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"forest characteristics"
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Physiological and Psychological Effects of Nature Experiences in Different Forests on Young People
Many studies have proved that having nature experiences in forests is conducive to human physiological and psychological health. However, currently there is little research focusing on the effects of forest characteristics and the experiential characteristics of nature experiences on changes in health. In the study, three types of forest (mixed forest; deciduous forest; coniferous forest) and an urban site were used to measure the effects of these environments on participants’ physiological and psychological restoration after nature experience activities (sitting and walking activities). The study participants were 30 young adult students from Beijing Forestry University. Restorative effects were measured by physiological indicators (blood pressure and heart rate) and four psychological questionnaires (Profile of Mood States (POMS); Restorative Outcome Scale (ROS); Subjective Vitality Scale (SVS); Warwick-Edinburgh Mental Well-being Scale (WEMWBS)). Results demonstrated that all types of forest were beneficial to lower blood pressure and heart rate as well as to reduce negative feelings while boosting positive emotions. The mixed forest was more effective in lowering blood pressure and heart rate as well as increasing vitality. The levels of restoration and positive mental health increased significantly, while all subscales of the POMS (with the exception of vigor) decreased greatly in the coniferous forest. Relative to the sitting activity, obvious decreases in blood pressure and negative emotions were observed, while significant increases in restoration, vitality and positive mental health were observed after the walking activity. In conclusion, the impact on subjects’ health restoration varied with different forest characteristics, and the experiential characteristics of exposure may be helpful for creating supportive interventions and lifting the benefits of forest therapy as people interact with the forest.
Journal Article
Effects of urbanization and forest type on species composition and diversity, forest characteristics, biomass carbon sink, and their associations in Changchun, Northeast China: implications for urban carbon stock improvement
Differences in forest attributes and carbon sequestration of each organ and layer between broadleaved and conifer forests of central and outer urban areas are not well-defined, hindering the precise management of urban forests and improvement of function. To clarify the effect of two forest types with different urbanization intensities, we determined differences in vegetation composition and diversity, structural traits, and carbon stocks of 152 plots (20 m × 20 m) in urban park forests in Changchun, which had the largest green quantity and carbon density effectiveness. We found that 1.1-fold thicker and healthier trees, and 1.6- to 2.0-fold higher, healthier, denser, and more various shrubs but with sparser trees and herbs occurred in the central urban forests (
p
< 0.05) than in the outer forests. The conifer forests exhibited 30–70% obviously higher tree aboveground carbon sequestration (including stem and leaf) and 20% bigger trees, especially in the outer forests (
p
< 0.05). In contrast, 1.1- to 1.5-fold higher branch stocks, healthier and more diverse trees were found in broadleaved forests of both the inner and outer forests (
p
< 0.05). Plant size and dominant species had similarly important roles in carbon stock improvement, especially big-sized woody plants and
Pinus tabuliformis.
In addition, a higher number of deciduous or needle species positively affected the broadleaved forest of the central urban area and conifer forest of the outer urban area, respectively. These findings can be used to guide precise management and accelerate the improvement of urban carbon function in Northeast China in the future.
Journal Article
Forest structure determines the abundance and distribution of large lianas in Gabon
Aim: Lianas are an important component of forest structure in the tropics, accounting for up to 45% of total stems. Mounting evidence that tropical forests are undergoing structural changes, with a growing abundance of lianas reducing forest carbon storage potential, imparts a sense of urgency to study the drivers that control liana abundance and biomass, particularly in Africa where data come from a few, small-scale studies. Location: Gabon, Africa. Methods: In the first countrywide study of lianas, we implemented the most ambitious, large-scale forest inventory in tropical Africa to date, quantifying the density, basal area and biomass of large lianas (≥10 cm in diameter) using a systematic, random design of 104 plots located across Gabon. Additionally, we examined the relative importance of environmental variables (mean annual precipitation, mean annual temperature, seasonality, soil nitrogen, soil fertility), disturbance (effect of gaps, forest type) and forest structure (large tree biomass) in driving macroscale variation in the abundance of large lianas. Results: In total, we surveyed 1354 large lianas, and found the density, basal area and biomass of large lianas in Gabon to be comparable to that in other tropical forests. The success of large lianas was positively related to soil N, but most strongly correlated with forest structure, particularly large tree biomass. The strength of the association between large lianas and large trees increased with tree size class. Main conclusions: Forest structure and the availability of large trees may be more important predictors of the abundance and distribution of large lianas in African tropical forests than environmental variables and disturbance. Changing environmental conditions are likely to have little direct effect on large lianas, but climate change, defaunation and land-use activities that diminish forest structure and reduce the number of large trees could have strong indirect effects on large lianas in Central African forests.
