Modeling Initial Recruitment of Mediterranean Pine Forests Using an Artificial Neural Network

Artificial neural networks (ANNs) have been recognized as powerful tools able to automatically learn complex relationships in data. To the best of our knowledge, ANNs have not yet been applied to forest regeneration modeling. Such models are essential to evaluate the effects of reforestation techniques. To fill this gap, the capacity of ANNs to simulate the initial recruitment of pine species in Mediterranean forests has been evaluated in this study. A feed-forward multilayer neural network has been applied to a case study of pine forests of Castilla La Mancha (Central–Eastern Spain), where seed germination and seedling survival, with or without seed protection, of four pine species (Pinus nigra, Pinus pinaster, Pinus halepensis, and Pinus sylvestris) were observed throughout 10 years under three soil conditions (scalped, wildfire-affected, and unaltered soils). The results we obtained have witnessed the good capacity of ANNs to predict both stages of pine initial recruitment. This may be of help in predicting the success of natural regeneration in Mediterranean pine forests under different tree species, soil characteristics, and management strategies.