Ecology

AbstractPhytoplankton blooms emerge from the interplay between nutrient availability, biomass growth, and inhibitory by-products such as toxins or exudates. Here, we develop a mechanistic nutrient–phytoplankton–by-product model that couples Beddington–DeAngelis nutrient uptake, by-product-mediated inhibition, and nutrient-dependent detoxification. Analytical results demonstrate that the system remains biologically feasible and bounded, and that a threshold condition governs bloom initiation. Linear stability and bifurcation analyses reveal how detoxification delays can trigger oscillatory bloom behaviour. Across ecologically realistic parameter regimes, the system tends to a stable coexistence state—either directly or through damped oscillations—rather than exhibiting repeated bloom–crash cycles. Global sensitivity analysis (PRCC and Sobol indices) highlights by-product production, inhibition strength, detoxification rate, toxin-linked mortality, and saturation effects as dominant regulators of stability and damping time. Introducing an explicit ecological delay exposes a critical threshold at which a Hopf bifurcation arises, converting the stable equilibrium into sustained oscillations. Numerical simulations confirm the transversality condition and indicate a supercritical onset. Collectively, these results provide a quantitative diagnostic for distinguishing transient from sustained bloom oscillations and identify measurable ecological processes—particularly detoxification and delayed feedback—that govern transitions between stable and oscillatory regimes.

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Data availability

The datasets used and/or analysed during the current study available from the corresponding author on reasonable request.
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Download referencesFundingOpen access funding provided by Manipal Academy of Higher Education, Manipal. This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.Author informationAuthors and AffiliationsDepartment of Mathematics, Poornima University, Jaipur, 303905, Rajasthan, IndiaRandhir Singh BaghelDepartment of Physics, Poornima University, Jaipur, 303905, Rajasthan, IndiaShrikant VermaDepartment of Mechatronics, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, 576104, IndiaNarendra KhatriAuthorsRandhir Singh BaghelView author publicationsSearch author on:PubMed Google ScholarShrikant VermaView author publicationsSearch author on:PubMed Google ScholarNarendra KhatriView author publicationsSearch author on:PubMed Google ScholarContributionsR.S.B. and S.V. conceptualized and designed the study. R.S.B. conducted the experimental work and data collection. S.V. performed the data analysis and interpretation. N.K. contributed to reviewing, editing, and redrafting the manuscript and handled the correspondence. All authors reviewed and approved the final version of the manuscript.Corresponding authorCorrespondence to
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Reprints and permissionsAbout this articleCite this articleBaghel, R.S., Verma, S. & Khatri, N. Delayed dynamics and detoxification in nutrient-phytoplankto-by-product systems: mechanisms driving bloom stability and oscillations.
Sci Rep (2025). https://doi.org/10.1038/s41598-025-32146-zDownload citationReceived: 15 September 2025Accepted: 08 December 2025Published: 21 December 2025DOI: https://doi.org/10.1038/s41598-025-32146-zShare 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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KeywordsPhytoplankton-nutrient dynamicsBeddington-DeAngelis uptakeBy-product interference (allelopathy)Stability and Hopf bifurcationGlobal sensitivity analysis (Sobol, PRCC)Delay More

AbstractClimate-induced stressors pose significant threats to fish growth, survival, and ecological stability. Identifying reliable molecular biomarkers is crucial for improving stress management and acclimation strategies. This study employed a comprehensive transcriptomic analysis to examine stress responses in rainbow trout (Oncorhynchus mykiss) exposed to five distinct environmental stressors—high and low temperatures, crowding, salinity, and low water quality (characterized by reduced dissolved oxygen and elevated CO2)—over six hours. A total of 21,580 differentially expressed transcripts (DETs) were identified, including 16,959 unique DETs. Heat stress and salinity induced the most pronounced transcriptomic responses, with most DETs being stressor-specific, highlighting distinct physiological acclimation mechanisms. Only 39 DETs were consistently regulated across all stress conditions. Key DETs associated with heat stress were further analyzed using machine learning models to evaluate their predictive potential in distinguishing control and heat-stressed fish from natural Redband trout populations. The logistic model tree (LMT) classifier demonstrated the highest accuracy with a set of 234 DETs. When the dataset was reduced to 50 or 2 DETs, the Random Forest model achieved optimal classification, consistently identifying two heat shock protein transcripts, hsp47 and hspa4l, as primary predictors across both short- and long-term stress responses. In contrast, core DETs shared across stressors exhibited limited predictive power, achieving only 52.78% classification accuracy. These findings underscore the specificity of molecular signatures to individual stressors and highlight the potential of transcriptomic biomarkers for monitoring climate-induced stress in fish populations. The study recommends the integration of these biomarkers into selective breeding programs and conservation strategies to enhance fish resilience and welfare in the face of environmental change.

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Data availability

The raw sequencing data from three strains of Redband trout were downloaded from the NCBI Short Read Archive (SRA) under accession number PRJNA233945. The rainbow trout genome annotation was obtained from NCBI (GCA_013265735.3, https://www.ncbi.nlm.nih.gov/assembly/ GCF_013265735.2/). Additionally, RNA-seq datasets from fish exposed to five different stress conditions were retrieved from the NCBI Sequence Read Archive (SRA) using the accession number SRP070774.
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Download referencesFundingNothing to report.Author informationAuthors and AffiliationsDepartment of Animal and Avian Sciences, University of Maryland, College Park, MD, 20742-231, USAAli Ali, Youssef Ali, Guglielmo Raymo & Mohamed SalemCollege of Computer, Mathematical, and Natural Sciences, University of Maryland, College Park, MD, 20742-231, USAAsutosh DaleiAuthorsAli AliView author publicationsSearch author on:PubMed Google ScholarYoussef AliView author publicationsSearch author on:PubMed Google ScholarGuglielmo RaymoView author publicationsSearch author on:PubMed Google ScholarAsutosh DaleiView author publicationsSearch author on:PubMed Google ScholarMohamed SalemView author publicationsSearch author on:PubMed Google ScholarContributionsY.A., A.A., and M.S. Conceived the study. Y.A., A.A., G.R., A.D. and M.S. analyzed the data. Y.A. Drafted the manuscript. All authors read and approved the final manuscript. Y.A. and A.A. contributed equally.Corresponding authorCorrespondence to
Mohamed Salem.Ethics declarations

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Reprints and permissionsAbout this articleCite this articleAli, A., Ali, Y., Raymo, G. et al. Molecular signatures and machine learning driven stress biomarkers for rainbow trout aquaculture and climate adaptation.
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KeywordsEnvironmental stressorsHeat stressFish welfarePredictive modelingAquaculture stress breeding More

AbstractThe deep-sea floor encompasses more than half of the surface of our planet, yet the extent and distribution of deep-sea biodiversity and its contribution to large biogeochemical cycles remain poorly understood. This knowledge gap stems from several factors, including sampling issues, the magnitude of the work required for morphological inventories, and the difficulty of integrating results from disparate local studies. The application of meta-omics to environmental DNA now makes it possible to assemble interoperable datasets at different spatial scales to move towards a global assessment of deep-sea biodiversity. We present a large-scale dataset on deep-sea biodiversity, with data and metadata openly accessible at ENA and Zenodo. The resource was generated using standardized protocols developed according to FAIR principles, covering fieldwork through bioinformatic analysis, within “Pourquoi Pas les Abysses?” and eDNAbyss projects. Together with information ensuring reproducibility, this dataset —combining metagenomics, metabarcoding across the Tree of Life and capture-by-hybridization— contributes to the international concerted effort to achieve a holistic view of the biodiversity in the largest biome on Earth.

