Automatic detection and classification of coastal Mediterranean fish from underwater images: Good practices for robust training

11 pages, 3 figures, 5 tables, supplementary material https://www.frontiersin.org/articles/10.3389/fmars.2023.1151758/full#supplementary-material.-- Data availability statement: The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/Supplementary MaterialFurther investigation is needed to improve the identification and classification of fish in underwater images using artificial intelligence, specifically deep learning. Questions that need to be explored include the importance of using diverse backgrounds, the effect of (not) labeling small fish on precision, the number of images needed for successful classification, and whether they should be randomly selected. To address these questions, a new labeled dataset was created with over 18,400 recorded Mediterranean fish from 20 species from over 1,600 underwater images with different backgrounds. Two state-of-the-art object detectors/classifiers, YOLOv5m and Faster RCNN, were compared for the detection of the ‘fish’ category in different datasets. YOLOv5m performed better and was thus selected for classifying an increasing number of species in six combinations of labeled datasets varying in background types, balanced or unbalanced number of fishes per background, number of labeled fish, and quality of labeling. Results showed that i) it is cost-efficient to work with a reduced labeled set (a few hundred labeled objects per category) if images are carefully selected, ii) the usefulness of the trained model for classifying unseen datasets improves with the use of different backgrounds in the training dataset, and iii) avoiding training with low-quality labels (e.g., small relative size or incomplete silhouettes) yields better classification metrics. These results and dataset will help select and label images in the most effective way to improve the use of deep learning in studying underwater organismsProject DEEP-ECOMAR. 10.13039/100018685-Comunitat Autonoma de les Illes Balears through the Direcció General de Política Universitària i Recerca with funds from the Tourist Stay Tax law ITS 2017-006 (Grant Number: PRD2018/26). [...] The present research was carried out within the framework of the activities of the Spanish Government through the “María de Maeztu Centre of Excellence” accreditation to IMEDEA (CSIC-UIB) (CEX2021-001198-M) and the “Severo Ochoa Centre Excellence” accreditation to ICM-CSIC (CEX2019-000928-S) and the Research Unit Tecnoterra (ICM-CSIC/UPC)Peer reviewe

SOCIB: the impact of new marine infrastructures in understanding and forecasting the coastal oceans: some examples from the Balearic Islands in the Mediterranean Sea

New monitoring technologies are being progressively implemented in coastal ocean observatories. As an example, gliders allow high resolution sampling, showing the existence of new features, such as submesoscale eddies with intense vertical motions that significantly affect upper ocean biogeochemical exchanges, an issue of worldwide relevance in a climate change context. SOCIB, is one of such systems, a new facility of facilities (covering from the coast to the open sea, and including among others a nearshore beach monitoring facility, HF radar, gliders and AUV’s, moorings, satellite, drifters and ARGO profilers, modelling), a scientific and technological infrastructure which is providing free, open, quality controlled and timely streams of oceanographic and coastal data and also modelling services. SOCIB takes profit of the strategic position of the Balearic Island at the Atlantic/Mediterranean transition area, one of the ‘hot spots’ of biodiversity in the world’s oceans. As an example of on-going SOCIB operations, since January 2011 sustained glider operations are in place in the Ibiza and Mallorca channels. The data centre is the core of SOCIB. The data management system created for gliders is an example of the new informatics capabilities for real time definition of mission planning, including adaptive sampling and real time monitoring using a Web tool that allows quick visualization and download. This type of new infrastructures, combined with new technologies and careful scientific analysis will allow new ways of international cooperation leading to major science breakthroughs in the very near future and new ways of science based coastal and ocean management.Peer Reviewe

Nearshore hydrodynamics and shoreline evolution in the Balearic Islands

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Wave energy and the upper depth limit distribution of Posidonia oceanica

