Abstract
Synthesizing trait observations and knowledge across the Tree of Life remains a grand challenge for biodiversity science. Species traits are widely used in ecological and evolutionary science, and new data and methods have proliferated rapidly. Yet accessing and integrating disparate data sources remains a considerable challenge, slowing progress toward a global synthesis to integrate trait data across organisms. Trait science needs a vision for achieving global integration across all organisms. Here, we outline how the adoption of key Open Science principles—open data, open source and open methods—is transforming trait science, increasing transparency, democratizing access and accelerating global synthesis. To enhance widespread adoption of these principles, we introduce the Open Traits Network (OTN), a global, decentralized community welcoming all researchers and institutions pursuing the collaborative goal of standardizing and integrating trait data across organisms. We demonstrate how adherence to Open Science principles is key to the OTN community and outline five activities that can accelerate the synthesis of trait data across the Tree of Life, thereby facilitating rapid advances to address scientific inquiries and environmental issues. Lessons learned along the path to a global synthesis of trait data will provide a framework for addressing similarly complex data science and informatics challenges.
This is a preview of subscription content, access via your institution
Access options
Access Nature and 54 other Nature Portfolio journals
Get Nature+, our best-value online-access subscription
27,99 € / 30 days
cancel any time
Subscribe to this journal
Receive 12 digital issues and online access to articles
118,99 € per year
only 9,92 € per issue
Buy this article
- Purchase on SpringerLink
- Instant access to full article PDF
Prices may be subject to local taxes which are calculated during checkout


Similar content being viewed by others
Change history
09 March 2020
A Correction to this paper has been published: https://doi.org/10.1038/s41559-020-1167-9
References
Adler, P. B. et al. Functional traits explain variation in plant life history strategies. Proc. Natl Acad. Sci. USA 111, 740–745 (2014).
Chapin, F. S. III, Autumn, K. & Pugnaire, F. Evolution of suites of traits in response to environmental stress. Am. Nat. 142, S78–S92 (1993).
Chown, S. L. & Gaston, K. J. Macrophysiology–progress and prospects. Funct. Ecol. 30, 330–344 (2016).
Kooijman, S. A. L. M. Dynamic Energy and Mass Budgets in Biological Systems (Cambridge Univ. Press, 2000).
Diaz, S., Cabido, M. & Casanoves, F. Plant functional traits and environmental filters at a regional scale. J. Veg. Sci. 9, 113–122 (1998).
Harmon, L. J. et al. Early bursts of body size and shape evolution are rare in comparative data. Evolution 64, 2385–2396 (2010).
Sauquet, H. & Magallón, S. Key questions and challenges in angiosperm macroevolution. New Phytol. 219, 1170–1187 (2018).
Sneath, P. H. & Sokal, R. R. Numerical Taxonomy: The Principles and Practice of Numerical Classification (W. H. Freeman & Co, 1973).
Edmunds, R. C. et al. Phenoscape: identifying candidate genes for evolutionary phenotypes. Mol. Biol. Evol. 33, 13–24 (2015).
Mungall, C. J. et al. The Monarch Initiative: an integrative data and analytic platform connecting phenotypes to genotypes across species. Nucleic Acids Res. 45, D712–D722 (2016).
Gkoutos, G. V., Schofield, P. N. & Hoehndorf, R. The anatomy of phenotype ontologies: principles, properties and applications. Brief. Bioinform. 19, 1008–1021 (2017).
Westoby, M., Falster, D. S., Moles, A. T., Vesk, P. A. & Wright, I. J. Plant ecological strategies: some leading dimensions of variation between species. Annu. Rev. Ecol. Syst. 33, 125–159 (2002).
Kiørboe, T., Visser, A. & Andersen, K. H. A trait-based approach to ocean ecology. ICES J. Mar. Sci. 75, 1849–1863 (2018).
Kunstler, G. et al. Plant functional traits have globally consistent effects on competition. Nature 529, 204–207 (2016).
