Abstract Purpose of Review Forests have a key role in global carbon dynamics, acting as both carbon sinks and sources. Yet, the intensification of global change-related natural and anthropogenic forest disturbances such as forest fires, deforestation, management practices, and biotic agents, among others, have the potential to compromise their carbon sink function. Here, we synthesize the current understanding of forest disturbances’ impact on forest carbon dynamics under varying spatial, temporal, and ecological contexts globally. Thereby, our goal is to address ongoing uncertainties around the pattern, magnitude, persistence, and variability of carbon emissions linked to forest disturbances and to identify underrepresented regions, disturbance types, or forest ecosystems that remain understudied. Recent Findings We present a synthesis of previous research from 2020 until early 2025. After screening 519 records, 90 studies were included for full synthesis following the PRISMA guidelines and PICOS framework. Data were extracted on forest type, carbon pool, disturbance type, geographic location, and study design. Recent studies have dominantly quantified carbon emissions from high-severity disturbances such as forest fires and deforestation. These disturbances have significant carbon impacts and have been amplifying under climate change. Furthermore, evidence from studies shows that compound disturbances often interact synergistically. However, the carbon impacts of low-intensity disturbances such as forest degradation, selective harvesting, or compound disturbances such as drought-fire interactions remain limited and fragmented. Moreover, the literature is biased toward aboveground pool estimates, with limited studies quantifying Total Ecosystem Carbon (TEC), as well as toward a remarkable underrepresentation of the Global South, with most research focused on areas and countries from the Global North. Summary This review identifies key gaps in the literature, particularly regarding underrepresented geographic regions, compound disturbance effects, and the integration of multiple carbon pools in carbon estimates. We conclude by offering recommendations to address these gaps, aiming to improve carbon flux estimates and support adaptive forest management.

Abstract Purpose of the Review Forest accessibility and road network density are important concerns not only for forest managers, but also for everyone who benefits from forests, whether for professional or personal use. A well-planned forest road network, integrated with the forest ecosystem, is a fundamental element of rational and efficient forest management. This review aims to describe the current state of forest road density and overall forest accessibility in Europe, as well as relevant global examples. Recent Findings When a forest road network is distributed adequately across an area, it can support many, if not all, tasks outlined in forest management plans, with high efficiency and minimal costs during construction and maintenance. Collecting data on forest accessibility and primary forest road density at the national level in many countries is a complex task. Diverse terrain conditions, economic factors, and forest management practices have led to variability in the data. A common thread, however, is the optimistic outlook on the use of modern technologies for road network planning and data acquisition. Summary Efficient forest management, especially harvesting operations, relies on a road network with appropriate density, load-bearing capacity, and alignment. Primary forest transport infrastructure helps reduce timber extraction costs by minimising extraction distances and shortening the travel time from the forest stand to the market. As such, forest road density and accessibility are crucial elements in planning forest operations. Despite differences in terrain conditions, harvesting systems, and economic contexts across Europe and globally, research consistently highlights the need to improve the efficiency of road network planning. Future research on forest accessibility should focus on integrating valuable data collection with scientific research and ensuring effective knowledge transfer to forest practitioners.

Abstract Purpose of Review This review synthesizes key insights emerging from wood-based circular bioeconomy research published between 2020 and 2025, with a focus on clarifying geographic, discipline, technology, application, and circular economy integration trends. Findings Analysis of reviewed literature ( N = 54) revealed that most wood-based circular bioeconomy publications originate at European institutions (83.4%), followed by institutions in China (11.1%). Research collaborations are widely interdisciplinary, with strong representation of forestry (11.6%), agricultural science (9.3%), and chemical engineering (8.1%) disciplines. Most studies focus on technologies that use wood-mixed biomass waste (26%) and forest residues (23%) as primary feedstocks, with 33% of these technologies currently at lab-scale. While 63% of studies adopt a technology or product-focused lens, 37% take a systems-view of circular bioeconomy and forest-wood supply chains, emphasizing stakeholder engagement and integration. Waste reduction is the primary stated environmental motivation for research (34%), followed by fossil fuel displacement (23%). Highlighted economic opportunities include new revenue streams for growth (32%) and cost savings (22%). Summary Wood-based circular bioeconomy has been advancing largely via research in biorefineries and co-production technologies, particularly lignin valorization, aromatic compound recovery, and end-of-life biomass waste valorization. Interdisciplinary collaboration and system-based approaches are improving the integration of bioeconomy with well-established circular economic methods. However, gaps remain: few studies address end-of-life (EOL) flows, chemically modified wood products, or circular reintegration of bio-based materials. Expanding research on underexplored flows and life cycle stages is essential to realize a fully circular wood-based bioeconomy.

Abstract Purpose of Review While 38% of tree species are at risk of extinction worldwide, their inventory and occurrence at ecologically and biogeographically meaningful scales is lacking in many parts of the world, including the biodiversity-rich Mediterranean region. Here, we provide presence/absence, extinction risk, biogeography and genetic diversity data of trees in 39 climatically and ecologically Mediterranean territories (so-called “botanical territories”) in North Africa, Western Asia and Southern Europe. Recent Findings The inventory includes 496 species and 147 subspecies from 50 families and 111 genera, including 48 species and 8 subspecies previously not considered as trees. We show that native tree species distribution is highly skewed across the tree of life with a few species-rich families such as the Rosaceae and the majority with less than 1% of all species. Endemism was not evenly distributed among botanical territories and neither was extinction risk, an assessment of which was lacking in almost half of the species. While no geographic trends were detectable, species richness was found to be positively correlated with botanical territory area and, when standardized by area, with habitat heterogeneity. Information on genetic diversity was lacking in two thirds of the species inventoried and mostly focused on species with economic importance. Summary Our data are open access and can be used by researchers and stakeholders for a wide range of purposes, including conservation and restoration. Our findings identified major native tree richness hotspots as well as key knowledge gaps and biases related to extinction risk and genetic diversity. Our findings also emphasize the importance of increased collaboration to support the conservation of Mediterranean forest trees.

