Volume 29, Issue 9 pp. 1495-1514
RESEARCH PAPER
Open Access

The global abundance of tree palms

Robert Muscarella

Corresponding Author

Robert Muscarella

Department of Plant Ecology and Evolution, Evolutionary Biology Centre, Uppsala University, Uppsala, Sweden

Section for Ecoinformatics and Biodiversity, Department of Biology, Aarhus University, Aarhus, Denmark

Correspondence

Robert Muscarella, Department of Plant Ecology and Evolution, Evolutionary Biology Centre, Uppsala University, Kåbovägen 4, house 7, SE-752 36, Uppsala, Sweden.

Email: [email protected]

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Thaise Emilio

Thaise Emilio

Department of Plant Biology, Institute of Biology, University of Campinas (UNICAMP), Campinas, Brazil

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

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Oliver L. Phillips

Oliver L. Phillips

School of Geography, University of Leeds, Leeds, UK

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Simon L. Lewis

Simon L. Lewis

School of Geography, University of Leeds, Leeds, UK

Department of Geography, University College London, London, UK

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Ferry Slik

Ferry Slik

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

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William J. Baker

William J. Baker

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

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Thomas L. P. Couvreur

Thomas L. P. Couvreur

IRD, DIADE, University of Montpellier, Montpellier, France

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Wolf L. Eiserhardt

Wolf L. Eiserhardt

Section for Ecoinformatics and Biodiversity, Department of Biology, Aarhus University, Aarhus, Denmark

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

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Jens-Christian Svenning

Jens-Christian Svenning

Section for Ecoinformatics and Biodiversity, Department of Biology, Aarhus University, Aarhus, Denmark

