Below, we summarise our modelling procedure. For more details, see our report.

Target species

We modelled habitat suitability for threatened landscape-managed and site-managed species (for more information see the Saving Our Species Program). Landscape-managed species are plants and animals that need broad landscape scale conservation projects. The objective for these species is to maximise the viability of species and their habitat by strategically investing in priority locations and management actions and working in partnership with stakeholders across NSW (OEH 2016). Some land-managed species might be widely distributed, and possess highly mobile or dispersed, or be affected by landscape-scale threats. Thus, recovery for these species should address threats such as habitat loss or degradation within a landscape. There are 98 landscape-managed species for which 81 had sufficient data for habitat suitability models to be developed.

Site-managed species are threatened plants and animals that can be secured by conservation projects at specific locations within NSW. For these species the objective is to maintain a 95% probability of having a viable population in the wild in 100 years from now, and ensure that the species' status under the TSC Act does not decline (OEH 2016). Different conservation actions can be implemented for those species, including weeding, controlling erosion or revegetation, and monitoring the results, among others. These actions allow the long term protection of these species securing their survival. There are approximately 440 site-managed species, and we developed models for 238 species (34 vertebrates and 204 plants).

Occurrence records for the species included in this study were obtained from (a) OEH Atlas; (b) Victoria's Biodiversity Atlas; and (c) the Australias Virtual Herbarium (AVH) hub of the Atlas of Living Australia (ALA, Note that we did not fit models for species with less than 20 unique location records after data cleaning (i.e. where we defined unique as 1 km x 1 km grid cells).

Climate data

While Global Climate Models (GCMs) project mean annual temperature to increase as the century progresses, there remains uncertainty in the magnitude of this change. Similarly, across alternate GCMs there can be considerable difference with respect to the magnitude and direction of precipitation changes. This means that multiple climate futures need to be considered when trying to understand future impacts and develop adaptation plans.

We used data derived from climate simulations performed as part of the NSW and ACT Regional Climate Modelling (NARCliM) project1. These data comprise climate surfaces projected by four GCMs (Table 1). Importantly, these scenarios have undergone a rigorous selection process1. The models project futures that we refer to as "Warmer/wetter", "Hotter/little change" in precipitation, "Hotter/wetter", and "Warmer/drier", given the future state with respect to mean annual temperature and annual precipitation of the baseline period (1990-2009). It is important to recognise that, at present, these futures are equally plausible.

Table 1. Climate futures used in this study. GCMs assumed the SRES A2 emissions scenario2.

Climate FutureGCMRepresents a future that is:
Warmer/Wetter MIROC3.2(medres) Warmer and wetter than present, particularly in NE NSW, although alpine regions are projected to become drier.
Hotter/Little change ECHAM5/MPI-OM Has the greatest increase in temperature, of the four scenarios. Precipitation trend varies across the state (slightly wetter in the NE and coastal regions, slightly drier elsewhere).
Hotter/Wetter CCCMA
Warmer than MIROC, and wetter across most of the state, although areas in NW and SE of the state may be slightly drier.
Warmer/Drier CSIRO-Mk3.0 Warmer than present, and the driest of the four models.

Projections from each climate scenario were downscaled, then summarised to a standard set of 19 bioclimatic variables commonly used in species distribution models (SDMs). These data were generated for each of the NARCliM time periods, representing baseline climate (1990-2009), near-future (2020-2039) and distant future (2060-2079). We then interpolated these data for intervening decades, resulting in climate data for 2000, 2010, 2020, 2030, 2040, 2050, 2060, and 2070. Finally, data were transformed to a 1 x 1 km resolution.

From the 19 bioclimatic variables, we selected a subset for model calibration: (1) mean diurnal temperature range; (2) temperature seasonality (the coefficient of variation of weekly mean temperature); (3) maximum temperature of the warmest week; (4) minimum temperature of the coldest week; (5) precipitation of the wettest week; (6) precipitation of the driest week; and (7) precipitation seasonality (the coefficient of variation of weekly total precipitation). These represent climatic variables that influence ecophysiological functions, and hence, species' distributions.

Static environmental data

To supplement bioclimate predictors, we also used data describing topsoil attributes, weathering intensity and topographic complexity. These data were originally at a spatial resolution of ~100m, and were resampled to 1 km.

Soil: These layers were based on spectral characteristics of soil samples from across the continent. Soil1 describes the distribution of highly weathered soils, soil2 the distribution of soils with large amounts of organic matter, and soil3 the distribution of low relief landscapes with soils containing abundant smectite (clay) minerals3.

