Search Results (83)

Ecosystem services—the benefits humans derive from nature—are foundational to environmental sustainability and economic well-being, with carbon sequestration and storage standing out as critical regulating services in the fight against climate change. This study presents a comprehensive financial valuation of the carbon sequestration, storage and product substitution ecosystem services provided by the Hungarian forest-based sector. Using a multi-scenario framework, four complementary valuation concepts are assessed: total carbon storage (biomass, soil, and harvested wood products), annual net sequestration, emissions avoided through material and energy substitution, and marketable carbon value under voluntary carbon market (VCM) and EU Carbon Removal Certification Framework (CRCF) mechanisms. Data sources include the National Forestry Database, the Hungarian Greenhouse Gas Inventory, and national estimates on substitution effects and soil carbon stocks. The total carbon stock of Hungarian forests is estimated at 1289 million tons of CO₂ eq, corresponding to a theoretical climate liability value of over EUR 64 billion. Annual sequestration is valued at approximately 380 million EUR/year, while avoided emissions contribute an additional 453 million EUR/year in mitigation benefits. A comparative analysis of two mutually exclusive crediting strategies—improved forest management projects (IFMs) avoiding final harvesting versus long-term carbon storage through the use of harvested wood products—reveals that intensified harvesting for durable wood use offers higher revenue potential (up to 90 million EUR/year) than non-harvesting IFM scenarios. These findings highlight the dual role of forests as both carbon sinks and sources of climate-smart materials and call for policy frameworks that integrate substitution benefits and long-term storage opportunities in support of effective climate and bioeconomy strategies. Full article

As China’s first systematic assessment of high-risk Amaranthaceae invaders, this study addresses a critical knowledge gap identified in the National Invasive Species Inventory, in which four invasive Amaranthaceae species (Dysphania ambrosioides, Celosia argentea, Amaranthus palmeri, and Amaranthus spinosus) are prioritized due to CNY 2.6 billion annual ecosystem damages in China. By coupling multi-species comparative analysis with a parameter-optimized Maximum Entropy (MaxEnt) model integrating climate, soil, and topographical variables in China under Shared Socioeconomic Pathways (SSP) 126/245/585 scenarios, we reveal divergent expansion mechanisms (e.g., 247 km faster northward shift in A. palmeri than D. ambrosioides) that redefine invasion corridors in the North China Plain. Under current conditions, the suitable habitats of these species span from 92° E to 129° E and 18° N to 49° N, with high-risk zones concentrated in central and southern China, including the Yunnan–Guizhou–Sichuan region and the North China Plain. Temperature variables (Bio: Bioclimatic Variables; Bio6, Bio11) were the primary contributors based on permutation importance (e.g., Bio11 explained 56.4% for C. argentea), while altitude (e.g., 27.3% for A. palmeri) and UV-B (e.g., 16.2% for A. palmeri) exerted lower influence. Model validation confirmed high accuracy (mean area under the curve (AUC) > 0.86 and true skill statistic (TSS) > 0.6). By the 2090s, all species showed net habitat expansion overall, although D. ambrosioides exhibited net total contractions during mid-century under the SSP126/245 scenarios, C. argentea experienced reduced total suitability during the 2050s–2070s despite high-suitability growth, and A. palmeri and A. spinosus expanded significantly in both total and highly suitable habitat. All species shifted their distribution centroids northward, aligning with warming trends. Overall, these findings highlight the critical role of temperature in driving range dynamics and underscore the need for latitude-specific monitoring strategies to mitigate invasion risks, providing a scientific basis for adaptive management under global climate change. Full article

Soil respiration (Rs) is a significant contributor to the global carbon cycle, with its two main sources—microbial (heterotrophic, Rh) and plant root (autotrophic, Ra) respiration—being sensitive to various environmental factors. This study investigates the impact of ecosystem disturbances (Ds), including fire, biogenic (insects and pathogens), and harvesting, on soil respiration in Russia’s forest ecosystems. We introduced response factors to account for the effects of these disturbances on Rh over three distinct stages of ecosystem recovery. Our analysis, based on data from case studies, remote sensing data, and the national forest inventory, revealed that Ds increase Rh by an average of 2.1 ± 3.2% during the restoration period. Biogenic disturbances showed the highest impacts, with average increases of 16.5 ± 3.2%, while the contributions of clearcuts and wildfires were, on average, less pronounced—2.0 ± 3.1% and 0.8 ± 3.3%, respectively. These disturbances modify forest soil dynamics by affecting soil temperature, moisture, and nutrient availability, influencing carbon fluxes over varying timescales. This research underscores the role of ecosystem disturbances in altering soil carbon dynamics and highlights the need for improved data and monitoring of forest disturbances to reduce uncertainty in soil carbon flux estimates. Full article

