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Showing results for: [ Vascular plants ]
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
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
***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
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
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
This collection contains a 9-second gridded dataset (ESRI binary float format in GDA94) showing the generalised predicted baseline (1990-centred) potential pre-clearing vegetation patterns of 77 Major... more Vegetation Sub-groups (MVS classes) derived from the maximum of their respective predicted probabilities for each grid cell (V_1990_MXC - MaxClass). Two additional datasets show the maximum probability in each gird cell that was used to assign that class (V_1990_MXP - MaxProb), and the number of classes with non-zero probabilities with potential to represent their type in each grid cell (V_1990_NMC - NumClasses). The predicted probabilities for each class were derived based on their distribution patterns and correlation with baseline ecological environments (c.1990 climates, substrate and landform). The pre-clearing vegetation patterns and classification derive from version 4.1 of “Australia - Estimated Pre1750 Major Vegetation Groups - NVIS Version 4.1 (Albers 100m analysis product)” developed by the Australian Government Department of the Environment and collaborating State agencies. A kernel regression was used with c.155,000 locations of training classes for the 77 MVS classes attributed with 17 GDM-scaled environmental predictors for Vascular Plants representing baseline ecological environments. These details are provided with the data package “Potential vegetation distribution: Australia - 9second gridded 1990-centred baseline predictions of the pre-clearing extents of 77 Major Vegetation Sub-groups using kernel regression with GDM-scaled environments for Vascular Plants (GDM: VAS_v5_r11)”. The GDM-scaled environmental predictors are available with the “VAS_v5_r11” data package. This dataset predicts the generalised potential pre-clearing vegetation patterns based on 1990-centred (30 year average) baseline climates derived from ANUCLIM v5.1 (Xu and Hutchinson 2011). The accuracy of projections is limited by the quality of the vegetation mapping used to train the models and the accuracy of environmental variables delimiting substrate boundaries and disturbance regimes. Uncertainty or errors in the underlying vegetation map and environmental data will be reproduced by the models. Furthermore, variables describing the relationship between extreme climatic events and ecological disturbance regimes, that have significant structural influences on vegetation, are not directly included in these models. The data are provided as 9-second (approximately 250m), ESRI binary float grid format in GDA94. This dataset series and its use is described in the AdaptNRM Guide “Helping biodiversity adapt to climate change: a community-level modelling approach”, available online at: www.adaptnrm.orgless
Proportional change in effective area of similar ecological environments for Vascular plants as a function of land clearing within the present long term (30 year average) climate (1990 centred) based... more on Generalised Dissimilarity Modelling (GDM) of compositional turnover. This metric describes the effects of land clearing on the area of similar environments to each grid cell as a proportion. Each cell is compared with a sample of 60,000 points in both uncleared landscape and degraded landscape (pairwise similarities summed (e.g. a completely similar cell will contribute 1, a dissimilar cell 0, with a range of values in between). The contribution of each cell is then multiplied by a 0 (cleared) to 1 (intact) condition index based on the natural areas layer. By dividing the test area by the current area, we are able to quantify the reduction in area as a function of land use/climate change. Values less than one indicate a reduction, values of 1 no change, and values greater than 1 (rare cases in the north) show an increase in similar environments. 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 Impact Analyses - Published 10 Dec 2014
