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Showing results for: [ generalised dissimilarity model ]
This collection contains 3-arcsecond gridded datasets (ESRI binary float format in WGS84) showing the baseline (1990-centred) predicted potential distribution of 102 (class numbers range between 1 and... more 125) "Keith" Vegetation Classes for New South Wales based on their correlation with baseline ecological environments (c.1990 climates, substrate and landform). The vegetation patterns and classification derive from a map for NSW compiled by David Keith. A kernel regression was used with a geographically even sample of 9,951 locations of training classes for the 102 classes attributed with 21 GDM-scaled environmental predictors for Vascular Plants representing baseline ecological environments. The training class data input to the kernel regression is provided with this package. The GDM-scaled environmental predictors, source biological data and model fit parameters are also provided with the data package. Using the 1990 baseline training class data, and without constraining the prediction to pre-existing map boundaries, the kernel regression predicted the potential distribution of the 102 Vegetation Classes using 1990-centred (30 year average) baseline climates derived from ANUCLIM v6.1 (Xu and Hutchinson 2011) and soil/geology/landform attributes. The kernel regression generates unconstrained probabilities varying in the range from 0 and up to 1 for each of the 102 classes. The data are provided as 3-arcsecond (approximately 90m), ESRI binary float grid format in WGS84. Each class is denoted “UNCON###”, where the number refers to the code originally assigned to that class in the vegetation map. A lookup table linking the vegetation classes to the output codes and descriptive title is provided. The methods are described in "Doerr, VAJ, Williams, KJ, Drielsma, M, Doerr, ED, Davies, MJ, Love, J, Langston, A, Low Choy, S, Manion, G, Cawsey, EM, McGinness, HM, Jovanovic, T, Crawford, D, Austin, M & Ferrier, S 2013, Designing landscapes for biodiversity under climate change: Final report, National Climate Change Adaptation Research Facility, Gold Coast, 260 pp.". A plain English description of the method used (but applied Nationally) can be found in the AdaptNRM Guide “Helping biodiversity adapt to climate change: a community-level modelling approach”, available online at: www.adaptnrm.org. Source of vegetation class data: KEITH, D. A. (2002) A compilation map of native vegetation for New South Wales. NSW Biodiversity Strategy, New South Wales Government. KEITH, D. A. (2004) Ocean shores to desert dunes, Hurstville, Department of Environment and Conservation (NSW). less
1157.1 TB NARP Best Practice Landscape - Macroecological modelling of vegetation patterns using GDM and kernel regression - Published 01 Aug 2017
Compositional turnover patterns in mammal species across continental Australia were derived using Generalised Dissimilarity Modelling (GDM). These models use best-available biological data extracted f... morerom the Atlas of Living Australia current to 26th February 2014 and spatial environmental predictor data compiled at 9 second resolution (with novel climate seasonality predictors, undersampling covariates and >3 species aggregated per 9-second grid cell). The models were developed to underpin continental assessments of biodiversity significance and identify gaps in biological surveys. GDM is a statistical technique that models the dissimilarity in composition of species between pairs of surveyed locations, as a function of environmental differences between these locations. The compositional dissimilarity between a given pair of locations can be thought of as the proportion of species occurring at one location that do not occur at the other location (averaged across the two locations) - ranging from ‘0’ if the two locations have exactly the same species through to ‘1’ if they have no species in common. GDM effectively weights and transforms the environmental variables such that distances between locations in this transformed multidimensional environmental space now correlate, as closely as possible, with the observed biological compositional dissimilarities between these same locations. Once a GDM model has been fitted to the biological data from the sampled locations using environmental predictor data, it can be used to predict compositional dissimilarity values for sites lacking biological data, based purely on their mapped environmental attributes. For this purpose, a set of GDM-scaled environmental grids are produced for use in subsequent spatial assessments of biodiversity significance. This collection includes the source biological and environmental data, the GDM-fitted model, the GDM-scaled environmental predictors for the fitted-model which comprises substrate (constant) and 1990-centred climates, and a derived classification. Projections using past and future climates are not included here (available upon request). This model was used in the AdaptNRM series of reports by Williams et al. (2013) and Prober et al. (2014). less
1173.3 Strat: WPC Global Prot Area Ass - Macroecological Modelling - Published 09 Jan 2017
Compositional turnover patterns in amphibian species across continental Australia were derived using Generalised Dissimilarity Modelling (GDM). These models use best-available biological data extracte... mored from the Atlas of Living Australia current to 27th February 2014 and spatial environmental predictor data compiled at 9 second resolution (with novel climate seasonality predictors, undersampling covariates and >3 species aggregated per 9-second grid cell). The models were developed to underpin continental assessments of biodiversity significance and identify gaps in biological surveys. GDM is a statistical technique that models the dissimilarity in composition of species between pairs of surveyed locations, as a function of environmental differences between these locations. The compositional dissimilarity between a given pair of locations can be thought of as the proportion of species occurring at one location that do not occur at the other location (averaged across the two locations) - ranging from ‘0’ if the two locations have exactly the same species through to ‘1’ if they have no species in common. GDM effectively weights and transforms the environmental variables such that distances between locations in this transformed multidimensional environmental space now correlate, as closely as possible, with the observed biological compositional dissimilarities between these same locations. Once a GDM model has been fitted to the biological data from the sampled locations using environmental predictor data, it can be used to predict compositional dissimilarity values for sites lacking biological data, based purely on their mapped environmental attributes. For this purpose, a set of GDM-scaled environmental grids are produced for use in subsequent spatial assessments of biodiversity significance. This collection includes the source biological and environmental data, the GDM-fitted model, the GDM-scaled environmental predictors for the fitted-model which comprises substrate (constant) and 1990-centred climates, and a derived classification. Projections using past and future climates are not included here (available upon request). This model was used in the AdaptNRM series of reports by Williams et al. (2013) and Prober et al. (2014). less
