THE PREDICTIVE VALIDATION OF ECOLOGICAL AND ENVIRONMENTAL-MODELS

The wide-spread use of numerical models for the study of ecological and environmental phenomena requires some means of assessing model correctness. While validation is such a process, it has tended to focus on the in-sample goodness-of-fit properties of models. Good performance at the model estimation and calibration stage, however, does not guarantee correct predictions. Despite the emphasis on prediction in many ecological and environmental models, predictive validation techniques are typically ignored in the modelling literature. Drawing on the econometric and statistical forecasting literature, this paper reviews the available methods and suggests that a structured approach to predictive model validation be adopted. The approach assumes the candidate models have been validated in-sample and proposes that the models first be evaluated for statistical adequacy before being compared and ranked. The evaluative phase eliminates models with poor statistical properties. The comparative phase selects, from the subset of statistically adequate models, the model with the best predictive properties. The approach is argued to increase user confidence and help ensure that the results of modelling exercises are effectively and appropriately used in ecological and environmental decision-making processes.