How to combine species distribution models based on different descriptors of the environment
Submitted by editor on 1 June 2016.
Get the paper! Specimens of Triturus pygmaeus, an endemic amphibian to the Iberian Peninsula, and environment in “San Pablo de Buceite” in 2011, Cádiz (Spain). Favourability model for T. pygmaeus in mainland Spain. Favourability ranges from 0 (white) to 1 (black).
by David Romero, Jesús Olivero, José Carlos Brito and Raimundo Real
In the context of the current biodiversity crisis, the distribution of a species, especially if threatened, must be preserved to conserve it. In order to know the processes that affect a species range, biogeographers are as interested in the individual role of the factors driving the species distribution as in the combined effect of several factors. However, methodological questions continue to arise regarding the way in which to combine different factors into comprehensive explanatory distribution models.
Our aim was to compare the different methods available for combining species distribution models. We used five approaches for model combination: Bayesian integration, Akaike weight averaging, stepwise variable selection, updating, and fuzzy logic. We demonstrated that different approaches to model combination give rise to disparities in the model outputs. Our conclusions were that Bayesian integration and the Akaike weight averaging should not be used unless their mathematical foundation is revised; the stepwise and updating approaches are recalibration methods that produce similar models useful if counterbalance between factors is permitted; and fuzzy logic is better when combining limiting factors that cannot be counterbalanced by more favourable factors.