It is widely accepted that light availability sets the lower limit of seagrass bathymetric distribution, while the upper limit depends on the level of disturbance by currents and waves. The establishment of light requirements for seagrass growth has been a major focus of research in marine ecology, and different quantitative models provide predictions for seagrass lower depth limits. In contrast, the influence of energy levels on the establishment, growth, and maintenance of seagrasses has received less attention, and to date there are no quantitative models predicting the evolution of seagrasses as a function of hydrodynamics at a large scale level. Hence, it is not possible to predict either the upper depth limit of the distribution of seagrasses or the effects that different energy regimes will have on these limits. The aim of this work is to provide a comprehensible methodology for obtaining quantitative knowledge and predictive capacity for estimating the upper depth limit of seagrasses as a response to wave energy dissipated on the seafloor. The methodology has been applied using wave data from 1958 to 2001 in order to obtain the mean wave climate in deep water seaward from an open sandy beach in the Balearic Islands, western Mediterranean Sea where the seagrass Posidonia oceanica forms an extensive meadow. Mean wave conditions were propagated to the shore using a two-dimensional parabolic model over the detailed bathymetry. The resulting hydrodynamics were correlated with bottom type and the distribution of P. oceanica. Results showed a predicted near-bottom orbital velocity of between 38 and 42 cm s-1 as a determinant of the upper depth limit of P. oceanica. This work shows the importance of interdisciplinary effort in ecological modeling and, in particular, the need for hydrodynamical studies to elucidate the distribution of seagrasses in shallow depths. Moreover, the use of predictive models would permit evaluation of the effects of coastal activities (construction of ports, artificial reefs, beach nutrient-input, dredging) on benthic ecosystems. © 2009 by Walter de Gruyter.E. Infantes would like to thank the Spanish Ministerio de Educación y Ciencia (MEC) FPI scholarship program (BES-2006-12850) for financial support. Financial support from grants CTM2005-01434, CTM2006-12072/MAR, and the Integrated Coastal Zone Management (UGIZC) project are acknowledgedPeer Reviewe

A nearshore wave and current operational forecasting system

7 páginas, 6 figuras.[EN]An operational forecasting system for nearshore waves and wave-induced currents is presented. The forecasting system (FS) has been built to provide real time information about nearshore conditions for beach safety purposes. The system has been built in a modular way with four different autonomous submodels providing, twice a day, a 36-hour wave and current forecast, with a temporal resolution of 1 hour. Making use of a mild slope parabolic model, the system propagates hourly deep water wave spectra to the shore. The resulting radiation stresses are introduced in a depth-integrated Navier-Stokes model to derive the resulting current fields. The system has been implemented in a beach located in the northeastern part of Mallorca Island (western Mediterranean), characterized by its high touristic pressure during summer season. The FS has been running for 3 years and is a valuable tool for local authorities for beach safety management.[ES]En este trabajo se presenta un sistema operacional para la predicción de las corrientes generadas por la rotura del oleaje en aguas someras. El sistema se ha desarrollado con el propósito de proporcionar las condiciones de oleaje y corrientes para la seguridad en playas. El sistema está construido en forma modular con cuatro submodelos funcionando de forma autónoma para proporcionar el oleaje y las corrientes dos veces al día con un horizonte predictivo de 36 horas. Las condiciones de oleaje en aguas profundas, se propagan hasta la costa mediante un modelo parabólico de pendiente suave y los tensores de radiación resultantes se introducen como forzamiento en un modelo de Navier Stokes verticalmente integrado. El sistema se ha aplicado en una zona piloto en la Isla de Malloca (Mediterráneo occidental). El sistema ha estado funcionando ininterrumpidamente durante tres años habiéndose mostrado como una herramienta muy válida para la gestión de la seguridad en la playa por parte de las Autoridades. La extensión del sistema a otras áreas del litoral es inmediata una vez se disponga de las batimetrías detalladas de las zonas de interés.Trabajo financiado por la Dirección General de Emergencias del Gobierno de las Islas Baleares y el Ministerio de España de Ciencia e Innovación(CTM2006-12072.Peer reviewe

Automatic, operational, high-resolution monitoring of fish length and catch numbers from landings using deep learning

Informed fishery management decisions require primary input data such as the fluctuations in the number of fish landed and fish length. Obtaining these data can be costly if conducted by hand, which is the case for length data in most fisheries. This cost often implies reduced sample sizes, which may introduce biases and lead to information loss at, for example, the boat level. The recent boost in artificial intelligence applied to fisheries provides a promising way to improve the assessment and management of stocks. We present an operational system using a deep convolutional network (Mask R-CNN) coupled with a statistical model that automatically estimates the number and the mean fork length of dolphinfish (Coryphaena hippurus) caught in a Mediterranean fishery with a resolution of each landed fish box from each boat. The system operates on images of fish boxes collected automatically at the centralized fish auction. The statistical model corrects for biases due to undetected fish using the convolutional network and estimates the mean fork length of the fish in a box from the number of fish and the box weight, allowing for high-resolution monitoring of fishery dynamics during the entire fishing season. The system predictions were empirically validated and showed good accuracy and precision. Our system could be readily incorporated into assessment schemes. We discuss how this type of monitoring system opens new opportunities for improving fishery management.This work was conducted with the collaboration of “Fundación Biodiversidad”, from the Spanish Ministry of Ecological Transition through the program Pleamar (co-financed with FEMP, EU). OPMallorcaMar and “Federació Balear de Confraries de Pescadors” are thanked for their support and collaboration. The Regional Directorate of Fisheries and Agriculture (Government of the Balearic Islands) is also thanked for its involvement

