Propagating uncertainty from physical and biogeochemical drivers through to top predators in dynamic Bayesian ecosystem models improves predictions

Trifonova, NedaWihsgott, JulianeScott, Beth2025-11-182025-12Trifonova, N, Wihsgott, J & Scott, B 2025, 'Propagating uncertainty from physical and biogeochemical drivers through to top predators in dynamic Bayesian ecosystem models improves predictions', Ecological Informatics, vol. 92, 103510. https://doi.org/10.1016/j.ecoinf.2025.1035101574-9541RIS: urn:16DEA2EB62B3BBF62E110B711BFD6414ORCID: /0000-0001-5412-3952/work/197191757https://hdl.handle.net/2164/26455Open Access via the Elsevier agreement The authors would also like to thank the following people for providing data to this study: Debbie Russel, James Waggitt, Amy Walker, Hannah Fougner, Andrew Logie, Mirko Hauswirth, Tim Dunn, Maria Pagla, Oliver Boisseau, Jared Wilson, Jen Graham, Kate Abbott, Sally Hamilton, Alex Banks, Phil Hammond, Peter Evans, Chelsea Bradbury, Paul Thompson, Nele Markones, Alice Walters, Andrea Salkeld. For detailed information on their organizations and contacts, please refer to the SI.142711958engSDG 7 - Affordable and Clean EnergySDG 13 - Climate ActionSDG 14 - Life Below WaterClimate changeHidden variableFunctional ecosystem changeBio-physicalIndicatorsMarine renewable energyQL ZoologySupplementary DataDASQLPropagating uncertainty from physical and biogeochemical drivers through to top predators in dynamic Bayesian ecosystem models improves predictionsJournal article10.1016/j.ecoinf.2025.103510https://github.com/bayesnet/bnt