Laughlin, D. C. Nitrification is linked to dominant leaf traits rather than functional diversity. J. Ecol. 99, 1091–1099 (2011).
Finegan, B. et al. Does functional trait diversity predict above-ground biomass and productivity of tropical forests? Testing three alternative hypotheses. J. Ecol. 103, 191–201 (2015).
Laigle, I. et al. Species traits as drivers of food web structure. Oikos 127, 316–326 (2018).
Brown, J. H., Gillooly, J. F., Allen, A. P., Savage, V. M. & West, G. B. Toward a metabolic theory of ecology. Ecology 85, 1771–1789 (2004).
West, G. B., Brown, J. H. & Enquist, B. J. A general model for the origin of allometric scaling laws in biology. Science 276, 122–126 (1997).
Iversen, C. M. et al. A global Fine‐Root Ecology Database to address below‐ground challenges in plant ecology. New Phytol. 215, 15–26 (2017).
Kattge, J. et al. TRY–a global database of plant traits. Glob. Change Biol. 17, 2905–2935 (2011).
Bernhardt‐Römermann, M., Poschlod, P. & Hentschel, J. BryForTrait–A life‐history trait database of forest bryophytes. J. Veg. Sci. 29, 798–800 (2018).
Bennett, J. M. et al. GlobTherm, a global database on thermal tolerances for aquatic and terrestrial organisms. Sci. Data 5, 180022 (2018).
Meiri, S. Traits of lizards of the world: variation around a successful evolutionary design. Glob. Ecol. Biogeogr. 27, 1168–1172 (2018).
Myhrvold, N. P. et al. An amniote life‐history database to perform comparative analyses with birds, mammals, and reptiles. Ecology 96, 3109–3109 (2015).
Schäfer, R. B. et al. A trait database of stream invertebrates for the ecological risk assessment of single and combined effects of salinity and pesticides in South-East Australia. Sci. Total Environ. 409, 2055–2063 (2011).
Bland, L. Global correlates of extinction risk in freshwater crayfish. Animal Conserv. 20, 532–542 (2017).
Brun, P., Payne, M. R. & Kiørboe, T. A trait database for marine copepods. Earth Syst. Sci. Data 9, 99–113 (2017).
Parr, C. L. et al. GlobalAnts: a new database on the geography of ant traits (Hymenoptera: Formicidae). Insect Conserv. Divers. 10, 5–20 (2017).
Froese, R. & Pauly, D. Progress Report on FishBase (Fisheries Centre, University of British Columbia, 2010).
Frimpong, E. A. & Angermeier, P. L. Fish traits: a database of ecological and life-history traits of freshwater fishes of the United States. Fisheries 34, 487–495 (2009).
Madin, J. S. et al. The Coral Trait Database, a curated database of trait information for coral species from the global oceans. Sci. Data 3, 160017 (2016).
Garnett, S. T. et al. Biological, ecological, conservation and legal information for all species and subspecies of Australian bird. Sci. Data 2, 150061 (2015).
Wilman, H. et al. EltonTraits 1.0: species‐level foraging attributes of the world’s birds and mammals: Ecological Archives E095‐178. Ecology 95, 2027 (2014).
Oliveira, B. F., São-Pedro, V. A., Santos-Barrera, G., Penone, C. & Costa, G. C. AmphiBIO, a global database for amphibian ecological traits. Sci. Data 4, 170123 (2017).
Jones, K. E. et al. PanTHERIA: a species‐level database of life history, ecology, and geography of extant and recently extinct mammals. Ecology 90, 2648–2648 (2009).
Faurby, S. et al. PHYLACINE 1.2: the phylogenetic atlas of mammal macroecology. Ecology 99, 2626 (2018).
Galán-Acedo, C., Arroyo-Rodríguez, V., Andresen, E. & Arasa-Gisbert, R. Ecological traits of the world’s primates. Sci. Data 6, 55 (2019).
