Philippa Kaur

AbstractMarine algae underpin entire ocean ecosystems. Yet algae in culture poorly represent their large environmental diversity, and we have a limited understanding of their convoluted evolution by endosymbiosis. Here, we perform a phylogeny-guided plastid genome-resolved metagenomic survey of Tara Oceans expeditions. We present a curated resource of 660 new non-redundant plastid genomes of environmental marine algae, vastly expanding plastid genome diversity within major algal groups, including many without closely related reference genomes. Notably, we recover four plastid genomes, including one near-complete, forming a deep-branching plastid lineage of nano-size algae that we informally name leptophytes. This group is globally distributed and generally rare, although it can reach relatively high abundance in the Arctic. A near-complete mitochondrial genome showing strong co-occurrence with leptophyte plastids is also recovered and assigned to this group. Leptophytes encompass the enigmatic plastid group DPL2, one of the very few known plastid groups not clearly belonging to major algal groups and previously known only from 16S rDNA sequences. Comparative organellar genomics and phylogenomics indicate that leptophytes are sister to haptophytes, and raise the intriguing possibility that cryptophytes acquired their plastids from haptophytes. Collectively, our study demonstrates that metagenomics can reveal hidden organellar diversity, and improve models of plastid evolution.

Data availability

The 937 metagenomes from Tara Oceans used in the study are publicly available at the EBI under project PRJEB402. Data our study generated has been deposited in an online repository: https://doi.org/10.17044/scilifelab.2821217381. This link provides access to the individual FASTA files from each plastid and mitochondrial genome used in our study (including the 660 non-redundant ptMAGs and 34 mtMAGs), the co-assembly of the top six samples where Lepto-01 was most abundant, individual gene alignments, concatenated and trimmed alignments, and maximum-likelihood and Bayesian tree files for the phylogenomic dataset. Source Data for Fig. 4, Supplementary Figs. 10-12, and Supplementary Fig. 22 can be found on the linked GitHub repository, while source data for Supplementary Figs. 2-6 is provided as Supplementary Data 1.
Code availability

