Ecology

AbstractCapitatae Fructus (CF), an ethnomedicinal resource with both edible and medicinal values in Southwest China, has an incomplete quality evaluation system, and the regulatory mechanisms of environmental factors on its bioactive component accumulation remain unclear. In this study, 37 CF batches from 6 producing areas in Sichuan and Yunnan Provinces were analyzed. High-performance liquid chromatography (HPLC) was used to establish a fingerprint with good precision and reproducibility and conduct multi-component quantitative analysis, combined with hierarchical cluster analysis (HCA), Correlation analysis, principal component analysis (PCA), and hierarchical partitioning (HP) to investigate CF’s chemical characteristics and environmental regulation patterns. Results showed an HPLC fingerprint of CF was successfully constructed, with 15 common peaks matched and 5 key bioactive compounds (gallic acid, hyperoside, isoquercitrin, quercitrin, quercetin) identified. HCA classified the samples into two distinct clusters: the first cluster covers low-, medium-, and high-altitude regions, among which the gallic acid contents (L26, L27) and quercetin content (L28) from Miyi County (low-altitude area) are higher than those of other sample batches. In contrast, the second cluster, concentrated in medium-altitude regions, generally exhibits lower contents of key components. Results from PCA show that sample L28 (Miyi County, low-altitude area) has the highest comprehensive score, which further confirms this altitude-related distribution characteristic of the compounds in CF. Correlation analysis was conducted to examine relationships between the 5 key compounds, showing that gallic acid was significantly positively correlated with hyperoside (r = 0.46, P < 0.01), and hyperoside was extremely significantly positively correlated with isoquercitrin (r = 0.91, P < 0.001). HP analysis was used to eliminate multicollinearity among environmental factors, confirming annual total solar radiation and annual cumulative ultraviolet radiation as core regulatory factors for CF component accumulation. Among the 5 compounds, gallic acid and hyperoside exhibited the highest sensitivity to environmental changes (R² = 0.76 and 0.71, respectively). Based on the above findings, Miyi County was identified as a high-quality producing area for CF, with gallic acid and hyperoside confirmed as the core quality indicators of CF. East longitude was determined to be the main environmental factor affecting CF component accumulation, while Annual Total Solar Radiation, Annual Cumulative Ultraviolet Radiation, and Annual Total Solar Radiation Difference were the synergistic driving factors. This study initially established a quality evaluation system for CF, providing support for its standardized development and sustainable utilization.

Data availability

The data presented in this study are available upon request from the corresponding author.
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Jinsong Ren or Xiaoli Eqi.Ethics declarations

Competing interests
The authors declare no competing interests.

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All experimental research and field studies on plants (both cultivated and wild), including the collection of plant materials, were performed in accordance with relevant institutional, national, and international guidelines and legislation. Specifically, the plant materials used in this study are wild plants, and their collection strictly adhered to the provisions of protected wild plant management regulations and ethical norms.

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Reprints and permissionsAbout this articleCite this articleZhong, H., Xia, Q., Li, Y. et al. Quality evaluation of Capitatae Fructus from different geographical regions in China.
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Keywords
Capitatae Fructus
HPLC fingerprintQuantitative analysisQuality evaluationEnvironmental regulation More

Abstract

Passive acoustic monitoring has become a widely used method to study cetaceans, especially for populations facing escalating threats from noisy human activities, including shipping traffic, fishing industry and marine constructions. Here, we conducted a study using an autonomous surface vehicle to explore the distribution and acoustic behavior of cetaceans and to characterize anthropogenic sound sources in the central Mediterranean Sea. A wave glider equipped with a single-towed acoustic recorder was deployed from 13th September 2022 to 3rd March 2023. The recording yielded 19,115 files of 460s each (approximately 2 TB), a third of which was kept for a preliminary analysis based on spectrogram visualization and audio listening. The results showed that nearly half of the dataset contained delphinid signals (Delphinidae), followed by sperm whales (Physeter macrocephalus) and fin whales (Balaenoptera physalus), with notable hotspots in the southern Tyrrhenian and the Ionian Sea. Moreover, the almost continuous detection of anthropogenic sources highlighted the widespread acoustic impact of human activities in the area. These findings demonstrate the value of passive acoustics in the use of autonomous vehicles as a versatile tool for large-scale and long-term monitoring, offering a promising approach to support conservation efforts for vulnerable species while advancing strategies to mitigate human impacts on marine ecosystems.

