Search Results (167)

Abies nephrolepis and Picea jezoensis are native Pinaceae trees distributed in high mountainous regions of Northeast Asia (typically above ~1000 m a.s.l. on the Korean peninsula, northeastern China, Sakhalin, and the Russian Far East) and southern boreal forests, vulnerable to climate change and human disturbances, necessitating accurate habitat identification for effective conservation. While protected areas (PAs) are essential, merely expanding existing ones often fail to protect populations under human pressure and climate change. Using species distribution models with current and projected climate data, we mapped potential habitats across Northeast Asia. Spatial clustering analyses integrated with PA and land cover data helped identify optimal sites and priorities for new conservation areas. Ensemble species distribution models indicated extensive suitable habitats, especially in southern Sikhote-Alin, influenced by maritime-continental climates. Specific climate variables strongly affected habitat suitability for both species. The Kamchatka peninsula consistently emerged as an optimal habitat under future climate scenarios. Our study highlights essential environmental characteristics shaping the habitats of these species, reinforcing the importance of strategically enhancing existing PAs, and establishing new ones. These insights inform proactive conservation strategies for current and future challenges, by focusing on climate refugia and future habitat stability. Full article

Extensive wildfires and logging have affected the Russian boreal forests in recent decades. Scots pine (Pinus sylvestris L.) forests are widespread in Russia and are one of the most disturbed tree species in Siberia. However, the effects of disturbance on soil CO₂ efflux in the vast Siberian forests are still poorly understood. We used the LI 8100A infrared gas analyzer to study changes in soil CO₂ efflux into the atmosphere in mature Scots pine forests in the Siberian central taiga five–six years following fires and logging. Measurements of soil CO₂ efflux rates were performed on sites where automatic weather stations have been continuously operational since 2022, which gives us temporal patterns of meteorological fluctuations across forests with different disturbance histories. We found significant differences in soil efflux rates depending on the site and disturbance characteristics. In the undisturbed dry lichen-dominated forest, CO₂ efflux was 4.8 ± 2.1 µmol m⁻² s⁻¹, while in the wet moss-dominated forest it was 2.3 ± 1.3 µmol m⁻² s⁻¹, with soil efflux in Sphagnum sp. being twofold of that in feather moss. Both fire and logging significantly reduced CO₂ efflux, with a smaller reduction in soil CO₂ efflux observed in the moss-dominated plots (5%–40%) compared to the lichen-dominated plots (36%–55%). The soil efflux rate increased exponentially with increasing topsoil temperatures in lichen-dominated Scots pine sites, with disturbed plots showing less dependence compared to undisturbed forest. In the wet moss-dominated Scots pine forest, we found no significant dependence of soil efflux on temperature for all disturbance types. We also found a positive moderate relationship between soil efflux and forest floor depth in both lichen- and moss-dominated Scots pine forests across all the plots studied. Our findings advance the understanding of the effects of fire and logging on the carbon cycle and highlight the importance of accounting for disturbance factors in Earth system models due to changing climate and anthropogenic patterns. Full article

The precise identification and classification of tree species in young forests during their early development stages are vital for forest management and silvicultural efforts that support their growth and renewal. However, achieving accurate geolocation and species classification through field-based surveys is often a labor-intensive and complicated task. Remote sensing technologies combined with machine learning techniques present an encouraging solution, offering a more efficient alternative to conventional field-based methods. This study aimed to detect and classify young forest tree species using remote sensing imagery and machine learning techniques. The study mainly involved two different objectives: first, tree species detection using the latest version of You Only Look Once (YOLOv12), and second, semantic segmentation (classification) using random forest, Categorical Boosting (CatBoost), and a Convolutional Neural Network (CNN). To the best of our knowledge, this marks the first exploration utilizing YOLOv12 for tree species identification, along with the study that integrates digital aerial photogrammetry with Planet imagery to achieve semantic segmentation in young forests. The study used two remote sensing datasets: RGB imagery from unmanned aerial vehicle (UAV) ortho photography and RGB-NIR from PlanetScope. For YOLOv12-based tree species detection, only RGB from ortho photography was used, while semantic segmentation was performed with three sets of data: (1) Ortho RGB (3 bands), (2) Ortho RGB + canopy height model (CHM) + Planet RGB-NIR (8 bands), and (3) ortho RGB + CHM + Planet RGB-NIR + 12 vegetation indices (20 bands). With three models applied to these datasets, nine machine learning models were trained and tested using 57 images (1024 × 1024 pixels) and their corresponding mask tiles. The YOLOv12 model achieved 79% overall accuracy, with Scots pine performing best (precision: 97%, recall: 92%, mAP50: 97%, mAP75: 80%) and Norway spruce showing slightly lower accuracy (precision: 94%, recall: 82%, mAP50: 90%, mAP75: 71%). For semantic segmentation, the CatBoost model with 20 bands outperformed other models, achieving 85% accuracy, 80% Kappa, and 81% MCC, with CHM, EVI, NIRPlanet, GreenPlanet, NDGI, GNDVI, and NDVI being the most influential variables. These results indicate that a simple boosting model like CatBoost can outperform more complex CNNs for semantic segmentation in young forests. Full article