All scripts used for genome annotation and phylogenetic analyses are available on GitHub: https://github.com/burki-lab/ptMAGs with the identifier: 10.5281/zenodo.1763560482.
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Download referencesAcknowledgementsWe thank Shinichi Sunagawa for having facilitated the recovery of relevant data from the mOTU metagenomic database maintained by his research group at the Department of Biology at ETH Zürich. We thank A. Roger, H. Baños, and C. McCarthey for discussions, and for kindly providing custom scripts for running the phylogenetic models MEOW and GF-MIX. We thank J.E. Dharamshi for discussions about phylogenetic analyses. Our survey was made possible by the sampling and sequencing efforts of the Tara Oceans Project. Tara Oceans (which includes the Tara Oceans and Tara Oceans Polar Circle expeditions) would not exist without the leadership of the Tara Oceans Foundation and the continuous support of 23 institutes (https://oceans.taraexpeditions.org/). This article is contribution number 164 of Tara Oceans. Phylogenetic analyses were enabled by resources provided by the National Academic Infrastructure for Supercomputing in Sweden (NAISS 2024/5-197), partially funded by the Swedish Research Council through grant agreement no. 2022-06725. M.J. was supported by the Swedish Research Council (International Postdoc grant 2022-00351). FB’s research is supported by grants from the European Research Council (ERC consolidator grant 101044505), the Swedish Research Council VR (2021-04055), and Science for Life Laboratory. TD’s research is supported by a grant from the l’Agence Nationale de la Recherche (ANR-23-CE02-0022). We also thank the commitment of the CNRS and Genoscope/CEA. Some of the computations were performed using the platine, titane and curie HPC machine provided through GENCI grants (t2011076389, t2012076389, t2013036389, t2014036389, t2015036389 and t2016036389).FundingOpen access funding provided by Uppsala University.Author informationAuthor notesEric Pelletier & Tom O. DelmontPresent address: Research Federation for the Study of Global Ocean Systems Ecology and Evolution, FR2022/Tara GOsee, Paris, FranceAuthors and AffiliationsDepartment of Aquatic Sciences and Assessment, Swedish University of Agricultural Sciences, Uppsala, SwedenMahwash JamyDepartment of Organismal Biology, Program in Systematic Biology, Uppsala University, Uppsala, SwedenThomas Huber & Fabien BurkiGénomique Métabolique, Genoscope, Institut François Jacob, CEA, CNRS, Univ Evry, Université Paris-Saclay, Evry, FranceThibault Antoine, Eric Pelletier & Tom O. DelmontResearch Federation for the Study of Global Ocean Systems Ecology and Evolution, FR2022/Tara GOsee, Paris, FranceThibault AntoineDepartment of Biology, Institute of Microbiology and Swiss Institute of Bioinformatics, ETH Zürich, Zürich, SwitzerlandHans-Joachim RuscheweyhGlobal Health Institute, School of Life Sciences, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, SwitzerlandLucas PaoliAuthorsMahwash JamyView author publicationsSearch author on:PubMed Google ScholarThomas HuberView author publicationsSearch author on:PubMed Google ScholarThibault AntoineView author publicationsSearch author on:PubMed Google ScholarHans-Joachim RuscheweyhView author publicationsSearch author on:PubMed Google ScholarLucas PaoliView author publicationsSearch author on:PubMed Google ScholarEric PelletierView author publicationsSearch author on:PubMed Google ScholarTom O. DelmontView author publicationsSearch author on:PubMed Google ScholarFabien BurkiView author publicationsSearch author on:PubMed Google ScholarContributionsF.B. and T.O.D. conceived the project. T.O.D. characterised the ptMAGs. M.J., F.B. and T.H., performed phylogenetic analyses. T.A., E.P. and T.O.D. created the plastid genomic database and performed surveys for nucleomorphs. H.J.R. and L.P. retrieved relevant data from mOTUs. M.J. and T.H. annotated the ptMAGs, and E.P. performed mapping analyses. F.B., M.J., and T.O.D. wrote the manuscript with input from all the authors.Corresponding authorsCorrespondence to
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Reprints and permissionsAbout this articleCite this articleJamy, M., Huber, T., Antoine, T. et al. Identification of a deep-branching lineage of algae using environmental plastid genomes.
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AbstractThe role of soil microorganisms in supporting multiple ecosystem functions (multifunctionality) remains poorly understood across diverse environmental conditions. Here, we investigate 484 soils from 27 European countries spanning a range of climatic and edaphic contexts. We assess the contribution of climate, soil properties, and soil microbiome traits (i.e., the relative abundance of co-occurring taxa) to explain six key functional proxies related to soil structure, biochemical activity, and productivity. We find the highest multifunctionality values in grasslands, woodlands, loamy and acidic soils, and temperate humid regions, and the lowest in croplands, alkaline soils, and drier regions. Soil properties explain 12–31% of variation in multifunctionality, with microbial biomass and nitrogen content emerging as the strongest predictors. The soil microbiome accounts for 2–14% of unique variance in multifunctionality but explains more than 25% of variation in enzymatic activities and primary productivity in clay-rich soils and soils originating from temperate dry regions. Specific taxa, particularly within Actinobacteria, Acidobacteria, and the fungal genus Mortierella consistently emerge as strong predictors of ecosystem multifunctionality. Our findings highlight that ecosystem multifunctionality is jointly shaped by soil properties and microbial communities. We argue that specific taxa hold potential as context-dependent indicators for multifunctionality monitoring across environmental gradients.

Data availability

The data that supports the findings of this study are freely available in figshare with the identifier https://doi.org/10.6084/m9.figshare.28645625. Raw DNA sequences can be accessed through the European Soil Data Centre (ESDAC) portal: https://esdac.jrc.ec.europa.eu/content/soil-biodiversity-dna-bacteria-and-fungi. The raw data (DNA sequences) generated in this study have been deposited in the Sequence Read Archive (SRA) database under BioProject ID PRJNA952168. Detailed information on the taxonomic composition of bacterial and fungal modules is available as Supplementary Dataset 1. Detailed information on Structural Equation Models (SEMs) results is availabel as Supplementary Dataset 2.
Code availability

