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Potential vegetation distribution: New South Wales - 3arcsecond gridded 1990-centred baseline predictions of the pre-clearing extents of "Keith" Vegetation Classes using kernel regression with GDM-scaled environments for Vascular Plants

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About this Collection

Potential vegetation distribution: New South Wales - 3arcsecond gridded 1990-centred baseline predictions of the pre-clearing extents of "Keith" Vegetation Classes using kernel regression with GDM-scaled environments for Vascular Plants


This collection contains 3-arcsecond gridded datasets (ESRI binary float format in WGS84) showing the baseline (1990-centred) predicted potential distribution of 102 (class numbers range between 1 and 125) "Keith" Vegetation Classes for New South Wales based on their correlation with baseline ecological environments (c.1990 climates, subs... more


Biogeography and Phylogeography Bioinformatics Community Ecology Conservation and Biodiversity


https://doi.org/10.4225/08/597fc4d49ee2d


01 Jan 1975


01 Jan 2005


CSIRO Enquiries
CSIROEnquiries@csiro.au
1300 363 400

Vascular plants kernel regression generalised dissimilarity model GDM-scaled environmental variables 1990-centred climates New South Wales Keith vegetation class biodiversity


Publication:

Designing landscapes for biodiversity under climate change: final report

Attribution

Predictive models of vegetation classes were derived using the two-step process originally developed for individual species distribution modelling with GDM (described in Elith et al. 2006). The first step uses a Generalised Dissimilarity Model (GDM) of vascular plants to derive a set of scaled environmental variables for current (e.g. 1990 baseline) climates. The second step applies this data in a kernel regression to predict each vegetation class using training data. The training data comprised 9,951 locations defined from a geographically even sample. These locations were then attributed with the baseline values of the GDM-scaled environmental variables. At any location (grid cell), the kernel regression considers the surrounding relative density of training sites of the target vegetation class as a proportion of other types and generates a predicted probability for that class for the focal grid cell. A probability surface for the predicted proportions, varying from 0 to 1, is generated for each of the 102 mapped Vegetation Classes. Some classes did not exist or were too rare to be represented in the sample. This method is infrequently used in ecology because of the need first to scale and reduce the dimensionality of the predictor variables (Lowe 1995). The GDM step reduces dimensionality (by choosing the variables to use) and scales the predictor variables using similarity-decay functions which equate to the multivariate distances expected by kernel regression. The kernel regression thus incorporates interactions by modelling ecological distances and vegetation class densities within a truly multivariate predictor space, with no assumption of additivity. Kernel regression aims to optimise model performance in terms of the accuracy of predictions at any single location according to the area predicted for each class. The predicted proportions of common vegetation types are typically greater than for rarer vegetation types. Therefore, cell by cell, the class with the maximum probability selected to represent spatially varying vegetation class mosaics on a single map (essentially one dimension) will often be the common type, at the expense of locally rare and nationally rare types. Therefore, the best way to view the results, and to inform planning, is the individual probability surfaces. These properly reveal where the rarer vegetation types have a likelihood of persistence. Higher probabilities associated with other vegetation types at the same location can be viewed as a measure of the extent to which those other vegetation types may compete. However the outcome, at least in the medium term, may be more driven by the extant occurrence of ecosystems and their ability to persist under marginal conditions. Source of vegetation class data: KEITH, D. A. (2002) A compilation map of native vegetation for New South Wales. NSW Biodiversity Strategy, New South Wales Government. KEITH, D. A. (2004) Ocean shores to desert dunes, Hurstville, Department of Environment and Conservation (NSW).


This dataset series and its use is described in Doerr VAJ, Williams KJ, Drielsma M, Doerr ED, Davies MJ, Love J, Langston A, Low Choy S, Manion G, Cawsey EM, McGinness HM, Jovanovic T, Crawford D, Austin M & Ferrier S 2013, Designing landscapes for biodiversity under climate change: Final report, National Climate Change Adaptation Research Facility, Gold Coast, 260 pp. References cited: Elith J, Graham CH, Anderson RP, Dudik M, Ferrier S, Guisan A, Hijmans RJ, Huettmann F, Leathwick JR, Lehmann A, Li J, Lohmann LG, Loiselle BA, Manion G, Moritz C, Nakamura M, Nakazawa Y, Overton JM, Peterson AT, Phillips SJ, Richardson K, Scachetti-Pereira R, Schapire RE, Soberon J, Williams S, Wisz MS, Zimmermann NE (2006) Novel methods improve prediction of species' distributions from occurrence data. Ecography 29(2), 129-151. Lowe DG (1995) Similarity metric learning for a variable-kernel classifier. Neural Computation 7(1), 72-85. Xu T, Hutchinson M (2011) 'ANUCLIM Version 6.1 User Guide.'


Creative Commons Attribution 4.0 International Licence


CSIRO (Australia), Office of Environment and Heritage, New South Wales (Australia)


Williams, Kristen; Manion, Glenn; Harwood, Tom (2017): Potential vegetation distribution: New South Wales - 3arcsecond gridded 1990-centred baseline predictions of the pre-clearing extents of "Keith" Vegetation Classes using kernel regression with GDM-scaled environments for Vascular Plants. v2. CSIRO. Data Collection. https://doi.org/10.4225/08/597fc4d49ee2d


All Rights (including copyright) CSIRO 2017.


Although the associated metadata is public, the files (if any) have not been approved for general release. Please phone or email the contact person for this collection to discuss access to the files.

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Location Details

27°0′17.2224″ S


40°0′1.5012″ S


153°59′41.3376″ E


139°59′58.4988″ E


WGS84


More about this Collection

Kristen Williams


Research Group Leader





Raster




eng


UTF8


Biota


About this Project

1157.1 TB NARP Best Practice Landscape


Landscape design – the particular placement of areas devoted to restoration of native vegetation at landscape scales – is a primary approach to climate adaptation for biodiversity management. It may facilitate the maintenance of larger populations as well as shifts in species distributions, both of which should help native species adjust to changi... more


Veronica Doerr


Macroecological modelling of vegetation patterns using GDM and kernel regression


Spatial models of present day native vegetation patterns were derived from estimated likelihoods of occurrence of vegetation classes using a simple kernel regression procedure (Lowe 1995) applied within the transformed environmental space generated by a generalised dissimilarity model (GDM, Ferrier et al. 2007) of vascular plant compositional turno... more


Modelling


GIS software, GDM and kernel regression software


Kristen Williams


Glenn Manion


Tom Harwood


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