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

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Kofi Affum-Baffoe

Kofi Affum-Baffoe

Ghana Forestry Commission, Kumasi, Ghana

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Shin-Ichiro Aiba

Shin-Ichiro Aiba

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

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Everton C. de Almeida

Everton C. de Almeida

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

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Samuel S. de Almeida

Samuel S. de Almeida

Museu Paraense Emilio Goeldi, Belém, Brazil

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Edmar Almeida de Oliveira

Edmar Almeida de Oliveira

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

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Esteban Álvarez-Dávila

Esteban Álvarez-Dávila

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

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Luciana F. Alves

Luciana F. Alves

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

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Carlos Mariano Alvez-Valles

Carlos Mariano Alvez-Valles

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

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Fabrício Alvim Carvalho

Fabrício Alvim Carvalho

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

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Fernando Alzate Guarin

Fernando Alzate Guarin

Instituto de Biología, Universidad de Antioquia, Medellín, Colombia

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Ana Andrade

Ana Andrade

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

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Luis E. O. C. Aragão

Luis E. O. C. Aragão

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

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

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Alejandro Araujo Murakami

Alejandro Araujo Murakami

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

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Luzmila Arroyo

Luzmila Arroyo

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

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Peter S. Ashton

Peter S. Ashton

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

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Gerardo A. Aymard Corredor

Gerardo A. Aymard Corredor

Compensation International Progress S.A, Bogotá, Colombia

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

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Timothy R. Baker

Timothy R. Baker

School of Geography, University of Leeds, Leeds, UK

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Plinio Barbosa de Camargo

Plinio Barbosa de Camargo

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

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Jos Barlow

Jos Barlow

Lancaster Environment Centre, Lancaster University, Lancaster, UK

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Jean-François Bastin

Jean-François Bastin

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

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Natacha Nssi Bengone

Natacha Nssi Bengone

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

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Erika Berenguer

Erika Berenguer

Lancaster Environment Centre, Lancaster University, Lancaster, UK

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

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Nicholas Berry

Nicholas Berry

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

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Lilian Blanc

Lilian Blanc

UR Forest & Societies, CIRAD, Montpellier, France

Forest & Societies, University of Montpellier, Montpellier, France

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Katrin Böhning-Gaese

Katrin Böhning-Gaese

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

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

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Damien Bonal

Damien Bonal

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

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Frans Bongers

Frans Bongers

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

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Matt Bradford

Matt Bradford

CSIRO Land and Water, Atherton, QLD, Australia

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Fabian Brambach

Fabian Brambach

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

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Francis Q. Brearley

Francis Q. Brearley

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

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Steven W. Brewer

Steven W. Brewer

Wild Earth Allies, Chevy Chase, MD, USA

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Jose L. C. Camargo

Jose L. C. Camargo

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

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David G. Campbell

David G. Campbell

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

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Carolina V. Castilho

Carolina V. Castilho

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

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Wendeson Castro

Wendeson Castro

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

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Damien Catchpole

Damien Catchpole

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

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Carlos E. Cerón Martínez

Carlos E. Cerón Martínez

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

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Shengbin Chen

Shengbin Chen

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

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

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Phourin Chhang

Phourin Chhang

Forestry Administration, Phnom Penh, Cambodia

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Percival Cho

Percival Cho

Forest Department, Belmopan, Belize

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Wanlop Chutipong

Wanlop Chutipong

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

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Connie Clark

Connie Clark

Nicholas School of the Environment, Duke University, Durham, NC, USA

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Murray Collins

Murray Collins

School of Geosciences, University of Edinburgh, Edinburgh, UK

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James A. Comiskey

James A. Comiskey

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

Smithsonian Institution, Washington, DC, USA

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Massiel Nataly Corrales Medina

Massiel Nataly Corrales Medina

Universidad Nacional de San Agustín de Arequipa, Arequipa, Peru

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Flávia R. C. Costa

Flávia R. C. Costa

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

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Heike Culmsee

Heike Culmsee

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

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Heriberto David-Higuita

Heriberto David-Higuita

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

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Priya Davidar

Priya Davidar

Sigur Nature Trust, Chadapatti Masinagudi PO, Nilgiris, India

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Jhon del Aguila-Pasquel

Jhon del Aguila-Pasquel

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

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Géraldine Derroire

Géraldine Derroire

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

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Anthony Di Fiore

Anthony Di Fiore

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

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Tran Van Do

Tran Van Do

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

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Jean-Louis Doucet

Jean-Louis Doucet

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

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Aurélie Dourdain

Aurélie Dourdain

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

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Donald R. Drake

Donald R. Drake

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

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Andreas Ensslin

Andreas Ensslin

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

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Terry Erwin

Terry Erwin

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

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Corneille E. N. Ewango

Corneille E. N. Ewango

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

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Robert M. Ewers

Robert M. Ewers

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

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Sophie Fauset

Sophie Fauset

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

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Ted R. Feldpausch

Ted R. Feldpausch

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

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Joice Ferreira

Joice Ferreira

EMBRAPA, Amazônia Oriental, Belém, Brazil

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Leandro Valle Ferreira

Leandro Valle Ferreira

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

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Markus Fischer

Markus Fischer

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

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Janet Franklin

Janet Franklin

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

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Gabriella M. Fredriksson

Gabriella M. Fredriksson

Pro Natura Foundation, Balikpapan, Indonesia

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Thomas W. Gillespie

Thomas W. Gillespie

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

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Martin Gilpin

Martin Gilpin

School of Geography, University of Leeds, Leeds, UK

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Christelle Gonmadje

Christelle Gonmadje

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

National Herbarium, Yaoundé, Cameroon

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Arachchige Upali Nimal Gunatilleke

Arachchige Upali Nimal Gunatilleke

University of Peradeniya, Peradeniya, Sri Lanka

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Khalid Rehman Hakeem

Khalid Rehman Hakeem

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

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Jefferson S. Hall

Jefferson S. Hall

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

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Keith C. Hamer

Keith C. Hamer

School of Biology, University of Leeds, Leeds, UK

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David J. Harris

David J. Harris

Royal Botanic Garden Edinburgh, Edinburgh, UK

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Rhett D. Harrison

Rhett D. Harrison

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

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Andrew Hector

Andrew Hector

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

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Andreas Hemp

Andreas Hemp

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

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Bruno Herault

Bruno Herault

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

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

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Carlos Gabriel Hidalgo Pizango

Carlos Gabriel Hidalgo Pizango

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

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Eurídice N. Honorio Coronado

