Generalized models: An application to identify environmental variables that significantly affect the abundance of three tree species

Abstract

In defining the environmental preferences of plant species, statistical models are part of the essential tools in the field of modern ecology. However, conventional linear models require compliance with some parametric assumptions and if these requirements are not met, imply a serious limitation of the applied model. In this study, the effectiveness of linear and nonlinear generalized models was examined to identify the unitary effect of the principal environmental variables on the abundance of three tree species growing in the natural temperate forests of Oaxaca, Mexico. The covariates that showed a significant effect on the distribution of tree species were the maximum and minimum temperatures and the precipitation during specific periods. Results suggest that the generalized models, particularly smoothed models, were able to detect the increase or decrease of the abundance against changes in an environmental variable; they also revealed the inflection of the regression. In addition, these models allow partial characterization of the realized niche of a given species according to some specific variables, regardless of the type of relationship.