Measuring biodiversity simultaneously in different locations, at different temporal scales, and over wide spatial scales is of strategic importance for the improvement of our understanding of the functioning of marine ecosystems and for the conservation of their biodiversity. Monitoring networks of cabled observatories, along with other docked autonomous systems (e.g., Remotely Operated Vehicles [ROVs], Autonomous Underwater Vehicles [AUVs], and crawlers), are being conceived and established at a spatial scale capable of tracking energy fluxes across benthic and pelagic compartments, as well as across geographic ecotones. At the same time, optoacoustic imaging is sustaining an unprecedented expansion in marine ecological monitoring, enabling the acquisition of new biological and environmental data at an appropriate spatiotemporal scale. At this stage, one of the main problems for an effective application of these technologies is the processing, storage, and treatment of the acquired complex ecological information. Here, we provide a conceptual overview on the technological developments in the multiparametric generation, storage, and automated hierarchic treatment of biological and environmental information required to capture the spatiotemporal complexity of a marine ecosystem. In doing so, we present a pipeline of ecological data acquisition and processing in different steps and prone to automation. We also give an example of population biomass, community richness and biodiversity data computation (as indicators for ecosystem functionality) with an Internet Operated Vehicle (a mobile crawler). Finally, we discuss the software requirements for that automated data processing at the level of cyber-infrastructures with sensor calibration and control, data banking, and ingestion into large data portals.

Seafloor multiparametric fibre-optic-cabled video observatories are emerging tools for standardized monitoring programmes, dedicated to the production of real-time fishery-independent stock assessment data. Here, we propose that a network of cabled cameras can be set up and optimized to ensure representative long-term monitoring of target commercial species and their surrounding habitats. We highlight the importance of adding the spatial dimension to fixed-point-cabled monitoring networks, and the need for close integration with Artificial Intelligence pipelines, that are necessary for fast and reliable biological data processing. We then describe two pilot studies, exemplary of using video imagery and environmental monitoring to derive robust data as a foundation for future ecosystem-based fish-stock and biodiversity management. The first example is from the NE Pacific Ocean where the deep-water sablefish (Anoplopoma fimbria) has been monitored since 2010 by the NEPTUNE cabled observatory operated by Ocean Networks Canada. The second example is from the NE Atlantic Ocean where the Norway lobster (Nephrops norvegicus) is being monitored using the SmartBay observatory developed for the European Multidisciplinary Seafloor and water column Observatories. Drawing from these two examples, we provide insights into the technological challenges and future steps required to develop full-scale fishery-independent stock assessments.

Behavioral rhythms are a key aspect of species fitness, since optimize ecological activities of animals in response to a constantly changing environment. Cabled observatories enable researchers to collect long-term biological and environmental data in real-time, providing relevant information on coastal fishes’ ecological niches and their temporal regulation (i.e., phenology). In this framework, the platform OBSEA (an EMSO Testing-Site in the NW coastal Mediterranean) was used to monitor the 24-h and seasonal occurrence of an ecologically iconic (i.e., top-predator) coastal fish species, the common dentex (Dentex dentex). By coupling image acquisition with oceanographic and meteorological data collection at a high-frequency (30 min), we compiled 8-years’ time-series of fish counts, showing daytime peaks by waveform analysis. Peaks of occurrence followed the photophase limits as an indication of photoperiodic regulation of behavior. At the same time, we evidenced a seasonal trend of counts variations under the form of significant major and minor increases in August and May, respectively. A progressive multiannual trend of counts increase was also evidenced in agreement with the NW Mediterranean expansion of the species. In GLM and GAM modeling, counts not only showed significant correlation with solar irradiance but also with water temperature and wind speed, providing hints on the species reaction to projected climate change scenarios. Grouping behavior was reported mostly at daytime. Results were discussed assuming a possible link between count patterns and behavioral activity, which may influence video observations at different temporal scales.