Eurídice N. Honorio Coronado

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

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Wannes Hubau

Wannes Hubau

School of Geography, University of Leeds, Leeds, UK

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

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Mohammad Shah Hussain

Mohammad Shah Hussain

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

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Faridah-Hanum Ibrahim

Faridah-Hanum Ibrahim

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

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Nobuo Imai

Nobuo Imai

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

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Carlos A. Joly

Carlos A. Joly

Department of Plant Biology, Institute of Biology, University of Campinas (UNICAMP), Campinas, Brazil

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Shijo Joseph

Shijo Joseph

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

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Anitha K

Anitha K

Rainforest Traditions, Kochi, India

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Kuswata Kartawinata

Kuswata Kartawinata

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

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

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Justin Kassi

Justin Kassi

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

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Timothy J. Killeen

Timothy J. Killeen

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

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Kanehiro Kitayama

Kanehiro Kitayama

Graduate School of Agriculture, Kyoto University, Kyoto, Japan

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Bente Bang Klitgård

Bente Bang Klitgård

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

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Robert Kooyman

Robert Kooyman

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

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

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Nicolas Labrière

Nicolas Labrière

Laboratoire Évolution et Diversité Biologique, CNRS, Toulouse, France

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Eileen Larney

Eileen Larney

TEAM, Ranomafana, Thailand

Conservation Programmes, Zoological Society of London, Kanchanaburi, Thailand

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Yves Laumonier

Yves Laumonier

UR105 Forêts et Sociétés, CIRAD, Montpellier, France

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Susan G. Laurance

Susan G. Laurance

Centre for Tropical Environmental and Sustainability Science, and College of Science and Engineering, James Cook University, Cairns, QLD, Australia

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William F. Laurance

William F. Laurance

Centre for Tropical Environmental and Sustainability Science, and College of Science and Engineering, James Cook University, Cairns, QLD, Australia

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Michael J. Lawes

Michael J. Lawes

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

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Aurora Levesley

Aurora Levesley

School of Geography, University of Leeds, Leeds, UK

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Janvier Lisingo

Janvier Lisingo

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

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Thomas Lovejoy

Thomas Lovejoy

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

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Jon C. Lovett

Jon C. Lovett

School of Geography, University of Leeds, Leeds, UK

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

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Xinghui Lu

Xinghui Lu

College of Agronomy, Liaocheng University, Liaocheng, China

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Anne Mette Lykke

Anne Mette Lykke

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

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William E. Magnusson

William E. Magnusson

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

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Ni Putu Diana Mahayani

Ni Putu Diana Mahayani

Faculty of Forestry, Universitas Gadjah Mada, Yogyakarta, Indonesia

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Yadvinder Malhi

Yadvinder Malhi

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

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Asyraf Mansor

Asyraf Mansor

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

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

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Jose Luis Marcelo Peña

Jose Luis Marcelo Peña

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

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

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Ben H. Marimon-Junior

Ben H. Marimon-Junior

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

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Andrew R. Marshall

Andrew R. Marshall

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

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

Flamingo Land, North Yorkshire, UK

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Karina Melgaco

Karina Melgaco

School of Geography, University of Leeds, Leeds, UK

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Casimiro Mendoza Bautista

Casimiro Mendoza Bautista

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

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Vianet Mihindou

Vianet Mihindou

Agence Nationale des Parcs Nationaux, Libreville, Gabon

Ministère de la Forêt et de l’Environnement, Libreville, Gabon

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Jérôme Millet

Jérôme Millet

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

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William Milliken

William Milliken

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

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D. Mohandass

D. Mohandass

Novel Research Academy, Puducherry, India

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Abel Lorenzo Monteagudo Mendoza

Abel Lorenzo Monteagudo Mendoza

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

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Badru Mugerwa

Badru Mugerwa

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

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Hidetoshi Nagamasu

Hidetoshi Nagamasu

The Kyoto University Museum, Kyoto University, Kyoto, Japan

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Laszlo Nagy

Laszlo Nagy

Department of Plant Biology, Institute of Biology, University of Campinas (UNICAMP), Campinas, Brazil

