Ecology

Abstract

Vibrio parahaemolyticus (Vp) poses a significant public health concern in marine environments. This study evaluated the occurrence and health risk of Vp in coastal waters of the southern Caspian Sea during summer 2022 using the WHO-recommended QMRA framework. Forty-eight seawater samples collected from two beaches in Guilan Province revealed Vp concentrations ranging from 1.9 × 10⁵ to 5.0 × 10⁵ CFU L⁻¹. Monte Carlo simulation was applied to quantify uncertainty, showing higher median probabilities of illness (Pill) at Beach B (children: 9.0 × 10⁻³; adults: 3.7–5.8 × 10⁻³) than at Beach A (children: 5.0 × 10⁻³; adults: 2.0–3.4 × 10⁻³), all below the US. EPA threshold (0.036). The estimated disability-adjusted life years (DALYs) exceeded the WHO reference level (10⁻⁶ pppy) but remained below the US. EPA benchmark (10⁻⁴ pppy), indicating a low but non-negligible health burden, particularly among children. In addition, statistical analysis revealed positive correlations between Vp and salinity, temperature, and turbidity, and a negative correlation with pH. Sensitivity analysis revealed that Vp concentration was the dominant factor at Beach A, while ingested water volume had the greatest influence at Beach B. These results support targeted management measures to mitigate microbial risks in recreational waters.

Data availability

All data generated or analyzed during this study are included in this published article.
Abbreviations
Vp
:
Vibrio parahaemolyticus
qPCR:
quantitative Polymerase Chain Reaction

tlh
:
thermolabile hemolysin
QMRA:
Quantitative Microbial Risk Assessment
FIB:
Fecal Indicator Bacteria
GI:
Gastrointestinal Illness
CDC:
Control Diseases Center
LOD:
Limit of Detection
DALY:
Disability-Adjusted Life Year
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Download referencesAcknowledgementsThe authors express their gratitude to the Iran University of Medical Sciences, Tehran, Iran, for their support and cooperation in conducting this study.FundingThis research was financially supported by the Iran University of Medical Sciences under grant number 20016.Author informationAuthors and AffiliationsDepartment of Environmental Health Engineering, School of Public Health, Tehran University of Medical Sciences, Tehran, IranMohammad AhmadiStudent’s Scientific Research Center, Tehran University of Medical Sciences, Tehran, IranMohammad AhmadiDepartment of Environmental Health Engineering, School of Public Health, Iran University of Medical Sciences, Tehran, IranAli Esrafili & Roshanak Rezaei KalantaryPhysiology Research Center, Iran University of Medical Sciences, Tehran, IranHamidreza Pazoki-Toroudi & Fazel GorjipourDepartment of Physiology, Faculty of Medicine, Iran University of Medical Sciences, Tehran, IranHamidreza Pazoki-ToroudiAuthorsMohammad AhmadiView author publicationsSearch author on:PubMed Google ScholarAli EsrafiliView author publicationsSearch author on:PubMed Google ScholarHamidreza Pazoki-ToroudiView author publicationsSearch author on:PubMed Google ScholarFazel GorjipourView author publicationsSearch author on:PubMed Google ScholarRoshanak Rezaei KalantaryView author publicationsSearch author on:PubMed Google ScholarContributionsMohammad Ahmadi: Methodology, Validation, Writing – original draft, Writing – review & editing, Visualization. Ali Esrafili: Methodology, Validation, Writing – review & editing, Visualization. Hamidreza Pazoki-Toroudi: Methodology, Validation. Fazel Gorjipour : Methodology, Validation, Writing – review & editing. Roshanak Rezaei Kalantary : Data curation, Supervision, Project administration, Conceptualization, Validation, Resources, Writing – review & editing.Corresponding authorCorrespondence to
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Reprints and permissionsAbout this articleCite this articleAhmadi, M., Esrafili, A., Pazoki-Toroudi, H. et al. Occurrence, environmental correlates, and risk assessment of Vibrio parahaemolyticus in Caspian sea coastal waters.
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KeywordsCaspian seaCoastal waterProbability of illnessQMRA
V. parahaemolyticus. DALY More

