**-i**- Compute EB for individual variables
**-m**- Use mean values of IES layers to compute MES
**-n**- Use median values of IES layers to compute MES
**-o**- Use minimum values of IES layers to compute MES
**--overwrite**- Allow output files to overwrite existing files
**--help**- Print usage summary
**--verbose**- Verbose module output
**--quiet**- Quiet module output
**--ui**- Force launching GUI dialog

**env**=*names[,**names*,...]**[required]**- Environmental layers
- Raster map(s) of environmental conditions
**ref**=*names***[required]**- Reference area
- Sub-area (1) within region (1+0) for which to compute the EB
**output**=*names*- Root of name output layers
- Output MES layer (and root for IES layers if kept)
**file**=*name*- Name of output text file
- Name of output text file (csv format)
**digits**=*string*- Precision of your input layers values
- Default:
*5*

The messure is based on the Multivariate Environmental Similarity
(*MES*) surface, which was proposed by Elith et al (2015). To compute
the MES first the similarity of a point *P* to the conditions
in *N* with respect to variable *V* is computed. The
similarity is expressed as the deviation from the median of *V*
in *P* to those in *N*. This is done for all variables of
interest (V_{1}, V_{2}, ...V_{j}).

In the original equation proposed by Elith et al (2010) the final
*MES* of *P* is computed as the minimum of the
similarity values (*IES _{minimum}*) of the individual
variables (

The *MEB* is computed as the absolute difference of the median
of the *MES* in the whole target area (MES_{n}) and the
median of the *MES* in the subset (*MES _{s}*),
divided by the median absolute deviation
(

The addon creates a MES layer and a table (saved to csv file)
with the median value of each variable in the region and in the
reference area, the median absolute deviation (mad) and the
environmental bias (eb). Optionally, this can be computed for the
individual variables as well. The user has the option to have the
addon compute the *MEB* based on the *MES
* computed using the minimum, average and/or median of the IES layers
(see above)

van Breugel P, Kindt R, LillesÃ¸ J-PB, van Breugel M (2015) Environmental Gap Analysis to Prioritize Conservation Efforts in Eastern Africa. PLoS ONE 10(4): e0121444. doi: 10.1371/journal.pone.0121444

r.meb -m -n -o env=bio_1,bio_3,bio_9 ref=forestmap output=Test file=Test Median Test_MES_mean (all region) = 47.338 Median Test_MES_mean (ref. area) = 69.798 MAD Test_MES_mean (all region) = 14.594 EB = 1.539 Median Test_MES_median (all region) = 45.712 Median Test_MES_median (ref. area) = 69.897 MAD Test_MES_median (all region) = 18.786 EB = 1.287 Median Test_MES_minimum (all region) = 20.364 Median Test_MES_minimum (ref. area) = 55.807 MAD Test_MES_minimum (all region) = 15.096 EB = 2.348 The results are written to Test.csv

- Elith, J, Kearney, M, and Phillips, S. 2010. The art of modelling range-shifting species. Methods in Ecology and Evolution 1:330-342.
- van Breugel P, Kindt R, Lillesø J-PB, van Breugel M. 2015. Environmental Gap Analysis to Prioritize Conservation Efforts in Eastern Africa. PLoS ONE 10(4): e0121444. doi: 10.1371/journal.pone.0121444.

Available at: r.meb source code (history)

Main index | Raster index | Topics index | Keywords index | Graphical index | Full index

© 2003-2019 GRASS Development Team, GRASS GIS 7.8.2dev Reference Manual