A spatially explicit dataset of upper canopy tree species composition of public forests of the Autonomous Province of Trento, Italy.

This article analyzes the emergence of modern forest management on the Polish lands from the late eighteenth to the mid-nineteenth century, framing it within Enlightenment economic thought, state-building, and delayed industrialization in Central and Eastern Europe. Based on written, cartographic, and administrative sources, it traces the transformation of forests from heterogeneous landscapes governed by customary rights into standardized, administratively legible spaces shaped by scientific forestry. The study compares three successive political regimes—the Prussian administration (1793–1806), the Duchy of Warsaw (1807–1815), and the Congress Kingdom of Poland after 1816—highlighting both continuity and rupture in forest governance. Although short-lived, the Prussian period established durable institutional and spatial frameworks, including large-scale surveying and centralized administration. Later reforms intensified bureaucratic control but unfolded under conditions of a delayed energy transition, with continued reliance on wood and charcoal. This dependence fostered the expansion of Scots pine monocultures, particularly in industrial regions such as the Holy Cross Mountains. The article argues that forest modernization in the Polish lands represents a case of multi-speed modernization, the ecological and social consequences of which remain visible in present-day forest landscapes. • Traces the emergence of modern forestry on Polish lands, 1790s–1850s. • Compares Prussian, Duchy of Warsaw, and Congress Kingdom regimes. • Shows forest management as a state-driven Enlightenment project. • Reveals delayed energy transition shaping pine monocultures. • Identifies long-term ecological and social legacies of forestry modernization.

Effective forest management and climate adaptation require a detailed understanding of tree species-specific disturbance dynamics. In recent years forest disturbances in Central Europe have intensified, driven by rising temperatures and recurring droughts. In Germany, drought and windthrow events since 2018 have triggered unprecedented Forest Canopy Cover Loss (FCCL). Here, we present the first national-scale assessment of FCCL for dominant tree species in Germany from 2018 to 2024, based on multi-temporal remote sensing data. We produced a dominant tree species map for 2016 by majority-voting on annual Sentinel-2 tree species classifications (2016–2024) and filtering with forest structure data. This yields a robust baseline for ten dominant species classes (F1-scores > 0.90 for nine pure-species classes). We quantified FCCL, derived from Sentinel-2 and Landsat time series at monthly intervals and 10 m resolution, by aggregating species-specific canopy loss pixels across different temporal (monthly, annual) and spatial (district, state, and national) scales. FCCL predominantly affected coniferous species: Spruce-dominated forests accounted for 4497 km² (51.3 % of total FCCL), corresponding to 18.6 % of initial spruce area, with peak FCCL in 2020–2021 and a distinct late-summer peak. Pine ranked second with 1893 km² (21.6 % of total FCCL and 7.4 % relative). Deciduous species such as beech and oak were less affected, with total FCCL below 300 km² each and relative declines of 1 %. Spatial hotspots of spruce FCCL were concentrated in central low mountain ranges. Our results provide a tree species-specific overview of forest loss dynamics in Germany, revealing tree species-dependent FCCL since 2018, thus supporting long-term forest management and adaptation to climate change. • First assessment of tree species-specific forest canopy cover loss in Germany • New 2016 dominant tree species map derived from multi-annual Sentinel-2 satellite data • Spruce-dominated forests experience the greatest forest canopy cover loss • Pine losses rank second, while beech and oak forests remain more stable • Species-specific disturbance patterns guide climate-adaptive forest management

