The establishment of plantations has become a critical approach for reducing greenhouse gas emissions, particularly in fragile environments with carbon sequestration potential. In karst areas, plantations based on fast-growing afforestation species made significant contributions to enhancing carbon sequestration. However, the impact of understory vegetation on carbon accumulation remains unclear. Especially, the carbon accumulation associated with litter produced during the replacement of understory species receives insufficient attention, which leads to the neglect of the carbon sequestration potential in plantations of karst areas. Leaf is a crucial organ that links the litter production. To explore how leaf traits adapt to competitive environments and drive litter carbon accumulation during understory species replacement, this study observed leaf traits and litter carbon content changes in three types of plantations in the Liujiang River Basin, a typical karst area. A total of 37 sampling plots were selected for field investigation over a two-year period. Leaf traits, species diversity, vegetation coverage, and litter carbon characteristics in understory vegetation were measured. Variance analysis, allometric equations, and path analysis were used for data analysis. The results showed that most understory species adopted a biomass conservation strategy under high-coverage conditions (> 44.27%) and expanded competitive leaf area under low-coverage conditions (< 44.27%). However, Bidens pilosa and Miscanthus floridulus exhibited strong competitiveness during understory species replacement. They showed an expansion of competitive leaf area under high-coverage conditions. This competitive strategy reduced species diversity and community specific leaf area. But the rapid expansion of Bidens pilosa and Miscanthus floridulus increased understory vegetation coverage, and their increased specific leaf area facilitated leaf shedding, resulting in significant litter weight accumulation (P < 0.05), thereby enhancing litter carbon content per unit area. These competitive strategies were key driving factors for the increase in litter carbon content per square meter, which reached a maximum of 49.6% higher than that in natural grasslands. And the maximum increase in litter carbon accumulation derived from understory vegetation reached 3.37 times from 2023 to 2024 in plantations. In the understory vegetation of plantations, the competitive strategies reflected by leaf adaptation of key competitive species are critical factors influencing litter carbon accumulation. Future research could deeply explore the carbon sequestration effects resulting from the dynamic changes in competition within the understory vegetation of plantations.
Urban green spaces have positive effects on both physical and mental wellbeing. However, few studies have focused on the trends and thresholds of the effects of different influences on restorative benefits when viewing scenes featuring plant communities. We measured subjective evaluations and objective physiological data from 44 participants who viewed images of plant communities in the yellow to green hue range to compare differences in restorative benefits among plant communities at different visual distances, as well as quantifying the influencing factors involved. The following results were found: (1) Coniferous and multi-layered plant communities were found to provide greater restorative benefits, and the restorative benefits grew with increasing visual distance. (2) Shape and color characteristics were significantly correlated with restorative benefits, but the relationship is not simply linear. (3) The restorative benefits were found to be greatest when crown proportion was 61.23%, trunk proportion ranged from 4.11 to 13.70%, and the value of color index value ranged from 25.44 to 35.56%; the restorative benefits gradually increased when sky proportion exceeded 12.95–13.19%, the fractal dimension exceeded 1.62–1.67, and hue index exceeded 91.64°–95.67°; additionally, the restorative benefits decreased when the saturation index increased. This study provides a scientific basis for the construction and improvement of plant landscapes in urban green spaces.
The forestry landscape is being climatically redefined due to global warming. Limited knowledge is available on whether introduced pine species will be viable for plantation forestry in South Africa. Existing global circulation models were scaled down to a finer resolution by incorporating historical climate data, global positioning, and terrain conditions (terrain scaling). Terrain scaling of mean annual maximum temperature (MAT-max), minimum temperature (MAT-min), and median annual precipitation rainfall (MAP-median) was statistically significant, achieving R2 values of 0.70, 0.78 and 0.90, respectively. Decadal climate change was analyzed for the period ranging from 2020 to 2060. Future decadal temperatures were found to increase and were generally greater in high-altitude regions compared to low-altitude regions. MAT-max increased by up to 1.7 °C and MAT-min by 0.4 °C by 2060. MAP-median decreased by up to 10% by 2060, with high-rainfall areas in low-altitude regions being more greatly impacted. Climate suitability was determined for Pinus elliottii, P. taeda, P. patula and the hybrid P. patula × P. tecunumanii by harnessing existing species-specific climate threshold models for the region. Current and future conditions were found to be most suitable for P. patula × P. tecunumanii plantations. Isolated climate niches with warmer, drier conditions were best suited for P. patula plantations, while warm, humid conditions favoured P. elliottii plantations. None of the current and future climatic conditions were suitable for P. taeda plantations. A similar approach can be applied to forestry regions globally to enable pre-emptive tree breeding and the introduction of new forest species due to climate change.
