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dc.contributor.authorSPAKE REBECCAen_GB
dc.contributor.authorLASSEUR REMYen_GB
dc.contributor.authorCROUZAT EMILYen_GB
dc.contributor.authorBULLOCK JAMESen_GB
dc.contributor.authorLAVOREL SANDRAen_GB
dc.contributor.authorPARKS KATHERINEen_GB
dc.contributor.authorSCHAAFSMA MARIJEen_GB
dc.contributor.authorBENNETT ELENAen_GB
dc.contributor.authorMAES JOACHIMen_GB
dc.contributor.authorMULLIGAN MARKen_GB
dc.contributor.authorMOUCHET MAUDen_GB
dc.contributor.authorPETERSON GARRYen_GB
dc.contributor.authorSCHULP C.J.E.en_GB
dc.contributor.authorTHUILLER WILFRIEDen_GB
dc.contributor.authorTURNER MONICAen_GB
dc.contributor.authorVERBURG PETERen_GB
dc.contributor.authorEIGENBROD FELIXen_GB
dc.date.accessioned2017-10-13T00:35:38Z-
dc.date.available2017-10-11en_GB
dc.date.available2017-10-13T00:35:38Z-
dc.date.created2017-10-06en_GB
dc.date.issued2017en_GB
dc.date.submitted2017-04-03en_GB
dc.identifier.citationGLOBAL ENVIRONMENTAL CHANGE-HUMAN AND POLICY DIMENSIONS vol. 47 p. 37-50en_GB
dc.identifier.issn0959-3780en_GB
dc.identifier.urihttp://www.sciencedirect.com/science/article/pii/S0959378016301893?via%3Dihuben_GB
dc.identifier.urihttp://publications.jrc.ec.europa.eu/repository/handle/JRC106437-
dc.description.abstractMultiple ecosystem services (ES) can respond similarly to social and ecological factors to form bundles. Identifying key social-ecological variables and understanding how they co-vary to produce these consistent sets of ES may ultimately allow the prediction and modelling of ES bundles, and thus, help us understand critical synergies and trade-offs across landscapes. Such an understanding is essential for informing better management of multi-functional landscapes and minimising costly trade-offs. However, the relative importance of different social and biophysical drivers of ES bundles in different types of social-ecological systems remains unclear. As such, a bottom-up understanding of the determinants of ES bundles is a critical research gap in ES and sustainability science. Here, we evaluate the current methods used in ES bundle science and synthesize these into four steps that capture the plurality of methods used to examine predictors of ES bundles. We then apply these four steps to a cross-study comparison (North and South French Alps) of relationships between social-ecological variables and ES bundles, as it is widely advocated that cross-study comparisons are necessary for achieving a general understanding of predictors of ES associations. We use the results of this case study to assess the strengths and limitations of current approaches for understanding distributions of ES bundles. We conclude that inconsistency of spatial scale remains the primary barrier for understanding and predicting ES bundles. We suggest a hypothesis-driven approach is required to predict relationships between ES, and we outline the research required for such an understanding to emerge.en_GB
dc.description.sponsorshipJRC.D.3-Land Resourcesen_GB
dc.format.mediumOnlineen_GB
dc.languageENGen_GB
dc.publisherELSEVIER SCI LTDen_GB
dc.relation.ispartofseriesJRC106437en_GB
dc.titleUnpacking ecosystem service bundles: towards predictive mapping of synergies and trade-offs between ecosystem servicesen_GB
dc.typeArticles in periodicals and booksen_GB
dc.identifier.doi10.1016/j.gloenvcha.2017.08.004en_GB
JRC Directorate:Sustainable Resources

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