Reliability of self-reported catch and effort data via a smartphone application in a multi-species recreational fishery

Abstract

The high spatial-temporal variability in fishing effort, combined with the difficulty of monitoring individual activities, hampers effective management of recreational fisheries. Angler smartphone applications (apps) offer a promising digital tool for self-reporting of fishing effort (E) and catch per unit of effort (CPUE). However, despite their growing use for data collection in recreational fisheries, the existing literature on their performance remains limited, raising concerns about potential biases in the data. Since 2019, daily trips inside the 12 partially protected Marine Protected Areas (MPAs) of the Balearic Islands (Spain) must be self-reported via the “Diari de Pesca Recreativa” app (the App), recording fishing E and CPUE. This study aimed to evaluate the App’s performance in reporting recreational fisheries data over a six-year period. Data obtained via the App (3672 trip self-reports) were compared to data collected through a standard method (360 on-site creel surveys). Importantly, the App represents complete fishing trips, whereas creel surveys record only partial trips, as they are conducted mid-activity. This methodological difference in trip duration reporting was expected to influence estimates of E (hours · angler · trip) and possibly CPUE (catch · E⁻¹). These estimates were compared across datasets overall, as well as stratified by month, fishing type, MPA, and for key target species. Data from the App tended to overestimate E, while creel surveys underestimated it, and significant differences were observed between whole datasets for E and CPUE. However, when stratified, most groups showed no statistically significant differences in E and CPUE estimates. With these generally comparable results, and given that the limitations of one are offset by the strengths of the other, combining both data sources will improve reliability. The App not only generates a higher volume of trip data but also digitizes data collection through a user-friendly platform for self-reporting, enabling automation and analytics for fisheries monitoring and management of recreational fisheries. Because reporting was mandatory in this case, biases commonly associated with voluntary apps (e.g. avidity, age bias) are unlikely to apply, making this study particularly relevant for assessing the utility of mandatory app-based data in fisheries management.