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This global spatial layer of contextual intactness aims to identify priority areas around the world where protection and management will best promote biodiversity persistence. This layer was derived b... morey integrating both the condition of each focal location and the condition of all other locations expected to have supported shared species with the focal location prior to any habitat degradation. The contextual intactness of each location (grid cell) is the proportion of habitat predicted to have once supported a similar assemblage of species but is now in worse condition than the focal location. This was derived using the BILBI global biodiversity assessment system, by integrating: (1) an updated map of the terrestrial human footprint on natural systems, and; (2) generalized dissimilarity models of species assemblage turnover for terrestrial vertebrates, invertebrates, and plants.less
The value of intact habitat for conserving biodiversity - - Published 23 Mar 2020
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 13 Jan 2020
Educational resources and lesson plans based on WA Northern Wheatbelt Bird Counts from Koobabbie 1987-2017 collection
Legacy data - Educational Datasets - Published 11 Sep 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
Global patterns of the contribution of biocultural approaches to pollinators and pollination to quality of life, from studies/sites identified in an analysis. These data were collected as part of, and... more in addition to, a contribution to the Intergovernmental Platform on Biodiversity and Ecosystem Services assessment of Pollinators and Pollination in Food Production. The data show locations of sites where Indigenous and Local Knowledge (ILK) contributes to: (a) beekeeping; (b) honey hunting; (c) Intangible Cultural Heritage listed as globally significant; (d) Cultural and mixed (cultural/natural) sites inscribed on the World Heritage List (WHL) with significance to pollinators. Also includes studies/sites identified where ILK contributions to practices that protect pollinators: Actions to foster pollinator nesting resources; mental maps of pollinators/resources; totems; taboos; manipulation of resources in the landscape; use of biotemporal indicators; and fire to stimulate pollination resources. Shows the locations of three types of diversified farming systems where ILK has been shown to contribute to pollinator conservation: shifting agriculture; home gardens; and commodity agroforestry. Globally Significant Agricultural Heritage Sites that contribute to pollinators and pollination are also included. This record includes the data underpinning maps that have been published in Nature Sustainability with full public access deposit. These maps did not appear in the IPBES assessment.less
Legacy data - IPBES - Published 18 Jan 2019
This dataset contains anonymised, expert elicited data about the management effectiveness of 19 Key Threatening Processes listed under the NSW Biodiversity Conservation Act 2016. Two key pieces of inf... moreormation are generated using the data and the associated R and Matlab code: (1) the expected effectiveness of management under current uncertainty, and (2) the expected value of removing uncertainty about management. Full details of the analysis are contained in the report: Nicol S, Brazill-Boast J, Gorrod E, McSorley A, Peyrard N, Chadès I (2018). Prioritising research and management of key threatening processes and listed species using value of information. CSIRO, Brisbane.less
A value of information analysis to determine the species that would benefit the most from an adaptive management program - - Published 24 Dec 2018
Magnified images of Eucalyptus seed from multiple accessions of multiple species were prepared. Automatic measurement tools used on the images then generated data for 35 different shape/image analysis... more parameters.less
Acquired - - Published 09 Apr 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
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
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
****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
Refugial potential index for Amphibians as a function of climate change based on Generalised Dissimilarity Modelling (GDM) of compositional turnover. This metric represents a relative measure of the ... morepotential of each grid cell to act as a climate change refugia for the local (100km radius) area, taking the representation of current ecological environments by the future state of the cell, and the area of similar ecological environments in the future into account. 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 plantsless
1173.3 Strat: WPC Global Prot Area Ass - Biodiversity Adaptation Analyses - Published 24 Jun 2015
Refugial potential index for Reptiles as a function of climate change based on Generalised Dissimilarity Modelling (GDM) of compositional turnover. This metric represents a relative measure of the po... moretential of each grid cell to act as a climate change refugia for the local (100km radius) area, taking the representation of current ecological environments by the future state of the cell, and the area of similar ecological environments in the future into account. 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 plantsless
Refugial potential index for Mammals as a function of climate change based on Generalised Dissimilarity Modelling (GDM) of compositional turnover. This metric represents a relative measure of the pot... moreential of each grid cell to act as a climate change refugia for the local (100km radius) area, taking the representation of current ecological environments by the future state of the cell, and the area of similar ecological environments in the future into account. 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 plantsless
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
