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Showing results for: [ Ecological Impacts of Climate Change ]
This Collection contains data and code that support the associated Godfree et al. Nature Communications paper: Implications of the 2019-2020 Megafires for the Biogeography and Conservation of Australi... morean Vegetation. The study was performed to understand the impacts of the Black Summer fires on plant species and communities in eastern Australia and provide information useful for associated biodiversity conservation work.less
Strategic CANBR Research - Data in support of Godfree et al. Nature Communications paper on impact of bushfires on Australian vegetation - Published 23 Dec 2020
A website for stakeholders and interested parties to plot and view the locations of turtles tagged with satellite tags as part of the Ningaloo Outlook project.
Ningaloo Outlook Phase II - Turtle Tracking - Published 05 Nov 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
Bio-physical, ecological and social information have been used to parameterise two computer models able to simulate (ALCES and Ecopath with Ecosim [EwE]) land, coastal and marine processes. A careful ... moreexamination of a large volume of publications from the academic, private and public sector has also allowed us to identify a number of climate and social economic development scenarios the Kimberly region may experience in the decades to comeless
WAMSI-Kim 2.2.8 MSE Modelling Knowledge - MSE modelling and ecosystem modelling - Published 22 Nov 2019
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
This collection provides additional analyses, figures and tables for an integrated risk assessment of natural, cultural and economic assets in the Kakadu Region of northern Australia, from the combine... mored threats of invasive species (feral animals & aquatic weeds) and climate change induced sea level rise saltwater inundation. It addresses cumulative multiple risks to multiple values over different time frames (Present-day, 2070 & 2100).less
Legacy data - Modelling - Published 25 Sep 2017
In this project (Kimberley Marine Research Project 2.2.7), historical data and numerical models have been used to identify the climate sensitivity of the Kimberley coast (Western Australia) to interan... morenual and decadal climate variability in the Pacific and Indian Ocean over the past several decades, especially on the variability of ocean temperature, precipitation and salinity, sea level, and shelf current associated with El Niño–Southern Oscillation (ENSO), Indian Ocean Dipole, and Pacific Decadal Oscillation. These analyses have provided the background understanding of the marine environment off the whole northwest shelf including the Kimberley coast and Scott Reef, to improve the predictability of climate-driven environmental variability, especially extreme events such as marine heatwaves. We have also utilised ocean downscaling models to project future climate change impacts on the marine environment such as the ocean temperature and internal wave characteristics. Data for model year 2069 onlyless
1177.1 WAMSI-Kim 2.2.7 Climate Modelling - Hydrodynamic modelling - Published 04 Sep 2017
In this project (Kimberley Marine Research Project 2.2.7), historical data and numerical models have been used to identify the climate sensitivity of the Kimberley coast (Western Australia) to interan... morenual and decadal climate variability in the Pacific and Indian Ocean over the past several decades, especially on the variability of ocean temperature, precipitation and salinity, sea level, and shelf current associated with El Niño–Southern Oscillation (ENSO), Indian Ocean Dipole, and Pacific Decadal Oscillation. These analyses have provided the background understanding of the marine environment off the whole northwest shelf including the Kimberley coast and Scott Reef, to improve the predictability of climate-driven environmental variability, especially extreme events such as marine heatwaves. We have also utilised ocean downscaling models to project future climate change impacts on the marine environment such as the ocean temperature and internal wave characteristics. Data for model year 2068 onlyless
In this project (Kimberley Marine Research Project 2.2.7), historical data and numerical models have been used to identify the climate sensitivity of the Kimberley coast (Western Australia) to interan... morenual and decadal climate variability in the Pacific and Indian Ocean over the past several decades, especially on the variability of ocean temperature, precipitation and salinity, sea level, and shelf current associated with El Niño–Southern Oscillation (ENSO), Indian Ocean Dipole, and Pacific Decadal Oscillation. These analyses have provided the background understanding of the marine environment off the whole northwest shelf including the Kimberley coast and Scott Reef, to improve the predictability of climate-driven environmental variability, especially extreme events such as marine heatwaves. We have also utilised ocean downscaling models to project future climate change impacts on the marine environment such as the ocean temperature and internal wave characteristics. Data for model year 2067 onlyless
In this project (Kimberley Marine Research Project 2.2.7), historical data and numerical models have been used to identify the climate sensitivity of the Kimberley coast (Western Australia) to interan... morenual and decadal climate variability in the Pacific and Indian Ocean over the past several decades, especially on the variability of ocean temperature, precipitation and salinity, sea level, and shelf current associated with El Niño–Southern Oscillation (ENSO), Indian Ocean Dipole, and Pacific Decadal Oscillation. These analyses have provided the background understanding of the marine environment off the whole northwest shelf including the Kimberley coast and Scott Reef, to improve the predictability of climate-driven environmental variability, especially extreme events such as marine heatwaves. We have also utilised ocean downscaling models to project future climate change impacts on the marine environment such as the ocean temperature and internal wave characteristics. Data for model year 2066 onlyless
