Determining forest volume increment, the potential of wood production in natural forests, is a complex issue but is of fundamental importance to sustainable forest management. Determining potential volume increment through growth and yield models is necessary for proper management and future prediction of forest characteristics (diameter, height, volume, etc.). Various methods have been used to determine the productive capacity and amount of acceptable harvest in a forest, and each has advantages and disadvantages. One of these methods involves the artificial neural network techniques, which can be effective in natural resource management due to its flexibility and potentially high accuracy in prediction. This research was conducted in the Ramsar forests of the Mazandaran Province of Iran. Volume increment was estimated using both an artificial neural network and regression methods, and these were directly compared with the actual increment of 20 one-hectare permanent sample plots. A sensitivity analysis for inputs was employed to determine which had the most effect in predicting increment. The actual average annual volume increment of beech was 4.52 m3ha−1 yr−1, the increment was predicted to be 4.35 and 4.02 m3ha−1 yr−1 through the best models developed using an artificial neural network and using regression, respectively. The results showed that an estimate of increment can be predicted relatively well using the artificial neural network method, and that the artificial neural network method is able to estimate the increment with higher accuracy than traditional regression models. The sensitivity analysis showed that the standing volume at the beginning of the measurement period and the diameter of trees had the greatest impact on the variation of volume increment.
Forest ecosystems play multiple important roles in meeting the habitat needs of different organisms and providing a variety of services to humans. Biodiversity is one of the structural features in dynamic and complex forest ecosystems. One of the most challenging issues in assessing forest ecosystems is understanding the relationship between biodiversity and environmental factors. The aim of this study was to investigate the effect of biotic and abiotic factors on tree diversity of Hyrcanian forests in northern Iran. For this purpose, we analyzed tree diversity in 8 forest sites in different locations from east to west of the Caspian Sea. 15,988 trees were measured in 655 circular permanent sample plots (0.1 ha). A combination of machine learning methods was used for modeling and investigating the relationship between tree diversity and biotic and abiotic factors. Machine learning models included generalized additive models (GAMs), support vector machine (SVM), random forest (RF) and K-nearest–neighbor (KNN). To determine the most important factors related to tree diversity we used from variables such as the average diameter at breast height (DBH) in the plot, basal area in largest trees (BAL), basal area (BA), number of trees per hectare, tree species, slope, aspect and elevation. A comparison of RMSEs, relative RMSEs, and the coefficients of determination of the different methods, showed that the random forest (RF) method resulted in the best models among all those tested. Based on the results of the RF method, elevation, BA and BAL were recognized as the most influential factors defining variation of tree diversity.
Simplified forest structures following even-age management have been associated with the loss of biodiversity, which may be avoided through disturbance-inspired silviculture. Here, we ask how much do gap characteristics in a managed old-growth differ from those in unmanaged old-growth subject only to natural dynamics? In this study, we compared important characteristics of gaps (e.g. canopy gap fraction, distribution of gap sizes) and gapmakers (e.g. size classes, frequency, decay classes) between a managed and an adjacent unmanaged old-growth Oriental beech (Fagus orientalis Lipsky) compartment in the Keladarsht region of northern Iran 10 years after a single harvest entry using single-tree selection. Canopy openings >100 m2 with visible remnants of gapmakers (i.e. stumps) were included in this study. Gap characteristics of both compartments were within typical ranges for old-growth beech. Nonetheless, small but potentially important differences between the two areas were observed. In the managed compartment, harvesting poor quality trees with structural defects and typical diameters at breast height >52.5 cm plus natural mortality resulted in 102 canopy gaps (1–6 gapmakers, averaging 3.5 gaps/ha, gap fraction 9.8 per cent) compared with 59 natural canopy gaps (1–7 gapmakers, averaging 2.6 gaps/ha, gap fraction 13.7 per cent) in the unmanaged compartment. In both compartments, medium-sized gaps (200–500 m2) were most prevalent. In the managed compartment, 60 per cent of gapmakers were large or very large (typically cut) compared with 39 per cent in the unmanaged compartment where large trees typically snapped and became snags. Uprooting, particularly of small and medium sized gapmakers, was less common in the managed than the unmanaged compartment. Our results indicate that even one single-tree selection harvest may lead to a short-term divergence in stand structure compared with the unmanaged forest. While such managed forests may no longer be considered as old-growth, divergences in canopy gap characteristics indicate that a more nuanced harvesting scheme that includes cutting some larger gaps may more closely mimic the canopy dynamics of this old-growth forest.
Background. Approximately 120 out of every 1 million children in the world develop cancer each year. In Turkey, 2500-3000 children are diagnosed with new cancer each year. The causes of childhood cancer have been studied for many years. It is known that many cancers in children, as in adults, cause uncontrolled cell growth, and develop as a result of mutations in genes that cause cancer. Methods. The investigation of family history within this context in the study, a total of 13 individuals consisting of all children and adults in the family were examined using the whole-exome sequencing (WES) with the individuals who were diagnosed with cancer in the family, who were detected to have different cancer profiles, and defined as high risk and to determine the gene or genes through which the disease has developed. Results. At the end of the study, a total of 30 variants with a pathogenic record in the family were identified. A total of 10 pathogenic variants belonging to 8 different genes from these variants have been associated with various cancer risks. Conclusions. A significant scientific contribution has been made to the mechanism of disease formation by studying a family with a high cancer burden and by finding the genes associated with the disease. In addition, by the results obtained, family members with cancer predisposition were selected after a risk analysis conducted in this family, and the necessary examinations and scans were recommended to provide an early diagnostic advantage.
1] Microbiology and immunology online [2] Medical Microbiology [3] Congenital cytomegalovirus infection: the relative importance of primery and recurrent maternal infection [4] Primer-directed enzymatic amplification of DNA with a thermostable DNA polymerase [5] Polymerase chain reaction [6] Inhibitory effects of urine on the polymerase chain reaction for cytomegalovirus DNA [7] Localization of DNA sequence analysis of the transforming domain (mtrII) of human cytomegalovirus [8] Cytomegalovirus (CMV) and Congenital CMV Infection [9] Red Book: 2006 Report of the Committee on Infectious Diseases [10] Congenital infection by human cytomegalovirus with a 65bp deletion in the morphological transforming region II [11] Sample preparation from paraffin-embedded tissues
This study was carried out to evaluate gap characteristics and gapmakers for different development stages of an oriental beech forest in northern Iran. Development stages of 1 ha square-shaped mosaic patches were identified using 100 × 100 m sampling grid and all gaps within these mosaics were recorded. Gap areas were calculated and classified into four classes and gapmakers were counted and classified into 4 decay and 4 diameter classes as well. Results showed that gaps comprised 13.7, 9.1 and 17.6% of the study area in initial, optimal and decay stages, respectively. There was a significant difference between development stages with respect to gap size and the highest amount was observed in decay stage. Medium-sized gaps were the most frequent in all three stages. Frequency distribution of gapmakers varied among development stages. Our findings revealed that 200–500 m<sup>2</sup> is the most preferable gap size for close-to-nature silvicultural approaches in Hyrcanian beech forests. To achieve this gap size 1–2 trees should be marked for harvesting operations.
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