Please use this identifier to cite or link to this item: http://buratest.brunel.ac.uk/handle/2438/10911
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dc.contributor.authorDuplisea, D-
dc.contributor.authorKenny, A-
dc.contributor.authorTucker, A-
dc.coverage.spatialBrussels-
dc.coverage.spatialBrussels-
dc.date.accessioned2015-05-26T14:09:05Z-
dc.date.available2014-11-01-
dc.date.available2015-05-26T14:09:05Z-
dc.date.issued2014-
dc.identifier.citationAdvances in Intelligent Data Analysis XIII, Lecture Notes in Computer Science, 8819: 298-308, (2014)en_US
dc.identifier.issn0302-9743-
dc.identifier.urihttp://link.springer.com/chapter/10.1007%2F978-3-319-12571-8_26-
dc.identifier.urihttp://bura.brunel.ac.uk/handle/2438/10911-
dc.description.abstractEcosystems consist of complex dynamic interactions among species and the environment, the understanding of which has implications for predicting the environmental response to changes in climate and biodiversity. Machine learning techniques can allow such complex, spatially varying interactions to be recovered from collected field data. In this study, we apply structure learning techniques to identify functional relationships between trophic groups of species that vary across space and time. Specifically, Bayesian networks are created on a window of data for each of the 20 geographically different and temporally varied sub-regions within an oceanic area. In addition, we explored the spatial and temporal variation of pre-defined functions (like predation, competition) that are generalisable by experts’ knowledge. We were able to discover meaningful ecological networks that were more precisely spatially-specific rather than temporally, as previously suggested for this region. To validate the discovered networks, we predict the biomass of the trophic groups by using dynamic Bayesian networks, and correcting for spatial autocorrelation by including a spatial node in our models.en_US
dc.language.isoenen_US
dc.publisherSpringer International Publishingen_US
dc.sourceSymposium on Intelligent Data Analysis-
dc.sourceSymposium on Intelligent Data Analysis-
dc.subjectEcological networksen_US
dc.subjectBayesian networksen_US
dc.subjectSpatial nodeen_US
dc.titleA spatio-temporal Bayesian network approach for revealing functional ecological networks in fisheriesen_US
dc.typeConference Paperen_US
dc.identifier.doihttp://dx.doi.org/10.1007/978-3-319-12571-8_26-
pubs.finish-date2014-11-01-
pubs.finish-date2014-11-01-
pubs.start-date2014-10-30-
pubs.start-date2014-10-30-
Appears in Collections:Dept of Life Sciences Research Papers

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