Epiphytic lichens are integral to boreal forest ecosystems, yet their allelopathic interactions with host trees, particularly in cryolithozone regions, remain poorly understood. This study elucidates the physiological and biochemical impacts of the epiphytic lichen Physcia alnophila on Larix gmelinii (Gmelin larch), a keystone species in Siberian permafrost forests. By combining dendrochronology, GC–MS metabolomic analysis, and HPLC–ESI–MS/MS analysis, we demonstrate that the lichen’s primary metabolite, atranorin (ATR), systemically migrates from thalli into the host’s cambium, roots, and needles, with root accumulation reaching 36.3 µg g⁻¹ DW. Lichen-colonized trees exhibited severe radial growth inhibition (27%–51% reduction over five years) and suppressed apical growth, despite comparable heights to controls, indicating chronic phytotoxicity. Metabolomic profiling revealed lichen-specific polyols (e.g., arabitol, mannitol) in larch tissues, alongside elevated stress biomarkers (terpenes, sterols, phenolic acids), and significant disruptions to the tricarboxylic acid cycle and oxidative phosphorylation. These metabolic perturbations correlate with reduced monosaccharide availability and impaired energy production, directly linking ATR translocation to growth suppression. L. gmelinii exhibited compensatory responses, including increased fatty acids and arabinogalactan synthesis, suggesting adaptive mechanisms to mitigate lichen-induced stress. Our findings suggest P. alnophila as a biotic stressor that affects tree physiology in extreme climates, with implications for boreal forest resilience. This work provides an insight to the rarely pointed out species interactions, which, when combined with climate change, may alter carbon cycling and forest dynamics in permafrost ecosystems. Full article

Forests, being the largest and most intricate terrestrial ecosystems, play an indispensable role in sustaining ecological balance. To effectively monitor forest productivity, it is imperative to accurately extract structural parameters such as the tree height and diameter at breast height (DBH). Airborne LiDAR technology, which possesses the capability to penetrate canopies, has demonstrated remarkable efficacy in extracting these forest structural parameters. However, current research rarely models different tree species separately, particularly lacking comparative evaluations of tree height-DBH models for diverse tree species. In this study, we chose sample plots within the Bila River basin, nestled in the Greater Hinggan Mountains of the Inner Mongolia Autonomous Region, as the research area. Utilizing both airborne LiDAR and field survey data, individual tree positions and heights were extracted based on the canopy height model (CHM) and normalized point cloud (NPC). Six tree height-DBH models were selected for fitting and validation, tailored to the dominant tree species within the sample plots. The results revealed that the CHM-based method achieved a lower RMSE of 1.97 m, compared to 2.27 m with the NPC-based method. Both methods exhibited a commendable performance in plots with lower average tree heights. However, the NPC-based method showed a more pronounced deficiency in capturing individual tree information. The precision of grid interpolation and the point cloud density emerged as pivotal factors influencing the accuracy of both methods. Among the six tree height-DBH models, a multiexponential model demonstrated a superior performance for both oak and ”birch–poplar” trees, with R² values of 0.479 and 0.341, respectively. This study furnishes a scientific foundation for extracting forest structural parameters in boreal forest ecosystems. Full article

