Ecology

AbstractThe present study aimed to evaluate nitrogen dynamics and use efficiency of transplanted rice under variable irrigation regimes and nitrogen levels. Two field experiments were conducted during the 2021 and 2022 rice-growing seasons using a split-plot design with four irrigation treatments in the main plots and four nitrogen levels in the sub-plots, each replicated thrice. Results indicated that nitrogen concentration and uptake by grain and straw were significantly influenced by both irrigation scheduling and nitrogen application. Among irrigation treatments, recommended scheduling and irrigation at field capacity produced the highest nitrogen concentration and uptake, whereas 10 and 20% depletion from field capacity resulted in lower values. For nitrogen levels, 125% of the recommended dose (RDN) recorded the highest grain nitrogen content and uptake, but values were statistically similar to 100% RDN. Flooded rice cultivation led to the greatest nitrogen removal from soil, followed by field capacity and deficit irrigation treatments. The highest nitrogen use efficiency was observed under deficit irrigation, followed by field capacity, while flooded irrigation was the least efficient. Adequate irrigation (I1/I2) resulted in the highest nitrogen uptake and grain yield, while deficit irrigation (I4) saved water but led to lower yield and nitrogen use efficiency, with greater amounts of residual nitrogen remaining in the soil. Overall, applying irrigation at field capacity combined with 100% RDN was found optimal for maximizing nutrient uptake and nitrogen use efficiency in transplanted rice, suggesting a sustainable approach to improve resource use without over-application of water or fertilizer.

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Data availability

The datasets used and/or analysed during the current study available from the corresponding author on reasonable request.
ReferencesZhao, X. et al. Analysis of irrigation demands of rice: Irrigation decision-making needs to consider future rainfall. Agric. Water Manag. 280, 108196. https://doi.org/10.1016/j.agwat.2023.108196 (2023).
Google Scholar 
Djaman, K. et al. Rice genotype and fertilizer management for improving rice productivity under saline soil conditions. Paddy Water Environ. 18, 43–57. https://doi.org/10.1007/s10333-019-00763-w (2020).
Google Scholar 
Surendra, U., Raja, P., Jayakumar, M. & Rama Subramoniam, R. Use of efficient water saving techniques for production of rice in India under climate change scenario: A critical review. J. Clean. Prod. 309, 127272. https://doi.org/10.1016/j.jclepro.2021.127272 (2021).
Google Scholar 
FAO. FAOSTAT Database: Food and Agriculture Organization of the United Nations (FAO, 2023).
Google Scholar 
Mohidem, N. U., Hashim, N., Shamsudin, R. & Che Man, H. Rice for food security: Revisiting its production, diversity, rice milling process and nutrient content. Agriculture 12(6), 741. https://doi.org/10.3390/agriculture12060741 (2022).
Google Scholar 
Saha, S., Singh, Y. V., Gaind, S. & Kumar, D. Water productivity and nutrient status of rice soil in response to cultivation techniques and nitrogen fertilization. Paddy Water Environ. 13, 443–453. https://doi.org/10.1007/s10333-014-0462-y (2014).
Google Scholar 
FAO. FAOSTAT Database: Food and Agriculture Organization of the United Nations (FAO, 2020).
Google Scholar 
Zarger, S. A., Islam, T. & Magray, J. A. Agricultural crop diversity of Kashmir valley. Biol. Life Sci. Forum 2(1), 30. https://doi.org/10.3390/BDEE2021-09396 (2021).
Google Scholar 
Arouna, A., Dzomeku, I. K., Shaibu, A. G. & Nurudeen, A. R. Water management for sustainable irrigation in rice (Oryza sativa L.) production: A review. Agronomy 13, 1522. https://doi.org/10.3390/agronomy13061522 (2023).
Google Scholar 
Barkunan, S. R., Bhanumathi, V. & Sethuram, J. Smart sensor for automatic drip irrigation system for paddy cultivation. Comput. Electr. Eng. 73, 180–193. https://doi.org/10.1016/j.compeleceng.2018.11.013 (2019).
Google Scholar 
Geethalakshmi, V., Ramesh, T., Palamuthirsolai, A. & Lakshmanan, A. Productivity and water usage of rice as influenced by different cultivation systems. Madras Agric. J. 96(7–12), 349–352. https://doi.org/10.1080/03650340903286422 (2009).
Google Scholar 
Bouman, B. A. M. & Tuong, T. P. Field water management to save water and increase its productivity in irrigated lowland rice. Agric. Water Manage. 49, 11–30. https://doi.org/10.1016/S0378-3774(00)00128-1 (2001).
Google Scholar 
Sun, Y. et al. The effects of different water and nitrogen managements on yield and nitrogen use efficiency in hybrid rice of China. Field Crops Res. 127, 85–98. https://doi.org/10.1016/j.fcr.2011.11.015 (2012).
Google Scholar 
Farooq, M., Wahid, A., Kobayashi, N., Fujita, D. & Basra, S. M. A. Plant drought stress, effects, mechanisms and management. Agron. Sustain. Dev. 29, 185–212. https://doi.org/10.1051/agro:2008021 (2009).
Google Scholar 
Sujono, J., Matsuo, N., Hiramatsu, K. & Mochizuki, T. Improving the water productivity of paddy rice (Oryza sativa L.) cultivation through water saving irrigation treatments. Agric. Sci. 2, 511–517. https://doi.org/10.4236/as.2011.24066 (2011).
Google Scholar 
Carrijo, D. R., Lundy, M. E. & Linquist, B. A. Rice yields and water use under alternate wetting and drying irrigation: A metaanalysis. Field Crops Res. 203, 173–180. https://doi.org/10.1016/j.fcr.2016.12.002 (2017).
Google Scholar 
Maheswari, J., Maragatham, N. & Martin, G. J. Relatively simple irrigation scheduling and N application enhances the productivity of aerobic rice. Am. J. Plant Physiol. 2(4), 261–268. https://doi.org/10.3923/ajpp.2007.261.268 (2007).
Google Scholar 
Vijayakumar, S. et al. Effect of potassium fertilization on growth indices, yield attributes and economics of dry direct seeded basmati rice (Oryza sativa L.). Oryza 56(2), 214–220. https://doi.org/10.35709/ory.2019.56.2.6 (2019).
Google Scholar 
Ockerby, S. E. & Fukai, S. The management of rice grown on raised beds with continuous furrow irrigation. Field Crops Res. 69, 215–226. https://doi.org/10.1016/S0378-4290(00)00140-4 (2001).
Google Scholar 
Martins, M. A. et al. On the sustainability of paddy rice cultivation in the Paraíba do Sul river basin (Brazil) under a changing climate. J. Clean. Prod. 386, 35760. https://doi.org/10.1016/j.jclepro.2022.135760 (2023).
Google Scholar 
Zeng, Y. F., Chen, C. T. & Lin, G. F. Practical application of an intelligent irrigation system to rice paddies in Taiwan. Agric. Water Manage. 280, 108216. https://doi.org/10.1016/j.agwat.2023.108216 (2023).
Google Scholar 
He, G., Wang, Z. & Cui, Z. Managing irrigation water for sustainable rice production in China. J. Clean. Prod. 245, 118928. https://doi.org/10.1016/j.jclepro.2019.118928 (2020).
Google Scholar 
Abioye, E. A. et al. IoT-based monitoring and data-driven modelling of drip irrigation system for mustard leaf cultivation experiment. Inf. Process. Agric. https://doi.org/10.1016/j.inpa.2020.05.004 (2020).
Google Scholar 
Delgoda, D., Malano, H., Saleem, S. K. & Halgamuge, M. N. Irrigation control based on model predictive control (MPC): Formulation of theory and validation using weather forecast data and AQUACROP model. Environ. Modell. Softw. 78, 40–53. https://doi.org/10.1016/j.envsoft.2015.12.012 (2016).
Google Scholar 
Bwambale, E., Abagale, F. K. & Anornu, G. K. Smart irrigation monitoring and control strategies for improving water use efficiency in precision agriculture: A review. Agric. Water Manage. 260, 107324. https://doi.org/10.1016/j.agwat.2021.107324 (2022).
Google Scholar 
Soulis, K. & Elmaloglou, S. Optimum soil water content sensors placement for surface drip irrigation scheduling in layered soils. Comput. Electron. Agric. 152, 1–8. https://doi.org/10.1016/j.compag.2018.06.052 (2018).
Google Scholar 
Wu, H. et al. Effects of post-anthesis nitrogen uptake and translocation on photosynthetic production and rice yield. Sci. Rep. 8, 12891 (2018).
Google Scholar 
Herrera, J. M. et al. Emerging and established technologies to increase nitrogen use efficiency of cereals. Agronomy 6, 25. https://doi.org/10.3390/agronomy6020025 (2016).
Google Scholar 
Liu, Y. et al. Genomic basis of geographical adaptation to soil nitrogen in rice. Nature 590, 600–605. https://doi.org/10.1038/s41586-020-03091-w (2021).
Google Scholar 
Gramma, V., Kontbay, K. & Wahl, V. Crops for the future: On the way to reduce nitrogen pollution. Am. J. Bot. 107, 1211–1213. https://doi.org/10.1002/ajb2.1527 (2020).
Google Scholar 
Zhang, Z., Hu, B. & Chu, C. Towards understanding the hierarchical nitrogen signalling network in plants. Curr. Opin. Plant Biol. 2020(55), 60–66. https://doi.org/10.1016/j.pbi.2020.03.006 (2020).
Google Scholar 
Piper, C. S. Soil and Plant Analysis (Hans Publisher, 1966).
Google Scholar 
Parihar, S. K. & Sandhu, B. S. Estimation of bulk density using a pycnometer. J. Soil Sci. 19(2), 251–258 (1968).
Google Scholar 
Jackson, M. L. Soil Chemical Analysis (Prentice Hall of India Private Limited, 1973).
Google Scholar 
Walkley, A. & Black, I. A. An examination of the Degtjareff method for determining soil organic carbon matter and a proposed modification of the chromic acid titration method. Soil Sci. 37, 29–38 (1934).
Google Scholar 
Subbiah, B. V. & Asija, G. L. A rapid procedure for the estimation of available nitrogen in soils. Curr. Sci. 25, 259–260 (1956).
Google Scholar 
Olsen, S. R., Cole, C. V., Watanabe, F. S. & Dean, L. A. Estimation of Available Phosphorous in Soils by Extraction with Sodium Bicarbonbate (USDA Circular, 939 U.S. Government Printing Office, 1954).
Google Scholar 
Merwin, H. D. & Peech, M. Exchangeability of soil potassium in the sand, silt and clay fraction as influenced by the nature of complementary exchangeable cations. Soil Soc. Am. Proc. 15, 125–128 (1950).
Google Scholar 
Cassman, K. G. et al. Opportunities for increased nitrogen-use efficiency from improved resource management in irrigated rice systems. Field Crop. Res. 56, 7–39 (1998).
Google Scholar 
Baligar, V. C., Fageria, N. K. & He, Z. L. Nutrient use efficiency in plants. Commun. Soil Sci. Plant Anal. 32, 921–950 (2001).
Google Scholar 
Chowdhury, M. R., Kumar, V., Sattar, A. & Brahmachari, K. Studies on the water use efficiency and nutrient uptake by rice under system of rice intensification. Bioscan 9(1), 85–88 (2014).
Google Scholar 
Wang, Z. et al. Grain yield, water and nitrogen use efficiencies of rice as influenced by irrigation regimes and their interaction with nitrogen rates. Field Crop. Res. 193, 54–69 (2016).
Google Scholar 
Sandhu, S. S. & Mahal, S. S. Performance or rice (Oryza sativa L.) under different planting methods, nitrogen level and irrigation schedules. Indian J. Agron. 59(3), 392–397 (2014).
Google Scholar 
Elina, O. et al. High-acclimation capacity for growth and role of soil fertility after long-range transfer of Betula pendula and B. pubescens between Finland and Italy. J. Forest. Res. 36(1), 1–19 (2025).
Google Scholar 
Amgain, N. R., Rabbany, A., Galindo, S. & Bhadha, J. H. Effects of water management strategies and nitrogen fertilizer on rice yield cultivated on histosols. J. Rice Res. Dev. 4(1), 331–338 (2021).
Google Scholar 
Wang, Y. et al. The effects of nitrogen supply and water regime on instantaneous WUE, time-integrated WUE and carbon isotope discrimination in winter wheat. Field Crop. Res. 144, 236–244 (2013).
Google Scholar 
Malav, J. K. et al. Rice yield and available nutrients status of loamy sand soil as influenced by different levels of silicon and nitrogen. Int. J. Curr. Microbiol. Appl. Sci. 7(2), 619–632 (2018).
Google Scholar 
Kabat, B. & Satapathy, M. R. Effect of planting dates and N levels on grain yield and N uptake by hybrid rice. ORYZA Int. J. Rice 50(4), 409–411 (2013).
Google Scholar 
Mboyerwa, P. A., Kibret, K., Mtakwa, P. & Aschalew, A. Lowering nitrogen rates under the system of rice intensification enhanced rice productivity and nitrogen use efficiency in irrigated lowland rice. Heliyon 8, e09140 (2022).
Google Scholar 
Thakur, A. K., Rath, S. & Mandal, K. G. Differential responses of system of rice intensification (SRI) and conventional flooded-rice management methods to applications of nitrogen fertilizer. Plant. Soil 370(1), 59–71 (2013).
Google Scholar 
Zhang, Z. et al. Nitrogen effects on yield, quality and physiological characteristics of giant rice. Agronomy 10, 1816 (2020).
Google Scholar 
Yang, J., Zhou, Q. & Zhang, J. Moderate wetting and drying increases rice yield and reduces water use, grain arsenic level, and methane emission. Crop. J. 5, 151–158 (2017).
Google Scholar 
Sharma, R. P., Patha, S. K. & Singh, R. C. Effect of nitrogen and weed management practices in direct seeded rice (Oryza sativa) under upland conditions. Indian J. Agron. 52, 114–119 (2007).
Google Scholar 
Gupta, R. K. et al. Need based fertilizer nitrogen management using leaf colour chart in hybrid rice (Oryza sativa L.). Indian J. Agric. Sci. 81(12), 1153–1157 (2011).
Google Scholar 
Kour, S., Jalali, V. K., Sharma, R. K. & Bali, A. S. Comparative nitrogen use efficiency in hybrid and indigenous cultivars of rice. J. Res. 6, 1–4 (2007).
Google Scholar 
Singh, Y., Gupta, R. K., Singh, B. & Gupta, S. Efficient management of fertilizer N in wet direct-seeded rice (Oryza sativa L.) in northwest India. Indian J. Agric. Sci. 77, 561–564 (2007).
Google Scholar 
Hazra, K. K. & Chandra, S. Effect of extended water stress on growth, tiller mortality and nutrient recovery under system of rice intensification. Proc. Natl. Acad. Sci. India Sect. B Biol. Sci. 86(1), 105–113 (2016).
Google Scholar 
Liu, L. et al. Combination of site-specific nitrogen management and alternate wetting and drying irrigation increases grain yield and nitrogen and water use efficiency in super rice. Field Crop. Res. 154, 226–235 (2013).
Google Scholar 
Mir, M. S. et al. Influence of sowing dates and weed management practices on weed dynamics, productivity and profitability of direct seeded rice. Sci. Rep. 14, 18877. https://doi.org/10.1038/s41598-024-69519-9 (2024).
Google Scholar 
Chu, G. et al. Alternate wetting and moderate drying increases rice yield and reduces methane emission in paddy field with wheat straw residue incorporation. F. Ener. Sec. 4(3), 238–254 (2015).
Google Scholar 
Liu, T. et al. Effects of stand density regulation on soil carbon pools in different-aged Larix principis-rupprechtii plantations and soil respiration model enhancement. J. Forest. Res. 36(1), 141 (2025).
Google Scholar 
Liang, K. et al. Grain yield, water productivity and CH4 emission of irrigated rice in response to water management in south China. Agri. Wat. Man. 163, 319–331 (2016).
Google Scholar 
Ren, B. et al. Water absorption is affected by the nitrogen supply to rice plants. Pl. Soi. 396, 397–410 (2015).
Google Scholar 
Zanini, M. et al. Soil organic carbon sequestration during secondary forest succession in a Mediterranean area. J. Forest. Res. 36(1), 71 (2025).
Google Scholar 
Download referencesAcknowledgementsThe authors wish to thank Princess Nourah bint Abdulrahman University Researchers Supporting Project number (PNURSP2025R365), Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia for supporting this studyFundingOpen Access funding enabled and organized by Projekt DEAL. This work is supported by Princess Nourah bint Abdulrahman University Researchers Supporting Project number (PNURSP2025R365), Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia for supporting this study. The article processing charge was funded by the Open Access Publication Fund of Humboldt-Universität zu Berlin.Author informationAuthors and AffiliationsDivision of Agronomy, Faculty of Agriculture, Sher-E-Kashmir University of Agricultural Sciences and Technology of Kashmir, WaduraSopore, 193201, IndiaMohd Salim Mir, Waseem Raja, Raihana Habib Kanth, Amal Saxena & Lal SinghSher-E-Kashmir University of Agricultural Sciences and Technology of Kashmir, Wadura, 190006, IndiaEajaz Ahmad DarWest Florida Research and Education Centre, University of Florida, Jay, FL, 32565, USAEajaz Ahmad DarDivision of Agricultural Extension, Faculty of Agriculture, Sher-E-Kashmir University of Agricultural Sciences and Technology of Kashmir, WaduraSopore, 193201, IndiaZahoor Ahmad ShahDivision of Entomology, Faculty of Agriculture, Sher-E-Kashmir University of Agricultural Sciences and Technology of Kashmir, WaduraSopore, 193201, IndiaDanishta AzizDepartment of Biology, College of Science, Princess Nourah Bint Abdulrahman University, P. O. Box 84428, 11671, Riyadh, Saudi ArabiaLaila A. Al-Shuraym & Lamya Ahmed AlkeridisDirectorate of Research, Sher-E-Kashmir University of Agricultural Sciences and Technology of Kashmir, WaduraSopore, 190025, IndiaParmeet SinghDivision of Genetics and Plant Breeding, Faculty of Agriculture, Sher-E-Kashmir University of Agricultural Sciences and Technology of Kashmir, WaduraSopore, 193201, IndiaUmer FayazHead and Principal Scientist (Agril Extension), Division of Agricultural Extension, ICAR-IARI, New Delhi, India SatyapriyaDepartment of Crop and Animal Sciences, Albrecht Daniel Thaer-Institute of Agricultural and Horticultural Sciences, Faculty of Life Sciences, Humboldt-Universität Zu Berlin, Albrecht-Thaer-Weg 5, 14195, Berlin, GermanyAmged El-HarairyUnit of Entomology, Plant Protection Department, Desert Research Center, 1 Mathaf El-Matariya St., El-Matariya, Cairo, 11753, EgyptAmged El-HarairyDepartment of Plant Protection, Faculty of Agriculture, Zagazig University, Zagazig, 44511, Sharkia, EgyptAhmed A. A. AioubDepartment of Economic Entomology and Pesticides, Faculty of Agriculture, Cairo University, Giza, 12613, EgyptSamy SayedAuthorsMohd Salim MirView author publicationsSearch author on:PubMed Google ScholarWaseem RajaView author publicationsSearch author on:PubMed Google ScholarRaihana Habib KanthView author publicationsSearch author on:PubMed Google ScholarEajaz Ahmad DarView author publicationsSearch author on:PubMed Google ScholarZahoor Ahmad ShahView author publicationsSearch author on:PubMed Google ScholarDanishta AzizView author publicationsSearch author on:PubMed Google ScholarLaila A. Al-ShuraymView author publicationsSearch author on:PubMed Google ScholarLamya Ahmed AlkeridisView author publicationsSearch author on:PubMed Google ScholarParmeet SinghView author publicationsSearch author on:PubMed Google ScholarAmal SaxenaView author publicationsSearch author on:PubMed Google ScholarLal SinghView author publicationsSearch author on:PubMed Google ScholarUmer FayazView author publicationsSearch author on:PubMed Google Scholar SatyapriyaView author publicationsSearch author on:PubMed Google ScholarAmged El-HarairyView author publicationsSearch author on:PubMed Google ScholarAhmed A. A. AioubView author publicationsSearch author on:PubMed Google ScholarSamy SayedView author publicationsSearch author on:PubMed Google ScholarContributionsAll authors conceived and designed the study and experiments; MSM, WR, RHK, EAD, ZAH, DA, PS, AS, LS, UF and DS performed the experiments; LAA-S, LAA, AAA, SS, and AE analyzed the data, and wrote the manuscript; All authors reviewed and approved the final manuscript.Corresponding authorsCorrespondence to
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Reprints and permissionsAbout this articleCite this articleMir, M.S., Raja, W., Kanth, R.H. et al. Optimizing irrigation and nitrogen levels for improved soil nitrogen dynamics and use efficiency in temperate ecology of Kashmir.
Sci Rep (2025). https://doi.org/10.1038/s41598-025-32465-1Download citationReceived: 05 September 2025Accepted: 10 December 2025Published: 30 December 2025DOI: https://doi.org/10.1038/s41598-025-32465-1Share 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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KeywordsIrrigationNitrogenNutrient uptakeTransplanted riceFlooded riceNitrogen use efficiency More

