Publications
Assessing Hyrcanian forest fire vulnerability: socioeconomic and environmental perspectives
A comprehensive analysis of the Hyrcanian Forests reveals critical vulnerabilities to forest fires due to climate change and human activities. The study identifies key indicators such as precipitation, elevation, biodiversity, and socioeconomic factors that significantly influence fire susceptibility in this region. Using advanced techniques like geographic information systems (GIS), remote sensing, and machine learning, researchers developed a forest fire vulnerability index that maps areas with high exposure and sensitivity but low adaptive capacity. These findings highlight urgent intervention points for policy-making, community engagement, and environmental management to mitigate the impacts of forest fires on this vital ecosystem.
Effects of forest roads on vegetation biodiversity and soil characteristics in Hyrcanian forests
Forest roads significantly impact vegetation biodiversity and soil characteristics in the Hyrcanian forests of northern Iran, with effects extending up to 45 meters from the road edge. The study found that harvesting activities led to a reduction in tree, herbaceous species, and lichen regeneration, while soil physical and chemical properties remained largely unaffected. However, distance from the road correlated with increased diversity and richness of vegetation and most soil characteristics (except carbon content). Notably, soil properties showed a strong relationship with tree and herbaceous regeneration, suggesting that roadside buffers play a critical role in mitigating these impacts. These findings underscore the broader influence of forest roads on ecosystem dynamics within UNESCO-listed Hyrcanian forests.
Challenges Facing the Improvement of Forest Management in the Hyrcanian Forests of Iran
A hierarchical framework, the Sustainable Hyrcanian Forest Management Model (SHFMM), emerges as a promising solution to address challenges hindering sustainable forest management (SFM) in Iran's Hyrcanian forests. These challenges include inconsistent policies, reliance on outdated techniques, and conflicts between policymakers and local communities. The SHFMM proposes a collaborative approach that empowers state and private sectors to make decisions while fostering cooperation among all stakeholders. Developed through independent studies of forest management in the region, this model offers site-specific lessons with broader applicability.
Estimating Aboveground Biomass in Dense Hyrcanian Forests by the Use of Sentinel-2 Data
A novel approach using Sentinel-2 satellite data demonstrates high accuracy in estimating aboveground biomass (AGB) in dense Hyrcanian forests, particularly for Carpinus betulus (common hornbeam). The study found that band 6, located in the red-edge spectrum, exhibited the strongest correlation with AGB (r = −0.723), outperforming other spectral and derived indices. Among machine learning methods tested—Multiple Regression (MR), Artificial Neural Network (ANN), k-Nearest Neighbor (kNN), and Random Forest (RF)—ANN achieved the best performance, with a %RMSE of 19.9%. This method offers a cost-effective alternative to traditional field measurements, which are often hindered by challenging terrain. The findings suggest that simple vegetation indices derived from Sentinel-2 imagery can reliably estimate AGB in temperate forests, providing a scalable solution for biomass assessment in remote or difficult-to-access areas.
The Response of Soil Physicochemical Properties in the Hyrcanian Forests of Iran to Forest Fire Events
Forest fires significantly alter soil physicochemical properties and hydrological processes in the Hyrcanian Forests of Iran, with burned areas showing increased sand, silt, aggregate stability, hydrophobicity, organic carbon, nitrogen, phosphorus, potassium, electrical conductivity, and pH, while clay content and infiltration rates decrease. A study conducted in the Zarrinabad watershed of Sari compared unburnt and burned natural and plantation forests, revealing drastic modifications that could activate land degradation. No significant changes were observed in true and bulk density, porosity, or soil moisture. These findings underscore the urgent need for land managers to address fire impacts in Iran, where such studies remain scarce.
Viscotoxin and lectin content in foliage and fruit of Viscum album L. on the main host trees of Hyrcanian forests
Mistletoe (Viscum album L.) on Hyrcanian forests' host trees exhibits varying levels of viscotoxins and lectins, with the highest concentrations found in December foliage. Notably, viscotoxin levels peaked at 9.25 mg/g dry weight in V. album on Carpinus betulus, while lectin content was highest (0.79 mg/g DW) in December foliage on Parrotia persica and C. betulus. Immature green berries contained significantly higher concentrations of both compounds than mature white berries. Four viscotoxin isoforms were identified across all samples, with A3 being the most predominant. This study highlights the seasonal and host-specific variability in bioactive compound production in mistletoe, offering insights into its potential medicinal properties.
