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    Widespread heat stress will become the norm in a warming Southeast Asia

    Abstract

    Southeast Asia faces growing risks from extreme heat under climate change, yet existing assessments do not fully account for physiological impacts and population-level exposure, particularly at resolutions relevant for planning. Here we present a comprehensive evaluation of future heat stress in the region by integrating three methodological innovations: physiologically relevant bio-climatic indices, spatially explicit population exposure, and sub-daily temporal resolution. Using the Universal Thermal Climate Index (UTCI) and Wet-Bulb Globe Temperature (WBGT), we assess heat stress across Southeast Asia at (22{times }22) km spatial and 3-hourly temporal resolution under low- and high-emissions scenarios. We combine these projections with population data to identify where and when risks are most acute. Our results show that even by the near-future (2030–2059), exposure to life-threatening extremes (UTCI > (46,^{circ })C, WBGT > (33,^{circ })C) increases sharply, by factors of 2.8–4.6 (UTCI) and 4.6–7.9 (WBGT) relative to historical levels. The number of people exposed to at least one consecutive week of extreme UTCI grows from 9 million historically to 23–28 million under RCP2.6 and RCP8.5, while extreme WBGT exposure increases from 0.1 million to 7–17 million. Continental Southeast Asia, including Myanmar, Thailand, and Cambodia, faces the most acute risks, with 6–9 hours of severe heat stress per day during peak months. By the far-future (2070–2099) under RCP8.5, exposure escalates to catastrophic levels, with up to 200 days per year of unsafe conditions and exposed populations increasing more than tenfold. Our findings show that dangerous levels of heat stress will emerge within decades, underscoring the urgency of adaptation and the benefits of strong mitigation.

    Data availability

    The UTCI and WBGT results computed in this study are partially available at https://doi.org/10.5281/zenodo.17020558 and the full dataset can be requested from the lead author (Sonali Manimaran, [email protected]). REMO RCM data are partially available through https://esgf-metagrid.cloud.dkrz.de/, and the full dataset can be requested from the Climate Service Centre Germany (GERICS). The historical population data can be accessed through https://human-settlement.emergency.copernicus.eu/download.php, and future population data through https://figshare.com/articles/dataset/Projecting_1_km-grid_population_distributions_from_2020_to_2100_globally_under_shared_socioeconomic_pathways/19608594/2.
    ReferencesDong, Z. et al. Heatwaves in Southeast Asia and their changes in a warmer world. Earth’s Future 9(7), e2021EF001992. https://doi.org/10.1029/2021EF001992 (2021).
    Google Scholar 
    Fan, Y., Li, J., Zhu, S., Li, H. & Zhou, B. Trends and variabilities of precipitation and temperature extremes over Southeast Asia during 1981–2017. Meteorol. Atmos. Phys. 134(4), 78. https://doi.org/10.1007/s00703-022-00913-6 (2022).
    Google Scholar 
    Li, X.-X. Heat wave trends in Southeast Asia during 1979–2018: The impact of humidity. Sci. Total Environ. 721, 137664. https://doi.org/10.1016/j.scitotenv.2020.137664 (2020).
    Google Scholar 
    Rogers, C. D. W. et al. Recent increases in exposure to extreme humid-heat events disproportionately affect populated regions. Geophys. Res. Lett. 48(19), 2021–094183. https://doi.org/10.1029/2021GL094183 (2021).
    Google Scholar 
    Thirumalai, K., DiNezio, P. N., Okumura, Y. & Deser, C. Extreme temperatures in Southeast Asia caused by El Niño and worsened by global warming. Nat. Commun. 8(1), 15531. https://doi.org/10.1038/ncomms15531 (2017).
    Google Scholar 
    CNN: Southeast Asia Heatwaves: Education and School Closures. https://edition.cnn.com/2024/05/09/asia/southeast-asia-heatwaves-education-school-closures-intl-hnk/index.html (2024).The Guardian: Inside an Oven: How Life in South-East Asia Is a Struggle Amid Sweltering Heat. https://www.theguardian.com/environment/article/2024/may/04/inside-an-oven-how-life-in-south-east-asia-is-a-struggle-amid-sweltering-heat (2024).South China Morning Post: Southeast Asia’s Brutal Heatwave: Daily Life and Agriculture Endangered by Rising Temperatures. https://www.scmp.com/week-asia/health-environment/article/3261584/southeast-asias-brutal-heatwave-daily-life-and-agriculture-endangered-rising-temperatures (2024).United Nations, Department of Economic and Social Affairs, Population Division: World population prospects 2024: Summary of results. Technical Report UN DESA/POP/2024/TR/NO.9, United Nations, New York. https://desapublications.un.org/publications/world-population-prospects-2024-summary-results (2024).Dahiya, B. Southeast Asia and sustainable urbanization. Global Asia 9(3), 84–91 (2014).
    Google Scholar 
    Sun, X., Ge, F., Fan, Y., Zhu, S. & Chen, Q. Will population exposure to heat extremes intensify over Southeast Asia in a warmer world?. Environ. Res. Lett. 17(4), 044006. https://doi.org/10.1088/1748-9326/ac48b6 (2022).
    Google Scholar 
    Sun, X., Ge, F., Chen, Q., Fraedrich, K. & Li, X. How striking is the intergenerational difference in exposure to compound heatwaves over Southeast Asia?. Earth’s Future 11(6), 2022–003179. https://doi.org/10.1029/2022EF003179 (2023).
    Google Scholar 
    Zhu, S. et al. Conspicuous temperature extremes over Southeast Asia: seasonal variations under (1.5,^{circ })C and (2,^{circ })C global warming. Clim. Change 160(3), 343–360. https://doi.org/10.1007/s10584-019-02640-1 (2020).
    Google Scholar 
    Jendritzky, G., De Dear, R. & Havenith, G. UTCI-why another thermal index?. Int. J. Biometeorol. 56(3), 421–428. https://doi.org/10.1007/s00484-011-0513-7 (2012).
    Google Scholar 
    International Organization for Standardization: Ergonomics of the thermal environment – assessment of heat stress using the WBGT (wet bulb globe temperature) index. Technical Report ISO 7243:2017, International Organization for Standardization, Geneva. https://www.iso.org/standard/67188.html (2017).Wong, M. C., Wang, J., Zhi, X. & Dong, L. A 1940–2020 spatiotemporal analysis of thermal discomfort days in Southeast Asian countries. Environ. Res. Commun. 6(10), 101009. https://doi.org/10.1088/2515-7620/ad810b (2024).
    Google Scholar 
    Kjellstrom, T., Lemke, B. & Otto, M. Mapping occupational heat exposure and effects in South-East Asia: Ongoing time trends 1980–2011 and future estimates to 2050. Ind. Health 51(1), 56–67. https://doi.org/10.2486/indhealth.2012-0174 (2013).
    Google Scholar 
    Liu, Z. et al. Global and regional changes in exposure to extreme heat and the relative contributions of climate and population change. Sci. Rep. 7, 43909. https://doi.org/10.1038/srep43909 (2017).
    Google Scholar 
    Chen, J. et al. Global socioeconomic exposure of heat extremes under climate change. J. Clean. Prod. 277, 123275. https://doi.org/10.1016/j.jclepro.2020.123275 (2020).
    Google Scholar 
    Freychet, N. et al. Robust increase in population exposure to heat stress with increasing global warming. Environ. Res. Lett. 17(6), 064049. https://doi.org/10.1088/1748-9326/ac71b9 (2022).
    Google Scholar 
    Li, D., Yuan, J. & Kopp, R. E. Escalating global exposure to compound heat-humidity extremes with warming. Environ. Res. Lett. 15(6), 064003. https://doi.org/10.1088/1748-9326/ab7d04 (2020).
    Google Scholar 
    ASEAN Secretariat: ASEAN state of climate change report: Current status and outlook of the ASEAN region toward the ASEAN Climate Vision 2050. Technical report, ASEAN Secretariat, Jakarta. https://asean.org/wp-content/uploads/2021/10/ASCCR-e-publication-Correction_8-June.pdf (2021).Kakaei, H., Omidi, F., Ghasemi, R., Sabet, M. R. & Golbabaei, F. Changes of WBGT as a heat stress index over the time: A systematic review and meta-analysis. Urban Clim. 27, 284–292. https://doi.org/10.1016/j.uclim.2018.12.009 (2019).
    Google Scholar 
    Błażejczyk, K. et al. An introduction to the universal thermal climate index (UTCI). Geogr. Pol. 86(1), 5–10. https://doi.org/10.7163/GPol.2013.1 (2013).
    Google Scholar 
    Jacob, D. et al. Assessing the transferability of the regional climate model REMO to different COordinated regional climate downscaling EXperiment (CORDEX) regions. Atmosphere 3(1), 181–199. https://doi.org/10.3390/atmos3010181 (2012).
    Google Scholar 
    Remedio, A. R. et al. Evaluation of new CORDEX simulations using an updated Köppen-Trewartha climate classification. Atmosphere 10(11), 726. https://doi.org/10.3390/atmos10110726 (2019).
    Google Scholar 
    Romaszko, J., Dragańska, E., Jalali, R., Cymes, I. & Glińska-Lewczuk, K. Universal climate thermal index as a prognostic tool in medical science in the context of climate change: A systematic review. Sci. Total Environ. 828, 154492. https://doi.org/10.1016/j.scitotenv.2022.154492 (2022).
    Google Scholar 
    Kjellstrom, T. et al. Heat, human performance, and occupational health: A key issue for the assessment of global climate change impacts. Annu. Rev. Public Health 37, 97–112. https://doi.org/10.1146/annurev-publhealth-032315-021740 (2016).
    Google Scholar 
    Hess, J. J. et al. Public health preparedness for extreme heat events. Annu. Rev. Public Health 44, 301–321. https://doi.org/10.1146/annurev-publhealth-071421-025508 (2023).
    Google Scholar 
    Flouris, A., Azzi, M., Graczyk, H., Nafradi, B. & Scott, N. Heat at work: Implications for safety and health. Technical report, International Labour Organization, Geneva. A global review of the science, policy, and practice. https://www.ilo.org/publications/heat-work-implications-safety-and-health (2024).Flato, G. et al. Evaluation of Climate Models 741–866 (Cambridge University Press, 2013). https://doi.org/10.1017/CBO9781107415324.020.
    Google Scholar 
    Gutowski, W. J. Jr. et al. WCRP COordinated regional downscaling EXperiment (CORDEX): A diagnostic MIP for CMIP6. Geosci. Model Dev. 9(11), 4087–4095. https://doi.org/10.5194/gmd-9-4087-2016 (2016).
    Google Scholar 
    Fiala, D., Havenith, G., Bröde, P., Kampmann, B. & Jendritzky, G. UTCI-Fiala multi-node model of human heat transfer and temperature regulation. Int. J. Biometeorol. 56(3), 429–441. https://doi.org/10.1007/s00484-011-0424-7 (2012).
    Google Scholar 
    Bourgault, P. et al. xclim: Xarray-based climate data analytics. J. Open Source Softw. 8(85), 5415. https://doi.org/10.21105/joss.05415 (2023).
    Google Scholar 
    Buntemeyer, L., Lierhammer, L., Pfeifer, S., Buelow, K. & Manimaran, S. index_calculator: v0.11.0. Software. https://doi.org/10.5281/zenodo.10159231 (2023).Minard, D. Prevention of heat casualties in marine corps recruits: Period of 1955–60, with comparative incidence rates and climatic heat stresses in other training categories. Mil. Med. 126(4), 261–272 (1961).
    Google Scholar 
    Brimicombe, C. et al. Wet bulb globe temperature: Indicating extreme heat risk on a global grid. GeoHealth 7(2), e2022GH000701. https://doi.org/10.1029/2022GH000701 (2023).
    Google Scholar 
    Brimicombe, C. et al. Thermofeel: A Python thermal comfort indices library. SoftwareX 18, 101005. https://doi.org/10.1016/j.softx.2022.101005 (2022).
    Google Scholar 
    Schiavina, M., Freire, S., Carioli, A. & MacManus, K. GHS-POP R2023A – GHS population grid multitemporal (1975-2030). European Commission, Joint Research Centre (JRC). https://doi.org/10.2905/2FF68A52-5B5B-4A22-8F40-C41DA8332CFE . http://data.europa.eu/89h/2ff68a52-5b5b-4a22-8f40-c41da8332cfe (2023).Wang, X., Meng, X. & Long, Y. Projecting 1 km-grid population distributions from 2020 to 2100 globally under shared socioeconomic pathways. Sci. Data 9(1), 563. https://doi.org/10.1038/s41597-022-01675-x (2022).
    Google Scholar 
    Jones, B. et al. Future population exposure to us heat extremes. Nat. Clim. Change 5, 652–655. https://doi.org/10.1038/nclimate2631 (2015).
    Google Scholar 
    Download referencesFundingThis work is supported by the following grants: the Helmholtz Information & Data Science Academy (HIDA) Visiting Researcher Grant to S.M.; the Ministry of Education, Singapore, under its MOE AcRF Tier 3 Award MOE2019-T3-1-004 to the Southeast Asia Sea-level (SEA2) Programme, supporting S.M., I.K., and D.L., and the MOE AcRF Tier 2 Award MOE-T2EP50222-0016, supporting D.W. and D.L.; and the Helmholtz Association under the programme “Changing Earth – Sustaining our Future”, funding C.N., L.L., and L.B.Author informationAuthors and AffiliationsEarth Observatory of Singapore, Nanyang Technological University, 50 Nanyang Ave, Singapore, 639798, SingaporeSonali Manimaran, Dennis Wagenaar, Indraneel Kasmalkar & David LallemantAsian School of the Environment, Nanyang Technological University, 50 Nanyang Ave, Singapore, 639798, SingaporeSonali Manimaran, Dennis Wagenaar & David LallemantClimate Service Center Germany (GERICS), Helmholtz-Zentrum Hereon, Fischertwiete 1, Hamburg, 20095, GermanyChristine Nam & Laurens M. BouwerCopernicus Climate Change Service, German Weather Service (DWD), Bernhard-Nocht-Straße 76, Hamburg, 20359, GermanyLudwig LierhammerInstitute of Geography, University of Hamburg, Bundesstraße 55, Hamburg, 20146, GermanyLaurens M. BouwerAuthorsSonali ManimaranView author publicationsSearch author on:PubMed Google ScholarDennis WagenaarView author publicationsSearch author on:PubMed Google ScholarChristine NamView author publicationsSearch author on:PubMed Google ScholarIndraneel KasmalkarView author publicationsSearch author on:PubMed Google ScholarLudwig LierhammerView author publicationsSearch author on:PubMed Google ScholarLaurens M. BouwerView author publicationsSearch author on:PubMed Google ScholarDavid LallemantView author publicationsSearch author on:PubMed Google ScholarContributionsS.M., D.W., C.N., L.B., and D.L. conceived and designed the study. S.M., D.W., C.N., and L.L. developed the methodology and software. S.M. performed the formal analysis and visualisation, and wrote the original draft. All authors contributed to the reviewing and editing of the manuscript. L.B. and D.L. supervised the project.Corresponding authorCorrespondence to
    Sonali Manimaran.Ethics declarations

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    The authors declare no competing interests.

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    Reprints and permissionsAbout this articleCite this articleManimaran, S., Wagenaar, D., Nam, C. et al. Widespread heat stress will become the norm in a warming Southeast Asia.
    Sci Rep (2025). https://doi.org/10.1038/s41598-025-28817-6Download citationReceived: 04 July 2025Accepted: 12 November 2025Published: 29 December 2025DOI: https://doi.org/10.1038/s41598-025-28817-6Share this articleAnyone you share the following link with will be able to read this content:Get shareable linkSorry, a shareable link is not currently available for this article.Copy shareable link to clipboard
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    KeywordsHeat stressExtreme heatClimate changeExposureSoutheast AsiaUTCIWBGT More

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    Reinforcement learning based dynamic vegetation index formulation for rice crop stress detection using satellite and mobile imagery

    AbstractTimely crop stress detection is essential for safeguarding yields and promoting sustainable agriculture. Traditional vegetation indices (e.g., NDVI, EVI) are widely used but remain static, crop-agnostic, and often insensitive to early stress signals. This study proposed RL-VI, a reinforcement learning-based framework that dynamically formulates vegetation indices optimized for rice stress detection. Unlike existing methods, RL-VI integrates Sentinel-2 multispectral imagery with smartphone-captured RGB data, creating the first cross-platform environment where vegetation indices are learned rather than predefined. The reinforcement learning agent adaptively selects stress-sensitive spectral band combinations guided by classification rewards. Experiments on real-world rice fields in Tamil Nadu, India, and benchmark datasets (Indian Pines, wheat salt stress) show that RL-VI achieves an overall accuracy of 89.4% and F1-score of 0.88, outperforming static and machine-learned indices by up to 12%. Importantly, RL-VI enables early stress detection up to 10 14 days before visible symptoms, providing actionable lead time for intervention. The proposed framework is computationally lightweight and scalable to UAV or edge devices, offering a farmer-ready tool for precision agriculture, bridging field-level mobile sensing with satellite monitoring for low-cost, real-time crop health management. Statistical validation using ANOVA (F = 88.24, p < 0.001) and pairwise t-tests (p < 0.001) confirmed RL-VI’s superiority, while SHAP analyses emphasized the physiological significance of red-edge and SWIR bands in stress discrimination.

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    Data availability

    All datasets used in this study are openly accessible. The Mobile RGB dataset, consisting of field-captured rice canopy images collected by the authors at Polur, Tamil Nadu, India, is publicly available on Kaggle under a CC BY-NC 4.0 license (DOI: [https://doi.org/10.34740/kaggle/dsv/14105754](https:/doi.org/10.34740/kaggle/dsv/14105754)). Sentinel-2 multispectral imagery was obtained from the European Space Agency Copernicus Open Access Hub via Google Earth Engine. Benchmark hyperspectral datasets (Indian Pines and Wheat Salt Stress) are publicly available from their respective repositories. All processed data generated during this study are available from the corresponding author upon reasonable request.
    Code availability

    All custom code developed for this work including the RL-VI (Reinforcement Learning–based Vegetation Index) formulation algorithm, image preprocessing scripts, vegetation index computation modules, model training pipelines, and evaluation routines is openly accessible in a public GitHub repository. The code is available without restriction for non-commercial research use and fully available at Github Repository (https://github.com/Poornisrm/Vegetation-Index.git).
    ReferencesMoharrami, M., Attarchi, S., Gloaguen, R. & Alavipanah, S. K. Integration of Sentinel-1 and Sentinel-2 Data for Ground Truth Sample Migration for Multi-Temporal Land Cover Mapping. Remote Sens., 16(9). https://doi.org/10.3390/rs16091566 (2024).Reis Pereira, M. et al. Plant disease diagnosis based on hyperspectral sensing: comparative analysis of parametric spectral vegetation indices and nonparametric Gaussian process classification approaches. Agronomy 14 (3), https://doi.org/10.3390/agronomy14030493 (2024).Fan, L. et al. A temporal-spatial deep learning network for winter wheat mapping using time-series Sentinel-2 imagery. ISPRS J. Photogrammetry Remote Sens. 214, 4864. https://doi.org/10.1016/j.isprsjprs.2024.06.005 (2024).
    Google Scholar 
    Jiang, X. et al. An automatic rice mapping method based on an integrated time-series gradient boosting tree using GF-6 and sentinel-2 images. GIScience Remote Sens. 61 (1), https://doi.org/10.1080/15481603.2024.2367807 (2024).Xu, H., Song, J. & Zhu, Y. Evaluation and Comparison of Semantic Segmentation Networks for Rice Identification Based on Sentinel-2 Imagery. Remote Sens. 15(6), https://doi.org/10.3390/rs15061499 (2023).Yu, L. et al. Research on machine Learning-Based extraction and classification of crop planting information in arid irrigated areas using Sentinel-1 and Sentinel-2 Time-Series data. Agric. (Switzerland). 15 (11), https://doi.org/10.3390/agriculture15111196 (2025).Herzig, P. et al. Evaluation of Rgb and multispectral unmanned aerial vehicle (Uav) imagery for high-throughput phenotyping and yield prediction in barley breeding. Remote Sens. 13 (14), https://doi.org/10.3390/rs13142670 (2021).Sun, Y., Wang, B. & Zhang, Z. Improving leaf area index Estimation with chlorophyll insensitive multispectral Red-Edge vegetation indices. IEEE J. Sel. Top. Appl. Earth Observations Remote Sens. 16, 35683582. https://doi.org/10.1109/JSTARS.2023.3262643 (2023).
    Google Scholar 
    Aslan, M. F., Sabanci, K. & Aslan, B. Artificial Intelligence Techniques in Crop Yield Estimation Based on Sentinel-2 Data: A Comprehensive Survey. In Sustainability (Switzerland) (Vol. 16, Issue 18). Multidisciplinary Digital Publishing Institute (MDPI). (2024). https://doi.org/10.3390/su16188277Samsuddin Sah, S., Abdul Maulud, K. N., Sharil, S., Karim, A., Pradhan, B. & O., & Monitoring of three stages of paddy growth using multispectral vegetation index derived from UAV images. Egypt. J. Remote Sens. Space Sci. 26 (4), 989998. https://doi.org/10.1016/j.ejrs.2023.11.005 (2023).
    Google Scholar 
    Sulaiman, N. et al. N., W. F. The Application of Hyperspectral Remote Sensing Imagery (HRSI) for Weed Detection Analysis in Rice Fields: A Review. In Applied Sciences (Switzerland) (12 (5), MDPI. https://doi.org/10.3390/app12052570 (2022).Zhang, G., Xu, T. & Tian, Y. Hyperspectral imaging-based classification of rice leaf blast severity over multiple growth stages. Plant. Methods. 18 (1), https://doi.org/10.1186/s13007-022-00955-2 (2022).Farmonov, N. et al. Crop Type Classification by DESIS Hyperspectral Imagery and Machine Learning Algorithms, in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 16, 1576–1588, https://doi.org/10.1109/JSTARS.2023.3239756 (2023).Singh, G. & Sharma, S. Enhancing precision agriculture through cloud based transformative crop recommendation model. Sci. Rep. 15, 9138. https://doi.org/10.1038/s41598-025-93417-3 (2025).
    Google Scholar 
    Singh, S. et al. A predictive framework using advanced machine learning approaches for measuring and analyzing the impact of synthetic agrochemicals on human health. Sci. Rep. 15, 15544. https://doi.org/10.1038/s41598-025-00509-1 (2025).
    Google Scholar 
    Singh, G. & Sharma, S. Revolutionizing cloud-IoT and UAV-assisted framework to analyze soil for cultivation in agricultural landscapes. Proc. Indian Natl. Sci. Acad. https://doi.org/10.1007/s43538-025-00489-w (2025).
    Google Scholar 
    Upadhyay, N. & Gupta, N. Detecting fungi-affected multi-crop disease on heterogeneous region dataset using modified resnext approach. Environ. Monit. Assess. 196, 610. https://doi.org/10.1007/s10661-024-12790-0 (2024).
    Google Scholar 
    Upadhyay, N. & Bhargava, A. Artificial intelligence in agriculture: applications, approaches, and adversities across pre-harvesting, harvesting, and post-harvesting phases. Iran. J. Comput. Sci. 8, 749772. https://doi.org/10.1007/s42044-025-00264-6 (2025).
    Google Scholar 
    Upadhyay, N., Gupta, N. & SegLearner A segmentation based approach for predicting disease severity in infected leaves. Multimed Tools Appl. 84, 42523 42546. https://doi.org/10.1007/s11042-025-20838-7 (2025).
    Google Scholar 
    Upadhyay, N., Sharma, D. K. & Bhargava, A. 3SW-Net: A feature fusion network for semantic weed detection in precision agriculture. Food Anal. Methods. 18, 2241 2257. https://doi.org/10.1007/s12161-025-02852-5 (2025).
    Google Scholar 
    Kurihara, J., Nagata, T. & Tomiyama, H. Rice yield prediction in different growth environments using unmanned aerial Vehicle-Based hyperspectral imaging. Remote Sens. 15 (8), https://doi.org/10.3390/rs15082004 (2023).Karmakar, P. et al. Crop monitoring by multimodal remote sensing: A review. In Remote Sensing Applications: Society and Environment (Vol. 33). Elsevier B.V. https://doi.org/10.1016/j.rsase.2023.101093 (2024).Albahar, M. A Survey on Deep Learning and Its Impact on Agriculture: Challenges and Opportunities. In Agriculture (Switzerland) (Vol. 13, Issue 3). MDPI. https://doi.org/10.3390/agriculture13030540 (2023).Mou, L. et al. Deep reinforcement learning for band selection in hyperspectral image classification. IEEE Trans. Geosci. Remote Sens. 60 https://doi.org/10.1109/TGRS.2021.3067096 (2022).Wu, B. et al. Challenges and opportunities in remote sensing-based crop monitoring: a review. In National Science Review (10 (4)). Oxford University Press. https://doi.org/10.1093/nsr/nwac290 (2023).Zhang, J. et al. UAV as a bridge: mapping key rice growth stage with Sentinel-2 imagery and novel vegetation indices. Remote Sens. 17 (13), 2180. https://doi.org/10.3390/rs17132180 (2025).
    Google Scholar 
    Virnodkar, S. S., Pachghare, V. K., Patil, V. C. & Jha, S. K. Remote sensing and machine learning for crop water stress determination in various crops: a critical review. In Precision Agriculture (21 (5), 1121 1155). Springer. (2020). https://doi.org/10.1007/s11119-020-09711-9Liu, L. et al. A disease index for efficiently detecting wheat fusarium head blight using Sentinel-2 multispectral imagery. IEEE Access. 8, 5218152191. https://doi.org/10.1109/ACCESS.2020.2980310 (2020).
    Google Scholar 
    Zhao, D. et al. Early detection of rice leaf blast disease using unmanned aerial vehicle remote sensing: A novel approach integrating a new spectral vegetation index and machine learning. Agronomy 14 (3), https://doi.org/10.3390/agronomy14030602 (2024).Sato, Y., Tsuji, T. & Matsuoka, M. Estimation of rice plant coverage using Sentinel-2 based on UAV-Observed data. Remote Sens. 16 (9), https://doi.org/10.3390/rs16091628 (2024).Yu, Y. et al. Early mapping method for different planting types of rice based on planet and Sentinel-2 satellite images. Agronomy 14 (1), https://doi.org/10.3390/agronomy14010137 (2024).Tian, H. et al. A novel spectral index for automatic Canola mapping by using Sentinel-2 imagery. Remote Sens. 14 (5). https://doi.org/10.3390/rs14051113 (2022).Choshi, T. J., Dhau, I. & Mashao, F. Enhancing maize streak virus detection: a comparative analysis of Sentinel-2 MSI and Landsat 9 OLI data across vegetative and reproductive growth stages. Geocarto Int. 40 (1). https://doi.org/10.1080/10106049.2025.2480701 (2025).Baldin, C. M. & Casella, V. M. Comparison of planetscope and Sentinel-2 spectral channels and their alignment via linear regression for enhanced index derivation. Geosci. (Switzerland). 15 (5). https://doi.org/10.3390/geosciences15050184 (2025).Liu, L., Xie, Y., Zhu, B. & Song, K. Rice leaf chlorophyll content Estimation with different crop coverages based on Sentinel-2. Ecol. Inf. 81 https://doi.org/10.1016/j.ecoinf.2024.102622 (2024).Cong, C. et al. Research on Monitoring Methods for the Appropriate Rice Harvest Period Based on Multispectral Remote Sensing. Discrete Dynamics in Nature and Society, 2022. https://doi.org/10.1155/2022/1519667 (2022).Zhang, H. et al. A novel red-edge spectral index for retrieving the leaf chlorophyll content. Methods Ecol. Evol. 13 (12), 27712787. https://doi.org/10.1111/2041-210X.13994 (2022).
    Google Scholar 
    Tian, J., Tian, Y., Cao, Y., Wan, W. & Liu, K. Research on rice fields extraction by NDVI difference method based on Sentinel data. Sensors 23 (13), https://doi.org/10.3390/s23135876 (2023).Luo, S. et al. Remotely sensed prediction of rice yield at different growth durations using UAV multispectral imagery. Agric. (Switzerland). 12 (9), https://doi.org/10.3390/agriculture12091447 (2022).Sharma, V., Honkavaara, E., Hayden, M. & Kant, S. UAV remote sensing phenotyping of wheat collection for response to water stress and yield prediction using machine learning. Plant. Stress. 12 https://doi.org/10.1016/j.stress.2024.100464 (2024).Ren, C., Kim, D. K. & Jeong, D. A survey of deep learning in agriculture: techniques and their applications. J. Inform. Process. Syst. 16 (5), 10151033. https://doi.org/10.3745/JIPS.04.0187 (2020).
    Google Scholar 
    Liao, Z. Q. et al. A double-layer model for improving the Estimation of wheat canopy nitrogen content from unmanned aerial vehicle multispectral imagery. J. Integr. Agric. 22 (7), 22482270. https://doi.org/10.1016/j.jia.2023.02.022 (2023).
    Google Scholar 
    Banerjee, B. P., Sharma, V., Spangenberg, G. & Kant, S. Machine learning regression analysis for Estimation of crop emergence using multispectral UAV imagery. Remote Sens. (Basel). 13 (15), 2918 (2021). https://www.mdpi.com/2072-4292/13/15/2918
    Google Scholar 
    Yu, W. et al. Evaluation of red-edge features for identifying subtropical tree species based on Sentinel-2 and Gaofen-6 time series. Int. J. Remote Sens. 43 (8), 3003. https://doi.org/10.1080/01431161.2022.2079018 (2022).
    Google Scholar 
    Qiu, Z. et al. Accurate prediction of 327 rice variety growth period based on unmanned aerial vehicle multispectral remote sensing. Drones 8 (11), 665. https://doi.org/10.3390/drones8110665 (2024).
    Google Scholar 
    Li, Z., Feng, X., Li, J., Wang, D., Hong, W., Qin, J., … Chen, S. (2024). Time Series Field Estimation of Rice Canopy Height Using an Unmanned Aerial Vehicle-Based RGB/Multispectral Platform. Agronomy, 14(5), 883. https://doi.org/10.3390/agronomy14050883.Brinkhoff, J. et al. Forecasting field rice grain moisture content using Sentinel-2 and weather data. Precision Agric. 26, 28. https://doi.org/10.1007/s11119-025-10228-2 (2025).
    Google Scholar 
    Sári-Barnácz, F. E. et al. Monitoring Helicoverpa armigera damage with PRISMA hyperspectral imagery: first experience in maize and comparison with Sentinel-2 imagery. Remote Sens. 16 (17), 3235. https://doi.org/10.3390/rs16173235 (2024).
    Google Scholar 
    Darra, N., Espejo-Garcia, B., Psiroukis, V., Psomiadis, E. & Fountas, S. Spectral bands vs. Vegetation indices: an automl approach for processing tomato yield predictions based on Sentinel-2 imagery. Smart Agricultural Technol. 100805. https://doi.org/10.1016/j.atech.2025.100805 (2025).Khosravi, I. Towards sustainable agriculture in Iran using a machine learning-driven crop mapping framework. Eur. J. Remote Sens. 58 (1), https://doi.org/10.1080/22797254.2025.2490787 (2025).Botero-Valencia, J. et al. Machine learning in sustainable agriculture: systematic review and research perspectives. Agriculture 15 (4), 377. https://doi.org/10.3390/agriculture15040377 (2025).
    Google Scholar 
    Sumantra Chatterjee, G. S., Baath, B. R., Sapkota, K. C., Flynn, D. R. & Smith Enhancing LAI estimation using multispectral imagery and machine learning: A comparison between reflectance-based and vegetation indices-based approaches, Computers and Electronics in Agriculture, 230, 2025, 109790,ISSN 0168–1699,https://doi.org/10.1016/j.compag.2024.109790Ou, C. et al. Using machine learning methods combined with vegetation indices and growth indicators to predict seed yield of Bromus inermis. Plants 13 (6), 773. https://doi.org/10.3390/plants13060773 (2024).
    Google Scholar 
    Yang Xu, Y. et al. A deep learning model based on RGB and hyperspectral images for efficiently detecting tea green leafhopper damage symptoms. Smart Agricultural Technol. Volume. 10, 2772–3755. https://doi.org/10.1016/j.atech.2025.100817 (2025).
    Google Scholar 
    Patel, U. & Patel, V. Active learning-based hyperspectral image classification: a reinforcement learning approach. J. Supercomput. 80, 2461 2486. https://doi.org/10.1007/s11227-023-05568-7 (2024).
    Google Scholar 
    Fu, B., Sun, X., Cui, C., Zhang, J. & Shang, X. Structure-preserved and weakly redundant band selection for hyperspectral imagery. IEEE J. Sel. Top. Appl. Earth Observations Remote Sens. https://doi.org/10.1109/JSTARS.2024.3425906 (2024).
    Google Scholar 
    Yang, J. X., Zhou, J., Wang, J., Tian, H. & Liew, A. W. C. LiDAR-Guided Cross-Attention Fusion for Hyperspectral Band Selection and Image Classification, in IEEE Transactions on Geoscience and Remote Sensing, 62, 1–15, 5515815, https://doi.org/10.1109/TGRS.2024.3389651 (2024).Download referencesAcknowledgementsThe authors would like to thank their supervisor for the guidance and constructive suggestions that significantly contributed to this work. The authors also acknowledge SRM Institute of Science and Technology, VADAPALANI campus for providing institutional support and research facilities essential for conducting this research.FundingThis research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.Author informationAuthors and AffiliationsDepartment of Electronics and Communication Engineering, Faculty of Engineering and Technology, SRM Institute of Science and Technology, Vadapalani Campus, Chennai, 600026, IndiaPoornima Seralathan & A. Shirly EdwardAuthorsPoornima SeralathanView author publicationsSearch author on:PubMed Google ScholarA. Shirly EdwardView author publicationsSearch author on:PubMed Google ScholarContributionsS.P conceived the study and developed the RL-VI framework and contributed to data collection, analysis, and validation. A.S assisted in implementation, visualization, and manuscript preparation. All authors reviewed and approved the final manuscript.Corresponding authorCorrespondence to
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    Reprints and permissionsAbout this articleCite this articleSeralathan, P., Edward, A.S. Reinforcement learning based dynamic vegetation index formulation for rice crop stress detection using satellite and mobile imagery.
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    Flora and vegetation of fallow lands invaded by the black Cherry Padus serotina (Ehrh.) Borkh. in Lower Silesia (SW Poland)