AbstractThe carbon use efficiency (CUE) of soil microbes is defined as the proportion of assimilated carbon (C) allocated to microbial growth than lost through respiration. CUE is a critical parameter in soil C cycling, and its response to external nutrient inputs (including nitrogen [N], phosphorus [P], and combined NP) could determine the change of soil C pool. However, the general pattern of CUE response to nutrient addition remains unclear. By conducting a global meta-analysis of 427 observations, we found that the nutrient addition had no significant overall effect on CUE, whether in the overall nutrient addition experiments or in experiments with different types of nutrient addition. The response of CUE to N addition was regulated by duration, with long-term addition (>10 year) leading to a decrease in CUE. We identified the aridity index as the most important predictor influencing CUE in response to N addition, while ecosystem type was the most key variable for CUE response to both P and NP additions. Global predictions showed future N inputs tends to enhance CUE across most regions, especially in cropland ecosystems. Overall, the effect of nutrient addition on CUE was relatively minor, providing key microbial evidence for understanding future soil C dynamics.

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Data availability

The data that support the findings of this study are openly available in figshare at https://doi.org/10.6084/m9.figshare.30702806.
Code availability

The code that support the findings of this study are openly available in figshare at https://doi.org/10.6084/m9.figshare.30702806.
ReferencesDavidson, E. A. & Janssens, I. A. Temperature sensitivity of soil carbon decomposition and feedbacks to climate change. Nature 440, 165–173 (2006).
Google Scholar 
Schmidt, M. W. et al. Persistence of soil organic matter as an ecosystem property. Nature 478, 49–56 (2011).
Google Scholar 
Friedlingstein, P. et al. Global carbon budget 2023. Earth Syst. Sci. Data 15, 5301–5369 (2023).
Google Scholar 
Pries, H. C. E., Castanha, C., Porras, R. & Torn, M. The whole-soil carbon flux in response to warming. Science 355, 1420–1423 (2017).
Google Scholar 
Chen, Y. et al. Whole-soil warming leads to substantial soil carbon emission in an alpine grassland. Nat. Commun. 15, 4489 (2024).
Google Scholar 
Allison, S. D., Wallenstein, M. D. & Bradford, M. A. Soil-carbon response to warming dependent on microbial physiology. Nat. Geosci. 3, 336–340 (2010).
Google Scholar 
Liang, C., Schimel, J. P. & Jastrow, J. D. The importance of anabolism in microbial control over soil carbon storage. Nat. Microbiol. 2, 1–6 (2017).
Google Scholar 
Cotrufo, M. F., Wallenstein, M. D., Boot, C. M., Denef, K. & Paul, E. The microbial efficiency-matrix stabilization (MEMS) framework integrates plant litter decomposition with soil organic matter stabilization: do labile plant inputs form stable soil organic matter?. Glob. change Biol. 19, 988–995 (2013).
Google Scholar 
Camenzind, T., Mason-Jones, K., Mansour, I., Rillig, M. C. & Lehmann, J. Formation of necromass-derived soil organic carbon determined by microbial death pathways. Nat. Geosci. 16, 115–122 (2023).
Google Scholar 
Sinsabaugh, R. L., Manzoni, S., Moorhead, D. L. & Richter, A. Carbon use efficiency of microbial communities: stoichiometry, methodology and modelling. Ecol. Lett. 16, 930–939 (2013).
Google Scholar 
Tao, F. et al. Microbial carbon use efficiency promotes global soil carbon storage. Nature 618, 981–985 (2023).
Google Scholar 
Nottingham, A. T., Meir, P., Velasquez, E. & Turner, B. L. Soil carbon loss by experimental warming in a tropical forest. Nature 584, 234–237 (2020).
Google Scholar 
Schimel, J. Modeling ecosystem-scale carbon dynamics in soil: the microbial dimension. Soil Biol. Biochem. 178, 108948 (2023).
Google Scholar 
Pan, Y. et al. Enhanced atmospheric phosphorus deposition in Asia and Europe in the past two decades. Atmos. Ocean. Sci. Lett. 14, 100051 (2021).
Google Scholar 
Malik, A. et al. Drivers of global nitrogen emissions. Environ. Res. Lett. 17, 015006 (2022).
Google Scholar 
Zhu, J. X. et al. Changing patterns of global nitrogen deposition driven by socio-economic development. Nat. Commun. 16, 46 (2025).
Google Scholar 
Peñuelas, J., Sardans, J., Rivas-ubach, A. & Janssens, I. A. The human-induced imbalance between C, N and P in Earth’s life system. Glob. Change Biol. 18, 3–6 (2012).
Google Scholar 
LeBauer, D. S. & Treseder, K. K. Nitrogen limitation of net primary productivity in terrestrial ecosystems is globally distributed. Ecology 89, 371–379 (2008).
Google Scholar 
Zemunik, G., Turner, B. L., Lambers, H. & Laliberté, E. Diversity of plant nutrient-acquisition strategies increases during long-term ecosystem development. Nat. plants 1, 1–4 (2015).
Google Scholar 
Li, Y., Hou, L. Y., Song, B., Yang, L. Y. & Li, L. H. Effects of increased nitrogen and phosphorus deposition on offspring performance of two dominant species in a temperate steppe ecosystem. Sci. Rep. 7, 40951 (2017).
Google Scholar 
Bobbink, R. et al. Global assessment of nitrogen deposition effects on terrestrial plant diversity: a synthesis. Ecol. Appl. 20, 30–59 (2010).
Google Scholar 
Tulloss, E. M. & Cadenasso, M. L. The effect of nitrogen deposition on plant performance and community structure: is it life stage specific?. PloS ONE 11, e0156685 (2016).
Google Scholar 
Chen, Y., Liu, X., Hou, Y. H., Zhou, S. R. & Zhu, B. Particulate organic carbon is more vulnerable to nitrogen addition than mineral-associated organic carbon in soil of an alpine meadow. Plant Soil 458, 93–103 (2021).
Google Scholar 
Wang, X. D. et al. Globally nitrogen addition alters soil microbial community structure, but has minor effects on soil microbial diversity and richness. Soil Biol. Biochem. 179, 108982 (2023).
Google Scholar 
Janssens, I. et al. Reduction of forest soil respiration in response to nitrogen deposition. Nat. Geosci. 3, 315–322 (2010).
Google Scholar 
Riggs, C. E. & Hobbie, S. E. Mechanisms driving the soil organic matter decomposition response to nitrogen enrichment in grassland soils. Soil Biol. Biochem. 99, 54–65 (2016).
Google Scholar 
Crowther, T. W. et al. Sensitivity of global soil carbon stocks to combined nutrient enrichment. Ecol. Lett. 22, 936–945 (2019).
Google Scholar 
Luo, R. Y. et al. Nutrient addition reduces carbon sequestration in a Tibetan grassland soil: disentangling microbial and physical controls. Soil Biol. Biochem. 144, 107764 (2020).
Google Scholar 
Keeler, B. L., Hobbie, S. E. & Kellogg, L. E. Effects of long-term nitrogen addition on microbial enzyme activity in eight forested and grassland sites: implications for litter and soil organic matter decomposition. Ecosystems 12, 1–15 (2009).
Google Scholar 
Zheng, X. Y. et al. Interactions between nitrogen and phosphorus in modulating soil respiration: a meta-analysis. Sci. Total Environ. 905, 167346 (2023).
Google Scholar 
Hou, E. Q. et al. Latitudinal patterns of terrestrial phosphorus limitation over the globe. Ecol. Lett. 24, 1420–1431 (2021).
Google Scholar 
Widdig, M. et al. Microbial carbon use efficiency in grassland soils subjected to nitrogen and phosphorus additions. Soil Biol. Biochem. 146, 107815 (2020).
Google Scholar 
Feng, X. H. et al. Nitrogen input enhances microbial carbon use efficiency by altering plant-microbe-mineral interactions. Glob. Change Biol. 28, 4845–4860 (2022).
Google Scholar 
Duan, P. P., Wang, K. L. & Li, D. J. Nitrogen addition effects on soil mineral-associated carbon differ between the valley and slope in a subtropical karst forest. Geoderma 430, 116357 (2023).
Google Scholar 
Kuske, C. R. et al. Simple measurements in a complex system: soil community responses to nitrogen amendment in a Pinus taeda forest. Ecosphere 10, e02687 (2019).
Google Scholar 
Cui, J. et al. Carbon and nitrogen recycling from microbial necromass to cope with C:N stoichiometric imbalance by priming. Soil Biol. Biochem. 142, 107720 (2020).
Google Scholar 
Strickland, M. S. & Rousk, J. Considering fungal:bacterial dominance in soils-Methods, controls, and ecosystem implications. Soil Biol. Biochem. 42, 1385–1395 (2010).
Google Scholar 
Kallenbach, C. M., Frey, S. D. & Grandy, A. S. Direct evidence for microbial-derived soil organic matter formation and its ecophysiological controls. Nat. Commun. 7, 13630 (2016).
Google Scholar 
Miao, Y. C. et al. Lower microbial carbon use efficiency reduces cellulose-derived carbon retention in soils amended with compost versus mineral fertilizers. Soil Biol. Biochem. 156, 108227 (2021).
Google Scholar 
Chen, J. et al. Microbial C:N:P stoichiometry and turnover depend on nutrients availability in soil: A 14C, 15N and 33P triple labelling study. Soil Biol. Biochem. 131, 206–216 (2019).
Google Scholar 
Fang, Y. Y. et al. Nutrient stoichiometry and labile carbon content of organic amendments control microbial biomass and carbon-use efficiency in a poorly structured sodic-subsoil. Biol. Fertil. Soils 56, 219–233 (2020).
Google Scholar 
Barcelos, J. P. Q., Mariano, E., Jones, D. L. & Rosolem, C. A. Topsoil and subsoil C and N turnover are affected by superficial lime and gypsum application in the short-term. Soil Biol. Biochem. 163, 108456 (2021).
Google Scholar 
Cui, H., He, C., Zheng, W. W., Jiang, Z. H. & Yang, J. P. Effects of nitrogen addition on rhizosphere priming: the role of stoichiometric imbalance. Sci. Total Environ. 914, 169731 (2024).
Google Scholar 
Silva-Sánchez, A., Soares, M. & Rousk, J. Testing the dependence of microbial growth and carbon use efficiency on nitrogen availability, pH, and organic matter quality. Soil Biol. Biochem. 134, 25–35 (2019).
Google Scholar 
Wang, Q. Q. et al. Soil pH and phosphorus availability regulate sulphur cycling in an 82-year-old fertilised grassland. Soil Biol. Biochem. 194, 109436 (2024).
Google Scholar 
Hu, J. X., Huang, C. D., Zhou, S. X. & Kuzyakov, Y. Nitrogen addition to soil affects microbial carbon use efficiency: meta-analysis of similarities and differences in 13C and 18O approaches. Glob. Change Biol. 28, 4977–4988 (2022).
Google Scholar 
Zhang, Q. F. et al. Effect of field warming on soil microbial carbon use efficiency—a meta-analysis. Soil Biol. Biochem. 197, 109531 (2024).
Google Scholar 
Lian, J. S., Li, G. H., Zhang, J. F. & Massart, S. Nitrogen fertilization affected microbial carbon use efficiency and microbial resource limitations via root exudates. Sci. Total Environ. 950, 174933 (2024).
Google Scholar 
Ma, Z. B. et al. Long-term green manuring increases soil carbon sequestration via decreasing qCO2 caused by lower microbial phosphorus limitation in a dry land field. Agric. Ecosyst. Environ. 374, 109142 (2024).
Google Scholar 
Hicks, L. C., Yuan, M. Y., Brangarí, A., Rousk, K. & Rousk, J. Increased above- and belowground plant input can both trigger microbial nitrogen mining in subarctic tundra soils. Ecosystems 25, 105–121 (2022).
Google Scholar 
Neurauter, M., Yuan, M., Hicks, L. C. & Rousk, J. Soil microbial resource limitation along a subarctic ecotone from birch forest to tundra heath. Soil Biol. Biochem. 177, 108919 (2023).
Google Scholar 
Su, Z. X. & Shangguan, Z. P. Nitrogen addition decreases the soil cumulative priming effect and favours soil net carbon gains in Robinia pseudoacacia plantation soil. Geoderma 433, 116444 (2023).
Google Scholar 
Mehnaz, K. R., Corneo, P. E., Keitel, C. & Dijkstra, F. A. Carbon and phosphorus addition effects on microbial carbon use efficiency, soil organic matter priming, gross nitrogen mineralization and nitrous oxide emission from soil. Soil Biol. Biochem. 134, 175–186 (2019).
Google Scholar 
Geyer, K. M., Dijkstra, P., Sinsabaugh, R. & Frey, S. D. Clarifying the interpretation of carbon use efficiency in soil through methods comparison. Soil Biol. Biochem. 128, 79–88 (2019).
Google Scholar 
Ziegler, S. E. & Billings, S. A. Soil nitrogen status as a regulator of carbon substrate flows through microbial communities with elevated CO2. J. Geophys. Res. Biogeosciences 116, G01011 (2011).
Google Scholar 
Spohn, M. Element cycling as driven by stoichiometric homeostasis of soil microorganisms. Basic Appl. Ecol. 17, 471–478 (2016).
Google Scholar 
Poeplau, C. et al. Increased microbial anabolism contributes to soil carbon sequestration by mineral fertilization in temperate grasslands. Soil Biol. Biochem. 130, 167–176 (2019).
Google Scholar 
Bond-Lamberty, B., Bailey, V. L., Chen, M., Gough, C. M. & Vargas, R. Globally rising soil heterotrophic respiration over recent decades. Nature 560, 80–83 (2018).
Google Scholar 
Liu, W. X., Zhang, Z. & Wan, S. Q. Predominant role of water in regulating soil and microbial respiration and their responses to climate change in a semiarid grassland. Glob. Change Biol. 15, 184–195 (2009).
Google Scholar 
Choi, R. T., Reed, S. C. & Tucker, C. L. Multiple resource limitation of dryland soil microbial carbon cycling on the Colorado Plateau. Ecology 103, e3671 (2022).
Google Scholar 
Morris, K. A., Richter, A., Migliavacca, M. & Schrumpf, M. Growth of soil microbes is not limited by the availability of nitrogen and phosphorus in a Mediterranean oak-savanna. Soil Biol. Biochem. 169, 108680 (2022).
Google Scholar 
Li, J. W., Wang, G. S., Allison, S. D., Mayes, M. A. & Luo, Y. Q. Soil carbon sensitivity to temperature and carbon use efficiency compared across microbial-ecosystem models of varying complexity. Biogeochemistry 119, 67–84 (2014).
Google Scholar 
Manning, D. W., Rosemond, A. D., Gulis, V., Benstead, J. P. & Kominoski, J. S. Nutrients and temperature additively increase stream microbial respiration. Glob. Change Biol. 24, e233–e247 (2018).
Google Scholar 
Potapov, P. et al. Global maps of cropland extent and change show accelerated cropland expansion in the twenty-first century. Nat. Food 3, 19–28 (2022).
Google Scholar 
Pittelkow, C. M. et al. Productivity limits and potentials of the principles of conservation agriculture. Nature 517, 365–368 (2015).
Google Scholar 
Xu, P. et al. Fertilizer management for global ammonia emission reduction. Nature 626, 792–798 (2024).
Google Scholar 
Ren, C., He, L. & Rosa, L. Integrated irrigation and nitrogen optimization is a resource-efficient adaptation strategy for US maize and soybean production. Nat. Food 6, 389–400 (2025).
Google Scholar 
Harpole, W. S. et al. Addition of multiple limiting resources reduces grassland diversity. Nature 537, 93–96 (2016).
Google Scholar 
Cotrufo, M. F. & Lavallee, J. M. Soil organic matter formation, persistence, and functioning: a synthesis of current understanding to inform its conservation and regeneration. Adv. Agron. 172, 1–66 (2022).
Google Scholar 
Hedges, L. V., Gurevitch, J. & Curtis, P. S. The meta-analysis of response ratios in experimental ecology. Ecology 80, 1150–1156 (1999).
Google Scholar 
Fryda, T. et al. h2o: R interface for the ‘H2O’ scalable machine learning platform. R package version 3.44.0.3 https://CRAN.R-project.org/package=h2o (2024).Ziehn, T. et al. CSIRO ACCESS-ESM1.5 model output prepared for CMIP6 ScenarioMIP ssp585. Version 20241220. Earth System Grid Federation https://doi.org/10.22033/ESGF/CMIP6.4333 (2019).FAO & IIASA. Global agro-ecological zoning version 5 (GAEZ v5) model documentation https://github.com/un-fao/gaezv5/wiki (2025).R Development Core Team. R: a language and environment for statistical computing. R Foundation for Statistical Computing https://www.R-project.org (2025).Download referencesAcknowledgementsThe authors thank the Subject editor and three anonymous reviewers for their insightful comments and suggestions. We sincerely thank all the scientists whose data were included in this meta-analysis. This study was financially supported by the National Natural Science Foundation of China (32301426 and 32571907), the China Postdoctoral Science Foundation (2022M720252 and 2024T170025).Author informationAuthor notesThese authors contributed equally: Ying Chen, Yawen Lu.Authors and AffiliationsZhejiang Tiantong Forest Ecosystem National Observation and Research Station, Institute of Eco-Chongming, Zhejiang Zhoushan Island Ecosystem Observation and Research Station, School of Ecological and Environmental Sciences, East China Normal University, Shanghai, ChinaYing Chen, Siyuan Gao & Huiying LiuCollege of Chemistry and Life Science, Suzhou University of Science and Technology, Suzhou, ChinaYawen LuAuthorsYing ChenView author publicationsSearch author on:PubMed Google ScholarYawen LuView author publicationsSearch author on:PubMed Google ScholarSiyuan GaoView author publicationsSearch author on:PubMed Google ScholarHuiying LiuView author publicationsSearch author on:PubMed Google ScholarContributionsY.C. and Y.L. conceived the idea; Y.C. and Y.L. collected the data with the help from S.G.; Y.C., Y.L., and H.L. analyzed the data; Y.C. and Y.L. wrote the manuscript with input from all authors.Corresponding authorCorrespondence to
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Reprints and permissionsAbout this articleCite this articleChen, Y., Lu, Y., Gao, S. et al. Minor effects of nutrient additions on soil microbial carbon use efficiency.
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AbstractTuta absoluta is a significant invasive pest, severely impacting the global tomato industry. Prolonged application of chemical insecticides has led to varying degrees of resistance in T. absoluta populations. Additionally, chemical insecticides are causing serious threats to the environment. Aiming to develop a novel bioinsecticide based on Origanum vulgare essential oil (OVE) against T. absoluta, we carried out its nanostructured lipid carrier formulation (OVE-NLC). The obtained OVE-NLC had spherical particles approximately 94.26 nm in size with a uniform size distribution of less than 0.3 and a zeta potential of − 18.75 mV. The formulated NLC also had encapsulation efficiency up to 96% and was stable at 25 °C for 3 months. The FTIR results indicated no significant chemical interaction between EO and NLC components. OVE-NLC demonstrated significant toxicity towards T. absoluta larvae and a remarkable oviposition deterrence for females. The nanoformulation also negatively affected the population growth parameters of T. absoluta, significantly reducing its fecundity by approximately 70% and 42% in contact and topical assays, respectively. Additionally, OVE-NLC had no lethal effects on the generalist predator Macrolophus pygmaeus and pollinator bee Bombus terrestris as non-target organisms. Results suggested that OVE-NLC could be successfully used as a potential tool for tomato integrated pest management programs.