Degradation of dibutyl phthalate by Paenarthrobacter sp. Shss isolated from Saravan landfill, Hyrcanian Forests, Iran
Researchers have identified Paenarthrobacter sp. Shss, a bacterium isolated from Saravan landfill within the Hyrcanian Forests of Iran, as one of the most efficient known degraders of dibutyl phthalate (DBP), a widely used plasticizer with harmful health effects. This bacterium can degrade 1 gram per liter of DBP in just 15 hours, with a doubling time of 5 hours, converting it into less toxic compounds like mono-n-butyl phthalate and ultimately carbon dioxide. The study also found that Paenarthrobacter sp. Shss can degrade other phthalic acid esters such as dimethyl phthalate, diethyl phthalate, and mono butyl phthalate, but not diethyl hexyl phthalate. The research employed enrichment culture techniques and advanced mass spectrometry to identify the bacterial strain and its esterase activities responsible for DBP degradation, highlighting its potential role in mitigating phthalic acid ester contamination in highly polluted environments like landfills.
Dynamics of humus forms and soil characteristics along a forest altitudinal gradient in Hyrcanian forest
Altitudinal gradients significantly influence humus form distribution and soil properties in Hyrcanian forests, with distinct mechanisms at play. While soil characteristics such as temperature, pH, calcium carbonate content, nitrogen levels, carbon-to-nitrogen ratio, and microbial biomass nitrogen show strong correlations with altitude, forest floor properties are more shaped by tree species composition. The study, which analyzed 225 humus profiles across altitudes from 0 to 2000 meters in the Alborz Mountains, found that Mull abundance decreases in plain mixed forests compared to mountain pure forests, whereas Amphi forms increase. Mixed beech forests exhibit high frequencies of Oligomull and Leptoamphi, while Eumacroamphi, Eumesoamphi, and Pachyamphi are exclusive to pure beech forests. These patterns impact soil fertility, with higher nitrogen, microbial biomass nitrogen, and pH observed at lower altitudes under mixed forests than in pure forests at higher altitudes.
Evaluating the Effectiveness of Mulching for Reducing Soil Erosion in Cut Slope and Fill Slope of Forest Roads in Hyrcanian Forests
Mulching significantly reduces soil erosion on forest roads, particularly on steep cut and fill slopes, with wood chips being the most effective material. The study found that on both slope types, wood chip cover (WCH) minimized runoff volume and sediment yield compared to sawdust or rice straw covers, as well as bare soil. Erosion decreased with road age but remained higher on steeper gradients (40–45% vs. 20–25%). These findings highlight mulching as a critical erosion control strategy in the Hyrcanian Forests, especially for newly constructed roads with high erosion potential.
Modeling of trees failure under windstorm in harvested Hyrcanian forests using machine learning techniques
A machine learning model, specifically the multi-layer perceptron (MLP) neural network, has demonstrated high accuracy—97.7% across all datasets—in predicting tree failure under windstorm conditions in harvested Hyrcanian forests. This model outperformed radial basis function neural networks and support vector machines, identifying key factors such as tree height, crown diameter, and target tree height as critical determinants of susceptibility to wind-induced damage. The findings underscore the potential for integrating artificial intelligence into forest management strategies to mitigate tree failure risks during timber harvesting operations, thereby optimizing wood extraction while reducing unscheduled clear-cutting costs.
Multi-Sensor Aboveground Biomass Estimation in the Broadleaved Hyrcanian Forest of Iran
A combination of Sentinel-2 (S2) and Sentinel-1 (S1) datasets using the k-Nearest Neighbor algorithm achieved the highest accuracy for estimating aboveground biomass (AGB) in the broadleaved Hyrcanian Forest, with an R² of 0.57 and a relative root mean square error (rRMSE) of 14.68%. This approach outperformed individual datasets, demonstrating that integrating optical data (Landsat-8 or Sentinel-2) with synthetic aperture radar (SAR) data improves AGB estimation in this UNESCO-listed forest ecosystem. The study also identified FAPAR and red-edge NDVI as the most correlated Sentinel-2 metrics for biomass prediction.
The combined effects of climate and canopy cover changes on understorey plants of the Hyrcanian forest biodiversity hotspot in northern Iran
Climate change is projected to significantly reduce the occurrence of understorey plant species in Iran's Hyrcanian Forests, but strategic canopy cover management could mitigate these effects for some groups. Using data from 512 plots and 95 understorey species, researchers modeled species distribution based on climate, soil variables, and canopy cover. Projections under two climate scenarios showed that while light-adapted and generalist forbs may increase with reduced canopy cover, graminoids and ferns would continue to decline. Conversely, increasing canopy cover could buffer the negative impact of climate change on cold-adapted species but is unlikely to help shade-adapted forest specialists. These findings highlight the potential for overstorey management to conserve biodiversity, though additional measures are needed for vulnerable species.