Different plants exist in preferences for ammonium (NH4+) and nitrate (NO3−) as the dominant N source, reflecting their adaptation to habitat environments. Elucidating plant specific nitrogen preferences and influencing factors is crucial for ecosystem management under climate change. In this study, we synthesized 216 observations from 15N isotopic tracer studies of diverse forests and grasslands in China to elucidate variations in and factors influencing soil inorganic nitrogen uptake by woody and herbaceous plants. Woody plants had significantly higher uptake rates and proportional contributions of 15NH4+ than 15NO3− (P < 0.05), while herbaceous plants exhibited a contrary trend (P < 0.05). Mean annual temperature played a significantly role in regulating both 15NH4+ and 15NO3− uptake rates in woody and herbaceous plants, followed by mean annual precipitation, mean annual inorganic nitrogen deposition rates, and soil total nitrogen content (P < 0.05). In addition, the key factors influencing nitrogen preference of woody and herbaceous plants were woody plant functional types (evergreen and deciduous plants) and soil inorganic nitrogen content, respectively. Based on the different functional types of woody plants, evergreen plants preferred 15NH4+, while deciduous plants preferred 15NO3−. The results reveal inorganic nitrogen patterns by different plant types in China and that mean annual temperature is a key determinant of plant nitrogen uptake rates. Thus, by matching species-specific nitrogen preferences with the environmental conditions of different regions, valuable insights can be gained to improve the uptake of inorganic N by different plant types and cope with N limitation.
Platycladus orientalis (L.) Franco seed orchards play an important role in sustainable forestry in China but balancing genetic gain and genetic diversity remains a significant challenge. Two key factors influence the success of seed orchards: parental breeding value and gamete contribution, as they determine both the genetic gain and diversity of the seed crops produced. This study aimed to optimize breeding strategies by analyzing parental breeding value, gamete contribution, and genetic gain across two growth periods (89 families in 2008 and 52 families in 2021). We evaluated height, diameter at breast height, and stem volume of progeny in a primary seed orchard, uncovering significant genetic variation among families. Interestingly, no correlation was found between growth traits and gamete contribution, indicating their independence. Using comprehensive scoring and PCA-biplot analysis, we consistently identified several elite families with superior growth performance in both years. We propose an optimal breeding strategy that combines 30% selective harvesting and 50% selective thinning to effectively balance genetic gain and genetic diversity, addressing a critical goal in tree improvement programs. The selected families and optimized strategy provide a scalable framework not only for P. orientalis but also for other conifer species globally, enhancing both productivity and genetic diversity in afforestation efforts.
Ecological and anthropogenic changes have reduced the area of Central Asian riparian forests (tugai), involving dieback of Populus pruinosa Schrenk, one of the tugai’s principal tree species. In a tugai forest on the Zarafshon River, Central Uzbekistan, we investigated the role of environmental factors in P. pruinosa dieback by comparing one healthy and one proximate declining stand. We measured the widths of tree rings of the past 25 years (1999–2023), analyzed their carbon isotope ratios (δ13C; 2004–2023), determined physical and chemical soil variables, and retrieved data on groundwater depths and SPEI (Standardised Precipitation Evapotranspiration Index). Over the 25-year period, radial growth did not differ between healthy and declining trees, but tree growth of the declining stand decreased, and in the last 6 years (2018–2023), during and after 2 exceptionally dry years (2018 and 2019), radial increment was significantly smaller. Correlations between radial growth, δ13C and SPEI, indicative of drought stress, were only found in the declining stand’s trees. Soil of the declining stand had a higher clay content in the subsoil (30–60 cm), higher salt concentrations in the uppermost layer (10 cm) and in the subsoil, and a lower field capacity across the entire soil profile. There was no groundwater decline during the study period. For the first time, evidence is provided that a drought spell in combination with predisposing unfavorable soil conditions can cause tree dieback in Central-Asian tugai forests at a relatively short distance from the water table. Our study may also contribute to initiate further research for analyzing interrelationships between hydrological, edaphic, ecophysiological and meteorological factors in dieback processes of Central-Asian riparian forests, especially in regions that are strongly underrepresented in ecological research.
