Open Collections

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.

<b>Abstract</b><br/><p style="border:none;margin-top:16px;margin-bottom:4px;text-align:justify;"><span style="page-break-after:avoid;">Arctic 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). </span></p>; <b>Methods</b><br /><p style="margin-bottom:11px;">We acquired both published and unpublished deciduous shrub-ring chronologies that were distributed throughout the Arctic region and covered, if possible, the entire 40 year-long period of passive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: <i>Betula</i> and <i>Salix</i>. 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 <i>Betula</i> spp. chronologies and 14 <i>Salix</i> spp. chronologies), 641 shrubs (306 <i>Betula</i> shrubs and 335 <i>Salix</i> shrubs), and 753 cross-sections.</p> <p style="margin-bottom:11px;">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.</p>; <b>Usage notes</b><br />

ReadMe_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</p>

Version_01</p>

Date: November 22, 2020</p>

Contact person: Agata Buchwal, Adam Mickiewicz University Poznan, Poland, ORCID ID: 0000-0001-6879-6656; kamzik@amu.edu.pl</p>

This repository consist of:</p>

#1: Table 1: Metadata file for 23 shrub ring chronologies used in the Buchwal et al. (2020) synthesis</p>

#2: 23 RWL files for 23 shrub ring chronologies.</p>

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).</p>

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</p>

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).</p>

Dataset

2020-12-15

This dataset is made available under a Creative Commons CC0 license with the following additional/modified terms and conditions: CC0 Waiver

https://doi.org/10.5683/SP2/BW4SEJ

Polish Ministry of Science and Higher Education*; EU-F7P INTERACT*; Scientific Exchange Program Sciex*; National Science Foundation; Arctic Observing Network*; Natural Environment Research Council; Canada 150 Research Chairs Program*; Natural Sciences and Engineering Research Council of Canada; Canadian Centennial Scholarship Fund*; Academy of Finland; JPI Climate*; ArcticNet; Polar Continental Shelf Program*; Fonds de recherche du Québec – Nature et technologies; Danmarks Grundforskningsfond; Northern Scientific Training Program*; UArctic Research Chairship*; European Commission Research and Innovation Action*

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