Showing results for: [ CanESM2 ]
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
Compositional turnover patterns in vascular plant species across continental Australia were derived using Generalised Dissimilarity Modelling (GDM). These models use best-available biological data ext... moreracted from the Australian Natural Heritage Assessment Tool (ANHAT) Database current to April 2013 (courtesy the Australian Government Department of the Environment and the BushBlitz program) and spatial environmental predictor data compiled at 9 second resolution. The models were developed to underpin continental assessments of biodiversity significance and identify gaps in biological surveys. GDM is a statistical technique that models the dissimilarity in composition of species between pairs of surveyed locations, as a function of environmental differences between these locations. The compositional dissimilarity between a given pair of locations can be thought of as the proportion of species occurring at one location that do not occur at the other location (averaged across the two locations) - ranging from ‘0’ if the two locations have exactly the same species through to ‘1’ if they have no species in common. GDM effectively weights and transforms the environmental variables such that distances between locations in this transformed multidimensional environmental space now correlate, as closely as possible, with the observed biological compositional dissimilarities between these same locations. Once a GDM model has been fitted to the biological data from the sampled locations using environmental predictor data, it can be used to predict compositional dissimilarity values for sites lacking biological data, based purely on their mapped environmental attributes. For this purpose, a set of GDM-scaled environmental grids are produced for use in subsequent spatial assessments of biodiversity significance. This collection describes the GDM-fitted model, the GDM-scaled environmental predictors for the fitted-model which comprises substrate (constant) and 1990-centred climates, and four projected models using past and future climates: 1960-centred climates and six 2050 climate change scenarios (3 GCMs, 3 RCPs). The past climate scenario for 1960 was generated using the c.75-year average monthly climate surfaces in ANUCLIM. The future climate projections (for two representative concentration pathways 8.5 and 4.5 greenhouse gas future emission scenario) were generated as 30 year averages centred on 2050 extracted from the CMIP5 database for three earth system models: MPI-ESM2 (Stevens (ed), 2013); CanESM2 (Chylek et al., 2011).; MIROC5 (Watanabe et al., 2010). Within model change grids (future minus 1990 ESM climates) were applied in ANUCLIM 6.1 and downscale to 0.0025 degrees by matching the spatial pattern of the 1990-centred surfaces (errors in the alignment of change grids have been corrected and the scenarios regenerated). Actual evapotranspiration was projected by modelling relative to the Budyko framework, using a topographically-scaled measure of soil water holding capacity (Claridge et al., 2000). Details are published in Reside et al. 2013 (http://www.nccarf.edu.au/publications/climate-change-refugia-terrestrial-biodiversity) and summarised in the methods summary report at related information. This GDM version was created 27 April 2014 with novel climate seasonality predictors and >10 species aggregated per 9-second grid cell, and used as the vascular plant model in the AdaptNRM biodiversity modules. The data are provided in ESRI binary float format, GDA 94. less
1173.3 WPC Global Protected Area Assessm - Macroecological Modelling - Published 16 Jun 2015
Composite ecological change as a function of three metrics (the potential degree of ecological change and of disappearing and novel ecological environments) shows where change might be greatest and di... morefferent types of vulnerability using 30-year climate averages between the present (1990:1976- 2005) and projected future (2050:2036-2065) under the CanESM2 global climate model (RCP 8.5), based on a Generalised Dissimilarity Modelling (GDM) of compositional turnover for vascular plants (VAS_v5_r11).
Wherever the Potential degree of ecological change is scored low, ecological environments can neither be novel nor disappearing and minimal change is expected. But when the Potential degree of ecological change is scored high, a variety of possible types of change can occur depending on whether scores for Novel and/or Disappearing ecological environments are also high.
To create a composite view, we assigned each of the three component measures to a colour band in a composite-band raster: local similarity as shades of green (inverted, 1-0 rescaled 0-255); novel as shades of blue (0-1 rescaled 0-255); and disappearing as shades of red (0-1 rescaled 0-255). The three layers can then be mapped simultaneously (red: band 3; green: band 1; blue: band 2) each scaled 0-255 to show the varying degrees of similar, novel and disappearing ecological environments and their combinations.
