Seguiment i recuperació d’espècies afectades per la pesca en ecosistemes marins d’aigües profundes: un esforç conjunt entre biologia i tecnologia

3 pages, 2 figures[EN] The oceans provide important ecosystem services, and protein provisioning is one of the main benefits for humanity. The millenarian Mediterranean fishing activity today accounts for almost half of all the fisheries in the EU, and the use of high-impact fishing methods has made this human activity one of the main drivers of ecosystem degradation, especially in demersal and benthic environments (Puig et al. 2012). Bottom otter gear (hereafter trawling) causes the removal of sediments and endangers demersal living resources and their ecosystems, with fragile epi-faunal species being replaced by mobile scavengers or predators and long-lived species being replaced by short-lived species. In the Mediterranean Sea, many commercial demersal populations are being overexploited, reducing the economic benefits of fisheries and the ecosystem services associated with cultural aspects of iconic species. […][ES] Los océanos proporcionan importantes servicios ecosistémicos, siendo el suministro de proteínas uno de los principales beneficios para la humanidad. La actividad pesquera del Mediterráneo constituye hoy en día casi la mitad de todas las pesquerías de la UE y el uso de métodos de pesca de alto impacto ha convertido a esta actividad en una de las principales impulsoras de la degradación de los ecosistemas de aguas profundas (Puig et al. 2012). El arte de pesca de arrastre provoca la eliminación de sedimentos y pone en peligro los recursos vivos demersales y sus ecosistemas, y las especies de epi-fauna frágiles son reemplazadas por especies carroñeras o depredadoras y las especies longevas son reemplazadas por especies de vida corta. En el mar Mediterráneo, muchas poblaciones de especies comerciales que habitan hábitats demersales están siendo sobreexplotadas, lo que reduce los beneficios económicos de la pesca y los servicios ecosistémicos asociados a los aspectos culturales de algunas especies consideradas icónicas. […][CAT] Els oceans proporcionen importants serveis ecosistèmics, i és el subministrament de proteïnes un dels principals beneficis per a la humanitat. L’activitat pesquera mil·lenària del Mediterrani constitueix avui en dia gairebé la meitat de totes les pesqueres de la UE i la utilització de mètodes de pesca d’alt impacte ha convertit aquesta activitat humana en un dels principals impulsors de la degradació dels ecosistemes, especialment en entorns bentònics (Puig et al. 2012). L’art de pesca d’arrossegament provoca l’eliminació de sediments i posa en perill els recursos vius demersals i els seus ecosistemes, i les espècies d’epi-fauna fràgils són substituïdes per carronyaires o depredadors mòbils i les espècies de llarga vida són substituïdes per espècies de vida curta. A la mar Mediterrània, moltes poblacions demersals comercials estan essent sobreexplotades, el que redueix per a les espècies icòniques, els beneficis econòmics de la pesca i els serveis ecosistèmics associats amb els aspectes culturals. […]Peer reviewe

Advancing fishery-independent stock assessments for the Norway lobster (Nephrops norvegicus) with new monitoring technologies

The Norway lobster, Nephrops norvegicus, supports a key European fishery. Stock assessments for this species are mostly based on trawling and UnderWater TeleVision (UWTV) surveys. However, N. norvegicus are burrowing organisms and these survey methods are unable to sample or observe individuals in their burrows. To account for this, UWTV surveys generally assume that "1 burrow system = 1 animal", due to the territorial behavior of N. norvegicus. Nevertheless, this assumption still requires in-situ validation. Here, we outline how to improve the accuracy of current stock assessments for N. norvegicus with novel ecological monitoring technologies, including: robotic fixed and mobile camera-platforms, telemetry, environmental DNA (eDNA), and Artificial Intelligence (AI). First, we outline the present status and threat for overexploitation in N. norvegicus stocks. Then, we discuss how the burrowing behavior of N. norvegicus biases current stock assessment methods. We propose that state-of-the-art stationary and mobile robotic platforms endowed with innovative sensors and complemented with AI tools could be used to count both animals and burrows systems in-situ, as well as to provide key insights into burrowing behavior. Next, we illustrate how multiparametric monitoring can be incorporated into assessments of physiology and burrowing behavior. Finally, we develop a flowchart for the appropriate treatment of multiparametric biological and environmental data required to improve current stock assessment methods

A new approach to use marine robotic networks for ecosystem monitoring and management: The PLOME Project

