Williams-Climate-change-refugia-for-terrestrial-biodiversity_0

lower deviance explained) at a site was selected. This ensured more sites were
available for site-pair sampling.
Environmental variables
A common set of environmental variables were developed and tested as candidate
predictors in GDM models. The spatial environmental data were compiled from best
available sources and resampled to a common resolution (9-second, 3-second). As
described in Supporting Information 3 (Harwood et al., terrain scaling) relevant climatic
variables were adjusted for local topography using a scaling factor derived from using
the r.sun routine in GRASS GIS (GRASS Development Team, 2011) with the
respective DEM. Representative monthly estimates of radiation relative to an
unshaded horizontal surface were obtained for the 15 th day. This ratio was used to
adjust monthly incoming shortwave radiation, net radiation, maximum temperature and
potential evaporation following the procedures in Wilson and Gallant (2000) and Allen
et al. (1998). The topographic adjustments were then propagated throughout relevant
climate predictors.
For the 3-second gridded predictors, a 3-second digital elevation model, available as a
derivative of the 1-second corrected and smoothed SRTM DEM (Gallant, 2011), was
used with ANUCLIM version 6.1 software (Xu & Hutchinson, 2011) to derive monthly
climate variables, representing 30 year averages centred on 1990, that were then used
to generate a series of climate predictors (described in Williams et al., 2012). The
climatic predictors describe minimum and maximum monthly conditions and different
facets of seasonality associated with temperature and water availability (Supporting
Table 1).
A series of primary and secondary derivatives of the 3-second smoothed and
hydrologically-enforced DEM, developed by J. Gallant and co-workers (Supporting
Table 1) were compiled as supplementary landform predictors, where these are
independent of the climate predictors. In addition to the substrate and landform
variables listed in Williams et al. (2012), soil attributes modelled from soil spectra
measurements (Viscarra-Rossel & Chen, 2011, Viscarra Rossel, 2011) and recent
250m gridded (9 second) composite national soil attributes (Jacquier, 2011a, Jacquier,
2011b, Jacquier, 2011c) were also compiled. Furthermore, a composite index of soil
water holding capacity was derived from the topographic wetness index (Gallant et al.,
2012), the soil depth variable derived from the Atlas of Australian soils (Bureau of Rural
Sciences, 2000, McKenzie et al., 2000) and soil water holding capacity (Jacquier,
2011a) using the method described in Claridge et al. (2000).
This compilation of best available, high-resolution climate, landform and substrate
variables resulted in 54 candidate predictors being tested for inclusion in the 3-second
GDM models and 61 in the 9-second models (Supporting Table 1).
GDM model fitting
We used the .NET GD Modeller software (Manion, 2012) to develop fitted models of
species compositional turnover. The .NET software has been developed by NSW OEH
Climate change refugia for terrestrial biodiversity 175