# The global abundance of tree palms

Robert Muscarella, Thaise Emílio, Oliver Phillips, Simon Lewis, Ferry Slik, William Baker, Thomas Couvreur, Wolf Eiserhardt, Jens-Christian Svenning, Kofi Affum-baffoe, et al.

► To cite this version:

Robert Muscarella, Thaise Emílio, Oliver Phillips, Simon Lewis, Ferry Slik, et al. The global abundance of tree palms. Global Ecology and Biogeography, 2020, 29 (9), pp.1495-1514. 10.1111/geb.13123. hal-02948753

HAL Id: hal-02948753

https://hal.inrae.fr/hal-02948753v1

Submitted on 20 Feb 2026

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# The global abundance of tree palms

Robert Muscarella¹,² ID | Thaise Emilio³,⁴ | Oliver L. Phillips⁵ | Simon L. Lewis⁵,⁶ |

Ferry Slik⁷ | William J. Baker⁴ | Thomas L. P. Couvreur⁸ | Wolf L. Eiserhardt²,⁴ |

Jens-Christian Svenning²,⁹ | Kofi Affum-Baffoe¹⁰ | Shin-Ichiro Aiba¹¹ |

Everton C. de Almeida¹² | Samuel S. de Almeida¹³ | Edmar Almeida de Oliveira¹⁴ ID |

Esteban Álvarez-Dávila¹⁵ | Luciana F. Alves¹⁶ | Carlos Mariano Alvez-Valles¹⁷ |

Fabrício Alvim Carvalho¹⁸ | Fernando Alzate Guarín¹⁹ | Ana Andrade²⁰ |

Luis E. O. C. Aragão²¹,²² | Alejandro Araujo Murakami²³ | Luzmila Arroyo²⁴ |

Peter S. Ashton²⁵ | Gerardo A. Aymard Corredor²⁶,²⁷ | Timothy R. Baker⁵ |

Plinio Barbosa de Camargo²⁸ | Jos Barlow²⁹ | Jean-François Bastin³⁰ ID |

Natacha Nssi Bengone³¹ | Erika Berenguer²⁹,³² | Nicholas Berry³³ | Lilian Blanc³⁴,³⁵ |

Katrin Böhning-Gaese³⁶,³⁷ | Damien Bonal³⁸ | Frans Bongers³⁹ | Matt Bradford⁴⁰ |

Fabian Brambach⁴¹ | Francis Q. Brearley⁴² | Steven W. Brewer⁴³ | Jose L. C. Camargo²⁰ |

David G. Campbell⁴⁴ | Carolina V. Castilho⁴⁵ | Wendeson Castro⁴⁶ |

Damien Catchpole⁴⁷ | Carlos E. Cerón Martínez⁴⁸ | Shengbin Chen⁴⁹,⁵⁰ |

Phourin Chhang⁵¹ | Percival Cho⁵² | Wanlop Chutipong⁵³ | Connie Clark⁵⁴ |

Murray Collins⁵⁵ | James A. Comiskey⁵⁶,⁵⁷ ID | Massiel Nataly Corrales Medina⁵⁸ |

Flávia R. C. Costa⁵⁹ | Heike Culmsee⁶⁰ | Heriberto David-Higuita⁶¹ | Priya Davidar⁶² |

Jhon del Aguila-Pasquel⁶³ | Géraldine Derroire⁶⁴ | Anthony Di Fiore⁶⁵ | Tran Van Do⁶⁶ ID |

Jean-Louis Doucet⁶⁷ | Aurélie Dourdain⁶⁴ | Donald R. Drake⁶⁸ | Andreas Ensslin⁶⁹ |

Terry Erwin⁷⁰ | Corneille E. N. Ewango⁷¹ | Robert M. Ewers⁷² | Sophie Fauset⁷³ |

Ted R. Feldpausch⁷⁴ | Joice Ferreira⁷⁵ | Leandro Valle Ferreira⁷⁶ | Markus Fischer⁶⁹ |

Janet Franklin⁷⁷ | Gabriella M. Fredriksson⁷⁸ | Thomas W. Gillespie⁷⁹ | Martin Gilpin⁵ |

Christelle Gonmadje⁸⁰,⁸¹ | Arachchige Upali Nimal Gunatilleke⁸² |

Khalid Rehman Hakeem⁸³ ID | Jefferson S. Hall⁸⁴ | Keith C. Hamer⁸⁵ |

David J. Harris⁸⁶ | Rhett D. Harrison⁸⁷ | Andrew Hector⁸⁸ | Andreas Hemp⁸⁹ ID |

Bruno Herault⁹⁰,⁹¹ ID | Carlos Gabriel Hidalgo Pizango⁶³ | Eurídice N. Honorio Coronado⁶³ |

Wannes Hubau⁵,⁹² | Mohammad Shah Hussain⁹³ | Faridah-Hanum Ibrahim⁹⁴ |

Nobuo Imai⁹⁵ | Carlos A. Joly³ | Shijo Joseph⁹⁶ | Anitha K⁹⁷ |

Kuswata Kartawinata⁹⁸,⁹⁹ | Justin Kassi¹⁰⁰ | Timothy J. Killeen²³ |

<footnote>Robert Muscarella and Thaise Emilio are joint first author.</footnote>

<footnote>This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium,
provided the original work is properly cited.</footnote>

<footnote>© 2020 The Authors. Global Ecology and Biogeography published by John Wiley & Sons Ltd</footnote>
Kanehiro Kitayama¹⁰¹ | Bente Bang Klitgård¹⁰² | Robert Kooyman¹⁰³,¹⁰⁴ |

Nicolas Labrière¹⁰⁵ | Eileen Larney¹⁰⁶,¹⁰⁷ | Yves Laumonier¹⁰⁸ | Susan G. Laurance¹⁰⁹ |

William F. Laurance¹⁰⁹ | Michael J. Lawes¹¹⁰ | Aurora Levesley⁵ | Janvier Lisingo¹¹¹ |

Thomas Lovejoy¹¹² | Jon C. Lovett⁵,¹¹³ | Xinghui Lu¹¹⁴ | Anne Mette Lykke¹¹⁵ |

William E. Magnusson⁵⁹ | Ni Putu Diana Mahayani¹¹⁶ | Yadvinder Malhi³² |

Asyraf Mansor¹¹⁷,¹¹⁸ | Jose Luis Marcelo Peña¹¹⁹,¹²⁰ | Ben H. Marimon-Junior¹²¹ |

Andrew R. Marshall¹²²,¹²³,¹²⁴ | Karina Melgaco⁵ | Casimiro Mendoza Bautista¹²⁵ |

Vianet Mihindou¹²⁶,¹²⁷ | Jérôme Millet¹²⁸ | William Milliken¹²⁹ | D. Mohandass¹³⁰ |

Abel Lorenzo Monteagudo Mendoza¹³¹ | Badru Mugerwa¹³² | Hidetoshi Nagamasu¹³³ |

Laszlo Nagy³ | Naret Seuaturien¹³⁴ | Marcelo T. Nascimento¹³⁵ | David A. Neill¹³⁶ |

Luiz Menini Neto¹³⁷ | Rueben Nilus¹³⁸ | Mario Percy Núñez Vargas¹³¹ |

Eddy Nurtjahya¹³⁹ | R. Nazaré O. de Araújo¹⁴⁰ | Onrizal Onrizal¹⁴¹ ID |

Walter A. Palacios¹⁴² | Sonia Palacios-Ramos¹⁴³ | Marc Parren³⁹ | Ekananda Paudel¹⁴⁴ |

Paulo S. Morandi¹⁴⁵ | R. Toby Pennington⁸⁶,¹⁴⁶ | Georgia Pickavance⁵ |

John J. Pipoly III¹⁴⁷ | Nigel C. A. Pitman¹⁴⁸ | Erny Poedjirahajoe¹¹⁶ | Lourens Poorter³⁹ ID |

John R. Poulsen⁵⁴ ID | P. Rama Chandra Prasad¹⁴⁹ | Adriana Prieto¹⁵⁰ |

Jean-Philippe Puyravaud⁶² | Lan Qie¹⁵¹ | Carlos A. Quesada¹⁵² | Hirma Ramírez-Angulo¹⁵³ |

Jean Claude Razafimahaimodison¹⁵⁴ | Jan Meindert Reitsma¹⁵⁵ | Edilson J. Requena-Rojas¹⁵⁶ |

Zorayda Restrepo Correa¹⁵⁷ | Carlos Reynel Rodriguez¹⁴³ | Anand Roopsind¹⁵⁸ |

Francesco Rovero¹⁵⁹,¹⁶⁰ ID | Andes Rozak¹⁶¹ | Agustín Rudas Lleras¹⁵⁰ |

Ervan Rutishauser¹⁶² | Gemma Rutten⁶⁹ | Ruwan Punchi-Manage¹⁶³ |

Rafael P. Salomão¹³,¹⁶⁴ | Hoang Van Sam¹⁶⁵ | Swapan Kumar Sarker¹⁶⁶ |

Manichanh Satdichanh¹⁶⁷,¹⁶⁸ | Juliana Schietti¹⁵² | Christine B. Schmitt¹⁶⁹,¹⁷⁰ |

Beatriz Schwantes Marimon¹²¹ | Feyera Senbeta¹⁷¹ | Lila Nath Sharma¹⁷² |

Douglas Sheil¹⁷³ | Rodrigo Sierra¹⁷⁴ | Javier E. Silva-Espejo¹⁷⁵ | Marcos Silveira¹⁷⁶ |

Bonaventure Sonké¹⁷⁷ | Marc K. Steininger¹⁷⁸ | Robert Steinmetz¹³⁴ ID |

Tariq Stévart¹⁷⁹ | Raman Sukumar¹⁸⁰,¹⁸¹ | Aisha Sultana⁹³ | Terry C. H. Sunderland¹⁸²,¹⁸³ |

Hebbalalu Satyanarayana Suresh¹⁸⁰,¹⁸¹ | Jianwei Tang¹⁸⁴ | Edmund Tanner¹⁸⁵ |

Hans ter Steege¹⁸⁶,¹⁸⁷ | John W. Terborgh¹⁸⁸,¹⁸⁹ | Ida Theilade¹⁹⁰ |

Jonathan Timberlake¹⁹¹ | Armando Torres-Lezama¹⁹² | Peter Umunay¹⁹³ |

María Uriarte¹⁹⁴ | Luis Valenzuela Gamarra¹⁹⁵ | Martin van de Bult¹⁹⁶ |

Peter van der Hout¹⁹⁷ | Rodolfo Vasquez Martinez¹⁹⁸ | Ima Célia Guimarães Vieira¹³ |

