Leveraging data from a private recreational fishing application to begin to understand potential impacts from offshore wind development

Abstract

Abstract The development of offshore wind energy in the United States necessitates a sound understanding of trade-offs across ocean uses. Location data on private recreational fishing have been a glaring gap in understanding how society uses marine resources, despite its economic importance. In this study, we use a novel data set to start to fill that knowledge gap. We employ a flexible restricted likelihood spatial scan statistic on data from Fish Rules, a smartphone application, which provides georeferenced species-level regulations, to understand whether species-level data of user queries are clustered spatially. Originally developed for epidemiological studies of disease clusters, the flexible scan statistic employed in this study uses a Bernoulli likelihood ratio test to assess the size, number, and significance of clusters in presence/absence data for recreational species. We use a second data set of known fishing locations to validate that the clusters identify private recreational fishing activity. We then discuss the analysis in the context of wind lease areas in the region, highlighting its value in supporting management decision-making. The results suggest that Fish Rules data identify areas with a high likelihood of being private angler fishing locations and can assess differential impacts of offshore wind development on private recreational fishing activities.