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)

Collection Title:

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)

Collection Description:

****UPDATED**** This collection contains a 9-second gridded dataset (ESRI binary float format in GDA94) showing the generalised projected future (2050-centred) potential pre-clearing vegetation patterns of 77 Major Vegetation Sub-groups (MVS classes) derived from the maximum of their respective predicted probabilities for each grid cell (V_85MIR50_... moreMXC - MaxClass). Two additional datasets show the maximum probability in each gird cell that was used to assign that class (V_85MIR50_MXP - MaxProb), and the number of classes with non-zero probabilities with potential to represent their type in each grid cell (V_85MIR50_NMC - NumClasses). The predicted probabilities for each class were derived based on their distribution patterns and correlation with baseline ecological 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. These details are provided with the data package “Potential 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)”. The GDM-scaled environmental predictors are available with the “VAS_v5_r11” data package. This dataset projects the generalised potential pre-clearing vegetation patterns based on 2050-centred (30 year average) future climates derived from the MIROC5 global climate model for the emission scenario defined by a representative concentration pathway of 8.5. The accuracy of projections is limited by the quality of the vegetation mapping used to train the models and the accuracy of environmental variables delimiting substrate boundaries and disturbance regimes. Uncertainty or errors in the underlying vegetation map and environmental data will be reproduced by the models. Furthermore, variables describing the relationship between extreme climatic events and ecological disturbance regimes, that have significant structural influences on vegetation, are not directly included in these models. The data are provided as 9-second (approximately 250m), ESRI binary float grid format in GDA94. 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/10.4225/08/55C7FD4376ACE

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 ;  AdaptNRM ;  biodiversity ;  MIROC5 ;  RCP 8.5

Related Links:

:	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)
:	Potential 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: 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: CanESM2 RCP 8.5)
:	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)
:	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)

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. Generalised maps assembled from individual projected vegetation class probabilities indicate which of the baseline vegetation classes may be most suited to the environment of a particular location in the future. However, the suitability of that vegetation class to the future environment may still be relatively low and a number of other vegetation classes may be almost equally suited. A more conservative view can be obtained from maps of the projected probabilities for individual vegetation classes (see related materials for the individual probability datasets).

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 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.' (The Australian National University, Fenner School of Environment and Society: Canberra) 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 (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). v2. CSIRO. Data Collection. https://doi.org/10.4225/08/55C7FD4376ACE

Rights Statement:

All Rights (including copyright) CSIRO Australia 2013.

Access:

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