Soil fauna are crucial for nutrient cycling and promoting plant growth. Plant species mixtures can enhance soil biodiversity and ecosystem functions, but their effects on soil fauna under changing water availability remain poorly understood. To address this gap, we combined a field experiment with a meta-analysis to examine how plant species mixtures influence springtail communities under varying water availability. In a field experiment in Ontario, Canada, we assessed springtail abundance, species richness, Simpson’s diversity index, and community composition in pure and mixed stands of trembling aspen (Populus tremuloides) and jack pine (Pinus banksiana) under ambient, reduced (− 25%), and increased (+ 25%) throughfall in young boreal forest. Tree mixtures enhanced springtail abundance and increased Simpson’s diversity index from − 8.3% under ambient water to + 12.3% under reduced water. Springtail community compositions varied significantly among stand types, with shifts in community composition strongly correlated with fine-root biomass and soil water content. A meta-analysis revealed the effects of plant mixtures on springtail abundance were more positive in sites with less precipitation. On the basis of these results, converting plant mixtures to monocultures will significantly decrease springtail abundance and diversity in areas with less water.
Cunninghamia lanceolata (Lamb.) Hook, a key species for forest plantations in subtropical China, is experiencing a critical decline in productivity due to management practices like long-term successive rotation. Within the C. lanceolata ecosystems, the vigour of the dominant trees reflects their growth potential under the prevailing site conditions. This is crucial for informing management strategies aimed at optimizing plantation productivity. This study focuses on dominant individuals of C. lanceolata, employing their basal area increment in the final year as a quantitative indicator of stand vigour. A dual-dimensional evaluation framework integrating crown structure and tree rings was developed to investigate the underlying mechanisms by which crown structural parameters, stand density and tree age influence stand vigour. This was based on stem analysis data from 76 dominant trees sampled across six southern Chinese provinces. Tree ring data were combined with crown structural parameters including length, width, ratio, volume, shape ratio, and crown projection ratio. A multi-method analytical framework incorporating correlation analysis, difference testing, subgroup analysis, and linear threshold regression modeling was employed to systematically examine these interactions. The results demonstrated that: (1) crown length exhibited a significant positive correlation with basal area increment, while crown shape and projection ratios had significant negative effects. (2) Middle-aged stands (11–20 years) and low-density stands (≤ 1000 ind. ha− 1) exhibited the highest vigour, with significantly greater basal area increment compared to other age classes and density gradients. (3) Linear threshold regression analysis identified a critical threshold for the clear bole ratio at approximately 0.5. Staying below this value optimizes crown morphology and boosts vigour. Therefore, silvicultural management of C. lanceolata plantations should prioritize density regulation to alleviate inter-tree competition, complemented by precision pruning during the critical 11−20-year phase. Strategic control of the clear bole ratio is recommended to enhance stand vigour.