Data availability

Acoustic data is available on request to the corresponding author.
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Download referencesAcknowledgementsThis research would not have been possible without the work of many colleagues at the Stazione Zoologica Anton Dohrn. In particular, the authors would like to thank Robin Caron, Florence Rappin and Gerardo Sorrentino. During the writing of this manuscript, S.F. was supported by the Stazione Zoologica Anton Dohrn and the University of Turin through a SUSTNET PhD scholarship (n°39-033-31-DOT21BX3F7-8932). The PhD funder had no role in study design, data collection, or data analysis. Part of the work conducted for this study by F.C., S. F. and A. E. was carried out within the framework of the projects PRIN DIVES (CUP: C53D23003430006, Grant Assignment Decree n°1015-07/07/2023) and PRIN PNRR KNOWhale (CUP: C53D23007160001, Grant Assignment Decree n°1370-01/09/2023), funded by the European Union – Next GenerationEU – under the National Recovery and Resilience Plan (NRRP), Mission 4, Component 2, Investment 1.1, Calls for tender No. 104 and 1409 published on 2022 by the Italian Ministry of University and Research (MUR). This work was also supported by NRRP, Mission 4, Component 2, Investment 1.4, Call for tender N. 3138 of 16/12/2021, rectified by Decree n.3175 of18/12/2021 of MUR, funded by the European Union – NextGenerationEU (Project code: CN_00000033, Concession Decree No. 1034 of 17/06/2022adopted by the MUR, CUP: C63C22000520001, project National Biodiversity Future Center – NBFC).Author informationAuthor notesSara Ferri and Anaëlle Evrard contributed equally to this work.Authors and AffiliationsDepartment of Marine Animal Conservation and Public Engagement, Stazione Zoologica Anton Dohrn, Villa Comunale, 80121, Naples, ItalySara Ferri, Anaëlle Evrard, Livio Favaro & Francesco CarusoDepartment of Life Sciences and System Biology, University of Torino, Via Accademia Albertina, 13, 10123, Torino, ItalySara Ferri & Livio FavaroDepartment of Research Infrastructures for Marine Biological Resources, Stazione Zoologica Anton Dohrn, Via Po’ 25c, 00189, Rome, ItalySimonepietro Canese & Augusto PassarelliDepartment of Biology and Evolution of Marine Organisms, Sicily Marine Centre, Stazione Zoologica Anton Dohrn, Villa Pace – Contrada Porticatello 29, 98167, Messina, ItalyTeresa RomeoNational Biodiversity Future Center, Piazza Marina 61, 90133, Palermo, ItalySimonepietro Canese, Teresa Romeo & Francesco CarusoNational Institute for Environmental Protection and Research, Via dei Mille 46, 98057, Milazzo, ItalyTeresa RomeoUniversity of Gastronomic Sciences, Piazza Vittorio Emanuele 9, 12060, Pollenzo, ItalySilvestro GrecoAuthorsSara FerriView author publicationsSearch author on:PubMed Google ScholarAnaëlle EvrardView author publicationsSearch author on:PubMed Google ScholarSimonepietro CaneseView author publicationsSearch author on:PubMed Google ScholarTeresa RomeoView author publicationsSearch author on:PubMed Google ScholarSilvestro GrecoView author publicationsSearch author on:PubMed Google ScholarAugusto PassarelliView author publicationsSearch author on:PubMed Google ScholarLivio FavaroView author publicationsSearch author on:PubMed Google ScholarFrancesco CarusoView author publicationsSearch author on:PubMed Google ScholarContributionsConceptualization, F.C, S.F. A.E.; methodology, F.C, S.C., A.P, S.F., A.E.; data analysis, F.C., S.F., A.E.; writing—original draft preparation, S.F., A.E.; writing—review and editing, F.C., L.F; supervision, F.C.; project administration, S.C., A.P., T.R; funding acquisition, T.R, S.G. All authors have read and agreed to the published version of the manuscript.Corresponding authorCorrespondence to
Francesco Caruso.Ethics declarations

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The authors declare no competing interests.

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Reprints and permissionsAbout this articleCite this articleFerri, S., Evrard, A., Canese, S. et al. A wave glider for passive acoustic monitoring of cetaceans and anthropogenic sources in the central Mediterranean Sea.
Sci Rep (2025). https://doi.org/10.1038/s41598-025-32142-3Download citationReceived: 13 March 2025Accepted: 08 December 2025Published: 21 December 2025DOI: https://doi.org/10.1038/s41598-025-32142-3Share 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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KeywordsAutonomous vehiclesMediterranean basinUnderwater acousticsMarine mammalsCetaceans’ distributionMarine conservation More

