Stochastic or deterministic single-tree models: is there any difference in growth predictions?

Introduction Deterministic single-tree models are commonly used in forestry. However, there is evidence that stochastic events may interact with the nonlinear mechanisms that underlie forest growth. As a consequence, stochastic and deterministic simulations could yield different results for the same single-tree model and the same initial conditions. This hypothesis was tested in this study. Material and methods We used a single-tree growth model that can be implemented either stochastically or deterministically. Two data sets of 186 and 342 plots each were used for the comparisons. For each plot, the simulations were run on a 100-year period using 10-year growth steps. Three different response variables were compared. Results The results showed that there were differences between the predictions from stochastic and deterministic simulations for some response variables and that randomness alone could not explain these differences. In the case of deterministic simulations, the fact that predictions are reinserted into the model at each growth step is a concern. These predictions are actually random variables and their transformations may result in biased quantities. Forest growth modellers should be aware that deterministic simulations may not correspond to the mathematical expectation of the natural dynamics.