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

Trifonova, Neda, Wihsgott, Juliane and Scott, Beth (2025) Propagating uncertainty from physical and biogeochemical drivers through to top predators in dynamic Bayesian ecosystem models improves predictions. Ecological Informatics, 92. ISSN 1574-9541

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Abstract

With the global rapid expansion of offshore renewable energies, there is an urgent need to assess and predict effects on marine species, habitats, and ecosystem functioning. Doing so will require dynamic, multitrophic, ecosystem-centric approaches coupled with oceanographic models that can allow for physical and/or biogeochemical indicators of marine ecosystem change to be included. However, in such coupled approaches, indicators carry uncertainties that can propagate and affect species higher up the trophic chain. Dynamic Bayesian networks (DBNs) are pragmatic approaches that probabilistically represent ecosystem-level interactions. They allow for uncertainties to be better estimated than mechanistic models that only account for expected values. In this study, we calculated variance as a measure of uncertainty from selected indicators and used them to build DBN models. A hidden variable was incorporated to model functional ecosystem change, where the underlying interactions dramatically change, following a disturbance. We wanted to assess whether propagating uncertainty into the modelling process affects the predictive accuracy of the models in the context of reconstructing the time series of the ecosystem dynamics. Model accuracy was improved for 60 % of the species once variance was added. The models were better in capturing the temporal inter-annual variability, once variance was calculated with a rolling window approach. The hidden variable successfully modelled previously identified ecosystem changes, however, now with the added uncertainty, the changes that implicated the ecosystem state were identified earlier in the time series. The results indicate that using DBNs is highly valuable as it gains accuracy with the addition of uncertainty.

Item Type:	Article
Additional Information:	Data availability: The sources (i.e., public links) for the data used in this study are shown in Table 1. Data were directly downloaded from the public links provided, except for the zooplankton data for which a data request process was needed, please see here: https://www.cprsurvey.org/data/our-data/. The harbour porpoise data is not publicly available. For specific request regarding access to the harbour porpoise data, please refer to the organizations involved in the collection of the data, provided in the SI. The source code is available in the SI. The BNT toolbox with build in functions to reproduce the work is available here: https://github.com/bayesnet/bnt.
Uncontrolled Keywords:	bio-physical,climate change,functional ecosystem change,hidden variable,indicators,marine renewable energy,ecology, evolution, behavior and systematics,modelling and simulation,ecology,ecological modelling,computer science applications,computational theory and mathematics,applied mathematics,sdg 7 - affordable and clean energy,sdg 13 - climate action,sdg 14 - life below water ,/dk/atira/pure/subjectarea/asjc/1100/1105
Faculty \ School:	Faculty of Science > School of Environmental Sciences
Related URLs:	http://www.scopus.com/inward/record.url?...
Depositing User:	LivePure Connector
Date Deposited:	23 Feb 2026 11:30
Last Modified:	01 Mar 2026 07:30
URI:	https://ueaeprints.uea.ac.uk/id/eprint/102005
DOI:	10.1016/j.ecoinf.2025.103510

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