Z. A. et al. Potential use of black soldier fly, Hermetia illucens larvae in chicken feed as a protein replacer: A review. J. Anim. Feed Sci. 32, 341–353 (2023).
Google Scholar 
Ndotono, E. W., Khamis, F. M., Bargul, J. L. & Tanga, C. M. Gut microbiota shift in layer pullets fed on black soldier fly larvae-based feeds towards enhancing healthy gut microbial community. Sci. Rep. 12, 16714 (2022).
Google Scholar 
Zhao, J. et al. Long-term dietary fish meal substitution with the black soldier fly larval meal modifies the caecal microbiota and microbial pathway in laying hens. Animals 13, 2629 (2023).
Google Scholar 
Mohan, K. et al. Use of black soldier fly (Hermetia illucens L.) larvae meal in Aquafeeds for a sustainable aquaculture industry: A review of past and future needs. Aquaculture 553, 738095 (2022).
Google Scholar 
Hong, J. & Kim, Y. Y. Insect as feed ingredients for pigs. Anim. Biosci. 35, 347–355 (2022).
Google Scholar 
Joosten, L. et al. Review of insect pathogen risks for the black soldier fly (Hermetia illucens) and guidelines for reliable production. Entomol. Exp. Appl. 168, 432–447 (2020).
Google Scholar 
Bala, B. K. Fungi, Insects and Other Organisms Associated with Stored Grain in Drying and Storage of Cereal Grains 263–272 (Wiley, 2016).Tanga, C. M. & Nakimbugwe, D. insfeed2: Insect feed for poultry, fish, and pig production in sub-Saharan Africa- phase 2. Technical Report at https://idl-bnc-idrc.dspacedirect.org/bitstream/handle/10625/60979/IDL%20-%2060979.pdf (2021).Friedrich, M. & Muqim, N. Sequence and phylogenetic analysis of the complete mitochondrial genome of the flour beetle Tribolium castanaeum. Mol. Phylogenet. Evol. 26, 502–512 (2003).
Google Scholar 
Agarwal, A. & Agashe, D. The red flour beetle Tribolium castaneum: A model for host-microbiome interactions. PLoS One. 15, e0239051 (2020).
Google Scholar 
Singh, N., Kaur, A., Shevkani, K. & Maize Grain Structure, Composition, Milling, and Starch Characteristics in Maize: Nutrition Dynamics and Novel Uses 65–76 (Springer India, New Delhi 2014).Tribolium Genome Sequencing Consortium. The genome of the model beetle and pest Tribolium castaneum. Nature 452, 949–955 (2008).
Google Scholar 
Ebeling, W. Pests of stored food products. In Urban Entomology 1st E.D. University of California, Division of Agricultural Sciences vol. 7, 275–309 (1975).Subramanyam, B., Campbell, J. & Kemp, K. It’s in the detail for retail: A study is underway to determine the best management methods in the retail environment. Pest Control. 69, 26–31 (2001).
Google Scholar 
Koç, N., Arslanbaş, M., Tiftikçioğlu, C., Nalbantoğlu, S. & Çakmak, A. Evaluation of relation with pet food and first record of necrobia rufipes (De Geer, 1775) (Coleoptera: Cleridae) associated with pet clinic in Turkey. Vet. Hekim Der Derg. 91, 44–48 (2019).
Google Scholar 
dos Santos, A. T., Pereira, T. N. & CL, B. Necrobia rufipes (Degeer, 1775) (Coleoptera: Cleridae) infesting industrialized foods for pet animals in campina Grande, Paraiba State, Brazil. Entomol. News. 129, 81–85 (2020).
Google Scholar 
Gredilha, R. & Lima A. F. First record of Necrobia rufipes (De Geer, 1775) (Coleoptera; Cleridae) associated with pet food in Brazil. Braz J. Biol. 67, 187 (2007).
Google Scholar 
Simmons, P. & Ellington, G. W. The Ham Beetle Necrobia rufipes De Geer. J. Agric. Res. 30, 845–863 (1925).
Google Scholar 
Hasan, M. M., Athanassiou, C. G., Schilling, M. W. & Phillips, T. W. Biology and management of the red-legged ham beetle, Necrobia rufipes DeGeer (Coleoptera: cleridae. J. Stored Prod. Res. 88, 101635 (2020).
Google Scholar 
Curole, J. P. & Kocher, T. D. Mitogenomics: Digging deeper with complete mitochondrial genomes. Trends Ecol. Evol. 14, 394–398 (1999).
Google Scholar 
Ballard, J. W. O. & Towarnicki, S. G. Mitochondria, the gut microbiome and ROS. Cell. Signal. 75, 109737 (2020).
Google Scholar 
Wu, C., Zhou, Y., Tian, T., Li, T. & Chen, B. First report of complete mitochondrial genome in the subfamily alleculinae and mitochondrial genome-based phylogenetics in Tenebrionidae (Coleoptera: Tenebrionoidea). Insect Sci. 29, 1226–1238 (2022).
Google Scholar 
Sheffield, N. C., Song, H., Cameron, S. L. & Whiting, M. F. A comparative analysis of mitochondrial genomes in coleoptera (Arthropoda: Insecta) and genome descriptions of six new beetles. Mol. Biol. Evol. 25, 2499–2509 (2008).
Google Scholar 
Meng, F. et al. The complete mitochondria genome of a forensic related beetle, Necrobia ruficollis (Fabricius, 1775). Mitochondrial DNA Part. B. 4, 1396–1397 (2019).
Google Scholar 
Liu, Q. N. et al. The complete mitochondrial genome of the red flour beetle, Tribolium castaneum (Coleoptera: Tenebrionidae). Mitochondrial DNA Part. A. 27, 1525–1527 (2016).
Google Scholar 
Lee, W. J. & Brey, P. T. How microbiomes influence metazoan development:insights from history and Drosophila modeling of gut-microbe interactions. Annu. Rev. Cell. Dev. Biol. 29, 571–592 (2013).
Google Scholar 
Johnson, K. V. A. & Foster, K. R. Why does the microbiome affect behaviour? Nat. Rev. Microbiol. 16, 647–655 (2018).
Google Scholar 
Kim, M. J., Wan, X. & Kim, I. Complete mitochondrial genome of the seven-spotted lady beetle, Coccinella septempunctata (Coleoptera: Coccinellidae). Mitochondrial DNA. 23, 179–181 (2012).
Google Scholar 
Liu, Q. N., Zhu, B. J., Dai, L. S. & Liu, C. L. The complete mitogenome of Bombyx Mori strain Dazao (Lepidoptera: Bombycidae) and comparison with other lepidopteran insects. Genomics 101, 64–73 (2013).
Google Scholar 
Liu, Q. N., Zhu, B. J., Dai, L. S., Wei, G. Q. & Liu, C. L. The complete mitochondrial genome of the wild silkworm moth, Actias selene. Gene 505, 291–299 (2012).
Google Scholar 
Ou, J., Liu, J. B., Yao, F. J., Wang, X. G. & Wei, Z. M. The complete mitochondrial genome of the American black flour beetle Tribolium audax (Coleoptera: Tenebrionidae). Mitochondrial DNA Part. A. 27, 958–959 (2016).
Google Scholar 
Masta, S. E. & Boore, J. L. The complete mitochondrial genome sequence of the spider Habronattus oregonensis reveals rearranged and extremely truncated tRNAs. Mol. Biol. Evol. 21, 893–902 (2004).
Google Scholar 
Xu, M., Zhou, S. & Wan, X. Phylogenetic implication of large intergenic spacers: Insights from a mitogenomic comparison of Prosopocoilus stag beetles (Coleoptera: Lucanidae). Animals 12, 1595 (2022).
Google Scholar 
Boore, J. L. Animal mitochondrial genomes. Nucleic Acids Res. 27, 1767–1780 (1999).
Google Scholar 
Yang, Z. On the best evolutionary rate for phylogenetic analysis. Syst. Biol. 47, 125–133 (1998).
Google Scholar 
Engel, P. & Moran, N. A. The gut microbiota of insects—diversity in structure and function. FEMS Microbiol. Rev. 37, 699–735 (2013).
Google Scholar 
Deb, R., Nair, A. & Agashe, D. Host dietary specialization and neutral assembly shape gut bacterial communities of wild dragonflies. PeerJ 7, e8058 (2019).
Google Scholar 
Phalnikar, K., Kunte, K. & Agashe, D. Dietary and developmental shifts in butterfly-associated bacterial communities. R. Soc. Open Sci. 5, 171559 (2018).
Google Scholar 
Jing, T. Z., Qi, F. H. & Wang, Z. Y. Most dominant roles of insect gut bacteria: Digestion, detoxification, or essential nutrient provision? Microbiome 8, 38 (2020).
Google Scholar 
Gutiérrez-García, K. et al. Gut microbiomes of cycad-feeding insects tolerant to β-methylamino-L-alanine (BMAA) are rich in siderophore biosynthesis. ISME Commun. 3, 122 (2023).
Google Scholar 
Morales-Jiménez, J., Zúñiga, G., Ramírez-Saad, H. C. & Hernández-Rodríguez, C. Gut-associated bacteria throughout the life cycle of the bark beetle Dendroctonus rhizophagus Thomas and bright (Curculionidae: Scolytinae) and their cellulolytic activities. Microb. Ecol. 64, 268–278 (2012).
Google Scholar 
Sevim, A., Sevim, E., Demirci, M. & Sandalli, C. The internal bacterial diversity of stored product pests. Ann. Microbiol. 66, 749–764 (2016).
Google Scholar 
Moldovan, O. T. et al. The gut microbiome mediates adaptation to scarce food in coleoptera. Environ. Microbiome. 18, 80 (2023).
Google Scholar 
McFarlane, D. J., Aitken, E. A. B., Ridley, A. W. & Walter, G. H. The dietary relationships of Tribolium castaneum (Herbst) with microfungi. J. Appl. Entomol. 145, 158–169 (2021).
Google Scholar 
Janson, E. M., Stireman, J. O., Singer, M. S. & Abbot, P. Phytophagous insect–microbe mutualisms and adaptive evolutionary diversification. Evolution 62, 997–1012 (2008).
Google Scholar 
Hirose, M. et al. Mitochondrial gene polymorphism is associated with gut microbial communities in mice. Sci. Rep. 7, 15293 (2017).
Google Scholar 
Martin, W. & Müller, M. The hydrogen hypothesis for the first eukaryote. Nature 392, 37–41 (1998).
Google Scholar 
Sagan, L. On the origin of mitosing cells. J. Theor. Biol. 14, 225–IN6 (1967).
Google Scholar 
Swanson, P. A. et al. Enteric commensal bacteria potentiate epithelial restitution via reactive oxygen species-mediated inactivation of focal adhesion kinase phosphatases. Proc. Natl. Acad. Sci. 108, 8803–8808 (2011).
Google Scholar 
Dong, Z., Wang, Y., Li, C., Li, L. & Men, X. Mitochondrial DNA as a molecular marker in insect ecology: Current status and future prospects. Ann. Entomol. Soc. Am. 114, 470–476 (2021).
Google Scholar 
Frézal, L. & Leblois, R. Four years of DNA barcoding: Current advances and prospects. Infect. Genet. Evol. 8, 727–736 (2008).
Google Scholar 
Girard, M., Luis, P., Valiente Moro, C. & Minard, G. Crosstalk between the microbiota and insect postembryonic development. Trends Microbiol. 31, 181–196 (2023).
Google Scholar 
Mason, C. J. Complex relationships at the intersection of insect gut microbiomes and plant defenses. J. Chem. Ecol. 46, 793–807 (2020).
Google Scholar 
Yuning, L. et al. The bacterial and fungal communities of the larval midgut of Spodoptera frugiperda (Lepidoptera: Noctuidae) varied by feeding on two cruciferous vegetables. Sci. Rep. 12, 13063 (2022).
Google Scholar 
Lange, C. et al. Impact of intraspecific variation in insect microbiomes on host phenotype and evolution. ISME J. 17, 1798–1807 (2023).
Google Scholar 
Vilanova, C., Baixeras, J., Latorre, A. & Porcar, M. The generalist inside the specialist: Gut bacterial communities of two insect Species feeding on toxic plants are dominated by Enterococcus sp. Front. Microbiol. 7, 1005 (2016).
Google Scholar 
Youle, R. J. Mitochondria—Striking a balance between host and endosymbiont. Science 365, eaaw9855 (2019).
Google Scholar 
Li, P. et al. Cytochrome c and dATP-dependent formation of Apaf-1/Caspase-9 complex initiates an apoptotic protease cascade. Cell 91, 479–489 (1997).
Google Scholar 
Liu, X., Kim, C. N., Yang, J., Jemmerson, R. & Wang, X. Induction of apoptotic program in cell-free extracts: Requirement for dATP and cytochrome c. Cell 86, 147–157 (1996).
Google Scholar 
Bankevich, A. et al. SPAdes: A new genome assembly algorithm and its applications to single-cell sequencing. J. Comput. Biol. 19, 455–477 (2012).
Google Scholar 
Altschul, S. F., Gish, W., Miller, W., Myers, E. W. & Lipman, D. J. Basic local alignment search tool. J. Mol. Biol. 215, 403–410 (1990).
Google Scholar 
Camacho, C. et al. BLAST+: Architecture and applications. BMC Bioinform. 10, 1–9 (2009).
Google Scholar 
Kearse, M. et al. Geneious basic: An integrated and extendable desktop software platform for the organization and analysis of sequence data. Bioinformatics 28, 1647–1649 (2012).
Google Scholar 
Laslett, D., Canbäck, B. & ARWEN. A program to detect tRNA genes in metazoan mitochondrial nucleotide sequences. Bioinformatics 24, 172–175 (2008).
Google Scholar 
Katoh, K. & Standley, D. M. MAFFT multiple sequence alignment software version 7: Improvements in performance and usability. Mol. Biol. Evol. 30, 772–780 (2013).
Google Scholar 
Tamura, K., Stecher, G. & Kumar, S. MEGA11: Molecular evolutionary genetics analysis version 11. Mol. Biol. Evol. 38, 3022–3027 (2021).
Google Scholar 
Kimura, M. A simple method for estimating evolutionary rates of base substitutions through comparative studies of nucleotide sequences. J. Mol. Evol. 16, 111–120 (1980).
Google Scholar 
Darriba, D., Taboada, G. L., Doallo, R. & Posada, D. jModelTest 2: more models, new heuristics and parallel computing. Nat. Methods. 9, 772 (2012).
Google Scholar 
R Development Core Team. R: A language and environment for statistical computing. In R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0. http://www.R-project.org (2008).Download referencesAcknowledgementsThe authors gratefully acknowledge the financial support for this research by the following organizations and agencies: Australian Centre for International Agricultural Research (ACIAR) (ProteinAfrica: LS/2020/154), Rockefeller Foundation (WAVE-IN: 2021 FOD 030); IKEA Foundation (G-2204-02144); European Commission (NESTLER Project: 101060762 and INNOECOFOOD project: 101136739), the Curt Bergfors Foundation Food Planet Prize Award; the Swedish International Development Cooperation Agency (Sida); the Swiss Agency for Development and Cooperation (SDC); the Australian Centre for International Agricultural Research (ACIAR); the Government of Norway; the German Federal Ministry for Economic Cooperation and Development (BMZ); and the Government of the Republic of Kenya. The views expressed herein do not necessarily reflect the official opinion of the donors. We thank Mr Fidelis Levi Ombura, Ms Maureen Adhiambo, and Mr Eric Rachami for their technical assistance.FundingThis research was funded by the following organizations and agencies: Australian Centre for International Agricultural Research (ACIAR) (ProteinAfrica: LS/2020/154), Rockefeller Foundation (WAVE-IN: 2021 FOD 030); IKEA Foundation (G-2204-02144); European Commission (NESTLER Project: 101060762 and INNOECOFOOD project: 101136739), the Curt Bergfors Foundation Food Planet Prize Award.Author informationAuthors and AffiliationsInternational Centre of Insect Physiology and Ecology, Nairobi, KenyaInusa Jacob Ajene, Chrysantus M. Tanga, Komivi S. Akutse, Samantha W. Karanu & Fathiya M. KhamisCollege of Tropical Agriculture and Human Resilience, Komohana Research and Extension Center, University of Hawaii at Manoa, Hilo, Hawai’i, 96720, USAInusa Jacob AjeneUnit for Environmental Sciences and Management, North-West University, Potchefstroom, 2520, South AfricaKomivi S. AkutseDepartment of Zoology and Entomology, University of Pretoria, Hatfield, South AfricaFathiya M. KhamisAuthorsInusa Jacob AjeneView author publicationsSearch author on:PubMed Google ScholarChrysantus M. TangaView author publicationsSearch author on:PubMed Google ScholarKomivi S. AkutseView author publicationsSearch author on:PubMed Google ScholarSamantha W. KaranuView author publicationsSearch author on:PubMed Google ScholarFathiya M. KhamisView author publicationsSearch author on:PubMed Google ScholarContributionsIJA: Conceptualization; data curation; investigation; formal analysis; methodology; validation; visualization; writing—original draft; writing— review and editing. CMT: Conceptualization; funding acquisition; resources; project administration; supervision; writing—original draft; writing—review and editing. KSA: Data curation; resources; validation; writing—review and editing. SWK: Data curation; investigation; formal analysis; methodology; writing—review and editing. FMK: Conceptualization; methodology; resources; supervision; validation; writing—review and editing. All authors have read and approved the manuscript.Corresponding authorsCorrespondence to
Inusa Jacob Ajene or Fathiya M. Khamis.Ethics declarations

