Spatio-temporal Bayesian network models with latent variables for revealing trophic dynamics and functional networks in fisheries ecology
| dc.contributor.author | Trifonova, Neda | |
| dc.contributor.author | Kenny, Andrew | |
| dc.contributor.author | Maxwell, David | |
| dc.contributor.author | Duplisea, Daniel | |
| dc.contributor.author | Fernandes, Jose | |
| dc.contributor.author | Tucker, Allan | |
| dc.contributor.institution | University of Aberdeen.Biological Sciences | en |
| dc.date.accessioned | 2020-04-02T15:05:01Z | |
| dc.date.available | 2020-04-02T15:05:01Z | |
| dc.date.issued | 2015-11 | |
| dc.description | We would like to thank Johan Van Der Molen from CEFAS for providing the ERSEM model outputs, the ICES DATRAS database for the North Sea IBTS data and Historical Catch Statistics, ICES North Sea Integrated Assessment Working Group (WGINOSE) and the organisations which provide data for the ICES assessment process, in particular SAHFOS who have provided the North Sea plankton data, Chiara Franco for general advice and the Natural Environment Research Council, UK (NE/J01642X/1) who has provided the funding of this research. We gratefully acknowledge support from the European Commission (OCEAN-CERTAIN, FP7-ENV-2013-6.1-1; no: 603773) for David Maxwell and support from CEFAS for Andrew Kenny and David Maxwell. | en |
| dc.description.status | Peer reviewed | en |
| dc.format.extent | 17 | |
| dc.format.extent | 2517982 | |
| dc.identifier | 157047739 | |
| dc.identifier | 9f1b0555-e7f6-4745-b7a1-9b07146354e4 | |
| dc.identifier | 84944889934 | |
| dc.identifier.citation | Trifonova, N, Kenny, A, Maxwell, D, Duplisea, D, Fernandes, J & Tucker, A 2015, 'Spatio-temporal Bayesian network models with latent variables for revealing trophic dynamics and functional networks in fisheries ecology', Ecological Informatics, vol. 30, pp. 142-158. https://doi.org/10.1016/j.ecoinf.2015.10.003 | en |
| dc.identifier.doi | 10.1016/j.ecoinf.2015.10.003 | |
| dc.identifier.issn | 1574-9541 | |
| dc.identifier.uri | https://hdl.handle.net/2164/13958 | |
| dc.identifier.vol | 30 | en |
| dc.language.iso | eng | |
| dc.relation.ispartof | Ecological Informatics | en |
| dc.subject | SDG 14 - Life Below Water | en |
| dc.subject | Bayesian network | en |
| dc.subject | Hidden variable | en |
| dc.subject | Spatial autocorrection | en |
| dc.subject | Biomass prediction | en |
| dc.subject | Functional network | en |
| dc.subject | Trophic interactions | en |
| dc.subject | Marine ecology | en |
| dc.subject | GC Oceanography | en |
| dc.subject | GE Environmental Sciences | en |
| dc.subject | Natural Environment Research Council (NERC) | en |
| dc.subject | NE/J01642X/1 | en |
| dc.subject | European Commission | en |
| dc.subject | OCEAN-CERTAIN, FP7-ENV-2013-6.1-1; no: 603773 | en |
| dc.subject.lcc | GC | en |
| dc.subject.lcc | GE | en |
| dc.title | Spatio-temporal Bayesian network models with latent variables for revealing trophic dynamics and functional networks in fisheries ecology | en |
| dc.type | Journal article | en |
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