Landslides are one of the most serious problems affecting large parts of the world. There are two approaches that are used to study the organization of these land cover features: firstly, an approach utilizing lithostratigraphic tools, where soils are described and interpreted in accordance with specific geological/lithological patterns, and, secondly, through pedological instruments, where the pedogenetic patterns are identified, and the sequences are identified via standardized criteria and organized according to modern classification systems. In the present review, a comparison between the two above approaches is outlined, using the Campania Apennine reliefs (Southern Italy) as the reference environment because they are periodically and dramatically affected by mass movements mainly associated with rainfall events. These reliefs are strongly influenced by the products emitted by the Phlegraean Fields and the Somma–Vesuvius volcanoes. These products affect surface structures either through their direct alteration, with the formation of pedogenized products, or through their reworking, mainly stimulated by rainfall events, which is also responsible for the movement of pedogenized materials along the slopes. This results in complex surface architectures, knowledge of which is a crucial step in the assessment of robust monitoring systems. This review covers the Cervinara area, located in the central portion of the Campania Apennines, which was overwhelmed by dramatic landslide events in 1999. Our aims were to critically analyze the impact and the potential of lithostratigraphic and pedological approaches in studying the soils of the area in question and to provide an inventory of the scientific papers in which, with different aims, descriptions and interpretations of the local soil covers are reported. We examined and selected the national and international literature available in major scientific online databases, and these were split into groups on the basis of citations and type of approach. The reviewed literature showed that the stratigraphic approach was by far the most preferred, although significant potential was offered by pedological tools in this field of investigation. A high number of hydraulic and geotechnical articles was also found, in comparison to geological and pedological papers, which confirmed the significant levels of interest in the land cover type in question, specifically regarding landslide processes, and in their role in risk mitigation practices. On the whole, the latter approach has been proven to offer a greater exploration potential through the use of rigorous classification systems and, thus, the possibility of identifying and correlating soil properties over large areas. Full article

Forest ecosystems are important for biodiversity conservation, climate regulation and climate change mitigation, soil and water protection, and the recreation and provision of raw materials. This paper presents a dataset on forest type and tree species composition for 934 georeferenced plots located in Italy. The forest type is classified in the field consistently with the Italian National Forest Inventory (NFI) based on the dominant tree species or species group. Tree species composition is provided by the percent crown cover of the main five species in the plot. Additional data on conifer and broadleaves pure/mixed condition, total tree and shrub cover, forest structure, sylvicultural system, development stage, and local land position are provided. The surveyed plots are distributed in the central–eastern Alps, in the central Apennines, and in the southern Apennines; they represent a wide range of species composition, ecological conditions, and silvicultural practices. Data were collected as part of a project aimed at developing a classification algorithm based on hyperspectral data. The dataset was made publicly available as it refers to forest types and species widespread in many countries of Central and Southern Europe and is potentially useful to other researchers for the study of forest biodiversity or for remote sensing applications. Full article

Climate variations in temperature and precipitation significantly impact forest productivity. Precipitation influences the physiology and growth of species, while temperature regulates photosynthesis, respiration, and transpiration. This study developed bioclimatic models to assess how climate change will affect the carbon density of aboveground biomass (_cdAGB) in Mexico’s coniferous forests for 2050 and 2070. We used _cdAGB data from the National Forest and Soils Inventory (INFyS) of Mexico and 19 bioclimatic variables from WorldClim ver. 2.0. The best predictors of _cdAGB were obtained using machine learning techniques with the “caret” library in R. The model was trained with 80% of the data and validated with the remaining 20% using Generalized Linear Models (GLMs). Current _cdAGB prediction maps were generated using the best predictors. Future _cdAGB was calculated with the average of three general circulation models (GCMs) of future climate projections from the Coupled Model Intercomparison Project Phase 5 (CMIP5), under four Representative Concentration Pathways (RCPs): 2.6, 4.5, 6.0, and 8.5 W/m². The results indicate _cdAGB losses in all climate scenarios, reaching up to 15 Mg C ha⁻¹, and could occur under the RCP 8.5 scenario by 2070 in the central region of the country. Temperature-related variables are more important than precipitation variables. Bioclimatic variables can explain up to 20% of the total variance in _cdAGB. The temperature in the study area is expected to increase by 2.66 °C by 2050 and 3.36 °C by 2070, while precipitation is expected to fluctuate by ±10% relative to the current values, which could geographically redistribute the _cdAGB of the country’s coniferous forests. These findings underscore the need for forest management to focus not only on biodiversity conservation but also on the carbon storage capacity of these ecosystems. Full article