Provenance effects in forest trees are well known for growth and productivity, but their influence on below-ground microbial partners remains underexplored. We investigated fungal communities associated with Scots pine from ten European provenances grown for 40 years in a common garden in Poland. Using high-throughput sequencing, we characterized both bulk-soil fungi and root endophytes and tested whether provenance identity shapes their taxonomic and functional composition. Additionally, to test the proposed provenance effect, we sourced provenance climatic conditions (PCC) data and investigated root traits and soil characteristics. Provenance explained up to 15 % of the variance in fungal community structure, with consistent effects in both soil and root-associated fractions. Fungal lifestyle groups also shifted with provenance. Soil saprotrophs were significantly less abundant in provenances originating from drier climates (spring: η 2 = 0.14, autumn: η 2 = 0.15). Ectomycorrhizal (ECM) fungi showed distinct lineage-level patterns (p < 0.001; spring: /boletus, /inocybe, /russula-lactarius, /tomentella-thelephora; autumn: /boletus, /cenococcum, /hydnotrya, /russula-lactarius, and /tomentella-thelephora). Indicator species analyses identified five ECM taxa associated with specific provenances, suggesting selective filtering by host origin. PCC variables, particularly precipitation, were significant predictors of fungal functional composition, suggesting legacy effects of seed origin environments. These findings demonstrate that tree provenance not only affects host growth performance but also shapes the composition and function of below-ground fungal communities. Considering the pivotal role of fungi in soil processes and forest resilience, provenance selection in reforestation and assisted migration strategies should account for below-ground biodiversity consequences, not solely above-ground growth traits. • Below-ground traits are key in Scots pine adaptation to distinct environments. • The community structure of below-ground fungi varies by the origin of the host tree. • Fungal community taxonomic structure drives variation in potential soil function. • Scots pines of various origins host communities with different ECM:saprotrophs ratios. • Forest management uses of provenance transfer should consider effects on soil ecology.

Integrated bio-structural slope stabilization using terracing, brushwood beams, and soil reinforcement by roots for sustainable forest road management

This study investigates the climate sensitivity and resilience of radial growth in eight coniferous and deciduous tree species in the Vienna Woods, Austria. Using dendrochronological methods, we analyzed tree-ring width data from 63 forest plots to assess growth responses to meteorological variability over the period 1933-2023. Historic climate records were used to develop a water balance model, from which we derived seasonal growth factors. Linear mixed effects models were applied to quantify species-specific relationships between tree-ring width and climatic conditions during the current and preceding two years. Tree-ring width responded not only to climatic conditions of the current growing season but also strongly to those of the previous year. Soil moisture and air temperature emerged as the principal drivers of radial growth, with soil moisture positively and temperature negatively affecting ring width. Climatic conditions during June-July of the current year exerted the strongest impact on ring formation. Using regional climate trends and projected air temperature and precipitation trajectories for Central Europe under RCP4.5 and RCP8.5, we forecast future growing conditions for the region. Both scenarios predict an extended growing season, increased transpiration demand, and heightened drought risk - more pronounced under RCP8.5. However, projected increases in precipitation partly offset the drought risk. By combining historical climate sensitivity of radial increment with future climate projections, we modelled expected tree-ring growth for eight tree species. Most species are predicted to experience notable declines in radial growth, with the strongest reductions in conifers, including European larch (Larix decidua), Norway spruce (Picea abies), Austrian pine (Pinus nigra) and Scots pine (Pinus sylvestris). Deciduous species - Sycamore maple (Acer pseudoplatanus), European beech (Fagus sylvatica), and sessile oak (Quercus petraea) - show moderate declines. In contrast, Turkey oak (Quercus cerris) is projected to increase radial growth under future climate scenarios. These findings suggest that forest management in the Vienna Woods and adjacent regions should prioritize the promotion of warm- and drought-tolerant tree species such as Quercus cerris to enhance forest resilience and sustainability in the face of climate change.

