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#When using the data, please cite the article for which the in situ data were collected for:
#Kemppinen, Niittynen, le Roux, Momberg, Happonen, Aalto, Rautakoski, Enquist, Vandvik, Halbritter, Maitner & Luoto (Accepted). Consistent trait-environment relationships within and across tundra plant communities. Nature Ecology and Evolution
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##Title
Data from: Consistent trait-environment relationships within and across tundra plant communities

##Authors
Kemppinen, Julia; Niittynen, Pekka; le Roux, Peter C.; Momberg, Mia; Happonen, Konsta; Aalto, Juha; Rautakoski, Helena; Enquist, Brian J.; Vandvik, Vigdis; Halbritter, Aud H.; Maitner, Brian & Miska Luoto

##ORCID
Julia Kemppinen 0000-0001-7521-7229
Pekka Niittynen 0000-0002-7290-029X
Peter C. le Roux 0000-0002-7941-7444
Mia Momberg 0000-0002-8901-9271
Konsta Happonen 0000-0002-3164-8868
Juha Aalto 0000-0001-6819-4911  	
Helena Rautakoski 0000-0002-5297-286X
Brian J. Enquist 0000-0002-6124-7096
Vigdis Vandvik 0000-0003-4651-4798
Aud H. Halbritter 0000-0003-2597-6328
Brian Maitner 0000-0002-2118-9880
Miska Luoto 0000-0001-6203-5143

##Contact Information
julia.kemppinen@gmail.com

##Abstract
A fundamental assumption in trait-based ecology is that relationships between traits and environmental conditions are globally consistent. We use field-quantified microclimate and soil data to explore if trait-environment relationships are generalisable across plant communities and spatial scales. We collected data from 6720 plots and 217 species across four distinct tundra regions from both hemispheres. We combine this data with over 76000 database trait records to relate local plant community trait composition to broad gradients of key environmental drivers: soil moisture, soil temperature, soil pH, and potential solar radiation. Results revealed strong, consistent trait-environment relationships across Arctic and Antarctic regions. This indicates that the detected relationships are transferable between tundra plant communities also when fine-scale environmental heterogeneity is accounted for, and that variation in local conditions heavily influences both structural and leaf economic traits. Our results strengthen the biological and mechanistic basis for climate change impact predictions of vulnerable high-latitude ecosystems.

##Acknowledgements
We thank the past and present members of the BioGeoClimate Modelling Lab and the le Roux lab for their hard work collecting the field data. We also thank the laboratory personnel at the University of Helsinki and University of Pretoria, as well as the staff at The University Centre in Svalbard, Kangerlussuaq International Support Services, Kilpisjärvi Biological research station, and Marion Island field assistants (specifically Elana Mostert, Nothando Mhlongo, Jenna van Berkel, and Janine Schoombie). We are also grateful to Pernille Eidesen for helping with the temperature loggers and Eric Pedersen for his help regarding HGAM.

##Funding
JK was funded by the Doctoral Programme in Geosciences at the University of Helsinki, PN by the Kone Foundation, MM by the National Research Foundation via the SANAP programme, and KH by the Doctoral Programme in Wildlife Biology Research at the University of Helsinki. The field campaigns were funded by the Academy of Finland (project numbers 307761 and 286950) and the National Research Foundation’s South African National Antarctic Program (unique grant numbers 93077 and 110726). We acknowledge the funding by the Finnish Ministry of Education and Culture (The FinCEAL Plus BRIDGES coordinated by the Finnish University Partnership for International Development).
 
##Permissions
Permission to carry out fieldwork was granted by the Governor of Svalbard for the high-Arctic site, the Government of Greenland for the low-Arctic site, Metsähallitus for the sub-Arctic site, and the Prince Edward Islands Management Committee (permit PEIMC1/2013) for the sub-Antarctic site.

##Structure of the datasets
#trait_genus.csv
The first column is the plot identification number. After which are the four fine-scale field-measured soil microclimate and chemistry variables, namely soil moisture, soil temperature, soil pH, and solar radiation. Finally, the last seven columns are plant functional traits, namely plant height, specific leaf area, leaf dry matter content, leaf nitrogen content, leaf phosphorous content and seed mass. The traits were derived from databases, namely Tundra Trait Team, TRY Plant Trait Database and the Botanical Information and Ecological Network. For a detailed description on the data, please see the Methods section in Kemppinen et al. (Accepted).

#trait.genus_coarse.csv
The first column is the plot identification number. After which are the four coarse-scale macroclimate and soil chemistry variables, namely precipitation, air temperature, soil pH, and solar radiation, which were derived from databases, namely Chelsa Climate database, SoilGrids database and NASA's Land Processes Distributed Active Archive Center. Finally, the last seven columns are plant functional traits, namely plant height, specific leaf area, leaf dry matter content, leaf nitrogen content, leaf phosphorous content and seed mass. The traits were derived from databases, namely Tundra Trait Team, TRY Plant Trait Database and the Botanical Information and Ecological Network. For a detailed description on the data, please see the Methods section in Kemppinen et al. (Accepted).

##Structure of the codes
The results were reproducible under R version 4.0.2 (2020-06-22).

#hgam
This code uses "trait_genus.csv" and produces the dataset needed in the codes "effects", "importance" and "pca". This code "hgam" produces the primary result of the study: trait-environment relationships are generalisable.

#hgam_coarse
This code uses "trait_genus_coarse.csv" and produces the dataset needed in the code "effects_coarse". This code "hgam_coarse" produces the secondary result of the study: coarse-scale data are outperformed by fine-scale data.

#effects
This code produces a side result of the study.

#importance
This code produces a side result of the study.

#pca
This code produces a side result of the study.

#effects_coarse
This code produces a side result of the study.

##References
#Bjorkman et al. Tundra Trait Team: A database of plant traits spanning the tundra biome. Glob. Ecol. Biogeogr. 27, 1402–1411 (2018).

#Hengl et al. SoilGrids250m: Global gridded soil information based on machine learning. PLoS One 12, e0169748 (2017).

#Karger et al. Climatologies at high resolution for the earth’s land surface areas. Scientific Data vol. 4 (2017).

#Kattge et al. TRY - a global database of plant traits. Glob. Chang. Biol. 17, 2905–2935 (2011).

#Kemppinen, Niittynen, le Roux, Momberg, Happonen, Aalto, Rautakoski, Enquist, Vandvik, Halbritter, Maitner & Luoto. Consistent trait-environment relationships within and across tundra plant communities. Nature Ecology and Evolution (Accepted).

#NASA’s Land Processes Distributed Active Archive Center. ASTER Global Digital Elevation Model (GDEM) Version 3 (ASTGTM). doi:10.5067/ASTER/ASTGTM.003.

#Maitner 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).

##Copyright
This work is licensed under Creative Commons Attribution 4.0 International Public License.