open access research journal of life sciences

Open Access Research Journal of Life Sciences

Issn 2783-025x (online).

Open Access Research Journal of Life Sciences (OARJLS) is a Peer Reviewed, Open Access, International Journal. It is a Referred, Indexed, Online International Journa l. Open Access Research Journal of Life Sciences (OARJLS)​   is published as a Quarterly Journal with 4 issues per year. Open Access Research Journal of Life Sciences (OARJLS)   offers fast publication of quality Research and Review articles. Open Access Research Journal of Life Sciences (OARJLS)   publishes manuscripts (Original research, review articles, Short communication and letter to editor) on original work, either experimental or theoretical) from all aspects of Life Sciences (Biology, Genetics, Biological Anthropology, Botany, Medical Sciences, Veterinary Sciences, Biochemical Genetics, Biometry, Clinical Genetics, Cytogenetics, Genetic Epidemiology, Genetic Testing, Evolution and Population Genetics, Immunogenetics and Molecular Genetics). The journal also covers ethical issues. To know more details about Open Access Research Journal of Life Sciences (OARJLS)  Click here...                               

Aims and Scope

Open Access Research Journal of Life Sciences (OARJLS)  aims to establish itself as a platform for exchanging ideas in new emerging trends in Biology and Allied Applied Sciences etc. It aims to serve as a forum for life scientists and health professionals. The journal publishes original papers on current research and practical programmes, short notes, news items, book reviews, reports of meetings and professional announcements. Constructive criticisms and discussions of published papers and letters of relevance and interest to the readership will be published at the discretion of the Managing Editor. The journal is committed to prompt review, and priority publication is given to manuscripts with novel or timely findings, and to manuscripts of unusual interests.  Since inception, Open Access Research Journal of Life Sciences (OARJLS)  is continuously publishing original and best quality research articles. To view full Aims and Scope of Open Access Research Journal of Life Sciences (OARJLS)   Click here...

Call for Paper

Open Access Research Journal of Life Sciences (OARJLS)  invites you to submit your research work via our Online Submission System or through Email at [email protected] . Make sure that the submitted manuscript should not have been submitted or published previously anywhere else for publication. It is strictly advised to submit original and plagiarism free articles only for possible consideration, else they will be rejected without any response. All received manuscripts will go through Double Blind Peer Review and final decision shall be based on the high level of quality, originality and additional contribution to the existing knowledge.  Special note : Once your article is submitted to Open Access Research Journal of Life Sciences (OARJLS) , you cannot submit / present this paper anywhere else, unless your article is rejected by Open Access Research Journal of Life Sciences (OARJLS) . Accepted submission will not be withdrawn or be presentable in any other journal / conference / magazine or any media without written permission of Open Access Research Journal of Life Sciences (OARJLS) . 

Why Publish with OARJLS?

• International Open Access Journal
• Peer Reviewed  Journal with ISSN (under process),
• Fast and Easy Publication of high quality Research and Review Papers
• Certificate of publication to each author at no cost
• Submission via e-mail /online 
• Online tracking of articles submitted.
• Open Access to all Articles: Anytime & Anywhere in the world
• Improved visibility of articles to  get more citations
• Immediate response to author queries.
• Highly experiences Editorial Board and Reviewer members ​

Open Access Research Journal of Life Sciences (OARJLS) Policies

Open access policy: Open Access Research Journal of Life Sciences (OARJLS) is an open access journal which means that all content is freely available without charge to the user or his/her institution. Users are allowed to read, download, copy, distribute, print, search, or link to the full texts of the articles in this journal without asking prior permission from the publisher or the author. This is in accordance with the Budapest Open Access Initiative (BOAI) definition of open access. To know more about Open access  Click here

Peer-review policy: The manuscript submitted to Open Access Research Journal of Life Sciences (OARJLS) will be reviewed by two suitable experts in respective subject area. The reports of both the reviewers will be considered when deciding on acceptance/revision or rejection of a manuscript. Editor-In-Chief will make the final decision, based on reviewer’s comments. To know more about Peer-review process Click here

Anti-Plagiarism policy: Open Access Research Journal of Life Sciences (OARJLS) has very strict policy against plagiarism. The authors should ensure that they have written entirely original works, and if the authors have used the work and/or words of others that this has been appropriately cited or quoted. To know more about Plagiarism Click here

Copyright policy: All the articles published in  Open Access Research Journal of Life Sciences (OARJLS)  are distributed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International license . The journal allows the author(s) to hold the copyright of their work (all usages allowed except for commercial purpose).

Author self-archiving policy: Open Access Research Journal of Life Sciences (OARJLS) allows the authors to self-archive pre-print, post-print and publisher’s version of the article in any Open Access Initiative (OAI) compliant repository.

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Open Access Research Journal of Life Sciences (OARJLS) is a Peer Reviewed, Open Access, International Journal. It is a Referred, Indexed, Online International Journal. Open Access Research Journal of Life Sciences (OARJLS)​ is published as a Quarterly Journal with 4 issues per year. Open Access Research Journal of Life Sciences (OARJLS) offers fast publication of quality Research and Review articles. Open Access Research Journal of Life Sciences (OARJLS) publishes manuscripts (Original research, review articles, Short communication and letter to editor) on original work, either experimental or theoretical) from all aspects of Life Sciences (Biology, Genetics, Biological Anthropology, Botany, Medical Sciences, Veterinary Sciences, Biochemical Genetics, Biometry, Clinical Genetics, Cytogenetics, Genetic Epidemiology, Genetic Testing, Evolution and Population Genetics, Immunogenetics and Molecular Genetics). The journal also covers ethical issues.

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Sage launches new open access journal of international life sciences research.

London , UK . SAGE , one of the world’s leading independent and academic publishers, has today announced that it has launched the Journal of International Life Sciences Research ( JILSR ) , a new open access (OA) journal dedicated to the rapid publication of high-quality articles in the form of life sciences research and review papers.

As a companion to the Journal of International Medical Research , which has been in publication for over 40 years, JILSR will similarly help authors from developing countries to get published, but with a focus on life sciences. JILSR will publish articles from a range of disciplines, and the journal is now open for submissions.

Editor-in-Chief, Malcolm Lader of King’s College London, commented that:

“The Journal of International Life Sciences Research builds on the success of its sister journal to facilitate rapid publication of high-quality life sciences research. It provides the same rapid and thorough peer review, followed by in-depth, high-quality technical editing. I look forward to working with SAGE to provide a valuable forum for this research.”

Karen Philips, Editorial Director, SAGE, further remarked on the launch:

“The launch of the Journal of International Life Sciences Research will provide a platform on which to publish life sciences research. At SAGE, one of our core publishing missions is to support the development of important research fields, ensuring the greatest access and dissemination of key research. We look forward to working with the editorial board and seeing the journal develop and grow internationally.”

More information about the journal can be found here .

SAGE Founded 50 years ago by Sara Miller McCune to support the dissemination of usable knowledge and educate a global community, SAGE publishes more than 850 journals and over 800 new books each year, spanning a wide range of subject areas. A growing selection of library products includes archives, data and video. SAGE remains majority owned by our founder and after her lifetime will become owned by a charitable trust that secures the company’s continued independence. Principal offices are located in Los Angeles, London, New Delhi, Singapore and Washington DC.

Journal of International Life Sciences Research is a journal for the rapid publication of high-quality life sciences research and review articles, on a page charge basis. It is a companion publication to the Journal of International Medical Research , which has been publishing for over 40 years.

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Harvard Library Is Launching Harvard Open Journals Program

Harvard Library is launching a new initiative called the Harvard Open Journals Program (HOJP), which will help researchers advance scholarly publishing that is open access, sustainable, and equitable. HOJP will provide publishing services, resources, and seed funding to participating Harvard researchers for new academic journals. All journal articles will be entirely free for authors and readers, with no barriers to publish or to access.

Martha Whitehead, Vice President for the Harvard Library and University Librarian, sees the initiative as an important step in championing open access. Whitehead said, “We want to model the original ethos of open access by reducing barriers and enabling the free flow of ideas and knowledge across the research ecosystem and beyond to the public at large.”

The Harvard Open Journals Program will offer publishing and hosting services to help the Harvard community launch new open access journals, or to convert existing journals to open access. The program will offer two support models: an overlay model which takes advantage of open access repositories, such as Harvard’s  DASH , and a brand-new academic press model. 

Yuan Li, University Scholarly Communication Officer and Director of Open Scholarship and Research Data Services at Harvard Library, pointed out the innovative nature of the program, “It is new for an institution to support faculty in seeking out an academic press to publish a no-fee open access journal and to provide assistance in securing its long-term funding. And offering a repository overlay journal model provides an alternative that appeals to some editorial boards and is gaining traction through initiatives such as Episciences. As we implement and refine this program on our campus, we hope it will inspire other universities to adopt such approaches to supporting barrier-free scholarly publishing.”

The program is a direct response to faculty interest in alternatives to the article-processing-charge model, in which journals charge author-side fees to publish papers open access. It also supports federal requirements that publications resulting from publicly-funded research be open access.

The open access movement in scholarly publishing seeks to grant free and public online access to publications and data. In recent decades, many researchers have become increasingly concerned that commercial rather than scholarly interests are driving the publishing ecosystem. With some publishers charging article processing fees of over $10,000 per article, skyrocketing costs inhibit many researchers and institutions from publishing in these journals. At the same time, research institutions continue to pay high subscription costs, even as their faculty provide editorial and peer review services mainly for free to the publishers. These practices have led to widespread outcry in the scholarly community, and tensions between publishers and editorial boards have led to the latter’s  mass resignations .

Scott Edwards, Professor of Organismic and Evolutionary Biology, and a member of the Harvard Library Faculty Advisory Council, applauds the library’s exploration of new models for supporting open access publishing. Edwards said, “In this increasingly challenging publishing ecosystem, the Harvard Open Journals Program is a welcome new approach.” 

“These are sustainable and equitable open access publishing models that allow scholars to take control of scholarly communication,” added Li. “I hope that many research-heavy institutions adopt our approach. The first  Harvard Open Access policy launched in 2008 has been adopted nationally and internationally, and it would be great to see similar reach.”

Under Harvard’s Open Access policies, Harvard faculty and researchers give the University a nonexclusive, irrevocable right to distribute their scholarly articles for any non-commercial purpose. Stored and preserved in  DASH , Harvard Library’s open access repository, these articles are made available to the scholarly community and the public—anyone with an internet connection can read them for free.

Harvard Library is working closely with the Office of the Vice Provost for Research on launching the HOJP program. John Shaw, Vice Provost for Research and Harry C. Dudley Professor of Structural and Economic Geology, is eager to promote the initiative in the suite of programs that support faculty research. Shaw said, “The launch of HOJP provides very encouraging options for removing barriers to making research results open and expanding their reach.”

The Harvard Open Journals Program will be open to all journals with a current Harvard affiliate on the editorial team or editorial board. Student-run journals are also eligible, as long as they are sponsored by a Harvard faculty member or administrator.

In preparing to launch HOJP this summer, Harvard Library is currently seeking input on program details from interested faculty. HOJP will begin accepting applications in the fall from journals and editorial boards. Colleen Cressman, Librarian for Open Publishing, will manage the program and can be reached by email for more information.

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Research Journal of Life Science

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Research Journal of Life Science is an open access publishes papers three times a year on April, August and December. The main objective of  Research Journal of Life Science  is to provide a platform for the international scholars, academicians and researchers to share the contemporary thoughts and innovation in the fields of life science. Research Journal of Life Science  aims to promote studies in life science and thus become the leading international journal in life science in the world.

Research Journal of Life Science has been accredited for five years as scientific journal based on Ministry of Education, Culture, Research and Technology of the Republic of Indonesia (SK No. 164/E/KPT/2021, 27 Desember 2021)

Research Journal of Life Science developed for being International Journal and provide postgraduate as a requirement graduation (University of Brawijaya Rector Decision Letter No. 113/UN10/AK/2017).

We accept submission from all over the world. All submitted articles shall never been published elsewhere, original and not under consideration for other publication.

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Vol 10, No 1 (2023): IN PRESS

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The Institut Pasteur adopts a Charter for Open Access to Scientific Publications

In 2021, the Institut Pasteur produced a Charter for Open Access to Publications. This charter is part of a broader commitment to open science, as reaffirmed in the 2019-2023 Strategic Plan.

In its 2019-2023 Strategic Plan , the Institut Pasteur confirms its long-term strategy to "promote open access to publications and research data." In 2021, in line with this commitment, it published a Charter for Open Access to Scientific Publications. In the charter, the Institut Pasteur calls for all its scientific publications (research articles, reviews, letters, books, conference papers and preprints) to be published as open access from 2021 onwards.

Find out more

Participation in open science is also one of the criteria taken into consideration when assessing scientists and research engineers. All publications for the period under assessment must be submitted to HAL-Pasteur , the open archive for Institut Pasteur publications.

Many research funding bodies, including the European Commission and the French National Research Agency (ANR), require any publications derived from projects they have funded to be made available as open access. To ensure compliance with these open access policies, the Institut Pasteur's scientists are asked to:

• Submit their publications to the HAL-Pasteur open archive.
• Publish with a CC-BY Creative Commons license and no embargo, in other words make articles available free of charge as soon as they are published , so that they can be read and reused by anyone.

For the latter requirement, there are two possibilities. Publications can be published in a fully open access journal or by applying the Rights Retention Strategy . This strategy was developed by several research funding bodies that have joined forces in cOAlition S .

Using the algorithm provided by the French Ministry of Higher Education, Research and Innovation to calculate the open access rate of French publications, the Institut Pasteur has published its open science barometer. As of July 2021, 85.1% of the Institut Pasteur's publications published in 2020 were available as open access.

The Institut Pasteur's commitment to open science began in 2004, the same year as the Berlin Declaration on Open Access to Knowledge. This strategy to promote open access to publications is in line with France's National Plan for Open Science and with ambitions at international level. The aim is to democratize access to the results of honest, quality research and to speed up innovation by facilitating the reuse of results.

In 2021, the Institut Pasteur also pledged its commitment to another area of open science by adopting a policy for managing and sharing research data and software codes .