Journal Article
Changes in Soil Fungal Diversity and Composition along a Rural–Urban Gradient
The functioning of forest ecosystems depends on the taxonomic and ecological diversity of soil fungi. Urbanization is increasing worldwide and is regarded as a key driver of environmental change altering local species assemblages in urban forest. We investigated whether the degree of urbanization and local forest characteristics affect the soil fungal community in 20 beech forests located along a rural–urban gradient in the city of Basel and its suburbs (Switzerland). We analyzed their soil fungal communities by DNA metabarcoding of the rDNA ITS2 region and related these data to local forest vegetation characteristics and soil properties. The number of fungal OTUs in the 20 forests examined ranged from 170 to 303. Richness, diversity and evenness of fungal communities were all significantly affected by the degree of urbanization, but in different ways. Soil fungal richness was highest in forests in areas with a low degree of urbanization and lowest in forests in rural areas. In contrast, the fungal community diversity increased with the increasing degree of urbanization. Different fungal phyla and fungal guilds showed distinct patterns in their relative abundance along the rural–urban gradient. The degree of urbanization reduced the relative abundance of symbiotrophic fungi, but increased that of saprotrophic and pathotrophic fungi. Our results show that urbanization changes soil fungal community, which in turn can lead to alterations in forest ecosystems.
Journal Article
Mixed Coniferous Broad-Leaved Forests as Road Shelter Forests: Increased Urban Traffic Noise Reduction Effects and Economic Benefits
Establishing road shelter forests is a key method to reduce traffic noise pollution. However, the characteristics of various types of road shelter forests and their effectiveness in reducing traffic noise remain extensively unexplored. This study focused on five types of pure road shelter forests (PFs) and one type of mixed coniferous broad-leaved forest (MCBLF). By conducting field noise monitoring and spectrum simulations, we analyzed average mass density, additional noise reduction and economic benefits. With a forest belt width of 60 m, the MCBLF reduced additional noise by 6.6 dB(A). Additionally, Forest height, crown shape, average mass density and noise frequency were all positively linked to noise reduction. The width of shelter forests was the main factor affecting noise reduction. Linear regression analysis results showed that cumulative mass surface density was a significant factor in noise reduction (p < 0.01, R2 = 0.93). Furthermore, the type and composition of the shelter forest had indirect effects on noise reduction. The MCBLF had better noise-reducing effects compared to both broad-leaved PFs and needle-leaved PFs due to its more complex structure. Interestingly, as the forest belt became wider, the noise reduction benefits per unit area decreased, implying that a 10 m wide forest belt offered higher economic returns. Considering that a 10 m wide shelter forest belt did not meet noise reduction requirements. This study suggested that the 20 m wide MCBLF was an optimal choice as an urban road shelter forest, providing both effective noise reduction and maximized economic benefits. Our findings provide a basis for the construction and sustainable development of road shelter forests with noise reduction functions.
Journal Article
Risk Modeling for the Emergence of the Primary Outbreak Area of the Siberian Moth Dendrolimus sibiricus Tschetv. in Coniferous Forests of Central Siberia
In the southern taiga of Siberia, periodic outbreaks of the Siberian moth Dendrolimus sibrircus Tschetv. have been observed. The outbreaks result in the defoliation of Siberian fir Abies sibirica Ledeb. and Siberian pine Pinus sibirica Du Tour. stands across approximately one million hectares, leading to dieback of the affected forests. This is largely attributable to the inability to promptly identify the onset of the pest population growth in a timely manner, particularly in the context of expansive forest areas with limited accessibility. It is feasible to enhance the efficacy of monitoring Siberian moth populations by discerning stands with the highest propensity for damage and concentrating efforts on these areas. To achieve this, we employed machine learning techniques, specifically gradient boosting, support vector machines, and decision trees, training models on two sets of predictors. One of the datasets was obtained through a field study conducted in forest stands during the previous outbreak of the Siberian moth (2015–2018), while the other was derived from the analysis of remote sensing data during the same period. In both 2015 and 2016, the defoliation was most accurately predicted using gradient boosting (XGB algorithm), with ROC-AUC values reaching 0.89–0.94. The most significant predictors derived from the ground data were the proportions of Siberian fir, Siberian spruce Picea obovata Ledeb., and Scots pine Pinus sylvestris L., phytosociological data, tree age, and site quality. Among the predictors obtained from the analysis of remote sensing data, the distance to disturbed forest stands was identified as the most significant, while the proportion of dark coniferous species (A. sibirica, P. sibirica, or Picea obovata Ledeb.), the influx of solar radiation (estimated through the CHILI index), and the position in the relief (mTPI index) were also determined to be important.