1173.3 Strat: WPC Global Prot Area Ass - Macroecological Modelling - Published 06 Jan 2017
Compositional turnover patterns in land snail species across continental Australia were derived using Generalised Dissimilarity Modelling (GDM). These models use best-available biological data extract... moreed from the ANHAT (Courtesy Australian Government Department of the Environment) current to April 2013 and spatial environmental predictor data compiled at 9 second resolution (with novel climate seasonality predictors, undersampling covariates and >2 species aggregated per 9-second grid cell). The models were developed to underpin continental assessments of biodiversity significance and identify gaps in biological surveys. GDM is a statistical technique that models the dissimilarity in composition of species between pairs of surveyed locations, as a function of environmental differences between these locations. The compositional dissimilarity between a given pair of locations can be thought of as the proportion of species occurring at one location that do not occur at the other location (averaged across the two locations) - ranging from ‘0’ if the two locations have exactly the same species through to ‘1’ if they have no species in common. GDM effectively weights and transforms the environmental variables such that distances between locations in this transformed multidimensional environmental space now correlate, as closely as possible, with the observed biological compositional dissimilarities between these same locations. Once a GDM model has been fitted to the biological data from the sampled locations using environmental predictor data, it can be used to predict compositional dissimilarity values for sites lacking biological data, based purely on their mapped environmental attributes. For this purpose, a set of GDM-scaled environmental grids are produced for use in subsequent spatial assessments of biodiversity significance. This collection includes the source biological and environmental data, the GDM-fitted model, the GDM-scaled environmental predictors for the fitted-model which comprises substrate (constant) and 1990-centred climates, and a derived classification. Projections using past and future climates are not included here (available upon request).less
Compositional turnover patterns in reptiles species across continental Australia were derived using Generalised Dissimilarity Modelling (GDM). These models use best-available biological data extracted... more from the Atlas of Living Australia current to 28th February 2014 and spatial environmental predictor data compiled at 9 second resolution (with novel climate seasonality predictors, undersampling covariates and >3 species aggregated per 9-second grid cell). The models were developed to underpin continental assessments of biodiversity significance and identify gaps in biological surveys. GDM is a statistical technique that models the dissimilarity in composition of species between pairs of surveyed locations, as a function of environmental differences between these locations. The compositional dissimilarity between a given pair of locations can be thought of as the proportion of species occurring at one location that do not occur at the other location (averaged across the two locations) - ranging from ‘0’ if the two locations have exactly the same species through to ‘1’ if they have no species in common. GDM effectively weights and transforms the environmental variables such that distances between locations in this transformed multidimensional environmental space now correlate, as closely as possible, with the observed biological compositional dissimilarities between these same locations. Once a GDM model has been fitted to the biological data from the sampled locations using environmental predictor data, it can be used to predict compositional dissimilarity values for sites lacking biological data, based purely on their mapped environmental attributes. For this purpose, a set of GDM-scaled environmental grids are produced for use in subsequent spatial assessments of biodiversity significance. This collection includes the source biological and environmental data, the GDM-fitted model, the GDM-scaled environmental predictors for the fitted-model which comprises substrate (constant) and 1990-centred climates, and a derived classification. Projections using past and future climates are not included here (available upon request). This model was used in the AdaptNRM series of reports by Williams et al. (2013) and Prober et al. (2014). less
This collection contains AdaptNRM biodiversity change datasets and maps contextualised for Tasmania and surrounding Islands, and specifically: novel ecological environments, disappearing ecological en... morevironments, and composite ecological change datasets and maps for amphibians, reptiles, mammals, and vascular plants. The Tasmania extent of the equivalent ‘Potential degree of ecological change’ datasets are also included for completeness, although identical to the national datasets. Ecological change is derived from change in long term (30 year average) climates between the present (1990 centred) and projected future (2050 centred) under the MIROC5 and CanESM2 global climate models (RCP 8.5), scaled using Generalised Dissimilarity Modelling (GDM) of compositional turnover for four biological groups (GDMs: AMP_r2_PTS1, MAM_r2, REP_r3_v2, and VAS_v5_r11). The source GDM models are listed in related materials below (AMP_V2_R2 is the same as the model also denoted ‘AMP_r2_PTS1’; REP_V2_R3 is the same as REP_r3_v2; MAM_V1_R2 is the same as MAM_r2). The equivalent national datasets for novel and disappearing ecological environments, composite ecological change and Potential degree of ecological change are also listed in related materials below. NOVEL ECOLOGICAL ENVIRONMENTS: this metric describes the nature of the projected 2050-centred future environmental conditions for each 9s grid cell. Using each cell of a GDM projection surface, the metric looks out to all other cells in the specified region, and records the ecological similarity of the future state of the cell to the most similar cell in the present (1990-centred). A novel ecological environment is a possible new ecological environment scaled by ecological similarity that may arise in the future but which doesn’t exist anywhere at present. DISAPPEARING ECOLOGICAL ENVIRONMENTS: this metric describes the extent to which the long term average environmental conditions for each 9s grid cell in the present (1990-centred) will be present in a projected 2050 centred future. For each cell of a GDM, the metric looks out to all other cells in a specified region, and records the ecological similarity of the present state of the cell to the most similar cell in the future. A disappearing ecological environment is a present-day ecological environment scaled by ecological similarity that may become absent in the future. COMPOSITE ECOLOGICAL CHANGE: this metric is a composite measure that integrates the Potential degree of ecological change with the degree to which ecological environments are becoming novel or disappearing, showing where different combinations of change may occur and how extreme that change may be. A technical report for the project provides details about the rationale, methods and data. Further details are 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). 