The Tundra Trait Team: Advancing Understanding and Model Representation of Tundra Plant Strategies

The largest database of tundra plant traits assembled to date allowed new insights into tundra plant strategies.

The Science

The Tundra Trait Team (TTT) compiled the largest ever database of key tundra plant traits to improve understanding and model representation of tundra plant strategies, trait-environment relationships, and trait variation across spatial scales.

The Impact

The TTT database serves as a foundation for several major new insights about tundra ecosystems, ranging from interactions between soil moisture and tundra plant responses to warming to the unique trait space occupied by tundra plant species growing in harsh environmental conditions that should be better represented by terrestrial biosphere models.


One of the major outcomes of the sTUNDRA working group at the German Centre for Integrative Biodiversity Research (iDiv) was the compilation of the TTT database—the largest ever compilation of key tundra plant traits (Bjorkman et al. 2018; Global Ecology and Biogeography). The TTT database contains more than 90,000 unique observations of 18 plant traits on 978 tundra species, with nearly twice as many high-latitude observations as the TRY Plant Trait Database for many key traits. Using the most commonly measured tundra plant traits in its database, the TTT developed several major new insights on tundra plant trait strategies: (1) soil moisture moderates increases in tundra plant size and altered resource acquisition strategies across space and over time in response to warming (Bjorkman et al. 2018; Nature); (2) tundra plant size characteristics, which are key drivers of tundra ecosystem function, were poorly captured by the plant functional groups traditionally used by terrestrial biosphere models (Thomas et al. 2019; GEB); and (3) tundra plants exhibit the same dimensions of plant trait variation as species around the world, but they are more constrained in the expression of size-related traits adapted for extreme environmental conditions in the tundra (Thomas et al. 2020; Nature Comm.). The most frequently measured traits in the TTT database were aboveground traits. Although the belowground trait data from Iversen et al. (“The Unseen Iceberg”; 2015) that served as the foundation for the development of the Fine-Root Ecology Database (FRED; Iversen et al. 2017; New Phytol.) were initially compiled as part of the TTT database, there simply were not enough data for global comparisons. This lack of belowground understanding of tundra plant traits has led to the development of a new international working group, the Arctic Underground, which will focus on improving global understanding and model representation of belowground tundra plant traits around the world.

Principal Investigator(s)

Colleen M. Iversen
Oak Ridge National Laboratory

Related Links


This paper is an outcome of the sTundra working group supported by the Synthesis Centre (sDiv) of the German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig (DFG FZT 118). Support to investigators was provided as follows: ADB by an iDiv postdoctoral fellowship and The Danish Council for Independent Research–Natural Sciences (DFF 4181-00565 to S.N.). ADB, IHM-S, HJDT, and SA-B. funded by the United Kingdom (U.K.) Natural Environment Research Council (ShrubTundra Project NE/M016323/1 to IHM-S). SN, ABO, SSN, and UAT by the Villum Foundation’s Young Investigator Programme (VKR023456 to SN) and the Carlsberg Foundation (2013-01-0825). NR by the DFG-Forschungszentrum iDiv Halle-Jena-Leipzig and Deutsche Forschungsgemeinschaft DFG (RU 1536/3-1). A. Buc. by EU-F7P INTERACT (262693) and MOBILITY PLUS (1072/MOB/2013/0). ABO additionally by the Danish Council for Independent Research–Natural Sciences (DFF 4181-00565 to SN). JMA by the Carl Tryggers stiftelse för vetenskaplig forskning. AH by the Research Council of Norway (244557/E50). BE and A. Mic. by the Danish National Research Foundation (CENPERM DNRF100). BM by the Soil Conservation Service of Iceland. ERF by the Swiss National Science Foundation (155554). BCF by the Academy of Finland (256991) and Joint Programming Initiative (JPI) Climate (291581). BJE by a National Science Foundation’s (NSF) Advancing Theory in Biology (ATB) CAREER and Macrosystems award. CMI by the Office of Biological and Environmental Research (BER), within the U.S. Department of Energy (DOE) Office of Science, as part of the Next-Generation Ecosystem Experiments (NGEE)–Arctic project. DB by The Swedish Research Council (2015-00465) and Marie Sklodowska Curie Actions co-funding (INCA 600398). EW by NSF (DEB-0415383), University of Wisconsin–Eau Claire (UWEC)–Office of Research and Sponsored Programs (ORSP), and UWEC–Blugold Commitment Differential Tuition (BCDT). GS-S and MI-G. by the University of Zurich Research Priority Program on Global Change and Biodiversity. HDA by NSF Division of Polar Programs (PLR; 1623764, 1304040). ISJ by the Icelandic Research Fund (70255021) and the University of Iceland Research Fund. JDMS by the Research Council of Norway (262064). JSP by the U.S. Fish and Wildlife Service. JCO by Klimaat voor ruimte, Dutch National Research Programme Climate changes Spatial Planning. JFJ, PG, GHRH, EL, NB-L, KAH, LSC, and TZ by the Natural Sciences and Engineering Research Council of Canada (NSERC). GHRH, NB-L., EL, LSC, and LH by ArcticNet. GHRH, NB-L, MTr, and LSC. by the Northern Scientific Training Program. GHRH, EL, and NB-L additionally by the Polar Continental Shelf Program. NB-L additionally by the Fonds de recherche du Quebec: Nature et Technologies and the Centre d’études Nordiques. JP by the European Research Council Synergy grant SyG-2013-610028 IMBALANCE-P. AA-R, OG, and JMN by the Spanish National Parks Autonomous Agency (OAPN; project 534S/2012) and European INTERACT project (262693 Transnational Access). KDT by NSF Arctic Natural Sciences (ANS)-1418123. LES and PAW by the U.K. Natural Environment Research Council Arctic Terrestrial Ecology Special Topic Programme and Arctic Programme (NE/K000284/1 to PAW). PAW additionally by the European Union Fourth Environment and Climate Framework Programme (Project Number ENV4-CT970586). MW by DFG RTG 2010. RDH by NSF. MJS and KNS by the Niwot Ridge Long-Term Ecological Research (LTER; NSF DEB-1637686). HJDT funded by a British Geological Survey’s Natural Environment Research Council (NERC) doctoral training partnership grant (NE/L002558/1). VGO by the Russian Science Foundation (14-50-00029). LB by NSF ANS (1661723). SJG by the National Aeronautics and Space Administration (NASA) Arctic-Boreal Vulnerability Experiment (ABoVE; NNX15AU03A/NNX17AE44G). BB-L as part of the Energy Exascale Earth System Model (E3SM) project of BER, within the DOE Office of Science. AE by the Academy of Finland (projects 253385 and 297191). EK was supported by Swedish Research Council (2015-00498). SDí by CONICET, FONCyT, and SECyT-UNC, Argentina. The study has been supported by the TRY initiative on plant traits (http://www.try-db.org), which is hosted at the Max Planck Institute for Biogeochemistry in Jena, Germany, and is currently supported by DIVERSITAS/Future Earth and the German iDiv Halle-Jena-Leipzig. AD and SCE thank NSF for support to receive training in Bayesian methods (grant 1145200 to N. Thompson Hobbs). The project thanks the governments, parks, field stations, and local and indigenous people for the opportunity to conduct research on their land.


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