Flores-Moreno, H. et al. fungaltraits aka funfun: a dynamic functional trait database for the world's fungi (GitHub, 2019); https://doi.org/10.5281/zenodo.1216257.
Sholler, D., Ram, K., Boettiger, C. & Katz, D. S. Enforcing public data archiving policies in academic publishing: A study of ecology journals. Big Data Soc. 6, 2053951719836258 (2019).
Fegraus, E. H., Andelman, S., Jones, M. B. & Schildhauer, M. Maximizing the value of ecological data with structured metadata: an introduction to Ecological Metadata Language (EML) and principles for metadata creation. Bull. Ecol. Soc. Am. 86, 158–168 (2005).
Parker, T. H. et al. Transparency in ecology and evolution: real problems, real solutions. Trends Ecol. Evol. 31, 711–719 (2016).
Hortal, J. et al. Seven shortfalls that beset large-scale knowledge of biodiversity. Annu. Rev. Ecol. Evol. Syst. 46, 523–549 (2015).
Cornwell, W. K., Pearse, W. D., Dalrymple, R. L. & Zanne, A. E. What we (don’t) know about global plant diversity. Ecography 42, 1819–1831 (2019).
Stodden, V., Seiler, J. & Ma, Z. An empirical analysis of journal policy effectiveness for computational reproducibility. Proc. Natl Acad. Sci. USA 115, 2584–2589 (2018).
Lowndes, J. S. S. et al. Our path to better science in less time using open data science tools. Nat. Ecol. Evol. 1, 0160 (2017).
Weigelt, P., König, C. & Kreft, H. GIFT–a global inventory of floras and traits for macroecology and biogeography. J. Biogeogr. https://doi.org/10.1111/jbi.13623 (2019).
Parker, T., Nakagawa, S. & Gurevitch, J., IIEE workshop participants. Promoting transparency in evolutionary biology and ecology. Ecol. Lett. 19, 726–728 (2016).
McKiernan, E. C. et al. Point of view: How open science helps researchers succeed. eLife 5, e16800 (2016).
Munafò, M. R. et al. A manifesto for reproducible science. Nat. Hum. Behav. 1, 0021 (2017).
Nosek, B. A. et al. Promoting an open research culture. Science 348, 1422–1425 (2015).
Farley, S. S., Dawson, A., Goring, S. J. & Williams, J. W. Situating ecology as a big-data science: current advances, challenges, and solutions. BioScience 68, 563–576 (2018).
Falster, D. S. et al. BAAD: a Biomass And Allometry Database for woody plants. Ecology 96, 1445–1445 (2015).
Salguero‐Gómez, R. et al. COMADRE: a global data base of animal demography. J. Anim. Ecol. 85, 371–384 (2016).
Salguero‐Gómez, R. et al. The COMPADRE Plant Matrix Database: an open online repository for plant demography. J. Ecol. 103, 202–218 (2015).
Marques, G. M. et al. The AmP project: comparing species on the basis of dynamic energy budget parameters. PLOS Comput. Biol. 14, e1006100 (2018).
Conde, D. A. et al. Data gaps and opportunities for comparative and conservation biology. Proc. Natl Acad. Sci. USA 116, 9658–9664 (2019).
Wieczorek, J. et al. Darwin Core: an evolving community-developed biodiversity data standard. PLOS ONE 7, e29715 (2012).
Guralnick, R., Walls, R. & Jetz, W. Humboldt Core–toward a standardized capture of biological inventories for biodiversity monitoring, modeling and assessment. Ecography 41, 713–725 (2018).
Deans, A. R. et al. Finding our way through phenotypes. PLOS Biol. 13, e1002033 (2015).
Haendel, M. A. et al. Unification of multi-species vertebrate anatomy ontologies for comparative biology in Uberon. J. Biomed. Semant. 5, 21 (2014).
Garnier, E. et al. Towards a thesaurus of plant characteristics: an ecological contribution. J. Ecol. 105, 298–309 (2017).