Multiparametric video-cabled marine observatories are becoming strategic to monitor remotely and in real-time the marine ecosystem. Those platforms can achieve continuous, high-frequency and long-lasting image data sets that require automation in order to extract biological time series. The OBSEA, located at 4 km from Vilanova i la Geltrú at 20 m depth, was used to produce coastal fish time series continuously over the 24-h during 2013–2014. The image content of the photos was extracted via tagging, resulting in 69917 fish tags of 30 taxa identified. We also provided a meteorological and oceanographic dataset filtered by a quality control procedure to define real-world conditions affecting image quality. The tagged fish dataset can be of great importance to develop Artificial Intelligence routines for the automated identification and classification of fishes in extensive time-lapse image sets.

The marine science community is engaged in the exploration and monitoring of biodiversity dynamics, with a special interest for understanding the ecosystem functioning and for tracking the growing anthropogenic impacts. The accurate monitoring of marine ecosystems requires the development of innovative and effective technological solutions to allow a remote and continuous collection of data. Cabled fixed observatories, equipped with camera systems and multiparametric sensors, allow for a non-invasive acquisition of valuable datasets, at a high-frequency rate and for periods extended in time. When large collections of visual data are acquired, the implementation of automated intelligent services is mandatory to automatically extract the relevant biological information from the gathered data. Nevertheless, the automated detection and classification of streamed visual data suffer from the “concept drift” phenomenon, consisting of a drop of performance over the time, mainly caused by the dynamic variation of the acquisition conditions. This work quantifies the degradation of the fish detection and classification performance on an image dataset acquired at the OBSEA cabled video-observatory over a one-year period and finally discusses the methodological solutions needed to implement an effective automated classification service operating in real time.

This study describes the distribution and composition of litter from the Gulf of Cadiz (Northeastern Atlantic, Spain), a region of confluence between the Atlantic and Mediterranean, with intense maritime traffic. Several geological features, such as canyons, open slopes and contourite furrows and channels, were surveyed by remotely operated vehicle (ROV) observations between depths of 220 and 1000 m. Marine litter was quantified by grouping the observations into six categories. Our results indicate the presence of markedly different habitats in which a complex collection of different types of litter accumulate in relation to bottom current flows and maritime and fishing routes. This result justifies a seascape approach in further anthropogenic impact studies within deep-sea areas.

The oceans are a fundamental source for climate balance, sustainability of resources and life on Earth, therefore society has a strong and pressing interest in maintaining and, where possible, restoring the health of the marine ecosystems. Effective, integrated ocean observation is key to suggesting actions to reduce anthropogenic impact from coastal to deep-sea environments and address the main challenges of the 21st century, which are summarized in the UN Sustainable Development Goals and Blue Growth strategies. The European Multidisciplinary Seafloor and water column Observatory (EMSO), is a European Research Infrastructure Consortium (ERIC), with the aim of providing long-term observations via fixed-point ocean observatories in key environmental locations across European seas from the Arctic to the Black Sea. These may be supported by ship-based observations and autonomous systems such as gliders. In this paper, we present the EMSO Generic Instrument Module (EGIM), a deployment ready multi-sensor instrumentation module, designed to measure physical, biogeochemical, biological and ecosystem variables consistently, in a range of marine environments, over long periods of time. Here, we describe the system, features, configuration, operation and data management. We demonstrate, through a series of coastal and oceanic pilot experiments that the EGIM is a valuable standard ocean observation module, which can significantly improve the capacity of existing ocean observatories and provides the basis for new observatories. The diverse examples of use included the monitoring of fish activity response upon oceanographic variability, hydrothermal vent fluids and particle dispersion, passive acoustic monitoring of marine mammals and time series of environmental variation in the water column. With the EGIM available to all the EMSO Regional Facilities, EMSO will be reaching a milestone in standardization and interoperability, marking a key capability advancement in addressing issues of sustainability in resource and habitat management of the oceans.

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