AbstractReliable identification of agricultural pests and beneficial insects is crucial for sustainable crop protection and ecological balance, yet most vision-based models remain black boxes and require high-dimensional features. This paper proposes an explainable hybrid insect-classification framework that combines convolutional neural network (CNN) feature extraction with a dual–XAI feature selection strategy. SHapley Additive exPlanations (SHAP) and Permutation Feature Importance (PFI) are applied in parallel to rank handcrafted and CNN-derived features, and their intersection yields a compact, biologically meaningful subset for final classification. The selected features are evaluated using lightweight classifiers and a hybrid ensemble, enabling accurate inference under field variability. Experiments on a curated, balanced dataset of four classes (Colorado potato beetle, green peach aphid, seven-spot ladybird, and healthy leaves) collected under diverse lighting and background conditions achieve 96.7% overall accuracy, with precision, recall, and F1-scores all above 96%. Importantly, performance remains stable when reducing dimensionality, retaining (ge)90% accuracy using only the top 11 hybrid-selected features. These results demonstrate that integrating SHAP and PFI improves both robustness and interpretability, supporting practical deployment for automated pest monitoring and precision agriculture.

Data availability

The selected data sets are available from free and open access sources using the following link:https://doi.org/10. 34740/kaggle/dsv/12745007
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Download referencesAcknowledgementsThe authors extend their appreciation to the Deanship of Scientific Research and Libraries in Multimedia University for funding this research work through the Program for Supporting Publication in Top-Impact Journals.FundingNot applicableAuthor informationAuthors and AffiliationsDepartment of Biotechnology Engineering, Bioenvironmental Engineering Research Center (BERC), International Islamic University Malaysia, 50728, Kuala Lumpur, MalaysiaNibedita DebDepartment of Electrical and Electronic Engineering, International University of Business Agriculture and Technology, Uttara, Dhaka, 1230, BangladeshTawfikur Rahman & Md. MoniruzzamanThe Saudi Technology Development and Investment Company (Taqnia), 12211, Riyadh, Saudi ArabiaAmeen Salem Bin ObadiFaculty of Artificial Intelligence and Engineering, Multimedia University, 63100, Cyberjaya, MalaysiaNoorlindawaty Md. JizatSpace Science Centre, Institute of Climate Change, Universiti Kebangsaan Malaysia (UKM), 43600, Bangi, MalaysiaSamir Salem Al-BawriDepartment of Electrical and Electronic Engineering, Southeast University, Dhaka, 1208, BangladeshAbdullah Al Mahfazur RahmanDepartment of Electronics & Communication Engineering, Faculty of Engineering & Petroleum, Hadhramout University, Mukalla, Hadhramout, YemenSamir Salem Al-BawriAuthorsNibedita DebView author publicationsSearch author on:PubMed Google ScholarTawfikur RahmanView author publicationsSearch author on:PubMed Google ScholarMd. MoniruzzamanView author publicationsSearch author on:PubMed Google ScholarAmeen Salem Bin ObadiView author publicationsSearch author on:PubMed Google ScholarNoorlindawaty Md. JizatView author publicationsSearch author on:PubMed Google ScholarSamir Salem Al-BawriView author publicationsSearch author on:PubMed Google ScholarAbdullah Al Mahfazur RahmanView author publicationsSearch author on:PubMed Google ScholarContributionsN. D. and T. R. Conceptualization, Methodology, Software, Visualization, Formal Analysis, Writing-original draft and review & editing. M.M. and A.S.B.O. Conceptualization, Methodology, Resource, Supervision, Visualization, Writing- review & editing. N.M.J. Data curation, Software, Resource, Formal Analysis. S. S. A. and A. A. M. R. Methodology, Data Curation, Visualization, Validation and Writing (Review & Editing)Corresponding authorsCorrespondence to
Md. Moniruzzaman, Noorlindawaty Md. Jizat or Samir Salem Al-Bawri.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 articleDeb, N., Rahman, T., Moniruzzaman, M. et al. Integrating feature selection and explainable CNN for identification and classification of pests and beneficial insects.
Sci Rep (2025). https://doi.org/10.1038/s41598-025-32520-xDownload citationReceived: 16 October 2025Accepted: 10 December 2025Published: 27 December 2025DOI: https://doi.org/10.1038/s41598-025-32520-xShare 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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KeywordsHybrid modelsFeature selectionPest detectionBeneficial insectsMachine learningAgricultural informatics. More

