Complete code example

Here are presented, in a full and complete example, all main functions (starting with BIOMOD_[...]) of biomod2.



Load dataset and variables

library(biomod2)
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 <- rast(bioclim_current)



Prepare data & parameters

Format data (observations & explanatory variables)

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

Pseudo-absences extraction

Single or multiple set of pseudo-absences can be selected with the BIOMOD_FormatingData function, which calls the bm_PseudoAbsences function to do so. More examples are presented on the Secundary functions webpage.

# # Transform true absences into potential pseudo-absences
# myResp.PA <- ifelse(myResp == 1, 1, NA)
# 
# # Format Data with pseudo-absences : random method
# myBiomodData.r <- BIOMOD_FormatingData(resp.var = myResp.PA,
#                                        expl.var = myExpl,
#                                        resp.xy = myRespXY,
#                                        resp.name = myRespName,
#                                        PA.nb.rep = 4,
#                                        PA.nb.absences = 1000,
#                                        PA.strategy = 'random')
# 
# myBiomodData.r
# plot(myBiomodData.r)
# # Select multiple sets of pseudo-absences
#
# # Transform true absences into potential pseudo-absences
# myResp.PA <- ifelse(myResp == 1, 1, NA)
# 
# # Format Data with pseudo-absences : random method
# myBiomodData.multi <- BIOMOD_FormatingData(resp.var = myResp.PA,
#                                            expl.var = myExpl,
#                                            resp.xy = myRespXY,
#                                            resp.name = myRespName,
#                                            PA.nb.rep = 4,
#                                            PA.nb.absences = c(1000, 500, 500, 200),
#                                            PA.strategy = 'random')
# myBiomodData.multi
# summary(myBiomodData.multi)
# plot(myBiomodData.multi)

Parameterize modeling options

# Print default modeling options
bm_DefaultModelingOptions()

# Create default modeling options
myBiomodOptions <- BIOMOD_ModelingOptions()
myBiomodOptions

# # Part (or totality) of the print can be copied and customized
# # Below is an example to compute quadratic GLM and select best model with 'BIC' criterium
# myBiomodOptions <- BIOMOD_ModelingOptions(
#   GLM = list(type = 'quadratic',
#              interaction.level = 0,
#              myFormula = NULL,
#              test = 'BIC',
#              family = 'binomial',
#              control = glm.control(epsilon = 1e-08,
#                                    maxit = 1000,
#                                    trace = FALSE)))
# myBiomodOptions
# 
# # It is also possible to give a specific GLM formula
# myForm <- 'Sp277 ~ bio3 + log(bio10) + poly(bio16, 2) + bio19 + bio3:bio19'
# myBiomodOptions <- BIOMOD_ModelingOptions(GLM = list(myFormula = formula(myForm)))
# myBiomodOptions
### Model parameters can also be automatically tuned for your specific 
### dataset with an optimization algorithm. The tuning can however 
### be quite long. Duration for tuning all models sequentially 
### with default optimization settings :
### on 1 x 2.5 GHz processor: approx. 45 min tuning all models 
### on 8 x 2.5 GHz processor: approx. 15 min tuning all models 
# 
# # library(doParallel)
# # cl <- makeCluster(8)
# # doParallel::registerDoParallel(cl) 
# 
# time.seq <- system.time(
#   bm.tuning <- BIOMOD_Tuning(bm.format = myBiomodData, ME.env = myExpl, ME.n.bg = ncell(myExpl))
# )
# 
# # stopCluster(cl)
# 
# plot(bm.tuning$tune.CTA.rpart)
# plot(bm.tuning$tune.CTA.rpart2)
# plot(bm.tuning$tune.RF)
# plot(bm.tuning$tune.ANN)
# plot(bm.tuning$tune.MARS)
# plot(bm.tuning$tune.FDA)
# plot(bm.tuning$tune.GBM)
# plot(bm.tuning$tune.GAM)
#
# # Get tuned modeling options
# myBiomodOptions <- bm.tuning$models.options

Cross-validation datasets

Several cross-validation methods are available and can be selected with the BIOMOD_Modeling function, which calls the bm_CrossValidation function to do so. More examples are presented on the Secundary functions webpage.

# # k-fold selection
# cv.k <- bm_CrossValidation(bm.format = myBiomodData,
#                            strategy = "kfold",
#                            nb.rep = 2,
#                            k = 3)
# 
# # stratified selection (geographic)
# cv.s <- bm_CrossValidation(bm.format = myBiomodData,
#                            strategy = "strat",
#                            k = 2,
#                            balance = "presences",
#                            strat = "x")
# head(cv.k)
# head(cv.s)



Run modelisation

Single models

# Model single models
myBiomodModelOut <- BIOMOD_Modeling(bm.format = myBiomodData,
                                    bm.options = myBiomodOptions,
                                    modeling.id = 'AllModels',
                                    CV.strategy = 'random',
                                    CV.nb.rep = 2,
                                    CV.perc = 0.8,
                                    var.import = 3,
                                    metric.eval = c('TSS','ROC'))
                                    # seed.val = 123)
                                    # nb.cpu = 8)
myBiomodModelOut

# Get evaluation scores & variables importance
get_evaluations(myBiomodModelOut)
get_variables_importance(myBiomodModelOut)

