Natural forests are the habitat of many plant and animal species and are the main source of genetic reserves. In addition to preserving biodiversity, forests play an important role in the livelihood and income of many indigenous communities. But, in the last few decades, due to the lack of proper management of the beneficiaries, they have been exposed to destruction, so that their protection requires the participation of all members of the society, especially the local people. Therefore, the main goal of this research was to discover the determinant factors on the intention of local communities to protect forests in Iran. An extended theory of planned behavior (TPB) by adding the variables of “risk perception” (RP) and “sense of belonging to place” (SBP) was used as the theoretical framework of the research. This research was done using the questionnaire survey method and with the help of structural equation modeling (SEM). The statistical population of this study included all the rural communities living in the Arasbaran forests (located in the northwest of Iran). The research findings indicated that the original TPB explains 66.9% of the variance of the behavioral intentions of locals toward forest protection. The three main constructs of the original TPB included attitude, perceived behavioral control (PBC) and subjective norms (SN), all of which had positive effects on the intention of local communities. Most importantly, the extended TPB by including RP and SBP increases the ability of the model to explain the intentions of local communities to forest protection by 11.8%. In this study, the variable of RP was identified as the most important factor, so it is suggested to the policy-makers of this field to increase the RP of local communities in relation to forest destruction using communication media. It is also suggested to promote safe behaviors in these natural areas through developing forest protection organizations and properly training locals on the protection of forest areas.

Estimating the diameter increment of forests is one of the most important relationships in forest management and planning. The aim of this study was to provide insight into the application of two machine learning methods, i.e., the multilayer perceptron artificial neural network (MLP) and adaptive neuro-fuzzy inference system (ANFIS), for developing diameter increment models for the Hyrcanian forests. For this purpose, the diameters at breast height (DBH) of seven tree species were recorded during two inventory periods. The trees were divided into four broad species groups, including beech (Fagus orientalis), chestnut-leaved oak (Quercus castaneifolia), hornbeam (Carpinus betulus), and other species. For each group, a separate model was developed. The k-fold strategy was used to evaluate these models. The Pearson correlation coefficient (r), coefficient of determination (R2), root mean square error (RMSE), Akaike information criterion (AIC), and Bayesian information criterion (BIC) were utilized to evaluate the models. RMSE and R2 of the MLP and ANFIS models were estimated for the four groups of beech ((1.61 and 0.23) and (1.57 and 0.26)), hornbeam ((1.42 and 0.13) and (1.49 and 0.10)), chestnut-leaved oak ((1.55 and 0.28) and (1.47 and 0.39)), and other species ((1.44 and 0.32) and (1.5 and 0.24)), respectively. Despite the low coefficient of determination, the correlation test in both techniques was significant at a 0.01 level for all four groups. In this study, we also determined optimal network parameters such as number of nodes of one or multiple hidden layers and the type of membership functions for modeling the diameter increment in the Hyrcanian forests. Comparison of the results of the two techniques showed that for the groups of beech and chestnut-leaved oak, the ANFIS technique performed better and that the modeling techniques have a deep relationship with the nature of the tree species.

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The Hyrcanian forests of Iran are mainly managed with the single-selection silvicultural technique. Despite significant ecological benefits associated with selection cutting, this type of forest management leads towards more challenging situations where it is difficult to maintain and practice successful forestry than in even-aged systems. Therefore, this study provides relevant management tools in the form of models to estimate low growth levels in Hyrcanian forests. In the present study, estimation of the population growth rate and then the allowable cut rate of these forests using a matrix model have been calculated in the Gorazbon district. For this purpose, the data of 256 permanent sample plots measured during the years between 2003 and 2012, as well as the data recorded about the trees harvested according to the forestry plan, have been used. As a first step, the most frequently occurring tree species were divided into four groups (beech, hornbeam, chestnut-leaved oak, and other species). Compartments of the district were divided into two groups of logged and unlogged compartments. The purpose of this division was to estimate the allowable cut and compare its volume with the volumes of observed and predicted allowable cuts obtained from forestry plans. The results showed that the total operated allowable cut (OAC) in logged compartments was more than the estimated allowable cut (EAC). In unlogged compartments, the total predicted allowable cut (PAC) was more than EAC. A comparison of EAC and OAC showed that hornbeam has been harvested more than its potential. However, chestnut-leaved oak and other species group have depicted opposite trends. Our models provide important advancements for estimating allowable cut that can enhance the goal of practicing sustainable forestry.