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. 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.