# Represent evaluation scores & variables importance
bm_PlotEvalMean(bm.out = myBiomodModelOut)
bm_PlotEvalBoxplot(bm.out = myBiomodModelOut, group.by = c('algo', 'algo'))
bm_PlotEvalBoxplot(bm.out = myBiomodModelOut, group.by = c('algo', 'run'))
bm_PlotVarImpBoxplot(bm.out = myBiomodModelOut, group.by = c('expl.var', 'algo', 'algo'))
bm_PlotVarImpBoxplot(bm.out = myBiomodModelOut, group.by = c('expl.var', 'algo', 'run'))
bm_PlotVarImpBoxplot(bm.out = myBiomodModelOut, group.by = c('algo', 'expl.var', 'run'))

# Represent response curves
bm_PlotResponseCurves(bm.out = myBiomodModelOut, 
                      models.chosen = get_built_models(myBiomodModelOut)[c(1:3, 12:14)],
                      fixed.var = 'median')
bm_PlotResponseCurves(bm.out = myBiomodModelOut, 
                      models.chosen = get_built_models(myBiomodModelOut)[c(1:3, 12:14)],
                      fixed.var = 'min')
bm_PlotResponseCurves(bm.out = myBiomodModelOut, 
                      models.chosen = get_built_models(myBiomodModelOut)[3],
                      fixed.var = 'median',
                      do.bivariate = TRUE)

Ensemble models

# Model ensemble models
myBiomodEM <- BIOMOD_EnsembleModeling(bm.mod = myBiomodModelOut,
                                      models.chosen = 'all',
                                      em.by = 'all',
                                      em.algo = c('EMmean', 'EMcv', 'EMci', 'EMmedian', 'EMca', 'EMwmean'),
                                      metric.select = c('TSS'),
                                      metric.select.thresh = c(0.7),
                                      metric.eval = c('TSS', 'ROC'),
                                      var.import = 3,
                                      EMci.alpha = 0.05,
                                      EMwmean.decay = 'proportional')
myBiomodEM

# Get evaluation scores & variables importance
get_evaluations(myBiomodEM)
get_variables_importance(myBiomodEM)

# Represent evaluation scores & variables importance
bm_PlotEvalMean(bm.out = myBiomodEM, group.by = 'full.name')
bm_PlotEvalBoxplot(bm.out = myBiomodEM, group.by = c('full.name', 'full.name'))
bm_PlotVarImpBoxplot(bm.out = myBiomodEM, group.by = c('expl.var', 'full.name', 'full.name'))
bm_PlotVarImpBoxplot(bm.out = myBiomodEM, group.by = c('expl.var', 'algo', 'merged.by.run'))
bm_PlotVarImpBoxplot(bm.out = myBiomodEM, group.by = c('algo', 'expl.var', 'merged.by.run'))

# Represent response curves
bm_PlotResponseCurves(bm.out = myBiomodEM, 
                      models.chosen = get_built_models(myBiomodEM)[c(1, 6, 7)],
                      fixed.var = 'median')
bm_PlotResponseCurves(bm.out = myBiomodEM, 
                      models.chosen = get_built_models(myBiomodEM)[c(1, 6, 7)],
                      fixed.var = 'min')
bm_PlotResponseCurves(bm.out = myBiomodEM, 
                      models.chosen = get_built_models(myBiomodEM)[7],
                      fixed.var = 'median',
                      do.bivariate = TRUE)

Presence-only evaluation

# Evaluate models with Boyce index and MPA
myBiomodPO <- BIOMOD_PresenceOnly(bm.mod = myBiomodModelOut,
                                  bm.em = myBiomodEM)
myBiomodPO

# Evaluate models with Boyce index and MPA (using background data)
myBiomodPO <- BIOMOD_PresenceOnly(bm.mod = myBiomodModelOut,
                                  bm.em = myBiomodEM, 
                                  bg.env = values(myExpl))
myBiomodPO



Project models

Single models

# Project single models
myBiomodProj <- BIOMOD_Projection(bm.mod = myBiomodModelOut,
                                  proj.name = 'Current',
                                  new.env = myExpl,
                                  models.chosen = 'all',
                                  metric.binary = 'all',
                                  metric.filter = 'all',
                                  build.clamping.mask = TRUE)
myBiomodProj
plot(myBiomodProj)

Ensemble models

# Project ensemble models (from single projections)
myBiomodEMProj <- BIOMOD_EnsembleForecasting(bm.em = myBiomodEM, 
                                             bm.proj = myBiomodProj,
                                             models.chosen = 'all',
                                             metric.binary = 'all',
                                             metric.filter = 'all')
                                             
# Project ensemble models (building single projections)
myBiomodEMProj <- BIOMOD_EnsembleForecasting(bm.em = myBiomodEM,
                                             proj.name = 'CurrentEM',
                                             new.env = myExpl,
                                             models.chosen = 'all',
                                             metric.binary = 'all',
                                             metric.filter = 'all')
myBiomodEMProj
plot(myBiomodEMProj)



Compare range sizes

# Load environmental variables extracted from BIOCLIM (bio_3, bio_4, bio_7, bio_11 & bio_12)
data("bioclim_future")
myExplFuture = rast(bioclim_future)

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

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

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

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

# Represent main results 
gg = bm_PlotRangeSize(bm.range = myBiomodRangeSize, 
                      do.count = TRUE,
                      do.perc = TRUE,
                      do.maps = TRUE,
                      do.mean = TRUE,
                      do.plot = TRUE,
                      row.names = c("Species", "Dataset", "Run", "Algo"))