Of 105 rhizobial isolates obtained from nodules of commonly cultivated legumes, we selected 19 strains on the basis of a high rate of symbiotic plant growth promotion. Individual strains within the species Rhizobium leguminosarum bv. trifolii , R. leguminosarum bv. viciae , and Rhizobium etli displayed variation not only in plasmid sizes and numbers but also in the chromosomal 16S–23S internal transcribed spacer. The strains were tagged with gusA gene and their competitiveness was examined in relation to an indigenous population of rhizobia under greenhouse conditions. A group of 9 strains was thus isolated that were competitive in relation to native rhizobia in pot experiments. Nineteen selected competitive and uncompetitive strains were examined with respect to their ability to utilize various carbon and energy sources by means of commercial Biolog GN2 microplate test. The ability of the selected strains to metabolize a wide range of nutrients differed markedly and the competitive strains were able to utilize more carbon and energy sources than uncompetitive ones. A major difference concerned the utilization of amino and organic acids, which were metabolized by most of the competitive and only a few uncompetitive strains, whereas sugars and their derivatives were commonly utilized by both groups of strains. A statistically significant correlation between the ability to metabolize a broad range of substrates and nodulation competitiveness was found, indicating that metabolic properties may be an essential trait in determining the competitiveness of rhizobia.

Analysis of pollen material obtained from the Hirst-type apparatus, which is a tedious and labor-intensive process, is usually performed by hand under a microscope by specialists in palynology. This research evaluated the automatic analysis of pollen material performed based on digital microscopic photos. A deep neural network called YOLO was used to analyze microscopic images containing the reference grains of three taxa typical of Central and Eastern Europe. YOLO networks perform recognition and detection; hence, there is no need to segment the image before classification. The obtained results were compared to other deep learning object detection methods, i.e., Faster R-CNN and RetinaNet. YOLO outperformed the other methods, as it gave the mean average precision (mAP@.5:.95) between 86.8% and 92.4% for the test sets included in the study. Among the difficulties related to the correct classification of the research material, the following should be noted: significant similarities of the grains of the analyzed taxa, the possibility of their simultaneous occurrence in one image, and mutual overlapping of objects.

Soil bacteria from the genus Rhizobium are characterized by a complex genomic architecture comprising chromosome and large plasmids. Genes responsible for symbiotic interactions with legumes are usually located on one of the plasmids, named the symbiotic plasmid (pSym). The plasmids have a great impact not only on the metabolic potential of rhizobia but also underlie genome rearrangements and plasticity.

Rhizobia are able to establish symbiosis with leguminous plants and usually occupy highly complex soil habitats. The large size and complexity of their genomes are considered advantageous, possibly enhancing their metabolic and adaptive potential and, in consequence, their competitiveness. A population ofRhizobium leguminosarumbv.trifoliiorganisms recovered from nodules of several clover plants growing in each other's vicinity in the soil was examined regarding possible relationships between their metabolic-physiological properties and their prevalence in such a local population. Genetic and metabolic variability within theR. leguminosarumbv.trifoliistrains occupying nodules of several plants was of special interest, and both types were found to be considerable. Moreover, a prevalence of metabolically versatile strains, i.e., those not specializing in utilization of any group of substrates, was observed by combining statistical analyses of Biolog test results with the frequency of occurrence of genetically distinct strains. Metabolic versatility with regard to nutritional requirements was not directly advantageous for effectiveness in the symbiotic interaction with clover: rhizobia with specialized metabolism were more effective in symbiosis but rarely occurred in the population. The significance of genetic and, especially, metabolic complexity of bacteria constituting a nodule population is discussed in the context of strategies employed by bacteria in competition.

The present study investigated the pattern of the birch atmospheric pollen seasons in Lublin in the period 2001–2010. Pollen monitoring was conducted using a Lanzoni VPPS 2000 sampler. The atmospheric pollen seasons were determined with the 98% method. Regression analysis was used to determine correlations between meteorological conditions and the pattern of the birch pollen season. On average, the birch pollen season started on 12 April, ended on 13 May, and lasted 32 days. The peak value and the Seasonal Pollen Index showed the greatest variation in particular years. All the seasons were right-skewed. During the study years, a trend was found towards earlier occurrence of the seasonal peak. Regression equations were developed for the following parameters of the atmospheric pollen season: start, duration, peak value and average pollen concentration during the season. The obtained model fit was at a level of 64–81%. Statistical analysis shows that minimum temperature of February and March and total rainfall in June in the year preceding pollen release have the greatest effect on the birch atmospheric pollen season in Lublin. Low temperatures in February promote the occurrence of high pollen concentrations.

During the spring period, Betula pollen is the main cause of inhalant allergies in Poland and therefore it is important to monitor and forecast airborne pollen concentrations of this taxon. This study conducted a comparative analysis of the basic characteristics of Betula pollen seasons at the regional scale. The study was carried out from 2001 to 2016 in five cities in Poland: Lublin, Warsaw, Cracow, Sosnowiec, and Szczecin. To find the attributes of birch pollen seasons that mostly differentiated the individual cities, a general discriminant analysis (GDA) was performed, while a principal component analysis (PCA) allowed us to reduce the data space and present a scatterplot of PCA scores in order to compare pollen seasons in the individual cities. The contingency table was also analyzed to check whether there was a significant relationship between pollen counts in the studied years and cities. At most of the sites, biennial cycles of low and high pollen concentrations can be observed. Due to the high variation in seasons in each of these cities, two data groups were distinguished: Group 1 was composed of seasons with high pollen deposition (2001, 2003, 2006, 2008, 2010, 2012, 2014, 2016), and Group 2 comprising the other seasons. Multivariate analyses were performed on both these groups as well as in the entire dataset. End98, Peak Value, and Annual Total had the highest discriminant power. In Group 1, Warsaw and Sosnowiec differed the most in the investigated parameters, while Cracow and Szczecin differed the least. In both groups, most seasons with the highest pollen birch concentration were observed in Lublin, followed by Warsaw, while in Cracow, the number of such seasons was the smallest.