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Showing results for: [ Williams, Kristen ]
A suite of 9s resolution climate surfaces for the Australian continent, with adjustment for the radiative effects of terrain. This collection represents a 30 year average centred on 2050 for the CAN E... moreSM2 circulation model under RCP 8.5. Projected future climates were generated by applying within-model changes (e.g. CAN ESM2 2036-2065 –CAN ESM2 (1976-2005) calculated at the native general circulation model grid resolution to these current surfaces, using ANUCLIM 6.1 prior to radiative adjustment. Precipitation, temperature, evaporation and water balance data are presented as annual means or totals and maximum and minimum monthly values. Data are provided as 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. Additionally a short methods summary is provided in the file 9sClimateMethodsSummary.pdf for further information, including a nomenclature for files. less
DEE: Enhancing landscape data and analytic capability through knowledge transfer of GDM technology - Australian 9s environmental surfaces - Published 07 Nov 2019
A suite of 9s resolution climate surfaces for the Australian continent, with adjustment for the radiative effects of terrain. This collection represents a 30 year average centred on 2050 for the MIROC... more 5 circulation model under RCP 8.5. Projected future climates were generated by applying within-model changes (e.g. MIROC 5 2036-2065 MIROC 5 (1976-2005) calculated at the native general circulation model grid resolution to these current surfaces, using ANUCLIM 6.1 prior to radiative adjustment. Precipitation, temperature, evaporation and water balance data are presented as annual means or totals and maximum and minimum monthly values. Data are provided as 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. Additionally a short methods summary is provided in the file 9sClimateMethodsSummary.pdf for further information, including a nomenclature for files. less
A suite of 9s resolution climate surfaces for the Australian continent, with adjustment for the radiative effects of terrain. This collection represents a 30 year average centred on 2050 for the MIROC... more 5 circulation model under RCP 4.5. Projected future climates were generated by applying within-model changes (e.g. MIROC 5 2036-2065 MIROC 5 (1976-2005) calculated at the native general circulation model grid resolution to these current surfaces, using ANUCLIM 6.1 prior to radiative adjustment. Precipitation, temperature, evaporation and water balance data are presented as annual means or totals and maximum and minimum monthly values. Data are provided as 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. Additionally a short methods summary is provided in the file 9sClimateMethodsSummary.pdf for further information, including a nomenclature for files. less
A suite of 9s resolution climate surfaces for the Australian continent, with adjustment for the radiative effects of terrain. This collection represents a 30 year average centred on 2050 for the CAN E... moreSM2 circulation model under RCP 4.5. Projected future climates were generated by applying within-model changes (e.g. CAN ESM2 2036-2065 –CAN ESM2 (1976-2005) calculated at the native general circulation model grid resolution to these current surfaces, using ANUCLIM 6.1 prior to radiative adjustment. Precipitation, temperature, evaporation and water balance data are presented as annual means or totals and maximum and minimum monthly values. Data are provided as 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. Additionally a short methods summary is provided in the file 9sClimateMethodsSummary.pdf for further information, including a nomenclature for files. less
A suite of 9s resolution climate surfaces for the Australian continent, with adjustment for the radiative effects of terrain. This collection represents a 30 year average centred on 1990. Precipitatio... moren, temperature, evaporation and water balance data are presented as annual means or totals and maximum and minimum monthly values. Data are provided as 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. Additionally a short methods summary is provided in the file 9sClimateMethodsSummary.pdf for further information, including a nomenclature for files. less
Source data for: Prober SM, Raisbeck-Brown N, Williams KJ, Porter N, Leviston Z, Dickson F. Recent climate-driven ecological change across a continent as perceived through local ecological knowledge. ... morePLoS ONE. less
Ecological change in Australia - - Published 01 Nov 2019
These data provide consistent rasterised layers of edaphic (physical and chemical conditions of the soil) and land surface physiography (landform and geomorphology) variables hypothesised to explain s... morepatial patterns in biological diversity at continental scales for immediate use with statistical modelling tools. These data are intended to be used along with a similarly compiled and spatially standardised set of climatic layers. Consistent "stacks" of raster variables are needed for spatially-explicit biodiversity modelling using tools such as MAXENT or Generalised Dissimilarity Modelling (GDM). Full details of each dataset, with a list of data sources and bibliography, are provided in a table as part of the data collection. Additional information provided with the 1km gridded raster is relevant to some these data and provided here also. Each dataset will need to be separately cited. These data have also been made available for use in the Atlas of Living Australia's Spatial Portal. less
Closed DIISR ALA Exp-Geosptl Data Mngmnt - - Published 21 Oct 2019
These images of terrestrial biodiversity habitats across Australia relate to a project that aimed to construct and test a method for habitat condition data capture across Australia using expert elicit... moreation. The images in this collection were assembled from various sources to represent habitats from a wide variety of vegetation types and climates in a variety of different condition states (from ‘good’ to ‘poor’). These images represent a continent-wide library suitable for various purposes, including training and validation of model-based approaches to habitat condition assessment.less
Habitat condition data capture using expert elicitation - National Reference Library of Expert Site Condition Assessments - Published 23 May 2019
These data relate to a project that aimed to construct and test a method for habitat condition data capture across Australia using expert elicitation. The data derived from experts are in two forms: (... more1) habitat condition scores for specified areas at a specified date range, and; (2) habitat condition scores based on images (photographs) of ecosystems. The image based habitat condition data were collected to enable cross-calibration of contributed site assessment data. These data represent the start of a continent-wide library of ecological condition data suitable for training and validation of model-based approaches to habitat condition assessment.less
