Showing results for: [ biodiversity, generalised vegetation patterns ]
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
NRM National Project - Macroecological Modelling - Published 16 Jun 2015