Coastal observatories for monitoring of fish behaviour and their responses to environmental changes

The inclusion of behavioural components in the analysis of a community is of key relevance in marine ecology. Diel and seasonal activity rhythms or more longlasting changes in behavioural responses determine shifts in population, which in turn affect measurable abundances. Here, we review the value of cabled videoobservatories as a new and reliable technology for the remote, long-term, and highfrequency monitoring of fishes and their environment in coastal temperate areas. We provide details on the methodological requirements and constraints to appropriately measure fish behaviour at day-night and seasonal temporal scales from fixed videostations. In doing so, we highlight the relevance of an accurate monitoring capacity of the surrounding environmental variability. We present examples of multiparametric video, oceanographic, and meteorological monitoring made with the western Mediterranean platform OBSEA (www.obsea.es; 20 m water depth). Results are reviewed in relation to future developments of cabled observatory science, which will greatly improve its monitoring capability due to: i. the application of Artificial Intelligence to aid in analysis of increasingly large, complex, and highly interrelated biological and environmental data, and ii. the design of future geographic observational networks to allow for reliable spatial analysis of observed populationsPostprint (published version

Expert, Crowd, Students or Algorithm: who holds the key to deep-sea imagery ‘big data’ processing?

1.Recent technological development has increased our capacity to study the deep sea and the marine benthic realm, particularly with the development of multidisciplinary seafloor observatories. Since 2006, Ocean Networks Canada cabled observatories, have acquired nearly 65 TB and over 90,000 hours of video data from seafloor cameras and Remotely Operated Vehicles (ROVs). Manual processing of these data is time-consuming and highly labour-intensive, and cannot be comprehensively undertaken by individual researchers. These videos are a crucial source of information for assessing natural variability and ecosystem responses to increasing human activity in the deep sea. 2.We compared the performance of three groups of humans and one computer vision algorithm in counting individuals of the commercially important sablefish (or black cod) Anoplopoma fimbria, in recorded video from a cabled camera platform at 900 m depth in a submarine canyon in the Northeast Pacific. The first group of human observers were untrained volunteers recruited via a crowdsourcing platform and the second were experienced university students, who performed the task for their ichthyology class. Results were validated against counts obtained from a scientific expert. 3.All groups produced relatively accurate results in comparison to the expert and all succeeded in detecting patterns and periodicities in fish abundance data. Trained volunteers displayed the highest accuracy and the algorithm the lowest. 4.As seafloor observatories increase in number around the world, this study demonstrates the value of a hybrid combination of crowdsourcing and computer vision techniques as a tool to help process large volumes of imagery to support basic research and environmental monitoring. Reciprocally, by engaging large numbers of online participants in deep-sea research, this approach can contribute significantly to ocean literacy and informed citizen input to policy development

Data from: Expert, crowd, students or algorithm: who holds the key to deep-sea imagery ‘big data’ processing?

1. Recent technological development has increased our capacity to study the deep sea and the marine benthic realm, particularly with the development of multidisciplinary seafloor observatories. Since 2006, Ocean Networks Canada cabled observatories, has acquired nearly 65 TB and over 90,000 hours of video data from seafloor cameras and Remotely Operated Vehicles (ROVs). Manual processing of these data is time-consuming and highly labour-intensive, and cannot be comprehensively undertaken by individual researchers. These videos contain valuable information for faunal and environmental monitoring, and are a crucial source of information for assessing natural variability and ecosystem responses to increasing human activity in the deep sea. 2. In this study, we compared the performance of three groups of humans and one computer vision algorithm in counting individuals of the commercially important sablefish (or black cod) Anoplopoma fimbria, in recorded video from a cabled camera platform at 900 m depth in a submarine canyon in the Northeast Pacific. The first group of human observers were untrained volunteers recruited via a crowdsourcing platform and the second were experienced university students, who performed the task in the context of an ichthyology class. Results were validated against counts obtained from a scientific expert. 3. All groups produced relatively accurate results in comparison to the expert and all succeeded in detecting patterns and periodicities in fish abundance data. Trained volunteers displayed the highest accuracy and the algorithm the lowest. 4. As seafloor observatories increase in number around the world, this study demonstrates the value of a hybrid combination of crowdsourcing and computer vision techniques, as a tool to help process large volumes of imagery to support basic research and environmental monitoring. Reciprocally, by engaging large numbers of online participants in deep-sea research, this approach can contribute significantly to ocean literacy and informed citizen input to policy development

students_data

Contains all the data acquired by the group of students. Details of the columns are as follows: UserID: unique ID attributed to each observer in the Ocean Networks Canada digital infrastructure database. Date: Date of video acquisition Time: Time of video acquisition Counts: Number of sablefish in the video MatchDateTime: Date and Time used to match the counts among the different group

crowd_data

Contains all the data acquired by Citizen Scientists using the Digital Fisher crowdsourcing platform. Details of the columns are as follows: UserID: unique ID attributed to each observer in the Ocean Networks Canada digital infrastructure database. Date: Date of video acquisition Time: Time of video acquisition Counts: Number of sablefish in the video MatchDateTime: Date and Time used to match the counts among the different group

expert_data

Contains all the data acquired by the PhD student referred as the expert. Details of the columns are as follows: Date: Date of video acquisition Time: Time of video acquisition Counts: Number of sablefish in the video MatchDateTime: Date and Time used to match the counts among the different group

algorithm_data

Contains all the data acquired using the computer vision algorithm. Details of the columns are as follows: Video: Name of the video in the Ocean Networks Canada digital infrastructure database. Date: Date of video acquisition Time: Time of video acquisition Counts: Number of sablefish in the video MatchDateTime: Date and Time used to match the counts among the different group