Development of individual tree growth and yield model across multiple contrasting species using nonparametric and parametric methods in the Hyrcanian forests of northern Iran

Abstract

The Hyrcanian forests of Iran contain many species-rich communities that can only be maintained through an understanding of the renewal and development of these forests. Located in the Jojadeh section of the Farim forest in northern Iran, individual tree growth of five distinct species [(Oriental beech (Fagus orientalis Lipsky), chestnut-leaved oak (Quercus castaneifolia Coss. ex J.Gay), Persian maple (Acer velutinum Boiss.), common hornbeam (Carpinus betulus L.) and Caucasian alder (Alnus subcordata C.A.Mey.)] were measured on 313 permanent sample plots (0.1 ha) over a 10-year period (2003–2013). In this analysis, various tree-level predictions were investigated using the available data with application of parametric models and two artificial neural networks [i.e., the multilayer perceptron (MLP) and radial basis function (RBF) networks]. Individual tree diameter growth models showed a robust negative relationship with basal area in larger trees, which was relatively consistent across species. A total height model indicated that the examined species did not differ for a given set of covariates. In the survival model, the survival probability of Oriental beech was lower than the other species, while the ingrowth model revealed sapling density of all species increased with greater basal area. The artificial neural network based on the MLP was superior for all models and predicted more accurately than the RBF. Furthermore, the models based on the MLP were also superior to the parametric individual tree models developed using mixed-effect regression. The use of these developed models in forest planning and management is imperative, particularly for uneven-aged stands, but assessment of long-term projection behavior across the contrasting statistical approaches used is warranted despite the general superiority of the nonparametric models.