Competing interests
The authors declare no competing interests.

Additional informationPublisher’s noteSpringer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.Rights and permissions
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
Reprints and permissionsAbout this articleCite this articleAjene, I.J., Tanga, C.M., Akutse, K.S. et al. Multi-omics comparison of two emerging storage pests (Necrobia rufipes and Tribolium castaneum) of dried black soldier fly larvae product.
Sci Rep (2026). https://doi.org/10.1038/s41598-025-34902-7Download citationReceived: 25 September 2025Accepted: 31 December 2025Published: 05 January 2026DOI: https://doi.org/10.1038/s41598-025-34902-7Share this articleAnyone you share the following link with will be able to read this content:Get shareable linkSorry, a shareable link is not currently available for this article.Copy shareable link to clipboard
Provided by the Springer Nature SharedIt content-sharing initiative
KeywordsGut microbiomeMitogenomePostharvest storage pestRed flour beetleRed-legged ham beetle More

AbstractAerosols can influence vegetation through multiple processes, yet the resulting biophysical climate feedback from the vegetation response remains poorly understood. Here, using an ensemble of Earth system models and an observation-based empirical model, we show that the vegetation response to complete removal of anthropogenic aerosols can either cool or warm the local climate by up to 0.039 ± 0.020 °C (multimodel mean ± intermodel standard deviation) through altering albedo and evapotranspiration. This feedback exhibits distinct latitudinal asymmetry, resulting, on average, in cooling (–0.0083 ± 0.0070 °C) in boreal regions, moderate cooling (–0.0036 ± 0.0017 °C) in temperate zones, and slight warming (0.0007 ± 0.0011 °C) in the tropics (excluding the Amazon). Future projections suggest that stringent aerosol control could amplify the local cooling effect of vegetation across most vegetated areas. These findings reveal a previously overlooked pathway by which aerosols influence vegetation climate effects, highlighting the need for integrated policies on air quality control and vegetation-based climate solutions.

