Application of artificial neural networks for predicting tree survival and mortality in the Hyrcanian forest of Iran

Tree survival and mortality play a critical role in forest ecosystems. Being able to predict the survival probability provides significant insights into forest management decision support. In this study, we employed an artificial neural network (ANN) to predict tree survival and mortality in an Oriental beech (Fagus orientalis Lipsky) forest in northern Iran, using long-term data and wide range of biotic (average tree diameter increment, and basal area in larger trees (BAL)) and abiotic variables (slope, average solar radiation during the growing season, wind velocity and direction, height above the nearest drainage point, topographic wetness index, temperature, and relative humidity). The ANN model revealed that the greatest survival rates belong to the trees with diameter > 20-100 cm. An increase in competition leads to reduced survival, and the Carpinus betulus species showed a slightly lower survival rate than the other species. Further, our findings demonstrated that BAL and diameter increment have a strong correlation with the probability of tree survival in the study area. The ANN performance was evaluated using the R-2 and RMSE metrics and compared to a multiple logistic regression that demonstrated the capability of the ANN model for the prediction of survival probability (R-2 = 0.92; RMSE = 0.985). From these results, we recommend the employment of ANN, and perhaps other machine learning methods, for the prediction of survival probability in many different ecosystems around the world.