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This spreadsheet presents the list of the policy elements assessed in the FAIR data policy checklist and allows policy makers to characterise their policies against each using standard set of options. By creating structured descriptions, depositing them with a repository, and making them visible through registry services, the community can support a more efficient and comparable means of assessing the policy landscape over time such as that envisaged by the EOSC Strategic Research and Innovation Agenda (https://www.eosc.eu/sria). FAIRsFAIR has developed guidance to help policy makers to work through the process (Creating and Sharing Structured Policy Descriptions - a step by step guide (1.0). Zenodo. https://doi.org/10.5281/zenodo.6281105).

This spreadsheet presents the list of the policy elements assessed in the FAIR data policy checklist and allows policy makers to characterise their policies against each using standard set of options. By creating structured descriptions, depositing them with a repository, and making them visible through registry services, the community can support a more efficient and comparable means of assessing the policy landscape over time such as that envisaged by the EOSC Strategic Research and Innovation Agenda (https://www.eosc.eu/sria). FAIRsFAIR has developed guidance to help policy makers to work through the process (Creating and Sharing Structured Policy Descriptions - a step by step guide (1.0). Zenodo. https://doi.org/10.5281/zenodo.6281105).

The study area is Šumava National Park (ŠNP), the largest of the four national parks located in the south-west of the Czech Republic, established as a special regime of environmental protection. A unique mosaic of forest habitats of exceptional natural value of European-wide significance, the ŠNP represents the largest terrestrial significant part of the Natura 2000 network in Czech Republic and Germany with unique mosaic of forest biotopes, rare, endemic and endangered species. It forms one of the largest forested areas between the Atlantic Ocean and the Ural. Recently, the ŠNP experienced changes in the landscapes and ecological community dynamics due to both natural climate changes and human impacts. Some parts of ŠNP are deforested and used for agriculture in the last decades., info:eu-repo/semantics/published

After summarizing experience of various countries in the field of landscape monitoring, it appears that the real changes of landscape have to be observed in a large scale (not smaller than 1:10,000) in order to avoid generalization of small landscape elements. Usually there are several levels of distributing the places of monitoring: 1) the level of sample area, when monitor territories are stratified by landscape types; 2) the level of investigation site that is distinguished inside the sample area for the more detailed research; 3) the level of transect or field investigation site, sometimes created in order to multiply the statistical data for some special aspect of monitoring. The paper offers an original method of distributing the landscape sample areas in Lithuanian territory, differing from most methods based on random choose of sample areas though thorough analysis of the analogous methods abroad was performed.

After summarizing experience of various countries in the field of landscape monitoring, it appears that the real changes of landscape have to be observed in a large scale (not smaller than 1:10,000) in order to avoid generalization of small landscape elements. Usually there are several levels of distributing the places of monitoring: 1) the level of sample area, when monitor territories are stratified by landscape types; 2) the level of investigation site that is distinguished inside the sample area for the more detailed research; 3) the level of transect or field investigation site, sometimes created in order to multiply the statistical data for some special aspect of monitoring. The paper offers an original method of distributing the landscape sample areas in Lithuanian territory, differing from most methods based on random choose of sample areas though thorough analysis of the analogous methods abroad was performed.

The structure of a landscape is highly relevant for research and planning (such as fulfilling the requirements of the Water Framework Directive – WFD – and for implementation of comprehensive catchment planning). There is a high potential for restoration of linear landscape elements in most European landscapes. By implementing the WFD in Germany, the restoration of linear landscape elements could be a valuable measure, for example to reduce nutrient input into rivers.Despite this importance of landscape structures for water and nutrients fluxes, biodiversity and the appearance of a landscape, specific studies of the linear elements are rare for larger catchment areas. Existing studies are limited because they either use remote sensing data, which does not adequately differentiate all types of linear landscape elements, or they focus only on a specific type of linear element. To address these limitations, we developed a framework allowing comprehensive quantification of linear landscape elements for catchment areas, using publicly available biotope type data. We analysed the dependence of landscape structures on natural regions and regional soil characteristics. Three data sets (differing in biotopes, soil parameters and natural regions) were generated for the catchment area of the middle Mulde River (2700 km2) in Germany, using overlay processes in geographic information systems (GIS), followed by statistical evaluation. The linear landscape components of the total catchment area are divided into roads (55%), flowing water (21%), tree rows (14%), avenues (5%), and hedges (2%). The occurrence of these landscape components varies regionally among natural units and different soil regions. For example, the mixed deciduous stands (3.5 m/ha) are far more frequent in foothills (6 m/ha) than in hill country (0.9 m/ha). In contrast, fruit trees are more frequent in hill country (5.2 m/ha) than in the cooler foothills (0.5 m/ha). Some 70% of avenues, and 4

This investigation tested the usefulness of geometry-based landscape metrics for monitoring landscapes in a heavily disturbed environment. Research was carried out in a 75 sq km study area in Saxony, eastern Germany, where the landscape has been affected by surface mining and agricultural intensification. Landscape metrics were calculated from digital maps (1912, 1944, 1973, 1989) for the entire study area and for subregions (river valleys, plains), which were defined using the original geology and topography of the region. Correlation and factor analyses were used to select a set of landscape metrics suitable for landscape monitoring. Little land-use change occurred in the first half of the century, but political decisions and technological developments led to considerable change later. Metrics showed a similar pattern with almost no change between 1912 and 1944, but dramatic changes after 1944. Nonparametric statistical methods were used to test whether metrics differed between river valleys and plains. Significant differences in the metrics for these regions were found in the early maps (1912, 1944), but these differences were not significant in 1973 or 1989. These findings indicate that anthropogenic influences created a more homogeneous landscape.