Network approaches for formalizing conceptual models in ecosystem-based management

Jonathan C P Reum* (Corresponding Author), Christopher R Kelble, Chris J Harvey, Robert P Wildermuth, Neda Trifonova, Sean M Lucey, P Sean McDonald, Howard Townsend

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


Qualitative Network Models (QNMs), Fuzzy Cognitive Maps (FCMs), and Bayesian Belief Networks (BBNs) have been proposed as methods to formalize conceptual models of social–ecological systems and project system responses to management interventions or environmental change. To explore how these different methods might influence conclusions about system dynamics, we assembled conceptual models representing three different coastal systems, adapted them to the network approaches, and evaluated outcomes under scenarios representing increased fishing effort and environmental warming. The sign of projected change was the same across the three network models for 31–60\ ranging from 33 to 92\ average levels of similarity were comparable between network pairs. Agreement measures based on both the sign and strength of change were substantially worse for all model comparisons. These general patterns were similar across systems and scenarios. Different outcomes between models led to different inferences regarding trade-offs under the scenarios. We recommend deployment of all three methods, when feasible, to better characterize structural uncertainty and leverage insights gained under one framework to inform the others. Improvements in precision will require model refinement through data integration and model validation.
Original languageEnglish
Article number fsab211
Number of pages13
JournalICES Journal of Marine Science
Early online date30 Oct 2021
Publication statusE-pub ahead of print - 30 Oct 2021


  • Bayesian Belief Network
  • ecosystem-based management
  • food webs
  • Fuzzy Cognitive Map
  • Georges Bank
  • Mid-Barataria Basin
  • Pribilof Islands
  • Qualitative Network Model


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