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MAM_V1_R2: Generalised dissimilarity model of compositional turnover in mammal species for continental Australia at 9 second resolution using ALA data extracted February 2014

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MAM_V1_R2: Generalised dissimilarity model of compositional turnover in mammal species for continental Australia at 9 second resolution using ALA data extracted February 2014


Compositional turnover patterns in mammal species across continental Australia were derived using Generalised Dissimilarity Modelling (GDM). These models use best-available biological data extracted from the Atlas of Living Australia current to 26th February 2014 and spatial environmental predictor data compiled at 9 second resolution (with novel c... more


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


https://doi.org/10.4225/08/5872c3174783c


01 Jan 1975


26 Feb 2014


Kristen Williams
Kristen.Williams@csiro.au

ALA Atlas of Living Australia mammals generalised dissimilarity model GDM scaled environmental predictors substrate, 1990 climates 1960 historical climate, 2050 future climates


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Williams et al, 2010: Harnessing Continent-Wide Biodiversity Datasets for Prioritising National Conservation Investment

Attribution
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Reside et al, 2013: Climate change refugia for terrestrial biodiversity

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Laffan et al., 2010: Biodiverse Software

Attribution
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Williams et al, 2012: Which environmental variables should I use in my biodiversity model

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AdaptNRM: Climate change adaption tools and resources for NRMs

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Rosauer et al, 2014: phlyGDM and perl scripts for site-pair sampling

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108 A0/A3 digital image of map posters accompanying AdaptNRM Guide: Implications of Climate Change for Biodiversity: a community-level modelling approach

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High resolution AdaptNRM Guide: Implications of Climate Change for Biodiversity: a community-level modelling approach

Attribution

1. Biological data for amphibians were extracted from the Atlas of Living Australia (26th February 2014), 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.


noted in lineage and listed in related materials and supporting attachments


CSIRO Data Licence


CSIRO (Australia)


Williams, Kristen; Manion, Glenn; Michael, O'Connor; Perry, Justin; Harwood, Tom; Rosauer, Dan; Laffan, Shawn; Ferrier, Simon (2014): MAM_V1_R2: Generalised dissimilarity model of compositional turnover in mammal species for continental Australia at 9 second resolution using ALA data extracted February 2014. v1. CSIRO. Data Collection. https://doi.org/10.4225/08/5872c3174783c


All Rights (including copyright) CSIRO 2014.


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

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Location Details

8°0′0″ S


43°44′33″ S


154°0′0″ E


112°54′0″ E


GDA94


More about this Collection

Kristen Williams


Research Group Leader





Grid




eng


UTF8


Farming


About this Project

1173.3 Strat: WPC Global Prot Area Ass


This project employs macroecological modelling and analysis techniques pioneered by CSIRO to assess present and future representation of terrestrial biodiversity within the world’s protected-area system. The project aims to address four main questions: 1. How adequately does the world’s protected-area system represent current patterns of compositio... more


Kristen Williams


Macroecological Modelling


This activity employed state-of-the-art techniques for community-level modelling integrating best-available existing biological and environmental data to map patterns of spatial turnover in terrestrial species composition (using generalised dissimilarity models) for a wide range of biological groups - vertebrates, invertebrates and vascular plants.... more


Modelling


Generalised Dissimilarity Modelling Software; ARCGIS mapping software; Biodiverse software


Kristen Williams


Glenn Manion


O'Connor Michael


Justin Perry


Tom Harwood


Dan Rosauer


Shawn Laffan


Simon Ferrier


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