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 as zipped ESRI tiff grids containing: raster image (*.tif) with associated header (*.tfw) and projection (*.xml) files. After extracting from the zip archive, these files can be imported into most GIS software packages. A readme file describes how to correctly reproduce the colour legend. In ArcGIS, the symbology statistics file can be used: "SND_display.stat.XML".
Reproducing RGB composite colours for 3-band raster in ArcGIS:
1. In file properties in ARCGIS, Symbology tab, Load XML "SND_display.stat.XML"
2. RED = BAND_3 (Disappearing)
3. GREEN = BAND_1 (Similarity )
4. BLUE = BAND_2 (Novel)
5. Always use min-max legend
6. Set each band in the custom range 0-255, mean = 126, std = 0
Layers in this 9s series use a consistent naming convention:
BIOLOGICAL GROUP _ FROM BASE TO SCENARIO _ ANALYSIS
e.g. A_90CAN85_SND or R_90MIR85_SND
where BIOLOGICAL GROUP is A: amphibians, M: mammals, R: reptiles and V: vascular plants
and scenario is CAN: CanESM2; MIR: MIROC5
analysis, SND refers to – similarity, novel, disappearing
1173.3 WPC Global Protected Area Assessm - Biodiversity Impact Analyses - Published 16 Jun 2015
This collection contains a 9-second gridded dataset (ESRI binary float format in GDA94) showing the generalised projected future (2050-centred) potential pre-clearing vegetation patterns of 77 Major V... moreegetation Sub-groups (MVS classes) derived from the maximum of their respective predicted probabilities for each grid cell (V_MXC_85Can50 - MaxClass). Two additional datasets show the maximum probability in each gird cell that was used to assign that class (V_MXP_85Can50 - MaxProb), and the number of classes with non-zero probabilities with potential to represent their type in each grid cell (V_NMC_85Can50 - NumClasses). The predicted probabilities for each class were derived based on their distribution patterns and correlation with baseline ecological environments (c.1990 climates, substrate and landform). The pre-clearing vegetation patterns and classification derive from version 4.1 of “Australia - Estimated Pre1750 Major Vegetation Groups - NVIS Version 4.1 (Albers 100m analysis product)” developed by the Australian Government Department of the Environment and collaborating State agencies. A kernel regression was used with c.155,000 locations of training classes for the 77 MVS classes attributed with 17 GDM-scaled environmental predictors for Vascular Plants representing baseline ecological environments. These details are provided with the data package “Potential vegetation redistribution: Australia - 9second gridded projection to 2050, pre-clearing extents of 77 Major Vegetation Sub-groups using kernel regression with GDM-scaled environments for Vascular Plants (GDM: VAS_v5_r11; CMIP5: CanESM2 RCP 8.5)”. The GDM-scaled environmental predictors are available with the “VAS_v5_r11” data package. This dataset projects the generalised potential pre-clearing vegetation patterns based on 2050-centred (30 year average) future climates derived from the CanESM2 global climate model for the emission scenario defined by a representative concentration pathway of 8.5.
The accuracy of projections is limited by the quality of the vegetation mapping used to train the models and the accuracy of environmental variables delimiting substrate boundaries and disturbance regimes. Uncertainty or errors in the underlying vegetation map and environmental data will be reproduced by the models. Furthermore, variables describing the relationship between extreme climatic events and ecological disturbance regimes, that have significant structural influences on vegetation, are not directly included in these models.
The data are provided as 9-second (approximately 250m), ESRI binary float grid format in GDA94. This dataset series and its use is described in the AdaptNRM Guide “Helping biodiversity adapt to climate change: a community-level modelling approach”, available online at: www.adaptnrm.orgless
NRM National Project - Macroecological Modelling - Published 16 Jun 2015