biomod2: BIOMOD_EnsembleForecasting – R documentation

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BIOMOD_EnsembleForecasting

Ensemble projections of species over space and time

Description

This function use projections of ‘individual models’ and ensemble models from BIOMOD_EnsembleModeling to build an ensemble of species' projections over space and time.

Usage

BIOMOD_EnsembleForecasting( EM.output,
                            projection.output = NULL,
                            new.env = NULL,
                            xy.new.env = NULL,
                            selected.models = 'all',
                            proj.name = NULL,
                            binary.meth = NULL,
                            filtered.meth = NULL,
                            compress = TRUE,
                            ...)

Arguments

`EM.output`	a `"BIOMOD.EnsembleModeling.out"` returned by `BIOMOD_EnsembleModeling`
`projection.output`	a `"BIOMOD.projection.out"` returned by `BIOMOD_Projection`
`new.env`	a `RasterStack` or a `data.frame` object containing explanatory data for the studied area. Needed only if `projection.output` is `NULL`. Prefer to use `"BIOMOD.projection.out"` if you have ever done the calculations.
`xy.new.env`	the matching coordinates of `new.env` if `new.env` is defined and if it is a `data.frame`
`selected.models`	if not 'all', a character vector containing a subset of ensemble-models you want make projection
`proj.name`	the projection name (results will be saved within proj_proj.name directory). Only needed if `projection.output` is `NULL`
`binary.meth`	vector specifying the names of evaluation metrics and associated thresholds to transform the probabilities of presence into presence and absence (binary transformation).
`filtered.meth`	vector specifying the names of evaluation metrics and associated thresholds to transform into 0 the probabilities of presence lower than the thresholds.
`compress`	boolean or character, the compression format of objects stored on your hard drive. May be one of ‘TRUE’, ‘FALSE’, ‘xz’ or ‘gzip’ (see `save`)
`...`	further arguments (see details)

Details

This function requires to have successfully run biomod2 modeling, ensemble-modeling and projection steps. Ensemble projections will be created in respect to projection.output projections, which are combined following EM.output ensemble-modeling rules.

The ‘total.consensus’ projection is basically the mean of all projections (for having only one output).

... may be :

on_0_1000:logical, if TRUE (default), 0 - 1 probabilities are converted into a 0 - 1000 integer scale. This implies a lot of memory saving. User that want to comeback on a 0 - 1 scale latter will just have to divide all projections by 1000

Value

Nothing returned but specific ‘projection files’ are saved on the hard drive projection folder. This files are either an array or a RasterStack depending the original projections data type. Load these created files to plot and work with them.

Author(s)

Wilfried Thuiller, Damien Georges, Robin Engler

Examples

# 0. Load data & Selecting Data
# species occurrences
DataSpecies <- read.csv(system.file("external/species/mammals_table.csv",
                                    package="biomod2"), row.names = 1)
head(DataSpecies)

# the name of studied species
myRespName <- 'GuloGulo'

# the presence/absences data for our species 
myResp <- as.numeric(DataSpecies[,myRespName])

# the XY coordinates of species data
myRespXY <- DataSpecies[,c("X_WGS84","Y_WGS84")]


# Environmental variables extracted from BIOCLIM (bio_3, bio_4, bio_7, bio_11 & bio_12)
myExpl = raster::stack( system.file( "external/bioclim/current/bio3.grd", 
                     package="biomod2"),
                system.file( "external/bioclim/current/bio4.grd", 
                             package="biomod2"), 
                system.file( "external/bioclim/current/bio7.grd", 
                             package="biomod2"),  
                system.file( "external/bioclim/current/bio11.grd", 
                             package="biomod2"), 
                system.file( "external/bioclim/current/bio12.grd", 
                             package="biomod2"))

# 1. Formatting Data
myBiomodData <- BIOMOD_FormatingData(resp.var = myResp,
                                     expl.var = myExpl,
                                     resp.xy = myRespXY,
                                     resp.name = myRespName)
                                                                     
# 2. Defining Models Options using default options.
myBiomodOption <- BIOMOD_ModelingOptions()

# 3. Running the models 
myBiomodModelOut <- BIOMOD_Modeling( myBiomodData, 
                                       models = c('RF'), 
                                       models.options = myBiomodOption, 
                                       NbRunEval=2, 
                                       DataSplit=60, 
                                       Yweights=NULL, 
                                       VarImport=0, 
                                       models.eval.meth = c('TSS'),
                                       SaveObj = TRUE,
                                       rescal.all.models = FALSE,
                                       do.full.models = FALSE)
                                       
# 4. Creating the ensemble models 
myBiomodEM <- BIOMOD_EnsembleModeling( 
                 modeling.output = myBiomodModelOut,
                 chosen.models = grep('_RF', get_built_models(myBiomodModelOut), 
                                  value=TRUE),
                 em.by = 'algo',
                 eval.metric = c('TSS'),
                 eval.metric.quality.threshold = c(0.7),
                 prob.mean = TRUE,
                 prob.cv = FALSE,
                 prob.ci = FALSE,
                 prob.ci.alpha = 0.05,
                 prob.median = FALSE,
                 committee.averaging = FALSE,
                 prob.mean.weight = FALSE,
                 prob.mean.weight.decay = 'proportional' )
                                       
# 5. Individual models projections on current environmental conditions
myBiomodProjection <- BIOMOD_Projection(
                        modeling.output = myBiomodModelOut,
                        new.env = myExpl,
                        proj.name = 'current',
                        selected.models = grep('_RF', get_built_models(
                                                myBiomodModelOut), value=TRUE),
                        compress = FALSE,
                        build.clamping.mask = FALSE)
                          

# 4. Creating the ensemble projections
BIOMOD_EnsembleForecasting( projection.output = myBiomodProjection,
                            EM.output = myBiomodEM)

biomod2

Ensemble Platform for Species Distribution Modeling

v3.4.6

GPL-2

Authors

Wilfried Thuiller [aut, cre], Damien Georges [aut, cre], Robin Engler [aut], Frank Breiner [aut]

Initial release

2020-02-24