- Quantification of novel uni- and multi-variate environments
r.exdet [-pde] reference=raster[,raster,...] [projection=raster[,raster,...]] [region=region] output=raster [--overwrite] [--help] [--verbose] [--quiet] [--ui]
- Most influential covariates (MIC)
- Mahalanobis distance in projection domain?
- Keep layer Mahalanobis distance in projection domain?
- Mahalanobis distance in reference domain
- Keep layer Mahalanobis distance in reference domain?
- Allow output files to overwrite existing files
- Print usage summary
- Verbose module output
- Quiet module output
- Force launching GUI dialog
- reference=raster[,raster,...] [required]
- Reference conditions
- Reference environmental conditions
- Projected conditions
- Projected conditions to be compared to reference conditions
- Projection region
- Region defining the area to be compared to the reference area
- output=raster [required]
- Suffix name output layers
- Root name of the output layers
Correlative species distribution models (SDMs) often involve some
degree of projection into conditions non-analogous to those under
which it has been calibrated. This projection into areas with novel
environmental conditions is risky as it may be ecologically and
statistically invalid. However, depending on the research question
it may be difficult to avoid or indeed the objective of research to
do so. An example is the prediction of potential distribution of
species under future climates. The latter may include conditions
hitherto not encountered in the area of interest. It is important to
identify such areas and to interpret model results with care.
The r.exdet function allows you to identify areas with
novel conditions, following methods developed by Mesgaran et al.
(2014) . This includes areas where conditions fall outside the
range of values observed in the reference / calibration data set (
NT1: Type 1 novelty) or areas with novel combinations between
the environmental variables (NT2: Type 2 novelty), which
Mesgaran et al. call the multivariate combination novelty index.
The type 1 (NT1) similarity is similar to how the
multi-environmental similarity measure (MESS) computes novel
climates . In both cases if a point is outside the range of a
given covariate, it gets a negative value based on its distance to
the minimum/maximum of that covariate. The difference is that the
MESS is based on the most negative value amongst these covariates.
The NT1, on the other hand, is the sum of all these distances. The NT1
thus accounts for all variables . The NT1 can have
infinite negative values to zero where zero indicates no
extrapolation beyond the univariate coverage of reference data.
The type 2 (NT2) similarity is based on the Mahalanobis
distance and is used to identify areas where conditions are within
the range of univariate variation but which exhibits novel
combinations between covariates. NT2 can range from zero up
to infinite positive values. Values ranging from 0 to 1 indicate
similarity (in terms of both univariate range and multivariate
combination), with values closer to zero being more similar. Values
larger than one are indicative of novel combinations.
r.exdet can also compute the most influential covariate (
MIC ). For areas with novel conditions, this is the variable
that has the lowest NT1 value. For areas with multivariate
combination novelty, this is the variable that yields the largest
percentage reduction in the Mahalanobis distance if dropped.
The function can be used to compare (1) conditions at two
different times (e.g., current climate conditions and climate
conditions in 2085). As input, the user needs to provide two
different sets of environmental variables, each representing
conditions at different times. The function can also be used to
compare the conditions in two different areas. This can be done in
three different ways:
- The user can provide two different sets of environmental
variables, each covering a different area.
- The user can provide a mask and a set of data layers describing
the reference conditions. Conditions outside the area defined by the
mask will then be compared with the conditions within the area defined
by the mask.
- The user can provide a
and a set of data layers describing the reference conditions.
Conditions in the region defined by the user are compared to the
conditions in the current computational region.
Some of the options can be combined. For example, the use can set
a mask, a set of layers describing current conditions (reference)
and a set of layers providing future conditions (projection). In
this case, the future conditions in the whole region are compared to
the current conditions within the area defined by the MASK. (todo:
provide some examples)
You can download a sample data set from http://www.climond.org/ExDet
The sample data contains 4 clipped Bioclim variable layers for
Australia and South Africa sourced from the CliMond dataset. In this
example we will use the Australia data as reference and the South
Africa data as projected or test. In the example below I will assume
you have downloaded the data and imported it in the currently open
location/mapset (the coordinate system is latlon, EPSG 4326).
r.exdet -p reference=AusBio13@example,AusBio14,AusBio5,AusBio6 projection=SaBio13,SaBio14,SaBio5,SaBio6 output=AusSa
When using this tool, please cite the paper describing the method in your publications or other derived products.
- Mesgaran, M.B., Cousens, R.D. and Webber, B.L. (2014) Here be dragons: a tool for quantifying novelty due to covariate range and correlation change when projecting species distribution models. Diversity & Distributions, 20: 1147-1159, DOI: 10.1111/ddi.12209.
If you want, in addition, to cite this tool, you can use:
- van Breugel, P. (2016) r.exdet, a GRASS GIS addon for the
quantification of novel uni- and multi-variate environments. URL:
 Mesgaran, M.B., Cousens, R.D. and Webber, B.L. (2014) Here be dragons: a tool for quantifying novelty due to covariate range and correlation change when projecting species distribution models. Diversity & Distributions, 20: 1147-1159, DOI: 10.1111/ddi.12209.
 ExDet: An stand alone extrapolation detection tool for the modelling of species distributions. URL: http://www.climond.org/ExDet.aspx
 Elith, J., Kearney, M. and Phillips, S. 2010. The art of modelling range-shifting species. Methods in Ecology and Evolution 1:330-342.
Paulo van Breugel, paulo at ecodiv.earth
r.exdet source code
Latest change: Thursday Feb 03 09:32:35 2022 in commit: f17c792f5de56c64ecfbe63ec315307872cf9d5c
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