A study of the relationship between forest distribution and environmental variables using information theory: A regional-scale model for predicting forest response to global warming

Many studies on forest or vegetation response to global warming have been done using the gap model or empirical models. Thus far, there is no good regional model allowing to predict forest change at an intermediate scale. In this study, we have developed a model of this type, called Knowledge Base Forest Model (KBFM), using an information analytical tool (P scEGASE) based on information theory.; Using this model and data from the Canadian Climatic Centre general circulation model, we could predict the future distribution of forest types in the research area: the Province of Manitoba. The study shows that the KBFM may well be used to predict the future regional distribution of forest types. Its main advantages are: (1) environmental variables used as predictors can be qualitatives (e.g. soil texture) as well as quantitative (e.g. temperature); (2) the KBFM provides the possibility to account for the role of soil factors in the forest response to global warming; (3) the KBFM can predict forest type distribution using various climatic scenarios; (4) the KBFM can predict forest type distribution with greater details than empirical models.