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The Institut Pasteur adopts a new data management and sharing policy

Strategic Plan: research advances in 2022

Innovative genomic data analysis method leads to the discovery of novel coronavirus families

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Research & Reviews : A Journal of Life Sciences

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Dr. H.Ramasubba Reddy

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A Comprehensive Review on COVID-19 Evolution and Development

Characterization of Plant Growth Promoting Bacillus subtilis (VBKT5) Isolated from Vermicompost

Estimation of Microbes on Phyllopalne Region of Healthy and Infected Leaf of Ipomoea cairica by Leaf Impression Method

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Research & Reviews : A Journal of Life Sciences (rrjols) : 2249-8656(e) is a peer-reviewed hybrid open-access journal launched in 2011 focused on the rapid publication of fundamental research papers on all areas of Life Sciences. View Full Focus and Scope…

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Vol-14 Issue-01 2024

Freshwater Resources in Delhi: A Decadal Analysis of Land Use Changes Saloni Sachdeva, Indira P Sarethy Keywords: LULC, E.coli, Water quality, Groundwater, Delhi, GIS

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Journal of Innovative Research in Life Sciences (JIRLS) is an open access, peer reviewed online and print journal, aimed at publishing latest research findings in life sciences. The Journal comprise techniques suitable in promoting the dissemination of research findings that is expected to be of benefit to the basic needs in health, agriculture, biotechnology, pharmaceutical and food industries. JIRLS publishes life science articles of evidence based practices, thus improving the quality of life.

JIRLS is an inherently life sciences research journal as such is constrained to life science research methods only. The journal, JIRLS  cut across subject areas like Biochemistry, Biology, Microbiology, Botany, Zoology, Biostatistics, Veterinary science, Health sciences, Agricultural sciences, Bioinformatics, Biotechnology and related fields.

2024, Vol: 6, Issue: 1

• STUDIES ON PREVALENCE OF SHIGELLA AND SALMONELLA SPECIES ON THE FOMITES OF SPECIALIST HOSPITAL, SOKOTO, NIGERIA Keta J.N., Bello I.M., Mubarak A., Keta N.M. JIRLS. 2024; 6(1): 1-11 » Abstract » PDF

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• Published: 24 April 2024

Phylogenomics and the rise of the angiosperms

• Alexandre R. Zuntini   ORCID: orcid.org/0000-0003-0705-8902 1   na1 ,
• Tom Carruthers 1   na1 ,
• Olivier Maurin   ORCID: orcid.org/0000-0002-4151-6164 1 ,
• Paul C. Bailey   ORCID: orcid.org/0000-0002-4650-9668 1 ,
• Kevin Leempoel   ORCID: orcid.org/0000-0001-7335-7930 1 ,
• Grace E. Brewer 1 ,
• Niroshini Epitawalage 1 ,
• Elaine Françoso   ORCID: orcid.org/0000-0002-6464-1240 1 , 2 ,
• Berta Gallego-Paramo   ORCID: orcid.org/0000-0002-2016-7161 1 ,
• Catherine McGinnie 1 ,
• Raquel Negrão   ORCID: orcid.org/0000-0002-4758-8038 1 ,
• Shyamali R. Roy 1 ,
• Lalita Simpson 3 ,
• Eduardo Toledo Romero 1 ,
• Vanessa M. A. Barber 1 ,
• Laura Botigué   ORCID: orcid.org/0000-0001-7114-5168 4 ,
• James J. Clarkson 1 ,
• Robyn S. Cowan 1 ,
• Steven Dodsworth   ORCID: orcid.org/0000-0001-6531-3540 5 ,
• Matthew G. Johnson   ORCID: orcid.org/0000-0002-1958-6334 6 ,
• Jan T. Kim 7 ,
• Lisa Pokorny   ORCID: orcid.org/0000-0002-2478-8555 1 , 8 ,
• Norman J. Wickett   ORCID: orcid.org/0000-0003-0944-1956 9 ,
• Guilherme M. Antar   ORCID: orcid.org/0000-0001-8109-4544 10 , 11 ,
• Lucinda DeBolt 12 ,
• Karime Gutierrez 12 ,
• Kasper P. Hendriks   ORCID: orcid.org/0000-0003-0245-8368 13 , 14 ,
• Alina Hoewener   ORCID: orcid.org/0009-0001-6938-9312 15 ,
• Ai-Qun Hu   ORCID: orcid.org/0000-0001-9564-878X 1 ,
• Elizabeth M. Joyce   ORCID: orcid.org/0000-0001-8291-8058 3 , 16 ,
• Izai A. B. S. Kikuchi   ORCID: orcid.org/0000-0002-0258-1537 17 ,
• Isabel Larridon   ORCID: orcid.org/0000-0003-0285-722X 1 ,
• Drew A. Larson   ORCID: orcid.org/0000-0002-7557-9999 18 ,
• Elton John de Lírio   ORCID: orcid.org/0000-0002-9986-9640 10 ,
• Jing-Xia Liu 19 ,
• Panagiota Malakasi 1 ,
• Natalia A. S. Przelomska   ORCID: orcid.org/0000-0001-9207-4565 1 , 5 ,
• Toral Shah 1 ,
• Juan Viruel   ORCID: orcid.org/0000-0001-5658-8411 1 ,
• Theodore R. Allnutt   ORCID: orcid.org/0000-0002-8258-0058 20 ,
• Gabriel K. Ameka   ORCID: orcid.org/0000-0001-6659-9982 21 ,
• Rose L. Andrew   ORCID: orcid.org/0000-0003-0099-8336 22 ,
• Marc S. Appelhans 23 ,
• Montserrat Arista   ORCID: orcid.org/0000-0003-0914-9525 24 ,
• María Jesús Ariza 25 ,
• Juan Arroyo 24 ,
• Watchara Arthan   ORCID: orcid.org/0000-0002-6941-2199 1 ,
• Julien B. Bachelier 26 ,
• C. Donovan Bailey 27 ,
• Helen F. Barnes 20 ,
• Matthew D. Barrett   ORCID: orcid.org/0000-0002-2926-4291 3 ,
• Russell L. Barrett   ORCID: orcid.org/0000-0003-0360-8321 28 ,
• Randall J. Bayer   ORCID: orcid.org/0000-0002-7827-5886 29 ,
• Michael J. Bayly   ORCID: orcid.org/0000-0001-6836-5493 30 ,
• Ed Biffin   ORCID: orcid.org/0000-0002-6582-716X 31 ,
• Nicky Biggs 1 ,
• Joanne L. Birch   ORCID: orcid.org/0000-0002-8226-6085 30 ,
• Diego Bogarín   ORCID: orcid.org/0000-0002-8408-8841 14 , 32 ,
• Renata Borosova   ORCID: orcid.org/0000-0002-3691-5005 1 ,
• Alexander M. C. Bowles   ORCID: orcid.org/0000-0002-7487-3811 33 ,
• Peter C. Boyce 34 ,
• Gemma L. C. Bramley 1 ,
• Marie Briggs   ORCID: orcid.org/0000-0003-2988-0032 1 ,
• Linda Broadhurst   ORCID: orcid.org/0000-0002-9853-3328 35 ,
• Gillian K. Brown 36 ,
• Jeremy J. Bruhl 22 ,
• Anne Bruneau   ORCID: orcid.org/0000-0001-5547-0796 37 ,
• Sven Buerki   ORCID: orcid.org/0000-0002-8299-6539 38 ,
• Edie Burns 1 ,
• Margaret Byrne   ORCID: orcid.org/0000-0002-7197-5409 39 ,
• Stuart Cable 1 ,
• Ainsley Calladine   ORCID: orcid.org/0000-0003-4724-8110 31 ,
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• Ángela Cano   ORCID: orcid.org/0000-0002-5090-7730 41 ,
• David J. Cantrill   ORCID: orcid.org/0000-0002-1185-4015 20 ,
• Warren M. Cardinal-McTeague   ORCID: orcid.org/0000-0003-3558-9794 42 ,
• Mónica M. Carlsen   ORCID: orcid.org/0000-0002-1663-0475 43 ,
• Abigail J. A. Carruthers   ORCID: orcid.org/0000-0003-2586-9733 1 ,
• Alejandra de Castro Mateo 24 ,
• Mark W. Chase   ORCID: orcid.org/0000-0002-9927-4938 1 , 44 ,
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• Shilin Chen   ORCID: orcid.org/0000-0002-0449-236X 46 , 47 ,
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• Pascal-Antoine Christin   ORCID: orcid.org/0000-0001-6292-8734 50 ,
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• Xavier Cornejo 53 ,
• Thomas L. P. Couvreur   ORCID: orcid.org/0000-0002-8509-6587 54 ,
• Ian D. Cowie 55 ,
• Laszlo Csiba 1 ,
• Iain Darbyshire   ORCID: orcid.org/0000-0002-5514-9561 1 ,
• Gerrit Davidse 43 ,
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• Kor-jent van Dijk   ORCID: orcid.org/0000-0002-6521-2843 56 ,
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• Marco F. Duretto   ORCID: orcid.org/0000-0003-1013-4291 28 ,
• Melvin R. Duvall 58 ,
• Sara L. Edwards 1 ,
• Urs Eggli   ORCID: orcid.org/0000-0002-8842-8188 59 ,
• Roy H. J. Erkens   ORCID: orcid.org/0000-0002-1093-0370 14 , 60 , 61 ,
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• Federico Fabriani   ORCID: orcid.org/0000-0002-5844-7484 45 ,
• Michael F. Fay   ORCID: orcid.org/0000-0003-3491-9093 1 ,
• Paola de L. Ferreira 63 , 64 ,
• Sarah Z. Ficinski 1 ,
• Rachael M. Fowler   ORCID: orcid.org/0000-0002-8953-7036 30 ,
• Sue Frisby 1 ,
• Lin Fu   ORCID: orcid.org/0000-0003-4933-221X 65 ,
• Tim Fulcher 1 ,
• Mercè Galbany-Casals   ORCID: orcid.org/0000-0002-7267-3330 66 ,
• Elliot M. Gardner   ORCID: orcid.org/0000-0003-1133-5167 67 ,
• Dmitry A. German   ORCID: orcid.org/0000-0001-7951-1644 68 ,
• Augusto Giaretta 69 ,
• Marc Gibernau   ORCID: orcid.org/0000-0003-3866-3099 70 ,
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• Laura Green   ORCID: orcid.org/0000-0003-0774-9498 1 ,
• Bee F. Gunn   ORCID: orcid.org/0000-0002-9591-3085 20 ,
• Diego G. Gutiérrez 73 ,
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• Jocelyn C. Hall   ORCID: orcid.org/0000-0002-5279-4346 76 ,
• Tony Hall 1 ,
• Melissa J. Harrison 3 ,
• Sebastian A. Hatt 1 ,
• Oriane Hidalgo   ORCID: orcid.org/0000-0002-1547-8627 77 ,
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• Gareth D. Holmes   ORCID: orcid.org/0000-0003-1120-8731 20 ,
• Helen C. F. Hopkins   ORCID: orcid.org/0000-0003-4984-8224 1 ,
• Christopher J. Jackson 20 ,
• Shelley A. James   ORCID: orcid.org/0000-0003-1105-1850 51 ,
• Richard W. Jobson   ORCID: orcid.org/0000-0002-1822-9634 28 ,
• Gudrun Kadereit 79 ,
• Imalka M. Kahandawala 1 ,
• Kent Kainulainen   ORCID: orcid.org/0000-0003-4271-1778 80 ,
• Masahiro Kato   ORCID: orcid.org/0000-0001-7752-2745 81 ,
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• Beata Klejevskaja 84 ,
• Bente B. Klitgaard 1 ,
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• Marcus A. Koch   ORCID: orcid.org/0000-0002-1693-6829 88 ,
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• De-Zhu Li   ORCID: orcid.org/0000-0002-4990-724X 19 ,
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• Sigrid Liede-Schumann   ORCID: orcid.org/0000-0003-2707-0335 92 ,
• Tatyana Livshultz   ORCID: orcid.org/0009-0000-7244-5955 93 , 94 ,
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• Patricia Lu-Irving   ORCID: orcid.org/0000-0003-1116-9402 28 ,
• Jaquelini Luber 96 ,
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• Manuel Luján 1 ,
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• Terry D. Macfarlane   ORCID: orcid.org/0000-0002-7023-9231 51 ,
• Carlos Magdalena 1 ,
• Vidal F. Mansano 96 ,
• Lizo E. Masters   ORCID: orcid.org/0000-0001-8632-0758 1 ,
• Simon J. Mayo 1 ,
• Kristina McColl 28 ,
• Angela J. McDonnell   ORCID: orcid.org/0000-0002-2549-9253 98 ,
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• Vincent S. F. T. Merckx   ORCID: orcid.org/0000-0002-3959-8623 14 ,
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• John D. Mitchell 100 ,
• Alexandre K. Monro   ORCID: orcid.org/0000-0003-4013-3804 1 ,
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• Ghillean T. Prance 1 ,
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• Richard K. Rabeler   ORCID: orcid.org/0000-0002-6765-0353 18 ,
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• Matthew Renner   ORCID: orcid.org/0000-0003-2286-7257 28 ,
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• William J. Baker   ORCID: orcid.org/0000-0001-6727-1831 1 , 64   na2  

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• Phylogenetics
• Phylogenomics
• Plant evolution

Angiosperms are the cornerstone of most terrestrial ecosystems and human livelihoods 1 , 2 . A robust understanding of angiosperm evolution is required to explain their rise to ecological dominance. So far, the angiosperm tree of life has been determined primarily by means of analyses of the plastid genome 3 , 4 . Many studies have drawn on this foundational work, such as classification and first insights into angiosperm diversification since their Mesozoic origins 5 , 6 , 7 . However, the limited and biased sampling of both taxa and genomes undermines confidence in the tree and its implications. Here, we build the tree of life for almost 8,000 (about 60%) angiosperm genera using a standardized set of 353 nuclear genes 8 . This 15-fold increase in genus-level sampling relative to comparable nuclear studies 9 provides a critical test of earlier results and brings notable change to key groups, especially in rosids, while substantiating many previously predicted relationships. Scaling this tree to time using 200 fossils, we discovered that early angiosperm evolution was characterized by high gene tree conflict and explosive diversification, giving rise to more than 80% of extant angiosperm orders. Steady diversification ensued through the remaining Mesozoic Era until rates resurged in the Cenozoic Era, concurrent with decreasing global temperatures and tightly linked with gene tree conflict. Taken together, our extensive sampling combined with advanced phylogenomic methods shows the deep history and full complexity in the evolution of a megadiverse clade.