Resolving the status of soil carbon with land cover is critical for addressing the impacts of climate change arising from land cover conversion in boreal regions. However, many conventional inferential approaches inadequately gauge statistical significance for this issue, due to limited sample sizes or skewness of soil properties. This study aimed to address this drawback by adopting inferential approaches suitable for smaller samples sizes, where normal distributions of soil properties were not assumed. A two-step inference process was proposed. The Kruskal–Wallis (KW) test was first employed to evaluate disparities amongst soil properties. Generalized estimating equations (GEEs) were then wielded for a more thorough analysis. The proposed method was applied to soil samples (n = 431) extracted within the southern transition zone of the boreal forest (49°–50° N, 80°40′–84° W) in northern Ontario, Canada. Sites representative of eight land cover types and seven dominant tree species were sampled, investigating the total carbon (C), carbon-to-nitrogen ratio (C:N), clay percentage, and bulk density (BD). The KW test analysis corroborated significance (p-values < 0.05) for median differences between soil properties across the cover types. GEEs supported refined robust statistical evidence of mean differences in soil C between specific tree species groupings and land covers, particularly for black spruce (Picea mariana) and wetlands. In addition to the proposed method, the results of this study provided application for the selection of appropriate predictors for C with digital soil mapping. Full article

For point cloud data compiled over larger spatial domains, the rasterization of features is effectively streamlined by means of structured query language (SQL). This comprises enhanced control with filtering data and implementing specific metrics for summarization to derive environmental covariates. LiDAR (light detection and ranging) point cloud data were analyzed via SQL to generate rasterized covariates of the digital terrain model (DTM), canopy height model (CHM), and a gap fraction for a boreal study region in Northern Ontario, Canada. These features, along with topographic covariates computed from the DTM, were later ascertained as important for subsequent tree species classification research. Full article

We evaluated the long-term impacts of various forest management practices on the structure and biodiversity of Estonian hemiboreal forests, a unique ecological transition zone between temperate and boreal forests, found primarily in regions with cold winters and moderately warm summers, such as the northern parts of Europe, Asia, and North America. The study examined 150 plots across stands of different ages (65–177 years), including commercial forests and Natura 2000 habitat 9010* “Western Taiga”. These plots varied in stand origin—multi-aged (trees of varying ages) versus even-aged (uniform tree ages), management history—historical (practices before the 1990s) and recent (post-1990s practices), and conservation status—protected forests (e.g., Natura 2000 areas) and commercial forests focused on timber production. Data on forest structure, including canopy tree diameters, deadwood volumes, and species richness, were collected alongside detailed field surveys of vascular plants and bryophytes. Management histories were assessed using historical maps and records. Statistical analyses, including General Linear Mixed Models (GLMMs), Multi-Response Permutation Procedures (MRPP), and Indicator Species Analysis (ISA), were used to evaluate the effects of origin, management history, and conservation status on forest structure and species composition. Results indicated that multi-aged origin forests had significantly higher canopy tree diameters and deadwood volumes compared to even-aged origin stands, highlighting the benefits of varied-age management for structural diversity. Historically managed forests showed increased tree species richness, but lower deadwood volumes, suggesting a biodiversity–structure trade-off. Recent management, however, negatively impacted both deadwood volume and understory diversity, reflecting short-term forestry consequences. Protected areas exhibited higher deadwood volumes and bryophyte richness compared to commercial forests, indicating a small yet persistent effect of conservation strategies in sustaining forest complexity and biodiversity. Indicator species analysis identified specific vascular plants and bryophytes as markers of long-term management impacts. These findings highlight the ecological significance of integrating historical legacies and conservation priorities into modern management to support forest resilience and biodiversity. Full article

Understanding how pollen assemblages represent the local and regional vegetation composition is crucial for palaeoecological research. Here, we analyze 102 surface moss/soil pollen samples collected from four study regions located in various boreal forest vegetation types in Central Siberia. Despite Larix being the most prevalent tree generus in the study area, the proportion of Larix pollen can be as low as 0.6–1.5% (0.4–4.7% on average) even in localities with a high canopy density of the species. No relationship between the quantity of Larix pollen in the spectra and the abundance of Larix in the local vegetation was found. The dominant components of the pollen assemblages are Betula and Alnus fruticosa. The pollen value of Picea is low (2.6–8.2% on average), with higher proportions at sample plots where spruce is abundant in forests. Pinus is a highly prevalent pollen species within its geographical range, comprising up to 40% of pollen assemblages. Outside of the range, the ratio of Pinus pollen was higher in habitats with low canopy density and in treeless ecosystems. The composition of herbaceous pollen and spores is significantly affected by the local plant community, offering more comprehensive insights into past vegetation patterns. Full article