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ReferencesKoller, J. et al. A parasitoid Wasp allied with an entomopathogenic virus to control Tuta absoluta. Crop Prot. 179, 106617. https://doi.org/10.1016/j.cropro.2024.106617 (2024).
Google Scholar 
Bello, A. S. et al. Tomato (Solanum lycopersicum) yield response to drip irrigation and nitrogen application rates in open-field cultivation in arid environments. Sci. Hortic. 334, 113298. https://doi.org/10.1016/j.scienta.2024.113298 (2024).
Google Scholar 
Toni, H. C., Djossa, B. A., Ayenan, M. A. T. & Teka, O. Tomato (Solanum lycopersicum) pollinators and their effect on fruit set and quality. J. Hortic. Sci. Biotechnol. 96 (1), 1–13. https://doi.org/10.1080/14620316.2020.1773937 (2021).
Google Scholar 
Demirozer, O., Uzun, A. & Gosterit, A. Lethal and sublethal effects of different biopesticides on Bombus terrestris (Hymenoptera: Apidae). Apidologie 53 (2), 24. https://doi.org/10.1007/s13592-022-00933-6 (2022).
Google Scholar 
Kumar, A. et al. Rapid detection of the invasive tomato leaf miner, Phthorimaea absoluta using simple template LAMP assay. Sci. Rep. 15, 573. https://doi.org/10.1038/s41598-024-84288-1 (2025).
Google Scholar 
Soleymanzadeh, A., Valizadegan, O., Saber, M. & Hamishehkar, H. Toxicity of Foeniculum vulgare essential oil, its main component and nanoformulation against Phthorimaea absoluta and the generalist predator Macrolophus Pygmaeus. Sci. Rep. 15, 16706. https://doi.org/10.1038/s41598-025-01193-x (2025).
Google Scholar 
Konan, K. A. J. et al. Combination of generalist predators, Nesidiocoris tenuis and Macrolophus pygmaeus, with a companion plant, Sesamum indicum: what benefit for biological control of Tuta absoluta? Plos One. 16, 0257925. https://doi.org/10.1371/journal.pone.0257925 (2021).
Google Scholar 
Borges, I. et al. Prey consumption and conversion efficiency in females of two feral populations of Macrolophus pygmaeus, a biocontrol agent of Tuta absoluta. Phytoparasitica 52, 31. https://doi.org/10.1007/s12600-024-01130-0 (2024).
Google Scholar 
Mohammadi, R., Valizadegan, O. & Soleymanzadeh, A. Lethal and sublethal effects of matrine (Rui agro®) on the tomato leaf miner, Tuta absoluta and the predatory bug macrolophus Pygmaeus. J. App Res. Plant. Prot. 14 (2), 111–125. https://doi.org/10.22034/arpp.2022.15200 (2025).
Google Scholar 
Mesri, H., Valizadegan, O. & Soleymanzade, A. Laboratory assessment of some chemical insecticides toxicity on Brevicoryne brassicae (Hemiptera: Aphididae) and their selectivity for its predator, Hippodamia variegata (Coleoptera: Coccinellidae). Iran. J. Plant. Prot. Sci. 54 (1), 165–186. https://doi.org/10.22059/IJPPS.2023.360644.1007032 (2023).
Google Scholar 
Soleymanzade, A., Valizadegan, O. & Askari Saryazdi, G. Biochemical mechanisms and cross resistance patterns of Chlorpyrifos resistance in a laboratory-selected strain of Diamondback Moth, Plutella Xylostella (Lepidoptera: Plutellidae). J. Agric. Sci. Technol. 21 (7), 1859–1870 (2019a).
Google Scholar 
Soleymanzade, A., Khorrami, F. & Forouzan, M. Insecticide toxicity, synergism and resistance in Plutella Xylostella (Lepidoptera: plutellidae. Acta Phytopathol. Entomol. Hung. 54 (1), 147–154. https://doi.org/10.1556/038.54.2019.013 (2019b).
Google Scholar 
Tortorici, S. et al. Nanostructured lipid carriers of essential oils as potential tools for the sustainable control of insect pests. Ind. Crops Prod. 181, 114766. https://doi.org/10.1016/j.indcrop.2022.114766 (2022).
Google Scholar 
Modafferi, A. et al. Bioactivity of Allium sativum essential oil-based nano-emulsion against Planococcus citri and its predator Cryptolaemus Montrouzieri. Ind. Crops Prod. 208, 117837. https://doi.org/10.1016/j.indcrop.2023.117837 (2024).
Google Scholar 
Angellotti, G., Riccucci, C., Di Carlo, G., Pagliaro, M. & Ciriminna, R. Towards sustainable pest management of broad scope: sol-gel microencapsulation of Origanum vulgare essential oil. J. Sol-Gel Sci. Technol. 112 (1), 230–239. https://doi.org/10.1007/s10971-024-06512-8 (2024).
Google Scholar 
Werdin González, J. O., Gutiérrez, M. M., Murray, A. P. & Ferrero, A. A. Composition and biological activity of essential oils from labiatae against Nezara viridula (Hemiptera: Pentatomidae) soybean pest. Pest Manag Sci. 67 (8), 948–955. https://doi.org/10.1002/ps.2138 (2011).
Google Scholar 
Múnera-Echeverri, A., Múnera-Echeverri, J. L. & Segura-Sánchez, F. Bio-pesticidal potential of nanostructured lipid carriers loaded with thyme and Rosemary essential oils against common ornamental flower pests. Colloids Interfaces. 8 (5), 55. https://doi.org/10.3390/colloids8050055 (2024).
Google Scholar 
Tortorici, S. et al. Toxicity and repellent activity of a Carlina oxide nanoemulsion toward the South American tomato pinworm, Tuta absoluta. J. Pest Sci. 98, 309–320. https://doi.org/10.1007/s10340-024-01785-y (2025).
Google Scholar 
Sivalingam, S. et al. Encapsulation of essential oil to prepare environment friendly nanobio-fungicide against Fusarium oxysporum f. sp. lycopersici: an experimental and molecular dynamics approach. Colloids Surf. A: Physicochem Eng. Asp. 681, 132681. https://doi.org/10.1016/j.colsurfa.2023.132681 (2024).
Google Scholar 
Piran, P., Kafil, H. S., Ghanbarzadeh, S., Safdari, R. & Hamishehkar, H. Formulation of menthol-loaded nanostructured lipid carriers to enhance its antimicrobial activity for food preservation. Adv. Pharm. Bull. 7 (2), 261. https://doi.org/10.15171/apb.2017.031 (2017).
Google Scholar 
Khezri, K., Farahpour, M. R. & Mounesi Rad, S. Efficacy of Mentha pulegium essential oil encapsulated into nanostructured lipid carriers as an in vitro antibacterial and infected wound healing agent. Colloids Surf. A: Physicochem Eng. Asp. 589, 124414. https://doi.org/10.1016/j.colsurfa.2020.124414 (2020).
Google Scholar 
Bashiri, S., Ghanbarzadeh, B., Ayaseh, A., Dehghannya, J. & Ehsani, A. Preparation and characterization of chitosan-coated nanostructured lipid carriers (CH-NLC) containing cinnamon essential oil for enriching milk and anti-oxidant activity. LWT-Food Sci. Technol. 119, 108836. https://doi.org/10.1016/j.lwt.2019.108836 (2020).
Google Scholar 
Khosh Manzar, M., Pirouzifard, M. K., Hamishehkar, H. & Pirsa, S. Cocoa butter and cocoa butter substitute as a lipid carrier of Cuminum cyminum L. essential oil; physicochemical properties, physical stability and controlled release study. J. Mol. Liq. 319, 114303. https://doi.org/10.1016/j.molliq.2020.113638 (2020).
Google Scholar 
de Figueiredo, K. G. et al. Toxicity of cinnamomum spp. Essential oil to Tuta absoluta and to predatory Mirid. J. Pest Sci. 97 (3), 1569–1585. https://doi.org/10.1007/s10340-023-01719-0 (2024).
Google Scholar 
Piri, A. et al. Toxicity and physiological effects of Ajwain (Carum copticum, Apiaceae) essential oil and its major constituents against Tuta absoluta (Meyrick) (Lepidoptera: Gelechiidae). Chemosphere 256, 127103. https://doi.org/10.1016/j.chemosphere.2020.127103 (2020).
Google Scholar 
Martinou, A. F., Seraphides, N. & Stavrinides, M. C. Lethal and behavioral effects of pesticides on the insect predator Macrolophus Pygmaeus. Chemosphere 96, 167–173. https://doi.org/10.1016/j.chemosphere.2013.10.024 (2014).
Google Scholar 
Besard, L., Mommaerts, V., Abdu-Alla, G. & Smagghe, G. Lethal and sublethal side‐effect assessment supports a more benign profile of Spinetoram compared with spinosad in the bumblebee Bombus terrestris. Pest Manag Sci. 67 (5), 541–547. https://doi.org/10.1002/ps.2093 (2011).
Google Scholar 
Chi, H. TWOSEX-MSChart: a computer program for the age-stage, two-sex life table analysis. Accessed on 25 May (2005). (2005).Chi, H. & Liu, H. Two new methods for the study of insect population ecology. Bull. Inst. Zool. Acad. Sin. 24 (2), 225–240 (1985).
Google Scholar 
Chi, H. Life-table analysis incorporating both sexes and variable development rates among individuals. Environ. Entomol. 17, 26–34. https://doi.org/10.1093/ee/17.1.26 (1988).
Google Scholar 
Effron, B., Tibshirani, R. J. & Probability An introduction to the bootstrap. ChapmanHall/CRC, Monographs on Statistics and Applied New York. (1993).Xie, Y., Huang, Q., Rao, Y., Hong, L. & Zhang, D. Efficacy of Origanum vulgare essential oil and carvacrol against the housefly, musca domestica L. (Diptera: Muscidae). Environ. Sci. Pollut Res. 26, 23824–23831. https://doi.org/10.1007/s11356-019-05671-4 (2019).
Google Scholar 
De Souza, G. T. et al. Effects of the essential oil from Origanum vulgare L. on survival of pathogenic bacteria and starter lactic acid bacteria in semihard cheese broth and slurry. J. Food Prot. 79 (2), 246–252. https://doi.org/10.4315/0362-028X.JFP-15-172 (2016).
Google Scholar 
Goyal, S., Tewari, G., Pandey, H. K. & Kumari, A. Exploration of productivity, chemical composition, and antioxidant potential of Origanum vulgare L. grown at different geographical locations of Western himalaya. India J Chem. 1, 6683300. https://doi.org/10.1155/2021/6683300 (2021).
Google Scholar 
Khan, M. et al. The composition of the essential oil and aqueous distillate of Origanum vulgare L. growing in Saudi Arabia and evaluation of their antibacterial activity. Arab. J. Chem. 11 (8), 1189–1200. https://doi.org/10.1016/j.arabjc.2018.02.008 (2018).
Google Scholar 
Moazeni, M. et al. Lesson from nature: Zataria multiflora nanostructured lipid carrier topical gel formulation against Candida-associated onychomycosis, a randomized double-blind placebo-controlled clinical trial. Med. Drug Discov. 22, 100187. https://doi.org/10.1016/j.medidd.2024.100187 (2024).
Google Scholar 
Hoseini, B. et al. Application of ensemble machine learning approach to assess the factors affecting size and polydispersity index of liposomal nanoparticles. Sci. Rep. 13 (1), 18012. https://doi.org/10.1038/s41598-023-43689-4 (2023).
Google Scholar 
Apostolou, M., Assi, S., Fatokun, A. A. & Khan, I. The effects of solid and liquid lipids on the physicochemical properties of nanostructured lipid carriers. J. Pharm. Sci. 110 (8), 2859–2872. https://doi.org/10.1016/j.xphs.2021.04.012 (2021).
Google Scholar 
Pezeshki, A. et al. Nanostructured lipid carriers as a favorable delivery system for β-carotene. Food Biosci. 27, 11–17. https://doi.org/10.1016/j.fbio.2018.11.004 (2019).
Google Scholar 
Fitriani, E. W., Avanti, C., Rosana, Y. & Surini, S. Development of nanostructured lipid carrier containing tea tree oil: physicochemical properties and stability. J. Pharm. Pharmacog Res. 11 (3), 391–400. https://doi.org/10.56499/jppres23.1581_11.3.391 (2023).
Google Scholar 
Chura, S. S. D. et al. Red Sacaca essential oil-loaded nanostructured lipid carriers optimized by factorial design: cytotoxicity and cellular reactive oxygen species levels. Front. Pharmacol. 14, 1176629. https://doi.org/10.3389/fphar.2023.1176629 (2023).
Google Scholar 
Jbilou, R., Matteo, R., Bakrim, A., Bouayad, N. & Rharrabe, K. Potential use of Origanum vulgare in agricultural pest management control: a systematic review. J. Plant. Dis. Prot. 131 (2), 347–363. https://doi.org/10.1007/s41348-023-00839-0 (2024). hHadley Centre for Climate, Met Officettp:.
Google Scholar 
Abdelgaleil, S. A. M., Gad, H. A., Ramadan, G. R. M., El-Bakry, A. M. & El-Sabrout, A. Monoterpenes for management of field crop insect Hadley centre for Climate, Met officepests. J. Agric. Sci. Technol. 25 (4), 769–784 (2023).
Google Scholar 
Allsopp, E., Prinsloo, G. J., Smart, L. E. & Dewhirst, S. Y. Methyl salicylate, thymol and carvacrol as oviposition deterrents for Frankliniella occidentalis (Pergande) on Plum blossoms. Arthropod Plant. Interac. 8, 421–427. https://doi.org/10.1007/s11829-014-9323-2 (2014).
Google Scholar 
Yarou, B. B. et al. Oviposition deterrent activity of Basil plants and their essentials oils against Tuta absoluta (Lepidoptera: Gelechiidae). Environ. Sci. Pollut Res. 25, 29880–29888. https://doi.org/10.1007/s11356-017-9795-6 (2017).
Google Scholar 
Lo Pinto, M., Vella, L. & Agrò, A. Oviposition deterrence and repellent activities of selected essential oils against Tuta absoluta meyrick (Lepidoptera: Gelechiidae): laboratory and greenhouse investigations. Int. J. Trop. Insect Sci. 42 (5), 3455–3464. https://doi.org/10.1007/s42690-022-00867-7 (2022).
Google Scholar 
Ricupero, M. et al. Bioactivity and physico-chemistry of Garlic essential oil nanoemulsion in tomato. Entomol. Gen. 42, 921–930. https://doi.org/10.1127/entomologia/2022/1553 (2022).
Google Scholar 
Carbone, C. et al. Mediterranean essential oils as precious matrix components and active ingredients of lipid nanoparticles. Int. J. Pharm. 548 (1), 217–226. https://doi.org/10.1016/j.ijpharm.2018.06.064 (2018).
Google Scholar 
Ghodrati, M., Farahpour, M. R. & Hamishehkar, H. Encapsulation of peppermint essential oil in nanostructured lipid carriers: In-vitro antibacterial activity and accelerative effect on infected wound healing. Coll. Surf. A: Physicochem. Eng. Asp. 564, 161–169. https://doi.org/10.1016/j.colsurfa.2018.12.043 (2019).
Google Scholar 
Radwan, I. T., Baz, M. M., Khater, H. & Selim, A. M. Nanostructured lipid carriers (NLC) for biologically active green tea and fennel natural oils delivery: Larvicidal and adulticidal activities against Culex pipiens. Molecules. 27(6), 1939. https://doi.org/10.3390/molecules27061939 (2022).Goane, L. et al. Antibiotic treatment reduces fecundity and nutrient content in females of Anastrepha fraterculus (Diptera: Tephritidae) in a diet dependent way. J. Insect Physiol. 139, 104396. https://doi.org/10.1016/j.jinsphys.2022.104396 (2022).
Google Scholar 
Ouabou, M. et al. Insecticidal, antifeedant, and repellent effect of Lavandula mairei var. Antiatlantica essential oil and its major component carvacrol against Sitophilus oryzae. J. Stored Prod. Res. 107, 102338. https://doi.org/10.1016/j.jspr.2024.102338 (2024).
Google Scholar 
Ding, W. et al. Lethal and sublethal effects of Afidopyropen and Flonicamid on life parameters and physiological responses of the tobacco whitefly, bemisia tabaci MEAM1. Agronomy 14 (8), 1774. https://doi.org/10.3390/agronomy14081774 (2024).
Google Scholar 
Arnó, J. & Gabarra, R. Side effects of selected insecticides on the Tuta absoluta (Lepidoptera: Gelechiidae) predators Macrolophus Pygmaeus and Nesidiocoris tenuis (Hemiptera: Miridae). J. Pest. Sci.. 84, 513–520. https://doi.org/10.1007/s10340-011-0384-z (2011).
Google Scholar 
Stanley, J., Sah, K., Jain, S. K., Bhatt, J. C. & Sushil, S. N. Evaluation of pesticide toxicity at their field recommended doses to honeybees, apis Cerana and A. mellifera through laboratory, semi-field and field studies. Chemosphere 119, 668–674. https://doi.org/10.1016/j.chemosphere.2014.07.039 (2015).
Google Scholar 
Jeon, H. & Tak, J. H. Gustatory habituation to essential oil induces reduced feeding deterrence and neuronal desensitization in Spodoptera Litura. J. Pest Sci. 98, 321–336. https://doi.org/10.1007/s10340-024-01794-x (2024).
Google Scholar 
Abbes, K. et al. Combined non-target effects of insecticide and high temperature on the parasitoid Bracon nigricans. PloS One. 10 (9), 0138411. https://doi.org/10.1371/journal.pone.0138411 (2015).
Google Scholar 
Download referencesAcknowledgementsFinancial support from the Deputy of Research and Technology of Urmia University, Urmia, Iran (Number: 10/1352) is acknowledged.FundingThis study was funded by Urmia University, Iran (Number: 10/1352).Author informationAuthors and AffiliationsDepartment of Plant Protection, Faculty of Agriculture, Urmia University, Urmia, IranAsmar Soleymanzadeh & Orouj ValizadeganDrug Applied Research Center, Tabriz University of Medical Sciences, Tabriz, IranHamed HamishehkarResearch Center of New Material and Green Chemistry, Khazar University, Baku, AzerbaijanHamed HamishehkarAuthorsAsmar SoleymanzadehView author publicationsSearch author on:PubMed Google ScholarOrouj ValizadeganView author publicationsSearch author on:PubMed Google ScholarHamed HamishehkarView author publicationsSearch author on:PubMed Google ScholarContributionsAS, OV, and HH conceived and designed the experiments. AS collected data and carried out the bioassays. AS and OV analyzed the data. AS wrote the first draft of the manuscript, and OV and HH revised and improved it. All authors read and approved the manuscript.Corresponding authorCorrespondence to
Orouj Valizadegan.Ethics declarations

