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Mapping and assessing ecosystem services (ES) projects at the national level have been implemented recently in the European Union in order to comply with the targets set out in the EU’s Biodiversity Strategy for 2020 and later in the Strategy for 2030. In Hungary this work has just been accomplished in a large-scale six-year project. The Hungarian assessment was structured along the ES cascade with each level described by a set of indicators. We present the selected and quantified indicators for 12 ES. For the assessment of cascade level 4, human well-being, a set of relevant well-being dimensions were selected. The whole process was supported by several forms of involvement, interviews, consultations and workshops and in thematic working groups performing the ES quantifications, followed by building scenarios and synthesizing maps and results. Here we give an overview of the main steps and results of the assessment, discuss related conceptual issues and recommend solutions that may be of international relevance. We refine some definitions of the cascade levels and suggest theoretical extensions to the cascade model. By finding a common basis for ES assessments and especially for national ones, we can ensure better comparability of results and better adoption in decision making.

This paper analyses and compares a set of case studies on ecosystem services (ES) mapping and assessment with the purpose of formulating lessons learned and recommendations. Fourteen case studies were selected during the EU Horizon 2020 “Coordination and Support Action” ESMERALDA to represent different policy- and decision-making processes throughout the European Union, across a wide range of themes, biomes and scales. The analysis is based on a framework that addresses the key steps of an ES mapping and assessment process, namely policy questions, stakeholder identification and involvement, application of mapping and assessment methods, dissemination and communication and implementation. The analysis revealed that most case studies were policy-orientated or gave explicit suggestions for policy implementation in different contexts, including urban, rural and natural areas. Amongst the findings, the importance of starting stakeholder engagement early in the process was confirmed in order to generate interest and confidence in the project and to increase their willingness to cooperate. Concerning mapping and assessment methods, it was found that the integration of methods and results is essential for providing a comprehensive overview from different perspectives (e.g. social, economic). Finally, lessons learned for effective implementation of ES mapping and assessment results are presented and discussed. biodiversity, EU Biodiversity Strategy, comparative analysis, ecosystem services, MAES, case studies

We present a ‘concept matching’ systematic review linking the classes of the Common International Classification for Ecosystem Services (CICES, v4.3) to the ways scientists define and apply ES indicators in published studies. With the dual aim of creating an overview how the different services are measured in the studies, and determining if CICES provides an appropriate structure to accommodate the ES assessed in the practical literature, we reviewed 85 scientific papers from which 440 indicators were identified. Almost all CICES classes were represented, with cultural and some regulating (e.g. global climate regulation, pollination) ES being the most frequently considered. The four most frequently studied CICES classes (or class clusters) were global climate regulation, aesthetic beauty, recreation, and bio-remediation. Regulating and cultural services were more often assessed than provisioning services. Normalisation to unit area and time was common for indicators of several regulating and provisioning ES. Scores were most frequently used for cultural ES (except recreation) and some regulating services (e.g. flood protection). Altogether 20% of the ES indicators were quantified as an economic value, and monetisation is most frequently done for cultural and provisioning ES. Few regulating services, on the other hand, were monetised (including ones, like global climate regulation, for which appropriate techniques are relatively easily available). The work enabled a library of indicators to be compiled and made available. The findings can be used to help improve CICES so that it can provide a more robust and comprehensive framework for ecosystem assessments.

This study compares biodiversity indicators based on plant and bird communities in eight mosaic landscapes in Hungary, dominated by a mixture of agro-ecosystems and grasslands. The eight landscapes were selected to represent the diversity of the mixed agricultural landscapes of South-East Europe, where a mosaic pattern of intensively managed farmlands and high nature value semi natural grasslands is still relatively prevalent. Bird communities were described using several assemblage-level (species number, total abundance, and Shannon diversity of the assemblage, based on 15 pre-selected key farmland bird species), as well as species-level (presence/absence of the 15 bird species) indicators, which were checked against a synthetic landscape quality indicator describing the degradation of the local plant communities with respect to an ideal baseline (vegetation-based natural capital index, NCI). The authors were interested if and how the assemblage- and species-level bird indicators can describe landscape quality in South-East European agricultural mosaic landscapes. It was found that assemblage-level bird indicators were poorly associated to the landscape quality measured in terms of NCI: only total abundance correlated significantly with NCI. On the other hand, species-level indicators were much more successful in predicting landscape quality. Six (Alauda arvensis, Emberiza calandra, Falco tinnunculus, Motacilla flava, Limosa limosa, Vanellus vanellus) of the 15 farmland bird species studied showed significant positive correlation with NCI, while three species (Emberiza citrinella, Galerida cristata, Sylvia communis) exhibited negative correlations. We also found that it was possible to draw conclusions about the landscape quality in an agricultural landscape based on the bird communities better, than to predict the bird assemblages from vegetation condition. The negative correlations for species that indicate good quality habitats in Western Europe, underline the context specificity of biodiversity indicators: whereas the conditions preferred by these species can be considered relatively natural in Western Europe, they correspond to relatively degraded habitats in South-East Europe. The nine farmland bird species which showed a significant connection to NCI can be seen as potential candidates for a regional Farmland Bird Index customized for agricultural landscapes in South-East Europe, in the Pannonian biogeographic region, or in Hungary.