AbstractIn recent world-wide studies on the autosomal genetic diversity of goats, Asian goats were represented only by Southwest Asian, Pakistani and Chinese breeds. We have collected 55 K genome-wide SNP genotypes for 12 South/Southeast Asian and 2 central Asian goat populations, and inferred the origin and evolutionary history of Asian goats based on the population genomic analyses. Breed relationships, diversity clines, and coancestry patterns revealed two distinct migration routes separated by the Himalayan mountains: a northern route (Kazakhstan–Mongolia–Xinjiang) and a southern route (Bangladesh–Indochina). These routes tentatively parallel major human migration events across Eurasia. The migrations of goats converge into the Indochina goat populations, which then became the ancestors of the Philippine and Indonesian goats. Previous data on Y-chromosomal haplogroups indicate within the first group a separate migration of cashmere goats in eastern and northern China. Similarly, the southern route has been followed by two subsequent waves of goats, the first carrying the mitochondrial B haplogroup and in eastern Indochina associated with that Katjang type, and a later wave carrying exclusively the mitochondrial A haplogroup and associated in western Indochina with the Indian lop-eared trait with a roman convex facial profile. Haplogroup B in Indochina and Indonesia seems to be associated with tropical adaptation, whereas the Y1AB haplotype in northern China occurs at high frequency in cashmere goats, suggesting potential adaptation to arid environments. Together, these patterns point to a complex demographic history and diverse adaptive trajectories in Asian goats.

Data availability

The datasets generated and analysed during the current study are available in the Figshare repository, (https://doi.org/10.6084/m9.figshare.29035598). The code developed for the Reduced Representation Admixture Analysis (RRAA) and the datasets used in this study are available at the following repository: https://github.com/wujiaqi06/RRAA.
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Download referencesAcknowledgementsThis work was supported in part by JSPS KAKENHI Grant Numbers 25304038 and 17H04643, 19K22367 and 21KK0122. I wish to thank The Society for Researches on Native Livestock for helping field research work. The authors would like to explain special thanks the late Dr. K. Nozawa for providing long-standing observations of goat morphological distribution in East Asia, related to Fig. S1.Author informationAuthor notesTakahiro Yonezawa and Jiaqi Wu contributed equally to this study.Authors and AffiliationsGraduate School of Integrated Sciences for Life, Hiroshima University, Higashi-Hiroshima, JapanTakahiro Yonezawa & Jiaqi WuDepartment of Molecular Life Science, Tokai University School of Medicine, Isehara, JapanJiaqi WuGraduate School of Agricultural Science, Kobe University, Kobe, 657-8501, JapanRyo Masuko, Kenta Iso, Yuto Nomura, Risa Tabata, Maho Masaoka,  Ayin, Fuki Kawaguchi, Shinji Sasazaki & Hideyuki MannenInstitute of Livestock and Grassland Science, National Agriculture and Food Research Organization, Tsukuba, JapanAisaku Arakawa & Eiji KobayashiFaculty of Agriculture, Tokyo University of Agriculture, Atsugi, JapanKoh Nomura & Yukimizu TakahashiNational Swine Research Program, Nepal Agricultural Research Council, Dhankuta, NepalManoj Kumar ShahAnimal Breeding and Genetics, Bangladesh Agricultural University, Mymensingh, BangladeshMuhammad Omar FaruqueUniversity of the Philippines, Los Banos, PhilippinesJoseph S. MasangkayInstitute of Radiobiology and Radiation Protection, Astana Medical University, Astana, KazakhstanMeirat Bakhtin & Polat KazymbetInternational Centre for Integrated Mountain Development, Kathmandu, NepalTashi DorjiFaculty of Animal Science, Hasanuddin University, Makassar, South Sulawesi, IndonesiaMuhammad Ihsan Andi Dagong & Sri Rachma Aprilita BugiwatiFaculty of Veterinary Medicine, Utrecht University, Utrecht, The NetherlandsJohannes A. LenstraAuthorsTakahiro YonezawaView author publicationsSearch author on:PubMed Google ScholarJiaqi WuView author publicationsSearch author on:PubMed Google ScholarRyo MasukoView author publicationsSearch author on:PubMed Google ScholarKenta IsoView author publicationsSearch author on:PubMed Google ScholarYuto NomuraView author publicationsSearch author on:PubMed Google ScholarRisa TabataView author publicationsSearch author on:PubMed Google ScholarMaho MasaokaView author publicationsSearch author on:PubMed Google Scholar AyinView author publicationsSearch author on:PubMed Google ScholarFuki KawaguchiView author publicationsSearch author on:PubMed Google ScholarShinji SasazakiView author publicationsSearch author on:PubMed Google ScholarAisaku ArakawaView author publicationsSearch author on:PubMed Google ScholarKoh NomuraView author publicationsSearch author on:PubMed Google ScholarYukimizu TakahashiView author publicationsSearch author on:PubMed Google ScholarEiji KobayashiView author publicationsSearch author on:PubMed Google ScholarManoj Kumar ShahView author publicationsSearch author on:PubMed Google ScholarMuhammad Omar FaruqueView author publicationsSearch author on:PubMed Google ScholarJoseph S. MasangkayView author publicationsSearch author on:PubMed Google ScholarMeirat BakhtinView author publicationsSearch author on:PubMed Google ScholarPolat KazymbetView author publicationsSearch author on:PubMed Google ScholarTashi DorjiView author publicationsSearch author on:PubMed Google ScholarMuhammad Ihsan Andi DagongView author publicationsSearch author on:PubMed Google ScholarSri Rachma Aprilita BugiwatiView author publicationsSearch author on:PubMed Google ScholarJohannes A. LenstraView author publicationsSearch author on:PubMed Google ScholarHideyuki MannenView author publicationsSearch author on:PubMed Google ScholarContributionsH.M., T.Y. and J.A.L conceived and designed the experiments and supervised the project. R.M., K.I., Y.N., R.T., M.M., A., E.K., F.K. and S.S. performed the genetic analyses. J.W., R.M., T.Y., A.A. and H.M. performed the phylogenetic analyses. K.N., Y.T., M.K.S., M.O.F., J.S.M., M.B., P.K., T.D., M.I.A.D. and S.R.A.B. conducted the field investigation and collected the samples. T.Y., J.A.L. and H.M. wrote the manuscript. All of the authors discussed the results and contributed to the final manuscript. Lenstra, JA played a fundamental role in this paper, including defining the research direction, structuring the paper, and developing the RRAA method.Corresponding authorCorrespondence to
Hideyuki Mannen.Ethics declarations

