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.

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 F_v/F_m (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 (F_v/F_m). 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 F_v/F_m (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 F_v/F_m 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, F_v/F_m 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.,

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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 (

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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.

Afforestation and reforestation, when aligned with site-specific ecological and socioeconomic conditions, can enhance ecosystem functions and services (ESs). In the Mediterranean, European black pine is widely used in such projects. While management strategies to maximize timber yield are well studied, the economic valuation of multiple ESs and their trade-offs remains limited. This study employed a process-based forest growth model, incorporating climate, soil and stand structure, to assess the effects of thinning intensity and frequency on the provision and economic value of ESs, namely carbon sequestration, erosion control and recreational/aesthetic value, in Italian black pine stands. Results show that while intense and frequent thinning boosts growth, optimal economic outcomes were achieved with 25% basal area removal every 25 years, yielding €57,000–69000 ha^–1, about 30% more than high-intensity, short-rotation regimes. Non-provisioning ESs declined with heavier thinning (up to 22% loss between 15 and 35% intensity) and improved with longer thinning intervals (up to 18% gain from 10 to 25 years). Strikingly speaking, aesthetic and carbon sequestration benefits dominated total value, accounting for up to 99%, regardless of regime. These findings underscore the importance of long-term, balanced thinning strategies to optimize both wood production and broader ESs. The modeling approach offers practical guidance for multifunctional forest management, supporting more sustainable and economically viable decisions. While tailored to Italy’s context, the insights are relevant to policy and practice across Mediterranean and comparable forest systems.

Optical greenness indices, such as the fraction of absorbed photosynthetically active radiation (fAPAR), are critical in constraining and guiding the modelling of forest carbon storage. However, as optical sensors have limited penetration capacity, it remains uncertain whether greenness indices accurately reflect the true response of aboveground biomass (AGB) to local climatic conditions. In this study, we integrate a wall-to-wall AGB dataset derived from microwave remote sensing to examine the consistency between AGB and fAPAR in their climatic responses. Meanwhile, we use an AGB-fAPAR Difference Index (AFDI) to quantify the driving mechanisms underlying their divergent responses, which is defined as the difference between AGB and fAPAR after standardization and normalization. We find that AGB is negatively associated with local precipitation, whereas fAPAR exhibits a positive correlation, leading to pronounced response differences in AFDI across precipitation gradients. Micro-topography contributes 75% to the spatial patterns in AGB and fAPAR (with a correlation coefficient of 0.75), further shaping AFDI through changes in elevation and slope. Using the AFDI, we demonstrate that topography-driven surface water redistributes water availability beyond local precipitation — proximity to surface water shows a significant positive relationship with higher AFDI, yet radiation after topographic correction shows no significant contribution. Moreover, more complex tree species composition and near-mature stand age amplify the AFDI due to their impact on vertical structure. Our results suggest that AGB and fAPAR exhibit inconsistent responses to precipitation. Local topographic effects, hydrological dynamics, and forest composition and age structure collectively drive the disparity between AGB and fAPAR, highlighting the constraints of greenness indices in capturing forest biomass dynamics and providing a new perspective to enhance the accuracy of forest carbon dynamics predictions.

Norway spruce, an ecologically and economically important conifer species, requires efficient propagation methods for mass production and for use in breeding programs. This review explores several propagation methods, including seed-based and vegetative approaches, with a particular emphasis on the cutting method. It examines key factors affecting rooting success, such as donor tree age, seasonal sampling effects, sample position within the crown, and surrounding rooting conditions. Unlike seed-based propagation, which faces major limitations, including long maturation times, irregular seeding year, and genetic variability, vegetative propagation methods can overcome the mentioned challenges. Vegetative propagation using the cutting method offers advantages such as genetic uniformity, higher genetic gain, and faster regeneration. Nonetheless, compared to seed-based propagation, its higher cost and obstacles, such as reduced rooting success in older trees and plagiotropism of cuttings must be considered. Hedging, serial propagation, and selecting the optimal sampling position within the crown can help overcome these constraints, and enhance rooting success. Achieving an acceptable rooting success rate even in older Norway spruce trees, and even without applying auxin hormones, presents a unique opportunity for propagating mature trees in breeding programs, especially for traits influenced by both additive and non-additive genetic effects. In addition, both additive and non-additive genetic effects can also be utilized through seed-based propagation methods, such as a complete diallel cross, which involves crossing all parental trees in every possible two-way combination and testing their progenies, leading to enhanced genetic gain. Somatic embryogenesis is an alternative propagation method that enables the long-term cryopreservation of cell lines and their mass propagation after evaluating the regenerated seedlings. By integrating different propagation methods, including cuttings, somatic embryogenesis, and seeds, Norway spruce breeding programs can be accelerated, enabling the efficient production and deployment of high-quality planting stock for both reforestation and breeding purposes.

