Received: 3 March 2021 | Revised: 10 April 2021 | Accepted: 10 September 2021 DOI: 10.1111/ele.13898 L E T T E R AVONET: morphological, ecological and geographical data for all birds Joseph A. Tobias1,2 | Catherine Sheard2,3 | Alex L. Pigot2,4 | Adam J. M. Devenish1 | Jingyi Yang1 | Ferran Sayol4 | Montague H. C. Neate- Clegg2,5 | Nico Alioravainen2,6 | Thomas L. Weeks1,7 | Robert A. Barber1 | Patrick A. Walkden1,7 | Hannah E. A. MacGregor2,8 | Samuel E. I. Jones2,9 | Claire Vincent10 | Anna G. Phillips11 | Nicola M. Marples12 | Flavia A. Montaño- Centellas13,14 | Victor Leandro- Silva15 | Santiago Claramunt16,17 | Bianca Darski18 | Benjamin G. Freeman19 | Tom P. Bregman2,20 | Christopher R. Cooney21 | Emma C. Hughes21 | Elliot J. R. Capp21 | Zoë K. Varley21,22 | Nicholas R. Friedman23 | Heiko Korntheuer24 | Andrea Corrales- Vargas25 | Christopher H. Trisos2,26,27 | Brian C. Weeks28,29 | Dagmar M. Hanz30 | Till Töpfer31 | Gustavo A. Bravo32,33 | Vladimír Remeš34,35 | Larissa Nowak11,36 | Lincoln S. Carneiro37 | Amilkar J. Moncada R.38 | Beata Matysioková34 | Daniel T. Baldassarre39 | Alejandra Martínez- Salinas38 | Jared D. Wolfe40 | Philip M. Chapman41 | Benjamin G. Daly2 | Marjorie C. Sorensen42 | Alexander Neu11,43 | Michael A. Ford44 | Rebekah J. Mayhew45 | Luis Fabio Silveira46 | David J. Kelly12 | Nathaniel N. D. Annorbah47 | Henry S. Pollock48 | Ada M. Grabowska- Zhang49 | Jay P. McEntee50 | Juan Carlos T. Gonzalez2,51,52 | Camila G. Meneses51 | Marcia C. Muñoz53 | Luke L. Powell54,55,56 | Gabriel A. Jamie57,58 | Thomas J. Matthews59,60 | Oscar Johnson61 | Guilherme R. R. Brito62 | Kristof Zyskowski63 | Ross Crates64 | Michael G. Harvey65 | Maura Jurado Zevallos66 | Peter A. Hosner67,68 | Tom Bradfer- Lawrence45 | James M. Maley69 | F. Gary Stiles70 | Hevana S. Lima71 | Kaiya L. Provost29,72 | Moses Chibesa73 | Mmatjie Mashao74 | Jeffrey T. Howard61,75 | Edson Mlamba76 | Marcus A. H. Chua77,78 | Bicheng Li79 | M. Isabel Gómez80 | Natalia C. García81 | Martin Päckert82 | Jérôme Fuchs83 | Jarome R. Ali84 | Elizabeth P. Derryberry85 | Monica L. Carlson84 | Rolly C. Urriza86 | Kristin E. Brzeski40 | Dewi M. Prawiradilaga87 | Matt J. Rayner88,89 | Eliot T. Miller90 | Rauri C. K. Bowie91 | René- Marie Lafontaine92 | R. Paul Scofield93 | Yingqiang Lou94 | Lankani Somarathna95 | Denis Lepage96 | Marshall Illif90 | Eike Lena Neuschulz11 | Mathias Templin11 | D. Matthias Dehling97 | This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. © 2022 The Authors. Ecology Letters published by John Wiley & Sons Ltd. Ecology Letters. 2022;25:581–597. wileyonlinelibrary.com/journal/ele | 581 582 | AVONET: MORPHOLOGICAL, ECOLOGICAL AND GEOGRAPHICAL DATA FOR ALL BIRDS Jacob C. Cooper98 | Olivier S. G. Pauwels99 | Kangkuso Analuddin100 | Jon Fjeldså67,68 | Nathalie Seddon101 | Paul R. Sweet29 | Fabrice A. J. DeClerck102 | Luciano N. Naka15 | Jeffrey D. Brawn48 | Alexandre Aleixo103 | Katrin Böhning- Gaese11,36 | Carsten Rahbek68,67,104,105 | Susanne A. Fritz11,106 | Gavin H. Thomas21,22 | Matthias Schleuning11 1Department of Life Sciences, Imperial College London, Ascot, UK 2Department of Zoology, University of Oxford, Oxford, UK 3School of Earth Sciences, University of Bristol, Bristol, UK 4Centre for Biodiversity and Environment Research, Department of Genetics, Evolution and Environment, University College London, London, UK 5School of Biological Sciences, University of Utah, Salt Lake City, Utah, USA 6Natural Resources Institute Finland, Natural resources - Migratory fish and regulated rivers, Oulu, Finland 7Department of Life Sciences, Natural History Museum, London, UK 8School of Biological Sciences, University of Bristol, Bristol, UK 9School of Biological Sciences, Royal Holloway, University of London, Egham, UK 10UN Environment Programme World Conservation Monitoring Centre (UNEP- WCMC), Cambridge, UK 11Senckenberg Biodiversity and Climate Research Centre (SBiK- F), Frankfurt am Main, Germany 12Department of Zoology, School of Natural Sciences, Trinity College Dublin, Dublin, Ireland 13Instituto de Ecología, Universidad Mayor de San Andres, La Paz, Bolivia 14Department of Wildlife Ecology and Conservation, University of Florida, Gainesville, Florida, USA 15Laboratório de Ecologia e Evolução de Aves, Departamento de Zoologia, Universidade Federal de Pernambuco, Recife, Brazil 16Department of Natural History, Royal Ontario Museum, Toronto, Ontario, Canada 17Department of Ecology and Evolutionary Biology, University of Toronto, Toronto, Ontario, Canada 18Departamento de Ecologia, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil 19Biodiversity Research Centre, University of British Columbia, Vancouver, British Columbia, Canada 20Future- Fit Foundation, Spitalfields, London, UK 21Department of Animal and Plant Sciences, University of Sheffield, Sheffield, UK 22Bird Group, Department of Life Sciences, The Natural History Museum, Tring, UK 23Biodiversity and Biocomplexity Unit, Okinawa Institute of Science and Technology Graduate University, Kunigami- gun, Okinawa, Japan 24Department of Ecology, Institute of Zoology, Johannes Gutenberg University Mainz, Mainz, Germany 25Central American Institute for Studies on Toxic Substances (IRET), Universidad Nacional de Costa Rica, Heredia, Costa Rica 26African Climate and Development Initiative, University of Cape Town, Cape Town, South Africa 27Centre for Statistics in Ecology, the Environment and Conservation, University of Cape Town, Cape Town, South Africa 28School for Environment and Sustainability, University of Michigan, Ann Arbor, Michigan, USA 29Department of Ornithology, American Museum of Natural History, New York, New York, USA 30Biogeography and Biodiversity Lab, Institute of Physical Geography, Goethe University Frankfurt, , Frankfurt am Main, Germany 31Ornithology Section, Zoological Research Museum Alexander Koenig, Bonn, Germany 32Museum of Comparative Zoology, Harvard University, Cambridge, Massachusetts, USA 33Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, Massachusetts, USA 34Department of Zoology, Palacký University, Olomouc, Czech Republic 35Department of Ecology, Faculty of Science, Charles University, Praha, Czech Republic 36Institute for Ecology, Evolution and Diversity, Goethe University Frankfurt, Frankfurt am Main, Germany 37Coordenação de Zoologia, Museu Paraense Emílio Goeldi, Belém, Pará, Brazil 38CATIE (Centro Agronómico Tropical de Investigación y Enseñanza), Cartago, Turrialba, Costa Rica 39Department of Biological Sciences, SUNY Oswego, Oswego, New York, USA 40College of Forest Resources and Environmental Science, Michigan Technological University, Houghton, Michigan, USA 41BSG Ecology, Worton Park, Worton, Witney, UK 42Department of Integrative Biology, University of Guelph, Guelph, Ontario, Canada 43Department of Biological Sciences, Goethe University Frankfurt, Frankfurt am Main, Germany 44South African Ringing Unit, University of Cape Town, Rondebosch, Cape Town, South Africa 45Biological and Environmental Sciences, University of Stirling, Stirling, UK 46Museu de Zoologia da Universidade de Sao Paulo (MZUSP), São Paulo, SP, Brazil 47Department of Biological, Physical and Mathematical Sciences, University of Environment and Sustainable Development, Somanya, Ghana 48Department of Natural Resources and Environmental Sciences, University of Illinois at Urbana- Champaign, Urbana, Illinois, USA 49Department for Continuing Education, University of Oxford, Oxford, UK 50Department of Biology, Missouri State University, Springfield, Missouri, USA 14610248, 2022, 3, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/ele.13898 by EBMG ACCESS - KENYA, Wiley Online Library on [30/05/2023]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License TOBIAS et al. | 583 51Museum of Natural History, University of the Philippines Los, Baños, Los Baños, Laguna, Philippines 52Animal Biology Division, Institute of