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Purpose Symbolic plants and animals are recognised as a cultural ecosystem service (CES), which is still underrepresented in ecosystem services assessments. Thus, this study aims at identifying and mapping important symbolic species in the European Alps, which are of cultural significance to large parts of the Alpine population. Methods Symbolic species were identified by ten expert groups, and their use was assessed in a qualitative way. The spatial distribution of all species across the Alpine Space area was mapped at the municipality level. Through hotspots analysis, we identified spatial patterns in the distribution of species. Spearman correlation was used to evaluate the relationship between symbolic species and selected environmental and social variables. Results Ten species were identified (edelweiss, gentian, alpenrose, larch, pine, Alpine ibex, chamois, marmot, brown bear, and golden eagle) that are widely used for symbolic representations, i.e., depiction on flags, emblems, logos, and naming of hotels and brands. Hotspots of symbolic species were found in several locations in the European Alps and could be related to high elevation, steep slopes, open land cover, and naturalness. Conclusions This study proposes a methodology to map and assess symbolic species as a CES. As the spatial distribution of symbolic species depends on environmental characteristics and human activities, our results provide important insights for landscape planning and management. However, it remains unclear whether associated cultural values depend on the presence of the species and further research is needed to understand the relationships between the distribution of symbolic species and social benefits.

A key challenge in the sustainable management of freshwater is related to non-stationary processes and transboundary requirements. The assessment of freshwater is often hampered due to small-scale analyses, lacking data and with the focus on only its provision. Based on the ecosystem service (ES) concept, this study aims at quantitatively comparing potential water supply with the demand for freshwater in the European Alps and their surrounding lowlands. We propose an easy-to-use combination of different mapping approaches, including a large-scale hydrologic model to estimate water supply and the downscaling of regional data to the local scale to map demand. Our results demonstrate spatial mismatches between supply and demand and a high dependency of the densely populated lowlands from water providing mountain areas. Under expected climate variations and future demographic changes, our results suggest increasing pressures on freshwater in the south of the Alps. Hence, sustainable water management strategies need to assure the supply of freshwater under changing environmental conditions to meet the increasing water demand of urbanized areas in the lowlands. Moreover, national water management strategies need to be optimally concerted at the international level, as transboundary policies and frameworks can strengthen future water provision.

Socio-economic indicators are key to understanding societal challenges. They disassemble complex phenomena to gain insights and deepen understanding. Specific subsets of indicators have been developed to describe sustainability, human development, vulnerability, risk, resilience and climate change adaptation. Nonetheless, insufficient quality and availability of data often limit their explanatory power. Spatial and temporal resolution are often not at a scale appropriate for monitoring. Socio-economic indicators are mostly provided by governmental institutions and are therefore limited to administrative boundaries. Furthermore, different methodological computation approaches for the same indicator impair comparability between countries and regions. OpenStreetMap (OSM) provides an unparalleled standardized global database with a high spatiotemporal resolution. Surprisingly, the potential of OSM seems largely unexplored in this context. In this study, we used machine learning to predict four exemplary socio-economic indicators for municipalities based on OSM. By comparing the predictive power of neural networks to statistical regression models, we evaluated the unhinged resources of OSM for indicator development. OSM provides prospects for monitoring across administrative boundaries, interdisciplinary topics, and semi-quantitative factors like social cohesion. Further research is still required to, for example, determine the impact of regional and international differences in user contributions on the outputs. Nonetheless, this database can provide meaningful insight into otherwise unknown spatial differences in social, environmental or economic inequalities.