3c636579-0389-4ba1-bf3d-d53f32892079 eng utf8 dataset dataset NERC EDS UK Polar Data Centre +44 (0)1223 221400 British Antarctic Survey, High Cross, Madingley Road Cambridge Cambridgeshire CB3 0ET United Kingdom polardatacentre@bas.ac.uk https://www.bas.ac.uk/team/business-teams/information-services/uk-polar-data-centre/ information pointOfContact 2024-02-22 ISO 19115 Geographic Information - Metadata ISO 19115:2003(E) European Petroleum Survey Group (EPSG) Geodetic Parameter Registry 2008-11-12 publication European Petroleum Survey Group EPSGadministrator@iogp.org https://www.epsg-registry.org/ EPSG Geodetic Parameter Dataset The EPSG Geodetic Parameter Dataset is a structured dataset of Coordinate Reference Systems and Coordinate Transformations, accessible through this online registry. information publisher urn:ogc:def:crs:EPSG::3031 6.18.3 Habitable area model output for green and red snow algae on the Antarctic peninsula with current and future climatic conditions, 2021 and 2100 2024-02-22 creation 2024-02-22 revision 2024-02-22 publication 2024-02-22 released 1.0 https://doi.org/10.5285/3c636579-0389-4ba1-bf3d-d53f32892079 doi GB/NERC/BAS/PDC/01821 https://data.bas.ac.uk/ NE/V000764/1 award Please cite this item as: Gray, A., Davey, M., Thomson, A., Colesie, C., Convey, P., Fretwell, P., & Peck, L. (2024). Habitable area model output for green and red snow algae on the Antarctic peninsula with current and future climatic conditions, 2021 and 2100 (Version 1.0) [Data set]. NERC EDS UK Polar Data Centre. https://doi.org/10.5285/3c636579-0389-4ba1-bf3d-d53f32892079 This dataset comprises raster model outputs from a Bayesian additive regression tree model predicting habitable area for snow algae on the Antarctic Peninsula. The dataset shows the likelihood that snow is habitable for snow algae growth and is presented for red snow algae (RSA) and green snow algae (GSA) for 2021 climatic conditions as well as for predicted 2100 climate conditions under the RCP8.5 warming scenario. The purpose of this model was to predict the potential coverage of snow algae under different temperatures, as well as to explore the climatic and environmental factors influencing their distribution. This work was carried out by researchers as part of the NERC-funded research project NE/V000764/1 investigating the historical, present and future snow algae distribution in Antarctica. No credit. completed Gray, Andrew The University of Edinburgh School of GeoSciences Researcher author Davey, Matthew P Scottish Association for Marine Science, Plant Sciences Researcher author Thomson, Alex I Scottish Association for Marine Science, Plant Sciences Researcher author Colesie, Claudia The University of Edinburgh School of GeoSciences Researcher author Convey, Peter British Antarctic Survey Researcher author Fretwell, Peter T British Antarctic Survey Researcher author Peck, Lloyd British Antarctic Survey Researcher author NERC EDS UK Polar Data Centre +44 (0)1223 221400 British Antarctic Survey, High Cross, Madingley Road Cambridge Cambridgeshire CB3 0ET United Kingdom PDCServiceDesk@bas.ac.uk https://www.bas.ac.uk/team/business-teams/information-services/uk-polar-data-centre/ information pointOfContact asNeeded completed EARTH SCIENCE > Biosphere > Ecological Dynamics > Survival EARTH SCIENCE > Biosphere > Microbiota theme Global Change Master Directory (GCMD) Science Keywords 2026-01-27 publication 23 Global Change Data Center, Science and Exploration Directorate, Goddard Space Flight Center (GSFC) National Aeronautics and Space Administration (NASA) Greenbelt MD United States of America https://earthdata.nasa.gov/about/gcmd/global-change-master-directory-gcmd-keywords Global Change Master Directory (GCMD) Keywords The information provided on this page seeks to define how the GCMD Keywords are structured, used and accessed. It also provides information on how users can participate in the further development of the keywords. information publisher Antarctic peninsula biogeography extreme weather snow algae species distribution model theme Antarctic Peninsula Antarctica place Habitats and biotopes theme GEMET - INSPIRE themes, version 1.0 2021-01-06 revision otherRestrictions no limitations to public access