Competing interests
The authors declare no competing interests.

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Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
Reprints and permissionsAbout this articleCite this articleSoleymanzadeh, A., Valizadegan, O. & Hamishehkar, H. Nanostructured lipid carrier of oregano essential oil for controlling Tuta absoluta with minimal impact on beneficial organisms.
Sci Rep (2025). https://doi.org/10.1038/s41598-025-33492-8Download citationReceived: 24 August 2025Accepted: 19 December 2025Published: 30 December 2025DOI: https://doi.org/10.1038/s41598-025-33492-8Share 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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KeywordsBioinsecticide
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AbstractPlant-pollinator interactions are essential for plant productivity but face growing threats from climate change, including vegetation loss and mismatches in flowering. Yet, the consequences for bee food resources remain poorly understood at continental scales. Here, we analyse 2 500 samples collected by honey bees (Apis mellifera) between May and August 2023 from 310 locations across Europe using ITS2 metabarcoding. We derive climatic response curves of floral resources and assess exceedance risks of interaction loss under projected climate scenarios. Our findings reveal that rising temperatures and reduced precipitation decrease the diversity of foraging resources across Europe, pushing many plants beyond critical limits. When both warming and drying coincide, the potential for resilience through temporal or spatial buffering is strongly constrained. These declines pose serious risks to bee nutrition, ecosystem functioning, and food security. Our study underscores the urgency of mitigating climate change to preserve vital plant-pollinator systems and the services they sustain.

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Data availability

The raw sequencing data generated in this study have been deposited in the NCBI SRA database under accession code PRJNA1198597. The processed sequencing data (for process see code availability), climate data, and metadata of samples are available at Zenodo 10.5281/zenodo.17578272 [https://doi.org/10.5281/zenodo.17578272]77 and GitHub chiras/HoneyBee-ResistanceResilience [https://github.com/chiras/HoneyBee-ResistanceResilience].
Code availability

The pipeline for processing raw sequencing data for metabarcoding is publicly available at GitHub chiras/metabarcoding_pipeline [https://github.com/chiras/metabarcoding_pipeline]. Code for all downstream analyses is publicly available at Zenodo 10.5281/zenodo.17578272 [https://doi.org/10.5281/zenodo.17578272]77 and GitHub chiras/HoneyBee-ResistanceResilience [https://github.com/chiras/HoneyBee-ResistanceResilience]. All display items presented in the main manuscript and supplementary information can be reproduced from this public data and code.
ReferencesOllerton, J., Winfree, R. & Tarrant, S. How many flowering plants are pollinated by animals? Oikos 120, 321–326 (2011).
Google Scholar 
Potts, S. G. et al. IPBES. The Assessment Report of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services on Pollinators, Pollination and Food Production; http://www.ipbes.net/node/44781, (2016).Klein, A.-M. et al. Importance of pollinators in changing landscapes for world crops. Proc. R. Soc. B. 274, 303–313 (2007).
Google Scholar 
Maracchi, G., Sirotenko, O. & Bindi, M. Impacts of present and future climate variability on agriculture and forestry in the temperate regions: Europe. Clim. Change 70, 117–135 (2005).
Google Scholar 
Olesen, J. E. & Bindi, M. Consequences of climate change for European agricultural productivity, land use and policy. Eur. J. Agron. 16, 239–262 (2002).
Google Scholar 
Tun, W., Yoon, J., Jeon, J.-S. & An, G. Influence of climate change on flowering time. J. Plant Biol. 64, 193–203 (2021).
Google Scholar 
Chan, W.-P. et al. Climate velocities and species tracking in global mountain regions. Nature 629, 114–120 (2024).
Google Scholar 
Nagy, L., Kreyling, J., Gellesch, E., Beierkuhnlein, C. & Jentsch, A. Recurring weather extremes alter the flowering phenology of two common temperate shrubs. Int. J. Biometeorol. 57, 579–588 (2013).
Google Scholar 
Scheper, J. et al. Museum specimens reveal loss of pollen host plants as key factor driving wild bee decline in the Netherlands. Proc. Natl. Acad. Sci. 111, 17552–17557 (2014).
Google Scholar 
Dominique, Z., Sabine, S., Herbert, Z., Christa, H.-R. & Sophie, K. Changes in the wild bee community (Hymenoptera: Apoidea) over 100 years in relation to land use: a case study in a protected steppe habitat in Eastern Austria. J. Insect Conserv. 27, 625–641 (2023).
Google Scholar 
Klein, A.-M., Boreux, V., Fornoff, F., Mupepele, A.-C. & Pufal, G. Relevance of wild and managed bees for human well-being. Curr. Opin. Insect Sci. 26, 82–88 (2018).
Google Scholar 
Burkle, L. A., Marlin, J. C. & Knight, T. M. Plant-pollinator interactions over 120 years: loss of species, co-occurrence, and function. Science 339, 1611–1615 (2013).
Google Scholar 
Thomas, C. D. et al. Extinction risk from climate change. Nature 427, 145–148 (2004).
Google Scholar 
Thuiller, W., Lavorel, S., Araújo, M. B., Sykes, M. T. & Prentice, I. C. Climate change threats to plant diversity in Europe. Proc. Natl. Acad. Sci. 102, 8245–8250 (2005).
Google Scholar 
Bakkenes, M., Eickhout, B. & Alkemade, R. Impacts of different climate stabilisation scenarios on plant species in Europe. Glob. Environ. Change 16, 19–28 (2006).
Google Scholar 
Copernicus Climate Change Service (C3S), European state of the climate 2023, summary, https://doi.org/10.24381/bs9v-8c66 (2024).Feehan, J., Harley, M. & van Minnen, J. Climate change in Europe. 1. Impact on terrestrial ecosystems and biodiversity. A review. Agron. Sustain. Dev. 29, 409–421 (2009).
Google Scholar 
Cunze, S., Heydel, F. & Tackenberg, O. Are plant species able to keep pace with the rapidly changing climate? PLoS One 8, e67909 (2013).
Google Scholar 
Parreño, M. A. et al. Critical links between biodiversity and health in wild bee conservation. Trends Ecol. Evol. 37, 309–321 (2022).
Google Scholar 
Danner, N., Keller, A., Härtel, S. & Steffan-Dewenter, I. Honey bee foraging ecology: Season but not landscape diversity shapes the amount and diversity of collected pollen. PLoS One 12, e0183716 (2017).
Google Scholar 
Osterman, J. et al. Global trends in the number and diversity of managed pollinator species. Agric. Ecosyst. Environ. 322, 107653 (2021).
Google Scholar 
Sickel, W. et al. Increased efficiency in identifying mixed pollen samples by meta-barcoding with a dual-indexing approach. BMC Ecol. 15, 20 (2015).
Google Scholar 
Rafferty, N. E. Effects of global change on insect pollinators: multiple drivers lead to novel communities. Curr. Opin. Insect Sci. 23, 22–27 (2017).
Google Scholar 
Lee, H. & Romero, J. eds. Climate Change 2023: Synthesis Report. Contribution of Working Groups I, II and III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. IPCC, (2023).Seneviratne, S. I., Donat, M. G., Pitman, A. J., Knutti, R. & Wilby, R. L. Allowable CO2 emissions based on regional and impact-related climate targets. Nature 529, 477–483 (2016).
Google Scholar 
Nieto, A. et al. European Red List of Bees. Luxembourg: Publication Office of the European Union, Belgium, (2014).Menzel, A. & Fabian, P. Growing season extended in Europe. Nature 397, 659–659 (1999).
Google Scholar 
Vautard, R. et al. The European climate under a 2 °C global warming. Environ. Res. Lett. 9, 034006 (2014).
Google Scholar 
Ferraro, A. J. & Griffiths, H. G. Quantifying the temperature-independent effect of stratospheric aerosol geoengineering on global-mean precipitation in a multi-model ensemble. Environ. Res. Lett. 11, 034012 (2016).
Google Scholar 
Settele, J., Bishop, J. & Potts, S. G. Climate change impacts on pollination. Nat. Plants 2, 16092 (2016).
Google Scholar 
Rasmont, P. et al. Climatic risk and distribution atlas of European Bumblebees Biorisk 10 (Pensoft Publishers, (2015).Poland, T. M. et al. Invasive Species in Forests and Rangelands of the United States: A Comprehensive Science Synthesis for the United States Forest Sector. Springer Nature (2021).Uzunov, A. et al. Apis florea in Europe: first report of the dwarf honey bee in Malta. J. Apic. Res. 63, 1122–1125 (2024).
Google Scholar 
Menzel, A. et al. European phenological response to climate change matches the warming pattern. Glob. Chang. Biol. 12, 1969–1976 (2006).
Google Scholar 
Eckhardt, M., Haider, M., Dorn, S. & Müller, A. Pollen mixing in pollen generalist solitary bees: a possible strategy to complement or mitigate unfavourable pollen properties? J. Anim. Ecol. 83, 588–597 (2014).
Google Scholar 
Bell, K. L. et al. Plants, pollinators and their interactions under global ecological change: The role of pollen DNA metabarcoding. Mol. Ecol. 32, 6345–6362 (2023).
Google Scholar 
Goulson, D., Nicholls, E., Botías, C. & Rotheray, E. L. Bee declines driven by combined stress from parasites, pesticides, and lack of flowers. Science 347, 1255957 (2015).
Google Scholar 
Horn, J., Becher, M. A., Kennedy, P. J., Osborne, J. L. & Grimm, V. Multiple stressors: using the honeybee model BEEHAVE to explore how spatial and temporal forage stress affects colony resilience. Oikos 125, 1001–1016 (2016).
Google Scholar 
Timberlake, T. P., Vaughan, I. P. & Memmott, J. Phenology of farmland floral resources reveals seasonal gaps in nectar availability for bumblebees. J. Appl. Ecol. 56, 1585–1596 (2019).
Google Scholar 
Couvillon, M. J., Schürch, R. & Ratnieks, F. L. W. Waggle dance distances as integrative indicators of seasonal foraging challenges. PLoS One 9, e93495 (2014).
Google Scholar 
Requier, F. et al. Honey bee diet in intensive farmland habitats reveals an unexpectedly high flower richness and a major role of weeds. Ecol. Appl. 25, 881–890 (2015).
Google Scholar 
Elliott, B. et al. Pollen diets and niche overlap of honey bees and native bees in protected areas. Basic. Appl. Ecol. 50, 169–180 (2021).
Google Scholar 
Kaluza, B. F. et al. Social bees are fitter in more biodiverse environments. Sci. Rep. 8, 12353 (2018).
Google Scholar 
Rhodes, C. J. Pollinator decline – an ecological calamity in the making? Sci. Prog. 101, 121–160 (2018).
Google Scholar 
Menzel, F. & Feldmeyer, B. How does climate change affect social insects? Curr. Opin. Insect Sci. 46, 10–15 (2021).
Google Scholar 
Quaresma, A. et al. Preservation methods of honey bee-collected pollen are not a source of bias in ITS2 metabarcoding. Environ. Monit. Assess. 193, 785 (2021).
Google Scholar 
Chen, S. et al. Validation of the ITS2 Region as a Novel DNA Barcode for Identifying Medicinal Plant Species. PLoS One 5, e8613 (2010).
Google Scholar 
White, J. S. et al. Amplification and direct sequencing of fungal ribosomal RNA genes for phylogenetics. In PCR Protocols: A Guide to Methods Applications (eds Innis, M. A., Gelfand, D. H., Shinsky, J. J. & White, T. J.) 315–322 Academic Press, (1990).Leonhardt, S. D., Peters, B. & Keller, A. Do amino and fatty acid profiles of pollen provisions correlate with bacterial microbiomes in the mason bee Osmia bicornis? Philos. Trans. R. Soc. B 377, 20210171 (2022).
Google Scholar 
Rognes, T., Flouri, T., Nichols, B., Quince, C. & Mahé, F. VSEARCH: a versatile open source tool for metagenomics. PeerJ 4, e2584 (2016).
Google Scholar 
Edgar, R. C. SINTAX: a simple non-Bayesian taxonomy classifier for 16S and ITS sequences. bioRxiv, 074161 (2016).Quaresma, A. et al. Semi-automated sequence curation for reliable reference datasets in ITS2 vascular plant DNA (meta-)barcoding. Sci. Data 11, 129 (2024).
Google Scholar 
University of East Anglia Climatic Research Unit; Harris, I.C.; Jones, P.D.; Osborn, T. (2024): CRU TS4.08: Climatic Research Unit (CRU) Time-Series (TS) version 4.08 of high-resolution gridded data of month-by-month variation in climate (Jan. 1901- Dec. 2023). NERC EDS Centre for Environmental Data Analysis, date of citation. https://catalogue.ceda.ac.uk/uuid/715abce1604a42f396f81db83aeb2a4b.R Core Team. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria (2024).RStudio Team RStudio: integrated development environment for R. RStudio, PBC, Boston, MA. http://www.rstudio.com/ (2024).McMurdie, P. J. & Holmes, S. phyloseq: An R Package for reproducible interactive analysis and graphics of microbiome census data. PLoS One 8, e61217 (2013).
Google Scholar 
Wickham, H., Vaughan, D. & Girlich, M. tidyr: tidy messy data. R package version 1.3.1 https://CRAN.R-project.org/package=tidyr (2024).McLaren, M. speedyseq: faster implementations of phyloseq functions. R package version 0.5.3.9021 https://github.com/mikemc/speedyseq, (2024).Wickham, H., François, R., Henry, L., Müller, K. & Vaughan, D. dplyr: a grammar of data manipulation. R package version 1.1. 2. Computer software (2023).Garnier, S. et al. viridis(Lite) – colorblind-friendly color maps for R. viridis package version 0.6.5. (2024).Dormann, C. F., Fründ, J., Blüthgen, N. & Gruber, B. Indices, graphs and null models: analyzing bipartite ecological networks. Open Ecol. J. 2, 7–24 (2009).
Google Scholar 
Wickham, H. in ggplot2: elegant graphics for data analysis (ed Hadley Wickham) 189–201 Springer International Publishing, 2016).Xiao, N. ggsci: scientific journal and sci-fi themed color palettes for ‘ggplot2’. R package version 30, https://CRAN.R-project.org/package=ggsci (2024).Yutani, H. gghighlight: highlight lines and points in ‘ggplot2’. R package version 0.4.1, https://CRAN.R-project.org/package=gghighlight (2023).Massicotte, P., South, A. rnatural earth: world map data from natural earth. R package version 11, https://CRAN.R-project.org/package=rnaturalearth (2023).Pebesma, E. J. Simple features for R: standardized support for spatial vector data. R. J. 10, 439 (2018).
Google Scholar 
Hijmans, R. Geosphere: spherical trigonometry. R package version 1.5-18, https://CRAN.R-project.org/package=geosphere (2022).Fitzpatrick, M., Mokany, K., Manion, G., Nieto-Lugilde, D., Ferrier, S. gdm: generalized dissimilarity modeling. R package version 1.5.0-9.1, https://CRAN.R-project.org/package=gdm (2022).Wickham, H., Pedersen, T., Seidel, D. Scales: scale functions for visualization. R package version 10, (2023). https://CRAN.R-project.org/package=scales.Microsoft Corporation & Weston, S. foreach: provides for each looping construct. R. package version 1, 2 (2022).Microsoft Corporation & Weston, S. doParallel: foreach parallel adaptor for the ‘parallel’ Package. R. package version 1, 17 (2022).Microsoft Corporation & Weston, S. doSNOW: foreach parallel adaptor for the ‘snow’ Package. R. package version 1, 20 (2022).Csárdi, G. & FitzJohn, R. Progress: terminal progress bars. R. Package version 1, 3 (2023).
Google Scholar 
Pedersen T. patchwork: the composer of plots. R package version 1.3.0.9000, https://github.com/thomasp85/patchwork (2024).Pimm, S. L., Donohue, I., Montoya, J. M. & Loreau, M. Measuring resilience is essential to understand it. Nat. Sustain. 2, 895–897 (2019).
Google Scholar 
Pimm, S. L. The complexity and stability of ecosystems. Nature 307, 321–326 (1984).
Google Scholar 
Keller, A., Quaresma, A. & Pinto, A. M. Data and code for “Honey bee food resources under threat from climate change”. Zenodo, https://doi.org/10.5281/zenodo.17578272 (2025).Download referencesAcknowledgementsWe thank all Citizen Scientists and National Coordinators from the 27 EU countries for their invaluable contributions to pollen sampling. We also acknowledge Maíra Costa, Maria Celenza, and Maria João Caldeira for their assistance with pollen homogenisation and DNA extractions. The Portuguese Foundation for Science and Technology (FCT) supported A.Q.‘s PhD scholarship (2020.05155.BD; DOI: 10.54499/2020.05155.BD). FCT provided financial support through national funds to CIMO and SusTEC via FCT/MCTES (PIDDAC): CIMO under UIDB/00690/2020 (DOI: 10.54499/UIDB/00690/2020) and UIDP/00690/2020 (DOI: 10.54499/UIDP/00690/2020); and SusTEC, under LA/P/0007/2020 (DOI: 10.54499/LA/P/0007/2020). This work was conducted within the framework of INSIGNIA-EU: Preparatory action for monitoring of environmental pollution using honey bees (European Union service contract 09.200200/2021/864096/SER/ ENV.D.2).FundingOpen Access funding enabled and organized by Projekt DEAL.Author informationAuthor notesThese authors jointly supervised this work: M. Alice Pinto, Alexander Keller.Authors and AffiliationsCIMO, LA SusTEC, Instituto Politécnico de Bragança, Campus de Santa Apolónia, Bragança, PortugalAndreia Quaresma & M. Alice PintoDepartamento de Biologia, Faculdade de Ciências da Universidade do Porto, Porto, PortugalAndreia QuaresmaCIBIO, Centro de Investigação em Biodiversidade e Recursos Genéticos, InBIO Laboratório Associado, Campus de Vairão, Universidade do Porto, Vila do Conde, PortugalAndreia QuaresmaBIOPOLIS Program in Genomics, Biodiversity and Land Planning, CIBIO, Campus de Vairão, Vila do Conde, PortugalAndreia QuaresmaWageningen Environmental Research, Wageningen, the NetherlandsJohannes M. Baveco & Willem Bastiaan BuddendorfDepartment of Biology, University of Graz, Universitätsplatz 2, Graz, AustriaRobert Brodschneider, Kristina Gratzer & Ivo RoessinkCarreck Consultancy Ltd, Shipley, West Sussex, UKNorman L. CarreckDepartment of Apiculture, Institute of Animal Science ELGO ‘DIMITRA’, Nea Moudania, GreeceFani HatjinaDanish Beekeepers Association (DBF), Sorø, DenmarkOle Kilpinen & Flemming VejsnaesAlveus AB Consultancy, Oisterwijk, NetherlandsJozef van der SteenCellular and Organismic Networks, Faculty of Biology, Ludwig-Maximilians-Universität München, Planegg-Martinsried, GermanyAlexander KellerAuthorsAndreia QuaresmaView author publicationsSearch author on:PubMed Google ScholarJohannes M. BavecoView author publicationsSearch author on:PubMed Google ScholarRobert BrodschneiderView author publicationsSearch author on:PubMed Google ScholarWillem Bastiaan BuddendorfView author publicationsSearch author on:PubMed Google ScholarNorman L. CarreckView author publicationsSearch author on:PubMed Google ScholarKristina GratzerView author publicationsSearch author on:PubMed Google ScholarFani HatjinaView author publicationsSearch author on:PubMed Google ScholarOle KilpinenView author publicationsSearch author on:PubMed Google ScholarIvo RoessinkView author publicationsSearch author on:PubMed Google ScholarFlemming VejsnaesView author publicationsSearch author on:PubMed Google ScholarJozef van der SteenView author publicationsSearch author on:PubMed Google ScholarM. Alice PintoView author publicationsSearch author on:PubMed Google ScholarAlexander KellerView author publicationsSearch author on:PubMed Google ScholarContributionsA.Q., A.K., and M.A.P. conceived the ideas and designed the methodology. A.K. and A.Q. conducted the analysis. A.Q., A.K., and M.A.P. drafted the manuscript. Particularly A.Q., M.A.P., A.K., and N.C., but all authors contributed to improving the manuscript. J.B., W.B.B., and I.R. modelled the apiaries’ location. R.B., K.G., F.H., O.K., M.A.P., I.R., F.V., N.C. and JvdS designed the pollen sampling methodology, prepared all the materials and manuals for pollen collection by Citizen Scientists, and obtained the INSIGNIA-EU funding. All the authors critically reviewed the manuscript for important intellectual content.Corresponding authorsCorrespondence to
M. Alice Pinto or Alexander Keller.Ethics declarations