Code used to perform all analyses described in this study is freely available at https://github.com/fromerob/Multifunctionality.git.
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Download referencesAcknowledgementsF.R. acknowledges funding from the Novo Nordisk Foundation through a Postdoctoral fellowship (grant reference number NNF24OC0094454). The LUCAS Survey is coordinated by Unit E4 of the Statistical Office of the European Union (EUROSTAT). The LUCAS Soil sample collection is supported by the Directorate-General Environment (DG-ENV), Directorate-General Agriculture and Rural Development (DG-AGRI) and Directorate-General Climate Action (DG-CLIMA) of the European Commission. M.D-B. acknowledges support from the Spanish Ministry of Science and Innovation for the I  +  D  + I project PID2020-115813RA-I00 funded by MCIN/AEI/10.13039/501100011033. M.v.d.H and F.R. acknowledge funding from the Swiss National Science Foundation (Switzerland) through grant no. 310030–188799 and from the European Union Horizon 2020 research and innovation program under grant agreement no. 862695 EJP SOIL-MINOTAUR.Author informationAuthors and AffiliationsPlant-Soil Interactions group, Agroscope, Zurich, SwitzerlandFerran Romero, Ido Rog & Marcel G. A. van der HeijdenDepartment of Plant and Microbial Biology, University of Zurich, Zurich, SwitzerlandFerran Romero, Ido Rog & Marcel G. A. van der HeijdenDepartment of Agroecology, Aarhus University, Slagelse, DenmarkFerran Romero & Mohammad BahramEuropean Commission, Joint Research Centre Ispra (JRC), Ispra, ItalyMaëva Labouyrie, Cristiano Ballabio, Panos Panagos & Arwyn JonesEuropean Dynamics, Brussels, BelgiumAlberto OrgiazziMycology and Microbiology Center, University of Tartu, Tartu, EstoniaLeho TedersooDepartment of Botany, Institute of Ecology and Earth Sciences, University of Tartu, Tartu, EstoniaMohammad BahramDepartment of Ecology, Swedish University of Agricultural Sciences, Uppsala, SwedenMohammad BahramGerman Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Leipzig, GermanyNico Eisenhauer, Marie Sünnemann & Carlos A. GuerraInstitute of Biology, Leipzig University, Leipzig, GermanyNico Eisenhauer & Marie SünnemannDepartment of Geography, University of Coimbra, Coimbra, PortugalCarlos A. GuerraLaboratorio de Biodiversidad y Funcionamiento Ecosistémico, Instituto de Recursos Naturales y Agrobiología de Sevilla (IRNAS), Consejo Superior de Investigaciones Científicas (CSIC), Sevilla, SpainDongxue Tao & Manuel Delgado-BaquerizoSchool of Ecology, Sun Yat-Sen University, Shenzhen, ChinaDongxue TaoState Key Laboratory for Crop Stress Resistance and High-Efficiency Production, College of Life Sciences, Northwest A&F University, Yangling, Shaanxi, ChinaShuo JiaoConsiglio per la ricerca in agricoltura e l’analisi dell’economia agraria – Centro di Ricerca Agricoltura e Ambiente (CREA-AA), Firenze, ItalyStefano MocaliFreie Universität Berlin, Institute of Biology, Berlin, GermanyMatthias C. Rillig & Anika LehmannAuthorsFerran RomeroView author publicationsSearch author on:PubMed Google ScholarMaëva LabouyrieView author publicationsSearch author on:PubMed Google ScholarAlberto OrgiazziView author publicationsSearch author on:PubMed Google ScholarCristiano BallabioView author publicationsSearch author on:PubMed Google ScholarPanos PanagosView author publicationsSearch author on:PubMed Google ScholarArwyn JonesView author publicationsSearch author on:PubMed Google ScholarLeho TedersooView author publicationsSearch author on:PubMed Google ScholarMohammad BahramView author publicationsSearch author on:PubMed Google ScholarNico EisenhauerView author publicationsSearch author on:PubMed Google ScholarMarie SünnemannView author publicationsSearch author on:PubMed Google ScholarCarlos A. GuerraView author publicationsSearch author on:PubMed Google ScholarDongxue TaoView author publicationsSearch author on:PubMed Google ScholarIdo RogView author publicationsSearch author on:PubMed Google ScholarShuo JiaoView author publicationsSearch author on:PubMed Google ScholarStefano MocaliView author publicationsSearch author on:PubMed Google ScholarMatthias C. RilligView author publicationsSearch author on:PubMed Google ScholarAnika LehmannView author publicationsSearch author on:PubMed Google ScholarManuel Delgado-BaquerizoView author publicationsSearch author on:PubMed Google ScholarMarcel G. A. van der HeijdenView author publicationsSearch author on:PubMed Google ScholarContributionsF.R. contributed to conceptualization, methodology, formal analysis, investigation, data curation, writing – original draft, writing – review & editing, and visualization. M.L. contributed to conceptualization, methodology, formal analysis, investigation, writing – review & editing, and data curation. A.O. contributed to conceptualization, methodology, resources, writing – review & editing, and data curation. C.B. contributed to methodology and resources. P.P. contributed to conceptualization, methodology, resources, writing – review & editing, project administration, and data curation. A.J. contributed to conceptualization, methodology, resources, writing – review & editing, project administration, and data curation. L.T. and M.B. contributed to resources, writing – review & editing, and data curation. N.E., M.S., and C.A.G. contributed to resources and writing – review & editing. D.T. contributed to methodology. I.R. and S.J. contributed to writing – review & editing. S.M. contributed to project administration and funding acquisition. M.C.R. and A.L. contributed to resources and writing – review & editing. M.D.B. contributed to writing – review & editing. M.v.d.H. contributed to conceptualization, methodology, resources, writing – original draft, writing – review & editing, supervision, project administration, and funding acquisition.Corresponding authorsCorrespondence to
Ferran Romero or Marcel G. A. van der Heijden.Ethics declarations

