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.

Despite extensive research into green microalgae belonging to the genus Chloroidium, their species diversity and biotechnological potential remain poorly characterized. The strain VKM Al-418, the subject of this study, was isolated from the soil of Duvannyi Yar (Russian Federation). The independent species status of this strain is supported by distinct morphological characteristics, robust phylogenetic placement based on the 18S-ITS1-5.8S-ITS2 fragment, and unique features in the secondary structures of both ITS1 and ITS2, including one compensatory base change (CBC) in the highly conserved helix III of ITS2. Additionally, the species delimitation was also confirmed using five independent algorithmic approaches analyzing four different DNA barcodes. The concatenated ITS1-5.8S-ITS2 fragment is more reliable for species discrimination than the individual ITS1 or ITS2 barcodes. Of the species delimitation methods evaluated, ASAP (Assemble Species by Automatic Partitioning) and GMYC (Generalized Mixed Yule Coalescent) performed best in distinguishing Chloroidium species across multiple barcode regions in our analysis. The fatty acid profile of strain VKM Al-418 was analyzed at 9 °C, 22 °C, and 27 °C and exhibited high plasticity in response to temperature, indicative of an adaptive strategy to its harsh environment. Using this integrative taxonomic approach, we describe Chloroidium pseudoellipsoideum sp. nov., a new species with a distinct phylogenetic positioning and promising biotechnological properties.

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.