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Naret Seuaturien

Naret Seuaturien

WWF Thailand, Bangkok, Thailand

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Marcelo T. Nascimento

Marcelo T. Nascimento

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

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David A. Neill

David A. Neill

Universidad Estatal Amazónica, Puyo, Ecuador

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Luiz Menini Neto

Luiz Menini Neto

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

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Rueben Nilus

Rueben Nilus

Forest Research Centre, Sabah Forestry Department, Sandakan, Malaysia

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Mario Percy Núñez Vargas

Mario Percy Núñez Vargas

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

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Eddy Nurtjahya

Eddy Nurtjahya

Department of Biology, Universitas Bangka Belitung, Merawang, Indonesia

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R. Nazaré O. de Araújo

R. Nazaré O. de Araújo

Instituto Nacional de Pesquisas da Amazônia, Manaus, Brazil

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Onrizal Onrizal

Onrizal Onrizal

Faculty of Forestry, Universitas Sumatera Utara, Medan, Indonesia

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Walter A. Palacios

Walter A. Palacios

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

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Sonia Palacios-Ramos

Sonia Palacios-Ramos

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

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Marc Parren

Marc Parren

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

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Ekananda Paudel

Ekananda Paudel

Nepal Academy of Science and Technology, Lalitpur, Nepal

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Paulo S. Morandi

Paulo S. Morandi

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

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R. Toby Pennington

R. Toby Pennington

Royal Botanic Garden Edinburgh, Edinburgh, UK

Department of Geography, University of Exeter, Exeter, UK

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Georgia Pickavance

Georgia Pickavance

School of Geography, University of Leeds, Leeds, UK

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John J. Pipoly III

John J. Pipoly III

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

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Nigel C. A. Pitman

Nigel C. A. Pitman

Field Museum, Chicago, IL, USA

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Erny Poedjirahajoe

Erny Poedjirahajoe

Faculty of Forestry, Universitas Gadjah Mada, Yogyakarta, Indonesia

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Lourens Poorter

Lourens Poorter

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

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John R. Poulsen

John R. Poulsen

Nicholas School of the Environment, Duke University, Durham, NC, USA

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P. Rama Chandra Prasad

P. Rama Chandra Prasad

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

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Adriana Prieto

Adriana Prieto

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

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Jean-Philippe Puyravaud

Jean-Philippe Puyravaud

Sigur Nature Trust, Chadapatti Masinagudi PO, Nilgiris, India

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Lan Qie

Lan Qie

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

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Carlos A. Quesada

Carlos A. Quesada

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

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Hirma Ramírez-Angulo

Hirma Ramírez-Angulo

INDEFOR, Universidad de Los Andes, Mérida, Venezuela

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Jean Claude Razafimahaimodison

Jean Claude Razafimahaimodison

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

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Jan Meindert Reitsma

Jan Meindert Reitsma

Bureau Waardenburg BV, Culemborg, the Netherlands

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Edilson J. Requena-Rojas

Edilson J. Requena-Rojas

Laboratorio de Dendrocronología, Universidad Continental, Huancayo, Peru

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Zorayda Restrepo Correa

Zorayda Restrepo Correa

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

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Carlos Reynel Rodriguez

Carlos Reynel Rodriguez

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

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Anand Roopsind

Anand Roopsind

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

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Francesco Rovero

Francesco Rovero

Department of Biology, University of Florence, Florence, Italy

Tropical Biodiversity Section, MUSE, Museo delle Scienze, Trento, Italy

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Andes Rozak

Andes Rozak

Research Center for Plant Conservation and Botanic Gardens, Cibodas Botanic Gardens, Indonesian Institute of Sciences (LIPI), Cianjur, Indonesia