Similar content being viewed by others

Widespread reduction of ozone extremes in storylines of future climate

Article
Open access
04 August 2025

Process-evaluation of forest aerosol-cloud-climate feedback shows clear evidence from observations and large uncertainty in models

Article
Open access
07 February 2024

Vegetation-based climate mitigation in a warmer and greener World

Article
Open access
01 February 2022

Data availability

The historical simulation of the Coupled Model Intercomparison Project Phase 6, the hist-piNTCF, hist-piAer and ssp370-lowNTCF simulations of the Aerosol Chemistry Model Intercomparison Project, and the ssp370 simulation of the Scenario Model Intercomparison Project are available at https://esgf-node.llnl.gov/search/cmip6/.
Code availability

The processing MATLAB codes are available at https://box.nju.edu.cn/f/6c15cf6a125e4a7eb0d4/.
ReferencesIPCC. Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA. https://doi.org/10.1017/9781009157896 (2021).Grantz, D. A., Garner, J. H. B. & Johnson, D. W. Ecological effects of particulate matter. Environ. Int. 29, 213–239 (2003).
Google Scholar 
Kanniah, K. D., Beringer, J., North, P. & Hutley, L. Control of atmospheric particles on diffuse radiation and terrestrial plant productivity: a review. Prog. Phys. Geogr. Earth Environ. 36, 209–237 (2012).
Google Scholar 
Zhou, H. et al. Aerosol radiative and climatic effects on ecosystem productivity and evapotranspiration. Curr. Opin. Environ. Sci. Health 19, 100218 (2021).
Google Scholar 
Gu, L. H. et al. Response of a deciduous forest to the Mount Pinatubo eruption: enhanced photosynthesis. Science 299, 2035–2038 (2003).
Google Scholar 
Mercado, L. M. et al. Impact of changes in diffuse radiation on the global land carbon sink. Nature 458, 1014–1017 (2009).
Google Scholar 
Rap, A. et al. Enhanced global primary production by biogenic aerosol via diffuse radiation fertilization. Nat. Geosci. 11, 640–644 (2018).
Google Scholar 
Unger, N., Yue, X. & Harper, K. L. Aerosol climate change effects on land ecosystem services. Faraday Discuss. 200, 121–142 (2017).
Google Scholar 
Yue, X. et al. Ozone and haze pollution weakens net primary productivity in China. Atmos. Chem. Phys. 17, 6073–6089 (2017).
Google Scholar 
Zhang, Y. et al. Increased global land carbon sink due to aerosol-induced cooling. Glob. Biogeochem. Cycles 33, 439–457 (2019).
Google Scholar 
Allen, R. J., Samset, B. H., Wilcox, L. J. & Fisher, R. A. Are Northern Hemisphere boreal forest fires more sensitive to future aerosol mitigation than to greenhouse gas–driven warming? Sci. Adv. 10, eadl4007 (2024).
Google Scholar 
Mahowald, N. M. et al. Aerosol deposition impacts on land and ocean carbon cycles. Curr. Clim. Change Rep. 3, 16–31 (2017).
Google Scholar 
Ainsworth, E. A., Yendrek, C. R., Sitch, S., Collins, W. J. & Emberson, L. D. The effects of tropospheric ozone on net primary productivity and implications for climate change. Annu. Rev. Plant Biol. 63, 637–661 (2012).
Google Scholar 
Farquhar, G. D. & Sharkey, T. D. Stomatal conductance and photosynthesis. Annu. Rev. Plant Physiol. Plant Mol. Biol. 33, 317–345 (1982).
Google Scholar 
Piao, S. L. et al. Characteristics, drivers and feedbacks of global greening. Nat. Rev. Earth Environ. 1, 14–27 (2021).
Google Scholar 
Forzieri, G., Alkama, R., Miralles, D. G. & Cescatti, A. Satellites reveal contrasting responses of regional climate to the widespread greening of Earth. Science 356, 1140–1144 (2017).
Google Scholar 
Zeng, Z. Z. et al. Climate mitigation from vegetation biophysical feedbacks during the past three decades. Nat. Clim. Change 7, 432–436 (2017).
Google Scholar 
Chen, C. et al. Biophysical impacts of Earth greening largely controlled by aerodynamic resistance. Sci. Adv. 6, eabb1981 (2020).
Google Scholar 
Keenan, T. F. et al. Increase in forest water-use efficiency as atmospheric carbon dioxide concentrations rise. Nature 499, 324–327 (2013).
Google Scholar 
Lemordant, L., Gentine, P., Swann, A. S., Cook, B. I. & Scheff, J. Critical impact of vegetation physiology on the continental hydrologic cycle in response to increasing CO2. Proc. Natl. Acad. Sci. USA 115, 4093–4098 (2018).
Google Scholar 
Yang, Y. T., Roderick, M. L., Zhang, S. L., McVicar, T. R. & Donohue, R. J. Hydrologic implications of vegetation response to elevated CO2 in climate projections. Nat. Clim. Change 9, 44–48 (2019).
Google Scholar 
Skinner, C. B., Poulsen, C. J. & Mankin, J. S. Amplification of heat extremes by plant CO2 physiological forcing. Nat. Commun. 9, 1094 (2018).
Google Scholar 
He, M. Z. et al. Amplified warming from physiological responses to carbon dioxide reduces the potential of vegetation for climate change mitigation. Commun. Earth Environ. 3, 160 (2022).
Google Scholar 
Allen, R. J. The biogeophysical effects of carbon fertilization of the terrestrial biosphere. Atmos. Chem. Phys. 25, 10361–10378 (2025).
Google Scholar 
Cui, J. P. et al. Vegetation forcing modulates global land monsoon and water resources in a CO2-enriched climate. Nat. Commun. 11, 5184 (2020).
Google Scholar 
Kooperman, G. J. et al. Forest response to rising CO2 drives zonally asymmetric rainfall change over tropical land. Nat. Clim. Change 8, 434–440 (2018).
Google Scholar 
Park, S. W., Kim, J. S. & Kug, J. S. The intensification of Arctic warming as a result of CO2 physiological forcing. Nat. Commun. 11, 2098 (2020).
Google Scholar 
Li, B. G. et al. The contribution of China’s emissions to global climate forcing. Nature 531, 357–361 (2016).
Google Scholar 
Allen, R. J. et al. Climate and air quality impacts due to mitigation of non-methane near-term climate forcers. Atmos. Chem. Phys. 20, 9641–9663 (2020).
Google Scholar 
Samset, B. H. et al. East Asian aerosol cleanup has likely contributed to the recent acceleration in global warming. Commun. Earth Environ. 6, 543 (2025).
Google Scholar 
Li, L. F. et al. Terrestrial carbon sink and clean air co-benefits from China’s carbon neutrality policy. Earth’s. Future 12, e2024EF004631 (2024).
Google Scholar 
Zhou, H. et al. Recovery of ecosystem productivity in China due to the Clean Air Action plan. Nat. Geosci. 17, 1233–1239 (2024).
Google Scholar 
Bala, G. et al. Combined climate and carbon-cycle effects of large-scale deforestation. Proc. Natl. Acad. Sci. USA 104, 6550–6555 (2007).
Google Scholar 
Pongratz, J., Reick, C. H., Raddatz, T. & Claussen, M. Biogeophysical versus biogeochemical climate response to historical anthropogenic land cover change. Geophys. Res. Lett. 37, L08702 (2010).
Google Scholar 
Windisch, M. G., Davin, E. L. & Seneviratne, S. I. Prioritizing forestation based on biogeochemical and local biogeophysical impacts. Nat. Clim. Change 11, 867–871 (2021).
Google Scholar 
Eyring, V. et al. Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization. Geosci.c Model Dev. 9, 1937–1958 (2016).
Google Scholar 
Collins, W. J. et al. AerChemMIP: quantifying the effects of chemistry and aerosols in CMIP6. Geosci. Model Dev. 10, 585–607 (2017).
Google Scholar 
Alkama, R. et al. Vegetation-based climate mitigation in a warmer and greener world. Nat. Commun. 13, 606 (2022).
Google Scholar 
Li, Y. T. et al. Biophysical impacts of earth greening can substantially mitigate regional land surface temperature warming. Nat. Commun. 14, 121 (2023).
Google Scholar 
O’Neill, B. C. et al. The Scenario Model Intercomparison Project (ScenarioMIP) for CMIP6. Geosci. Model Dev. 9, 3461–3482 (2016).
Google Scholar 
Chen, C. et al. China and India lead in greening of the world through land-use management. Nat. Sustain. 2, 122–129 (2019).
Google Scholar 
Zhu, Z. C. et al. Greening of the Earth and its drivers. Nat. Clim. Change 6, 791–795 (2016).
Google Scholar 
Winckler, J. et al. Different response of surface temperature and air temperature to deforestation in climate models. Earth Syst. Dyn. 10, 473–484 (2019).
Google Scholar 
Cao, Y. P. et al. Greening vegetation cools mean and extreme near-surface air temperature in China. Environ. Res. Lett. 19, 014040 (2024).
Google Scholar 
Li, Y. T. et al. Observed different impacts of potential tree restoration on local surface and air temperature. Nat. Commun. 16, 2335 (2025).
Google Scholar 
Ge, J. et al. Local surface cooling from afforestation amplified by lower aerosol pollution. Nat. Geosci. 16, 781–788 (2023).
Google Scholar 
Sitch, S., Cox, P. M., Collins, W. J. & Huntingford, C. Indirect radiative forcing of climate change through ozone effects on the land-carbon sink. Nature 448, 791–794 (2007).
Google Scholar 
