Towards Naples Ecological REsearch for Augmented Observatories (NEREA): The NEREA-Fix Module, a Stand-Alone Platform for Long-Term Deep-Sea Ecosystem Monitoring

The NEREA Augmented Observatory: an integrative approach to marine coastal ecology

The NEREA (Naples Ecological REsearch for Augmented observatories) initiative aims to establish an augmented observatory in the Gulf of Naples (GoN), designed to advance the understanding of marine ecosystems through a holistic approach. Inspired by the Tara Oceans expedition and building on the scientific legacy of the MareChiara Long-Term Ecological Research (LTER-MC) site, NEREA integrates traditional physical, chemical, and biological measurements with state-of-the-art methodologies such as metabarcoding and metagenomics. Here we present the first 10 months of NEREA data, collected from April 2019 to January 2020, encompassing physico-chemical parameters, plankton biodiversity (e.g., microscopy and flow cytometry), prokaryotic and eukaryotic metabarcoding, a prokaryotic gene catalogue, and a collection of 3818 prokaryotic Metagenome-Assembled Genomes (MAGs). NEREA's efforts produce a significant volume of multifaceted data, which enhances our understanding of marine ecosystems and promotes the development of scientific hypotheses and ideas.

Environmental DNA metabarcoding serves as a promising method for aquatic species monitoring and management: A review focused on its workflow, applications, challenges and prospects

Marine and freshwater biodiversity is under threat from both natural and manmade causes. Biological monitoring is currently a top priority for biodiversity protection. Given present limitations, traditional biological monitoring methods may not achieve the proposed monitoring aims. Environmental DNA metabarcoding technology reflects species information by capturing and extracting DNA from environmental samples, using molecular biology techniques to sequence and analyze the DNA, and comparing the obtained information with existing reference libraries to obtain species identification. However, its practical application has highlighted several limitations. This paper summarizes the main steps in the environmental application of eDNA metabarcoding technology in aquatic ecosystems, including the discovery of unknown species, the detection of invasive species, and evaluations of biodiversity. At present, with the rapid development of big data and artificial intelligence, certain advanced technologies and devices can be combined with environmental DNA metabarcoding technology to promote further development of aquatic species monitoring and management.

Don't catch me if you can - Using cabled observatories as multidisciplinary platforms for marine fish community monitoring: An in situ case study combining Underwater Video and environmental DNA data

Cabled observatories are marine infrastructures equipped with biogeochemical and oceanographic sensors as well as High-Definition video and audio equipment, hence providing unprecedented opportunities to study marine biotic and abiotic components. Additionally, non-invasive monitoring approaches such as environmental DNA (eDNA) metabarcoding have further enhanced the ability to characterize marine life. Although the use of non-invasive tools beholds great potential for the sustainable monitoring of biodiversity and declining natural resources, such techniques are rarely used in parallel and understanding their limitations is challenging. Thus, this study combined Underwater Video (UV) with eDNA metabarcoding data to produce marine fish community profiles over a 2 months period in situ at a cabled observatory in the northeast Atlantic (SmartBay Ireland). By combining both approaches, an increased number of fish could be identified to the species level (total of 22 species), including ecologically and economically important species such as Atlantic cod, whiting, mackerel and monkfish. The eDNA approach alone successfully identified a higher number of species (59%) compared to the UV approach (18%), whereby 23% of species were detected by both methods. The parallel implementation of point collection eDNA and time series UV data not only confirmed expectations of the corroborative effect of using multiple disciplines in fish community composition, but also enabled the assessment of limitations intrinsic to each technique including the identification of false-negative detections in one sampling technology relative to the other. This work showcased the usefulness of cabled observatories as key platforms for in situ empirical assessment of both challenges and prospects of novel technologies in aid to future monitoring of marine life.

Smart Fishery: A Systematic Review and Research Agenda for Sustainable Fisheries in the Age of AI

Applications of artificial intelligence (AI) technologies for improving the sustainability of the smart fishery have become widespread. While sustainability is often claimed to be the desired outcome of AI applications, there is as yet little evidence on how AI contributes to the sustainable fishery. The purpose of this paper is to perform a systematic review of the literature on the smart fishery and to identify upcoming themes for future research on the sustainable fishery in the Age of AI. The findings of the review reveal that scholarly attention in AI-inspired fishery literature focuses mostly on automation of fishery resources monitoring, mainly detection, identification, and classification. Some papers list marine health and primary production which are vital dimensions for Large Marine Ecosystems to recycle nutrients to sustain anticipated production levels. Very few reviewed articles refer to assessing individual needs, particularly fishers, from AI deployment in fisheries and policy response from governments. We call for future AI for sustainable fishery studies on how fishers perceive AI needs, and how governments possess a tangible strategy or depth of understanding on the regulation of AI concerning smart fishery systems and research on resilience-enhancing policies to promote the value and potentials of the AI-inspired smart fishery in different locations.