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Agustín Rudas Lleras

Agustín Rudas Lleras

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

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Ervan Rutishauser

Ervan Rutishauser

Smithsonian Tropical Research Institute, Balboa, Panama

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Gemma Rutten

Gemma Rutten

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

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Ruwan Punchi-Manage

Ruwan Punchi-Manage

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

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Rafael P. Salomão

Rafael P. Salomão

Museu Paraense Emilio Goeldi, Belém, Brazil

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

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Hoang Van Sam

Hoang Van Sam

Vietnam National University of Forestry, Hanoi, Vietnam

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Swapan Kumar Sarker

Swapan Kumar Sarker

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

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Manichanh Satdichanh

Manichanh Satdichanh

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

World Agroforestry Centre, Kunming, China

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Juliana Schietti

Juliana Schietti

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

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Christine B. Schmitt

Christine B. Schmitt

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

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

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Beatriz Schwantes Marimon

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

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Feyera Senbeta

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

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Lila Nath Sharma

Lila Nath Sharma

ForestAction Nepal, Kathmandu, Nepal

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Douglas Sheil

Douglas Sheil

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

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Rodrigo Sierra

GeoIS, Cedar Park, TX, USA

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Javier E. Silva-Espejo

Javier E. Silva-Espejo

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

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Marcos Silveira

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

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Bonaventure Sonké

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

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Marc K. Steininger

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

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Robert Steinmetz

Robert Steinmetz

WWF Thailand, Bangkok, Thailand

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Tariq Stévart

Tariq Stévart

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

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Raman Sukumar

Raman Sukumar

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

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

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Aisha Sultana

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

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

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

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Hebbalalu Satyanarayana Suresh

Hebbalalu Satyanarayana Suresh

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

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

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Jianwei Tang

Jianwei Tang

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

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Edmund Tanner

Edmund Tanner

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

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Hans ter Steege

Hans ter Steege

Naturalis Biodiversity Center, Leiden, the Netherlands

Systems Ecology, Vrije University, Amsterdam, the Netherlands

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John W. Terborgh

John W. Terborgh

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

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

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Ida Theilade

Ida Theilade

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

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Jonathan Timberlake

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Warren Lane, East Dean, East Sussex, UK

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Armando Torres-Lezama

Armando Torres-Lezama

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

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Peter Umunay

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

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María Uriarte

María Uriarte

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

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Luis Valenzuela Gamarra

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

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Martin van de Bult

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

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Peter van der Hout

Van der Hout Förestry Consulting, Rotterdam, the Netherlands

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Rodolfo Vasquez Martinez

Rodolfo Vasquez Martinez

Herbario Selva Central Oxapampa, Oxapampa, Peru

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Ima Célia Guimarães Vieira

Museu Paraense Emilio Goeldi, Belém, Brazil

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Simone A. Vieira

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

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Emilio Vilanova

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

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Jeanneth Villalobos Cayo

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

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Ophelia Wang

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

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Campbell O. Webb

Campbell O. Webb

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

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Edward L. Webb

Edward L. Webb

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

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Lee White

Lee White

Agence Nationale des Parcs Nationaux, Libreville, Gabon

Institut de Recherche en Ecologie Tropicale, Libreville, Gabon

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

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Timothy J. S. Whitfeld

Timothy J. S. Whitfeld

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

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Serge Wich

Serge Wich

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

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

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Simon Willcock

Simon Willcock

School of Natural Sciences, Bangor University, Bangor, UK

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Susan K. Wiser

Susan K. Wiser

Manaaki Whenua, Landcare Research, Lincoln, New Zealand

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Kenneth R. Young

Kenneth R. Young

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

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Rahmad Zakaria

Rahmad Zakaria

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

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Runguo Zang

Runguo Zang

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

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Charles E. Zartman

Charles E. Zartman

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

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Irié Casimir Zo-Bi

Irié Casimir Zo-Bi

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

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Henrik Balslev

Henrik Balslev

Section for Ecoinformatics and Biodiversity, Department of Biology, Aarhus University, Aarhus, Denmark

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First published: 08 July 2020
Citations: 79

Robert Muscarella and Thaise Emilio are joint first author.

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.

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 defined 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 arborescent palms that reach ≥10 cm d.b.h. (henceforth, “tree palms”), because smaller-diameter stems are excluded in the standard protocol 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 original 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 aggregated 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 biogeographical realms defined by Olson et al. (2004), but we combined locations in Oceania with Australasia because of their strong historical connection (Muellner, Pannell, Coleman, & Chase, 2008) and their relatively low sample sizes (n = 32 and 37, respectively).