Yue, X. & Unger, N. Fire air pollution reduces global terrestrial productivity. Nat. Commun. 9, 5413 (2019).
Google Scholar 
Ma, Y. M. et al. Implementation of trait-based ozone plant sensitivity in the Yale Interactive terrestrial Biosphere model v1.0 to assess global vegetation damage. Geosci. Model Dev. 16, 2261–2276 (2023).
Google Scholar 
Yue, X. et al. Development and evaluation of the interactive Model for Air Pollution and Land Ecosystems (iMAPLE) version 1.0. Geosci. Model Dev. 17, 4621–4642 (2024).
Google Scholar 
Winckler, J., Reick, C. H. & Pongratz, J. Robust identification of local biogeophysical effects of land-cover change in a global climate model. J. Climate 30, 1159–1176 (2017).
Google Scholar 
Chen, L. & Dirmeyer, P. A. Reconciling the disagreement between observed and simulated temperature responses to deforestation. Nat. Commun. 11, 202 (2020).
Google Scholar 
Chen, C. R. et al. The biophysical impacts of idealized afforestation on surface temperature in China: local and nonlocal effects. J. Climate 35, 4233–4253 (2023).
Google Scholar 
Teuling, A. J. et al. Observational evidence for cloud cover enhancement over western European forests. Nat. Commun. 8, 14065 (2017).
Google Scholar 
Duveiller, G. et al. Revealing the widespread potential of forests to increase low level cloud cover. Nat. Commun. 12, 4337 (2021).
Google Scholar 
Xu, R. et al. Contrasting impacts of forests on cloud cover based on satellite observations. Nat. Commun. 13, 670 (2022).
Google Scholar 
Ge, J. et al. Deforestation intensifies daily temperature variability in the northern extratropics. Nat. Commun. 13, 5955 (2022).
Google Scholar 
Portmann, R. et al. Global forestation and deforestation affect remote climate via adjusted atmosphere and ocean circulation. Nat. Commun. 13, 5569 (2022).
Google Scholar 
Zan, B. L. et al. Spatiotemporal inequality in land water availability amplified by global tree restoration. Nat. Water 2, 863–874 (2024).
Google Scholar 
Bonan, G. B. Forests, climate, and public policy: A 500-year interdisciplinary odyssey. Annu. Rev. Ecol. Evolut. Syst. 47, 97–121 (2016).
Google Scholar 
Arneth, A. et al. Terrestrial biogeochemical feedbacks in the climate system. Nat. Geosci. 3, 525–532 (2010).
Google Scholar 
Gomez, J. et al. The projected future degradation in air quality is caused by more abundant natural aerosols in a warmer world. Commun. Earth Environ. 4, 22 (2023).
Google Scholar 
Zhu, J. L. et al. Reforestation-induced aerosol cooling effects divergently modulated by various types of biogeophysical feedback. Natl. Sci. Rev. 12, nwaf323 (2025).
Google Scholar 
Unger, N. Human land-use-driven reduction of forest volatiles cools global climate. Nat. Clim. Change 4, 907–910 (2014).
Google Scholar 
Scott, C. E. et al. Impact on short-lived climate forcers increases projected warming due to deforestation. Nat. Commun. 9, 157.Allen, R. J. et al. Atmospheric chemistry enhances the climate mitigation potential of tree restoration. Commun. Earth Environ. 6, 367 (2025).
Google Scholar 
Zhao, Q., Zhu, Z. C., Zeng, H., Zhao, W. Q. & Myneni, R. B. Future greening of the Earth may not be as large as previously predicted. Agric. For. Meteorol. 292, 108111 (2020).
Google Scholar 
Song, X., Wang, D. Y., Li, F. & Zeng, X. D. Evaluating the performance of CMIP6 Earth system models in simulating global vegetation structure and distribution. Adv. Clim. Change Res. 12, 584–595 (2021).
Google Scholar 
Wu, J., Wang, D. S., Li, L. Z. X. & Zeng, Z. Z. Hydrological feedback from projected Earth greening in the 21st century. Sustain. Horiz. 1, 100007 (2022).
Google Scholar 
Boysen, L. R. et al. Global climate response to idealized deforestation in CMIP6 models. Biogeosciences 17, 5615–5638 (2020).
Google Scholar 
Luo, X. et al. An evaluation of CMIP6 models in representing the biophysical effects of deforestation with satellite-based observations. J. Geophys. Res. Atmos. 128, e2022JD038198 (2023).
Google Scholar 
Luo, X. et al. Local and nonlocal biophysical effects of historical land use and land cover changes in CMIP6 models and the intermodel uncertainty. Earth’s. Future 12, e2023EF004220 (2024).
Google Scholar 
Ganguly, D., Rasch, P. J., Wang, H. L. & Yoon, J. H. Fast and slow responses of the South Asian monsoon system to anthropogenic aerosols. Geophys. Res. Lett. 39, L18804 (2012).
Google Scholar 
Samset, B. H. et al. Fast and slow precipitation responses to individual climate forcers: A PDRMIP multimodel study. Geophys. Res. Lett. 43, 2782–2791 (2016).
Google Scholar 
Gao, J. Y. et al. Climate responses in China to domestic and foreign aerosol changes due to clean air actions during 2013-2019. npj Clim. Atmos. Sci. 6, 160 (2023).
Google Scholar 
Vicente-Serrano, S. M. et al. Response of vegetation to drought time-scales across global land biomes. Proc. Natl. Acad. Sci. USA 110, 52–57 (2013).
Google Scholar 
Wu, D. H. et al. Time-lag effects of global vegetation responses to climate change. Glob. Change Biol. 21, 3520–3531 (2015).
Google Scholar 
Seddon, A. W. R., Macias-Fauria, M., Long, P. R., Benz, D. & Willis, K. J. Sensitivity of global terrestrial ecosystems to climate variability. Nature 531, 229–232 (2016).
Google Scholar 
Hoesly, R. M. et al. Historical (1750-2014) anthropogenic emissions of reactive gases and aerosols from the Community Emissions Data System (CEDS). Geosci. Model Dev. 11, 369–408 (2018).
Google Scholar 
Alkama, R. & Cescatti, A. Biophysical climate impacts of recent changes in global forest cover. Science 351, 600–604 (2015).
Google Scholar 
Winckler, J., Reick, C. H., Bright, R. M. & Pongratz, J. Importance of surface roughness for the local biogeophysical effects of deforestation. J. Geophys. Res. Atmos. 124, 8605–8618 (2019).
Google Scholar 
Bright, R. et al. Local temperature response to land cover and management change driven by non-radiative processes. Nat. Clim. Change 7, 296–302 (2017).
Google Scholar 
Li, Y. et al. Local cooling and warming effects of forests based on satellite observations. Nat. Commun. 6, 6603 (2015).
Google Scholar 
Duveiller, G., Hooker, J. & Cescatti, A. The mark of vegetation change on Earth’s surface energy balance. Nat. Commun. 9, 679 (2018).
Google Scholar 
Beck, H. E. et al. Present and future Köppen-Geiger climate classification maps at 1-km resolution. Sci. Data 5, 180214 (2018).
Google Scholar 
Download referencesAcknowledgementsThis research is supported by the Natural Science Foundation of China (42375115 and 42130602), the Basic Research Program of Jiangsu Province (BK20240170), the ‘GeoX’ Interdisciplinary Project of Frontiers Science Center for Critical Earth Material Cycling (20250104), and the Jiangsu Collaborative Innovation Center of Climate Change. The authors thank Dr. Ramdane Alkama for providing assistance in using the empirical model to estimate biophysical feedback from vegetation changes.Author informationAuthors and AffiliationsSchool of Atmospheric Sciences, Joint International Research Laboratory of Atmospheric and Earth System Sciences, Nanjing University, Nanjing, ChinaJun Ge, Xin Miao, Xin Huang, Bo Qiu & Weidong GuoFrontiers Science Center for Critical Earth Material Cycling, Nanjing University, Nanjing, ChinaJun Ge, Bo Qiu & Weidong GuoJiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, School of Environmental Science and Engineering, Nanjing University of Information Science & Technology, Nanjing, ChinaXu YueDepartment of Physical Geography and Ecosystem Science, Lund University, Lund, SwedenMengyuan MuARC Centre of Excellence for Climate Extremes and Climate Change Research Centre, University of New South Wales, Sydney, NSW, AustraliaMengyuan MuAuthorsJun GeView author publicationsSearch author on:PubMed Google ScholarXu YueView author publicationsSearch author on:PubMed Google ScholarMengyuan MuView author publicationsSearch author on:PubMed Google ScholarXin MiaoView author publicationsSearch author on:PubMed Google ScholarXin HuangView author publicationsSearch author on:PubMed Google ScholarBo QiuView author publicationsSearch author on:PubMed Google ScholarWeidong GuoView author publicationsSearch author on:PubMed Google ScholarContributionsJ.G. conceived and designed the overall study. J.G. performed the data analysis with help from X.Y., M.M., X.M., X.H., B.Q., and W.G. in the interpretation of the results. J.G. drafted the manuscript. All the authors discussed and revised the manuscript.Corresponding authorCorrespondence to
Jun Ge.Ethics declarations

Competing interests
The authors declare no competing interests.