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AbstractThe universality of the allometric model for describing the length–weight relationship in marine species has been questioned, particularly for some invertebrates such as sea urchins, clams, and barnacles. In such cases, nonparametric regression models may offer improved flexibility and capture specific patterns-such as inflection points in growth curves-not identified by standard parametric models. These features can support the identification of biologically meaningful thresholds relevant to fisheries, including size-dependent yield. Nonparametric quantile regression further enhances inference by characterizing variability across the entire distribution of body condition. Here, we assess the comparative performance of parametric and nonparametric regression models for the Atlantic surfclam, Spisula solidissima, using data collected from three regions along the U.S. Atlantic coast (Virginia, Delaware/Maryland, and New Jersey). First, we compare two mean regression approaches —a classic allometric model and a kernel-based nonparametric alternative— using a bootstrap-based procedure. Second, we apply quantile regression to both parametric and nonparametric frameworks to investigate size-dependent variation in growth patterns. Model selection for mean regressions was based on a hypothesis test contrasting the allometric model versus a general nonparametric alternative, while the quantile regressions were evaluated using a goodness-of-fit test derived from the cumulative sum of the gradient vector. Our results indicate that the allometric model provides a better fit in the mean regression context, while the nonparametric model proves more effective for quantile regression, particularly in detecting condition-dependent deviations and regional variability. Other long-lived marine bivalves, such as Arctica islandica and Mercenaria mercenaria, which show environmentally driven variation in growth and condition, may similarly benefit from modeling approaches that distinguish central from marginal populations.

Data availability

The datasets used and/or analysed during the current study available from the corresponding author on reasonable request. The code used to estimate the models and perform the goodness-of-fit testing procedure is publicly available on GitHub at: https://github.com/sestelo/surfclam_length_weight_code.
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Gorka Bidegain.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 articleBidegain, G., Sestelo, M., Luque, P.L. et al. Nonparametric quantile regression captures regional variability and scaling deviations in Atlantic surfclam length–weight relationships.
Sci Rep (2025). https://doi.org/10.1038/s41598-025-31936-9Download citationReceived: 03 July 2025Accepted: 05 December 2025Published: 14 December 2025DOI: https://doi.org/10.1038/s41598-025-31936-9Share 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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KeywordsBivalvesGrowth modellingBootstrap methodsNonparametric smoothing More

AbstractHigh-quality datasets are crucial for computer vision in endangered fish monitoring. Species similarities and dynamic environments pose challenges. The Pomeranian region, a key salmonid refuge with ongoing restoration efforts, necessitates robust monitoring to assess restoration success. To facilitate environmentally sustainable research, we introduce a dataset, Pomerania Fish (PomerFish), comprising two sub-datasets: PomerFishObj and PomerFishSeg, each with corresponding annotations. This dataset, compiled by salmonid experts, focuses on endangered Central European salmon populations. It was collected from 2015 to 2024 in the Pomerania Region, Central Europe, using a GoPro Hero 5 camera. The dataset comprises: (1) PomerFishObj, containing 14,989 high-resolution images with manually annotated bounding boxes and 3,273 negative samples (fish absence); and (2) PomerFishSeg, containing 1,115 images, including 1,038 with polygon-based segmentation masks and 77 negative samples. Characterized by high-resolution imagery, large data volume, and comprehensive habitat records beyond species, it enables training for detailed underwater observations and precise species growth estimations. This dataset supports targeted conservation and habitat management, providing crucial resources for research, species detection, and conservation practices.