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Flowering plants (angiosperms) represent about 90% of all terrestrial plant species 2 but, despite their remarkable diversity and ecological importance underpinning almost all main terrestrial ecosystems, their evolutionary history remains incompletely known. Since their Mesozoic origins 5 , 10 , 11 , angiosperms have had a pervasive influence on the biosphere of Earth, shaping climatic changes at global and local scales 12 , supporting the structure and assembly of biomes 13 and influencing the diversification of other organisms, such as insects, fungi and birds 14 . The evolution of terrestrial biodiversity is thus inextricably linked with the macroevolution of angiosperms, which can only be shown using a robust and comprehensive tree of life. Reconstructing such a tree, however, is challenging because of the sheer diversity of angiosperms and the complex phylogenetic signal in their genomes.

High-throughput DNA sequencing methods now enable us to reconstruct phylogenetic trees that broadly represent the evolutionary signal across entire genomes. Target sequence capture 15 has revolutionized plant phylogenetics by unlocking herbarium specimens as a source of sequenceable DNA 16 , thus removing the chief sampling bottleneck that has obstructed the completion of the tree of life. Although previous work on plants has relied primarily on the widely sequenced plastid genome 3 , 4 , 7 , these technologies now allow us to tap into the evolutionary signal of the much larger and more complex nuclear genome. Universal nuclear probe sets, such as Angiosperms353 (ref. 8 ), have made target sequence capture consistently applicable across broad taxonomic scales, opening doors to collaboration and data integration 17 . As a result, opportunities now present themselves to address fundamental questions in plant evolutionary biology, such as the origin of angiosperms, the tempo and mode of their diversification and the classification of main lineages.

Here, we present a nuclear phylogenomic tree that includes all 64 orders and 416 families of angiosperms recognized by the prevailing classification 18 , using the Angiosperms353 (ref. 8 ) gene panel. Our sampling of 7,923 angiosperm genera (represented by 9,506 species) amounts to a 15-fold increase compared to previous work 9 . Leveraging a dataset of 200 fossil calibrations, we scale the tree to time, effectively capturing evolutionary divergences for all but the most recent 15% of angiosperm history. Although our tree broadly supports relationships predicted by previous studies primarily based on plastid data, it also shows previously unknown relationships and highlights some that remain intractable despite a vast increase in data. Gene tree conflict is tightly linked to diversification across the tree. We find evidence for high levels of conflict associated with an early burst of diversification, which is followed by an extended period of constant diversification rates underpinned by a tapestry of varied lineage-specific patterns. Diversification then increases in the Cenozoic Era, potentially driven by global climatic cooling. Our results highlight the fundamental role of botanical collections in reconstructing the tree of life to illuminate long-standing questions in angiosperm macroevolution.

The angiosperm tree of life

Our phylogenetic tree includes 58% of the approximately 13,600 currently accepted genera of angiosperms (Fig. 1 and Supplementary Table 1 ; ref. 2 ). Together, the 7,923 genera encompass 85.7% of total known angiosperm species diversity. We produced data for 6,777 of these genera; before this study, 3,154 of these lacked publicly available genomic data, of which 393 lacked any form of DNA sequence data. For the remaining genera, data were obtained from public repositories. Sampling for this project was possible thanks to the collaborative effort of many biodiversity institutions from around the world, including 163 herbaria in 48 countries. More than one-third of species were sourced directly from herbarium specimens, some dating back nearly 200 years. Many phylogenetically problematic lineages with unconventional genome evolution were sampled, such as holoparasites, mycoheterotrophs and aquatics. Many of the species included are threatened and four are extinct (or extinct in the wild). The resulting tree of life presented here is one of the largest genomic trees generated yet for angiosperms as a whole.

All 64 orders, all 416 families and 58% (7,923) of genera are represented. The young tree is illustrated here (maximum constraint at the root node of 154 Ma), with branch colours representing net diversification rates. Black dots at nodes indicate the phylogenetic placement of fossil calibrations based on the updated AngioCal fossil calibration dataset. Note that calibrated nodes can be older than the age of the corresponding fossils owing to the use of minimum age constraints. Arcs around the tree indicate the main clades of angiosperms as circumscribed in this paper. ANA grade refers to the three consecutively diverging orders Amborellales, Nymphaeales and Austrobaileyales. Plant portraits illustrating key orders were sourced from Curtis’s Botanical Magazine (Biodiversity Heritage Library). These portraits, by S. Edwards, W. H. Fitch, W. J. Hooker, J. McNab and M. Smith, were first published between 1804 and 1916 (for a key to illustrations see Supplementary Table 2 ). A high-resolution version of this figure can be downloaded from https://doi.org/10.5281/zenodo.10778206 (ref. 55 ).

The phylogenomic challenge

Large genomic datasets present challenges to phylogenetic inference. One issue is accurate homology assessment, which proved intractable across the full span of our dataset, even with the most advanced multiple sequence alignment methods. Another challenge is the efficient search of tree space based on gene matrices that have many more taxa than characters. We overcame both challenges with a divide-and-conquer approach (Supplementary Fig. 1 ). First, we computed a backbone species tree with sampling limited to five species per family (1,336 (15%) samples in total) and targeted to represent their deepest nodes (Supplementary Fig. 2 ). We used the backbone species tree to delimit taxon subsets for the construction of order-level gene alignments, which were then merged into global alignments. We then computed global gene trees from the global alignments, using backbone gene trees (inferred during the estimation of the backbone species tree) as topological constraints to reduce tree space while still letting gene trees differ from each other. The smaller number of samples in the backbone dataset permits a more thorough search of tree space, resulting in greater confidence at deeper nodes than could be achieved in an unconstrained global analysis. This approach allows a trade-off between comprehensive sampling and tree search robustness while accommodating putative discordance among gene trees. Finally, we used the global gene trees to generate a global species tree in a multispecies coalescent framework (Supplementary Fig. 3 ).

A widespread concern in phylogenomic analysis is the presence of undetected gene copies. Our findings are unlikely to be affected by this because we used genes that have been selected to be mostly single-copy across green plants 8 , 9 . Although gene duplication cannot be ruled out 19 , the methods we used have been shown to be robust to the presence of paralogues 20 . In addition, a full assessment of orthologues was not computationally tractable but should be undertaken when methods become available to fully unravel the complexity of genome evolution at this scale 21 .

Phylogenetic insights from nuclear data

Our results broadly corroborate the prevailing understanding of angiosperm phylogenetic relationships, which rests on three decades of molecular systematic research largely built on data from the plastid genome 3 , 4 , 18 , 22 . We recover all main lineages of angiosperms, namely Amborellales, Nymphaeales, Austrobaileyales, Ceratophyllales and the three larger clades, monocots, magnoliids (including Chloranthales) and eudicots (Figs. 1 and 2 ). Although some of the relationships among those groups, such as the placement of Amborellales as sister group to all other angiosperms, are well-established and confirmed here, others, such as the placement of Ceratophyllales, which have been unstable in previous work 4 , 9 , remain inconclusive in our results. Despite the contrasting biological properties of the nuclear and plastid genomes (for example, size, copy number, mode of inheritance, recombination and evolutionary rate), which can lead to conflicting phylogenetic results, our findings largely support the mostly plastid-based phylogenetic classification of the Angiosperm Phylogeny Group 18 (Extended Data Fig. 1 ). For example, 58 of the 64 now accepted orders and 406 of the 416 families are recovered as monophyletic (excluding artefacts; Supplementary Table 1 ). The most striking exception is the non-monophyly of Asteraceae, the largest angiosperm family comprising the sunflowers and their relatives. Our tree also confirms 85% of the relationships among families recovered by ref. 4 using plastid genomes (Supplementary Fig. 4 ).

The overall stability of established relationships is unevenly distributed across the tree, as observed in contrasting patterns in the main eudicot clades, the asterids and rosids, which account for 35% and 29% of angiosperm diversity, respectively 2 . The relationships among main orders of asterids are stable 9 , with a clade comprising Ericales and Cornales sister to all other asterids and the remaining 15 orders divided in two main clades (campanulids and lamiids), both long characterized by their contrasting floral ontogeny 23 . Relationships contrasting with the status quo are mostly restricted to small orders, such as the paraphyly of Aquifoliales, Bruniales and Icacinales. These DNA-defined orders were consistently recovered as highly supported clades in plastome analyses 4 , 24 but they lack morphological cohesion. Given their placement in our phylogenetic tree and unique morphologies, these changes, although small, will alter our understanding of the evolution of asterids.

By contrast to asterids, our findings in rosids conflict markedly with plastid-based evidence. First, we resolve Saxifragales, rather than Vitales 4 , as sister to the remainder of rosids. In rosids, the fabid and malvid subclades, recovered as reciprocally monophyletic by plastid data 4 , 22 , are substantially rearranged into a grade of four orders subtending two well-supported sister clades, which we designate here as the recircumscribed fabids and malvids. The new fabid clade (Cucurbitales, Fabales, Fagales and Rosales) has long been characterized by symbiotic nitrogen fixation 25 . In the new malvids (Brassicales, Celastrales, Huerteales, Malpighiales, Malvales, Oxalidales, Picramniales and Sapindales), Oxalidales is resolved as two independent lineages, the core emerging closer to Brassicales, Malvales and Sapindales, whereas Huaceae emerges in the position conventionally occupied by Oxalidales, that is, closer to Malpighiales and Celastrales (the former Celastrales–Oxalidales–Malpighiales (COM) clade 18 ).

Notwithstanding the many well-supported confirmatory and new findings, some key relationships remain contentious and cannot be resolved by our data. These areas of high gene tree conflict often coincide with biological processes that confound phylogenetic inference. For example, the uncertain placements of eudicot orders Caryophyllales, Dilleniales and Gunnerales are probably impacted by key whole genome duplications 9 , 26 . The poor support for relationships among magnoliids, monocots, eudicots and Ceratophyllales might be explained by ancient hybridization events, such as that recently proposed for the origin of the monocots 27 . These examples highlight the importance of areas of poor resolution as waymarkers to biological events meriting further study.

Time frame for angiosperm macroevolution

Our tree was analysed in combination with a dataset of 200 fossil calibrations (originally described in ref. 5 , with modifications) to estimate divergence times and rates of diversification. Because the age of angiosperms is uncertain 28 , we dated the tree with two different maximum constraints at the angiosperm crown node (154 and 247 million years ago (Ma), termed the young tree and old tree, respectively), which reflect realistic upper and lower bounds for the maximum age of this node 5 , 28 . These different constraints affected age estimates across angiosperms (Extended Data Fig. 2 , Supplementary Fig. 5 and Supplementary Table 3 ). For example, in the young tree, stem node age estimates for Nymphaeales, Austrobaileyales and Ceratophyllales were 153, 152 and 152 Ma, respectively, whereas in the old tree the equivalent age estimates were 245, 244 and 243 Ma. Likewise, for larger clades such as magnoliids, monocots and eudicots, crown node age estimates were 151, 149 and 151 Ma in the young tree and 238, 237 and 241 Ma in the old tree. This range in age estimates is consistent with the most comprehensive comparable study 5 (Extended Data Fig. 3 ) but our trees provide age estimates for a further 7,000 nodes. In subsequent analyses, we indicate if differing age estimates between the young tree and old tree cause substantially different interpretations of angiosperm diversification.

With our sampling across angiosperms, we ensured that deeper branching events leading to extant lineages are comprehensively represented, while recognizing that extinct lineages are inaccessible to genomic methods. However, our dated trees are sparsely sampled at the species-level, meaning that branching events are incompletely represented towards the present, limiting diversification inferences in that time window. To address this, we developed a simulation-based approach to quantify the sampling fraction through time. For both dated trees, the lineage representation begins to drop substantially (below 75%) around 50 Ma (Supplementary Fig. 6 ). However, the most dramatic fall in lineage representation occurs in the most recent 20 Myr, in which it falls from around 50% to slightly more than 1% at present. Our investigation of angiosperm diversification should be interpreted with this broader context in mind. In particular, inferences in the most recent 20 Myr may be updated in the future with denser species sampling.

The diversification of angiosperms

Diversification linked to gene conflict.

We used our dated trees to reconstruct both diversification and gene tree conflict across a broad range of temporal and phylogenetic scales and investigate the relationship between them. We show that throughout angiosperm macroevolution, elevated gene tree conflict was tightly associated with elevated diversification. At a general level, this relationship is visible by simply comparing estimated diversification rates with gene tree conflict across all angiosperms through time (Fig. 3a ). Meanwhile, in a branch-specific analysis using the temporal duration of branches as a proxy for the rate at which branches are diversifying, we also show that conflict and diversification rate are positively correlated (Extended Data Fig. 4 ) ( P  < 0.001, r 2  = 0.51).

To characterize the theoretical basis of this relationship, we simulated species trees with corresponding gene trees under different diversification scenarios in a multispecies coalescent framework. These simulations showed that gene tree conflict is positively correlated with diversification when caused by incomplete lineage sorting, assuming that effective population size is constant (Supplementary Fig. 7 ). Our empirical results are largely consistent with such a scenario. Other potential causes of gene tree conflict such as whole genome duplication and hybridization may also be associated with rapid diversification and have been recorded extensively throughout angiosperms 29 , 30 . Overall, however, gene tree conflict seems to be reliable corroborating evidence for investigating temporal patterns of angiosperm diversification.

Early burst of angiosperm diversification

Our lineage-through-time (LTT) heatmap and diversification rate estimates through time both indicate an explosive early phase of diversification of extant lineages during the Late Jurassic and Early Cretaceous Periods (Fig. 2b and Fig. 3a ). An early burst of angiosperm diversification, popularized as ‘Darwin’s abominable mystery’ 31 , 32 , is expected given the sudden emergence of diverse angiosperm fossils during the Early Cretaceous 11 , 33 , 34 , 35 . Phylogenetic studies based on single or few genes have also implied that angiosperms diversified rapidly in the Early Cretaceous 7 , 36 , 37 , 38 . Our dated tree corroborates the existence of a distinct early burst of diversification, associated with high levels of gene tree conflict (Fig. 3a and Supplementary Fig. 8 ), further increasing our confidence in this finding.

The results illustrated are based on the young tree (maximum constraint at the root node of 154 Ma). a , Time-calibrated summary phylogenetic tree with LTT plots rendered as heatmaps for all orders with four or more sampled genera. The log-transformed increase in the number of lineages is depicted in 5 Myr intervals, omitting crown nodes, which disproportionately altered the visualization; crown node locations are indicated by vertical lines. The yellow to blue colour scale represents steep to shallow slopes. For each order, the numbers of sampled and total genera are provided. b , A global LTT heatmap for all angiosperms is shown at the bottom of the figure as a whole.

