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.

中文翻译：

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使用非参数和参数方法在伊朗北部的希尔卡尼亚森林中开发多个对比物种的个体树木生长和产量模型

伊朗的希尔卡尼亚森林包含许多物种丰富的群落，只有通过了解这些森林的更新和发展才能维持这些群落。位于伊朗北部 Farim 森林的 Jojadeh 部分，5 个不同树种 [（东方山毛榉 (Fagus orientalis Lipsky)、栗叶橡树 (Quercus castaneifolia Coss. ex J.Gay)、波斯枫 (Acer velutinum) Boiss.)、普通角树 (Carpinus betulus L.) 和高加索桤木 (Alnus subcordata CAMey.)] 在 10 年期间（2003-2013 年）的 313 个永久样本地块（0.1 公顷）上进行了测量。在该分析中，各种使用可用数据和参数模型和两个人工神经网络[即多层感知器 (MLP) 和径向基函数 (RBF) 网络] 研究了树级预测。个体树木直径生长模型与较大树木的基面积呈强烈的负相关，这在物种间相对一致。总高度模型表明，对于一组给定的协变量，所检查的物种没有差异。在生存模型中，东方山毛榉的生存概率低于其他物种，而向内生长模型显示所有物种的树苗密度随着基面积的增加而增加。基于 MLP 的人工神经网络对所有模型都具有优势，并且比 RBF 预测更准确。此外，基于 MLP 的模型也优于使用混合效应回归开发的参数化个体树模型。在森林规划和管理中使用这些开发的模型势在必行，特别是对于年龄不均匀的林分，