The charcoal disease agents, Biscogniauxia mediterranea and Obolarina persica are two latent, ascomycetous oak pathogens in the Middle Eastern Zagros forests, where they have devastating effects, particularly during drought. Under greenhouse conditions, we investigated the effects of the two charcoal disease agents individually and in combination with drought on survival, growth, foliar gas-exchange, pigment content, oxidative stress and the antioxidant response of Quercus infectoria and Q. libani, two of the dominant tree species in this region. Commonly, the strongest negative effects emerged in the drought–pathogen interaction treatments. Q. infectoria showed less severe lesions, higher survival, more growth, and less leaf loss than Q. libani under combined biotic and abiotic stress. In both oak species, the combination of pathogen infection and drought resulted in more than 50% reduction in foliar gas-exchange parameters with partial recovery over time in Q. infectoria suggesting a superior defense system. Indeed, enhanced foliar anthocyanin, total soluble protein and glutathione concentrations imply an upregulation of the antioxidant defense system in Q. infectoria under stress while none of these parameters showed a significant treatment response in Q. libani. Consequently, Q. infectoria foliage showed no significant increase in superoxide, lower lipoxygenase activity, and less electrolyte leakage compared to the highly elevated levels seen in Q. libani indicating oxidative damage. Our findings indicate greater drought tolerance and pathogen resilience in Q. infectoria compared to Q. libani. Under future climate scenarios, we therefore expect changes in forest community structure driven by a decline in Q. libani and closely associated organisms.

The economics of the forestry enterprise are largely measured by their performance in road construction and management. The construction of forest roads requires tremendous capital outlays and usually constitutes a major component of the construction industry. The availability of cost estimation models assisting in the early stages of a project would therefore be of great help for timely costing of alternatives and more economical solutions. This study describes the development and application of such cost estimation models. First, the main cost elements and variables affecting total construction costs were determined for which the real-world data were derived from the project bids and an analysis of 300 segments of a three kilometer road constructed in the Hyrcanian Forests of Iran. Then, five state-of-the-art machine learning methods, i.e., linear regression (LR), K-Star, multilayer perceptron neural network (MLP), support vector machine (SVM), and Instance-based learning (IBL) were applied to develop models that would estimate construction costs from the real-world data. The performance of the models was measured using the correlation coefficient (R), root mean square error (RMSE), and percent of relative error index (PREI). The results showed that the IBL model had the highest training performance (R = 0.998, RMSE = 1.4%), whereas the SVM model had the highest estimation capability (R = 0.993, RMSE = 2.44%). PREI indicated that all models but IBL (mean PREI = 0.0021%) slightly underestimated the construction costs. Despite these few differences, the results demonstrated that the cost estimations developed here were consistent with the project bids, and our models thus can serve as a guideline for better allocating financial resources in the early stages of the bidding process.

This study examined the spatial variability of throughfall (T_f) and its implications for sampling throughfall during the leafless period of oak trees. To do this, we measured T_f under five single Brant’s oak trees (Quercus brantii var. Persica), in the Zagros region of Iran, spanning a six-month-long study period. Overall, the T_f amounted to 85.7% of gross rainfall. The spatial coefficient of variation (CV) for rainstorm total T_f volumes was 25%, on average, and it decreased as the magnitude of rainfall increased. During the leafless period, T_f was spatially autocorrelated over distances of 1 to 3.5 m, indicating the benefits of sampling with relatively elongated troughs. Our findings highlight the great variability of T_f under the canopies of Brant’s oaks during their leafless period. We may also conclude that the 29 T_f collectors used in the present study were sufficient to robustly estimate tree-scale T_f values within a 10% error of the mean at the 95% confidence level. Given that a ±10% uncertainty in T_f is associated with a ±100% uncertainty in interception loss, this underscores the challenges in its measurement at the individual tree level in the leafless season. These results are valuable for determining the number and placement of T_f collectors, and their expected level of confidence, when measuring tree-level T_f of scattered oak trees and those in forest stands.

Splash erosion, as the first step of soil erosion, causes the movement of the soil particles and lumps and is considered an important process in soil erosion. Given the complexity of this process in nature, one way of identifying and modeling the process is to use a rainfall simulator and to study it under laboratory circumstances. For this purpose, transported material was measured with various rainfall intensities and different amounts of poly-acryl-amide. In the next step, artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS), and support vector machine (SVM) were used to model the transported materials. The results showed that among the three methods, the best values of evaluation criteria were related to SVM, and ANFIS respectively. Among the three studied durations, the experiment with a duration of 30 minutes received the best results. The results based on available data showed by increasing the number of membership functions, over-fitting happens in the ANFIS method. To reduce the complexity of the model and the likelihood of over-fitting, some rules were eliminated. The results showed that the performance of the model improved by eliminating some rules.