In this project (Kimberley Marine Research Project 2.2.7), historical data and numerical models have been used to identify the climate sensitivity of the Kimberley coast (Western Australia) to interan... morenual and decadal climate variability in the Pacific and Indian Ocean over the past several decades, especially on the variability of ocean temperature, precipitation and salinity, sea level, and shelf current associated with El Niño–Southern Oscillation (ENSO), Indian Ocean Dipole, and Pacific Decadal Oscillation. These analyses have provided the background understanding of the marine environment off the whole northwest shelf including the Kimberley coast and Scott Reef, to improve the predictability of climate-driven environmental variability, especially extreme events such as marine heatwaves. We have also utilised ocean downscaling models to project future climate change impacts on the marine environment such as the ocean temperature and internal wave characteristics. Data for model year 2012 onlyless
In this project (Kimberley Marine Research Project 2.2.7), historical data and numerical models have been used to identify the climate sensitivity of the Kimberley coast (Western Australia) to interan... morenual and decadal climate variability in the Pacific and Indian Ocean over the past several decades, especially on the variability of ocean temperature, precipitation and salinity, sea level, and shelf current associated with El Niño–Southern Oscillation (ENSO), Indian Ocean Dipole, and Pacific Decadal Oscillation. These analyses have provided the background understanding of the marine environment off the whole northwest shelf including the Kimberley coast and Scott Reef, to improve the predictability of climate-driven environmental variability, especially extreme events such as marine heatwaves. We have also utilised ocean downscaling models to project future climate change impacts on the marine environment such as the ocean temperature and internal wave characteristics. Data for model year 2011 onlyless
In this project (Kimberley Marine Research Project 2.2.7), historical data and numerical models have been used to identify the climate sensitivity of the Kimberley coast (Western Australia) to interan... morenual and decadal climate variability in the Pacific and Indian Ocean over the past several decades, especially on the variability of ocean temperature, precipitation and salinity, sea level, and shelf current associated with El Niño–Southern Oscillation (ENSO), Indian Ocean Dipole, and Pacific Decadal Oscillation. These analyses have provided the background understanding of the marine environment off the whole northwest shelf including the Kimberley coast and Scott Reef, to improve the predictability of climate-driven environmental variability, especially extreme events such as marine heatwaves. We have also utilised ocean downscaling models to project future climate change impacts on the marine environment such as the ocean temperature and internal wave characteristics. Data for model year 2010 onlyless
In this project (Kimberley Marine Research Project 2.2.7), historical data and numerical models have been used to identify the climate sensitivity of the Kimberley coast (Western Australia) to interan... morenual and decadal climate variability in the Pacific and Indian Ocean over the past several decades, especially on the variability of ocean temperature, precipitation and salinity, sea level, and shelf current associated with El Niño–Southern Oscillation (ENSO), Indian Ocean Dipole, and Pacific Decadal Oscillation. These analyses have provided the background understanding of the marine environment off the whole northwest shelf including the Kimberley coast and Scott Reef, to improve the predictability of climate-driven environmental variability, especially extreme events such as marine heatwaves. We have also utilised ocean downscaling models to project future climate change impacts on the marine environment such as the ocean temperature and internal wave characteristics. Data for model year 2009 onlyless
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
In April 2014 and March 2015 surveys of coral populations were undertaken at Enderby and West Lewis Islands in the Dampier Archipelago, Western Australia. The corals investigated in this study were Ac... moreropora millepora, Turbinaria mesenterina and massive Porites spp. (mainly P. lobata and P. lutea). For each species, population size-frequency distributions were obtained by recording the size of all colonies within a one metre distance on either side of permanent transects (60 m2). Using the permanent transect as a reference point, the locations of all colonies were recorded, and all colonies were tagged, measured and photographed. Tagged colonies were re-located and re-measured approximately one year later. A total of 737 corals were examined; 473 corals from Enderby Island and 264 from West Lewis Islands. Similar numbers of massive Porites (279), T. mesenterina (229) and A. millepora (272) colonies were tagged. Of the colonies tagged 733 corals were re-located a year later and assessed for survival, growth, partial mortality and fission.less
WAMSI-Dredg T4 Coral response - Demographic processes in corals of Dampier Archipelago - Published 16 May 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
