This internal biomod2 function allows the user to run a rectilinear surface range envelop (SRE) (equivalent to BIOCLIM) using the extreme percentiles (as recommended by Nix or Busby, see References and Details).
bm_SRE(
resp.var = NULL,
expl.var = NULL,
new.env = NULL,
quant = 0.025,
do.extrem = FALSE
)a vector, a SpatVector
without associated data (if presence-only),
or a SpatVector object containing binary data
(0 : absence, 1 : presence, NA : indeterminate)
for a single species that will be used to build the species distribution model(s)
Note that old format from sp are still supported such as
SpatialPoints (if presence-only) or SpatialPointsDataFrame
object containing binary data.
a matrix, data.frame, SpatVector
or SpatRaster object containing the explanatory variables (in
columns or layers) that will be used to build the SRE model
Note that old format from raster and sp are still supported such as
RasterStack and SpatialPointsDataFrame objects.
a matrix, data.frame, SpatVector
or SpatRaster object containing the explanatory variables (in
columns or layers) that will be used to predict the SRE model
Note that old format from raster and sp are still supported such as
RasterStack and SpatialPointsDataFrame objects.
a numeric between 0 and 0.5 defining the half-quantile
corresponding to the most extreme value for each variable not to be taken into account for
determining the tolerance boundaries of the considered species (see Details)
(optional, default FALSE)
A logical value defining whether a matrix containing extreme conditions
supported should be returned or not
A vector or a SpatRaster object, containing binary
(0 or 1) values.
Please refer to References to get more information about surface range envelop models.
This method is highly influenced by the extremes of the data input. Whereas a linear model
can discriminate the extreme values from the main tendency, the SRE considers them as
important as any other data point leading to changes in predictions.
The more (non-collinear) variables, the more restrictive the model will be.
Predictions are returned as binary (0 or 1) values, a site being either
potentially suitable for all the variables, or out of bounds for at least one variable and
therefore considered unsuitable.
quant determines the threshold from which the data will be taken into account for
calibration. The default value of 0.05 induces that the 5% most extreme values
will be avoided for each variable on each side of its distribution along the gradient, meaning
that a total of 10% of the data will not be considered.
Nix, H.A., 1986. A biogeographic analysis of Australian elapid snakes. In: Atlas of Elapid Snakes of Australia. (Ed.) R. Longmore, pp. 4-15. Australian Flora and Fauna Series Number 7. Australian Government Publishing Service: Canberra.
Busby, Jeremy. BIOCLIM - a bioclimate analysis and prediction system. Plant protection quarterly 6 (1991): 8-9.
bm_PseudoAbsences, BIOMOD_FormatingData,
bm_ModelingOptions, bm_Tuning,
bm_RunModelsLoop, BIOMOD_Modeling,
Other Secondary functions:
bm_BinaryTransformation(),
bm_CrossValidation(),
bm_FindOptimStat(),
bm_MakeFormula(),
bm_ModelingOptions(),
bm_PlotEvalBoxplot(),
bm_PlotEvalMean(),
bm_PlotRangeSize(),
bm_PlotResponseCurves(),
bm_PlotVarImpBoxplot(),
bm_PseudoAbsences(),
bm_RunModelsLoop(),
bm_SampleBinaryVector(),
bm_SampleFactorLevels(),
bm_Tuning(),
bm_VariablesImportance()
library(terra)
## Load real data
data(DataSpecies)
myResp.r <- as.numeric(DataSpecies[, 'GuloGulo'])
data(bioclim_current)
myExpl.r <- rast(bioclim_current)
myRespXY <- DataSpecies[which(myResp.r == 1), c('X_WGS84', 'Y_WGS84')]
myResp.v <- classify(subset(myExpl.r, 1),
matrix(c(-Inf, Inf, 0), ncol = 3, byrow = TRUE))
myResp.v[cellFromXY(myResp.v, myRespXY)] <- 1
## Compute SRE for several quantile values
sre.100 <- bm_SRE(resp.var = myResp.v,
expl.var = myExpl.r,
new.env = myExpl.r,
quant = 0)
sre.095 <- bm_SRE(resp.var = myResp.v,
expl.var = myExpl.r,
new.env = myExpl.r,
quant = 0.025)
sre.090 <- bm_SRE(resp.var = myResp.v,
expl.var = myExpl.r,
new.env = myExpl.r,
quant = 0.05)
## Visualize results
res <- c(myResp.v, sre.100, sre.095, sre.090)
names(res) <- c("Original distribution", "Full data calibration"
, "Over 95 percent", "Over 90 percent")
plot(res)