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Data availability

The global dataset has been deposited in European Nucleotide Archive (ENA) as project PRJEB 39225 (https://www.ebi.ac.uk/ena/browser/view/PRJEB39225), with metadata available on Zenodo (https://zenodo.org/records/6815677).
Code availability

1. Time Analysis software: https://www.illumina.com/search.html?filter=support&q=RTA%20download&p=1.
2. Bcl2fastq Conversion: https://support.illumina.com/downloads/bcl2fastq-conversion-software-v2-20.html
3. Cutadapt, https://github.com/marcelm/cutadapt/releases/tag/v1.18
4. Fastx_clean software, http://www.genoscope.cns.fr/fastxtend
5. FASTX-Toolkit, http://hannonlab.cshl.edu/fastx_toolkit/index.html
6. SortMeRNA v2.1, https://github.com/biocore/sortmerna
7. fastx_estimate_duplicate software, http://www.genoscope.cns.fr/fastxtend
8. fastx_mergepairs software, http://www.genoscope.cns.fr/fastxtend
9. Usearch, https://www.drive5.com/usearch/
10. Trimmomatic: https://github.com/usadellab/Trimmomatic
11. Decontam: https://github.com/benjjneb/decontam
12. Prinseq: https://github.com/uwb-linux/prinseq
13. Qiime2 feature classifier: https://github.com/qiime2/q2-feature-classifier
14. FastQC: https://github.com/s-andrews/FastQC
15. BBTools: https://github.com/kbaseapps/BBTools
16. MultiQC: https://github.com/MultiQC/MultiQC
17. MetaRib: https://github.com/yxxue/MetaRib
18. EMIRGE: https://github.com/csmiller/EMIRGE
19. VSearch: https://github.com/torognes/vsearch
20. 1IDBA_UD: https://github.com/1928d/idba_ud
21. CAP3: https://faculty.sites.iastate.edu/xqhuang/cap3-and-pcap-sequence-and-genome-assemblyprograms
22. eDNAbyss pipeline: https://gitlab.ifremer.fr/abyss-project/
23. MUMU algorithm: https://github.com/frederic-mahe/mumu
24. bbmap: https://sourceforge.net/projects/bbmap/
26. RiboTaxa: https://github.com/oschakoory/RiboTaxa
27. eDNAbyss pipeline(s): https://gitlab.ifremer.fr/abyss-project/),
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Download referencesAcknowledgementsWe express special gratitude to the scientific direction and scientific committee of the “Pourquoi Pas les Abysses?” project and to the board of the French Oceanographic Fleet for allowing unusual use of boat time (including transits) through the AMIGO series and to the mission chiefs of all the crews who kindly sampled for the project. This work was supported by Ifremer during the development of prototypes and protocols in “Pourquoi Pas les Abysses?” and by Genoscope, the Commissariat à l’Energie Atomique et aux Energies Alternatives (CEA) and France Génomique (ANR-10-INBS-09) for high-throughput sequencing in eDNAbyss (AP2016–228 France Génomique). We also thank the HADES-ERC Advanced grant (#669947) and the EU Atlas project (678760) and benefited from State aid managed by the National Research Agency under France 2030 for the LIFEDEEPER project (ANR-22-POCE-0007) and the ANR Cerberus (ANR-17-CE02-0003) for samples gathered during the associated cruises and the project MarEEE (MUSE, Montpellier, ANR-16-IDEX-0006) for the improvement of the original bioinformatic pipeline. We warmly acknowledge all the crews, mission chiefs and colleagues who contributed gathering this widespread sampling collection: Covadonga Orejas, Martin Ludvigsen and Eva Ramirez-Llodra, Jean-Paul Justiniano, Yves Fouquet and Ewan Pelleter, Ewen Raugel, Wayne Crawford, Cécile Guieu, Sophie Bonnet, Sophie Arnaud-Haond, François Bonhomme, Pierre-Marie Sarradin, Carlos Duarte, Franck Wenzhoefer, Mathilde Cannat, Norbert Franck, Marie-Anne Cambon, Stéphane Hourdez and Didier Jollivet. We would like gratefully acknowledge the entire Genoscope technical team: Julie Batisse, Odette Beluche, Isabelle Bordelais, Elodie Brun, Maria Dubois, Corinne Dumont, Zineb El Hajji, Barbara Estrada, Thomas Guérin, Chadia Hamon, Sandrine Lebled, Patricia Lenoble and Marine Lepretre, Claudine Louesse, Ghislaine Magdelenat, Eric Mahieu, Claire Milani, Sophie Oztas, Emilie Payen, Emmanuelle Petit, Muriel Ronsin and Benoît Vacherie, for their invaluable work in producing the data. We thank the editorial team and the referees for the improvements suggested to previous versions of this manuscript.Author informationAuthor notesBabett GüntherPresent address: GEOMAR, Helmholtz Centre for Ocean Research Kiel, Wischhofstraße 1-3, 24148, Kiel, GermanyThese authors contributed equally: Julie Poulain, Caroline BelserThe Genoscope technical team members are listed in the Acknowledgements section.Authors and AffiliationsMARBEC, Univ Montpellier, IFREMER, IRD, CNRS, Sète, FranceSophie Arnaud-Haond, Cathy Liautard-Haag, Miriam I. Brandt, Annaëlle Caillarec-Joly, Florence Cornette, Christine Felix, Babett Günther, Adrien Tran Lu Y & Sandrine VazUniv Brest, Ifremer, BEEP, F-29280, Plouzané, FranceBlandine Trouche, Karine Alain, Johanne Aubé, Marie-Anne Cambon, Valérie Cueff-Gauchard, Sandra Fuchs, Françoise Lesongeur, Loïs Maignien, Marjolaine Matabos, Emmanuelle Omnes, Florence Pradillon, Jozée Sarrazin & Daniela ZeppiliISEM, Univ Montpellier, CNRS, IRD, Montpellier, FranceFrançois Bonhomme & Frédérique ViardNORCE, Norwegian Research Centre AS, Climate & Environment department, Bergen, NorwayMiriam I. BrandtIfremer, IRSI, SeBiMER Service de Bioinformatique de l’Ifremer, Plouzané, FrancePatrick DurandSorbonne Université, CNRS, Station Biologique de Roscoff, UMR 7144 Adaptation and diversity in the marine environment, Place Georges Teissier, Roscoff, FranceColomban de Vargas, Didier Jollivet & Anne-Sophie Le PortResearch Federation for the Study of Global Ocean Systems Ecology and Evolution, FR2022 GOSEE, 3 rue Michel-Ange, Paris, 75016, FranceColomban de Vargas & Nicolas HenryCNRS, Sorbonne Université, FR2424, ABiMS, Station Biologique de Roscoff, Roscoff, 29680, FranceNicolas HenryUMR 8222 LECOB CNRS-Sorbonne Université, Observatoire Océanologique de Banyuls, Avenue du Fontaulé, 66650, Banyuls-sur-mer, FranceStéphane HourdezUniversité Clermont Auvergne, INRAE, UMR 0454 MEDIS, Clermont-Ferrand, FranceSophie Comtet-Marre & Pierre PeyretHadal & Nordcee, Department of Biology, University of Southern Denmark, Odense, DenmarkClemens SchaubergerInstituto Milenio de Oceanografía (IMO), Universidad de Concepción, Concepción, ChileOsvaldo UlloaGénomique Métabolique, Genoscope, Institut François Jacob, CEA, CNRS, Univ Evry, Université Paris-Saclay, Evry, FranceFrédérick Gavory, Jean-Marc Aury, Patrick Wincker, Julie Poulain & Caroline BelserGenoscope, Institut François Jacob, Commissariat à l’Energie Atomique (CEA), Université Paris-Saclay, 2 Rue Gaston Crémieux, Evry, FranceShahinaz Gaz, Julie Guy, E’Krame Jacoby, Pedro H. Oliveira & Gaëlle SamsonEuropean Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SD, UKStéphane PesantAuthorsSophie