Simone A. Vieira¹⁹⁹ | Emilio Vilanova²⁰⁰ | Jeanneth Villalobos Cayo²⁰¹ |

Ophelia Wang²⁰² | Campbell O. Webb²⁰³ | Edward L. Webb²⁰⁴ | Lee White¹²⁶,²⁰⁵,²⁰⁶ |

Timothy J. S. Whitfeld²⁰⁷ | Serge Wich²⁰⁸,²⁰⁹ | Simon Willcock²¹⁰ | Susan K. Wiser²¹¹ |

Kenneth R. Young²¹² | Rahmad Zakaria¹¹⁷ | Runguo Zang²¹³ | Charles E. Zartman⁵⁹ |

Irié Casimir Zo-Bi⁹¹ | Henrik Balslev<fnref n="2" />

<footnote n="1">¹Department of Plant Ecology and Evolution, Evolutionary Biology Centre, Uppsala University, Uppsala, Sweden</footnote>

<footnote n="2">²Section for Ecoinformatics and Biodiversity, Department of Biology, Aarhus University, Aarhus, Denmark</footnote>
³Department of Plant Biology, Institute of Biology, University of Campinas (UNICAMP), Campinas, Brazil

⁴Comparative Plant and Fungal Biology Department, Royal Botanic Gardens Kew, Richmond, UK

⁵School of Geography, University of Leeds, Leeds, UK

⁶Department of Geography, University College London, London, UK

⁷Department of Environmental and Life Sciences, Faculty of Science, Universiti Brunei Darussalam, Gadong, Brunei Darussalam

⁸IRD, DIADE, University of Montpellier, Montpellier, France

⁹Center for Biodiversity Dynamics in a Changing World (BIOCHANGE), Department of Biology, Aarhus University, Aarhus, Denmark

¹⁰Ghana Forestry Commission, Kumasi, Ghana

¹¹Faculty of Environmental Earth Science, Hokkaido University Sapporo, Sapporo, Japan

¹²Instituto de Biodiversidade e Floresta, Universidade Federal do Oeste do Pará, Santarém, Brazil

¹³Museu Paraense Emilio Goeldi, Belém, Brazil

¹⁴Campus de Nova Xavantina, Universidade do Estado de Mato Grosso, Nova Xavantina, Brazil

¹⁵Escuela de Ciencias Ambientales, Universidad Nacional Abierta y a Distancia, Bogotá, Colombia

¹⁶Center for Tropical Research, Institute of the Environment and Sustainability, University of California, Los Angeles, CA, USA

¹⁷Instituto Veterinario de Investigaciones Tropicales y de Altura (IVITA), Estación Experimental Pucallpa, Universidad Nacional Mayor de San Marcos (UNMSM), Lima, Peru

¹⁸Departamento de Botânica, Instituto de Ciências Biológicas, Universidade Federal de Juiz de Fora, Juiz de Fora, Brazil

¹⁹Instituto de Biología, Universidad de Antioquia, Medellín, Colombia

²⁰Biological Dynamics of Forest Fragments Project (BDFFP), National Institute for Amazonian Research (INPA) and Smithsonian Tropical Research Institute, Manaus, Brazil

²¹Remote Sensing Division, National Institute for Space Research, São José dos Campos, Brazil

²²College of Life and Environmental Sciences, University of Exeter, Exeter, UK

²³Museo de Historia Natural Noel Kempff Mercado, Universidad Autonoma Gabriel Rene Moreno, Santa Cruz, Bolivia

²⁴Biology Career, Gabriel René Moreno Autonomous University, Santa Cruz, Bolivia

²⁵Department of Organismal and Evolutionary Biology, Harvard University, Cambridge, MA, USA

²⁶Compensation International Progress S.A, Bogotá, Colombia

²⁷UNELLEZ-Guanare, Programa de Ciencias del Agro y el Mar, Herbario Universitario (PORT), Mesa de Cavacas, Venezuela

²⁸Ecology Isotope Lab, Center for Nuclear Energy in Agriculture, São Paulo, Brazil

²⁹Lancaster Environment Centre, Lancaster University, Lancaster, UK

³⁰Department of Environmental Systems Science, Institute of Integrative Biology, ETH Zürich, Zürich, Switzerland

³¹National Agency of National Parks of Gabon, ANPN, Libreville, Gabon

³²Environmental Change Institute, School of Geography and the Environment, University of Oxford, Oxford, UK

³³Forest and Landscape Ecology, The Landscapes and Livelihoods Group, Edinburgh, UK

³⁴UR Forest & Societies, CIRAD, Montpellier, France

³⁵Forest & Societies, University of Montpellier, Montpellier, France

³⁶Senckenberg Biodiversity and Climate Research Centre, Frankfurt am Main, Germany

³⁷Institute of Ecology, Evolution & Diversity, Goethe University, Frankfurt, Frankfurt am Main, Germany

³⁸Université de Lorraine, AgroParisTech, INRAE, UMR Silva, Nancy, France

³⁹Forest Ecology and Forest Management, Wageningen University & Research, Wageningen, the Netherlands

⁴⁰CSIRO Land and Water, Atherton, QLD, Australia

⁴¹Biodiversity, Macroecology and Biogeography, University of Goettingen, Goettingen, Germany

⁴²School of Science and the Environment, Manchester Metropolitan University, Manchester, UK

⁴³Wild Earth Allies, Chevy Chase, MD, USA

⁴⁴Department of Biology, Grinnell College, Grinnell, IA, USA

⁴⁵Embrapa Roraima & Programa de Pós-graduação em Recursos Naturais, Universidade Federal de Roraima, Boa Vista, Brazil

⁴⁶Amazonia Green Landscape Protection and Governance Programme, SOS Amazônia, 61 Pará St., Rio Branco, AC 69905-082, Brazil

⁴⁷School of Technology, Environments and Design, University of Tasmania, Hobart, TAS, Australia

⁴⁸Herbario Alfredo Paredes (QAP), Universidad Central del Ecuador, Quito, Ecuador

⁴⁹College of Ecology and Environment, Chengdu University of Technology, Chengdu, China

⁵⁰State Key Laboratory of Geohazard Prevention and Geoenvironment Protection, Chengdu University of Technology, Chengdu, China

⁵¹Forestry Administration, Phnom Penh, Cambodia

⁵²Forest Department, Belmopan, Belize

⁵³Conservation Ecology Program, Pilot Plant Development and Training Institute, King Mongut's Institute of Technology Thonburi, Bangkok, Thailand

⁵⁴Nicholas School of the Environment, Duke University, Durham, NC, USA
⁵⁵School of Geosciences, University of Edinburgh, Edinburgh, UK

⁵⁶Inventory and Monitoring Division, National Park Service, Frederickburg, VA, USA

⁵⁷Smithsonian Institution, Washington, DC, USA

⁵⁸Universidad Nacional de San Agustín de Arequipa, Arequipa, Peru

⁵⁹Biodiversity Department, National Institute for Amazonian Research (INPA), Manaus, Brazil

⁶⁰DBU Natural Heritage, German Federal Foundation for the Environment, Osnabrück, Germany

⁶¹Herbario Universidad de Antioquia (HUA), Universidad de Antioquia, Medellín, Colombia

⁶²Sigur Nature Trust, Chadapatti Masinagudi PO, Nilgiris, India

⁶³Instituto de Investigaciones de la Amazonía Peruana (IIAP), Iquitos, Peru

⁶⁴Cirad, UMR EcoFoG, (AgroParisTech, CNRS, INRAE, Université de la Guyane, Université des Antilles), Kourou, French Guiana

⁶⁵Department of Anthropology, University of Texas at Austin, Austin, TX, USA

⁶⁶Silviculture Research Institute, Vietnamese Academy of Forest Sciences, Hanoi, Vietnam

⁶⁷Gembloux Agro-Bio Tech, Liège University, Gembloux, Belgium

⁶⁸Botany Department, University of Hawai'i at Mānoa, Honolulu, HI, USA

⁶⁹Institute of Plant Sciences, University of Bern, Bern, Switzerland

⁷⁰Department of Entomology, Smithsonian Institution, Washington, DC, USA

⁷¹Faculty of Renewable Natural Resources Management & Faculty of Science, University of Kisangani, Kisangani, Democratic Republic of the Congo

⁷²Department of Life Sciences, Imperial College London, Ascot, UK

⁷³School of Geography, Earth and Environmental Sciences, University of Plymouth, Plymouth, UK

⁷⁴Geography, College of Life and Environmental Sciences, University of Exeter, Exeter, UK

⁷⁵EMBRAPA, Amazônia Oriental, Belém, Brazil

⁷⁶Coordenação de Botânica, Museu Paraense Emílio Goeldi, Belém, Brazil

⁷⁷Department of Botany and Plant Sciences, University of California – Riverside, Riverside, CA, USA

⁷⁸Pro Natura Foundation, Balikpapan, Indonesia

⁷⁹Department of Geography, University of California, Los Angeles, Los Angeles, CA, USA

⁸⁰Department of Plant Biology, University of Yaoundé, Yaoundé, Cameroon

⁸¹National Herbarium, Yaoundé, Cameroon

⁸²University of Peradeniya, Peradeniya, Sri Lanka

⁸³Department of Biological Sciences, Faculty of Science, King Abdulaziz University, Jeddah, Saudi Arabia

⁸⁴Forest GEO, Smithsonian Tropical Research Institute, Panama City, Panama

⁸⁵School of Biology, University of Leeds, Leeds, UK

⁸⁶Royal Botanic Garden Edinburgh, Edinburgh, UK

⁸⁷World Agroforestry, East and Southern Africa Region, Lusaka, Zambia

⁸⁸Department of Plant Sciences, University of Oxford, Oxford, UK

⁸⁹Department of Plant Systematics, University of Bayreuth, Bayreuth, Germany

⁹⁰Centre International de Recherche en Agronomie pour le Développement, Montpellier, France

⁹¹Département Eaux, Forêts et Environnement, Institut National Polytechnique Félix Houphouët-Boigny, Yamoussoukro, Ivory Coast

⁹²Service of Wood Biology, Royal Museum for Central Africa, Tervuren, Belgium

⁹³Biodiversity Parks Programme, Centre for Environmental Management of Degraded Ecosystems, University of Delhi, Delhi, India

⁹⁴Institut Ekosains Borneo, Universiti Putra Malaysia Bintulu Campus, Bintulu, Sarawak, Malaysia

⁹⁵Department of Forest Science, Tokyo University of Agriculture, Tokyo, Japan

⁹⁶Department of Remote Sensing and GIS, School of Environment, Kerala University of Fisheries and Ocean Studies, Kochi, India