The Gene Ontology Consortium. The Gene Ontology resource: 20 years and still GOing strong. Nucleic Acids Res. 47, D330–D338 (2018).
Buttigieg, P. L., Morrison, N., Smith, B., Mungall, C. J. & Lewis, S. E. The environment ontology: contextualising biological and biomedical entities. J. Biomed. Semant. 4, 43 (2013).
Becker, J., Brackbill, D. & Centola, D. Network dynamics of social influence in the wisdom of crowds. Proc. Natl Acad. Sci. USA 114, E5070–E5076 (2017).
Page, S. E. The Difference: How the Power of Diversity Creates Better Groups, Firms, Schools, and Societies - New Edition (Princeton Univ. Press, 2008).
Tenopir, C. et al. Data sharing by scientists: practices and perceptions. PLOS ONE 6, e21101 (2011).
Tyler, E. H. et al. Extensive gaps and biases in our knowledge of a well‐known fauna: implications for integrating biological traits into macroecology. Glob. Ecol. Biogeogr. 21, 922–934 (2012).
Kissling, W. D. et al. Towards global data products of Essential Biodiversity Variables on species traits. Nat. Ecol. Evol. 2, 1531–1540 (2018).
Lajoie, G. & Kembel, S. W. Making the most of trait-based approaches for microbial ecology. Trends Microbiol. 27, 814–823 (2019).
Dawson, S. K. et al. Handbook for the measurement of macrofungal functional traits: a start with basidiomycete wood fungi. Funct. Ecol. 33, 372–387 (2019).
Ankenbrand, M. J., Hohlfeld, S. C., Weber, L., Förster, F. & Keller, A. Functional exploration of natural networks and ecological communities. Methods Ecol. Evol. 9, 2028–2033 (2018).
Gaillard, J.-M. et al. Generation time: a reliable metric to measure life-history variation among mammalian populations. Am. Nat. 166, 119–123 (2005).
Steiner, U. K., Tuljapurkar, S. & Coulson, T. Generation time, net reproductive rate, and growth in stage-age-structured populations. Am. Nat. 183, 771–783 (2014).
Andelman, S. J., Bowles, C. M., Willig, M. R. & Waide, R. B. Understanding environmental complexity through a distributed knowledge network. BioScience 54, 240–246 (2004).
Schneider, F. D. et al. Towards an ecological trait-data standard. Methods Ecol. Evol. 10, 2006–2019 (2019).
Perez-Harguindeguy, N. et al. A new handbook for standardised measurement of plant functional traits worldwide. Aust. J. Bot. 64, 715–716 (2013).
Fang, J. et al. Methods and protocols for plant community inventory. Biodivers. Sci. 17, 533–548 (2009).
Sack, L. et al. A unique web resource for physiology, ecology and the environmental sciences: PrometheusWiki. Funct. Plant Biol. 37, 687–693 (2010).
Bjorkman, A. D. et al. Tundra Trait Team: a database of plant traits spanning the tundra biome. Glob. Ecol. Biogeogr. 27, 1402–1411 (2018).
Moretti, M. et al. Handbook of protocols for standardized measurement of terrestrial invertebrate functional traits. Funct. Ecol. 31, 558–567 (2017).
Ferris, H. NEMAPLEX: The Nematode-Plant Expert Information System (Univ. California Davis, 2005); http://nemaplex.ucdavis.edu/
Tennessen, J. M., Barry, W. E., Cox, J. & Thummel, C. S. Methods for studying metabolism in Drosophila. Methods 68, 105–115 (2014).
Palomares, M. L. D. & Pauly, D. SeaLifeBase v.12/2010 (2010); www.sealifebase.org
Le Bagousse‐Pinguet, Y. et al. Traits of neighbouring plants and space limitation determine intraspecific trait variability in semi‐arid shrublands. J. Ecol. 103, 1647–1657 (2015).