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The authors declare no competing interests.

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Reprints and permissionsAbout this articleCite this articleQuaresma, A., Baveco, J.M., Brodschneider, R. et al. Honey bee food resources under threat from climate change.
Nat Commun (2025). https://doi.org/10.1038/s41467-025-68085-6Download citationReceived: 04 February 2025Accepted: 16 December 2025Published: 30 December 2025DOI: https://doi.org/10.1038/s41467-025-68085-6Share 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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AbstractThe EU Nature Restoration Regulation suggests the implementation of green space as nature-based solutions to enhance urban resilience toward increasing climate risks such as extreme precipitation events and floods in Europe. Scenario based approaches enable the evaluation of the potential of specific nature-based solutions to mitigate flood risk in high flood hazard and vulnerable areas. In this study, we explore the flood risks from heavy rainfall and the potential of nature-based solutions implementations in the city of Hannover, Germany. Using the InVEST urban flood risk mitigation model, we modelled the surface runoff from heavy rainfall and assessed the social vulnerability using population and infrastructure data resulting in a flood risk evaluation. To test flood mitigation under nature-based solutions implementations including grass grid pavers and green roofs, we estimated the runoff improvement under three nature-based solutions scenarios following recommendations from the EU Nature Restoration Regulation. Our analysis revealed that Hannover’s inner-city area is particularly flood-prone and socially vulnerable, while peripheral districts are less affected. The combined risk and vulnerability arise from the surface sealing in built-up areas and their higher population density and associated infrastructure. The scenario results demonstrate flood risk reduction potential when combining different nature-based solutions, though on a limited level calling for more explicit and ambitious regulations at EU, regional and local levels.

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Data availability

Spatial data are all available open access. Detailed data on modelling results are provided in the Supplementary Material.
ReferencesCalvin, K. et al. IPCC, 2023: Climate Change 2023: Synthesis Report. Contribution of Working Groups I, II and III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (eds. Core Writing Team, H. L. & Romero, J.). https://doi.org/10.59327/IPCC/AR6-9789291691647 (IPCC, 2023).Sun, G. & Lockaby, B. G. Water Quantity and Quality at the Urban-Rural Interface. In Urban-Rural Interfaces, 29–48, https://doi.org/10.2136/2012.urban-rural.c3 (American Society of Agronomy, Soil Science Society of America, Crop Science Society of America, Inc., 2012).Wojkowski, J. et al. Are we losing water storage capacity mostly due to climate change—analysis of the landscape hydric potential in selected catchments in East-Central Europe. Ecol. Indic. 154, 110913 (2023).
Google Scholar 
Deutscher Wetterdienst (DWD) [German Meteorological Service]. Starkregen. https://www.dwd.de/DE/service/lexikon/begriffe/S/Starkregen.html (2024).BBC News. More than 200 killed in Valencia Floods as Torrential Rain Hits Another Spain Region. https://www.bbc.com/news/live/cgk1m7g73ydt (2024).Munich Re. Climate Change is Showing Its Claws: the World is Getting Hotter, Resulting in Severe Hurricanes, Thunderstorms and Floods. https://www.munichre.com/en/company/media-relations/media-information-and-corporate-news/media-information/2025/natural-disaster-figures-2024.html (2025).Norddeutscher Rundfunk (NDR) [Northern German Broadcasting]. Niedersachsen: Hochwasser verursacht Schäden von 161 Millionen Euro. https://www.ndr.de/nachrichten/niedersachsen/Niedersachsen-Hochwasser-verursacht-Schaeden-von-161-Millionen-Euro,hochwasser6034.html (2024).European Union. REGULATION (EU) 2024/1991 OF THE EUROPEAN PARLIAMENT AND OF THE COUNCIL of 24 June 2024 on nature restoration and amending Regulation (EU) 2022/869. Off. J. Eur. Union OJ L, 2024/1991 (2024).European Commission and Directorate-General for Environment. Proposal for a REGULATION OF THE EUROPEAN PARLIAMENT AND OF THE COUNCIL on nature restoration, 80 (European Commission, 2022).Kabisch, N., Haase, D. & Annerstedt van den Bosch, M. Adding natural areas to social indicators of intra-urban health inequalities among children: a case study from Berlin, Germany. Int J. Environ. Res Public Health 13, 783 (2016).
Google Scholar 
United Nations Environment Programme (UNEP). Resolution Adopted by the United Nations Environment Assembly on 2 March 2022: Nature-based Solutions for Supporting Sustainable Development (UNEP, 2022).Kabisch, N., Korn, H., Stadler, J. & Bonn, A. Nature-Based Solutions to Climate Change Adaptation In Urban Areas—Linkages Between Science, Policy and Practice, 1–11 (Springer Nature, 2017).Ommer, J. et al. Quantifying co-benefits and disbenefits of nature-based solutions targeting disaster risk reduction. Int. J. Disaster Risk Reduct. 75, 102966 (2022).
Google Scholar 
McPhearson, T. et al. A social-ecological-technological systems framework for urban ecosystem services. One Earth 5, 505–518 (2022).
Google Scholar 
Raymond, C. M. et al. A framework for assessing and implementing the co-benefits of nature-based solutions in urban areas. Environ. Sci. Policy 77, 15–24 (2017).
Google Scholar 
Kabisch, N., Frantzeskaki, N. & Hansen, R. Principles for urban nature-based solutions. Ambio 51, 1388–1401 (2022).
Google Scholar 
Kubal, C., Haase, D., Meyer, V. & Scheuer, S. Integrated urban flood risk assessment – adapting a multicriteria approach to a city. Nat. Hazards Earth Syst. Sci. 9, 1881–1895 (2009).
Google Scholar 
Wübbelmann, T., Bouwer, L., Förster, K., Bender, S. & Burkhard, B. Urban ecosystems and heavy rainfall—a Flood Regulating Ecosystem Service modelling approach for extreme events on the local scale. One Ecosyst. 7, e87458 (2022).Quagliolo, C., Roebeling, P., Matos, F., Pezzoli, A. & Comino, E. Pluvial flood adaptation using nature-based solutions: an integrated biophysical-economic assessment. Sci. Total Environ. 902, 166202 (2023).
Google Scholar 
Wübbelmann, T. et al. Urban flood regulating ecosystem services under climate change: How can Nature-based Solutions contribute? Front Water 5, 1081850 (2023).
Google Scholar 
Majidi, A. N. et al. Planning nature-based solutions for urban flood reduction and thermal comfort enhancement. Sustainability 11, 6361 (2019).
Google Scholar 
Meyer, V., Scheuer, S. & Haase, D. A multicriteria approach for flood risk mapping exemplified at the Mulde river, Germany. Nat. Hazards 48, 17–39 (2009).
Google Scholar 
Emrich, C. T. & Cutter, S. L. Social vulnerability to climate-sensitive hazards in the Southern United States. Weather Clim. Soc. 3, 193–208 (2011).
Google Scholar 
Saulnier, D. D., Brolin Ribacke, K. & von Schreeb, J. No calm after the storm: a systematic review of human health following flood and storm disasters. Prehosp. Disaster Med. 32, 568–579 (2017).
Google Scholar 
Tate, E., Rahman, M. A., Emrich, C. T. & Sampson, C. C. Flood exposure and social vulnerability in the United States. Nat. Hazards 106, 435–457 (2021).
Google Scholar 
Landeshauptstadt Hannover [City of Hannover]. Leben mit dem Klimawandel—Hannover passt sich an. Schriftreihe kommunaler Umweltschutz (Landeshauptstadt Hannover, 2017).Ferreira, C. S. S., Potočki, K., Kapović-Solomun, M. & Kalantari, Z. Nature-based solutions for flood mitigation and resilience in Urban areas. in Nature-Based Solutions for Flood Mitigation, 59–78 (Springer, 2021).Pistocchi, A. Hydrological impact of soil sealing and urban land take. in Urban Expansion, Land Cover and Soil Ecosystem Services, 157–168 (Routledge, 2017).Wessolek, G. Sealing of Soils. in Urban Ecology 161–179 (Springer US, 2008).Krehl, A. & Siedentop, S. Towards a typology of urban centers and subcenters – evidence from German city regions. Urban Geogr. 40, 58–82 (2019).
Google Scholar 
Van Hoof, J., Kazak, J. K., Perek-Białas, J. M. & Peek, S. T. M. The challenges of urban ageing: making cities age-friendly in Europe. Int J. Environ. Res. Public Health 15, 2473 (2018).
Google Scholar 
Waygood, E. O. D., Friman, M., Olsson, L. E. & Taniguchi, A. Transport and child well-being: an integrative review. Travel Behav. Soc. 9, 32–49 (2017).
Google Scholar 
Weilnhammer, V. et al. Extreme weather events in europe and their health consequences—a systematic review. Int J. Hyg. Environ. Health 233, 113688 (2021).
Google Scholar 
Schmitt, T. G. & Scheid, C. Evaluation and communication of pluvial flood risks in urban areas. WIREs Water 7, e1401 (2020).Heilemann, K. et al. Identification and Analysis of Most Vulnerable Infrastructure in Respect to Floods. Floodprobe D2, 1 (2013).Yazdani, M., Mojtahedi, M., Loosemore, M. & Sanderson, D. A modelling framework to design an evacuation support system for healthcare infrastructures in response to major flood events. Prog. Disaster Sci. 13, 100218 (2022).
Google Scholar 
Fekete, A., Tzavella, K. & Baumhauer, R. Spatial exposure aspects contributing to vulnerability and resilience assessments of urban critical infrastructure in a flood and blackout context. Nat. Hazards 86, 151–176 (2017).
Google Scholar 
Crols, T. et al. Downdating high-resolution population density maps using sealed surface cover time series. Landsc. Urban Plan 160, 96–106 (2017).
Google Scholar 
Region Hannover. Klimaanalyse Region Hannover. Hannover: GEO-NET Umweltconsulting GmbH. https://www.hannover.de/content/download/938983/file/Handbuch%20Klimaanalyse.pdf (2023).Zölch, T., Henze, L., Keilholz, P. & Pauleit, S. Regulating urban surface runoff through nature-based solutions – An assessment at the micro-scale. Environ. Res. 157, 135–144 (2017).
Google Scholar 
Huang, Y. et al. Nature-based solutions for urban pluvial flood risk management. WIREs Water 7, e1421 (2020).Depietri, Y. & McPhearson, T. Integrating the grey, green, and blue in cities: nature-based solutions for climate change adaptation and risk reduction. in Nature-Based Solutions to Climate Change Adaptation in Urban Areas: Linkages Between Science, Policy and Practice (eds. Kabisch, N., Korn, H., Stadler, J. & Bonn, A.) 91–109 (Springer, 2017).Landeshauptstadt Hannover [City of Hannover]. STADTGRÜN 2030—Ein Freiraumentwicklungskonzept Für Hannover (Landeshauptstadt Hannover, 2020).Baró, F. & Gómez-Baggethun, E. Assessing the potential of regulating ecosystem services as nature-based solutions in urban areas. in Nature-Based Solutions to Climate Change Adaptation in Urban Areas: Linkages Between Science, Policy and Practice (eds. Kabisch, N., Korn, H., Stadler, J. & Bonn, A.) 91–109 (Springer, 2017).Kabisch, N., Basu, S., van den Bosch, M., Bratman, G. N. & Masztalerz, O. Nature-based solutions and mental health. in Nature-Based Solutions for Cities, 192–212 (Edward Elgar Publishing, 2023).Medina Camarena, K. S., Wübbelmann, T. & Förster, K. What is the contribution of urban trees to mitigate pluvial flooding?Hydrology 9, 108 (2022).
Google Scholar 
Quagliolo, C., Comino, E. & Pezzoli, A. Experimental flash floods assessment through urban flood risk mitigation (UFRM) model: the case study of Ligurian coastal cities. Front. Water 3, 663378 (2021).
Google Scholar 
Landeshauptstadt Hannover [City of Hannover]. Starkregenhinweiskarten. https://stadtmodell-prod4.hannover-stadt.de/DT5/#/ (2025).Grayson, R. & Blöschl, G. Spatial modelling of catchment dynamics. in Spatial Patterns in Catchment Hydrology: Observations and Modelling (eds. Grayson, R. & Blöschl, G.) 51–81 (Cambridge: Cambridge University Press, 2000).Natural Capital Project. InVEST 3.14.1. https://naturalcapitalproject.stanford.edu/software/invest (Stanford University, 2023).Reinstaller, S. et al. Resilient urban flood management: a multi-objective assessment of mitigation strategies. Sustainability 16, 4123 (2024).
Google Scholar 
Chang, H. et al. Assessment of urban flood vulnerability using the social-ecological-technological systems framework in six US cities. Sustain Cities Soc. 68, 102786 (2021).
Google Scholar 
Wübbelmann, T. & Kabisch, N. Backcasting—a scenario approach in urban climate adaptation planning. npj Urban Sustain. 5, 69 (2025).
Google Scholar 
Alves, A., van Opstal, C., Keijzer, N., Sutton, N. & Chen, W.-S. Planning the multifunctionality of nature-based solutions in urban spaces. Cities 146, 104751 (2024).
Google Scholar 
Kadaverugu, A., Nageshwar Rao, C. & Viswanadh, G. K. Quantification of flood mitigation services by urban green spaces using InVEST model: a case study of Hyderabad city, India. Model Earth Syst. Environ. 7, 589–602 (2021).
Google Scholar 
Ekeu-wei, I. T. & Blackburn, G. A. Applications of open-access remotely sensed data for flood modelling and mapping in developing regions. Hydrology 5, 39 (2018).
Google Scholar 
Zhou, K. et al. Urban flood risk management needs nature-based solutions: a coupled social-ecological system perspective. npj Urban Sustain. 4, 25 (2024).
Google Scholar 
Bundesamt für Naturschutz (BfN) [Federal Agency for Nature Conservation, Germany]. Naturräumliche Gliederung (WFS). https://gdk.gdi-de.org/geonetwork/srv/api/records/325bfe9a-21f0-4fc4-9dae-a8feb4668a08 (2022).Landeshauptstadt Hannover [City of Hannover]. Strukturdaten der Stadtteile und Stadtbezirke 2023. Statistische Berichte der Landeshauptstadt Hannover (Landeshauptstadt Hannover, 2024).Deutscher Wetterdienst (DWD) [German Meteorological Service]. Open Data Service. https://opendata.dwd.de/climate_environment/CDC/observations_germany/climate (2024).Do, S. & Besnier, K. InVEST Urban Development Incorporating Earth Observation Data into the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) Urban Flood Risk Mitigation Model Python API. NASA DEVELOP Closeout (2023).Salata, S., Giaimo, C., Alberto Barbieri, C. & Garnero, G. The utilization of ecosystem services mapping in land use planning: the experience of LIFE SAM4CP project. J. Environ. Plan. Manag. 63, 523–545 (2020).
Google Scholar 
United States Department of Agriculture – Natural Resources Conservation Service (USDA-NRCS). Estimation of Direct Runoff from Storm Rainfall. in Part 630 Hydrology National Engineering Handbook (U.S. Department of Agriculture, Washington DC, 2004).Ross, C. W. et al. HYSOGs250m, global gridded hydrologic soil groups for curve-number-based runoff modeling. Sci. Data 5, 180091 (2018).
Google Scholar 
Copernicus Land Monitoring Service. CLCplus Backbone 2021 (raster 10 m), Europe, 3-yearly. European Environment Agency. https://doi.org/10.2909/71fc9d1b-479f-4da1-aa66-662a2fff2cf7 (2024).Kuehler, E., Hathaway, J. & Tirpak, A. Quantifying the benefits of urban forest systems as a component of the green infrastructure stormwater treatment network. Ecohydrology 10, e1813 (2017).United States Department of Agriculture—Natural Resources Conservation Service (USDA-NRCS). Hydrologic Soil-Cover Complexes. in Part 630 Hydrology National Engineering Handbook (U.S. Department of Agriculture, 2004).Landesamt für Geoinformation und Landesvermessung Niedersachsen (LGLN) [Lower Saxony Office for Geoinformation and Surveying, Germany]. Digitales Landschaftsmodell (Basis-DLM). Open Data, CC-BY 4.0. https://ni-lgln-opengeodata.hub.arcgis.com/documents/cd1867f0356d4ed2a102f9bdfb52eb88/about (2024).Deutscher Wetterdienst (DWD) [German Meteorological Service]. Starkniederschlagshöhen und -spenden gemäß KOSTRA-DWD-2020: Rasterfeld 109140 (2022).Tapsell, S. M., Penning-Rowsell, E. C., Tunstall, S. M. & Wilson, T. L. Vulnerability to flooding: health and social dimensions. Philos. Trans. R. Soc. Lond. Ser. A Math. Phys. Eng. Sci. 360, 1511–1525 (2002).
Google Scholar 
Hamidi, A. R. et al. Flood exposure and social vulnerability analysis in rural areas of developing countries: an empirical study of Charsadda District, Pakistan. Water 14, 1176 (2022).
Google Scholar 
Landeshauptstadt Hannover [City of Hannover]. Strukturdaten der statistischen Bezirke 2023 (Landeshauptstadt Hannover, 2024).OpenStreetMap contributors. Planet OSM. (2024).Scheuer, S., Haase, D. & Meyer, V. Exploring multicriteria flood vulnerability by integrating economic, social and ecological dimensions of flood risk and coping capacity: from a starting point view towards an end point view of vulnerability. Nat. Hazards 58, 731–751 (2011).
Google Scholar 
Eisenberg, B., Lindow, K. C. & Smith, D. R. Permeable Pavements (American Society of Civil Engineers, 2015).Hunt, W. F. & Bean, E. Z. NC State University Permeable Pavement Research and Changes to the State of NC Runoff Credit System. 8th International Conference on Concrete Block Paving, San Francisco, California, USA (2006).Eisenberg, B. & Polcher, V. Nature Based Solutions—Technical Handbook—Part II. https://doi.org/10.13140/RG.2.2.24970.54726 (2019).Russell, V. L. The green roof manual: professional guide to design, installation, and maintenance, by Edmund C. Snodgrass and Linda McIntyre. Landsc. Archit. 100, 92–93 (2019).
Google Scholar 
Pätzold, S. Green Roofs in Würzburg: GIS-Based Potential Analysis as a Planning Basis in Urban Greening Instruments. Master’s thesis, Julius-Maximilians-University Würzburg. https://doi.org/10.25972/OPUS-21067 (2020).Scholz-Barth, K. Green roofs: Stormwater management from the top down. Environmental Design & Construction 4, 63–69 (2001).
Google Scholar 
Lohwasser, J., Bolognesi, T. & Schaffer, A. Impacts of population, affluence and urbanization on local air pollution and land transformation—a regional STIRPAT analysis for German districts. Ecol. Econ. 227, 108416 (2025).
Google Scholar 
Wang, B., Wang, B., Lv, B. & Wang, R. Impact of motor vehicle exhaust on the air quality of an urban city. Aerosol Air Qual. Res. 22, 220213 (2022).
Google Scholar 
Bundesministerium für Umwelt, Naturschutz, Bau und Reaktorsicherheit (BMUB) [Federal Ministry for the Environment, Nature Conservation, Building and Nuclear Safety, Germany]. Weißbuch Stadtgrün: Grün in der Stadt–Für eine lebenswerte Zukunft (BMUB, 2017).Leibniz Universität Hannover (LUH) [Leibniz University Hannover, Germany]. Integriertes Klimaschutzkonzept der Leibniz Universität Hannover (Leibniz Universität Hannover, 2022).Nguyen Dang, H.-A. et al. Users’ perceptions of the contribution of a university green roof to sustainable development. Sustainability 15, 6772 (2023).
Google Scholar 
Fassman-Beck, E. et al. Curve number and runoff coefficients for extensive living roofs. J. Hydrol. Eng. 21, 04015073 (2016).
Google Scholar 
Slowikowski, K. ggrepel: automatically position non-overlapping text labels with ggplot2. CRAN: Contributed Packages. https://doi.org/10.32614/CRAN.package.ggrepel (2024).Wickham, H. Data analysis. in ggplot2: Elegant Graphics for Data Analysis, 189–201 (Cham: Springer International Publishing, 2016).Landeshauptstadt Hannover [City of Hannover]. Statistische Gliederung SKH20 (Landeshauptstadt Hannover, 2024).Download referencesAcknowledgementsThis research was supported by the GoGreenNext project which has received funding from the European Union (Grant Agreement number 101137209). Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or the European Health and Digital Executive Agency (HADEA, granting authority). Neither the European Union nor the granting authority can be held responsible for them.FundingOpen Access funding enabled and organized by Projekt DEAL.Author informationAuthors and AffiliationsInstitute of Earth System Sciences, Physical Geography and Landscape Ecology Section, Leibniz University Hannover, Hannover, GermanyPaula Therese Schröder, Thea Wübbelmann & Nadja KabischAuthorsPaula Therese SchröderView author publicationsSearch author on:PubMed Google ScholarThea WübbelmannView author publicationsSearch author on:PubMed Google ScholarNadja KabischView author publicationsSearch author on:PubMed Google ScholarContributionsP.S., T.W., and N.K. designed and conceptualised the study. P.S. wrote the draft manuscript text, did the modelling and designed and developed the Figures. T.W. and N.K. edited the manuscript, figures and tables.Corresponding authorCorrespondence to
Nadja Kabisch.Ethics declarations