otherRestrictions no limitations license Open Government Licence v3.0 otherRestrictions This data is governed by the NERC Data Policy: https://www.ukri.org/who-we-are/nerc/our-policies-and-standards/nerc-data-policy/ otherRestrictions This data is governed by the NERC data policy and supplied under Open Government Licence v.3 otherRestrictions There are no access constraints on this dataset. url url crossReference url url crossReference url url crossReference textTable eng utf8 biota -86.42888 -36.07761 -64.52356 -63.37907 2020-11-01 2021-01-21 It is recommended that careful attention be paid to the contents of any data, and that the author be contacted with any questions regarding appropriate use. If you find any errors or omissions, please report them to polardatacentre@bas.ac.uk. NERC EDS UK Polar Data Centre +44 (0)1223 221400 British Antarctic Survey, High Cross, Madingley Road Cambridge Cambridgeshire CB3 0ET United Kingdom PDCServiceDesk@bas.ac.uk https://www.bas.ac.uk/team/business-teams/information-services/uk-polar-data-centre/ information distributor application/netcdf bytes 5948529705 http://ramadda.data.bas.ac.uk/repository/entry/show?entryid=3c636579-0389-4ba1-bf3d-d53f32892079 WWW:LINK-1.0-http--link Get Data Download data download dataset Methodology: Data is the posterior output of a Bayesian additive regression tree model using remotely sensed snow algal observations as training data, with slope, aspect, distance from the coast, distance from an animal colony, surface type, snow melt and season length predictor variables. Remote imagery was accessed from WorldView 2 (Maxar Technology). Presence and absence observations of green snow algae and red snow algae blooms were derived from six locations on the Antarctic peninsula (Robert Island, Nelson Island, Trinity Island, Melchior Island, Neumayer Channel and Ryder Bay) with six summer growth seasons (2013, 2017, 2019, 2020, 2021, 2023). Snow algae were identified following methods in Gray et al. (2021), where algae were identified using chlorophyll-a pigments. To distinguish green snow algae from red, an adapted red-green normalised difference (RGND) index was used (please see Engstorm et al. 2022 for original red-green RGND). Outputs show modelled probability of occurrence using actual climate conditions in 2021 and projected climate conditions in 2100, The model resolution is 100m for both scenarios. Please see the companion paper (Modelling Antarctic Peninsula snow algal habitat distribution through extreme temperature events and projected climate change) for further details on methods. Data for the model was prepared in GDAL (version 3.4.3), SAGA (v8.5.1) and RStudio (version 2022.02.03). Modelling was preformed in RStudio (version 2022.02.03). Data collection: Variables used in the training dataset are listed below along with their reference: Aspect: 8m (training) or 100m (Peninsula-scale posterior analysis) Reference Elevation Model of Antarctica (REMA) Slope: 8m (training) or 100m (Peninsula-scale posterior analysis) REMA Cumulative snow melt: ECMWF, ERA5 dataset (Hersbach et al. 2020) for 2021 and CMIP5 daily data RCP 8.5 for 2100 (Lopez 2015) Days of snow melt: Glacier SMBM output (8m resolution) Surface type: Rock outcrop shapefile for 2021 (Quantarctica) and Modelled ice-free area under RCP 8.5 for 2100 (Lee et al. 2018) Distance to coastline: Quantarctica (Gerrish et al. 2020) Distance to colonies: MAPPPD (Humphires et al. 2018), Important Bird Areas (BirdLife Data Zone), emperor penguins (Fretwell and Trathan 2020), Matsuoka et al 2018 (emperor penguins), petrel breeding sites (Schwaller et al. 2018), Weddell seal populations (LaRue et al. 2021) 2021 rock outcrop/soil: Landsat 8 (Burton-Johnson et al. 2016) 2100 ice free areas: Lee et al. 2017 Physical observations: Davey et al. 2025 Data for the model was prepared in GDAL (version 3.4.3), SAGA (v8.5.1) and RStudio (version 2022.02.03). Modelling was preformed in RStudio (version 2022.02.03). Data quality: 95 percent confidence interval data are included within the supplied .nc file. Model confidence and remote sensing uncertainty are presented and discussed in the companion paper. NetCDF file complies with CF conventions. asNeeded completed