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

Abstract

The spatial and temporal heterogeneity of recreational anglers and the challenges associated with monitoring their activities often complicate their effective management. Angler smartphone applications (apps) offer a promising digital tool for self-reporting fishing effort (E) and captures 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 collected data. Since 2019, self-reporting of E and CPUE has been mandatory for recreational fishing trips within the network of partially-protected Marine Protected Areas (MPAs) in the Balearic Islands (Spain). A regional-scale App ("Diari de Pesca Recreativa") was developed to streamline data collection. This study aimed to evaluate the Apps performance in reporting recreational fisheries data over a six-year period. Data obtained via the App (3,672 trip self-reports) were compared to data collected through a traditional method (360 on-site creel surveys). Estimates of E and CPUE were compared across datasets overall, as well as by month, fishing modality, MPA, and for key target species. Significant differences were observed for E (hours {middle dot} angler {middle dot} trip, p < 0.001) and CPUE (captures{middle dot} E-1, p = 0.001) across datasets. However, when stratified by species, month, fishing modality, and MPA, most groups showed no statistically significant differences in E and CPUE estimates. Data from the App tended to overestimate E compared to creel surveys, likely due to inaccuracies in self-reported trip durations. Conversely, creel surveys appeared to underestimate E, reflecting intrinsic biases in trip duration collection. Assuming that the true values of E and CPUE lie between the estimates derived from the App and creel surveys, integrating both methods is recommended to improve the accuracy and reliability of data. The App not only generates a higher volume of trip data but also provides a user-friendly platform for self-reporting. Moreover, it digitizes data collection, enabling automation and advanced analytics for fisheries monitoring and management of recreational fisheries.