Close-range remote sensing (CRRS) technologies are increasingly used in forestry, but there is a lack of awareness of the challenges, needs and expectations of both service providers and end users. We used a customised online questionnaire to interview professionals in the field, recruited through direct (existing networks) and indirect channels (social media). The main barriers we identified include the cost of equipment, the complexity of data processing workflows and insufficient access to specialised training. Our findings emphasise the need for interdisciplinary collaboration, the development of more intuitive and user-friendly tools and the expansion of specialised training programmes. The results of the questionnaire suggest that stronger partnerships between industry and academia should be encouraged to drive innovation and knowledge sharing. In addition, the development of standardised protocols for CRRS applications and the creation of accessible educational resources proved essential to support both novice and experienced users. Scientific conferences are the most important platform to gather all stakeholders in one place, and have underutilised potential to narrow the gap between theory and application. The recommendations we have made aim to facilitate the widespread adoption and efficient utilisation of CRRS technologies in practical forestry.

To clarify the carbon sinks and their uncertainty components in the temperate secondary forest ecosystem (mosaics of natural secondary forests and plantations). We selected three typical forest stands including secondary mixed broadleaved forest (T1-MBF), secondary Mongolian oak forest (T2-MOF), and larch plantation (T3-LPF). The net primary productivity (NPP) and soil heterotrophic respiration (R_h) were monitored for 4 years (2020–2023) by both inventory and chamber methods, and net ecosystem productivity (NEP, carbon sink; NEP=NPP−R_h) and its uncertainty were further calculated for three forest stands. The results showed that the NEP were 1.99±1.78, 1.87±2.06 and 2.68±1.42 t ha⁻¹·a⁻¹ for T1-MBF, T2-MOF, and T3-LPF, respectively, with high relative uncertainties (89.33%, 109.98% and 52.93%, accordingly). Specifically, fine root NPP dominated uncertainty (58.05–78.63%), followed by R_h (2.20–30.45%) and leaf (2.47–9.58%), while stable pools (e.g., stem, coarse root) contributed minimally. Importantly, to improve reliability, we developed a revised method to estimate low-uncertainty carbon sink, which focuses on low-uncertainty carbon pools, including stem, coarse root, and soil carbon. Low-uncertainty carbon sinks reached 1.82±0.21, 1.79±0.34, and 2.56±0.54 t ha⁻¹·a⁻¹ for T1-MBF, T2-MOF, and T3-LPF, respectively. Notably, relative uncertainties dropped sharply to 11.52%, 18.77%, and 21.15%. This study provides a robust framework for quantifying low-uncertainty carbon sinks in the temperate secondary forest ecosystem by integrating resilient carbon pools, significantly improving estimation reliability while reducing uncertainty by 60.0–87.1% compared to conventional approaches.

Understanding tree mortality is crucial to understand forest dynamics and is essential for growth models and simulators. Although factors such as competition, drought, and pathogens drive mortality, their underlying mechanisms remain difficult to model. While substantial attention has focused on selecting appropriate algorithms and covariates, evaluating individual tree mortality models also requires careful selection of performance criteria. This study compares seven different metrics to assess their impact on model evaluation and selection. Results show that candidate models exhibited varying performances across metrics and that the choice of metric significantly influences the selection of the best model. When no confusion matrix was available, the area under the precision-recall curve (AUCPR) emerged as a more reliable alternative to the area under the ROC curve (AUC), offering a more informative assessment for imbalanced datasets. When a confusion matrix was available, Cohen’s Kappa coefficient (K) and Matthews correlation coefficient (MCC) outperformed accuracy-based metrics, providing a fairer evaluation of both live and dead tree classifications. These findings emphasize the importance of choosing appropriate evaluation standards to enhance mortality model assessment and ensure reliable predictions in forestry applications.

Soil aggregates are crucial for evaluating soil quality and fertility, as they significantly contribute to the storage of soil organic carbon and support the functional stability of forest ecosystems. Nitrogen and phosphorus deposition can influence the carbon cycle within forest ecosystems, thereby altering nutrient inputs that affect soil microbial communities and enzymatic activities. In this study, soil samples were gathered from the 0–20 cm depth in both Broad-leaved Korean pine (Pinus koraiensis) forests and KPP, which were representative in northeast China. After wet screening, soil aggregates of 4 different fractions were obtained. Soil aggregates of >2, 2–0.25, 0.25–0.053 and <0.053 mm were included. Corresponding forest litter was added to each fraction, and three levels of nitrogen and phosphorus (NP) addition involving low (L), medium (M), and high (H) along with a control treatment (CK), were applied. A 360–d laboratory incubation experiment was conducted under controlled conditions. The following parameters were measured: soil total organic carbon (TOC), microbial carbon (MBC), dissolved organic carbon (DOC), readily oxidized carbon (ROC), particulate organic carbon (POC), mineral bound organic carbon (MOC), light group organic carbon (LOC), mean weight diameter (MWD), geometric mean diameter (GMD), to assess alterations in soil carbon storage and the stability of soil aggregates. High NP addition markedly decreased soil aggregate stability and inhibited soil mineralization. However, it enhanced soil organic carbon accumulation, particularly DOC and LOC, in most aggregate fractions, while potentially suppressing MBC in small aggregate fractions. Carbon stabilization mechanisms differed between forest types: natural forests relied more on large aggregates (>0.25 mm), whereas plantation depended more on microaggregates (<0.25 mm). Consequently, soils dominated by large aggregates are better suited for M and L NP additions, whereas soils rich in microaggregates should avoid H NP inputs. Overall, the results indicate that medium and low levels of NP enhance aggregate stability and promote mineralization rates, whereas H NP application compromises structural stability and suppresses mineralization. These results enhance our understanding of how nitrogen and phosphorus deposition influence the organic carbon cycle in temperate forest ecosystems.