The results illustrated are based on the young tree (maximum constraint at the root node of 154 Ma). See Extended Data Fig. 5 for results based on the old tree. a , Estimated net diversification rate through time (yellow, left y axis) and the level of gene tree conflict through time (blue, right y axis). Net diversification rates are estimated with a model that enables speciation rates to vary between time intervals; the line is the posterior mean and the yellow shaded area is the 95% highest posterior density. Gene tree conflict is calculated from the percentage of gene trees that do not share a congruent bipartition with each species tree branch, with the plotted value being the mean across all species tree branches that cross each 2.5 Myr time slice. b , Cumulative percentage of extant orders and families that have originated through time. In both a and b , the background grey-scale gradient is the estimated percentage of extant lineages represented in the species tree through time (sampling fraction).

More than 80% of extant angiosperm orders originated during the early burst of diversification (Fig. 3b ). Although not strictly comparable because of their subjective delimitation, orders represent the main components of angiosperm feature diversity, which have arisen rapidly after the crown node of angiosperms. In the young tree (Fig. 3 ), the early burst occurs during the Cretaceous, consistent with the hypothesis that a Cretaceous terrestrial revolution was triggered by the establishment of main angiosperm lineages 14 , 39 , 40 . More controversially, the old tree places the early burst in the Triassic Period (Extended Data Fig. 5 ), which is dramatically at variance with the palaeobotanical record 33 , 34 , highlighting that current molecular dating methods are unable to resolve the age of angiosperms 28 .

A tapestry of lineage-specific histories

Following the early burst, overall rates of diversification across angiosperms continued at a lower, constant pace for at least 80 Myr (Fig. 3a ), during which time around three-quarters of all families originated (Fig. 3b ). As expected, this phase of slower diversification was associated with lower levels of gene tree conflict. Despite the constancy of overall rates, diversification during this period was underpinned by a complex tapestry of lineage-specific patterns. This is illustrated by the LTT heatmap, which shows profound differences in diversification trajectories among orders (Fig. 2 ) and by the estimation of around 160 lineage-specific diversification rate shifts in angiosperms, most of which occur during this period. These rate shifts have a widespread phylogenetic distribution, with most orders containing at least one rate shift and many containing several nested shifts (Supplementary Table 4 ). The importance of nested rate shifts is highlighted extensively in discussions of evolutionary radiation 41 , 42 and underpins the continual response of diversification to dynamic extrinsic and intrinsic conditions. However, because these rate shifts are temporally scattered, as also shown by ref. 43 , they do not lead to observable global rate shifts across angiosperms.

A Cenozoic diversification surge

A second surge in angiosperm diversification occurred during the Cenozoic Era (Fig. 3a ). The occurrence of this surge, despite the already high standing diversity of angiosperms at the time, suggests that diversification was unaffected by diversity-dependent processes, that is, the filling of available niche space as clades diversify 44 . Instead, this finding is consistent with previously proposed positive feedbacks between increased diversity and increased rates of diversification in angiosperms 14 , alongside more positive feedbacks, for example, between angiosperm and insect diversification 45 , 46 . Alternatively, global climatic cooling during the Cenozoic acting as a driver of angiosperm diversification could explain this finding 7 , 47 , 48 , 49 . Importantly, an even larger Cenozoic surge would probably be inferred with increased sampling that addresses the under-representation of branching events in the recent time window. The temporal distribution of lineage-specific diversification rate shifts may offer some insight into the cause of the Cenozoic surge. Many of the largest diversification rate increases occur during the Cenozoic, whereas the number of diversification rate decreases declines markedly during this period (Fig. 4 ). These large rate increases may underpin the Cenozoic surge. The expansion of taxon sampling should be given priority to confirm these patterns.

The results illustrated are based on the young tree (maximum constraint at the root node of 154 Ma). See Extended Data Fig. 6 for results based on the old tree. a , Diversification rate increases per LTT. The colour corresponds to the average magnitude of the rate increases during the time period. b , Equivalent to a but for rate decreases. c , Equivalent to a but focusing on the largest 25% of diversification rate increases. In a , b and c , the number of shifts is from the maximum a posteriori shift configuration with the prior for the number of shifts set to 10 and the background grey-scale gradient is the estimated percentage of extant lineages represented in the species tree through time (sampling fraction).

The nuclear phylogenomic framework presented here is the result of an ongoing initiative to complete the tree of life for all angiosperm genera 50 , a milestone in our understanding of angiosperm evolutionary relationships. This study not only sheds light on much of the deep diversification history of the angiosperms but also lays foundations for future work towards a species-level tree 50 . The standardized panel of nuclear genes in our dataset paves the way for more collaborations and data integration 17 , 51 , while the open availability of universal tools to sequence them (that is, Angiosperms353 probes 8 ) has made nuclear genomic data more accessible at relatively low cost. The accelerating uptake of this approach 52 , 53 , 54 , which is readily applicable to herbarium collections 16 , indicates that large volumes of data will soon become available for a wide range of applications in plant diversity, systematic and macroevolutionary research.

Our fossil-calibrated, phylogenomic tree enables a range of unique insights into broad-scale diversification dynamics of angiosperms, substantiating the early burst of diversification anticipated by Darwin while illuminating the complexity and conflict in the lineage histories underlying it. This sets the scene for future research, extending these investigations to shallower phylogenetic scales or digging more deeply into the data to discover the processes driving angiosperm diversification, such as genomic conflict, polyploidy, selection, trait evolution and adaptation. The challenges brought by the scale of this dataset and its ongoing expansion may also catalyse the development of methods which take full advantage of the global proliferation of genomic data.

As part of the Plant and Fungal Trees of Life (PAFTOL) Project at the Royal Botanic Gardens, Kew 50 , we assembled a nuclear genomic dataset consisting of newly generated data and data mined from public repositories. Our objective was to sample at least 50% of all angiosperm genera, with genera selected in a phylogenetically representative manner on the basis of published research. To avoid excessive imbalance in the tree, we included only one sample per species and a maximum of three species per genus. When several samples were available for the same species, we selected those with the largest amount of data, that is, more genes and a higher sum of gene length. For genera with several species available, the criterion for selection was primarily phylogenetic representation followed by amount of data. One species of each gymnosperm family was selected to form the outgroup, totalling 12 samples.

We produced target sequence capture data for 7,561 samples using the universal Angiosperms353 probe set 8 following established laboratory protocols 50 , 56 . We complemented our dataset with publicly available data for 2,054 species, sourced from the One Thousand Plant Transcriptomes Initiative 9 (OneKP; 564 samples), annotated and unannotated genomes (151 samples) and the sequence read archive (SRA; 1,339 samples), the last including transcriptomes (for example, see refs.  57 , 58 ) and target capture data (for example, see refs.  59 , 60 ). To standardize taxonomy and nomenclature, all species names and families were harmonized with the World Checklist of Vascular Plants 2 and orders with APG IV if possible 18 .

Sequence recovery

Sequence recovery was carried out in two ways, depending on the type of input data. For recovery on the basis of raw reads, that is, Angiosperms353 data or data mined from the SRA, we used HybPiper v.1.31 (ref. 61 ), embedded in a bespoke pipeline ( https://github.com/baileyp1/PhylogenomicsPipelines ). Raw reads were trimmed using Trimmomatic 62 to remove low-quality bases and short sequences. In HybPiper, reads were initially binned into genes using BLASTN and an amino acid target file as reference (Supplementary File  1 ). Individual genes were assembled de novo using SPADES 63 and refined by joining and trimming gene contigs to match coding regions using Exonerate 64 . For genes with paralogue warnings, only the putative orthologue as identified by HybPiper was used. Exclusion of genes with several copies per species has been shown to have negligible impact on species tree inference when it is performed under a multispecies coalescent framework, as described below 20 . Conversely, the inclusion of several copies per species would have rendered our study computationally intractable. Gene sequences from assembled genomes and OneKP transcriptomes were recovered using custom scripts described in ref. 50 . Briefly, the assembled sequences were searched against the target file mentioned above using BLASTN, selecting the best match for each gene and trimming it to the BLAST hit. For a few Angiosperms353 samples that represented the sole accession of their respective families ( Ixonanthes reticulata , Mitrastemon matudae and Tetracarpaea tasmannica ) and had poor recovery from HybPiper (that is, below 5 kilobase pairs (kb) in total sum of contig length), recovery was undertaken following ref. 50 , using less stringent recovery thresholds. The average recovery per order is presented in Supplementary Fig. 9 .

Phylogenetic inference

To analyse the dataset, we devised a divide-and-conquer approach. First, we computed a backbone tree, sampling up to five species per family, to test the monophyly of orders and to rigorously explore deep relationships. We used the backbone tree to identify groups (orders or groups of orders) for multiple sequence alignment, with the aim of producing refined subalignments among closely related taxa. Subsequently, the subalignments were merged into global gene alignments and global gene trees were inferred from these using the respective gene trees from the backbone analysis as constraints. Finally, we inferred a multispecies coalescent tree using the estimated gene trees. The inference pipeline is summarized in Supplementary Fig. 1 .

Backbone tree inference

The samples for the backbone were selected so as to represent the crown node and deepest divergences in each family. For families with five or fewer samples (279 families), all samples were included. For those with more than five samples (156), we selected the best sample (most genes and longest sequence) of each consecutively diverging clade (based on published phylogenetic evidence and preliminary analyses of our own data), until five samples were included. To evaluate the extent to which sample selection might affect the backbone tree topology, we inferred 20 backbone replicates, randomly selecting five samples for each family with more than five samples (among the 50% best samples in terms of gene number and gene length recovered). We then summarized the trees to family level and computed Robinson–Foulds distances between the backbone and the 20 replicates (Supplementary Fig. 10 ).

The phylogenetic reconstruction of the backbone involved up to two iterations of gene alignment and gene tree estimation, with an intermediate step of outlier removal. This was followed by species tree inference in a multispecies coalescent framework. In the first iteration, all sequences for a given gene were aligned using MAFFT v.7.480 (ref. 65 ) (with ffnsi method, that is, --retree 2 --maxiterate 1000) and with the direction of the sequence adjusted (--adjustdirection). After removing sites with more than 90% missing data with Phyutility 66 , gene trees were estimated using IQ-TREE v.2.2.0-beta 67 , keeping identical sequences in the analysis (--keep-ident), setting the substitution model to GTR + G and estimating branch support with 1,000 ultrafast bootstrap replicates 68 . Before the second iteration, we identified long branch outliers using TreeShrink 69 in ‘all-genes’ mode and rerooting at the centroids of the trees. A second iteration of gene alignment, removal of gappy sites and gene tree estimation was performed for genes with outliers after the removal of outlier sequences. Subsequently, the resulting gene trees were summarized into a species tree using ASTRAL III v.5.7.3, a quartet-based species tree estimation method statistically consistent with the multispecies coalescent model 70 , enabling the full annotation option (-t 2), having first collapsed poorly supported nodes (ultrafast bootstrap ≤ 30%) in the input gene trees using Newick utilities 71 .

Order-level subalignments

For the order-level subalignments, most orders were analysed individually, following the same method described for the backbone. In some cases, smaller orders (fewer than 50 samples) were analysed together with larger ones if they formed monophyletic groups in the backbone. These groups are: (1) Commelinales with Zingiberales, (2) Dioscoreales with Pandanales, (3) Fagales with Fabales, (4) Columelliales, Dipsacales, Escalloniales and Paracryphiales with Apiales, (5) all magnoliids (Canellales, Laurales, Magnoliales and Piperales) and (6) all gymnosperms together (Cycadales, Ephedrales, Gnetales, Ginkgoales and Pinales). Conversely, orders emerging as non-monophyletic in the backbone were split into monophyletic subgroups as follows: (1) Cardiopteridaceae and Stemonuraceae separate from the rest of Aquifoliales, (2) Dasypogonaceae separate from the rest of Arecales, (3) Collumelliaceae separate from the rest of Bruniales, (4) Oncothecaceae separate from the rest of Icacinales and (5) Huaceae separate from the rest of Oxalidales. The groupings of samples used in the order-level subalignments are provided in Supplementary Table 1 . Very small groups, comprising one or two samples (termed orphan sequences), were not included in subalignments and were incorporated directly in global analyses.

Global gene alignments and trees

We produced global gene alignments by merging the order-level subalignments (before removal of gappy sites) and adding the orphan non-aligned sequences using MAFFT 65 , with up to 100 refinement iterations. This approach yields alignment across the order-level subalignments without disrupting the structure in the subalignments. The final gene alignments were cleaned by removing gappy sites. A summary of the alignments was produced with AMAS 72 (Supplementary Table 5 ) and the average occupancy per gene per order is presented in Supplementary Fig. 11 .

We then estimated gene trees in Fasttree v.2.1.10 (ref. 73 ), setting the model to GTR + G, using pseudocounts to avoid biases from fragmentary sequences and increasing search thoroughness (-spr 4, -mlacc 2 and -slownni). We used the gene trees from the backbone analysis to constrain the topology of each respective global gene tree. To avoid propagating error from the backbone analysis to the global analysis, we removed potentially misleading signal from the backbone gene trees before applying them as constraints. First, branches with bootstrap values below 80% were collapsed to avoid enforcing poorly supported relationships. Second, tips placed far from the rest of their order were algorithmically removed (but retained in global gene alignments). Once global gene trees were estimated, outlier long branches were removed using TreeShrink and the set of pruned gene trees was used to compute the global species tree using ASTRAL-MP v.5.15.5 (ref. 74 ), after collapsing branches with poor support (that is, those with support lower than 10% in the Shimodaira–Hasegawa test).

Divergence time estimation

Divergence times were estimated by penalized likelihood in treePL 75 , 76 . This method is computationally efficient for datasets of this scale and typically estimates similar divergence times to more computationally intensive Bayesian analyses. The coalescent species tree topology was used as the input tree with molecular branch lengths estimated in IQ-TREE, on the basis of a concatenated alignment of the top 25 genes selected by SortaDate 77 . Genes were selected by ranking their corresponding gene trees according to the number of congruent bipartitions with the species tree. We selected genes on this basis because high gene tree conflict leads to error in divergence time estimates 78 , 79 .

