Presentation

Data formatting : BIOMOD_FormatingData

• Formatting data : combine observations, coordinates and explanatory variables,
  and sample pseudo-absences for presence-only data (through bm_PseudoAbsences)

• Cross-validation : create calibration / validation datasets (through bm_CrossValidation)

• Modeling options (with the help of ModelsTable and OptionsBigboss datasets)

  • Define specific options for single models (through bm_ModelingOptions)
  • And tune with your own data (through bm_Tuning)

Single models : BIOMOD_Modeling

• Evaluation : transform predicted probabilities in binary values (through bm_BinaryTransformation),
  and compute evaluation metrics (through bm_FindOptimStat)

• Variables’ importance : estimate the impact of each explanatory variable on predictions (through bm_VariablesImportance)

Ensemble models : BIOMOD_EnsembleModeling

• Filter single models based on one evaluation metric
• Combine them according to different combinations
• And build ensemble models

Exploring outputs

• Evaluation : bm_PlotEvalMean, bm_PlotEvalBoxplot
• Variables’ importance : bm_PlotVarImpBoxplot
• Response curves : bm_PlotResponseCurves

Projecting models : BIOMOD_Projection, BIOMOD_EnsembleForecasting

• Project models onto new conditions
• Transform predicted probabilities in binary values (through bm_BinaryTransformation)

Species range change : BIOMOD_RangeSize