Weathering intensity: This layer characterises the regolith, which has a major influence on geomorphic and hydrologic processes4.

Topographic complexity: We used two layers characterising topographic complexity. The Topographic Wetness Index5 (TWI) estimates the relative wetness within a catchment, while the Topographic Position Index6 (TPI) categorises grid cells as belonging to the upper, middle and lower parts of the landscape.

Habitat suitability models

We modelled habitat suitability with Maxent version 3.3.3k7,8, a machine learning approach to habitat suitability modelling known for its high performance9. Maxent produces a grid, where the value of each grid cell may range between 0 to 1. Values can be interpreted as a relative index of habitat suitability with respect to the included predictors. Locations with higher values are deemed to have greater suitability for the modelled species7,10. More details on the modelling procedure can be found in our report to OEH.

For each species, we calibrated models using three sets of the predictor variables described above. Of the three models run per species, the model resulting in the highest predictive power was selected to project onto future climate scenarios. When generating projections of future habitat suitability, soil predictors were assumed to remain static.

Current and future habitat suitability

Using Maxent, habitat suitability for each species was estimated for the 'current' period (based on 2000), as well as for the future climates for each decade from 2010 to 2070. These maps can be viewed on our website, by searching for a given species, selecting one of the four climate scenarios from the layers icon at the top right, and playing the animation across the time periods.

Maps with cell values ranging from 0 (unsuitable) to 1 (highly suitable) were then converted to binary layers indicating suitable/unsuitable habitat. Thresholded maps can be viewed in the website by selecting the Threshold option.

For each decade after 2000, the habitat suitability surfaces for each species were summarised to a single layer indicating the number of scenarios in which a given grid cell was classified as suitable. On the website, these maps can be visualised using the Consensus button under the climate scenarios.

The website can also be used to identify populations that may be at more or less risk from climate change. Suitable regions that currently contain populations, and are projected to remain suitable in the future, can be classified as internal refugia. Conversely, regions from which populations are currently absent but that become or remain suitable in the future can be classified as external refugia. Populations likely to be least at risk from climate change are those with suitable habitat under all of the scenarios for a given time period (and can be identified by selecting the Consensus option on the website). In our reports, we refer to these regions as areas of consensus.

Identifying multi-species refugia for threatened species

Localities that are likely to continue to have suitable climate for multiple threatened species represent areas that are particularly valuable for conservation. To identify these higher value refugia, we combined maps of each species' internal refugia for each climate scenario, thereby calculating the number of species for which each grid cell was suitable. In addition, we repeated this process for maps of areas of consensus, calculating the number of species for which each grid cell was suitable under all climate scenarios. These results can be viewed for landscape- and site-managed species separately.

1. Evans, J. P. et al. Design of a regional climate modelling projection ensemble experiment--NARCliM. Geoscientific Model Development 7, 621-629 (2014).
2. Nakicenovic, N. et al. Special report on emissions scenarios (SRES), a special report of Working Group III of the Intergovernmental Panel on Climate Change. (Cambridge University Press, 2000).
3. Viscarra Rossel, R. & Chen, C. Digitally mapping the information content of visible-near infrared spectra of surficial Australian soils. Remote Sensing of Environment 115, 1443-1455 (2011).
4. Wilford, J. A weathering intensity index for the Australian continent using airborne gamma-ray spectrometry and digital terrain analysis. Geoderma 183-184, 124-142 (2012/8).
5. CSIRO. Topographic Wetness Index (3" resolution) derived from 1 second DEM-H version 1.0. (2012).
6. CSIRO. Topographic position index (3" resolution) derived from 1 second DEM-S version 0.1. (2012).
7. Phillips, S. J., Anderson, R. P. & Schapire, R. E. Maximum entropy modeling of species geographic distributions. Ecological Modelling 190, 231-259 (2006).
8. Elith, J. & Leathwick, J. R. Species distribution models: ecological explanation and prediction across space and time. Annual Review of Ecology, Evolution and Systematics 40, 677-697 (2009).
9. Elith, J. et al. Novel methods improve prediction of species' distributions from occurrence data. Ecography 29, 129-151 (2006).
10. Phillips, S. J. & Dudík, M. Modeling of species distributions with Maxent: new extensions and a comprehensive evaluation. Ecography 31, 161-175 (2008).