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Ethical approval
This study is reported in accordance with ARRIVE guidelines (https:// arriv eguid elines. org). All experiments were carried out according to the Kobe University Animal Experimentation Regulations, and all protocols were approved by the Institutional Animal Care and Use Committee of Kobe University and by Association for the Promotion of Research Integrity (Tokyo, Japan) (Approval Number: AP0000436777). All blood samples collections were approved by animal owners with signed informed consent.

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Reprints and permissionsAbout this articleCite this articleYonezawa, T., Wu, J., Masuko, R. et al. Genome-wide variation reveal that goats were introduced into Asia via multiple migrations.
Sci Rep (2025). https://doi.org/10.1038/s41598-025-29204-xDownload citationReceived: 30 April 2025Accepted: 14 November 2025Published: 21 December 2025DOI: https://doi.org/10.1038/s41598-025-29204-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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AbstractAccurate species identification is crucial for managing ex-situ populations, especially in cryptic species complexes where taxonomic uncertainty may compromise conservation. The sugar glider (Petaurus breviceps s.l.) is a small nocturnal marsupial commonly bred in zoos and is popular in the exotic pet trade. Recent taxonomic revisions revealed substantial cryptic diversity within the complex, raising concerns about species identity and geographic origin of captive individuals. We used an integrative approach combining genetic and acoustic analyses to assess the taxonomic status of captive P. breviceps s.l. populations. Phylogenetic analyses of mitochondrial ND2 and ND4 genes showed a strong genetic affinity between European and United States captive populations, suggesting a shared origin within the New Guinean lineage. These findings support their reclassification as Petaurus papuanus. Despite their captive origin, these populations showed unexpectedly high haplotype diversity, likely due to repeated introductions from genetically distinct but geographically close wild populations. However, within-group homogeneity indicates limited genetic exchange among breeding lines. Acoustic analyses of the barking call revealed intraspecific variability but little species-specificity, indicating a minor role in reproductive isolation. Our findings underscore the importance of taxonomic clarity and structured genetic management for conserving captive population integrity.

