
Relating Ocean Condition Forecasts to the Process of End-User Decision Making: A Case Study of the Oregon Commercial Fishing Community
Author(s) -
Jessica Kuonen,
Flaxen Conway,
P. Ted Strub
Publication year - 2019
Publication title -
marine technology society journal/marine technology society journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.23
H-Index - 43
eISSN - 1948-1209
pISSN - 0025-3324
DOI - 10.4031/mtsj.53.1.1
Subject(s) - process (computing) , fishing , computer science , perception , environmental resource management , commercial fishing , value of information , environmental science , operations research , engineering , fishery , neuroscience , biology , operating system , artificial intelligence
This case study is in response to a recognized need to transform short-term regional ocean condition forecast information into useful data products for a range of end users, considering their perceptions of uncertainty and risk associated with these forecasts. It demonstrates the value of user engagement in achieving long-term goals for data providers. Commercial fishermen from Oregon are selected as key information users due to the physically risky and economically uncertain nature of their profession, their expertise at navigating the marine environment, and their important economic and cultural role at the Oregon coast. Semistructured interviews ( n = 16) are used to clarify the processes that govern decision making, in terms of risk perception and comfort with uncertainty. The results characterize a community "mental model" in regard to ocean use and ocean forecasts. Findings reveal that commercial fishermen consume and interpret forecast data in a nonlinear fashion by combining multiple sources and data types and with a heavy reliance on real-time data. Our assessment is that improving accuracy at temporal and spatial scales that are relevant to decision making, improving the accessibility of forecasts, and increasing forecast lead time could potentially add more value to forecasts than quantifying and communicating the types of uncertainty metrics used within the scientific community.