Arnaud-HaondView author publicationsSearch author on:PubMed Google ScholarBlandine TroucheView author publicationsSearch author on:PubMed Google ScholarCathy Liautard-HaagView author publicationsSearch author on:PubMed Google ScholarKarine AlainView author publicationsSearch author on:PubMed Google ScholarJohanne AubéView author publicationsSearch author on:PubMed Google ScholarFrançois BonhommeView author publicationsSearch author on:PubMed Google ScholarMiriam I. BrandtView author publicationsSearch author on:PubMed Google ScholarAnnaëlle Caillarec-JolyView author publicationsSearch author on:PubMed Google ScholarMarie-Anne CambonView author publicationsSearch author on:PubMed Google ScholarFlorence CornetteView author publicationsSearch author on:PubMed Google ScholarValérie Cueff-GauchardView author publicationsSearch author on:PubMed Google ScholarPatrick DurandView author publicationsSearch author on:PubMed Google ScholarColomban de VargasView author publicationsSearch author on:PubMed Google ScholarChristine FelixView author publicationsSearch author on:PubMed Google ScholarSandra FuchsView author publicationsSearch author on:PubMed Google ScholarBabett GüntherView author publicationsSearch author on:PubMed Google ScholarNicolas HenryView author publicationsSearch author on:PubMed Google ScholarStéphane HourdezView author publicationsSearch author on:PubMed Google ScholarDidier JollivetView author publicationsSearch author on:PubMed Google ScholarAnne-Sophie Le PortView author publicationsSearch author on:PubMed Google ScholarFrançoise LesongeurView author publicationsSearch author on:PubMed Google ScholarLoïs MaignienView author publicationsSearch author on:PubMed Google ScholarSophie Comtet-MarreView author publicationsSearch author on:PubMed Google ScholarMarjolaine MatabosView author publicationsSearch author on:PubMed Google ScholarEmmanuelle OmnesView author publicationsSearch author on:PubMed Google ScholarPierre PeyretView author publicationsSearch author on:PubMed Google ScholarFlorence PradillonView author publicationsSearch author on:PubMed Google ScholarJozée SarrazinView author publicationsSearch author on:PubMed Google ScholarClemens SchaubergerView author publicationsSearch author on:PubMed Google ScholarAdrien Tran Lu YView author publicationsSearch author on:PubMed Google ScholarOsvaldo UlloaView author publicationsSearch author on:PubMed Google ScholarSandrine VazView author publicationsSearch author on:PubMed Google ScholarDaniela ZeppiliView author publicationsSearch author on:PubMed Google ScholarFrédérique ViardView author publicationsSearch author on:PubMed Google ScholarFrédérick GavoryView author publicationsSearch author on:PubMed Google ScholarShahinaz GazView author publicationsSearch author on:PubMed Google ScholarJulie GuyView author publicationsSearch author on:PubMed Google ScholarE’Krame JacobyView author publicationsSearch author on:PubMed Google ScholarPedro H. OliveiraView author publicationsSearch author on:PubMed Google ScholarGaëlle SamsonView author publicationsSearch author on:PubMed Google ScholarJean-Marc AuryView author publicationsSearch author on:PubMed Google ScholarPatrick WinckerView author publicationsSearch author on:PubMed Google ScholarStéphane PesantView author publicationsSearch author on:PubMed Google ScholarJulie PoulainView author publicationsSearch author on:PubMed Google ScholarCaroline BelserView author publicationsSearch author on:PubMed Google ScholarConsortiaGenoscope Technical TeamContributionsS.A.H., C.B., J.P., S.C.M. and F.P. wrote the manuscript with the help of F.V., S.H. and M.M. All coauthors reviewed the manuscript. S.A.H., F.P., J.S., C.dV., J.P. and P.W. conceptualized the project. S.A.H. and P.W. obtained funding and administrated the project. B.T., C.L.H., J.A., M.I.B., M.C., S.F., V.C.G., D.J., A.S.L., F.P., J.S., P.M.S., C.S., M.C., A.T.L., S.V., F.B., D.Z., O.U. and J.P., as well as all mission chiefs, contributed to the collection of the environmental samples. B.T., C.L.H., K.A., J.A., M.I.B., F.C., V.C.G., B.G., C.F., S.F., F.L., E.O., G.T.T. and S.A.H. performed the DNA extractions. J.P., C.B., M.I.B. and C.L.H. developed the amplicon sequencing protocol, and S.C.M. and P.P. developed the C.B.H. protocol. J.P., K.L., F.G., P.H.O. and all the Genoscope technical teams were involved in the library preparations and sequencing tasks for metagenomics and metabarcoding, and data curation, S.C.M. and PP for the libraries and sequencing for C.B.H. C.B. and J.M.A. developed Data validation and visualization softwares. S.A.H., B.T., M.V., J.M.A., J.P. and C.B. contributed to Validation. M.I.B., A.C.J., B.G., B.T., L.M., P.D., S.A.H., N.H., K.A., F.V., S.C.M. and P.P. developed the bioinformatics pipelines and/or performed the data analysis. S.P., C.B., S.A.H. and S.V. provided the metadata and data. C.B.H., S.G., J.G., G.S., E.K.J., S.P., S.C.M. and P.D. managed the data to be transmitted to a public repository.Corresponding authorsCorrespondence to
Sophie Arnaud-Haond or Julie Poulain.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 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 articleArnaud-Haond, S., Trouche, B., Liautard-Haag, C. et al. Omics exploration of deep-sea biodiversity: data from the “Pourquoi Pas les Abysses?” and eDNAbyss projects.
Sci Data (2025). https://doi.org/10.1038/s41597-025-06009-1Download citationReceived: 17 May 2024Accepted: 22 September 2025Published: 20 December 2025DOI: https://doi.org/10.1038/s41597-025-06009-1Share 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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AbstractWild birds are key natural reservoirs and play a central role in the global spread of avian influenza viruses (AIVs). However, the absence of a standardized global list of wild bird hosts has limited comprehensive AIV risk monitoring and assessment within the One Health framework. Here, we generate a taxonomically harmonized dataset of AIV wild bird hosts, derived from 23,358 viral isolates of wild bird origin reported in the GISAID EpiFluTM database from 1973 to 2023. Host names were systematically extracted, validated, and harmonized to resolve reporting inconsistencies and unify taxonomy across records. The dataset comprises 394 wild bird species spanning 26 orders, with Anseriformes and Charadriiformes representing a substantial share of host diversity. By clarifying the global spectrum of wild bird hosts for AIVs, this dataset provides a foundation for host identification, phylogenetic annotation, and ecological trait-based analysis. Structured in machine-readable formats, it enables reproducible and large-scale, species-level studies spanning virology, epidemiology, and biodiversity.

