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

现有的森林生长模型预测了良好的林分水平过程，如生长。然而，它们很少提供关于森林结构的信息，以及这种结构如何影响商品林产品、人工林生长风险和林分动态，这些动态决定了碳的固定和水的使用，并导致与年龄有关的生产力下降和自我稀疏。通过使用新开发的技术来量化树木之间的竞争、树木水平的资源供应、树木遗传差异和偶发事件的重要性，该项目将利用复杂性理论开发一个创新的模型，将林分水平生产划分为预测单个树木的生长和大小。

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.