Fossil calibrations were based on the AngioCal fossil calibration dataset described in ref. 5 . We used an updated version of this dataset, referred to as AngioCal v.1.1 (Supplementary Table 6 and Supplementary File  2 ). Assigning fossil calibrations in this dataset to our tree topology led to 200 unique minimum age calibrations at internal nodes (Supplementary Table 7 and Supplementary Fig. 12 ). A maximum constraint of 154 or 247 Ma was used at the angiosperm crown node. These two values, respectively, represent a young and old constraint for the maximum age of the angiosperm crown node 5 , 28 . Both values are nonetheless considerably older than the oldest known crown group angiosperm fossils of around 127.2 Ma (ref. 80 ). Both maximum constraints, in combination with all the minimum age constraints, were used to time-calibrate the species tree. Depending on the maximum constraint at the root node, these dated phylogenetic trees are referred to as young tree and old tree, respectively. For both the young tree and old tree, four analyses were performed in treePL, using smoothing values of 0.1, 1, 10 or 100. These different smoothing values assume high to low levels of among-branch substitution rate variation.

Sampling extant lineages through time

At 1 Myr intervals from the root age of the dated phylogenetic trees to the present, we calculated how many angiosperm lineages would have been present in a hypothetical tree that sampled 100% of extant angiosperm species diversity. We used this to quantify the proportion of extant lineages incorporated by our phylogenetic trees through time ( Supplementary Methods ). To do this we simulated unsampled diversity on the dated trees: the species diversity of unsampled genera was simulated as a constant-rate birth–death branching process originating in the crown group of its respective family, whilst unsampled species diversity in sampled genera was simulated as a constant-rate birth–death branching process originating at the stem node of the relevant genus. The extant diversity of each simulated branching process was determined using the World Checklist of Vascular Plants 2 . At each time interval, we then calculated the proportional difference between the number of lineages in our dated phylogenetic tree and the hypothetical fully sampled tree.

Diversification rate estimation

Dated trees estimated with alternative smoothing values were very similar (Extended Data Fig. 2 and Supplementary Fig. 5 ), so diversification rate estimates were only performed with the dated trees estimated with a smoothing value of 10. By contrast, age estimates in the young and old trees differed markedly. Diversification rate estimates were therefore performed for both these dated trees. In each case, the dated trees were pruned such that there was a maximum of one tip for each genus.

An initial analysis of diversification rates was performed by generating LTT plots as heatmaps for angiosperms as a whole, as well as for each order, with colours representing the steepness of each LTT curve at 5 Myr intervals. To calculate the steepness of the curve, we calculated the running difference between logarithmic corrected cumulative sums of lineages and applied Tukey’s running median smoothing to avoid excessive noise. For order plots, the cumulative sum starts at the first branching point, that is, order crown nodes.

Time-dependent diversification parameters (speciation and extinction rates) were also explicitly estimated across all angiosperms. These analyses were performed in RevBayes with the dnEpisodicBirthDeath function 81 . The smallest time windows in which rates were estimated were 5 Ma. However, larger windows were used toward the root of the tree such that there were at least 50 branching events in each time window. Three different models were used: equal rates of speciation and extinction across all windows; variable rates of speciation across windows but equal rates of extinction; and equal rates of speciation across windows but variable rates of extinction. Bayes factor comparison was used to compare models and offered strong support for the variable rate models but could not distinguish between the two variable rate models ( Supplementary Information ), indicating that they are probably from the same congruent set of models for the species tree 82 . In subsequent discussion we primarily refer to results from the variable speciation rate model (for justification see  Supplementary Information ), although both variable rate models estimate similar patterns of net diversification rates through time ( Supplementary Information ).

Lineage-specific diversification rate estimation was performed in BAMM 83 and RevBayes. For analyses in BAMM, the setBammPriors function from the R package BAMMtools 84 was used to define appropriate priors. Different sets of analyses were performed with the prior for the expected number of shifts set to either 10 or 100. These different prior settings had minimal effect on parameter estimates. Clade-specific sampling fractions were specified for each sampled family and a backbone sampling fraction of 1 was used. We therefore accounted for incomplete sampling within families alongside comprehensive sampling of the backbone of the tree. For analyses in RevBayes, the dnCDBDP function was used and the prior for the total number of rate shifts was set to either 10 or 100. Clade-specific sampling fractions cannot be specified with this function. Therefore, the sampling fraction was set to 1 meaning that estimates will become inaccurate toward the present because of unsampled within-family diversity.

Simulations on gene tree conflict

Simulations were based on a multispecies coalescent process. Each species tree contained 100 tips and was simulated as a birth–death branching process with time-dependent rates of speciation and extinction. In experiment 1, the extinction rate was always 0. The speciation rate was 0.75 species Myr −1 at times over 6 Ma, between 6 and 2 Ma the speciation rate was 0.075 species Myr −1 and less than 2 Ma the speciation rate was 0.75 species Myr −1 . In experiment 2, the net diversification rates were the same as in experiment 1; however, in this case changes to the extinction rate led to the net diversification rate shifts. Therefore, for all time intervals, the speciation rate was 0.75 species Myr −1 . At times over 6 Ma the extinction rate was 0 species Myr −1 , between 6 and 2 Ma the extinction rate was 0.675 species Myr −1 and at times less than 2 Ma the extinction rate was 0 species Myr −1 .

Species trees with extinct lineages have extra complexities: first, changes in the extinction rate have a less direct impact on the duration of extant lineages in the species tree compared to changes in the speciation rate ( Supplementary Information ); and second, the effect of extinction is reduced at times close to the present. This causes shorter branches in the species tree, leading to the so-called ‘pull of the present’. We therefore performed a further analysis that was similar to experiment 2 but with no decrease in the extinction rate at the present. This offered insight into the effect of the ‘pull of the present’ on inferences of gene tree conflict and diversification rates and the relationship between these variables and the timing of rate shifts.

One-hundred gene trees were simulated along the branches of the birth–death branching processes according to a multispecies coalescent process. For most experiments, the effective population size was 5,000. In one further experiment, which was otherwise the same as experiment 1, the effective population size was 50,000. For each simulated dataset, the degree to which the simulated gene trees exhibited conflicting topologies with the species tree was plotted through time ( Supplementary Information ). This enabled characterization of the relationship between gene tree conflict caused by incomplete lineage sorting and shifts in speciation and extinction rates in the species tree.

More methods, results and discussion are available ( Supplementary Information ; Supplementary Figs. 13 – 24 and Supplementary Table 8 ).

Inclusion and ethics statement

The research described here results from a highly inclusive, large-scale, international collaboration, which has actively encouraged the participation of many individuals from around the world. The authorship comprises many nationalities and is representative in terms of gender, career stage and career path. A total of 163 herbaria from 48 countries provided samples and/or house herbarium vouchers related to samples used in the study (see Acknowledgements). These samples originated from many countries, including Indigenous lands. We recognize the complex histories underlying all natural history collections and the global challenge we face in acknowledging them. We gave priority to recently collected samples and, as a result, most (85%) date from the postcolonial era (estimated here as 1970 onward). To share the benefits of our research, all data generated through this collaboration have been made publicly available before the submission of this work in several data releases, starting in 2019 (see Data availability).

Reporting summary

Further information on research design is available in the  Nature Portfolio Reporting Summary linked to this article.

Data availability

All raw DNA sequence data generated for this study are deposited in the European Nucleotide Archive under the following bioprojects PRJNA478314 , PRJEB35285 , PRJEB49212 and PRJNA678873 . All analysed data and metadata are available in Zenodo at https://doi.org/10.5281/zenodo.10778206 (ref. 55 ). The resulting trees and metadata are also available in GBIF ( https://doi.org/10.15468/4njn8b ) and Open Tree of Life ( https://tree.opentreeoflife.org/curator/study/view/ot_2304 ). The names used in this work match the World Checklist of Vascular Plants ( https://doi.org/10.34885/jdh2-dr22 ).

Code availability

The code used and developed to perform analyses is available in GitHub at https://github.com/RBGKew/AngiospermPhylogenomics and Zenodo at https://doi.org/10.5281/zenodo.10778206 (ref. 55 ).

Diamond, J. Evolution, consequences and future of plant and animal domestication. Nature 418 , 700–707 (2002).

Article   ADS   CAS   PubMed   Google Scholar  

Govaerts, R., Nic Lughadha, E., Black, N., Turner, R. & Paton, A. The World Checklist of Vascular Plants, a continuously updated resource for exploring global plant diversity. Sci. Data 8 , 215 (2021).

Article   PubMed   PubMed Central   Google Scholar  

Chase, M. W. et al. Phylogenetics of seed plants: an analysis of nucleotide sequences from the plastid gene rbcL. Ann. Missouri. Bot. Gard. 80 , 528–580 (1993).

Article   Google Scholar  

Li, H.-T. et al. Plastid phylogenomic insights into relationships of all flowering plant families. BMC Biol. 19 , 232 (2021).

Ramírez-Barahona, S., Sauquet, H. & Magallón, S. The delayed and geographically heterogeneous diversification of flowering plant families. Nat. Ecol. Evol. 4 , 1232–1238 (2020).

Article   PubMed   Google Scholar  

Li, H.-T. et al. Origin of angiosperms and the puzzle of the Jurassic gap. Nat. Plants 5 , 461–470 (2019).

Dimitrov, D. et al. Diversification of flowering plants in space and time. Nat. Commun. 14 , 7609 (2023).

Article   ADS   CAS   PubMed   PubMed Central   Google Scholar  

Johnson, M. G. et al. A universal probe set for targeted sequencing of 353 nuclear genes from any flowering plant designed using k-medoids clustering. Syst. Biol. 68 , 594–606 (2019).

Article   CAS   PubMed   Google Scholar  

One Thousand Plant Transcriptomes Initiative. One thousand plant transcriptomes and the phylogenomics of green plants. Nature 574 , 679–685 (2019).

Article   CAS   Google Scholar  

Barba-Montoya, J., dos Reis, M., Schneider, H., Donoghue, P. C. J. & Yang, Z. Constraining uncertainty in the timescale of angiosperm evolution and the veracity of a Cretaceous Terrestrial Revolution. New Phytol. 218 , 819–834 (2018).

Doyle, J. A. Molecular and fossil evidence on the origin of angiosperms. Annu. Rev. Earth Planet. Sci. 40 , 301–326 (2012).

Article   ADS   CAS   Google Scholar  

Holbourn, A. E. et al. Late Miocene climate cooling and intensification of southeast Asian winter monsoon. Nat. Commun. 9 , 1584 (2018).

Article   ADS   PubMed   PubMed Central   Google Scholar  

Pennington, R. T., Cronk, Q. C. B. & Richardson, J. A. Introduction and synthesis: plant phylogeny and the origin of major biomes. Philos. Trans. R. Soc. Lond. B 359 , 1455–1464 (2004).

Benton, M. J., Wilf, P. & Sauquet, H. The Angiosperm terrestrial revolution and the origins of modern biodiversity. New Phytol. 233 , 2017–2035 (2022).

Dodsworth, S. et al. Hyb-Seq for flowering plant systematics. Trends Plant Sci. 24 , 887–891 (2019).

Brewer, G. E. et al. Factors affecting targeted sequencing of 353 nuclear genes from herbarium specimens spanning the diversity of angiosperms. Front. Plant Sci. 10 , 1102 (2019).

Baker, W. J. et al. Exploring Angiosperms353: an open, community toolkit for collaborative phylogenomic research on flowering plants. Am. J. Bot. 108 , 1059–1065 (2021).

The Angiosperm Phylogeny Group. et al. An update of the Angiosperm Phylogeny Group classification for the orders and families of flowering plants: APG IV. Bot. J. Linn. Soc. 181 , 1–20 (2016).

Joyce, E. M. et al. Phylogenomic analyses of Sapindales support new family relationships, rapid Mid-Cretaceous Hothouse diversification and heterogeneous histories of gene duplication. Front. Plant Sci. 14 , 1063174 (2023).

Yan, Z., Smith, M. L., Du, P., Hahn, M. W. & Nakhleh, L. Species tree inference methods intended to deal with iIncomplete lineage sorting are robust to the presence of paralogs. Syst. Biol. 71 , 367–381 (2022).

Watanabe, T., Kure, A. & Horiike, T. OrthoPhy: a program to construct ortholog data sets using taxonomic information. Genome Biol. Evol. 15 , evad026 (2023).

Ruhfel, B. R., Gitzendanner, M. A., Soltis, P. S., Soltis, D. E. & Burleigh, J. G. From algae to angiosperms—inferring the phylogeny of green plants (Viridiplantae) from 360 plastid genomes. BMC Evol. Biol. 14 , 23 (2014).

Endress, P. K. Origins of flower morphology. J. Exp. Zool. 291 , 105–115 (2001).

Stull, G. W., Duno de Stefano, R., Soltis, D. E. & Soltis, P. S. Resolving basal lamiid phylogeny and the circumscription of Icacinaceae with a plastome-scale data set. Am. J. Bot. 102 , 1794–1813 (2015).

Soltis, D. E. et al. Chloroplast gene sequence data suggest a single origin of the predisposition for symbiotic nitrogen fixation in angiosperms. Proc. Natl Acad. Sci. USA 92 , 2647–2651 (1995).

Walker, J. F. et al. From cacti to carnivores: improved phylotranscriptomic sampling and hierarchical homology inference provide further insight into the evolution of Caryophyllales. Am. J. Bot. 105 , 446–462 (2018).

Guo, X. et al. Chloranthus genome provides insights into the early diversification of angiosperms. Nat. Commun. 12 , 6930 (2021).

Sauquet, H., Ramírez-Barahona, S. & Magallón, S. What is the age of flowering plants? J. Exp. Bot. 73 , 3840–3853 (2022).

Landis, J. B. et al. Impact of whole-genome duplication events on diversification rates in angiosperms. Am. J. Bot. 105 , 348–363 (2018).

Tank, D. C. et al. Nested radiations and the pulse of angiosperm diversification: increased diversification rates often follow whole genome duplications. New Phytol. 207 , 454–467 (2015).

Darwin, C. The Correspondence of Charles Darwin (Cambridge Univ. Press, 1879).

Buggs, R. J. A. Reconfiguring Darwin’s abominable mystery. Nat. Plants 8 , 194–195 (2022).

Coiro, M., Doyle, J. A. & Hilton, J. How deep is the conflict between molecular and fossil evidence on the age of angiosperms? New Phytol. 223 , 83–99 (2019).

Herendeen, P. S., Friis, E. M., Pedersen, K. R. & Crane, P. R. Palaeobotanical redux: revisiting the age of the angiosperms. Nat. Plants 3 , 17015 (2017).