Data availability

The dataset associated with this study is publicly avaiable on Zenodo: https://zenodo.org/records/15970977. All data are provided in CSV format. Further details regarding the dataset structure and variable descriptions are avaiable in the Data Records section.
Code availability

The code for the study can be accessed at https://github.com/gogofxd/InfluenzaWildBirdHost.
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Download referencesAcknowledgementsThis work was supported by the National Key Research and Development Program of China (grant number: 2022YFF0802400) and the National Natural Science Foundation of China (grant number: 81961128002). We gratefully acknowledge all the data contributors, i.e., the authors and their originating laboratories responsible for obtaining the specimens and their submitting laboratories for generating the genetic sequences and metadata and sharing via the GISAID Initiative, on which this research is based. We acknowledge all the members of the AviList Core Team for collecting and providing the bird data. We also acknowledge Yue Wu for help with the bird taxonomic assignment.Author informationAuthor notesThese authors contributed equally: Fanshu Du, Qiang Zhang.Authors and AffiliationsNational Key Laboratory of Veterinary Public Health and Safety, Key Laboratory for Prevention and Control of Avian Influenza and Other Major Poultry Diseases, Ministry of Agriculture and Rural Affairs, College of Veterinary Medicine, China Agricultural University, Beijing, 100193, P. R. ChinaFanshu Du, Lu Wang, Honglei Sun, Yipeng Sun, Jinhua Liu & Juan PuInstitute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, P. R. ChinaQiang Zhang & Zhichao LiUniversity of Chinese Academy of Sciences, Beijing, 100101, P. R. ChinaQiang Zhang & Zhichao LiThird Institute of Oceanography, Ministry of Natural Resources, PRC, Daxue Road No. 178, Siming District, Xiamen, Fujian, 361005, P. R. ChinaYachang ChengState Key Laboratory of Biocontrol, School of Ecology, Sun Yat-Sen University, Shenzhen, 518107, P. R. ChinaYang LiuKey Laboratory for Biodiversity Science and Ecological Engineering, Demonstration Center for Experimental Life Sciences & Biotechnology Education, College of Life Sciences, Beijing Normal University, Beijing, 100875, P. R. ChinaWeipan LeiAuthorsFanshu DuView author publicationsSearch author on:PubMed Google ScholarQiang ZhangView author publicationsSearch author on:PubMed Google ScholarYachang ChengView author publicationsSearch author on:PubMed Google ScholarYang LiuView author publicationsSearch author on:PubMed Google ScholarWeipan LeiView author publicationsSearch author on:PubMed Google ScholarLu WangView author publicationsSearch author on:PubMed Google ScholarHonglei SunView author publicationsSearch author on:PubMed Google ScholarYipeng SunView author publicationsSearch author on:PubMed Google ScholarJinhua LiuView author publicationsSearch author on:PubMed Google ScholarZhichao LiView author publicationsSearch author on:PubMed Google ScholarJuan PuView author publicationsSearch author on:PubMed Google ScholarContributionsF.D. and Q.Z. jointly conceived and designed the study. F.D. led the data extraction, performed the core analyses, interpreted the results, and drafted the initial manuscript. Q.Z. independently conducted a parallel round of host classification and contributed to the methodological design and critical revision of the manuscript. Y.C., Y.L., and W.L. participated in species-level data validation and cross-verification of host taxonomy. L.W. contributed to background research and assisted in data integration. Z.L. and J.P. supervised the overall research process, provided guidance on result interpretation, and revised the manuscript for important intellectual content. All the authors reviewed and approved the final version of the manuscript.Corresponding authorsCorrespondence to
Zhichao Li or Juan Pu.Ethics declarations

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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 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 articleDu, F., Zhang, Q., Cheng, Y. et al. A taxonomically harmonized global dataset of wild bird hosts for avian influenza virus surveillance.
Sci Data (2025). https://doi.org/10.1038/s41597-025-06451-1Download citationReceived: 21 July 2025Accepted: 09 December 2025Published: 19 December 2025DOI: https://doi.org/10.1038/s41597-025-06451-1Share 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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AbstractStudying the diet of penguins is essential for understanding their role in marine ecosystems, evaluating their responses to environmental changes, and informing conservation strategies. This manuscript presents the ‘Penguin Diet Dataset-v2’, a comprehensive compilation of dietary data for 17 out of 19 currently recorded penguin species, obtained through a systematic review of the scientific literature. Spanning the 1980–2019 period and covering 149 global locations, the dataset includes 2.749 dietary entries with both qualitative and quantitative information on prey types. The dataset is enriched with metadata such as geographic locations, time periods, seasons, and life stages, providing insights into species-specific dietary patterns and ecological trends. Key prey groups include fish from the class Teleostei, crustaceans from the class Malacostraca, and mollusks from the class Cephalopoda. The Adélie Penguin and the Magellanic Penguin are the most extensively studied species. This dataset serves as a valuable resource for advancing research on penguin feeding ecology and addressing gaps in knowledge on understudied species, regions, and time periods.