⁹⁷Rainforest Traditions, Kochi, India

⁹⁸Integrative Research Center, The Field Museum of Natural History, Chicago, IL, USA

⁹⁹Herbarium Bogoriense, Research Center for Biology, Indonesian Institute of Sciences, Cibinong, Indonesia

¹⁰⁰Laboratoire de Botanique, UFR Biosciences, Université Félix Houphouët-Boigny, Abidjan, Ivory Coast

¹⁰¹Graduate School of Agriculture, Kyoto University, Kyoto, Japan

¹⁰²Department for Identification & Naming, Royal Botanic Gardens Kew, Richmond, UK

¹⁰³Department of Biological Sciences, Macquarie University, North Ryde, NSW, Australia

¹⁰⁴National Herbarium of New South Wales, Royal Botanic Gardens, Sydney, NSW, Australia

¹⁰⁵Laboratoire Évolution et Diversité Biologique, CNRS, Toulouse, France

¹⁰⁶TEAM, Ranomafana, Thailand

¹⁰⁷Conservation Programmes, Zoological Society of London, Kanchanaburi, Thailand

¹⁰⁸UR¹⁰⁵ Forêts et Sociétés, CIRAD, Montpellier, France
¹⁰⁹Centre for Tropical Environmental and Sustainability Science, and College of Science and Engineering, James Cook University, Cairns, QLD, Australia

¹¹⁰School of Life Sciences, University of KwaZulu-Natal, Scottsville, South Africa

¹¹¹Laboratory of Ecology and Forest Management, Department of Ecology and Plant Resource Management, Kisangani University, Kisangani, Democratic Republic of the Congo

¹¹²Environmental Science and Policy, George Mason University, Washington, DC, USA

¹¹³Natural Capital and Plant Health, Royal Botanic Gardens Kew, Richmond, UK

¹¹⁴College of Agronomy, Liaocheng University, Liaocheng, China

¹¹⁵Plant and Insect Ecology, Department of Bioscience, Aarhus University, Silkeborg, Denmark

¹¹⁶Faculty of Forestry, Universitas Gadjah Mada, Yogyakarta, Indonesia

¹¹⁷School of Biological Sciences, Universiti Sains Malaysia, George Town, Malaysia

¹¹⁸Centre for Marine and Coastal Studies, Universiti Sains Malaysia, George Town, Malaysia

¹¹⁹Departamento de Manejo Forestal, Herbario MOL-Forestales, Universidad Nacional Agraria La Molina, Lima, Peru

¹²⁰Departamento de Ciências Florestais, Escola Superior de Agricultura Luiz de Queiroz, Universidade de São Paulo, São Paulo, Brazil

¹²¹Postgraduate Program in Ecology and Conservation, UNEMAT State University of Mato Grosso, Nova Xavantina, Brazil

¹²²Tropical Forests and People Research Centre, University of the Sunshine Coast, Sippy Downs, QLD, Australia

¹²³Department of Environment and Geography, University of York, York, UK

¹²⁴Flamingo Land, North Yorkshire, UK

¹²⁵Departamento Cochabamba, Escuela de Ciencias Forestales de la Universidad Mayor de San Simón, Cochabamba, Bolivia

¹²⁶Agence Nationale des Parcs Nationaux, Libreville, Gabon

¹²⁷Ministère de la Forêt et de l'Environnement, Libreville, Gabon

¹²⁸National Botanical Conservatories Division, French Agency for Biodiversity, Vincennes, France

¹²⁹Natural Capital and Plant Health, Royal Botanic Gardens Kew, Richmond, United Kingdom

¹³⁰Novel Research Academy, Puducherry, India

¹³¹Universidad Nacional de San Antonio Abad del Cusco, Cusco, Peru

¹³²Institute of Tropical Forest Conservation, Mbarara University of Science and Technology, Kabale, Uganda

¹³³The Kyoto University Museum, Kyoto University, Kyoto, Japan

¹³⁴WWF Thailand, Bangkok, Thailand

¹³⁵Laboratório de Ciências Ambientais, Centro de Biociências e Biotecnologia, Universidade Estadual do Norte Fluminense Darcy Ribeiro (UENF), Campos dos Goytacazes, Brazil

¹³⁶Universidad Estatal Amazónica, Puyo, Ecuador

¹³⁷Departamento de Botânica, Instituto de Ciências Biológicas, Universidade Federal de Juiz de Fora, Juiz de Fora, Brazil

¹³⁸Forest Research Centre, Sabah Forestry Department, Sandakan, Malaysia

¹³⁹Department of Biology, Universitas Bangka Belitung, Merawang, Indonesia

¹⁴⁰Instituto Nacional de Pesquisas da Amazônia, Manaus, Brazil

¹⁴¹Faculty of Forestry, Universitas Sumatera Utara, Medan, Indonesia

¹⁴²Herbario Nacional del Ecuador, Universidad Técnica del Norte, Quito, Ecuador

¹⁴³Facultad de Ciencias Forestales, Universidad Nacional Agraria La Molina, Lima, Peru

¹⁴⁴Nepal Academy of Science and Technology, Lalitpur, Nepal

¹⁴⁵Departamento de Ciências Biológicas, Universidade do Estado de Mato Grosso, Nova Xavantina, Brazil

¹⁴⁶Department of Geography, University of Exeter, Exeter, UK

¹⁴⁷Broward County Parks and Recreation Division, Oakland Park, FL, USA

¹⁴⁸Field Museum, Chicago, IL, USA

¹⁴⁹Lab for Spatial Informatics, International Institute of Information Technology, Hyderabad, India

¹⁵⁰Instituto de Ciencias Naturales, Universidad Nacional de Colombia, Bogotá, Colombia

¹⁵¹School of Life Sciences, University of Lincoln, Lincoln, UK

¹⁵²INPA (Instituto Nacional de Pesquisas da Amazônia), Manaus, Brazil

¹⁵³INDEFOR, Universidad de Los Andes, Mérida, Venezuela

¹⁵⁴Centre ValBio Ranomafana CP 312 Fianarantsoa & Science Faculty, University of Fianarantsoa, Fianarantsoa, Madagascar

¹⁵⁵Bureau Waardenburg BV, Culemborg, the Netherlands

¹⁵⁶Laboratorio de Dendrocronología, Universidad Continental, Huancayo, Peru

¹⁵⁷Ecosystems Services and Climate Change (SECC) Group, COL-TREE Corporation, Medellín, Colombia

¹⁵⁸Biology Department, Boise State University, Boise, ID, USA

¹⁵⁹Department of Biology, University of Florence, Florence, Italy

¹⁶⁰Tropical Biodiversity Section, MUSE, Museo delle Scienze, Trento, Italy
¹⁶¹Research Center for Plant Conservation and Botanic Gardens, Cibodas Botanic Gardens, Indonesian Institute of Sciences (LIPI), Cianjur, Indonesia

¹⁶²Smithsonian Tropical Research Institute, Balboa, Panama

¹⁶³Department of Statistics and Computer Science, Faculty of Science, University of Peradeniya, Peradeniya, Sri Lanka

¹⁶⁴CAPES, Universidade Federal Rural da Amazônia-UFRA, Belém, Brazil

¹⁶⁵Vietnam National University of Forestry, Hanoi, Vietnam

¹⁶⁶Department of Forestry & Environmental Science, Shahjalal University of Science & Technology, Sylhet, Bangladesh

¹⁶⁷Key Laboratory of Plant Diversity and Biogeography of East Asia, Kunming Institute of Botany, Chinese Academy of Sciences, Kunming, China

¹⁶⁸World Agroforestry Centre, Kunming, China

¹⁶⁹Center for Development Research (ZEF), University of Bonn, Bonn, Germany

¹⁷⁰Chair of Nature Conservation and Landscape Ecology, University of Freiburg, Freiburg, Germany

¹⁷¹College of Development Studies, Addis Ababa University, Addis Ababa, Ethiopia

¹⁷²ForestAction Nepal, Kathmandu, Nepal

¹⁷³Faculty of Environmental Sciences and Natural Resource Management, Norwegian University of Life Sciences, Ås, Norway

¹⁷⁴GeoIS, Cedar Park, TX, USA

¹⁷⁵Departamento de Biología, Universidad de La Serena, La Serena, Chile

¹⁷⁶Centro de Ciências Biológicas e da Natureza, Universidade Federal do Acre, Rio Branco, Brazil

¹⁷⁷Plant Systematic and Ecology Laboratory, Department of Biology, Higher Teachers' Training College, University of Yaoundé, Yaoundé, Cameroon

¹⁷⁸Department of Geographical Sciences, University of Maryland, College Park, MD, USA

¹⁷⁹Africa and Madagascar Department, Missouri Botanical Garden, St. Louis, MO, USA

¹⁸⁰Centre for Ecological Sciences, Indian Institute of Science, Bangalore, India

¹⁸¹Divecha Center for Climate Change, Indian Institute of Science, Bangalore, India

¹⁸²Faculty of Forestry, University of British Columbia, Vancouver, BC, Canada

¹⁸³Centre for International Forestry Research (CIFOR), Bogor, Indonesia

¹⁸⁴Key Laboratory of Tropical Plant Resources and Sustainable Use, Xishuangbanna Tropical Botanical Garden, Chinese Academy of Sciences, Menglun, China

¹⁸⁵Department of Plant Sciences, University of Cambridge, Cambridge, UK

¹⁸⁶Naturalis Biodiversity Center, Leiden, the Netherlands

¹⁸⁷Systems Ecology, Vrije University, Amsterdam, the Netherlands

¹⁸⁸Department of Biology and Florida Museum of Natural History, University of Florida, Gainsville, FL, USA

¹⁸⁹College of Science and Engineering, James Cook University, Cairns, QLD, Australia

¹⁹⁰Department of Food and Resource Economics, University of Copenhagen, Copenhagen, Denmark

¹⁹¹Warren Lane, East Dean, East Sussex, UK

¹⁹²INDEFOR, Facultad de Ciencias Forestales y Ambientales, Universidad de Los Andes, Mérida, Venezuela

¹⁹³School of Forestry and Environmental Studies, Yale University, New Haven, CT, USA

¹⁹⁴Department of Ecology, Evolution and Environmental Biology, Columbia University, New York, NY, USA

¹⁹⁵Investigación Botánica y Ecología, Jardín Botánico de Missouri, Oxapampa, Peru

¹⁹⁶Doi Tung Development Project, Social Development Department, Chiang Rai, Thailand

¹⁹⁷Van der Hout Forestry Consulting, Rotterdam, the Netherlands

¹⁹⁸Herbario Selva Central Oxapampa, Oxapampa, Peru

¹⁹⁹Núcleo de Estudos e Pesquisas Ambientais (NEPAM), University of Campinas (UNICAMP), Campinas, Brazil