    AbstractSecondary succession of abandoned agricultural land is a long-term process, shaped by numerous biotic and abiotic factors. One stage in this process is the encroachment of pioneer tree species, among which the black cherry Padus serotina is increasingly common. This invasive species is widespread in temperate forests in Europe and is beginning to colonize open habitats, including fallow lands. In this study, we evaluated the effects of P. serotina on floristic composition, vegetation, and species diversity. Ten fallows in Lower Silesia (SW Poland) were selected. Within each field, five study plots invaded by P. serotina and five without this species were established. Botanical composition and area covered by individual plant species were determined in total of 100 plots. Our results indicate that the P. serotina seedlings and saplings have the strongest impact on species composition. The increase in P. serotina cover in the herb layer is accompanied by a reduction in the spread of expansive species such as Calamagrostis epigejos and Solidago gigantea. The low thickets formed by P. serotina provide perching sites for birds and facilitate seed dispersal, which promotes the development of multispecies shrub communities and enhances species diversity in study fallows at the current stage of succession.

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    IntroductionAbandoned agricultural lands, along with prolonged periods of non-cultivation, are undergoing transformations in vegetation and changes in soil physicochemical properties1,2. The trajectory and rate of secondary succession depend on the degree of vegetation damage and the soil properties caused by previous cultivation practices3. At the same time, this complex process is influenced by many abiotic (topography, climate, soil fertility, and moisture) and biotic (parent material and soil seed bank) factors4. In Central Europe, the initial phase of secondary succession of fallow land is dominated by annual weed species (e.g., Apera spica-venti, Echinochloa crus-galli), which are then replaced by perennial weeds (e.g., Elymus repens) and encroaching ruderal species (e.g., Artemisia vulgaris, Convolvulus arvensis, and Solidago canadensis)5,6. Colonisation by woody vegetation is a long-term phenomenon that lasts from 140 to 290 years7 and indicates the final stage of fallow land transformation8. In Poland, pioneering tree species encroaching on former agricultural land include Betula pendula, Populus tremula, Pinus sylvestris, and Alnus glutinosa. They play an important role because by shading the area, they create favourable conditions for the development of shade-loving forest-forming tree species, including Tilia cordata, Carpinus betulus, and Fraxinus excelsior9. The development of tree formations on fallow lands has been initiated, in addition to native, light-seeded, and wind-seeded species, as well as by alien invasive species10,11. Regardless of the stage of secondary succession, their presence often contributes to the disturbance of plant communities, the functioning of which is more complex and dynamic than that in natural ecosystems12,13.One of the most widespread invasive tree species in Europe, including Poland, is black cherry Padus serotina (Ehrh.) Borkh., which is native to North America14,15. This deciduous tree exhibits considerable morphological variation across their range. In its native land, the eastern part of the USA, it can reach heights of up to 38 m with trunk diameters exceeding 1.2 m, whereas southwestern varieties and introduced European populations are significantly smaller, often remaining shrubby forms or low tress16. Padus serotina is characterised by a dark brown bark marked with numerous white lenticels and a distinctive almond-like scent. Its simple leaves are glossy, serrated, and oval to lanceolate. Black cherry blooms in late May and June, producing small white flowers arranged in loose racemes, followed by black drupes that ripen in late summer17 This species was deliberately introduced to European temperate forests, initially to obtain good-quality timber, and when it failed, as a biocenotic additive in poorly fertile habitats in the understory of Scots pine forests17,18. In Poland, the main period of planting P. serotina in forests occurred during the 1970s and the 1980s15. Several decades later, the invasive nature of this species became apparent19. Many ecological traits of P. serotina facilitate its invasion. These include: high resistance to unfavorable environmental conditions, high potential for generative and vegetative reproduction, seed dispersal by birds and mammals, the formation of long-lived seedling banks, allelopathic interactions, and a small number of natural enemies in the initial phase of invasion17,20,21,22,23,24. Currently, in disturbed forest phytocoenoses, the dense understory of P. serotina, together with the leaf litter layer of this species, strongly shades the forest floor18,25. This results in a reduction in the cover area and the number of common, light-loving plant species in the herb and moss layer17,26,27,28. The decomposition of black cherry litter contributes to the enrichment of the organic soil level with phosphorus and nitrogen29,30, and changes in the soil macronutrient content initiate vegetation transformations. Species with lower nutritional requirements withdraw, whereas plants from fertile habitats simultaneously encroach31,32,33.Black cherry spread in forests around seed sources, fill available habitats, and then begin to colonise open areas, including abandoned farmlands34,35,36. The scope and effects of P. serotina colonisation of fallows remains to be determined. One of the first published studies on this topic indicates that the presence of P. serotina on fallow lands can significantly alter soil properties37. Knowledge of vegetation changes in abandoned agricultural lands is crucial for developing management strategies for such areas4. Active regeneration processes should direct succession towards a vegetation structure that enhances ecosystem services and/or livestock production (for example, through species adapted to grazing).This paper is a continuation of our previous work37 and concerns the effect of P. serotina on fallow vegetation. The aim of this study was to assess the relationship between the occurrence of P. serotina and variations in floristic composition, vegetation, and species diversity of selected fallow lands. As the main focus of this study, we used the area covered by individual plant species and species richness. We based our research on the results obtained from the study plots, both invaded and uninvaded by black cherry. In this study, we tested the following hypotheses:

    dispersal of Padus serotina in fallows triggers changes in secondary succession process,

    development of P. serotina shrubs and trees affects the plant species composition,

    in fallow lands invided by P. serotina significant changes in vegetation diversity occur.

    Materials and methodsStudy areaThis research was conducted on fallow lands in Lower Silesia, a region located in southwestern Poland. The selected area is characterised by significant farm fragmentation, with fallow land constituting a significant part of the agricultural landscape. Lower Silesia is characterised by a temperate climate, influenced by its inland location and proximity to the Sudetes Mountains. Summers are long and warm (average 18 °C in July), while winters are short and mild (average − 1.4 °C in January). The average annual air temperature is 8 °C. The annual precipitation in the study area ranges from 500 to 600 mm, with an average of approximately 350 mm during the growing season. The growing season lasts 220 days, making it the longest in the country38.We selected 10 fallow lands (study sites) representing plant communities of approximately 2.000 m2, containing self-seeded, now mature (flowering) Padus serotina trees. The main sources of black cherry propagules in the analyzed fallow lands were forests, field woodlots, or nearby home gardens. These phytocenoses have been following a natural succession path without external interference for the past 10 years. The chosen study sites represent a variety of soil types (podzols, phaeozems, cambisols, fluvisols). The description of selected fallow lands is provided in the Fig. 1; Table 1 (for more detailes see37 ).Fig. 1Location of study fallow lands with their numbering (1–10) in accordance with Table 1.Full size imageTable 1 General information of selected fallow lands.Full size tableData collectionA total of 10 study plots (10 × 10 m squares) were designated on each of the selected fallow lands, of which five were invaded by black cherry and five were free of this species. The study plots were randomly placed within homogeneous patches of vegetation, free from areas disturbed by animal activity. The distance between the edges of the squares was not less than 10 m. This rule was also applied to the locations of the study plots in relation to the boundaries of the fallow lands, defined by dirt roads, ditches, or woodlots. Determining such a margin allowed the elimination of the edge-effect interactions on the analysed vegetation patches.Observations were conducted in 2017 during the period of the richest vegetation (June–July). Botanical composition and the area occupied by individual plant species (species cover express in %) with species abundance were determined using the Braun-Blanquet procedure. To avoid varying interpretations of vegetation cover, observations were conducted by the same person, who visually estimated the percentage cover (5% estimate intervals) of each plant species in the study plots. The total vegetation cover was determined for three layers: the tree layer (> 1.5 m; marked with the symbol a), the shrub layer (0.5–1.5 m; b), and the lowest, ground-level layer of plants (< 0.5 m) including herbaceous plants, tree and shrub seedlings (c), and leafy bryophytes (d). A total of 100 vegetation surveys (relevé) were obtained from the study plots, creating a database used for further analysis. We determined the taxonomic affiliation of vascular plants directly in the field. Individual bryophytes were collected for laboratory identification by bryologist Ewa Fudali, PhD (Department of Botany and Plant Ecology, Wrocław University of Environmental and Life Sciences, Poland). The identified moss material is in the private collection of Aleksandra Halarewicz. No species protected in Poland were found in the analysed flora. Vascular plant nomenclature follows Mirek et al. 39, and moss species names are based on Ochyra et al.40. The Latin species name Padus serotina (Ehrh.) Borkh., is consistent with the publication of flowering plants and pteridophytes of Poland, a checklist39, and is the equivalent of Prunus serotina Ehrh., the species name of the black cherry used in Flora Europea41. The plant communities were identified according to the method described by Matuszkiewicz42.Data analysisPrincipal component analysis (PCA) with rotation using non-metric multidimensional scaling (NMDS)43 was used to assess the diversity of plant species in the selected fallows. Analyses were conducted using a floristic database of 100 study plots, representing the area occupied by individual plant species. Species occurring simultaneously in multiple layers within the same study plot were analysed separately for each layer. In the analyses, the distances between samples were estimated using Euclidean distance. The stress value was used to assess the model quality.To reveal the main environmental gradients for the variation in the herb layers, a detrended correspondence analysis (DCA) was conducted44. All ordination analyses were performed without data transformation. The length of the gradient, represented by the first ordination axis, had a standard deviation of four units, which indicated the unimodal nature of the floristic database and recommended the choice of a specific direct ordination technique. Based on this, we decided to use canonical correspondence analysis CCA44 for further analyses. This allowed us to assess the influence of habitat variables (represented by the cover of Padus serotina separately in three vegetation layers) on the floristic composition of the study plots. The significance of the variables was tested using the Monte Carlo permutation test with stepwise variable selection45. The influence of individual habitat variables on species composition (marginal effects) as well as their combined influence (conditional effects) was tested.Additionally, to illustrate the relationships between black cherry in the tree and herb layers (significant habitat variables) and individual plant species in the database, a generalised additive model (GAM) was used46. The GAM plots included only the species selected after analysing the mean cover and frequency values. These taxa had an average cover of above 5% and occurred in at least 10% of the study plots. All ordination analyses were performed using the CANOCO v. 5.03 software.The impact of black cherry on fallow vegetation diversity was assessed based on species richness and diversity indices, which were calculated separately for the study plots invaded by black cherry and those free of this species. The Shannon diversity index was calculated as follows: (:{H}^{{prime:}}=sum:_{i=1}^{s}(pi:times:text{ln}pi)), and the evenness index was calculated as: (:J{prime:}=frac{H{prime:}}{lnS::}), where (:pi=:frac{ni}{N}), ni is the abundance of the ith species expressed as its cover, N is the sum of abundances of all species, and S is the total species richness. The value of each diversity parameter was calculated for all the layers considered together. In the next stage of the study, we focused on the relationship between P. serotina, present in the three vegetation layers, and diversity parameters determined for the herb and moss layers. The vertical structure of the vegetation was not well developed in all 100 study plots; the presence of a tree layer (excluding P. serotina) was found in six study plots, and the shrub layer in four (Table S1 (Supplementary)). Therefore, we decided to refer only to the vegetation layers present in all the plots. The MVSP v. The 3.131 package47 was used to calculate all diversity parameters. The normal distribution of variables was evaluated using the Shapiro–Wilk test. Levene’s test was used to check for the homogeneity of variance. The parametric Student’s t-test was used to determine the significance of differences between the parameters. Correlations between the analysed parameters were examined using Pearson’s rank correlation. The strengths of the correlations were interpreted according to Stanisz48. Calculations were performed at a significance level of p ≤ 0.05, for the entire database. The above analyses were conducted using the STATISTICA v. 1349 software.ResultsDirections of vegetation changes in the studied fallow landsA total of 191 plant species were recorded across all the study plots (Table S1 (Supplementary), Table 2). They form floristically impoverished communities, in particular, devoid of species that are characteristic of lower syntaxa. Most of the identified species represent semi-natural and anthropogenic meadow communities of the class Molinio-Arrhenatheretea and communities of perennial plants in ruderal areas of the class Artemisietea vulgaris.Table 2 The number of plant species found in the study plots uninhabited (0) and inhabited (1) by Padus serotina on individual fallows (from F1 to F10), taking into account their affiliation to the phytosociological class.Full size tableThe analysed fallow lands are similar in terms of the succession processes occurring within them. The main type of transformation is the encroachment of invasive and expansive native species, particularly Solidago gigantea and Calamagrostis epigejos. Well-developed patches of the Rudbeckio-Solidaginetum association, dominated by S. gigantea, were found throughout the study plots of F5, F6, F7, F9, F10, and F2 plots without P. serotina. Patches of the Calamagrostietum epigeji association were also clearly visible, either as independent patches of herbaceous vegetation or mosaic with S. gigantea. Well-developed patches of this association were found in both F8 and F1 plots without black cherry. Less developed patches, co-occurring with patches of the Rudbeckio-Solidaginetum association, were found within the entire F9 fallow and in F5 plots without P. serotina.The remaining fallow lands are dominated by meadow vegetation (class Molinio-Arrhenatheretea, especially the order Arrhenatheretalia elatioris), shrubland (subclass Galio-Urticenea and class Epilobietea angustifolii), and ruderal vegetation (subclass Artemisienea vulgaris and class Agropyretea intermedio-repentis). Relatively well-developed patches of meadow communities, characterised by fresh meadow characteristics but with a clear tendency to desiccation, were found throughout the F3 fallow. In the remaining fallows, the presence of truncated meadow-ruderal or meadow-shrub communities was observed, mostly with a tendency for desiccation (suggested by the presence of Artemisia campestris, Euphorbia cyparissias, Hieracium pilosella).Another direction of change is the gradual succession of the woody species. This is particularly visible in the F9 fallow, where seedlings and saplings of Syringa vulgaris form a mosaic with other woody species, C. epigejos, and the dominant S. gigantea. In turn, on fallow F4 (study plots with P. serotina), as well as F7 and F9 (all study plots), large numbers of Rubus caesius were recorded. The initial stages of woody species succession, with the encroachment of representatives of Rhamno-Prunetea, Querco-Fagetea, and other tree and shrub species, were observed in fallow F1 and F3 (only in plots with P. serotina) and in both types of study plots of all the remaining fallows.The least visible trend was the appearance of segetal species, primarily in areas with disturbed soil cover, which generally disappeared from the studied post-agricultural fields. Vicia hirsuta and Viola arvensis were found on most of the studied fallow lands, often occurring with considerable phytosociological stability (even V), but at marginal cover abundance.Analysis of the impact of Padus serotina on plant species compositionComparisons of plant species composition with their quantity expressed in percentage scale, which were made on 50 study plots invaded by Padus serotina and 50 without the presence of this species, were subjected to PCA analysis with rotation using the NMDS method. In the analyses, the distance between the samples was estimated using the Euclidean distance with the three-dimensional final solution obtained from 42 interactions. The stress value of the resulting model was 14.86%, indicating a good quality. The eigenvalues for the first two canonical axes were 0.4308 and 0.3001, explaining 43% and 30% of the total variation in the vegetation data, respectively. The diagrams present the location of the study plots within the fallows (Fig. 2) and the arrangement of all plant species, considering their belonging to the plant layer (Fig. 3). In the ordination space created by the first two axes, the separation of the study plots along the first and second canonical axes was clearly visible (Fig. 2). The study plots not inhabited by P. serotina were located on the left side of the ordination space, and those where P. serotina was present were on the right. The exception is study plot 51, dominated by giant goldenrod, the only plot where black cherry is absent from the shrub layer but occurs in the tree and herb layers (Table S1 (Supplementary)). The disturbance in species composition caused by the presence of Solidago gigantea and Calamagrostis epigejos is shown in Fig. 3. The above species are located at the extreme left of the first ordination axis, whereas P. serotina occupies the farthest position on the right of the same axis. Along the second canonical axis, a separate position in the ordination space was occupied by P. serotina in the shrub layer and, on the opposite side of the axis, by the nitrophilic species Arrhenatherum elatius and Tanacetum vulgare.Fig. 2Principal component analysis (PCA) diagram with rotation using nonmetric multidimensional scaling (NMDS) for study plots with Padus serotina (red points) and study plots without this species (black points). The study plot numbers are presented in Table 2.Full size imageFig. 3Principal component analysis (PCA) diagram with rotation using nonmetric multidimensional scaling (NMDS) for all plant species recorded in all study plots. The symbols preceding the species name abbreviation indicate the vegetation layer (a—trees, b—shrubs, d—moss layer, no symbol—herb layer). Species names and their abbreviations are provided in Table S2 (Supplementary).Full size imageCCA analyses allowed for the identification of general relationships between plant species in the ground-level layer and environmental variables, represented by P. serotina cover in the herb, shrub, and tree layers. Based on the calculations, the eigenvalues of the first two canonical axes were found to be 0.265 (first axis) and 0.186 (second axis), respectively. These axes explained 3.3% and 2.3% of the total variation in plant species composition, and 49.4% and 34.7% of the variation in the relationships between species and the presence of P. serotina, respectively. The results of the stepwise selection of variables using the Monte Carlo permutation test showed a significant relationship between the presence of black cherry and the species composition of herbaceous plants, tree and shrub seedlings, and leafy bryophytes (Table 3). Analysing the influence of variables acting together, the strongest relationship was found between species composition and P. serotina in the shrub layer and a slightly weaker relationship between species composition and P. serotina in the tree layer. The ordination diagram using the first two canonical axes shows the distribution of species and environmental variable vectors (Fig. 4). The black cherry vector in the tree layer was weakly and positively correlated with the second canonical axis, whereas the vector illustrating P. serotina in the herb layer exhibited a slightly stronger correlation with the first axis. The absence of species near the ends of the environmental variable vectors indicated a lack of positive relationships with these variables. Note the abundant species occurring in opposite positions, extending the beginning of the vectors of the analysed habitat variables. Their location indicated a negative relationship with the area covered by P. serotina in the tree and herb layers.Table 3 Results of canonical analysis (CCA and Stepwise selection) for the impact of environmental variables, represented by Prunus Serotina cover in plant layers, on undergrowth community composition in fallow lands. The designations preceding the abbreviation of the species name P. Serotina (Pru_se) indicate affiliation to a—tree layer, b—shrub layer, and c—herb layer.Full size tableFig. 4Canonical correspondence analysis (CCA) diagram for plant species recorded in the herb and moss layers (taken together) and environmental variables, represented by Prunus serotina cover in plant layers, are shown as vectors. The designations preceding the abbreviation of Prunus serotina (Pru_se) indicate affiliation to a—tree layer, b—shrub layer, and c—herb layer. The names of the other species and their abbreviations are provided in Table S2 (Supplementary).Full size imageTo more precisely examine the relationships between the occurrence of black cherry in the herb and tree layers and between specific species in the herb and moss layers, generalised additive models (GAMs) were used (Fig. 5). Three species exhibited a clear response to an increase in cover by P. serotina in the tree layer. The curve for Solidago gigantea was the most dynamic, demonstrating both a reduction in the cover by approximately 20% (with an increase in the cover by P. serotina in the tree layer to 40%) and an increase of approximately 15% (with a canopy closure of P. serotina ranging from 40% to 80%). The curve for Calamagrostis epigejos provides information on the decrease in the area occupied by this species, from approximately 13% to less than 5%. In the case of the grass Arrhenatherum elatius, an approximately 10% increase in cover was observed in response to canopy closure of P. serotina ranging from 60 to 100%. The strongest response to P. serotina cover in the herb layer was observed in S. gigantea. Coverage by this species decreased from approximately 22% to zero with increasing seedling and P. serotina sapling density. Similarly, the curve for C. epigejos showed an overall decline in species cover from approximately 14%.Fig. 5Generalised additive models (GAM) illustrating the effect of Padus serotina cover in the tree (a_Pad_ser) and herb layers (c_Pad_ser) on the responses of species in the herb and moss layers, expressed by average cover. Only species with coverage higher than 5% were included. Species names and their abbreviations are provided in Table S2 (Supplementary).Full size imageThe impact of Padus serotina on the species diversity of fallows vegetationThe analysis of diversity parameters indices for the study plots inhabited by black cherry and those free of this species revealed significant differences. Higher values of the diversity index (H’) were observed (t = 4.215; p ≤ 0.0001). Shannon-Wiener evenness index (J′) (t = 3.496; p = 0.001) and species richness (s) (t = 3.956; p ≤ 0.0001) were observed in the plots invaded by Padus serotina. Additional information on the relationship between P. serotina considering its proportion in the individual vegetation layers and the parameters describing the diversity of the herb and moss layers were considered together. is provided by correlation analyses (Table 4) and figures prepared for statistically significant correlations (Fig. 6).Table 4 Pearson’s correlations between the cover of Padus serotina in the layers of trees, shrubs, and herbs and the Shannon-Wiener diversity (H′), evenness (J′), and species richness (s) calculated for the vegetation in the herb and moss layers (taken together). The Pearson correlation coefficient value (R) are given (n = 100). Variables significant at p ≤ 0.05 are marked in red.Full size tableFig. 6The effect of Padus serotina cover in the shrub layer on diversity (graph A) and the effect of Padus serotina cover in the herb layer on diversity (B), evenness (C), and species richness (D) calculated for vegetation in the herb and moss layers.Full size imageBased on these data, a weak correlation was found between the occurrence of P. serotina shrubs and the value of the diversity index (H’) for vegetation in the herb and moss layers. A stronger effect was associated with the presence of black cherry in the herb layer and the value of each of the analysed diversity parameters (H′, J′, and s).DiscussionThe presence of Padus serotina in Europe dates back to almost four centuries. The intensive spread of this species, particularly over the last few decades, has influenced the floristic composition, spatial structure, dynamics, and stability of colonised phytocoenoses, particularly in forests18,26,29. Our floristic studies confirmed that P. serotina can also play a significant role in the early stages of secondary succession in abandoned agricultural lands. In most of the study plots, P. serotina was observed to have a well-developed shrub layer and abundant seedlings and saplings in the herb layer. New populations of black cherry are expanding, occupying areas immediately adjacent to previously colonised sites. A long-lived seed bank, deposited annually near parent plants20, and the ability to spontaneously produce root suckers50 result in a rapid increase in the density of juveniles34,36. Fruits are eaten willingly and dispersed by birds51. Birds carry fruit up to approximately 100 m from the parent plant36. Without bird involvement, most fruits fall no further than 5–10 m from the seed source52. Under favourable conditions, colonisation of fallow lands can proceed so rapidly that a period of less than 10 years is sufficient to form a dense layer of P. serotina shrubs growing up to 2 m in height11. At the same time, new foci of species invasion in already colonised areas have been assessed by scientists as sporadic36,51.The fallow lands we selected were dominated by meadows, shrublands, and ruderal vegetation. Fallow flora did not form clear patterns associated with the presence or absence of P. serotina. Because of the relatively short time that has passed since the abandonment of cultivation in fields, the soil seed bank of segetal species, especially from the class Stellarietea mediae, should still be rich and shallow. However, the proportion of weed species from the segetal communities in the fallow flora was low. This may be due to the strong competition and possible allelopathic effects of other species, but also to the lack of cyclical disturbances in the topsoil. The actual vegetation of fallows does not always reflect the species present in the soil seed bank53. The presence of representatives of classes such as Phragmitetea, Koelerio-Corynephoretea, and Festuco-Brometea within the studied fallows appeared to be accidental. According to previous research, plant communities developing on fallow lands constitute heterogeneous groups and are usually dominated by one or several expansive plant species that reproduce mainly vegetatively (for example: Agrostis capillaris, Holcus mollis, Elymus repens)54.Fallow fields are among the most disturbed phytocoenoses, which further promotes the encroachment and establishment of alien species, including invasive ones55. Our observations confirm the colonisation of fallow lands by invasive species, especially Solidago gigantea. The average cover of S. gigantea in the study plots with and without P. serotina was comparable. However, the response patterns of S. gigantea to an increase in P. serotina cover are not clear. The effect of black cherry, illustrated by GAM, was both limited (realised by the youngest developmental stages of P. serotina, probably through competition for resources and allelopathy) and stimulating (concerning P. serotina trees, which, with a crown closure of 40–80%, create a favourable microclimate and provide nutrient-rich litter). Studies by other authors have indicated that the varied response of S. gigantea may also result from the ecological properties of the species itself, which are less competitive and grow slower in drier locations56. Invasive species of the genus Solidago often dominate fallows and form dense stands57,58. Such degenerated plant communities are also invaded by the native expansive species Calamagrostis epigejos58,59. In the fallow lands examined, the occurrence of C. epigejos was also observed, and the presence of P. serotina in vegetation patches contributed to the decline of this species.This contradicts the results of our study, in which P. serotina was not the dominant species in the fallows. In the fallow lands we selected, the average cover by P. serotina shrubs did not exceed 46%, whereas in the cited study by Hejda et al.60 the cover by S. gigantea was 70–100%, for H. mantegazzianum 90–100%, and for R. japonica 100%. The impacts of invasive species worsen as the invasion process continues, and the cover of the alien species increases12. An increase in the density and spread of the P. serotina population is a continuous process that results in a continuum of changes in plant community structure. Furthermore, the invasion of herbaceous species modifies the course of secondary succession in fallows in contrast to the invasion of non-native trees. Black cherry participates in the formation of mid-field shelterbelts, which favour bird migration and propagule dispersal, helping to maintain a high level of biodiversity in agricultural areas61. According to Benton et al.62, the presence of non-native woody plants is more beneficial for local species richness than the complete absence of such landscape elements in agrophytocenoses.Scientific studies lack information on the impact of P. serotina on plant biodiversity in synanthropic habitats. In open communities (meadows, wastelands, roadsides), invasive herbaceous species such as Reynoutria spp., Heracleum mantegazzianum, Solidago spp., and Rudbeckia laciniata contribute to a decline in species richness in inhabited vegetation patches60,63. Our results indicate that P. serotina has a positive impact on the diversity of fallow vegetation. It should be emphasised that we analysed the vegetation condition at the initial phase of secondary succession, and black cherry was not yet the dominant species in the fallows. The cover by P. serotina shrubs did not exceed 46%, whereas in the cited study by Hejda et al. 60 the cover by S. gigantea was 70–100%, for H. mantegazzianum 90–100%, and for R. japonica 100%.The invasive species’ impacts worsen as the invasion process continues and the cover of the alien species increases12. Because the increase in the density and spread of P. serotina populations is a continuous process, further changes in the plant community structure can be expected. Furthermore, the invasion of herbaceous species modifies the course of secondary succession on fallows in contrast to the invasion of alien trees. Black cherry participates in the formation of mid-field shelterbelts, which favor bird migration and propagule dispersal, helping to maintain a high level of biodiversity in agricultural areas61. According to Benton et al.62, the presence of non-native woody plants is more beneficial for local species richness than the complete absence of such landscape elements in agrophytocenoses.The high level of species diversity observed in the plots with P. serotina in our study may also be related to their greater fertility following the decomposition of black cherry litter compared to the plots not inhabited by the studied species. Fallow sites are generally relatively fertile habitats64, where an increase in the number of flora species was first observed during the process of directional environmental changes59,65. These originate from the rich seed bank, as well as the introduction of propagules of other species66. According to Hochół et al.67 the floristic richness of segetal communities is associated with a large number of species with a low degree of cover. This may be true for species whose presence is entirely accidental and/or related to deliberate human activity (dumping garden waste), such as Iris sibirica and Fragaria × ananasa, found in our study on black cherry thickets. The presented results allowed us to identify temporary relationships accompanying secondary succession in selected agrophytocenoses, justifying the need for further research in this area.ConclusionsThe influence of Padus serotina on fallow vegetation represents a complex long-term ecological process. Our study demonstrated that seedlings and saplings of this species have the strongest impact on the species composition of fallow lands. As P. serotina cover increases in the herb layer, expansive species such as Calamagrostis epigejos and Solidago gigantea decline, allowing for the development of more diverse plant communities. Additionally, black cherry forms low thickets that provide perching sites for birds, promoting seed dispersal and the development of multispecies shrubs. Our results indicated a positive effect of P. serotina on species diversity in the early to mid-stages of secondary succession. To the best of our knowledge, this is the first study to assess the impact of P. serotina on perennial fallow lands, making it an important reference point for the future. The impact of the analysed species on vegetation composition and biodiversity may change as succession progresses. Therefore, further long-term monitoring is necessary to fully assess how this invasive species influences secondary succession dynamics and biodiversity in fallow lands, thereby providing a basis for future management and conservation strategies.

