Angling counts: Harnessing the power of technological advances for recreational fishing surveys

As the popularity of recreational fishing gathers global momentum, so does the importance of knowing the number of active anglers and their spatial behaviour. Conventional counting methods, however, can be inaccurate and time-consuming. Here we present two novel methods to monitor recreational fishing applied in Kaunas water reservoir (ca 65 km(2)), Lithuania, comparing their performance to a conventional visual count. First, we employed a remotely piloted fixed wing drone which conducted 39 missions distributed over one year and compared its accuracy to conventional visual land or boat-based counts. With these data we developed a linear model to predict the annual number of anglers depending on weekday and ice conditions. Second, we used anonymous data from a popular GPS-enabled sonar device Deeper (R) , used by anglers to explore underwater landscapes and to find fish. The sonar usage probability was calibrated with angler observations from drones using Bayesian methods, demonstrating that at any given time ~2 % of anglers are using the sonar device during the open water season and ~15 % during the ice fishing season. The calibrated values were then used to estimate the total number of anglers, given the daily records of sonar usage in Kaunas water reservoir. The predicted annual number of anglers from both linear drone-based and Bayesian sonar-based methods gave similar results of 25 and 27 thousand anglers within the area during the period of day surveyed, which corresponded to nearly 110 thousand angling trips in the total reservoir area annually. Our study shows high potential of both drone and fish finder digital devices for assessing recreational fishing activities through space and time.

Automated summary

As recreational fishing becomes more popular, it is important to understand the number of anglers and their behavior. This study presents two novel methods, using drones and GPS devices, to monitor recreational fishing. The results show that both methods are accurate and have high potential for assessing fishing activities.