Species specific effects of ants on microbial activity and N-availability in the soil of an old-field

Microbial biomass and activity as well as N-availability were measured in the mounds of three ant species strongly differing in foraging strategy and mound architecture: Myrmica scabrinodis, Lasius niger and L. flavus. Soil microbial biomass (C_mic) was significantly increased in the mounds of all three ant species. This positive effect was due to the accumulation of organic matter (C_org) within the mounds. Microbial activity was increased in M. scabrinodis mounds only. Available N_min was accumulated in all mound types, independent of the feeding mode of the ants, with Lasius mounds having significantly higher N_min content than M. scabrinodis mounds. It is hypothesised that the differences between the mound types are due to differences in nest architecture, especially the integration of grassy vegetation. Higher microbial activity in M. scabrinodis mounds may be a consequence of supplementary energy provided by root exudates. The amount of N_min in M. scabrinodis mounds may be reduced by increased plant uptake and by immobilisation within the microbial biomass.     

Cloudwater chemistry in the subcooled droplet regime at Mount Sonnblick (3106 M A.S.L., Salzburg,Austria)

Cloudwater and wet precipitation (snow) samples were collected at Mount Sonnblick during two field campaigns in May and November 1991. A newly designed active cloud water samples was used. Concentrations of major anions, cations and carboxylic acids were determined. Cloudwater and wet precipitation samples were generally more acidic in the warm season than in the cold season. Average cloudwater pH was 4.2 in May and 4.5 in November, average pH in snow was 4.4 in May and 5.1 in November. Average levels for sulfate (May: 96 μeq L^?1, November: 64 μeq L^?1) and nitrate (May: 27 μeq L^?1, November: 32 μeq L^?1) in cloudwater at SBO (3 km altitude) were considerably lower than at high mountain sites (0.9–2 km altitude) in the Eastern U.S.A. Cold season levels of sulfate in cloud water at SBO were as low as cloud water levels observed in Alaska. Equivalent concentrations of sulfate, nitrate and ammonium in snow precipitation were basically lower or equal compared to cloudwater but showed higher concentrations and stronger acidity in both phases in May than in November. Cloud to snow ratios for major ions were higher in November showing a wider spread than in May. Average cloud to snow ratios for sulfate were 2.4 in May and 3.5 in November. For nitrate the ratio was 1.7 in May and 2.1 in November. The lower cloud to snow ratios for nitrate are explained by the ability of the ice phase to scavenge nitric acid. Cloud to snow ratios were similar to measurements from the Swiss Alps and generally equal or lower than high elevation cloud to rain ratios from the U.S.A. Cloud to snow ratios for sulfate were used to reconstruct the mixing ratio of sublimation grown ice phase and cloud water droplets during the riming process of the ice particles in the seeder-feeder mechanism. The mixing ratio of ice phase and cloud droplets was estimated to be 1.4 in May and 2.5 in November. Sulfate to nitrate ratios were higher in cloud water than in snow and within the range of values found in North America. Generally, sulfate was more concentrated than nitrate at an equivalent basis for both cloudwater and rainwater. Total equivalent concentrations of acetate were generally higher than those of formate which is in contrast to measurements at remote high elevation sites in the U.S.A.     

First report of Enterocytozoon bieneusi infection on a pig farm in the Czech Republic

Enterocytozoon bieneusi infects humans and animals and can cause life-threatening diarrhea in immunocompromised people. The routes of transmission and its zoonotic potential are not fully understood. Pigs have been frequently reported to have E. bieneusi; therefore, we surveyed farm-raised pigs in the Czech Republic to determine its presence and genetic diversity. Spores were detected by microscopy in the faeces of 65 out of 79 examined animals (82%). A species-specific polymerase chain reaction (PCR) identified E. bieneusi in 94% of samples. Genotyping based on the ITS regions of the SSU rRNA gene identified that most pigs were infected with the species-specific genotype F, while two animals had the zoonotic genotype D and two had genotype Peru 9. This is the first report of E. bieneusi in swine in the Czech Republic, and demonstrated that most infections were with pig-specific genotypes. Nonetheless, swine may still play a role in the transmission of E. bieneusi to humans.     

Quantification of the effect of various patient and image factors on ultrasonographic detection of select canine abdominal organs.

