Cellular automata model of forest stands to predict size-class distribution and survival.

现有的森林生长模型可以很好地预测林分水平过程，例如生长。 然而，这些报告提供的资料很少，无法说明森林结构以及森林结构如何影响商业森林产品、种植园的风险以及决定碳固存和用水并导致与年龄有关的生产力下降和自疏的林分动态。 通过使用新开发的技术来量化树间竞争，树级资源供应，树间遗传差异和机会事件的重要性，该项目将利用复杂性理论开发一个创新的模型，划分林分水平生产来预测个体树木的生长和大小。

Existing forest growth models predict well stand level processes such as growth. However, they provide little information on forest structure and how this affects commercial forest products, risks of growing plantations and stand dynamics that determine carbon sequestration and water-use and result in age-related decline in productivity and self-thinning. By using newly developed technology to quantify inter-tree competition, tree level resource supply, between tree genetic differences and the importance of chance events this project will draw on complexity theory to develop an innovative model that partitions stand level production to forecast the growth and size of individual trees.