    Data availability

    The datasets generated and analysed during the current study are available from the corresponding author on reasonable request.
    ReferencesLasanta, T., Nadal-Romero, E. & Arnáez, J. Managing abandoned farmland to control the impact of re-vegetation on the environment. The state of the Art in Europe. Environ. Sci. Policy. 52, 99–109 (2015).Article 

    Google Scholar 
    Ustaoglu, E. & Collier, M. J. Farmland abandonment in europe: an overview of drivers, consequences, and assessment of the sustainability implications. Environ. Rev. 26, 396–416 (2018).Article 

    Google Scholar 
    Cramer, V., Hobbs, R. & Standish, R. What’s new about old fields? Land abandonment and ecosystem assembly. Trends Ecol. Evol. 23, 104–112 (2008).Article 
    PubMed 

    Google Scholar 
    Moyo, B. & Ravhuhali, K. E. Abandoned croplands: drivers and secondary succession trajectories under livestock grazing in communal areas of South Africa. Sustainability 14, 6168 (2022).Article 
    ADS 
    CAS 

    Google Scholar 
    Kinhal, V. & Parthasarathy, N. Secondary succession and resource use in tropical fallows: A case study from the Coromandel Coast of South India. Land. Degrad. Dev. 19, 649–662 (2008).Article 

    Google Scholar 
    Sojneková, M. & Chytrý, M. From arable land to species-rich semi-natural grasslands: succession in abandoned fields in a dry region of central Europe. Ecol. Eng. 77, 373–381 (2015).Article 

    Google Scholar 
    Faliński, J. Procesy Ekologiczne w Zbiorowiskach leśnych. (Ecological processes in forest communities). Phytocoenosis 3, 17–41 (1991).
    Google Scholar 
    Prach, K. et al. Vegetation succession in restoration of disturbed sites in central europe: the direction of succession and species richness across 19 Seres. Appl. Veg. Sci. 17, 193–200 (2014).Article 

    Google Scholar 
    Bernadzki, E. & Kowalski, M. Brzoza na gruntach porolnych. Sylwan. 12, 33–42 (1983).Meiners, S. J., Pickett, S. T. A. & Cadenasso, M. L. Exotic plant invasions over 40 years of old field successions: community patterns and associations. Ecography 25, 215–223 (2002).Article 
    ADS 

    Google Scholar 
    Adamczak, A. Acer negundo L. and Padus Serotina (Ehrh.) Borkh. As kenophytes initiatingthe development of Wildwoods on fallow lands. Przegląd Przyrodniczy. 17, 1–2 (2007).
    Google Scholar 
    Meiners, S. J., Pickett, S. T. A. & Cadenasso, M. L. Effects of plant invasions on the species richness of abandoned agricultural land. Ecography 24, 633–644 (2001).Article 
    ADS 

    Google Scholar 
    Yurkonis, K. A. & Meiners, S. J. Invasion impacts local species turnover in a successional system. Ecol. Lett. 7, 764–769 (2004).Article 

    Google Scholar 
    Engel, M. et al. Managing black Cherry (Prunus Serotina Ehrh.) in European forests: insights from native and non-native ranges. For. Ecol. Manag. 562, 121959 (2024).Article 

    Google Scholar 
    Bijak, S., Czajkowski, M. & Ludwisiak, Ł. Occurrence of black Cherry (Prunus Serotina Ehrh.) in the state forests in Poland. For. Res. Papers. 75, 359–365 (2014).
    Google Scholar 
    Terwei, A. Prunus serotina (black cherry). https://www.cabi.org/fc/datasheet/44360 (2021). https://doi.org/10.1079/FC.44360.20210113967Starfinger, U. Introduction and naturalization of Prunus serotina in central Europe. In Plant Invasions: Studies from North America and Europe, 161–171 (Backhuys, 1997).Starfinger, U., Kowarik, I., Rode, M. & Schepker, H. From desirable ornamental plant to pest to accepted addition to the Flora? – the perception of an alien tree species through the centuries. Biol. Invasions. 5, 323–335 (2003).Article 

    Google Scholar 
    Kowarik, I. Time lags in biological invasion with regard to the success and failure of alien species. In Plant Invasions-General Aspects and Special Problems (SPB Academic Publishing, 1995).Pairon, M., Chabrerie, O., Casado, C. M. & Jacquemart, A. L. Sexual regeneration traits linked to black Cherry (Prunus Serotina Ehrh.) invasiveness. Acta Oecol. 30, 238–247 (2006).Article 
    ADS 

    Google Scholar 
    Closset-Kopp, D., Chabrerie, O., Valentin, B., Delachapelle, H. & Decocq, G. When Oskar Meets alice: does a lack of trade-off in r/K-strategies make Prunus Serotina a successful invader of European forests? For. Ecol. Manag. 247, 120–130 (2007).Article 

    Google Scholar 
    Halarewicz, A. & Jackowski, J. Leaf damage of the black cherry, Prunus Serotina Ehrh., by the leaf flea beetle, gonioctena quinquepunctata Fabr.: an accidental foraging on a neophytic host, or an established trophic link? Pol. J. Ecol. 59, 587–595 (2011).
    Google Scholar 
    Bączek, P. & Halarewicz, A. Effect of black Cherry (Prunus serotina) litter extracts on germination and growth of Scots pine (Pinus sylvestris) seedlings. Pol. J. Ecol. 67, 137 (2019).
    Google Scholar 
    Bączek, P. & Halarewicz, A. Allelopathic effect of black cherry (Prunus serotina Ehrh.) on early growth of white mustard (Sinapis alba L.) and Common Buckwheat (Fagopyrum esculentum Moench): Is the Invader a Threat to Restoration of Fallow Lands? Agronomy. 12, 2103 (2022).Godefroid, S., Phartyal, S. S., Weyembergh, G. & Koedam, N. Ecological factors controlling the abundance of non-native invasive black Cherry (Prunus serotina) in deciduous forest understory in Belgium. For. Ecol. Manag. 210, 91–105 (2005).Article 

    Google Scholar 
    Chabrerie, O., Loinard, J., Perrin, S., Saguez, R. & Decocq, G. Impact of Prunus serotina invasion on understory functional diversity in a European temperate forest. Biol. Invasions. 12, 1891–1907 (2010).Article 

    Google Scholar 
    Halarewicz, A. & Pruchniewicz, D. Vegetation and environmental changes in a Scots pine forest invaded by Prunus serotina: what is the threat to terricolous bryophytes? Eur. J. For. Res. 134, 793–801 (2015).Article 
    CAS 

    Google Scholar 
    Bury, S. & Dyderski, M. K. Invasive tree species affect terricolous bryophytes biomass and biodiversity in nutrient-poor but not nutrient-rich temperate forests. Sci. Rep. 15, 5272 (2025).Article 
    ADS 
    PubMed 
    PubMed Central 
    CAS 

    Google Scholar 
    Koutika, L. S., Vanderhoeven, S., Chapuis-Lardy, L., Dassonville, N. & Meerts, P. Assessment of changes in soil organic matter after invasion by exotic plant species. Biol. Fertil. Soils. 44, 331–341 (2007).Article 

    Google Scholar 
    Chabrerie, O., Verheyen, K., Saguez, R. & Decocq, G. Disentangling relationships between habitat conditions, disturbance history, plant diversity, and American black Cherry (Prunus Serotina Ehrh.) invasion in a European temperate forest. Divers. Distrib. 14, 204–212 (2008).Article 

    Google Scholar 
    Verheyen, K., Vanhellemont, M., Stock, T. & Hermy, M. Predicting patterns of invasion by black Cherry (Prunus serotina Ehrh.) in Flanders (Belgium) and its impact on the forest understorey community. Divers. Distrib. 13, 487–497 (2007).Article 

    Google Scholar 
    Halarewicz, A. & Żołnierz, L. Changes in the understorey of mixed coniferous forest plant communities dominated by the American black Cherry (Prunus serotina Ehrh). For. Ecol. Manag. 313, 91–97 (2014).Article 

    Google Scholar 
    Halarewicz, A., Pruchniewicz, D. & Kawałko, D. Black Cherry (Prunus serotina) invasion in a Scots pine forest: relationships between soil properties and vegetation. Pol. J. Ecol. 65, 295–302 (2017).
    Google Scholar 
    Bomanowska, A. & Adamowski, W. Alien plant species in secondary succession. Thaiszia J. Bot. Košice. 22, 121–141 (2012).
    Google Scholar 
    Bułaj, B., Okpisz, K., Rutkowski, P. & Tomczak, A. Occurrence of invasive black Cherry (Prunus serotina Ehrh.) on abandoned farmland in west–central Poland. Forestry Lett. 110, 26–31 (2017).
    Google Scholar 
    Wołkowycki, D. & Próchnicki, P. Spatial expansion pattern of black Cherry Padus serotina Ehrh. In suburban zone of Białystok (NE Poland). Biodivers. Res. Conserv. 40, 59–67 (2015).Article 

    Google Scholar 
    Bączek, P., Halarewicz, A., Pruchniewicz, D., Podlaska, M. & Kawałko, D. Soil properties of fallow land invaded by black Cherry (Padus serotina (Ehrh.) Borkh). Agriculture. 13, 2148 (2023).Article 

    Google Scholar 
    Sobik, M. Klimat. In Przyroda Dolnego Śląska, 39–57 (Polska Akademia Nauk Oddział we Wrocławiu, 2005).
    Google Scholar 
    Mirek, Z., Piękoś-Mirkowa, H., Zając, A. & Zając, M. Vascular Plants of Poland. An Annotated Checklist (Rośliny Naczyniowe Polski. Adnotowany Wykaz gatunków) (Szafer Institute of Botany. Polish Academy of Sciences, 2020).Ochyra, R., Żarnowiec, J. & Bednarek-Ochyra, H. Census Catalogue of Polish Mosses (Polish Academy of Sciences, Institute of Botany, 2003).Tutin, T. G. et al. Flora Europaea. Vol. 2. Rosaceae To Umbelliferae (Cambridge University Press, 1968).Matuszkiewicz, W. Przewodnik Do Oznaczania Zbiorowisk roślinnych Polski (Wydawnictwo Naukowe PWN, 2007).McCune, B. P. & Grace, J. B. Analysis of ecological communities. J. Exp. Mar. Biol. Ecol. 289, 303–305 (2002).
    Google Scholar 
    ter Braak, C. J. F. & Šmilauer, C. Canoco reference manual and user’s guide: software for ordination. Version 5.0. In Microcomputer Power Ithaca USA (2012).Manly, B. F. J. Randomization and Monte Carlo Methods in Biology (Chapman and Hall, 1990).Hastie, T. J. & Tibshirani, R. J. Generalized Additive Models (Chapman and Hall, 1990).Kovach, W. L. MVSP—a multivariate statistical package for windows. Kovach computing services (2010).Stanisz, A. Przystępny Kurs Statystyki Z Zastosowaniem STATISTICA PL Na przykładach Z medycyny. Tom 1. Statystyki Podstawowe (Kraków, 2006).Tibico Software Inc. Statistica. Tibico Software Inc. (2017).Halarewicz, A. Odnawianie się Czeremchy amerykańskiej Prunus Serotina Ehrh. Na Siedliskach Borowych. Sylwan. 155, 530–534 (2011).
    Google Scholar 
    Deckers, B. et al. Impact of avian frugivores on dispersal and recruitment of the invasive Prunus serotina in an agricultural landscape. Biol. Invasions. 10, 717–727 (2008).Article 

    Google Scholar 
    Hoppes, W. G. Seedfall pattern of several species of bird-dispersed plants in an Illinois woodland. Ecology. 69, 320–329 (1988).Article 

    Google Scholar 
    Sekutowski, T. R., Włodek, S., Biskupski, A. & Sienkiewicz-Cholewa, U. Porównanie odłogu I sąsiadującego Pola Uprawnego pod względem zasobności w Nasiona i rośliny nawłoci (Solidago sp). In Zeszyty Naukowe Uniwersytetu Przyrodniczego We Wrocławiu Rolnictwo C, 99–112 (2012).Bucała, A., Budek, A. & Kozak, M. The impact of land use and land cover changes on soil properties and plant communities in the Gorce mountains (Western Polish Carpathians), during the past 50 years. zfg_suppl 59, 41–74 (2015).Article 

    Google Scholar 
    Tokarska-Guzik, B. The expansion of some alien plant species (neophytes) in Poland. In Plant Invasions: Ecological Treats and Management Solutions, 147–167 (Backhuys, 2003).
    Google Scholar 
    Weber, E. & Jakobs, G. Biological flora of central europe: Solidago gigantea Aiton. Flora Morphol. Distrib. Funct. Ecol. Plants. 200, 109–118 (2005).
    Google Scholar 
    Węgrzynek, B., Urbisz, A. & Nowak, T. Participation of Solidago candensis L. and S. gigantea Aiton in abandoned field communities in the Silesian upland (Poland). Thaiszia J. Bot. Košice. 15, 267–275 (2005).
    Google Scholar 
    Babczyńska-Sendek, B., Błońska, A. & Hejdysz, J. Characteristics of the flora of fallow lands on Rendzina soils on the Twardowice plateau (Silesian Upland). Acta Agrobot. 65, 75–90 (2012).Article 

    Google Scholar 
    Sosnowska, J. A. Changes of vegetation effects in soil properties in the post-agriculture landscapes (south-eastern Poland). Miscellanea Geogr. 23, 63–70 (2019).Article 

    Google Scholar 
    Hejda, M., Pyšek, P. & Jarošík, V. Impact of invasive plants on the species richness, diversity and composition of invaded communities. J. Ecol. 97, 393–403 (2009).Article 

    Google Scholar 
    Marshall, E. J. P. & Moonen, A. C. Field margins in Northern europe: their functions and interactions with agriculture. Agric. Ecosyst. Environ. 89, 5–21 (2002).Article 

    Google Scholar 
    Benton, T. G., Vickery, J. A. & Wilson, J. D. Farmland biodiversity: is habitat heterogeneity the key? Trends Ecol. Evol. 18, 182–188 (2003).Article 

    Google Scholar 
    Szymura, M. & Szymura, T. H. Rozmieszczenie Nawłoci (Solidago spp.) Na obszarze Dolnego Śląska Oraz Ich wpływ Na różnorodność biologiczną Zasiedlanych Fitocenoz. In Synantropizacja W Dobie Zmian różnorodności Biologicznej, 195–212 (2011).Chmolowska, D., Kozak, M. & Laskowski, R. Soil physicochemical properties and floristic composition of two ecosystems differing in plant diversity: fallows and meadows. Plant. Soil. 402, 317–329 (2016).Article 
    CAS 

    Google Scholar 
    Ruprecht, E. Successfully recovered grassland: A promising example from Romanian old-fields. Restor. Ecol. 14, 473–480 (2006).Article 

    Google Scholar 
    Kostuch, R. Sukcesja roślinna Na odłogowanych Gruntach Ornych. Woda Środowisko Obszary Wiejskie. 3, 57–78 (2003).
    Google Scholar 
    Hochół, T., Łabza, T. & Stupnicka-Rodzynkiewicz, E. Zachwaszczenie Wieloletnich odłogów w porównaniu do Stanu Na Polach Uprawnych. Bibl. Fragmenta Agron. 5, 115–123 (1998).
    Google Scholar 
    Download referencesFundingThe APC/BPC was funded by the Wrocław University of Environmental and Life Sciences.Author informationAuthors and AffiliationsDepartment of Plant Protection, Wrocław University of Environmental and Life Sciences, Grunwaldzki Sq. 24a, 50-363, Wrocław, PolandPaulina BączekDepartment of Botany and Plant Ecology, Wrocław University of Environmental and Life Sciences, Grunwaldzki Sq. 24a, 50-363, Wrocław, PolandAleksandra Halarewicz, Daniel Pruchniewicz & Magda PodlaskaAuthorsPaulina BączekView author publicationsSearch author on:PubMed Google ScholarAleksandra HalarewiczView author publicationsSearch author on:PubMed Google ScholarDaniel PruchniewiczView author publicationsSearch author on:PubMed Google ScholarMagda PodlaskaView author publicationsSearch author on:PubMed Google ScholarContributionsA.H. and P.B. developed the concept and design of the study. A.H. and M.P. developed a detailed methodology. Data collection were performed by A.H., P.B. and M.P. Analysis were performed by D.P. The first draft of the manuscript was written by A.H. and all authors commented on previous versions of the manuscript. All authors read and approved the manuscript.Corresponding authorsCorrespondence to
    Paulina Bączek or Aleksandra Halarewicz.Ethics declarations

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    Reprints and permissionsAbout this articleCite this articleBączek, P., Halarewicz, A., Pruchniewicz, D. et al. Flora and vegetation of fallow lands invaded by the black Cherry Padus serotina (Ehrh.) Borkh. in Lower Silesia (SW Poland).
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    Assessment of herbaceous vegetation species composition growing around Kleinkopje opencast coal mine, Mpumalanga Province, South Africa

    AbstractCoal mining involves the removal of natural vegetation, heavy excavation; combustion and ignition accompanied by a release of coal dust to the atmosphere that drastically reduce the ecosystem services. A study was conducted to assess of veld grasses around a coal mine in Emalahleni in Mpumalanga. Six 100 m line transects were established on rehabilitated site and natural veld in Kleinkopje coal mine. Plant identification, leaf and tiller counting, and biomass harvesting were conducted on each of five 1 m2 sampling quadrats placed at intervals of 20 m along the transect. Nineteen (19) grass species, some (07) forbs and (03) sedges were recorded. Therefore, Eragrostis curvula (31.5%) was the most frequent on the natural veld, followed by Cynodon dactylon (28.36%) and Panicum maximum (22.25%) on the rehabilitated. On the natural veld, Aristida congesta had higher tiller production (17 tillers per plant) while E. curvula averaged 11 tillers per plant on the rehabilitated site. On the rehabilitated site, U. mosambicensis produced averaged 94 leaves per plant than all other species on both sites. Basal cover, species diversity and richness did not differ (p > 0.05) between rehabilitated site and natural veld. Rehabilitated site had a generally higher biomass production of 4.35 kg DM ha− 1 than natural veld with 3.24 kg DM ha− 1. E. curvula and C. dactylon had higher contribution to the total biomass production on both sites. However, their biomass production was insignificantly different (p > 0.05) from that of Urochloa mozambicensis and P. maximum on rehabilitated site and Heteropogon contortus on natural site. Our results indicated that E. curvula and C. dactylon are highly persistent and productive hence they are ideal for mine rehabilitation.

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    IntroductionSouth Africa’s economy is largely dependent on fossil fuel as a major energy source for electricity that is commonly generated by burning the coal. In addition, South Africa also plays a significant role in the global coal market. Most of the mines in South Africa are concentrated in Mpumalanga Province with most of them around Emalahleni (Witbank) and Middleburg. However, coal mining has negative effects consequences on the environment and land use. Since opencast coal mining is associated with stripping and removal of the topsoil with organic matter and vegetation cover mined areas are usually characterized by poor soil structure1. This leads to serious negative impacts on the ecology of the land under coal mining. The land deteriorates further if there is no comprehensive rehabilitation practice aimed at re-establishment and reclamation of the mined land back to its original state2. Furthermore, one of the most problematic occurrences is the exposure of the coal to air and water which reacts resulting in spontaneous combustion, ignition and coal oxidation3. The leachate that comes out of the tailings that emanate during coal mining is usually characterized by low pH level, escalating the large volume of acid mine drainage. Acid mine drainage has been reported to have a potential to contaminate groundwater and surface water4.Rangeland degradation around coal mining areas of Mpumalanga Province remains a critical issue. Various plant species have been suggested for phytoremediation of the mined areas. For a rapid and successful rehabilitation program herbaceous species, as opposed to trees, have been promoted because of their rapid growth rate, high biomass production and good stabilization of degraded soils5,6,7,8. Grasses are well adapted when growing in their favorable environmental conditions, however, once their specific environmental conditions are disturbed either through natural or anthropogenic activities such as mining, their relative abundance, diversity and fitness is compromised9. The disturbance adversely affects agricultural production particularly rangelands. Over a period, the agricultural land available is either directly or indirectly affected leading to a reduction in productivity of the area.Despite the loss of agricultural land, surface coal mining also leads to more complicated environmental problems such as air, water pollution, destruction of the soil structure and massive biodiversity loss10. Consequently, when soil structure, texture and most importantly soil nutrient composition are disturbed, the concentration of suitable plant nutrients gradually decreases11. As a result, rehabilitation of mined areas turns to be slow due to poor soil structure and low soil nutrient content to support plant growth and development. Furthermore, the effect on soil chemical, physical properties and nutrient loss is exacerbated by prolonged period of the stockpiles12.Mushia et al.1 compared stockpiles with different ages of between 10 and 20 years and found high plant height, fresh and dry biomass on the 10-year-old stockpile. Stockpiling, therefore, leads to ecological changes in the mined areas thus affecting species composition in terms of abundance, diversity and fitness even after rehabilitation has been completed. This study was conducted to assess growth performance of veld grasses growing around a coal mine. It was, therefore, hypothesized that there is variation in tolerance to elemental contaminants among grasses growing around opencast mine areas and this would result in differences in species abundance, diversity and fitness.Materials and methodsSite descriptionThe study was conducted at Kleinkopje Colliery (26˚23’S, 29˚21’E; altitude 1570 m) located approximately 20 km south of Emalahleni in Mpumalanga Province of South Africa (Fig. 1). Summer average temperature ranges between 12 °C and 29 °C with winter average temperatures of between 3 °C and 20 °C. This area is a summer rainfall region, with the most rainfall occurring in summer between November through to February and winter season occurring during May to July with the average annual rainfall of approximately 696 mm. The soil characteristics of the study site were a sandy clay loam characterized by 67.8% sand, 11.5% clay, 12.9% loam and with a pH of 5.3. The natural veld was predominantly grassland with some patches of forbs as well as sedge plant species. On the rehabilitated area several grass species, Eragrostis teff, Themeda triandra, Digitaria eriantha, Panicum maximum, Chloris gayana, Melinis repens and Cynodon dactylon, were planted during the rehabilitation program13. The rehabilitated site was initially fertilized and irrigated with treated mine water (Table 1) but later converted into a naturalized pasture that is fully dependent on natural rainfall.Table 1 Chemical composition of treated mine water used for irrigation of sites under rehabilitation.Full size tableFig. 1The map displays the location of the Anglo-American Coal Mine, which covers an area of 375 hectares in Mpumalanga Province, South Africa, just outside Emalahleni Town14. In the bottom right corner, there is a Google Map image from Imagery ©2025 Airbus, CNES/Arbus Maxar Technologies, Map data ©2025 AfriGIS (Pty) Ltd of the study area at Kleinkopje Colliery (https://www.google.com/maps/place/Kleinkopje+Colliery+offices), with two sites represented by the yellow and red triangles on the map. The yellow triangles represent the sampling points (transects) in the natural sites while red triangles represent sapling points (transects) in the rehabilitated.Full size imageData collectionAssessment of herbaceous vegetationVegetation assessment was conducted during the growing season in February and March 2019. A total of six transects, each 100 m long, were established: three on the natural site and three on the rehabilitated site and were inter-spaced by 100 m from each other. Along each transect, five 0.5 m2 sampling quadrats were placed at intervals of 20 m. Plants in each quadrat were identified and their representative tiller and leaf numbers were counted. Total biomass was determined by harvesting all the herbage in the transect lines along all sampling quadrat (Fig. 2) followed by separation by species and oven dying at 65 °C to a constant weight.Fig. 2A representation of one of the six transects, each 100 m long, which were established: three on the natural site and three on the rehabilitated site. Each transect had five 1 m2 quadrats with 20 m interspace whereas transect lines were inter-spaced by 100 m from each other.Full size imageStatistical analysisFor quantitative field data, a completely randomized design (CRD) was used in the analysis of variance. Each of the two sites had 3 replicates. The outline of the model used for analysis was:({{text{Y}}_{{text{ij}}({text{k}})}},=,upmu ,+,{upalpha _{text{j}}},+,{upvarepsilon _{{text{ij}}left( {text{k}} right)}})Where Yij(k) = response variables (species composition, biomass basal cover, tiller and leaf numbers). µ = overall mean. αij = the effect of the ith treatment on ith site. ειj(k) = random error.FrequencyPercentage frequency was also calculated using Eq. 1 for species composition and is defined as an amount of how often a specific data point or count occurs in a dataset, presented as a percentage of the total number of observations.Grasses species frequency.$$:%=frac{f}{N}:times100$$
    (1)
    Where % = percentage, f = frequency. N = total number of occurrences.Basal coverBasal cover for each site was recorded using a measuring tape to measure the tuft size (minimum and maximum diameter) of each of identified living plant species per quadrat. The inter-tuft distance from the tuft of a living plant to the nearest tuft of another living plant was also measured.Basal cover was calculated using the formula developed by Hardy and Tinton15:$${text{BC}},=,{text{19}}.{text{8}},+,0.{text{39 }}left( {text{D}} right)–{text{11}}.{text{87 }}left( {{text{lo}}{{text{g}}_{text{e}}}{text{D}}} right),+,0.{text{64 }}left( {text{d}} right),+,{text{2}}.{text{93 }}left( {{text{lo}}{{text{g}}_{text{e}}}{text{d}}} right)$$
    (2)
    Where BC denotes Basal Cover, D the distance (cm) of the tuft of living plant to the nearest living plant, d was the mean basal diameter of the tuft of the living plant.Shannon – Wiener indexA Shannon – Wiener index formula (diversity, richness) was applied to calculate species diversity per site. The grass species were classified into, grass species were classified into their ecological value such as Decreaser species (these are the most desirable species commonly found in a properly managed rangeland), Increaser I species (less wanted species that increase in abundance with light grazing), Increaser II species (this group of grasses is noticed to increase in yield with over/heavy grazing), Invader species (none native grasses that grow and out-compete native species). The grass species were also categorized into life forms that is, annuals and perennials. Forbs and sedges were recorded. The equation that was used to calculate the species diversity is as follows16:$$:{H}^{{prime:}}=-{sum:}_{i=1}^{}left(text{q}text{i}/text{Q})text{l}text{o}text{g}(text{q}text{i}/text{Q}right)$$
    (3)
    Where H’ is the Shannon-Weiner diversity index, qi is the number of individuals of each ith species, Q is the total number of the individual species for each site. Grass species evenness was measured by Pielou’s equation, where evenness (E) is represented as follows17:$$:E={H}^{{prime:}}/text{ln}S$$
    (4)
    Where H’ is the Shannon-Wiener diversity index and S is the number of species recorded per site.Biomass production, basal cover, tiller and leaf numbers of different sites were analyzed at 95% confidence level (p ≤ 0.05) using t-test and one way ANOVA. General Linear Model (GLM) using p-diff. procedure of Statistical Analyses Systems of18 was conducted for mean separation at (p ≤ 0.05) on herbaceous species composition.Results and discussionHerbaceous species abundanceThe mean values for herbaceous species composition recorded in both rehabilitated and natural sites are presented in Table 2. This study recorded a total number of about 19 different grass species, 7 forbs and 3 sedges from both sites divided into 2 families and 16 genera. Species frequency ranging between 32% and 25% indicated most frequent species, less frequent species ranged between 16% and 24% whereas scarce species were all those that recorded less than 15%. Eragrostis curvula was the most frequent species (31.50%) recorded on the natural veld and it was higher by 12% relative to that which was recorded on the rehabilitation site. E. curvular was the most frequent species than all recorded plant species on both sites. On the rehabilitated site Cynodon dactylon and Panicum maximum were significantly more frequent than all recorded species on the rehabilitated site. There was also higher comparative to those that were recorded on the natural veld. All the other recorded species on both sites were scarce species ranging between 0.80 and 15.10% (Table 2).Generally, there were more Increaser IIb species as opposed to Decreaser species on both sites. However, the herbaceous vegetation comprised of 47% Increaser IIb species, 20% Increaser IIc species, 13% Increaser IIa species, 20% Decreaser species and there were no Increaser I species recorded on the rehabilitated. On the natural veld 6% were Increaser I species, 25% Increaser IIa, 31% Increaser IIb, 25% Increaser IIc and 13% Decreaser species. In general, perennial species were more prevalent on both sites relative to the abundance of annual species. Out of 15 species recorded on the rehabilitated, 80% were perennial species and 20% were annual species. On the natural veld 75% were perennial and 25% were annual species. The results show that perennial species were more abundant on the rehabilitated site that on the natural site whereas, annual plants were prevalent on the natural veld.Diversity index combines both the species richness and abundance. There were differences in Shannan – Wiener diversity index between the natural veld and the rehabilitated site and the indexes were 0.84 and 0.82 respectively. More species diversity was observed on the natural veld relative to the rehabilitated site. Both sites had low species evenness, and this was due low diversity component19 (Table 2).Table 2 Mean frequency (%) of herbaceous species composition at Kleinkopje colliery.Full size tableHowever, the prevalence of C. dactylon in the rehabilitated site might be encouraged by its ability to reduce the accumulation of toxic ions that could be found on the rehabilitated site. Such plants sequester toxic ions into their vacuoles to minimize toxicity level. Our results agree with the findings by Platt20 who reported that C. dactylon was amongst most prevalent grass species on the rehabilitation site. This demonstrates that the most frequent grasses exhibit tolerance and/or avoidance adaptation mechanism for them to be able to survive under hostile environmental condition such as mined areas where mine coal dust, heavy metal and disturbed soil structure is a huge problem. Our results are also still in agreement with the results by Truter2 and Limpitlaw et al.21 who reported that these two grass species i.e., C. dactylon as the main dominating grass species around the rehabilitated site amongst grass species that were identified in their studies. In our study P. maximum appears amongst the most prevalent species on the rehabilitated site. This suggests that this grass would also be ideal for restoration of mined areas unlike E. curvula that only colonized the natural veld in abundance.Observed species composition in both sites had more Increaser II species and Forbs, a feature that depicts a mismanaged rangeland22. Furthermore, Increaser II grass species colonize an area that have been disturbed either through natural or anthropogenic activities. Our results are still consistent with the results reported by Snyman23 and Firn24, who reported that successful establishment for E. curvula, was by exacerbated by disturbances such as selective/light grazing. Therefore, since there were no grazing management practices employed on these two sites, E. curvula grows to its full potential and out compete other grass species that were recorded on the natural site. On the rehabilitated site, reduction in the abundance of the plant species recorded in this study including E. curvula could be linked to poor adaptation strategies under disturbed areas and possible with elemental contaminants. This leads to failure in re-vegetating mined lands, and this might also be due to poor soil nutrient content or soil compaction around the rehabilitated site25.Changes in soil nutrient content and introduction or removal of the vegetation on the rehabilitated site might be responsible for differences in species diversity among the two sites with low diversity on the rehabilitated site than the natural veld. Soil properties are important in plant growth and survival and once disturbed vegetation chances from best to worst (Oluwole and Dube 2008). The presence of salt ions in treated mine water (Table 1) could reduce the uptake of potassium due to the nature of their chemical reaction; therefore, this inhibits growth in plants on the rehabilitated site. The results in Table 2 also showed that the natural veld had high species evenness compared to the rehabilitated site. This could be attributed to elemental contaminants that might affect growth and development of plant species on the rehabilitated site.The basal cover for the natural and rehabilitated sites ranged from 1.78 to 20.90 cm2/m2 and 4.12–20.09 cm2/m2, respectively. Furthermore, this varied greatly with species wherein forbs had a significantly high basal cover area of 20.81 cm2/m2 relative to all recorded species on the natural veld. This was followed by C. dactylon and P. maximum with 20.29 cm2/m2 and 18.59 cm2/m2 basal cover recorded on the rehabilitated site (Fig. 3). The largest portion of land on the natural veld was covered by forb species and this show that the area more likely to be susceptible to soil erosion due to the short life span of forb species. On the rehabilitated site the observed grasses species covering a large area were C. dactylon, P. maximum, A. adcessionis and sedge species were perennial species which have longer lifespan that annual species. A. congesta, E. plana D. amplectens and P. squarossa recorded on the natural veld had the smallest basal cover and therefore this site is more exposed to soil erosion than the rehabilitated site.Generally, both these sites were of poor veld condition since they were dominated by forbs on the natural veld and tufted species on the rehabilitated sites. Basal cover of the two sites might have been affected severely by soil disturbance, soil compaction, heavy metals and coal dust within the coal mine. Coal dust released during coal extraction when the coal seam is cut. The major contributing activities in the dispersal of coal dust are blasting, drilling, hauling, shunting and transportation which affect vegetation around the mine. Furthermore, this does not only pollute the air but also areas in the surrounding of the coal mining operation26. Spencer27 reported that coal dust significantly increased soil temperatures, reduced pH, and increased concentration of heavy metals in the soil. Therefore, all these have a potential to induce serious negative effects on plant growth, development and recruitment of news seedlings. The presence of heavy metals in the soil causes visible damage to flora and fauna and yet limiting soil use26.Fig. 3Basal cover (cm) in the natural and rehabilitated site.Full size imageTiller and leaf production of grass species growing around a coal mineThe main tiller producing species was Aristida congesta (17 tillers per plant) followed by E. plana and E. villosa (12 tillers per plant) recorded on the natural site. This was followed by E. curvula recorded on the natural veld with 11 tillers per plant (Table 3). Grass species such as D. amplectens, P. notatum, M. repens and the other presented grasses in Table 3 produced low tiller numbers ranging from (2–9 tillers per plant) on both sites, C. dactylon the lowest being tiller producer on both sites.Urochloa mosambicensis produced significantly high leaf numbers (94 leaves per plant) on the rehabilitated site (Table 3) compared to all other species recorded on both sites. However, some plant species that were recorded on the natural veld also demonstrated significant high (p < 0.05) leaf numbers. These plant species include E. plana (59 leaves per plant), Forbs (56 leaves per plant), A. congesta (43 leaves per plant) and D. amplectens (41 leaves per plant). The number of leaves of other herbaceous species that were recorded on both sites were lower ranging between (5–40 leaves per plant) with A. adscensionis being the least leaf producing grass species on the rehabilitated site. This clearly showed that this grass has good adaptation strategies even under harsh environmental conditions within the rehabilitated site where soil nutrient composition is low.Grasses were noticeable for their relatively high tiller/leaf production with an increasing linear relationship in both sites. Among the recorded grass species on the natural veld C. dactylon and E. curvula leaf production increased linearly in response to increasing tiller numbers (r2 > 60) followed by E. lehmanniana (r2 > 70). D. amplectens and A. congesta were observed to produce a smaller number of leaves on the natural veld (Fig. 4). On the rehabilitated site, P. maximum and D. eriantha tiller leaf relationship was (r2 > 0.79). While M. repens and E. chloromelas were (r2 ≥ 0.50) (Fig. 5).Our results showed that A. congesta produced high tillers than all recorded grasses. The difference in phenological cycle following growing season of these grasses influence the growth and establishment of tillers and leaf production per plant (Livela et al. 2013). High relationship values among these grass species are possible because of the morphological characteristics expressed during growth and development28. However, this might also be influenced by many factors such as edaphic and soil contamination associated with coal dust generated during mining.The average dust particle size that can be carried away by wind is reported to be 0.106 mm and the concentration of heavy metals carried along are often determined by the quantity of the dust particles that have settled on the surface29. Therefore, when coal dust settles back to the soil and landing on the aerial parts of the plant, it changes the physiology of the plant leading to reduction of leaf size, leaf mass and rate of photosynthesis27. Therefore, leading to reduced carbon dioxide (CO2) exchange into and out of the stomatal opening, electron transport rate (ETR). Barre et al.30 reported that leaf length is an important part for the survival of the plant within a sward. However, when plants are exposed to coal dust leaf areas is affected and reduced. Therefore, reduction in photosynthesis rate due to reduced light occurrence on the photosynthetic tissues as a result of the shade caused by coal dust on plant leaves. Additionally, plants with hairy leaves are more susceptible to coal dust attachment as compared to those with glabrous leaves. This reduces tiller to leaf production as some plants are more suscuptable to toxic elements. Dust induced effects vary greatly amongst plant species31.Table 3 Mean tiller/leaf production per plant.Full size tableFig. 4Tiller and leaf production per plant for common, moderate and few veld grass species in the natural veld at KleinKopje coal mine.Full size imageFig. 5Tiller and leaf production per plant for common, moderate and few veld grass species in the rehabilitated site at KleinKopje coal mine.Full size imageHerbaceous biomass production harvested from Kleinkopje colliery Broadly, the total biomass production was higher on the rehabilitated site comparative to that which was recorded on the natural veld (Fig. 6). The results showed that U. mosambicensis, P. maximum and C. dactylon produced high biomass of 3.87, 4.12 and 4.35 kg DM h− 1 on the rehabilitated site, respectively. This was followed by significant different (p˃0.05) biomass production recorded for D. eriantha (3.32 kg DM h− 1, p < 0.001) on the rehabilitated site. Whereas on the natural veld C. dyctylon, H. contortus and E. curvula produced high biomass of 3.30, 3.62 and 3.93 kg DM h− 1. Amongst the biomass producing grass species on the natural site, E. lemaniana, produced high total dry biomass of 2.62 kg DM h− 1. Other presented plant species in Fig. 3.2.7 produced low biomass on both sites.In general, the results have shown that biomass production was generally higher in the rehabilitated site than natural site. Firstly, this could be ascribed to additional soil nutrients that are added to support germination, growth and development of planted species during mine rehabilitation. Secondly, the additional grass species such as D. eriantha and C. gayana were absent on the natural veld but present on the rehabilitated site produce high biomass production per hectare (Fig. 6). However, in most cases coal mine disturbed areas are characterized by low organic matter in the soil due to leaching of essential elements. Nevertheless, some plants have a homeostatic mechanism that controls the uptake of toxic ions and reduce potential damage in the plant cell. This phenomenon is known as compartmentalization of toxic ions and these plants are referred to as ion excluders32. Grass species such as Cynodon dactylon have been widely investigated and reported to be tolerant in contaminated soils by Wong et al.33 in China, by Wu et al.34 in Hong Kong and by Boshoff et al.35 in Belgium. Furthermore, Cynodon dactylon was considered as forage grasses with a potential to produce high biomass under contaminated soils. Biomass production differs in forage grasses due to different characteristics of tolerance and recovery pathways in heavy metal toxicity during the process36. In Southern Africa, the most popular forage grasses are Chloris gayana, Cynodon dactylon Panicum maximum Cynodon aethiopicus, Atriplex nummularia and Eragrostis curvula etc. and could be used in restoration programs of degraded lands37.Fig. 6Oven dried herbaceous biomass production harvested at Kleinkopje colliery.Full size imageConclusionThe results have shown that the most common species such as E. curvula in natural sites have long life span. E. curvula is a perennial grass species and this was amongst most desirable plant traits required for re-vegetation of mined areas. Furthermore, the use of veld condition indicators such as basal cover, ecological status, and species diversity provides guidelines for the procedures that need to be followed towards preservation of the present ecosystem services on both sites. This would also play an important role in the selection of performing grass species for reseeding in the rehabilitated site. More leaf producing grasses and high biomass production were recorded in the rehabilitated site and therefore, the built up of organic matter would be expected in the rehabilitated site as the time progresses. In addition, the highest basal cover percentage was recorded in the rehabilitated site; therefore, this site is less prone to detrimental effects that might arise as a result of soil erosion.Recommendations