Moso bamboo [Phyllostachys edulis (Carrière) J. Houz.] expansion into adjacent forests affects plant species diversity and associations with soil microorganisms, which will likely have significant impacts on soil phosphorus (P) bioavailability. However, our understanding of how moso bamboo invasion changes soil P bioavailability and its linkage with fungal communities, particularly during expansion into different native forest types, remains limited. Here, we compared soil acid phosphatase (ACP) activity, available P (AP) content, four bioavailable P fractions (CaCl2-P, citrate-P, enzyme-P and HCl-P), and fungal community composition among stands of moso bamboo forest (BF), bamboo-broadleaf mixed forest (MLF), bamboo-coniferous mixed forest (MCF), adjacent evergreen broadleaf forest and coniferous forest (CF). Our results indicate that moso bamboo invasion significantly altered bioavailability of soil P. Specifically, its invasion into CFs significantly increased the AP, CaCl2-P, citrate-P, and HCl-P and reduced soil ACP activity, whereas enzyme-P content significantly increased in the MCF. In contrast, its invasion into broadleaf forests significantly reduced soil enzyme-P content and ACP activity and increased HCl-P content, whereas citrate-P content did not change. In the MLF, the contents of AP and CaCl2-P significantly decreased after the invasion. The invasion also reshaped the composition of soil fungal communities; fungal biomass increased by 128.92% in broadleaf forests compared to 65.67% in the CF. The beta diversity and biomass of soil fungal communities in the CF invaded by moso bamboo were significantly correlated with various P forms, such as AP, citrate-P, and HCl-P, whereas in the BF, they were only significantly correlated with soil ACP activity. These findings demonstrate that the divergent responses of soil P fractions and fungal community traits are primarily driven by the forest type preinvasion, highlighting the importance of baseline ecosystem characteristics in predicting invasion outcomes.
COP29 stepped forward in operationalizing the critical Paris Agreement Article 6 mechanism, which poses new opportunities for forest-based carbon crediting projects. However, these projects, especially those with forest conservation activities, once deemed promising nature-based solutions to climate change, have been facing unique over-crediting challenges, which raised significant concerns in the public and academia. This paper provides recommendations for adopting a dynamic matched baseline accounting approach to enhance integrity and rebuild trust in the industry and the upcoming new global carbon market.
Forest therapy has emerged as a promising intervention for chronic health conditions, yet the underlying mechanisms that govern its efficacy remain poorly understood. The study’s objective was to explore the therapeutic potential for four chronic diseases- hypertension, diabetes, chronic obstructive pulmonary disease (COPD) and subhealth through the interaction of environmental and structural factors of three subtropical forest types of deciduous broadleaf, evergreen broadleaf, and mixed coniferous-broadleaf forests in Zhejiang Province, China. The forest environmental factors we have selected include the biogenic volatile organic compounds (BVOCs), illumination, temperature, humidity, wind speed, CO2, ozone (O3), PM2.5, PM10, and negative air ions (NAI) in forest stands. The forest structural factors we have selected include diameter at breast height, tree height, clear bole height, canopy density, leaf area index, stand density, altitude, aggregation index, competition index, and mingling index. Physiological and psychological indicators of four chronic diseases groups were used as dependent variables. The methods of random forest analysis and factor importance ranking were employed to identify the predominant drivers of forest therapy efficacy. The key findings indicate that beneficial BVOCs promoted therapeutic effects across all four patient groups, with the antihypertensive effect is significant in the hypertension group (P ≤ 0.01). Environmental factors, especially humidity and O3, had negative impacts on therapeutic outcomes across all four groups. In contrast, illumination and NAI had positive therapeutic effects on three groups but not the subhealth group Structural factors, especially stand density and altitude were key drivers of treatment effectiveness. Forest type was also crucial, with deciduous broadleaf forests yielding the best outcomes for four chronic conditions. The three forest types all exhibited significant therapeutic effects on emotional scores (P ≤ 0.001). In addition, deciduous broadleaf forest significantly reduced systolic blood pressure (P ≤ 0.001), while evergreen broadleaf forest significantly reduced systolic and diastolic blood pressure (P ≤ 0.001). This study provides critical insights into the interactions between the forest environment, structures, and therapeutic effects, offering a foundation for optimizing forest therapy strategies and forest management practices to improve public health. The findings have implications for personalized therapeutic interventions and sustainable forest management in subtropical regions.