Data availability

Data is provided within the manuscript or supplementary material files. Sequenced fragments of the mitochondrial ND2, ND4, and nuclear ω-globin are available on GenBank (https://www.ncbi.nlm.nih.gov/) under accession numbers: PV701819-PV701993. Recordings of wild individuals are available on iNaturalist (https://www.inaturalist.org/). Additional acoustic data recorded during this study are available from the corresponding author upon request.
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Download referencesAcknowledgementsWe thank all the ex-situ institutions that have contributed to our study, namely the Berlin Zoological Garden, Riga National Zoological Garden, Zoological and Botanical Garden Plzen, Prague Zoological Garden, and the Zoological Garden Brno. We also thank the private breeders for their contributions. The following thanks go to the iNaturalist website and mostly to all the authors of recordings from Australia. We also thank Denisa Stejskalová for her amazing drawing of Petaurus papuanus.FundingThe research was supported by the Internal Grant Agency of FTZ CZU (IGA20253125).Author informationAuthors and AffiliationsFaculty of Tropical AgriSciences, Czech University of Life Sciences Prague, Prague, Czech RepublicMiroslav Mulko & Barbora Černá BolfíkováResearch Institute for Gene Pool Conservation, Safari Park Dvůr Králové, Dvůr Králové nad Labem, Czech RepublicMiroslav MulkoFaculty of Science, Charles University, Prague, Czech RepublicIrena SchneiderováAuthorsMiroslav MulkoView author publicationsSearch author on:PubMed Google ScholarIrena SchneiderováView author publicationsSearch author on:PubMed Google ScholarBarbora Černá BolfíkováView author publicationsSearch author on:PubMed Google ScholarContributionsAll authors contributed to the study conception and design. Material preparation, data collection, and analysis were performed by M. M., I. S., and B. Č. B. The first draft of the manuscript was written by M. M., and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.Corresponding authorCorrespondence to
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Reprints and permissionsAbout this articleCite this articleMulko, M., Schneiderová, I. & Černá Bolfíková, B. Taxonomic reassessment of captive sugar gliders using genetic analyses and complementary acoustic data.
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KeywordsBioacousticsCaptive breedingGenetic diversityMarsupialiaPet tradePetauridae More

AbstractFrost events can cause severe and often irreversible damage to leaves and canopies. The negative impacts of isolated frost events, defined as discontinuous days of extreme cold, on forests are well documented. However, a critical aspect of frost damage—its prolonged duration—is largely overlooked, even though freezing temperatures often persist for multiple consecutive days in natural environments. The impact of continuous frost events, characterized by consecutive days of extreme cold, on forest growth remains poorly understood. Using multiple remote-sensing data sources, herein we demonstrate that continuous frost events result in significantly greater declines in forest growth compared to isolated frost events across the Northern Hemisphere. Our frost-controlled experiments using seven tree species further confirm that continuous frost causes more severe damage to cell integrity and photosynthetic rate. Using GPP and climate data simulated by CMIP6 models, we find that continuous frost events significantly reduce forest growth by the end of this century, with the largest reductions observed under high-emission scenarios. Our findings underscore the importance of accounting for the prolonged duration of frost events to fully capture their impacts on forest ecosystems, as failing to consider this factor may lead to underestimation of frost-induced effects on forest growth and carbon cycling under climate change.

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Data availability

The MODIS gross primary productivity and net primary production data sourced from https://lpdaac.usgs.gov/products/mod17a2hv006/, and the global all-sky daily average solar-induced fluorescence dataset was obtained from https://doi.org/10.6084/m9.figshare.6387494. MODIS enhanced vegetation index and evapotranspiration were obtained from https://www.glass.hku.hk/index.html. The palmer drought severity index was sourced from https://crudata.uea.ac.uk/cru/, and the global aridity index was sourced from https://cgiarcsi.community/data/global-aridity-and-pet-database/. Future gross primary productivity projections were sourced from https://esgf-node.llnl.gov/projects/esgf-llnl/. Daily minimum temperature data were acquired from the climatic research unit and Japanese reanalysis climate dataset (CRU JRA v2.4) (https://catalogue.ceda.ac.uk/), with future temperature projections obtained from the NorESM2-MM and CMCC-ESM2 model (https://esgf-node.llnl.gov/projects/esgf-llnl/). The frost-damage experimental data generated in this study have been deposited in the Source data (Fig. 4). Source data are provided with this paper.
Code availability

The primary codes used in this study are available at https://doi.org/10.6084/m9.figshare.30069994.v1.
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Download referencesAcknowledgementsThis research was funded and supported by the National Key R&D Program of China (2023YFF0806600) and the National Natural Science Foundation of China (32271833, 32401380).Author informationAuthor notesThese authors contributed equally: Hongjun Yang, Wenjing Tao, Juan Chen.Authors and AffiliationsKey Laboratory of Bio-Resource and Eco-Environment of Ministry of Education, College of Life Sciences, Sichuan University, Chengdu, ChinaHongjun Yang, Qin Yan, Yuchuan Yang, Haoyu Qiu, Junkai Ma & Lei ChenKey Laboratory of Eco-environments in Three Gorges Reservoir Region of Ministry of Education, School of Life Sciences, Southwest University, Chongqing, ChinaWenjing Tao & Juan ChenCSIC, Global Ecology Unit CREAF-CSIC-UAB, Barcelona, SpainJosep PeñuelasCREAF, Cerdanyola del Vallès, Catalonia, SpainJosep PeñuelasAuthorsHongjun YangView author publicationsSearch author on:PubMed Google ScholarWenjing TaoView author publicationsSearch author on:PubMed Google ScholarJuan ChenView author publicationsSearch author on:PubMed Google ScholarQin YanView author publicationsSearch author on:PubMed Google ScholarYuchuan YangView author publicationsSearch author on:PubMed Google ScholarHaoyu QiuView author publicationsSearch author on:PubMed Google ScholarJunkai MaView author publicationsSearch author on:PubMed Google ScholarJosep PeñuelasView author publicationsSearch author on:PubMed Google ScholarLei ChenView author publicationsSearch author on:PubMed Google ScholarContributionsL.C. conceived and designed the research. H.Y., W.T., J.C., and L.C. performed the data analysis. H.Y. wrote the paper with the inputs of W.T., J.C., Q.Y., Y.Y., H.Q., J.M., J.P., and L.C. All authors contributed to the interpretation of the results and approved the final manuscript.Corresponding authorCorrespondence to
Lei Chen.Ethics declarations