Habitat condition data capture using expert elicitation - National Reference Library of Expert Site Condition Assessments - Published 05 Mar 2019
These data provide rasterised layers of edaphic (physical and chemical conditions of the soil) and land surface physiography (landform and geomorphology) attributes hypothesised to explain spatial pat... moreterns in biological diversity at continental scales for immediate use with statistical modelling tools. These data are intended to be used along with a similarly compiled and spatially standardised set of climatic layers (See " 0.01 degree stack of climate layers for continental analysis of biodiversity pattern: version 1.0 " in related materials).NOTE: Full details of the data, with a list of data sources and bibliography, are provided in a PDF file included as part of the data collection.less
Closed DEWHA Harness Cntnnt-wide Bdvrsty - Spatial Environmental Data Preparation - Published 11 Dec 2018
A selection of 9sec gridded National climate change variables for biodiversity modelling. This collection represents 30-year averages centred on each of 1990, 2050, 2070, 2090. Projected future climat... morees were generated by applying within-model changes for two circulation model outputs: GFDL and ACCESS1.0; and for two representative concentration pathways (RCP 4.5, 8.5), calculated at the native general circulation model grid resolution to these current surfaces, using ANUCLIM 6.1 prior to radiative adjustment. That the maximum temperature variables have been adjusted for topographic slope/aspect and shading effects. A short methods summary is provided in the file 9sClimateMethodsSummary.pdf for further information, including a nomenclature for files. The selected climate variables provided in this collection are: TNM - mean annual minimum temperature TXM - mean annual maximum temperature TXX - mean maximum monthly maximum temperature TXI - mean minimum monthly maximum temperature TNI - mean minimum monthly minimum temperature TNX - mean maximum monthly minimum temperature PTA - Average total annual rainfall PTX - mean maximum monthly rainfall PTI - mean minimum monthly rainfall Other variables (evaporation and water balance, temperature range, and seasonality, etc) are available upon application. The data are provided in ESRI binary float grid format (*.hdr, *.flt), Projection is geographic GDA94. less
1173.3 WPC Global Protected Area Assessm - climate change scenarios - Published 12 Sep 2018
This data collection is the source data for the manuscript "Shifting the conservation paradigm - a synthesis of options for renovating nature under climate change" by Suzanne M. Prober, Veronica A. J.... more Doerr, Linda M. Broadhurst, Kristen J. Williams, Fiona Dickson and published in the peer-reviewed journal "Ecological Monographs" in 2018. The data are provided as an excel spreadsheet with three sheets. The results are provided in "SourceData_Prober_etal_EcolMono" including the cited references (peer-reviewed journal articles). The full citation of each reference is given in Appendix S1 of the manuscript (and on tab 3). less
Decision Pathways - Ecosystem Engineers - A synthesis component to review established and emerging ecological engineering options - Published 31 Aug 2018
A selection of 9-arcsecond resolution substrate surfaces (soil and landform) for the Australian continent, aggregated from 3-arcsecond source data. These substrate surfaces have been selected because ... morethey have been found to be relevant to biodiversity modelling using generalised dissimilarity modelling. These data are intended to be used along with a similarly compiled and spatially standardised 9-arcsecond gridded climatic layers. See links for related collections. less
DEE: Enhancing landscape data and analytic capability through knowledge transfer of GDM technology - Australian 9s environmental surfaces - Published 19 Jun 2018
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
Representation within the National Reserve System 2015 for Vascular Plants as a function of current climate and climate change based on Generalised Dissimilarity Modelling (GDM) of compositional turno... morever. This metric represents a measure of the support provided for the ecological environments of each grid cell by the NRS. A full description of the project can be found in the report "Assessing the ecological representativeness of Australia’s terrestrial National Reserve System: A community-level modelling approach" by KJ Williams, TD Harwood & S Ferrier (2016) at https://publications.csiro.au/rpr/pub?pid=csiro:EP163634 Four subfolders are provided: 1. P maps: 9s resolution mapping of cellwise P metric for representation of the environment of each cell within the NRS based on a GDM model of Vascular Plants. 2. IBRA maps: 9s resolution mapping of summary statistics (17: proportion 17% represented and Geometric Mean: P metric summarised by region) with single value applied to all cells within each IBRA bioregion. 3. IBRASUB maps: 9s resolution mapping of summary statistics (17: proportion 17% represented and Geometric Mean: P metric summarised by region) with single value applied to all cells within each IBRA subregion. Format ESRI float grids 4. Summary statistics: Regional statistics and histograms of distribution of values within IBRA bioregions and IBRA subregions. Format: Microsoft Excel spreadsheets. Files contain a row for each numbered region. Statistics files show the Geometric and Arithmetic Mean, the proportion of each region achieving target representation and the Maximum and Minimum cellwise representation within each region. less
Assessing present and future representation of terrestrial biodiversity within Australia's National Reserve System - GDM-based assessment of National Reserve System Representativeness - Published 14 Jun 2017
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
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
A global map of 5 land use types at 30s (approx. 1km) resolution for 2005. The data set was generated through the statistical downscaling of the Land-use Harmonisation data set (Hurt et al 2011) at ht... moretp://luh.umd.edu/. Five land use types (primary, secondary, pasture, crop, urban) are provided as separate raster layers, with the value of each cell representing the proportion of the grid cell occupied by that land use type. An additional layer representing cells defined as permanent ice (value of 1) is also provided.less
1173.3 Strat: WPC Global Prot Area Ass - Land-use downscaling - Published 04 Apr 2016
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
NRM National Project - Macroecological Modelling - Published 01 Feb 2016
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
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
***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
NRM National Project - Macroecological Modelling - Published 10 Aug 2015