Vegetation Growth Analysis of UNESCO World Heritage Hyrcanian Forests Using Multi-Sensor Optical Remote Sensing Data
A significant longitudinal gradient drives vegetation growth in the UNESCO-listed Hyrcanian Forests, with faster growth observed in lowland and upland western zones compared to mid-elevations and western outskirts. Environmental factors, including elevation, surface temperature, and precipitation, also contribute differently but significantly (p < 0.0001) along this gradient. The study leveraged multi-sensor optical remote sensing data—specifically MODIS-NDVI and Sentinel 2-derived Temporal Normalized Phenology Index—analyzed via Google Earth Engine (GEE) to model seasonal phenology and growth across ca. 1.9 million hectares, including 15 World Heritage Sites. This approach provides a scalable benchmark for assessing environmental impacts on vegetation in these inaccessible mountainous regions.
A Comparison of the Formation Rates and Composition of Tree-Related Microhabitats in Beech-Dominated Primeval Carpathian and Hyrcanian Forests
Primeval beech forests, critical reference areas for conservation, exhibit distinct differences in tree-related microhabitats (TreMs) between the Hyrcanian and Carpathian regions. A study comparing Oriental beech (Fagus orientalis) in Iran's Hyrcanian Forest with European beech (Fagus sylvatica) in Ukraine's Uholka Forest revealed that Hyrcanian trees, despite similar individual TreM richness, had twice as many TreMs per hectare and a faster formation rate. These forests also showed higher proportions of rot holes, root-buttress concavities, and crown deadwood, while Carpathian beech dominated in bark losses and exposed heartwood. Environmental factors likely drove these differences, underscoring the Hyrcanian Forest's unique ecological value as a UNESCO World Heritage Site, not only for its relict status but also for its rich and distinct TreMs supporting endemic saproxylic species.
Analyzing the Biological and Structural Diversity of Hyrcanian Forests Dominated by Taxus baccata L.
A groundbreaking analysis of the Hyrcanian Forests, a UNESCO World Heritage Site in Northern Iran, reveals complex biological and structural diversity patterns dominated by Taxus baccata L. Researchers identified non-linear relationships between tree density and species mingling, as well as between average tree size and neighborhood dominance. Additionally, they found a linear relationship between neighborhood dominance and mean neighborhood differentiation, offering novel insights into forest ecology. The study employed innovative methods—species abundance distribution models and species-area relations—to analyze 15 tree species across three large field plots, providing detailed metrics such as tree densities, sizes, and structural indices. These findings underscore the importance of Taxus baccata in these ecosystems while recommending similar analytical approaches for global forest studies.
Effectiveness of Three Post-Harvest Rehabilitation Treatments for Runoff and Sediment Reduction on Skid Trails in the Hyrcanian Forests
Contour-felled logs (CFL) are significantly more effective than straw or leaf litter mulch in reducing runoff and sediment yield on skid trails in the Hyrcanian Forests, with CFL treatments showing up to 94.7% reduction in sediment yield compared to untreated areas. The study, conducted by Jourgholami et al. (2020), tested three post-harvest rehabilitation treatments—litter mulch (LM), straw mulch (SM), and contour-felled logs (CFL)—at varying rates or distances on 30 runoff plots in machine operating trails. Results indicated that while both LM and SM reduced runoff by 41.5–60.6% and sediment yield by 42.9–71.8%, CFL treatments were more efficient, particularly at a distance of 20 meters, which achieved the highest reduction in runoff and sediment. The study also developed polynomial regression models to predict runoff and sediment yield based on mulching rates, log distances, and rainfall intensity. These findings suggest that contour-felled logs could be an optimal management strategy for stabilizing skid trails and mitigating soil erosion in post-harvest forest areas.
Four millennia of vegetation and environmental history above the Hyrcanian forest, northern Iran
A groundbreaking reconstruction of vegetation, fire, and climate dynamics over the past 4,300 years in the highlands of Gilan province, northern Iran, reveals significant environmental shifts above the UNESCO-listed Hyrcanian forest. The study, employing multi-proxy analysis including pollen, spores, charcoal, and geochemical data, identifies a transition from dominant steppe vegetation to wetter conditions between 4,300 and 1,700 years ago, followed by a period of aridity (1,700–1,000 years ago) marked by reduced vegetation cover and increased soil erosion. Human activities, such as livestock grazing and deforestation, are linked to the decline of tree species like Fagus and Quercus, while frequent fires suggest long-term anthropogenic influence. Recent centuries show signs of forest recovery alongside continued grazing pressure, highlighting the resilience and vulnerability of this unique ecosystem.