    Due to sensitivity of the rehabilitated site, it is recommended that species such as C. dactylon, should be selected for restoration.

    This is based on its creeping growth form and the ability to quickly grow and cover large surface areas. Additionally, this grass could be planted on steep areas to reduce potential soil erosion.

    Soil chemical analysis within the rehabilitated site is essential in monitoring the built-up of salt ions which have been reported to cause serious adverse effects on seed germination.

    In this regard, more grasses need to be evaluated with the idea of identifying more adaptive species.

    These would then be used to improve rehabilitation seed pack mixes.

    Data availability

    Data used in this study are available on request.
    ReferencesMushia, M. N., Ramoelo, A. & Ayisi, K. K. The Impact of the Quality of Coal Mine Stockpile Soils on Sustainable Vegetation Growth and Productivity. Sustainability 8, 546 (2016).Article 
    ADS 

    Google Scholar 
    Truter, W.F., Rethman, N.F.G., Potgieter, C.E. & Kruger, R.A. Re-vegetation of cover soils and coal discard material ameliorated with class F-fly ash. In Proceedings of the 2009 World Coal Ash Conference, Lexington, KY, USA, 4 – 7 May 2000 (2009).Pone, J. D. N. et al. The spontaneous combustion of coal and its by-products in the Witbank and Sasolburg coalfields of South Africa. International Journal Coal Geology. 72, 124–140 (2007).Article 
    CAS 

    Google Scholar 
    Munnik, V. The Social and Environmental Consequences of Coal Mining in South Africa: A Case Study (2010).Gilbert, M. Mine site rehabilitation. Trop. Grassl. J. 34, 147–154 (2000).Loch, R. J. Effects of vegetation cover on runoff and erosion under simulated rain and overland flow on a rehabilitated site on the Meandu Minejj, Tarong, Queensland. Aust. J. Soil Res. 38, 299–312 (2000).Article 

    Google Scholar 
    Xia, H. P., Wang, Q. L. & Kong, G. H. Phyto-toxicity of garbage leachates and effectiveness of plant purification for them. Acta Phytoeco. Sinica 23, 289–301 (1999).
    Google Scholar 
    Ye, Z. H., Wong, J. W. C. & Wong, M. H. Vegetation response to lime and manure compost amendments on acid lead/zinc mine tailings: a greenhouse study. Ecol. Restor. 8, 289–295 (2000).Article 

    Google Scholar 
    Manssour, K.M.Y. Rangeland degradation assessment using remote sensing and vegetation species. PhD Dissertation, University of KwaZulu Natal (2013).McCarthy, T.S. & Pretorius, K. Coal mining on the highveld and its implication on future water quality in the vaal system. IMWA Mine Water Forum (2009).Kundu, N. K. & Ghose, M. K. Shelf Life of Stock-piled Topsoil of an Open Cast Coal Mine. Environmental Conservation Journal 24, 24–30 (2012).Article 

    Google Scholar 
    Favas, P. J. C., Pratas, J., Elisa Gomes, M. & Cala, V. Selective chemical extraction of heavy metals in tailings and soils contaminated by mining activities: Environmental implications. J. Geochem. Explor. 111, 160–171 (2011).Article 
    CAS 

    Google Scholar 
    Mentis, M. T. Diagnosis of the Rehabilitation of Opencast Coal Mines on the Highveld of South Africa. S. Afr. J. Sci. 95, 210–217 (1999).
    Google Scholar 
    Cudjoe, M. N. M., Kwarteng, E. V. S., Anning, E., Bodunrin, I. R. & Andam-Akorful, S. A. Application of Remote Sensing and Geographic Information System Technologies to Assess the Impact of Mining: A Case Study at Emalahleni. Appl. Sci. 14, 1739 (2024).Article 
    CAS 

    Google Scholar 
    Hardy M, Tainton N 2(007) Towards a technique for determining basal cover in tufted grasslands. Afr. J. Range Forage Sci. 10(2), 77 – 81.Shannon, C. E. & Wiener, W. The mathematical theory of communication (University of Illinois Press, 1963).
    Google Scholar 
    Pielou, E. C. The measurement of diversity in different types of biological collections. J. Theor. Biol. 13, 131–144 (1966).Article 
    ADS 

    Google Scholar 
    SAS. SAS 9.1.1 (2003) Qualification Tools User’s Guide. (SAS Institute Inc.).Rutherford, M. C. & Powrie, L. W. Impacts of heavy grazing on plant species richness: A comparison across rangeland biomes of South Africa. S. Afr. J. Bot. 87, 146–156 (2013).Article 

    Google Scholar 
    Platt, M. The Evaluation of Three Native Grass Species and a Tree Species as a Vegetation Option for Coal Mine Rehabilitation on the Mpumalanga Highveld of South Africa. Submitted to COALTECH Research Association Native Species Trial (2009).Limpitlaw, D., Aken, M., Lodewijks, H. & Viljoen, J. Post-mining rehabilitation, land use and pollution at collieries in South Africa. In Sustainable Development in the Life of Coal Mining in South Africa. Boksburg, South Africa, 13 July (2005).Oluwole, F. A. & Dube, S. Land degradation evaluation in a game reserve in Eastern Cape of South Africa: soil properties and vegetation cover. Scientific Research and Essay 3, 111–119 (2008).
    Google Scholar 
    Snyman, H. A. Soil seed bank evaluation and seedlings establishment along a degradation gradient in a semi-arid rangeland. African Journal of Range and Forest Science 21, 263–268 (2004).
    Google Scholar 
    Firn, J. African lovegrass in Australia: a valuable pasture species or embarrassing invader? Trop. Grassl. 43, 86–97 (2009).Truter, W. F., Rethman, N. F. G., Potgieter, C. E., Reynolds, K. A. & Kruger, R. A. Re-vegetation of cover soils and coal discard material ameliorated with class F fly ash. Biores. Technol. 21, 23–45 (2000).
    Google Scholar 
    Avila, M., Perez, G., Eshaimi, M., Mandi, L., Ouazzani, N., Brianso, J.L. & Valiente, M. Heavy metal contamination and mobility at the mine area of Draa Lasfar (Morocco). Open Environ. Pollut. Toxicol. J. 3, 2 – 12 (2012).Spencer, S. Effects of coal dust on species composition of mosses and lichens in an arid environment. J. Arid Environ. 49, 843–853 (2001).Article 
    ADS 

    Google Scholar 
    Lemaire-Chamle, M. et al. Changes in transcriptional profiles are associated with early fruit tissue specialization in tomato. J. Plant Physiol. 139, 750–769 (2005).Article 

    Google Scholar 
    Armbrust, D. V. Effect of Particulates (Dust) on Cotton Growth, Photosynthesis, and Respiration. Agron. J. 6, 85–439 (1986).
    Google Scholar 
    Barre, P., Turner, L. B. & Escobar-Gutiérrez, A. J. Leaf length variation in perennial forage grasses (Review). Agriculture 5, 682–696 (2015).Article 

    Google Scholar 
    Naidoo, Y. & Naidoo, G. Coal dust pollution effects on wetland tree species in Richards Bay, South Africa. Wetlands Ecol. Manage. 13, 509–515 (2005).Article 

    Google Scholar 
    McGrath, S. P., Zhao, F. J. & Lombi, E. Plant and rhizosphere processes involved in phytoremediation of metal-contaminated soils. Plant Soil J. 232, 207–214 (2001).Article 
    CAS 

    Google Scholar 
    Wong, C. S. C., Li, X. D., Zhang, G., Qi, S. H. & Peng, X. Z. Atmospheric deposition of heavy metals in the Pearl River Delta, China. Atmos. Environ. 6, 767–776 (2003).Article 
    ADS 

    Google Scholar 
    Wu, F. C., Tseng, R. L. & Juang, R. S. A review and experimental verification of using chitosan and its derivatives as adsorbents for selected heavy metals. J. Environ. Manage. 91, 798–806 (2010).Article 
    PubMed 
    CAS 

    Google Scholar 
    Boshoff, M., De Jonge, M., Scheifler, R. & Bervoets, L. Predicting As, Cd, Cu, Pb and Zn levels in grasses (Agrostis sp. and Poa sp.) and stinging nettle (Urtica dioica) applying soil plant transfer models. Sci. Total Environ. 493, 862–871 (2014).Article 
    ADS 
    PubMed 
    CAS 

    Google Scholar 
    Teng, Y. et al. Tolerance of Grasses to Heavy Metals and Microbial Functional Diversity in Soils Contaminated with Copper Mine Tai. Pedosphere 18, 363–370 (2008).Article 
    CAS 

    Google Scholar 
    Truter, W. F. et al. Southern African pasture and forage science entering the 21st century: past to present. African Journal of Range & Forage Science 32, 73–89 (2015).Article 

    Google Scholar 
    Download referencesAcknowledgementsThe authors are grateful to the Agricultural Research Council for financially supporting this study and a special gratitude to the University of Pretoria for availing its resources to the authors to successfully complete the study. A special thanks also goes to Francouis Muller, Mzamo Mndela, Aphelele Mangwane, Bafana Jerom Mncina, and Dolly Mthethwa for their technical support during the inception and data collection of the study.FundingThe author(s) declare that financial support was received for the research and/or publication of this article. This study was supported by Agricultural Research Council Range and Forage (ARC- National Department of Agriculture, grant number API012403000089).Author informationAuthors and AffiliationsDepartment of Plant and Soil Sciences, University of Pretoria, Pretoria, South AfricaM. Mangwane & I. C. MadakadzeAgricultural Research Council (ARC), Irene, Pretoria, South AfricaM. Mangwane & H. T. PuleNational Emergent Red Meat Producers’ Organisation (NERPO), Pretoria, South AfricaF. V. Nherera-ChokudaDepartment of Livestock & Pasture Science, University of Fort Hare, Dikeni, South AfricaM. MndelaInternational Livestock Research Institute (ILRI), Mount Pleasant, Harare, ZimbabweS. DubeDepartment of Agriculture and Animal Health, College of Agriculture & Environmental Sciences, University of South Africa, Florida Campus, Johannesburg, South AfricaT. J. TjeleleAuthorsM. MangwaneView author publicationsSearch author on:PubMed Google ScholarI. C. MadakadzeView author publicationsSearch author on:PubMed Google ScholarF. V. Nherera-ChokudaView author publicationsSearch author on:PubMed Google ScholarS. DubeView author publicationsSearch author on:PubMed Google ScholarM. MndelaView author publicationsSearch author on:PubMed Google ScholarT. J. TjeleleView author publicationsSearch author on:PubMed Google ScholarH. T. PuleView author publicationsSearch author on:PubMed Google ScholarContributionsAuthor Contributions: Conceptualization, M.M. (Mziwanda Mangwane), I.C.M., M.M. (Mthunzi Mndela), S.D. and F.V.N.-C.; methodology, formal analysis, investigation, and data curation, M.M. (Mziwanda Mangwane), I.C.M., M.M. (Mthunzi Mndela), F.V.N.-C., S.D. and H.T.P.; writing—original draft preparation, M.M. (Mziwanda Mangwane), I.C.M., M.M. (Mthunzi Mndela), S.D., F.V.N.-C. and T.J.T.; project administration, writing—review and editing, M.M. (Mziwanda Mangwane), F.V.N.-C., M.M. (Mthunzi Mndela), T.J.T. and H.T.P.; funding acquisition, T.J.T. and H.T.P. All authors have read and agreed to the published version of the manuscript.Corresponding authorCorrespondence to
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    Reprints and permissionsAbout this articleCite this articleMangwane, M., Madakadze, I.C., Nherera-Chokuda, F.V. et al. Assessment of herbaceous vegetation species composition growing around Kleinkopje opencast coal mine, Mpumalanga Province, South Africa.
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    Less-invasive age estimation using hair based on DNA methylation in brown bears

    AbstractInformation on chronological age is essential for exploring the life history, conservation, and management of wildlife. Recently, DNA methylation-based methods using blood or skin have been established as alternatives to the traditional tooth-based method in bear species. However, the collection of these tissues is limited to captured or dead individuals. In the present study, we established the first hair-based age estimation model based on DNA methylation levels in brown bears, aiming for future application to less-invasively obtained hair of wild individuals. We performed bisulfite pyrosequencing and measured the methylation levels of hair root DNA. The methylation levels of cytosine-phosphate-guanine sites adjacent to the genes VGF, KCNK12, and ELOVL2 were found to be correlated with age. The best age estimation model used three cytosine-phosphate-guanine sites adjacent to two genes, VGF and KCNK12, with a mean absolute error of 3.2 years and median absolute error of 2.2 years after leave-one-out cross-validation. Our method is innovative because of the simplicity of sampling and the lack of requirement to capture bears. If this method can be widely applied to hair samples obtained in the field, the age structure of wild populations can be understood, contributing to ecological research, conservation, and management of bear species.