Friis, E. M., Crane, P. R. & Pedersen, K. R. Early Flowers and Angiosperm Evolution (Cambridge Univ. Press, 2011).

Davies, T. J. et al. Darwin’s abominable mystery: insights from a supertree of the angiosperms. Proc. Natl Acad. Sci. USA 101 , 1904–1909 (2004).

Magallón, S., Gómez-Acevedo, S., Sánchez-Reyes, L. L. & Hernández-Hernández, T. A metacalibrated time-tree documents the early rise of flowering plant phylogenetic diversity. New Phytol. 207 , 437–453 (2015).

Mathews, S. & Donoghue, M. J. The root of angiosperm phylogeny inferred from duplicate phytochrome genes. Science 286 , 947–950 (1999).

Dilcher, D. Toward a new synthesis: major evolutionary trends in the angiosperm fossil record. Proc. Natl Acad. Sci. USA 97 , 7030–7036 (2000).

Meredith, R. W. et al. Impacts of the Cretaceous terrestrial revolution and KPg extinction on mammal diversification. Science 334 , 521–524 (2011).

Bouchenak-Khelladi, Y., Onstein, R. E., Xing, Y., Schwery, O. & Linder, H. P. On the complexity of triggering evolutionary radiations. New Phytol. 207 , 313–326 (2015).

Donoghue, M. J. & Sanderson, M. J. Confluence, synnovation and depauperons in plant diversification. New Phytol. 207 , 260–274 (2015).

Magallón, S., Sánchez-Reyes, L. L. & Gómez-Acevedo, S. L. Thirty clues to the exceptional diversification of flowering plants. Ann. Bot. 123 , 491–503 (2019).

Rabosky, D. L. Diversity-dependence, ecological speciation and the role of competition in macroevolution. Annu. Rev. Ecol. Evol. Syst. 44 , 481–502 (2013).

Asar, Y., Ho, S. Y. W. & Sauquet, H. Early diversifications of angiosperms and their insect pollinators: were they unlinked? Trends Plant Sci. 27 , 858–869 (2022).

Peris, D. & Condamine, F. L. The dual role of the angiosperm radiation on insect diversification. Nat. Commun. 15 , 552 (2024).

Folk, R. A. et al. Rates of niche and phenotype evolution lag behind diversification in a temperate radiation. Proc. Natl Acad. Sci. USA 116 , 10874–10882 (2019).

Sun, M. et al. Recent accelerated diversification in rosids occurred outside the tropics. Nat. Commun. 11 , 3333 (2020).

Soltis, P. S., Folk, R. A. & Soltis, D. E. Darwin review: angiosperm phylogeny and evolutionary radiations. Proc. R. Soc. B 286 , 20190099 (2019).

Article   PubMed Central   Google Scholar  

Baker, W. J. et al. A comprehensive phylogenomic platform for exploring the angiosperm tree of life. Syst. Biol. 71 , 301–319 (2022).

McDonnell, A. J. et al. Exploring Angiosperms353: developing and applying a universal toolkit for flowering plant phylogenomics. Appl. Plant Sci. 9 , e11443 (2021).

Bratzel, F. et al. Target-enrichment sequencing reveals for the first time a well-resolved phylogeny of the core Bromelioideae (family Bromeliaceae). TAXON 72 , 47–63 (2023).

Gagnon, E. et al. Phylogenomic discordance suggests polytomies along the backbone of the large genus Solanum. Am. J. Bot. 109 , 580–601 (2022).

Murillo-A, J., Valencia-D, J., Orozco, C. I., Parra-O, C. & Neubig, K. M. Incomplete lineage sorting and reticulate evolution mask species relationships in Brunelliaceae, an Andean family with rapid, recent diversification. Am. J. Bot. 109 , 1139–1156 (2022).

Zuntini, A.R. & Carruthers, T. Phylogenomics and the rise of the angiosperms. Zenodo https://doi.org/10.5281/zenodo.10778206 (2024).

Hendriks, K. P. et al. Global Brassicaceae phylogeny based on filtering of 1,000-gene dataset. Curr. Biol 33 , 4052–4068 (2023).

Chen, L.-Y. et al. Phylogenomic analyses of Alismatales shed light into adaptations to aquatic environments. Mol. Biol. Evol. 39 , msac079 (2022).

Article   CAS   PubMed   PubMed Central   Google Scholar  

Timilsena, P. R. et al. Phylogenomic resolution of order- and family-level monocot relationships using 602 single-copy nuclear genes and 1375 BUSCO genes. Front. Plant Sci. 13 , 876779 (2022).

Ogutcen, E. et al. Phylogenomics of Gesneriaceae using targeted capture of nuclear genes. Mol. Phylogenet. Evol. 157 , 107068 (2021).

Yardeni, G. et al. Taxon-specific or universal? Using target capture to study the evolutionary history of rapid radiations. Mol. Ecol. Resour. 22 , 927–945 (2022).

Johnson, M. G. et al. HybPiper: extracting coding sequence and introns for phylogenetics from high-throughput sequencing reads using target enrichment. Appl. Plant Sci. 4 , 1600016 (2016).

Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30 , 2114–2120 (2014).

Bankevich, A. et al. SPAdes: a new genome assembly algorithm and its applications to single-cell sequencing. J. Comput. Biol. 19 , 455–477 (2012).

Article   MathSciNet   CAS   PubMed   PubMed Central   Google Scholar  

Slater, G. S. C. & Birney, E. Automated generation of heuristics for biological sequence comparison. BMC Bioinformatics 6 , 31 (2005).

Katoh, K. & Standley, D. M. MAFFT Multiple Sequence Alignment Software Version 7: improvements in performance and usability. Mol. Biol. Evol. 30 , 772–780 (2013).

Smith, S. A. & Dunn, C. W. Phyutility: a phyloinformatics tool for trees, alignments and molecular data. Bioinformatics 24 , 715–716 (2008).

Minh, B. Q. et al. IQ-TREE 2: new models and efficient methods for phylogenetic inference in the genomic era. Mol. Biol. Evol. 37 , 1530–1534 (2020).

Hoang, D. T., Chernomor, O., von Haeseler, A., Minh, B. Q. & Vinh, L. S. UFBoot2: improving the ultrafast bootstrap approximation. Mol. Biol. Evol. 35 , 518–522 (2018).

Mai, U. & Mirarab, S. TreeShrink: fast and accurate detection of outlier long branches in collections of phylogenetic trees. BMC Genomics 19 , 272 (2018).

Zhang, C., Rabiee, M., Sayyari, E. & Mirarab, S. ASTRAL-III: polynomial time species tree reconstruction from partially resolved gene trees. BMC Bioinformatics 19 , 153 (2018).

Junier, T. & Zdobnov, E. M. The Newick utilities: high-throughput phylogenetic tree processing in the Unix shell. Bioinformatics 26 , 1669–1670 (2010).

Borowiec, M. L. AMAS: a fast tool for alignment manipulation and computing of summary statistics. PeerJ 4 , e1660 (2016).

Price, M. N., Dehal, P. S. & Arkin, A. P. FastTree 2—approximately maximum-likelihood trees for large alignments. PLoS ONE 5 , e9490 (2010).

Yin, J., Zhang, C. & Mirarab, S. ASTRAL-MP: scaling ASTRAL to very large datasets using randomization and parallelization. Bioinformatics 35 , 3961–3969 (2019).

Sanderson, M. J. Estimating absolute rates of molecular evolution and divergence times: a penalized likelihood approach. Mol. Biol. Evol. 19 , 101–109 (2002).

Smith, S. A. & O’Meara, B. C. treePL: divergence time estimation using penalized likelihood for large phylogenies. Bioinformatics 28 , 2689–2690 (2012).

Smith, S. A., Brown, J. W. & Walker, J. F. So many genes, so little time: a practical approach to divergence-time estimation in the genomic era. PLoS ONE 13 , e0197433 (2018).

Britton, T. Estimating divergence times in phylogenetic trees without a molecular vlock. Syst. Biol. 54 , 500–507 (2005).

Carruthers, T. et al. The implications of incongruence between gene tree and species tree topologies for divergence time estimation. Syst. Biol. 71 , 1124–1146 (2022).

Gomez, B., Daviero-Gomez, V., Coiffard, C., Martín-Closas, C. & Dilcher, D. L. Montsechia, an ancient aquatic angiosperm. Proc. Natl Acad. Sci. USA 112 , 10985–10988 (2015).

Höhna, S. et al. RevBayes: Bayesian phylogenetic inference using graphical models and an interactive model-specification language. Syst. Biol. 65 , 726–736 (2016).

Louca, S. & Pennell, M. W. Extant timetrees are consistent with a myriad of diversification histories. Nature 580 , 502–505 (2020).

Rabosky, D. L. Automatic detection of key innovations, rate shifts and diversity-dependence on phylogenetic trees. PLoS ONE 9 , e89543 (2014).

Rabosky, D. L. et al. BAMMtools: an R package for the analysis of evolutionary dynamics on phylogenetic trees. Methods Ecol. Evol. 5 , 701–707 (2014).

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Acknowledgements

The PAFTOL project was funded by grants from the Calleva Foundation to the Royal Botanic Gardens, Kew. Data were also contributed by the Genomics for Australian Plants Framework Initiative consortium funded by Bioplatforms Australia (enabled by the National Collaborative Research Infrastructure Strategy) and partner organizations. The work was further supported by research grants from VILLUM FONDEN (grant no. 00025354) and the Aarhus University Research Foundation (grant no. AUFF-E-2017-7-19) to W.L.E. and from grant nos NSF DBI 1930030 and DEB 1917146 to S.A.S. Computational resources and technical support were provided by the Research/Scientific Computing teams at The James Hutton Institute and the National Institute of Agricultural Botany (NIAB) through the ‘UK’s Crop Diversity Bioinformatics HPC’ (BBSRC grant no. BB/S019669/1). The following provided technical assistance to the project at various stages: O. Berry, N. Black, M. Corcoran, S. Dequiret, I. Fairlie, L. Frankel, T. Freeth, A. Gilbert, B. Lepschi, D. Lewis, L. May, A. McArdle, E. O’Loughlin, S. Phillips, T. Sarkinen, L. Simmons, N. Walsh and M.-H. Weech. We thank all institutions who made their biological collections available and the many botanists and co-workers in the field who have collected, identified and curated the specimens used in this project. Specifically, we thank the following herbaria and their staff for providing samples for genomic analysis and/or for housing voucher specimens associated with analysed samples: A, ABH, AD, ALTB, APSC, B, BA, BC, BCN, BCRU, BG, BH, BHCB, BISH, BJFC, BKF, BM, BNRH, BOL, BONN, BR, BRI, BRIT, BRLU, BRUN, C, CAN, CANB, CAS, CBG, CNS, COL, CONC, CORD, CS, CTES, CUVC, DNA, E, EA, F, FI, FLAS, FMB, FTG, G, GB, GC, GENT, GH, GOET, GUAY, GZU, HAW, HEID, HITBC, HNG, HO, HPUJ, HRCB, HTW, HUA, HUAL, HUAZ, HUB, HUEFS, HUFU, IBSC, IBUG, ICN, IEB, INB, INPA, JBB, JBL, JRAU, K, KAS, KLU, KRB, KUN, L, LE, LISC, LP, LPB, LYJB, M, MA, MAU, MBA, MBML, MEDEL, MEL, MELU, MHA, MICH, MIN, MJG, MO, MSUN, MT, MY, N, NBG, NCU, NCY, NE, NH, NHM, NHMR, NMNL, NOU, NSW, NU, NY, OS, OSBU, P, PERTH, PG, PH, PRE, PTBG, QBG, QCA, QRS, RB, REU, S, SALA, SAR, SEV, SGO, SI, SING, SP, SPF, SPFR, SUVA, TCD, TEX, TNS, TUH, TUM, U, UAPC, UB, UBT, UDW, UEC, UPCB, UPR, UPS, UPTC, US, USM, W, WAG, WS, WTU, YA and ZSS; acronyms follow Index Herbariorum ( https://sweetgum.nybg.org/science/ih/ ). We also thank the Millennium Seed Bank Partnership for supporting access to samples. We acknowledge all national, state and regional authorities who authorized and facilitated the sourcing of these specimens. See also extended acknowledgements in the  Supplementary Information .

Author information

These authors contributed equally: Alexandre R. Zuntini, Tom Carruthers

These authors jointly supervised this work: Stephen A. Smith, Wolf L. Eiserhardt, Félix Forest, William J. Baker

Authors and Affiliations

Royal Botanic Gardens, Kew, Richmond, UK

Alexandre R. Zuntini, Tom Carruthers, Olivier Maurin, Paul C. Bailey, Kevin Leempoel, Grace E. Brewer, Niroshini Epitawalage, Elaine Françoso, Berta Gallego-Paramo, Catherine McGinnie, Raquel Negrão, Shyamali R. Roy, Eduardo Toledo Romero, Vanessa M. A. Barber, James J. Clarkson, Robyn S. Cowan, Lisa Pokorny, Ai-Qun Hu, Isabel Larridon, Panagiota Malakasi, Natalia A. S. Przelomska, Toral Shah, Juan Viruel, Watchara Arthan, Nicky Biggs, Renata Borosova, Gemma L. C. Bramley, Marie Briggs, Edie Burns, Stuart Cable, Abigail J. A. Carruthers, Mark W. Chase, Martin Cheek, Maarten J. M. Christenhusz, Laszlo Csiba, Iain Darbyshire, Nina M. J. Davies, Aaron P. Davis, Sara L. Edwards, Michael F. Fay, Sarah Z. Ficinski, Sue Frisby, Tim Fulcher, David J. Goyder, Aurélie Grall, Laura Green, Jan Hackel, Anna Haigh, Tony Hall, Sebastian A. Hatt, Helen C. F. Hopkins, Imalka M. Kahandawala, Bente B. Klitgaard, Christine J. Leon, Gwilym P. Lewis, Meng Lu, Eve J. Lucas, Manuel Luján, Carlos Magdalena, Lizo E. Masters, Simon J. Mayo, Alexandre K. Monro, Oscar A. Perez-Escobar, Robyn F. Powell, Ghillean T. Prance, Carmen Puglisi, Paul E. J. Rees, Saba Rokni, Ana Rita G. Simões, Michelle Siros, Cynthia A. Sothers, Anna Trias-Blasi, Timothy M. A. Utteridge, Maria S. Vorontsova, Noor Al-Wattar, Roseina Woods, Martin Xanthos, Sue Zmarzty, Alexandre Antonelli, Sidonie Bellot, Olwen M. Grace, Paul J. Kersey, Ilia J. Leitch, Wolf L. Eiserhardt, Félix Forest & William J. Baker