Cornelissen, J. et al. A handbook of protocols for standardised and easy measurement of plant functional traits worldwide. Aust. J. Bot. 51, 335–380 (2003).
Maitner, B. S. et al. The bien r package: a tool to access the Botanical Information and Ecology Network (BIEN) database. Methods Ecol. Evol. 9, 373–379 (2018).
Jetz, W., Thomas, G., Joy, J., Hartmann, K. & Mooers, A. The global diversity of birds in space and time. Nature 491, 444–448 (2012).
Smith, S. A. & Brown, J. W. Constructing a broadly inclusive seed plant phylogeny. Am. J. Bot. 105, 302–314 (2018).
Revell, L. J. phytools: an R package for phylogenetic comparative biology (and other things). Methods Ecol. Evol. 3, 217–223 (2012).
Díaz, S. et al. The global spectrum of plant form and function. Nature 529, 167–171 (2016).
Andersen, K. H. et al. Characteristic sizes of life in the oceans, from bacteria to whales. Annu. Rev. Mar. Sci. 8, 217–241 (2016).
Neuheimer, A. B. et al. Adult and offspring size in the ocean over 17 orders of magnitude follows two life history strategies. Ecology 96, 3303–3311 (2015).
Ernest, S. M. et al. Thermodynamic and metabolic effects on the scaling of production and population energy use. Ecol. Lett. 6, 990–995 (2003).
Weiss, K. C. & Ray, C. A. Unifying functional trait approaches to understand the assemblage of ecological communities: synthesizing taxonomic divides. Ecography 42, 2012–2020 (2019).
Ball, I. R., Possingham, H. P. & Watts, M. in Spatial Conservation Prioritisation: Quantitative Methods and Computational Tools (eds Moilanen, A. et al.) 185–195 (Oxford Univ. Press, 2009).
Pollock, L. J., Thuiller, W. & Jetz, W. Large conservation gains possible for global biodiversity facets. Nature 546, 141–144 (2017).
Margules, C. R. & Pressey, R. L. Systematic conservation planning. Nature 405, 243–253 (2000).
Gross, N. et al. Functional trait diversity maximizes ecosystem multifunctionality. Nat. Ecol. Evol. 1, 0132 (2017).
Loreau, M. Does functional redundancy exist? Oikos 104, 606–611 (2004).
van Bodegom, P. M., Douma, J. C. & Verheijen, L. M. A fully traits-based approach to modeling global vegetation distribution. Proc. Natl Acad. Sci. USA 111, 13733–13738 (2014).
Sakschewski, B. et al. Leaf and stem economics spectra drive diversity of functional plant traits in a dynamic global vegetation model. Glob. Change Biol. 21, 2711–2725 (2015).
Butler, E. E. et al. Mapping local and global variability in plant trait distributions. Proc. Natl Acad. Sci. USA 114, E10937–E10946 (2017).
Kearney, M. & Porter, W. Mechanistic niche modelling: combining physiological and spatial data to predict species’ ranges. Ecol. Lett. 12, 334–350 (2009).
Fordham, D. A. et al. How complex should models be? Comparing correlative and mechanistic range dynamics models. Glob. Change Biol. 24, 1357–1370 (2018).
Enriquez‐Urzelai, U., Kearney, M. R., Nicieza, A. G. & Tingley, R. Integrating mechanistic and correlative niche models to unravel range‐limiting processes in a temperate amphibian. Glob. Change Biol. 25, 2633–2647 (2019).
Benito Garzón, M., Robson, T. M. & Hampe, A. ΔTrait SDMs: species distribution models that account for local adaptation and phenotypic plasticity. New Phytol. 222, 1757–1765 (2019).
Berzaghi, F. et al. Assessing the role of megafauna in tropical forest ecosystems and biogeochemical cycles–the potential of vegetation models. Ecography 41, 1934–1954 (2018).
Galetti, M. & Dirzo, R. Ecological and evolutionary consequences of living in a defaunated world. Biol. Conserv. 163, 1–6 (2013).