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Nature Communications thanks Seyed Alavi, Juan Alvarez-Yepiz and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. A peer review file is available.

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Reprints and permissionsAbout this articleCite this articleYang, H., Tao, W., Chen, J. et al. Continuous frost causes a greater reduction in forest growth than isolated frost in the Northern Hemisphere.
Nat Commun (2025). https://doi.org/10.1038/s41467-025-67861-8Download citationReceived: 08 January 2025Accepted: 10 December 2025Published: 21 December 2025DOI: https://doi.org/10.1038/s41467-025-67861-8Share 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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AbstractWheat growth monitoring plays a vital role in agricultural decision-making and food security. This study aims to develop an accurate and efficient monitoring method for wheat growth by integrating satellite remote sensing and machine learning techniques. Based on preprocessed Sentinel-2 satellite images and measured wheat leaf area index (LAI) data, a set of 11 vegetation indices—such as NDVI, NDRE, and RVI—were selected and ranked through Pearson correlation analysis. A comprehensive index system was then constructed by selecting the top eight indices using a stepwise optimization approach. Three machine learning models—Linear Regression (LR), Backpropagation Neural Network (BPNN), and XGBoost—were applied to evaluate the performance of the index system, with the Particle Swarm Optimization (PSO) algorithm employed to optimize each model. The results demonstrate that the PSO-optimized XGBoost model achieved the highest accuracy (R² = 0.94, MSE = 0.075), exhibiting strong stability and robustness to data fluctuations. These findings suggest that the proposed approach provides a reliable solution for wheat growth monitoring.

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Data availability

This Sentinel-2 remote sensing image data are available at the Copernicus Data Space Ecosystem, https://dataspace.copernicus.eu/. The wheat Leaf Area Index (LAI) dataset originates from the canopy chlorophyll and ground validation dataset of winter wheat in Yucheng, Shandong, https://www.geodoi.ac.cn/edoi.aspx? DOI=10.3974/geodb.2020.08.01.V1.
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Mingguang Diao or Chuyan Zhang.Ethics declarations

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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 articleDiao, M., Liang, S., Chen, J. et al. Optimization of comprehensive wheat growth index system and monitoring model based on LAI.
Sci Rep (2025). https://doi.org/10.1038/s41598-025-25313-9Download citationReceived: 20 December 2024Accepted: 20 October 2025Published: 21 December 2025DOI: https://doi.org/10.1038/s41598-025-25313-9Share 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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KeywordsWheat growthRemote sensingVegetation indexMachine learningExtreme gradient boosting More

AbstractSugar beet is a vital sugar-producing crop, and continuous cropping poses a significant threat to its growth, leading to a decline in yield and quality. This study aimed to investigate the effects of two bacterial agents, Bacillus subtilis and Bacillus mucilaginosus, on the growth, soil physicochemical properties, and rhizosphere microbial community of sugar beet seedlings. We employed pot experiments and amplicon sequencing to analyze the impact of applying two different Bacillus agents on the microbial community structure in the rhizosphere soil of continuously cropped sugar beet and explore the microbial composition, environmental driving factors, and potential functions present within the microbial communities. The results showed that both Bacillus agents and their combination significantly promoted the growth of continuous cropping sugar beet seedlings, reaching or even surpassing the levels observed in crop rotation, improved soil pH, and enhanced soil environment. High-throughput sequencing analysis of the rhizosphere soil revealed that all Bacillus treatments induced changes in the diversity and structural composition of the rhizosphere microbial community, and significantly increased the relative abundance of Proteobacteria, thereby enriching beneficial microorganisms such as Pseudomonas, Novosphingobium, and Sphingomonas compared with that in the control group. Additionally, the application of Bacillus inoculants significantly enhanced the nitrate respiration, nitrogen respiration, and chitinolytic functions. These two bacterial agents optimized soil physicochemical properties and improved the rhizosphere soil microbial community structure, promoting sugar beet seedling growth and effectively mitigating the negative effects of continuous cropping.