Restoration efforts to maintain oak-dominated habitats and enhance biodiversity often employ selective thinning, but its long-term effect on multiple taxa remains unclear. This study examines the effects of halo thinning around pedunculate oak on epiphytic and tree-related microhabitat (TreM) diversity in the boreal–nemoral forest zone. We revisited nine sites in Latvia with 150–331-year-old oaks, where thinning was conducted in 2003–2004. Epiphyte species composition and cover were assessed, and TreMs were evaluated using standardised methods. Diversity indices (Hill numbers, q = 0; 1; 2; 3) and statistical models (LMMs, Poisson GLMMs) were used to analyse the effects of thinning on species richness and TreM occurrence. Halo thinning over the past 20 years has not caused significant differences in epiphyte or TreM diversity, though managed trees exhibited a higher occurrence of the most common microhabitats. These findings suggest halo thinning may enhance specific TreM features but do not substantially promote epiphyte and TreM diversity. Future research should implement systematic monitoring, deriving the relationships between the conditions after the thinning and their effects, thus serving as the basis for adaptive habitat management strategies. Expanding the scope of such studies is essential for developing evidence-based forest management strategies. Full article

Soil stoichiometry is essential for determining the ecological functioning of terrestrial ecosystems. Understanding the stoichiometric relationships in mixed forests could enhance our knowledge of nutrient cycling. In a mixed forest zone of Larix principis-rupprechtii (LP) and Betula Platyphylla (BP) in Hebei China, we conducted a study at six different sites with varying levels of tree species mixing. The proportion of L. principis-rupprechtii ranged from 0% to 100%, with intermediate values of 8.58%, 10.44%, 18.62%, and 38.32%. We compared soil stoichiometry, including carbon (C), nitrogen (N), and phosphorus (P), as well as chemical and physical properties across these sites. Piecewise structural equation modeling (piecewiseSEM) was used to assess the direct and indirect links between key ecosystem factors and their effects on soil stoichiometry. In mixed forests, the soil exhibited higher contents of soil organic matter (SOM), total nitrogen (TN), and total phosphorus (TP) compared to those in pure LP forests. Additionally, the soil C: N ratio in the 8.58% and 18.62% mixed forests as well as pure BP forests was significantly higher than that in LP forests. Structural equation modeling (SEM) revealed that the contents and ratios of soil C, N, and P exhibited different responses to mixed species proportions. The effect of mixed species proportions on soil nutrients was predominantly indirect, mediated primarily by variations in soil-available nutrients and, to a lesser extent, by physical properties and pH. Specifically, an increase in the proportion of LP in mixed forests had a direct negative effect on soil-available nutrients, which in turn had a positive effect on the content of SOM, TN, and TP and their respective ratios. Based on these findings, we can predict that soil nutrient limitation becomes more pronounced with increasing proportions of Larix principis-rupprechtii in the mixed forest. Our results emphasized the significance of changes in mixed species proportions on soil stoichiometry, providing valuable references for the sustainable development of forests. Full article

A dataset collected from central South Finland was used to investigate the mortality of boreal trees. The mortality rate was found to be the order of three times that predicted by earlier Nordic mortality models, being in the upper range of international literature observations. Small subpopulations of any tree species tend to die out. The mortality of downy birch increases with stand basal area, as well as with stand age. The mortality of Norway spruce and silver birch increases after 100 years, while that of Scots pine is invariant to age. It is suspected that the high mortality of conifers is due to climatic phenomena of anthropogenic origin. As the relative loss rate of basal area is insensitive to stand basal area, the mortality of trees does not strongly regulate thinning practices, but stand-replacing damage can be avoided by retaining a larger timber stock, along with an enhanced proportion of deciduous trees. Full article

