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Main functions: binary data
In biomod2: Ensemble Platform for Species Distribution Modeling
Complete code example
Here are presented, in a full and complete example, all main functions (starting with BIOMOD_[...]) of biomod2.
The data set used is the DataSpecies containing presence/absence data.
Similar examples are presented for count data on the Main functions (abund) webpage.
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.name = myRespName,
resp.var = myResp,
resp.xy = myRespXY,
expl.var = myExpl)
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 Auxiliary 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)
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 Auxiliary 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)
Retrieve modeling options
Modeling options are automatically retrieved from selected models within the BIOMOD_Modeling function, which calls the bm_ModelingOptions function to do so. Model parameters can also be automatically tuned to a specific dataset, by calling the bm_Tuning function, however it can be quite long. More examples are presented on the Auxiliary functions webpage.
# # bigboss parameters
# opt.b <- bm_ModelingOptions(data.type = 'binary',
# models = c('SRE', 'XGBOOST'),
# strategy = 'bigboss')
#
# # tuned parameters with formated data
# opt.t <- bm_ModelingOptions(data.type = 'binary',
# models = c('SRE', 'XGBOOST'),
# strategy = 'tuned',
# bm.format = myBiomodData)
#
# opt.b
# opt.t
Run modeling
Single models
# Model single models
myBiomodModelOut <- BIOMOD_Modeling(bm.format = myBiomodData,
modeling.id = 'AllModels',
CV.strategy = 'random',
CV.nb.rep = 2,
CV.perc = 0.8,
OPT.strategy = 'bigboss',
metric.eval = c('TSS', 'AUCroc', 'BOYCE'),
var.import = 3)
# 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, dataset = 'calibration')
bm_PlotEvalMean(bm.out = myBiomodModelOut, dataset = 'validation')
bm_PlotEvalBoxplot(bm.out = myBiomodModelOut, dataset = 'calibration', group.by = c('algo', 'algo'))
bm_PlotEvalBoxplot(bm.out = myBiomodModelOut, dataset = 'calibration', 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, 4, 8, 10, 13)],
fixed.var = 'median')
bm_PlotResponseCurves(bm.out = myBiomodModelOut,
models.chosen = get_built_models(myBiomodModelOut)[c(1, 4, 8, 10, 13)],
fixed.var = 'min')
bm_PlotResponseCurves(bm.out = myBiomodModelOut,
models.chosen = get_built_models(myBiomodModelOut)[4],
fixed.var = 'median',
do.bivariate = TRUE)
# Explore models' outliers & residuals
bm_ModelAnalysis(bm.mod = myBiomodModelOut,
models.chosen = get_built_models(myBiomodModelOut)[c(1, 4, 8, 10, 13)])
Ensemble models
# Model ensemble models
myBiomodEM <- BIOMOD_EnsembleModeling(bm.mod = myBiomodModelOut,
models.chosen = 'all',
em.by = 'all',
em.algo = c('EMmedian', 'EMmean', 'EMwmean',
'EMca', 'EMci', 'EMcv'),
metric.select = c('TSS'),
metric.select.thresh = c(0.7),
metric.eval = c('TSS', 'AUCroc', 'BOYCE'),
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, dataset = 'calibration', group.by = 'full.name')
bm_PlotEvalBoxplot(bm.out = myBiomodEM, dataset = 'calibration', 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)
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
myBiomodProjFuture <- BIOMOD_Projection(bm.mod = myBiomodModelOut,
proj.name = 'Future',
new.env = myExplFuture,
models.chosen = 'all',
metric.binary = 'TSS',
build.clamping.mask = TRUE)
# Compute differences
myBiomodRangeSize <- BIOMOD_RangeSize(proj.current = myBiomodProj,
proj.future = myBiomodProjFuture,
metric.binary = 'TSS')
myBiomodRangeSize@Compt.By.Models
plot(myBiomodRangeSize@Diff.By.Pixel)
# Represent main results
bm_PlotRangeSize(bm.range = myBiomodRangeSize,
do.count = TRUE,
do.perc = TRUE,
do.maps = TRUE,
do.mean = TRUE)
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biomod2 documentation built on Dec. 22, 2025, 5:10 p.m.