Biological Sciences, College of Arts and Sciences, University of the Philippines Los, Baños, Los Baños, Laguna, Philippines 53Programa de Biología, Universidad de la Salle, Bogotá, Colombia 54Institute of Animal Health and Comparative Medicine, Graham Kerr Building, University of Glasgow, Glasgow, UK 55Biodiversity Initiative, Houghton, Michigan, USA 56CIBIO- InBIO, Research Centre in Biodiversity and Genetic Resources, University of Porto, Vairão, Portugal 57Department of Zoology, University of Cambridge, Cambridge, UK 58FitzPatrick Institute of African Ornithology, University of Cape Town, Rondebosch, Cape Town, South Africa 59GEES (School of Geography, Earth and Environmental Sciences) and Birmingham Institute of Forest Research, University of Birmingham, Birmingham, UK 60CE3C (Centre for Ecology, Evolution and Environmental Changes/Azorean Biodiversity Group and Universidade, dos Açores), Depto de Ciências Agráriase Engenharia do Ambiente, Angra do Heroísmo, Açores, Portugal 61Department of Biological Sciences and Museum of Natural Science, Louisiana State University, Baton Rouge, Louisina, USA 62Depto. de Ecologia e Zoologia, Centro de Ciências Biológicas, Universidade Federal de Santa Catarina, Florianópolis, Brazil 63Peabody Museum of Natural History, Yale University, New Haven, Connecticut, USA 64Fenner School of Environment and Society, Australian National University, Canberra, Australia 65Department of Biological Sciences and Biodiversity Collections, The University of Texas at El Paso, El Paso, Texas, USA 66Universidad Nacional Agraria La Molina, Av. La Molina s/n, La Molina, Lima, Peru 67Natural History Museum of Denmark, University of Copenhagen, Copenhagen, Denmark 68Center for Macroecology, Evolution and Climate, GLOBE Institute, University of Copenhagen, Copenhagen, Denmark 69Moore Laboratory of Zoology, Occidental College, Los Angeles, California, USA 70Instituto de Ciencias Naturales, Universidad Nacional de Colombia, Bogotá, Colombia 71Instituto Nacional de Pesquisas da Amazônia, Manaus, Brazil 72Department of Evolution, Ecology and Organismal Biology, Ohio State University, Columbus, Ohio, USA 73Department of Zoology and Aquatic Sciences, Copperbelt University, Kitwe, Zambia 74Durban Natural Science Museum, Durban, South Africa 75Louisiana State University, Health Sciences Center Shreveport, Shreveport, Louisina, USA 76Department of Zoology, National Museums of Kenya, Nairobi, Kenya 77Lee Kong Chian Natural History Museum, National University of Singapore, Singapore, Singapore 78Department of Environmental Science and Policy, George Mason University, Fairfax, Virginia, USA 79Natural History Research Center, Shanghai Natural History Museum, Shanghai, China 80Colección Boliviana de Fauna – Museo Nacional de Historia Natural, Ministerio de Medio Ambiente y Agua, La Paz, Bolivia 81División Ornitología, Museo Argentino de Ciencias Naturales “Bernardino Rivadavia”, CONICET, Buenos Aires, Argentina 82Senckenberg Natural History Collections, Museum of Zoology, Dresden, Germany 83Institut de Systématique, Evolution, Biodiversité (ISYEB), Muséum National d’Histoire Naturelle, CNRS, SU, EPHE, UA, Paris, France 84Department of Ecology and Evolutionary Biology, Princeton University, Princeton, New Jersey, USA 85Department of Ecology and Evolutionary Biology, University of Tennessee, Knoxville, Tennessee, USA 86Ornithology Section, Zoology Division, Philippine National Museum, Rizal Park, Manila, Philippines 87Museum Zoologicum Bogoriense, Research Centre for Biology, Indonesian Institute of Sciences (LIPI), Bogor, Indonesia 88Auckland Museum, Auckland, New Zealand 89School of Biological Sciences, University of Auckland, Auckland, New Zealand 90Cornell Lab of Ornithology, Ithaca, New York, USA 91Museum of Vertebrate Zoology and Department of Integrative Biology, University of California Berkeley, Berkeley, California, USA 92Operational Directorate Natural Environment, Royal Belgian Institute of Natural Sciences (RBINS), Brussels, Belgium 93Canterbury Museum, Christchurch, New Zealand 94Institute of Zoology, Chinese Academy of Sciences, Beijing, China 95Natural History Section, Department of National Museum, Colombo, Sri Lanka 96Birds Canada, Port Rowan, Ontario, Canada 97Swiss Federal Institute for Forest, Snow and Landscape Research WSL, Birmensdorf, Switzerland 98University of Kansas, Lawrence, Kansas, USA 99Department of Recent Vertebrates, Royal Belgian Institute of Natural Sciences (RBINS), Brussels, Belgium 100Department of Biotechnology, Halu Oleo University, Kendari, Sulawesi Tenggara, Indonesia 101Nature- based Solutions Initiative, Department of Zoology, University of Oxford, Oxford, UK 102Bioversity International, CGIAR, Parc Scientifique Agropolis II, Montpellier, France 103Finnish Museum of Natural History, University of Helsinki, Helsinki, Finland 104Danish Institute for Advanced Study, University of Southern Denmark, Odense, Denmark 105Institute of Ecology, Peking University, Beijing, China 106Institut für Geowissenschaften, Goethe University, Frankfurt, Frankfurt am Main, Germany 14610248, 2022, 3, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/ele.13898 by EBMG ACCESS - KENYA, Wiley Online Library on [30/05/2023]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License 584 | AVONET: MORPHOLOGICAL, ECOLOGICAL AND GEOGRAPHICAL DATA FOR ALL BIRDS Correspondence Abstract Joseph Tobias, Department of Life Sciences, Imperial College London, Functional traits offer a rich quantitative framework for developing and testing Silwood Park, Buckhurst Road, Berkshire SL5 7PY, UK. theories in evolutionary biology, ecology and ecosystem science. However, the po- Email: j.tobias@imperial.ac.uk tential of functional traits to drive theoretical advances and refine models of global Funding information change can only be fully realised when species- level information is complete. Here Natural Environment Research Council, Grant/Award Number: NE/I028068/1 and we present the AVONET dataset containing comprehensive functional trait data NE/P004512/1 for all birds, including six ecological variables, 11 continuous morphological traits, Editor: Tim Coulson and information on range size and location. Raw morphological measurements are presented from 90,020 individuals of 11,009 extant bird species sampled from 181 countries. These data are also summarised as species averages in three taxonomic formats, allowing integration with a global phylogeny, geographical range maps, IUCN Red List data and the eBird citizen science database. The AVONET dataset provides the most detailed picture of continuous trait variation for any major ra- diation of organisms, offering a global template for testing hypotheses and explor- ing the evolutionary origins, structure and functioning of biodiversity. K E Y W O R D S avian traits, continuous variables, data integration, ecomorphology, functional diversity, macroecology, macroevolution, trait- based ecology INTRODUCTION advanced for plants, and plant trait data are therefore readily available to the scientific community at a global Functional traits— the morphological and ecological scale (Kattge et al., 2020). However, while these datasets characteristics that influence organismal performance have provided insightful tests of theory, including the or fitness— have driven innovation in the field of ecology mechanisms underlying community assembly (Mayfield for the last two decades (McGill et al., 2006; Violle et al., & Levine, 2010), species coexistence (Kraft et al., 2008) 2007). Preliminary analyses of functional trait variation and the scaling from traits to ecosystems (Enquist et al., across lineages or within species assemblages have led 2015), it is difficult to know whether these results can be to the development of influential models and metrics in applied more generally to non- plant systems. The same a range of fields, including macroevolution (FitzJohn, caveat applies to many fundamental trait- based con- 2010), community ecology (Petchey & Gaston, 2002) and cepts designed from a plants- only perspective (Suding ecosystem science (Suding et al., 2008). Perhaps, the most et al., 2008). alluring factor that draws researchers from these differ- Plant functional ecology has f lourished, yet trait ent fields to functional traits is the hope they offer of mov- datasets of plants are nonetheless patchy in terms ing beyond species to a more mechanistic understanding of species and trait coverage at a global scale. Out of ecosystem structure and function (Cadotte et al., 2011; of roughly 352,000  f lowering plant species, the TRY Díaz et al., 2013; Funk et al., 2017; Hooper et al., 2002; database currently contains fewer than 60,000  spe- Tilman et al., 1997). Another recurring theme is the idea cies (17%) with 10 or more traits (Kattge et al., 2020). that functional traits can help us to devise a quantita- This creates problems for analytical approaches that tive framework for understanding and predicting eco- assume coverage is complete, including phyloge- logical communities (Schleuning et al., 2020; Winemiller netic comparative analyses and evolutionary models. et al., 2015). However, unlocking the true potential of Missing species in partially sampled trait datasets functional traits is highly dependent on comprehensive can radically alter the trait structure of communities sampling at the species level, whereas coverage remains and the fit of evolutionary models, reducing predic- patchy for all major taxonomic groups, particularly for tive power and restricting studies to a biased sample continuous morphological traits (Cernansky, 2017; Kohli of well- known clades (Kohli & Jarzyna, 2021; Weiss & Jarzyna, 2021). & Ray, 2019). The main obstacle to the completion of Progress to date in amassing trait data has been species sampling for plant traits is the sheer diversity weighted towards plant systems, partly because of the of plants themselves. One solution for the next genera- fundamental importance of plants to critical ecological tion of trait- based models in ecology and evolutionary functions (Funk et al., 2017), and also because plant traits biology is to switch some attention to vertebrate clades are relatively easy to access and measure. International containing a more manageable number of species. In initiatives for data sharing and synthesis are relatively any case, a catch- up phase for animal trait databases 14610248, 2022, 3, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/ele.13898 by EBMG ACCESS - KENYA, Wiley Online Library on [30/05/2023]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License TOBIAS et al. | 585 is a high priority because this would open up research The study of avian morphological traits helped to into a range of novel questions. For example, integra- inspire core theoretical concepts in ecology and evolu- tion across animal and plant trait data may provide tionary biology, from adaptive radiation and ecological the key to understanding complex trophic interaction speciation (Darwin, 1859), to the mechanisms underlying networks at the heart of ecological processes, such as community assembly (Diamond, 1975) and large- scale nutrient and energy transfer, seed dispersal, pollina- biodiversity gradients (MacArthur, 1972). In the last tion and predation (Bartomeus et al., 2016; Harfoot two decades of the twentieth century, the field of eco- et al., 2014; Schleuning et al., 2015). morphology rose to prominence in avian ecology (Bock, 1994; Leisler & Winkler, 1985; Miles & Ricklefs, 1984; Ricklefs & Travis, 1980), laying the foundations for all A brief history of animal functional ecology recent research into avian functional traits (see Figure 1). Most of the focus has been on beak size and shape, given To date, the most prominent species- level trait datasets the apparent association of beak traits with dietary niche published for animals at a global scale— PanTHERIA and resource competition (Cooney et al., 2017; Pigot & (Jones et al., 2009), EltonTraits (Wilman et al., 2014) Tobias, 2013). Some studies focusing on smaller spatial and the amniote database (Myhrvold et al., 2015)— have or taxonomic scales have reported only weak predictive focused mainly on life history and ecological traits for power for avian morphological traits, including beaks the world's mammals, birds and reptiles. These data- (Bright et al., 2016; Miller et al., 2017; Weeks et al., 2020). sets have been highly influential, yet the only complete However, other recent analyses have shown that a more continuous morphological trait data presented in each holistic combination of morphological traits can predict case is body mass. Body mass has long been the main- avian trophic niches or interactions far more powerfully stay of ecological and evolutionary research, but it pro- than either body mass or beak shape alone (Pigot et al., vides only limited information about ecological niches 2020). and trophic interactions (Bender et al., 2018; Pigot et al., Despite the historical emphasis on ecomorphology, 2020). Moving beyond body mass in animal functional most trait- based studies using this conceptual frame- ecology has proved challenging, with most progress work still focus on a few well- sampled clades, including made in the validation and compilation of avian traits ovenbirds (Tobias et al., 2014), tanagers (Drury et al., (Tobias et al., 2020). 2018) and corvoid passerines (Kennedy et al., 2020). In 12,000 11,000 1. Hartman 1961 10,000 2. Ricklefs 1977 9000 3. James 1982 8000 4. Miles et al 1987 7000 5. Leisler & Winkler 1991 6. Dunn et al 2001 6000 7. Korner Nievergelt & Leisler 2004 5000 8. Ricklefs 2004 4000 9. Dawideit et al 2009 10. Claramunt 2010 3000 2000 1000 0 10,000 20,000 11. Kennedy et al 2016 30,000 12. Miller et al 2017 40,000 13. Ricklefs 2017 50,000 14. Cooney et al 2017 60,000 15. Zanata et al 2018 16. Phillips et al 2018 70,000 17. Chira et al 2018 80,000 18. Rodrigues et al 2019 90,000 19. Chira et al 2020 20. Pigot et al 2020 100,000 1960 1970 1980 1990 2000 2010 2016 2018 2020 Year F I G U R E 1 The sampling of avian morphological traits over time. The number of species (above x axis) and the number of specimens (below x axis) measured for landmark studies along with their year of publication is indicated by the vertical bars. Each bar indicates the maximum number of species and specimens measured for any trait. The number of traits in each study is represented by circle sizes (continuous from 1 to 15, with examples shown in the legend). Studies openly providing raw trait data are indicated in black. AVONET contains the raw specimen- level data for Pigot et al. (2020), along with substantial expansion in coverage of both species and specimens- per- species. To provide historical context, coloured time periods correspond roughly to interest in ‘ecomorphology’ (blue) and ‘functional traits’ (red). Citations for studies not used in the main text are provided in the Supplementary Material # specimens # species 14610248, 2022, 3, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/ele.13898 by EBMG ACCESS - KENYA, Wiley Online Library on [30/05/2023]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License 586 | AVONET: MORPHOLOGICAL, ECOLOGICAL AND GEOGRAPHICAL DATA FOR ALL BIRDS addition, many trait- based analyses testing ideas relat- treatments— BirdLife International, eBird and BirdTree ing to community assembly, ecosystem function or re- (Supplementary Dataset 1). We hope this removes a sponses to land- use change are focused on local study major obstacle to future analyses, allowing integration systems containing a few hundred species at most with IUCN Red List data and geographical range maps, (Bregman et al., 2016; Dehling et al., 2016; Pigot et al., as well as eBird citizen science