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This study investigated the relationship between landscape structure at different distances from the Drava River in South Hungary and the food composition of the Common Barn-owl. Pellets were collected from 15 villages between 2006 and 2008. Based on the CORINE land cover elements, five land use types were determined, and five landscape metrics were calculated to compare land use and landscape structure in the three distance zones. There were significant differences in the Shannon and Simpson diversity of small mammal assemblages between the three areas. A positive relationship was detected between the distance categories and the abundance distribution of the Striped Field Mouse and Field Vole. The relative abundance of the Striped Field Mouse in the diet of Common Barn-owl was influenced by the increase in the mean perimeter/area ratio and the mean of the contiguity index. The value of the trophic level index was negatively influenced by the decrease in crop patches and the increase in pasture and grassland areas, which land use types facilitate the distribution of insectivores. Our results suggest that landscape characteristics influence prey occurrence in hunting areas and the frequency-dependent availability of small mammal prey, which determines the resource utilization of Common Barn-owl. [ABSTRACT FROM AUTHOR]

Highlights • The socio-political transformation in Hungary and Poland after 1989 have contributed significantly to the land use change. • Surface areas of individual land use types in Hungary and Poland show similar trends but the degree of change is different. • A reduction of agricultural land and increased forest and uncultivated land coverage was observed in Poland and Hungary. • The most intensive anthropogenic changes were identified near the cities (Budapest and Kraków). • The decrease in agricultural land noted in the land cover of the study area coincided with European and global trends. Abstract This study has analysed changes in land use and land cover (LUCC) in two post-socialist countries – Hungary and Poland, based upon cadastral data, statistical data, and CORINE Land Cover data. This is a new approach aimed to perform a synergy analysis carried out in accordance with the 'top-down' principle, i.e. from the level of countries (in the case of land use) to the level of provinces/counties (land cover). This approach facilitates more reliable results. The authors analysed LUCC in northern Hungary (the Pest County) and southern Poland (the Małopolska Province) using GIS and statistical methods. The main aim of the research was to identify and assess the ongoing LUCC changes and compare them in the assumed field of research. The results indicate the existence of a trend in each time series. A downward trend was revealed for agricultural land in Hungary and Poland as well as for uncultivated land in Hungary. An upward trend was found in other variables (uncultivated land in Poland and forest land in both countries). Changes in land cover in the Pest County and the Małopolska Province (in 2000 and 2012) show a decrease in agricultural areas and an increase in artificial surfaces, forest and semi-natural areas with the change in Poland being more intensive than that in Hungary. [ABSTRACT FROM AUTHOR]

As soil erosion is still a global threat to soil resources, the estimation of soil loss, particularly at a spatiotemporal setting, is still an existing challenge. The primary aim of our study is the assessment of changes in soil erosion potential in Hungary from 1990 to 2018, induced by the changes in land use and land cover based on CORINE Land Cover data. The modeling scheme included the application and cross-valuation of two internationally applied methods, the Universal Soil Loss Equation (USLE) and the Pan-European Soil Erosion Risk Assessment (PESERA) models. Results indicate that the changes in land cover resulted in a general reduction in predicted erosion rates, by up to 0.28 t/ha/year on average. Analysis has also revealed that the combined application of the two models has reduced the occurrence of extreme predictions, thus, increasing the robustness of the method. Random Forest regression analysis has revealed that the differences between the two models are mainly driven by their sensitivity to slope and land cover, followed by soil parameters. The resulting spatial predictions can be readily applied for qualitative spatial analysis. However, the question of extreme predictions still indicates that quantitative use of the output results should only be carried out with sufficient care. [ABSTRACT FROM AUTHOR]