Data availability

The Penguin Diet Dataset-v2, now publicly available on Digital.CSIC (https://doi.org/10.20350/digitalCSIC/17565, URL: http://hdl.handle.net/10261/399953), brings together dietary data for 17 of the 19 known penguin species. Compiled in an accessible.xlsx format, the dataset includes detailed predator–prey interactions, along with essential metadata to support interpretation and reuse. Users will find clearly organized sheets for diet entries, variable definitions, species and prey codes, and geographic coordinates.
Code availability

No computational code is associated with this dataset, as all information has been exclusively sourced from available literature.
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Download referencesAcknowledgementsWe would like to thank Helena Buil for her invaluable assistance with literature screening and data extraction. We also sincerely thank the Handling Editor and the two anonymous reviewers for their insightful and constructive feedback on the manuscript and accompanying dataset. This work is supported by the Spanish government through the following projects: SOSPEN (Spanish National Plan for Scientific and Technical Research and Innovation, 2021, PID2021-124831OA-I00), SEASentinels (Spanish National Plan for Scientific and Technical Research and Innovation, 2023, CNS2022-135631), and ProOceans (Spanish National Plan for Scientific and Technical Research and Innovation, 2020, PID2020-118097RB-I00). Additionally, this research is part of the Integrated Marine Ecosystem Assessments (iMARES) research group, funded by the Agència de Gestió d’Ajuts Universitaris i de Recerca (Generalitat de Catalunya), Grant No. 2021 SGR 00435. MG was supported by the FPI-SO fellowship (PRE2022-101875). This study is a contribution to the ICM-TEC (Trophic Ecology and Connectivity Scientific-Technical service of the Institut de Ciències del Mar CSIC; https://www.icm.csic.es/en/service/trophic-ecology-and-connectivity).Author informationAuthors and AffiliationsInstitut de Ciències del Mar, Recursos Marins Renovables, Barcelona, SpainFrancisco Ramírez, Claudia Aparicio-Estalella, Míriam Gimeno & Marta CollAuthorsFrancisco RamírezView author publicationsSearch author on:PubMed Google ScholarClaudia Aparicio-EstalellaView author publicationsSearch author on:PubMed Google ScholarMíriam GimenoView author publicationsSearch author on:PubMed Google ScholarMarta CollView author publicationsSearch author on:PubMed Google ScholarContributionsF.R.: conceptualization, methodology, investigation, writing – original draft, supervision, project administration, funding acquisition; C.A.: methodology, investigation, data curation, writing – review & editing; M.G.: visualization, writing review & editing; M.C.: conceptualization, methodology, supervision, writing – review & editing, funding acquisition.Corresponding authorCorrespondence to
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Sci Data (2025). https://doi.org/10.1038/s41597-025-06458-8Download citationReceived: 19 March 2025Accepted: 11 December 2025Published: 19 December 2025DOI: https://doi.org/10.1038/s41597-025-06458-8Share 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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AbstractEnvironmental changes including urbanization significantly influence the spatial distribution and the ecology of mosquito vectors, such as Aedes albopictus and Aedes aegypti, which are responsible of the transmitting of dengue, chikungunya, and Zika arboviruses. While studies often focus on breeding site typology, the physicochemical characteristics of these habitats remain underexplored, especially in sub-Saharan Africa. This study investigates (i) the larval ecology of Ae. albopictus and Ae. aegypti in Franceville, an equatorial forest region undergoing urbanization, south-eastern Gabon, and (ii) emphasizing habitat typology and the physicochemical attributes influencing their proliferation. Field larval surveys were conducted across central, intermediate, and peripheral settings of the town, documenting the diversity of larval habitats and their physical features (nature, substrate material and size) and the mosquito species recovered. Water samples were analysed to determine physicochemical properties including pH, salinity, conductivity, and the presence of organic matter. The results reveal significant physicochemical heterogeneity across settings, with central urban areas more characterised by plastic (12.9%) and rubber (10.7%) breeding sites while peripheral areas were dominated by cement microhabitats (15.7%). Notably, the findings have clarified the ecological niche of these two species (Ae. albopictus and Ae. aegypti), revealing a preference for anthropogenic water bodies composed of rubber, plastic, or cement materials, with small to medium surface areas (< 1,250 cm2) and low to medium salinity levels (< 0.4 ppt). These findings underscore the importance of integrating physicochemical analyses into vector ecology studies to enhance our understanding of vector proliferation in rapidly urbanizing regions. By addressing this knowledge gap, the study provides critical insights to inform public health strategies and urban planning, offering a foundation for targeted vector control interventions.

