Forests have a key role in mitigating both non-biological and biological ecological disturbances. However, major disturbances (soil pollution, shift from native forest species to exoticones, forested watersheds and climate changes) can have different impacts on a forest’s soil microbiome. Because the soil microbial community of forests has a key role in a variety of ecosystem services that promote the forest’s health, this review tries to answer the following questions: (i) Which are the main ecological disturbances that drive the responses of the forest soil microbiome? (ii) How can we measure these changes? For this aim, the review summarizes details on the tree vegetation type, the microbial communities in forest ecosystems, and the mutual influence between plants, soil, and microbiomes. Microbial communities are shaped by factors such as soil type and composition, plant and vegetation types, nutrient levels and soil fertility, disturbance patterns, symbiotic associations, biotic interactions, and the progression of forest succession. Anthropogenic activities produce a rapid response in the microbial communities, leading to both short- and long-term alterations. Harvesting processes reduce drastically the microbiome diversity, forcing a shift from specialized to more generalist microorganisms. Restoration scenarios indicate a re-establishment of microbial communities to a level similar to the native forest, but with a high percentage of replaced native microorganisms. This review emphasizes that the forest soil microbiome is shaped by a range of environmental, ecological, and biotic factors. The primary drivers of the soil microbiome in forest ecosystems discussed in this review include soil composition and nutrient availability, plant community structure, microbial interactions within the soil, disturbances, succession, and temporal dynamics. When considered together, these factors interact in complex ways, influencing the diversity, function, and resilience of the soil microbiome in forest ecosystems.

The purpose of this study, which contains historical data recorded over a period of 40 years, was to identify the main factors that influence and control the level of wood mass production. The main reason was to optimize the management of forest areas and was driven by the necessity to identify factors that can influence most of the volume produced by coniferous forests located in southeast Europe. The data was collected between1980 and 2005 at the National Institute for Research and Development in Forestry, for forests located in the Southern Carpathians, Romania. The studied data refer to the parameters that model forest structure for spruce, fir, pine, and larch. These are the main resinous species found in the Southern Carpathians. The total area covered by these forests is 143,431 ha. At the forest species level, the analysis consists of 16,162 records (corresponding to the elements of the trees), covering an area of 45,008 ha for fir, 4711 ha for larch, 81,995 ha for spruce, and 11,717 ha for pine. The aim of this research has been to investigate and to assess the impact and magnitude of abiotic factors such as altitude and field aspect on forest structures from the main resinous stands located in the Southern Carpathians. Taking into account the size of the database as well as the duration for collecting data, a complete statistical and systematic approach was considered optimum. This resulted from our wish to emphasize and evaluate the influence of each analysed factor on the wood mass production level. The relationship between abiotic factors and forest structure has been analysed by using a systematic statistical approach in order to provide a useful theoretical reference for the improvement of forest management practices in the context of multiple climatic, environmental, and socio-economic challenges. These common characteristics have been found by applying ANOVA and multivariate statistical methods such as PCA and FA methods. A series of parameters were considered in this investigation, namely altitude (ALT), forest site type (TS), forest type (TP), consistency (CONS) etc. In order to obtain a complete image, we have also applied multivariate analysis methods that emphasize the effect size for each database parameter. At such a level of recorded data, the statistical approach ensures a factor level of p <0.001 while the accuracy in evaluating effect size is increased. As such, they influence the spreading and structure of the studied resinous stands to a higher degree, regardless of species.

Spent mushroom substrate (SMS), often disregarded as waste despite its abundance in organic matter and mineral micronutrients, has emerged as a valuable resource for diverse applications. While Europe and Asia have witnessed extensive research in this field over the past decade, Ukraine’s exploration remains limited. This study conducts a thorough investigation into SMS recycling and reutilization over a 4-year period. Employing experimental and comparative methods, this research unveils compelling insights into the potential of SMS for reintroduction into the primary production cycle and secondary activities. The main conclusions reveal the success of SMS valorization in the production of additives, fertilizers, and alternative fuels. Furthermore, the application of SMS in agroecosystems significantly enhances the soil biological activity. The integration of these methods into production chains not only yields economic benefits for companies but also fosters environmental stewardship, aligning with waste-free practices and the principles of bioeconomy and sustainability.

