Search bioRxiv⌕ Search

Biology subjects

Vienozinskis, V.

Publications and source records attributed to Vienozinskis, V..

2 recordsLinked to original sources

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

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. Significance statementRecreational fishing is a popular and widespread activity with ecological, social and economic impacts, though problematic to assess and manage due to a paucity of information regarding effort and catch. Here, we use high-resolution data from a personal angler sonar device to show how the COVID-19 pandemic changed angler behaviour and fishing effort across Europe. We demonstrate that angling effort doubled and remained higher at the end of 2021 than before the first lockdowns. Such rapid and profound changes could have significant consequences for aquatic ecosystems, possibly requiring new management approaches. We encourage the adoption of novel data from angler devices, citizen science, and more active science-industry collaborations to improve recreational fishing assessment and management.

ecology↗

A scalable open-source framework for machine learning based image collection, annotation and classification: a case study for automatic fish species identification

Citizen science platforms, social media and multiple smart phone applications enable collection of large amounts of georeferenced images. This provides a huge opportunity in biodiversity and ecological research, but also creates challenges for efficient data handling and processing. Recreational and small-scale fisheries is one of the fields that could be revolutionised by efficient, widely accessible and machine learning based processing of georeferenced images. The majority of non-commercial inland and coastal fisheries are considered data poor and are rarely assessed, yet they provide multiple societal benefits and can have large ecological impacts. Given that large quantities of fish observations and images are being collected by fishers every day, artificial intelligence (AI) and computer vision applications offer a great opportunity to improve data collection, automate analyses and inform management. Yet, to date, many AI image analysis applications in fisheries are focused on the commercial sector and are not publicly available for community use. In this study we present an open-source modular framework for large scale image storage, handling, annotation and automatic classification, using cost- and labour-efficient methodologies. The tool is based on TensorFlow Lite Model Maker library and includes data augmentation and transfer learning techniques, applied to different convolutional neural network models. We demonstrate the implementation of this framework in an example case study for automatic fish species identification from images taken through a recreational fishing smartphone application. The framework presented here is highly customisable for further advancement and community based image collection and annotation.

ecology↗