Data availability

The code, model, and associated notebooks for data processing and baseline computation are available in our Zenodo repository: https://zenodo.org/records/17432128.42. The software toolkit, developed using Python 3.10 and the PyTorch 2.3 deep learning framework, offers the following functionalities, including data preprocessing, model training, model evaluation, deployment and inference, and visualization.
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Xiaohao Shi.Ethics declarations

Competing interest
The authors declare no competing interests.

Additional informationPublisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.Rights and permissions
Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
Reprints and permissionsAbout this articleCite this articleShi, X., Czerniawski, R., Tanwari, K. et al. Pomerania Fish: A dataset for fishes across Pomerania freshwater waterbodies in-situ environments.
Sci Data (2025). https://doi.org/10.1038/s41597-025-06393-8Download citationReceived: 24 March 2025Accepted: 28 November 2025Published: 13 December 2025DOI: https://doi.org/10.1038/s41597-025-06393-8Share 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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AbstractSpiders, a keystone predatory group for terrestrial ecosystem balance, have underexplored gut microbiotas. We collected 1090 spiders from 34 families in southwestern China, performing 16S rRNA sequencing to investigate their gut microbiota. Wandering and ambushing spiders exhibited higher α-diversity, while web-building spiders showed the lowest α-diversity with the highest endosymbiont infection rates. Gut microbiota diversity was significantly higher in Guizhou-region spiders than in Sichuan-region spiders. All spiders showed high amount of endosymbiont ASVs, which varied with foraging strategies and regions. Additionally, closer geographic distances between spiders were associated with more similar gut microbiota diversity levels. Environmental factor analysis preliminary revealed a positive correlation between precipitation and gut microbiota diversity, though its generalizability is limited by geographic sampling. Random processes were the primary drivers of spiders’ gut microbial community assembly. Our findings highlight that spider gut microbiota assembly is predominantly driven by stochastic processes but regulated by foraging strategies and geographic factors, providing a framework for understanding predator-microbe interactions in spiders.

Data availability

The raw sequencing reads from this study have been submitted to the China National GeneBank Database (CNP0007324; CNGBdb, https://db.cngb.org/) and Genome Sequence Archive database (PRJCA051897: CRA034046; GSA, https://ngdc.cncb.ac.cn/gsa/).
Code availability

The analysis code has been submitted to GitHub (https://github.com/WJiao95/16S-spider) and Zenodo85 (DOI: 10.5281/zenodo.17640349).
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Zhenxin Fan or Yucheng lin.Ethics declarations

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The authors declare no competing interests.

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Communications Biology thanks Marc Domènech, Evgeniia Propistsova, Hirokazu Toju, Jordan Cuff and the other anonymous reviewer(s) for their contribution to the peer review of this work. Primary handling editors: Hannes Schuler and Tobias Goris.

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Reprints and permissionsAbout this articleCite this articleWang, J., Wang, S., Chen, Q. et al. Foraging strategies and geographic factors jointly shape gut microbiota of spiders in the Sichuan and Guizhou regions of China.
Commun Biol (2025). https://doi.org/10.1038/s42003-025-09358-0Download citationReceived: 27 May 2025Accepted: 02 December 2025Published: 13 December 2025DOI: https://doi.org/10.1038/s42003-025-09358-0Share 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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AbstractIn this study, the spatial and temporal variations and distribution characteristics of the carbon dioxide (CO2) concentration on Hainan Island are analyzed using GOSAT L3 data from 2011 to 2024, and the effects of various factors impacting the CO2 concentration on Hainan Island are discussed. The results indicate that from 2011 to 2024, the CO2 concentration on Hainan Island showed an increasing trend, with a fast growth rate in the early period and a slow growth rate in recent years with the implementation of the dual-carbon strategy. The spatial distribution is affected by anthropogenic activities, topography, vegetation and solar radiation, and the overall CO2 concentration pattern is high in the north and low in the south. Human activities are the most important source of carbon on Hainan Island, vegetation is the most important carbon sink, and elements such as surface temperature, precipitation, and total solar radiation play roles in suppressing CO2. The CO2 concentration on Hainan Island is expected to continue to increase at a slow rate and may display a decreasing trend in the future.