2. GeoTIFF files (*.tif). After extracting from the zip archive, these files can be imported into most GIS software packages. Component measures are provided in both ESRI float and GeoTiff formats, while composite rasters are provided in GeoTiff format. Datasets in this series use a consistent naming convention: see the file readme_filenames.txt for a full explanation. Readme and xml files for how to reproduce the 3-band colours in the composite measure are also provided. Higher resolution images used in the technical report are also provided. less
Customised AdaptNRM biodiversity impact datasets for Tasmania - Ecological Change Modelling - Published 26 Apr 2017
This dataset is a series of digital map-posters accompanying the AdaptNRM Guide: Helping Biodiversity Adapt: supporting climate adaptation planning using a community-level modelling approach. These ... morerepresent supporting materials and information about the community-level biodiversity models applied to climate change. Map posters are organised by four biological groups (vascular plants, mammals, reptiles and amphibians), two climate change scenario (1990-2050 MIROC5 and CanESM2 for RCP8.5), and five measures of change in biodiversity. The map-posters present the nationally consistent data at locally relevant resolutions in eight parts – representing broad groupings of NRM regions based on the cluster boundaries used for climate adaptation planning (http://www.environment.gov.au/climate-change/adaptation) and also Nationally. Map-posters are provided in PNG image format at moderate resolution (300dpi) to suit A0 printing. The posters were designed to meet A0 print size and digital viewing resolution of map detail. An additional set in PDF image format has been created for ease of download for initial exploration and printing on A3 paper. Some text elements and map features may be fuzzy at this resolution. Each map-poster contains four dataset images coloured using standard legends encompassing the potential range of the measure, even if that range is not represented in the dataset itself or across the map extent. Most map series are provided in two parts: part 1 shows the two climate scenarios for vascular plants and mammals and part 2 shows reptiles and amphibians. Eight cluster maps for each series have a different colour theme and map extent. A national series is also provided. Annotation briefly outlines the topics presented in the Guide so that each poster stands alone for quick reference. An additional 77 National maps presenting the probability distributions of each of 77 vegetation types – NVIS 4.1 major vegetation subgroups (NVIS subgroups) - are currently in preparation. Example citations: Williams KJ, Raisbeck-Brown N, Prober S, Harwood T (2015) Generalised projected distribution of vegetation types – NVIS 4.1 major vegetation subgroups (1990 and 2050), A0 map-poster 8.1 - East Coast NRM regions. CSIRO Land and Water Flagship, Canberra. Available online at www.AdaptNRM.org and https://data.csiro.au/dap/. Williams KJ, Raisbeck-Brown N, Harwood T, Prober S (2015) Revegetation benefit (cleared natural areas) for vascular plants and mammals (1990-2050), A0 map-poster 9.1 - East Coast NRM regions. CSIRO Land and Water Flagship, Canberra. Available online at www.AdaptNRM.org and https://data.csiro.au/dap/. This dataset has been delivered incrementally. Please check that you are accessing the latest version of the dataset. less
NRM National Project - AdaptNRM Biodiversity Module - Published 18 Jan 2016
Need for assisted dispersal for Vascular Plants as a function of change in long term (30 year average) climates between the present (1990 centred) and projected future (2050 centred) under the MIROC5 ... moremodel (RCP 8.5) based on Generalised Dissimilarity Modelling (GDM) of compositional turnover. The distance to the nearest grid cell with ecological similarity of at least 0.5 is given. This metric describes the nature of the projected 2050 centred future environmental conditions for each 9s grid square. Using a Generalised Dissimilarity Model of compositional turnover (the effects of changing environment on changing species), each future location is compared with the continent in the present. For each cell, the metric looks out to all other cells in the continent, and records the ecological similarity of the future state of the cell to the most similar cell in the present. A value of 1 indicates that the future environment is similar to a current location in the present, and perfect analogue can found somewhere in Australia. A value of 0 indicates that the most similar environment to be found in the present is ecologically so different that we would expect no species in common, i.e. there are no current analogues for this environment; it is novel. Intermediate values show how ecologically similar the most similar cell is. However, no weight is given to the proximity of the most similar cell. The environment may be similar, but the cells thousands of kilometres apart. 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. 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: amphibians, M: mammals, R: reptiles and V: vascular plants less
1173.3 Strat: WPC Global Prot Area Ass - Biodiversity Adaptation Analyses - Published 23 Jun 2015