²⁰⁰Department of Environmental Science, Policy, and Management, University of California, Berkeley, CA, USA

²⁰¹Herbario del Sur de Bolivia, Universidad Mayor Real and Pontifical de San Francisco Xavier de Chuquisaca, Sucre, Bolivia

²⁰²School of Earth and Sustainability, Northern Arizona University, Flagstaff, AZ, USA

²⁰³UA Museum of the North, University of Alaska, Fairbanks, AK, USA

²⁰⁴Department of Biological Sciences, National University of Singapore, Singapore, Singapore

²⁰⁵Institut de Recherche en Ecologie Tropicale, Libreville, Gabon

²⁰⁶School of Natural Sciences, University of Stirling, Stirling, UK

²⁰⁷Bell Museum, University of Minnesota, St. Paul, MN, USA

²⁰⁸School of Natural Sciences and Psychology, Liverpool John Moores University, Liverpool, UK

²⁰⁹Institute for Biodiversity and Ecosystem Dynamics, University of Amsterdam, Amsterdam, the Netherlands

²¹⁰School of Natural Sciences, Bangor University, Bangor, UK

²¹¹Manaaki Whenua, Landcare Research, Lincoln, New Zealand

²¹²Department of Geography and the Environment, University of Texas at Austin, Austin, TX, USA

²¹³Key Laboratory of Forest Ecology and Environment of State Forestry Administration, Institute of Forest Ecology, Environment and Protection, Chinese Academy of Forestry, Beijing, China
## Correspondence

Robert Muscarella, Department of Plant
Ecology and Evolution, Evolutionary Biology
Centre, Uppsala University, Kåbovägen 4,
house 7, SE-752 36, Uppsala, Sweden.
Email: bob.muscarella@gmail.com

## Funding information

Natur og Univers, Det Frie Forskningsråd,
Grant/Award Number: 4181-00158;
H2020 Marie Skłodowska-Curie Actions,
Grant/Award Number: 706011; Brazilian
National Research Council, Grant/Award
Number: CNPq/PELD 403710/2012-0
and Universal 459941/2014-3; Natural
Environment Research Council, Grant/
Award Number: NE/N012542/1; H2020
European Research Council, Grant/Award
Number: 291585; Vetenskapsrådet, Grant/
Award Number: 2019-03758; Coordenação
de Aperfeiçoamento de Pessoal de Nível
Superior, Grant/Award Number: Finance
Code 001; Fundação de Amparo à Pesquisa
do Estado de São Paulo, Grant/Award
Number: 2003/12595-7 and 2012/51509-
8; Villum Fonden, Grant/Award Number:
00025354 and 16549

Editor: Melodie McGeoch

## Abstract

**Aim:** Palms are an iconic, diverse and often abundant component of tropical ecosystems that provide many ecosystem services. Being monocots, tree palms are evolutionarily, morphologically and physiologically distinct from other trees, and these differences have important consequences for ecosystem services (e.g., carbon sequestration and storage) and in terms of responses to climate change. We quantified global patterns of tree palm relative abundance to help improve understanding of tropical forests and reduce uncertainty about these ecosystems under climate change.

**Location:** Tropical and subtropical moist forests.

**Time period:** Current.

**Major taxa studied:** Palms (Arecaceae).

**Methods:** We assembled a pantropical dataset of 2,548 forest plots (covering 1,191 ha) and quantified tree palm (i.e., ≥10 cm diameter at breast height) abundance relative to co-occurring non-palm trees. We compared the relative abundance of tree palms across biogeographical realms and tested for associations with palaeoclimate stability, current climate, edaphic conditions and metrics of forest structure.

**Results:** On average, the relative abundance of tree palms was more than five times larger between Neotropical locations and other biogeographical realms. Tree palms were absent in most locations outside the Neotropics but present in >80% of Neotropical locations. The relative abundance of tree palms was more strongly associated with local conditions (e.g., higher mean annual precipitation, lower soil fertility, shallower water table and lower plot mean wood density) than metrics of long-term climate stability. Life-form diversity also influenced the patterns; palm assemblages outside the Neotropics comprise many non-tree (e.g., climbing) palms. Finally, we show that tree palms can influence estimates of above-ground biomass, but the magnitude and direction of the effect require additional work.

**Conclusions:** Tree palms are not only quintessentially tropical, but they are also overwhelmingly Neotropical. Future work to understand the contributions of tree palms to biomass estimates and carbon cycling will be particularly crucial in Neotropical forests.

## KEYWORDS

above-ground biomass, abundance patterns, Arecaceae, local abiotic conditions, Neotropics, pantropical biogeography, tropical rainforest, wood density

# 1 | INTRODUCTION

Palms (Arecaceae/Palmae) are an iconic and diverse group (>2,500 recognized species worldwide) that have long delivered a wide range of provisioning services to humankind (Cámara-Leret et al., 2017; Eiserhardt, Svenning, Kissling, & Balslev, 2011; Levis et al., 2017; Tomlinson, 2006). Many palms are considered ecological keystone species because large numbers of animals depend on their fruit and flower resources (Onstein et al., 2017). In some areas, palms are also remarkably abundant. For instance, six of the 10 most common tree species in the Amazon rain forest are palms (ter Steege et al., 2013).

Given the morphological and physiological distinctiveness of palms (which are monocots; Renninger & Phillips, 2016), palm abundance can have important consequences for tropical forest ecosystem function, including carbon sequestration. However, we currently lack a quantitative analysis of the biogeographical patterns and conditions associated with palm abundance.

As a family, palms exhibit a variety of growth forms, ranging from small shrubs to lianas and large trees. Based on available data (Kissling et al., 2019), c. 40% of palm species are capable of growing stems ≥10 cm in diameter at 1.3 m above the ground (here defined as "tree palms"). Tree palms have often been pooled with non-palm
trees in forest inventory plots that are commonly used to measure terrestrial carbon stocks and parameterize vegetation models (Phillips et al., 2013). This raises several issues. First, biomass estimates are typically based on allometric equations developed for non-monocot trees (e.g., Chave et al., 2014; Feldpausch et al., 2012). These equations tend to perform poorly for palms because they lack secondary growth, which decouples diameter-height relationships (Goodman et al., 2013). Second, physiological and morphological differences between tree palms and other trees suggest the potential for large differences in terms of the responses of tree palms to drivers of environmental change compared with non-palm trees (Emilio et al., 2014; Renninger & Phillips, 2016). From an ecosystem functioning perspective, these issues are clearly most crucial in areas where palms account for a relatively high proportion of forest biomas, such as some swamp forests (Dargie et al., 2017). Here, we provide the first global analysis of tree palm abundance relative to other co-occurring trees to help reduce uncertainty about tropical ecosystem function.

Patterns of tree palm abundance may be associated with variation in contemporary ecological conditions that favour the establishment and persistence of palms over other types of trees (the contemporary conditions hypothesis). Most existing evidence for palm abundance distributions along contemporary environmental gradients comes from studies in Amazonian forests, where palms tend to be more abundant in areas with ample soil moisture and relatively high soil fertility (Castilho et al., 2006; Costa, Guillaumet, Lima, & Pereira, 2009; Emilio et al., 2014; Kahn & Mejia, 1990; Schietti et al., 2014; Svenning, 1999, 2001). Palm root architecture is the most likely explanation for the observed patterns in relationship to hydrological and soil properties for at least two reasons. First, palms tend to have dense, superficial root systems that may provide them with better anchorage compared with relatively deep-rooted trees in dynamic fluvial systems. In an Ecuadorian forest, for example, Gale and Barford (1999) found that uprooting was c. 50% higher for dicotyledonous trees than for the dominant palm, *Iriartea deltoidea*. Second, higher investment in root biomass towards the soil surface might be beneficial in terms of competition for nutrients but could represent a limitation for growth in regions where plants must rely on seasonal access to deeper water. In these conditions, high annual precipitation and low precipitation seasonality should maximize the advantages of shallow root systems, and therefore, promote relative abundance of palms (Eiserhardt et al., 2011).

However, local edaphic conditions in the Amazon region are also correlated with longer-term landscape evolution (Higgins et al., 2011; Hoorn et al., 2010) and forest stem turnover rates, which may also affect palm abundance indirectly (e.g., Emilio et al., 2014). For instance, several studies have shown that at least some palm species can be successful in relatively dynamic forests by capitalizing on high-resource conditions after disturbance (Eiserhardt et al., 2011; Emilio et al., 2014; Salm, 2005). Additionally, some palms are resilient to certain types of disturbance, including hurricanes and blowdowns (Lugo & Scatena, 1996) and, in some cases, fire. As a result, we might expect tree palm relative abundance to exhibit a positive association

with contemporary rates of forest turnover. In general, the extent to which relatively local ecological factors can help to explain biogeographical scale variation in tree palm abundance remains unknown. In summary, under the contemporary conditions hypothesis, we expect significant associations between local ecological conditions and tree palm relative abundance. In particular, we expect higher tree palm relative abundance in areas with higher annual and dry season rainfall, more fertile soils with shallower depth to the water table, and faster stem turnover rates.

It is also possible that patterns of palm abundance are associated with historical distributions of conditions that allowed the persistence of palm species and populations over long time periods (the climate stability hypothesis). Previous work on macroecological patterns of palm diversity provides context for hypotheses about historical drivers of tree palm abundance at biogeographical scales (Eiserhardt et al., 2011; Kissling, Eiserhardt, et al., 2012; Svenning, Borchsenius, Bjorholm, & Balslev, 2008). For example, Kissling, Eiserhardt, et al. (2012) reported that the extent of tropical rain forest biome in different biogeographical realms during the Cenozoic period (based on palaeoclimate reconstructions) was positively associated with current palm diversity. Relatively low palm species richness in Africa has been attributed to extinctions during rain forest contraction (Faye et al., 2016; Kissling, Eiserhardt, et al., 2012). However, Baker and Couvreur (2013) argued that higher speciation rates outside Africa (as opposed to higher extinction rates in Africa) might better explain contemporary richness patterns. In Madagascar, Rakotoarinivo et al. (2013) reported higher palm diversity in areas that had higher precipitation during the Last Glacial Maximum (LGM; 21,000 years ago) compared with present-day precipitation. In general, palaeoclimatic variability could also be associated with spatial variation of palm abundance. For example, larger areas with more stable tropical climates could facilitate larger populations of palms, which could also be associated with higher diversification rates (Blach-Overgaard, Kissling, Dransfield, Balslev, & Svenning, 2013; Couvreur et al., 2015; Kisel, McInnes, Toomey, & Orme, 2011; Rakotoarinivo et al., 2013; Rosenzweig, 1995). Therefore, under the climate stability hypothesis, we expect a positive association between metrics of long-term climatic stability and tree palm relative abundance.