We assessed factors that affected ultrasonographic visualization of the pylorus, duodenal papilla, pancreas, adrenal glands, and jejunal and medial iliac lymph nodes in the dog. An abdominal ultrasonographic examination was performed on 100 canine patients, equally divided between two facilities. The pylorus was visible in 64% of the dogs, the major duodenal papilla in 42%, the left pancreatic lobe in 56%, the body of the pancreas in 60%, the right pancreatic lobe in 87%, the left adrenal gland in 91%, the right adrenal gland in 86%, the medial iliac lymph nodes in 54%, and the jejunal lymph nodes in 51%. The parameters that negatively influenced the visibility of these organs were the presence of air or food in the gastrointestinal tract (pancreas, duodenal papilla), age (lymph nodes), and body weight (pancreas, duodenal papilla). The parameters that positively influenced their visibility were the presence of air or food in the gastrointestinal tract (lymph nodes), body weight (lymph nodes), body condition score (right adrenal gland), and inherent image quality (left pancreatic lobe). There was a significant difference between the two institutes for the visualization of the pylorus, pancreas, and lymph nodes, which was probably related to different body positions used for scanning in each institution.     

Antioxidative and EROD activities of osajin and pomiferin

The major constituents of fruits of Maclura pomifera are the prenylated isoflavones, osajin (1) and pomiferin (2). Since significant biological activities of extracts from the wood of M. pomifera were previously reported, the peroxynitrite scavenging activity, inhibition of lipid peroxidation, scavenging of DPPH and EROD activity of these two major substances were studied.     

Variability of soil water content controlled by evapotranspiration and groundwater–root zone interaction

The central moments of soil water content (SWC) variability at the field scale are determined by soil texture, considering both smooth topography and groundwater table position. The characteristics of variability are governed by other soil factors like soil structure, micro relief, preferred water flow paths, root system characteristics, rock content, etc. This paper shows the integral effect of all these hardly quantifiable factors on SWC variability simulated by the processes of evapotranspiration and groundwater–root zone interaction using the HYDRUS ET model. SWC and soil hydraulic characteristics were spatially determined over a 4.5 ha field during two sampling campaigns under different atmospheric and groundwater conditions, and data distributions were compared to SWC distributions provided by mathematical modeling. The entire spring–summer period of 2003 was then examined for changes of SWC spatial variability. It was found that evapotranspiration influences SWC spatial variability only if SWC is under the critical value when wetter parts of the field evaporate more water than drier parts, resulting in smoothed SWC variability. Under wet conditions the spatial variability of SWC increases by drainage, as those parts of the soil with coarser texture drain faster than finer-textured parts.     

Fuzzy classification of microbial biomass and enzyme activities in grassland soils

Soil microbial properties are widely used as indicators of soil quality. The interpretation of soil microbial processes, however, is difficult because of their regional and seasonal heterogeneity as well as the lack of reference values. One possibility to overcome these limitations to apply the fuzzy set theory. This approach more realistically describes ecological systems because it considers natural ambiguity and complexity. The present study applies a fuzzy rule-based classification model to define soil quality based on soil microbial biomass, N-mineralisation, enzyme activity data (urease, xylanase, phosphatase, arylsulfatase) and soil organic matter. The data have been collected from different grassland sites in the European Union over a period of 20 years. The fuzzy model is based on a rule system derived from a training set using simulated annealing as an optimisation algorithm. For each variable, nine triangular fuzzy sets were defined for consideration as possible rule arguments. The model uses the t-norm for combination of arguments, product inference, the weighted sum as rule combination and the maximum method for defuzzification. The output is the assignment of membership of the object to a given soil quality class. The soil quality classes (very poor, poor, medium, high, very high) were defined by five heavy metal contamination levels (very high, high, medium, low, no). A predefined number of fuzzy rules were assessed using a simulated annealing algorithm. The fuzzy model was validated by a test file by assigning the soils to the quality class with the highest response value. The fuzzy model yielded an overall coincidence of 88.8% between observed and simulated results. The most sensitive index of soil quality was microbial biomass. N-mineralisation was a good indicator for the high-quality soils, while urease and arylsulfatase were important indicators for heavily contaminated, poor soil quality. Xylanase and phosphatase behaved ambivalently. Including soil organic carbon in the model decreased its effectiveness by 6.5%. We suggest that the presented fuzzy model based on soil microbial properties could be applied not only to soil degradation, upscaling and prediction, but also to judge the response of soils to environmental changes.