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    Data availability

    The data obtained from pyrosequencing analyses are available in Dryad at: https://doi.org/10.5061/dryad.h44j0zpzc.
    ReferencesShimozuru, M. et al. Reproductive parameters and Cub survival of brown bears in the Rusha area of the Shiretoko Peninsula, Hokkaido, Japan. PLOS ONE. 12, e0176251. https://doi.org/10.1371/journal.pone.0176251 (2017).
    Google Scholar 
    Shimozuru, M. et al. Estimation of breeding population size using DNA-based pedigree reconstruction in brown bears. Ecol. Evol. 12 https://doi.org/10.1002/ece3.9246 (2022).Yagi, G. et al. Non-invasive age Estimation based on faecal DNA using methylation-sensitive high-resolution melting for Indo-Pacific bottlenose dolphins. Mol. Ecol. Resour. 24 https://doi.org/10.1111/1755-0998.13906 (2024).Thomas, D. C. Metachromatic staining of dental cementum for mammalian age determination. J. Wildl. Manag. 41, 207–210. https://doi.org/10.2307/3800596 (1977).
    Google Scholar 
    Laws, R. M. A new method of age determination for mammals. Nature 169, 972–973. https://doi.org/10.1038/169972b0 (1952).
    Google Scholar 
    Lundervold, M., Langvatn, R. & Milner-Gulland, E. J. A comparison of age Estimation methods for the Saiga antelope Saiga Tatarica. Wildl. Biology. 9, 219–227. https://doi.org/10.2981/wlb.2003.054 (2003).
    Google Scholar 
    Zhang, Y., Bi, J., Ning, Y. & Feng, J. Methodology advances in vertebrate age Estimation. Animals 14, 343. https://doi.org/10.3390/ani14020343 (2024).
    Google Scholar 
    Kimura, D. K., Mandapat, R. R. & Oxford, S. L. Method, validity, and variability in the age determination of Yellowtail rockfish (Sebastes flavidus), using otoliths. J. Fish. Res. Board Can. 36, 377–383. https://doi.org/10.1139/f79-057 (1979).
    Google Scholar 
    Cattet, M. et al. Can concentrations of steroid hormones in brown bear hair reveal age class? Conserv. Physiol. 6 (2018). https://doi.org/10.1093/conphys/coy001Wiedower, E. E. et al. Fecal near infrared spectroscopy to discriminate physiological status in giant pandas. PLoS ONE. 7, e38908. https://doi.org/10.1371/journal.pone.0038908 (2012).
    Google Scholar 
    Horvath, S. DNA methylation age of human tissues and cell types. Genome Biol. 14, R115. https://doi.org/10.1186/gb-2013-14-10-r115 (2013).
    Google Scholar 
    Bogdanović, O. & Veenstra, G. J. C. DNA methylation and methyl-CpG binding proteins: developmental requirements and function. Chromosoma 118, 549–565. https://doi.org/10.1007/s00412-009-0221-9 (2009).
    Google Scholar 
    Jones, P. A. Functions of DNA methylation: islands, start sites, gene bodies and beyond. Nat. Rev. Genet. 13, 484–492. https://doi.org/10.1038/nrg3230 (2012).
    Google Scholar 
    Moore, L. D., Le, T. & Fan, G. D. N. A. Methylation and its basic function. Neuropsychopharmacology 38, 23–38. https://doi.org/10.1038/npp.2012.112 (2013).
    Google Scholar 
    Jones, M. J., Goodman, S. J. & Kobor, M. S. DNA methylation and healthy human aging. Aging Cell. 14, 924–932. https://doi.org/10.1111/acel.12349 (2015).
    Google Scholar 
    Bocklandt, S. et al. Epigenetic predictor of age. PLoS ONE. 6, e14821. https://doi.org/10.1371/journal.pone.0014821 (2011).
    Google Scholar 
    Koch, C. M. & Wagner, W. Epigenetic-aging-signature to determine age in different tissues. Aging 3, 1018–1027. https://doi.org/10.18632/aging.100395 (2011).
    Google Scholar 
    Le Clercq, L. S., Kotzé, A., Grobler, J. P. & Dalton, D. L. Biological clocks as age Estimation markers in animals: a systematic review and meta-analysis. Biol. Rev. 98, 1972–2011. https://doi.org/10.1111/brv.12992 (2023).
    Google Scholar 
    Riekkola, L. et al. Application of a multi-disciplinary approach to reveal population structure and Southern ocean feeding grounds of humpback whales. Ecol. Ind. 89, 455–465. https://doi.org/10.1016/j.ecolind.2018.02.030 (2018).
    Google Scholar 
    Slieker, R. C., Relton, C. L., Gaunt, T. R., Slagboom, P. E. & Heijmans, B. T. Age-related DNA methylation changes are tissue-specific with ELOVL2 promoter methylation as exception. Epigenetics Chromatin. 11 https://doi.org/10.1186/s13072-018-0191-3 (2018).Lowe, R. et al. Ageing-associated DNA methylation dynamics are a molecular readout of lifespan variation among mammalian species. Genome Biol. 19 https://doi.org/10.1186/s13059-018-1397-1 (2018).Klughammer, J. et al. Comparative analysis of genome-scale, base-resolution DNA methylation profiles across 580 animal species. Nat. Commun. 14, 232. https://doi.org/10.1038/s41467-022-34828-y (2023).
    Google Scholar 
    Haghani, A. et al. DNA methylation networks underlying mammalian traits. Science 381, eabq5693. https://doi.org/10.1126/science.abq5693 (2023).
    Google Scholar 
    Polanowski, A. M., Robbins, J., Chandler, D. & Jarman, S. N. Epigenetic Estimation of age in humpback whales. Mol. Ecol. Resour. 14, 976–987. https://doi.org/10.1111/1755-0998.12247 (2014).
    Google Scholar 
    Petkovich, D. A. et al. Using DNA methylation profiling to evaluate biological age and longevity interventions. Cell Metabol. 25, 954–960e956. https://doi.org/10.1016/j.cmet.2017.03.016 (2017).
    Google Scholar 
    Ito, H., Udono, T., Hirata, S. & Inoue-Murayama, M. Estimation of chimpanzee age based on DNA methylation. Sci. Rep. 8, 9998. https://doi.org/10.1038/s41598-018-28318-9 (2018).
    Google Scholar 
    De Paoli-Iseppi, R. et al. Age Estimation in a long-lived seabird (Ardenna tenuirostris) using DNA methylation-based biomarkers. Mol. Ecol. Resour. 19, 411–425. https://doi.org/10.1111/1755-0998.12981 (2019).
    Google Scholar 
    Arai, K., Qi, H. & Inoue-Murayama, M. Age Estimation of captive Asian elephants (Elephas maximus) based on DNA methylation: an exploratory analysis using methylation-sensitive high-resolution melting (MS-HRM). PLOS ONE. 18, e0294994. https://doi.org/10.1371/journal.pone.0294994 (2023).
    Google Scholar 
    Thompson, M. J., Vonholdt, B., Horvath, S. & Pellegrini, M. An epigenetic aging clock for dogs and wolves. Aging 9, 1055–1068. https://doi.org/10.18632/aging.101211 (2017).
    Google Scholar 
    Prado, N. A. et al. Epigenetic clock and methylation studies in elephants. Aging Cell. 20 https://doi.org/10.1111/acel.13414 (2021).Mayne, B. et al. Age prediction of green turtles with an epigenetic clock. Mol. Ecol. Resour. 22, 2275–2284. https://doi.org/10.1111/1755-0998.13621 (2022).
    Google Scholar 
    Qi, H., Lim, Q. L., Kinoshita, K., Nakajima, N. & Inoue-Murayama, M. A cost-effective blood DNA methylation-based age Estimation method in domestic cats, Tsushima Leopard cats (Prionailurus bengalensis euptilurus) and Panthera species, using targeted bisulphite sequencing and machine learning model. Mol. Ecol. Resour. https://doi.org/10.1111/1755-0998.13928 (2024).
    Google Scholar 
    Shimozuru, M. et al. Epigenetic clock in bears: A simple Cost-Effective blood DNA Methylation-Based age Estimation method applicable to multiple bear species. Ecol. Evol. 15 https://doi.org/10.1002/ece3.71424 (2025).Stubbs, T. M. et al. Multi-tissue DNA methylation age predictor in mouse. Genome Biol. 18, 1–14. https://doi.org/10.1186/s13059-017-1203-5 (2017).
    Google Scholar 
    Arneson, A. et al. A mammalian methylation array for profiling methylation levels at conserved sequences. Nat. Commun. 13 https://doi.org/10.1038/s41467-022-28355-z (2022).Lu, A. T. et al. Universal DNA methylation age across mammalian tissues. Nat. Aging. 3, 1144–1166. https://doi.org/10.1038/s43587-023-00462-6 (2023).
    Google Scholar 
    Han, Y. et al. Epigenetic age-predictor for mice based on three CpG sites. eLife 7, e37462 (2018). https://doi.org/10.7554/eLife.37462IUCN. The IUCN Red List of Threatened Species. Version 2025-1. https://www.iucnredlist.org (accessed 26 Apr 2025).Can, Ö. E., D’Cruze, N., Garshelis, D. L., Beecham, J. & Macdonald, D. W. Resolving human-bear conflict: A global survey of countries, experts, and key factors. Conserv. Lett. 7, 501–513. https://doi.org/10.1111/conl.12117 (2014).
    Google Scholar 
    Hatter, I. W., Mowat, G. & Mclellan, B. N. Statistical population reconstruction to evaluate Grizzly bear trends in British Columbia. Can. Ursus. 29, 1. https://doi.org/10.2192/ursus-d-18-00001.1 (2018).
    Google Scholar 
    Mano, T., Matsuda, H., Natsume, S. & Tsuruga, H. Harvest-based demographic Estimation of a brown bear population on the Oshima Peninsula, Hokkaido. Ursus 2025 https://doi.org/10.2192/ursus-d-23-00016.1 (2024).Mundy, K. R. D. & Fuller, W. A. Age determination in the Grizzly bear. J. Wildl. Manag. 28, 863–866 (1964).
    Google Scholar 
    Marks, S., Erickson, A. W. & A. & Age determination in the black bear. J. Wildl. Manag. 30, 389–410 (1966).
    Google Scholar 
    Stoneberg, R., Jonkel, C. J. & P. & Age determination of black bears by cementum layers. J. Wildl. Manag. 30, 411–414 (1966).
    Google Scholar 
    Mclaughlin, C. R. et al. Precision and accuracy of estimating age of Maine black bears by cementum annuli. Bears: their Biology Manage. 8, 415–419 (1990).
    Google Scholar 
    Costello, C. M. et al. Reliability of the cementum annuli technique for estimating age of black bears in new Mexico. Wildl. Soc. Bull. 32, 169–176. https://doi.org/10.2193/0091-7648(2004)32[169:rotcat]2.0.co;2 (2004).
    Google Scholar 
    Nakamura, S. et al. Age Estimation based on blood DNA methylation levels in brown bears. Mol. Ecol. Resour. 23, 1211–1225. https://doi.org/10.1111/1755-0998.13788 (2023).
    Google Scholar 
    Czajka, N., Northrup, J. M., Jones, M. J. & Shafer, A. B. A. Epigenetic clocks, sex markers and age-class diagnostics in three harvested large mammals. Mol. Ecol. Resour. 24 https://doi.org/10.1111/1755-0998.13956 (2024).Hao, T. et al. Predicting human age by detecting DNA methylation status in hair. ELECTROPHORESIS 42, 1255–1261. https://doi.org/10.1002/elps.202000349 (2021).
    Google Scholar 
    Hayes, B. J. et al. An epigenetic aging clock for cattle using portable sequencing technology. Front. Genet. 12 https://doi.org/10.3389/fgene.2021.760450 (2021).Hanski, E. et al. Epigenetic age Estimation of wild mice using faecal samples. Mol. Ecol. 33 https://doi.org/10.1111/mec.17330 (2024).Waits, L. P. & Paetkau, D. Noninvasive genetic sampling tools for wildlife biologists: A review of applications and recommendations for accurate data collection. J. Wildl. Manag. 69, 1419–1433 (2005).
    Google Scholar 
    Jimbo, M. et al. Diet selection and asocial learning: Natal habitat influence on lifelong foraging strategies in solitary large mammals. Ecosphere 13 https://doi.org/10.1002/ecs2.4105 (2022).Mowat, G. & Strobeck, C. Estimating population size of Grizzly bears using hair Capture, DNA Profiling, and Mark-Recapture analysis. J. Wildl. Manage. 64, 183–193. https://doi.org/10.2307/3802989 (2000).
    Google Scholar 
    Sato, Y. et al. Evaluation of the effectiveness of scented wooden posts for DNA hair snagging of brown bears. Mammal Study. 45, 213. https://doi.org/10.3106/ms2018-0045 (2020).
    Google Scholar 
    Hannum, G. et al. Genome-wide methylation profiles reveal quantitative views of human aging rates. Mol. Cell. 49, 359–367. https://doi.org/10.1016/j.molcel.2012.10.016 (2013).
    Google Scholar 
    Tsubota, T., Takahashi, Y. & Kanagawa, H. Changes in serum progesterone levels and growth of fetuses in Hokkaido brown bears. Bears: their Biology Manage. 7, 355–358. https://doi.org/10.2307/3872643 (1987).
    Google Scholar 
    Tsubota, T., Maeda, N. & Kanagawa, H. Parturition and postnatal development in the captive Hokkaido brown bear Ursus Arctos Yesoensis. J. Mammalogical Soc. Japan. 19, 75–82. https://doi.org/10.11238/jmammsocjapan.19.75 (1994).
    Google Scholar 
    Friebe, A. et al. Factors affecting date of Implantation, Parturition, and Den entry estimated from activity and body temperature in Free-Ranging brown bears. PLoS ONE. 9, e101410. https://doi.org/10.1371/journal.pone.0101410 (2014).
    Google Scholar 
    Jimbo, M. et al. Hair growth in brown bears and its application to ecological studies on wild bears. Mammal Study. 45 https://doi.org/10.3106/ms2020-0021 (2020).Day, K. et al. Differential DNA methylation with age displays both common and dynamic features across human tissues that are influenced by CpG landscape. Genome Biol. 14, R102. https://doi.org/10.1186/gb-2013-14-9-r102 (2013).
    Google Scholar 
    Florath, I., Butterbach, K., Muller, H., Bewerunge-Hudler, M. & Brenner, H. Cross-sectional and longitudinal changes in DNA methylation with age: an epigenome-wide analysis revealing over 60 novel age-associated CpG sites. Hum. Mol. Genet. 23, 1186–1201. https://doi.org/10.1093/hmg/ddt531 (2014).
    Google Scholar 
    Bekaert, B., Kamalandua, A., Zapico, S. C., Van De Voorde, W. & Decorte, R. Improved age determination of blood and teeth samples using a selected set of DNA methylation markers. Epigenetics 10, 922–930. https://doi.org/10.1080/15592294.2015.1080413 (2015).
    Google Scholar 
    Lowe, R. et al. Ageing-associated DNA methylation dynamics are a molecular readout of lifespan variation among mammalian species. Genome Biol. 19, 22. https://doi.org/10.1186/s13059-018-1397-1 (2018).
    Google Scholar 
    Ito, G., Yoshimura, K. & Momoi, Y. Analysis of DNA methylation of potential age-related methylation sites in canine peripheral blood leukocytes. J. Vet. Med. Sci. 79, 745–750. https://doi.org/10.1292/jvms.16-0341 (2017).
    Google Scholar 
    Yamazaki, J. et al. Obese status is associated with accelerated DNA methylation change in peripheral blood of senior dogs. Res. Vet. Sci. 139, 193–199. https://doi.org/10.1016/j.rvsc.2021.07.024 (2021).
    Google Scholar 
    Qi, H. et al. Age Estimation using methylation-sensitive high-resolution melting (MS-HRM) in both healthy felines and those with chronic kidney disease. Sci. Rep. 11 https://doi.org/10.1038/s41598-021-99424-4 (2021).Core Team, R. R: A Language and environment for statistical computing. (R Foundation for Statistical Computing, 2022). https://www.R-project.org/Friedman, J., Hastie, T. & Tibshirani, R. Regularization paths for generalized linear models via coordinate descent. J. Stat. Softw. 33, 1–22 (2010).
    Google Scholar 
    Tay, J. K., Narasimhan, B. & Hastie, T. Elastic net regularization paths for all generalized linear models. J. Stat. Softw. 106 https://doi.org/10.18637/jss.v106.i01 (2023).Meyer, D., Dimitriadou, E., Hornik, K., Weingessel, A. & Leisch, F. e1071: Misc Functions of the Department of Statistics, Probability Theory Group (Formerly: E1071), TU Wien. R package version 1.7–13 (2023). https://CRAN.R-project.org/package=e1071Raj, K. et al. Epigenetic clock and methylation studies in cats. GeroScience 43, 2363–2378. https://doi.org/10.1007/s11357-021-00445-8 (2021).
    Google Scholar 
    Xu, C. et al. A novel strategy for forensic age prediction by DNA methylation and support vector regression model. Sci. Rep. 5, 17788. https://doi.org/10.1038/srep17788 (2015).
    Google Scholar 
    Bartoń, K. MuMIn: Multi-Model Inference. R package version 1.47.5 (2023). https://CRAN.R-project.org/package=MuMInBates, D., Mächler, M., Bolker, B. & Walker, S. Fitting linear Mixed-Effects models using lme4. J. Stat. Softw. 67, 1–48. https://doi.org/10.18637/jss.v067.i01 (2015).
    Google Scholar 
    Interagency Grizzly Bear Committee. Grizzly Bear Compendium (The National Wildlife Federation, 1987).de Magalhães, J. P. et al. Human ageing genomic resources: updates on key databases in ageing research. Nucleic Acids Res. 52, D900–d908. https://doi.org/10.1093/nar/gkad927 (2024).
    Google Scholar 
    Fokias, K., Dierckx, L., Van de Voorde, W. & Bekaert, B. Age determination through DNA methylation patterns in fingernails and toenails. Forensic Sci. International: Genet. 64, 102846. https://doi.org/10.1016/j.fsigen.2023.102846 (2023).
    Google Scholar 
    Fokias, K., Dierckx, L., Van de Voorde, W. & Bekaert, B. Improving the age Estimation model for toenails. Forensic Sci. International: Genet. 66, 102911. https://doi.org/10.1016/j.fsigen.2023.102911 (2023).
    Google Scholar 
    Ciucci, P. et al. Estimating abundance of the remnant apennine brown bear population using multiple noninvasive genetic data sources. J. Mammal. 96, 206–220. https://doi.org/10.1093/jmammal/gyu029 (2015).
    Google Scholar 
    El Khoury, L. Y. et al. Systematic underestimation of the epigenetic clock and age acceleration in older subjects. Genome Biol. 20 https://doi.org/10.1186/s13059-019-1810-4 (2019).Snir, S., Farrell, C. & Pellegrini, M. Human epigenetic ageing is logarithmic with time across the entire lifespan. Epigenetics 14, 912–926. https://doi.org/10.1080/15592294.2019.1623634 (2019).
    Google Scholar 
    Gentilini, D. et al. Role of epigenetics in human aging and longevity: genome-wide DNA methylation profile in centenarians and centenarians’ offspring. AGE 35, 1961–1973. https://doi.org/10.1007/s11357-012-9463-1 (2013).
    Google Scholar 
    Horvath, S. et al. Decreased epigenetic age of PBMCs from Italian semi-supercentenarians and their offspring. Aging 7, 1159–1170. https://doi.org/10.18632/aging.100861 (2015).
    Google Scholar 
    Teschendorff, A. E. & Horvath, S. Epigenetic ageing clocks: statistical methods and emerging computational challenges. Nat. Rev. Genet. 26, 350–368. https://doi.org/10.1038/s41576-024-00807-w (2025).
    Google Scholar 
    Zbieć-Piekarska, R. et al. Development of a forensically useful age prediction method based on DNA methylation analysis. Forensic Sci. International: Genet. 17, 173–179. https://doi.org/10.1016/j.fsigen.2015.05.001 (2015). 
    Google Scholar 
    Hamano, Y. et al. Forensic age prediction for dead or living samples by use of methylation-sensitive high resolution melting. Leg. Med. 21, 5–10. https://doi.org/10.1016/j.legalmed.2016.05.001 (2016).
    Google Scholar 
    Spiers, H. et al. Age-associated changes in DNA methylation across multiple tissues in an inbred mouse model. Mech. Ageing Dev. 154, 20–23. https://doi.org/10.1016/j.mad.2016.02.001 (2016).
    Google Scholar 
    Johnson, A. A., Torosin, N. S., Shokhirev, M. N. & Cuellar, T. L. A set of common buccal CpGs that predict epigenetic age and associate with lifespan-regulating genes. iScience 25, 105304. https://doi.org/10.1016/j.isci.2022.105304 (2022).
    Google Scholar 
    Li, X. et al. Lipid metabolism dysfunction induced by age-dependent DNA methylation accelerates aging. Signal. Transduct. Target. Therapy. 7 https://doi.org/10.1038/s41392-022-00964-6 (2022).Li, J. et al. Progressive alopecia reveals decreasing stem cell activation probability during aging of mice with epidermal deletion of DNA methyltransferase 1. J. Invest. Dermatology. 132, 2681–2690. https://doi.org/10.1038/jid.2012.206 (2012).
    Google Scholar 
    Wang, S. et al. Integrative analysis of methylome and transcriptome reveals the regulatory mechanisms of hair follicle morphogenesis in cashmere goat. Cells 9, 969. https://doi.org/10.3390/cells9040969 (2020).
    Google Scholar 
    Shirane, Y. et al. Sex-biased dispersal and inbreeding avoidance in Hokkaido brown bears. J. Mammal. 100, 1317–1326. https://doi.org/10.1093/jmammal/gyz097 (2019).
    Google Scholar 
    Shih, C. C., Sung-Lin, W. U., Hwang, M. H. & Ling-Ling, L. Evaluation on the effects of ageing factor, sampling and preservation methods on Asiatic black bear (Ursus thibetanus) noninvasive DNA amplification. Taiwania 62, 363 (2017).
    Google Scholar 
    Download referencesAcknowledgementsWe would like to express our sincere thanks to all staff at Noboribetsu Bear Park for providing bear hair and sample information. We wish to thank Hatsusaburo Ose and all the members of the Shiretoko Fishery Productive Association for their kind support. We are deeply grateful to all the members of the Shiretoko Nature Foundation for their generous support. We also thank everyone involved in sample collection. We would like to express our sincere gratitude to Dr. Miho Inoue-Murayama from Wildlife Research Center, Kyoto University for her support and helpful advice. Finally, we thank Editage (www.editage.jp) for English language editing.FundingThis study was supported by funding from the Japan Society for the Promotion of Science (JSPS) (https://www.jsps.go.jp/english/e-grants/index.html) KAKENHI grant numbers JP19K06833, JP23K05312, JP25H01002, JP24KJ0304, and JP22K14910, Grant for Basic Science Research Projects from The Sumitomo Foundation (grant no. 200561), a grants-in-aid of The Inui Memorial Trust for Research on Animal Science, Japan, JST SPRING (grant no. JPMJSP2119), and World-leading Innovative and Smart Education (WISE) Program from the Ministry of Education, Culture, Sports, Science and Technology (MEXT) (grant no. 1801).Author informationAuthors and AffiliationsFaculty of Veterinary Medicine, Hokkaido University, Kita-18, Nishi-9, Kita-ku, Sapporo, 060-0818, Hokkaido, JapanShiori Nakamura, Jumpei Yamazaki, Mina Jimbo, Yojiro Yanagawa, Toshio Tsubota & Michito ShimozuruOne Health Research Center, Hokkaido University, Kita-18, Nishi-9, Kita-ku, Sapporo, 060-0818, Hokkaido, JapanJumpei Yamazaki & Michito ShimozuruAzabu University, 1-17-71 Fuchinobe, Chuo-ku, Sagamihara, 252-5201, Kanagawa, JapanNaoya MatsumotoNoboribetsu Bear Park, 224 Noboribetsuonsencho, Noboribetsu, 059-0551, Hokkaido, JapanKyogo Hagino & Hideyuki SakamotoShiretoko Nature Foundation, 531 Iwaobetsu, Shari, 099-4356, Hokkaido, JapanMasami Yamanaka & Masanao NakanishiHokkaido Research Organization, Sapporo, 060-0819, Hokkaido, JapanMina JimboKyoto City Zoo, Okazaki Hosshojicho, Kyoto, 606-8333, JapanHideyuki ItoAuthorsShiori NakamuraView author publicationsSearch author on:PubMed Google ScholarJumpei YamazakiView author publicationsSearch author on:PubMed Google ScholarNaoya MatsumotoView author publicationsSearch author on:PubMed Google ScholarKyogo HaginoView author publicationsSearch author on:PubMed Google ScholarHideyuki SakamotoView author publicationsSearch author on:PubMed Google ScholarMasami YamanakaView author publicationsSearch author on:PubMed Google ScholarMasanao NakanishiView author publicationsSearch author on:PubMed Google ScholarMina JimboView author publicationsSearch author on:PubMed Google ScholarYojiro YanagawaView author publicationsSearch author on:PubMed Google ScholarHideyuki ItoView author publicationsSearch author on:PubMed Google ScholarToshio TsubotaView author publicationsSearch author on:PubMed Google ScholarMichito ShimozuruView author publicationsSearch author on:PubMed Google ScholarContributionsS.N. designed the study, performed laboratory work, and constructed each age estimation model. S.N., N.M., K.H., H.S., M.Y., M.N., M.J., Y.Y., and M.S. were involved in sample collection. J.Y. and H.I. supported with the technical aspects of the experiment. S.N. and M.S. wrote the article with inputs from J.Y., H.I., and T.T. All authors reviewed the article.Corresponding authorCorrespondence to
    Michito Shimozuru.Ethics declarations

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    The authors declare no competing interests.

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    All procedures involved in sample collection from animals were conducted in accordance with the Guidelines for Animal Care and Use, Hokkaido University, and were approved by the Animal Care and Use Committee of the Graduate School of Veterinary Medicine, Hokkaido University (Permit Number: 1152, 15009, 17005, 18–0083, 19–0021, 20–0146, and 23 − 0014). In addition, all methods were carried out in compliance with ARRIVE guidelines.

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    Reprints and permissionsAbout this articleCite this articleNakamura, S., Yamazaki, J., Matsumoto, N. et al. Less-invasive age estimation using hair based on DNA methylation in brown bears.
    Sci Rep (2025). https://doi.org/10.1038/s41598-025-27455-2Download citationReceived: 04 July 2025Accepted: 04 November 2025Published: 29 December 2025DOI: https://doi.org/10.1038/s41598-025-27455-2Share this articleAnyone you share the following link with will be able to read this content:Get shareable linkSorry, a shareable link is not currently available for this article.Copy shareable link to clipboard
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    Vertebrate scavenging patterns during extreme winter conditions in North Dakota

    AbstractThe decomposition process for above-the-ground human remains is usually divided into five stages: fresh, bloat, active decay, advanced decay, and skeletal remains. The chronology of these stages is influenced by numerous factors, including environmental temperature, microbial proliferation, necrophagous insect species activity, and vertebrate scavenging. The current research aimed to investigate the vertebrate scavenger’s activity during North Dakota winters, using pig carcasses as human analogues. The carcasses were placed outdoors in December 2022, on Mekinock Field Station, Grand Forks County ND, and monitored until the snow melted in May 2023. The monitoring took place daily via trail cameras with motion sensors, while the temperature was recorded hourly using temperature data loggers. The temperature fluctuated between − 32℃ and 30.3℃ along the six experimental months, with a snow cover of up to 130 cm. While the carcasses were covered by snow most of the time, the main vertebrate scavengers observed, represented by coyote (Canis latrans Say, 1823) and the red fox (Vulpes vulpes (Linnaeus, 1758)), dug up corridors to reach and consume the tissues, mostly after sunset. Field based monitoring studies are of tremendous help to understand the factors that induce variation in the chronology of the decomposition stages, invertebrate and vertebrate diversity, and colonization patterns. The current data will be of use to the forensic science field, as it relates to death investigations and search and recovery efforts, by providing an inventory of the primary vertebrate scavengers in this far North location.

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    IntroductionThe decomposition process begins in the gastrointestinal tract1, and, during warmer months, is primarily driven by microbial and insect activity. Vertebrate scavengers also play a significant role during the decomposition process, particularly during the winter months when insects are absent2. Although scavengers have been studied in ecological contexts since the 70’s3,4, most research focuses on food webs rather than forensic implications5,6,7. Moreover, predation and scavenging are discussed separately, though, most predators can scavenge in various circumstances, with more energy being transferred per scavenger link when compared to predation5,8. The decomposition process is influenced by multiple biotic and abiotic factors, and vertebrate scavenging will introduce additional variability by accelerating tissue removal and scattering of remains. These actions will alter the overall decomposition pattern, having significant forensic implications.Competition for decomposed remains occurs not only among vertebrate scavengers but also between scavengers, microbes, and insects during warmer months. During higher temperatures, microbial proliferation can make the entire carrion undesirable to most scavengers2, while chemical cues are the main factor of carrion detection and attraction, acting as attractants or repellents depending on concentration9,10,11. While during the warmer months the interspecific competition between vertebrate scavengers, microbes, and insects is more intense, it decreases in intensity during the winter months, especially at sub-zero temperatures when insect activity ceases. Although these interspecific interactions are investigated and discussed in the ecology arena, analyzing them from a forensic perspective will provide a better understanding for interpreting outdoor death scenes involving scavenging.To understand scavengers behavior, experimental studies have used different animal carcasses such as chickens or pigs, across various geographical locations12,13 and environments, including farmlands and undisturbed natural habitats14. As previously demonstrated, scavengers efficacy varies by season and habitat3, and one key factor influencing the frequency of scavenging is carrion availability. The environment and carrion accessibility further affect scavenging activity2. Often, scavenging is observed on animals that died from accidents, malnutrition, or diseases, rather than from a predator kill, with ungulates representing the main source of carrion in many terrestrial environments5. Irrespective of the cause of death, carrion constitutes an ephemeral food resource in a given environment and attracts a range of obligate and facultative scavengers2,5,7.During winter periods with high snow depths, scavengers may increase their reliance on carrion as a food source15. Although most available carcasses are subject to scavenging, quantifying the amount of carrion consumed before decomposition remains challenging. Consequently, systematic monitoring using trail cameras can provide crucial information about the scavenging behavior, which is highly relevant for death investigations. Since a large proportion of terrestrial vertebrates can be facultative scavengers, the presence and scavenging debut and activity must be considered when investigating an outdoor death scene, as animal scavenging is a frequent occurrence in forensic cases.Most of the studies focusing on scavenging from the forensic science perspective are reported from North America16,17,18,19,20,21,22, however, the topic is under-researched23. Most of the studies are from Texas, Colorado, Tennessee, and North Carolina, with no records for scavenging data for North Dakota winter months24. Scavenging of human remains affects the microbiome and insect activity, hindering forensic investigations by removing the soft tissues, disarticulating the skeletal remains and even remove personal effects25,26. These scavenging activities can produce changes that could lead to the obliteration of trauma sites and misinterpretation of events related to the death of the individual. For example, in a previous study from southern Texas, animal claw marks were misinterpreted as defense wounds27. Moreover, monitoring studies related to vertebrate scavenging are extremely valuable in gathering data on scavengers species, temperature, season, and environment28,29.According to the latest data30, 2,300 Americans are reported missing every day, while 4,400 unidentified bodies are recovered every year from both urban and rural outdoor areas. Given the high number of cases that end in fatalities, it is important to have data on all factors that might influence the decomposition of human remains, including vertebrate scavengers. Furthermore, understanding scavenging in a forensic context will also provide valuable data for postmortem interval (PMI) estimation, as in several cases scavenging caused errors in the estimation of the time elapsed since death, by accelerating the decomposition process via removal of soft tissues31,32, or delaying the process by removing or consuming the insects33.Most of the time, detection dogs are used to locate human remains34. Data on scavengers and scavenging behavior could enhance the search strategies used by human detection dogs’ teams, by improving and developing updated search and recovery protocols.In light of these implications, and considering that vertebrate scavenging data can provide valuable information regarding the PMI estimation through predictable scavenging patterns17, the current research aimed to address two questions: (1) What are the primary scavengers of outdoor remains in Grand Forks County, ND? (2) When are these scavengers active during the extreme winter season?ResultsEnvironmental dataDuring the investigated months the temperature recorded a minimum of -32℃ and a maximum of 30.3℃ (Fig. 1). During the first four experimental months, December 2022 – March 2023, the temperature records were sub-zero, with most values in the range of -10℃ and − 20℃. December and February were characterized by very low temperatures, dropping to -32℃ (Fig. 1), and significant snowfall (Fig. 2).Fig. 1Environmental temperature variation from December 2022 to May 2023.Full size imageFig. 2Snow precipitation records from December 2022 to May 2023.Full size imageDuring early December the snow precipitation was relatively low (7–10 cm), after which a gradual increase can be observed, with a high snow precipitation recorded in mid-December (38.1 cm) (Fig. 2). After mid-December the snow precipitation was relatively constant, reaching a maximum record of 48 cm. The last snow was recorded between April 21 and 24, decreasing from 10 to 3 cm (Fig. 2). Total precipitation during the experimental time frame was higher than the historical average of the region35, with a snow cover of up to 130 cm in certain field areas.Vertebrate scavenging patternThe pig carcasses were placed in the field on December 5, 2022, at 13:50, and were covered by snow for most of the experimental time. Scavenging was observed starting on December 31, 2022, with the main scavengers being the coyote (Canis latrans Say, 1823), the red fox (Vulpes vulpes (Linnaeus, 1758)), and the skunk (Mephitis mephitis (Schreber, 1776)) (Fig. 3). Scavenging primarily occurred after sunset, with both canid scavengers often digging corridors to reach and consume the carcasses. The presence of these three species was expected, as they are commonly found in the Northern United States. A raccoon (Procyon lotor (Linnaeus, 1758)) and several blue jays (Cyanocitta cristata (Linnaeus, 1758)) were recorded as infrequent visitors (Supplementary video).Fig. 3Main scavengers recorded during the experimental period: (A) Canis latrans Say, 1823; (B) Vulpes vulpes (Linnaeus, 1758); (C) Mephitis mephitis (Schreber, 1776).Full size imageThe removal of pig remains, including tissue and bones, was hindered by freezing conditions, resulting in all scavenging activities occurring at the initial carcass deployment area. Due to snow coverage, it was not always possible to note which side of the carcass was scavenged first. The winter conditions slowed decomposition, keeping the remains fresh and edible for a longer period.From the 1800 photographs and 1200 videos analyzed, the red fox triggered the camera 50 times out of 150 for the first pig carcass, 315 times out of 768 for the second pig carcass, and 139 times out of 368 for the third pig carcass. These 504 camera triggers covered 13 distinct scavenging days for the first pig carcass (Fig. 4), 21 days for the second pig carcass, and 18 days for the third pig carcass, with scavenging hotspots clustered around the first and last week of January and the first week of February (Supplementary Figs. 1, 2 and 3). During this period, air temperatures ranged between − 20 and − 30 °C.Fig. 4Occurrence of vertebrate scavengers at pig carcass 1 from December 2022 to May 2023.Full size imageThe coyote triggered the camera 33 times for the first pig carcass, accounting for 9 distinct days. For the second pig carcass, 419 camera triggers accounted for 22 days (Fig. 5), and for the third pig carcass, 183 camera triggers corresponded to 18 distinct days. Coyote scavenging behavior was nocturnal, with a single late afternoon appearance.Fig. 5Occurrence of vertebrate scavengers at pig carcass 2 from December 2022 to May 2023.Full size imageSkunk activity towards the end of the scavenging period coincided with the end of winter (Fig. 6). This late presence is explained by the fact that skunks enter a torpor state from November until March, significantly reducing their activity to conserve energy and survive the harsh North Dakota winter conditions.Fig. 6Occurrence of vertebrate scavengers at pig carcass 3 from December 2022 to May 2023.Full size imageThe scavengers primarily exhibited solitary behaviors, with the exception of coyotes, which scavenged in pairs during March and April. The most intense scavenging activity was recorded between 20:00 and 06:00. Scavenging occurred intermittently among different species. The red fox was the sole scavenger for the first pig carcass until late March (Fig. 6). In contrast, the second pig carcass was predominantly scavenged by coyotes. The third carcass experienced alternating scavenging between the red fox and coyotes, with distinct times recorded for each species. The chi-square test indicated a significant association between the pig carcasses and scavenger species (χ² = 56.67, df = 4, p < 0.001). Furthermore, the correspondence analysis confirmed the visual observations for the first pig carcass association with the red fox scavenging, followed by the second pig carcass balanced distribution with slight association with the coyote scavenging, and the third pig carcass with an intermediate distribution for both fox and coyote scavenging.DiscussionThe current study aimed to provide key information related to vertebrate scavenging activities during North Dakota winter months, to be of use for death investigations in outdoor locations in Grand Forks County and other areas with similar environments.The taphonomic impact of scavenging changes with season and habitat36. A few studies targeted the monitoring of scavengers’ activities in the wetlands37 and sunflower fields38 of North and South Dakota, primarily during mid and late summer. These early studies evaluated the scavenging activity on blackbirds, which are considered pests, causing significant crop damage in the Northern Great Plains. The research focused on the ecological impact of poisoned blackbirds, particularly on vertebrate scavengers. Both studies focused on the importance of understanding non-target animal mortalities that can be associated with agricultural pesticide applications. Further, the aim of one of these earlier studies was to assess the search efficiency and carcass removal by scavengers in cattail marshes37. It is noteworthy that the experiment took place from August to September 1987. The researchers analyzed the removal time data, defined as the time between carrion placement and complete removal37. The results suggested that the water depth influenced the removal times, however, the scavenger’s activity was substantial in the North Dakota’s cattail marshes, including the scavenger species involved. Further, in the second study38, the researchers focused on song bird carrion removal times from sunflower and corn fields in late summer and early spring. The rates of carcasses persistence varied between fields, emphasizing that the removal times are dependent on the season and habitat. This later study38 focused on areas from Grand Forks and Nelson counties (ND), and Miner and Brookings counties (SD). Coyotes, red foxes, and raccoons fed on the birds’ carcasses in spring and fall. Scavenging was more pronounced in the sunflower fields compared to harvested cornfields. The same scavenger species were recorded in the current study; however, the raccoon was not recorded as a dominant species, having a sporadic presence. Moreover, the skunk followed in the scavenging succession, after the fox and coyote, while the persistent snow cover and subzero temperatures significantly slowed decomposition.As in the case of necrophagous insect species colonization, the vertebrate scavenging activity is influenced by biotic and abiotic factors14. When considering carrion removal in addition to inter- and intra-competition, and biotic and abiotic factors, the secondary scavengers’ densities within a specific habitat must be investigated and considered. In a recent study, the authors investigated the carrion removal rates by scavengers in a farmed region in Indiana, USA14. The study recorded raccoons and opossums as the main scavengers. The authors emphasized that the carrion competition is different in agricultural landscapes as compared to other habitats, and that the temperature influenced the carcass removal times, decreasing with an increase in temperature. This aspect is reflected by the current study, as the pig carcasses were not foraged immediately after field placement, but two weeks later, which corresponded with a drop in temperatures and an increase in snow accumulation. This could suggest that scavenging occurs when other food alternatives are no longer available. Additionally, habitat fragmentation must be investigated as well, as this will also affect scavenging patterns14. Noteworthy, the current research recorded only three main species responsible for the carcass forging, in agreement with similar studies showing that these species thrive in fragmented, human-altered landscapes39,40,41. In contrast to warmer environments, where carcass relocation or dispersal is common, the freezing conditions recorded during this experiment prevented the scavengers from removing large portions of tissues or bones, resulting in all feeding occurring at the original deposition site.Obligate scavengers are very rare, when compared to facultative scavengers2. Vulture species are considered obligatory scavengers, as they are completely adapted to scavenge, and they can outcompete other vertebrate scavengers. In the current experiment, no vultures were recorded. Even though Turkey vultures can be found in North Dakota, including Grand Forks County, these species migrate south for the winter. It is important to consider that, if present, vultures can detect and consume carrion faster than other scavengers5. Moreover, vultures will not forage the carrion during nighttime, when other mammalian scavengers will be present5.Several previous studies focused on carrion removal14,37,38 showed that this activity occurs in different percentages across different habitats. In the current experiment, no carrion removal was noted, which can be attributed to the carrion mass and frozen conditions. Smaller carrions are easier to remove from the field and carry away. During death investigations of bodies found in an open field, animal paths and dens should be searched for scattered remains, as well as personal belongings. As previously reported42, foxes can scatter remains 10 to 45 m from the deposition site.In another earlier study in Northern Virgina USA19, experiments carried out on exposed and buried carcasses identified red foxes, opossums, skunks, raccoons, crows, and turkey vultures as scavengers. It is worth mentioning that scavengers avoided feeding on carcasses colonized by insects. Nevertheless, the scavenging behavior of foxes was nocturnal, as reported by the current results. A study performed in Ontario, Canada43 reported the coyote, red fox, fisher, pine marten, bald eagle, turkey vulture, and corvids as scavengers, and found that the season impacted the scavengers presence and the decomposition process, with a fast tissue consumption during the summer months.Coyotes and foxes are more likely to scavenge exposed remains in a rural area like Grand Forks County. However, snow depth and temperature must be considered, as scavenging was predominantly observed during the lowest temperatures. As previously reported42,44, the coyote will focus on scavenging the remains where they are deposited, a behavior recorded in the current study as well. Moreover, both studies noted that the coyote presence has the potential to disrupt fox scavenging activities. Nevertheless, other previous study43 reported that the coyote was responsible for the dispersion of the remains, thus, these different behaviors must be considered when comparing data from different locations. The coyote scavenging behavior can expose soft tissues, otherwise inaccessible to the fox. The coyote was among the scavengers identified from a recent experiment in Yukon, Canada45, however, the study aimed to test the difference between carnivore and herbivore carcasses scavenging. The research revealed that the carnivore carcasses were scavenged later by the secondary scavengers to avoid parasites transmission and diseases.Coyotes have been reported to interfere with the activity of other scavengers, like the bobcats46, and are considered a source of risk for other small scavengers. Coyotes will kill fox species and sometimes raccoons46. In the current experiment, the coyote presence might have reduced the feeding times of the red fox. For example, for the second pig carcass the fox activity declined from 310 camera triggers before coyote arrival to just 5 afterward. However, for the first and third pig carcasses, the fox activity remained stable after coyotes began scavenging. This can suggest that the influence of the coyote presence on fox scavenging behavior could be context dependent, influenced by carcass location or resource availability.Skunk scavenging activities on human remains were previously reported from studies carried out in Colorado, USA, where they were observed feeding on tissues from the limbs47. While skunk scavenging is not often reported47, this species is very common in North America48,49, being less active during the winter months in the northern regions of the USA. In the current study, skunks were observed nocturnally, consistent to prior reports47. However, when they were observed near the remains, most of the soft tissue had already been consumed by coyotes and foxes.The limitations of the current study are represented by the use of only one camera for each carcass and the usage of unclothed carcasses. Future studies on scavenging behavior should be region-specific to develop a local database for outdoor death scene investigations. Moreover, studies should consider the role of clothing, including the fiber type, as it can act as a barrier for scavengers, causing a reduction of the decomposition rate50.Materials and methodsExperimental designThree pig (Sus domesticus Erxleben, 1777) carcasses, each weighing 55 kg, were used as human analogues during the current winter field based scavenging study. The carcasses were placed in the field without protective cages and monitored from December 2022 until May 2023. This approach was based on a previous winter experiment (2021–2022)51, where carcasses were protected by cages, and scavengers’ presence was observed from March throughout the end of April. The current experiment aimed to observe the vertebrate scavengers’ activity without such barriers. The pig carcasses were euthanized by captive blitz bolt at a local pig farm and then transported to the experimental location within 45 min and placed in proximity to the cages used in the previous experiment, 20 m apart from one another and more than 30 m from the secondary road.The study was conducted at Mekinock Field Station (47°57’11.5″N 97°25’42.3″W) (Fig. 7), Grand Forks County ND, with approval from the Field Station Committee. The Institutional Animal Care and Use Committee (IACUC) protocol approval was not required, as the pigs were euthanized at the pig farm prior to the study. Vertebrate scavenger activity was monitored passively via trail cameras, with no direct animal interaction. All methods were carried out in accordance with relevant institutional, national, and international guidelines and regulations.Fig. 7Mekinock Field Station, North Dakota, December 2022.Full size imageField monitoringFor monitoring the decomposition and scavengers’ activity, trail cameras (TACTACAM Reveal) were placed at a height of 1.5 m, and 3 m away from each pig carcass, at a 30° angle. The trail cameras have a detection range of 29 m, with a trigger speed of less than ½ second, and minimal motion blur. Photographs and videos were triggered by motion sensors, with a Passive Infra-Red delay interval of 15 s, in addition to the preset ones taken at 12-hour intervals. Over the six-month period, 1800 photographs and 1200 videos were recorded and analyzed. The scavenging data, including dates and times, has been compiled into an Excel file. Cameras captured color photographs during the day and black and white photographs during the night.Both photographs and videos were manually analyzed to record the presence times of scavengers. The data, including the specific days and hours when presence was detected, were documented and recorded in Microsoft Excel.A chi-square test of independence was performed to assess the association between the pig carcasses and the scavenger species. Observed frequencies were compared to expected frequencies under the assumption of independence. Additionally, a correspondence analysis was conducted to visualize and further explore the relationship between the pig carcasses and the scavenger species. The analysis produced a two-dimensional solution summarizing the variation in the contingency table.The hourly temperature was recorded with temperature data loggers (thermo button 22 L, Plug&Track, USA) placed 1.5 m above ground level, under each trail camera. Daily relative humidity and precipitation records were obtained from Grand Forks International Airport Station, located 10 km from the research site35. The winter conditions at Mekinock Field Station are typical for the northern Great Plains environment, with consistent extreme low temperatures and strong winds, which can frequently exceed 60 km/h35.