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AbstractMalaria remains a leading cause of morbidity and mortality in the developing world, particularly in sub-Saharan Africa. Spatial variability significantly influences efforts to control malaria and its incidence, which remains a serious public health concern in Ethiopia. Using geostatistical methods, this study investigates the environmental factors and spatial distribution of malaria incidence in the Hadiya Zone across woredas in 2022 and 2023. Descriptive analyses revealed consistent spatial heterogeneity, with high incidence rates in Shashogo, Soro, and Misrak Badawacho. Global spatial autocorrelation measures Moran’s I (0.558 in 2022 and 0.483 in 2023; p < 0.01) and Geary’s C (0.63 and 0.69, respectively) confirmed statistically significant clustering of malaria cases. Local Moran’s I analysis identified hot spots in Shashogo, Soro, and Misrak Badawacho, and cold spots in Misha, Duna, and Gombora, indicating localized spatial dependence. Spatial regression analysis, comparing Ordinary Least Squares (OLS) and Spatial Autoregressive (SAR) models, highlighted average maximum temperature (β = 0.945, p = 0.017) and proportion of highland terrain (β = 0.543, p = 0.040) as key predictors of malaria incidence. The SAR model showed superior fit, evidenced by lower AIC and higher log-likelihood values, confirming the influence of spatial dependence. These findings support geographically targeted malaria interventions in high-risk woredas. Limitations include the short study period (2022–2023) and the absence of socioeconomic variables due to lack of household survey and secondary data.

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Data availability

The datasets generated and/or analyzed during the current study are not publicly available and must be obtained from the corresponding author upon reasonable request. The data were sourced from the Hadiyya Zone Health Bureau, the SNNPR Meteorological Center, and the Hadiyya Zone Agricultural and Finance Bureaus, and access requires official permission from the respective agencies.
AbbreviationsSAR:
Spatial autoregressive model
OLS:
Ordinary least squares
AIC:
Akaike information criterion
LM:
Lagrange multiplier
SNNPR:
Southern nations, nationalities, and peoples’ region
WHO:
World health organization
GIS:
Geographic information system
ARC GIS:
A specific GIS software platform
ITN:
Insecticide-treated nets
IRS:
Indoor residual spraying
RF:
Rainfall
MIT:
Minimum temperature
MAT:
Maximum temperature
LL:
Log-likelihood
GMI:
Global Moran’s I
LCI:
Local Moran’s I
Geary’s C:
Geary’s contiguity ratio
API:
Annual parasite incidence
MoH:
Ministry of health
ReferencesWorld Health Organization. World malaria report 2019. Geneva: World Health Organization. (2019). Available from: https://www.who.int/publications/i/item/9789241565721Federal Democratic Republic of Ethiopia Ministry of Health. Malaria Surveillance and Response Report (Federal Democratic Republic of Ethiopia Ministry of Health, 2024).World Health Organization. Ethiopia Malaria Risk Assessment. Geneva: World Health Organization. (2024). Available from: https://www.who.intSmith, D. L. Spatial dynamics of malaria transmission. J. Trop. Med. 2014, 1–10 hkgsWArticle ID 123456 (2014).Alemu, A., Abebe, G., Tsegaye, W. & Golassa, L. Climatic variables and malaria transmission dynamics in Jimma town, South West Ethiopia. Parasites Vectors. 4, 30. https://doi.org/10.1186/1756-3305-4-30 (2011).
Google Scholar 
Central Statistical Agency (CSA). Population and Housing Census Projections for Ethiopia, 2017–2037 (CSA, 2021).Ethiopian Meteorological Institute (EMI). Climate Data Summary for Southern Ethiopia: Rainfall and Temperature Records (EMI, 2020).Anselin, L. Under the hood: issues in the specification and interpretation of Spatial regression models. Agric. Econ. 27 (3), 247–267. https://doi.org/10.1111/j.1574-0862.2002.tb00120.x (2002).
Google Scholar 
Anselin, L. & Bera, A. K. Spatial dependence in linear regression models with an introduction to Spatial econometrics. In Handbook of Applied Economic Statistics (eds Ullah, A. & Giles, D. E. A.) 237–289 (Marcel Dekker, 1998).
Google Scholar 
Cliff, A. D. & Ord, J. K. Spatial Processes: Models & Applications (Taylor & Francis, 2012) ((Originally published 1981 by Pion Limited; reprinted by Routledge)).Tobler, W. R. Cellular geography. In Philosophy in Geography (eds Gale, S. & Olsson, G.) 379–386 (D. Reidel Publishing Company, 1979). https://doi.org/10.1007/978-94-009-9394-5_21.
Google Scholar 
LeSage, J. P. & Pace, R. K. Introduction to Spatial Econometrics (CRC, 2009). https://doi.org/10.1201/9781420064254.Anselin, L. Spatial effects in econometric practice in environmental and resource economics. Am. J. Agric. Econ. 83 (3), 705–710. https://doi.org/10.1111/0002-9092.00194 (2001).
Google Scholar 
Anselin, L. & Rey, S. Properties of tests for Spatial dependence in linear regression models. Geogr. Anal. 23 (2), 112–131. https://doi.org/10.1111/j.1538-4632.1991.tb00228.x (1991).
Google Scholar 
Gimpel, J. G. & Cho, W. K. T. Fitting Spatial error and lag models for political network analysis. Political Geogr. 23 (8), 987–1008 (2004).
Google Scholar 
Ayele, D. G., Zewotir, T. T. & Mwambi, H. G. Spatial distribution of malaria problem in three regions of Ethiopia. Malar. J. 12, 207. https://doi.org/10.1186/1475-2875-12-207 (2013).
Google Scholar 
Oluyemi, A. A. & Oyeyemi, O. T. Spatio-temporal analysis of malaria incidence and environmental predictors in Nigeria. Sci. Rep. 9, 17500. https://doi.org/10.1038/s41598-019-53814-x (2019).
Google Scholar 
Yeshiwondim, A. K., Gopal, S., Hailemariam, A. T., Dengela, D. O. & Patel, H. P. Spatial analysis of malaria incidence at the village level in areas with unstable transmission in Ethiopia. Int. J. Health Geogr. 8, 5. https://doi.org/10.1186/1476-072X-8-5 (2009).
Google Scholar 
Dessie, D. B. Spatial modelling of malaria prevalence and its risk factors in rural SNNPR, ethiopia: classical and bayesian approaches. Am. J. Theor. Appl. Stat. 6 (6), 254–269. https://doi.org/10.11648/j.ajtas.20170606.11 (2017).
Google Scholar 
Abeku, T. A., Van Oortmarssen, G. J., Borsboom, G., de Vlas, S. J. & Habbema, J. D. Spatial and Temporal variations of malaria epidemic risk in ethiopia: factors involved and implications. Acta Trop. 87 (3), 331–340. https://doi.org/10.1016/S0001-706X(03)00123-2 (2003).
Google Scholar 
Howes, R. E., Battle, K. E., Golding, N. & Hay, S. I. Plasmodium vivax thematic review: Epidemiology (World Health Organization, 2014). https://doi.org/10.1016/B978-0-12-407826-0.00050-3.Tsegaye, B., Ayele, D. & Kebede, T. Influence of altitude and climate on malaria transmission dynamics in ethiopia: a systematic review. Malar. J. 22 (1), 134. https://doi.org/10.1186/s12936-023-04512-9 (2023).
Google Scholar 
Taffese, H. S. et al. Malaria epidemiology and interventions in Ethiopia from 2001 to 2016. Infect. Dis. Poverty. 7, 103. https://doi.org/10.1186/s40249-018-0487-3 (2018).
Google Scholar 
Ssempiira, J. et al. The influence of Spatial heterogeneity on malaria intervention impact in Uganda. Malar. J. 20, 108. https://doi.org/10.1186/s12936-021-03652-w (2021).
Google Scholar 
Tusting, L. S. et al. Housing improvements and malaria risk in sub-Saharan africa: a multi-country analysis of survey data. Lancet Planet. Health. 1 (2), e83–e93 (2017).
Google Scholar 
Prothero, R. M. Disease and mobility: a neglected factor in epidemiology. Soc. Sci. Med. 11 (1), 9–25 (1977).
Google Scholar 
R Core Team. R: A language and environment for statistical computing. Vienna: R Foundation for Statistical Computing. (2023). Available from: https://www.R-project.org/Download referencesAcknowledgementsThe author gratefully acknowledges Wachemo University (WCU) for granting ethical approval and institutional support for this study. Appreciation is extended to the Hadiyya Zone Health Bureau, the SNNPR Meteorological Center, and the Hadiyya Zone Agricultural and Finance Bureaus for providing the necessary data. Special thanks are due to the health facility staff and local administrators in Hadiyya Zone for their cooperation during data collection.FundingThis research was conducted without any external funding support.Author informationAuthors and AffiliationsDepartment of Statistics, College of Natural and Computational Sciences, Wachemo University, P.O. Box 667, Hossana, EthiopiaShambel Selman AbdoAuthorsShambel Selman AbdoView author publicationsSearch author on:PubMed Google ScholarContributionsS.S.A. conceived and designed the study, acquired and analyzed the data, interpreted the results, prepared all figures and tables, and wrote the entire manuscript. S.S.A. also reviewed and approved the final version of the manuscript.Corresponding authorCorrespondence to
Shambel Selman Abdo.Ethics declarations

Competing interests
The authors declare no competing interests.

Ethical approval
Ethical clearance for this study was obtained from the Institutional Review Board (IRB) of Wachemo University (WCU 17.2023). The study was based on secondary data without personal identifiers. All methods were performed in accordance with the relevant guidelines and regulations of Wachemo University and national research ethics standards. All necessary permissions were secured from relevant health and administrative offices in the Hadiyya Zone to ensure the ethical use of the data.

Informed consent
As the study utilized secondary, de-identified data and did not involve direct interaction with human participants, informed consent was not applicable. However, permission to use the data was formally obtained from the appropriate authorities.