Analysing pollen dispersal dynamics in natural ecosystems is essential to unravel the effects of meteorological conditions on pollen dispersion and decipher the reproductive strategies of threatened species. However, most aerobiological studies have been conducted in cities. In this study, the intradiurnal airborne pollen dynamics of Sierra de las Nieves National Park (southern Spain) were studied by applying a novel methodology based on the combination of decision trees and clustering to identify the meteorological drivers of intradiurnal pollen dispersion. To that end, pollen patterns were studied in the main pollen types of this protected area during 2018–2024. The days with high pollen detection for each pollen type were clustered according to their intradiurnal pattern. The meteorological conditions of the grouped days were analysed using decision tree algorithms to identify possible causes of their intradiurnal pollen pattern. The highest pollen detection usually occurred around 12:00 and 14:00 h. Most pollen types exhibited a peak during daylight, corresponding to the typical pattern of local pollen sources. Some pollen types, such as Castanea and Urticaceae, exhibited a nocturnal peak characteristic of distant pollen transport. Most pollen types had two different intradiurnal patterns triggered by different meteorological conditions, except Plantago, Poaceae and Quercus. The most relevant variables determining the intradiurnal patterns observed were the frequency of winds blowing from the northwest and northeast quadrants, relative humidity and maximum temperatures. Combining cluster analysis with decision trees proved to be of great utility to analyse the influence of weather conditions on intradiurnal pollen patterns.

Tropical forests contribute over half of the global primary productivity, playing a critical role in regulating the global carbon cycle. In recent decades, global tropical forests have shown a widespread increase in vegetation cover. However, how tropical forests respond to spatiotemporal climate change under increasing vegetation cover remains a critical question, limiting the development of effective conservation strategies. To address this, we evaluated the sensitivity of tropical forests to spatiotemporal climate variability using the vegetation sensitivity index (VSI) across greening in 2000–2021 and identified dominant climate drivers based on solar-induced chlorophyll fluorescence, enhanced vegetation index, and leaf area index data. Results indicate that over 84% of global tropical forests show an increase in vegetation cover, while a decrease in vegetation cover appeared in the southern Congo and southeastern Amazon. VSI showed a latitudinal gradient, with high values (>60) near the equator and lower ones (<40) in higher latitudes. Tropical forests in the Congo had the highest VSI, followed by the Amazon and Southeast Asia. VSI decreased in over half of the Amazon tropical forests, whereas the Congo and Southeast Asian forests showed comparable proportion of pixels with decreasing and increasing VSI trends. Notably, tropical forests typically exhibited increasing variability, indicated by the detrended variance along the greening trend gradient. In addition, tropical forests are highly vulnerable to climate change in the western Amazon, equatorial Congo Basin, and equatorial Southeast Asia based on the aspects of VSI magnitude and VSI trend. Precipitation is the dominant climate driver regulating global tropical forest variability to climate change, followed by temperature and solar radiation. Temperature dominated tropical forests variability in Southeast Asia. Our findings highlight the instability and vulnerability of tropical forests under climate change despite widespread increase in vegetation cover.

Chinese fir (Cunninghamia lanceolata) is a key resource in China’s timber industry. However, there are no site-specific cultivation guidelines for producing large-diameter trees and enhancing forest productivity and timber quality while addressing the complexities of Chinese tropical terrains. This study identifies key site factors using the Boruta algorithm and establishes diameter growth curves through K-means clustering and mixed-effects modeling to estimate how many years are required to reach target diameters. It is based on sample plot data from Chinese fir plantations across seven provinces in subtropical China. The results show that: (1) soil type and depth, and elevation have a strong influence on growth; (2) of the six basic growth models compared, the Korf model performed best, with the coefficient of determination (R²) of 0.6154. The predictive accuracy was significantly improved by introducing a mixed-effects model, which increased the R² value to 0.7535; (3) at a designated harvesting age of 26 years, diameters at breast height (DBH) were approximately 30, 27, and 26 cm under STG6, STG4, and STG8, respectively, all exceeding the large-size timber threshold (DBH≥25 cm), demonstrating strong cultivation potential; and, (4) to attain a 25 cm DBH, STG6, STG4, and STG8 required 18, 21, and 24 years, respectively. Growth rate slowed significantly after 30 years, suggesting that the rotation period should be limited to within 30 years to optimize management efficiency. In this study, we developed a robust growth prediction model suitable for diverse site conditions and propose a site-specific management strategy that provides the basis for the precision cultivation and sustainable management of large-size Chinese fir plantations.

Soil bacteria, by participating in the soil nutrient cycling process, directly or indirectly regulate tree regeneration and survival. We established a 1-ha plot in the mixed forest of Picea asperata and Larix principis-rupprechtii and conducted a location-based survey of the regeneration seedlings, combining with collecting soil samples from 75 sampling points. The results showed that (1) Regeneration seedlings (n=275) showed a patchy distribution, with density decreasing across height classes and smaller seedlings aggregating over shorter distances. As the scale increasing, the distribution pattern tends to become random; (2) Soil bacteria and nutrients also exhibited spatial heterogeneity and primarily shaped by structural spatial factors (range: 8.4–17.1 m); (3) Bacterial effects on tree regeneration were indirect by mediating through changes in soil nutrient availability (pc=0.28). Specifically, bacteria competed with seedlings for beneficial macronutrients (pc=1.19*, –0.85*) but mitigated toxicity by absorbing harmful micronutrients (pc=–0.48*, –0.75*). These findings highlight the role of bacteria-mediated nutrient dynamics in shaping regeneration patterns in warm-temperate mixed forests.