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Download referencesFundingThis study is supported by the National Key Research and Development Program (2023YFC3008001);Natural science foundation of China(Grant No.42465006, Grant No.U21A6001).Hainan Provincial Natural Science Foundation of China(424QN364).Data availability statement.The Data used and during the current study are publicly available in repositories:1. GOSAT data is available at https://data2.gosat.nies.go.jp/index_en.html.2. EVI、PAR 、LST and GPP data are available at https://modis.gsfc.nasa.gov/.3. Meteorological data are available at https://cds.climate.copernicus.eu/datasets/reanalysis-era5-land-monthly-means.4. Population, GDP, and energy data are available at https://en.hainan.gov.cn/hainan/tjnj/list3.shtml.Author informationAuthors and AffiliationsHainan Institute of Meteorological Science, Haikou, 570203, ChinaQi Luo, Jing Han & Shaojun LiuSansha Marine Meteorology Field Experiment Station of CMA, Sansha, 573199, ChinaQi LuoSouth China Sea Marine Meteorology Hainan Observation and Research Station, Sansha, 573199, ChinaJing Han & Shaojun LiuAuthorsQi LuoView author publicationsSearch author on:PubMed Google ScholarJing HanView author publicationsSearch author on:PubMed Google ScholarShaojun LiuView author publicationsSearch author on:PubMed Google ScholarContributionsLuo wrote the main manuscript text and prepared the figures. Han dowload the satellite and reanlysis data. Liu provided technical guidance and revised the article.All authors reviewed the manuscript.Corresponding authorCorrespondence to
Qi Luo.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 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 articleLuo, Q., Han, J. & Liu, S. Characterization of spatial and temporal variations of CO2 concentration on tropical Island and analysis of influencing factors.
Sci Rep (2025). https://doi.org/10.1038/s41598-025-32647-xDownload citationReceived: 15 May 2025Accepted: 11 December 2025Published: 13 December 2025DOI: https://doi.org/10.1038/s41598-025-32647-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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KeywordsTropical islandCO2 concentrationInfluencing factorsVariation trend More

AbstractThe endosymbiont Wolbachia can both benefit host nutrition and manipulate host reproduction to its own advantage. However, the mechanisms of its nutritional benefits remain unclear. We show that Wolbachia enhances ovarian development in the small brown planthopper Laodelphax striatellus by boosting energy production. Wolbachia-infected females have increased fecundity, accelerated ovarian development, and prolonged oviposition. Enhanced activity of mitochondrial complex I is linked to increased ATP production and the expression of energy metabolism-related genes. We further identify that Wolbachia-synthesized riboflavin is crucial for ATP production and ovarian development. A riboflavin transporter, slc52a3a, positively correlates with Wolbachia density and is required for normal ovarian maturation. Our findings demonstrate that Wolbachia-produced riboflavin drives energy production and accelerates ovarian maturation, thus improving host fecundity. This research reveals insights into symbiont-host metabolic interactions and underscores the role of nutrient delivery in symbiosis.

Data availability

The RNA-seq data generated in this study have been deposited in the NCBI GenBank database under accession code PRJNA1195149, PRJNA1195150, and PRJNA1195152. Source data are provided with this paper.
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Reprints and permissionsAbout this articleCite this articleNiu, YD., Fan, QH., Wang, ZH. et al. Wolbachia enhances ovarian development in the rice planthopper Laodelphax striatellus through elevated energy production.
Nat Commun (2025). https://doi.org/10.1038/s41467-025-67660-1Download citationReceived: 02 March 2025Accepted: 05 December 2025Published: 13 December 2025DOI: https://doi.org/10.1038/s41467-025-67660-1Share 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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AbstractMosquitoes of the Anopheles, Aedes, and Culex genera are responsible for transmitting major vector-borne diseases. Malaria remains a significant public health concern in Odisha, primarily due to the state’s conducive environment for Anopheles mosquito breeding. This study, conducted between March 2021 and February 2023 across 11 traditionally hyper-endemic districts in southern Odisha, aimed to assess seasonal variations in Anopheles diversity, composition, and abundance. A total of 10,807 Anopheles mosquito’s species were collected manually indoors (house dwellings and cattle sheds) and outdoors (burrows, vegetation, tree holes, and culverts). Morphological identification revealed 18 Anopheles species. An. subpictus was the predominant species during the summer of 2021, with (328; 42.99%), and during the rainy season, with (1151; 46.60%), although its prevalence declined in subsequent years. An. culicifacies, a primary malaria vector, exhibited a consistent presence with (780; 31.58%) in the rainy season of 2021 and (798; 38.35%) in the rainy season of 2022. An. varuna remained scarce during summer and rainy seasons but peaked sharply in winter, with the highest prevalence in winter 2021–2022 (730; 35.56%) and winter 2022–2023 (485; 25.18%). Diversity indices (Shannon’s, Simpson’s, Pielou’s) and Correspondence Analysis identified Ganjam as the district with the highest species diversity (1.26–2.2). Seasonal variation had a statistically significant impact on species diversity (p < 0.001), surpassing the influence of district level factors. These findings show that seasonality strongly influences Anopheles populations and highlight the need for localized, evidence-based vector control. Monitoring of mosquito diversity is vital for shaping malaria interventions suited to Odisha’s transmission ecology.