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Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
Reprints and permissionsAbout this articleCite this articleAbdo, S.S. Spatial analysis of malaria incidence and environmental determinants in Hadiya Zone, Ethiopia.
Sci Rep (2025). https://doi.org/10.1038/s41598-025-33236-8Download citationReceived: 09 July 2025Accepted: 17 December 2025Published: 30 December 2025DOI: https://doi.org/10.1038/s41598-025-33236-8Share 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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KeywordsMalariaIncidenceGeostatistics and spatial autocorrelation More

Abstract

The abundance-biomass comparison curves (ABC curves) method was adopted to analyze the temporal stability of the fish and nekton community structure in Laoshan Bay, China, during the spring and summer seasons from 2013 to 2015. This study aimed to explore the feasibility of using stable species in the nekton structure as stock enhancement candidates and to enrich the guidelines for responsible stock enhancement. Results showed that the W-statistic values of ABC curves for nekton were generally higher than those for fish across the three years’ spring and summer seasons. For fish, the biomass curves were often located below their abundance curves; in contrast, the biomass curves of nekton were generally positioned above their abundance curves or intersected with them. These findings indicated that the nekton community structure was more complex and stable than that of the fish community. Portunus trituberculatus played an important role in maintaining the stability of nekton community structure during spring and summer. Therefore, stock enhancement of P. trituberculatus in Laoshan Bay every spring could improve the stability of nekton community structure in the subsequent summer through trophic relationships. Given that ABC curves have a solid ecological theoretical basis, the stability of nekton structure could be one of joint ecological elements for selecting stock enhancement species.

Data availability

The datasets used and analysed during the current study available from the corresponding author on reasonable request.
ReferencesKitada, S. Economic, ecological and genetic impacts of marine stock enhancement and sea ranching: A systematic review. Fish Fish. 19 (2), 511–532 (2018).
Google Scholar 
Lorenzen, K. Population dynamics and potential of fisheries stock enhancement: practical theory for assessment and policy analysis. Philosophical Trans. Royal Soc. B. 360, 171–189 (2005).
Google Scholar 
Lorenzen, K., Leber, K. M. & Blankenship, H. L. Responsible approach to marine stock enhancement: an update 2010. Rev. Fish. Sci. 18 (2), 189–210 (2010).
Google Scholar 
Feng, S. Y. Structure, function, succession and ecological law of ecosystem. Water Resour. Electr. Power. 20, 1–10 (1993).
Google Scholar 
Loreau, M. Biodiversity and ecosystem functioning: recent theoretical advances. Oikos 91, 3–17 (2000).
Google Scholar 
Tilman, D. Causes, consequences and ethics of biodiversity. Nature 405, 363–365 (2000).
Google Scholar 
Rosenfeld, J. S. Functional redundancy in ecology and conservation. Oikos 98, 156–162 (2002).
Google Scholar 
Cummins, K. W. Field Procedures for Analysis of Functional Feeding Groups of Stream Macroinvertebrates (ed. Cummins K W) (University of Pittsburgh,1998).Pearson, T. & Mannvik, H. P. Long-term changes in the diversity and faunal structure of benthic communities in the Northern North sea: natural variability or induced instability? Hydrobiologia 375–376, 317–329 (1998).
Google Scholar 
Lotze, H. K. & Milewski, I. Two centuries of multiple human impacts and successive changes in a North Atlantic food web. Ecol. Appl. 14, 1428–1447 (2004).
Google Scholar 
Lotze, H. K. et al. Recovery of marine animal populations and ecosystems. Trends Ecol. Evol. 26, 595–605 (2011).
Google Scholar 
Wichert, G. A. & Rapport, D. J. Fish community structure as a measure of degradation and rehabilitation of riparian systems in an agricultural drainage basin. Environ. Manage. 22, 425–443 (1998).
Google Scholar 
Sanjib, K. D., Dibyendu, B. & Sudipto, R. Use of biotic community structure as a measure of ecological degradation. Chin. J. Appl. Environ. Biology. 13, 662–667 (2007).
Google Scholar 
Warwick, R. M. A new method for detecting pollution effects on marine macrobenthic communities. Mar. Biol. 92, 557–562 (1986).
Google Scholar 
Li, Z. Y. et al. Interannual variations in fish community structure in the Bohai sea. J. Fish. Sci. China. 24 (2), 403–413 (2017).
Google Scholar 
Lee, S. G. & Rahimi Midani. A. National comprehensive approaches for rebuilding fisheries in South Korea. Mar. Policy. 45, 156–162 (2014).
Google Scholar 
Blanchard, F. et al. Fishing effects on diversity, size and community structure of the benthic invertebrate and fish megafauna on the Bay of Biscay Coast of France. Mar. Ecol. Prog. Ser. 280, 249–260 (2004).
Google Scholar 
Li, Z., Qiu, S. Y., Zhang, J. H. & Gen, B. L. Preliminary analysis of fish community structure in Southern Shandong Peninsula before and after portunus trituberctdatus releasing. J. Yantai Univ. (Natural Sci. Eng. Edition). 27, 184–190 (2014).
Google Scholar 
Li, C. Y. et al. Comparative analysis of effect of resource-replenishment on portunus trituberculatus enhancements in Jinghai Bay and Wuleidao Bay. J. Yantai Univ. (Natural Sci. Eng. Edition). 32, 159–164 (2019).
Google Scholar 
Deng, J. Y., Meng, T. X. & Ren, S. M. Food web of fishes in Bohai sea. Acta Ecol. Sinica. 6, 356–364 (1986).
Google Scholar 
Tang, Q. S. & Ye, M. Z. In Development and Protection of Offshore Fishery Resources in Shandong Province (eds Tang, Q. S. & Ye, M. Z.). 90–119 (Agriculture, 1990). Shimano, A. Stocking effects on the swimming crab portunus trituberculatus population in a community model. Nippon Suisan Gakkaishi (Japanese Edition). 61, 13–20 (1995).
Google Scholar 
Chen, J. S. & Zhu, J. S. A study on the feeding and trophic levels of major economic invertebrates in the yellow sea. Acta Oceanol. Sin. 19, 102–108 (1997).
Google Scholar 
Jiang, W. M. et al. Diet of Charybdis Japonic and portunus trituberculatus in the Bohai sea. Mar. Fisheries Res. 19, 53–59 (1998).
Google Scholar 
Yang, J. M. A study on food and trophic levels of Bohai sea invertebrates. Mod. Fisheries Inform. 16, 8–16 (2001).
Google Scholar 
Turner, M. G. Landscape Heterogeneity and Disturbance(ed. Turner M G). 239 (Springer, 1987).Johnson, E. A. Fire and Vegetation Dynamics: Studies from the North American Boreal Forests (ed. Johnson E A). 129Cambridge University Press, (1992).Peng, S. In The Dynamics of Community in Southern Subtropical Forest (ed. Peng, S.)15–16 (Science, 1996). Kimmins, J. P. Forest Ecology. 2 edition. 596Prince Hall, (1996).Dai, B. L. & He, Y. L. Temporal-spatial scale problem of offshore ecosystem health assessment. Water Sav. Irrig. 60, 60–63 (2012). Brodeur, R. D. & Pearcy, W. G. Effects of environmental variability on trophic interactions and food web structure in a pelagic upwelling ecosystem. Mar. Ecosyst. Process. Ser. 84, 101–119 (1992).
Google Scholar 
Tilman, D. Biodiversity: population versus ecosystem stability. Ecology 77, 350–363 (1996).
Google Scholar 
Liu, B. W. et al. Monthly recapture rate and reproduction contribution rate of released swimming crab. Fish. Sci. 40, 10–19 (2021).
Google Scholar 
Karr, J. R. & Dudley, D. R. Ecological perspective on water quality goals. Environ. Manage. 5, 55–68 (1981).
Google Scholar 
Mendoca, J. C., Clemente, S. & Hernandez, J. C. Modeling the role of marine protected areas on the recovery of shallow Rocky reef ecosystem after a catastrophic submarine volcanic eruption. Mar. Environ. Res. 155, 1–14 (2020).
Google Scholar 
Dunton, K. H., Goodall, J. L., Schonberg, S. V., Grebmeier, J. M. & Maidment, D. R. Multi-decadal synthesis of benthic–pelagic coupling in the Western arctic: role of cross-shelf advective processes. Deep-Sea Res. II. 52, 3462–3477 (2005).
Google Scholar 
Blanchard, A. L. et al. Ecosystem variability in the offshore Northeastern Chukchi sea. Prog. Oceanogr. 159, 130–153 (2017).
Google Scholar 
Folke, C. Energy economy of salmon aquaculture in the Baltic sea. Environ. Manage. 12, 525–537 (1988).
Google Scholar 
Cohen, J. E. Concluding remarks. in: Kawanabe H, Cohen J E, and Iwasaki K. Mutualism and Community Organization. 412–415Oxford University Press, (1993).Meysman, F. J. R., Middelburg, J. J., Heip, C. H. R. & Bioturbation A fresh look at darwin’s last Idea. Trends Ecol. Evol. 21, 688–695 (2006).
Google Scholar 
Calbet, A. & Saize, E. The ciliate-copepod link in marine ecosystems. Aquat. Microb. Ecol. 38, 157–167 (2005).
Google Scholar 
Mermillod-Blondin, F. et al. Influence of bioturbation by three benthic infaunal species on microbial community and biogeochemical processes in marine sediment. Aquat. Microb. Ecol. 36, 271–284 (2004).
Google Scholar 
Adamek, Z. & Marsalek, B. Bioturbation of sediments by benthic macroinvertebrates and fish and its implication for pond ecosystems: a review. Aquacult. Int. 21, 1–17 (2013).
Google Scholar 
Mccann, K. S.The diversity-stability debate. Nature 405, 228–233 (2000).
Google Scholar 
Han, D. Y., Xue, Y., Zhang, C. L. & Ren, Y. A mass balanced model of trophic structure and energy flows of a semiclosed marine ecosystem. Acta Oceanol. Sin. 36, 60–69 (2017).
Google Scholar 
Li, C. W. et al. Difference of trophic niche between Sebastiscus marmoratus and Larimichthys polyactis in marine ranching of ma’an Archipelago, China. Chin. J. Appl. Ecol. 29, 1489–1493 (2018).
Google Scholar 
Shen, X. Q. & Yuan, Q. Characteristic analysis of nekton community structure in the enhancement area of Hangzhou Bay. Mar. Fisheries. 33, 251–257 (2011).
Google Scholar 
Jouffre, D. & Inejih, C. A. Assessing the impact of fisheries on demersal fish assemblages of the Mauritanian continental shelf, 1987–1999, using dominance curves. ICES J. Mar. Sci. 62, 380–383 (2005).
Google Scholar 
Download referencesAcknowledgementsThe authors are grateful to the staff in the Key Laboratory of Sustainable Development of Marine Fisheries at the Yellow Sea Fisheries Research Institute for their cooperation during the sampling operation. FundingThis work was supported by the National Key Research and Development Program of China (Grant No.: 2023YFD2401101).Author informationAuthors and AffiliationsYellow Sea Fisheries Research Institute, Key Laboratory of Sustainable Development of Marine Fisheries, Ministry of Agriculture and Rural Affairs, Chinese Academy of Fishery Science, 106 Nanjing Road, 266071, Qingdao, ChinaZhong Yi Li & Qun LinShandong Provincial Key Laboratory for Fishery Resources and Eco-environment, Yellow Sea Fisheries Research Institute, Chinese Academy of Fishery Sciences, Qingdao, 266071, ChinaZhong Yi Li & Qun LinAuthorsZhong Yi LiView author publicationsSearch author on:PubMed Google ScholarQun LinView author publicationsSearch author on:PubMed Google ScholarContributionsZhong Yi Li wrote the main manuscript text. Zhong Yi Li prepared Figs. 1, 2 and 3. Qun Lin performed data analysis.Both authors reviewed the manuscript.Corresponding authorCorrespondence to
Zhong Yi Li.Ethics declarations

Competing interests
The authors declare no competing interests.

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Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
Reprints and permissionsAbout this articleCite this articleLi, Z., Lin, Q. An ecological element for selecting enhancement stock based on the stability of nekton community structure.
Sci Rep (2025). https://doi.org/10.1038/s41598-025-33783-0Download citationReceived: 10 April 2025Accepted: 22 December 2025Published: 30 December 2025DOI: https://doi.org/10.1038/s41598-025-33783-0Share 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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KeywordsLaoshan bayCommunity structureABC curvesStock enhancementSpecies selection More

AbstractExtreme temperature events are becoming widespread with global warming, impacting phytoplankton, the foundation of the marine ecosystem. In the Southern Ocean, these impacts are not well understood, despite the key role of phytoplankton in global carbon cycling and climate. Here, we use 26 years of satellite observations and confirm previously identified impacts of marine heatwaves (MHWs) on phytoplankton in the Southern Ocean, while systematically comparing the opposite impacts of marine cold spells (MCSs). MHWs decrease phytoplankton chlorophyll-a (Chl-a) in subtropical regions (−21.11%) but less so in polar regions, with Chl-a even increasing in the Sub-Antarctic Zone ( + 22.26%). MCSs exhibit opposite patterns, enhancing Chl-a in subtropical regions ( + 32.37%) while inhibiting it in southern regions (−21.19%). These regional differences in Chl-a anomalies are mediated by distinct responses in phytoplankton size composition to MHWs and MCSs. As extreme events intensify with global warming, Southern Ocean’s phytoplankton will be disrupted, with implications for global biogeochemical cycles. These findings highlight the importance of simultaneously considering both MHWs and MCSs when assessing the ecological impacts of climate extremes.

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Data availability

The daily SST-CCI (version 3.0) and SIC data are provided by ESA from their website: https://doi.org/10.5285/4a9654136a7148e39b7feb56f8bb02d2. The daily Rrs-CCI and kd (version 6.0) data are provided by ESA from their website: https://rsg.pml.ac.uk/thredds/catalog-cci.html. The PAR data are provided by GlobColour from their website: https://hermes.acri.fr/index.php?class=archive. The nitrate data provided by CMEMS from their website: https://doi.org/10.48670/moi-00019. The MLD (GLORYS12V1) data provided by CMEMS from their website: https://doi.org/10.48670/moi-00021. The HPLC data provided by PANGAEA from their website: https://doi.org/10.1594/PANGAEA.938703. The HPLC data provided by ADON from their website: https://portal.aodn.org.au/search. The data used to generate the figures presented in this study are available via figshare at https://doi.org/10.6084/m9.figshare.3021702187.
Code availability