Spatiotemporal patterns of forest fires in the Eastern Okavango Panhandle, Botswana: Implications for vegetation cover and forest management

Estimating forest surface fuel loads along the China–Mongolia border using a multi-source remote sensing model optimized by active learning

Canopy closure and litter management in coniferous plantation modulate the bacterial community to increase soil nitrogen mineralization and understory medicinal tuber yield

A review and conceptual framework for a new era of forest management planning: integrating hybrid digital twin systems toward sustainable forest ecosystems

Forestry extension: Contributions and challenges for sustainable forest management in Ethiopia

Habitat selection studies in large mammals typically rely on photo-interpreted forest maps to link the telemetry locations of individuals to environmental data. Such forest maps mainly provide information on forest composition, age, and disturbance history but say little about the structure of stands. In contrast, airborne LiDAR (Light Detection and Ranging) can provide 3D metrics of vegetation structure, but its application to the study of wildlife–habitat relationships remains limited. We aim to determine if combining these products could improve our capacity to understand the habitat selection patterns of large mammal species representative of the eastern Canadian boreal forest: caribou ( Rangifer tarandus caribou ), moose ( Alces alces americana ) and eastern coyote ( Canis latrans ). We built resource selection functions with mixed logistic regressions to characterize habitat selection patterns, using telemetry data and the different sources of information on forest composition and structure. We evaluated model performance with a k -fold cross-validation. Our results suggest that integrating LiDAR data with forest maps substantially improves the ability to characterize habitat selection patterns across species, though benefits varied with periods and study areas. For example, vegetation structure, mostly detailed by LiDAR data, was the main determinant for caribou and eastern coyotes in the snow-covered period, whereas forest composition, described in the forest maps, was most important to characterize habitat selection patterns for moose in both periods. These differences may be partly attributed to contrasting compositions between northernmost and southernmost forests in our study area (i.e. province of Quebec), species ecology, as well as potential temporal discrepancies between data sources. When used appropriately, combining LiDAR with traditional forest maps provides richer ecological insight and a more comprehensive characterization of habitat selection patterns of large boreal mammals. Based on an average population-level description of habitat selection patterns, this integrated approach can guide practitioners to preserve sparse understory to support caribou, promote complex shrub structure for moose, and limit dense cover that may favor eastern coyotes. • Forest maps inform on composition and disturbance while LiDAR captures structure. • We combined LiDAR and forest maps to model habitat selection for three species. • We compared model parsimony and evaluated performance with k -fold cross-validation. • Combining data sources outperformed simpler models across species and seasons. • Considering stand structure variables could improve forest management for wildlife.

Assessing the economic tradeoffs of various forest management activities to enhance carbon sequestration efforts in Pennsylvania and Maryland

This article examines the problem of applying predictive analytics, anomaly detection, and carbon risk scoring to make timber supply chains more resilient and sustainable in the climate change context. On a predictive model of possible disruption of atmospheric characteristics caused by climate-related outcomes like extreme weather and deforestation, we have utilized 1992-2020 data on Global Forest and Carbon Metrics and made a predictive model with the help of the Random Forest. Anomaly detection was used to detect any deviations of carbon stocks and forest area which disclosed that there were big anomalies in particular areas. Moreover, a carbon risk scoring system has been designed to identify carbon integrity in timber sourcing with more insights given to regions with more sustainability risks. The results indicate that the implementation of these methods in timber supply chain management system is likely to enhance the accuracy of forecasting, early detection of a disruption, and sustainability of sourcing timber. The project proposes additional composite incorporation of granular climatic information and rule structures to enhance wood forest management and forest timber supply mechanics.

Natural disturbances are fundamentalin shaping forest ecosystems, yet management decisions in production forests can aggravate or mitigate their impacts. Understanding the interplay between forest adaptation strategies and susceptibility to damage is important for sustainable forest management. Here, we review the scientific literature to explore the relationship between natural disturbances and the impact of forest adaptation strategies on the forest susceptibility to damage. To do so we reviewed seven different adaptation strategies (mixed-species stands, shorter rotations, longer rotations, few or no thinnings, logging residue removal, prescribed burning and uneven-aged forestry), and how they affect forest susceptibility to damage by ten different disturbances. Each of the strategies offers opportunities to improve the forest's resilience to damage in different ways and against specific disturbances. A combination of these strategies may have a greater impact on reducing forest susceptibility to damage. The review informs the development of adaptation strategies considering relevant forest characteristics coupled to natural disturbances.