Additional informationPublisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.Supplementary informationSupplementary InformationRights and permissions
Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
Reprints and permissionsAbout this articleCite this articleGe, J., Yue, X., Mu, M. et al. Local biophysical climate feedback from vegetation responses to lower aerosol pollution.
npj Clim Atmos Sci (2026). https://doi.org/10.1038/s41612-025-01310-7Download citationReceived: 13 September 2025Accepted: 21 December 2025Published: 05 January 2026DOI: https://doi.org/10.1038/s41612-025-01310-7Share this articleAnyone you share the following link with will be able to read this content:Get shareable linkSorry, a shareable link is not currently available for this article.Copy shareable link to clipboard
Provided by the Springer Nature SharedIt content-sharing initiative More

Abstract

This study focuses on describing and assessing the genetic structure and diversity among populations, as well as constructing a dendrogram of genetic similarity of the hawksbill turtle (Eretmochelys imbricata) in four nesting habitats along the Persian Gulf: Kharkoo, Nakhiloo, Shidvar, and Ommolgorm. For this purpose, we collected 14 samples of dead hawksbill turtle hatchlings from these locations and utilized six ISSR markers for genetic analysis. Results showed that 71% of the observed polymorphism was related to within-population diversity, while 29% was linked to among-population diversity. The percentage of polymorphism at the loci (AG)8 C, (AG)8G, (GA)8AC, (GA)8AG, (GACA)4, and (GTG)5GC for the Kharkoo, Nakhiloo, Ommolgorm, and Shidvar nasting group was 0%, 4.35%, 8.7%, and 30.43%, respectively. In the resulting dendrogram of genetic similarity, individuals from each nesting group were placed on separate branches, with different nesting groups situated close to or far from each other based on genetic similarity. According to population structure analysis, the Kharkoo and Shidvar nesting groups formed one subpopulation, whereas the Nakhiloo and Ommolgorm populations constituted separate groups. Hawksbill turtles are typically observed along these shores for 3 to 4 months during the nesting season, after which they emigrate to feeding areas far from the nesting habitats. The natal homing hypothesis states that female hawksbill turtles return to the nesting habitat where they were born, often years later. This hypothesis supports the genetic findings obtained from our study samples.Therefore, each of these habitats is critically important for conservation, and any adverse conditions affecting them could lead to the loss of unique genetic compositions and bring the population of this species one step closer to extinction.

Data availability

All data generated and analyzed during this study, including ISSR band scoring matrices and gel images, are provided in the Supplementary Material (Supplementary Figures S1–S16; Supplementary Tables S1–S6) associated with this article; all ISSR primers used were previously published.
ReferencesChen, C. et al. Global assessment of current extinction risks and future challenges for turtles and tortoises. Nat. Commun. https://doi.org/10.1038/s41467-025-62441-2 (2025).Patel, E., Kotera, M. & Phillott, A. The roles of sea turtles in ecosystem processes and services. Indian Ocean. Turt. Newsl. 36, 23–31 (2022).
Google Scholar 
Lovich, J., Ennen, J., Agha, M. & Gibbons, J. Where have all the turtles gone, and why does it matter? BioScience 68, 771–781 (2018).
Google Scholar 
Miller, J. Reproduction in sea turtles. In The Biology of Sea Turtles, vol. 1, 51–81 (CRC, Boca Raton, FL, USA, 2017).
Google Scholar 
Mobaraki, A. & Elmi, A. First sea turtle tagging program in Iran. Mar. Turt. Newsl. 110, 6–7 (2005).
Google Scholar 
Tabib, M., Frootan, F. & Askari Hesni, M. Genetic diversity and phylogeography of Hawksbill turtle in the Persian Gulf. J. Biodivers. Environ. Sci. 4, 51–57 (2014).
Google Scholar 
Askari-Hesni, M., Tabib, M. & Ramaki, A. Nesting ecology and reproductive biology of the Hawksbill turtle, eretmochelys imbricata, at Kish island, Persian Gulf. J. Mar. Biol. Assoc. United Kingd. 96, 1373–1378 (2016).
Google Scholar 
Sinaei, M. et al. On a poorly known rookery of green turtles (chelonia mydas) nesting at the Chabahar beach, Northeastern Gulf of Oman. Russ J. Mar. Biol. 44, 254–261 (2018).
Google Scholar 
Pilcher, N. et al. Identification of important sea turtle areas (itas) for Hawksbill turtles in the Arabian region. J. Exp. Mar. Biol. Ecol. 460, 89–99 (2014).
Google Scholar 
Mortimer, J. & Donnelly, M. Eretmochelys imbricata. The IUCN Red List Threat: Species. https://doi.org/10.2305/IUCN.UK.2008.RLTS.T8005A12881238.en (2008)Askari-Hesni, M. Restoration and rehabilitation of sea turtle nesting habitats in Bushehr Province with Emphasis on the Islands of nakhiloo, omolgorme, and kharkoo, and Nayband National Park. Project Report, Department of Environment, Tehran, Iran (2015).
Google Scholar 
Askari-Hesni, M. et al. Monitoring Hawksbill turtle nesting sites in some protected areas from the Persian Gulf. Acta Oceanol. Sinica. 38, 43–51 (2019).
Google Scholar 
Ahmadi, F., Pazira, A., Tabatabaei, T. & Askari-Hesni, M. Trace elements accumulation and maternal transfer in critically endangered sea turtle, eretmochelys imbricata. Pol. J. Environ. Stud. 33, 3555–3566 (2024).
Google Scholar 
Razaghian, H. et al. Distribution patterns of epibiotic barnacles on the Hawksbill turtle, eretmochelys imbricata, nesting in Iran. Reg. Stud. Mar. Sci. 27, 100527 (2019).
Google Scholar 
Saadatabadi, L. et al. Unraveling candidate genes related to heat tolerance and immune response traits in some native sheep using whole genome sequencing data. Small Rumin Res. 225, 107018 (2023).
Google Scholar 
Forien, R., Schertzer, E., Talyigás, Z. & Tourniaire, J. Stochastic neutral fractions and the effective population size. arXiv preprint Submitted (2025). Groeneveld, L. et al. Genetic diversity in farm animals – a review. Anim. Genet. 41, 6–31 (2010).
Google Scholar 
Brugman, E., Widiastuti, A. & Wibowo, A. Population genetics of phytophthora species based on short sequence repeat (ssr) marker: a review of its importance and recent studies. IOP Conf. Series: Earth Environ. Sci. 1230, 012102 (2023).
Google Scholar 
Mohammadabadi, M., Oleshko, V., Oleshko, O. & Roudbari, Z. Using inter simple sequence repeat multi-loci markers for studying genetic diversity in Guppy fish. Turkish J. Fish. Aquat. Sci. 21, 603–613 (2021).
Google Scholar 
Bahador, Y., Mohammadabadi, M., Khezri, A. & Asadi, M. Study of genetic diversity in honey bee populations in Kerman Province using Issr markers. Res. Anim. Prod. 7, 186–192 (2016).
Google Scholar 
Askari, N., Mohammadabadi, M. & Baghizadeh, A. Issr markers for assessing Dna polymorphism and genetic characteriza- Tion of cattle, goat, and sheep populations. Iran. J. Biotechnol. 9, 222–229 (2011).
Google Scholar 
Ghasemi, M., Baghizadeh, A. & Mohammadabadi, M. Determination of genetic polymorphism in Kerman Holstein and Jersey cattle population using Issr markers. Aust J. Basic. Appl. Sci. 4, 5758–5760 (2010).
Google Scholar 
Mohammadabadi, M. & Askari, N. Characterization of Genetic Structure Using ISSR-PCR Markers: Cattle, Goat and Sheep Populations (LAP LAMBERT Academic Publishing, 2012). Zamani, P., Akhondi, M., Mohammadabadi, M., Saki, A. & Ershadi, A. Genetic variation of Mehraban sheep using two inter-simple sequence repeat (issr) markers. Afr. J. Biotechnol. 10, 1812–1817 (2011).
Google Scholar 
Bornet, B., Muller, C., Paulus, F. & Branchard, M. Highly informative nature of inter simple sequence repeat (issr) sequences amplified with tri- and tetra-nucleotide primers from cauliflower (Brassica oleracea var. Botrytis l.) Dna. Genome 45, 890–896 (2002).
Google Scholar 
Jin, L. & Lee, J. H. Genetic diversity and structure of the endangered lady’s slipper Orchid (Cypripedium japonicum) in Korea using Issr markers. Plant. Syst. Evol. 269, 37–46 (2007).
Google Scholar 
Guicking, D., Blattner, F., Fiala, B. & Weising, K. Comparative analysis of genetic diversity in Macaranga (euphorbiaceae) from Borneo using Issr and Aflp markers. Bot. J. Linn. Soc. 157, 447–457 (2008).
Google Scholar 
Mobaraki, A. Nesting of the Hawksbill turtle at Shidvar island, Hormozgan province, Iran. Mar. Turt. Newsl. 103, 13–14 (2004).
Google Scholar 
Broderick, D., Moritz, C., Miller, J. & Guine, M. Genetic studies of the Hawksbill turtle (Eretmochelys imbricata): evidence for multiple stocks in Australian waters. Pac. Conserv. Biol. 1, 123–131 (1994).
Google Scholar 
Bowen, B. et al. Origin of Hawksbill turtles in a Caribbean feeding area as indicated by genetic markers. J. Ecol. Appl. 6, 566–572 (1996).
Google Scholar 
Okayama, T., Diaz-Fernandez, R., Baba, Y. & Halim, M. Genetic diversity of the Hawksbill turtle in the indo-pacific and Caribbean regions. Chelonian Conserv. Biol. 3, 362–367 (1999).
Google Scholar 
Kazemi Nezhad, S., Modheji, E. & Zolgharnein, H. Polymorphism analysis of mitochondrial Dna control region of Hawksbill turtles (Eretmochelys imbricata) in the Persian Gulf. J. Fish. Aquat. Sci. 7, 339–345 (2012).
Google Scholar 
Sani, L. et al. Unraveling fine-scale genetic structure in endangered Hawksbill turtle (Eretmochelys imbricata) in indonesia: implications for management strategies. Front. Mar. Sci. 11, 1358695 (2024).
Google Scholar 
Simões, T. et al. Low diversity and strong genetic structure between feeding and nesting areas in Brazil for the critically endangered Hawksbill sea turtle. Front. Ecol. Evol. 9, 704838 (2021).
Google Scholar 
Ghavam Mostafavi, P. et al. Genetic study of the Hawksbill turtle (Eretmochelys imbricata) in Hengam, Hormoz, and Nakhiloo Islands in the Persian Gulf using microsatellite markers. Anim. Environ. 2, 43–48 (2010).
Google Scholar 
Nasiri, Z., Gholamalifard, M. & Mohammadi, M. Population genetics of the Hawksbill sea turtle (Eretmochelys imbricata; Linnaeus, 1766) in the Persian gulf: structure and historical demography. Aquat. Sci. 87, 30 (2025).
Google Scholar 
Zolgharnein, H., Salari, A., Forougmand, A. & Roshani, S. Genetic population structure of Hawksbill turtle (Eretmochelys imbricata) using microsatellite analysis. Iran. J. Biotechnol. 9, 56–62 (2011).
Google Scholar 
Carr, A. So Excellent a Fish: A Natural History of Sea Turtles (Scribner, 1967).Pous, S., Lazure, P. & Carton, X. Hydrology and circulation in the Strait of Hormuz and the Gulf of oman-results from the gogp99 experiment: 1—Strait of Hormuz. J. Geophys. Res. Ocean. 109, C12037 (2004).
Google Scholar 
Jafari, A. & Rahnama, R. Nay-band coastal – marine National park; missed opportunity for Iran. Int. J. Vet. Anim. Res. 1, 43–45 (2018).
Google Scholar 
Rupali, S. et al. Dna, Rna isolation, primer designing, sequence submission, and phylogenetic analysis. In (eds Bhatt, A., Bhatia, R. & Bhalla, T.) Basic Biotechniques for Bioprocess and Bioentrepreneurship, 197–206 (Academic, London, UK, (2023).
Google Scholar 
Labastida, E. et al. The use of Issr markers for species determination and a genetic study of the invasive Lionfish in guanahacabibes. Lat Am. J. Aquat. Res. 43, 1011–1018 (2015).
Google Scholar 
Excoffier, L., Smouse, P. & Quattro, J. Analysis of molecular variance inferred from metric distances among Dna haplotypes: application to human mitochondrial Dna restriction sites. Genetics 131, 479–491 (1992).
Google Scholar 
Peakall, R. & Smouse, P. Genalex 6.5: genetic analysis in excel—Population genetic software for teaching and research-an update. Bioinformatics 28, 2537–2539 (2012).
Google Scholar 
Perrier, X., Flori, A. & Bonnot, F. Data analysis methods. In (eds Hamon, P., Seguin, M., Perrier, X. & Glaszmann, J.) Genetic Diversity of Cultivated Tropical Plants, 43–76 (Science, Enfield, NH, USA, (2003).
Google Scholar 
Pritchard, J., Wen, X. & Falush, D. Documentation for structure software: version 2.3. (accessed on 4 May 2025).http://pritchardlab.stanford.edu/structure.html (2025).Evanno, G., Regnaut, S. & Goudet, J. Detecting the number of clusters of individuals using the software structure: a simulation study. Mol. Ecol. 14, 2611–2620 (2005).
Google Scholar 
Earl, D. & VonHoldt, B. Structure harvester: a website and program for visualizing structure output and implementing the Evanno method. Conserv. Genet. Resour. 4, 359–361 (2012).
Google Scholar 
Download referencesAcknowledgementsThe authors thank the local communities of Dayer City, Aslouyeh Port, and Kharg Island (Bushehr Province) for their assistance in coastal monitoring. We are especially grateful to DOE Bushehr for their invaluable collaboration in sample collection, particularly Mr. Hossein Jafari, Amin Tolab, Mostafa Moazeni, Abdolrahman Moradzadeh, Mahdi Iranmanesh, and Amirmozafar Hoseini.FundingThis work was sponsored by Persian Gulf Mobin Energy Company (Grant Nos. 99/871 and 1403/1224) and Shahid Bahonar University of Kerman.Author informationAuthors and AffiliationsDepartment of Animal Science, Faculty of Agriculture, Shahid Bahonar University of Kerman, Kerman, IranMohammadali Farahvashi & Mohammadreza MohammadabadiDepartment of Biology, Faculty of Sciences, Shahid Bahonar University of Kerman, Kerman, IranMajid Askari-HesniAnimal Science Research Department, Fars Agricultural and Natural Resources Research and Education Center, Agricultural Research, Education and Extension Organization (AREEO), Shiraz, IranZeynab Amiri Ghanatsaman & Hojjat Asadollahpoor NanaeeAuthorsMohammadali FarahvashiView author publicationsSearch author on:PubMed Google ScholarMohammadreza MohammadabadiView author publicationsSearch author on:PubMed Google ScholarMajid Askari-HesniView author publicationsSearch author on:PubMed Google ScholarZeynab Amiri GhanatsamanView author publicationsSearch author on:PubMed Google ScholarHojjat Asadollahpoor NanaeeView author publicationsSearch author on:PubMed Google ScholarContributionsConceptualization: M.M. and M.A.H.; Writing original draft preparation: M.F., Z.A., and H.A.N.; Visualization: M.M. and M.A.H.; Software: M.F., Z.A., and H.A.N.; Data curation: M.F., Z.A., and H.A.N.; Resources: M.M. and M.A.H.All authors read and approved the final manuscript.Corresponding authorCorrespondence to
Mohammadreza Mohammadabadi.Ethics declarations