The analyses were performed using MATLAB and Python; the main code used in this study is available at https://doi.org/10.5281/zenodo.1722329288, with BGC-Argo data processing based on code from https://github.com/NOAA-PMEL/OneArgo-Mat and MHWs/MCSs detection using code from https://github.com/ZijieZhaoMMHW/m_m hw1.0.
ReferencesOliver, E. C. J. et al. Longer and more frequent marine heatwaves over the past century. Nat. Commun. 9, 1324 (2018).
Google Scholar 
Frölicher, T. L., Fischer, E. M. & Gruber, N. Marine heatwaves under global warming. Nature 560, 360–364 (2018).
Google Scholar 
Hobday, A. J. et al. A hierarchical approach to defining marine heatwaves. Prog. Oceanogr. 141, 227–238 (2016).
Google Scholar 
Wang, Y., Kajtar, J. B., Alexander, L. V., Pilo, G. S. & Holbrook, N. J. Understanding the changing nature of marine cold-spell. Geophys. Res. Lett. 49, e2021GL097002 (2022).
Google Scholar 
Dispert, B. oeningC. W., Visbeck, A., Rintoul, M. & Schwarzkopf, S. R. FU. The response of the Antarctic Circumpolar Current to recent climate change. Nat. Geosci. 1, 864–869 (2008).
Google Scholar 
Swart, N. C., Gille, S. T., Fyfe, J. C. & Gillett, N. P. Recent Southern Ocean warming and freshening driven by greenhouse gas emissions and ozone depletion. Nat. Geosci. 11, 836–841 (2018).
Google Scholar 
Shi, J.-R., Talley, L. D., Xie, S.-P., Peng, Q. & Liu, W. Ocean warming and accelerating Southern Ocean zonal flow. Nat. Clim. Chang. 11, 1090–1097 (2021).
Google Scholar 
Schlegel, R. W., Darmaraki, S., Benthuysen, J. A., Filbee-Dexter, K. & Oliver, E. C. J. Marine cold-spells. Prog. Oceanogr. 198, 102684 (2021).
Google Scholar 
He, Q. et al. Enhancing impacts of mesoscale eddies on Southern Ocean temperature variability and extremes. Proc. Natl. Acad. Sci. USA 120, e2302292120–e2302292120 (2023).
Google Scholar 
Montie, S., Thomsen, M. S., Rack, W. & Broady, P. A. Extreme summer marine heatwaves increase chlorophyll a in the Southern Ocean. Antarct. Sci. 32, 508–509 (2020).
Google Scholar 
Jones, T. et al. Massive mortality of a Planktivorous seabird in response to a marine heatwave. Geophys. Res. Lett. 45, 3193–3202 (2018).
Google Scholar 
Gruber, N. et al. Trends and variability in the ocean carbon sink. Nat. Rev. Earth Environ. 4, 119–134 (2023).
Google Scholar 
Hauck, J. et al. The Southern Ocean Carbon Cycle 1985–2018: mean, seasonal cycle, trends, and storage. Glob. Biogeochem. Cycles 37, e2023GB007848 (2023).
Google Scholar 
Frölicher, T. L. et al. Dominance of the Southern Ocean in anthropogenic carbon and heat uptake in CMIP5 models. J. Clim. 28, 862–886 (2015).
Google Scholar 
Roemmich, D. et al. Unabated planetary warming and its ocean structure since 2006. Nat. Clim. Chang. 5, 240–245 (2015).
Google Scholar 
Buesseler, K. O. & Boyd, P. W. Shedding light on processes that control particle export and flux attenuation in the twilight zone of the open ocean. Limnol. Oceanogr. 54, 1210–1232 (2009).
Google Scholar 
Boyd, P. W., Claustre, H., Levy, M., Siegel, D. A. & Weber, T. Multi-faceted particle pumps drive carbon sequestration in the ocean. Nature 568, 327–335 (2019).
Google Scholar 
Decima, M. et al. Salp blooms drive strong increases in passive carbon export in the Southern Ocean. Nat. Commun. 14, 425 (2023).
Google Scholar 
Boyce, D. G., Lewis, M. R. & Worm, B. Global phytoplankton decline over the past century. Nature 466, 591–596 (2010).
Google Scholar 
Thomas, M. K., Kremer, C. T., Klausmeier, C. A. & Litchman, E. A global pattern of thermal adaptation in marine phytoplankton. Science 338, 1085–1088 (2012).
Google Scholar 
Rodrigues, R. R., Taschetto, A. S., Sen Gupta, A. & Foltz, G. R. Common cause for severe droughts in South America and marine heatwaves in the South Atlantic. Nat. Geosci. 12, 620–626 (2019).
Google Scholar 
Crampton, J. S. et al. Southern Ocean phytoplankton turnover in response to stepwise Antarctic cooling over the past 15 million years. Proc. Natl. Acad. Sci. USA 113, 6868–6873 (2016).
Google Scholar 
Thomalla, S. J., Nicholson, S.-A., Ryan-Keogh, T. J. & Smith, M. E. Widespread changes in Southern Ocean phytoplankton blooms linked to climate drivers. Nat. Clim. Chang. 13, 975–984 (2023).
Google Scholar 
Pinkerton, M. H. et al. Evidence for the impact of climate change on primary producers in the Southern Ocean. Front. Ecol. Evol. 9, 592027 (2021).
Google Scholar 
Martin, J. H., Gordon, R. M. & Fitzwater, S. E. Iron in Antarctic waters. Nature 345, 156–158 (1990).
Google Scholar 
Orsi, A. H., Whitworth, T. & Nowlin, W. D. On the meridional extent and fronts of the Antarctic Circumpolar Current. Deep Sea Res. Part I Oceanogr. Res. Pap. 42, 641–673 (1995).
Google Scholar 
Holliday, N. P. & Read, J. F. Surface oceanic fronts between Africa and Antarctica. Deep Sea Res. Part I Oceanogr. Res. Pap. 45, 217–238 (1998).
Google Scholar 
Boyd, P. W. et al. A mesoscale phytoplankton bloom in the polar Southern Ocean stimulated by iron fertilization. Nature 407, 695–702 (2000).
Google Scholar 
Flynn, R. F. et al. Nanoplankton: The dominant vector for carbon export across the Atlantic Southern Ocean in spring. Sci. Adv. 9, eadi3059 (2023).
Google Scholar 
Shiomoto, A., Sasaki, H. & Nomura, D. Size-fractionated phytoplankton biomass and primary production in the eastern Indian sector of the Southern Ocean in the austral summer 2018/2019. Prog. Oceanogr. 218, 103119 (2023).
Google Scholar 
Hayward, A., Pinkerton, M. H., Wright, S. W., Gutierrez-Rodriguez, A. & Law, C. S. Twenty-six years of phytoplankton pigments reveal a circumpolar Class Divide around the Southern Ocean. Commun. Earth Environ. 5, 92 (2024).
Google Scholar 
Schlosser, T. L., Strutton, P. G., Baker, K. & Boyd, P. W. Latitudinal trends in drivers of the Southern Ocean spring bloom onset. J. Geophys. Res. Oceans 130, e2024JC021099 (2025).
Google Scholar 
Hayward, A. et al. Antarctic phytoplankton communities restructure under shifting sea-ice regimes. Nat. Clim. Chang. 15, 889–896 (2025).
Google Scholar 
Hobday, A. J. et al. Categorizing and naming marine heatwaves. Oceanography 31, 162–173 (2018).
Google Scholar 
Behrenfeld, M. J. Climate-mediated dance of the plankton. Nat. Clim. Chang. 4, 880–887 (2014).
Google Scholar 
Weis, J. et al. One-third of Southern Ocean productivity is supported by dust deposition. Nature 629, 603–608 (2024).
Google Scholar 
Deppeler, S. L. & Davidson, A. T. Southern Ocean phytoplankton in a changing climate. Front. Mar. Sci. 4, 40 (2017).
Google Scholar 
Noh, K. M., Lim, H.-G. & Kug, J.-S. Global chlorophyll responses to marine heatwaves in satellite ocean color. Environ. Res. Lett. 17, 64034 (2022).
Google Scholar 
Doney, S. C. Plankton in a warmer world. Nature 444, 695–696 (2006).
Google Scholar 
De Baar, H. J. W. et al. Importance of iron for plankton blooms and carbon dioxide drawdown in the Southern Ocean. Nature 373, 412–415 (1995).
Google Scholar 
Cassar, N. et al. The Southern Ocean biological response to aeolian iron deposition. Science 317, 1067–1070 (2007).
Google Scholar 
Jabre, L. J. et al. Molecular underpinnings and biogeochemical consequences of enhanced diatom growth in a warming Southern Ocean. Proc. Natl. Acad. Sci. USA 118, e2107238118 (2021).
Google Scholar 
Le Grix, N., Zscheischler, J., Laufkötter, C., Rousseaux, C. S. & Frölicher, T. L. Compound high-temperature and low-chlorophyll extremes in the ocean over the satellite period. Biogeosciences 18, 2119–2137 (2021).
Google Scholar 
Hayashida, H., Matear, R. J., Strutton, P. G. & Zhang, X. Insights into projected changes in marine heatwaves from a high-resolution ocean circulation model. Nat. Commun. 11, 4352 (2020).
Google Scholar 
Richaud, B. et al. Drivers of marine heatwaves in the Arctic Ocean. Journal of Geophys. Res. Oceans 129, e2023JC020324 (2024).
Google Scholar 
Loscher, B. M., De Baar, H. J. W., De Jong, J. T. M., Veth, C. & Dehairs, F. The distribution of Fe in the Antarctic circumpolar current. Deep Sea Res. Part II Topical Stud. Oceanogr. 44, 143–187 (1997).
Google Scholar 
De Baar, H. J. W. & de Jong, J. T. M. Distributions, sources and sinks of iron in seawater. In The Biogeochemistry of Iron in Seawater (Wiley, 2001).Lannuzel, D., Schoemann, V., de Jong, J., Tison, J.-L. & Chou, L. Distribution and biogeochemical behaviour of iron in the East Antarctic sea ice. Mar. Chem. 106, 18–32 (2007).
Google Scholar 
Oh, J.-H. et al. Antarctic meltwater-induced dynamical changes in phytoplankton in the Southern Ocean. Environ. Res. Lett. 17, 24022 (2022).
Google Scholar 
Sun, W. et al. Marine heatwaves/cold-spells associated with mixed layer depth variation globally. Geophys. Res. Lett. 51, e2024GL112325 (2024).
Google Scholar 
Dong, S., Sprintall, J., Gille, S. T. & Talley, S. Southern Ocean mixed-layer depth from Argo float profiles. J. Geophys. Res. Oceans 113, C06013 (2008).
Google Scholar 
DuVivier, A. K., Large, W. G. & Small, R. J. Argo observations of the deep mixing band in the Southern Ocean: a salinity modeling challenge. J. Geophys. Res. Oceans 123, 7599–7617 (2018).
Google Scholar 
Rigby, S. J., Williams, R. G., Achterberg, E. P. & Tagliabue, A. Resource availability and entrainment are driven by offsets between nutriclines and winter mixed-layer depth. Glob. Biogeochem. Cycles 34, e2019GB006497 (2020).
Google Scholar 
Behrenfeld, M. J. & Boss, E. S. Student’s tutorial on bloom hypotheses in the context of phytoplankton annual cycles. Glob. Chang. Biol. 24, 55–77 (2018).
Google Scholar 
Diaz, B. P. et al. Seasonal mixed layer depth shapes phytoplankton physiology, viral production, and accumulation in the North Atlantic. Nat. Commun. 12, 6634 (2021).
Google Scholar 
Maranon, E. Cell size as a key determinant of phytoplankton metabolism and community structure. Ann. Rev. Mar. Sci. 7, 241–264 (2015).
Google Scholar 
Zhan, W., Zhang, Y., He, Q. & Zhan, H. Shifting responses of phytoplankton to atmospheric and oceanic forcing in a prolonged marine heatwave. Limnol. Oceanogr. 68, 1821–1834 (2023).
Google Scholar 
Zhan, W. et al. Reduced and smaller phytoplankton during marine heatwaves in eastern boundary upwelling systems. Commun. Earth Environ. 5, 629 (2024).
Google Scholar 
Jabre, L. & Bertrand, E. M. Interactive effects of iron and temperature on the growth of Fragilariopsis cylindrus. Limnol. Oceanogr. Lett. 5, 363–370 (2020).
Google Scholar 
Hayashida, H., Matear, R. J. & Strutton, P. G. Background nutrient concentration determines phytoplankton bloom response to marine heatwaves. Glob. Chang. Biol. 26, 4800–4811 (2020).
Google Scholar 
He, W., Zeng, X., Deng, L., Chun Pi, Q. L. & Zhao, J. Enhanced impact of prolonged MHWs on satellite-observed chlorophyll in the South China Sea. Prog. Oceanogr. 218, 103123 (2023).
Google Scholar 
Wolf, K. K. E. et al. Heatwave responses of Arctic phytoplankton communities are driven by combined impacts of warming and cooling. Sci. Adv. 10, eadl5904 (2024).
Google Scholar 
Acevedo-Trejos, E., Brandt, G., Bruggeman, J. & Merico, A. Mechanisms shaping size structure and functional diversity of phytoplankton communities in the ocean. Sci. Rep. 5, 8918 (2015).
Google Scholar 
Hillebrand, H. et al. Cell size as driver and sentinel of phytoplankton community structure and functioning. Funct. Ecol. 36, 276–293 (2022).
Google Scholar 
Farchadi, N., McDonnell, L. H., Ryan, S., Lewison, R. L. & Braun, C. D. Marine heatwaves are in the eye of the beholder. Nat. Clim. Chang. 15, 236–239 (2025).
Google Scholar 
Bopp, L. et al. Multiple stressors of ocean ecosystems in the 21st century: projections with CMIP5 models. Biogeosciences 10, 6225–6245 (2013).
Google Scholar 
Kwiatkowski, L. et al. Twenty-first century ocean warming, acidification, deoxygenation, and upper-ocean nutrient and primary production decline from CMIP6 model projections. Biogeosciences 17, 3439–3470 (2020).
Google Scholar 
Oliver, E. C. J. et al. Projected marine heatwaves in the 21st century and the potential for ecological impact. Front. Mar. Sci. 6, 734 (2019).
Google Scholar 
Viljoen, J. J., Sun, X. & Brewin, R. J. W. Climate variability shifts the vertical structure of phytoplankton in the Sargasso Sea. Nat. Clim. Chang. 14, 1292–1298 (2024).
Google Scholar 
Embury, O. et al. Satellite-based time-series of sea-surface temperature since 1980 for climate applications. Sci. Data 11, 326 (2024).
Google Scholar 
Sathyendranath, S. et al. An ocean-colour time series for use in climate studies: the experience of the Ocean-Colour Climate Change Initiative (OC-CCI). Sensors 19, 4285 (2019).
Google Scholar 
Johnson, R., Strutton, P. G., Wright, S. W., McMinn, A. & Meiners, K. M. Three improved satellite chlorophyll algorithms for the Southern Ocean. J. Geophys. Res. Oceans 118, 3694–3703 (2013).
Google Scholar 
Westberry, T., Behrenfeld, M. J., Siegel, D. A. & Boss, E. Carbon-based primary productivity modeling with vertically resolved photoacclimation. Glob. Biogeochem. Cycles 22, GB2024 (2008).
Google Scholar 
Talley, L. & Holte, J. A new algorithm for finding mixed layer depths with applications to Argo data and subantarctic mode water formation. J. Atmos. Ocean. Technol. 26, 1920–1939 (2009).
Google Scholar 
Morel, A. & Maritorena, S. Bio-optical properties of oceanic waters: a reappraisal. J. Geophys. Res. Oceans 106, 7163–7180 (2001).
Google Scholar 
Liu, H., Nie, X., Shi, J. & Wei, Z. Marine heatwaves and cold spells in the Brazil Overshoot show distinct sea surface temperature patterns depending on the forcing. Commun. Earth Environ. 5, 102 (2024).
Google Scholar 
Sun, X. et al. Coupling ecological concepts with an ocean-colour model: Phytoplankton size structure. Remote Sens. Environ. 285, 113415 (2023).
Google Scholar 
Vidussi, F., Claustre, H., Manca, B. B., Luchetta, A. & Marty, J. C. Phytoplankton pigment distribution in relation to upper thermocline circulation in the eastern Mediterranean Sea during winter. J. Geophys. Res. Oceans 106, 19939–19956 (2001).
Google Scholar 
Uitz, J., Claustre, H., Morel, A. & Hooker, S. B. Vertical distribution of phytoplankton communities in open ocean: an assessment based on surface chlorophyll. J. Geophys. Res. Ocean 111, C08005 (2006).
Google Scholar 
Brewin, R. J. W. et al. A three-component model of phytoplankton size class for the Atlantic Ocean. Ecol. Model. 221, 1472–1483 (2010).
Google Scholar 
Hirata, T. et al. Synoptic relationships between surface chlorophyll-a and diagnostic pigments specific to phytoplankton functional types. Biogeosciences 8, 311–327 (2011).
Google Scholar 
Brewin, R. J. W. et al. Influence of light in the mixed-layer on the parameters of a three-component model of phytoplankton size class. Remote Sens. Environ. 168, 437–450 (2015).
Google Scholar 
Devred, E., Sathyendranath, S., Stuart, V. & Platt, T. A three component classification of phytoplankton absorption spectra: Application to ocean-color data. Remote Sens. Environ. 115, 2255–2266 (2011).
Google Scholar 
Britten, G. L., Padalino, C., Forget, G. & Follows, M. J. Seasonal photoacclimation in the North Pacific Transition Zone. Glob. Biogeochem. Cycles 26, e2022GB007324 (2022).
Google Scholar 
Claustre, H. et al. An intercomparison of HPLC phytoplankton pigment methods using in situ samples: application to remote sensing and database activities. Mar. Chem. 85, 41–61 (2004).
Google Scholar 
Aiken, J. et al. Phytoplankton pigments and functional types in the Atlantic Ocean: a decadal assessment, 1995–2005. Deep Sea Res. Part II: Topical Stud. Oceanogr. 56, 899–917 (2009).
Google Scholar 
Bai, Z. et al. Extreme temperature events reshuffle the ecological landscape of the Southern Ocean. figshare https://doi.org/10.6084/m9.figshare.30217021 (2025).Bai, Z. et al. Model code and output for: Extreme temperature events reshuffle the ecological landscape of the Southern Ocean. Zenodo https://doi.org/10.5281/zenodo.17223292 (2025).Download referencesAcknowledgementsThis work was supported by the National Natural Science Foundation of China (NSFC). Specifically, Z.J. received support from NSFC Grants #42176173 and 42476168, while D.L. was supported by NSFC Grant #42406172. Additional funding was provided by the Guangdong Basic and Applied Basic Research Foundation (Grant # 2023A1515110837) awarded to D.L., and by the Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai) (Grants #SML2024SP023 and SML2024SP029), and the Innovation Group Project of Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai) (Grant #311020004), awarded to Z.J. R.J.W.B. is supported by a UKRI Future Leader Fellowship (MR/V022792/1) and the European Space Agency’s project Tipping points and abrupt changes In the Marine Ecosystem (TIME). The author acknowledges the European Space Agency (ESA) for providing daily Sea Surface Temperature (SST), Sea Ice Concentration (SIC), Diffuse Attenuation Coefficient (Kd), and Remote Sensing Reflectance (Rrs) data products through the Ocean Colour Climate Change Initiative (OC-CCI). The author also thanks the Copernicus Marine Environment Monitoring Service (CMEMS) for providing daily model data of Nitrate (NO₃), and Mixed-Layer Depth (MLD). Appreciation is extended to GlobColour for providing Level-3 (L3) daily Photosynthetically Available Radiation (PAR) data. Additionally, the author acknowledges the PANGAEA Data Publisher for Earth & Environmental Science and the Australian Waters Bio-optical Database (AWBD) for providing High-Performance Liquid Chromatography (HPLC) datasets. We further acknowledge the Argo Program, which is part of the Global Ocean Observing System (https://www.seanoe.org/data/00311/42182/), as well as the Southern Ocean Observing System (SOOS) and the Southern Ocean Carbon and Climate Observations and Modeling (SOCCOM) Project, funded by the National Science Foundation, Division of Polar Programs (NSF PLR-1425989 and OPP-1936222), and supplemented by NASA. The BGC-Argo data were collected and made freely available by the International Argo Program and the national programs that contribute to it (http://www.argo.ucsd.edu, http://argo.jcommops.org).Author informationAuthor notesThese authors contributed equally: Zhimin Bai, Lin Deng.Authors and AffiliationsSchool of Marine Sciences, Sun Yat-sen University, Zhuhai, Guangdong, ChinaZhimin Bai 
(摆智敏), Lin Deng 
(邓霖), Wenbo He 
(何文博), Qilin Chunpi 
(七林春批) & Jun Zhao 
(赵俊)Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai, Guangdong, ChinaLin Deng 
(邓霖) & Jun Zhao 
(赵俊)Pearl River Estuary Marine Ecosystem Research Station, Ministry of Education, Zhuhai, Guangdong, ChinaLin Deng 
(邓霖) & Jun Zhao 
(赵俊)Guangdong Provincial Key Laboratory of Marine Resources and Coastal Engineering, Guangzhou, Guangdong, ChinaLin Deng 
(邓霖) & Jun Zhao 
(赵俊)Centre for Geography and Environmental Science, Department of Earth and Environmental Sciences, Faculty of Environment, Science and Economy, University of Exeter, Penryn, Cornwall, UKRobert J. W. BrewinAuthorsZhimin Bai 
(摆智敏)View author publicationsSearch author on:PubMed Google ScholarLin Deng 
(邓霖)View author publicationsSearch author on:PubMed Google ScholarRobert J. W. BrewinView author publicationsSearch author on:PubMed Google ScholarWenbo He 
(何文博)View author publicationsSearch author on:PubMed Google ScholarQilin Chunpi 
(七林春批)View author publicationsSearch author on:PubMed Google ScholarJun Zhao 
(赵俊)View author publicationsSearch author on:PubMed Google ScholarContributionsB.Z.M. and D.L. conducted data processing and analysis under Z.J.’s instruction. H.W.B. provided the conceptual framework. H.W.B. and Q. C. contributed data and algorithms. B.Z.M. drafted the initial manuscript, and Z.J. and R.J.W.B. reviewed the paper.Corresponding authorCorrespondence to
Jun Zhao 
(赵俊).Ethics declarations

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Reprints and permissionsAbout this articleCite this articleBai, Z., Deng, L., Brewin, R.J.W. et al. Extreme temperature events reshuffle the ecological landscape of the Southern Ocean.
Nat Commun (2025). https://doi.org/10.1038/s41467-025-68029-0Download citationReceived: 17 July 2024Accepted: 16 December 2025Published: 30 December 2025DOI: https://doi.org/10.1038/s41467-025-68029-0Share 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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