The genus Klebsormidium is a cosmopolitan filamentous alga that belongs to the phylum Charophyta and inhabits a wide range of environments, primarily freshwater, soil, and aerial habitats. Its basal position on the Streptophyta phylogenetic tree, widespread distribution and significant functional role make the genus Klebsormidium an important object for phycological research. To advance the molecular phylogeny of Klebsormidium , we conducted the first comparative evaluation of six species delimitation methods – ASAP (Assemble Species by Automatic Partitioning), LocMin (Local Minima), KoT (K over Theta), GMYC (Generalized mixed Yule-coalescent), mlPTP (Poisson Tree Process with Maximum Likelihood), and bPTP (Poisson Tree Process with Bayesian inference) – using rbc L and concatenated ITS2– rbc L fragments. The accuracy of these methods was further validated against molecular, morphological, and ecological data. According to an integrative approach, Klebsormidium mirabile sp. nov. was proposed as a new charophyte species from soil of the Fergana Valley, Uzbekistan. In addition, a study on the fatty acid composition showed that the authentic strain VKM Al-436 of this new species of Klebsormidium is a potential source of polyunsaturated fatty acids, including linoleic, α-linolenic, and hexadecatrienoic acids.

A neural network model for individual tree detection was developed based on the YOLOv4 architecture, which underwent additional preprocessing and postprocessing steps. The preprocessing step involved expanding the dataset by randomly cutting fragments from images, calculating anchor box sizes using the K-means clustering algorithm, and discarding anchor boxes that were too small a priori. The existing post-processing block of the YOLO architecture was modified by giving more weight to false positives in the error function and using the non-maximum suppression algorithm. Baseline neural networks from the YOLOv4 and YOLOv5 architectures, each in two versions (pre-trained and not pre-trained on the MS COCO dataset), were used for comparison without any additional modifications. In the overgrown experimental field, multi-season aerial copter surveys and ground counts were conducted on several sample plots to gather data. Comparison of multi-season aerial photographs with ground-count data showed that the best images in terms of the percentage of visually identifiable trees were those taken during the snowy season and when there was no foliage. Using these images and some additional images, we manually created a dataset on which we trained and tested neural network models. The model we developed showed significantly better results (2 to 10 times better) on the mAP 0.5 metric compared to the alternatives we considered.

Across eleven administrative districts of the Moscow region and the Moscow metropolitan area, 559 survey sites were studied from 2001 to 2020. These sites were grouped by land use types (forest, meadow and fallow land, arable land, and urban soil) and the main soil groups: Albic Podzols, Folic Retisols, Greyzemic Phaeozems, and Luvic Chernozems. At all survey sites, the dominant vegetation and geographic coordinates were recorded, and soil samples were collected from the upper 10 cm of the humus-accumulative layer. Forest soils were represented by 204 sites, meadows and fallow lands by 120, arable land by 43, soils of small towns (8 in total) by 108 sites, and those of the Moscow metropolitan area, by 84. In towns and cities, survey sites were selected in recreational, residential, and industrial land uses. The soil samples were analyzed for organic carbon (C_org), microbial biomass carbon (C_mic), basal (microbial) respiration (BR), and pH. The C_mic/C_org and BR/C_org ratios were calculated. It has been shown that the microbial parameters of soils for different land uses in small towns did not differ significantly, but in the megalopolis they did. In forest soils, the C_mic, BR, C_mic/C_org, and BR/C_org values were the highest and amounted, on average, to 589 μg C/g, 1.07 μg C–CO₂/(g h), 1.99%, and 36.0 μg C–CO₂/(g C_org h), respectively; in meadows they were lower, and in arable lands and in small towns they were the lowest (on average, 249 and 328 μg C_mic/g, 0.41 and 0.60 μg C–CO₂/(g h), 1.14 and 0.82%, 18.3 and 15.1 μg C–CO₂/(g C_org h), respectively). The spatial distribution of microbial indicators of soils in the region is visualized using the spatial interpolation method, separately for natural (forest, meadow, and fallow lands) and anthropogenically transformed (arable land, small towns) ecosystems.

We performed a quantitative and qualitative assessment of the dynamics of gap formation in the canopy of intact old-growth polydominant broadleaved forest in a permanent sample area in the Kaluga Zaseki Nature Reserve. Digital elevation models were obtained from aerial photography data of the forest in 2018 and 2021, from which gap schemas of several height classes were compiled. The resulting schemas were expertly analyzed using orthophotomosaic images, and the gap areas were estimated. A selective ground survey of gaps was carried out, and a regression analysis of the relationship between the relative gap area and the species composition of the stand based on the primary ground survey data was conducted. It was shown that the phenophase during the survey can significantly change the estimation of gap areas, and the height of the stand in the gap cannot serve as a reliable indicator of its age. It was also found that aerial photography reveals a more complex gap structure than ground-based surveys.