AbstractThere are serious ecological and radiological risks associated with the release and buildup of man-made and natural radionuclides. These risks are particularly relevant for fossil fuel power plants located in residential and agricultural areas. High-purity germanium (HPGe) detectors were employed to analyze environmental samples, including soil, water, and plants collected around the West Delta fossil fuel power station in Egypt. The activity levels of both man-made and naturally occurring radionuclides, such as 226Ra, 228Ra, and40K, were measured, and the corresponding ecological and radiological hazards were assessed using several radiological hazard indices. The findings showed elevated concentrations of 226Ra, 228Ra, and40K specifically in agricultural areas near the power station, with some values exceeding internationally recommended guideline values. The calculated radioecological indicators highlight potential long-term exposure risks for nearby populations and ecosystems. These results indicate the need for targeted monitoring and site-specific mitigation measures in the most impacted areas. while providing essential baseline data for future environmental monitoring. This study provides the first comprehensive radiological and radioecological assessment around the West Delta power station, offering new baseline data for environmental monitoring and risk management.

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Data availability

The datasets used and/or analysed during the current study available from the corresponding author on reasonable request.
Abbreviations
238U:
Uranium-238

235U:
Uranium-235

232Th:
Thorium-232

40K:
Potassium-40

137Cs:
Cesium-137

226Ra:
Radium-226

228Ra:
Radium-228 (representative of the Th-232 decay series)

214Pb:
Lead-214
Raeq
:
Radium equivalent
Hex
:
External hazard index
Hin
:
Internal hazard index
D:
Absorbed dose rate in air
AED:
Annual effective dose rate
ELCR:
Excess lifetime cancer risk
ICP-OES:
Inductively coupled plasma-optical emission spectroscopy
HPGe:
High-purity germanium detector
EPA:
Environmental Protection Agency
IAEA:
International Atomic Energy Agency
ICRP:
International commission on radiological protection
NORMs:
Naturally occurring radioactive materials
UNSCEAR:
United nation scientific committee on the effects of atomic radiation
WHO:
World Health Organization
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Shimaa M. Elgingihy.Ethics declarations

Competing interests
The authors declare no competing interests.

Additional informationPublisher’s noteSpringer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.Rights and permissions
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
Reprints and permissionsAbout this articleCite this articleElgingihy, S.M., Abdelsalam, A.A. & Saleh, I.H. Radiological and radioecological risk assessment around the West Delta fossil-fuel power station in Egypt.
Sci Rep (2025). https://doi.org/10.1038/s41598-025-31092-0Download citationReceived: 07 September 2025Accepted: 28 November 2025Published: 27 December 2025DOI: https://doi.org/10.1038/s41598-025-31092-0Share 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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KeywordsRadioactivityEnvironmental riskRadiological assessmentRadioecological riskFossil fuel power plants More