Accurate quantification of forest structural parameters, such as tree height (H), crown vertical projection area (CPA) and crown volume (CV), is essential for precise estimation of forest carbon sequestration, monitoring succession dynamics, and improving carbon cycle models. In natural forests characterized by high species diversity and complex stand structures, the capability of terrestrial laser scanning (TLS) and unmanned aerial vehicle laser scanning (UAV-LS) to measure forest structural parameters across different tree heights for coniferous and broadleaved species, remains unevaluated under the influence of canopy shading effects. This study investigated deciduous broadleaf -Korean pine forests by integrating TLS and UAV-LS point clouds using geographic coordinates and combining inventory data to identify tree species from individual tree point clouds. The fused point cloud of forest structural parameters served as a baseline dataset to evaluate TLS and UAV-LS accuracy during the period of no leaf cover. The results showed a strong correlation between TLS and UAV-LS with the fused point cloud (R²=0.96–0.99) TLS and UAV-LS had greater accuracy in measuring H, CPA and CV for coniferous trees than for broadleaf trees, with smaller D-rRMSE differences for conifers (0.7%–3.6%) than for broadleaves (1.1%–21.1%) Across height categories, TLS maintained relatively stable rRMSE values except when height exceeded 25 m, where rRMSE increased. Conversely, UAV-LS showed a significant reduction in rRMSE and RMSE as height increased (88.0% to 1.4%) These results highlight the greater stability of TLS than UAV-LS in measuring the structural parameters of the forest during the period of no foliage.

The survival of urban forests is increasingly challenged by prolonged droughts which adversely affect the function of urban trees. Drought resistance in tree species is determined by their plant-water relationship. There are significant differences in xylem structure between ring-porous and diffuse-porous species, and these structures are closely linked to their hydrodynamic functional traits. This study examined the relationship between branch xylem anatomy and hydrodynamic traits in two timber species and analyzing xylem samples from eight broadleaved species. A water-efficiency safety trade-off was observed in diffuse-porous species, while ring-porous species adapt to their environment by adjusting water transport traits and varying tissue types. Two distinct hydraulic strategies were identified: ring-porous species with high water demand, formed a large conduit area, and axial parenchyma to improve water transfer efficiency, while increasing the thickness of the conduit wall to improve the implosion resistance. Diffuse-porous species formed an independent conduit distribution pattern with greater conduit density and proportion of conduit tissue hydraulic security. The physiological roles of conduit structures system directly determine the dynamic balance between efficiency and safety of water transport in the xylem hydraulic system, spatial distribution and the allocation of resources to thin-walled and fibrous tissues. Overall, woody species in urban environments exhibit considerable variation in drought tolerance, forming complex three-dimensional systems where conduit structure, spatial distribution and functional tissue allocation work together to determine their drought resistance strategies.

Forest measurements, including genetic trials, have relied on traditional measurement methods, an approach affected by different types of errors. To assess genetic trials, Terrestrial Laser Scanning (TLS) devices offer potential to improve accuracy. This study aimed to implement an approach for analyzing forest genetics trial measurements using TLS data. A 15-year-old Pinus taeda L. progeny test in North Carolina USA was assessed using both TLS data and traditional field measurements. Accuracy was assessed using adjusted R², bias, percent bias, and RMSE. Genetic parameters were estimated via BLUP for diameter at breast height (DBH). The

values were 0.56 for DBH and 0.29 for total height (HT). Field-measured DBH had higher heritability (h²=0.32) than raw TLS data (h²=0.17). However, “cleaned” TLS estimates (DBH_R) improved heritability (h²=0.27) and showed stronger phenotypic correlation with DBH_F (R=0.84) than DBH_L (R=0.75). GCA predictions using BLUP showed high correlation (R=0.92) between field and TLS DBH estimates. Estimated gains using DBH_F were 11.3% and 12.1% for selecting the top 1st progeny (30 families) and the top 1st and 2nd progenies (15 families), respectively. Estimated gains using DBH_F were 11.3% and 12.1% for selecting the top 1st progeny (30 families) and the top 1st and 2nd progenies (15 families), respectively. Corresponding gains from DBH_L were 6.9% and 9.6%, and from DBH_R, 8.5% and 10.3%. The results demonstrate that TLS, combined with the proposed methodology, is a reliable alternative for genetic analysis in forest trials.

