The probability of mortality for an individual tree with certain characteristics growing under certain conditions was modeled. A particular algorithm SCREEN was used to fmd the best set of predictors variables. This algorithm was specially created to be used when the dependent variable can take only two values like in this binary case (dead or alive tree). The logistic model with different independent variables, which were found to be significant through the SCREEN algorithm, was fitted to the data. For the unthinned plots the logistic model with the following variables, CR (crown ratio), HH (total height/height of dominant and co-dominant trees) and CI (competition index) was compared with the survival model applied in a published distance-dependent model PTAEDA. The logistic model with CR, HH and DD (quadratic mean diameter/dbh) was compared with the survival model already used in a distance-independent model TRULOB. In both cases the behavior of the logistic model was quite similar to the published models. For the thinned plots the predictor variables DDt HH, CI and CR raised to 1.5 were used in the logistic model to predict mortality for individual trees. Mortality is difficult to predict. In this particular study the logistic model was used. The final distance-dependent model for unthinned plots includes as predictor variables CR, HH and CI. For thinned plots the final logistic model employs HH, CI and CR raised to 1.5 as independent variables. The final distance-independent model for unthinned plots includes as predictor variables HH, DD and CR. For thinned plots the final logistic model uses HH, DD and CR raised to 1.5 as independent variables. Differences between deterministic and the stochastic treatments of mortality were also studied. No practical differences in several stand characteristics such as average height, total volume, basal area were found when using these two approaches. Further, no significant differences were found in the diameter distribution for dead or alive trees. / Master of Science