The FishPath harvest strategy decision support tool has been applied to hundreds of fisheries and across the globe to help users identify viable harvest strategy options given their fishery’s context. The tool’s website has accumulated a large repository of case study fisheries, the characteristics of each of which have been elicited by the user questionnaire. While each fishery is unique, common principles may emerge, which can help characterise fisheries and ultimately help inform and improve the FishPath Tool and guidance for harvest strategy design globally.
We here undertake a meta-analysis of the data within the FishPath to identify emergent patterns and commonalities. We first identify where, and to which species FishPath has been most used and undertake an exploratory analysis of the most and least commonly selected harvest strategy component options. We then use statistical modelling to identify any regional and social-economic commonalities in the profiles of options selected. We finally attempt to determine whether, given a fishery’s questionnaire response profile, we can statistically predict the options that are most likely to be selected. Such predictions can improve user experience with the tool by prioritising those options most likely to be selected given the fishery’s similarity to others within the database. Given the range of attributes elicited by the FishPath questionnaire, our database provides an informed way to identify fishery “archetypes” and to ultimately assist with providing viable options for fisheries management.