Details are in the caption following the image
(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 (BApalm) 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 BApalm at that location [Colour figure can be viewed at wileyonlinelibrary.com]

For each location, we calculated the relative basal area (BApalm) and the relative abundance (RApalm) 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 metrics of tree palm abundance reduce variation caused by differences in sample area and stem density across locations. Results based on BApalm and RApalm were highly correlated, and we present and discuss BApalm results in the main text (results based on RApalm are provided 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), 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 covariatesbefore 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 urn:x-wiley:1466822X:media:geb13123:geb13123-math-0001~ BeZI ( urn:x-wiley:1466822X:media:geb13123:geb13123-math-0002, urn:x-wiley:1466822X:media:geb13123:geb13123-math-0003, urn:x-wiley:1466822X:media:geb13123:geb13123-math-0004), which represents a mixture of beta and Bernoulli distributions defined as:
urn:x-wiley:1466822X:media:geb13123:geb13123-math-0005
The parameter urn:x-wiley:1466822X:media:geb13123:geb13123-math-0006 is the probability of tree palms being absent from a location, and urn:x-wiley:1466822X:media:geb13123:geb13123-math-0007, urn:x-wiley:1466822X:media:geb13123:geb13123-math-0008 is the beta density function of BApalm at locations where tree palms are present: urn:x-wiley:1466822X:media:geb13123:geb13123-math-0009 is the expected value and urn:x-wiley:1466822X:media:geb13123:geb13123-math-0010 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, urn:x-wiley:1466822X:media:geb13123:geb13123-math-0011. We used the following model:
urn:x-wiley:1466822X:media:geb13123:geb13123-math-0012

Where y is palm occurrence (for the Bernoulli part of the model) and BApalm (for the beta part of the model), urn:x-wiley:1466822X:media:geb13123:geb13123-math-0013 is the intercept, and the terms urn:x-wiley:1466822X:media:geb13123:geb13123-math-0014 and urn:x-wiley:1466822X:media:geb13123:geb13123-math-0015 correspond to the anomalies between the LGM and contemporary mean annual and dry quarter precipitation, respectively; urn:x-wiley:1466822X:media:geb13123:geb13123-math-0016 and urn:x-wiley:1466822X:media:geb13123:geb13123-math-0017 represent current climatological means for mean annual and dry quarter precipitation; urn:x-wiley:1466822X:media:geb13123:geb13123-math-0018, urn:x-wiley:1466822X:media:geb13123:geb13123-math-0019, urn:x-wiley:1466822X:media:geb13123:geb13123-math-0020 and urn:x-wiley:1466822X:media:geb13123:geb13123-math-0021 correspond to cation exchange capacity, water table depth, canopy height and CWM wood density, respectively; and urn:x-wiley:1466822X:media:geb13123:geb13123-math-0022, corresponds to the sum of area sampled in component plots at location i. We included the area term to account for possible correlation between the area sampled and BApalm. 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 urn:x-wiley:1466822X:media:geb13123:geb13123-math-0023 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 R2 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 conducted 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 environmental 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): AGBChave-only. Ideally, we could compare these estimates with values where tree palm biomass was calculated using species-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 measurements. 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: AGBGoodman+Chave. We report the ratio of AGBGoodman+Chave to AGBChave-only 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 forest inventories) could affect conclusions about palm abundance patterns, 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 BApalm (and RApalm) 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 abundance (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, locations in the Neotropics had higher BApalm (5.5%) and RApalm (8.4%) than locations in all other realms (mean of BApalm 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, BApalm was highly variable, ranging globally from 0 to 60%.

Details are in the caption following the image
(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 (BApalm, as a percentage, shown on a log10 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]

3.1 Tree palm occurrence, abundance and environmental conditions

Based on t tests, there were some significant differences in environmental 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 locations 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 significant in Australasia/Oceania. Results from the occurrence component 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 contemporary dry season precipitation. Palms also tended to be recorded in locations with larger total area sampled.

Details are in the caption following the image
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 (BApalm). 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

In contrast, palm relative basal area (BApalm) was not significantly associated with either palaeoclimate stability or current precipitation in the driest quarter (Figures 3b and 4). However, BApalm was positively associated with current mean annual precipitation (Figures 3b and 4c). There were also significant negative associations between BApalm and CEC (soil fertility), depth to water table and CWM wood density (Figures 3b and 4e–h). The relationships between BApalm and canopy height and area sampled were not statistically significant. Together, these results indicated that tree palms accounted for a greater proportion of total basal area in locations with lower soil fertility, closer access to groundwater and lower CWM wood density. The R2 of the full model was .26 [95% credible intervals (CIs) = .21–.32; Supporting Information Tables S2-3].