Competing interests
The authors declare no competing interests.

Additional informationPublisher’s noteSpringer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.Supplementary InformationBelow is the link to the electronic supplementary material.Supplementary Material 1Supplementary Material 2Rights and permissions
Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
Reprints and permissionsAbout this articleCite this articleFarahvashi, M., Mohammadabadi, M., Askari-Hesni, M. et al. Population structure of Hawksbill turtles (Eretmochelys imbricata) nesting along the Persian Gulf coastline revealed by inter-simple sequence repeat (ISSR) markers.
Sci Rep (2026). https://doi.org/10.1038/s41598-025-34749-yDownload citationReceived: 14 October 2025Accepted: 31 December 2025Published: 05 January 2026DOI: https://doi.org/10.1038/s41598-025-34749-yShare this articleAnyone you share the following link with will be able to read this content:Get shareable linkSorry, a shareable link is not currently available for this article.Copy shareable link to clipboard
Provided by the Springer Nature SharedIt content-sharing initiative More

AbstractTo explore the synergistic effects of biochar and nitrogen fertilizer on soil carbon emission and microbial diversity in acidic orchards were studied. A 300-day pot experiment was conducted, including control (CK), nitrogen fertilizer (N), 1% biochar (B1), 3% biochar (B3), nitrogen fertilizer with 1% biochar (NB1), and nitrogen fertilizer with 3% biochar application (NB3). After biochar and nitrogen fertilizer treatments, soil pH increased from 4.73 to 6.75 unit, soil organic carbon (SOC), mineral-associated organic carbon (MAOC) and particulate organic carbon (POC) contents increased 17.88%–41.14%, 31.95%–73.44% and 15.50%–48.90%, respectively, Dissolved organic carbon (DOC) content decreased by 33.56%–55.35%. The release of CO2–C increased by 0.73%–232.43%, with the synergistic effect of NB3 being the most significant. NB1 and B1 reduced VOCs-C release, while NB3 and B3 increased VOCs-C release. B1 and B3 significantly enhanced the abundance of Bradyrhizobium, while decreasing the abundance of Streptomyces and Streptacidiphilus, NB3 exhibited opposite trends. Compared with CK, B1 and B3 increased the abundance of acyl-CoA dehydrogenase (acdA), and NB1 and NB3 reduced the abundance of β-galactosidase (β-gaL) and glucosidase (GA). Correlation analysis showed that the release of CO2-C was significantly positively correlated with MAOC and negatively correlated with DOC, while VOCs-C was significantly negatively correlated with DOC. This synergistic effect of biochar and nitrogen fertilizer has positive implications for improving soil health and represents a viable strategy for sustainable agricultural practices.