Need for assisted dispersal for Vascular Plants as a function of change in long term (30 year average) climates between the present (1990 centred) and projected future (2050 centred) under the CAN ESM... more2 model (RCP 8.5) based on Generalised Dissimilarity Modelling (GDM) of compositional turnover. The distance to the nearest grid cell with ecological similarity of at least 0.5 is given. This metric describes the nature of the projected 2050 centred future environmental conditions for each 9s grid square. Using a Generalised Dissimilarity Model of compositional turnover (the effects of changing environment on changing species), each future location is compared with the continent in the present. For each cell, the metric looks out to all other cells in the continent, and records the ecological similarity of the future state of the cell to the most similar cell in the present. A value of 1 indicates that the future environment is similar to a current location in the present, and perfect analogue can found somewhere in Australia. A value of 0 indicates that the most similar environment to be found in the present is ecologically so different that we would expect no species in common, i.e. there are no current analogues for this environment; it is novel. Intermediate values show how ecologically similar the most similar cell is. However, no weight is given to the proximity of the most similar cell. The environment may be similar, but the cells thousands of kilometres apart. 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. 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: amphibians, M: mammals, R: reptiles and V: vascular plants less
Need for assisted dispersal for Reptiles as a function of change in long term (30 year average) climates between the present (1990 centred) and projected future (2050 centred) under the MIROC5 model (... moreRCP 8.5) based on Generalised Dissimilarity Modelling (GDM) of compositional turnover. The distance to the nearest grid cell with ecological similarity of at least 0.5 is given. This metric describes the nature of the projected 2050 centred future environmental conditions for each 9s grid square. Using a Generalised Dissimilarity Model of compositional turnover (the effects of changing environment on changing species), each future location is compared with the continent in the present. For each cell, the metric looks out to all other cells in the continent, and records the ecological similarity of the future state of the cell to the most similar cell in the present. A value of 1 indicates that the future environment is similar to a current location in the present, and perfect analogue can found somewhere in Australia. A value of 0 indicates that the most similar environment to be found in the present is ecologically so different that we would expect no species in common, i.e. there are no current analogues for this environment; it is novel. Intermediate values show how ecologically similar the most similar cell is. However, no weight is given to the proximity of the most similar cell. The environment may be similar, but the cells thousands of kilometres apart. 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. 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: amphibians, M: mammals, R: reptiles and V: vascular plants less
Need for assisted dispersal for Reptiles as a function of change in long term (30 year average) climates between the present (1990 centred) and projected future (2050 centred) under the CAN ESM2 model... more (RCP 8.5) based on Generalised Dissimilarity Modelling (GDM) of compositional turnover. The distance to the nearest grid cell with ecological similarity of at least 0.5 is given. This metric describes the nature of the projected 2050 centred future environmental conditions for each 9s grid square. Using a Generalised Dissimilarity Model of compositional turnover (the effects of changing environment on changing species), each future location is compared with the continent in the present. For each cell, the metric looks out to all other cells in the continent, and records the ecological similarity of the future state of the cell to the most similar cell in the present. A value of 1 indicates that the future environment is similar to a current location in the present, and perfect analogue can found somewhere in Australia. A value of 0 indicates that the most similar environment to be found in the present is ecologically so different that we would expect no species in common, i.e. there are no current analogues for this environment; it is novel. Intermediate values show how ecologically similar the most similar cell is. However, no weight is given to the proximity of the most similar cell. The environment may be similar, but the cells thousands of kilometres apart. 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. 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: amphibians, M: mammals, R: reptiles and V: vascular plants less
Need for assisted dispersal for 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 CAN ESM2 model ... more(RCP 8.5) based on Generalised Dissimilarity Modelling (GDM) of compositional turnover. The distance to the nearest grid cell with ecological similarity of at least 0.5 is given. This metric describes the nature of the projected 2050 centred future environmental conditions for each 9s grid square. Using a Generalised Dissimilarity Model of compositional turnover (the effects of changing environment on changing species), each future location is compared with the continent in the present. For each cell, the metric looks out to all other cells in the continent, and records the ecological similarity of the future state of the cell to the most similar cell in the present. A value of 1 indicates that the future environment is similar to a current location in the present, and perfect analogue can found somewhere in Australia. A value of 0 indicates that the most similar environment to be found in the present is ecologically so different that we would expect no species in common, i.e. there are no current analogues for this environment; it is novel. Intermediate values show how ecologically similar the most similar cell is. However, no weight is given to the proximity of the most similar cell. The environment may be similar, but the cells thousands of kilometres apart. 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. 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: amphibians, M: mammals, R: reptiles and V: vascular plants less
Need for assisted dispersal for 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 MIROC5 model (R... moreCP 8.5) based on Generalised Dissimilarity Modelling (GDM) of compositional turnover. The distance to the nearest grid cell with ecological similarity of at least 0.5 is given. This metric describes the nature of the projected 2050 centred future environmental conditions for each 9s grid square. Using a Generalised Dissimilarity Model of compositional turnover (the effects of changing environment on changing species), each future location is compared with the continent in the present. For each cell, the metric looks out to all other cells in the continent, and records the ecological similarity of the future state of the cell to the most similar cell in the present. A value of 1 indicates that the future environment is similar to a current location in the present, and perfect analogue can found somewhere in Australia. A value of 0 indicates that the most similar environment to be found in the present is ecologically so different that we would expect no species in common, i.e. there are no current analogues for this environment; it is novel. Intermediate values show how ecologically similar the most similar cell is. However, no weight is given to the proximity of the most similar cell. The environment may be similar, but the cells thousands of kilometres apart. 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. 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: amphibians, M: mammals, R: reptiles and V: vascular plants less