data (Sullivan et al., 2014) 2016). All datasets presenting compilations of avian mor- and the global bird phylogeny (Jetz et al., 2012). The phological traits were similarly restricted in geograph- AVONET database represents a collaborative effort by ical or taxonomic sampling until 2017 (Figure 1). Since specimen collectors, museum workers and field ornithol- then, a series of global analyses (e.g. Chira et al., 2018, ogists over many decades. In the spirit of the Open Traits 2020; Cooney et al., 2017) have included data from many network (Gallagher et al., 2020), the data are released for more species, but relatively few measurements per spe- use by the wider research community, in conjunction cies of a limited number of traits (Figure 1). The recent with additional information describing the ecology and publication of macroevolutionary analyses by Pigot et al. geographical context of all bird species. (2020) marked another step- change in scale, with com- prehensive species- level sampling and deeper individual sampling of multiple morphological traits. However, all M ATERI A LS A N D M ETHODS these datasets have limited utility for research because they present measurements summarised as principal Morphological traits components aligned with the BirdTree taxonomy (Jetz et al., 2012). External morphological traits were measured from live In this paper, we provide a global overview of the individuals and preserved museum skins. For each indi- AVONET database, a compilation of individual- level vidual, we measured nine traits (generally to the nearest trait measurements for all the world's bird species. 0.1 mm): four beak measurements, three wing measure- AVONET contains the raw trait data used by Pigot ments, tarsus length and tail length (see Figure 2 and et al. (2020), focusing on the same set of phenotypic Supplementary Dataset 1 for details). Traits were se- traits with well- established connections to diet, dis- lected for the information they provide about ecological persal and locomotion (Pigot et al., 2016, 2020; Sheard niches. The beak is the primary apparatus used by birds et al., 2020; Figure 2, Supplementary Dataset 1). To to capture and process food, while morphological dif- improve intraspecific sampling, we added measure- ferences in wings, tails and legs are related to locomo- ments taken from a further 37,150 individual birds— a tion, providing insight into the way birds move through 71% increase (Figure 1). To improve interoperability their environment and forage for resources (Leisler & with external datasets, we also averaged all traits at the Winkler, 1985; Miles & Ricklefs, 1984; Pigot et al., 2020; species level according to three alternative taxonomic Ricklefs & Travis, 1980). We targeted four individuals F I G U R E 2 Diagram of linear measurements of avian morphology presented in AVONET. (a) Resident frugivorous tropical passerine (fiery- capped manakin, Machaeropterus pyrocephalus) showing four beak measurements: (1) beak length measured from tip to skull along the culmen; (2) beak length measured from the tip to the anterior edge of the nares; (3) beak depth; (4) beak width. (b) Insectivorous migratory temperate- zone passerine (redwing, Turdus iliacus) showing five body measurements: (5) tarsus length; (6) wing length from carpal joint to wingtip measured on the unflattened wing; (7) secondary length from carpal joint to tip of the outermost secondary; (8) Kipp's distance, measured directly or calculated as wing length minus first- secondary length; (9) tail length. Protocols for measuring these traits are provided in Supplementary material. AVONET also includes body mass, and Hand- wing index (calculated from 6 to 8), making 11 traits in total. Illustration by Richard Johnson 14610248, 2022, 3, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/ele.13898 by EBMG ACCESS - KENYA, Wiley Online Library on [30/05/2023]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License TOBIAS et al. | 587 (two of each sex) as our minimum sample wherever pos- numbers of specimens- per- taxon, with specimens re- sible. Further data for these traits were added by merg- allocated among newly split species. This can lead to ing independent datasets, excluding data measured in a variation in trait averages even when taxonomic names substantially different way. The final version contains are the same (see Supplementary Material). Levels of measurements from 90,020 individual birds with an aver- matching are similar between BirdLife and eBird ver- age of 8.1– 9.0 individuals per species (variable depending sions. To facilitate navigation between datasets, we cre- on taxonomy; Table S1). All raw data and species aver- ated crosswalks between them (Supplementary Dataset ages are provided in Supplementary Dataset 1. 1). We also included Avibase ID where possible to pro- In addition to direct measurements, we also calcu- vide a more stable resolution of taxonomic concepts over lated the hand- wing index (HWI), a metric of flight ef- time (Lepage et al., 2014). ficiency and dispersal ability in birds (Claramunt, 2021). Although a global HWI dataset following BirdTree taxonomy has been published previously (Sheard et al., Geographical sampling 2020), we update HWI scores to reflect additional sam- pling under the BirdTree taxomomy, and provide species All individual birds measured by field teams were as- averages aligned with BirdLife and eBird species lists. signed to the country in which sampling localities were Finally, we also update published body mass data for situated. For museum data, we identified the country 1985 species, either by inferring from close relatives or, where each specimen was collected based on the label. in the case of 716 species, from literature, fieldwork and For specimens with no locality data, we assigned them museum specimen labels (Supplementary Material). In to their respective countries in the case of single- country many of these cases, previously published mass data had endemics. Where possible, we matched the transcription been inferred with low accuracy on the basis of predic- with country names and boundary data aligned with tive models (Wilman et al., 2014), so AVONET provides a country shapefiles published by the World Resources much- improved body mass dataset for the world's birds. Institute (https://github.com/wri/wri- bounds, accessed on 14/10/2020). Further details of methods for identify- ing localities and assigning specimens to countries are Data curation given in Supplementary Material. We included a series of checks to identify and remove errors before merging trait datasets (Supplementary Ecological categories Material). To assess the potential influence of observer biases, we collected duplicate measurements from 4799 For each species, we used the information on propor- individual specimens of 3421 species measured by 64 dif- tional dietary categories used by Pigot et al. (2020) to ferent people. We then used mixed effects models to as- score the proportion of diet obtained from three trophic sess concordance between independent measures of the levels (herbivore; carnivore; scavenger). Herbivores rep- same trait. To assess whether trait data were significantly resent primary consumers; carnivores (including inver- different in museum specimens versus live samples, we tivores) represent secondary and tertiary consumers. compared measurements for 962 species with both types Following Pigot et al. (2020), we assigned all species to of data. nine trophic niches (frugivore; granivore; nectarivore; terrestrial herbivore; aquatic herbivore; invertivore; ver- tivore; aquatic predator; scavenger) encompassing major Taxonomic classification resource types utilised by birds. Our scoring of species diets is primarily based on data from Wilman et al. Taxonomic classification is in constant flux, particu- (2014), extensively updated