Landslides and their resulting impacts on property and human life have become an ongoing challenge in the hilly regions of Bangladesh. This study aims to systematically review diverse landslide studies in Bangladesh, particularly focusing on landslide disaster management (LDM) from 2008 to 2023, encompassing the pre-disaster, syn-disaster, and post-disaster phases. Several key attributes of landslide studies were considered, including general trends, data types, study scales, contributing factors, methodologies, results, and validation approaches, to investigate challenges and subsequently identify research gaps. This study evaluated 51 research articles on LDM using a systematic literature review (SLR) technique that adhered to the Preferred Reporting Item for Systematic Reviews and Meta-Analyses (PRISMA) framework. Our finding revealed that articles on LDM were dominated by the pre-disaster (76%) and the syn-disaster phases (12%), with the post-disaster phase (12%) receiving equal attention. The SLR revealed a growing number of studies since 2020 that used data-driven methods and secondary spatial data, often focused on medium-scale analyses (district level) that, however, often lacked field-based validation. From the factors examined in various landslide studies, topographical and hydrological factors were found to be the most significant attributes in assessment. This study identified key challenges, such as insufficient landslide inventories including poor site accessibility and a lack of high-resolution geological, soil, and rainfall data. It also highlighted critical research gaps, including the need for advanced technologies in susceptibility mapping for national hazard atlas, the investigation of underexplored causative factors, effective early warning systems, detailed post-event characterization, health impact assessment, risk-sensitive land use planning, and interactive web portals for landslide prone areas. This study would thus aid researchers in understanding the depth of existing knowledge and provide insights into how landslides fit into broader disaster management frameworks, facilitating interdisciplinary approaches. Full article

The standardized methods used in carbon markets require measurement of the biomass and carbon stored in trees, which can be quantified through allometric equations. The objective of this study was to analyze aboveground biomass estimates with allometric models in three mangrove species and compare them with those used by the Climate Action Reserve (CAR) standard. The mangrove forest in Tabasco, Mexico, was certified with the Forest Protocol for Mexico Version 2.0 (FPM) of the CAR standard. Allometric equations for mangrove species were reviewed to determine the most suitable equation for the calculation of biomass. The predictions of the allometric equations of the FPM were analyzed with data from Tabasco from the National Forest and Soil Inventory 2015–2020, and the percentages of trees within the ranges of diameters of the FPM equations were determined. The FPM equations generated higher biomass values for Rhizophora mangle and lower values for Avicennia germinans than the seven equations with which they were compared. In the mangrove swamp of Ejido Úrsulo Galván, Tabasco, 81.8% of the biomass of A. germinans, 34.4% of Laguncularia racemosa and 24.0% of R. mangle were within the diameter range of the FPM equations, and in Tabasco, 28.5% of A. germinans, 16.7% of L. racemosa and 5.7% of R. mangle were within the diameter range. For A. germinans and R. mangle, we recommend using the equation that considers greater maximum diameters. The allometric equations of the FPM do not adequately predict a large percentage of the biomass. Full article

The main aim of this study was to assess the amount of carbon (C) stored in the upper 30 cm layer of mineral soils in eucalypt plantations in Portugal, with a Mediterranean-type climate. Soil sampling data (2468 samples), field evaluations (soil profile description) and relevant information on the particle size distribution, climate, bedrock and reference soil group were accomplished. Bulk density per sample was assessed using pedo-transfer functions and soil C stock was estimated. The results showed an average of 41.2 t C ha⁻¹ stored in the soil. In the northern regions of Portugal, the coldest and wettest areas of the country with better stand productivity, a higher soil organic carbon (SOC) is achieved (median SOC of 39.2 g kg⁻¹ and soil C stock of 55 t ha⁻¹) than in southern and inland regions, with a warmer and drier climate (median SOC of 15.2 g kg⁻¹ and soil C stock of 28 t ha⁻¹). The assessment of mean soil C stock per bedrock type revealed higher C stored in granites followed by conglomerates, coal shales and clay shales. Regarding soil type, the results showed a higher C stock in Cambisols, Leptosols and Fluvisols (>50 t C ha⁻¹), whereas Regosols and Luvisols stored less, following the same trend presented for reference soil groups in Europe. Comparing the geographic distribution of the C stock in the upper layer of the mineral soils with the amount of C in eucalyptus stands (root and aboveground biomass—data from national forest inventory), the mineral soil pool can represent more than two-thirds of the total C stored in eucalyptus plantations in Portugal. Further studies should focus on the evolution of C stocks in eucalypt plantations during different stages of stand growth and under different management practices. Full article