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Complete code example
Here are presented, in a full and complete example, all main functions (starting with BIOMOD_[...]) of biomod2.
The data set used is the DataSpecies containing presence/absence data.
Similar examples are presented for count data on the Main functions (abund) webpage.
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.name = myRespName, resp.var = myResp, resp.xy = myRespXY, expl.var = myExpl) 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 Auxiliary 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)
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 Auxiliary 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)
Retrieve modeling options
Modeling options are automatically retrieved from selected models within the BIOMOD_Modeling function, which calls the bm_ModelingOptions function to do so. Model parameters can also be automatically tuned to a specific dataset, by calling the bm_Tuning function, however it can be quite long. More examples are presented on the Auxiliary functions webpage.
# # bigboss parameters # opt.b <- bm_ModelingOptions(data.type = 'binary', # models = c('SRE', 'XGBOOST'), # strategy = 'bigboss') # # # tuned parameters with formated data # opt.t <- bm_ModelingOptions(data.type = 'binary', # models = c('SRE', 'XGBOOST'), # strategy = 'tuned', # bm.format = myBiomodData) # # opt.b # opt.t
Run modeling
Single models
# Model single models myBiomodModelOut <- BIOMOD_Modeling(bm.format = myBiomodData, modeling.id = 'AllModels', CV.strategy = 'random', CV.nb.rep = 2, CV.perc = 0.8, OPT.strategy = 'bigboss', metric.eval = c('TSS', 'AUCroc', 'BOYCE'), var.import = 3) # 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, dataset = 'calibration') bm_PlotEvalMean(bm.out = myBiomodModelOut, dataset = 'validation') bm_PlotEvalBoxplot(bm.out = myBiomodModelOut, dataset = 'calibration', group.by = c('algo', 'algo')) bm_PlotEvalBoxplot(bm.out = myBiomodModelOut, dataset = 'calibration', 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, 4, 8, 10, 13)], fixed.var = 'median') bm_PlotResponseCurves(bm.out = myBiomodModelOut, models.chosen = get_built_models(myBiomodModelOut)[c(1, 4, 8, 10, 13)], fixed.var = 'min') bm_PlotResponseCurves(bm.out = myBiomodModelOut, models.chosen = get_built_models(myBiomodModelOut)[4], fixed.var = 'median', do.bivariate = TRUE) # Explore models' outliers & residuals bm_ModelAnalysis(bm.mod = myBiomodModelOut, models.chosen = get_built_models(myBiomodModelOut)[c(1, 4, 8, 10, 13)])
Ensemble models
# Model ensemble models myBiomodEM <- BIOMOD_EnsembleModeling(bm.mod = myBiomodModelOut, models.chosen = 'all', em.by = 'all', em.algo = c('EMmedian', 'EMmean', 'EMwmean', 'EMca', 'EMci', 'EMcv'), metric.select = c('TSS'), metric.select.thresh = c(0.7), metric.eval = c('TSS', 'AUCroc', 'BOYCE'), 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, dataset = 'calibration', group.by = 'full.name') bm_PlotEvalBoxplot(bm.out = myBiomodEM, dataset = 'calibration', 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)
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 myBiomodProjFuture <- BIOMOD_Projection(bm.mod = myBiomodModelOut, proj.name = 'Future', new.env = myExplFuture, models.chosen = 'all', metric.binary = 'TSS', build.clamping.mask = TRUE) # Compute differences myBiomodRangeSize <- BIOMOD_RangeSize(proj.current = myBiomodProj, proj.future = myBiomodProjFuture, metric.binary = 'TSS') myBiomodRangeSize@Compt.By.Models plot(myBiomodRangeSize@Diff.By.Pixel) # Represent main results bm_PlotRangeSize(bm.range = myBiomodRangeSize, do.count = TRUE, do.perc = TRUE, do.maps = TRUE, do.mean = TRUE)
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