Drought affects forest productivity and tree radial growth in multiple ways. Two major impacts are growth decline and loss of resilience, i.e., the capacity to recover normal growth rates after a drought, which may indicate impending death. Growth decline and dieback processes have been reported for Mediterranean conifers, but information for natural and planted stands under semi-arid conditions is still scarce, particularly across the increasingly arid Maghreb. We addressed this by assessing growth rates, variability and resilience indices in Algerian Aleppo pine (Pinus halepensis Mill.) stands under Mediterranean sub-humid to semi-arid conditions. Several climate variables and teleconnection patterns (NAO, North Atlantic Oscillation; WeMO, Western Mediterranean Oscillation) were investigated to determine the main drivers of growth decline. Growth resilience indices were calculated at site and tree levels and related to growth trends. Mean basal area increment (BAI) during 2000–2023 was 16.6 cm2 a−1. Negative BAI trends occurred for all sites since 2013, as aridification intensified. All stands showed growth decreases during dry years regardless of site conditions or growth rates. Growth was constrained by cold January conditions, dry conditions from the previous winter to summer, and elevated temperatures from late spring to late summer. Long (12-month) droughts peaking in summer suppressed growth, which was also inversely associated with NAO June indices. Growth decline responded to recovery and resistance indices during the 2012 and 2017 droughts. The results show that long-term aridification triggers growth decline despite short-term, post-drought recovery.
Remote sensing technology has become increasingly effective for forest mapping but its operational use in forest management and planning is still in its infancy. One of the most critical concerns is that remotely sensed forest attributes are not compatible with those traditionally defined in forestry practice. Tree species composition as a fundamental forest attribute is referred to by per-species tree volume or basal area proportion in conventional forestry but is quantified as tree counts or canopy cover percentage in remote sensing. These differences in the definition of tree species composition imply a barrier for effectively applying remote sensing in forestry decision-making. This study developed a remote sensing framework to derive tree species composition in a mixed-species, complex forest landscape based on tree attributes obtained by integrating UAV LiDAR and hyperspectral data. We classified 11 tree species with machine learning and obtained F-score values of 0.43–0.95. By incorporating tree species into tree diameter at breast height (DBH) prediction models, DBH was estimated with accuracy much higher than a general model of all tree species. The magnitude of increase in DBH-estimation accuracy was proportional to tree species-classification accuracy. Consequently, species composition coefficient estimation error was largely below 20% in the plots where forest type classification accuracy exceeded 90%. The error propagation from tree crown detection to DBH modeling cannot be overlooked for the integrated use of UAV LiDAR and hyperspectral data toward automatic, model-imbedded forestry-oriented surveys.
Forest insects and pathogens, in addition to fire, contribute to structural diversity by creating snags (dead trees) and dead tops on live trees or “topkill” in conifers throughout western North America. Snags and top-killed trees are important sources of wildlife habitat but quantifying their presence can be challenging at broad scales. Multiple approaches for detecting snags have been developed, but limited methods exist for detecting topkill via remote sensing. There is a need to monitor overlapping disturbances across time and space in addition to the structural diversity in mature and old-growth forests. We used airborne light detection and ranging (lidar) and associated field observations to map individual dead trees and live trees with topkill across a forest dominated by Pinus ponderosa in central Oregon. The lidar point cloud was segmented into individual tree objects (polygons representing tree crown extents as viewed from nadir), for which lidar metrics were computed. Lidar-detected tree objects were paired with 647 field-observed trees with corresponding tree health measurements, and a random forest classifier was developed that separated trees into: (1) live without topkill; (2) live with topkill; and (3) dead classes with 87% accuracy using lidar metrics. Tree mortality was mainly due to fire injury and native bark beetles, such as western pine beetle (Dendroctonus brevicomis), while topkill was primarily due to comandra blister rust (caused by the native fungal pathogen Cronartium comandrae). The classifier was applied to map individual tree health status for 46,444 tree objects from which tree health class density maps were created across the 229-ha study area. Approximately 43% were classified as live without topkill, 22% of trees were classified as live with topkill, and 35% of trees were classified as dead. This approach can be used to improve detection of topkill in conifers, along with tree mortality, caused by disturbances associated with forest insects and pathogens.