Spatial–temporal variation in soil organic carbon is an important factor for national targets to mitigate climate change and land degradation impacts. In this research, we took Guangdong Province of China as the study area, evaluated the spatial–temporal distributions of soil organic carbon using data from three China Geochemical Baseline projects (conducted in 2009, 2016, and 2023, respectively), and quantified the main driving factors of spatial–temporal variations in soil organic carbon using the random forest algorithm, further predicting the density and inventories of soil organic carbon. The results demonstrate that the mean value of SOC in Guangdong in 2009 was 0.81%; in 2016 it was 1.13%; and in 2023 it was 1.02%. The inventories of soil organic carbon (0–30 cm) in Guangdong Province were 0.61 Pg in 2009, 0.74 Pg in 2016, and 0.62 Pg in 2023. Soil in Guangdong acted as a carbon sink from 2009 to 2023 as a whole, and the most important driving force behind spatial–temporal variations in soil organic carbon was temperature, followed by precipitation and vegetation cover. Full article

Over a period of 10 years, 418 forested plots within the US National Capital Region parks were visited for morphological descriptions and to inventory carbon (C) stocks. Samples were collected from organic horizons, the loose leaf litter, and, using a hand auger, from each mineral horizon to a depth of 1 m. Soil C concentration was determined using high-temperature combustion, and organic carbon (OC) stocks were then calculated for each master horizon. Soil bulk density (Db) was determined using the core method for O and A horizons. For deeper mineral horizons, a strong linear relationship between NRCS SSURGO representative values and measured Db values averaged according to soil series (R² = 0.75) was observed. Thus, the NRCS SSURGO representative Db values were used for mineral horizons below the A horizon. An average of 0.5 ± 0.0 kg C m⁻² was contained in the loose leaf litter. For plots with O horizons, the organic layer contained 2.9 ± 0.3 kg C m⁻². An average of 4.6 ± 0.2 kg C m⁻² was stored in the A horizon, down to an average lower boundary of 18.8 cm. The mineral horizons below the A horizon averaged 8.5 kg C m⁻². In these forested soil profiles, 52.8% of the TOC is found below the A horizon and 18.0% of the TOC is in the organic horizons. The predictive strength of the thickness of and SOC in the A horizon was also evaluated in terms of explaining and predicting TOC in the profile and in the subsoil. The thickness and SOC in the A horizon explained 54% of the variation in TOC stock; however, it was a poor predictor of OC stored in the subsoil (R² = 0.04). This study demonstrates the importance of deeper sampling to encompass more of the rooting depth when investigating SOC stocks. Full article

The native pine species of Mexico, constituting 55% of all pine species, play a crucial economic role for local populations. Climatic factors affected by climate change, such as temperature and precipitation, influence tree physiology and distribution. Our study focused on the aboveground biomass density (AGB_d) distribution of ten Mexican pine species and its correlation with bioclimatic variables. Dendrometric data were obtained from National Forest and Soil Inventory (INFyS) (period: 2009 and 2014) while data on bioclimatic variables were obtained from WorldClim2. AGB_d distribution maps were generated for the ten species. Spearman and Bayesian correlations were determined between AGB_d and the 19 bioclimatic variables. Six species showed a significant correlation (p < 0.05) between AGB_d and bioclimatic variables. The results did not show geographical regionalization for AGB_d and highlighted the complexity of responses in each species. Temperature variables showed the highest number of correlations with AGB_d (76%), which varied between species. Regarding precipitation, correlations were mostly positive. In general, our findings suggest an important link between climate and AGB_d, from which relevant strategies can be developed for sustainable forest management of the country’s forests in relation to expected climate change. Full article