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    All data generated or analyzed during this study are included in this published article.
    ReferencesForensic Microbiology. (Wiley, Chichester, West Sussex, UK, (2017).DeVault, T. L., Rhodes, O. E. Jr. & Shivik, J. A. Scavenging by vertebrates: Behavioral, ecological, and evolutionary perspectives on an important energy transfer pathway in terrestrial ecosystems. Oikos 102, 225–234 (2003).Article 
    ADS 

    Google Scholar 
    Putman, R. J. Patterns of carbon dioxide evolution from decaying carrion decomposition of small mammal carrion in temperate systems. Oikos 31, 47 (1978).Article 
    ADS 

    Google Scholar 
    Putman, R. J. Carrion and Dung: the Decomposition of Animal Wastes (Arnold, 1983).Moleón, M., Sánchez-Zapata, J. A., Selva, N. & Donázar, J. A. Owen‐Smith, N. Inter‐specific interactions linking predation and scavenging in terrestrial vertebrate assemblages. Biol. Rev. 89, 1042–1054 (2014).Article 
    PubMed 

    Google Scholar 
    Olson, Z. H. et al. Foraging risk in scavenging ecology: A study of scavenger behavior and patterns of bacterial growth. Basic Appl. Ecol. 61, 10–19 (2022).Article 

    Google Scholar 
    Pereira, L. M., Owen-Smith, N. & Moleón, M. Facultative predation and scavenging by mammalian carnivores: seasonal, regional and intra-guild comparisons. Mammal Rev. 44, 44–55 (2014).Article 

    Google Scholar 
    Wilson, E. E. & Wolkovich, E. M. Scavenging: how carnivores and carrion structure communities. Trends Ecol. Evol. 26, 129–135 (2011).Article 
    PubMed 

    Google Scholar 
    DeVault, T. L. & Rhodes, O. E. Identification of vertebrate scavengers of small mammal carcasses in a forested landscape. Acta Theriol. 47, 185–192 (2002).Article 

    Google Scholar 
    DeVault, T. L. & Krochmal, A. R. Scavenging by snakes: an examination of the literature. Herpetologica 58, 429–436 (2002).Article 

    Google Scholar 
    Wetmore, A. The role of olfaction in food location by the Turkey Vulture (Cathartes aura) Kenneth E. Stager. Auk 82, 661–662 (1965).Article 

    Google Scholar 
    Houston, D. C. Competition for food between Neotropical vultures in forest. Ibis 130, 402–417 (1988).Article 

    Google Scholar 
    Magoun, A. J. Summer Scavenging Activity in Northeastern Alaska (University of Alaska, 1976).DeVault, T. L., Olson, Z. H., Beasley, J. C. & Rhodes, O. E. Mesopredators dominate competition for carrion in an agricultural landscape. Basic Appl. Ecol. 12, 268–274 (2011).Article 

    Google Scholar 
    Huggard, D. J. Effect of snow depth on predation and scavenging by Gray wolves. J. Wildl. Manag. 57, 382 (1993).Article 

    Google Scholar 
    Spradley, M. K., Hamilton, M. D. & Giordano, A. Spatial patterning of Vulture scavenged human remains. Forensic Sci. Int. 219, 57–63 (2012).Article 
    PubMed 

    Google Scholar 
    Kjorlien, Y. P., Beattie, O. B. & Peterson, A. E. Scavenging activity can produce predictable patterns in surface skeletal remains scattering: observations and comments from two experiments. Forensic Sci. Int. 188, 103–106 (2009).Article 
    PubMed 

    Google Scholar 
    Reeves, N. M. Taphonomic effects of Vulture Scavenging*. J. Forensic Sci. 54, 523–528 (2009).Article 
    PubMed 

    Google Scholar 
    Morton, R. J. & Lord, W. D. Taphonomy of Child-Sized remains: A study of scattering and scavenging in Virginia, USA*. J. Forensic Sci. 51, 475–479 (2006).Article 
    PubMed 

    Google Scholar 
    Haglund, W. Dogs and coyotes: postmortem involvement with human remains. in Forensic Taphonomy (eds (eds Haglund, W. & Sorg, M.) (CRC, doi:https://doi.org/10.1201/9781439821923.sec3. (1996).Haglund, W. D., Reay, D. T. & Swindler, D. R. Tooth mark artifacts and survival of bones in animal scavenged human skeletons. J. Forensic Sci. 33, 985–997 (1988).Article 
    PubMed 

    Google Scholar 
    Haglund, W. D., Reay, D. T. & Swindler, D. R. Canid scavenging/disarticulation sequence of human remains in the Pacific Northwest. J. Forensic Sci. 34, 587–606 (1989).Article 
    PubMed 

    Google Scholar 
    Spies, M. J., Finaughty, D. A. & Gibbon, V. E. Portion size matters: carrion ecology lessons for medicolegal death investigations—A study in cape Town, South Africa. J. Forensic Sci. 69, 28–39 (2024).Article 
    PubMed 

    Google Scholar 
    Indra, L., Lösch, S., Errickson, D. & Finaughty, D. Forensic experiments on animal scavenging: A systematic literature review on what we have and what we need. Forensic Sci. Int. 353, 111862 (2023).Article 
    PubMed 

    Google Scholar 
    Mann, R. W., Bass, W. M. & Meadows, L. Time since death and decomposition of the human body: variables and observations in case and experimental field studies. J. Forensic Sci. 35, 103–111 (1990).Article 
    PubMed 

    Google Scholar 
    Asamura, H., Takayanagi, K., Ota, M., Kobayashi, K. & Fukushima, H. Unusual characteristic patterns of postmortem injuries. J. Forensic Sci. 49, 592–594 (2004).Article 
    PubMed 

    Google Scholar 
    Rippley, A., Larison, N. C., Moss, K. E., Kelly, J. D. & Bytheway, J. A. Scavenging behavior of Lynx Rufus on human remains during the winter months of Southeast Texas. J. Forensic Sci. 57, 699–705 (2012).Article 
    PubMed 

    Google Scholar 
    O’Brien, R. C., Forbes, S. L., Meyer, J. & Dadour I. R. A preliminary investigation into the scavenging activity on pig carcasses in Western Australia. Forensic Sci. Med. Pathol. 3, 194–199 (2007).Article 
    PubMed 

    Google Scholar 
    Steadman, D. W. et al. Differential scavenging among Pig, Rabbit, and human subjects. J. Forensic Sci. 63, 1684–1691 (2018).Article 
    PubMed 

    Google Scholar 
    National Missing and Unidentified Persons System (NamUs). NamUs Statistics – October 2024. (2024). https://namus.nij.ojp.gov/sites/g/files/xyckuh336/files/media/document/namus-stats-all-october-2024.pdfByard, R. W., James, R. A. & Gilbert, J. D. Diagnostic problems associated with cadaveric trauma from animal activity. Am. J. Forensic Med. Pathol. 23, 238–244 (2002).Article 
    PubMed 

    Google Scholar 
    Suckling, J. K., Spradley, M. K. & Godde, K. A longitudinal study on human outdoor decomposition in central Texas. J. Forensic Sci. 61, 19–25 (2016).Article 
    PubMed 

    Google Scholar 
    Bass, W. M. Outdoor decomposition rates in Tennessee. in Forensic Taphonomy: the Postmortem Fait of Human Remains (eds (eds Sorg, M. H. & Haglund, W. D.) 181–186 (CRC: Boca Raton, (1997).
    Google Scholar 
    Dargan, R. & Forbes, S. L. Cadaver-detection dogs: A review of their capabilities and the volatile organic compound profile of their associated training aids. WIREs Forensic Sci. 3, e1409 (2021).Article 

    Google Scholar 
    National Weather Service. Weather.gov. U.S. National Oceanic and Atmospheric Administration.https://www.weather.govSéguin, K. et al. The taphonomic impact of scavenger guilds in Southern Quebec during summer and fall in two distinct habitats. J. Forensic Sci. 67, 460–470 (2022).Article 
    PubMed 

    Google Scholar 
    Linz, G. M., Davis, R. M. Jr., Otis, D. L. & Avery, M. L. Estimating survival of bird carcasses in cattail marshes. Wildl. Soc. Bull. 19, 195–199 (1991).
    Google Scholar 
    Linz, G. M., Bergman, D. L. & Bleier, W. J. Estimating survival of song bird carcasses in crops and woodlots. Prairie Naturalist. 29, 7–13 (1997).
    Google Scholar 
    Oehler, J. D. & Litvaitis, J. A. The role of Spatial scale in Understanding responses of medium-sized carnivores to forest fragmentation. Can. J. Zool. 74, 2070–2079 (1996).Article 

    Google Scholar 
    Prange, S. & Gehrt, S. D. Changes in mesopredator-community structure in response to urbanization. Can. J. Zool. 82, 1804–1817 (2004).Article 

    Google Scholar 
    Ritchie, E. G. & Johnson, C. N. Predator interactions, mesopredator release and biodiversity conservation. Ecol. Lett. 12, 982–998 (2009).Article 
    PubMed 

    Google Scholar 
    Young, A., Márquez-Grant, N., Stillman, R., Smith, M. J. & Korstjens, A. H. An investigation of red Fox (Vulpes vulpes) and Eurasian Badger (Meles meles) Scavenging, Scattering, and removal of deer remains: forensic implications and applications. J. For. Sci. 60, (2015).Forbes, S. L., Samson, C. & Watson, C. J. Seasonal impact of scavenger guilds as taphonomic agents in central and Northern Ontario, Canada. J. Forensic Sci. 67, 2203–2217 (2022).Article 
    PubMed 

    Google Scholar 
    Selva, N. & Fortuna, M. A. The nested structure of a scavenger community. Proc. R Soc. B. 274, 1101–1108 (2007).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Peers, M. J. L. et al. Vertebrate scavenging dynamics differ between carnivore and herbivore carcasses in the Northern boreal forest. Ecosphere 12, e03691 (2021).Article 

    Google Scholar 
    Jensen, A. J., Saldo, E. A., Chapman, Z. G., Butfiloski, J. W. & Jachowski, D. S. Risk from a top predator and forest structure influence scavenging by smaller carnivores. Ecosphere 14, e4596 (2023).Article 

    Google Scholar 
    Smith, A. Patterns of striped skunk scavenging on human remains. J. Forensic Sci. 66, 1420–1426 (2021).Article 
    PubMed 

    Google Scholar 
    Wade-Smith, J. & Verts, B. J. Mephitis mephitis. Mammalian Species. 1 https://doi.org/10.2307/3503883 (1982).Dragoo, J. W. Nutrition and behavior of striped skunks. Veterinary Clin. North. America: Exotic Anim. Pract. 12, 313–326 (2009).
    Google Scholar 
    Spies, M. J., Finaughty, D. A., Friedling, L. J. & Gibbon, V. E. The effect of clothing on decomposition and vertebrate scavengers in cooler months of the temperate Southwestern Cape, South Africa. Forensic Sci. Int. 309, 110197 (2020).Article 
    PubMed 

    Google Scholar 
    Iancu, L., Bonicelli, A. & Procopio, N. Decomposition in an extreme cold environment and associated microbiome—prediction model implications for the postmortem interval Estimation. Front. Microbiol. 15, 1392716 (2024).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Download referencesAcknowledgementsWe would like to thank the University of North Dakota Field Station Committee for approving the experiment implementation.FundingThis research received financial support from the Start-up research funding (LI), College of Arts and Sciences, University of North Dakota.Author informationAuthors and AffiliationsForensic Science Program, University of North Dakota, 221 Centennial Drive, Grand Forks, ND, 58202, USALavinia Iancu & Nicolette RasAuthorsLavinia IancuView author publicationsSearch author on:PubMed Google ScholarNicolette RasView author publicationsSearch author on:PubMed Google ScholarContributionsLI : Conceptualization; Data curation; Formal analysis; Funding acquisition; Investigation; Methodology; Project administration; Resources; Supervision; Validation; Visualization; Roles/Writing – original draft; Writing – review & editing. **NR** : Data curation; Formal analysis. Corresponding authorCorrespondence to
    Lavinia Iancu.Ethics declarations

    Competing interests
    The authors declare no competing interests.

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    Reprints and permissionsAbout this articleCite this articleIancu, L., Ras, N. Vertebrate scavenging patterns during extreme winter conditions in North Dakota.
    Sci Rep 15, 44905 (2025). https://doi.org/10.1038/s41598-025-28834-5Download citationReceived: 22 July 2025Accepted: 12 November 2025Published: 29 December 2025Version of record: 29 December 2025DOI: https://doi.org/10.1038/s41598-025-28834-5Share this articleAnyone you share the following link with will be able to read this content:Get shareable linkSorry, a shareable link is not currently available for this article.Copy shareable link to clipboard
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    KeywordsDecompositionWinterExtreme environmentVertebrate scavengingNorth dakota

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    Mycorrhizal colonization of dryland tree establishment depends on soil microbial cooperation

    AbstractMycorrhizal fungi serve as fundamental agents in forest establishment and progression, underpinning critical ecosystem functions through symbiotic root associations. Drylands, which cover nearly half of Earth’s land, have limited forest establishment, and factors influencing mycorrhization in these stressful environments remain unclear. Here, we integrate large-scale field surveys along aridity gradients with greenhouse experiments and over 33,000 microscopic mycorrhizal observations, revealing that aridity significantly enhances mycorrhization. Mycorrhizal fungi undergo niche modification, whereby facilitative microbial interactions promote mycorrhization under aridity stress. We identify a core synthetic microbial community linked to mycorrhization and provide mechanistic evidence that this community facilitates mycorrhization through physical attachment to fungal hyphae and by alleviating soil metabolite inhibition that otherwise suppresses mycorrhization under arid conditions. In this work, our findings highlight the role of microbial interkingdom interactions in driving tree mycorrhizal colonization in arid regions, offering critical insights for guiding tree planting and restoration efforts in drylands.

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    Data availability

    All raw plant RNA-seq data, amplicon sequencing data generated in this study have been deposited in the Sequence Read Archive (http://www.ncbi.nlm.nih.gov/sra). Raw amplicon sequences derived from the field survey are publicly available under NCBI BioProject number PRJNA1311795 (16S rRNA gene) and PRJNA1311818 (ITS). All 16S rRNA sequence data of bacterial strains are publicly available under NCBI BioProject number PRJNA1312253. The RNA-seq data for Tuber are publicly available under NCBI BioProject number PRJNA1312988. Source data are available in the Figshare database (https://doi.org/10.6084/m9.figshare.30775883). Source data are provided with this paper.
    Code availability

    Codes used in this study are available in the Figshare database (https://doi.org/10.6084/m9.figshare.30685235).
    ReferencesRen, S. et al. Storage potential of soil functional carbon fractions in the World’s largest plantations. Adv. Sci. 12, e04995 (2025).
    Google Scholar 
    Wang, Y. J. et al. Land availability and policy commitments limit global climate mitigation from forestation. Science 389, 931–934 (2025).
    Google Scholar 
    Garrett, L., Lévite, H., Besacier, C., Alekseeva, N. & Duchelle, M. The key role of forest and landscape restoration in climate action. (FAO, 2022).Reynolds, J. F. et al. Global desertification: building a science for dryland development. Science 316, 847–851 (2007).
    Google Scholar 
    Ma, L. L. et al. Planted forests in China have higher drought risk than natural forests. Glob. Change Biol. 31, e70055 (2025).
    Google Scholar 
    Koppa, A. et al. Dryland self-expansion enabled by land-atmosphere feedbacks. Science 385, 967–972 (2024).
    Google Scholar 
    Gu, L., Schumacher, D. L., Wang, H. M., Yin, J. & Fischer, E. M. Land-atmosphere feedbacks drive dryland drought and expansion under climate warming. Innovation 6, 100863 (2025).
    Google Scholar 
    Cosme, M. Mycorrhizas drive the evolution of plant adaptation to drought. Commun. Biol. 6, 346 (2023).
    Google Scholar 
    Sachsenmaier, L. et al. Forest growth resistance and resilience to the 2018–2020 drought depend on tree diversity and mycorrhizal type. J. Ecol. 112, 1787–1803 (2024).
    Google Scholar 
    Poudel, M. et al. The role of plant-associated bacteria, fungi, and viruses in drought stress mitigation. Front. Microbiol. 12, 743512 (2021).
    Google Scholar 
    Hu, M. Y. et al. Plant functional traits affect biomass responses to global change: a meta-analysis. J. Ecol. 113, 2046–2065 (2025).
    Google Scholar 
    Van Nuland, M. E. et al. Global hotspots of mycorrhizal fungal richness are poorly protected. Nature 645, 414–422 (2025).
    Google Scholar 
    Jiang, F. et al. Mycorrhizal symbioses and tree diversity in global forest communities. Sci. Adv. 11, eadt5743 (2025).
    Google Scholar 
    Berdugo, M. et al. Global ecosystem thresholds driven by aridity. Science 367, 787–790 (2020).
    Google Scholar 
    Stuart, E. K. et al. Acquisition of host-derived carbon in biomass of the ectomycorrhizal fungus Pisolithus microcarpus is correlated to fungal carbon demand and plant defences. FEMS Microbiol. Ecol. 99, fiad037 (2023).
    Google Scholar 
    Canarini, A. et al. Soil fungi remain active and invest in storage compounds during drought, independent of future climate conditions. Nat. Commun. 15, 10410 (2024).
    Google Scholar 
    Xie, L. L. et al. Variations in ectomycorrhizal exploration types parallel seedling fine root traits of two temperate tree species under extreme drought and contrasting solar radiation treatments. Plant Cell Environ. 47, 5053–5066 (2024).
    Google Scholar 
    Guarnizo, A. L., Navarro-Ródenas, A., Calvo-Polanco, M., Marqués-Gálvez, J. E. & Morte, A. A mycorrhizal helper bacterium alleviates drought stress in mycorrhizal Helianthemum almeriense plants by regulating water relations and plant hormones. Environ. Exp. Bot. 207, 105228 (2023).
    Google Scholar 
    Berrios, L. et al. Positive interactions between mycorrhizal fungi and bacteria are widespread and benefit plant growth. Curr. Biol. 33, 2878–2887.e2874 (2023).
    Google Scholar 
    Navarro-Rodenas, A., Berna, L. M., Lozano-Carrillo, C., Andrino, A. & Morte, A. Beneficial native bacteria improve survival and mycorrhization of desert truffle mycorrhizal plants in nursery conditions. Mycorrhiza 26, 769–779 (2016).
    Google Scholar 
    Jorgensen, K., Clemmensen, K. E., Wallander, H. & Lindahl, B. D. Do ectomycorrhizal exploration types reflect mycelial foraging strategies? New Phytol. 237, 576–584 (2023).
    Google Scholar 
    Durán, P. et al. Microbial interkingdom interactions in roots promote Arabidopsis survival. Cell 175, 973–983.e914 (2018).
    Google Scholar 
    Bertness, M. D. & Callaway, R. Positive interactions in communities. Trends Ecol. Evol. 9, 191–193 (1994).
    Google Scholar 
    Hammarlund, S. P. & Harcombe, W. R. Refining the stress gradient hypothesis in a microbial community. Proc. Natl. Acad. Sci. USA 116, 15760–15762 (2019).
    Google Scholar 
    Hernandez, D. J., David, A. S., Menges, E. S., Searcy, C. A. & Afkhami, M. E. Environmental stress destabilizes microbial networks. ISME J. 15, 1722–1734 (2021).
    Google Scholar 
    Gao, C. et al. Co-occurrence networks reveal more complexity than community composition in resistance and resilience of microbial communities. Nat. Commun. 13, 3867 (2022).
    Google Scholar 
    Berrios, L., Ansell, T. B., Dahlberg, P. D. & Peay, K. G. Standardizing experimental approaches to investigate interactions between bacteria and ectomycorrhizal fungi. FEMS Microbiol. Rev. 49, fuae035 (2025).
    Google Scholar 
    Hao, Z. G. et al. Thresholds in aridity and soil carbon-to-nitrogen ratio govern the accumulation of soil microbial residues. Commun. Earth Environ. 2, 236 (2021).
    Google Scholar 
    Miyamoto, Y., Maximov, T. C., Kononov, A. & Sugimoto, A. Soil propagule banks of ectomycorrhizal fungi associated with Larix cajanderi above the treeline in the Siberian Arctic. Mycoscience 63, 142–148 (2022).
    Google Scholar 
    Shemesh, H., Bruns, T. D., Peay, K. G., Kennedy, P. G. & Nguyen, N. H. Changing balance between dormancy and mortality determines the trajectory of ectomycorrhizal fungal spore longevity over a 15-yr burial experiment. New Phytol. 238, 11–15 (2023).
    Google Scholar 
    Williams, A., Sinanaj, B. & Hoysted, G. A. Plant-microbe interactions through a lens: tales from the mycorrhizosphere. Ann. Bot. 133, 399–412 (2024).
    Google Scholar 
    Hoek, T. A. et al. Resource availability modulates the cooperative and competitive nature of a microbial cross-feeding mutualism. PLoS Biol. 14, e1002540 (2016).
    Google Scholar 
    Piccardi, P., Vessman, B. & Mitri, S. Toxicity drives facilitation between 4 bacterial species. Proc. Natl. Acad. Sci. USA 116, 15979–15984 (2019).
    Google Scholar 
    Lofgren, L. et al. Suillus: an emerging model for the study of ectomycorrhizal ecology and evolution. New Phytol. 242, 1448–1475 (2024).
    Google Scholar 
    Voller, F., Ardanuy, A., Taylor, A. F. S. & Johnson, D. Maintenance of host specialisation gradients in ectomycorrhizal symbionts. New Phytol. 242, 1426–1435 (2024).
    Google Scholar 
    Frey-Klett, P., Garbaye, J. & Tarkka, M. The mycorrhiza helper bacteria revisited. New Phytol. 176, 22–36 (2007).
    Google Scholar 
    Tedersoo, L., Bahram, M. & Zobel, M. How mycorrhizal associations drive plant population and community biology. Science 367, eaba1223 (2020).
    Google Scholar 
    Peng, L. et al. A facultative ectomycorrhizal association is triggered by organic nitrogen. Curr. Biol. 32, 5235–5249.e5237 (2022).
    Google Scholar 
    Rasheed, Z. et al. Production of some secondary metabolites of antibiotic nature from mycorrhizal helper bacteria (MHB) associated with conifers. Pol. J. Environ. Stud. 33, 4325–4334 (2024).
    Google Scholar 
    Singh, B. K. & Walker, A. Microbial degradation of organophosphorus compounds. FEMS Microbiol. Rev. 30, 428–471 (2006).
    Google Scholar 
    Yang, F. et al. Genome-wide analysis reveals genetic potential for aromatic compounds biodegradation of Sphingopyxis. Biomed. Res. Int. 2020, 5849123 (2020).
    Google Scholar 
    Zhao, Q. et al. Comparative genomic analysis of 26 Sphingomonas and Sphingobium strains: dissemination of bioremediation capabilities, biodegradation potential and horizontal gene transfer. Sci. Total Environ. 609, 1238–1247 (2017).
    Google Scholar 
    Tong, S. et al. Characterization of a fungal competition factor: production of a conidial cell-wall-associated antifungal peptide. PLoS Pathog. 16, e1008518 (2020).
    Google Scholar 
    Zhong, Z. X. et al. Differential gene expression profiling analysis in Pleurotus ostreatus during interspecific antagonistic interactions with Dichomitus squalens and Trametes versicolor. Fungal Biol. 121, 1025–1036 (2017).
    Google Scholar 
    Mochizuki, K. et al. The ASCT/SCS cycle fuels mitochondrial ATP and acetate production in Trypanosoma brucei. Biochim. Biophys. Acta Bioenerg. 1861, 148283 (2020).
    Google Scholar 
    Labbe, J. L., Weston, D. J., Dunkirk, N., Pelletier, D. A. & Tuskan, G. A. Newly identified helper bacteria stimulate ectomycorrhizal formation in Populus. Front. Plant Sci. 5, 579 (2014).
    Google Scholar 
    Al Farraj, D. A., Alkufeidy, R. M., Alkubaisi, N. A. & Alshammari, M. K. Polynuclear aromatic anthracene biodegradation by psychrophilic Sphingomonas sp., cultivated with Tween-80. Chemosphere 263, 128115 (2021).
    Google Scholar 
    Park, J. et al. Sulfur metabolism-mediated fungal glutathione biosynthesis is essential for oxidative stress resistance and pathogenicity in the plant pathogenic fungus Fusarium graminearum. mBio 15, e0240123 (2024).Hanna, V. S., Abd El-Ghany, M. N., Ibrahim, M. I. M., Abdel-Rahman, T. M. & Tallima, H. Novel approaches to Mortierella alpina identification and arachidonic acid production optimization. ACS Omega 9, 34456–34463 (2024).
    Google Scholar 
    Agerer, R. Exploration types of ectomycorrhizae: a proposal to classify ectomycorrhizal mycelial systems according to their patterns of differentiation and putative ecological importance. Mycorrhiza 11, 107–114 (2001).
    Google Scholar 
    van der Linde, S. et al. Environment and host as large-scale controls of ectomycorrhizal fungi. Nature 558, 243–248 (2018).
    Google Scholar 
    Põlme, S. et al. Biogeography of ectomycorrhizal fungi associated with alders (Alnus spp.) in relation to biotic and abiotic variables at the global scale. New Phytol. 198, 1239–1249 (2013).
    Google Scholar 
    Reis, F. et al. Ectomycorrhizal fungal diversity and community structure associated with cork oak in different landscapes. Mycorrhiza 28, 357–368 (2018).
    Google Scholar 
    Edwards, J. et al. Structure, variation, and assembly of the root-associated microbiomes of rice. Proc. Natl. Acad. Sci. USA 112, E911–E920 (2015).
    Google Scholar 
    He, X. H., Yang, Y. Z. & Yuang, Z. L. Protocol for sampling of root and rhizosphere soils from trees in natural fields. Bio 101, e2003655 (2021).
    Google Scholar 
    Beckers, B., Op De Beeck, M., Weyens, N., Boerjan, W. & Vangronsveld, J. Structural variability and niche differentiation in the rhizosphere and endosphere bacterial microbiome of field-grown poplar trees. Microbiome 5, 25 (2017).
    Google Scholar 
    Chelius, M. K. & Triplett, E. W. The diversity of archaea and bacteria in association with the roots of Zea mays L. Microb. Ecol. 41, 252–263 (2001).
    Google Scholar 
    Bodenhausen, N., Horton, M. W. & Bergelson, J. Bacterial communities associated with the leaves and the roots of Arabidopsis thaliana. PloS ONE 8, e56329 (2013).
    Google Scholar 
    Gardes, M. & Bruns, T. D. ITS primers with enhanced specificity for basidiomycetes-application to the identification of mycorrhizae and rusts. Mol. Ecol. 2, 113–118 (1993).
    Google Scholar 
    White, T. J., Bruns, T., Lee, S. & Taylor, J. Amplification and direct sequencing of fungal ribosomal RNA genes for phylogenetics. PCR Protocols. https://doi.org/10.1016/b978-0-12-372180-8.50042-1 (Academic Press, 1990).Põlme, S. et al. FungalTraits: a user-friendly traits database of fungi and fungus-like stramenopiles. Fungal Divers. 105, 1–16 (2021).
    Google Scholar 
    Kim, M., Oh, H. S., Park, S. C. & Chun, J. Towards a taxonomic coherence between average nucleotide identity and 16S rRNA gene sequence similarity for species demarcation of prokaryotes. Int. J. Syst. Evol. Microbiol. 64, 346–351 (2014).
    Google Scholar 
    Deveau, A. et al. The mycorrhiza helper Pseudomonas fluorescens BBc6R8 has a specific priming effect on the growth, morphology and gene expression of the ectomycorrhizal fungus Laccaria bicolor S238N. New Phytol. 175, 743–755 (2007).
    Google Scholar 
    Zhao, L., Wu, X. Q., Ye, J. R., Li, H. & Li, G. E. Isolation and characterization of a mycorrhiza helper bacterium from rhizosphere soils of poplar stands. Biol. Fertil. Soils 50, 593–601 (2014).
    Google Scholar 
    Founoune, H. et al. Interactions between ectomycorrhizal symbiosis and Fluorescent pseudomonads on Acacia holosericea: isolation of mycorrhiza helper bacteria (MHB) from a Soudano-Sahelian soil. FEMS Microbiol. Ecol. 41, 37–46 (2002).
    Google Scholar 
    Zi, H. Y. et al. Co-inoculation with rhizobacterial community and an ectomycorrhizal fungus promotes poplar ectomycorrhization. Appl. Soil Ecol. 206, 105833 (2025).
    Google Scholar 
    Yu, L. et al. Rhizosphere microbiome of forest trees is connected to their resistance to soil-borne pathogens. Plant Soil 479, 143–158 (2022).
    Google Scholar 
    Cavanaugh, J. E. & Neath, A. A. The Akaike information criterion: background, derivation, properties, application, interpretation, and refinements. Wiley Interdiscip. Rev. Comput. Stat. 11, e1460 (2019).Muggeo, V. Segmented: an R package to fit regression models with broken-line relationships. R New. 8, 20–25 (2008).Hu, W. G. et al. Aridity-driven shift in biodiversity-soil multifunctionality relationships. Nat. Commun. 12, 5350 (2021).
    Google Scholar 
    Friedman, J. & Alm, E. J. Inferring correlation networks from genomic survey data. PLoS Comput. Biol. 8, e1002687 (2012).
    Google Scholar 
    Herren, C. M. & McMahon, K. D. Cohesion: a method for quantifying the connectivity of microbial communities. ISME J. 11, 2426–2438 (2017).
    Google Scholar 
    Dufrêne, M. & Legendre, P. Species assemblages and indicator species: the need for a flexible asymmetrical approach. Ecol. Monogr. 67, 345–366 (1997).
    Google Scholar 
    Gweon, H. S. et al. Contrasting community assembly processes structure lotic bacteria metacommunities along the river continuum. Environ. Microbiol. 23, 484–498 (2021).
    Google Scholar 
    Download referencesAcknowledgementsThis work was supported by 32430069, W2412011 (to X.L.); The Jiangsu Special Fund on Technology Innovation of Carbon Dioxide Peaking and Carbon Neutrality BE2022420 (to X.L.). We appreciate Professor Nan Yang of Nanjing Forestry University for providing the Tuber strains.Author informationAuthors and AffiliationsState Key Laboratory of Tree Genetics and Breeding, Nanjing Forestry University, Nanjing, ChinaHaiyun Zi, Zhe Hua, Yun Wang, Yangwenke Liao, Shuikuan Bei, Fuliang Cao & Xiaogang LiSchool of Tea and Coffee, Puer University, Puer, ChinaHaiyun ZiLaboratorio de Biodiversidad y Funcionamiento Ecosistémico, Instituto de Recursos Naturales y Agrobiología de Sevilla (IRNAS), CSIC, Sevilla, SpainManuel Delgado-BaquerizoAuthorsHaiyun ZiView author publicationsSearch author on:PubMed Google ScholarZhe HuaView author publicationsSearch author on:PubMed Google ScholarYun WangView author publicationsSearch author on:PubMed Google ScholarYangwenke LiaoView author publicationsSearch author on:PubMed Google ScholarShuikuan BeiView author publicationsSearch author on:PubMed Google ScholarFuliang CaoView author publicationsSearch author on:PubMed Google ScholarManuel Delgado-BaquerizoView author publicationsSearch author on:PubMed Google ScholarXiaogang LiView author publicationsSearch author on:PubMed Google ScholarContributionsX.L. conceived the project, designed the experiments. H.Z. and Y.W. conducted data curation, methodology, and writing of the original draft. M.D.-B. contributed to data interpretation, writing – review & editing. Z.H., Y.L., S.B., F.C., and M.D.-B. worked on the manuscript. All authors have discussed the results, read and approved the contents of the manuscript.Corresponding authorCorrespondence to
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    Comparative effects of synthetic and natural hydrogels enriched with fertilizer on poppy yield and soil health in drought-prone conditions