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All data generated or analysed during this study are included in this published article.
AbbreviationsPCA:
Principal component analysis
GLM:
Generalized linear model
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Download referencesAcknowledgementsWe would like to thank the institutions that helped us carry out this study, in particular the Interdisciplinary Centre for Medical Research of Franceville (CIRMF) and the Masuku University of Science and Technology (USTM), for the technical support they provided. We would particularly like to thank the Biology Department of the Faculty of Science of the USTM, and the staff of the Health Ecology Research Unit of the CIRMF and the Zoology and Entomology Department of the UFS, who welcomed us.FundingThis study has been conducted with the financial support of the European Union (Grant no. ARISE-PP-FA-072 to JON), through the African Research Initiative for Scientific Excellence (ARISE), pilot program. ARISE is implemented by the African Academy of Sciences with support from the European Commission and the African Union Commission. This study also benefited from the internal support of the University of the Free State, South Africa (to PVO), for English editing services in addition to the salary support provided to the corresponding author by the University of Science and Technology of Masuku and the Interdisciplinary Centre for Medical Research, Gabon. We benefited from the support GDRI-GRAVIR network (led by CP) in conceptualizing the study. The contents of this document are the sole responsibility of the authors and can under no circumstances be regarded as reflecting the position of the European Union, the African Academy of Sciences, the African Union Commission, or the institutions to which the authors are affiliated. The funders played no role in the design of the study, the collection and analysis of the data, the decision to publish or the preparation of the manuscript.Author informationAuthors and AffiliationsLab-MC, Département de Biologie, Faculté Des Sciences de L, Université Des Sciences Et Techniques de Masuku (USTM), BP 901, Franceville, GabonJudicaël Obame-Nkoghe, Faël Moudoumi Kondji, Brad Ghaven Niangui & Landry Erik MomboEcotoxicology Research Laboratory, Department of Zoology and Entomology, Faculty of Natural and Agricultural Sciences, University of the Free State, Private Bag x13, Phuthaditjhaba, 9866, Republic of South AfricaJudicaël Obame-Nkoghe & Patricks Voua OtomoAgence Nationale Des Parc Nationaux, Libreville, GabonRicardo Ewak ObameCentre Interdisciplinaire de Recherches Médicales de Franceville (CIRMF), BP 769, Franceville, GabonArnauld Ondo Oyono, Natif Yapet Koumlah, Patrick Yangari, Neil Michel Longo-Pendy, Lynda Chancelya Nkoghe Nkoghe, Marc-Flaubert Ngangue & Yasmine Okomo NguemaMIVEGEC, Univ. Montpellier, CNRS, Montpellier, IRD, FranceChristophe Paupy & Pierre KengneCentre for Global Change, University of the Free State, Private Bag x13, Phuthaditjhaba, 9866, Republic of South AfricaPatricks Voua OtomoAuthorsJudicaël Obame-NkogheView author publicationsSearch author on:PubMed Google ScholarFaël Moudoumi KondjiView author publicationsSearch author on:PubMed Google ScholarBrad Ghaven NianguiView author publicationsSearch author on:PubMed Google ScholarRicardo Ewak ObameView author publicationsSearch author on:PubMed Google ScholarArnauld Ondo OyonoView author publicationsSearch author on:PubMed Google ScholarNatif Yapet KoumlahView author publicationsSearch author on:PubMed Google ScholarPatrick YangariView author publicationsSearch author on:PubMed Google ScholarNeil Michel Longo-PendyView author publicationsSearch author on:PubMed Google ScholarLynda Chancelya Nkoghe NkogheView author publicationsSearch author on:PubMed Google ScholarMarc-Flaubert NgangueView author publicationsSearch author on:PubMed Google ScholarYasmine Okomo NguemaView author publicationsSearch author on:PubMed Google ScholarLandry Erik MomboView author publicationsSearch author on:PubMed Google ScholarChristophe PaupyView author publicationsSearch author on:PubMed Google ScholarPatricks Voua OtomoView author publicationsSearch author on:PubMed Google ScholarPierre KengneView author publicationsSearch author on:PubMed Google ScholarContributionsConceptualisation: JO-N, PK, CP; Data curation: NYK, AOO, JO-N, FMK, BGN, MFN, LCNN, PY, NMLP; Formal analysis: JO-N, AOO, YON, NYK, FMK, REO; Data visualization: FMK, LEM, PVO, REO, JO-N; First article drafting: JO-N, FMK, AOO; Reviewing and editing: PK, PVO, LEM, YON, Acquisition of funding: JO-N, PVO.Corresponding authorCorrespondence to
Judicaël Obame-Nkoghe.Ethics declarations

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Reprints and permissionsAbout this articleCite this articleObame-Nkoghe, J., Moudoumi Kondji, F., Niangui, B.G. et al. Physicochemical and typological insights into Aedes albopictus and Aedes aegypti larval habitats in a sub-Saharan African urban gradient setting.
Sci Rep (2025). https://doi.org/10.1038/s41598-025-32398-9Download citationReceived: 17 June 2025Accepted: 10 December 2025Published: 19 December 2025DOI: https://doi.org/10.1038/s41598-025-32398-9Share 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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Larval habitatsPhysicochemical featuresAfrican urban settingGabonArbovirus transmission More

AbstractTree longevity is thought to increase in growth-limiting, adverse environments, but a quantitative assessment of drivers of global variation in tree longevity is lacking. We assemble a global database of maximum longevity for 739 tree species and analyse associations between longevity and climate, soil, and species’ functional traits. Our results show two primary pathways towards long lifespans. The first is slow growth in resource-limited environments, consistent with the “adversity begets longevity” paradigm. The second pathway is through relief from abiotic constraints in productive environments. Despite notable exceptions, long-lived gymnosperms tend to follow the first path through slow growth in cold environments, whereas long-lived angiosperms tend to follow the second (“productivity”) path reaching maximum longevity generally in humid environments. For angiosperms, we identify two mechanisms for increased longevity under humid conditions. First, higher water availability increases species’ maximum tree height which is associated with greater longevities. Secondly, greater water availability increases stand density and inter-tree competition, limiting growth which may increase tree lifespan. The documented differences between gymnosperm and angiosperm longevity are likely rooted in intrinsic differences in hydraulic architecture that provide fitness advantages for gymnosperms under high abiotic stress, and for angiosperms under increased productivity or competition.

Data availability

Data on species’ maximum longevity, traits, and climate that support the findings of this study are available from https://doi.org/10.6084/m9.figshare.29876984. Original raw tree ring data from the ITRDB can be downloaded from https://www.ncei.noaa.gov/products/paleoclimatology/tree-ring, and tropical tree ring data compilations from https://figshare.com/articles/dataset/Locoselli_et_al_2020_Global_tree-ring_analysis_reveals_rapid_decrease_in_tropical_tree_longevity_with_temperature_PNAS/13119842?file=25178405. Individual longevity records from following oldlists http://www.rmtrr.org/oldlist.htm, https://www.ldeo.columbia.edu/~adk/oldlisteast/, http://www.nativetreesociety.org/dendro/ents_maximum_ages.htm, https://www.oldgrowth.ca/oldtrees/. Tree height data can be downloaded from https://zenodo.org/record/6637599, and maximum height measurements were obtained from https://www.conifers.org and https://Monumentaltrees.com. Wood density data can be obtained from https://zenodo.org/records/13322441, and from https://doi.org/10.18167/DVN1/KRVF0E. Conduit density from https://doi.org/10.5061/dryad.1138, and conduit density, P50 and HSM from https://doi.org/10.5061/dryad.1138, and from https://doi.org/10.1126/sciadv.aav1332. Leaf traits from https://www.nature.com/articles/nature02403#Sec15, and seedmass data from https://www.try-db.org/TryWeb/dp.php, database request No 30569. Mean climate and soil data for a species were obtained from the TreeGOER database https://zenodo.org/records/10008994, and gridded climate and elevation data from https://www.worldclim.org/data/worldclim21.html, growing season length and site level Net Primary Productivity (NPP) from https://chelsa-climate.org/. Species occurrence data from https://doi.org/10.15468/dl.77gcvq.
Code availability