In this study, we use a large pantropical dataset of forest plots to quantify global-scale variation in tree palm abundance (quantified as the basal area and the number of stems) relative to co-occurring trees. We examine spatial patterns of tree palm relative abundance across major biogeographical realms, in addition to correlations with abiotic and biotic variables in light of the contemporary conditions and climate stability hypotheses outlined above. As a step towards assessing the potential ecosystem-level consequences of tree palm relative abundance patterns, we estimate the amount of error introduced to standard calculations of above-ground biomass (AGB) when tree palms are pooled with other trees versus treated separately. Finally, in light of the broad range of palm growth forms, we assess how the diameter size threshold commonly used in forest inventory plots (10 cm) affects inferences of tree palm relative abundance in different biogeographical realms. Our overarching aim is
to develop a quantitative understanding of patterns and drivers of tree palm relative abundance across broad geographical and environmental scales that can help us to gain a better understanding of this important and unique group and to reduce uncertainty about tropical ecosystem functioning and dynamics.

2 | METHODS

2.1 | Forest inventory data

Our analysis is based primarily on data from ForestPlots.net (Lewis
et al., 2009; Lewis et al., 2013; Lopez-Gonzalez et al., 2009; Lopez-
Gonzalez, Lewis, Burkitt, & Phillips, 2011; Malhi et al., 2002),
which integrates data from research plot networks active in the
Neotropics (RAINFOR and PPBio), Africa (AfriTRON) and Southeast
Asia (T-FORCES), in addition to other networks and researchers, and
also uses the pan-tropical Gentry 0.1-ha transect dataset (Phillips &
Miller, 2002) and the database compiled by Slik et al. (2018).

We assembled data for 2,624 individual forest plots located in
the subtropical and tropical moist broadleaf forest biomes as de-
fined by Olson et al. (2004). A list of publications associated with
data used in this paper is found in the Supporting Information
Appendix S1. In each plot, all individual stems with a diameter of
≥10 cm in diameter at breast height (d.b.h., 1.3 m above the ground)
were identified and measured for d.b.h. Our analyses focus on ar-
borescent palms that reach ≥10 cm d.b.h. (henceforth, “tree palms”),
because smaller-diameter stems are excluded in the standard pro-
tocol of most forest inventories. Although palms occur in a wide
range of tropical ecosystems (including rain forests, savannas and
dry forests), we focused on moist forests because this biome houses

the greatest palm diversity and it is where the majority of plots in
our dataset are located. We restricted our analyses further to plots
reported as “old-growth”, “primary” or “undisturbed” by the orig-
nal data collectors and excluded 57 plots described as swamps or
“monodominant” palm forests. We analysed a total of 2,548 plots
(covering a total of 1,191 ha; Supporting Information Appendix S2).
To reduce spatial autocorrelation, we pooled data from plots within
the same 10 km × 10 km grid cell; hereafter, we refer to these as ag-
gregated plots as “locations”. After aggregation, our dataset included
842 locations (Figure 1a). The area sampled per location ranged from
0.1 to 51.8 ha (median ± SD = 0.4 ± 3.5 ha), with 95% of the locations
sampled were ≥0.1 ha. We assigned each location to one of the bio-
geographical realms defined by Olson et al. (2004), but we combined
locations in Oceania with Australasia because of their strong his-
torical connection (Muellner, Pannell, Coleman, & Chase, 2008) and
their relatively low sample sizes (n = 32 and 37, respectively).

For each location, we calculated the relative basal area (BA<sub>palm</sub>)
and the relative abundance (RA<sub>palm</sub>) of tree palms as the sum of the
tree palm basal area or number of tree palm individuals divided by
the total basal area or total number of stems. These relative met-
rics of tree palm abundance reduce variation caused by differences
in sample area and stem density across locations. Results based on
BA<sub>palm</sub> and RA<sub>palm</sub> were highly correlated, and we present and dis-
cuss BA<sub>palm</sub> results in the main text (results based on RA<sub>palm</sub> are pro-
vided in the Supporting Information Figure S4).

2.2 | Environmental variables

To address our hypothesis about the link between long-term climatic
stability and tree palm abundance (the climate-stability hypothesis),

<figure><img src="image_3.png" /></figure>

FIGURE 1 (a) Locations of study plots coloured to reflect four biogeographical realms. The total number of locations is given along with the total area (in hectares) sampled in each realm. (b) Tree palm relative basal area (BA<sub>palm</sub>) in each location defined as palms that reach 10 cm diameter at breast height (d.b.h.), and thus, are included in typical forest inventory plots. Locations without tree palms present are shown as blue crosses. Red circles represent plots with tree palms present, and circle size is proportional to the BA<sub>palm</sub> at that location [Colour figure can be viewed at wileyonlinelibrary.com]
we used several variables that are likely to have had major impacts on the distributions of palms and their habitats (e.g., Melo, Freitas, Bacon, & Collevatti, 2018). Specifically, we calculated the absolute difference (or “anomaly”) between the LGM (21,000 years ago) and modern climatological averages (1979–2013), for both the mean annual precipitation (in millimetres per year) and the precipitation in the driest quarter (in millimetres per quarter) using 30 arc s (c. 1 km²) resolution data from CHELSA (Karger et al., 2017). We used LGM variables based on data from the Palaeoclimate Modelling Intercomparison Project (PMIP3) and output from the Community Climate System Model (CCSM4) (Karger et al., 2017).

We used several datasets to address our hypothesis about contemporary ecological controls on tree palm relative abundance (the contemporary conditions hypothesis). For contemporary climate, we extracted the mean annual precipitation (in millimetres per year) and precipitation in the driest quarter (in millimetres per quarter) for each location from the 30 arc s (c. 1 km²) resolution CHELSA dataset (Karger et al., 2017). For edaphic conditions, we extracted cation exchange capacity (CEC; a general proxy for soil fertility; cmol+ kg⁻¹) at 250 m resolution from the SoilGrids website (https://soilgrids.org/). We focused on CEC because prior work has shown associations between forest composition (including palm diversity) and soil fertility (Muscarella et al., 2019). We extracted the depth to the water table (in metres) from the 30 arc s (c. 1 km²) resolution database of Fan, Li, and Miguez-Macho (2013). We examined how conditions at the study locations reflect the range of conditions in their respective biogeographical realms by sampling each variable at 10,000 random points in each realm (Supporting Information Figure S1). We extracted values for the predictor variables listed above based on the mean latitude and longitude of plots in each location.

We calculated two proxies for turnover rates in each location. First, because forests with faster turnover rates tend to have shorter canopies (Feldpausch et al., 2011), we estimated the maximal canopy height for each location based on the tree inventory data. More specifically, given that most plots do not include measured data on tree height, we used the “BIOMASS” package (Réjou-Méchain, Tanguy, Piponiot, Chave, & Hérault, 2017) to estimate the height of each individual tree based on its diameter using the geographically based allometric equation of Chave et al. (2014). We used the 95th percentile of estimated tree height in each location as a metric of maximal canopy height (comparable results were obtained when using the 99th percentile). Second, we calculated the basal area-weighted mean (CWM) wood density for each location by matching wood density data from the global wood density database (Chave et al., 2009; Zanne et al., 2009) with the relative basal area of each species in each location. The CWM wood density reflects life-history strategies of trees, and stands with lower values of CWM wood density tend to have more rapid turnover rates (Chave et al., 2009; Phillips et al., 2004, 2019). Given that we were interested in the relationship between local environmental conditions and palm relative abundance patterns, we excluded palms when calculating CWM wood density. However, note that CWM wood density values per location were highly correlated whether or not palms were included

(Pearson's r = .98). Species-level mean wood density values were available for 51% of the individual stems [representing 61% of the total basal area (BA)], genus-level mean values were used for 37% of the individuals (30% of the total BA), family-level mean values were used for 9% of the individuals (6% of the total BA), and we excluded the remaining 3% of individuals (representing 3% of the total BA) that were not identified to the family level. The computed CWM wood density values were highly correlated with CWM wood density values computed based only on species-level data (Pearson's r = .87), genus-level data (Pearson's r = .99) or family-level data (Pearson's r = .99).

## 2.3 | Statistical analyses

As a first step to assess general patterns of tree palm occurrence, we used t tests to compare each environmental covariate described above between locations with and without tree palms, separately for each realm. We log₁₀-transformed all covariates before analysis, except for CWM wood density.

In order to assess associations between tree palm abundance and covariates, we fitted a Bayesian zero-inflated version of the beta regression (BeZI; Ospina & Ferrari, 2010), because our response variable of interest (BApalm) is a proportion, and tree palms were absent from many (61%) locations. We modelled BApalm at each location, i, using a beta-distributed random variable yᵢ ~ BeZI (p₀ᵢ, μᵢ, φ), which represents a mixture of beta and Bernoulli distributions defined as:

$$ \text{BeZI}(p_{0i}, \mu_i, \phi) = \begin{cases} p_0, & y=0 \\ (1-p_0) f(y; \mu, \phi), & y \in (0,1) \end{cases} $$

The parameter p₀ is the probability of tree palms being absent from a location, and f(y; μ, φ) is the beta density function of BApalm at locations where tree palms are present: μ is the expected value and φ is a precision parameter (Ospina & Ferrari, 2010). For both components of the model (occurrence and relative basal area), we modelled the relationship between the response variable, y, at each location, i, including biogeographical realm as a random grouping variable, eᵢ. We used the following model:

$$ y_i \sim \beta_0 + \beta_1 \text{MAP}_{\text{LGM-curr}_i} + \beta_2 \text{DRY}_{\text{LGM-curr}_i} + \beta_3 \text{MAP}_{\text{curr}_i} + \beta_4 \text{DRY}_{\text{curr}_i} \\ + \beta_5 \text{CEC}_i + \beta_6 \text{WTD}_i + \beta_7 \text{CH}_i + \beta_8 \text{WD}_i + \beta_9 \text{Area}_i + e_i $$