Journal Article
Comparative ecological responses to cloud seeding in three forested basins of South Korea
Cloud seeding has emerged as a promising strategy for artificially enhancing water availability in forest ecosystems, thereby supporting hydrological processes and promoting ecological function. While previous studies have reported improvements in hydrological indicators such as soil moisture and runoff following cloud seeding-induced precipitation, there remains a critical knowledge gap regarding the extent of these ecological effects including GPP and NPP across basins with differing forest characteristics—such as total area, forest coverage, and dominant tree species. This study addresses this gap by conducting a comparative assessment of the ecological benefits of cloud seeding across three basins in South Korea with varying forest properties. An ecohydrological model, RHESSys, is first calibrated for each basin to simulate both hydrological and ecological responses under different cloud seeding scenarios from 2020 to 2023. The results indicate that cloud seeding improves hydrological conditions—including runoff and soil moisture—proportional to the degree of precipitation enhancement, These hydrological improvements, in turn, lead to nonlinear yet consistent increases in GPP and NPP. Notably, the magnitude and persistence of these ecological benefits are more strongly influenced by forest characteristics than by basin size. Under cloud seeding scenarios simulating a 20% annual increase in precipitation, an expansion in forest cover from 78% to 84% results in a 3.1% to 4.2% increase in GPP. Moreover, basins dominated by broadleaf forests exhibit extended vegetation productivity responses, lasting approximately 20–30 days longer. These findings underscore the importance of incorporating basin-specific forest characteristics into the design and implementation of cloud seeding strategies aimed at ecological conservation and productivity enhancement.
Journal Article
Forest and population characteristics of vulnerable relict Pseudotsuga sinensis in southwestern China
Pseudotsuga sinensis, a vulnerable relict coniferous tree species, faces threats from overexploitation and habitat loss. Despite its status, the forest types, community structure, and population characteristics of this species in China have remained largely unknown. In our study, we conducted analyses of 69 vegetation plots where P. sinensis was the dominant species in southwestern China. We identified five forest types, i.e., Type 1: Pseudotsuga sinensis evergreen coniferous forest; Type 2: Pseudotsuga sinensis - Pinus yunnanensis evergreen coniferous forest; Type 3: Pseudotsuga sinensis - Cyclobalanopsis glaucoides evergreen coniferous and broad-leaved mixed forest; Type 4: Pseudotsuga sinensis - Platycarya strobilacea evergreen coniferous and deciduous broad-leaved mixed forest; Type 5: Pseudotsuga sinensis - Calocedrus macrolepis evergreen coniferous forest. These forests exhibited a multilayered vertical structure. The Simpson diversity index ranged between 0.58-0.86, while the Shannon- Wiener diversity index ranged from 1.24 to 2.3. P. sinensis trees' average growth rate of ring width decreased from 5.86 to 0.33 mm/year as increasing ages. Observed maximum age of P. sinensis was 115 years with 78 cm diameter at the breast height. Age frequency distribution of P. sinensis was multimodal. There was a great lack of individuals at 0-20 years old. Forests with moderate disturbance showed a significantly higher number of P. sinensis individuals younger than 30 years compared to areas with no, slight, or severe disturbance. P. sinensis' regeneration depends on moderate disturbance. We recommended delineating buffer zones in the protected areas at certain sites and in such zones can be allowed to sustainable firewood cutting by selectively marked species other than P. sinensis.
Journal Article
Estimating Forest Stock Volume Based on Airborne Lidar Data
Forest stock volume (FSV) stands as an important indicator in evaluating the potential for carbon sequestration. It is crucial for forest resource management at local, regional, and national scales. In order to achieve an accurate estimation of FSV, this article takes Mengyin County, Shandong Province, China as the research area, builds a random forest (RF) model for four tree species based on airborne Lidar data, and forms a monitoring system of \"individual tree - grid - county\" granularities. The results demonstrated that all four models exhibited excellent generalization capabilities, with no signs of overfitting. In the test phase, the R2 of the poplar and pine models exceeded 0.9, while the R2 of the cypress model was 0.81, and the rRMSE was controlled within 20%, indicating that the fitting effect of the three tree species models was better; the accuracy of the robinia pseudoacacia model was relatively poor, with R2 of 0.60 and rRMSE of 20.60%. This study provides a feasible method for estimating forest stock volume within the county, which provides strong technical support for forest resource management and planning, and helps promote sustainable forestry development.
Journal Article