Variability in results
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 modeling 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
parametrization 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.