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Data availability

The datasets generated during and/or analyzed during the current study are available in the NCBI Sequence Read Archive (SRA) database under accession numbers PRJNA1310802 (ITS) and PRJNA1310801 (16S).
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Download referencesAcknowledgementsThis research was made possible through the collaboration and support of the National Sugar Crop Improvement Centre of Heilongjiang University and Heilongjiang Provincial Key Laboratory of Ecological Restoration and Resource Utilization for Cold Region.FundingThis research was funded by the Heilongjiang Provincial Postdoctoral Research Funding Program (LBH-Z23255).Author informationAuthors and AffiliationsCollege of Advanced Agriculture and Ecological Environment, Heilongjiang University, Harbin, 150000, ChinaYanchun Sun, Qun Song, Zenghao Wang, Youkai Gao, Liuli Wei, Yuguang Wang, Rui Chen & Gui GengAuthorsYanchun SunView author publicationsSearch author on:PubMed Google ScholarQun SongView author publicationsSearch author on:PubMed Google ScholarZenghao WangView author publicationsSearch author on:PubMed Google ScholarYoukai GaoView author publicationsSearch author on:PubMed Google ScholarLiuli WeiView author publicationsSearch author on:PubMed Google ScholarYuguang WangView author publicationsSearch author on:PubMed Google ScholarRui ChenView author publicationsSearch author on:PubMed Google ScholarGui GengView author publicationsSearch author on:PubMed Google ScholarContributionsQun Song and Zenghao Wang: Conceptualization, Methodology, Writing-Original draft preparation; Youkai Gao, Liuli Wei, Rui Chen: Data curation, Sample analysis; Yanchun Sun: Conceptualization, Resources, Supervision, Writing-Reviewing; Gui Geng, Yuguang Wang: Methodology, Writing-Reviewing. All authors contributed to the article and approved the submitted version.Corresponding authorsCorrespondence to
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Reprints and permissionsAbout this articleCite this articleSun, Y., Song, Q., Wang, Z. et al. Effects of bacillus on continuous cropping of sugar beets and their rhizosphere microbial community.
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AbstractMarine fishery resource prediction is crucial for sustainable fishery management and ecosystem conservation, yet traditional statistical methods face limitations in capturing the complex non-linear relationships and multi-scale temporal dependencies inherent in marine environmental systems. This study proposes a novel CNN-XGBoost fusion model that integrates convolutional neural networks’ temporal pattern recognition capabilities with extreme gradient boosting’s ensemble learning strengths for enhanced marine fishery resource forecasting. The fusion architecture employs a hierarchical two-stage framework where CNN components extract high-level temporal features from multi-source marine environmental data, while XGBoost modules process both extracted features and engineered variables to generate final predictions. Comprehensive experiments demonstrate that the proposed fusion model achieves superior performance compared to standalone CNN, XGBoost, and traditional ARIMA approaches, with 19.1% improvement in RMSE and statistically significant enhancements across all evaluation metrics. The optimal fusion weight analysis reveals that CNN-extracted features and XGBoost-processed features are weighted at 40 and 60% respectively in the final prediction fusion, achieving RMSE of 2.847, MAE of 2.184, and R2 of 0.846. These percentages represent fusion weight allocation rather than prediction accuracy values. Time series analysis confirms robust performance across seasonal variations and exceptional capability in predicting extreme abundance events critical for adaptive fishery management. The results provide valuable insights for sustainable marine resource management and offer practical tools for fishery policymakers and resource managers.

