bm_PlotRangeSize {biomod2}

Plot species range change

Description

This function represents species range change from object that can be obtained from BIOMOD_RangeSize function. Several graphics can be obtained, representing global counts or proportions of gains / losses, as well as spatial representations (see Details).

Usage

bm_PlotRangeSize(
  bm.range,
  do.count = TRUE,
  do.perc = TRUE,
  do.maps = TRUE,
  do.mean = TRUE,
  do.plot = TRUE,
  row.names = c("Species", "Dataset", "Run", "Algo")
)

Arguments

bm.range

an object returned by the BIOMOD_RangeSize function

do.count

(optional, default TRUE)
A logical value defining whether the count plot is to be computed or not

do.perc

(optional, default TRUE)
A logical value defining whether the percentage plot is to be computed or not

do.maps

(optional, default TRUE)
A logical value defining whether the maps plot is to be computed or not

do.mean

(optional, default TRUE)
A logical value defining whether the mean maps plot is to be computed or not

do.plot

(optional, default TRUE)
A logical value defining whether the plots are to be rendered or not

row.names

(optional, default c('Species', 'Dataset', 'Run', 'Algo'))
A vector containing tags matching bm.range$Compt.By.Models rownames splitted by '_' character

Details

4 plots can be obtained with this function :

Count barplot

representing absolute number of locations (pixels) lost, stable and gained

Percentage barplot

representing percentage of locations (pixels) lost, stable, and the corresponding Species Range Change (PercGain - PercLoss)

SRC models maps

representing spatially locations (pixels) lost, stable and gained for each single distribution model

SRC community averaging maps

representing spatially locations (pixels) lost, stable and gained, taking the majoritary value across single distribution models (and representing the percentage of models' agreement)

Please see BIOMOD_RangeSize function for more details about the values.

Value

A list containing one or several data.frame and the corresponding ggplot object representing species range change.

Author(s)

Maya Gueguen

See Also

BIOMOD_RangeSize

Other Secundary functions: bm_BinaryTransformation(), bm_CrossValidation(), bm_FindOptimStat(), bm_MakeFormula(), bm_ModelingOptions(), bm_PlotEvalBoxplot(), bm_PlotEvalMean(), bm_PlotResponseCurves(), bm_PlotVarImpBoxplot(), bm_PseudoAbsences(), bm_RunModelsLoop(), bm_SRE(), bm_SampleBinaryVector(), bm_SampleFactorLevels(), bm_Tuning(), bm_VariablesImportance()

Other Plot functions: bm_PlotEvalBoxplot(), bm_PlotEvalMean(), bm_PlotResponseCurves(), bm_PlotVarImpBoxplot()

Examples

library(terra)

# Load species occurrences (6 species available)
data(DataSpecies)
head(DataSpecies)

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

# Get corresponding presence/absence data
myResp <- as.numeric(DataSpecies[, myRespName])

# Get corresponding XY coordinates
myRespXY <- DataSpecies[, c('X_WGS84', 'Y_WGS84')]

# Load environmental variables extracted from BIOCLIM (bio_3, bio_4, bio_7, bio_11 & bio_12)
data(bioclim_current)
myExpl <- terra::rast(bioclim_current)



# ---------------------------------------------------------------#
file.out <- paste0(myRespName, "/", myRespName, ".AllModels.models.out")
if (file.exists(file.out)) {
  myBiomodModelOut <- get(load(file.out))
} else {

  # Format Data with true absences
  myBiomodData <- BIOMOD_FormatingData(resp.var = myResp,
                                       expl.var = myExpl, 
                                       resp.xy = myRespXY,
                                       resp.name = myRespName)

  # Model single models
  myBiomodModelOut <- BIOMOD_Modeling(bm.format = myBiomodData,
                                      modeling.id = 'AllModels',
                                      models = c('RF', 'GLM'),
                                      CV.strategy = 'random',
                                      CV.nb.rep = 2,
                                      CV.perc = 0.8,
                                      OPT.strategy = 'bigboss',
                                      metric.eval = c('TSS','ROC'),
                                      var.import = 3,
                                      seed.val = 42)
}

models.proj <- get_built_models(myBiomodModelOut, algo = "RF")
  # Project single models
  myBiomodProj <- BIOMOD_Projection(bm.mod = myBiomodModelOut,
                                    proj.name = 'CurrentRangeSize',
                                    new.env = myExpl,
                                    models.chosen = models.proj,
                                    metric.binary = 'all')



# ---------------------------------------------------------------#
# Load environmental variables extracted from BIOCLIM (bio_3, bio_4, bio_7, bio_11 & bio_12)
data(bioclim_future)
myExplFuture <- terra::rast(bioclim_future)


# Project onto future conditions
myBiomodProjectionFuture <- BIOMOD_Projection(bm.mod = myBiomodModelOut,
                                              proj.name = 'FutureRangeSize',
                                              new.env = myExplFuture,
                                              models.chosen = models.proj,
                                              metric.binary = 'TSS')

# Load current and future binary projections
CurrentProj <- get_predictions(myBiomodProj,
                               metric.binary = "TSS",
                               model.as.col = TRUE)
FutureProj <- get_predictions(myBiomodProjectionFuture,
                               metric.binary = "TSS",
                               model.as.col = TRUE)

# Compute differences
myBiomodRangeSize <- BIOMOD_RangeSize(proj.current = CurrentProj, proj.future = FutureProj)


# ---------------------------------------------------------------#
myBiomodRangeSize$Compt.By.Models
plot(myBiomodRangeSize$Diff.By.Pixel)

# Represent main results 
bm_PlotRangeSize(bm.range = myBiomodRangeSize)