Need for assisted dispersal for Amphibians as a function of change in long term (30 year average) climates between the present (1990 centred) and projected future (2050 centred) under the MIROC5 model... more (RCP 8.5) based on Generalised Dissimilarity Modelling (GDM) of compositional turnover. The distance to the nearest grid cell with ecological similarity of at least 0.5 is given. This metric describes the nature of the projected 2050 centred future environmental conditions for each 9s grid square. Using a Generalised Dissimilarity Model of compositional turnover (the effects of changing environment on changing species), each future location is compared with the continent in the present. For each cell, the metric looks out to all other cells in the continent, and records the ecological similarity of the future state of the cell to the most similar cell in the present. A value of 1 indicates that the future environment is similar to a current location in the present, and perfect analogue can found somewhere in Australia. A value of 0 indicates that the most similar environment to be found in the present is ecologically so different that we would expect no species in common, i.e. there are no current analogues for this environment; it is novel. Intermediate values show how ecologically similar the most similar cell is. However, no weight is given to the proximity of the most similar cell. The environment may be similar, but the cells thousands of kilometres apart. 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. 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: amphibians, M: mammals, R: reptiles and V: vascular plants less
Need for assisted dispersal for Amphibians 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 mode... morel (RCP 8.5) based on Generalised Dissimilarity Modelling (GDM) of compositional turnover. The distance to the nearest grid cell with ecological similarity of at least 0.5 is given. This metric describes the nature of the projected 2050 centred future environmental conditions for each 9s grid square. Using a Generalised Dissimilarity Model of compositional turnover (the effects of changing environment on changing species), each future location is compared with the continent in the present. For each cell, the metric looks out to all other cells in the continent, and records the ecological similarity of the future state of the cell to the most similar cell in the present. A value of 1 indicates that the future environment is similar to a current location in the present, and perfect analogue can found somewhere in Australia. A value of 0 indicates that the most similar environment to be found in the present is ecologically so different that we would expect no species in common, i.e. there are no current analogues for this environment; it is novel. Intermediate values show how ecologically similar the most similar cell is. However, no weight is given to the proximity of the most similar cell. The environment may be similar, but the cells thousands of kilometres apart. 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. 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: amphibians, M: mammals, R: reptiles and V: vascular plants less
1173.3 Strat: WPC Global Prot Area Ass - Biodiversity Adaptation Analyses - Published 22 Jun 2015
Benefits of revegetation index for Mammals as a function of land clearing and climate change based on Generalised Dissimilarity Modelling (GDM) of compositional turnover. This metric represents the m... morearginal benefit from a unit increase of vegetation at the site, which is a direct function of the slope of the species area curve at the test state of the site. In practice, revegetation of the whole cell is likely to be impractical due to the availability of cleared land within the cell, and practical limitations such as land ownership and revegetation cost. The metric therefore excludes these factors from the analysis, allowing direct comparison of the relative benefit of a given area of revegetation between cells. The values of the index generated according to the above formula are generally low (since a significant area is required to support additional species) and the index is rescaled by multiplying by 1000 to bring it into an approximate 0-1 range. 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 “Helping Biodiversity Adapt: Supporting climate adaptation planning using 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 BiodiversityModellingMethodsSummary.pdf for further information. 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: amphibians, M: mammals, R: reptiles and V: vascular plants less
Benefits of revegetation index for Mammals as a function of land clearing within the present long term (30 year average) climate (1990 centred) based on Generalised Dissimilarity Modelling (GDM) of c... moreompositional turnover. This metric represents the marginal benefit from a unit increase of vegetation at the site, which is a direct function of the slope of the species area curve at the test state of the site. In practice, revegetation of the whole cell is likely to be impractical due to the availability of cleared land within the cell, and practical limitations such as land ownership and revegetation cost. The metric therefore excludes these factors from the analysis, allowing direct comparison of the relative benefit of a given area of revegetation between cells. The values of the index generated according to the above formula are generally low (since a significant area is required to support additional species) and the index is rescaled by multiplying by 1000 to bring it into an approximate 0-1 range. 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 “Helping Biodiversity Adapt: Supporting climate adaptation planning using 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 BiodiversityModellingMethodsSummary.pdf for further information. 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: amphibians, M: mammals, R: reptiles and V: vascular plants less