AbstractAquaculture is increasingly relied upon for global seafood production, projected to be the leading supplier by 2030. Climate change impacts on species health and industry productivity are already evident, creating uncertainties around long-term aquaculture development. While these impacts have been projected for some species, around 62% of aquaculture production remains unassessed. We utilized climate dissimilarity to assess the exposure of 327 species—including those previously unassessed—in their native ranges to changing climates under three climate scenarios: SSP1-1.9, SSP3-7.0, and SSP5-8.5. We projected that under a sustainability scenario (SSP1-1.9), 41% of Exclusive Economic Zones (EEZ) remained unexposed, including high-value aquaculture regions. However, under increased emissions scenarios (SSP3-7.0 and SSP5-8.5) all current aquaculture EEZ are projected to be exposed. Semi-enclosed seas, like the Baltic, Black, and Red Seas, experience the largest dissimilarity, alongside equatorial regions. Our findings suggest widespread mitigation efforts are necessary to ensure the long-term resilience of marine aquaculture.

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Data availability

Climate data were retrieved from the Copernicus Marine Service at [http://resources.marine.copernicus.eu/products] and the Coupled Model Intercomparison Project (Phase 6) at [https://esgf-node.llnl.gov/projects/cmip6/] in May 2023. Species’ range maps were retrieved from AquaMaps at [https://www.aquamaps.org] in May 2023.
Code availability

Our manually-derived range maps are available on figshare89 and our code on GitHub (https://github.com/jorgeassis/climateAnalogs).
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Download referencesAcknowledgementsThis study was supported by the Foundation for Science and Technology (FCT) of Portugal through projects UIDB/04326/2020 (https://doi.org/10.54499/UIDB/04326/2020), UIDP/04326/2020 (https://doi.org/10.54499/UIDP/04326/2020), LA/P/0101/2020 (https://doi.org/10.54499/LA/P/0101/2020), and the Individual Call to Scientific Employment Stimulus 2022.00861.CEECIND/CP1729/CT0003 (https://doi.org/10.54499/2022.00861.CEECIND/CP1729/CT0003). The authors thank the anonymous reviewers for their helpful comments on the manuscript.FundingOpen access funding provided by Nord University.Author informationAuthors and AffiliationsFaculty of Biosciences and Aquaculture, Nord University, Bodø, NorwayAmy Leigh Mackintosh, Griffin Goldstein Hill, Mark John Costello & Jorge AssisCentre of Marine Sciences, CCMAR, University of Algarve, Faro, PortugalJorge AssisAuthorsAmy Leigh MackintoshView author publicationsSearch author on:PubMed Google ScholarGriffin Goldstein HillView author publicationsSearch author on:PubMed Google ScholarMark John CostelloView author publicationsSearch author on:PubMed Google ScholarJorge AssisView author publicationsSearch author on:PubMed Google ScholarContributionsA.L.M., G.G.H.: Conceptualization, Methodology, Interpretive Analysis, Investigation, Data Curation, Writing—Original Draft, Writing—Review & Editing, and Visualization. M.J.C.: Conceptualization, Investigation, Writing—Review & Editing, and Funding Acquisition. J.A.: Conceptualization, Formal Analysis, Writing—Review & Editing, and Funding Acquisition. A.L.M. and G.G.H. contributed equally to this manuscript.Corresponding authorCorrespondence to
Amy Leigh Mackintosh.Ethics declarations

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Reprints and permissionsAbout this articleCite this articleMackintosh, A.L., Hill, G.G., Costello, M.J. et al. No significant projected climate change effects on the geographic ranges of marine aquaculture species under the sustainable scenario (SSP 1-1.9, 1.5°C warming).
npj Ocean Sustain (2025). https://doi.org/10.1038/s44183-025-00178-7Download citationReceived: 20 March 2025Accepted: 03 December 2025Published: 27 December 2025DOI: https://doi.org/10.1038/s44183-025-00178-7Share this articleAnyone you share the following link with will be able to read this content:Get shareable linkSorry, a shareable link is not currently available for this article.Copy shareable link to clipboard
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AbstractSelection mosaic theory explains observations of polymorphism in host-pathogen interactions in terms of spatially variable natural selection but does not account for population dynamics. In contrast, classical host-pathogen theory easily explains observations of population cycles, but does not explain the persistence of pathogen polymorphism. Here, we synthesize these two frameworks to understand the effects of population cycles on pathogen polymorphism. We show that geographic variation in the frequency of two morphotypes of a baculovirus that infects the Douglas-fir tussock moth (Orgyia pseudotsugata) depends on the frequency of Douglas-fir (Pseudotsuga menziesii), an important tussock moth host tree. The morphotype frequency data are best explained by host-pathogen models that combine a selection mosaic with population cycles. In our model, population cycles intensify pathogen competition across a selection mosaic, leading to a strong effect of Douglas-fir frequency on morphotype frequency that matches the data. Models without host-pathogen cycles or a selection mosaic project only weak effects of varying Douglas-fir frequency. Our model further projects that a biopesticide made up of both viral morphotypes would be more effective than the current single-morphotype biopesticide, demonstrating that our synthesis of selection mosaic theory and host-pathogen theory provides useful insights into pest management.

