High-resolution app data reveal sustained increases in recreational fishing effort in Europe during and after COVID-19 lockdowns

Abstract

It is manifest that COVID-19 lockdowns extensively impacted human interactions with natural ecosystems. One example is recreational fishing, an activity which involves nearly 1 in 10 people in developed countries. Fishing licence sales and direct observations at popular angling locations suggest that recreational fishing effort increased substantially during lockdowns. However, the extent and duration of this increase remain largely unknown due to a lack of objective data. We used four years (2018 to 2021) of anonymous, high-resolution data from a personal fish-finder device to explore the impact of COVID-19 lockdowns on recreational fishing effort in four European countries (Lithuania, the Czech Republic, Denmark, and Germany). We show that device use and, by extension, angling effort increased 1.2-3.8 fold during March-May 2020 and remained elevated even at the end of 2021 in all countries except Denmark. Fishing during the first lockdown also became more frequent during weekdays. Statistical models with the full set of fixed (weekdays, lockdown, population) and random (season, year, administrative unit) factors typically explained 50-70% of the variation, suggesting that device use and angling effort were relatively consistent and predictable through space and time. Our study demonstrates that recreational fishing behaviour can change substantially and rapidly in response to societal shifts, with profound ecological, human well-being and economic implications. We also show the potential of angler devices and smartphone applications to supply data for high-resolution fishing effort analysis and encourage more extensive science and industry collaborations to take advantage of this information.

High-resolution app data reveal sustained increases in recreational fishing effort in Europe during and after COVID-19 lockdowns

“…Requests for raw data should be directed to Vincentas Vienožinskis at Deeper (vincentas.vienozinskis@deeper.eu). Derived data and relevant code for all analyses are stored in GitHub: https:// github.com/astaaudzi/covid_angling and have been archived within the Zenodo repository: https://zenodo.org/ record/8072917 [84].…”