European Journal of Forest Research最新文献

The quantification of tree growth and carbon storage over time is an important task for sustainable forest management and carbon sequestration projects. For the South African short-rotation Pinus radiata (D. Don) forests, this knowledge is lacking. We developed allometric equations and compared the estimated weights to previously published biomass studies and we used Dirichlet Regression (DR) modelling to ensure additivity of the component proportions. The biomass components and their contribution to carbon storage depend strongly on forest structure and mean tree size but also on-site conditions and tree architecture. Our first two hypotheses were that the (1) best model for stemwood (SW), bark and total mass will include the combined variable DBH²H and (2) that the DR will yield statistically similar estimates for all components when compared to the best models. Our third hypothesis was that allometric equations developed for sites with high resource availability (e.g. wet, fertile sites) will yield biased estimates when extrapolated to sites with lower levels of resource availability (drier and/or infertile sites). The results indicated that DBH²H was the best variable to describe SW, bark and total mass and the DR yield similar estimates for all component proportions when compared to the best models. There were strong similarities in the SW and total mass of independent test sites in comparison to the SW and total mass of this study but greater variability in the bark, needle and branch mass. This can be associated to site and seasonal differences as well as variability in tree architecture brought about by different silvicultural operations on individual sites. Previously developed equations by other authors for sites with high resource availability overpredicted the SW and total mass of the models developed in this study. Our set of additive component equations performed well even when applied to sites of similar productivity over a climate gradient. The presented new equations bridge the gap in knowledge where allometric equations for short rotation Radiata pine stands are lacking.

Tree growth is a multifaceted process influenced by various factors at different spatial and temporal scales, including intrinsic tree traits and environmental conditions. Climate factors have a significant impact on tree growth dynamics, while geological controls can also play a crucial role. However, our understanding of the interplay between these factors concerning tree growth is currently limited. This study focuses on Norway spruce (Picea abies [L.] Karst.), one of the economically most important coniferous tree species in Europe, to investigate the interplay of growth, climate, and environment at the forest and corresponding treeline sites in the High Tatra Mountains of Slovakia. Specifically, we developed chronologies of tree-ring width (TRW) and late-wood density (MXD) for different tree size classes across two limestone and granitic sites. Growth rates of Norway spruce trees have been increasing in forests since the 1930s and from the 1950s at treelines. Growth rates were consistently higher on limestone bedrock compared to granitic bedrock conditions. Variability of radial growth is primarily driven by climate at both geological settings with trees on granitic bedrock displaying more pronounced responses to climatic variables. We observed weakening (non-stationarity) in climate signals over time and across all size classes in both geological settings. The magnitude of these effects is small, but varies across size classes, with larger trees generally displaying stronger climate sensitivities compared to smaller ones. Therefore, our findings accentuate the potential implications of geological settings, climate, and environmental factors on the absolute growth and growth dynamics of Norway spruce, highlighting the need for further research to fully understand and manage forest ecosystems in mountainous regions.

The present study examined Pinus sylvestris L. growth responses to climatic variations and its relationship with intrinsic water-use efficiency (iWUE) across a water availability gradient and also in pure P. sylvestris and P. sylvestris-Quercus species mixed forests. Study sites were selected in the Mediterranean, temperate, and temperate continental climates in Spain, Italy, and Poland, respectively. A combined tree-ring dendrochronological and stable carbon isotope analysis was used to assess the relationship between tree growth and climate variation. Results showed that P. sylvestris growth is critically affected by summer water availability, regardless of study site and species mixing. Warming temperatures during the early growing season benefit tree growth in Mediterranean and temperate continental climates, while no significant effect was observed in the temperate climatic conditions. At the Mediterranean site, trees in mixed stands showed enhanced growth during wet years when moisture is not limiting. At the temperate continental site, trees in the mixed stand grew at a lower rate than those in pure stands, which suggests that intense interspecific competition for water could overwhelm the benefits of species mixing. Also, we found a divergent growth-iWUE relationship of non-significant and significantly positive and significantly negative correlations at the Polish, Italian, and Spanish sites, respectively. Overall, the negative growth-iWUE relationship at the drier Mediterranean site signifies the risk of tree growth decline, particularly in drier climate conditions. Despite that, elevated iWUE levels would benefit tree radial growth when water is not limited and the admixing tree species have compatible light and water use strategies.

