Network approaches for formalizing conceptual models in ecosystem-based management

Author:

Reum Jonathan C P12ORCID,Kelble Christopher R3,Harvey Chris J4,Wildermuth Robert P567,Trifonova Neda8,Lucey Sean M9,McDonald P Sean1011,Townsend Howard12ORCID

Affiliation:

1. Alaska Fisheries Science Center, National Marine Fisheries Service, NOAA, Seattle, WA, USA

2. Institute for Marine and Antarctic Studies and Centre for Marine Socioecology, University of Tasmania, Hobart, TAS, Australia

3. Atlantic Oceanographic and Meteorological Laboratory, NOAA, Miami, FL, USA

4. Northwest Fisheries Science Center, National Marine Fisheries Service, NOAA, Seattle, WA, USA

5. Department of Fisheries Oceanography, School for Marine Science and Technology, University of Massachusetts Dartmouth, New Bedford, MA, USA

6. Institute of Marine Sciences, University of California, Santa Cruz, CA, USA

7. Southwest Fisheries Science Center, National Marine Fisheries Service, NOAA, La Jolla, CA, USA

8. School of Biological Sciences, University of Aberdeen, Aberdeen AB24 2TZ, UK

9. Northeast Fisheries Science Center, National Marine Fisheries Service, NOAA, Woods Hole, MA, USA

10. School of Aquatic and Fishery Sciences, University of Washington, Seattle, WA 98122, USA

11. Program on the Environment, University of Washington, Seattle, WA 98195-5679, USA

12. Office of Science and Technology, National Marine Fisheries Service, NOAA, Silver Spring, MD, USA

Abstract

Abstract 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% of system variables on average. Pairwise agreement between network models was higher, 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.

Funder

NOAA

Washington Sea Grant, University of Washington

National Oceanic and Atmospheric Administration

National Marine Fisheries Service

Publisher

Oxford University Press (OUP)

Subject

Ecology,Aquatic Science,Ecology, Evolution, Behavior and Systematics,Oceanography

Reference54 articles.

1. Practical solutions for making models indispensable in conservation decision-making;Addison;Diversity and Distributions,2013

2. Bayesian networks in environmental modelling;Aguilera;Environmental Modelling and Software,2011

3. Informing network management using fuzzy cognitive maps;Baker;Biological Conservation,2018

4. Recent advances of quantitative modeling to support invasive species eradication on islands;Baker;Conservation Science and Practice,2021

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