The impact of frequent water deficits on dominant tree species in boreal forests has received increased attention, particularly towards addressing the global climate change scenarios. However, the impacts of coupled light intensity and water deficit in the regeneration and growth of Larix gmelinii seedlings, a dominant species in China’s boreal forests, are still unclear. We conducted a dual-factor controlled experiment with four light intensities (natural sunlight, 50% shading, 75% shading, and 90% shading) and three soil water conditions (80%, 60%, and 40% soil saturated water content). The results showed that the coupling of light and water has a significant effect on the growth and development of Larix gmelinii seedlings. In 40% of the saturated soil moisture content, net photosynthetic rate, transpiration rate, chlorophyll a, and total phenol—leaf were significantly lower than the same light conditions under 80% soil saturated water content. Under the coupling treatment of 60% soil saturated water content and 50% shading treatment, the plant height increment, net photosynthetic rate, stomatal conductance, transpiration rate, chlorophyll a, and phenolic compound content were significantly higher than those of other coupling treatments; however, more than 75% shading inhibited photosynthetic parameters, chlorophyll a, total flavonoid—leaf, and total flavonoid—branch. Our results have important implications for forest management practices; they provide a scientific reference for the early growth of Larix gmelinii seedlings under the coupling of light and water and promote the survival and growth of seedlings. Full article

Dahurian larch (Larix gmelinii) is the dominant tree species in boreal forests, and its photosynthetic response to climate warming is important in modeling and predicting carbon cycling for boreal forest ecosystems. In 1983, seedlings of L. gmelinii from 11 provenances were transplanted into two common gardens with different climate conditions (control and warming climate). Forty years after the transplant, we investigated the response of leaf photosynthetic capacity to climate warming and its variation among provenances. The warming treatment significantly increased the maximum net photosynthetic rate (P_max-a), photosynthetic nitrogen use efficiency (PNUE), maximum carboxylation rate (V_cmax), maximum electron transport rate (J_max), triose phosphate utilization rate (TPU), mesophyll conductance (g_m), leaf nitrogen content (N_area), and chlorophyll content (Chl_m). P_max-a was significantly positively associated with V_cmax, J_max, TPU, g_m, and N_area, and the slope of the linear regression between P_max-a and V_cmax, J_max, and TPU was greater in the warming treatment. The responses of P_max-a, PNUE, V_cmax, J_max, TPU, N_area, and Chl_m to warming differed among provenances. As the aridity index of the original site increased, the magnitude of the warming treatment’s effect on P_max-a, V_cmax, J_max, and TPU represented a varying form of a bell-shaped curve. Overall, the warming treatment improved the photosynthetic capacity of L. gmelinii, but the extent of the improvement varied among provenances. These findings provide insights into the mechanisms underlying the responses of L. gmelinii to climate warming. Full article

Allometry reflects the quantitative relationship between the allocation of resources among different organs. Understanding patterns of forest biomass allocation is critical to comprehending global climate change and the response of terrestrial vegetation to climate change. By collecting and reorganizing the existing allometric models of tree species in China, we established a database containing over 3000 empirical allometric models. Based on this database, we analyzed the model parameters and the effect of climate on forest biomass allocation under the context of ‘optimal allocation theory’. We showed that (1) the average and median exponent of power functions for above-ground biomass were 2.344 and 2.385, respectively, which significantly deviated from the theoretical prediction of 2.667 by metabolic theory (p < 0.01). (2) The parameters of the allometric model were not constant, and not significantly correlated with temperature, precipitation, latitude, and elevation (p > 0.05), but were more closely related to individual size (p < 0.01). (3) Among different types of forests, the proportion of above-ground biomass in tropical rainforests and subtropical evergreen rainforests was significantly higher than that in temperate forests and boreal forests (p < 0.05). The proportion of trunk and branch biomass allocated to tropical rainforest was significantly higher than that of boreal forest (p < 0.05), while the proportion of root and leaf biomass allocated to tropical rainforest was significantly lower than that of boreal forest (p < 0.05). (4) The abiotic environment plays a crucial role in determining the allocation of plant biomass. The ratio of below-ground/above-ground biomass is significantly and negatively correlated with both temperature and rainfall (p < 0.01), and significantly and positively correlated with altitude and latitude (p < 0.01). This means that as temperature and rainfall increase, there is a decrease in the amount of biomass allocated to below-ground structures such as roots. On the other hand, as altitude and latitude increase, there is an increase in below-ground biomass allocation. These findings highlight the importance of considering the influence of abiotic factors on plant growth and development. Full article