This collection contains a 9-second gridded dataset (ESRI binary float format in GDA94) showing the generalised predicted baseline (1990-centred) potential pre-clearing vegetation patterns of 77 Major... more Vegetation Sub-groups (MVS classes) derived from the maximum of their respective predicted probabilities for each grid cell (V_1990_MXC - MaxClass). Two additional datasets show the maximum probability in each gird cell that was used to assign that class (V_1990_MXP - MaxProb), and the number of classes with non-zero probabilities with potential to represent their type in each grid cell (V_1990_NMC - NumClasses). The predicted probabilities for each class were derived based on their distribution patterns and correlation with baseline ecological environments (c.1990 climates, substrate and landform). The pre-clearing vegetation patterns and classification derive from version 4.1 of “Australia - Estimated Pre1750 Major Vegetation Groups - NVIS Version 4.1 (Albers 100m analysis product)” developed by the Australian Government Department of the Environment and collaborating State agencies. A kernel regression was used with c.155,000 locations of training classes for the 77 MVS classes attributed with 17 GDM-scaled environmental predictors for Vascular Plants representing baseline ecological environments. These details are provided with the data package “Potential vegetation distribution: Australia - 9second gridded 1990-centred baseline predictions of the pre-clearing extents of 77 Major Vegetation Sub-groups using kernel regression with GDM-scaled environments for Vascular Plants (GDM: VAS_v5_r11)”. The GDM-scaled environmental predictors are available with the “VAS_v5_r11” data package. This dataset predicts the generalised potential pre-clearing vegetation patterns based on 1990-centred (30 year average) baseline climates derived from ANUCLIM v5.1 (Xu and Hutchinson 2011). The accuracy of projections is limited by the quality of the vegetation mapping used to train the models and the accuracy of environmental variables delimiting substrate boundaries and disturbance regimes. Uncertainty or errors in the underlying vegetation map and environmental data will be reproduced by the models. Furthermore, variables describing the relationship between extreme climatic events and ecological disturbance regimes, that have significant structural influences on vegetation, are not directly included in these models. The data are provided as 9-second (approximately 250m), ESRI binary float grid format in GDA94. This dataset series and its use is described in the AdaptNRM Guide “Helping biodiversity adapt to climate change: a community-level modelling approach”, available online at: www.adaptnrm.orgless
NRM National Project - Macroecological Modelling - Published 16 Jun 2015
****UPDATED**** This collection contains a 9-second gridded dataset (ESRI binary float format in GDA94) showing the generalised projected future (2050-centred) potential pre-clearing vegetation patter... morens of 77 Major Vegetation Sub-groups (MVS classes) derived from the maximum of their respective predicted probabilities for each grid cell (V_85MIR50_MXC - MaxClass). Two additional datasets show the maximum probability in each gird cell that was used to assign that class (V_85MIR50_MXP - MaxProb), and the number of classes with non-zero probabilities with potential to represent their type in each grid cell (V_85MIR50_NMC - NumClasses). The predicted probabilities for each class were derived based on their distribution patterns and correlation with baseline ecological environments (c.1990 climates, substrate and landform). The pre-clearing vegetation patterns and classification derive from version 4.1 of “Australia - Estimated Pre1750 Major Vegetation Groups - NVIS Version 4.1 (Albers 100m analysis product)” developed by the Australian Government Department of the Environment and collaborating State agencies. A kernel regression was used with c.155,000 locations of training classes for the 77 MVS classes attributed with 17 GDM-scaled environmental predictors for Vascular Plants representing baseline ecological environments. These details are provided with the data package “Potential vegetation redistribution: Australia - 9second gridded projection to 2050, pre-clearing extents of 77 Major Vegetation Sub-groups using kernel regression with GDM-scaled environments for Vascular Plants (GDM: VAS_v5_r11; CMIP5: MIROC5 RCP 8.5)”. The GDM-scaled environmental predictors are available with the “VAS_v5_r11” data package. This dataset projects the generalised potential pre-clearing vegetation patterns based on 2050-centred (30 year average) future climates derived from the MIROC5 global climate model for the emission scenario defined by a representative concentration pathway of 8.5. The accuracy of projections is limited by the quality of the vegetation mapping used to train the models and the accuracy of environmental variables delimiting substrate boundaries and disturbance regimes. Uncertainty or errors in the underlying vegetation map and environmental data will be reproduced by the models. Furthermore, variables describing the relationship between extreme climatic events and ecological disturbance regimes, that have significant structural influences on vegetation, are not directly included in these models. The data are provided as 9-second (approximately 250m), ESRI binary float grid format in GDA94. This dataset series and its use is described in the AdaptNRM Guide “Helping biodiversity adapt to climate change: a community-level modelling approach”, available online at: www.adaptnrm.orgless
NRM National Project - Macroecological Modelling - Published 10 Aug 2015
This collection contains a 9-second gridded dataset (ESRI binary float format in GDA94) showing the generalised projected future (2050-centred) potential pre-clearing vegetation patterns of 77 Major V... moreegetation Sub-groups (MVS classes) derived from the maximum of their respective predicted probabilities for each grid cell (V_MXC_85Can50 - MaxClass). Two additional datasets show the maximum probability in each gird cell that was used to assign that class (V_MXP_85Can50 - MaxProb), and the number of classes with non-zero probabilities with potential to represent their type in each grid cell (V_NMC_85Can50 - NumClasses). The predicted probabilities for each class were derived based on their distribution patterns and correlation with baseline ecological environments (c.1990 climates, substrate and landform). The pre-clearing vegetation patterns and classification derive from version 4.1 of “Australia - Estimated Pre1750 Major Vegetation Groups - NVIS Version 4.1 (Albers 100m analysis product)” developed by the Australian Government Department of the Environment and collaborating State agencies. A kernel regression was used with c.155,000 locations of training classes for the 77 MVS classes attributed with 17 GDM-scaled environmental predictors for Vascular Plants representing baseline ecological environments. These details are provided with the data package “Potential vegetation redistribution: Australia - 9second gridded projection to 2050, pre-clearing extents of 77 Major Vegetation Sub-groups using kernel regression with GDM-scaled environments for Vascular Plants (GDM: VAS_v5_r11; CMIP5: CanESM2 RCP 8.5)”. The GDM-scaled environmental predictors are available with the “VAS_v5_r11” data package. This