Pollination is a key ecosystem service as many crops but in particular, fruits and vegetables are partially dependent on pollinating insects to produce food for human consumption. Here we assessed how pollination services are delivered at the European scale. We used this assessment to estimate the relative contribution of wild pollinators to crop production. We developed an index of relative pollination potential, which is defined as the relative potential or relative capacity of ecosystems to support crop pollination. The model for relative pollination potential is based on the assumption that different habitats, but in particular forest edges, grasslands rich in flowers and riparian areas, offer suitable sites for wild pollinator insects. Using data of the foraging range of wild bees with short flight distances, we linked relative pollination potential to regional statistics of crop production. At aggregated EU level, the absence of insect pollination would result in a reduction of between 25% and 32% of the total production of crops which are partially dependent on insect pollination, depending on the data source used for the assessment. This production deficit decreases to 2.5% if only the relative pollination potential of a single guild of pollinators is considered. A strength of our approach is the spatially-explicit link between land cover based relative pollination potential and crop yield which enables a general assessment of the benefits that are derived from pollination services in Europe while providing insight where pollination gaps in the landscape occur.

• A structured protocol for adapting a spatial ecosystem service model to local contexts is proposed. • Decision context, the final users and uses of maps should drive the way the spatial ecosystem service models are structured. • Simply increasing spatial resolution is not sufficient to increase legitimacy and the ultimate utility of maps. • The type and level of stakeholders’ involvement is a determinant of spatial model usefulness.

Nature-based recreation is among the most relevant ecosystem services supplied by urban green infrastructure, affecting citizens’ physical and mental wellbeing. Providing adequate green spaces for nature-based recreation is among the main goals of urban planning, but commonly-used indicators offer a partial view on the issue. Innovative methods and approaches, such as the ESTIMAP-recreation model, appear as promising ways to increase the quality of information available for decision-makers by considering both the range of green spaces that provide the service and the locally-specific demand. The article presents an application of the ESTIMAP-recreation model to the city of Trento (Italy), aimed at testing its adaptation to the local context and the potential improvements brought to urban planning. The comparison of the results with traditional indicators based on the availability and accessibility to urban parks shows significant differences in terms of priority of intervention across the city, with implications on planning decisions. The application demonstrates that innovative methods can enhance the understanding of nature-based recreation in cities beyond the focus on urban parks, revealing a wide portfolio of actions that planners can put in place to promote nature-based recreation through a multifunctional green infrastructure.

Animals, plants, and other organisms unintentionally or deliberately brought into a natural environment where they are not normally found, and where they cause harmful effects on that environment, are known also as invasive alien species (IAS). They represent a major threat to native biodiversity and ecosystem functioning, and can affect negatively human health and the economy. We assessed the presence and potential pressure by IAS on terrestrial and freshwater ecosystems across 27 European countries, for 66 IAS of policy concern. We computed a spatial indicator that accounts for the number of IAS present in an area and the extent of the ecosystems affected; for each ecosystem, we also looked at the pattern of invasions in the different biogeographical regions. We found disproportionally greater invasion in the Atlantic region, followed by Continental and Mediterranean regions, possibly related to historical patterns of first introductions. Urban and freshwater ecosystems were the most invaded (nearly 68% and ca. 52% of their extent respectively), followed by forest and woodland (nearly 44%). The average potential pressure of IAS was greater across cropland and forests, where we also found the lowest coefficient of variation. This assessment can be repeated over time to derive trends and monitor progress towards environmental policy objectives.

The European Union (EU) Horizon 2020 Coordination and Support Action ESMERALDA aimed at developing guidance and a flexible methodology for Mapping and Assessment of Ecosystems and their Services (MAES) to support the EU member states in the implementation of the EU Biodiversity Strategy’s Target 2 Action 5. ESMERALDA’s key tasks included network creation, stakeholder engagement, enhancing ecosystem services mapping and assessment methods across various spatial scales and value domains, work in case studies and support of EU member states in MAES implementation. Thus ESMERALDA aimed at integrating various project outcomes around four major strands: i) Networking, ii) Policy, iii) Research and iv) Application. The objective was to provide guidance for integrated ecosystem service mapping and assessment that can be used for sustainable decision-making in policy, business, society, practice and science at EU, national and regional levels. This article presents the overall ESMERALDA approach of integrating the above-mentioned project components and outcomes and provides an overview of how the enhanced methods were applied and how they can be used to support MAES implementation in the EU member states. Experiences with implementing such a large pan-European Coordination and Support Action in the context of EU policy are discussed and recommendations for future actions are given.