Invasive alien species of policy concerns show widespread patterns of invasion and potential pressure across European ecosystems.

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.

Ecosystem services accounts: Valuing the actual flow of nature-based recreation from ecosystems to people.

Natural capital accounting aims to measure changes in the stock of natural assets (i.e., soil, air, water and all living things) and to integrate the value of ecosystem services into accounting systems that will contribute to better ecosystems management. This study develops ecosystem services accounts at the European Union level, using nature-based recreation as a case study and following the current international accounting framework: System of Environmental-Economic Accounting - Experimental Ecosystem Accounting (SEEA EEA). We adapt and integrate different biophysical and socio-economic models, illustrating the workflow necessary for ecosystem services accounts: from a biophysical assessment of nature-based recreation to an economic valuation and compilation of the accounting tables. The biophysical assessment of nature-based recreation is based on spatially explicit models for assessing different components of ecosystem services: potential, demand and actual flow. Deriving maps of ecosystem service potential and demand is a key step in quantifying the actual flow of the service used, which is determined by the spatial relationship (i.e., proximity in the case of nature-based recreation) between service potential and demand. The nature-based recreation accounts for 2012 show an actual flow of 40 million potential visits to 'high-quality areas for daily recreation', with a total value of EUR 50 billion. This constitutes an important contribution of ecosystems to people's lives that has increased by 26% since 2000. Practical examples of ecosystem services accounts, as shown in this study, are required to derive recommendations and further develop the conceptual and methodological framework proposed by the SEEA EEA. This paper highlights the importance of using spatially explicit models for ecosystem services accounts. Mapping the different components of ecosystem services allows proper identification of the drivers of changes in the actual service flow derived from ecosystems, socio-economic systems and/or their spatial relationship. This will contribute to achieving one of the main goals of ecosystem accounts, namely measuring changes in natural capital, but it will also support decision-making that targets the enhancement of ecosystems, their services and the benefits they provide.

Multiscale socio-ecological networks in the age of information.

Interactions between people and ecological systems, through leisure or tourism activities, form a complex socio-ecological spatial network. The analysis of the benefits people derive from their interactions with nature-also referred to as cultural ecosystem services (CES)-enables a better understanding of these socio-ecological systems. In the age of information, the increasing availability of large social media databases enables a better understanding of complex socio-ecological interactions at an unprecedented spatio-temporal resolution. Within this context, we model and analyze these interactions based on information extracted from geotagged photographs embedded into a multiscale socio-ecological network. We apply this approach to 16 case study sites in Europe using a social media database (Flickr) containing more than 150,000 validated and classified photographs. After evaluating the representativeness of the network, we investigate the impact of visitors' origin on the distribution of socio-ecological interactions at different scales. First at a global scale, we develop a spatial measure of attractiveness and use this to identify four groups of sites. Then, at a local scale, we explore how the distance traveled by the users to reach a site affects the way they interact with this site in space and time. The approach developed here, integrating social media data into a network-based framework, offers a new way of visualizing and modeling interactions between humans and landscapes. Results provide valuable insights for understanding relationships between social demands for CES and the places of their realization, thus allowing for the development of more efficient conservation and planning strategies.

Practical application of spatial ecosystem service models to aid decision support.

Ecosystem service (ES) spatial modelling is a key component of the integrated assessments designed to support policies and management practices aiming at environmental sustainability. ESTIMAP ("Ecosystem Service Mapping Tool") is a collection of spatially explicit models, originally developed to support policies at a European scale. We based our analysis on 10 case studies, and 3 ES models. Each case study applied at least one model at a local scale. We analyzed the applications with respect to: the adaptation process; the "precision differential" which we define as the variation generated in the model between the degree of spatial variation within the spatial distribution of ES and what the model captures; the stakeholders' opinions on the usefulness of models. We propose a protocol for adapting ESTIMAP to the local conditions. We present the precision differential as a means of assessing how the type of model and level of model adaptation generate variation among model outputs. We then present the opinion of stakeholders; that in general considered the approach useful for stimulating discussion and supporting communication. Major constraints identified were the lack of spatial data with sufficient level of detail, and the level of expertise needed to set up and compute the models.

Ecosystem services sustainability in the Mediterranean Sea: assessment of status and trends using multiple modelling approaches.

Mediterranean ecosystems support important processes and functions that bring direct benefits to human society. Yet, marine ecosystem services are usually overlooked due to the challenges in identifying and quantifying them. This paper proposes the application of several biophysical and ecosystem modelling approaches to assess spatially and temporally the sustainable use and supply of selected marine ecosystem services. Such services include food provision, water purification, coastal protection, lifecycle maintenance and recreation, focusing on the Mediterranean region. Overall, our study found a higher number of decreasing than increasing trends in the natural capacity of the ecosystems to provide marine and coastal services, while in contrast the opposite was observed to be true for the realised flow of services to humans. Such a study paves the way towards an effective support for Blue Growth and the European maritime policies, although little attention is paid to the quantification of marine ecosystem services in this context. We identify a key challenge of integrating biophysical and socio-economic models as a necessary step to further this research.