Data availability

The raw data for the morphotype frequency dataset, field experiments, and line search results supporting the findings of this study are openly available in the GitHub repository at https://github.com/kpd19/Two_Pathogen_Evolution/, with a persistent identifier assigned to version 1.0.0 via Zenodo: https://doi.org/10.5281/zenodo.17574036. The Bayesian model outputs from Stan are very large and are available from the first author upon request. The data from the increased realizations from the line search results are also very large and are available from the first author upon request. The previously published data included as part of our morphotype frequency dataset can be found in Fig. 1 from Hughes37, Table 1 from Williams and Otvos65, and Table 1 from Williams et al.40. The National Forest Type Dataset for the continental United States was previously publicly available from the USDA Forest Service. The dataset is no longer hosted by the USDA and is available from the first author upon request. State and Province administrative boundaries for the United States and Canada used in the maps for this paper are publicly available for download from GADM v4.1 via https://geodata.ucdavis.edu/gadm/gadm4.1/shp/. Source data are provided with this paper.
Code availability

All code to perform the simulations and statistical analysis, as well as for plotting Figs. 1–6 and the supplementary data figures, is openly available in the GitHub repository at https://github.com/kpd19/Two_Pathogen_Evolution/, with a persistent identifier assigned to version 1.0.0 via Zenodo: https://doi.org/10.5281/zenodo.17574036.
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Download referencesAcknowledgementsWe are extremely grateful for the support of many dedicated and talented field technicians: Rhiannon Archerelle, Ari Freedman, Amy Gannon, Laurel Haavik, Sophia Horigan, Amy Huang, Alison Hunter, Jessica Johnson, August Kramer, Allie Kreitman, Kate-Lynne Logan, and Chelsea Miller. Special thanks to the field technicians who made the work possible in the Summer of 2020, as well as Mary Johnson and Luis Marmolejo, who managed shipping. We thank Roy Magelssen and Connie Mehmel at the Forestry Sciences Laboratory in Wenatchee, Washington, for providing important institutional and biological knowledge of the system, and Joe Mihaljevic for important guidance in doing transmission experiments. We would like to thank Cara Brook, Sarah Cobey, and Tim Wootton for providing important feedback on earlier drafts of this work. Computational Resources were provided by the Research Computing Center at the University of Chicago. Help in morphotyping isolates was provided by the Electron Microscopy Center at the University of Chicago. Our work was supported by EEID NSF grant DEB-2109774 to G.D. and V.D. K.P.D. was supported by the University of Chicago Data Science for Energy and Environmental Research (DSEER) training grant as part of an NSF Research Traineeship program (1735359) and the U.S. Department of Education Quantitative Ecology GAANN training grant (P200A150101). W.T.K. and K.P.D. received separate awards from the University of Chicago Hinds Fund for Student Research. Our work was further supported by a grant from the Western Wildlands Environmental Threat Assessment Center to C.M.P., by NIFA Biological Sciences grant 2019-67014-29919 to V.D., by an ARCS Foundation Fellowship to W.T.K., and by a Theodore Roosevelt Memorial Grant through the American Museum of Natural History to W.T.K.Author informationAuthors and AffiliationsDepartment of Ecology and Evolution, University of Chicago, Chicago, IL, USAKatherine P. Dixon, William T. Koval & Greg DwyerPacific Northwest Research Station, USDA Forest Service, Wenatchee, WA, USACarlos M. PolivkaDepartment of Biology, Lewis & Clark College, Portland, OR, USAGrace BirdDepartment of Applied Mathematics, University of Colorado, Boulder, CO, USAVanja DukicAuthorsKatherine P. DixonView author publicationsSearch author on:PubMed Google ScholarWilliam T. KovalView author publicationsSearch author on:PubMed Google ScholarCarlos M. PolivkaView author publicationsSearch author on:PubMed Google ScholarGrace BirdView author publicationsSearch author on:PubMed Google ScholarVanja DukicView author publicationsSearch author on:PubMed Google ScholarGreg DwyerView author publicationsSearch author on:PubMed Google ScholarContributionsG.D. and K.P.D. planned and designed the research. W.T.K., C.M.P., G.B., G.D., and K.P.D. collected the data. K.P.D. analyzed the data. K.P.D., G.D., V.D., and C.M.P. contributed substantially to the discussion of the results and the writing of the manuscript.Corresponding authorCorrespondence to
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Reprints and permissionsAbout this articleCite this articleDixon, K.P., Koval, W.T., Polivka, C.M. et al. Synthesizing selection mosaic theory and host-pathogen theory to explain large-scale pathogen coexistence.
Nat Commun (2025). https://doi.org/10.1038/s41467-025-67952-6Download citationReceived: 14 November 2024Accepted: 12 December 2025Published: 26 December 2025DOI: https://doi.org/10.1038/s41467-025-67952-6Share 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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Abstract

