Northwest Fisheries Science Center

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Document Type: Journal Article
Center: NWFSC
Document ID: 4730
Title: A test of the use of computer generated visualizations in support of ecosystem-based management
Author: Amanda Pearl Rehr, G. D. Williams, P. S. Levin
Publication Year: 2014
Journal: Marine Policy
Volume: 46
Pages: 14-18
Keywords: scenario analysis,visualization,ecosystem management
Abstract:

Systematic scenario analysis is increasingly being used as an approach to evaluate ecosystem-based management options, often using “storylines” communicated through computer-generated (CG) images or visualizations. To explore potential issues associated with using CG imagery for conveying scenarios of habitat restoration we performed experiments in the Puget Sound, Washington region in which we asked whether respondents could differentiate among images of varying seagrass density and spatial extent, and if the presence of humans in the images affected these assessments and their perceptions of ecosystem health. Respondents were able to grossly determine relative seagrass density in the images, but only about 50% of them were able to determine this perfectly. Most errors occurred when the difference in density was small: approximately 20 shoot m-2. The ability to correctly distinguish among images was inversely correlated with educational level. The presence or absence of people in the imagery did not influence the ability of respondents to correctly sort images, nor did it affect perceptions of ecosystem “health”. Taken together, the results suggest that such imagery can be useful as the basis for communicating large differences in ecological conditions, but may be less informative as a means to convey marginal changes in ecological structure. This work begins to highlight some of the pitfalls, but also the promise, of the use of CG visualization in marine resource management.

Theme: Ecosystem Approach to Management for the California Current Large Marine Ecosystem
Foci: Conduct integrated ecosystem assessments that produce metrics and criteria that will improve ecosystem forecasts and predictions.