Review of forest dynamics simulation under the background of climate change:a case study of Forest Gap Model and Dynamic Global Vegetation Model

Under the background of global climate change, forest geographic distribution and ecological processes have attracted increasingly more attention of the research community. Response of forest to climate change is a complex process. In the past decade, a growing number of researchers have simulated the climate effects on plant species or vegetation by using various models. Among the simulation models, the Forest Gap Models(FGMs) and Dynamic Global Vegetation Models(DGVMs) consider mechanism like vegetation succession and regeneration under climatic impact, and can reasonably simulate the transient response of forest in the context of climate change, which is one of the hot topics in climate change research. FGMs simulate forest change over time using spatially referenced data on a broad spatial scale(i.e. landscape scale), generally larger than a single forest stand. Spatial interaction among forest stands is a key component of such models. These models can incorporate other spatiotemporal processes such as natural disturbances(e.g. wildfires, hurricanes, outbreaks of native and exotic invasive pests and diseases) and human interference. These models are increasingly used as tools for studying forest management, ecological assessment, restoration planning and climate change. DGVMs are powerful tools to simulate past and future vegetation patterns and associated biogeochemical cycles. Most DGVMs are limited by how they define vegetation and their simplistic representation of competition. They use Plant Functional Types(PFTs) to show regional forest vegetation composition, and simulate proportional changes of PFTs to reflect forest geographic pattern dynamics. DGVMs have shown that forest dynamics could dramatically alter the response of the global climate system over the next century. But there is little agreement among different DGVMs, making forest dynamic one of the greatest sources of uncertainty in predicting future climate. DGVMs' predictions could be strengthened by integrating the ecological realities of biodiversity and height-structured competition for light, facilitated with recent advances in the mathematics of forest modeling,ecological understanding of diverse forest communities, and the availability of forest inventory data. In this paper, we review the development, components and types of the models and discuss the application of simulating potential response of forest geographic distribution and ecological processes to past and future climate change.As for deficiencies in current development and application of FGMs and DGVMs, key research should be carried out in multi-scale study of dynamic models and enhancement of the model description of the non-climatic factors, comparison among models, and assimilation of multi-source remote sensing data and models, among others. In conclusion, only when these models reflect more accurately realistic relationships between plant species or vegetation and climate variables, can they be employed to simulate responses of plant species or vegetation to rapid change in climate.