Accurate prediction of plants’ flowering onset date (FOD) is vital for maintaining ecosystem functions and boosting forestry economic gains. While the Spring Warming (SW) model is commonly used to predict flowering phenology, its traditional fixed setting of the heat accumulation threshold (HAT), measured by growing degree days (GDD), fails to account for the spatial variation in preseason thermal requirements reported in previous studies. This limitation reduces the accuracy of FOD predictions across large spatial areas. In this study, we hypothesized that the HAT in the SW model varies spatially with habitat-specific temperature due to thermal acclimation. To test this, we systematically quantified the spatial differences in HAT using observed FOD data of Robinia pseudoacacia, which is a keystone species for afforestation and a vital nectar source, from 58 stations across China between 1963 and 2008. We identified the key temperature variables influencing HAT variability and developed a simplified, spatially dynamic HAT scheme. The updated SW model, incorporating this variable HAT, was evaluated with cross-site FOD observations. Results showed significant variation of HAT across different climate zones. A geodetector analysis found that the mean temperature from February to May was the main factor driving HAT heterogeneity, supporting our hypothesis. Additionally, spatial factors such as elevation and longitude also contributed to HAT variation alongside thermal factors. Incorporating this spatially variable HAT, predicted from preseason temperatures, into the SW model significantly improved FOD prediction accuracy, decreasing the root mean square error (RMSE) by 11.91% compared to a model with a constant HAT. Future climate scenario predictions indicated that the SW mode with the fixed HAT underestimated FOD advances in warmer areas and overestimated the rate of change, especially when compared to the heterogeneous HAT model. Overall, we emphasize the importance of considering spatial thermal acclimation in broad-scale flowering onset predictions.

Under the dual pressures of climate change and human activities, the frequency and intensity of global wildfires have significantly increased. While seasonal differences profoundly affect the intensity and spatial patterns of wildfire driving factors, past research has largely focused on annual scales, with insufficient attention paid to the dynamic changes and deeper impacts of driving factors in the seasonal dimension. Taking seasonal variations as the core entry point, this study integrated cross-border resources in the Sino-Mongolian border area, adopted satellite fire point data from 2001 to 2022, fused multi-source data including meteorological, topographic, vegetation, socioeconomic and anthropogenic activity data, incorporated meteorological data under three future climate scenarios, and compared the applicability of six models (Logistic Regression (LR), Gompit Regression (GR), Random Forest (RF), Boosted Regression Trees (BRT), XGBoost, and Support Vector Machine (SVM)) in wildfire prediction on the Mongolian Plateau. The results indicate that the Boosted Regression Trees model is the optimal model. Daily average relative humidity (Hum) and yearly average wind speed (Y_win) are the primary driving factors. The eastern provinces of Mongolia, Khovd Province, Selenge Province, and Hulunbuir City in China are identified as extremely high-risk areas for wildfires, with an increasing trend in wildfire incidents on the Mongolian Plateau in the future. This study improves the analysis of fire risk level zoning to accurately identify the spatial characteristics of high-risk areas and clarifies critical thresholds through the marginal benefit analysis of driving factors. Based on this, differentiated early warning systems can be initiated in conjunction with the specific conditions of high-risk areas, supported by targeted prevention and control measures, enhancing the foresight and effectiveness of wildfire risk management in cross-border regions.

Global climate change is impacting organisms and ecosystems on a wide scale, with increasingly visible effects. This ongoing process is anticipated to significantly threaten species and populations, especially plants that lack mobility, potentially causing large-scale losses in the near future. To mitigate these impacts, it is essential to understand how long-lived forest trees will respond to climate shifts and to facilitate necessary migration mechanisms through human intervention. This study aims to model the suitable habitat distribution of Scots pine (Pinus sylvestris), a crucial forest tree species in Türkiye, under two climate scenarios (SSP245 and SSP585) for the present and future years (2040, 2060, 2080, and 2100) using the Maxent entropy model, with mapping support from ArcGIS software. Habitat suitability was analyzed with 21 parameters (19 bioclimatic and 2 topographic). Jackknife test results indicated that Mean Temperature of the Driest Quarter (Bio9) and Mean Temperature of the Warmest Quarter (Bio10) were the most influential parameters on the species’ distribution. The findings showed that under the SSP245 scenario, the suitable habitat for Scots pine is projected to decline to 83.63% of its current range by 2060, then increase to 106.02% by 2100. For the SSP585 scenario, the area is projected to decrease to 81.89% by 2060 and reach 96.13% by 2100. Populations in Türkiye’s southern and Marmara regions face high risks of near-total loss. To sustain Scots pine in new suitable habitats, adjustments to current forest management plans and silvicultural practices are needed to align with climate change projections.

Accurate tree height measurement is crucial for assessing forest health and management strategies, as it directly correlates with biomass and carbon storage capabilities. Thus, drones are increasingly used in forest remote sensing due to their high spatial resolution and operational flexibility. They support individual tree detection and detailed structural mapping, yet the reliability of tree height estimates remains influenced by multiple factors. We present a meta-analysis of 36 case studies to evaluate how environmental complexity and sample size influence tree height estimation accuracy. Forest environments, characterised by heterogeneous canopy structures, occlusions, and species diversity, were associated with higher errors and more significant variability. Conversely, plantations and urban sites yielded lower and more consistent errors, reflecting their structural simplicity and spatial regularity. Although lidar and image-based methods performed similarly in forests (1.58 m vs. 1.69 m mean error), lidar clearly outperformed image matching in plantations (0.44 m vs. 0.91 m). Regarding sample size, mean errors were not consistently lowest in larger datasets, but studies with more than 300 trees exhibited the lowest variability, indicating more stable performance. These findings highlight that both environmental structure and sample size affect the robustness of drone-based height estimation. We recommend the implementation of standardised workflows that account for environmental and technical limitations, including terrain complexity, sensor configuration, and weather conditions. Although drones provide considerable benefits, their successful application depends on careful mission planning and grounded operational expectations. Addressing these challenges through improved mission planning and methodological consistency is essential to ensure the robustness and scalability of drone applications in long-term forest monitoring.