Patterns of density and structure of natural populations of Taxus baccata in the Hyrcanian forests of Iran
European yew (Taxus baccata) populations in Iran's Hyrcanian forests exhibit similar structural patterns across two reserves despite varying elevations and slopes. Analysis of diameter distributions revealed no significant differences between Gazou and Afratakhteh, with the highest tree densities found in specific height classes (12–16 m in Afratakhteh and 18–24 m in Gazou). Static life tables highlighted critical mortality in early age classes, suggesting that targeted management—such as thinning and selective removal of competing vegetation—could enhance survival rates. These findings underscore the importance of proactive conservation strategies to sustain this ecologically and economically valuable Tertiary relict species.
Classification of the Hyrcanian forest vegetation, Northern Iran
Researchers have developed the first comprehensive classification of Hyrcanian forest vegetation, identifying 21 associations and seven alliances across Northern Iran. This study, which analyzed 1,597 vegetation plots covering altitudes from -22 to 2,850 meters, revealed that the distribution of these syntaxa is primarily influenced by altitude and mean annual temperature. Notably, this classification scheme links Hyrcanian forests to European temperate forest vegetation, supported by an expert system for automatic plot classification. The findings propose a new syntaxonomic framework, distinguishing between lowland swamp forests, submontane mesic and wet forests, montane beech forests, and upper-montane oak and hornbeam forests.
Improving Accuracy Estimation of Forest Aboveground Biomass Based on Incorporation of ALOS-2 PALSAR-2 and Sentinel-2A Imagery and Machine Learning: A Case Study of the Hyrcanian Forest Area (Iran)
Combining Sentinel-2A optical imagery with ALOS-2 PALSAR-2 radar data significantly improves the accuracy of estimating forest aboveground biomass (AGB) compared to using either dataset alone. A case study in Iran’s Hyrcanian Forest demonstrated that Support Vector Regression (SVR) models, when trained on combined Sentinel-ALOS datasets, achieved the highest prediction accuracy (R² = 0.73, RMSE = 38.68), outperforming Gaussian Processes (GP), Random Forests (RF), and Multi-Layer Perceptron Neural Networks (MPL). While Sentinel-2A alone provided reasonable results, ALOS-2 PALSAR-2 data yielded poor performance when used independently. This study highlights the potential of integrating multi-sensor remote sensing with machine learning to enhance biomass estimation in UNESCO World Heritage Sites like the Hyrcanian Forests.
Genetic diversity of Castanea sativa an endangered species in the Hyrcanian forest
A study on the genetic diversity of Castanea sativa, an endangered tree species in Iran's Hyrcanian Forest, reveals significant genetic erosion and population differentiation. Using 18 simple sequence repeat (SSR) loci—10 nuclear and 8 chloroplastic—the research found low nuclear genetic diversity with observed heterozygosity ranging from 0.125 to 1.000, while chloroplast SSRs showed no polymorphism. Analysis of molecular variance (AMOVA) indicated high within-population variation (84%) but notable between-population differentiation (16%). Structure and UPGMA analyses separated the Shafaroud population from others, suggesting a genetic extinction vortex due to limited gene flow over short distances. The findings underscore the urgent need for stricter protection measures across all four known populations of this species in Iran.
Classifying Complex Mountainous Forests with L-Band SAR and Landsat Data Integration: A Comparison among Different Machine Learning Methods in the Hyrcanian Forest
Integrating L-band SAR and Landsat data significantly improves the classification accuracy of forest stand age classes in complex mountainous regions, such as the Hyrcanian Forest. The study found that combining these datasets achieves an overall accuracy (OA) of 86%, a notable improvement over using either dataset alone or traditional classifiers like maximum likelihood classification (MLC). Among machine learning methods, support vector machines (SVM) and random forest (RF) outperformed neural networks (NN), with SVM and RF showing the highest robustness and accuracy. Terrain correction was crucial, as non-corrected data failed to differentiate forest classes effectively (OA = 65%). The research highlights the potential of multisource remote sensing for precise forest classification in challenging terrains.
Regional and local patterns of ectomycorrhizal fungal diversity and community structure along an altitudinal gradient in the Hyrcanian forests of northern Iran
A study of the Hyrcanian Forests, one of Eurasia's last temperate old-growth forests, reveals that ectomycorrhizal fungal diversity declines monotonically with altitude. This pattern mirrors broader trends seen in macroorganisms and is influenced by environmental energy limitations, which reduce competitive ability for rare species. The research combined morphological and molecular identification methods to analyze 367 fungal species across three altitudinal transects, finding that host species and altitude were the primary drivers of community composition at both local and regional scales. Climatic variables like mean annual temperature and precipitation, strongly correlated with altitude, also played a significant role in shaping these patterns. The study suggests further investigation into direct effects of climatic variables and their seasonality, particularly at larger spatial scales.
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