Made available in DSpace on 2018-12-11T17:15:22Z (GMT). No. of bitstreams: 0 Previous issue date: 2018-02-01 The ecosystem service (ES) concept is becoming mainstream in policy and planning, but operational influence on practice is seldom reported. Here, we report the practitioners’ perspectives on the practical implementation of the ES concept in 27 case studies. A standardised anonymous survey (n = 246), was used, focusing on the science-practice interaction process, perceived impact and expected use of the case study assessments. Operationalisation of the concept was shown to achieve a gradual change in practices: 13% of the case studies reported a change in action (e.g. management or policy change), and a further 40% anticipated that a change would result from the work. To a large extent the impact was attributed to a well conducted science-practice interaction process (>70%). The main reported advantages of the concept included: increased concept awareness and communication; enhanced participation and collaboration; production of comprehensive science-based knowledge; and production of spatially referenced knowledge for input to planning (91% indicated they had acquired new knowledge). The limitations were mostly case-specific and centred on methodology, data, and challenges with result implementation. The survey highlighted the crucial role of communication, participation and collaboration across different stakeholders, to implement the ES concept and enhance the democratisation of nature and landscape planning. Centre for Ecology and Hydrology, Bush Estate Research Institute for Nature and Forest (INBO), Kliniekstraat 25 Social-ecological Systems Lab Department of Ecology Universidad Autónoma de Madrid, Calle Darwin no. 2, Campus de Cantoblanco Finnish Environment Institute, P.O. Box 140 Institute of Landscape Ecology Slovak Academy of Sciences, Akademická 2 Department of Ecology and Environmental Sciences Constantine the Philosopher University, Trieda A. Hlinku 1 Environmental Social Science Research Group (ESSRG Ltd.), Rómer Flóris u. 38. Department of Decision Sciences Corvinus University of Budapest, Fővám tér 8. Helmholtz-Centre for Environmental Research – UFZ, Permoserstraße 15 CoKnow Consulting – Coproducing Knowledge for Sustainability, Jesewitz CENSE – Centre for Environmental and Sustainability Research Faculdade de Ciências e Tecnologia Universidade Nova de Lisboa, 2829-516 Caparica Institute of Ecology and Botany MTA Centre for Ecological Research, Alkotmány u. 2-4. Institute of Environmental Science and Technology (ICTA) Universitat Autònoma de Barcelona (UAB) Edifici Z (ICTA-ICP), Carrer de les Columnes s/n, Campus de la UAB Norwegian Institute for Nature Research (NINA), Gaustadalléen 21 Environmental Change Institute, Dyson Perrins Building, South Parks Road Wageningen University and Research Environmental Research (Alterra), P.O. Box 47 European Topic Centre on Biological Diversity Muséum national d'Histoire naturelle, 57 rue Cuvier, FR-75231 Paris Centre for Ecology & Hydrology, Maclean Building, Benson Lane, Crowmarsh Gifford Urban Environment and Territorial Sustainability Area Energy and Environment Dividision Parque Tecnológico de Bizkaia C/Geldo, Edificio 700 University of Bucharest - Research Center in Systems Ecology and Sustainability, Splaiul Independentei 91-95 European Commission – Joint Research Centre (JRC), Via E. Fermi 2749 Institute of Landscape Ecology Slovak Academy of Sciences, Štefánikova 3 UNESP – Universidade Estadual Paulista Ecology Department – LabTerra, Av. 24-A, 1515 CEP: 13506-900, Rio Claro IRSTEA National Research Institute of Science and Technology for Environment and Agriculture UMR TETIS, 500 rue JF BRETON Centro Austral de Investigaciones Científicas (CADIC CONICET), Houssay 200 Leuphana University of Lüneburg Faculty of Sustainability Institute of Ethics and Transdisciplinary Sustainability Research, Scharnhorststraβe 1 IBRAD (Indian Institute of Bio Social Research and Development), VIP Road, Kestopur, Prafulla Kanan Department of Environmental Sciences, P.O. Box 65, FI-00014 University of Helsinki Kenya Forestry Research Institute (KEFRI), P.O. Box 20412-0200 Instituto Nacional de Tecnología Agropecuaria (INTA), CC 332 (9400), Río Gallegos Universidad Nacional de la Patagonia Austral (UNPA) Río Gallegos Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET) INCLINE – Interdisciplinary Climate Change Research Group Instituto de Astronomia e Geofísica USP – Universidade de São Paulo, R. do Matão, 1226 - Butantã Wageningen University & Research – Marine (WUR), P.O. Box 57 National Institute for Research and Development in Forestry “Marin Dracea”, Eroilor Blvd 128 Institute of Nature Conservation and Landscape Management Szent István University, Páter Károly u. 1. Norwegian University of Life Science (NMBU), Universitetstunet 3 Department of Applied Research and Agricultural Extension Madrid Institute for Rural Agricultural and Food Research and Development (IMIDRA), Ctra. Madrid-Barcelona (N-II), KM. 38.200, 28802 Alcalá De Henares Department of International Environment and Development Studies (Noragric) Norwegian University of Life Sciences (NMBU), P.O. Box 5003 Norwegian Institute for Nature Research (NINA), Fakkelgården VISTA Analyse A/S, Meltzers gate 4 University of Girona, Plaça de Sant Domènec, 3 Basque Centre for Climate Change, Alameda de Urquijo 4 Norwegian Institute for Nature Research (NINA), Postboks 5685 Sluppen De Økonomiske Råd, Amaliegade 44 UNESP – Universidade Estadual Paulista Ecology Department – LabTerra, Av. 24-A, 1515 CEP: 13506-900, Rio Claro

Highlights • 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., 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.