The conversion of Norway spruce stands into mixed-species forests is currently one of the most pressing challenges to ensure the stability of forest ecosystems in Central Europe. Recently, direct seeding as a method of artificial regeneration and species (re-)introduction has received increased attention in forestry. Considering that environmental conditions have a strong influence on the growth performance of direct-seeded plants, we investigated how differences in soil and environmental conditions affect the growth performance of silver fir (Abies alba Mill.) and pedunculate oak (Quercus robur L.) seedlings. Our study focused on closed-canopy and open-canopy (canopy removal) Norway spruce stands in a low mountain forest in central Germany. Our data indicates that the growth performance of A. alba and Q. robur seedlings is mainly influenced by the availability of photosynthetically active radiation (PAR). The growth of A. alba increased with a higher PAR-ratio, whereas the photosynthetic efficiency, as measured by Fv/Fm (chlorophyll fluorescence), showed sensitivity to it. Conversely, the growth performance of Q. robur showed a linear increase with light availability. Nutrient availability was the second most important factor, while soil pH alone showed no significant effect. The volumetric water content showed no direct effect, though drought appeared to reduce growth. The results stress that A. alba is sensitive to abrupt changes in the light regime at this early stage of development, highlighting the key role of canopy longevity in facilitating growth. Q. robur, on the other hand, appears to be well suited to sites at high risk of canopy loss due to disturbance or where the canopy has previously been removed.
Climate change has altered the global temperature regimes leading to warmer temperatures occurring earlier in spring in many temperate regions. This has induced an earlier budbreak making trees susceptible to late spring-frost events, however, information on species-specific late spring-frost tolerance is only available from observational studies. Here, we implemented a quantitative study on late spring-frost tolerance determined by in vitro leaf-level measurements of three temperate broad-leaved tree species via the assessment of the maximum quantum yield efficiency of the photosystem II (Fv/Fm). We investigated to what extent in vitro measurements conducted one day before a late spring-frost event can predict the in vivo damage caused by a cold snap. Fraxinus excelsior showed the lowest in vitro tested tolerance to late spring-frost, and the leaves lost 50% of Fv/Fm (LT50) at + 0.60 ± 0.26 °C. The damage induced by the cold snap the following day (minimum temperature of − 3.28 °C) was a fatal decline of Fv/Fm to 5.8% of the maximum. The other two species, namely Fagus sylvatica and Quercus robur, were characterized by LT50 of − 0.17 ± 9.99 °C and − 2.29 ± 1.11 °C, respectively. The cold snap induced less damage, Fv/Fm values declined to 46.9% and 53.5% of the maximum in the two species, respectively. The in vitro measurements precisely predicted the damage caused by the late spring-frost event. We suggest that in vitro estimated LT50 values can be used as a comparative leaf trait as it has high predictive power for tree species performance after late spring-frost.
Timber quality modeling is essential for value-oriented forest management since the traditional, volume-only yield models often ignore internal defects (notably knots) and overestimate usable wood. In this study, we developed an integrated framework to quantify knot-free and knotty core volumes in Korean pine (Pinus koraiensis) plantations in Northeast China. The framework couples a re-parameterized Kozak (For Chron 80:507-515, 2004) taper equation with a bark factor model to convert outside- to inside-bark diameters and two height-dependent functions to describe sound- and loose-knot vertical distribution. Nonlinear mixed-effects models were employed with climatic, stand, competition, and tree predictors (e.g., , , index,, ) to partition each stem into knot-free and knotty core sections (sound- and loose-knot) based on the heights to crown base () and lowest dead branch (), ensuring consistency with observed stem–crown structure. Model evaluation showed high accuracy for all three curves ( 0.70–1.04 cm; 0.72–0.98 cm), supporting reliable prediction of stem profiles and internal knot distributions. We further identified the drivers of section yields at tree and stand scales: at the tree scale, climate, stand, competition, and tree attributes contributed comparably to knot-free yield; for the loose-knot (lowest-quality) section, climate (51%) and tree factors (37%) dominated. At the stand scale, knot-free proportion was accurately predicted using only basal area (), dominant height (), and mean annual temperature (), with an of 0.03. The framework delivers fast, reliable estimates of timber quantity and quality under varying stand and climate conditions, supporting higher-value management of Korean pine.