and re- organised (Tobias & larly in birds (Garnett & Christidis, 2017), causing Pigot, 2019; Supplementary Material). major problems for compilers and users of functional Next, we classified each species into five lifestyles trait datasets. To facilitate integrative analyses, we av- (or domains) according to their predominant locomo- erage our trait data according to three different taxo- tory niche while foraging: aerial, insessorial, terrestrial, nomic treatments differing by roughly 1000  species: aquatic and generalist. This is a separate dimension to BirdLife International (2020), 10,999  species; eBird diet inasmuch as species eating fish may be aquatic (e.g. (Clements et al., 2021), 10,661 species; and BirdTree (Jetz pelican), aerial (e.g. tern), terrestrial (e.g. heron) or inses- et al., 2012), 9993 species. Most extant BirdLife species sorial (e.g. kingfisher). Insessorial denotes a perching (n = 8949, 81.4%) are one- to- one matches with BirdTree lifestyle, including arboreal species, but also any species species. This leaves roughly a fifth of species with imper- habitually perching on other substrates, including cliffs fect matches across these two datasets. In many cases, or manmade structures. Further explanations of all eco- BirdTree species have been split into multiple BirdLife logical categories are given as metadata (Supplementary species, such that traits are averaged across smaller Dataset 1). 14610248, 2022, 3, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/ele.13898 by EBMG ACCESS - KENYA, Wiley Online Library on [30/05/2023]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License 588 | AVONET: MORPHOLOGICAL, ECOLOGICAL AND GEOGRAPHICAL DATA FOR ALL BIRDS Biogeographical variables samples (Figure S5) suggesting that data from different sources can be pooled (Supplementary Material). For the BirdLife dataset, species geographical range maps were obtained from Birdlife International. We se- lected breeding and resident ranges in areas where the Species coverage species is coded as extant and either native or reintro- duced. Mapping species under the BirdTree taxonomy Discounting duplicate measurements, we compiled dataset often required the combination of maps for mul- morphological trait data from 90,020 individual birds, tiple BirdLife species to form an expanded range map for including previously unpublished raw data from a single BirdTree species (Supplementary Dataset 1). We 75,287  museum specimens and 11,424  living individu- did not generate new maps for the eBird dataset which als. We added further measurements from 556 museum can be integrated directly with citizen- science locality specimens and 2753 living individuals published in pre- data and a suite of spatial research tools (Sullivan et al., vious data papers (Supplementary Dataset 1). Overall, 2014). morphological trait sampling was conducted by 153 re- Using BirdLife and BirdTree maps, we extracted range searchers, of which 88 used the AVONET protocol (see size (km2), longitude (of range centroid) and three latitu- Supplementary Material). In addition, we integrated sev- dinal variables (maximum, minimum and centroid). See eral unpublished trait datasets including morphological Supplementary Material for details of mapping methods traits of 37,150 individual birds measured using alterna- used to generate these data. We included maximum and tive protocols by 65 researchers. minimum latitude because these values may be import- In total, 99.4% (n  =  89,434) of measured individ- ant in studies of latitudinal gradients, constraints or cli- ual birds were included in all three datasets (BirdLife, matic effects. Note that range size based on interpolated eBird and BirdTree), with the remaining 0.6% (n = 586) range polygons tends to over- estimate the true extent appearing in 1– 2 datasets. The AVONET database of occurrence (Jetz et al., 2008). To map morphological greatly increases coverage and availability of morpho- traits, we extracted species ranges onto an equal area logical trait data for birds in comparison with previous grid (Behrmann projection) with a resolution of 110 km studies (Figure 1; Table S1). All morphological traits (≈1° at the equator). are now sampled for 95.7– 96.8% species under BirdLife; 96.4– 97.1% under eBird; and 99.4– 99.7% species under BirdTree (percentages varying by trait; see Table S2). RESU LTS Regardless of taxonomy, 100% of avian families are sam- pled, with species- level sampling near- complete for most Repeatability of morphological measurements families, particularly for the BirdTree dataset (Table S1; Figure 3). The few remaining undersampled families are When all repeated trait measurements were pooled re- either small (low species richness), particularly when gardless of measurement protocol used, data were highly subject to recent taxonomic splitting (e.g. ostriches), or concordant among individual measurers (Figures S1 and difficult to collect (e.g. owls; Figure 3; Table S3). At the S2), indicating that different methods produce compa- species level, sampling was uneven according to speci- rable measurements of the traits included in our data- men availability, with some common species sampled set. Repeatability (R) scores differed across traits (GLM: intensively and rarer species falling short of the targeted F = 523,963, Df = 7, p < 0.0001; Table S4) suggesting that four individuals (Figure 3). some trait measurements are more repeatable than oth- The AVONET dataset currently lacks direct mea- ers. For example, estimated variation in beak width was surements of any trait for 351  species under BirdLife, relatively high (6.6%; Figure S1), suggesting low repeata- 308 species under eBird and 26 species under BirdTree. bility arising from the use of different trait definitions by An additional 56– 193 species have data missing for one contributors (see Supplementary Material). Nonetheless, or more traits, depending on the taxonomy used (Table the overall concordance of trait data collected by differ- S3). We fill these gaps using inference by identifying the ent measurers was high for all traits (R  =  0.928– 0.996; closest relative with the most similar ecology and mor- Table S4). When we analysed variance separately for phology (often this was the parent species in the case duplicate pairs measured using the AVONET protocol of daughter species arising from taxonomic splits). We (n = 277), we found that they usually had higher corre- highlight inferred traits and the surrogate species from spondence (Figure S3) than duplicates for which at least which inferences were made (Supplementary Dataset 1) one measurement was taken using a different protocol to allow future users to decide whether to include inferred (n = 4527; Figure S4). While this suggests that repeatabil- trait data. Their decision may vary with context since, on ity improves when measurement protocol is standardised, the one hand, data inferred by this method may invali- the effects are marginal and only found in some traits. date evolutionary models, but on the other hand they can Finally, we found only minor differences when compar- refine models of community assembly wherein the inclu- ing measurements taken on museum specimens and live sion of like- for- like proxies is better than deleting species 14610248, 2022, 3, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/ele.13898 by EBMG ACCESS - KENYA, Wiley Online Library on [30/05/2023]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License TOBIAS et al. | 589 F I G U R E 3 Morphological trait sampling for all bird families. AVONET contains 718,662 individual trait measurements, all of which are used to calculate species averages. However, sampling per species varies across families depending on taxonomy. Upper phylogram shows sampling under BirdLife International (11,009 species in 243 families). Families where sampling