Assessment and representation of variability in ecological niche model predictions

Abstract

Ecological niche models are popular tools used in fields such as ecology, biogeography, conservation biology, and epidemiology. These models are used commonly to produce representations of species potential distributions, which are then used to answer other research questions; for instance, where species richness is highest, where potential impacts of climate change can be anticipated, or where to expect spread of invasive species or disease vectors. Although these representations of potential distributions are variable which contributes to uncertainty in these predictions, model variability is neglected when presenting results of ecological niche model analyses. Here, we present examples of how to quantify and represent variability in models, particularly when models are transferred in space and time. To facilitate implementations of analyses of variability, we developed R functions and made them freely available. We demonstrate means of understanding how much variation exists and where this variation is manifested in geographic space. Representing model variability in geographic space gives a reference of the uncertainty in predictions, so analyzing this aspect of model outcomes must be a priority when policy is to be set or decisions taken based on these models. Our open access tools also facilitate post modeling process that otherwise could take days of manual work.