Forests play a critical role in global carbon sequestration, however the mechanisms linking biodiversity to carbon sinks across environmental gradients remain poorly understood. Using 735 permanent plots across subtropical China’s Zhejiang and Fujian provinces, we investigated how elevation mediates biodiversity-carbon relationships (BCRs) in natural forests compared to plantations. Our results show that natural forests maintained 16% higher carbon sequestration and had 23% lower mortality than plantations, with peak productivity at mid-elevations (400–800 m). Community-weighted specific leaf area (CWM_SLA) and tree size inequality (Gini coefficient) explained 43.6% of the carbon sink variation, while Shannon diversity showed negligible effects (P>0.05). Structural equation modeling revealed that initial carbon stocks mediated BCRs, particularly in natural forests, with plantations showing significant carbon-mortality trade-offs at low and mid- elevations. Significant BCRs were only at low elevations, where CWM_SLA and Gini coefficients negatively affected carbon sinks, providing no support for consistently positive BCRs across elevation zones. To optimize forest carbon sequestration, we suggest species selection based on complementary functional traits, increasing the complexity of stand structure in medium and high elevation areas, and planting stress-resistant genotypes at low elevations to reduce mortality. This study provides insight for optimizing carbon-biodiversity co-benefits in subtropical forest restoration.

Key differences in biological legacy exist between clear-cutting and natural disturbances. One way to enhance similarity is by preserving structural features of old forests, such as retention trees, within harvested areas. Norwegian forest certification standards set by the Programme for the Endorsement of Forest Certification (PEFC) and the Forest Stewardship Council (FSC) require both the preservation and mapping of retention trees within harvested area for eventual reporting in a central database. This study, conducted in a managed forest in southeast Norway, evaluates the accuracy of retention tree identification, including density and volume predictions, using airborne laser scanning (ALS) data with low (2 pulses m^–2) and high (approximately 100 pulses m^–2) pulse densities, with and without spectral data. We also assess whether ex-situ reference data, such as diameter–height measurements from sample plots in similar forests or species annotations from aerial images, can fully or partially replace in-situ data collected within harvested stands.Three reference datasets were used, fully or partially: (1) 630 in-situ retention trees across 27 stands (for species classification and diameter at breast height (DBH) prediction), (2) 1064 ex-situ sample trees (for DBH prediction), and (3) 150 ex-situ annotated segments (for species classification). Using an individual tree segmentation approach with adaptative local maxima window size and regeneration height filtering, 65% of the in-situ retention trees were correctly identified, increasing to 74% when excluding snags. ALS at 2 pulses m^–2 alone provided reliable total density and volume predictions, while spectral data improved species-specific accuracy. Species classifications remained consistent across data source (kappa=0.556 for in-situ retention trees, 0.519 for ex-situ annotated segments), but DBH were underpredicted with ex-situ sample trees (RMSE=9.4 cm, MSD=−4.6 cm) compared to using 40 in-situ retention trees (RMSE=8.8 cm, MSD=0.2 cm). We recommend sampling approximately 40 in-situ retention trees to calibrate diameter-height models and using ex-situ annotated segments for species classification. This approach, based on low-density ALS and orthophoto datasets, meets the PEFC requirement to provide the locations of retention trees and may also support retrospective detection, thereby contributing to semi-automated certification reporting and virtual audits. In the eventuality of a publicly accessible database of measured retention trees were available, in-situ sampling for diameter-height model calibration could be omitted.

Phenology is crucial for assessing the effect of climate change on the survival and growth dynamics of temperate and boreal plants. Warmer temperatures induce earlier budbreak, possibly increasing the risk of late frost, while warmer winters may fail to fulfill the chilling requirement delay budbreak. In our study, we simulate early spring warming on the seedlings and branch cuttings of sugar maple (Acer saccharum Marsh.) from two provenances (Cantley, more southern, and Duchesnay, more northern) originating from different bioclimatic zones in Quebec, Canada. We assessed budbreak in seedlings and branch cuttings after transfer to controlled forcing temperatures (15 or 20 °C) on two dates (DOY 61 and 115). We also calculated chilling accumulation using three commonly applied models including the Chilling Hours, Utah, and Dynamic models. We tested either direct transfer from natural conditions or transfer after a period in artificial chilling temperatures (4 or 7 °C). Seedlings transferred to 20 °C on DOY 61 required 12 additional days to complete budbreak compared to those transferred to the same temperature on DOY 115. The northern provenance (Duchesnay) completed budbreak 11 d faster than the southern provenance (Cantley). Seedlings exposed to 7 °C chilling and 20 °C forcing performed budbreak 7 d faster than those submitted to 4 °C chilling and 15 °C forcing, and 4 d faster than seedlings at 4 °C chilling and 20 °C forcing. The tested chilling metrics models were not able to fully explain the difference in budbreak timing between the treatments. No difference in budbreak was found between branch cuttings and seedlings, validating the branch cuttings as a reliable proxy for phenological studies. Our findings demonstrate the role of chilling and forcing accumulation on budbreak during late winter and early spring. We also show that current chilling models need to be modified to incorporate subzero temperatures to better represent and predict budbreak in boreal and northern temperate species. Warming during winter and spring could advance the timing of budbreak in sugar maple, thus lengthening the growing season, but possibly exposing the trees to damage by late frosts. The warmer provenance (Cantley) showed later budbreak, suggesting a potential for spring frost avoidance that is relevant from a forest management perspective.

