Projected vegetation redistribution: Australia - 9second gridded projection to 2050, pre-clearing extents of 77 Major Vegetation Sub-groups using kernel regression with GDM-scaled environments for Vascular Plants (GDM: VAS_v5_r11; CMIP5: CanESM2 RCP 8.5)

Collection Title:

Projected vegetation redistribution: Australia - 9second gridded projection to 2050, pre-clearing extents of 77 Major Vegetation Sub-groups using kernel regression with GDM-scaled environments for Vascular Plants (GDM: VAS_v5_r11; CMIP5: CanESM2 RCP 8.5)

Collection Description:

Updated to include UNCON079 This collection contains 9-second gridded datasets (ESRI binary float format in GDA94) showing the projected future (2050-centred) potential vegetation redistribution of 77 Major Vegetation Sub-groups (MVS classes) for continental Australia based on their pre-clearing distribution patterns and correlation with baseline e... morecological environments (c.1990 climates, substrate and landform). The pre-clearing vegetation patterns and classification derive from version 4.1 of “Australia - Estimated Pre1750 Major Vegetation Groups - NVIS Version 4.1 (Albers 100m analysis product)” developed by the Australian Government Department of the Environment and collaborating State agencies. A kernel regression was used with c.155,000 locations of training classes for the 77 MVS classes attributed with 17 GDM-scaled environmental predictors for Vascular Plants representing baseline ecological environments. The training class data input to the kernel regression is provided with this package. The GDM-scaled environmental predictors are available with the “VAS_v5_r11” data package. Using the 1990 baseline training MVS class data, and without constraining the prediction to pre-existing map boundaries, the kernel regression projected to 2050 the distribution of the 77 Major Vegetation Sub-groups using 2050-centred (30 year average) future climates derived from the CanESM2 global climate model for the emission scenario defined by a representative concentration pathway of 8.5. The kernel regression generates unconstrained probabilities varying in the range from 0 and up to 1 for each of the 77 MVS classes. The data are provided as 9-second (approximately 250m), ESRI binary float grid format in GDA94. Each class is denoted “UNCON###”, where the number refers to the code originally assigned to that MVS class by the supplier. A lookup table linking the MVS classes to the output codes and descriptive title is provided. Generalised representations of the vegetation classes derived from the individual class probabilities as the maximum probability in any grid cell are provided separately (see related information). There are three dataset packages in this series: 1) 1990 predictions of MVS classes; 2) 2050 CanESM2 RCP 8.5 predictions of MVS classes; 3) 2050 MIROC5 RCP 8.5 predictions of MVS classes. This dataset series and its use is described in the AdaptNRM Guide “Helping biodiversity adapt to climate change: a community-level modelling approach”, available online at: www.adaptnrm.org less

Field of Research:

Biogeography and Phylogeography ;  Bioinformatics ;  Community Ecology ;  Conservation and Biodiversity ;  Ecological Impacts of Climate Change

DOI:

https://doi.org/10.4225/08/56AEF0F339BDE

Start Date:

01 Jan 1975

End Date:

01 Jan 2065

Contact:CSIRO Enquiries
 CSIROEnquiries@csiro.au
 1300 363 400
Keywords:

Vascular plants ;  VAS_v5_r11 ;  ANHAT ;  kernel regression ;  generalised dissimilarity model ;  GDM-scaled environmental variables ;  1990-centred climates ;  2050-centred future climates ;  AdaptNRM ;  biodiversity ;  CanESM2 ;  RCP 8.5

Related Links:

:	AdaptNRM: Climate change adaption tools and resources for NRMs
:	VAS_v5_r11: Generalised dissimilarity model of compositional turnover in vascular plant species for continental Australia at 9 second resolution using ANHAT data extracted April 2013
:	Australia - Estimated Pre1750 Major Vegetation Subgroups - NVIS Version 4.1 (Albers 100m analysis product)
:	Projected vegetation redistribution: Australia - 9second gridded projection to 2050, pre-clearing extents of 77 Major Vegetation Sub-groups using kernel regression with GDM-scaled environments for Vascular Plants (GDM: VAS_v5_r11; CMIP5: MIROC5 RCP 8.5)
:	Potential vegetation distribution: Australia - 9second gridded 1990-centred baseline predictions of the pre-clearing extents of 77 Major Vegetation Sub-groups using kernel regression with GDM-scaled environments for Vascular Plants (GDM: VAS_v5_r11)
:	AdaptNRM Module - Helping Biodiversity Adapt: Supporting climate adaptation planning using a community-level modelling approach
:	Potential vegetation distribution (MaxClass): Australia - 9s gridded 1990-centred baseline prediction, maximum probability class generalised pre-clearing patterns of Major Vegetation Sub-groups using kernel regression with Vascular Plants GDM (VAS_v5_r11)
:	Projected vegetation redistribution (MaxClass): Australia - 9sec gridded projection to 2050, maximum probability class generalised pre-clearing patterns of Major Vegetation Sub-groups using kernel regression with GDM (VAS_v5_r11) (CMIP5: CanESM2 RCP 8.5)
:	Projected vegetation redistribution (MaxClass): Australia - 9sec gridded projection to 2050, maximum probability class generalised pre-clearing patterns of Major Vegetation Sub-groups using kernel regression with GDM (VAS_v5_r11) (CMIP5: MIROC5 RCP 8.5)

Supporting Files:

MVSp_Class_LUT.csv

 

LookupTable_for_NVIS41_MVS_pre1750.docx

Lineage:

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 (VAS_V5_R11) to derive a set of scaled environmental variables for current (e.g. 1990 baseline) and future climates (e.g. 2050). The second step applies this data in a kernel regression to predict each vegetation class using training data derived from the pre-clearing mapping of 77 Major Vegetation Sub-groups. The training data comprised c.155,000 locations defined by randomly sampling within each vegetation class, proportional to their observed areal extent. These locations were then attributed with the baseline values of the GDM-scaled environmental variables. Separate kernel regressions were then run for the baseline and future climate scenarios using the baseline training data. In this way, the future distribution of each vegetation class was projected based on its affinity with present-day ecological environments. 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 77 mapped Major Vegetation Sub-groups. 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.

Credit:

This dataset series and its use is described in the AdaptNRM Guide “Helping biodiversity adapt to climate change: a community-level modelling approach”, available online at: www.adaptnrm.org The lineage identifies datasets that are elsewhere described: html links for those datasets are given in the related materials. references cited in lineage: 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.

Licence:

CSIRO Data Licence

Organisations:

Atlas of Living Australia (Australia), Australian Government Department of the Environment (Australia), CSIRO (Australia), NSW Office of Environment and Heritage (Australia)

Attribution Statement:

Williams, Kristen; Manion, Glenn; Ferrier, Simon; Prober, Suzanne; Harwood, Tom; Perry, Justin; Ota, Noboru (2013): Projected vegetation redistribution: Australia - 9second gridded projection to 2050, pre-clearing extents of 77 Major Vegetation Sub-groups using kernel regression with GDM-scaled environments for Vascular Plants (GDM: VAS_v5_r11; CMIP5: CanESM2 RCP 8.5). v2. CSIRO. Data Collection. https://doi.org/10.4225/08/56AEF0F339BDE

Rights Statement:

All Rights (including copyright) CSIRO 2013.

Access:

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