dataset projects the generalised potential pre-clearing vegetation patterns based on 2050-centred (30 year average) future climates derived from the CanESM2 global climate model for the emission scenario defined by a representative concentration pathway of 8.5. The accuracy of projections is limited by the quality of the vegetation mapping used to train the models and the accuracy of environmental variables delimiting substrate boundaries and disturbance regimes. Uncertainty or errors in the underlying vegetation map and environmental data will be reproduced by the models. Furthermore, variables describing the relationship between extreme climatic events and ecological disturbance regimes, that have significant structural influences on vegetation, are not directly included in these models. The data are provided as 9-second (approximately 250m), ESRI binary float grid format in GDA94. This dataset series and its use is described in the AdaptNRM Guide “Helping biodiversity adapt to climate change: a community-level modelling approach”, available online at: www.adaptnrm.orgless
Updated to include missing UNCON079 This collection contains 9-second gridded datasets (ESRI binary float format in GDA94) showing the baseline (1990-centred) predicted potential distribution of 77 Ma... morejor Vegetation Sub-groups (MVS classes) for continental Australia based on their pre-clearing distribution patterns and correlation with baseline ecological environments (c.1990 climates, substrate and landform). The pre-clearing vegetation patterns and classification derive from version 4.1 of “Australia - Estimated Pre1750 Major Vegetation Groups - NVIS Version 4.1 (Albers 100m analysis product)” developed by the Australian Government Department of the Environment and collaborating State agencies. A kernel regression was used with c.155,000 locations of training classes for the 77 MVS classes attributed with 17 GDM-scaled environmental predictors for Vascular Plants representing baseline ecological environments. The training class data input to the kernel regression is provided with this package. The GDM-scaled environmental predictors are available with the “VAS_v5_r11” data package. Using the 1990 baseline training MVS class data, and without constraining the prediction to pre-existing map boundaries, the kernel regression predicted the potential distribution of the 77 Major Vegetation Sub-groups using 1990-centred (30 year average) baseline climates derived from ANUCLIM v6.1 (Xu and Hutchinson 2011). The kernel regression generates unconstrained probabilities varying in the range from 0 and up to 1 for each of the 77 MVS classes. The data are provided as 9-second (approximately 250m), ESRI binary float grid format in GDA94. Each class is denoted “UNCON###”, where the number refers to the code originally assigned to that MVS class by the supplier. A lookup table linking the MVS classes to the output codes and descriptive title is provided. Generalised representations of the vegetation classes derived from the individual class probabilities as the maximum probability in any grid cell are provided separately (see related information). There are three dataset packages in this series: 1) 1990 predictions of MVS classes; 2) 2050 CanESM2 RCP 8.5 predictions of MVS classes; 3) 2050 MIROC5 RCP 8.5 predictions of MVS classes. This dataset series and its use is described in the AdaptNRM Guide “Helping biodiversity adapt to climate change: a community-level modelling approach”, available online at: www.adaptnrm.orgless
NRM National Project - Macroecological Modelling - Published 01 Feb 2016
***UPDATED*** This collection contains 9-second gridded datasets (ESRI binary float format in GDA94) showing the projected future (2050-centred) potential vegetation redistribution of 77 Major Vegetat... moreion Sub-groups (MVS classes) for continental Australia based on their pre-clearing distribution patterns and correlation with baseline ecological environments (c.1990 climates, substrate and landform). The pre-clearing vegetation patterns and classification derive from version 4.1 of “Australia - Estimated Pre1750 Major Vegetation Groups - NVIS Version 4.1 (Albers 100m analysis product)” developed by the Australian Government Department of the Environment and collaborating State agencies. A kernel regression was used with c.155,000 locations of training classes for the 77 MVS classes attributed with 17 GDM-scaled environmental predictors for Vascular Plants representing baseline ecological environments. The training class data input to the kernel regression is provided with this package. The GDM-scaled environmental predictors are available with the “VAS_v5_r11” data package. Using the 1990 baseline training MVS class data, and without constraining the prediction to pre-existing map boundaries, the kernel regression projected to 2050 the distribution of the 77 Major Vegetation Sub-groups using 2050-centred (30 year average) future climates derived from the MIROC5 global climate model for the emission scenario defined by a representative concentration pathway of 8.5. The kernel regression generates unconstrained probabilities varying in the range from 0 and up to 1 for each of the 77 MVS classes. The data are provided as 9-second (approximately 250m), ESRI binary float grid format in GDA94. Each class is denoted “UNCON###”, where the number refers to the code originally assigned to that MVS class by the supplier. A lookup table linking the MVS classes to the output codes and descriptive title is provided. Generalised representations of the vegetation classes derived from the individual class probabilities as the maximum probability in any grid cell are provided separately (see related information). There are three dataset packages in this series: 1) 1990 predictions of MVS classes; 2) 2050 CanESM2 RCP 8.5 predictions of MVS classes; 3) 2050 MIROC5 RCP 8.5 predictions of MVS classes. This dataset series and its use is described in the AdaptNRM Guide “Helping biodiversity adapt to climate change: a community-level modelling approach”, available online at: www.adaptnrm.orgless
Updated to include UNCON079 This collection contains 9-second gridded datasets (ESRI binary float format in GDA94) showing the projected future (2050-centred) potential vegetation redistribution of 77... more Major Vegetation Sub-groups (MVS classes) for continental Australia based on their pre-clearing distribution patterns and correlation with baseline ecological environments (c.1990 climates, substrate and landform). The pre-clearing vegetation patterns and classification derive from version 4.1 of “Australia - Estimated Pre1750 Major Vegetation Groups - NVIS Version 4.1 (Albers 100m analysis product)” developed by the Australian Government Department of the Environment and collaborating State agencies. A kernel regression was used with c.155,000 locations of training classes for the 77 MVS classes attributed with 17 