BIOMOD_RangeSize {biomod2}R Documentation

Analyze the range size differences between projections of species distribution models

Description

This function allows to calculate the absolute number of locations (pixels) lost, stable and gained, as well as the corresponding relative proportions, between two (or more) binary projections of (ensemble) species distribution models (which can represent new time scales or environmental scenarios for example).

Usage

BIOMOD_RangeSize(proj.current, proj.future)

## S4 method for signature 'data.frame,data.frame'
BIOMOD_RangeSize(proj.current, proj.future)

## S4 method for signature 'SpatRaster,SpatRaster'
BIOMOD_RangeSize(proj.current, proj.future)

Arguments

proj.current

a data.frame, RasterLayer or SpatRaster object containing the initial binary projection(s) of the (ensemble) species distribution model(s)

proj.future

a data.frame, RasterLayer or SpatRaster object containing the final binary projection(s) of the (ensemble) species distribution model(s)

Details

Note that this function is only relevant to compare binary projections, made on the same area with the same resolution.

Comparison between proj.current and proj.future depends on the number of projection in both objects:

proj.current proj.future Comparison
1 projection (e.g. data.frame with 1 column, SpatRaster with 1 layer) 1 projection (e.g. data.frame with 1 column, SpatRaster with 1 layer) comparison of both projection (e.g. current vs future conditions for the same model ; current vs current condition for two different models)
n projections (e.g. data.frame with n column, SpatRaster with n layer) n projections (e.g. data.frame with n column, SpatRaster with n layer) comparing projection i in proj.current to projection i in proj.future (e.g. comparing current vs future condition for n models)
1 projection (e.g. data.frame with 1 column, SpatRaster with 1 layer) n projections (e.g. data.frame with n column, SpatRaster with n layer) comparing projection in proj.current to each projection in proj.future (e.g. comparing current vs n different future condition (e.g. climate change scenario) for 1 model)

Diff.By.Pixel object is obtained by applying the simple following formula :

proj.future - 2 * proj.current

Value

A list containing two objects :

Compt.By.Species

a data.frame containing the summary of range change for each comparison

  • Loss : number of pixels predicted to be lost

  • Stable0 : number of pixels not currently occupied and not predicted to be

  • Stable1 : number of pixels currently occupied and predicted to remain occupied

  • Gain : number of pixels predicted to be gained

  • PercLoss : percentage of pixels currently occupied and predicted to be lost (Loss / (Loss + Stable1))

  • PercGain : percentage of pixels predicted to be gained compare to the number of pixels currently occupied (Gain / (Loss + Stable1))

  • SpeciesRangeChange : percentage of pixels predicted to change (loss or gain) compare to the number of pixels currently occupied (PercGain - PercLoss)

  • CurrentRangeSize : number of pixels currently occupied

  • FutureRangeSize0Disp : number of pixels predicted to be occupied, assuming no migration

  • FutureRangeSize1Disp : number of pixels predicted to be occupied, assuming migration

Diff.By.Pixel

an object in the same form than the input data (proj.current and proj.future) and containing a value for each point/pixel of each comparison among :

  • -2 : predicted to be lost

  • -1 : predicted to remain occupied

  • 0 : predicted to remain unoccupied

  • 1 : predicted to be gained

Author(s)

Wilfried Thuiller, Damien Georges, Bruno Lafourcade

See Also

BIOMOD_Projection, BIOMOD_EnsembleForecasting, bm_PlotRangeSize

Other Main functions: BIOMOD_EnsembleForecasting(), BIOMOD_EnsembleModeling(), BIOMOD_FormatingData(), BIOMOD_LoadModels(), BIOMOD_ModelingOptions(), BIOMOD_Modeling(), BIOMOD_PresenceOnly(), BIOMOD_Projection(), BIOMOD_Tuning()

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)

  # Create default modeling options
  myBiomodOptions <- BIOMOD_ModelingOptions()

  # Model single models
  myBiomodModelOut <- BIOMOD_Modeling(bm.format = myBiomodData,
                                      modeling.id = 'AllModels',
                                      models = c('RF', 'GLM'),
                                      bm.options = myBiomodOptions,
                                      CV.strategy = 'random',
                                      CV.nb.rep = 2,
                                      CV.perc = 0.8,
                                      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',
                                    build.clamping.mask = TRUE)


# --------------------------------------------------------------- #
# 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)
[Package biomod2 version 4.2-4 Index]