The sensitivity of vegetation productivity and ecosystem respiration to precipitation, namely S_GPP and S_ER, is an intrinsic characteristic of vegetation and a key metric for understanding the variations in ecosystem carbon cycle under changing climate. Previous studies typically treat S_GPP or S_ER independently, overlooking their potential interrelationship. Besides, beyond the direct effects of precipitation, temperature likely plays a significant role in shaping both S_GPP and S_ER. In this study, we leveraged the FLUXNET2015 dataset to analyze the global spatiotemporal patterns of S_GPP and S_ER, and applied a mixture regression model to investigate their hydrothermal regulations. The results showed that S_GPP and S_ER varied greatly across biomes, with the higher values in arid ecosystems, while forest ecosystems exhibited relatively low values. Temporal analysis indicated that S_GPP and S_ER significantly increased over time in shrubland, while S_ER in evergreen needleleaf forest and S_ER in grassland significantly increased and decreased, respectively. The relationship between S_GPP and S_ER decoupled with increasing leaf area index, with a breakpoint occurring at 1.61 m² m⁻². Across ecosystem types, the decoupling of S_GPP and S_ER was primarily observed in closed-canopy forests, with hydrothermal conditions identified as the key drivers of this phenomenon. Specifically, in forests, S_GPP was predominantly regulated by the joint influence of temperature and precipitation, whereas S_ER was mainly controlled by precipitation alone. Across all ecosystems, S_ER shifted from being co-regulated by precipitation and temperature to predominantly precipitation-driven as mean annual precipitation (MAP) exceeded 1000 mm, while S_GPP transitioned to primarily temperature-dependent above 1500 mm MAP. This study underscores how hydrothermal conditions shape the complex S_GPP-S_ER interplay, providing critical insights for future forest ecosystem research.

The soil desiccation tends to occur after afforestation in arid and semi-arid areas, exacerbated by increasing precipitation uncertainty under climate change. To clarify the water consumption patterns and drought response mechanisms of key silvicultural species, a manipulated drought experiment (50% throughfall exclusion) was conducted in Robinia pseudoacacia plantation on the Loess Plateau of China in 2021–2022. The stem sap flow, soil moisture, surface runoff and meteorological variables were monitored to investigate the drought responses of sap flux and transpiration. The sap flow under drought peaked 1−h earlier diurnally and one month earlier seasonally than in the control after 1−a drought treatment, as showed heightened sensitivity to increasing air temperature. Furthermore, the daily stand transpiration (T) of drought exhibited stronger dependence on soil moisture, relative humidity and vapour pressure deficit than in control. In this study, T calculated by the original Granier equation and by the revised method (accounting for inactive xylem length) was still underestimated by 65% and 41%, respectively, compared to the water balance method. After applying a calibrated correction coefficient (×2.875) to the original Grainer equation, the mean T of R. pseudoacacia plantation was 209.60 mm for control and 144.15 mm for drought treatment during May to August 2021–2022. Nevertheless. both treatments extracted more than 70% of soil water from the 100–200 cm soil layer, suggesting an intensification of deep soil desiccation, particularly under drought stress. This study contributes to further understanding the water consumption characteristics of plantations to drought on the Loess Plateau, providing a scientific basis for sustainable forest management in semi-arid area.

Lodgepole pine (Pinus contorta ssp. latifolia) is the most widely distributed and commercially valuable subspecies among the four recognized subspecies of P. contorta. This study explored the genetic potential of seven quantitative traits using a comprehensive 14-provenance trial that included family structure, with collections from five geographic zones across British Columbia. The analysis focused on wood specific gravity, branch characteristics (angle, diameter, and length) as well as growth parameters (height, diameter, and volume). While inter-provenance differences within zones remained generally modest at age 12, substantial variation at the family level within provenances became apparent, showing significant genetic diversity within populations. Heritability estimates showed considerable variation across both traits and geographic regions, reflecting the complex genetic basis of these characteristics. Quantitative genetic differentiation (Q_st) for all traits except branch angle indicated that natural selection rather than genetic drift drove the evolution of these traits. The lack of significant isolation-by-distance effects indicated that patterns of genetic variation were not geographically structured in a straightforward linear way. Correlation analyses among traits uncovered important evolutionary trade-offs, with specific gravity showing negative associations with growth traits and branch length, but no link to branch angle. Path analysis pinpointed branch length as a key mediating factor directly decreasing wood density while also enhancing growth performance. These results highlight the hierarchical nature of trait co-evolution, where branch angle develops independently, and branch length plays a central role in mediating covariations among other features. The comprehensive results improve our understanding of lodgepole pine’s genetic potential and offer valuable insights for breeding programs aiming to balance growth performance with wood quality objectives.