Purpose of ReviewForest roads, which are important for accessing and managing forest areas, are particularly vulnerable to damaging impacts of severe climatic events. Understanding how weather changes affect forest roads is important for their efficient management and to ensure their reliability in supporting forest products supply chains. This paper reviews research conducted on the impact of climate factors on forest roads over the past two decades. The aim of our study was to develop a conceptual framework to support adaptation and mitigation strategies in forest road network management, ensuring sustainable wood flow despite a changing climate.Recent FindingsThrough a review of scientific articles and their results, we provided insights and recommendations to increase the resiliency of forest road infrastructures against the effects of climate change. Framed within the principles of climate-smart forestry, this study also offers practical suggestions to maintain the efficiency and safety of wood transportation networks under changing weather conditions, supporting sustainable forest operations and climate adaptation.SummaryThis review highlights how changes in precipitation and temperature patterns caused by climate change can impact forest road infrastructure and wood transportation. Based on the analysis of the reviewed articles, we identified key consequences such as increased erosion, road deformation, and reduced frozen periods. The research provides dedicated actions to ensure sustainability of forest resources and their infrastructure. This review is a key step towards more resilient and adaptive forest road management practices, helping to reduce the impacts of climate change on forest transportation and ecological systems.

Purpose of ReviewForest biodiversity is heavily influenced by structural conditions. In the past, forest stand structure was primarily quantified by traditional one- or two-dimensional metrics and indices. Close-range remote sensing enables researchers for the first time to reproduce and digitalize the three-dimensional structure of forests in high-resolution. The technological progress creates new possibilities in the field of biodiversity assessments. Since a structured overview of this development is still missing, the current review examines the potential of close-range technologies and elucidates the current state of the art and future perspectives.Recent FindingsA systematic literature review was conducted within the Web of Science and yielded 2204 papers which were further assessed according to our scope. Only 31 of these articles used close-range remote sensing to monitor forest structure in biodiversity assessments. Terrestrial laser scanners were the most popular platform, followed by drone-based and handheld solutions. Most authors calculated density or openness measures to describe forest structure based on point clouds. Mammals, insects, and plants were the most represented organism groups studied by the researchers. Classical biodiversity parameters such as abundance and species richness or diversity, although in various forms, were most frequently used for quantification.SummaryThe low number of available studies on the topic points to a significant knowledge gap. The analysis suggests a positive trend for close-range remote sensing in forest biodiversity research, as a great portion of the reviewed studies was released in the last two years. The great diversity of approaches and sampled metrics reveals potential for standardization, especially as the number of studies emerging in this field is expected to increase. While the reviewed studies highlight the added value of close-range remote sensing, the potential of other modern approaches, such as machine learning or different sensors, remain hitherto unexplored.

Purpose of ReviewForests are integral to global ecological stability, climate regulation, and economic resilience. They function as major carbon sinks, act as biodiversity reservoirs, and provide ecosystem services. Accurately modeling forest growth is essential to predict ecosystem responses to climate change and optimize ecosystem services. However, predicting forest growth remains challenging due to complex interactions between ecological processes, external drivers like climate change, and intrinsic dynamics, such as legacy effects and emergent properties, that influence forest responses over time.This work provides a systematic in-depth analysis of both established and emerging theories as found in the literature, exploring their integration into modern forest growth modeling with a special focus on new approaches, as implemented in 18 forest growth models which vary in their structure, objectives, and overarching goals.Recent FindingsForest modeling requires a deep understanding of forest growth theories driven by multiple interacting processes. Over time, numerous eco-physiological theories have been developed to predict forest growth under both current and future climatic conditions via dynamic vegetation models. While some were established in the past, new approaches continue to emerge, refining the complexity, predictive accuracy, and practical applicability of models. This ongoing evolution has resulted in models that are theoretically diversified but also increasingly relevant for real-world case studies dealing with both anthropogenic and natural disturbances. Machine learning, trained on increasingly large datasets, is emerging as a powerful complement to traditional forest models. Rather than replacing process-based approaches, it can be combined with them in hybrid frameworks that integrate mechanistic understanding with data-driven flexibility. This combination improves predictive performance, extends model applicability, and supports more robust decision-making in forest management.SummaryAmid the ongoing’chicken-and-egg’ debate on whether photosynthesis drives growth or growth drives photosynthesis, our review synthesizes key interconnected theories, including Functional Balance, Local Determination of Growth, and Optimality Principles of forest growth. By integrating these perspectives, we offer a clear and comprehensive overview of the main frameworks governing growth and resource allocation in plants.As multiple studies emphasize the importance of integrating different and recent theories to better capture growth dynamics, we build on a state-of-the-art multi-modelling comparison to discuss what the implications of different theories might be at different temporal and spatial resolutions. Finally, we explore how emerging technologies, such as machine learning, can enhance predictive accuracy and help address current modeling limitations.