PDF Hosted at the Radboud Repository of the Radboud University Nijmegen
PDF hosted at the Radboud Repository of the Radboud University Nijmegen The following full text is a publisher's version. For additional information about this publication click this link. https://hdl.handle.net/2066/228804 Please be advised that this information was generated on 2021-10-02 and may be subject to change. Ecological Modelling 438 (2020) 109331 Contents lists available at ScienceDirect Ecological Modelling journal homepage: www.elsevier.com/locate/ecolmodel Integration of local knowledge and data for spatially quantifying ecosystem services in the Hoeksche Waard, the Netherlands M.J. Paulin a,b,*, M. Rutgers a, T. de Nijs a, A.J. Hendriks b, K.R. Koopman a, T. Van Buul c, M. Frambach d, G. Sardano e, A.M. Breure a,b a National Institute for Public Health and the Environment (RIVM), Bilthoven, Netherlands b Radboud University, Nijmegen, Netherlands c Utrecht University, Utrecht, Netherlands d Tauw, Utrecht, Netherlands e University of Amsterdam (UvA), Amsterdam, Netherlands ARTICLE INFO ABSTRACT Keywords: Enhancing consideration of the ecosystem services concept within decision-making calls for integration of local Field margins knowledge and data within assessments to capture context-specific spatial and thematic detail. This paper Ecosystem services modelling demonstrates how local knowledge and (biophysical, sociocultural, economic) data can be integrated within Local information ecosystem service assessments by use of well-established spatial quantification methods. We hypothesise that Biophysical ecosystem services can be spatially quantified at high spatial and thematic detail by integration of local Sociocultural Monetary knowledge and data within models, contributing to identificationof key factors that influencetheir delivery. We demonstrate this by making use of local knowledge and data to assess ecosystem services in the Hoeksche Waard, a Dutch municipality characterised by historically-rich cultural landscapes and predominant agriculture.
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