Title: Introducing AIC model averaging in ecological niche modeling: a single-algorithm multi-model strategy to account for uncertainty in suitability predictions,
Abstract: Aim: The Akaike information Criterion (AIC) is widely used science to make
predictions about complex phenomena based on an entire set of models weighted
by Akaike weights. This approach (AIC model averaging; hereafter AvgAICc) is
often preferable than alternatives based on the selection of a single model.
Surprisingly, AvgAICc has not yet been introduced in ecological niche modeling
(ENM). We aimed to introduce AvgAICc in the context of ENM to serve both as an
optimality criterion in analyses that tune-up model parameters and as a
multi-model prediction strategy.
Innovation: Results from the AvgAICc approach differed from those of
alternative approaches with respect to model complexity, contribution of
environmental variables, and predicted amount and geographic location of
suitable conditions for the focal species. Two theoretical properties of the
AvgAICc approach might justify that future studies will prefer its use over
alternative approaches: (1) it is not limited to make predictions based on a
single model, but it also uses secondary models that might have important
predictive power absent in a given single model favored by alternative
optimality criteria; (2) it balances goodness of fit and model accuracy, this
being of critical importance in applications of ENM that require model
transference.
Main conclusions: Our introduction of the AvgAICc approach in ENM; its
theoretical properties, which are expected to confer advantages over
alternatives approaches; and the differences we found when comparing the
AvgAICc approach with alternative ones; should eventually lead to a wider use
of the AvgAICc approach. Our work should also promote further methodological
research comparing properties of the AvgAICc approach with respect to those of
alternative procedures.