Where y is palm occurrence (for the Bernoulli part of the model) and BApalm (for the beta part of the model), β₀ is the intercept, and the terms MAPLGM-currᵢ and DRYLGM-currᵢ correspond to the anomalies between the LGM and contemporary mean annual and dry quarter precipitation, respectively; MAPcurrᵢ and DRYcurrᵢ represent current climatological means for mean annual and dry quarter precipitation; CECᵢ, WTDᵢ, CHᵢ and WDᵢ correspond to cation exchange capacity, water table depth, canopy height and CWM wood density, respectively; and Areaᵢ corresponds to the sum of
<figure><img src="image_4.png" /><figcaption>FIGURE 2 (a) Percentage of locations in each realm where tree palms (≥10 cm diameter at breast height) are present. Text on bars shows the number of locations with tree palms, total number of locations and total area (in hectares) sampled. (b) Relative basal area of tree palms (BA<sub>palm</sub>, as a percentage, shown on a log<sub>10</sub> scale) in each realm among locations where at least one tree palm was present. Circles show data for individual locations, with the size being proportional to the location sample area. The dotted horizontal line is a reference line delineating 5% relative tree palm basal area. In boxplots, the bold horizontal line indicates the median; top and bottom edges of the boxes reflect the first and third quartiles, respectively; and whiskers extend to the most extreme data point that is not > 1.5 times the interquartile range. Circles show the values for individual locations [Colour figure can be viewed at wileyonlinelibrary.com]</figcaption></figure>

area sampled in component plots at location i. We included the area
term to account for possible correlation between the area sampled
and BA<sub>palm</sub>. Before fitting the model, we centred and scaled each
predictor variable by subtracting its mean and dividing it by its
standard deviation. This procedure facilitates model convergence
and allows for the direct comparison of relative effect sizes across
predictor variables (Gelman & Hill, 2006). The majority of model
covariates were little correlated (Pearson's |r| < .3), but historical
climate anomalies and current climate conditions were moderately
correlated (Pearson's r = .47–.71; Supporting Information Table S1).
Nonetheless, models fitted separately with either historical climate
anomalies or current climate conditions gave similar estimates for
all parameters, and we report results from the full model. We fitted
models using STAN (Stan Development Team, 2016) via the “brms”
R package (Bürkner, 2017) and with default (uninformative) priors
(see model code in the Supporting Information Appendix S3). We
used four chains with 1,000 burn-in samples and 1,000 sampling
iterations per chain. All parameters had R̂ values < 1.1, indicating
successful convergence. Plots showing posterior distributions, trace
plots and a posterior predictive check are provided in the Supporting
Information (Figures S5 and S6). We computed R<sup>2</sup> of the model using
the “add_ic” function of the “brms” R package (Bürkner, 2017). To
evaluate spatial autocorrelation, we computed Moran's I for model
residuals using geographical coordinates of locations. Observed and
expected values did not differ significantly (p = .83), indicating a lack
of spatial autocorrelation among residuals. All analyses were con-
ducted in R v.3.5.1 (R Development Core Team 2019).

## 2.4 | Implications for above-ground biomass estimates

To quantify the magnitude of error introduced to estimations
of AGB if tree palms are pooled with other trees, we first used

the geographically based allometric equation (i.e. using the en-
vironmental factor, *E*) of Chave et al. (2014), implemented in the
BIOMASS R package (Réjou-Méchain et al., 2017) to estimate the
total AGB for each location (including tree palms and non-palm
trees): AGB<sub>Chave-only</sub>. Ideally, we could compare these estimates
with values where tree palm biomass was calculated using spe-
cies-specific allometric equations developed specifically for palms
that include information on plant height (Feldpausch et al., 2012;
Marshall et al., 2012). Unfortunately, allometric equations have
not yet been developed for most palm species, and most ground-
based forest inventory datasets do not include height measure-
ments. As an alternative, we calculated tree palm AGB using the
family-level allometric equation (based on diameter only) from the
study by Goodman et al. (2013). We then added this value of tree
palm AGB to non-palm tree AGB calculated with the method of
Chave et al. (2014) described above to arrive at a hybrid estimate
of AGB for each location: AGB<sub>Goodman+Chave</sub>. We report the ratio of
AGB<sub>Goodman+Chave</sub> to AGB<sub>Chave-only</sub> as a step towards quantifying the
error in AGB estimates introduced by palms.

## 2.5 | Effect of size threshold on tree palm abundance

To examine how the 10 cm d.b.h. threshold (commonly used by for-
est inventories) could affect conclusions about palm abundance pat-
terns, we analysed separately Alwyn Gentry's transect data (Phillips
& Miller, 2002), which includes all woody stems ≥2.5 cm d.b.h. in
144 locations (0.1 ha each) distributed globally throughout the (sub)
tropical moist broadleaf biome (Supporting Information Figure S2).
For each transect, we compared BA<sub>palm</sub> (and RA<sub>palm</sub>) based on the
full dataset (i.e., all stems ≥2.5 cm d.b.h., *n* = 80,712 individual stems)
and a subset of the data using a ≥10 cm d.b.h. threshold (*n* = 16,665
individual stems).
3 | RESULTS

Across all 842 locations, tree palms (≥10 cm d.b.h.) accounted for a total of 20,029 out of 661,194 individual stems (3%). The majority of tree palms (95%) belonged to 126 species and 71 genera (representing c. 5 and c. 39% of globally accepted palm species and genera, respectively). The remaining 5% of tree palm individuals in our dataset were identified to genus only. Across all locations, tree palms accounted for c. 1.4% of the total basal area sampled.

We found a striking pattern across biogeographical realms in
terms of tree palm occurrence, relative basal area and relative abun-
dance (Figures 1b and 2). The proportion of locations where tree
palms were recorded was, by far, highest in the Neotropics (84%)
compared with any other realm (41% in Australasia/Oceania, 13%
in Afrotropics and 11% in IndoMalaya; Figure 2a). On average, loca-
tions in the Neotropics had higher BA<sub>palm</sub> (5.5%) and RA<sub>palm</sub> (8.4%)
than locations in all other realms (mean of BA<sub>palm</sub> across other
realms ranged from .15 to .84%; Figure 2b). In other words, palms
represent <5% of the total basal area for trees ≥10 cm d.b.h. in 99%
of the sampled locations outside of the Neotropics. Nonetheless,
especially within the Neotropics, BA<sub>palm</sub> was highly variable, rang-
ing globally from 0 to 60%.

3.1 | Tree palm occurrence, abundance and environmental conditions

Based on t tests, there were some significant differences in environ-
mental variables between locations where tree palms were present
versus those where they were absent, but these were not always
consistent or significant across realms (Supporting Information
Figure S3). For example, soil fertility (CEC) and palm occurrence were
negatively associated among Neotropical locations and positively
associated among Afrotropical locations. Palms were more likely to
occur in locations with shallower water tables in Neotropical loca-
tions and deeper water tables in IndoMalayan locations. Notably,
tree palm occurrence was associated with higher dry season rainfall
in all realms, although the relationship was not statistically signifi-
cant in Australasia/Oceania. Results from the occurrence compo-
nent of our zero-inflated model were consistent, in part, with results
from the t tests (Figure 3a). Specifically, palms were more likely to
occur in locations with higher precipitation in the driest quarter and
in locations with smaller anomalies between historical and contem-
porary dry season precipitation. Palms also tended to be recorded in
locations with larger total area sampled.

In contrast, palm relative basal area (BA<sub>palm</sub>) was not signifi-
cantly associated with either palaeoclimate stability or current

<figure><img src="image_5.png" /></figure>

FIGURE 3 Standardized effect size (estimated coefficients) with 95 and 90% credible intervals (thinner and thicker lines, respectively) for variables explaining (a) palm occurrence and (b) relative palm basal area (BA<sub>palm</sub>). For visualization, estimated coefficients in (a) were multiplied by minus one to allow a more intuitive interpretation (i.e., meaning that they correspond to the probability of palms being present rather than absent). Points represent median values of posterior distributions and are filled when 95% credible intervals do not overlap zero. See the Supporting Information Figure S5 for full posterior parameter distributions. The mean annual precipitation anomaly and the driest quarter precipitation anomaly refer to the absolute difference between the Last Glacial Maximum (21,000 years ago) and modern climatological averages (1979–2013) for each of those climate variables, respectively. The CWM wood density refers to the basal area-weighted mean wood density value among non-palm trees recorded in each location
<figure><img src="image_6.png" /></figure>

**FIGURE 5** Palm relative basal area (BA<sub>palm</sub>, as a percentage, shown on a log<sub>10</sub> scale) versus the ratio of estimated total above-ground biomass (AGB) in each location calculated using the family-level (diameter-only) equation from Goodman et al. (2013) for palms and the equation from Chave et al. (2014) for trees (AGB<sub>Goodman+Chave</sub>) versus total AGB when palms are treated as trees using the equations of Chave et al. (2014) (AGB<sub>Chave-only</sub>). Values on the y axis less than one indicate that using the equation from Goodman et al. (2013) for palms leads to a decrease of the estimated plot-level biomass, and vice versa [Colour figure can be viewed at wileyonlinelibrary.com]

# 4 | DISCUSSION

## 4.1 | Patterns and drivers of tree palm abundance and diversity

Tree palms were clearly most abundant in Neotropical forests, where they composed ≤ 60% of the total forest basal area and stem abundance. We consider these figures to be conservative estimates because we did not consider areas of tree palm monodominance, which will require a separate and focused treatment. Reinforcing prior work (e.g., Dransfield et al., 2008; Moore, 1973; ter Steege et al., 2013, 2019), tree palms are particularly abundant components of forests in western Amazonia, which is also a hotspot of palm diversity (Svenning et al., 2008). In contrast, tree palms play a relatively minor role in terms of abundance in other biogeographical realms, especially the Afrotropics and IndoMalaya [with notable exceptions of tree palm monodominance in some swamps of these regions (Dargie et al., 2017)]. Tree palms do, however, reach relatively high levels of relative abundance in some plots in Madagascar and on the northeast coast of Australia (≤ 14% of the total basal area), but these locations were exceptional in comparison to most locations in those realms.

In our study, local conditions, including current climate, edaphic properties and proxies for turnover rates, were more strongly related to tree palm abundance within biogeographical realms than our metrics of palaeoclimate stability. In particular, tree palms had

<figure><img src="image_7.png" /></figure>

**FIGURE 6** Effect of size threshold on palm abundance. Difference of BA<sub>palm</sub> (as a percentage) for 116 Gentry transects when using a 2.5 versus 10 cm diameter at breast height (d.b.h.) size threshold. Each bar represents one transect where at least one palm was present. Positive values indicate that BA<sub>palm</sub> was higher when stems down to 2.5 cm d.b.h. were included, and vice versa. Asterisks indicate transects where palms were recorded in the 2.5 cm d.b.h. data but absent from the 10 cm d.b.h. data. The inset shows the percentage of transects where palms were present with a 2.5 cm d.b.h. threshold and absent at the 10 cm d.b.h. threshold [Colour figure can be viewed at wileyonlinelibrary.com]

greater relative abundance in locations with higher mean annual rainfall, lower soil fertility, shallower water tables and lighter CWM wood density. These results are broadly consistent with a number of previous studies at smaller spatial scales that have linked spatial distributions of palms to hydrology, edaphic conditions and stand turnover rates (Costa et al., 2009; Eiserhardt et al., 2011; Emilio et al., 2014; Svenning, 2001).

