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:
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)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).
g.region raster=AusBio13 r.exdet -p reference=AusBio13@example,AusBio14,AusBio5,AusBio6 projection=SaBio13,SaBio14,SaBio5,SaBio6 output=AusSa
If you want, in addition, to cite this tool, you can use:
 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.r.mess
Latest change: Thursday Feb 03 09:32:35 2022 in commit: f17c792f5de56c64ecfbe63ec315307872cf9d5c
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