4th Marine Imaging Workshop, 3-6 October 2022, Brest, FranceOur understanding of marine ecosystem functioning and processes relies on adequate spatio-temporal multiparametric monitoring procedures. Over the next 3 years, the Project PLOME (Platforms for Long-lasting Observation of Marine Ecosystems) will implement a spatially adaptive and autonomous network of easy-to-use benthic landers with dockable Autonomous Underwater Vehicles (AUVs)ñ This network will be used to intelligently video-monitor and map marine ecosystems and their environment from coastal to deep-sea areas. All platforms will be connected via acoustic or optical communication and will operate over periods of weeks to months with real-time supervision. Stations will provide continuous and intensive temporal observations, while dockable AUVs (with battery recharge and data downloading capability) will provide intensive measurements at various spatial scales, using intelligent and adaptive trajectories to explore surrounding areas. Biological, geochemical and oceanographic data will be generated by an array of sensors including acoustic receivers and cameras. Images will be processed in real-time for species classification and tracking, using advanced data analysis and Deep Learning techniques. Metadata will be communicated between landers and AUVs and transmitted opportunistically whenever an Unmanned Surface Vehicle (USV) connects the platform via aerial communications (i.e. GSM and satellite communications, depending on form distance to shore). The unattended operation will also be possible with an innovation of pop-up buoys that will allow data transfer to the surface from landers and UAVs to be relayed once the pop-up buoys reach the surface. Complex ecological indicators for ecosystem management will be computed from the collected data, by applying advanced computer vision techniques to classify, count and size individuals in video images and to generate multimodal maps of the seabed. A pipeline for automated data treatment will be tailored for multiparametric analyses to derive cause-effect relationships between biological variables and the physical habitatsPeer reviewe

Advancing fishery-independent stock assessments for the Norway lobster (Nephrops norvegicus) with new monitoring technologies

The Norway lobster, Nephrops norvegicus, supports a key European fishery. Stock assessments for this species are mostly based on trawling and UnderWater TeleVision (UWTV) surveys. However, N. norvegicus are burrowing organisms and these survey methods are unable to sample or observe individuals in their burrows. To account for this, UWTV surveys generally assume that “1 burrow system = 1 animal”, due to the territorial behavior of N. norvegicus. Nevertheless, this assumption still requires in-situ validation. Here, we outline how to improve the accuracy of current stock assessments for N. norvegicus with novel ecological monitoring technologies, including: robotic fixed and mobile camera-platforms, telemetry, environmental DNA (eDNA), and Artificial Intelligence (AI). First, we outline the present status and threat for overexploitation in N. norvegicus stocks. Then, we discuss how the burrowing behavior of N. norvegicus biases current stock assessment methods. We propose that state-of-the-art stationary and mobile robotic platforms endowed with innovative sensors and complemented with AI tools could be used to count both animals and burrows systems in-situ, as well as to provide key insights into burrowing behavior. Next, we illustrate how multiparametric monitoring can be incorporated into assessments of physiology and burrowing behavior. Finally, we develop a flowchart for the appropriate treatment of multiparametric biological and environmental data required to improve current stock assessment methods

La robòtica submarina ens permet portar un laboratori científic fins el lloc on passa l'acció

[EN] Today we interview Ivan Masmitja, ICM postdoctoral student and expert in underwater robotics[ES] Hoy entrevistamos a Ivan Masmitja, estudiante postdoctoral del ICM y experto en robótica submarina[CAT] Avui entrevistem l’Ivan Masmitja, estudiant postdoctoral de l’ICM i expert en robòtica submarinaPeer reviewe

Utilització d'intel·ligència artificial per localitzar i perseguir objectes mitjançant robòtica submarina

L'exploració de l'última frontera de la Terra, l'oceà, cada vegada és de més importància a causa de les conseqüència que té l’activitat humana sobre el funcionament dels ecosistemes i la biodiversitat, com ara l'escalfament dels oceans i la disminució de les espècies, en major part relacionada amb el canvi climàtic i la sobrepesca, un problema social important que cal abordar. Des de microbis fins a grans depredadors, hi ha cada cop més evidència que la vida marina està formada per corrents oceànics de curta durada (0,1-10 km) que són difícils d'observar, modelar i explicar teòricament. Com aquests intensos corrents tridimensionals estructuren la productivitat i la diversitat dels ecosistemes marins és un tema de debat actiu. A més, la megafauna marina té un paper clau en el funcionament dels ecosistemes. No obstant això, un terç d'aquests animals estan en risc d'extinció. Estudis recents han demostrat la importància d'utilitzar plataformes robòtiques per avançar en l'estudi d'àrees clau de recerca marina com l'ecologia física i del moviment. Tot i així, hi ha una manca de coordinació total entre els vehicles, reduint-ne el rendiment i limitant-ne l'aplicabilitat, cosa que es podria solucionar amb l'aprenentatge automàtic (reinforcement learning). Un nou enfocament mitjançant l'ús de mètodes d'aprenentatge de reforç multi-agent (multi-agent reinforcement learning) per resoldre els problemes de coordinació de les flotes robòtiques marines, podria suposar un important pas endavant. Estic imaginant un futur on l’ús d’entorns de simulació i el machine-learning potenciarà l'ús de flotes robòtiques marines per abordar algun dels principals problemes socials, aportant noves idees per millorar la salut de l'oceàPeer reviewe