Our finding that palms were relatively more abundant in locations with low soil fertility, shallow water tables and lighter CWM wood density might reflect related underlying processes. For instance, Schietti et al. (2014) reported a strong relationship between water table depth and palm community composition in forests of central Brazilian Amazonia. Likewise, Castilho et al. (2006) found greater palm biomass in sites with poorly drained, sand-rich soils. Quesada et al. (2012) reported that forests with lighter CWM wood density are associated with shorter stem residency times, and palms may be more likely to be abundant at sites that face more severe or frequent disturbances. In fact, some areas regularly exposed to wind disturbance, for example, show high levels of tree palm abundance (e.g., Caribbean islands, east coast of Australia). Emilio et al. (2014) linked palm abundance in Amazonian forests with aspects of soil structure and suggested that edaphic properties, such as soil depth and texture, might affect plant composition and abundance indirectly by influencing stem turnover rates. Additionally, CWM wood density is geographically structured in our dataset; locations in western Amazonia tend to have lighter CWM wood density than locations in eastern Amazonia,
potentially reflecting gradients in drought and nutrient stress (ter
Steege et al., 2006).

We hypothesized that long-term climatic conditions could also
influence current patterns of tree palm abundance based on the
climatic sensitivity of palms (Reichgelt, West, & Greenwood, 2018;
Tomlinson, 2006). In some respects, our results are broadly similar
to global patterns of palm diversity (i.e., high diversity and relative
abundance of tree palms in the Neotropics but not the Afrotropics).
However, variation of tree palm relative abundance within realms was
not significantly related to the anomaly of either mean annual precip-
itation or dry season precipitation between the LGM (21,000 years
ago) and the current period. It appears that local environmental het-
erogeneity mediates palm relative abundance more strongly within
biogeographical realms, whereas legacy effects of palaeoclimate
might be more apparent at larger scales. Interestingly, palaeoclimate
stability (anomaly of dry quarter precipitation) was (negatively) asso-
ciated with palm occurrence. We might generally expect abundance
to exhibit less coupling to historical legacies than diversity, given that
species present in a locality have the potential to respond in a rel-
atively rapid manner to current environmental conditions through
population growth. In other words, processes governing abundance
are likely to occur along shorter time-scales than processes affecting
species richness (i.e., speciation, extinction, immigration).

In this study, we were unable to assess the role of several po-
tentially important drivers of palm abundance directly. First, hu-
mans have affected tropical landscapes for millennia (Roberts, Hunt,
Arroyo-Kalin, Evans, & Boivin, 2017) and, especially given the many

uses of palms (Cámara-Leret et al., 2017), past and recent human
activity could influence observed contemporary patterns of palm
abundance. Levis et al. (2017) showed that domesticated tree spe-
cies, including several tree palms, were more abundant in forests
near archaeological sites in Amazon forests, suggesting long-term
human impacts on the composition and structure of tropical for-
ests. In contrast, Piperno, McMichael, and Bush (2019) reported a
lack of evidence for ancient human impacts on palm abundance in
Amazonian terra firme forests. Humans have clearly impacted forests
in other biogeographical realms also (Hunt & Rabbett, 2014; Malhi,
Adu-Bredu, Asare, Lewis, & Mayaux, 2013; Roberts et al., 2017), but
we do not expect human activity to have altered the broad biogeо-
graphical patterns we report. The degree to which human activities
have impacted populations of palms and other trees requires more
study.

Second, we cannot rule out the possibility that the historical
presence or absence of certain palm lineages influences patterns of
palm abundance. Palms display strong spatial phylogenetic structure
(Kissling, Baker, et al., 2012; Figure 7), showing that different regions
are characterized by different lineages. If certain lineages have a
particular tendency to evolve highly abundant tree palms, this could
drive patterns like the ones observed here. For example, the palm
tribes Euterpeae, Iriarteae and Cocoseae, which include the most
abundant palms in Amazonia (ter Steege et al., 2013), are absent out-
side the Neotropics (except for a few Attaleinae species in South
Africa and Madagascar, which, interestingly, are not particularly
abundant there). Also, non-human animal seed dispersers influence
palm diversification and diversity via eco-evolutionary interactions
(Onstein et al., 2017), and these mutualisms could also potentially
influence contemporary palm abundance patterns. For instance,
Onstein et al. (2018) argued that palms with megafaunal fruits (some
of which could also be large tree palms) may have reduced abun-
dance and increased extinctions owing to the extinction of their
megafaunal dispersers (also see Doughty et al., 2016). Quantifying
these effects will require additional phylogenetic analyses, which
will be attempted soon, once a comprehensive species-level phylog-
eny of the palm family is available.

Finally, our dataset does not include swamp habitats, and some
tree palms are known for forming monodominant stands, especially
in swampy conditions associated with large carbon pools. For exam-
ple, palm swamps in the Congo basin, Amazonia and southeast Asia
cover millions of hectares and store huge amounts of biomass both
above and below ground (Dargie et al., 2017; Kahn & Mejia, 1990).
Better understanding of these unique habitats requires additional
work; recent progress has been made using various remote sensing
techniques (e.g., Dargie et al., 2017).

4.2 | Implications for above-ground biomass estimates

One motivation for our study was to gain a better understanding of
the potential error introduced to estimates of AGB when tree palms

<figure><img src="image_8.png" /><figcaption>FIGURE 7 Phylogeny of palms (Faurby, Eiserhardt, Baker, & Svenning, 2016) showing the presence (in bold colour) or absence (in faint colour) for each species in each biogeographical realm based on the world checklist of palms (WCSP, 2017) in the "level 3" geographical units defined by the International Working Group on Taxonomic Databases (TDWG; Brummitt, 2001) and biogeographical realms defined by Olson et al. (2004). The outermost ring is black for species known to reach a maximal diameter at breast height (d.b.h.) ≥ 10 cm ("tree palms" in this study), grey for species with maximal d.b.h. < 10 cm, and white for species for which maximum d.b.h. information was not available (28%) (Kissling et al., 2019) [Colour figure can be viewed at wileyonlinelibrary.com]</figcaption></figure>
are pooled with other trees. Specifically, standard allometric equa-
tions generated for trees that estimate AGB from stem diameter per-
form in a relative poor manner when estimating biomass for palms
because, unlike other trees, growth in diameter and growth in height
of palms are largely decoupled. The magnitude of error this intro-
duces clearly depends, in part, on tree palm relative abundance. In
our dataset, pooling tree palms with other trees added error to AGB
estimates, but the direction of the error was not consistent. Pooling
palms with dicots most often led to an overestimation of AGB [com-
pared with estimates based on using the family-level model from
the study by Goodman et al. (2013) for palms and the dicot models
of Chave et al. (2014) for non-palm trees]. Variation in the magni-
tude and direction of these estimates emerged, in part, from the
geographical variation in the equation of Chave et al. (2014). The
differences in AGB estimates between the two methods used here
(AGB<sub>Goodman+Chave</sub> and AGB<sub>Chave-only</sub>) were <1% in >90% of locations
where BA<sub>palm</sub> was <5%, indicating that error from palms is prob-
ably negligible when palms are minor components of the total for-
est basal area. However, the difference between the two methods
increases with BA<sub>palm</sub>, and the difference ranged from -15 to 6% in
our dataset. Future analyses using data on stem height are needed
to refine these estimates, and additional species- and region-spe-
cific palm allometric models will be important to improve AGB esti-
mates in many Neotropical forests where tree palms are abundant.
Fortunately, increasingly available remote sensing data on canopy
height (e.g., NASA GEDI) will improve this situation when paired with
appropriate ground-based data. Additionally, recent advances in re-
mote sensing to identify palms (Tagle Casapia et al., 2020) will also
help to identify areas where attention to palm abundance is impor-
tant to reduce the error in biomass estimates.

## 4.3 | Palm life-forms and the effect of size threshold

To some extent, the patterns we report reflect these differences in palm life diversity across biogeographical realms. For example, clades that assume a climbing form (e.g., rattans in the subfamily Calamoideae) constitute a major component of palm diversity in the IndoMalayan and Australasian biogeographical realms (Couvreur et al., 2015). In the Neotropics, in contrast, many abundant palm species grow as large canopy trees (e.g., *Astrocaryum chambira*, *Euterpe precatoria*, *Iriartea deltoidea*, *Oenocarpus bataua*), which are among the hyperdominant species of Amazonia (ter Steege et al., 2013). Meanwhile, understorey species also make up a large portion of palm diversity in the Neotropical, IndoMalayan and Australasian biogeographical realms (e.g., *Bactris* and *Geonoma* species in Amazonia; *Dypsis* in Madagascar; *Pinanga* and *Licuala* in IndoMalaya; *Pritchardia* in parts of Oceania).

Contrasting patterns of life-form diversity influence the patterns
of abundance we report, because the forest inventory datasets we
used only include trees with a large diameter (e.g., 10 cm). On the
one hand, plots censused with this size threshold are adequate for
quantifying important forest properties (Bastin et al., 2018; Lutz
et al., 2018; Slik et al., 2013; Stephenson et al., 2014). For example,

Lutz et al. (2018) showed that half the above-ground living biomass
in a global dataset of forest plots was stored in the largest trees,
which made up 1% of the total stems. On the other hand, major
components of biodiversity are neglected by using a size thresh-
old of 10 cm d.b.h., because a substantial amount of tropical rain
forest diversity exists as understorey and slender climbing plants
(Cicuzza et al., 2013; Hubau et al., 2019). For palms, we estimate that
c. 40% of all species reach a diameter of 10 cm (Kissling et al., 2019;
Figure 7). The plot dataset we analysed included a total of 126 palm
species, which represent c. 5% of the total known palm species and
nearly 20% of the species known to reach 10 cm d.b.h. Meanwhile,
lowering the diameter cut-off for the Gentry transect dataset from
10 to 2.5 cm resulted in the inclusion of 51 additional palm species
despite containing < 25% the number of palm individuals.

Our analysis of the Gentry transect data provides additional con-
firmation that biogeographical differences in palm life-form diversity
can affect the perception of palm abundance. For example, small-di-
ameter climbing palms are diverse and more common in IndoMalaya
compared with arborescent palms (Couvreur et al., 2015). In fact,
many IndoMalayan palm species (e.g., rattans) are unlikely to reach
even the 2.5 cm d.b.h. used by Gentry; a major part of palm di-
versity in the IndoMalayan realm is contributed by the subfamily
Calamoideae, which comprises mostly climbers. Notably, acaules-
cent (or stemless) palms can also be common in Neotropical forests.
Overall, a comprehensive understanding of the abundance of palms
across the entire phylogenetic tree will require additional data that
include small size classes and different life-forms.