Huang, Y. et al. Orchimic (v1. 0), a microbe-mediated model for soil organic matter decomposition. Geosci. Model Dev. 11, 2111–2138 (2018).
McGuire, K. L. & Treseder, K. K. Microbial communities and their relevance for ecosystem models: decomposition as a case study. Soil Biol. Biochem. 42, 529–535 (2010).
Todd-Brown, K. E., Hopkins, F. M., Kivlin, S. N., Talbot, J. M. & Allison, S. D. A framework for representing microbial decomposition in coupled climate models. Biogeochemistry 109, 19–33 (2012).
Hardisty, A. R. et al. The Bari Manifesto: an interoperability framework for essential biodiversity variables. Ecol. Inform. 49, 22–31 (2019).
Acknowledgements
Ideas presented stem from initial discussions at three international meetings—the Australian National Climate Change Adaptation Research Facility Roundtable on Species Traits, the iDigBio ALA Traits workshop, and the preliminary Open Traits workshop held at the Ecological Society of America. R.V.G. is supported by an Australian Research Council DECRA Fellowship (DE170100208). D.S.F. is supported by an Australian Research Council Future Fellowship (FT160100113). R.S.-G. is supported by NERC R/142195-11-1. W.D.P. is supported by NSF ABI-1759965, NSF EF-1802605, and USDA Forest Service agreement 18-CS-11046000-041. A.K. received financial support for M.J.A. by the German Research Foundation (DFG KE1743/7-1). C.M.I. was supported by the Biological and Environmental Research program in the United States Department of Energy’s Office of Science. C.P. is supported by the DFG Priority Program 1374. M.J. was supported by the German Research Foundation within the framework of the Jena Experiment (FOR 1451) and by the Swiss National Science Foundation. S.P.-M. was supported by the Benson Fund from the Department of Paleobiology, National Museum of Natural History. S.T.M. is supported by SERDP project RC18-1346. B.J.E. was supported by NSF Grants DEB0133974, HDR1934790 and EF1065844, a Leverhulme Trust Visiting Professorship Grant, and an Oxford Martin School Fellowship.
Author information
Authors and Affiliations
Contributions
R.V.G. wrote the manuscript with contributions from D.S.F., B.S.M., R.S.-G., V.V., W.D.P., F.D.S., J.K., J.H.P., J.S.M., M.J.A., C.P., X.F., V.M.A., J.A., S.C.A., M.A.B., L.M.B., B.L.B., C.H.B.-A., I.B., A.J.R.C., R.C., B.R.C., D.A.C., S.L.C., B.F., H.G., A.H.H., J.H., J.A.H., H.H., M.H., C.M.I., M.J., M.K., A.K., P.Mabee, P.Manning, L.M., S.T.M., D.S.P., T.M.P., S.P.-M., C.A.R., M.R., H.S., B.S., M.J.S., R.J.T., J.A.T., C.V., R.W., K.C.B.W., M.W., I.J.W. and B.J.E.
Corresponding author
Ethics declarations
Competing interests
The authors declare no competing interests.
Additional information
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Gallagher, R.V., Falster, D.S., Maitner, B.S. et al. Open Science principles for accelerating trait-based science across the Tree of Life. Nat Ecol Evol 4, 294–303 (2020). https://doi.org/10.1038/s41559-020-1109-6
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1038/s41559-020-1109-6
This article is cited by
-
The Octocoral Trait Database: a global database of trait information for octocoral species
Scientific Data (2025)
-
Factors influencing open science participation through research data sharing and reuse among researchers: a systematic literature review
Knowledge and Information Systems (2025)
-
Canopy functional trait variation across Earth’s tropical forests
Nature (2025)
-
DEBBIES Dataset to study Life Histories across Ectotherms
Scientific Data (2024)
-
The Pelagic Species Trait Database, an open data resource to support trait-based ocean research
Scientific Data (2024)