Determining the significance of threshold values uncertainty in rule-based classification models

Ecological models not only represent a tool for studying ecosystems, but also function as a key source for informing environmental policy. As such, these models are expected to present and convey information and uncertainty as accurately as possible. As the complexity of these models increases, the resultant output also becomes more complex and difficult to interpret. However, understanding model output is not limited to interpreting complex dynamics; scientists must also contend with the possibility of model error and uncertainty. It is the uncertainty associated with the output that provides information about the reliability of model predictions, and therefore plays a pivotal role in model evaluation and interpretation. Despite its importance, uncertainty frequently is ignored. We present a methodology to determine the significance of uncertainty in rule-based classification models. This is a stepwise methodology that, using a genetic algorithm, determines the effects of uncertainty in model output and identifies the most likely alternative results. It also computes a measure of confidence to help the modeler evaluate how the predictions are affected by uncertainties. We present a case study applying the methodology to the global vegetation model Mapped Atmosphere-Plant-Soil System (MAPSS) where the measure of confidence contains information about the dissimilarity between alternative outcome classes, and their spatial configuration on the landscape. Fuzzy sets are used to portray most likely alternative results as integral part of model output in a spatially explicit format.