This study examines the development and preliminary validation of a protocol for fully automated production analysis in forest harvesting operations, utilizing onboard computer data. By integrating ignition status, motion status, and machine location data from FPDat II data loggers with LiDAR forest inventory data, this research aims to accurately predict key production metrics such as productive time, area covered, volume harvested, and overall productivity for individual machines. The efficacy of this fully automated data collection and analysis approach is scrutinized using a direct comparison with traditional in-field data collection methods, with a focus on feller buncher operations. Findings indicate minimal discrepancies in productive time recordings (0.9%) and area covered by machines (-1.9%), with slightly larger discrepancies observed in volume harvested (-4.4%) and productivity (-5.3%). More significant disparities in area coverage estimations were noted during individual shifts, particularly when multiple machines operated simultaneously or when there was incomplete coverage of machine tracking by FPDat II data loggers. This study is a crucial step towards understanding the capabilities and limitations of onboard computer data for remote production analysis in forest operations. Through comprehensive analysis, it contributes to the digital transformation of forestry, underscoring both the challenges and opportunities of automated tools in enhancing harvesting efficiency.

Fine root decomposition is an important driver of forest carbon (C) and nutrient cycling. Harvesting operations may affect fine root decomposition rates by altering root properties and environmental conditions, but our understanding of root dynamics is limited. In this study, we investigated the chemistry, mass loss, element release (C, nitrogen (N), and phosphorus (P)), and compound release (lignin and cellulose) of decaying fine roots in a 26 year-old Chinese fir plantation seven years after low- and high-intensity thinning (30% and 70% tree removal) using two root size classes (< 1 mm and 1–2 mm diameter). Low-intensity thinning (LIT) did not affect mass loss in either fine root class or the release of fine root elements or compounds during decomposition. Similarly, high-intensity thinning (HIT) had no effect on the decomposition of large fine roots. However, compared with LIT and no thinning, HIT reduced the decay rates and lignin and cellulose losses of small fine roots. This reduction was related to an increase in the root lignocellulose index (lignin/[lignin + cellulose]) and a decrease in soil invertase activity. Interestingly, thinning did not affect root C, N, or P loss during decomposition. In summary, our results suggest that thinning intensity as well as root size and chemistry should be considered when studying fine root dynamics in managed forests.

Knowledge of how different drivers affect tree responses to drought is unprecedentedly imperative in the context of increasing frequency and severity of climatic droughts. Here, to fully understand the drought response complexity of trees, we assessed drought resilience (resistance and recovery) for Chinese fir (Cunninghamia lanceolata) in Southeast China based on tree ring from 324 trees, and used mixed effects model and machine learning (ML) to examine the roles of tree size, predrought growth performances, multiple drought dimensions, and microtopography in affecting tree drought responses. ML were interpreted using a novel of SHapley Additive exPlanations (SHAP) method. Tree responses to drought were primarily driven by tree characteristics (tree size and predrought growth), rather than drought dimensions (intensity, duration and occurrence Timing) and microtopography (elevation and slope aspect). Resistance and resilience increased with tree size and pre-drought growth variability but decreased with drought intensity- quantified by negative climate water balance. Recovery increased with predrought growth rates but decreased with drought duration. The drought intensity threshold for trees fully recovery of tree growth was about -80 mm. Higher elevations and shady slopes favored resistance (resilience) and recovery respectively, which combined with a greater impact of drought in the dry season suggested that the trees suffered more from droughts that only occurred in the dry season, especially at low- and medium-elevation sunny slopes. This study provided a comprehensive insight into tree growth response to drought, and contributed to the understanding of the mechanisms underlying the complexity of drought response. Increasing size diversity in Chinese fir plantations at sunny lower-elevation slopes is a promising measure to cope with the negative effects of drought.

Over the past decade, community-based forest landscape restoration (FLR) has gained policy attention in Pakistan. However, there is a lack of information on how FLR affects the livelihood capital of mountain communities. To fill this gap, this study investigated the livelihood impacts of FLR in the Hindu Kush Himalaya (HKH) region of Pakistan. To understand the role of FLR in livelihood restoration, we utilized focus group discussions (FGDs) and field observations. The study revealed that community members perceived various impacts of FLR on their livelihood assets. FLR helped restore the ecology, improved access to food, education, and health facilities, developed infrastructure, and initiated ecotourism in the study area. FLR activities were executed through several institutions, particularly extension services, Village Development Committees (VDCs), and Joint Forest Management Committees (JFMCs), through the process of equity, accountability, and collaboration. We recommend continuing the implementation of FLR initiatives through extension services to maximize positive impacts on social, natural, human, financial, and physical capitals.