Water management in irrigation networks is crucial for sustainable agriculture under conditions of water scarcity and climate variability. This study applies the water accounting plus (WA+) framework, integrating meteorological and remote sensing data (WaPOR), to analyze water fluxes, productivity, and spatial heterogeneity in the Qazvin Plain irrigation network from 2009 to 2021. The total net inflow during this period was approximately 10,582 MCM, with contributions from precipitation (≈ 20%), surface inflow (≈ 27%), and storage changes (≈ 53%). Analysis of evapotranspiration revealed that transpiration accounted for 80% of total ET, with 72% classified as beneficial (transpiration plus interception) and 28% as non-beneficial (soil evaporation and canopy interception). Spatial patterns indicate higher water availability in the eastern part of the network and deficits in the western region, highlighting the potential for improving water productivity through targeted interventions such as soil moisture conservation and optimized irrigation scheduling. These findings demonstrate the applicability of the WA + framework for enhancing water use efficiency and informing sustainable irrigation management in semi-arid regions.

Data availability

The datasets generated and/or analyzed during the current study are not publicly available due to privacy concerns and proprietary constraints, but they are available from the corresponding author on reasonable request.
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Download referencesFundingThe authors received support to conduct the study but the support did not include support for the submitted work.Author informationAuthors and AffiliationsDepartment of Water Sciences and Engineering, Imam Khomeini International University, P.O. Box 3414896818, Qazvin, IranMahkameh Sadat NaeiniDepartment of Irrigation and Reclamation Engineering, Faculty of Agricultural Engineering and Technology, College of Agriculture and Natural Resources, University of Tehran, P. O. Box 4111, Karaj, 31587-77871, IranBijan NazariWater Engineering Department, Imam Khomeini International University, Qazvin, IranBijan NazariDepartment of Water Sciences and Engineering, Imam Khomeini International University, P.O. Box 3414896818, Qazvin, IranAbbas SotoodehniaAuthorsMahkameh Sadat NaeiniView author publicationsSearch author on:PubMed Google ScholarBijan NazariView author publicationsSearch author on:PubMed Google ScholarAbbas SotoodehniaView author publicationsSearch author on:PubMed Google ScholarContributionsMahkameh Sadat Naeini: Conceptualization; Data downloading and processing; Writing the original draft; Finalization. Bijan Nazari and Abbas Sotoodehnia: Supervision; Editing drafts; Providing suggestions and additions to improve the findings and their practical applicability.Corresponding authorCorrespondence to
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Reprints and permissionsAbout this articleCite this articleNaeini, M.S., Nazari, B. & Sotoodehnia, A. A comprehensive approach to enhancing irrigation network management through the water accounting plus framework.
Sci Rep (2025). https://doi.org/10.1038/s41598-025-33024-4Download citationReceived: 15 December 2024Accepted: 15 December 2025Published: 26 December 2025DOI: https://doi.org/10.1038/s41598-025-33024-4Share this articleAnyone you share the following link with will be able to read this content:Get shareable linkSorry, a shareable link is not currently available for this article.Copy shareable link to clipboard