More green infrastructure is required to maintain ecosystem services under current trends in land-use change in Europe.

Green infrastructure (GI), a network of nature, semi-natural areas and green space, delivers essential ecosystem services which underpin human well-being and quality of life. Maintaining ecosystem services through the development of GI is therefore increasingly recognized by policies as a strategy to cope with potentially changing conditions in the future. This paper assessed how current trends of land-use change have an impact on the aggregated provision of eight ecosystem services at the regional scale of the European Union, measured by the Total Ecosystem Services Index (TESI8). Moreover, the paper reports how further implementation of GI across Europe can help maintain ecosystem services at baseline levels. Current demographic, economic and agricultural trends, which affect land use, were derived from the so called Reference Scenario. This scenario is established by the European Commission to assess the impact of energy and climate policy up to 2050. Under the Reference Scenario, economic growth, coupled with the total population, stimulates increasing urban and industrial expansion. TESI8 is expected to decrease across Europe between 0 and 5 % by 2020 and between 10 and 15 % by 2050 relative to the base year 2010. Based on regression analysis, we estimated that every additional percent increase of the proportion of artificial land needs to be compensated with an increase of 2.2 % of land that qualifies as green infrastructure in order to maintain ecosystem services at 2010 levels.

How are caseload and service utilisation of psychiatric services influenced by distance? A geographical approach to the study of community-based mental health services.

INTRODUCTION: The aim of this study was to assess how the caseload and the utilisation of community-based mental health services is influenced by distance and to socioeconomic characteristics. METHODS: Spatial and statistical analyses were conducted with a sample of 12,347 patients, with ICD-10 psychiatric diagnosis, who had at least one contact with psychiatric services in Verona, Italy, between 2000 and 2006. Three types of mental health facility were considered: acute inpatient wards, outpatient clinics, and community mental health centres (CMHC). To measure distance and accessibility, the locations of static mental health facilities and patients' homes were geocoded. Data were organised in a spatial database, which included census blocks, catchment areas locations, road network graphs, patients' and facilities' locations. In order to calculate travel distances, patients' and facilities' locations were connected to the road network. Accessibility was modelled by using the Network Analyst Service Area Function and 13 Service Areas were created around all facility locations, by measuring distances along the street network. For the epidemiological analyses, patients and census block centroids were linked to the service areas by using spatial join techniques. Epidemiological and utilisation analyses were performed for each type of setting. RESULTS: The facilities were not equally located in the catchment areas. Of particular significance, rural areas appear to be poorly served by mental health services. The distance decay effect exists, with different trends for the three types of facility. The caseload (number of patients using services) decreased with increasing distance; at a distance of 10 km, there was a decrease of 80, 60 and 85%, respectively, for CMHCs, inpatients wards and outpatients clinics. From the Poisson regression models, distance was significantly correlated (p value < 0.0001) with service use. Also univariate analyses showed a statistically significant association between distance and caseload for each type of setting (p value < 0.05), with a decrease in service use for each service area increase in distance (1.5% for acute inpatient wards, 2.0% for CMHC, and 2.1% outpatient clinics). By adding other predictors in the Poisson regression models, these percentages increased. CONCLUSIONS: Further studies are needed to evaluate the influence of other factors, such as environmental variables, that may influence the use of mental health services.

DRGs and other patient-, service- and area-level factors influencing length of stay in acute psychiatric wards: the Veneto Region experience.

PURPOSE: This study aimed to identify services-related and area-based measures together with socio-demographic factors that could improve diagnosis-related groups in explaining length of stay variability in general hospital psychiatric units in Veneto Region (North East of Italy). METHODS: Data were collected from the regional hospital discharge records database. A hierarchical multiple regression model with only diagnosis-related groups as predictors of actual and ln-transformed length of stay was compared with a second model in which patient-, service- and area-level variables were included. Local health district was used as group-level in the hierarchical multiple regression analysis. RESULTS: The only diagnosis explains 6.4% of actual length of stay total variance (14.8% for ln-transformation). In the second model length of stay resulted related also to gender, age, severity of hospitalization, patient's local health district, number of psychiatrists, psychologists, hospital attendants/nurses, social workers and educators in the general hospital psychiatric units, number of outpatients in each local health district and percentages of divorced and single people, with almost a 2% point increase on actual length of stay in explained variance (5% point increase for ln-transformation). CONCLUSIONS: For the first time the hospital discharge card regional survey of all public acute inpatient psychiatric facilities in Veneto Region were used. The innovative aspect of this study was the attempt to investigate the relationship between length of stay and other indexes, characterizing not only the inpatient facilities, but also the resident population structure in each area. The information about factors that influence length of stay can be useful to inform service planning and resource allocation.