Projected distribution of vegetation types datasets accompanying AdaptNRM Helping Biodiversity Adapt

Metadata

Potential vegetation distribution: Australia - 1990-centred baseline predictions of the pre-clearing extents of 77 Major Vegetation Sub-groups

This collection contains 9-second gridded datasets (ESRI binary float format in GDA94) showing the baseline (1990-centred) predicted potential distribution of 77 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).

Potential vegetation distribution (MaxClass): Australia - 1990-centred baseline prediction, maximum probability class generalised pre-clearing patterns of Major Vegetation Sub-groups

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 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).

Projected vegetation redistribution (MaxClass): Australia - projection to 2050, maximum probability class generalised pre-clearing patterns of Major Vegetation Sub-groups (CMIP5: MIROC5 RCP 8.5)

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 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).

Projected vegetation redistribution (MaxClass): Australia - projection to 2050, maximum probability class generalised pre-clearing patterns of Major Vegetation Sub-groups (CMIP5: CanESM2 RCP 8.5)

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 Vegetation 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).

Projected vegetation redistribution: Australia - projection to 2050, pre-clearing extents of 77 Major Vegetation Sub-groups (CMIP5: MIROC5 RCP 8.5)

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 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).

Projected vegetation redistribution: Australia - projection to 2050, pre-clearing extents of 77 Major Vegetation Sub-groups (CMIP5: CanESM2 RCP 8.5)

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 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).

VAS_v5_r11: Generalised dissimilarity model of compositional turnover in vascular plant species for continental Australia at 9 second resolution using ANHAT data extracted April 2013

Compositional turnover patterns in vascular plant species across continental Australia were derived using Generalised Dissimilarity Modelling (GDM). These models use best-available biological data extracted 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.