Mapping individual dead trees from aerial imagery is useful for assessing habitat quality, monitoring forest health, identifying risks to infrastructure, and guiding forest management strategies. Dead trees can be identified in summertime aerial imagery but computer-assisted methods are needed for efficient mapping across large areas. This study evaluated pixel- and object-based unsupervised classification and Deep Learning for mapping individual dead trees using summertime true-color aerial imagery. The study area was located in Rhode Island forests which had high rates of deciduous tree mortality due to a Spongy moth outbreak in 2015–2017. The unsupervised approaches included spectral and morphological filters to reduce commission error. The Deep Learning approach used a training/validation dataset with 22,504-point features representing dead trees. The pixel-based method had the best performance (F1 = 0.84), the object-based method had slightly lower performance (F1 = 0.79), and Deep Learning had the worst performance (F1 = 0.67). The pixel-based method had the added benefits of being automatable and not requiring training data whereas the object-based method could not be automated and Deep Learning required a large training dataset. The study showed that dead deciduous trees can be mapped from true-color aerial imagery and that an automated classification can yield high accuracy.
Carbon sequestration is an important management objective in China’s plantation forests, which are the most extensive in the world. Planning of plantation management often employs simulators that use either tree- or stand-level models. The use of individual-tree models is increasing in Heilongjiang Province, northeastern China, as these models enable flexible analyses of different stand structures, species compositions, and cutting types. So far, management optimization with individual-tree simulators has considered only the carbon storage of living tree biomass. This study proposed a method for expanding the simulators to accommodate the carbon storage of dead organic materials (DOM) and wood products. A dead tree is transferred to the dead-tree list, and a cut tree is transferred to the harvested tree list. Each new dead tree is characterized by the biomass of stem, branches, foliage, and roots, and the annual decomposition rate of each biomass component. A harvested tree is partitioned into harvest residue and wood product components. A residue component obtains biomass and the annual decomposition rate, and a wood product component obtains biomass and the yearly product disposal rate. From these lists, the remaining biomass of any DOM or product category can be easily computed for any future year. The method can be used in any country. In the example analyses conducted for Chinese larch (Larix gmelinii) plantations, the live tree carbon stock accounted for 75–80% of the total carbon storage, and wood products accounted for a maximum of 10–12%. Increasing the importance of carbon storage decreased cutting and led to longer rotations. Maximizing carbon storage as the sole objective resulted in very low harvest and long rotations (> 200 years).
Low-carbon synergistic governance in the forestry industry constitutes a critical component of agricultural engineering. It holds substantial practical significance for China in addressing the strategic challenges posed by the “dual-carbon” goals and fostering high-quality regional economic development. From the perspective of the digital economy and drawing on Germany’s experience, this paper investigates the logical framework, practical challenges, and implementation mechanisms through which the digital economy can synergistically enhance low-carbon development in forestry. The findings reveal that the upstream enabling pathway is realized via a forest information monitoring mechanism, a resource analysis and utilization management mechanism, and an ecological restoration management mechanism, all of which are underpinned by the operation of intelligent equipment and natural resource regeneration. The midstream pathway primarily focuses on a raw material management mechanism for forestry inputs during processing and a supply-chain synergy mechanism that facilitates cross-sectoral coordination. The downstream pathway centers on a forest product marketing mechanism and a consumer-oriented service management mechanism. In-depth analysis indicates that, under current conditions, the digital economy’s enablement of forestry for low-carbon objectives still faces governance challenges. These include data security risks for target entities, “market failure” in the digital transformation of enabling actors, and insufficient regulatory policies within the enabling framework, necessitating further targeted policy recommendations.
