Bridging ecology and technology: Using citizen science and artificial intelligence to track fish health

Crucial to conservation and management of wild fisheries are data on fish distribution and their disease status. Anglers have reported an increasing burden of welfare issues, particularly for those prominent skin pathogens which cause skin lesions, and are often a key indicator of general health. Oomycetes of Saprolegnia species are particularly problematic; increasing in prevalence as fish become more stressed and immunocompromised in the light of climate change, and the resulting fungal-like growths on the skin of the fish are visible by eye. In the UK, wild fisheries data are collected via government agencies, such as Natural Resources Wales (NRW) and the Environment Agency (EA) using methods that can be timeconsuming, laborious, and expensive. Data on the distribution and extent of visible fish diseases are, as such, sparse, leading to large gaps in knowledge even for common species. There exists, however largely untapped sources of publicly contributed data via images shared on social media that could fill this knowledge gap by providing extensive spatial data on fish distribution and disease status. Social networks have a user base of over 2 billion, reflecting the observed reality of 25% of the human population. Using AI machine learning techniques from images submitted to social media we will develop an automatic monitoring systems (AMS) to identify, across the UK, fish species, their location and signs of disease.

As an alternative to directed and structured surveys, we will explore the value of social media content in which members of the public photograph and describe wildlife ad hoc, and with no particular scientific intent. Such images, taken with smart phones are geo-tagged and so could provide a rich source of validated wildlife distribution data, constituting what we term 'passive citizen science'. We will conduct a systematic evaluation of the quality of social media observations for wild freshwater fish mapping across the UK to predict species distribution data using the textual tags and image content of photos on online photo sharing sites, Flickr, Twitter, etc. Using machine learning and computer vision processes, we will identify fish species and quantify any visible disease on the fish (e.g. Saprolegnia). Such data will allow us to map the distribution of wild fish whilst simultaneously mapping their disease status, so allowing us to rapidly identify disease distribution.

As a second objective we will initiate a dedicated citizen science project to map fish distribution and health intensively on Welsh rivers. Working with stakeholders 'Wye and Usk Foundation' we will use their 'fishing passport' to roll out a citizen science project. Anglers on the Wye and Usk are required to register for a day pass so providing an infrastructure via which we can provide instructions to angler on how to upload images to the photo-sharing website Flickr. From this very low citizen science effort we will extract location data and use the machine learning tools to identify both the species in the image and the presence and extent of any disease. We will evaluate this technique as a real-time reporter of disease on this catchment.