completeness is below 75% indicated by lighter shading. Most families with lower sampling are species poor (numbers in black circles show species richness). Lower panels show that sampling improves under more conservative taxonomic treatments of eBird (10,661 species in 249 families) and BirdTree (9993 species in 194 families). Coloured bars indicate the proportion of species in each family measured to different levels of completeness. ‘Complete set’ means a full set of all 9 core morphological traits (not necessarily from the same individual). ‘Individuals’ means any individual bird with one or more traits measured 14610248, 2022, 3, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/ele.13898 by EBMG ACCESS - KENYA, Wiley Online Library on [30/05/2023]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License 590 | AVONET: MORPHOLOGICAL, ECOLOGICAL AND GEOGRAPHICAL DATA FOR ALL BIRDS altogether. Our rationale for using proxies rather than including 181  sovereign countries. Given the prepon- phylogenetic inference is presented in the Supplementary derance in our dataset of specimens from the Natural Material. History Museum, London, it is no surprise to see a rela- tively high sampling in regions associated with the former British empire, including South and East Africa, India, Geographical sampling Malaysia, Australia and New Zealand (Figure 5b,c). Other regions of dense sampling are North America, The 75,843  specimens contributing measurements to Brazil and China, while the main targets for future sam- this dataset are held in 78 institutional or private col- pling are in Central and West Africa, the Middle East lections in 31 countries worldwide (Figure 4). There is and North Asia (Figure 5b,c). a strong bias to the Global North, particularly Europe and North America. Further coordinated sampling of museum collections is most urgently required in Africa Intraspecific trait variation and across much of Asia. Pooling across both museum and field data, we were able to assign 77,245 (85.8%) of Previous analyses based on earlier versions of this data- 90,020 sampled individuals to a specific source country, set indicate that most variance in trait values occurs leaving 12,775 (14.2%) unassigned. Mapping these coun- among (98.25%) rather than within (1.75%) species (Pigot tries of origin, and overlaying with species geographical et al., 2020), suggesting that species are a valid sampling ranges, revealed that our morphological traits have been unit for bird traits at large taxonomic scales. This con- sampled from populations in 206 administrative units, trasts with the situation in plant traits, which typically (a) (b) (c) F I G U R E 4 Geographical distribution of morphological data sampling. (a) Location of collections sampled (n = 78 museums or scientific collections in 31 countries), with the number of specimens per collection indicated by bubble size (excluding seven specimens from unknown museums). Sampling of live- caught and released individuals (n = 14,177) is not shown. (b) The number of individual birds sampled from each of 206 administrative units (181 countries), combining museum and field sampling (removing cases not assignable to administrative units). Darker colours indicate a larger number of specimens; specimens lacking precise information on the country of origin (n = 12,775) are not included. (c) The completeness of species sampling in each 100 km grid cell. Colours show the proportion of species present in that cell with specimens sampled from the same country in which the cell is located; warmer colours indicate higher proportions. Species presence was mapped as the portion of the species range occurring within the country, because the specimen is unlikely to have originated from outside the natural range 14610248, 2022, 3, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/ele.13898 by EBMG ACCESS - KENYA, Wiley Online Library on [30/05/2023]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License TOBIAS et al. | 591 F I G U R E 5 AVONET presents raw morphological data for 90,020 individual birds at an average of 8.1– 9.0 individuals per species (varying by taxonomy), providing a foundation for a new generation of studies investigating or accounting for intraspecific variance. This figure illustrates how variance is partitioned for a key morphological trait (beak length). Left- hand panels show that most variance is explained at higher taxonomic levels (orders, family and species), whereas intraspecific (individual) variation is contrastingly low, supporting the use of species averages in comparative studies. Curves are normal distributions based on SD; percentages (%) show proportion of variance at each level. Right- hand panels show beak length variance within families and within species (restricting to families with >5 species and species with >5 individuals measured; note different axis scales in upper and lower panel). Sequential ranks show a ‘hockey- stick’ distribution with examples of the most extreme outlier family (Scolopacidae) illustrated. Extreme within- species values for beak variance may reflect polymorphism or, in some cases, measurement error vary within species according to local conditions (light, reflecting adaptation to a more terrestrial lifestyle. soil chemistry, hydrology, etc.). Since the publication of Relative beak length shows a mirror image to this pat- Pigot et al. (2020) we have added multiple parallel data- tern, with increases in well- vegetated regions, including sets, increasing intraspecific sampling by 71%, rising tropical rainforests. Definitions for relative beak and from around 5.0 individuals per species to 9.0 individu- tarsus length are given in Figure 6. Previous work has als per species. In tandem, coverage by sex has increased shown the tight link between avian morphological traits to an average of 3.0 females, 4.6 males and 1.4 unsexed and trophic niches (Pigot et al., 2020), whereas these individuals per species (BirdTree dataset; Table S1). patterns additionally reveal strong geographical trends Using this expanded sample, and partitioning morpho- in conjunction with a geographical clustering of differ- logical variation among taxonomic levels, we show that ent lifestyles. Our data also pinpoint certain trait syn- almost all variance can be explained at the level of order, dromes. For example, species with aerial lifestyles have family and genus, whereas intraspecific variance is com- the shortest tarsi but the highest HWI, in line with their paratively small (Figure 5). We conclude that the use of adaptation for frequent flight (Figure 6). species average trait values is appropriate at a range of taxonomic scales in birds, although we note that much higher levels of inter- and intraspecific variance are de- DISCUSSION tected for some traits in a minority of taxonomic groups (Figure 5). Our understanding of biodiversity is ultimately limited by the availability of data in a few critical areas, includ- ing species geographical distributions (the so- called Macroecological patterns of trait variation ‘Wallacean shortfall’) and traits (the ‘Raunkiæran short- fall’; Hortal et al., 2015). AVONET goes a long way to- We focus here on presenting an overview of the AVONET wards addressing these shortfalls by summarising a data rather than testing particular hypotheses. To il- complete set of morphological trait measurements, along lustrate potential uses of the data, we plotted macro- with discrete ecological and geographical variables, for scale geographical and ecological patterns for three key all extant species of birds. This resource provides a foun- traits— HWI, tarsus length and beak length (Figure 6). dation for theoretical tests and technical innovations in At a global scale, spatial patterns show that HWI in- ecology, evolution and conservation biology, yet also creases towards the poles, indicating strong variation in highlights the need for further sampling at the intersec- dispersal ability across latitudes and in relation to par- tion of Wallacean and Raunkiæran shortfalls, where ticular lifestyles. Relative tarsus length increases in less many gaps remain in the intraspecific and geographical vegetated biomes (i.e. steppes, grasslands and deserts), sampling of bird traits. 