Code to reproduce the Figs. 1–3 and Supplementary Figs. 3–6, 8, 9 and statistics are available from https://doi.org/10.6084/m9.figshare.29876984.
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This study was supported by the following grants; National Environmental Research Council grants NE/S008659/1 (R.B.), NE/N012542/1 (E.G.), and NE/R005079/1 (E.G., R.S.); FAPESP grants 12/50457-4, 2019/08783-0 (G.L., G.C.) and 17/5008-3 (G.L., G.C.); Conselho Nacional de Desenvolvimento Científico e Tecnológico, CNPq, grants 478503/2009 (G.L., G.C.), 311247/2021-0 (J.S.) and 441811/2020-5 (J.S.); CNPq/ FAPEAM, Fundação de Amparo à Pesquisa do Estado do Amazonas, grant number 01.02.016301.02630/2022-76 (J.S.); Czech Science Foundation research grants 24-12210 K (J.P. and M.S.) and 23-05272S (J.A., J.D., K.K., N.A., P.F., V.B.); Mobility Plus between the Czech Republic and Taiwan, NSTC-24-08 (J.A., J.D., K.K., N.A., P.F., V.B.); Czech Academy of Sciences long-term research development project No. RVO 67985939 (J.A., J.D., K.K., N.A., P.F., V.B.); Utah Agricultural Experiment Station, Utah State University, and approved as journal paper number 9803 (R.J.D.); Academy of Finland, #339788 (S.H.); European Union, NextGenerationEU, Italian Ministry of University and Research under PNRR – M4C2-I1.4 Project code: CN00000033, Title: NBFC – National Biodiversity Future Center, CUP: J83C22000860007 (G.P.); Ministry of University and Research (MUR) via the Agritech National Research Centre, European Union Next-GenerationEU PNRR M4C2-I1.4 Project Code: CN00000022 (A.D.); Departments of Excellence (Law 232/2016) Project 2023-27 “Digital, Intelligent, Green and Sustainable (D.I.Ver.So)” (A.D.); National Science Foundation, Division of Environmental Biology, award #1945910 (N.P.); Directorate for Biological Sciences, Emerging Frontiers, award #1241870 (N.P.); Redes Federales de Alto Impacto, Bosque-Clima CN32 (L.L., R.V.); MSMT INTER-EXCELLENCE, # LUAUS24258 (J.D.), Estonian Research Council, grant PSG1044 (J.A.).Author informationAuthors and AffiliationsSchool of Geography, University of Leeds, Leeds, UKRoel J. W. Brienen & Emanuel GloorCenter of Nuclear Energy in Agriculture, University of São Paulo, Piracicaba, BrazilGiuliano Maselli LocosselliPhysical Geography, University of Passau, Passau, GermanyStefan KrottenthalerSchool of Earth and Environment, University of Leeds, Leeds, UKRobyn WrigleyCollege of Forest Resources and Environmental Science, Michigan Technological University, Houghton, MI, USASteven L. VoelkerInstitute of Botany of the Czech Academy of Sciences, Třeboň, Czech RepublicJan Altman, Nela Altmanova, Jiri Dolezal, Pavel Fibich & Kirill KorznikovFaculty of Forestry and Wood Sciences, Czech University of Life Sciences, Prague, Czech RepublicJan Altman, Vaclav Bazant, Jakob Pavlin & Miroslav SvobodaDepartment of Geography, Institute of Ecology and Earth Sciences, University of Tartu, Tartu, EstoniaJan AltmanFaculty of Science, University of South Bohemia, České Budějovice, Czech RepublicNela Altmanova, Jiri Dolezal & Pavel FibichDepartment of Ecology, Evolution & Marine Biology, University of California Santa Barbara, Santa Barbara, CA, USALeander D. L. Anderegg & Gianluca PiovesanDepartment of ecological and biological science (DEB), Università della Tuscia, Viterbo, ItalyMichele BalivaDepartment of Biology, Indian Institute of Science Education and Research, Pune, IndiaDeepak BaruaLaboratory of Tree Ring Research, University of Arizona, Tucson, AZ, USABryan BlackRocky Mountain Tree-Ring Research, Fort Collins, CO, USAPeter M. BrownDepartment of Botany, University of São Paulo, Institute of Biosciences, São Paulo, SP, BrazilGregorio CeccantiniDepartment of Wildland Resources and Ecology Center, Logan, UT, USAR. Justin DeRoseLaboratorio de Dendrocronologia, Instituto Nacional de Investigaciones Forestales, Agricolas y Pecuarias, Gomez Palacio, MexicoJose Villanueva DiazDepartment of Agriculture and Forest Science (DAFNE), Università della Tuscia, Viterbo, ItalyAlfredo Di FilippoMinistère des Ressources naturelles et des Forêts, Direction de la recherche forestière, Quebec city, QC, CanadaLouis DuchesneGymnosperm Database, Olympia, WA, USAChristopher EarleBritish Columbia Ministry of Forests, Prince George, BC, CanadaHardy GriesbauerNatural Resources Institute Finland, Rovaniemi, FinlandSamuli HelamaForest and Soil Ecology, Swiss Federal Institute for Forest, Snow and Landscape Research WSL, Birmensdorf, SwitzerlandStefan KlesseFenner School of Environment and Society, The Australian National University, Canberra, ACT, AustraliaDavid LindenmayerResearch Center of Forest Management Engineering of State Forestry and Grassland Administration, Beijing Forestry University, Beijing, ChinaShuhui Liu & Chunyu ZhangLaboratorio de Dendrocronología e Historia Ambiental IANIGLA/CONICET, Mendoza, ArgentinaLidio Lopez & Ricardo VillalbaCREAF, Bellaterra, SpainMaurizio MencucciniICREA, Barcelona, SpainMaurizio MencucciniDepartment of forestry and renewable forest resources, University of Ljubljana, Ljubljana, SloveniaThomas A. NagelIndependent Scholar, Maynard, MA, USANeil PedersonHarvard Forest, Harvard University, Petersham, MA, USANeil PedersonUniversity of Nevada, Reno, Reno, NV, USAChristina RestainoInstitute for Global Change Biology, University of Michigan, Ann Arbor, MI, USAPeter B. ReichDepartment of Forest Resources, University of Minnesota, St. Paul, MN, USAPeter B. ReichPrairie Adaptation Research Collaborative, Geography and Environmental Studies, University of Regina, Regina, CanadaDavid SauchynInstituto Nacional de Pesquisas da Amazônia (INPA), Ecologia, Monitoramento e Uso Sustentável de Áreas Úmidas (MAUA), Manaus, AM, BrazilJochen SchöngartRocky Mountain Research Station, USDA Forest Service, Ogden, UT, USAJohn D. ShawDepartment of Geography, University of Victoria, Victoria, BC, CanadaDan SmithDepartment of Botany, St Joseph’s College (Autonomous), Devagiri, Calicut, Kerala, IndiaRon SunnyUniversity of Northern British Columbia, Faculty of Environment, Prince George, BC, CanadaLisa J. WoodAuthorsRoel J. W. BrienenView author publicationsSearch author on:PubMed Google ScholarGiuliano Maselli LocosselliView author publicationsSearch author on:PubMed Google ScholarStefan KrottenthalerView author publicationsSearch author on:PubMed Google ScholarEmanuel GloorView author publicationsSearch author on:PubMed Google ScholarRobyn WrigleyView author publicationsSearch author on:PubMed Google ScholarSteven L. VoelkerView author publicationsSearch author on:PubMed Google ScholarJan AltmanView author publicationsSearch author on:PubMed Google ScholarNela AltmanovaView author publicationsSearch author on:PubMed Google ScholarLeander D. L. AndereggView author publicationsSearch author on:PubMed Google ScholarMichele BalivaView author publicationsSearch author on:PubMed Google ScholarDeepak BaruaView author publicationsSearch author on:PubMed Google ScholarVaclav BazantView author publicationsSearch author on:PubMed Google ScholarBryan BlackView author publicationsSearch author on:PubMed Google ScholarPeter M. BrownView author publicationsSearch author on:PubMed Google ScholarGregorio CeccantiniView author publicationsSearch author on:PubMed Google ScholarR. Justin DeRoseView author publicationsSearch author on:PubMed Google ScholarJose Villanueva DiazView author publicationsSearch author on:PubMed Google ScholarAlfredo Di FilippoView author publicationsSearch author on:PubMed Google ScholarJiri DolezalView author publicationsSearch author on:PubMed Google ScholarLouis DuchesneView author publicationsSearch author on:PubMed Google ScholarChristopher EarleView author publicationsSearch author on:PubMed Google ScholarPavel FibichView author publicationsSearch author on:PubMed Google ScholarHardy GriesbauerView author publicationsSearch author on:PubMed Google ScholarSamuli HelamaView author publicationsSearch author on:PubMed Google ScholarStefan KlesseView author publicationsSearch author on:PubMed Google ScholarKirill KorznikovView author publicationsSearch author on:PubMed Google ScholarDavid LindenmayerView author publicationsSearch author on:PubMed Google ScholarShuhui LiuView author publicationsSearch author on:PubMed Google ScholarLidio LopezView author publicationsSearch author on:PubMed Google ScholarMaurizio MencucciniView author publicationsSearch author on:PubMed Google ScholarThomas A. NagelView author publicationsSearch author on:PubMed Google ScholarJakob PavlinView author publicationsSearch author on:PubMed Google ScholarNeil PedersonView author publicationsSearch author on:PubMed Google ScholarGianluca PiovesanView author publicationsSearch author on:PubMed Google ScholarChristina RestainoView author publicationsSearch author on:PubMed Google ScholarPeter B. ReichView author publicationsSearch author on:PubMed Google ScholarDavid SauchynView author publicationsSearch author on:PubMed Google ScholarJochen SchöngartView author publicationsSearch author on:PubMed Google ScholarJohn D. ShawView author publicationsSearch author on:PubMed Google ScholarDan SmithView author publicationsSearch author on:PubMed Google ScholarRon SunnyView author publicationsSearch author on:PubMed Google ScholarMiroslav SvobodaView author publicationsSearch author on:PubMed Google ScholarRicardo VillalbaView author publicationsSearch author on:PubMed Google ScholarLisa J. WoodView author publicationsSearch author on:PubMed Google ScholarChunyu ZhangView author publicationsSearch author on:PubMed Google ScholarContributionsR.B., G.L., S.K., E.G., and R.W. designed the study, R.B., R.W. and S.K. downloaded and compiled functional traits and ITRDB datasets, R.B., R.W. and S.K. analysed data, G.L. and S.K. compiled the tropical longevity datasets, M.M., D.B., R.S. and P.R. provided functional traits data, R.B., G.L., S.K., S.V., C.E., G.P. and N.P. revised and improved the longevity database, R.B., G.L., S.V., J.A., N.A., L.A., M.B., V.B., B.B., P.B., G.C., J.dR., J.V.D., A.D., J.D., L.D., C.E., P.F., H.G., S.H., S.K.l., K.K., D.L., S.L., L.L., T.N., J.P., N.P., G.P., C.R., D.S., J.S., J.D.S., D.S., M.S., R.V., L.W., and C.Z. contributed original longevity data, R.B. wrote the first draft of the manuscript and all authors revised the manuscript.Corresponding authorCorrespondence to
Roel J. W. Brienen.Ethics declarations

