GAS_V3_R2: Generalised dissimilarity model of compositional turnover in land snail species for continental Australia at 9 second resolution using ANHAT data extracted April 2013

Collection Title:

GAS_V3_R2: Generalised dissimilarity model of compositional turnover in land snail species for continental Australia at 9 second resolution using ANHAT data extracted April 2013

Collection Description:

Compositional turnover patterns in land snail species across continental Australia were derived using Generalised Dissimilarity Modelling (GDM). These models use best-available biological data extracted from the ANHAT (Courtesy Australian Government Department of the Environment) current to April 2013 and spatial environmental predictor data compil... moreed at 9 second resolution (with novel climate seasonality predictors, undersampling covariates and >2 species aggregated per 9-second grid cell). 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 includes the source biological and environmental data, the GDM-fitted model, the GDM-scaled environmental predictors for the fitted-model which comprises substrate (constant) and 1990-centred climates, and a derived classification. Projections using past and future climates are not included here (available upon request). less

Field of Research:

Biogeography and Phylogeography ;  Bioinformatics ;  Climate Change Processes ;  Community Ecology ;  Conservation and Biodiversity ;  Natural Resource Management

DOI:

https://doi.org/10.4225/08/586ec6e4246ac

Start Date:

01 Jan 1900

End Date:

01 Apr 2013

Contact:Kristen Williams
 Kristen.Williams@csiro.au
Keywords:

ANHAT ;  land snails ;  gastropods ;  generalised dissimilarity model ;  GDM ;  scaled environmental predictors ;  substrate, 1990 climates ;  1960 historical climate, 2050 future climates

Related Links:

:	Williams et al, 2010: Harnessing Continent-Wide Biodiversity Datasets for Prioritising National Conservation Investment
:	Reside et al, 2013: Climate change refugia for terrestrial biodiversity
:	Laffan et al., 2010: Biodiverse Software
:	Williams et al, 2012: Which environmental variables should I use in my biodiversity model
:	Rosauer et al, 2014: phlyGDM and perl scripts for site-pair sampling

Lineage:

1. Biological data for land snails were extracted from the ANHAT database (April 2013) at the species taxonomic rank (matched to the census of Australian fauna), excluding alien/introduced taxa. Unmatched or uncertain taxa were excluded. Locations with a geoaccuracy > +/-1000m were excluded; locations that lacked a geoaccuracy estimate were included. Approximately 1.5M site-pairs (of potentially millions) were generated using Biodiverse software (in groups of 0.0025 degrees latitude and longitude) and Perl scripts (courtesy S Laffan and D Rosauer) as a stratified random sample of 85 IBRA v7 continental Australia bioregions (85 x 85 strata) weighted by 10% for selection of site-pairs within bioregions. Only grid cells with at least 2 species occurrences represented were used. A response weight variable was generated as the sum of the number of species at the two sites in the pair, irrespective of species in common). Two independent sets of site pairs were generated of the same size and site-pair selection parameters – for model “training” and “test” (validation). 2. The biological data grouped into 0.0025 degrees of latitude and longitude (the same as the raster grid centroids) using Biodiverse software (Laffan et al, 2010) were exported and summarized to generate the number of (unique) species in each group, the number of original (unique) latitude and longitude per group, and the number of records (unique species latitude and longitude). Three “under-sampling” covariates were subsequently generated for inclusion as candidate covariate predictors in the GDM models. The covariate calculation is detailed in the report by Williams et al. 2010 (see link in related materials, https://publications.csiro.au/rpr/pub?list=BRO&pid=csiro:EP102983). 2. Environmental data were compiled from best available sources of geology, soil, landform (including DEM derivatives) and climate. Climate data were derived using ANUCLIM v6.1 software with the 1990-centred (30 year average) surfaces and version 3.1, 9 second digital elevation model for Australia. Climate predictors were generated as the monthly minimum or maximum values (including a range of seasonality predictors as described in Williams et al, 2012; IJGIS, 26:2009). The ratio of topographically-shaded and slope/aspect-corrected incoming shortwave radiation relative to the unshaded radiation on a horizontal surface was used to adjust both monthly radiation and maximum temperature. Details pertaining to these calculations are published in Reside et al. 2013 (see link in related materials http://www.nccarf.edu.au/publications/climate-change-refugia-terrestrial-biodiversity). 3. GDM models were fitted using .NET software v3.x (courtesy G Manion, NSW Office of Environment and Heritage), selecting environmental predictors using backward elimination by testing the partial contribution of each predictor and removing the least significant predictor until all predictors explained at least 0.05% of the model deviance in the presence of all other included predictors. These models excluded Geographic Distance between site pairs. The potential for a 4th spline to better define the shape of the predictors was tested selectively for the dominant predictors using the model fit criterion of at least 0.05% additional partial deviance explained. Following these tests the significance of the predictors was again tested using backward elimination. Details of the resulting fitted model and the input data table are provided with the data package. 4. Transformed grids for the environmental predictor variables were generated for the final fitted model. The climatic predictors were replaced with past (1960-centred 75 year average) and six future (2050-centred 30 year averages) scenarios and the transformed grids generated also. The covariate predictors are not included in the set of transformed grids, and so assumed to have optimal values. Extrapolation error grids are also provided.

Credit:

noted in lineage and listed in related materials and supporting attachments

Licence:

CSIRO Data Licence

Organisations:

CSIRO (Australia)

Attribution Statement:

Williams, Kristen; Manion, Glenn; Perry, Justin; Harwood, Tom; Rosauer, Dan; Laffan, Shawn; Ferrier, Simon (2014): GAS_V3_R2: Generalised dissimilarity model of compositional turnover in land snail species for continental Australia at 9 second resolution using ANHAT data extracted April 2013. v1. CSIRO. Data Collection. https://doi.org/10.4225/08/586ec6e4246ac

Rights Statement:

All Rights (including copyright) CSIRO 2014.

Access:

The metadata and files (if any) are available to the public.