### Definition

When modelling species distribution, there is most of the time an uncertainty of what is actually modelled :

• the fundamental niche is most of the time unknown
• and there might be an uncertainty in the realized niche, except in the case of an exhaustive sampling.

What can be evaluated is how much the modelisation results are consistent with what is known and observed, and how much variability is present in the results in function of modelling choices.

### Variability - within the evaluation / importance of variables

Single and ensemble models can be evaluated by several available evaluation metrics, and the importance of variables can be calculated through several repetitions (see `metric.eval` and `var.import` parameters in `BIOMOD_Modeling` and `BIOMOD_EnsembleModeling`).

Variability in evaluation and importance values can come from the parametrisation of different elements of the modelling :

• observed dataset,
• through the number of repetitions of calibration / validation splitting,
• illustrating the robustness of the computed model based on the data
• pseudo-absence dataset,
• through the number of repetitions of PA sampling,
• to check the choice of the pseudo-absence strategy
• modelling technique,
• through the models selected, among 10 models available,
• to spot the most adapted modelling methods

### Variability - within the predictions

Making projections, either for single or ensemble models, can produce two additional sources of variability in results that can be explored through two parameters :

• For single models, `build.clamping.mask` in `BIOMOD_Projection` function :

• it produces a map of the studied area with, in each pixel, the number of variables whose value is outside the range of values used to calibrate the models.
• It identifies potential extrapolation areas.
• For ensemble models, `prob.cv` in `BIOMOD_EnsembleForecasting` function :

• it calculates the coefficient of variation between models’ projection,
• identifying areas of disagreement in predictions.
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