**-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 measure is based on the Multivariate Environmental Similarity
(*MES*) surface, which was proposed by Elith et al (2010). The MESS
measures the similarity in a set of variables between any given location in
an area and the locations in a reference area.

The first step to compute the MES for a location *P* is to calculate
how similar conditions in *P* are compared to the conditions
in the reference area *N*, based on variable *V _{i}*.
The similarity
is expressed as the deviation from

In the original equation proposed by Elith et al (2010) the final
*MES* in location *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)

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)

Latest change: Tue May 3 10:17:58 2022 in commit: 40917f70917f4c7de58b1f3cedd0f47905eba03e

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