Plant indicators are important in studies related to the environment, and mountain forests are rich in such plants. We conducted this study using the Web of Science Core Collection tools and the VOSviewer program combined with a classic review, based on the specialty literature. The topic of indicator plants in mountain forests has been (the first article published in a prestigious journal dates back to 1980) and remains relevant (after 2015, between 40 and 60 articles were published annually), with the total number of publications found being 665 articles and 22 review articles. These publications were authored by researchers from 96 countries, the most representative being the USA, China, and Germany (which also have large areas of mountain forests), and were published in 306 journals, with the most important being Ecological Indicators, Forest Ecology and Management, Forests, Journal of Vegetation Science, and Plant Ecology. They belong to the main scientific fields of Ecology, Forestry, Plant Sciences, and Environmental Sciences. The most frequently used keywords are vegetation, diversity, biodiversity, and forests. Their evolution over the past decade shows that the focus has shifted from keywords specific to this topic to those related to biodiversity and conservation, and more recently to climate change. Indicator plants in mountain forests are extremely varied but can be used successfully in the monitoring activity. Climatic conditions or human interventions lead to the dynamic of these indicator plants.

More than a third of Romania’s surface has low-productive soils, at the same time exposed to risks of climatic phenomena and generating high economic loss. Afforestation with pine has been the most common solution for the recovery of sheet erosion. Many of the pines grown on such land have run down. This paper presents the results of the first dendroecological investigation of degraded lands in Romania, 80 years after the first ecological reconstruction. In this way, the effects of reconstruction were assessed, supporting the adoption of future solutions for the improvement and efficiency of recovered ecosystems. Reconstructed radial growth was set against rainfall, air temperature, and management history. A total of 330 black pine and Scots pine trees (Pinus sylvestris L. and Pinus nigra Arn.) of different ages and social positions from 11 stands of different densities were cored for retrospective tree-ring analysis. Scots pine has made better use of these sites, with a better growth rate than black pine especially in plantations with lower survival and on dominant trees. The dynamics of radial growth distinguish the two pine species, with Scots pine showing an accentuated juvenile growth spurt and bigger growth range. The growth decline is predominantly a maturation effect that begins when the tree is around 40 years old and seems to be irreversible. After this age, weak or moderated removal is not enough to revive growth. The contribution of climate (air temperature and rainfall) to the last radial increments in decline is 3–57% and is higher than in the previous decades. On moderately degraded land by farming and grazing, the mixture of Scots pine and black pine, rather than monocultures, proved to be a sustainable solution. Dendrochronological surveying of restored ecosystems allows development of management strategies, which becomes critically important in the circumstances of climate warming.

Introduction: Mapping tree species is an important activity that provides the information necessary for sustainable forest management. Remote sensing is a effective tool that offers data at different spatial and spectral resolutions over large areas. Free and open acces Sentinel satellite imagery and Google Earth Engine, which is a powerful cloud computing platform, can be used together to map tree species.Methods: In this study we mapped tree species at a local scale using recent Sentinel-1 (S-1) and Sentinel-2 (S-2) time-series imagery, various vegetation indices (Normalized Difference Vegetation Index - NDVI, Enhanced Vegetation Index - EVI, Green Leaf Index - GLI, and Green Normalized Difference Vegetation Index - GNDVI) and topographic features (elevation, aspect and slope). Five sets of data were used, in different combinations, together with the Random Forest classifier in order to determine seven tree species (spruce, beech, larch, fir, pine, mixed, and other broadleaves [BLs]) in the studied area.Results and discussion: Dataset 1 was a combination of S-2 images (bands 2, 3, 4, 5, 6, 7, 8, 8a, 11 and 12), for which an overall accuracy of 76.74% was obtained. Dataset 2 comprised S-2 images and vegetation indices, leading to an overall accuracy of 78.24%. Dataset 3 included S-2 images and topographic features, which lead to an overall accuracy of 89.51%. Dataset 4 included S-2 images, vegetation indices, and topographic features, that have determined an overall accuracy of 89.36%. Dataset 5 was composed of S-2 images, S-1 images (VV and VH polarization), vegetation indices, and topographic features that lead to an overall accuracy of 89.68%. Among the five sets of data, Dataset 3 produced the most significant increase in accuracy, of 12.77%, compared to Dataset 1. Including the vegetation indices with the S-2 images (Dataset 2) gave an accuracy increase of only 1.50%. By combining the S-1 and S-2 images, vegetation indices and topographic features (Dataset 5) there was an accuracy increase of only 0.17%, compared with the S-2 images plus topographic features combination (Dataset 3). However, the input brought by the S-1 images was apparent in the increase in classification accuracy for the mixed and other BL species that were mostly found in hilly locations. Our findings confirm the potential of S-2 images, used together with other variables, for classifying tree species at the local scale.