Details are in the caption following the image
Bivariate plots of tree palm relative basal area (BApalm, as a percentage, shown on a log10 scale) versus environmental covariates for locations in four biogeographical realms. The tick marks along the x axis represent locations without tree palms recorded; symbol size is proportional to the sample area of the location. Covariates with significant effects (i.e., 95% credible intervals did not overlap zero) are indicated with an asterisk (*), and fitted slopes are shown; covariates with non-significant effects are indicated with NS. Mean annual precipitation anomaly and 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 [Colour figure can be viewed at wileyonlinelibrary.com]

3.2 Implications for above-ground biomass estimates

As expected, the difference in AGB estimated by the two methods (AGBGoodman+Chave versus AGBChave-only) increased with BApalm (Figure 5). However, the direction and magnitude of the change in AGB predictions were not consistent. For example, where palms accounted for >10% of total basal area, the ratio of AGB calculated using the two methods ranged from 0.84 to 1.05. Notably, for non-Neotropical locations with BApalm > 1%, values of AGBChave-only were always higher than those of AGBGoodman+Chave (Figure 5).

Details are in the caption following the image
Palm relative basal area (BApalm, as a percentage, shown on a log10 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 (AGBGoodman+Chave) versus total AGB when palms are treated as trees using the equations of Chave et al. (2014) (AGBChave-only). 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]

3.3 Effect of size threshold

In total, 116 of the 144 Gentry transects considered included at least one palm. Across all transects and using a 2.5 cm d.b.h. threshold, 4,502 palm individuals (from 177 species) accounted for 5.5% of the total individuals (80,712) and 2.8% of the total basal area. When using a 10 cm d.b.h. threshold, there were 880 palm individuals (from 76 species) that accounted for 5.3% of the total individuals (16,665) and 2.5% of the total basal area. Among Neotropical transects, changing the size threshold from 2.5 to 10 cm d.b.h. caused palm relative basal area (BApalm) to increase or decrease in 60 and 40% of the transects, respectively (Figure 6). In the other realms, however, BApalm increased in 16 out of 17 transects (94%) when stems down to 2.5 cm d.b.h. were included. In fact, outside of the Neotropics, 53% of the transects with at least one palm present at the 2.5 cm d.b.h. threshold had no palms recorded above the 10 cm d.b.h. threshold. These results indicate that the majority of palm abundance in locations outside the Neotropics occurs in the 2.5–10 cm d.b.h. size class. The different d.b.h. thresholds changed the value of BApalm in each transect by ≤ 14% (Figure 6).

Details are in the caption following the image
Effect of size threshold on palm abundance. Difference of BApalm (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 BApalm 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]

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 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 precipitation or dry season precipitation between the LGM (21,000 years ago) and the current period. It appears that local environmental heterogeneity 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) associated 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 relatively 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 potentially important drivers of palm abundance directly. First, humans 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 species, 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 forests. 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 & Rabett, 2014; Malhi, Adu-Bredu, Asare, Lewis, & Mayaux, 2013; Roberts et al., 2017), but we do not expect human activity to have altered the broad biogeographical 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 outside 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 abundance 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 phylogeny of the palm family is available.

Details are in the caption following the image
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]

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 example, 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 are pooled with other trees. Specifically, standard allometric equations generated for trees that estimate AGB from stem diameter perform 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 introduces 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 [compared 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 magnitude 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 (AGBGoodman+Chave and AGBChave-only) were <1% in >90% of locations where BApalm was <5%, indicating that error from palms is probably negligible when palms are minor components of the total forest basal area. However, the difference between the two methods increases with BApalm, 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-specific palm allometric models will be important to improve AGB estimates 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 remote sensing to identify palms (Tagle Casapia et al., 2020) will also help to identify areas where attention to palm abundance is important 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 threshold 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 confirmation that biogeographical differences in palm life-form diversity can affect the perception of palm abundance. For example, small-diameter 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 diversity in the IndoMalayan realm is contributed by the subfamily Calamoideae, which comprises mostly climbers. Notably, acaulescent (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, especially IndoMalaya, compared with the Neotropics (Banin et al., 2014). These conditions may favour the evolution of alternative life-forms, including 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 variation 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 generally 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 Vleminckx, 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.

    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.

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