Similar content being viewed by others

Effects of biochar and nitrogen fertilizer on microbial communities, CO2 emissions, and organic carbon content in soil

Article
Open access
21 March 2025

Nutrient retention after crop harvest in a typic hapludults amended with biochar types under no-tillage system

Article
Open access
01 March 2024

A microbial ecosystem enhanced by regulating soil carbon and nitrogen balance using biochar and nitrogen fertiliser five years after application

Article
Open access
14 December 2023

Data availability

The datasets used and/or analyzed during the current study available from the corresponding author on reasonable request.
ReferencesSchmidt, H. P. et al. Pyrogenic carbon capture and storage. 11, 573-591. (Wiley, 2019).Hu, X. J. et al. Metagenomics reveals divergent functional profiles of soil carbon and nitrogen cycling under long-term addition of chemical and organic fertilizers in the black soil region. Geoderma 418, 115846 (2022).
Google Scholar 
Liang, C., Amelung, W., Lehmann, J. & Kästner, M. Quantitative assessment of microbial necromass contribution to soil organic matter. Glob. Change Biol. 25, 3578–3590 (2019).
Google Scholar 
Zhang, C. Y. et al. Spatial-temporal characteristics of carbon emissions from land use change in Yellow River Delta region, China. Ecol. Indic. 136, 108623 (2022).
Google Scholar 
Zhao, Y. P. et al. Sphagnum increases soil’s sequestration capacity of mineral-associated organic carbon via activating metal oxides. Nat. Commun. 14, 5052 (2023).
Google Scholar 
Andrea, J. et al. Minerals in the rhizosphere: Overlooked mediators of soil nitrogen availability to plants and microbes. Biogeochemistry 139, 103–122 (2018).
Google Scholar 
Wu, H. W. et al. Unveiling the crucial role of soil microorganisms in carbon cycling: A review. Sci. Total Environ. 909, 168627 (2024).
Google Scholar 
Chen, H. Y., Zhu, T., Li, B., Fang, C. M. & Nie, M. The thermal response of soil microbial methanogenesis decreases in magnitude with changing temperature. Nat. Commun. 11, 5733 (2020).
Google Scholar 
Kuśtrowski, P., Rokicińska, A. & Kondratowicz, T. Chapter nine – abatement of volatile organic compounds emission as a target for various human activities including energy production. Adv. Inorg. Chem. 72, 385–419 (2018).
Google Scholar 
WMO Greenhouse Gas Bulletin: the state of greenhouse gases in the atmosphere based on global observations through 2020. (2021).Basheer, S. et al. A review of greenhouse gas emissions from agricultural soil. Sustainability 16, 4789 (2024).
Google Scholar 
Zheng, G. Y. et al. A new attempt to control volatile organic compounds (VOCs) pollution—Modification technology of biomass for adsorption of VOCs gas. Environ. Pollut. 336, 122451 (2023).
Google Scholar 
Nguyen, T.-P., Koyama, M. & Nakasaki, K. Effects of oxygen supply rate on organic matter decomposition and microbial communities during composting in a controlled lab-scale composting system. Waste Manag. 153, 275–282 (2022).
Google Scholar 
Xiang, S. Z. et al. Antibiotics adaptation costs alter carbon sequestration strategies of microorganisms in karst river. Environ. Pollut. 288, 117819 (2021).
Google Scholar 
Ma, S. S. et al. Effects of the functional membrane covering on the gas emissions and bacterial community during aerobic composting. Biores. Technol. 340, 125660 (2021).
Google Scholar 
Wang, K., Mao, H. L. & Li, X. K. Functional characteristics and influence factors of microbial community in sewage sludge composting with inorganic bulking agent. Biores. Technol. 249, 527–535 (2018).
Google Scholar 
Chen, J. et al. Differential responses of carbon-degrading enzymes activities to warming: implications for soil respiration. Glob. Change Biol. 24, 4816–4826 (2018).
Google Scholar 
Tang, L. et al. Warming counteracts grazing effects on the functional structure of the soil microbial community in a Tibetan grassland. Soil Biol. Biochem. 134, 113–121 (2019).
Google Scholar 
Zhao, H. Y. et al. Global reactive nitrogen loss in orchard systems: A review. Sci. Total Environ. 821, 153462 (2022).
Google Scholar 
Repullo-Ruibérriz de Torres, M. A. et al. Cover crop contributions to Improve the soil nitrogen and carbon sequestration in Almond Orchards (SW Spain). Agronomy 11, 387 (2021).
Google Scholar 
Rodriguez-Ramos, J. C., Scott, N., Marty, J., Kaiser, D. & Hale, L. Cover crops enhance resource availability for soil microorganisms in a pecan orchard. Agric. Ecosyst. Environ. 337, 108049 (2022).
Google Scholar 
Lal, R. Soil health and carbon management. Food Energy Secur. 5, 212–222 (2016).
Google Scholar 
Jing, H. et al. The effects of nitrogen addition on soil organic carbon decomposition and microbial C-degradation functional genes abundance in a Pinus tabulaeformis forest. For. Ecol. Manag. 489, 119098 (2021).
Google Scholar 
Huang, J. X. et al. Organic carbon mineralization in soils of a natural forest and a forest plantation of southeastern China. Geoderma 344, 119–126 (2019).
Google Scholar 
Neogi, S. et al. Sustainable biochar: A facile strategy for soil and environmental restoration, energy generation, mitigation of global climate change and circular bioeconomy. Chemosphere 293, 133474 (2022).
Google Scholar 
Wang, C. et al. Effects of biochar amendment on net greenhouse gas emissions and soil fertility in a double rice cropping system: A 4-year field experiment. Agr. Ecosyst. Environ. 262, 83–96 (2018).
Google Scholar 
Yang, W. et al. Impact of biochar on greenhouse gas emissions and soil carbon sequestration in corn grown under drip irrigation with mulching. Sci. Total Environ. 729, 138752 (2020).
Google Scholar 
Zeng, L. B. et al. Seaweed-derived nitrogen-rich porous biomass carbon as bifunctional materials for effective electrocatalytic oxygen reduction and high-performance gaseous toluene absorbent. ACS Sustainable Chemistry & Engineering. 7, 5057–5064 (2019).
Google Scholar 
Wang, J. et al. Effects of ammonium-based nitrogen addition on soil nitrification and nitrogen gas emissions depend on fertilizer-induced changes in pH in a tea plantation soil. Sci. Total Environ. 747, 141340 (2020).
Google Scholar 
Li, T. T. et al. Contrasting impacts of manure and inorganic fertilizer applications for nine years on soil organic carbon and its labile fractions in bulk soil and soil aggregates. CATENA 194, 104739 (2020).
Google Scholar 
Oladele, S. O. & Adetunji, A. T. Agro-residue biochar and N fertilizer addition mitigates CO2-C emission and stabilized soil organic carbon pools in a rain-fed agricultural cropland. Int. Soil Water Conserv. Res. 9, 76–86 (2021).
Google Scholar 
Wang, Y. H. et al. Straw-derived biochar regulates soil enzyme activities, reduces greenhouse gas emissions, and enhances carbon accumulation in farmland under mulching. Field Crop Res. 317, 109547 (2024).
Google Scholar 
Llusià, J. et al. Contrasting nitrogen and phosphorus fertilization effects on soil terpene exchanges in a tropical forest. Sci. Total Environ. 802, 149769 (2022).
Google Scholar 
Qiu, Z. J. et al. Distribution characteristics and pollution assessment of phosphorus forms, TOC, and TN in the sediments of Daye Lake, Central China. J. Soils Sediments 23, 1023–1036 (2023).
Google Scholar 
Ghani, M. I. et al. Variations of soil organic carbon fractions in response to conservative vegetation successions on the Loess Plateau of China. Int. Soil Water Conserv. Res. 11, 561–571 (2023).
Google Scholar 
Yu, W., Huang, W., Weintraub-Leff, S. R. & Hall, S. J. Where and why do particulate organic matter (POM) and mineral-associated organic matter (MAOM) differ among diverse soils?. Soil Biol. Biochem. 172, 108756 (2022).
Google Scholar 
Wang, S. Z. et al. Rhizosphere microbial roles in phosphorus cycling during successive plantings of Chinese fir plantations. For. Ecol. Manag. 570, 122227 (2024).
Google Scholar 
Buchfink, B., Xie, C. & Huson, D. H. Fast and sensitive protein alignment using DIAMOND. Nat. Methods. 12, 59–60 (2015).
Google Scholar 
Collier, S. M., Ruark, M. D., Oates, L. G., Jokela, W. E., Dell, C. J. Measurement of greenhouse gas flux from agricultural soils using static chambers. J Vis. Exp. JoVE. e52110 (2014).Gray, C. M., Monson, R. K. & Fierer, N. Biotic and abiotic controls on biogenic volatile organic compound fluxes from a subalpine forest floor. J. Geophys. Res. Biogeosci. 119, 547–556 (2014).
Google Scholar 
Bai, J. Z. et al. Biochar combined with N fertilization and straw return in wheat-maize agroecosystem: Key practices to enhance crop yields and minimize carbon and nitrogen footprints. Agric. Ecosyst. Environ. 347, 108366 (2023).
Google Scholar 
Zhu, L. X., Xiao, Q., Fang, S. Y. & Li, S. Q. Effects of biochar and maize straw on the short-term carbon and nitrogen dynamics in a cultivated silty loam in China. Environ. Sci. Pollut. Res. Int. 24, 1019–1029 (2017).
Google Scholar 
Darby, I. et al. Short-term dynamics of carbon and nitrogen using compost, compost-biochar mixture and organo-mineral biochar. Environ. Sci. Pollut. Res. 23, 11267–11278 (2016).
Google Scholar 
Yang, Y. et al. Biochar stability and impact on soil organic carbon mineralization depend on biochar processing, aging and soil clay content. Soil Biol. Biochem. 169, 108657 (2022).
Google Scholar 