While tree species in phosphorus (P) impoverished subtropical forests exhibit divergent foliar P allocation strategies to maximize phosphorus-use efficiency, how these strategies integrate with the leaf economics spectrum (LES) and are modulated by leaf habit (deciduous vs. evergreen) remains unclear. Five foliar P fractions involving inorganic, metabolite, nucleic acid, lipid, and residual were quantified alongside LES traits across 10 deciduous and 22 evergreen woody species grown in standardized monocultures under uniform conditions. Deciduous species had 30% higher total foliar P than evergreens due to 50% and 35% greater metabolite P and nucleic acid P fractions, respectively. Evergreens allocated more P to residual fractions associated with higher leaf mass per area and conservative traits, whereas deciduous species linked lower residual P to rapid photosynthetic turnover. Evergreen species reduced allocation to inorganic P and residual P, together with increased nucleic acid P allocation, corresponding closely with their LES positioning. In deciduous species, however, LES variation was largely independent of foliar P allocation patterns. Importantly, deciduous species exhibited 31% higher photosynthetic P-use efficiency (PPUE) than evergreens. Overall, PPUE was shaped by the combined effects of leaf habit and LES, with foliar P fractions the primary proximate driver and LES functioning as the central mediator. These findings demonstrate that leaf habit governs the physiological coupling of regional LES trade-offs with foliar P biochemistry, underpinning niche differentiation in P-limited soils and enhancing trait-based predictions of nutrient utilization across tree functional types.
Global diversification of the structure and composition of artificial forests and partial or complete conversion to natural forests is an important task for improving long-term biodiversity and counteracting climate change. Larix kaempferi is a tree species used widely in forests throughout northeast Asia that plays an important role in converting artificial forests to mixed forests. However, the phylogenetic diversity (PD) of the species remains unclear. We investigated L. kaempferi forests formed in Gayasan National Park, South Korea, categorized the community types, and quantified species composition, PD, and phylogenetic community structure depending on the vegetation type. Furthermore, we explored the factors regulating biodiversity in L. kaempferi forests to provide insights for promoting forests with high structural diversity. We observed unique vegetation characteristics and community formation mechanisms depending on the local environment, with vegetation types located in valleys and at the bottom of slopes having the highest PD. We revealed how the structural properties and local conditions of forests affect phylogenetic community structure for each vegetation type, leading to competitive interactions and competitive exclusion. For all vegetation types, PD showed a gradually increasing trend with older stand age, but piecewise structural equation modeling analysis showed that topographic environmental factors are the main factors regulating PD. Our findings highlight the need to introduce customized management approaches suited to the characteristics of each community rather than using the same method for all communities. This approach is crucial because species composition, ecological properties, rate of succession, and surrounding environmental conditions differ between vegetation types. In addition, by presenting management strategies to improve biodiversity depending on vegetation type, we expanded existing knowledge on the conversion of artificial forests to mixed forests. Our study provides important insights into establishing strategies for managing artificial coniferous forests and the mechanisms of community formation with changes in species composition after forest creation.
The Saihanba Forest Farm (SHB), China’s largest plantation base, has experienced significant habitat quality services (HQS) changes following six decades of afforestation. This study developed an integrated “CLUE-S–InVEST–BBN” framework to simulate and optimize HQS spatial patterns under alternative land use/cover change (LUCC) scenarios for 2035. Using Landsat imagery (2002–2022) and twelve biophysical and socio-economic drivers, we projected LUCC under three scenarios: ecological protection (EP), natural development (ND), and economic development (ED). HQS exhibited a “decline-then-recovery” trend from 2002 to 2022, with forests and grasslands contributing 79.6% and 48.2% of total HQS, respectively. The EP scenario projected 89.96% forest cover by 2035 with the highest mean HQS (0.8925), while the ED scenario showed a 1.54% decrease due to urban expansion. Bayesian belief network analysis identified LUCC, NDVI, road proximity, and precipitation as primary HQS determinants and delineated three management zones: key optimization, ecological conservation, and general management. This framework provides a replicable approach for enhancing HQS and supporting sustainable land-use planning in ecologically sensitive mountainous forest areas.