    AbstractThe negative effects of agricultural drought are particularly pronounced in spring crops, which are generally less tolerant to dry periods. One such crop frequently affected by drought is poppy (Papaver somniferum L.). Hydrogels enriched with fertilizer represent a promising technology to enhance water availability for plants and improve nutrient uptake from applied fertilizers. The aim of this research was to compare the effects of standard fertilizer (NPKS), a natural-based (NHA) hydrogel, a synthetic hydrogel (SAP), and both hydrogels enriched with fertilizer (NHA-NPKS and SAP-NPKS) on culinary poppy yield, the agronomic efficiency of N fertilization (AEN) and soil microbial activity. Each treatment was applied in two dosages (I and II). Results from a three-year field experiment showed that the application of SAP-NPKS at the lower dose (I) significantly increased seed yield. The highest AEN was also observed in the SAP-NPKS I treatment. The highest seed yield overall was achieved with the higher dose of the natural-based hydrogel enriched with fertilizer (NHA-NPKS II). Furthermore, the use of NHA and NHA-NPKS significantly increased soil microbial activity. These findings suggest that fertilizer-enriched natural-based hydrogels are a promising approach for improving soil moisture retention and nutrient availability, particularly under drought conditions in poppy cultivation.

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    Introduction Climate change is increasingly altering environmental conditions, directly affecting the cultivation of field crops. The rise in temperature and shifts in precipitation patterns have led to a higher incidence of droughts1, which adversely impact crop production and ecosystem services. Yield losses due to drought stress depend on the timing, duration and severity of the drought2. The negative effects of agricultural drought are particularly pronounced in spring-sown crops, which are generally less tolerant to water deficit. One such a crop frequently affected by drought is poppy (Papaver somniferum L.). This oilseed crop is especially vulnerable to unfavourable weather conditions early in the growing season, particularly during germination and emergence3. Efficient soil moisture management is therefore crucial for successful poppy cultivation.The use of synthetic superabsorbent polymers (SAPs) to enhance the water retention capacity of topsoil has been practiced for over two decades4. These polymers can absorb and subsequently release many times their weight in water to support plant growth over time5. As a result, they increase the soil´s water holding capacity6 and help to mitigate plant drought stress7. In addition, hydrogels reduce erosion and nutrients leaching during heavy rainfall, improve soil structure and prevent compaction8. Despite the efforts to use hydrogels based on natural polymers or their organic-inorganic hybrids, yet, the mostly used hydrogels are of synthetic origin (abbreviated here as SAPs), such as acrylate and acrylamide monomers9. The popularity of SAPs is caused particularly due to their low production demands and costs10. The beneficial properties of SAPs are well-documented, but their introduction into soil systems may also pose several risks. A major concern is the persistence of polyacrylic acid due to its extremely low biodegradation rates in soil (e.g., 0.2–0.5% over year)11,12.In accordance to Commission Delegated Regulation (EU) 2024/277013, continuous use of SAPs for water retention improvement in soil is conditional. It is required an ultimate degradation of at least 90% of SAPs (relative to the reference material) within 48 months plus the indicated functionality period. Second option includes mineralization of at least 90% measured by evolved CO2, within the same timeframe (according to the test method EN ISO 17556:201914). Nevertheless, due to the low biodegradability of SAPs, current research efforts turned to developing biodegradable, environment-friendly alternatives15,16,17,18,19.Indeed, over the last decade, natural-based hydrogel alternatives (NHAs) have shown promise as eco- friendly and cost-effective substitutes. A specific group, inorganic hydrogels, appeared to be limited by low swelling capacity and adverse effects on soil fertility20,21. This shifted the attention to NHAs derived from biopolymers such as polysaccharides22 (e.g. cellulose, starch, chitosan or various gums) or proteins7 (e.g. gelatine).In addition to water availability, adequate nutrient supply is critical for optimal plant growth. For poppy, the most important nutrients include nitrogen (N), phosphorus (P), potassium (K), and sulphur (S). Nitrogen is essential for synthesis of amino acids, nucleic acids, enzymes and chlorophyll, playing a key role in biomass production23,24. Phosphorus is involved in synthesis of nucleic acids and phospholipids, respiration, glycolysis, lipid metabolism and energy transfer25. Potassium contributes to ion homeostasis, osmoregulation, enzyme activation, and membrane protein transport26. Sulphur is critical for the synthesis of sulphur-containing amino acids (e.g. cysteine, methionine) and certain vitamins27 and it supports vegetative growth28.These macronutrients are usually supplied through mineral fertilizers. However, a significant portion is often lost through leaching into deeper soil layers, immobilization in soil, volatilization, runoff29, thereby reducing nutrient-use efficiency. As a result, only about 45% of applied nitrogen fertilizer is typically utilized by crops30. Therefore, improving synchronization between nutrient availability and crop demand is crucial for both economic and environmental sustainability.Sustainable agriculture aims to introduce innovative plant nutrition systems that enhance fertilizer efficiency. One such strategy involves the application of NHA-based hydrogels in combination with mineral fertilizers to simultaneously improve soil water retention and nutrient availability. These bio-based polymers, when combined with conventional mineral fertilizers, can potentially hold substantial quantities of water and nutrients, releasing them in sync with plant demand. In case of SAPs their capacity to serve as carriers and regulators of nutrient release, reducing nutrient losses while sustaining plant growth have already been well-documented31,32. In contrast, broader adoption of NHAs is still limited by gaps in understanding the mechanism of nutrient release, the impacts on soil physical, chemical and biological properties as well as on plant root development33. Nonetheless, several studies have already indicated that NHAs can bind nutrients and release them in a controlled manner22. Furthermore, multicomponent NHAs have been found to slow nitrogen release, enhance soil moisture retention, and partially mitigate the environmental risks associated with SAPs18,34.The aim of this study was to evaluate the multi-year effect of SAPs and NHAs enriched with fertilizer (NPKS) on the seed yield of culinary poppy. To the best of our knowledge, the impact of specific nutrient-enriched hydrogels on culinary poppy has not yet been investigated in this context. The main hypothesis was that fertilizer-enriched natural hydrogels would achieve equal or superior yield outcomes compared to synthetic SAPs. To test this hypothesis, a three-year field experiment (2022–2024) was conducted under real agricultural conditions.Materials and methodsExperimental locality and climate-soil conditionsThe effect of fertilizer-enriched natural and synthetic hydrogels on poppy yield was evaluated in a three-year (2022–2024) small-plot field experiment. The trial was conducted at the Žabčice experimental station in South Moravia, Czech Republic (49°1′18.658″ N, 16°36′56.003″ E), at an elevation 184 m above sea level. The site is characterized by mild, wet winters and warm, somewhat dry summers, with an average annual temperature 10.1 °C and annual precipitation of approximately 490 mm. According to the Köppen climate classification, the region falls within the “Cfb” category (temperate oceanic climate). The total precipitation and average air temperature during the experimental growing seasons were 121 mm/11.8 °C (2022), 159 mm/11.0 °C (2023), and 205 mm/14.4 °C (2024). Average monthly temperatures and precipitations during the experimental period, along with the 1991–2020 climatic norm, are presented in Fig. 1.Fig. 1Weather conditions during the field experiment (2022–2024).Full size imageThe experiment was conducted on a single field (240 m × 150 m), which was divided into three experimental Sect. (80 m × 150 m). Each year, poppy was grown on a different section, always following a spring barley pre-crop. Key physicochemical properties of the topsoil (0–30 cm) over the three years are presented in Table 1.Table 1 Physicochemical properties of the experimental soil (0–30 cm depth).Full size tableExperimental design and treatmentsThe objective was to evaluate the effect of two types of hydrogels (synthetic and natural) enriched with nutrients (N, P, K, S) on poppy (Papaver somniferum L.) yield. The natural hydrogel (NHA) was prepared from potato starch (AGRANA Beteiligungs-AG, Konstanz, Germany), glycerol (PENTA, Ltd., Prague, Czechia), clinoptilolite zeolite (particle size 2 mm; Rosteto, Jindrichuv Hradec, Czechia) and potassium polyacrylate (Falconry, Kozmice, Czechia). The final NHA composition consisted of 86wt.% starch- glycerol mixture (43wt.% glycerol), 7 wt% potassium polyacrylate, and 7 wt% zeolite. NHA was prepared by thermoplasticization at 140 °C by passing one cycle in a hot-melt extruder using citric acid as a crosslinking agent. The synthetic superabsorber polymer (SAP) treatment consisted of 100% potassium polyacrylate. Details on NHA preparation and nitrogen release characteristics are described by Skarpa et al.22.The nutrient source was NPKS fertilizer (YARA Mila Complex 12-11-18-8; YARA Agri Czech Republic, Prague, Czechia). The hydrogels, fertilizer and fertilizer-enriched hydrogels were applied in two dosage levels (I and II). The ratio of hydrogel to fertilizer was adjusted to ensure equal application rates of nitrogen (24 and 48 kg/ha) and hydrogel (15 and 30 kg/ha) in both dosage levels (Table 2).Table 2 Treatment design and application rates.Full size tableThe blue-seed poppy variety “MS Harlekyn” (National Agricultural and Food Center, Luzianky, Slovak Republic) was used as a model crop. The experiment followed a Randomized Complete Block Design. Each treatment was replicated 4 times (4 plots per treatment) each year (experimental section). The area of each plot was 15 m2 (10 × 1,5 m). The allocation of replicates across the area of each experimental section was consistent for all years (Figure S1).Fertilizers and hydrogels were manually applied to individual plots one day before sowing and incorporated into the soil immediately after application. Sowing took place on 28 February 2022, 1 March 2023, and 19 March 2024.Harvesting was carried out after physiological maturity (27 July 2022, 20 July 2023, and 22 July 2024) using a Haldrup C-85 plot harvester (Haldrup GmbH, Ilshofen, Germany). Seed yield was measured using a digital scale (KERN DS 60K0.2, KERN and Sohn GmbH, Germany). Grain moisture content was determined with a portable grain moisture meter Wile 78 Crusher (Farmcomp Oy, Tuusula, Finland) and yield was standardized to 8.0% moisture and expressed in tons per hectare (t/ha). The 1000-seeds weight was determined using a scale KERN ARJ 220-4 M, KERN and Sohn GmbH (Balingen, Germany).The agronomic efficiency (AE)37 was expressed for the fertilized treatments as the increase in seed yield (kg) per unit of nitrogen applied (kg) according to the Eq. 1) and per unit of hydrogel applied (kg) according to the Eq. 2):$$:{text{AE}}_{text{N}}text{(kg/kg)=}frac{{text{Y}}_{text{FERT}}text{(t/ha)}:-:{text{Y}}_{text{CONTROL}}text{(t/ha)}}{{text{N}}_{text{DOSE}}text{(kg/ha)}}timestext{1000}$$
    (1)
    $$:{text{AE}}_{text{H}}text{(kg/kg)=}frac{{text{Y}}_{text{FERT}}text{(t/ha)}:-:{text{Y}}_{text{CONTROL}}text{(t/ha)}}{{text{Hydrogel}}_{text{DOSE}}text{(kg/ha)}}times text{1000}$$
    (2)
    where AEN is agronomic efficiency of nitrogen, AEH is agronomic efficiency of hydrogel, YFERT is fertilized treatment yield of poppy seed, YCONTROL is unfertilized treatment yield of poppy seed, NDOSE is nitrogen dose applied by fertilizer, and HydrogelDOSE is hydrogel dose applied.Soil sampling (0–15 cm depth) was carried out after poppy each year. Fresh fine samples were stored at 4 °C and analyzed for dehydrogenase activity (DHA) using 2,3,5-triphenyltetrazolium chloride (TTC) method38, and basal respiration (BR) using MicroResp® device (The James Hutton Institute, Scotland) according to Campbell et al.39.Economic analysisA partial budget analysis40 was performed to assess the cost effectiveness of hydrogels treatments on poppy seed production. This procedure considers only major differences between treatments (fertilization) without considering all costs and benefits. Therefore, only the cost of fertilizer or/and hydrogels, their applications (12 € for each treatment) and price of poppy seed were considered. All non-fertility costs (e.g., seed costs, field operations, plant protection) were held constant across treatments and were not included in the calculation. The cost of 1 ton of used fertilizer (YARA Mila Complex) was 840 €; the cost of 1 ton of SAP and NHA hydrogels was 12 000 and 5 400 € respectively. The prices of fertilizers for treatments were recalculated according to the corresponding applied doses (Table 2). The price of harvested commodity (culinary poppy seed) was 2400 €/t. The prices were based on the actual market values at the end of 2024.However, the inherent volatility input (fertilizer/hydrogels) and output (seed of poppy) prices represents a challenge for accurate economic analysis. This approach focuses on practical economic variability; therefore, scenario-based sensitivity analysis was preferred over statistical confidence intervals, as it more realistically represents uncertainty arising from market and climatic fluctuations. Therefore, additional sensitivity analysis41 was performed under three different scenarios to accommodate possible market dynamics and to assess the effects of input and output price changes on the compared treatments of fertilization. These scenarios were: (Sc. 1) increase cost of hydrogels/fertilizer by 10% but fixed commodity price, (Sc. 2) increase commodity price by 10% with fixed hydrogels/fertilizer cost and (Sc. 3) increase cost of hydrogels/fertilizer by 10% and decrease commodity price by 10% (worst case scenario from farmers’ perspective). The average yield of poppy over the three years of the experiment was used for the economic evaluation. Confidence intervals for average yields were not included, as they would represent within-year experimental variability rather than the economic uncertainty captured by the scenario-based sensitivity analysis, which better reflects market and climatic risks influencing profitability.Statistical analysisThe effect of the hydrogels, fertilizer and their mixtures were assessed using ANOVA. Before performing ANOVA, the assumptions of normality and homogeneity of variances were tested using the Shapiro–Wilk and Levene’s tests, respectively. ANOVA was used to evaluate the effects of hydrogel type, dose, fertilizer, and their combinations. Two model structures were used:

    (i)

    Per-year analyses, performed as a one-way ANOVA with Treatment as a fixed factor and Plot as a random factor (yield, 1000 seed weight, AEN, AEH, DHA, and BR), and.

    (ii)

    Combined analyses, performed as a mixed two-way ANOVA with Year and Treatment as fixed factors and Plot (Year) as a random factor.

