Fuzzy Cognitive Mapping Data for Perceptions of Lake Erie Eutrophication: Relationships among Critical Concepts

The current data bring together maps (flowcharts of relationships) created by participants at three workshop series, held in 2009, 2010, and 2013-2014. The maps depict relationships among concepts (+ or -) relationship strength of 1-5 with 5 being strong and 1 being weak) identified as important by workshop participants. Maps were then translated into sparse matrices by project HQP, where the value in Cij represents the strength of the relationship between concepts i and j, which is ith row and jth column of the matrix. The data from each workshop have been combined into an overall consensual fuzzy cognitive map using matrix algebra, depicting a broad understanding of eutrophication in Lake Erie as of 2014. Future workshops are being planned to update this dataset. The updated dataset will allow an assessment of how our understanding of the causes of eutrophication has evolved over time, as well as comparisons of the perceptions of various groups (stakeholders, researchers, etc.).

This data set is collected for the project titled “Lake Futures: Enhancing Adaptive Capacity and Resilience of Lakes and Their Watersheds: Work Package 3.1". Lake Futures is a Pillar 3 project under the Global Water Futures Program funded by Canada First Research Excellence Fund. The main goal of this project is to identify links between land-based human activities and the in-lake manifestations of eutrophication. The project uses fuzzy cognitive mapping (FCM) to translate expert knowledge into semi-quantitative data that can be used to link ultimate causes with effects based on best personal judgement, compare perceptions of the cause-effect relationships among groups (researchers, managers, stakeholders, the public), identify important concepts in understanding eutrophication, and run scenarios to understand potential outcomes of management actions.

Links between land-based human activities and in-lake manifestations of eutrophication are identified. The project uses fuzzy cognitive mapping (FCM) to translate expert knowledge into quantitative data to link causes with effects based on - personal judgement, - compare perceptions of the cause-effect relationships among groups of researchers, managers, stakeholders, and the public, - identification of concepts for understanding eutrophication, and - running of scenarios to understand possible outcomes of management actions.

Rooney, R., Ciborowski, J., and Ives, J. (2019). Fuzzy Cognitive Mapping Data for Perceptions of Lake Erie Eutrophication: Relationships among Critical Concepts. Waterloo, Canada: Canadian Cryospheric Information Network (CCIN). Unpublished Data.

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