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AbstractTraditional medicinal plants remain vital healthcare resources for rural communities, particularly in areas with limited access to modern medical services. This study documents and quantitatively analyzes the ethnobotanical use of medicinal plants in Meketewa District, northwestern Ethiopia. Ethnobotanical data were collected from 360 informants (20 key informants and 340 general informants) across five kebeles (Sub-Districts) representing different agroecological zones. Data were analyzed using preference ranking, direct matrix ranking (DMR), informant consensus factor (ICF), fidelity level (FL), Jaccard similarity index (JSI), Rahman’s similarity index (RSI), t-tests, and one-way ANOVA. The distribution of indigenous medicinal plant knowledge was significantly influenced by agroecology and socio-demographic factors, including age, gender, education, and knowledge experience. A total of 76 medicinal plant species belonging to 46 families were documented, with Fabaceae as the dominant family (7.9%) and herbs as the most common growth form (38.16%). Most species were used for human ailments (63.2%), while 9.2% were used for livestock and 27.6% for both. Natural forests were the primary source of medicinal plants (61.84%). Crushing was the dominant preparation method (38.4%), and oral administration was the most common route (47.7%). The use of additives, antidotes, and localized dosage systems reflects advanced therapeutic knowledge. Rhamnus prinoides was the most preferred species for treating human tonsillitis, whereas Euphorbia abyssinica was widely used for livestock swelling. High ICF values (up to 0.92) indicated strong informant agreement, while JSI (2.29–45.19%) and RSI (0.00–16.67%) reflected largely localized ethnomedicinal knowledge; similarly, high fidelity levels for Asparagus africanus var. puberulus (83.3%), Rhamnus prinoides (75%), and Cucumis ficifolius and Euphorbia abyssinica (73.3%) underscore strong cultural consensus and priority for phytochemical validation. Olea europaea subsp. cuspidata was the highest-ranked multipurpose species but faces increasing anthropogenic threats. These findings emphasize the need for in situ and ex situ conservation and further phytochemical and pharmacological validation.

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The data supporting the findings of this study are presented in the tables and figures within the manuscript and supplementary file.
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Download referencesAcknowledgementsThe authors sincerely thank the Meketewa community members and key informants for generously sharing their invaluable knowledge of medicinal plants used for human and livestock healthcare. We are grateful to Dr. Getinet Masresha for his expert botanical identification of plant specimens. We also acknowledge the local authorities and community elders for their invaluable support and guidance during data collection.Author informationAuthors and AffiliationsDepartment of Biology, Debre Tabor University, Debre Tabor, EthiopiaFentaye Kassawmar, Endale Adamu, Worku Misganaw & Kindu GetaDepartment of Biology, Debark University, Debark, EthiopiaFentaye KassawmarAuthorsFentaye KassawmarView author publicationsSearch author on:PubMed Google ScholarEndale AdamuView author publicationsSearch author on:PubMed Google ScholarWorku MisganawView author publicationsSearch author on:PubMed Google ScholarKindu GetaView author publicationsSearch author on:PubMed Google ScholarContributionsFK led data collection, analysis, and manuscript writing, while EA supervised fieldwork and plant identification. WM contributed to analyses, interpreting results, and manuscript writing. KG managed data curation. All authors reviewed and approved the final manuscript.Corresponding authorCorrespondence to
Worku Misganaw.Ethics declarations

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The authors declare no competing interests.

