Toward Predicting Effects Of Fire On Vegetation Dynamics Under Changed Climate Scheme— Landscape Scale Models

More and More efforts are given to landscape scale models in ecological researches recently, for they are crucial in bridging the gap between broad scale generalization and fine scale measurements in global change studies. Disturbances, such as: fire, wind, drought, insect and disease, are hot topics of landscape scale models, for they are the main agents in creating patchiness to form heterogeneous landscape and shifting mosaics, which are considered to be typical at this scale. Fire, the most common disturbances in most terrestrial ecosystems, and with relative predictable regime (frequency, mean area, return interval) compare to other types of disturbances, is often chosen by researchers as example to show the importance of effects of disturbance on vegetation dynamics. It is now commonly accepted that fire disturbance may plays a major role in shaping and maintaining many terrestrial ecosystems. Models dealing with these ecosystems without considering fire disturbance are thought to be at lest inadequate. Many researchers further point out fire may play a more important role in the responses of vegetation to rapid climate change. Fire regime change may have more drastic effects on vegetation response then the effects of physiological change such as growth rate and mortality during the period of rapid climate change. Many influential research projects, such as IGBP, give high priority in developing an appropriate fire disturbance model. However, it is not a easy task to predict fire effects on vegetation dynamics, because the relationships among weather, fire, and the dynamics of vegetation require an understanding of fine-grained details of fire ignition, fire spread, the patterns of vegetation and their response to fire, and the fast changing weather conditions imbedded in the long term-trends and potential shifts in climate. Additionally, the complexity of human influences and the uncertainty of their trends make the problem more difficult.