14610248, 2022, 3, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/ele.13898 by EBMG ACCESS - KENYA, Wiley Online Library on [30/05/2023]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License 592 | AVONET: MORPHOLOGICAL, ECOLOGICAL AND GEOGRAPHICAL DATA FOR ALL BIRDS F I G U R E 6 Species- level variation in avian functional traits in relation to geography and lifestyle. Hand- wing index (wing elongation) peaks towards high latitudes (a), and in species with aquatic and aerial lifestyles (b); relative tarsus length peaks at mid- latitudes and non- forest regions (c), and in species with terrestrial lifestyles (d); relative beak length peaks in the tropics, including rainforests (e), and in nectar feeders and aquatic predators (f). For maps, median trait values were calculated for 18,709 grid- cell assemblages worldwide. Darker colours indicate larger trait values. Assemblages were delimited by extracting species native resident or breeding distributions (n = 10,964 species for which both trait and geographical range data are available) onto an equal area grid with a cell resolution of ~100 km (Behrmann projection). Relative beak and tarsus length are the residuals of a linear regression of log- transformed tarsus and beak length (mm) against log- transformed body mass (grams). Species in (b,d) are classified according to primary lifestyle (predominant locomotory niche; insessorial = perching lifestyle). Species in (f) are classified according to primary diet following Pigot et al. (2020). Sample sizes (b,d,f) are numbers of species in each category Our analyses show that morphological trait data in to undermine the utility of avian morphological traits AVONET were sampled from bird populations through- given they predict trophic niches with remarkable accu- out the world, with intensive sampling in many tropical racy (Pigot et al., 2020), including species interactions regions. However, we also show that access to specimens across trophic levels (Dehling et al., 2016). Moreover, we underlying the dataset was distributed very unevenly, show that the same traits are also strongly indicative of with most reference material stored in the collections of habitat biomes and primary lifestyle. These results sug- North American and European museums (Figure 5a). gest that avian morphology is not only ecologically in- We hope that the publication of AVONET helps to re- formative, but that the relationships between form and move these longstanding biases in access to museum col- function are sufficiently general to develop the kind of lections by mobilising specimen data for global use. predictive framework envisaged by Winemiller et al. Morphological traits of animals may fail to fulfil their (2015). Furthermore, we show substantial trait variation promise of delivering ecological insights and theoretical within taxonomic groups, lifestyles and diet categories, advances if their connection to function varies idiosyn- highlighting the importance of quantitative trait data as cratically across evolutionary lineages or if they are unin- a basis for moving beyond the subjective and ultimately formative about niche- based processes and interactions coarse categorical data used in most previous analyses (Didham et al., 2016). These concerns are particularly of vertebrate functional traits (Kohli & Jarzyna, 2021). relevant for global datasets wherein species coverage is The earliest phase of AVONET development involved so high that the set of traits assembled is relatively lim- different research groups independently collecting ited. Fortunately, these potential issues do not appear smaller subsets of trait data to explore a range of topics 14610248, 2022, 3, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/ele.13898 by EBMG ACCESS - KENYA, Wiley Online Library on [30/05/2023]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License TOBIAS et al. | 593 in ecology and evolutionary biology, including adap- increase rapidly as the quality of avian phylogenetic tive radiation (Claramunt, 2010), character displace- data continues to improve. ment (Tobias et al., 2014), community assembly (Trisos In the fields of macroecology and biogeography, et al., 2014) and the predictive properties of traits in re- AVONET data have immediate uses in quantifying the lation to ecological networks (Schleuning et al., 2015). trophic structuring of diversity gradients (Hanz et al., The maturation and integration of these trait datasets 2019; Pigot et al., 2016) and understanding the mecha- into AVONET allows hypotheses to be tested at an nisms of species coexistence and community assembly ever- larger scale. For example, previous applications of (Pigot et al., 2018). In the context of ecosystem science, trait- based indices of dispersal focused on avian sister species- level variation can now be applied to under- species or individual clades (Kennedy et al., 2016; Pigot standing the trophic interaction networks regulating key & Tobias, 2015) whereas the data are now available for ecological processes, such as insect predation, pollina- global analyses (Sheard et al., 2020). Similarly, trait- tion and seed dispersal (Bender et al., 2018; Schleuning based assessments of the impacts of global change on et al., 2015). Comprehensive functional trait data for spe- avian functional diversity have shifted focus from sur- cies delivering these ‘services’ may, over time, provide veys of plots or transects (Bender et al., 2018; Bregman valuable insight into the impacts of biodiversity loss on et al., 2015, 2016) to the global scale (Sol et al., 2020; ecosystem function (Tobias et al., 2020). Stewart et al., 2022). Avian functional traits, in conjunction with detailed information on bird distribution and movements, pro- vide an opportunity for monitoring and assessing the Applications and future directions impacts of global change. Integrating functional per- spectives may help make sense of the variation in species Given the visibility and popular interest in birds, we responses to change (Bender et al., 2019), enabling the already know more about their distribution and sea- development of more general and mechanistic models of sonal movement than any other major group of organ- current and future changes in distribution and diversity isms (Tobias et al., 2020). If anything, the pace at which (Estrada et al., 2016). For instance because the morpho- knowledge is accumulating for birds has accelerated over logical data in AVONET cover different aspects of spe- recent years through vast citizen science programmes cies ecology, from movement (reflected in wing shape) generating hundreds of millions of distributional data to feeding (reflected in beak shape), these data can be points (La Sorte & Somveille, 2020; McEntee et al., 2018), used to parameterise global range shift models with while efforts are well underway to sequence the genomes morphological indices of dispersal and trophic niche of all bird species (Stiller & Zhang, 2019). The scale of structure respectively (Stewart et al., 2022). In addi- these emerging resources suggests that morphological tion, trait- based analyses show promise in understand- trait data for birds have numerous potential uses and ap- ing and predicting the effects of environmental change plications. We map out these opportunities in more de- on trophic interaction networks (Gravel et al., 2016; tail elsewhere, providing a short summary here. Schleuning et al., 2020), with implications for the con- Evolutionary