Ethics approval
Ethical approval was obtained from the Biology Department of Debre Tabor University and permissions from the Meketewa District administrative offices before data collection. All informants were informed about the study’s objectives and provided verbal consent prior to participation.

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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 articleKassawmar, F., Adamu, E., Misganaw, W. et al. Ethnobotanical study of medicinal plants in Meketewa District, northwestern Ethiopia.
Sci Rep (2025). https://doi.org/10.1038/s41598-025-33571-wDownload citationReceived: 14 August 2025Accepted: 19 December 2025Published: 26 December 2025DOI: https://doi.org/10.1038/s41598-025-33571-wShare this articleAnyone you share the following link with will be able to read this content:Get shareable linkSorry, a shareable link is not currently available for this article.Copy shareable link to clipboard
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KeywordsConservationIndigenous knowledgeHerbal medicineMedicinal plantsMeketewa DistrictVeterinary More

AbstractMicroplastics (MPs), increasingly common in freshwater ecosystems, pose serious ecological threats to the Formosan landlocked salmon (Oncorhynchus masou formosanus), an endangered salmonid endemic to Taiwan. This study presents the first comprehensive investigation into how seasonal variation and land-use patterns influence MP abundance, composition, and distribution in the salmon’s exclusive habitats within Shei-Pa National Park, Taiwan. Using µ‑FTIR spectroscopy and fluorescence microscopy, we quantified microplastic concentrations and observed higher levels in the dry season (48–93 items/L) than in the wet season (45–72 items/L). Principal component analysis (PCA) further indicated spatial gradients aligned with land‑use contrasts, with higher concentrations associated with intensive agriculture and recreational tourism relative to pristine forest areas. Seasonal variation profoundly influenced MPs’ composition, with synthetic fibers (Rayon and Polyester) predominating in the wet season and common plastic polymers (PET, PE, PP) increasing in the dry season. Smaller-sized MPs (< 25 µm), potentially more hazardous due to their capacity for trophic transfer, were predominant in upstream reaches during critical salmon breeding periods.

Data availability

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.
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Download referencesFundingThe authors are appreciative of the Shei-Pa National Park, Taiwan for supporting this work with Grant No. PG11212-0178.Author informationAuthors and AffiliationsDepartment of Safety, Health and Environmental Engineering, Ming Chi University of Technology, New Taipei City, 243303, Taiwan, ROCTsan-Yang Hu & Wen-Hui KuanR&D Center of Biochemical Engineering Technology, Ming Chi University of Technology, New Taipei City, 243303, Taiwan, ROCWen-Hui KuanChronic Diseases and Health Promotion Research Center, Chang Gung University of Science and Technology, Chiayi, 61363, Taiwan, ROCWen-Hui KuanAuthorsTsan-Yang HuView author publicationsSearch author on:PubMed Google ScholarWen-Hui KuanView author publicationsSearch author on:PubMed Google ScholarContributionsTsan-Yang Hu: Writing—original draft, Visualization, Software, Methodology, Investigation, Formal analysis. Wen-Hui Kuan: Conceptualization, Funding acquisition, Methodology, Investigation, Resources, Project administration, Supervision, Writing—review & editing.Corresponding authorCorrespondence to
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Reprints and permissionsAbout this articleCite this articleHu, TY., Kuan, WH. Seasonal rainfall and land-use impacts on microplastic characteristics in an endangered salmon stream.
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KeywordsFluorescence microscopyFormosan landlocked salmonMicroplastics (MPs)Seasonal dynamics
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