Forests and woodlands are the major source of wild medicinal plants worldwide. In our study, we aimed to identify the factors influencing the yield and polyphenol content of Aegopodium podagraria L., Galium aparine L., Rubus fruticosus L., Rubus idaeus L., Stachys sylvatica L. and Urtica dioica L., the common and abundant medicinal plant species in the study areas. We showed that European temperate forests are potentially an important source of the medicinal resources. Light availability, controlled by canopy cover, proportion of deciduous trees and stand basal area were the most important factors positively influencing both abundance and quality of medicinal plants. The C/N ratio and pH of the topsoil were the most important factors positively influencing the content of phenolic compounds. The phenolic content was highly species-specific and varied according to local environmental conditions. A high proportion of deciduous species and a high canopy openness increased the yield and quality of medicinal plants by ensuring high light availability. Plants with high total polyphenol content should also be sought on biologically active (non-acidic) soils with a high C/N ratio. Our results can be used to guide forest management in areas where harvesting of understory medicinal plants is an important provisioning ecosystem service. In many cases a forest management scenario friendly to medicinal plants may require only a minor changes in forest management intensity, as cultivation or enhanced growth of MD plants can take place in intensively thinned forests and cleared forest patches, without competing with timber production.

Soil microbial biomass carbon (MBC) plays an essential role in driving and regulating global cycling of carbon (C) which is critically important to climate system. However, the pivotal transect-scale determinant factor for the content of soil MBC, along with the variation pattern in different natural forests and soils has not been sufficiently investigated. In this study, 252 soils samples (6 replicates and 3 depths for each site) were sampled from 14 forests which lie in 7 soil types along the 4000 km North-South transect of Eastern China. We found that the highest content of soil MBC in the whole soil profile with 556.69 ± 14.59 mg C kg^− 1 occurred in the subalpine coniferous forest, and the lowest content was observed with 53.11 ± 10.22 mg C kg^− 1 in warm coniferous forest. From the angle of soil type, in the whole soil profile Haplic Andosol had the highest content of soil MBC with 480 mg C kg^− 1, while Haplic ferralsol possessed the lowest content of MBC with 102 mg C kg^− 1. In addition, over the North-South transect of Eastern China the strongest correlation was observed in between the content of soil MBC and altitude (ALT) (R² = 0.64), followed by soil type (R² = -0.61), mean annual temperature (MAT) (R² = -0.56). We concluded that the content of soil MBC in natural forests is mainly controlled by ALT on a transect scale. We believed that the soil C in Yue spruce-fir forest was relatively stable, which can be an option to be a specific forest to optimize forest management and to contribute to mitigating climate change.

Above-ground biomass (AGB) is one of the most popular forest attribute used to estimating, monitoring and evaluating global carbon storage. Accurately estimating AGB is one of the most significant steps in decision-making regarding sustainable forest management, climate policy and management efficiency. Thus, developing accurate AGB estimation models using satellite data is essential. In the present study, the capability of Phased array type L-band synthetic aperture radar (ALOS-PALSAR) and SPOT-6 data to model AGB using Deep learning (DL) and Random forest (RF) and Multiple linear regression (MLR) algorithms were evaluated in coniferous planted area, northern Iran. The systematic cluster sampling method was applied to collect field plot data. A total of 180 circular plots were measured to calculate AGB per hectare. The DL, RF and MLR algorithms were used for AGB modeling. The relative root mean squared error (rRMSE) and R² using ALOS-PALSAR data were 21.99% and 0.21 for the DL, 48.46% and 0.18 for RF and 50.20% and 0.11 for MLR, respectively. Also, the RMSE% and R² using SPOT-6 data were 18.31% and 0.44 for DL, 39.64% and 0.43 for the RF and 44.08% and 0.38 for MLR, respectively. Compared to modeling AGB using ALOS-PALSAR and SPOT-6 data separately, the combination of ALOS-PALSAR and SPOT-6 improved AGB prediction (1.14–23% decrease in RMSE% and 0.11–0.33 increase in R²).The results showed that using of DL provided an increase in prediction accuracy compared to RF and MLR. Based on the results, we conclude that modeling AGB using a combination of ALOS-PALSAR and SPOT-6 data and DL can be useful for estimating AGB in the coniferous planted forests.