Decarbonization options for buildings include using low-carbon cement and engineered timber. However, the long-term cumulative effects of urbanization, destinations of building materials at end-of-life, CO2 uptake from cement carbonation, and biogenic sequestration from biomass regrowth on their climate change impacts remain unclear. Here, we assess the climate change impacts of these dynamic factors on future urban buildings for urban growth between 2025 and 2100, using dynamic life cycle assessment across 14 pathways under various short- and long-term scenarios. Construction of urban buildings using timber ('timber cities') can lead to a global temperature increase that is up to 0.023 K lower by 2100 than that caused by their construction using reinforced concrete ('reinforced concrete cities'). After 2100, timber cities can lead to a temperature increase similar to or higher than reinforced concrete cities if there is poor forest regrowth, high landfill gas release, and incineration. If timber recycling leads to forest aging or deforestation due to reduced motivation for forest regrowth, global temperature can significantly rise compared to a scenario in which timber is recycled while simultaneously maintaining the forest carbon sink, which is the most climate-friendly option. Important global actions to minimize the climate impacts of future cities are (1) to support rapid and large-scale implementation of timber buildings in response to current high urbanization; (2) to proactively develop land, forest, and waste policies that limit future temperature increases caused by poor forest regrowth, landfill gas release, and wood incineration; and (3) to adopt dynamic life cycle assessment and related indicators such as absolute global warming potential in the built environment for climate-related policymaking, rather than using only global warming potential.

Accurate and timely information on forest disturbance drivers is important for sustainable forest management, global carbon cycle accounting, and climate change response. However, forest disturbance classification is difficult due to two major challenges: limited labeled samples and highly imbalanced disturbance class distribution. In this article, a new framework for multi-type forest disturbance classification based on collaborative semi-supervised learning and sample generation was proposed. First, forest disturbance is detected using long-term remote sensing time series data and disturbance detection algorithms. Spatiotemporal, spectral and terrain features of different disturbance types are extracted. On this basis, to address the problem of imbalanced and small-sample conditions, a collaborative classification strategy is developed. Based on a small number of labeled samples, Support Vector Machine (SVM) and Random Forest (RF) are used to build dual base classifiers. A confident learning (CL) framework is applied to select high-confidence pseudo-labeled samples from unlabeled data. Then, a latent diffusion model (LDM) is introduced to generate high-fidelity pseudo-samples. This increases the sample size and balances the class distribution. Based on the augmented dataset, the dual classifiers are iteratively optimized using a co-training strategy, which improves model generalization under complex conditions. The results show that the proposed framework could generate high-quality pseudo-samples and effectively reduce class imbalance. The overall accuracy (OA) of the proposed framework reaches 93.2%, which is 5.7% and 4.4% higher than single classifier baselines, respectively. After introducing the LDM-based balancing mechanism, performance is further improved by 1.8% compared with the pure semi-supervised framework. This study provides an efficient and reliable solution for large-scale forest ecosystem monitoring.

ABSTRACT Conventional forestry is a major driver of forest degradation, creating a pressing need for sustainable alternatives like Reduced Impact Exploration (RIE). However, the claim that RIE conserves biodiversity requires empirical validation. This study directly addresses this problem by investigating if the biodiversity of game birds and mammals is consistent across the management environments of a forest implementing RIE, thereby testing its conservation effectiveness. Accordingly, we assessed potential impacts on the richness, abundance, and composition of game birds and mammals using camera trap sampling and statistical analyses across three forest management areas: Forest Environments, Managed Areas, and Skid Trails. Eighteen camera traps were deployed at 12 points, spaced 1 km apart, resulting in a total of 2,122 camera days. We recorded 19 species, including five game birds and 14 medium to large terrestrial mammals. No significant differences were observed in game bird and mammal populations among the three sampled environments or across Annual Production Units. Our studies indicate that managed forest areas and skid trails appear to differ from forests. Notably, the presence of species with large home ranges and extensive hunting areas highlights the critical role of managed areas in conserving these species groups in the study area.