Centre for Ecology, Evolution and Behaviour, Department of Biological Sciences, School of Life Sciences and the Environment, Royal Holloway University of London, London, UK

Elaine Françoso

Australian Tropical Herbarium, James Cook University, Smithfield, Queensland, Australia

Lalita Simpson, Elizabeth M. Joyce, Matthew D. Barrett, Melissa J. Harrison, Katharina Nargar, Lars Nauheimer, Stuart Worboys & Darren M. Crayn

Centre for Research in Agricultural Genomics (CRAG), CSIC-IRTA-UAB-UB, Campus UAB, Barcelona, Spain

Laura Botigué

School of Biological Sciences, University of Portsmouth, Portsmouth, UK

Steven Dodsworth & Natalia A. S. Przelomska

Texas Tech University, Lubbock, TX, USA

Matthew G. Johnson

School of Physics, Engineering and Computer Science, University of Hertfordshire, Hatfield, UK

Department of Biodiversity and Conservation, Real Jardín Botánico (RJB-CSIC), Madrid, Spain

Lisa Pokorny

Department of Biological Sciences, Clemson University, Clemson, SC, USA

Norman J. Wickett

Departamento de Botânica, Instituto de Biociências, Universidade de São Paulo, São Paulo, Brazil

Guilherme M. Antar, Elton John de Lírio & José R. Pirani

Departamento de Ciências Agrárias e Biológicas, Centro Universitário Norte do Espírito Santo, Universidade Federal do Espírito Santo, São Mateus, Brazil

Guilherme M. Antar

Smith College, Northampton, MA, USA

Lucinda DeBolt & Karime Gutierrez

Department of Biology, University of Osnabrück, Osnabrück, Germany

Kasper P. Hendriks & Klaus Mummenhoff

Naturalis Biodiversity Center, Leiden, The Netherlands

Kasper P. Hendriks, Diego Bogarín, Roy H. J. Erkens, Frederic Lens, Vincent S. F. T. Merckx & Jan J. Wieringa

Plant Biodiversity, Technical University Munich, Freising, Germany

Alina Hoewener, Edgardo M. Ortiz & Hanno Schaefer

Systematic, Biodiversity and Evolution of Plants, Ludwig Maximilian University of Munich, Munich, Germany

Elizabeth M. Joyce

Department of Botany, University of British Columbia, Vancouver, British Columbia, Canada

Izai A. B. S. Kikuchi & Sean W. Graham

Department of Ecology & Evolutionary Biology, University of Michigan, Ann Arbor, MI, USA

Drew A. Larson, Richard K. Rabeler & Stephen A. Smith

Germplasm Bank of Wild Species, Kunming Institute of Botany, Chinese Academy of Sciences, Kunming, China

Jing-Xia Liu, De-Zhu Li & Meng-Yuan Zhou

Royal Botanic Gardens Victoria, Melbourne, Victoria, Australia

Theodore R. Allnutt, Helen F. Barnes, David J. Cantrill, Bee F. Gunn, Gareth D. Holmes, Christopher J. Jackson, Todd G. B. McLay, Daniel J. Murphy & Frank Udovicic

Department of Plant and Environmental Biology, University of Ghana, Accra, Ghana

Gabriel K. Ameka

Botany and N.C.W. Beadle Herbarium, University of New England, Armidale, New South Wales, Australia

Rose L. Andrew, Jeremy J. Bruhl, Ian R. H. Telford & Andrew H. Thornhill

Department of Systematics, Biodiversity and Evolution of Plants, Albrecht-von-Haller Institute of Plant Sciences, University of Göttingen, Göttingen, Germany

Marc S. Appelhans

Departamento de Biología Vegetal y Ecología, Facultad de Biología, Universidad de Sevilla, Seville, Spain

Montserrat Arista, Juan Arroyo, Alejandra de Castro Mateo, Marcial Escudero & Jose C. Del Valle

General Research Services, Herbario SEV, CITIUS, Universidad de Sevilla, Seville, Spain

María Jesús Ariza

Institute of Biology, Freie Universität, Berlin, Germany

Julien B. Bachelier

Department of Biology, New Mexico State University, Las Cruces, NM, USA

C. Donovan Bailey

National Herbarium of NSW, Botanic Gardens of Sydney, Mount Annan, New South Wales, Australia

Russell L. Barrett, Marco F. Duretto, Richard W. Jobson, Patricia Lu-Irving, Kristina McColl, Hannah McPherson, Matthew Renner, Ifeanna Tooth, Trevor C. Wilson, Lisa A. Woods & Hervé Sauquet

Department of Biological Sciences, University of Memphis, Memphis, TN, USA

Randall J. Bayer

School of BioSciences, The University of Melbourne, Parkville, Victoria, Australia

Michael J. Bayly, Joanne L. Birch & Rachael M. Fowler

State Herbarium of South Australia, Botanic Gardens and State Herbarium, Adelaide, South Australia, Australia

Ed Biffin, Ainsley Calladine, Francis J. Nge, Andrew H. Thornhill, Helen P. Vonow & Michelle Waycott

Jardín Botánico Lankester, Universidad de Costa Rica, Cartago, Costa Rica

Diego Bogarín

School of Geographical Sciences, University of Bristol, Bristol, UK

Alexander M. C. Bowles

Centro Studi Erbario Tropicale, Dipartimento di Biologia, University of Florence, Florence, Italy

Peter C. Boyce

Centre for Australian National Biodiversity Research, National Research Collections Australia, CSIRO, Canberra, Australian Capital Territory, Australia

Linda Broadhurst, Mark A. Clements, Katharina Nargar & Alexander Schmidt-Lebuhn

Queensland Herbarium and Biodiversity Science, Brisbane Botanic Gardens, Toowong, Queensland, Australia

Gillian K. Brown

Institut de Recherche en Biologie Végétale and Département de Sciences Biologiques, University of Montreal, Montreal, Quebec, Canada

Anne Bruneau

Department of Biological Sciences, Boise State University, Boise, ID, USA

Sven Buerki & James F. Smith

Biodiversity and Conservation Science, Department of Biodiversity, Conservation and Attractions, Government of Western Australia, Kensington, Western Australia, Australia

Margaret Byrne

Conservatoire et Jardin Botaniques de Genève, Chambésy, Switzerland

Martin W. Callmander

Cambridge University Botanic Garden, Cambridge, UK

Ángela Cano

Department of Forest and Conservation Sciences, University of British Columbia, Vancouver, British Columbia, Canada

Warren M. Cardinal-McTeague

Missouri Botanical Garden, St. Louis, MO, USA

Mónica M. Carlsen, Gerrit Davidse, Carmen Puglisi, Ihsan Al-Shehbaz & Peter F. Stevens

Department of Environment and Agriculture, Curtin University, Bentley, Western Australia, Australia

Mark W. Chase

Department of Biology, Ghent University, Ghent, Belgium

Lars W. Chatrou & Federico Fabriani

Institute of Herbgenomics, Chengdu University of Traditional Chinese Medicine, Chengdu, China

Shilin Chen

Institute of Medicinal Plant Development, Chinese Academy of Medical Sciences, Beijing, China

Department of Environment and Agriculture, Curtin University, Perth, Western Australia, Australia

Maarten J. M. Christenhusz

Plant Gateway, Den Haag, The Netherlands

Ecology and Evolutionary Biology, School of Biosciences, University of Sheffield, Sheffield, UK

Pascal-Antoine Christin

Western Australian Herbarium, Department of Biodiversity, Conservation and Attractions, Government of Western Australia, Kensington, Western Australia, Australia

Skye C. Coffey, Shelley A. James, Terry D. Macfarlane & Kelly A. Shepherd

School of Biological Sciences, The University of Adelaide, Adelaide, South Australia, Australia

John G. Conran, Andrew H. Thornhill & Michelle Waycott

Herbario GUAY, Facultad de Ciencias Naturales, Universidad de Guayaquil, Guayaquil, Ecuador

Xavier Cornejo

DIADE, Université Montpellier, CIRAD IRD, Montpellier, France

Thomas L. P. Couvreur

Northern Territory Herbarium Department of Environment Parks & Water Security, Northern Territory Government, Palmerston, Northern Territory, Australia

Ian D. Cowie

The University of Adelaide, North Terrace Campus, Adelaide, South Australia, Australia

Kor-jent van Dijk, Andrew E. McDougall & Luis T. Williamson

Department of Plant Biology, University of Illinois at Urbana-Champaign, Urbana, IL, USA

Stephen R. Downie

Department of Biological Sciences and Institute for the Study of the Environment, Sustainability and Energy, Northern Illinois University, DeKalb, IL, USA

Melvin R. Duvall

Sukkulenten-Sammlung Zürich/ Grün Stadt Zürich, Zürich, Switzerland

Maastricht Science Programme, Maastricht University, Maastricht, The Netherlands

Roy H. J. Erkens

System Earth Science, Maastricht University, Venlo, The Netherlands

Departamento de Botánica, Ecología y Fisiología Vegetal, Facultad de Ciencias, Universidad de Córdoba, Córdoba, Spain

Manuel de la Estrella

Departamento de Biologia, Faculdade de Ciências e Letras de Ribeirão Preto, Universidade de São Paulo, São Paulo, Brazil

Paola de L. Ferreira

Department of Biology, Aarhus University, Aarhus, Denmark

Paola de L. Ferreira, Wolf L. Eiserhardt & William J. Baker

South China Botanical Garden, Chinese Academy of Sciences, Guangzhou, China

Lin Fu & Ming Qin

Systematics and Evolution of Vascular Plants (UAB)—Associated Unit to CSIC by IBB, Departament de Biologia Animal, Biologia Vegetal i Ecologia, Facultat de Biociències, Universitat Autònoma de Barcelona, Bellaterra, Spain

Mercè Galbany-Casals

Department of Biology, Case Western Reserve University, Cleveland, OH, USA

Elliot M. Gardner

Altai State University, Barnaul, Russia

Dmitry A. German

Faculdade de Ciências Biológicas e Ambientais, Universidade Federal da Grande Dourados, Dourados, Brazil

Augusto Giaretta

Laboratoire Sciences Pour l’Environnement, Université de Corse, Ajaccio, France

Marc Gibernau

Canadian Museum of Nature, Ottawa, Ontario, Canada

Lynn J. Gillespie

Herbario Trelew, Universidad Nacional de la Patagonia San Juan Bosco, Trelew, Argentina

Cynthia C. González

Museo Argentino de Ciencias Naturales (MACN-CONICET), Buenos Aires, Argentina

Diego G. Gutiérrez & Luis Palazzesi

Department of Biology, Universität Marburg, Marburg, Germany

Institut de Systématique, Evolution, Biodiversité, Muséum National d’Histoire Naturelle, Paris, France

Thomas Haevermans & Marc Pignal

Department of Biological Sciences, University of Alberta, Edmonton, Alberta, Canada

Jocelyn C. Hall

Institut Botànic de Barcelona (IBB CSIC-Ajuntament de Barcelona), Barcelona, Spain

Oriane Hidalgo & Jaume Pellicer

Botany, School of Natural Sciences, Trinity College Dublin, The University of Dublin, Dublin, Ireland

Trevor R. Hodkinson

Prinzessin Therese von Bayern-Lehrstuhl für Systematik, Biodiversität & Evolution der Pflanzen, Ludwig-Maximilians-Universität München, Botanische Staatssammlung München, Botanischer Garten München-Nymphenburg, Munich, Germany

Gudrun Kadereit

Gothenburg Botanical Garden, Gothenburg, Sweden

Kent Kainulainen

National Museum of Nature and Science, Tsukuba, Japan

Masahiro Kato

Donald Danforth Plant Science Center, St. Louis, MO, USA

Elizabeth A. Kellogg

Southern Cross University, Lismore, New South Wales, Australia

Graham J. King

Synergy SRG, Luton, UK

Beata Klejevskaja

Foundational Biodiversity Science Division, South African National Biodiversity Institute, Pretoria, South Africa

Ronell R. Klopper

Department of Plant and Soil Sciences, University of Pretoria, Pretoria, South Africa

Natural History Museum, London, UK

Sandra Knapp

Centre for Organismal Studies, Biodiversity and Plant Systematics, Heidelberg University, Heidelberg, Germany

Marcus A. Koch

Department of Plant Biology, University of Georgia, Athens, GA, USA

James H. Leebens-Mack

Institut de Recherche en Biologie Végétale, University of Montreal, Montreal, Quebec, Canada

Étienne Léveillé-Bourret

CSIRO, Canberra, Australian Capital Territory, Australia

Lan Li & Jennifer M. Taylor

Department of Plant Systematics, University of Bayreuth, Bayreuth, Germany

Sigrid Liede-Schumann

Department of Biodiversity, Earth and Environmental Sciences, Drexel University, Philadelphia, PA, USA

Tatyana Livshultz

Academy of Natural Science, Drexel University, Philadelphia, PA, USA

National Tropical Botanical Garden, Kalaheo, HI, USA

David Lorence

Instituto de Pesquisas Jardim Botânico do Rio de Janeiro, Rio de Janeiro, Brazil

Jaquelini Luber, Vidal F. Mansano & Ariane Luna Peixoto

Bioplatforms Australia Ltd, Sydney, New South Wales, Australia

Department of Biological Sciences, Saint Cloud State University, Saint Cloud, MN, USA

Angela J. McDonnell

Instituto de Arqueología y Antropología, Universidad Católica del Norte, San Pedro de Atacama, Chile

Rosa I. Meneses

New York Botanical Garden, Bronx, NY, USA

Fabián A. Michelangeli, John D. Mitchell & Gregory M. Plunkett

Department of Biology, Oberlin College, Oberlin, OH, USA

Michael J. Moore

Department of Ecology, Evolution & Behavior, University of Minnesota, St. Paul, MN, USA

Taryn L. Mueller

AMAP Lab, Université Montpellier, IRD, CIRAD, CNRS INRAE, Montpellier, France

Jérôme Munzinger

Laboratorio de Ecofisiología, Escuela de Ciencias Biológicas, Pontificia Universidad Católica del Ecuador, Quito, Ecuador

Priscilla Muriel

Department of Systematic and Evolutionary Botany, University of Zürich, Zürich, Switzerland