The composition, distribution, and growth of native natural forests are important references for the restoration, structural adjustment, and close-to-nature transformation of artificial forests. The joint species distribution model is a powerful tool for analyzing community structure and interspecific relationships. It has been widely used in biogeography, community ecology, and animal ecology, but it has not been extended to natural forest conservation and restoration in China. Therefore, based on the 9th National Forest Inventory data in Jilin Province, combined with environmental factors and functional traits of tree species, this study adopted the joint species distribution model—including a model with all variables (model FULL), a model with environmental factors (model ENV), and a model with spatial factors (model SPACE)—to examine the distribution of multiple tree species. The results show that, in models FULL and ENV, the environmental factors explaining the model variation were ranked as follows, climate > site > soil. The explanatory power was as follows: model FULL (AUC = 0.8325, Tjur R² = 0.2326) > model ENV (AUC = 0.7664, Tjur R² = 0.1454) > model SPACE (AUC = 0.7297, Tjur R² = 0.1346). Tree species niches in model ENV were similar to those in model FULL. Compared to predictive power, we found that the information transmitted by environmental and spatial predictors overlaps, so the choice between model FULL and ENV should be based on the purpose of the model, rather than the difference in predictive ability. Both models can be used to study the adaptive distribution of multiple tree species in northeast China. Full article

Modern forestry systems rely on typologies of forest types (FTs). In Argentina, several proposals have been developed, but they lack unified criteria. The objective was to compare different approaches, specifically focusing on (i) phenoclusters (functional forests based on vegetation phenology variations and climate variables) and (ii) forest canopy cover composition by tree species. We conducted comparative uni-variate analyses using data from national forest inventories, forest models (biodiversity, carbon, structure), and regional climate. We assessed the performance of phenoclusters in differentiating the variability of native forests (proxy: forest structure), biodiversity (proxy: indicator species), and environmental factors (proxies: soil carbon stock, elevation, climate). Additionally, we proposed a simple FT classification methodology based on species composition, considering the basal area of tree species. Finally, we compared the performance of both proposals. Our findings showed that classifications based on forest canopy cover composition are feasible to implement in regions dominated by mono-specific forests. However, phenoclusters allowed for the increased complexity of categories at the landscape level. Conversely, in regions where multi-specific stands prevailed, classifications based on forest canopy cover composition proved ineffective; however, phenoclusters facilitated a reduction in complexity at the landscape level. These results offer a pathway to harmonize national FT classifications by employing criteria and indicators to achieve sustainable forest management and conservation initiatives. Full article

As forest-based climate change mitigation has become a crucial element of international climate policy it is of increasing importance to understand the processes leading to the carbon offsetting capacity of the sector. In our study, we assessed the climate benefits of contrasting forest management strategies: decreasing harvest and enlarging the forest carbon stock, or increasing harvest to increase carbon uptake, wood product carbon pools, and substitution effects. We developed the Forest Industry Carbon Model (FICM) which is a new carbon accounting tool covering forest biomass, dead organic matter, soil, and harvested wood product pools, as well as avoided emissions through product and energy substitution. We modeled the carbon balance of the Hungarian forest industry under three different scenarios. In the business as usual (BAU) scenario, we assumed no changes in the current harvest and afforestation levels. In the extensification scenario, we assumed that the harvest and afforestation levels drop to half, while in the intensification scenario, we assumed an increase in afforestation, improved industrial wood assortments, and a gradual increase in logging, reaching the highest level as per sustainability criteria by 2050. Our results show that the intensification scenario is characterized by the largest net removals and the maximized product and energy substitution effects. By 2050, the net forest industry carbon balance reaches −8447 kt CO₂ eq under the BAU scenario, while −7011 kt CO₂ eq is reached under the extensification scenario and −22,135 kt CO₂ eq is reached under the intensification scenario. Although substitution effects are not accounted for under the land-based (LULUCF) sector in the greenhouse gas inventory, the emission reductions in the industry and energy sectors have beneficial effects on the national carbon balance. Modeling results show that the 2030 LULUCF greenhouse gas removal target set by EU legislation for Hungary is reached under the intensification scenario. To achieve this outcome, widespread innovation is needed in the wood sector. The modeling results show that nonutilization of forests can only be a very short-term solution; however, its favorable effects will be reversed by 2050 resulting in additional emissions compared to the BAU scenario. Full article