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12 pages, 3550 KiB

Open AccessArticle

Deep Learning Based Characterization of Cold-Water Coral Habitat at Central Cantabrian Natura 2000 Sites Using YOLOv8

by Alberto Gayá-Vilar, Alberto Abad-Uribarren, Augusto Rodríguez-Basalo, Pilar Ríos, Javier Cristobo and Elena Prado

J. Mar. Sci. Eng. 2024, 12(9), 1617; https://doi.org/10.3390/jmse12091617 - 11 Sep 2024

Cited by 2 | Viewed by 1570

Abstract

Cold-water coral (CWC) reefs, such as those formed by Desmophyllum pertusum and Madrepora oculata, are vital yet vulnerable marine ecosystems (VMEs). The need for accurate and efficient monitoring of these habitats has driven the exploration of innovative approaches. This study presents a novel application of the YOLOv8l-seg deep learning model for the automated detection and segmentation of these key CWC species in underwater imagery. The model was trained and validated on images collected at two Natura 2000 sites in the Cantabrian Sea: the Avilés Canyon System (ACS) and El Cachucho Seamount (CSM). Results demonstrate the model’s high accuracy in identifying and delineating individual coral colonies, enabling the assessment of coral cover and spatial distribution. The study revealed significant variability in coral cover between and within the study areas, highlighting the patchy nature of CWC habitats. Three distinct coral community groups were identified based on percentage coverage composition and abundance, with the highest coral cover group being located exclusively in the La Gaviera canyon head within the ACS. This research underscores the potential of deep learning models for efficient and accurate monitoring of VMEs, facilitating the acquisition of high-resolution data essential for understanding CWC distribution, abundance, and community structure, and ultimately contributing to the development of effective conservation strategies. Full article

(This article belongs to the Special Issue Application of Deep Learning in Underwater Image Processing)

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21 pages, 5790 KiB

Open AccessArticle

Deep-Sea Ophiuroids (Echinodermata; Ophiuroidea) from the Avilés Canyon System: Seven New Records for the Spanish North Atlantic Marine Subdivision

by Aurora Macías-Ramírez, Laura M. García-Guillén and M. Eugenia Manjón-Cabeza

Diversity 2024, 16(7), 407; https://doi.org/10.3390/d16070407 - 14 Jul 2024

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Abstract

The Avilés Canyon System (ACS) is located in the southern Bay of Biscay (northern Spain, Cantabrian Sea). It has been declared a Site of Community Importance (SCI: C ESZZ12003) within the Natura 2000 Network and recognized as a Vulnerable Marine Ecosystem (VME). This area is included in the North Atlantic Marine Subdivision (NAMD). The present study reviews ophiuroid fauna collected during the INDEMARES–ACS project and compares the new findings with previous studies using the Official Spanish Checklist (“Inventario Español de Especies Marinas”) to update our knowledge on the diversity and distribution of these species. During the surveys carried out within the LIFE + INDEMARES–Avilés Canyon System project (2010–2012), a total of 7413 specimens belonging to 45 ophiuroid species were collected from 50 stations in a depth range between 266 and 2291 m. The most frequent species was Ophiactis abyssicola (M. Sars, 1861). Comparing the identified species with public datasets, seven species should be considered as new records for NAMD: Ophiocten centobi Paterson, Tyler & Gage, 1982, Amphiura borealis (G.O. Sars, 1872), Amphiura fragilis Verrill, 1885, Ophiochondrus armatus (Koehler, 1907), Ophiosabine parcita (Koehler, 1906), Ophiophrixus spinosus (Storm, 1881), Ophiotreta valenciennesi (Lyman, 1879). Furthermore, one species has expanded its bathymetric range: Ophiosabine parcita (Koehler, 1906). Full article

(This article belongs to the Special Issue Deep-Sea Echinoderms of the European Seas)

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14 pages, 8323 KiB

Open AccessArticle

Soft-Bottom Infaunal Macrobenthos of the Avilés Canyon System (Cantabrian Sea)

by Antía Lourido, Santiago Parra and Francisco Sánchez

Diversity 2023, 15(1), 53; https://doi.org/10.3390/d15010053 - 2 Jan 2023

Cited by 3 | Viewed by 2246

Abstract

The Aviles Canyon System (Northern Atlantic coast of Spain) is one of the ten marine regions studied in the Spanish seas by the LIFE+ INDEMARES project, which aims to identify special areas of conservation within the Natura 2000 Network. This study aims to characterize the composition and distribution of the macrobenthic fauna in order to provide baseline data to obtain a basic knowledge of the environment. Three oceanographic surveys were carried out to investigate species and habitats of this deep-sea ecosystem. The stations were sampled using a box corer, in order to evaluate the distribution and biodiversity of the macroinfauna, and to analyse the granulometric composition and the organic matter content. Sediments were mainly sandy in nature, the finest sediments with the highest organic matter content were found in the deepest areas, while coarser sediments were located in shallow stations. Polychaetes were the best represented group in total number of species and individuals, followed by crustaceans and molluscs. Five major macrobenthic assemblages were determined through multivariate analyses. Bathymetry and sedimentary composition were the main factors structuring the benthic community separating shallow and coarser stations from deeper and finer ones. Full article

(This article belongs to the Special Issue Ecology and Biogeography of Marine Benthos)

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28 pages, 11009 KiB

Open AccessArticle

3D Fine-scale Terrain Variables from Underwater Photogrammetry: A New Approach to Benthic Microhabitat Modeling in a Circalittoral Rocky Shelf

by Elena Prado, Augusto Rodríguez-Basalo, Adolfo Cobo, Pilar Ríos and Francisco Sánchez

Remote Sens. 2020, 12(15), 2466; https://doi.org/10.3390/rs12152466 - 31 Jul 2020

Cited by 17 | Viewed by 5775

Abstract

The relationship between 3D terrain complexity and fine-scale localization and distribution of species is poorly understood. Here we present a very fine-scale 3D reconstruction model of three zones of circalittoral rocky shelf in the Bay of Biscay. Detailed terrain variables are extracted from 3D models using a structure-from-motion (SfM) approach applied to ROTV images. Significant terrain variables that explain species location were selected using general additive models (GAMs) and micro-distribution of the species were predicted. Two models combining BPI, curvature and rugosity can explain 55% and 77% of the Ophiuroidea and Crinoidea distribution, respectively. The third model contributes to explaining the terrain variables that induce the localization of Dendrophyllia cornigera. GAM univariate models detect the terrain variables for each structural species in this third zone (Artemisina transiens, D. cornigera and Phakellia ventilabrum). To avoid the time-consuming task of manual annotation of presence, a deep-learning algorithm (YOLO v4) is proposed. This approach achieves very high reliability and low uncertainty in automatic object detection, identification and location. These new advances applied to underwater imagery (SfM and deep-learning) can resolve the very-high resolution information needed for predictive microhabitat modeling in a very complex zone. Full article

(This article belongs to the Special Issue Underwater 3D Recording & Modelling)

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