Compatible Deterministic and Stochastic Predictions by Probabilistic Modeling of Individual Trees

摘要

A single growth model can provide both deterministic and stochastic predictions which are compatible. Change may be expressed using probabilistic functions which can represent proportions of populations or probabilities for individuals. The former represents determinism while the latter enables the stochastic implementation. The same functional relationships may thus be used to generate compatible deterministic and stochastic predictions. All components of forest growth and change, including diameter increment, can be expressed as probabilistic functions, enabling construction of a single model which provides compatible stochastic and deterministic outcomes. Users may specify the minimum expansion factor corresponding to the simulated plot size and thus control the granularity of predictions. Such a model may facilitate numerical estimation of confidence intervals about yield forecasts and sustained yield estimates.