BGS Hosted Metadata
Habitable area model output for green and red snow algae on the Antarctic peninsula with current and future climatic conditions, 2021 and 2100
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.
Simple
- Date (Creation)
- 2024-02-22
- Date (Revision)
- 2024-02-22
- Date (Publication)
- 2024-02-22
- Date (released)
- 2024-02-22
- Edition
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1.0
- Unique resource identifier
- https://doi.org/10.5285/3c636579-0389-4ba1-bf3d-d53f32892079
- Codespace
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doi
- Unique resource identifier
- GB/NERC/BAS/PDC/01821
- Codespace
- Unique resource identifier
- NE/V000764/1
- Codespace
-
award
- Other citation details
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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
- Credit
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No credit.
- Status
- Completed
- Point of contact
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Organisation name Individual name Electronic mail address Role The University of Edinburgh School of GeoSciences Gray, Andrew Author Scottish Association for Marine Science, Plant Sciences Davey, Matthew P Author Scottish Association for Marine Science, Plant Sciences Thomson, Alex I Author The University of Edinburgh School of GeoSciences Colesie, Claudia Author British Antarctic Survey Convey, Peter Author British Antarctic Survey Fretwell, Peter T Author British Antarctic Survey Peck, Lloyd Author NERC EDS UK Polar Data Centre Point of contact
- Maintenance and update frequency
- As needed
- Maintenance note
- Completed
- Theme
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Antarctic peninsula
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biogeography
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extreme weather
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snow algae
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species distribution model
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- Place
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Antarctic Peninsula Antarctica
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GEMET - INSPIRE themes, version 1.0
- Access constraints
- Other restrictions
- Other constraints
- no limitations to public access
- Access constraints
- Other restrictions
- Other constraints
- no limitations
- Use constraints
- License
- Other constraints
- Open Government Licence v3.0
- Use constraints
- Other restrictions
- Other constraints
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This data is governed by the NERC Data Policy: https://www.ukri.org/who-we-are/nerc/our-policies-and-standards/nerc-data-policy/
- Use constraints
- Other restrictions
- Other constraints
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This data is governed by the NERC data policy and supplied under Open Government Licence v.3
- Use constraints
- Other restrictions
- Other constraints
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There are no access constraints on this dataset.
- Unique resource identifier
- url
- Codespace
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url
- Association Type
- Cross reference
- Unique resource identifier
- url
- Codespace
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url
- Association Type
- Cross reference
- Unique resource identifier
- url
- Codespace
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url
- Association Type
- Cross reference
- Spatial representation type
- Text, table
- Language
- English
- Character set
- UTF8
- Topic category
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- Biota
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- Begin date
- 2020-11-01
- End date
- 2021-01-21
- Supplemental Information
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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.
- Title
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European Petroleum Survey Group (EPSG) Geodetic Parameter Registry
- Date (Publication)
- 2008-11-12
- Cited responsible party
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Organisation name Individual name Electronic mail address Role European Petroleum Survey Group
Publisher
- Unique resource identifier
- urn:ogc:def:crs:EPSG::3031
- Version
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6.18.3
Distributor
- Distributor contact
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Organisation name Individual name Electronic mail address Role NERC EDS UK Polar Data Centre Distributor
- Distributor format
-
Name Version application/netcdf
- Units of distribution
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bytes
- Transfer size
- 5948529705
- OnLine resource
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Protocol Linkage Name WWW:LINK-1.0-http--link
http://ramadda.data.bas.ac.uk/repository/entry/show?entryid=3c636579-0389-4ba1-bf3d-d53f32892079 Get Data
- Hierarchy level
- Dataset
- Statement
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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.
Metadata
- File identifier
- 3c636579-0389-4ba1-bf3d-d53f32892079 XML
- Metadata language
- English
- Character set
- UTF8
- Hierarchy level
- Dataset
- Hierarchy level name
-
dataset
- Date stamp
- 2024-02-22
- Metadata standard name
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ISO 19115 Geographic Information - Metadata
- Metadata standard version
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ISO 19115:2003(E)
- Metadata author
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Organisation name Individual name Electronic mail address Role NERC EDS UK Polar Data Centre Point of contact
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