GDM-scaled environmental predictors for Vascular Plants representing baseline ecological environments. The training class data input to the kernel regression is provided with this package. The GDM-scaled environmental predictors are available with the “VAS_v5_r11” data package. Using the 1990 baseline training MVS class data, and without constraining the prediction to pre-existing map boundaries, the kernel regression projected to 2050 the distribution of the 77 Major Vegetation Sub-groups using 2050-centred (30 year average) future climates derived from the CanESM2 global climate model for the emission scenario defined by a representative concentration pathway of 8.5. The kernel regression generates unconstrained probabilities varying in the range from 0 and up to 1 for each of the 77 MVS classes. The data are provided as 9-second (approximately 250m), ESRI binary float grid format in GDA94. Each class is denoted “UNCON###”, where the number refers to the code originally assigned to that MVS class by the supplier. A lookup table linking the MVS classes to the output codes and descriptive title is provided. Generalised representations of the vegetation classes derived from the individual class probabilities as the maximum probability in any grid cell are provided separately (see related information). There are three dataset packages in this series: 1) 1990 predictions of MVS classes; 2) 2050 CanESM2 RCP 8.5 predictions of MVS classes; 3) 2050 MIROC5 RCP 8.5 predictions of MVS classes. This dataset series and its use is described in the AdaptNRM Guide “Helping biodiversity adapt to climate change: a community-level modelling approach”, available online at: www.adaptnrm.orgless
Compositional turnover patterns in vascular plant species across continental Australia were derived using Generalised Dissimilarity Modelling (GDM). These models use best-available biological data ext... moreracted from the Australian Natural Heritage Assessment Tool (ANHAT) Database current to April 2013 (courtesy the Australian Government Department of the Environment and the BushBlitz program) and spatial environmental predictor data compiled at 9 second resolution. The models were developed to underpin continental assessments of biodiversity significance and identify gaps in biological surveys. GDM is a statistical technique that models the dissimilarity in composition of species between pairs of surveyed locations, as a function of environmental differences between these locations. The compositional dissimilarity between a given pair of locations can be thought of as the proportion of species occurring at one location that do not occur at the other location (averaged across the two locations) - ranging from ‘0’ if the two locations have exactly the same species through to ‘1’ if they have no species in common. GDM effectively weights and transforms the environmental variables such that distances between locations in this transformed multidimensional environmental space now correlate, as closely as possible, with the observed biological compositional dissimilarities between these same locations. Once a GDM model has been fitted to the biological data from the sampled locations using environmental predictor data, it can be used to predict compositional dissimilarity values for sites lacking biological data, based purely on their mapped environmental attributes. For this purpose, a set of GDM-scaled environmental grids are produced for use in subsequent spatial assessments of biodiversity significance. This collection describes the GDM-fitted model, the GDM-scaled environmental predictors for the fitted-model which comprises substrate (constant) and 1990-centred climates, and four projected models using past and future climates: 1960-centred climates and six 2050 climate change scenarios (3 GCMs, 3 RCPs). The past climate scenario for 1960 was generated using the c.75-year average monthly climate surfaces in ANUCLIM. The future climate projections (for two representative concentration pathways 8.5 and 4.5 greenhouse gas future emission scenario) were generated as 30 year averages centred on 2050 extracted from the CMIP5 database for three earth system models: MPI-ESM2 (Stevens (ed), 2013); CanESM2 (Chylek et al., 2011).; MIROC5 (Watanabe et al., 2010). Within model change grids (future minus 1990 ESM climates) were applied in ANUCLIM 6.1 and downscale to 0.0025 degrees by matching the spatial pattern of the 1990-centred surfaces (errors in the alignment of change grids have been corrected and the scenarios regenerated). Actual evapotranspiration was projected by modelling relative to the Budyko framework, using a topographically-scaled measure of soil water holding capacity (Claridge et al., 2000). Details are published in Reside et al. 2013 (http://www.nccarf.edu.au/publications/climate-change-refugia-terrestrial-biodiversity) and summarised in the methods summary report at related information. This GDM version was created 27 April 2014 with novel climate seasonality predictors and >10 species aggregated per 9-second grid cell, and used as the vascular plant model in the AdaptNRM biodiversity modules. The data are provided in ESRI binary float format, GDA 94. less
1173.3 WPC Global Protected Area Assessm - Macroecological Modelling - Published 16 Jun 2015
Disappearing ecological environments for Plants as a function of change in long term (30 year average) climates between the present (1990 centred) and projected future (2050 centred) under the MIROC5 ... moremodel (RCP 8.5) based on Generalised Dissimilarity Modelling (GDM) of compositional turnover. This metric describes the extent to which the long term average environmental conditions for each 9s grid square in the present (1990 centred) will be present in a projected 2050 centred future. Using a Generalised Dissimilarity Model of compositional turnover (the effects of changing environment on changing species), each location is compared with the continent in the future. For each cell, the metric looks out to all other cells in the continent, and records the ecological similarity of the present state of the cell to the most similar cell in the future. A value of 1 indicates that the environment is not disappearing, and perfect analogue is found somewhere in Australia. A value of 0 indicates that the most similar environment to be found in the future is ecologically so different that we would expect no species in common. Intermediate values show how ecologically similar the most similar cell is. However, no weight is given to the proximity of the most similar cell. The environment may be similar, but the cells could be thousands of kilometres apart. 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. 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: Vascular Plants, M: Vascular Plants, R: Vascular Plants and V: vascular plantsless
1173.3 Strat: WPC Global Prot Area Ass - Biodiversity Impact Analyses - Published 09 Dec 2014
