Effect of Single-Selection Method on Soil Quality Index in the Hyrcanian Forests

Introduction and Objectives: The Hyrcanian forests, as one of the most important broadleaf forests in the Northern Hemisphere with a history of millions of years, play a crucial role in biodiversity conservation, regulation of environmental cycles, and provision of ecosystem services. These forests, particularly in their eastern region, are increasingly under pressure from unsustainable exploitation, climate change, and habitat degradation. Among the various components of forest ecosystems, soil holds a vital role in ecological stability, as it forms the main platform for biological and chemical cycles. Soil quality, as a composite indicator, reflects the soil’s ability to perform biological, chemical, and physical functions. Therefore, the assessment of the Soil Quality Index (SQI) can serve as an effective tool to analyze the impacts of forest management practices on forest sustainability. The main objective of this study was to investigate the effect of single-tree selection silvicultural management on the physical, chemical, and biological properties of soil, as well as the Soil Quality Index, in the eastern Hyrcanian forests.
Materials and Methods: This study was conducted in the forest region of Khalilshahr, located in the eastern part of Mazandaran Province, northern Iran. Two distinct management zones were selected: (1) a managed area under the single-tree selection method, and (2) an unmanaged control area without any harvesting operations. Soil sampling was carried out at the 0–10 cm depth using a completely randomized design in each area. A total of 20 soil samples were collected from each site. Soil physical properties included texture (percentages of sand, silt, and clay) and bulk density (BD); chemical properties included pH, electrical conductivity (EC), total nitrogen (TN), phosphorus (P), potassium (K), and organic carbon (OC); and biological indicators included the production rates of ammonium (〖NH〗_4^+) and nitrate (〖NO〗_3^-), all measured using standard laboratory methods. To determine the SQI, Principal Component Analysis (PCA) was used to select key indicators, and the index was then calculated using a weighted additive approach. To identify the most influential variables affecting SQI, a Generalized Linear Model (GLM) was employed.
Results: The results showed that forest management through the single-tree selection method significantly improved several soil properties compared to the control area. Specifically, in the managed area, the percentage of sand, volumetric moisture content, pH, EC, total nitrogen, phosphorus, potassium, and ammonium production rate were significantly higher (P < 0.05). In contrast, no significant differences were observed in bulk density, organic carbon, or nitrate production between the two areas. PCA results revealed that the first two components explained over 67% of the total variance; the first component represented soil fertility and physicochemical quality, while the second was related to nitrogen cycling processes. The Soil Quality Index was significantly higher in the managed area than in the control. The GLM results indicated that potassium, bulk density, and phosphorus were the most influential variables in explaining SQI variation, and the model performed well with a coefficient of determination (R²) of 0.87.
Conclusion: The findings of this study suggest that single-tree selection forest management, when properly implemented based on sustainable harvesting principles, can have positive impacts on key soil properties and ultimately enhance soil quality. This indicates that selective harvesting, unlike large-scale clearcutting, has the potential to maintain or even improve the biological and chemical functioning of the soil. Since soil is a key factor in the sustainability and regeneration of forest ecosystems, its proper management can play a vital role in maintaining ecosystem health. Future studies are recommended to investigate the long-term effects of this type of management on soil carbon and nitrogen dynamics, the influence of vegetation species composition, and the role of soil microbial biodiversity. Additionally, the use of advanced modeling approaches to better predict spatial and temporal variations in SQI can support the development of more precise and adaptive forest management strategies.