Data from: Divergence of Arctic shrub growth associated with sea ice decline

• Main Authors: Buchwal, Agata, Sullivan, Patrick F., Macias-Fauria, Marc, Post, Eric, Myers-Smith, Isla H., Stroeve, Julienne C., Blok, Daan, Tape, Ken D., Forbes, Bruce C., Ropars, Pascale, Lévesque, Esther, Elberling, Bo, Angers-Blondin, Sandra, Boyle, Joseph S., Boudreau, Stéphane, Boulanger-Lapointe, Noémie, Gamm, Cassandra, Hallinger, Martin, Rachlewicz, Grzegorz, Young, Amanda, Zetterberg, Pentti, Welker, Jeffrey M.
• Format: Dataset
• Language: unknown
• Published: Borealis 2021
• Subjects: Other; Agata; Arctic; Sea ice; Tundra
• Online Access: https://search.dataone.org/view/sha256:c120a0428fcfb8fd4e0429b649301e9b546211864abb8c6adf4672c1224ba330

AbstractArctic sea ice extent (SIE) is declining at an accelerating rate with a wide range of ecological consequences. However, determining sea ice effects on tundra vegetation remains a challenge. In this study, we examined the universality or lack thereof in tundra shrub growth responses to changes in SIE and summer climate across the Pan-Arctic, taking advantage of 23 tundra shrub-ring chronologies from 19 widely distributed sites (56⁰-83⁰N)., MethodsWe acquired both published and unpublished deciduous shrub-ring chronologies that were distributed throughout the Arctic region and covered, if possible, the entire 40 year-long period of passive microwave satellite-based estimates of arctic sea ice extent (SIE) (1979-present). In order to perform a comparable study at the biome level, our synthesis focused on two shrub genera of commonly studied and widespread deciduous shrubs: Betula and Salix. Shrub-ring data were included in our analyses if the corresponding chronologies i) covered the common period (1979-2008) and ii) had an EPS (a theoretical indicator of how well the chronology represents the population mean) greater than 0.75. Our final dataset consisted of 23 chronologies (9 Betula spp. chronologies and 14 Salix spp. chronologies), 641 shrubs (306 Betula shrubs and 335 Salix shrubs), and 753 cross-sections. This dataset consists of 23 RWL files for 23 shrub ring chronologies. Each RWL file (Tucson format, unit = mm, resolution 0.001) contains raw data for all individual shrub growth series used to established each site chronology. Raw data are averaged at the plant level for the shrubs that were subjected to serial sectioning or when more than one cross-section was sampled per individual shrub. These are raw individual shrub data, not detrended or standardized., Usage notesReadMe_v01: File description for manuscript Buchwal et al. 2020: Divergence of Arctic shrub growth associated with sea ice decline. https://www.pnas.org/content/early/2020/12/09/2013311117 Version_01 Date: November 22, 2020 Contact person: Agata Buchwal, Adam Mickiewicz University Poznan, Poland, ORCID ID: 0000-0001-6879-6656; [e-mail] This repository consist of: #1: Table 1: Metadata file for 23 shrub ring chronologies used in the Buchwal et al. (2020) synthesis #2: 23 RWL files for 23 shrub ring chronologies. Each RWL file (Tucson format, unit = mm, resolution 0.001) contains raw data for all individual shrub growth series used to established each site chronology. Raw data are averaged at the plant level for the shrubs that were subjected to serial sectioning or when more than one cross-section was sampled per individual shrub. These data are not detrended or standardized. If You need shrub ring data at the cross-sectional level, please contact a relevant data contributor/-s (Table 1, column Q). In order to open RWL files (.rwl) you can use ‘read.rwl’ function in dplR package (Bunn 2008) in R (R Core Team). List of RWL files attached: AF_SAR.rwl BL_BGL.rwl BS_BGL.rwl BT_BGL.rwl DE_SGL.rwl DK_BNA.rwl EB_SPO.rwl GR_BGL.rwl HE_SAR.rwl HE_SRI.rwl KG_BNA.rwl KG_SGL.rwl KY_BNA.rwl KY_SPU.rwl LA_SAR.rwl LB_SRI.rwl PL_SAR.rwl RE_SAR.rwl TL_BNA.rwl UM_BGL.rwl VA_SRI.rwl YR_SRI.rwl ZA_SAR.rwl References: A. G. Bunn, A dendrochronology program library in R (dplR). Dendrochronologia 26, 115-124 (2008). R Core Team R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria.URL https://www.R-project.org/ (2018).