Reto Nyffeler & Rolf Rutishauser

Royal Botanic Garden Edinburgh, Edinburgh, UK

Andrés Orejuela & Olwen M. Grace

Grupo de Investigación en Recursos Naturales Amazónicos, Instituto Tecnológico del Putumayo, Mocoa, Colombia

Andrés Orejuela

US Botanic Garden, Washington, DC, USA

Susan K. Pell

Department of Biology and Marine Biology, University of North Carolina Wilmington, Wilmington, NC, USA

Darin S. Penneys

Department of Biological and Environmental Sciences, University of Gothenburg, Gothenburg, Sweden

Claes Persson & Alexandre Antonelli

LSTM Université Montpellier, CIRADIRD, Montpellier, France

Yohan Pillon

School of Biological Sciences, Washington State University, Pullman, WA, USA

Eric H. Roalson

National Parks Board, Singapore Botanic Gardens, Singapore, Singapore

Michele Rodda

New Mexico State University, Las Cruces, NM, USA

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Tasmanian Herbarium, University of Tasmania, Sandy Bay, Tasmania, Australia

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University of Exeter, Exeter, UK

Rowan J. Schley

School of Science Technology and Engineering, Center for Bioinnovation, University Sunshine Coast, Sippy Downs, Queensland, Australia

Alison Shapcott

Department of Biology, Colorado State University, Fort Collins, CO, USA

Mark P. Simmons

Departamento de Biologia Vegetal, Universidade Estadual de Campinas, Campinas, Brazil

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University of California, San Francisco, San Francisco, CA, USA

Michelle Siros

Departamento de Botânica, Universidade Federal do Paraná, Curitiba, Brazil

Eric C. Smidt

Pittsburg State University, Pittsburg, KS, USA

Florida Museum of Natural History, University of Florida, Gainesville, FL, USA

Douglas E. Soltis & Pamela S. Soltis

Smithsonian Institution, Washington, DC, USA

Robert J. Soreng

Department of Biology, University of Ottawa, Ottawa, Ontario, Canada

Julian R. Starr

Hobart and William Smith Colleges, Geneva, NY, USA

Shannon C. K. Straub

Rutgers University, New Brunswick, NJ, USA

Lena Struwe

Department of Biological Sciences and Bolus Herbarium, University of Cape Town, Cape Town, South Africa

G. Anthony Verboom

Department of Environmental Sciences—Botany, University of Basel, Basel, Switzerland

Jurriaan M. de Vos

Instituto de Biologia, Universidade Federal de Uberlândia, Uberlândia, Brazil

Cassiano A. D. Welker

Australian National Herbarium, Centre for Australian National Biodiversity Research, National Research Collections Australia, CSIRO, Canberra, Australian Capital Territory, Australia

Adam J. White

Institute of Biodiversity And Environmental Conservation, Universiti Malaysia Sarawak, Samarahan, Malaysia

Sin Yeng Wong

University of Minnesota-Twin Cities, St. Paul, MN, USA

Southwest Forestry University, Kunming, China

Yu-Xiao Zhang

Instituto de Botánica Darwinion, San Isidro, Argentina

Fernando O. Zuloaga

Gothenburg Global Biodiversity Centre, University of Gothenburg, Gothenburg, Sweden

Alexandre Antonelli

Department of Biology, University of Oxford, Oxford, UK

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A.R.Z., T.C., A.A., S. Bellot, D.M.C., O.M.G., P.J.K., I.J.L., H. Sauquet, S.A.S., W.L.E., F. Forest and W.J.B were involved in conceptualization of this work. A.R.Z., T.C., A.A., S. Bellot, D.M.C., O.M.G., H. Sauquet, S.A.S., W.L.E., F. Forest and W.J.B. contributed to the methodology. A.R.Z., O.M., E.F., C. McGinnie, S.R.R., L. Simpson, J.J.C., R.S.C., S.D., L. Pokorny, G.M.A., K.G., K.P.H., A. Hoewener, A.-Q.H., E.M.J., I.A.B.S.K., I.L., D.A.L., E. J. Lírio, J.-X.L., P. Malakasi, N.A.S.P., T.S., J.V., G.K.A., R.L.A., M.S.A., M.A., M.J.A., J.A., W.A., J.B.B., C.D.B., H.F.B., M.D.B., R.L.B., R.J.B., M.J.B., E. Biffin, N.B., J.L.B., D.B., R.B., A.M.C.B., P. C. Boyce, G.L.C.B., M. Briggs, L. Broadhurst, G.K.B., J.J.B., A.B., S. Buerki, E. Burns, M. Byrne, S. Cable, A.C., M. W. Callmander, Á.C., D.J.C., W.M.C.-M., M.M.C., A.J.A.C., A.C.M., M. W. Chase, L.W.C., M.C., S. Chen, M.J.M.C., P.-A.C., M.A.C., S.C.C., J.G.C., X.C., T.L.P.C., I.D.C., L.C., I.D., G.D., N.M.J.D., A.P.D., K.-J.D., S.R.D., M.F.D., M.R.D., S.L.E., U.E., R.H.J.E., M. Escudero, M. Estrella, F. Fabriani, M.F.F., P.L.F., S.Z.F., R.M.F., S.F., L.F., T.F., M.G.-C., E.M.G., D.A.G., A. Giaretta, M.G., L.J.G., C.C.G., D.J.G., S.W.G., A. Grall, L.G., B.F.G., D.G.G., J.H., T. Haevermans, A. Haigh, J.C.H., T. Hall, M.J.H., S.A.H., O.H., T.R.H., G.D.H., H.C.F.H., C.J.J., S.A.J., R.W.J., G.K., I.M.K., K.K., M.K., E.A.K., G.J.K., B.K., B.B.K., R.R.K., S.K., M.A.K., J.H.L.-M., F.L., C.J.L., É.L.-B., G.P.L., D.-Z.L., L.L., S.L.-S., T.L., D.L., M. Lu, P.L.-I., J.L., E. J. Lucas, M. Luján, M. Lum, T.D.M., C. Magdalena, V.F.M., L.E.M., S.J.M., K. McColl, A.J.M., A.E.M., T.G.B.M., H.M., R.I.M., V.S.F.T.M., F.A.M., J.D.M., A.K.M., M.J.M., T.L.M., K. Mummenhoff, J.M., P. Muriel, D.J.M., K.N., L.N., F.J.N., R. Nyffeler, A.O., E.M.O., L. Palazzesi, A.L.P., S.K.P., J.P., D.S.P., O.A.P.-E., C. Persson, M.P., Y.P., J.R.P., G.M.P., R.F.P., G.T.P., C. Puglisi, M.Q., R.K.R., P.E.J.R., M. Renner, E.H.R., M. Rodda, Z.S.R., S.R., R.R., M.F.S., H. Schaefer, R. J. Schley, A.S.-L., A.S., I.S., K.A.S., M.P.S., A.O.S., A.R.G.S., M.S., E.C.S., J.F.S., N.S., D.E.S., P.S.S., R. J. Soreng, C.A.S., J.R.S., P.F.S., S.C.K.S., L. Struwe, J.M.T., I.R.H.T., A.H.T., I.T., A.T.-B., F.U., T.M.A.U., J.C.V., G.A.V., H.P.V., M.S.V., J.M.V., N.W., M.W., C.A.D.W., A.J.W., J.J.W., L.T.W., T.C.W., S.Y.W., L.A.W., R.W., S.W., M.X., Y.Y., Y.-X.Z., M.-Y.Z., S.Z., F.O.Z., S. Bellot, D.M.C., O.M.G., H. Sauquet, W.L.E., F. Forest and W.J.B. provided resources. A.R.Z., T.C., O.M., P. C. Bailey, K.L., G.E.B., N.E., E.F., B.G.-P., C. McGinnie, S.R.R., L. Simpson, L. Botigué, J.J.C., R.S.C., S.D., M.G.J., J.T.K., L. Pokorny, N.J.W., G.M.A., L.D., K.G., K.P.H., A. Hoewener, A.-Q.H., E.M.J., I.A.B.S.K., I.L., D.A.L., E. J. Lírio, J.-X.L., P. Malakasi, N.A.S.P., T.S., J.V. and S. Bellot carried out the investigations. A.R.Z., T.C., O.M., G.E.B., N.E., E.F., B.G.-P., C. McGinnie, R. Negrão, S.R.R., L. Simpson, E.T.R., V.M.A.B., K.P.H., J.V., T.R.A. and H. Sauquet were responsible for data curation. A.R.Z., T.C., P. C. Bailey and K.L. conducted the formal analysis. A.R.Z., T.C., P. C. Bailey, K.L., M.G.J. and J.T.K. developed the software. A.R.Z., T.C. and R. Negrão prepared the visualizations. A.R.Z., T.C., W.L.E., F. Forest and W.J.B. wrote the original manuscript with support from A.A., S. Bellot, D.M.C., O.M.G., H. Sauquet and S.A.S. K.L., E.F., J.J.C., J.T.K., L. Pokorny, N.J.W., A.-Q.H., E.M.J., I.L., J.V., M.S.A., J.B.B., M.D.B., R.L.B., A.M.C.B., L. Broadhurst, A.B., D.J.C., M.M.C., M. W. Chase, L.W.C., M.J.M.C., P.-A.C., T.L.P.C., U.E., R.H.J.E., M. Estrella, M.F.F., P.L.F., M.G., L.J.G., S.W.G., J.H., T. Haevermans, J.C.H., O.H., T.R.H., K.K., E.A.K., M.A.K., F.L., C.J.L., D.-Z.L., S.L.-S., T.D.M., V.S.F.T.M., F.A.M., A.K.M., M.J.M., D.J.M., F.J.N., L. Palazzesi, J.P., D.S.P., O.A.P.-E., Y.P., G.M.P., R.K.R., R.R., H. Schaefer, A.S.-L., M.P.S., A.R.G.S., N.S., D.E.S., P.S.S., R. J. Soreng, P.F.S., S.C.K.S., A.H.T., T.M.A.U., J.M.V., J.J.W., T.C.W., Y.Y., S.Z. and I.J.L. reviewed the final manuscript. S.A.S., W.L.E., F. Forest and W.J.B. undertook supervision. P.J.K., I.J.L., F. Forest and W.J.B. acquired funding. V.M.A.B., P.J.K., I.J.L., F. Forest and W.J.B. were responsible for project administration.

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Correspondence to William J. Baker .

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Extended data figures and tables

Extended data fig. 1 tanglegram at ordinal level between this work (left) and the apg iv schematic tree (right)..

Branches colours represent the clades according to the composition proposed in each work. Posterior probability is presented only for nodes without maximum support. Coloured circles in the left tree represent the posterior probability of each node as: maximum (absent), between 1 and 0.95 (green), between 0.95 and 0.75 (yellow), between 0.75 and 0.5 (red), below 0.5 (black).

Extended Data Fig. 2 Comparison of node age estimates in the eight time-calibrated phylogenetic trees.

Each point represents a node and corresponds to the percentage difference in age estimates for that node between the two trees that are compared in each plot.

Extended Data Fig. 3 Comparison of stem ages of families and orders inferred in this study and Ramírez-Barahona et al. 5 .

a and b , Stem age comparison between our young tree (maximum constraint at the root node of 154 Ma) and the dataset CC_complete of Ramírez-Barahona et al. 5 . a , Ages in each study, coloured according to taxonomic rank and b , Age differences, calculated as age in this study minus age in Ramírez-Barahona et al. 5 c and d , Stem ages comparison between our old tree (maximum constraint at the root node of 247 Ma) and the dataset UC_complete from Ramírez-Barahona et al. 5 c , Ages in each study, coloured according to taxonomic rank and d , Age differences, calculated as age in this study minus age in Ramírez-Barahona et al. 5 .

Extended Data Fig. 4 Correlation between branch time duration and percentage of gene trees that do not share a congruent bipartition for the branch.

The results are based on the young tree (maximum constraint at the root node of 154 Ma). For each branch in the young tree, the percentage of gene trees that do not share a congruent bipartition with the species tree branch is plotted against the logarithm of the time duration for the branch.

Extended Data Fig. 5 Angiosperm-wide diversification and gene tree conflict through time.

This is equivalent to Fig. 3 but for the old tree (maximum constraint at the root node of 247 Ma). a , Estimated net diversification rate through time (yellow, left y-axis) and the level of gene tree conflict through time (blue, right y-axis). Net diversification rates are estimated with a model that enables speciation rates to vary between time intervals; the line is the posterior mean and the yellow shaded area is the 95% highest posterior density. Gene tree conflict is calculated from the percentage of gene trees that do not share a congruent bipartition with each species tree branch, with the plotted value being the mean across all species tree branches that cross each 2.5 Myr time slice. b , Cumulative percentage of extant orders and families that have originated through time. In both a and b, the background grey-scale gradient is the estimated percentage of extant lineages represented in the species tree through time (“sampling fraction”).

Extended Data Fig. 6 Summary of lineage-specific diversification rate shifts estimated by BAMM.

This is equivalent to Fig. 4 , but for the old tree (maximum constraint at the root node of 247 Ma). a , Diversification rate increases per lineage through time. The colour corresponds to the average magnitude of the rate increases during the time period. b , Equivalent to a, but for rate decreases. c , Equivalent to a, but focusing on the largest 25% of diversification rate increases. In a , b and c , the number of shifts is extracted from the maximum a posteriori shift configuration, the prior for the number of shifts is set to 10 and the background grey-scale gradient is the estimated percentage of extant lineages represented in the species tree through time (“sampling fraction”).

Supplementary information

Supplementary information.

Supplementary Methods, Results, Discussion, references, Figs. 1–24, captions for Tables 1–8, Files 1 and 2 and extended acknowledgements.

Reporting Summary

Peer review file, supplementary material, supplementary tables.

Supplementary Tables 1–8.

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Zuntini, A.R., Carruthers, T., Maurin, O. et al. Phylogenomics and the rise of the angiosperms. Nature (2024). https://doi.org/10.1038/s41586-024-07324-0

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DOI : https://doi.org/10.1038/s41586-024-07324-0

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Erkata Yandri 1 ,2 * , Kukuh Priyo Pramono 1 , Very Sihombing 1 , Luqmanul Hakim Effendi 1 , Denis Ardianto 1 , Roy Hendroko Setyobudi 1 , Suherman Suherman 3 , Satriyo Krido Wahono 4 , Haryo Wibowo 5 , Marchel Putra Garfansa 6 and Afrida Rizka Farzana 7

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