ABSTRACT Forest certification schemes such as the Forest Stewardship Council (FSC) and the Programme for the Endorsement of Forest Certification (PEFC) are widely promoted as tools to improve environmental, social and economic outcomes in timber production forestry. While international research on certification effects is substantial, evidence from Australia remains limited and fragmented. This study synthesises peer-reviewed and grey literature published since 1993 to assess certification effects with a focus on Australian contexts, while situating findings within the broader international evidence base. Four research questions guided the review: (1) What methods have been used to assess the effects of implementing certification by forest managers?; (2) What types of environmental, social and economic outcomes have been empirically attributed to forest certification and to what extent do these represent observable differences in management practices or forest conditions?; (3) Where along forest product value chains have effects been observed?; and (4) What types of impacts (e.g. market access, reporting, chain-of-custody integrity) have been documented beyond the forest gate? Searches were conducted in Google Scholar and Scopus in May 2025, limited to the first 50 results per query, and supplemented by targeted snowballing. Eligible studies included those that evaluated actual or perceived certification effects in forestry. Forest certification effect assessments vary across environmental, social and economic dimensions. Few international studies establish a credible causal link between certification and on-ground environmental effects using explicit counterfactuals and the findings have been mixed. Australian studies generally point to procedural improvements rather than demonstrated environmental outcomes, although the counterfactual with no certification has not been investigated. Social outcomes have been investigated using qualitative methods, revealing context-dependent effects, including strengthening of tenure rights internationally and improved stakeholder engagement in Australia, but potential burdens on smallholders. Economic effects were investigated using interviews, questionnaires and surveys and reveal mixed outcomes. While there is limited evidence of price premiums in both international and Australian contexts, certification improves access to certain forest product markets and provides assurance to investors. Key barriers to certification include high compliance costs; key enablers include reputational benefits, access to markets, and alignment with existing regulations.

ABSTRACTSoil respiration (RS) represents a major process of release of carbon dioxide (CO2) from soil to atmospheric carbon pools. Measurements of soil respiration help to understand the dynamics of carbon in ecosystems. This study examines the soil respiration rate and the effect of environmental variables in different forests along elevation gradient. This study was conducted in three distinct montane forest types distributed along an elevational gradient in the middle mountain region of Nepal, namely Schima‐Castanopsis Forest, Oak Laurel Forest, and Evergreen Oak Forest. In each forest type, 10 circular chambers were installed for measuring soil respiration. Soil CO2 efflux was measured monthly for 1 year, using the “closed chamber method” with an infrared gas analyzer. Soil respiration rate was modeled as a function of soil temperature and moisture using a generalized linear model (GLM). Soil respiration rate varied significantly among the forest types, ranging from 274.7 to 352.4 mg CO2 m−2 h−1 and demonstrated seasonal changes with a summer peak. Soil respiration was significantly higher in Evergreen Oak Forest than Oak Laurel Forest and Schima‐Castanopsis Forest. Response of soil respiration to soil temperature and soil water content indicated a significant exponential relationship in all the forests. Soil respiration showed a strong correlation with soil temperature than soil water content. Temperature sensitivity (Q10) of soil respiration was higher in the forest of upper elevation (Evergreen Oak Forest, Q10 = 3.9) than in the lower elevation forests (Oak Laurel Forest, Q10 = 2.7; Schima Castanopsis Forest, Q10 = 2.5), indicating that soil respiration in the Evergreen Oak Forest is more responsive to temperature changes. Hence, forests at higher elevation are highly susceptible in the context of future climate warming due to enhanced efflux of soil CO2. This study highlights the necessity of incorporating belowground carbon processes into climate policy and sustainable forest management frameworks in Nepal.