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

关于鱼类分布及其疾病状况的数据对野生渔业的养护和管理至关重要。垂钓者报告说，福利问题的负担越来越重，特别是那些引起皮肤病变的突出皮肤病原体，它们往往是一般健康状况的一个关键指标。水霉属物种的卵菌特别成问题;随着鱼类在气候变化的影响下变得更加紧张和免疫力低下，其流行率增加，并且由此产生的鱼类皮肤上的真菌样生长物是肉眼可见的。在英国，野生渔业数据是通过政府机构收集的，如威尔士自然资源局（NRW）和环境局（EA），使用的方法可能耗时，费力和昂贵。因此，关于可见鱼病的分布和程度的数据很少，导致即使是对常见物种的知识也存在很大差距。然而，通过在社交媒体上分享的图像，存在着大量尚未开发的公共贡献数据来源，这些数据可以通过提供有关鱼类分布和疾病状况的广泛空间数据来填补这一知识空白。社交网络拥有超过20亿的用户群，反映了25%的人口的观察现实。我们将从提交给社交媒体的图像中使用人工智能机器学习技术，开发一个自动监测系统（AMS），以识别英国各地的鱼类物种，它们的位置和疾病迹象。作为定向和结构化调查的替代方案，我们将探索社交媒体内容的价值，其中公众成员拍摄和描述野生动物临时，没有特别的科学意图。这些用智能手机拍摄的图像带有地理标签，因此可以提供丰富的经验证的野生动物分布数据来源，构成我们所说的“被动公民科学”。我们将对英国各地野生淡水鱼地图的社交媒体观察质量进行系统评估，以使用在线照片共享网站，Flickr，Twitter等照片的文本标签和图像内容预测物种分布数据。使用机器学习和计算机视觉过程，我们将识别鱼类物种并量化鱼类（例如水霉）上的任何可见疾病。这些数据将使我们能够绘制野生鱼类的分布图，同时绘制它们的疾病状况，从而使我们能够快速确定疾病分布。作为第二个目标，我们将启动一个专门的公民科学项目，以绘制威尔士河流上鱼类分布和健康状况的密集图。与利益相关者“怀伊和乌斯克基金会”合作，我们将使用他们的“钓鱼护照”推出一个公民科学项目。怀伊河和乌斯克河上的垂钓者必须注册一天的通行证，这样我们就可以通过一个基础设施向垂钓者提供如何将图像上传到照片共享网站Flickr的指导。从这个非常低的公民科学努力中，我们将提取位置数据，并使用机器学习工具来识别图像中的物种以及任何疾病的存在和程度。我们将评估这种技术作为一个实时报告的疾病在这个集水区。

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.