El uso de la robótica submarina como herramienta en la investigación oceanográfica

El uso de la robótica submarina como herramienta en la investigación oceanográfica ya es una realidad, según el último estudio liderado por el CSICPeer reviewe

De qué forma salvar un submarino: un reto para la tecnología actual que sí consiguieron los espías de la CIA hace cincuenta años | Tecnología

En plena Guerra Fría, el submarino lanzamisiles soviético K-ciento veintinueve se hundió el ocho de marzo de mil novecientos sesenta y ocho y fue localizado a prácticamente cinco mil metros de profundidad, en el medio del Oceáno Pacífico, en algún punto jamás revelado entre la península de Kamchatka y las islas Hawái. [...]Peer reviewe

Artificial intelligence models applied to the tracking of marine vehicles (and organisms) with machine learning methodologies

ICM-CRM Meeting 2023: New Bridges between Marine Sciences and Mathematics, 2-10 November 2023Reinforcement learning (RL) techniques can be used as a path-planning method to localize and track underwater targets using range-only techniques and autonomous vehicles. Topics of interest cover the optimization of RL algorithms in terms of processing and execution time in order to train agents more quickly. In order to reduce the sim-to-real gap, some strategies use a high-fidelity simulation environment that includes characteristics and dynamics of the marine environment. Then the transfer of control policies learned in this simulated environment are suitable for the real environment, guaranteeing certain control dynamics. Further information can be found at • Masmitja, I.; Gomariz, S.; Del Rio, J.; Kieft, B.; O’Reilly, T.C.; Bouvet, P.; Aguzzi, J. Optimal path shape for range-only underwater target localization using a Wave Glider. International journal of robotics research (IJRR). ISSN 0278-3649 pp. 1 – 16 (2018). • Masmitja, I.; Martin, M.; Katija, K.; Gomariz, S.; Navarro, J. A reinforcement learning path planning approach for range-only underwater target localization with autonomous vehicles. IEEE 18th International Conference on Automation Science and Engineering (CASE), August 20-24, 2022. Mexico City, Mexico (2022)Peer reviewe

TransfQMix: Transformers for Leveraging the Graph Structure of Multi-Agent Reinforcement Learning Problems

International Conference on Autonomous Agents and Multiagent Systems (AAMAS '23), 29 May - 2 June 2023, London, United Kingdom.-- 9 pages, 6 figures, 1 table, supplementary materialCoordination is one of the most difficult aspects of multi-agent reinforcement learning (MARL). One reason is that agents normally choose their actions independently of one another. In order to see coordination strategies emerging from the combination of independent policies, the recent research has focused on the use of a centralized function (CF) that learns each agent's contribution to the team reward. However, the structure in which the environment is presented to the agents and to the CF is typically overlooked. We have observed that the features used to describe the coordination problem can be represented as vertex features of a latent graph structure. Here, we present TransfQMix, a new approach that uses transformers to leverage this latent structure and learn better coordination policies. Our transformer agents perform a graph reasoning over the state of the observable entities. Our transformer Q-mixer learns a monotonic mixing-function from a larger graph that includes the internal and external states of the agents. TransfQMix is designed to be entirely transferable, meaning that same parameters can be used to control and train larger or smaller teams of agents. This enables to deploy promising approaches to save training time and derive general policies in MARL, such as transfer learning, zero-shot transfer, and curriculum learning. We report TransfQMix's performances in the Spread and StarCraft II environments. In both settings, it outperforms state-of-the-art Q-Learning models, and it demonstrates effectiveness in solving problems that other methods can not solveThis project has received funding from the EU’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 893089. This work acknowledges the ‘Severo Ochoa Centre of Excellence’ accreditation (CEX2019-000928-S)Peer reviewe