Yang, F. J. et al. Fertilizer reduction and biochar amendment promote soil mineral-associated organic carbon, bacterial activity, and enzyme activity in a jasmine garden in southeast China. Sci. Total Environ. 954, 176300 (2024).
Google Scholar 
Xu, Z. B. & Tsang, D. C. W. Mineral-mediated stability of organic carbon in soil and relevant interaction mechanisms. Eco-Environ. Health. 3, 59–76 (2024).
Google Scholar 
Yi, Z. et al. Influence mechanisms of iron, aluminum and manganese oxides on the mineralization of organic matter in paddy soil. J. Environ. Manag. 301, 113916 (2022).
Google Scholar 
Jia, X. Y., Ma, H. Z., Yan, W. M., Shang Guan, Z. P. & Zhong, Y. Q. W. Effects of co-application of biochar and nitrogen fertilizer on soil profile carbon and nitrogen stocks and their fractions in wheat field. J. Environ. Manag. 368, 122140 (2024).
Google Scholar 
Eykelbosh, A. J., Johnson, M. S. & Couto, E. G. Biochar decreases dissolved organic carbon but not nitrate leaching in relation to vinasse application in a Brazilian sugarcane soil. J. Environ. Manag. 149, 9–16 (2015).
Google Scholar 
Hu, Q. Y. et al. The effects of straw returning and nitrogen fertilizer application on soil labile organic carbon fractions and carbon pool management index in a rice – wheat rotation system. Pedobiol. J. Soil Ecol. 101, 150913 (2023).
Google Scholar 
Jia, X. Y., Ma, H. Z., Yan, W. M., Shang, G. P. & Zhong, Y. Q. W. Effects of co-application of biochar and nitrogen fertilizer on soil profile carbon and nitrogen stocks and their fractions in wheat field. J. Environ. Manag. 368, 122140 (2024).
Google Scholar 
Li, H. Z. et al. Biochar’s dual role in greenhouse gas emissions: Nitrogen fertilization dependency and mitigation potential. Sci. Total Environ. 917, 170293 (2024).
Google Scholar 
Bamminger, C. et al. Short-term response of soil microorganisms to biochar addition in a temperate agroecosystem under soil warming. Agr. Ecosyst. Environ. 233, 308–317 (2016).
Google Scholar 
Pan, Y., Yin, Y. J., Sharma, P., Zhu, S. H. & Shang, J. Y. Field aging slows down biochar-mediated soil carbon dioxide emissions. J. Environ. Manag. 370, 122811 (2024).
Google Scholar 
Yan, T. T., Xue, J. H., Zhou, Z. D. & Wu, Y. B. Biochar-based fertilizer amendments improve the soil microbial community structure in a karst mountainous area. Sci. Total Environ. 794, 148757 (2021).
Google Scholar 
Chagas, J. K. M., Figueiredo, C. C. D. & Ramos, M. L. G. Biochar increases soil carbon pools: Evidence from a global meta-analysis. J. Environ. Manag. 305, 114403 (2022).
Google Scholar 
Tang, E., Liao, W. X. & Thomas, S. C. Optimizing biochar particle size for plant growth and mitigation of soil salinization. Agronomy 13, 1394 (2023).
Google Scholar 
Ayaz, M. et al. Biochar with inorganic nitrogen fertilizer reduces direct greenhouse gas emission flux from soil. Plants. 12, 1002 (2023).
Google Scholar 
Nan, Q., Fang, C. X., Cheng, L. Q., Hao, W. & Wu, W. X. Elevation of NO3−-N from biochar amendment facilitates mitigating paddy CH4 emission stably over seven years. Environ. Pollut. 295, 118707 (2022).
Google Scholar 
Case, S. D. C., Mcnamara, N. P., Reay, D. S. & Whitaker, J. The effect of biochar addition on N2O and CO2 emissions from a sandy loam soil—The role of soil aeration. Soil Biol. Biochem. 51, 125–134 (2012).
Google Scholar 
Pi, X. X., Bin, Q. Z., Sun, F., Zhang, Z. K. & Gao, J. H. Catalytic activation preparation of nitrogen-doped hierarchical porous bio-char for efficient adsorption of dichloromethane and toluene. J. Anal. Appl. Pyrol. 156, 105150 (2021).
Google Scholar 
Niu, J. et al. Biomass-derived mesopore-dominant porous carbons with large specific surface area and high defect density as high performance electrode materials for Li-ion batteries and supercapacitors. Nano Energy 36, 322–330 (2017).
Google Scholar 
Ding, Y. et al. Recyclable regeneration of NiO/NaF catalyst: Hydrogen evolution via steam reforming of oxygen-containing volatile organic compounds. Energy Convers. Manag. 258, 115456 (2022).
Google Scholar 
Hu, X. J. et al. Changes in soil microbial functions involved in the carbon cycle response to conventional and biodegradable microplastics. Appl. Soil. Ecol. 195, 105269 (2024).
Google Scholar 
Chen, W. F., Meng, J., Han, X. R., Lan, Y. & Zhang, W. M. Past, present, and future of biochar. Biochar. 1, 75–87 (2019).
Google Scholar 
Philippot, L., Chenu, C., Kappler, A., Rillig, M. & Fierer, N. The interplay between microbial communities and soil properties. Nat. Rev. Microbiol. 22, 226–239 (2023).
Google Scholar 
Cao, X. C. et al. Optimum organic fertilization enhances rice productivity and ecological multifunctionality via regulating soil microbial diversity in a double rice cropping system. Field Crop Res. 318, 109569 (2024).
Google Scholar 
Kang, E. et al. Soil pH and nutrients shape the vertical distribution of microbial communities in an alpine wetland. Sci. Total Environ. 774, 145780 (2021).
Google Scholar 
Zhang, G. X. et al. The effects of different biochars on microbial quantity, microbial community shift, enzyme activity, and biodegradation of polycyclic aromatic hydrocarbons in soil. Geoderma 328, 100–108 (2018).
Google Scholar 
Zhang, L. Y., Jing, Y. M., Xiang, Y. Z., Zhang, R. D. & Lu, H. B. Responses of soil microbial community structure changes and activities to biochar addition: A meta-analysis. Sci. Total Environ. 643, 926–935 (2018).
Google Scholar 
Ji, G. X. et al. Response of soil microbes to Carex meyeriana meadow degeneration caused by overgrazing in inner Mongolia. Acta Oecologica. 117, 103860 (2022).
Google Scholar 
Zhang, W. B. et al. Recovery through proper grazing exclusion promotes the carbon cycle and increases carbon sequestration in semiarid steppe. Sci. Total Environ. 892, 164423 (2023).
Google Scholar 
Zhang, G. X., Liu, Y. Y., Zheng, S. L. & Hashisho, Z. Adsorption of volatile organic compounds onto natural porous minerals. J. Hazard. Mater. 364, 317–324 (2019).
Google Scholar 
Download referencesAcknowledgementsThis study was funded by three organizations: Fujian Provincial Natural Science Foundation Project (2025J011231) ,Special Project for Public Welfare Research Institutes (2025R1023004) and Fujian Provincial Key Guiding Project for Agriculture (2024N0057).FundingCollaborative Innovation Project, XTCXGC2021009Author informationAuthors and AffiliationsInstitute of Resources, Environment and Soil Fertilizer, Fujian Academy of Agricultural Sciences, Fuzhou, 350013, ChinaHongmei Chen, Xinyang Bian, Tingting Li, Xiaojie Qian, Lin Zhao, Xiaolin Chen, Qinghua Li & Fei WangCollege of Resources and Environment, Fujian Agriculture and Forestry University, Fuzhou, 350002, ChinaHongmei Chen, Xinyang Bian, Tingting Li, Xiaojie Qian, Lin Zhao, Xiaolin Chen & Zhigang YiSha County Agriculture and Rural Bureau, Sanming, 365001, ChinaZhu LiuAuthorsHongmei ChenView author publicationsSearch author on:PubMed Google ScholarXinyang BianView author publicationsSearch author on:PubMed Google ScholarTingting LiView author publicationsSearch author on:PubMed Google ScholarXiaojie QianView author publicationsSearch author on:PubMed Google ScholarLin ZhaoView author publicationsSearch author on:PubMed Google ScholarXiaolin ChenView author publicationsSearch author on:PubMed Google ScholarZhu LiuView author publicationsSearch author on:PubMed Google ScholarQinghua LiView author publicationsSearch author on:PubMed Google ScholarFei WangView author publicationsSearch author on:PubMed Google ScholarZhigang YiView author publicationsSearch author on:PubMed Google ScholarContributionsHongmei Chen: Writing—original draft, Writing—review and editing, Methodology, Investigation, Formal analysis, Data Curation, Conceptualization. Xinyang Bian: Visualization, Validation, Formal analysis. Tingting Li: Methodology, Formal analysis, Data Curation. Xiaojie Qian: Supervision, Resources, Formal analysis, Data Curation. Lin Zhao: Investigation, Resources, Formal analysis. Xiaoling Chen: Validation, Formal analysis, Visualization. Zhu Liu：Resources, Supervision. Qinghua Li: Visualization, Project administration, Writing—Review & Editing, Funding acquisition. Fei Wang: Resources, Supervision, Funding acquisition. Zhgigang Yi: Conceptualization, Supervision, Methodology, Project administration.Corresponding authorCorrespondence to
Qinghua Li.Ethics declarations

Competing interests
The authors declare no competing interests.

Additional informationPublisher’s noteSpringer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.Supplementary InformationSupplementary Information.Rights and permissions
Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
Reprints and permissionsAbout this articleCite this articleChen, H., Bian, X., Li, T. et al. Synergistic effects of biochar and nitrogen fertilizer enhance soil carbon emissions and microbial diversity in acidic orchard soils.
Sci Rep (2026). https://doi.org/10.1038/s41598-025-07374-yDownload citationReceived: 27 November 2024Accepted: 13 June 2025Published: 05 January 2026DOI: https://doi.org/10.1038/s41598-025-07374-yShare this articleAnyone you share the following link with will be able to read this content:Get shareable linkSorry, a shareable link is not currently available for this article.Copy shareable link to clipboard
Provided by the Springer Nature SharedIt content-sharing initiative
KeywordsAcidic soilBiocharCarbon emissionsMineral-associated organic carbon
β-galactosidase More