A Stepwise Assessment of Parsimony and Fuzzy Entropy in Species Distribution Modelling.

ABSTRACT: Entropy is intrinsic to the geographical distribution of a biological species. A species distribution with higher entropy involves more uncertainty, i.e., is more gradually constrained by the environment. Species distribution modelling tries to yield models with low uncertainty but normally has to reduce uncertainty by increasing their complexity, which is detrimental for another desirable property of the models, parsimony. By modelling the distribution of 18 vertebrate species in mainland Spain, we show that entropy may be computed along the forward-backwards stepwise selection of variables in Logistic Regression Models to check whether uncertainty is reduced at each step. In general, a reduction of entropy was produced asymptotically at each step of the model. This asymptote could be use

SUBMITTER: Estrada A 

PROVIDER: S-EPMC8392680 | biostudies-literature | 2021 Aug

REPOSITORIES: biostudies-literature