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Plant traits and associated data from a warming experiment, a seabird colony, and along elevation in Svalbard.

Vigdis VandvikAud H HalbritterInge H J AlthuizenCasper T ChristiansenJonathan J HennIngibjörg Svala JónsdóttirKari KlanderudMarc Macias-FauriaYadvinder MalhiBrian Salvin MaitnerSean T MichaletzRuben E RoosRichard J TelfordPolly BassKatrín BjörnsdóttirLucely Lucero Vilca BustamanteAdam ChmurzynskiShuli ChenSiri Vatsø HaugumJulia KemppinenKai LepleyYaoqi LiMary LinaburyIlaíne Silveira MatosBarbara M Neto-BradleyMolly NgPekka NiittynenSilje ÖstmanKarolína PánkováNina RothMatiss CastorenaMarcus SpiegelEleanor ThomsonAlexander Sæle VågenesBrian J Enquist
Published in: Scientific data (2023)
The Arctic is warming at a rate four times the global average, while also being exposed to other global environmental changes, resulting in widespread vegetation and ecosystem change. Integrating functional trait-based approaches with multi-level vegetation, ecosystem, and landscape data enables a holistic understanding of the drivers and consequences of these changes. In two High Arctic study systems near Longyearbyen, Svalbard, a 20-year ITEX warming experiment and elevational gradients with and without nutrient input from nesting seabirds, we collected data on vegetation composition and structure, plant functional traits, ecosystem fluxes, multispectral remote sensing, and microclimate. The dataset contains 1,962 plant records and 16,160 trait measurements from 34 vascular plant taxa, for 9 of which these are the first published trait data. By integrating these comprehensive data, we bridge knowledge gaps and expand trait data coverage, including on intraspecific trait variation. These data can offer insights into ecosystem functioning and provide baselines to assess climate and environmental change impacts. Such knowledge is crucial for effective conservation and management in these vulnerable regions.
Keyphrases
  • climate change
  • electronic health record
  • genome wide
  • big data
  • healthcare
  • randomized controlled trial
  • systematic review
  • data analysis
  • risk assessment
  • deep learning