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All data generated or analysed during this study are included in this published article.
AbbreviationsDDT:
Dichloro-diphenyl-trichloroethane
IVM:
Integrated vector management
H’:
Shannon’s diversity index
D:
Simpson’s index
J’:
Pielou index
CA:
Correspondence analysis
OWMS:
Odisha weather monitoring systems
LLINs:
Long-lasting insecticidal nets
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Download referencesAcknowledgementsWe extend sincere appreciation to Malaria Elimination Research Alliance (MERA) India for providing the essential funding that made this project possible. We are also deeply grateful to the technical staff for their invaluable assistance throughout the study. Furthermore, we acknowledge the support and cooperation of the inhabitants of all sampling sites and the dedicated volunteers whose significant contributions during fieldwork were indispensable. This research would not have been possible without their collective efforts.Author informationAuthor notesThese authors contributed equally: Muhammed Mustafa Baig and Divya Teja Koppula.Authors and AffiliationsICMR-Vector Control Research Centre Field Unit, Near Hati lane, Koraput, Odisha, 764 020, IndiaMuhammed Mustafa Baig, Dilip Kumar Panigrahi, Dolly Choudhary, Premalatha Acharya & Manoj PatnaikICMR-Vector Control Research Centre, Medical Complex, Indira Nagar, Puducherry, IndiaDivya Teja Koppula, B. Vijayakumar, K. Gunasekaran, Ashwani Kumar, Manju Rahi & A. N. ShriramAuthorsMuhammed Mustafa BaigView author publicationsSearch author on:PubMed Google ScholarDivya Teja KoppulaView author publicationsSearch author on:PubMed Google ScholarDilip Kumar PanigrahiView author publicationsSearch author on:PubMed Google ScholarB. VijayakumarView author publicationsSearch author on:PubMed Google ScholarDolly ChoudharyView author publicationsSearch author on:PubMed Google ScholarPremalatha AcharyaView author publicationsSearch author on:PubMed Google ScholarManoj PatnaikView author publicationsSearch author on:PubMed Google ScholarK. GunasekaranView author publicationsSearch author on:PubMed Google ScholarAshwani KumarView author publicationsSearch author on:PubMed Google ScholarManju RahiView author publicationsSearch author on:PubMed Google ScholarA. N. ShriramView author publicationsSearch author on:PubMed Google ScholarContributionsConceptualization and design: ANS, and AK.Writing – original draft preparation: DTK, MMB and ANS; Conducting the field study and supervision: DKP, DC, PA, MP and MMBWriting – review and editing with inputs from all other authors: ANS, AK, DTK and MR; Acquisition of data, analysis and interpretation: VK, DTK and ANSRevising it critically for intellectual content and the final approval of the version to be published: AK, ANS and MR. All authors provided critical feedback. All authors have read and agreed to the final version of the manuscript and to be accountable for all aspects of the work.Corresponding authorCorrespondence to
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Reprints and permissionsAbout this articleCite this articleBaig, M.M., Koppula, D.T., Panigrahi, D.K. et al. Seasonal dynamics and species diversity of Anopheles mosquitoes in malaria endemic districts of Southern Odisha India.
Sci Rep (2025). https://doi.org/10.1038/s41598-025-28997-1Download citationReceived: 09 June 2025Accepted: 13 November 2025Published: 13 December 2025DOI: https://doi.org/10.1038/s41598-025-28997-1Share 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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KeywordsMalariaOdishaShannon’sSimpson’sAnd pielou’s
Anopheles diversity More