We propose several additional potential reasons for generally low
tree palm abundance in the Afrotropics and IndoMalaya in addition
to the differences in life-forms described above. The first reason may
be related to the generally tall forest canopies in these realms, espe-
cially IndoMalaya, compared with the Neotropics (Banin et al., 2014).
These conditions may favour the evolution of alternative life-forms, in-
cluding climbers (Couvreur et al., 2015). In fact, Couvreur et al. (2015)
reported a link between diversification of the climbing life-form in
palms and forest canopy height. Additional synthetic work integrating
abundance patterns with evolutionary drivers of palm life-form varia-
tion could be a fruitful research avenue. Second, for IndoMalaya, we
note that dry quarter precipitation is greater in locations with palms
than in locations where palms were absent (Supporting Information
Figure S2d). On average, locations in this realm without palms have
only c. 50 mm rain during the driest quarter, whereas sites with palms
have >100 mm. It is possible that the minimum monthly rainfall gener-
ally limits palm occurrence in many areas that have months of rainfall
<100 mm, which includes much of mainland Southeast Asia.

## 4.4 | Conclusions and future directions

The results of our analysis of tree palm abundance show that tree
palms are not only quintessentially tropical, but also overwhelmingly
Neotropical. Although palaeoclimatic conditions appear to have a
strong influence on global palm diversity, tree palm relative abundance
was more strongly related to current ecological conditions, and tree palm abundance patterns might be particularly sensitive to future climate change. Future research should focus on specific drivers (and interactions among drivers) linked to tree palm abundance within biogeographical realms. We suggest that stronger consideration of the influence of palms can reduce uncertainty in biomass estimates. Especially in the Neotropics, improvement of our understanding of carbon cycling will require additional fieldwork to measure palm height and develop new allometric equations for palms. High tree palm relative abundance in locations with low average wood density (presumably, high-turnover forests) might also dramatically impact field estimates of forest productivity, because measurements of palm height growth are typically neglected. We also show that understanding the general patterns of abundance across the entire palm family will require additional work to understand macroevolutionary drivers of palm life-form diversity. Finally, our study illustrates the synergistic research potential of large data-sharing networks such as RAINFOR in South America and AfriTRON in Africa (Hubau et al., 2013; Lewis et al., 2013, Lopez-Gonzalez et al., 2009; Lopez-Gonzalez et al., 2011). Besides storage and organization of data to facilitate research, these networks strengthen the entire research chain (seeking field funding, fieldwork, developing protocols, allometries, collaborations, training, database design and quality control). Networks such as these are essential for conducting broad-scale and data-rich analyses. As development of these networks continues, it will be especially useful if they are even denser than today and even more balanced in terms of biomes, biogeographical realms and natural and disturbed sites.

## ACKNOWLEDGMENTS

This study would not have been possible without the ambitious and dedicated work of many colleagues, including Emmanuel Akampulira, Miguel N. Alexiades, William Balée, Olaf Banki, Serge K. Begne, Desmo Betian, Wemo Betian, Michael I. Bird, Neil M. Bird, George A. Blackburn, Rene Boot, Roel J. W. Brienen, Foster Brown, Ezequiel Chavez, Eric Chezeaux, Manoela F. F. Da Silva, Douglas C. Daly, Kyle G. Dexter, Luisa Fernanda Duque, Jose Farreras, Nina Farwig, Toby Gardner, Alwyn Gentry, Francisco Gómez, Rachel Graham, René Guillén Villaroel, Olivier J. Hardy, Terese Hart, Miriam van Heist, Mireille Breuer Ndoundou Hockemba, Kathryn Brun-Jeffery, Valerie Kapos, Jeanette Kemp, Miguel Leal, Eddie Lenza, Antonio S. Lima, Maurício Lima Dan, Pedro Lisboa, Jon Lloyd, Jhon Mario Lopez, Ubirajara N. Maciel, Jean-Remy Makana, Antti Marjokorpi, Toby Marthews, Emanual H. Martin, James Franklin Maxwell, Irina Mendoza Polo, Edi Mirmanto, Kazuki Miyamoto, Franklin Molina, Sam Moore, Pantaleo K. T. Munishi, Helen Murphy, David M. Newbery, Vojtech Novotny, Navendu Page, Karla Pedra de Abreu, Maria C. Peñuela-Mora, Ghillean T. Prance, John Proctor, Wilfredo Ramirez Salas, Adela Reatigui Ismodes, Eliana Riascos, Terhi Riutta, Nelson A. Rosa, Philippe Saner, Lars Schmidt, Marcela Serna, Michael Swaine, James Taplin, Peguy Tchouto, Johan van Valkenburg, Peter van de Meer, Cesar Velasquez, Jason Vleminclckx, George Weiblen and Roderick Zagt. We also depend on the centuries of work completed by palm taxonomists. W.L.E.'s contribution was supported by a research grant (00025354) from VILLUM FONDEN. J.C.S.

considers this work a contribution to his VILLUM Investigator project "Biodiversity Dynamics in a Changing World" funded by VILLUM FONDEN (grant 16549). R.M. was supported by Vetenskapsrådet (2019-03758). This work was supported by the Danish Council for Independent Research Natural Sciences (grant 4181-00158) to H.B, the European Union's Horizon 2020 research and innovation program under the Marie Sklodowska-Curie grant agreement No. 706011, and Coordenação de Aperfeiçoamento de Pessoal de Nível Superior-Brasil (CAPES)-Finance Code 001. This paper is a product of the RAINFOR, AfriTRON and T-FORCES networks and other partner networks in ForestPlots.net, which together support long-term forest science and monitoring across tropical countries. These initiatives have been supported by numerous people and grants since their inception. We are particularly indebted to >1,400 field assistants for their help in establishing and maintaining the plots, in addition to hundreds of rural communities and institutions. Collection and management of data analysed here from the RAINFOR, AfriTRON and T-FORCES networks have been supported by multiple grants, most notably the European Research Council (ERC Advanced Grant 291585, "T-FORCES"), the Gordon and Betty Moore Foundation (#1656, "RAINFOR"), the David and Lucile Packard Foundation, the European Union's Fifth, Sixth, and Seventh Framework Programme (EVK2-CT-1999-00023, "CARBONSINK-LBA"; 283080, "GEOCARBON"; and 282664, "AMAZALERT"), the Natural Environment Research Council (NERC grants: NE/D005590/1, "TROBIT"; NE/F005806/1, "AMAZONICA"; E/M0022021/1, "PPFOR"; NERC Urgency Grants and NERC New Investigators Grants), the NERC/State of São Paulo Research Foundation (FAPESP) consortium grants "BIO-RED" (NE/N012542/1, 2012/51872-5) and "ECOFOR" (NE/K016431/1, 2012/51509-8), the Royal Society (University Research Fellowships and Global Challenges Awards "FORAMA", ICA/R1/180100), the National Geographic Society, the Centre for International Forestry (CIFOR), Gabon's National Parks Agency (ANPN) and Colombia's Colciencias. We thank the National Council for Science and Technology Development of Brazil (CNPq) for support to the Cerrado/Amazonia Transition Long-Term Ecology Project (PELD/403725/2012-7), the PPBio Phytogeography of Amazonia/ Cerrado Transition project (CNPq/PPBio/457602/2012-0), PVE grants, and Productivity Grants to several colleagues. Atlantic Forest plots in Brazil were supported by the State of São Paulo Research Foundation (FAPESP 2003/12595-7 and 2012/51509-8, BIOTA/FAPESP Program) and by the Brazilian National Research Council (CNPq/ PELD 403710/2012-0; Universal 459941/2014-3) under COTEC/ IF 41.065/2005 and IBAMA/CGEN 093/2005 permits. Some of the data were provided by the Tropical Ecology Assessment and Monitoring (TEAM) Network, a collaboration between Conservation International, the Smithsonian Institution and the Wildlife Conservation Society, and partially funded by these institutions, the Gordon and Betty Moore Foundation and other donors. RAPELD plots in Brazil were supported by the Program for Biodiversity Research (PPBio), the National Institute for Amazonian Biodiversity (INCT-CENBAM) and BDFFP (INPA-STRI). Grant USM-RUI-1001/PBIologI/8011031 also supported fieldwork. This is publication 788 of the BDFFP Technical Series and is an outcome of the ForestPlots.net approved research project #2, "Global Patterns
of Palm Abundance". We acknowledge the support of the European Space Agency. We thank several anonymous reviewers and the editor for help improving our manuscript.

## CONFLICT OF INTEREST

The authors have no conflict of interest to declare.

## DATA AVAILABILITY STATEMENT

Raw data are available from ForestPlots.net and through contact with the authors. Restrictions apply to the availability of these data, which were used under licence for this study. Summarized data to replicate the main analyses presented in the paper are provided in the Supporting Information.

## ORCID

Robert Muscarella ID https://orcid.org/0000-0003-3039-1076

Edmar Almeida de Oliveira ID https://orcid.org/0000-0002-6446-3376

Jean-François Bastin ID https://orcid.org/0000-0003-2602-7247

James A. Comiskey ID https://orcid.org/0000-0001-6710-1269

Tran Van Do ID https://orcid.org/0000-0001-9059-5842

Khalid Rehman Hakeem ID https://orcid.org/0000-0001-7824-4695

Andreas Hemp ID https://orcid.org/0000-0002-5369-2122

Bruno Herault ID https://orcid.org/0000-0002-6950-7286

Onrizal Onrizal ID https://orcid.org/0000-0003-1899-2397

Lourens Poorter ID https://orcid.org/0000-0003-1391-4875

John R. Poulsen ID https://orcid.org/0000-0002-1532-9808

Francesco Rovero ID https://orcid.org/0000-0001-6688-1494

Robert Steinmetz ID https://orcid.org/0000-0001-6860-8715

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## BIOSKETCH

Our group represents a broad range of researchers and land managers interested in the composition and dynamics of tropical forests worldwide. All authors are responsible for long-term field data-collection efforts, which are crucial for global syntheses.

## SUPPORTING INFORMATION

Additional supporting information may be found online in the Supporting Information section.

How to cite this article: Muscarella R, Emilio T, Phillips OL, et al. The global abundance of tree palms. *Global Ecol Biogeogr.* 2020;29:1495-1514. https://doi.org/10.1111/geb.13123