The recent adoption of Bayesian networks (BNs) in ecology provides an opportunity to make advances because complex interactions can be recovered from field data and then used to predict the environmental response to changes in climate and biodiversity. In this study, we use a dynamic BN model with a hidden variable and spatial autocorrelation to explore the future of different fish and zooplankton species, given alternate scenarios, and across spatial scales within the North Sea. For most fish species, we were able to predict a trend of increase or decline in response to change in fisheries catch; however, this varied across the different areas, outlining the importance of trophic interactions and the spatial relationship between neighbouring areas. We were able to predict trends in zooplankton biomass in response to temperature change, with the spatial patterns of these effects varying by species. In contrast, there was high variability in terms of response to productivity changes and consequently knock-on effects on higher level trophic species. Finally, we were able to provide a new data-driven modelling approach that accounts for multispecies associations and interactions and their changes over space and time, which might be beneficial to give strategic advice on potential response of the system to pressure.
Bibliographical noteWe would like to thank Daniel Duplisea from DFO, Canada and Simon Jennings from CEFAS for providing comments and feedback. 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 organizations which provide data for the ICES assessment process, in particular SAHFOS who have provided the North Sea plankton data. We gratefully acknowledge the Natural Environment Research Council UK that has funded this research, along with support from the European Commission (OCEAN-CERTAIN, FP7-ENV-2013-6.1-1; no: 603773) for David Maxwell and from CEFAS for Andrew Kenny and David Maxwell
- Bayesian network
- fisheries catch
- species dynamics
- temperature and productivity scenarios