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Data availability

The marine fishery resource datasets used in this study were obtained from multiple sources, with access information provided below to facilitate reproducibility and follow-up research. Fishery catch records were obtained from the National Fisheries Database of China (http://www.cnfm.gov.cn/) operated by the Ministry of Agriculture and Rural Affairs, available upon reasonable request with appropriate data sharing agreements that comply with commercial confidentiality requirements. Researchers interested in accessing these data should contact the Fisheries Bureau (email: [email protected]) with a formal data request describing the research purpose, intended use, and data protection measures.Satellite-derived oceanographic data are publicly accessible through the following sources: (1) MODIS sea surface temperature and chlorophyll-a concentration data were downloaded from NASA’s Ocean Color Web portal (https://oceancolor.gsfc.nasa.gov/), specifically utilizing MODIS Aqua Level-3 mapped products (dataset identifiers: AQUA_MODIS.20080101_20231231.L3m.MO.SST.sst.4 km and AQUA_MODIS.20080101_20231231.L3m.8D.CHL.chlor_a.4 km); (2) SeaWiFS chlorophyll-a concentration data for the earlier period (1997-2010) were obtained from the same NASA Ocean Color portal (https://oceancolor.gsfc.nasa.gov/data/seawifs/). All satellite data are freely available without registration and can be accessed through the portal’s data browser or bulk download protocols.Meteorological data including wind speed, wind direction, and precipitation measurements were provided by the China Meteorological Administration through their National Meteorological Information Center data portal (http://data.cma.cn/). Access requires registration (free for research purposes) and adherence to the CMA data policy (http://data.cma.cn/en/site/index.html). Station-level daily observations can be requested through the portal’s data ordering system, with typical processing time of 3-5 business days for historical data requests. Ocean current velocity data were obtained from the China High-Frequency Radar Ocean Observation Network operated by the State Oceanic Administration, available through collaborative research agreements. Researchers should contact the National Marine Data and Information Service (email: [email protected], website: http://www.nmdis.org.cn/) to inquire about data access procedures.The processed datasets supporting the conclusions of this article, including the preprocessed and harmonized multi-source data, engineered features, and model predictions, are available from the corresponding author (Mingqi Zhang, email: [email protected]) upon reasonable request, subject to privacy and confidentiality restrictions imposed by original data providers. The Python code implementing the CNN-XGBoost fusion model, including data preprocessing scripts, model architecture definitions, training procedures, and evaluation metrics, will be made publicly available on GitHub (https://github.com/[username]/CNN-XGBoost-Marine-Fishery) upon publication acceptance, licensed under MIT License to facilitate reproducibility and encourage further methodological development by the research community.
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Klyashtorin, L. B. Climate change and long-term fluctuations of commercial catches: the possibility of forecasting. FAO Fisheries Technical Paper No. 410. Rome, FAO. (2001).Download referencesAuthor informationAuthors and AffiliationsSchool of Economics, Hebei University, Baoding, 071000, ChinaMingqi ZhangAuthorsMingqi ZhangView author publicationsSearch author on:PubMed Google ScholarContributionsMingqi Zhang conceived and designed the study, developed the CNN-XGBoost fusion model architecture, collected and preprocessed the marine fishery resource datasets, implemented the computational algorithms, conducted the experimental analysis, performed statistical evaluations, interpreted the results, and wrote the manuscript. The author read and approved the final manuscript.Corresponding authorCorrespondence to
Mingqi Zhang.Ethics declarations

Competing interests
The authors declare no competing interests.

Ethics approval
This study utilized publicly available marine fishery resource data and satellite-derived oceanographic measurements that do not involve human subjects or animal experimentation, thereby satisfying ethical requirements for environmental data analysis. All data sources employed in this research were accessed through legitimate channels with appropriate authorization and compliance with data provider policies. Fishery catch records obtained from the National Fisheries Database of China were used under formal data sharing agreements that stipulate data confidentiality, appropriate use restrictions, and acknowledgment requirements. The research team adhered to all terms of these agreements, including anonymization of vessel-specific information and aggregation of data to temporal resolutions that protect commercially sensitive information. Satellite-derived oceanographic data from NASA’s Ocean Color Web portal are publicly available without restrictions and were used in accordance with NASA’s Earth Science Data and Information Policy. Meteorological data from the China Meteorological Administration were accessed following registration procedures and compliance with stated data use policies. The research was conducted in accordance with institutional guidelines for environmental data analysis at Hebei University and adheres to international standards for responsible conduct of research. Ethical approval was obtained from the Research Ethics Committee of the School of Economics, Hebei University (Approval Number: HBU-ECO-2024-015, Date: March 15, 2024), which reviewed the study protocol, data management procedures, and potential societal impacts of the research findings. The ethics review confirmed that the study poses no risks to human subjects, animal welfare, or environmental integrity, and that the research objectives align with principles of sustainable marine resource management and ecosystem conservation. All research activities were performed in compliance with relevant guidelines and regulations governing environmental research in China.

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Reprints and permissionsAbout this articleCite this articleZhang, M. Marine fishery resource dynamic prediction based on CNN-XGBoost fusion model.
Sci Rep (2025). https://doi.org/10.1038/s41598-025-33175-4Download citationReceived: 05 August 2025Accepted: 16 December 2025Published: 21 December 2025DOI: https://doi.org/10.1038/s41598-025-33175-4Share 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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KeywordsMarine fishery resourcesCNN-XGBoost fusionResource predictionDeep learningEnsemble learningTime series forecasting More