applications span from understand- servation of key ecosystem processes regulated by birds, ing selection at the level of genes and populations to such as seed dispersal and pest control (Bregman et al., trait diversification over deep timescales. The oppor- 2015, 2016). In a more general sense, since AVONET tunity to integrate complete trait information with data are explicitly aligned with the taxonomy underpin- high- quality genomes now available for hundreds of ning the IUCN Red List, they will provide a rich seam bird species (Feng et al., 2020) offers a model system to explore for conservation biologists wishing to under- for unlocking the genomic basis of adaptation (Stiller & stand the causes and consequences of biodiversity loss Zhang, 2019). In parallel, AVONET trait data can now (Weeks et al., 2022). support more extensive testing of macroevolutionary Despite attaining comprehensive species- level cover- hypotheses (e.g. Crouch & Tobias, 2022; Freeman et al., age, our results reveal uneven representation of avian 2022) which have until recently been limited or invali- diversity across and within countries (Figure 5b,c). The dated by incomplete trait sampling (Drury et al., 2018; next step is to improve intraspecific sampling and geo- Phillips et al., 2018). The availability of comprehensive graphical coverage for all species— common and ra- data will improve models of trait evolution and allow a re— to provide a better description of individual trait more thorough examination of how evolutionary pro- variation across space and time (Des Roches et al., cesses have led to birds exploring niche space and trait 2018). Ideally, this will involve continued sampling of space. Large- scale phylogenetic comparative analyses historical museum collections, as well as wild individ- will also benefit because many of these were previously uals mist- netted and released by field projects, to pro- constrained by the availability of key morphological vide the most complete time series and widest spatial indices, such as HWI or beak size (Derryberry et al., sampling. To these ends, we have included missing spe- 2018). In both evolutionary models and comparative cies to highlight sampling coldspots in Supplementary analyses, the power of trait- based approaches will Dataset 1, and supplied our trait sampling protocol 14610248, 2022, 3, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/ele.13898 by EBMG ACCESS - KENYA, Wiley Online Library on [30/05/2023]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License 594 | AVONET: MORPHOLOGICAL, ECOLOGICAL AND GEOGRAPHICAL DATA FOR ALL BIRDS (Supplementary Materials) to improve the standardisa- PEER R EV I EW tion of morphological trait data for incorporation into The peer review history for this article is available at AVONET. https://publo ns.com/publo n/10.1111/ele.13898. OPEN R E SEA RCH BA DGE S CONCLUSIONS We present a complete description of morphological This article has earned Open Data and Open Materials and ecological trait diversity for all birds (Aves), the badges. Data and materials are available at: https://figsh largest class of tetrapod vertebrates. The achieve- are.com/s/b9907 22d72 a26b5 bfead ment of total species coverage at this scale sets a new standard for morphological trait data. All specimen- DATA AVA I LA BI LI T Y STAT EM EN T level data and metadata are included. Our results con- The AVONET dataset and all code for figures and analy- firm that avian traits have distinct associations with ses in this manuscript are archived on Figshare (https:// diet, environment and lifestyle, offering a framework figsh are.com/s/b9907 22d72 a26b5 bfead). for testing ecological theory, identifying underlying mechanisms and predicting the response of biodiver- ORCI D sity to global change. Although we have focused ex- Joseph A. Tobias  https://orcid. clusively on birds, we hope this venture will inspire org/0000-0003-2429-6179 similar efforts across other animal systems, opening Catherine Sheard  https://orcid. up the possibility of future integration across verte- org/0000-0002-8259-1275 brate databases. Alex L. Pigot  https://orcid.org/0000-0002-6490-6261 Adam J. M. Devenish  https://orcid. ACK NOW LEDGEM EN TS org/0000-0001-5240-622X We thank the Natural History Museum, Tring, UK, the Jingyi Yang  https://orcid.org/0000-0001-7471-7635 American Museum of Natural History, USA, and 76 Ferran Sayol  https://orcid.org/0000-0003-3540-7487 other research collections for providing access to speci- Montague H. C. Neate- Clegg  https://orcid. mens. Illustrations are reproduced with permission of org/0000-0001-9753-6765 Lynx Edicions/Cornell Lab of Ornithology. Financial Nico Alioravainen  https://orcid. support was received from numerous sources, with ex- org/0000-0003-2723-1012 tensive support from Natural Environment Research Thomas L. Weeks  https://orcid. Council grants NE/I028068/1, NE/P004512/1 and org/0000-0001-7145-3187 UKRI Global Challenges Research Fund grant ES/ Robert A. Barber  https://orcid. P011306/1 (JAT). All major sources of funding are listed org/0000-0001-6735-5250 in Supplementary Material, along with a complete list of Patrick A. Walkden  https://orcid. institutions and individuals that contributed to data col- org/0000-0002-5922-8777 lection, logistics and specimen access. Hannah E. A. MacGregor  https://orcid. org/0000-0001-5379-8392 AU T HORSH I P Samuel E. I. Jones  https://orcid. JAT initiated and developed the core dataset and org/0000-0002-8123-986X conceived the publication. JAT, MS, GHT and SAF Flavia A. Montaño- Centellas  https://orcid. worked on the conceptual framework for the paper. org/0000-0003-3115-3950 CS coordinated the initial phase of data collection Victor Leandro- Silva  https://orcid. and measured over 10000  specimens. MHCN, NA, org/0000-0001-9985-5532 HEAM, PAW, SEIJ, CV, AGP, NMM, FMC, VLS, Santiago Claramunt  https://orcid. SC, BD, BGF and TPB also collected substantial trait org/0000-0002-8926-5974 datasets (>1500  specimens measured). LNN, KBG, Bianca Darski  https://orcid.org/0000-0001-9714-3662 CR, GHT, SAF and MS coordinated independent trait Benjamin G. Freeman  https://orcid. datasets and helped with merging data. Additional au- org/0000-0001-6131-6832 thors contributed smaller trait datasets. AJMD, TLW, Christopher R. Cooney  https://orcid. RAB, JY, PAW and FS helped with data management org/0000-0002-4872-9146 and integration of datasets. ALP, AJMD, JY, FS, SAF Emma C. Hughes  https://orcid. and JAT conducted analyses and produced figures. org/0000-0003-4682-6257 JAT wrote the first version of the manuscript with Christopher H. Trisos  https://orcid. input from MS and ALP. All authors contributed criti- org/0000-0002-5854-1489 cally to subsequent drafts and gave final permission Brian C. Weeks  https://orcid. for publication. org/0000-0003-2967-2970 14610248, 2022, 3, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/ele.13898 by EBMG ACCESS - KENYA, Wiley Online Library on [30/05/2023]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License TOBIAS et al. | 595 Till Töpfer  https://orcid.org/0000-0002-4866-2703 R. Paul Scofield  https://orcid. Gustavo A. Bravo  https://orcid. org/0000-0002-7510-6980 org/0000-0001-5889-2767 Eike Lena Neuschulz  https://orcid. Larissa Nowak  https://orcid.org/0000-0002-1910-8041 org/0000-0001-7526-2580 Lincoln S. Carneiro  https://orcid. D. Matthias Dehling  https://orcid. org/0000-0002-7680-2291 org/0000-0002-2863-5580 Daniel T. Baldassarre  https://orcid. Jacob C. Cooper  https://orcid. org/0000-0002-1749-548X org/0000-0003-2182-3236 Alejandra Martínez- Salinas  https://orcid. Jon Fjeldså  https://orcid.org/0000-0003-0790-3600 org/0000-0003-2557-0635 Nathalie Seddon  https://orcid. Rebekah J. Mayhew  https://orcid. org/0000-0002-1880-6104 org/0000-0003-4463-0787 Fabrice A. J. 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