    When appropriate, a factorial ANOVA including the main effects of Hydrogel type and Dose, and their interaction (Type × Dose), was used to partition the total variability. For each model, the F-statistic, degrees of freedom (df), and p-values for main effects and their interactions were calculated and are reported in Supplementary Tables S1–S3. The effect sizes were expressed as eta-squared (η² = SSeffect/SStotal) and partial eta-squared (partial η² = SSeffect/(SSeffect + SSerror)), representing the proportion of total variance explained by each factor. After a significant omnibus F-test (p ≤ 0.05), Fisher’s Least Significant Difference (LSD) test was applied for post-hoc multiple comparisons among treatment means. All statistical analyses were conducted using Statistica 14 CZ software42. Results are expressed as means ± standard deviations (SD) or standard errors (SE), as appropriate.ResultsSeed yield of poppy and agronomic efficiencyThe effects of hydrogels (SAP, NHA) enriched and not-enriched with fertilizer applied at two doses (I and II) on poppy seed yield are presented in Fig. 2. In all years, it is evident that the higher nutrient dose (II) applied in conventional fertilizer (NPKS) relatively increased seed yield in comparison with the lower dose (NPKS I) by 1.6% (2023), 4.7% (2022) and 5% (2024). The increase in yield of poppy seed caused by the higher NPKS dose was significant compared to the control in 2023 and 2024.The yield response was also influenced by hydrogel dose, which accounted for 32.3% (η² = 0.323, Partial η² = 0.487, p = 0.335) of total seed yield variability, while hydrogel type explained 7.6% (η² = 0.076, Partial η² = 0.183, p = 0.638). A higher dose of synthetic SAP relatively reduced seed production (by 5.3% on average), whereas a higher dose of a natural-based hydrogel increased poppy seed yield: Control (1.15 t/ha, 100%) ˂ NHA I (1.22 t/ha, 106.5%) ˂ NHA II (1.29 t/ha, 112.3%).The highest seed yields were observed in all years with fertilizer-enriched hydrogels. At the lower fertilizer rate (I), except in 2022, the combination of fertilizer with synthetic SAP had a higher effect on production compared to NHA (Fig. 2). This corresponded to the effect of pure SAP. At the higher nutrient rate (II), a higher increase in poppy yield for the fertilizer-NHA combination was observed (in 2023 and 2024). The effect of soil application of the tested fertilizers on seed yield, expressed as a mean for lower dose of hydrogels/fertilizer (I), was as follows: 1.15 ± 0.12 t/ha (Control) ˂ 1.22 ± 0.14 t/ha (NHA) ˂ 1.27 ± 0.11 t/ha (NPKS) ˂ 1.29 ± 0.15 t/ha (NHA-NPKS) ˂ 1.32 ± 0.18 t/ha (SAP) ˂ 1.36 ± 0.22 t/ha (SAP-NPKS). In contrast, when higher rates (II) were used, the effect of the tested fertilizer types was as follows: 1.15 ± 0.12 t/ha (Control) ˂ 1.26 ± 0.17 t/ha (SAP) ˂ 1.29 ± 0.17 t/ha (NHA) ˂ 1.32 ± 0.10 t/ha (NPKS) ˂ 1.36 ± 0.13 t/ha (SAP-NPKS) ˂ 1.38 ± 0.18 t/ha (NHA-NPKS).Fig. 2Effects of fertilization on poppy seed yield (t/ha) in 2022, 2023, 2024 (a), and average of three years (b). Control: treatment without fertilization; NHA: fertilized with bio- natural-based hydrogel; SAP: fertilized with synthetic hydrogel; NPKS: fertilized with NPKS fertilizer; NHA-NPKS: fertilized with NPKS fertilizer-enriched bio- natural-based hydrogel; SAP-NPKS: fertilized with NPKS fertilizer-enriched synthetic hydrogels. Roman numerals I and II indicate hydrogels/fertilizer rates. The columns marked by different lower-case letters indicate significant differences among treatments (each year was evaluated separately). The columns represent the arithmetic means (n = 4), standard deviation is expressed by error bars. The values F, df, and P for main effects and interactions are given in Tables S1.Full size imageThe 1000-seed weight was not significantly affected by fertilization in 2022 and 2023 (Table 3). In 2024, the significantly highest poppy seed weight was found in the higher fertilizer rate (II) treatments as follows: NHA-NPKS ˂ NPKS ˂ SAP-NPKS. Consistent with the results of the 3rd year of testing, the relatively highest average seed weight was found on the treatments fertilized with higher rates of pure fertilizer and hydrogels enriched by fertilizer. Their increased doses (II) resulted in an increase of poppy seed weight compared to the lower doses (I), by 6.6% (NPKS), 7.2% (NHA-NPKS) and 11.4% (SAP-NPKS), respectively. Pure hydrogels did not affect seed weight significantly.Table 3 Effects of fertilization on 1000 seed weight (g).Full size tableThe agronomic efficiency of nitrogen (AEN) and hydrogel (AEH) is shown in Table 4.The lower rate of nitrogen applied by NPKS fertilizer resulted in significantly higher AEN compared to the higher rate in the average of three years (Table 4). The largest difference in AEN between nitrogen rates was observed in 2023, where 1 kg of nitrogen applied at the lower rate increased poppy seed yield by 6.9 kg, while the yield increase at the higher rate was 3.8 kg of seed.The agronomic nitrogen efficiency of fertilizer-enriched hydrogels applied at a lower rate was significantly higher when using synthetic SAP. Significant increase in AEN for SAP-NPKS I compared to NPKS I was found in 2023 (+ 46.4%) and 2024 (+ 160.7%), averaging 69.2% over the three years (Table 4). An average increase in AEN (+ 15.4%) was also observed with the use of fertilizer-enriched NHA (NHA-NPKS I), but not significant. Higher rates of hydrogels enriched by fertilizer did not statistically affect the efficiency of applied nitrogen. The relatively highest AEN value was found for the NHA-NPKS II treatment (4.9; 100%), followed by SAP-NPKS II (91.8%) and NPKS II (71.4%). The total variability of AEN was significantly influenced mainly by the dose of hydrogel (η² = 0.570, Partial η² = 0.851, p = 0.021) while the type of hydrogel explained 11.7% (η² = 0.117, Partial η² = 0.539, p = 0.286).With pure hydrogel applied at a lower rate, poppy seed yield increased significantly with synthetic SAP. The average AEH for SAP I was 11.7 (i.e. the seed yield increased by 11.7 kg due to the application of 1 kg of SAP). The agronomic efficiency of the synthetic hydrogel was more than twice as high compared to NHA (Table 4). In contrast, for the higher dose of hydrogel, the efficiency of NHA was similar to the effect of its lower dose, whereas in the case of SAP, AEH was significantly reduced (more than threefold decrease).The effect of hydrogels (AEH) on poppy yield increased when used in combination with fertilizers. In the case of natural hydrogel enriched by fertilizer (NHA-NPKS) compared to its pure form (NHA), a significant increase in AEH was observed for both doses (I + 92%, II + 68.1%). In the case of synthetic hydrogel, an increase in AEH was also observed between the SAP-NPKS and SAP, but significantly only for the higher dose (+ 89.5%).Table 4 Effects of fertilization on agronomic efficiency of nitrogen (AEN), and agronomic efficiency of hydrogel (AEH).Full size tableSoil microbial activity and biomassNo significant effects of any treatment of hydrogels (SAP, NHA) applied either solely or with NPS was observed on soil dehydrogenase activity (DHA) in 2022 (Table 5). In the next year 2023, all types of amendments (except of NHA I) increased DHA in comparison to Control value, and increased value in SAP I, which was even higher in combination with higher NPKS (SAP-NPKS II) as well as in all treatments with higher or/and combined NHA amendment (NHA-NPKS I, NHA II, NHA-NPKS II). Moreover, NHA II enhanced soil DHA significantly more than both doses of NPKS (I and II), showing the highest enzyme values in 2023, 2024 and in 3-year average (Table 5). In 2024, only treatments with sole NHA (I, II), NHA-NPKS I, and NPKS I were increased over Control, while SAP II was decreased. In 3-year average, SAP applied solely (in low dose I) or combined (I, II) increased DHA over Control values but not compared to NPKS (I, II). Only NHA-NPKS I and NHA II enhanced DHA more than amendment of fertilizers.Table 5 Effects of fertilization on dehydrogenase activity (DHA) of microbial biomass and basal soil respiration (BR).Full size tableIn 2022, soil basal respiration (BR) was decreased compared to Control in treatments with high fertilizer dose (NPKS II), applied solely or combined with NHA (Table 5). In 2023, only NPKS I (low dose) exerted negative effect on BR. In 2024, NPKS II again solely or combined (SAP-NPKS II) showed significant decrease in BR, while NPKS I and NHA II increased the values over Control. In 3-year average, mainly insignificantly different or negative effects of tested amendments on BR were found, the strongest BR reduction was derived by NPKS II, SAP I, NHA-NPKS I and II (both; Table 5).Economic evaluation of poppy productionTable 6 describes the results of the economic evaluation of poppy production for the tested fertilization treatments under different price scenarios, considering the variable price of fertilizers and commodity (poppy seed). At lower fertilizer doses (I), the application of pure SAP (SAP I) and SAP enriched by fertilizer (SAP-NPKS I) was the most profitable in each scenario. The net profit of the NHA I treatment was approximately 38% of the SAP I profit. In the case of fertilizer-enriched hydrogels, the difference between net profit of SAP and NHA was lower (Table 6).Higher doses of hydrogels and their fertilizer-enriched forms increased the cost of poppy production and reduced profits. The use of higher doses of SAP (SAP II, SAP-NPKS II) was unprofitable (loss-making in all scenarios). The higher poppy yield obtained after applying natural hydrogels at a higher dose (II) increased the profit, in the case of using pure NHA in the range of 115–199 €/ha, when using NHA-NPKS depending on the type of scenario, as shown in Table 6 (highest profit in Sc. 2, conversely, loss in the case of Sc. 3).Table 6 Economic evaluation of poppy fertilization.Full size tableDiscussionThe effect of the tested types of hydrogels on poppy (Papaver somniferum L.) seed yield varied depending on the type and application rate. At a lower dose, the synthetic SAP increased seed yield more effectively compared to the natural hydrogel (NHA), whereas the opposite trend was observed at the higher dose. This pattern was consistent for both pure hydrogels and their fertilizer-enriched formulations.Specifically, the lower application rate of pure SAP and SAP combined with fertilizer (SAP-NPKS I) significantly improved poppy seed yield. These results align with prior studies that demonstrated increased crop yields following SAP application due to their high water absorption and retention capacity43, as well as improvements in soil physical and chemical properties44,45. Additionally, SAPs have been shown to reduce nutrient losses46,47, and enhance fertilizer use efficiency45. In the present study, the agronomic efficiency of nitrogen (AEN) was significantly higher in the SAP-NPKS treatment compared to NPKS alone (Table 4).Yield differences following hydrogel application are attributed to the feedstock’s composition, hydrogel formulation and method of application48. Synthetic SAP generally show stronger positive effects on yield than natural hydrogels9. Zheng et al.9 reported an average 12.8% in crop yield from SAP application (95% confidence intervals: 12.1–13.4%, p < 0.01). In our trial, pure SAP application increased poppy yield by 12.4% o average across both doses, whereas pure NHA increased yield by 9.4%. Zheng et al.9 also found a 15.2% increase in oilseed-yields, including poppy, after SAP application, although data specific to poppy remain scarce. One of the few relevant studies showed improved oilseed rape yield under drought and irrigation conditions with the application of anionic cellulose-based polyacrylate hydrogel at 40 kg/ha49.However, high doses of synthetic SAPs may negatively affect plant growth. Situ et al.34 found that excessive use of synthetic SAP reduced biomass and roots and stems–leaves, likely due to ion imbalances (elevated K+ and Na+, reduced Ca2+ and Mg2+). This may explain observed reductions in germination rate, plant height and yield. In our study the SAP II treatment led to a relative yield decline in two of the three trial years. On average, the SAP II treatment yielded 5.6% less than the NHA II treatment.The effect of hydrogels on seed weight and poppy production also depended on weather conditions during vegetation periods. In particular, in relatively dry and cool years (2022 and 2023) seed weight was not significantly affected by fertilization (Table 3). On the contrary, 2024 growing period was characteristic of sufficient water supply and higher temperatures, which supported soil microbial activity (as reflected in DHA and BR, Table 5), in particular in the presence of bio-SAP-based fertilizers. The elevated microbial activity increases soil organic matter turnover and contribute to soil fertility50, with a direct impact on crop yields51,52, as observed in this work.However, at higher SAP rates the reduced growth was observed, which may stem (apart from ion imbalance), from the presence of acrylic acid. Chen et al.53 reported damage to the organizational structure and cellular morphology of maize roots and the membrane system of root cells. Puoci et al.54 described acrylate hydrogel intermediates as cytotoxic. Additionally, excessive SAP can compete with plants for water under drought conditions, potentially exacerbating water stress in plants and thereby reducing yields55,56. This aligns with Zheng et al.9 who concluded that SAP rates > 90 kg/ha do not significantly improve yields and may even be detrimental, while the application rate 45–90 kg/ha exhibited positive results.Although recent research focuses increasingly on natural polymers or organic-inorganic hybrid compounds, most studies still involve synthetic SAPs (acrylate and acrylamide-based)9. Nevertheless, several studies confirm yield benefits from bio-based hydrogels22,57,58,59,60, which offer key advances such as biocompatibility, non-toxicity, and biodegradability. However, “biodegradability” is a broad term and does not necessarily reflect degradation rates. The NHA used in this study, composed primarily of potato starch (86 wt%), is highly biodegradable. Therefore, NHA was superior in enhancing microbial activity as starch degradation products provide carbon source for microorganisms. Based on CO2 released during analysis, Guo et al.61 reported that 78.34% of starch degraded within 14 days. The starch decomposition rate reported in laboratory tests is difficult to achieve under field conditions. Starch and starch based polyurethane materials (starch-polyhydroxyurethanes) lost about 44.1 and 66.4% of their weight, respectively, after 60 days burial in soil62. The biodegradability of NHA was also supported by elevated dehydrogenase activity and basal respiration measured in soil after harvest (Table 5). Soil microbial activity was more strongly stimulated by NHA and NHA-NPKS than by NPKS alone, likely due to excellent biodegradability of starch63 and glycerol64.While SAP also stimulated soil microbiome which is reflected in elevated DHA65, the stimulation was weaker, likely due to slow biodegradation of polyacrylic gels that are primarily degraded fungi such as Phanerochaete chrysosporium66. Although NHA-NPKS enhanced DHA, it did not significantly increase basal respiration over the three-year average. This is likely due to the limited stimulator effect of NPKS amendment, in agreement with study of Kulachkova et al.67, who reported minimal BR after Nitroammofoska-1 application (NPKS 21-10-10-2, similar to the composition of YARA Mila Complex 12-11-18-8) to urban lawn soil.On average, BR was higher following NHA application compared to SAP, by 4.6% at the lower dose, and 5.0% at the higher dose. The poor biodegradability of synthetic SAPs raises environmental concerns68. Their degradation rate is typically 0.45 to 0.82% over 24 weeks depending on soil type but not on soil temperature. Detailed study showed that the polyacrylate superabsorbent main chain degraded in the soils at rates of 0.12–0.24% per 6 months11. Aging via chemical, photolytic, and mechanical stress can lead to fragmentation and formation of microplastic particles, which may leach into deeper soil layers or into adjacent ecosystems, potentially impacting microbial communities and plant growth69,70.Dehydrogenase activity is a well-established indicator of overall microbial activity71. Significantly higher DHA values were observed found in two of the three years and on average in the NHA II treatment (Table 4). Soil treated with NHA and NHA-NPKS exhibited the highest DHA (7.1 a ± 1.8) significantly greater than SAP treatments (6.8 b ± 1.3) and non-hydrogel controls (6.6 c ± 1.5) (p˂0.05). Excessive doses of SAP (based on polyacrylic acid) have been reported to supress microbial respiration in sandy soils72. Soil microbial biomass plays a crucial role in nutrient cycling and natural based hydrogel may further support microbial growth by providing degradable organic substances73, enhancing microbial diversity74, and ultimately improving soil vitality, plant growth and survival rates73.In addition to agronomic and environmental performance, economic viability is crucial for hydrogels adoption. Although economic analysis of hydrogel use remain limited, they are essential to evaluate practical constrains and inform farmers. Commercial synthetic SAPs based on polyacrylic acid are costly despite their high swelling capacity68. Natural hydrogels represent a lower-cost, faster-degrading alternatives with promising market potential48.In this work, a lower dose of potassium polyacrylate (SAP I) resulted in the highest net profit up to 269 €/ha (Sc. 2, Table 5) due to the increase in poppy seed yield. The fertilizer-enriched SAP (SAP-NPKS I) was profitable compared to fertilizer (NPKS I) (average of all calculated scenarios: +20.8 €/ha). The increase in yield and net profit at a lower dose of NHA (NHA I) was also economically beneficial (61–105 €/ha). The use of NHA enriched with fertilizer (NHA-NPKS I) did not exhibit an increased profit compared to SAP. In this context, however, it is also important to consider the environmental compatibility of natural-based hydrogels, even though they may be less economically attractive.These results suggest that poppy is among the crops for which hydrogel application can be economically justified. Yet, profitability depends on crop type. For example, despite yield increases, SAP costs were not offset by revenues in grain crops (net loss of 11 €/ha)9. On the other hand, net profit gains have been documented in maize75, sugarcane76, potatoes44, Indian mustard77, and summer pearl millet78. In our study high-dose NHA (NHA II) indicate an net profit increased by 115 to 199 €/ha, while high-dose SAP (SAP II) appeared to be economically unviable.ConclusionThis study demonstrates that the application of hydrogels, particularly when enriched with fertilizers, can significantly enhance the yield and nutrient-use efficiency of culinary poppy cultivated under drought-prone conditions. While low-dose synthetic SAP treatments provided the highest net economic returns, high-dose SAP applications proved less effective and potentially detrimental due to reduced biodegradability and possible phytotoxicity. In contrast, natural-based hydrogels (NHA), especially when combined with fertilizer, promoted soil microbial activity and showed consistent yield benefits at both application rates. Although the economic return from NHA was generally lower than from SAP, its environmental advantages, such as enhanced biodegradability and stimulation of beneficial soil microbiota, make it a compelling alternative for sustainable agriculture.Overall, natural starch-based hydrogels enriched with fertilizer represent a viable, environmentally friendly strategy for improving soil water retention, nutrient efficiency, and crop performance in poppy cultivation. However, the composition of hydrogels (source of nutrients, e.g., potassium), site-specific conditions such as soil type, climate, and crop response variability must be considered when selecting the appropriate hydrogel type and dose for field application.

    Data availability

    The datasets generated and/or analysed during the current study are available from the corresponding author on reasonable request.
    AbbreviationsNHA:
    natural-based hydrogel
    SAP:
    synthetic hydrogel
    NPKS:
    mineral fertilizer YARA Mila Complex
    NHA-NPKS:
    natural-based hydrogel enriched with fertilizer
    SAP-NPKS:
    synthetic hydrogel enriched with fertilizer
    AEN
    :
    agronomic efficiency of nitrogen fertilization
    AEH
    :
    agronomic efficiency of hydrogel
    DHA:
    dehydrogenase activity
    BR:
    basal respiration
    ReferencesFurtak, K. & Wolińska, A. The impact of extreme weather events as a consequence of climate change on the soil moisture and on the quality of the soil environment and agriculture – A review. Catena (Amst). 231, 107378 (2023).Article 

    Google Scholar 
    Pandey, R. K., Maranville, J. W. & Admou, A. Tropical wheat response to irrigation and nitrogen in a Sahelian environment. I. Grain yield, yield components and water use efficiency. Eur. J. Agron. 15, 93–105 (2001).Article 

    Google Scholar 
    Kundrátová, K. et al. Transcriptomic and proteomic analysis of drought stress response in opium poppy plants during the first week of germination. Plants 10, 1878 (2021).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Behera, S. & Mahanwar, P. A. Superabsorbent polymers in agriculture and other applications: a review. Polymer-Plastics Technol. Mater. 59, 341–356 (2020).Article 

    Google Scholar 
    Ghobashy, M. M. et al. Synthesis and application of a multifunctional Poly (vinyl pyrrolidone)-based superabsorbent hydrogel for controlled fertilizer release and enhanced water retention in drought-stressed Pisum sativum plants. Sci. Rep. 14, 27734 (2024).Article 
    ADS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Yu, J. et al. Superabsorbent polymer properties and concentration effects on water retention under drying conditions. Soil Sci. Soc. Am. J. 81, 889–901 (2017).Article 
    ADS 

    Google Scholar 
    Sarmah, D. & Karak, N. Biodegradable superabsorbent hydrogel for water holding in soil and controlled-release fertilizer. J Appl. Polym. Sci 137, 48495 (2020).Cao, Y., Wang, B., Guo, H., Xiao, H. & Wei, T. The effect of super absorbent polymers on soil and water conservation on the terraces of the loess plateau. Ecol. Eng. 102, 270–279 (2017).Article 

    Google Scholar 
    Zheng, H. et al. Effects of super absorbent polymer on crop yield, water productivity and soil properties: A global meta-analysis. Agric. Water Manag. 282, 108290 (2023).Article 

    Google Scholar 
    Yang, Y. et al. Research advances in superabsorbent polymers. Polym. (Basel). 16, 501 (2024).Article 

    Google Scholar 
    Wilske, B. et al. Biodegradability of a polyacrylate superabsorbent in agricultural soil. Environ. Sci. Pollut. Res. 21, 9453–9460 (2014).Article 

    Google Scholar 
    Liang, D. et al. Degradation of polyacrylate in the outdoor agricultural soil measured by FTIR-PAS and LIBS. Polym. (Basel). 10, 1296 (2018).Article 
    ADS 

    Google Scholar 
    European Union Delegated Regulation (EU) 2024/2770 of the Commission of July 15 2024. Amending Regulation (EU) 2019/1009 of the European Parliament and of the Council with Respect to the Biodegradability Criteria Applicable to Coating Agents and Water Retention Polymers; European Union: Brussels, Belgium, 2024. Preprint at (2024).ISO 17556 Plastics—determination of. the ultimate aerobic biodegradability of plastic materials in soil by measuring the oxygen demand in a respirometer or the amount of carbon dioxide evolved. ISO https://www.iso.org/cms/render/live/en/sites/isoorg/contents/data/standard/07/49/74993.html Preprint at (2019). Dodangeh, F., Nabipour, H., Rohani, S. & Xu, C. Applications, challenges and prospects of superabsorbent polymers based on cellulose derived from lignocellulosic biomass. Bioresour Technol. 408, 131204 (2024).Article 
    PubMed 

    Google Scholar 
    Firmanda, A. et al. Biopolymer-based slow/controlled-release fertilizer (SRF/CRF): nutrient release mechanism and agricultural sustainability. J. Environ. Chem. Eng. 12, 112177 (2024).Article 

    Google Scholar 
    Xiao, X. et al. One-step method to prepare starch-based superabsorbent polymer for slow release of fertilizer. Chem. Eng. J. 309, 607–616 (2017).Article 

    Google Scholar 
    Ribeiro, A. B. et al. Bio-based superabsorbent hydrogels for nutrient release. J. Environ. Chem. Eng. 12, 112031 (2024).Article 

    Google Scholar 
    Wen, P. et al. Rapid synthesis of a corncob-based semi-interpenetrating polymer network slow-release nitrogen fertilizer by microwave irradiation to control water and nutrient losses. Arab. J. Chem. 10, 922–934 (2017).Article 
    ADS 

    Google Scholar 
    Chen, M. et al. Kaolin-Enhanced superabsorbent composites: Synthesis, characterization and swelling behaviors. Polym. (Basel). 13, 1204 (2021).Article 

    Google Scholar 
    Noh, Y. D., Komarneni, S. & Park, M. Mineral-Based slow release fertilizers: A review. Korean J. Soil Sci. Fertil. 48, 1–7 (2015).Article 

    Google Scholar 
    Skarpa, P. et al. Effect of fertilizers enriched with bio-based carriers on selected growth parameters, grain yield and grain quality of maize (Zea Mays L). Eur. J. Agron. 143, 126714 (2023).Article 

    Google Scholar 
    Wang, Q., Li, S., Li, J. & Huang, D. The utilization and roles of nitrogen in plants. Forests 15, 1191 (2024).Article 

    Google Scholar 
    Mohammed, Y. A. et al. Nitrogen fertilizer management for improved grain quality and yield in winter wheat in Oklahoma. J. Plant. Nutr. 36, 749–761 (2013).Article 

    Google Scholar 
    Lovio-Fragoso, J. P. et al. Biochemical and molecular aspects of phosphorus limitation in diatoms and their relationship with biomolecule accumulation. Biology (Basel). 10, 565 (2021).PubMed 

    Google Scholar 
    Johnson, R. et al. Potassium in plants: growth regulation, signaling, and environmental stress tolerance. Plant Physiol. Biochem. 172, 56–69 (2022).Article 
    PubMed 

    Google Scholar 
    Antošovský, J., Škarpa, P., Ryant, P. & Brtnický, M. Waste sulfur from biogas desulphurization: a supplement of Brassica Napus L. nutrition. J. Plant. Nutr. 47, 296–313 (2024).Article 

    Google Scholar 
    Narayan, O. P., Kumar, P., Yadav, B., Dua, M. & Johri, A. K. Sulfur nutrition and its role in plant growth and development. Plant. Signal. Behav. https://doi.org/10.1080/15592324.2022.2030082 (2022).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Lehmann, J. & Schroth, G. Nutrient leaching. In Trees, Crops and Soil Fertility: Concepts and Research Methods 151–166 (CABI Publishing, 2002). https://doi.org/10.1079/9780851995939.0151.Chapter 

    Google Scholar 
    Yu, X., Keitel, C., Zhang, Y., Wangeci, A. N. & Dijkstra, F. A. Global meta-analysis of nitrogen fertilizer use efficiency in rice, wheat and maize. Agric. Ecosyst. Environ. 338, 108089 (2022).Article 

    Google Scholar 
    Islam, M. R. et al. Effects of water-saving superabsorbent polymer on antioxidant enzyme activities and lipid peroxidation in corn (Zea Mays L.) under drought stress. J. Sci. Food Agric. 91, 813–819 (2011).Article 
    PubMed 

    Google Scholar 
    Liu, F. et al. Effects of super-absorbent polymer on dry matter accumulation and nutrient uptake of Pinus pinaster container seedlings. J. For. Res. 18, 220–227 (2013).Article 

    Google Scholar 
    Liao, R., Yang, P., Wu, W. & Ren, S. An inverse method to estimate the root water uptake Source-Sink term in soil water transport equation under the effect of superabsorbent polymer. PLoS One. 11, e0159936 (2016).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Situ, Y. et al. Effects of several superabsorbent polymers on soil exchangeable cations and crop growth. Environ. Technol. Innov. 30, 103126 (2023).Article 

    Google Scholar 
    Gee, G. W. & Bauder, J. W. Particle-size analysis. in 383–411 https://doi.org/10.2136/sssabookser5.1.2ed.c15 (2018).Zbíral, J., Malý, S. & Váňa, M. Soil Analysis 3rd Edn (Central Institute for Supervising and Testing in Agriculture, 2011).Antošovský, J., Škarpa, P. & Ryant, P. The effect of nitrification inhibitor on the yield and quality of triticum aestivum L. and brassica Napus L. – A long-term experiment. Field Crops Res. 328, 109906 (2025).Article 

    Google Scholar 
    Dick, R. P., Breakwell, D. P. & Turco, R. F. Soil enzyme activities and biodiversity measurements as integrative Microbiological indicators. in 247–271 (2015) https://doi.org/10.2136/sssaspecpub49.c15Campbell, C. D., Chapman, S. J., Cameron, C. M., Davidson, M. S. & Potts, J. M. A rapid microtiter plate method to measure carbon dioxide evolved from carbon substrate amendments so as to determine the physiological profiles of soil microbial communities by using whole soil. Appl. Environ. Microbiol. 69, 3593–3599 (2003).Article 
    ADS 
    PubMed 
    PubMed Central 

    Google Scholar 
    CIMMYT Economics Program. From Agronomic Data To Farmer Recommendations: an Economics Training Manual (CIMMYT Economics Program, 1988).Mohammed, Y. A., Gesch, R. W., Johnson, J. M. F. & Wagner, S. W. Agronomic and economic evaluations of N fertilization in maize under recent market dynamics. Nitrogen 3, 514–527 (2022).Article 

    Google Scholar 
    StatSoft, I. STATISTICA (Data Analysis Software System) Preprint at (2022).Feng, W. et al. Effects of Polyacrylamide-Based super absorbent polymer and corn straw Biochar on the arid and Semi-Arid salinized soil. Agriculture 10, 519 (2020).Article 

    Google Scholar 
    Hou, X. et al. Superabsorbent polymers influence soil physical properties and increase potato tuber yield in a dry-farming region. J. Soils Sediments. 18, 816–826 (2018).Article 

    Google Scholar 
    Egrinya Eneji, A., Islam, R., An, P. & Amalu, U. C. Nitrate retention and physiological adjustment of maize to soil amendment with superabsorbent polymers. J. Clean. Prod. 52, 474–480 (2013).Article 

    Google Scholar 
    Mikkelsen, R. L. Using hydrophilic polymers to control nutrient release. Fertilizer Res. 38, 53–59 (1994).Article 

    Google Scholar 
    Zheng, T., Liang, Y., Ye, S. & He, Z. Superabsorbent hydrogels as carriers for the controlled-release of urea: experiments and a mathematical model describing the release rate. Biosyst Eng. 102, 44–50 (2009).Article 

    Google Scholar 
    Grabowska-Polanowska, B., Garbowski, T., Bar-Michalczyk, D. & Kowalczyk, A. The benefits of synthetic or natural hydrogels application in agriculture: an overview Article. J. Water Land. Dev. 208–224 https://doi.org/10.24425/jwld.2021.139032 (2022).Badr, E. A. et al. Enhancing Canola yield and photosynthesis under water stress with hydrogel polymers. Phyton (B Aires). 93, 1623–1645 (2024).Article 

    Google Scholar 
    Lin, J. et al. Effect of degradable microplastics, Biochar and their coexistence on soil organic matter decomposition: A critical review. TRAC Trends Anal. Chem. 183, 118082 (2025).Article 

    Google Scholar 
    Shu, X. et al. Organic amendments enhance soil microbial diversity, microbial functionality and crop yields: A meta-analysis. Sci. Total Environ. 829, 154627 (2022).Article 
    PubMed 

    Google Scholar 
    Tautges, N. E., Sullivan, T. S., Reardon, C. L. & Burke, I. C. Soil microbial diversity and activity linked to crop yield and quality in a dryland organic wheat production system. Appl. Soil. Ecol. 108, 258–268 (2016).Article 

    Google Scholar 
    Chen, X., Huang, L., Mao, X., Liao, Z. & He, Z. A comparative study of the cellular microscopic characteristics and mechanisms of maize seedling damage from superabsorbent polymers. Pedosphere 27, 274–282 (2017).Article 

    Google Scholar 
    Puoci, F. et al. Polymer in agriculture: a review. Am. J. Agric. Biol. Sci. 3, 299–314 (2008).Article 

    Google Scholar 
    Shahram, S. & Felora, R. Investigation of superabsorbent polymer and water stress on physiological indexes of maize. J. Adv. Biol. 4, 455–460 (2014).
    Google Scholar 
    Li, H. Y., Zhang, R. & Wang, F. X. Effects of water-retaining agent on soil water movement and water use efficiency of maize sowed with absolved water-storing irrigation. Trans. Chin. Soc. Agricultural Eng. 27, 37–42 (2011).
    Google Scholar 
    Iftime, M. M., Ailiesei, G. L., Ungureanu, E. & Marin, L. Designing Chitosan based eco-friendly multifunctional soil conditioner systems with Urea controlled release and water retention. Carbohydr. Polym. 223, 115040 (2019).Article 
    PubMed 

    Google Scholar 
    Mohamady Ghobashy, M. The application of natural polymer-based hydrogels for agriculture. in Hydrogels Based on Natural Polymers 329–356 (Elsevier, doi:https://doi.org/10.1016/B978-0-12-816421-1.00013-6 (2020).López-Velázquez, J. C. et al. Gelatin–chitosan–PVA hydrogels and their application in agriculture. J. Chem. Technol. Biotechnol. 94, 3495–3504 (2019).Article 

    Google Scholar 
    Supare, K. & Mahanwar, P. A. Starch-derived superabsorbent polymers in agriculture applications: an overview. Polym. Bull. 79, 5795–5824 (2022).Article 

    Google Scholar 
    Guo, W. et al. Introduction of environmentally degradable parameters to evaluate the biodegradability of biodegradable polymers. PLoS One. 7, e38341 (2012).Article 
    ADS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Ghasemlou, M. et al. Biodegradation of novel bioplastics made of starch, polyhydroxyurethanes and cellulose nanocrystals in soil environment. Sci. Total Environ. 815, 152684 (2022).Article 
    PubMed 

    Google Scholar 
    Cui, C. et al. Recent advances in the preparation, characterization, and food application of starch-based hydrogels. Carbohydr. Polym. 291, 119624 (2022).Article 
    PubMed 

    Google Scholar 
    Pathak, V. Effect of Starch-based Hydrogel on Early Growth of Corn (School of Agricultural & Biological Engineering, 2018).Khodadadi Dehkordi, D. Effects of hydrophilic polymers on soil Water, wheat plant and microorganisms. Appl. Ecol. Environ. Res. 16, 1711–1724 (2018).Article 

    Google Scholar 
    Stahl, J. D., Cameron, M. D., Haselbach, J. & Aust, S. D. Biodegradation of superabsorbent polymers in soil. Environ. Sci. Pollut. Res. 7, 83–88 (2000).Article 

    Google Scholar 
    Kulachkova, S. A., Derevenets, E. N., Korolev, P. S. & Pronina, V. V. The effect of mineral fertilizers on soil respiration in urban lawns. Mosc. Univ. Soil. Sci. Bull. 78, 281–291 (2023).Article 

    Google Scholar 
    Ha, J. et al. Direct measurement of crosslinked surface layer in superabsorbent poly(acrylic acid). Mater. Lett. 228, 33–36 (2018).Article 
    ADS 

    Google Scholar 
    Sojka, R. E., Bjorneberg, D. L., Entry, J. A., Lentz, R. D. & Orts, W. J. Polyacrylamide in agriculture and environmental land management. in 75–162 https://doi.org/10.1016/S0065-2113(04)92002-0 (2007).Ren, X., Wang, L., Tang, J., Sun, H. & Giesy, J. P. Combined effects of degradable film fragments and micro/nanoplastics on growth of wheat seedling and rhizosphere microbes. Environ. Pollut. 294, 118516 (2022).Article 
    PubMed 

    Google Scholar 
    Wolinska, A. & Stepniewsk, Z. Dehydrogenase Activity in the Soil Environment. in DehydrogenasesInTech, https://doi.org/10.5772/48294 (2012).Buchmann, C., Rudolph, S., Neff, J. & Steinmetz, Z. Impact of polyacrylic acid as soil amendment on soil microbial activity under different moisture regimes. Sci. Rep. 15, 19422 (2025).Article 
    ADS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Bana, R. S. et al. Enhanced Pearl millet yield stability, water use efficiency and soil microbial activity using superabsorbent polymers and crop residue recycling across diverse ecologies. Eur. J. Agron. 148, 126876 (2023).Article 

    Google Scholar 
    Li, X., He, J. Z., Hughes, J. M., Liu, Y. R. & Zheng, Y. M. Effects of super-absorbent polymers on a soil–wheat (Triticum aestivum L.) system in the field. Appl. Soil. Ecol. 73, 58–63 (2014).Article 

    Google Scholar 
    Li, R., Hou, X., Li, P. & Wang, X. Multifunctional superabsorbent polymer under residue incorporation increased maize productivity through improving sandy soil properties. Adv. Polym. Technol. 2022, 1–12 (2022).Article 
    ADS 

    Google Scholar 
    Singh, I., Verma, R. R., Srivastava, T. K. & Growth Yield, irrigation water use Efficiency, juice quality and economics of sugarcane in Pusa hydrogel application under different irrigation scheduling. Sugar Tech. 20, 29–35 (2018).Article 

    Google Scholar 
    Jat, A. L., Rathore, B. S., Desai, A. G. & Shah, S. K. Production potential, water productivity and economic feasibility of Indian mustard (Brassica juncea) under deficit and adequate irrigation scheduling with hydrogel. Indian J. Agricultural Sci. 88, 212–215 (2018).Article 

    Google Scholar 
    Saini, A. K., Patel, A. M., Chaudhary, P. P. & Saini, L. H. Impact assessment of Irrigation, fertility and hydrogel levels on growth Attributes, yield and economics of summer Pearl millet (Pennisetum glaucum L.) under North Gu-jarat conditions. J. Pharmacogn Phytochem. 7, 2914–2918 (2018).
    Google Scholar 
    Download referencesFundingThe work was supported by the projects of Technology Agency of the Czech Republic SS06020468 „Development of natural nutrient-releasing controlled-release hydroabsorbents for use in crop production”.Author informationAuthors and AffiliationsDepartment of Agrochemistry, Soil Science, Microbiology and Plant Nutrition, Faculty of AgriSciences, Mendel University in Brno, Zemědělská 1, Brno, 61300, Czech RepublicTomáš Kriška, Jiří Antošovský, Martin Brtnický, Jiří Kučerík, Jiří Holátko & Petr ŠkarpaInstitute of Materials Science, Faculty of Chemistry, Brno University of Technology, Purkyňova 118, Brno, 61200, Czech RepublicJosef JančářAuthorsTomáš KriškaView author publicationsSearch author on:PubMed Google ScholarJiří AntošovskýView author publicationsSearch author on:PubMed Google ScholarMartin BrtnickýView author publicationsSearch author on:PubMed Google ScholarJiří KučeríkView author publicationsSearch author on:PubMed Google ScholarJiří HolátkoView author publicationsSearch author on:PubMed Google ScholarJosef JančářView author publicationsSearch author on:PubMed Google ScholarPetr ŠkarpaView author publicationsSearch author on:PubMed Google ScholarContributionsTK was involved in conceptualization, investigation, data curation, software, writing—original draft. JA was involved in investigation, writing—review and editing. MB was involved in investigation, data curation, validation, and writing—review and editing. JK was involved in writing—review and editing. JH was involved in writing—review and editing. JJ was involved in methodology, and investigation. PS was involved in conceptualization, methodology, formal analysis, funding acquisition, supervision, software, writing—original draft. All authors read and approved the final manuscript.Corresponding authorCorrespondence to
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    Reprints and permissionsAbout this articleCite this articleKriška, T., Antošovský, J., Brtnický, M. et al. Comparative effects of synthetic and natural hydrogels enriched with fertilizer on poppy yield and soil health in drought-prone conditions.
    Sci Rep 15, 44694 (2025). https://doi.org/10.1038/s41598-025-28213-0Download citationReceived: 07 July 2025Accepted: 10 November 2025Published: 29 December 2025Version of record: 29 December 2025DOI: https://doi.org/10.1038/s41598-025-28213-0Share this articleAnyone you share the following link with will be able to read this content:Get shareable linkSorry, a shareable link is not currently available for this article.Copy shareable link to clipboard
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    KeywordsHydrogelCulinary poppyYieldSoil healthProfitability of fertilization More