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Bayesian Network Modelling provides Spatial and Temporal Understanding of Ecosystem Dynamics within Shallow Shelf Seas

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dc.contributor.authorTrifonova, Neda
dc.contributor.authorScott, Beth
dc.contributor.authorDominicis, Michela De
dc.contributor.authorWaggitt, James J.
dc.contributor.authorWolf, Judith
dc.contributor.institutionUniversity of Aberdeen.Biological Sciencesen
dc.contributor.institutionUniversity of Aberdeen.Centre for Energy Transitionen
dc.contributor.institutionUniversity of Aberdeen.Marine Alliance for Science and Technology for Scotland (MASTS)en
dc.date.accessioned2021-07-27T10:24:01Z
dc.date.available2021-07-27T10:24:01Z
dc.date.issued2021-10
dc.descriptionAcknowledgements This work was supported by the Supergen Offshore Renewable Energy (ORE) Hub, funded by the Engineering and Physical Sciences Research Council (EPSRC EP/S000747/1) and the NERC/DEFRA funded Marine Ecosystems Research Programme (MERP: NE/L003201/1). The authors would also like to thank the following people for providing data to this study: Debbie Russel, Signe Sveegaard, Mirko Hauswirth, Ruben Fijn, Chelsea Bradbury, Mark Lewis, Steve Geelhoed, Nicolas Vanermen, Oliver Boisseau, Dave Wall, Mark Jessopp, Jared Wilson, Alex Banks, Graham Pierce, Sally Hamilton, Jan Haelters, Suzanne Henderson, Peter Evans, Anita Gilles, Eric Stienen, Paul Thompson, Nicola Hodgins and Andrea Salkeld. For detailed information on their organizations and contacts, please refer to the SI. The authors would like to thank Ella-Sophia Benninghaus (University of Aberdeen) for providing the images in Fig. 6.en
dc.description.statusPeer revieweden
dc.format.extent16
dc.format.extent9756980
dc.identifier197053570
dc.identifierffb73771-1f6e-4a39-b5f2-5308af36c91b
dc.identifier85111043152
dc.identifier.citationTrifonova, N, Scott, B, Dominicis, M D, Waggitt, J J & Wolf, J 2021, 'Bayesian Network Modelling provides Spatial and Temporal Understanding of Ecosystem Dynamics within Shallow Shelf Seas', Ecological Indicators, vol. 129, 107997. https://doi.org/10.1016/j.ecolind.2021.107997en
dc.identifier.doi10.1016/j.ecolind.2021.107997
dc.identifier.issn1470-160X
dc.identifier.otherORCID: /0000-0001-5412-3952/work/162251669
dc.identifier.urihttps://hdl.handle.net/2164/16869
dc.identifier.vol129en
dc.language.isoeng
dc.relation.ispartofEcological Indicatorsen
dc.subjectSDG 7 - Affordable and Clean Energyen
dc.subjectSDG 13 - Climate Actionen
dc.subjectSDG 14 - Life Below Wateren
dc.subjectclimate changeen
dc.subjectHidden variableen
dc.subjectfunctional ecosystem changeen
dc.subjecttop predator dynamicsen
dc.subjectFisheries effectsen
dc.subjectQH301 Biologyen
dc.subjectEngineering and Physical Sciences Research Council (EPSRC)en
dc.subjectEP/S000747/1en
dc.subjectNatural Environment Research Council (NERC)en
dc.subjectNE/L003201/1en
dc.subject.lccQH301en
dc.titleBayesian Network Modelling provides Spatial and Temporal Understanding of Ecosystem Dynamics within Shallow Shelf Seasen
dc.typeJournal articleen

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