Spatio-temporal Bayesian network models with latent variables for revealing trophic dynamics and functional networks in fisheries ecology

Neda Trifonova, Andrew Kenny, David Maxwell, Daniel Duplisea, Jose Fernandes, Allan Tucker

Research output: Contribution to journalArticlepeer-review

49 Citations (Scopus)
8 Downloads (Pure)


Ecosystems 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. However, with the recent adoption of more explorative tools, like Bayesian networks, in predictive ecology, few assumptions can be made about the data and complex, spatially varying interactions can be recovered from collected field data. In this study, we compare Bayesian network modelling approaches accounting for latent effects to reveal species dynamics for 7 geographically and temporally varied areas within the North Sea. We also apply structure learning techniques to identify functional relationships such as prey–predator between trophic groups of species that vary across space and time. We examine if the use of a general hidden variable can reflect overall changes in the trophic dynamics of each spatial system and whether the inclusion of a specific hidden variable can model unmeasured group of species. The general hidden variable appears to capture changes in the variance of different groups of species biomass. Models that include both general and specific hidden variables resulted in identifying similarity with the underlying food web dynamics and modelling spatial unmeasured effect. We predict the biomass of the trophic groups and find that predictive accuracy varies with the models' features and across the different spatial areas thus proposing a model that allows for spatial autocorrelation and two hidden variables. Our proposed model was able to produce novel insights on this ecosystem's dynamics and ecological interactions mainly because we account for the heterogeneous nature of the driving factors within each area and their changes over time. Our findings demonstrate that accounting for additional sources of variation, by combining structure learning from data and experts' knowledge in the model architecture, has the potential for gaining deeper insights into the structure and stability of ecosystems. Finally, we were able to discover meaningful functional networks that were spatially and temporally differentiated with the particular mechanisms varying from trophic associations through interactions with climate and commercial fisheries.

Original languageEnglish
Pages (from-to)142-158
Number of pages17
JournalEcological Informatics
Early online date19 Oct 2015
Publication statusPublished - Nov 2015

Bibliographical note

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.


  • Bayesian network
  • Hidden variable
  • Spatial autocorrection
  • Biomass prediction
  • Functional network
  • Trophic interactions
  • Marine ecology


Dive into the research topics of 'Spatio-temporal Bayesian network models with latent variables for revealing trophic dynamics and functional networks in fisheries ecology'. Together they form a unique fingerprint.

Cite this