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Reprints and permissionsAbout this articleCite this articleBrienen, R.J.W., Locosselli, G.M., Krottenthaler, S. et al. Contrasting pathways to tree longevity in gymnosperms and angiosperms.
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AbstractThe eastern margin of the Qinghai-Xizang Plateau, as a critical transition zone between the plateau and the Sichuan Basin, poses substantial challenges for geographic regionalization, primarily due to its intricate terrain and climatic heterogeneity. Traditional spatial clustering methods often struggle to balance spatial continuity and attribute similarity, suffering from subjectivity and inadequate representation of topographic complexity. This study proposes a novel mountainous geographic regionalization framework that integrates topographic and climatic characteristics, using Kangding county as a typical case. Principal Component Analysis (PCA) was employed to perform dimensionality reduction on multiple environmental variables and assign relative weights. A Gaussian-weighted function was further applied to adjust attribute distances to capture spatial non-stationarity, while the geographic distance weight was systematically optimized. The partitioning outcomes were evaluated using clustering quality indicators (Davies-Bouldin index, Silhouette index, Calinski-Harabasz index) and spatial autocorrelation indicators (Moran’s I index, Moran’s Z-score). Results indicated that when the number of clusters was set to five and the geographic distance weight was 0.5, the clustering algorithm optimized the trade-off between spatial continuity and attribute similarity (Davies-Bouldin index = 1.14, Silhouette index = 0.30, Calinski-Harabasz index = 25150.91, Moran’s I = 0.97, Moran’s Z-score = 292.28). Compared to the traditional K-means clustering, this approach enhanced intra-cluster similarity (Sil) by 259% and improved spatial continuity (Moran’s I, Moran’s Z-score) by approximately 44%. This method effectively addresses the challenge of coordinating spatial constraints with attribute heterogeneity in mountainous environmental zoning, in a county scale, providing an automated, data-driven approach for geographic partitioning in complex terrains. The findings offer valuable insights for mountain ecosystem management and regional geographic studies. Our study provides a set of effective methods of demarcation of regional boundaries based on raster data, offering important insights for ecological zoning management and regional studies in mountainous environments at a small scale.

Data availability

The datasets analyzed in this study are publicly available. Climate data were obtained from the National Tibetan Plateau Data Center (https://data.tpdc.ac.cn). The DEM was acquired from the Shuttle Radar Topography Mission (SRTM, https://srtm.csi.cgiar.org/).
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Xiaoguo Wang.Ethics declarations

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Reprints and permissionsAbout this articleCite this articleLiu, X., Hong, D., Dong, H. et al. Enhancing demarcation in regionalization in the eastern Qinghai-Xizang Plateau through geographically weighted.
Sci Rep (2025). https://doi.org/10.1038/s41598-025-32098-4Download citationReceived: 31 August 2025Accepted: 08 December 2025Published: 19 December 2025DOI: https://doi.org/10.1038/s41598-025-32098-4Share 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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KeywordsRegionalizationSpatial clusterGaussian-weightedEuclidean distanceEastern Qinghai-Xizang plateau More