Tailoring ecological restoration policies to local conditions, such as determining whether to prioritize afforestation programs or grassland restoration, is critical for the sustainable implementation of China’s Grain for Green Project (GGP). However, there remain significant knowledge gaps to understanding the divergent hydrological responses of these programs. This study integrates remote sensing and meteorological data to detect spatiotemporal variations of evapotranspiration and its influencing factors. The eco-hydrological dynamics between two distinct vegetation restoration zones are compared: the northwestern region (grassland restoration) and the southeastern region (afforestation). After implementation of the GGP, a notable greening trend with a significant increase in leaf area index was observed, exhibiting spatial heterogeneity in both regions. At the same time, evapotranspiration showed a distinct NW–SE increasing gradient. Regional drivers also diverged: evapotranspiration in the southeast was primarily leaf area index-driven, while in the northwest, it was controlled by precipitation-to-potential evapotranspiration ratios. This water-carbon coupling disparity amid regional climate variability caused contrasting water yield declines, significant in the southeast compared to the insignificant yields in the northwest. This study contrasts forest versus grassland restoration at a regional scale. The findings underscore ecohydrological feedbacks from ecological restoration, advocating spatially adaptive ecosystem management to balance water-carbon trade-offs.

The indiscriminate expansion of rubber plantations in Southeast Asia has catalyzed extensive tropical primary deforestation, yet its cascading impacts on ecosystem services and the underlying eco-economic trade-offs remain inadequately explained. By integrating multi-source remote sensing data with ecosystem service valuation models, this study unveiled the spatial dynamics, ecological repercussions, and economic costs of primary forests converted to rubber plantations in Southeast Asia from 2001 to 2021. The results showed that 12.7% of the current 14.15×10⁶ ha rubber plantations were converted from primary forests, with Indonesia, Thailand, and Cambodia collectively contributing 70% of the conversion area. Cambodia exhibited the highest conversion intensity, with 35.3% of its rubber plantations displacing primary forests. Such land-use transformations precipitated catastrophic ecosystem services degradation in conversion, with 66.3–98.2% of areas experiencing declines. Although rubber cultivation generated 69.3 billion USD in economic benefits in conversion, this amount offset only approximately 1/19 of ecosystem service losses, which were valued at 1,344.9 billion USD. Indonesia, accounting for 46% of aggregate ecological value loss, is the regional degradation epicenter. Benefit-loss ratios remained alarmingly low (<6%) in major producing countries, exposing considerable imbalances in eco-economic relationships. This study draws the attention of policymakers and researchers to the substantial ecological costs associated with rubber-driven primary forest deforestation and calls for collective efforts to explore balanced pathways for rubber expansion and ecosystem conservation.

Forest restoration plays an essential role in mitigating climate change, conserving biodiversity, and maintaining ecosystem services. Altough global initiatives emphasize the urgency of developing effective strategies for ecosystem recovery, assessing the success of these strategies remains a considerable challenge. In this context, we investigated the growth dynamics of Cedrela odorata L. (Meliaceae) over more than 160 years in two Atlantic Forest areas in Rio de Janeiro, Brazil, characterized by distinct historical recuperation pathways: active reforestation in Parque Nacional da Tijuca (PNT) and natural regeneration following land protection in Reserva Biológica do Tinguá (RBT). Dendrochronological analyses revealed significant differences in age structure and tree size between areas. Trees in PNT were older and larger (169 years, DBH 78 cm) than those in RBT (132 years, DBH 56 cm), reflecting differences in management intensity. Higher growth rates during early development of trees in PNT are likely associated with silvicultural practices implemented during reforestation. However, from the 1940s onwards, a convergence in growth rates indicates structural recovery in both areas. This pattern reflects increased competition among trees, characteristic of more advanced stages of forest dynamics. After 1990, both areas experienced a decline in growth, potentially linked to increasing urban-related environmental stressors. Overall, our results highlight the resilience of C. odorata and emphasize the importance of forest recuperation efforts in both areas, demonstrating their long-term success. Additionally, they underscore the effectiveness of restoration practices in PNT, as demonstrated by the acceleration of structural recovery in the forest, with older trees, increased above-ground biomass, and radial increment rates comparable to those of mature forests. This success also implies the restoration of ecosystem services, as sought in the historical recovery proposals for these two tropical forest areas.

Taper models are widely used to estimate log assortments and, consequently, forest yield from inventory data. However, for Pinus taeda, few studies have employed mixed-effects taper models that explicitly account for the hierarchical structure of forestry data and heterogeneity of variances. This study addresses this gap by developing and evaluating mixed-effects taper models based on modified versions of Kozak’s (1969) equation. The models incorporate random effects at the farm/forest region, stand, and tree levels and allow for different variance structures, enabling them to capture the heterogeneity commonly observed in P. taeda stands. Diagnostic procedures using least confounded residuals were applied to assess model adequacy. Compared with traditional fixed-effects taper models, the selected mixed-effects model achieved superior performance, including reduced bias, improved fit across stem sections, and better predictive accuracy. Additionally, in Appendices, we provide a tutorial outlining the computational procedures in R software for statistical modeling of data related to this species within the mixed-effects model framework.