Data

9-second gridded continental Australia potential degree of ecological change for Mammals 1990:2050 CanESM2 RCP 8.5 (CMIP5) (GDM: MAM_R2)

Commonwealth Scientific and Industrial Research Organisation
Harwood, Tom
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ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Adc&rfr_id=info%3Asid%2FANDS&rft_id=info:doi10.4225/08/548696E96D997&rft.title=9-second gridded continental Australia potential degree of ecological change for Mammals 1990:2050 CanESM2 RCP 8.5 (CMIP5) (GDM: MAM_R2)&rft.identifier=https://doi.org/10.4225/08/548696E96D997&rft.publisher=Commonwealth Scientific and Industrial Research Organisation&rft.description=Potential degree of ecological change in Mammals as a function of change in long term (30 year average) climates between the present (1990 centred) and projected future (2050 centred) under the CanESM2 model (RCP 8.5) based on Generalised Dissimilarity Modelling (GDM) of compositional turnover. \n\nThis metric describes the change in long term average environmental conditions at a single location (9s grid square) from the present (1990 centred) to a 2050 centred future, scaled in terms of its expected effects on the turnover of species. Compositional turnover patterns in amphibian species across continental Australia were derived using Generalised Dissimilarity Modelling (GDM). These models use best-available biological data extracted from the Atlas of Living Australia (ALA) in 2013, and spatial environmental predictor data compiled at 9 second resolution. GDM-scaled environmental grids were used as the basis for pairwise cell comparisons across space and time using the highly parallel CSIRO Muru software to derive the potential degree of ecological change. Each location is compared with its future state. The difference in environment is presented as an expected ecological similarity, ranging from 1 (completely similar) to 0, for which we would expect no species in common. If this environmental difference was observed in a different spatial location within the present, we would expect to observe such a difference if we visited both sites. \n\nThis metric was developed along with others for use in an assessment of the efficacy of the protected area system for biodiversity under climate change at continental and global scales, presented at the IUCN World Parks Congress 2014. It is described in the AdaptNRM Guide “Implications of Climate Change for Biodiversity: a community-level modelling approach”, available online at: www.adaptnrm.org. \n\nData are provided in two forms:\n1. Zipped ESRI float grids: Binary float grids (*.flt) with associated ESRI header files (*.hdr) and projection files (*.prj). After extracting from the zip archive, these files can be imported into most GIS software packages, and can be used as other binary file formats by substituting the appropriate header file.\n2. ArcGIS layer package (*.lpk): These packages contain can be unpacked by ArcGIS as a raster with associated legend.\n\nAdditionally a short methods summary is provided in the file 9sMethodsSummary.pdf for further information.Tom HA\n\nLayers in this 9s series use a consistent naming convention:\nBIOLOGICAL GROUP _ FROM BASE_ TO SCENARIO_ ANALYSIS\ne.g. A_90_CAN85_S or R_90_MIR85_L\nwhere BIOLOGICAL GROUP is A: Mammals, M: mammals, R: reptiles and V: vascular plants\n\nLineage: Potential degree of ecological change was calculated using the highly parallel bespoke CSIRO Muru software running on a LINUX high-performance-computing cluster, taking GDM model transformed environmental grids as inputs. The similarity of each cell in the present to its future state was calculated. More detail of the calculations and methods are given in the document “9sMethodsSummary.pdf” provided with the data download. \nGDM Model:&rft.creator=Harwood, Tom &rft.date=2014&rft.edition=v2&rft.coverage=westlimit=112.9; southlimit=-43.7425; eastlimit=154.0; northlimit=-8.0; projection=WGS84&rft_rights=CSIRO Data Licence https://research.csiro.au/dap/licences/csiro-data-licence/&rft_rights=Data is accessible online and may be reused in accordance with licence conditions&rft_rights=All Rights (including copyright) CSIRO Australia 2014.&rft_subject=Mammals&rft_subject=generalised dissimilarity model&rft_subject=scaled environmental variables&rft_subject=1990 climates&rft_subject=historical climates, 2050 future climates&rft_subject=adaptnrm&rft_subject=biodiversity&rft_subject=Community ecology (excl. invasive species ecology)&rft_subject=Ecology&rft_subject=BIOLOGICAL SCIENCES&rft_subject=Global change biology&rft_subject=Other biological sciences&rft_subject=Ecological impacts of climate change and ecological adaptation&rft_subject=Climate change impacts and adaptation&rft_subject=ENVIRONMENTAL SCIENCES&rft_subject=Conservation and biodiversity&rft_subject=Environmental management&rft.type=dataset&rft.language=English Access the data

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Data is accessible online and may be reused in accordance with licence conditions

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Brief description

Potential degree of ecological change in Mammals as a function of change in long term (30 year average) climates between the present (1990 centred) and projected future (2050 centred) under the CanESM2 model (RCP 8.5) based on Generalised Dissimilarity Modelling (GDM) of compositional turnover.

This metric describes the change in long term average environmental conditions at a single location (9s grid square) from the present (1990 centred) to a 2050 centred future, scaled in terms of its expected effects on the turnover of species. Compositional turnover patterns in amphibian species across continental Australia were derived using Generalised Dissimilarity Modelling (GDM). These models use best-available biological data extracted from the Atlas of Living Australia (ALA) in 2013, and spatial environmental predictor data compiled at 9 second resolution. GDM-scaled environmental grids were used as the basis for pairwise cell comparisons across space and time using the highly parallel CSIRO Muru software to derive the potential degree of ecological change. Each location is compared with its future state. The difference in environment is presented as an expected ecological similarity, ranging from 1 (completely similar) to 0, for which we would expect no species in common. If this environmental difference was observed in a different spatial location within the present, we would expect to observe such a difference if we visited both sites.

This metric was developed along with others for use in an assessment of the efficacy of the protected area system for biodiversity under climate change at continental and global scales, presented at the IUCN World Parks Congress 2014. It is described in the AdaptNRM Guide “Implications of Climate Change for Biodiversity: a community-level modelling approach”, available online at: www.adaptnrm.org.

Data are provided in two forms:
1. Zipped ESRI float grids: Binary float grids (*.flt) with associated ESRI header files (*.hdr) and projection files (*.prj). After extracting from the zip archive, these files can be imported into most GIS software packages, and can be used as other binary file formats by substituting the appropriate header file.
2. ArcGIS layer package (*.lpk): These packages contain can be unpacked by ArcGIS as a raster with associated legend.

Additionally a short methods summary is provided in the file 9sMethodsSummary.pdf for further information.Tom HA

Layers in this 9s series use a consistent naming convention:
BIOLOGICAL GROUP _ FROM BASE_ TO SCENARIO_ ANALYSIS
e.g. A_90_CAN85_S or R_90_MIR85_L
where BIOLOGICAL GROUP is A: Mammals, M: mammals, R: reptiles and V: vascular plants

Lineage: Potential degree of ecological change was calculated using the highly parallel bespoke CSIRO Muru software running on a LINUX high-performance-computing cluster, taking GDM model transformed environmental grids as inputs. The similarity of each cell in the present to its future state was calculated. More detail of the calculations and methods are given in the document “9sMethodsSummary.pdf” provided with the data download.
GDM Model:

Available: 2014-12-09

Data time period: 2014-11-30 to 2014-11-30

154,-8 154,-43.7425 112.9,-43.7425 112.9,-8 154,-8

133.45,-25.87125