Modeling Tree Growth Responses to Climate Change: A Case Study in Natural Deciduous Mountain Forests

Climate change has significant effects on forest ecosystems around the world. Since tree diameter increment determines volume and ultimately production, an accurate estimate of this variable under future climate is great importance for sustainable management. In study, we modeled current expected change, using multilayer perceptron (MLP) artificial neural networks linear mixed-effect model in two sites Hyrcanian Forest, northern Iran. Using 573 monitoring fixed-area (0.1 ha) plots, measured calculated biotic abiotic factors (i.e., at breast height (DBH), basal area largest trees (BAL), (BA), elevation, aspect, slope, precipitation, temperature). We investigated effect year 2070 reference scenarios; RCP 4.5 (an intermediate scenario) 8.5 extreme due to uncertainty caused by general circulation models. According scenarios amount annual precipitation temperature during study period will increase 12.18 mm 1.77 °C, respectively. Further, results showed that impact predicted was not very noticeable growth end decreased only about 7% annually. The rate, fact, neutralize each other, therefore, rate does significantly compared beginning. Based models’ predictions, MLP performed better predicting increment.