Controls on sediment dynamics and medium-term morphological change in a barred microtidal beach (Cala Millor, Mallorca, Western Mediterranean)

This paper describes the sedimentological and morphological evolution of a microtidal beach over an eight-month period under varying hydrodynamic conditions. During the monitoring a set of transverse to crescentic bars migrated onshore welded to the upper beach and then they were flattened under energetic wave conditions. The grain size distribution of surficial sediments did vary consistently across the beach profile and temporal changes in the sedimentology were mostly related to the seasonal morphological response. From our results we can state that changes in the beach morphology resulting from erosion and deposition might induce, at least to some degree, concomitant changes in the beach when hydrodynamics exceed some intensity and duration levels (Hs > 1. m). Wave climate, rather than wave forcing is the major control on sediment and morphological change co-variation. © 2011 Elsevier B.V.This research was sponsored by the “Conselleria de Medi Ambient” from the Government of the Balearic Islands and by the project CTM2006-12072 from the MICINNPeer Reviewe

Western Mediterranean operational ocean forecasts at SOCIB

Trabajo presentado en el International Liège Colloquium on Ocean Dynamics, celebrado en Liege, Bélgica, del 23 al 27 de mayo de 2015Peer Reviewe

Image-based, unsupervised estimation of fish size from commercial landings using deep learning

The dynamics of fish length distribution is a key input for understanding the fish population dynamics and taking informed management decisions on exploited stocks. Nevertheless, in most fisheries, the length of landed fish is still made by hand. As a result, length estimation is precise at fish level, but due to the inherent high costs of manual sampling, the sample size tends to be small. Accordingly, the precision of population-level estimates is often suboptimal and prone to bias when properly stratified sampling programmes are not affordable. Recent applications of artificial intelligence to fisheries science are opening a promising opportunity for the massive sampling of fish catches. Here, we present the results obtained using a deep convolutional network (Mask R-CNN) for unsupervised (i.e. fully automatic) European hake length estimation from images of fish boxes automatically collected at the auction centre. The estimated mean of fish lengths at the box level is accurate; for average lengths ranging 20–40 cm, the root-mean-square deviation was 1.9 cm, and maximum deviation between the estimated and the measured mean body length was 4.0 cm. We discuss the challenges and opportunities that arise with the use of this technology to improve data acquisition in fisheries.This work has been funded by the projects FOTOPEIX and FOTOPEX2 (2017/2279 and 2018/2002) from Fundación Biodiversidad, through the Pleamar Program. We specially thank OPMALLORCAMAR and Direcció General de Pesca del Govern de les Illes Balears for supporting these projects. The work of J-LL was partially supported by grants TIN2017-85572-P and DPI2017-86372-C3-3-R (MINECO/AEI/FEDERUE)

Urban beach coastline evolution (s'Arenal, Mallorca): human impact and natural dynamics

Capítulo publicado en: Montoya Montes, Isabel; Rodríguez Santalla, Inmaculada; Sánchez García, María José (eds.). Avances recientes en geomorfología litoral: actas de las VI Jornadas de Geomorfología Litoral, celebradas del 7 al 9 de septiembre de 2011 en Tarragona. [S.l.]: Universidad Rey Juan Carlos, 2011[EN] This work deals with the coastline evolution of a microtidal, low energy (Hs= 1 m y Tp=4 s) urban beach. The methodological approach couples large-term (1956-2008) and short-term (2009-2010) studies, using monthly survey data, historical aerial photography and numerical models. Results show that s'Arenal beach is in a dynamic equilibrium resulting in short-term coastline variability. Historical approach highlights that the system is in equilibrium because there is not sediment losses. In early 90's the beach experienced a nourishment project and the present coastline has accreted or remains at former positions in respect of the beach nourishment.[ES] Se analiza la evolución de la línea de costa de una playa urbana, micromareal y en un ambiente poco energético (Hs= 1 m y Tp =4 s), combinando estudios de largo plazo (1956-2008) y corto plazo (2009-2010) en base a levantamientos topográficos, análisis de fotografía aérea y modelos numéricos. El análisis pone manifiesto que la playa de s'Arenal mantiene un equilibrio dinámico en el que existe una variabilidad natural de la playa significativa en el corto plazo. El análisis histórico refuerza el diagnóstico de estabilidad del sistema, puesto que no hay una pérdida importante del volumen de playa alcanzado tras la regeneración de la playa, a principios de los años 90, y que en toda la playa la posición de la línea de costa es, como mínimo, igual o mayor que la que presentaba la playa en fechas anteriores a la regeneraciónEl presente trabajo fue financiado por el Consorcio para la Rehabilitación Integral de la Playa de Palma. LGP disfruta de un contrato del programa JAE-Doc del CSICPeer Reviewe