Deep learning-assisted high resolution mapping of vulnerable habitats within the Capbreton Canyon System, Bay of Biscay

Abad-Uribarren, Alberto; Prado, Elena; Sierra, Sergio; Cobo, A.; Rodríguez-Básalo, Augusto; Gómez-Ballesteros, M.; Sánchez, Francisco Arreguín

doi:10.1016/j.ecss.2022.107957

Cited by 12 publications

(6 citation statements)

References 73 publications

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“…As reported by several other authors, slope, depth and rugosity are among the factors that most influence the habitat preferences of CWCs (Garcıá-Alegre et al, 2014;Lauria et al, 2021;Abad-Uribarren et al, 2022).…”

Section: Discussionmentioning

confidence: 54%

“…Nowadays, modern technology, such as multi-beam echosounders and ROVs, are used for much more accurate prediction modelling (Yesson et al, 2012;Buhl-Mortensen et al, 2015;Sundahl et al, 2020;Abad-Uribarren et al, 2022). Habitat Suitability Models (HSMs) have grown significantly in resource management and conservation biology in the last few years.…”

Section: Introductionmentioning

confidence: 99%

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Effect of environmental and anthropogenic factors on the distribution and co-occurrence of cold-water corals

Palummo,

Milisenda,

Canese

et al. 2023

Front. Mar. Sci.

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Cold-water corals (CWCs) are bioengineering species that can increase habitat heterogeneity and improve the deep sea’s biological diversity and ecosystem functioning. Knowledge of their distribution provides a critical baseline for assessing the effect of natural and anthropogenic impacts on these important deep-sea habitats. The aims of this study are: i) provide new data on the spatial distribution of six CWCs species in the Strait of Sicily, ii) describe the principal environmental and anthropogenic variables that play a role in shaping their distribution, iii) identify hotspots in which individuals belonging to the various species co-occur. Presence-only data of six CWCs species, ten environmental variables (depth, slope, rugosity, aspect, flowdir, temperature, salinity, north bottom current, east bottom current, chlorophyll-a), and one variable relating to bottom trawling effort (Automatic Information System – AIS) were used to predict the suitable habitats. We used Maximum Entropy modelling (MaxEnt) approach and used the AUC (area under the receiver operating characteristic curve) and TSS (true skill statistics) to evaluate the model performance. The results showed excellent AUC, TSS and AUC’s standard deviation mean values for all six species. The validation show high predictive performance. MaxEnt identified slope, depth, and rugosity as the most important predictors, showing the highest percentage contribution for all six species considered. Throughout the study area, highlyinterspecific persistent density hotspot of CWCs co-occurrence were discovered, with a total extension of 4.05 km2 where all species co-occur. Although studies on the effect of environmental and anthropogenic factors that impact the distribution of these species of conservation interest remain scarce, the results of this study offer useful guidance for decision-makers to develop necessary conservation measures.

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Section: Discussionmentioning

confidence: 54%

Section: Introductionmentioning

confidence: 99%

Effect of environmental and anthropogenic factors on the distribution and co-occurrence of cold-water corals

Palummo,

Milisenda,

Canese

et al. 2023

Front. Mar. Sci.

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“…These ecosystems are crucial for maintaining marine biodiversity and play a vital role in the provision of essential ecosystem services ( Ríos et al, 2022 ). Incorporating automatic image analysis techniques represents a significant advancement in the field of benthic community studies ( Abad-Uribarren et al, 2022 ). The tremendous diversity of species present poses a formidable challenge for an exhaustive species analysis approach.…”

Section: Discussionmentioning

confidence: 99%

“…This approach entails the application of multiple layers of highly interconnected machine learning algorithms to achieve improved results from raw images ( Olden, Lawler & Poff, 2008 ; LeCun, Bengio & Hinton, 2015 ). These techniques have already achieved formidable results in different marine ecology tasks such as coral classification ( Bhandarkar, Kathirvelu & Hopkinson, 2022 ; Mahmood et al, 2017 ; Raphael et al, 2020 ), fish detection and classification ( Zhong et al, 2022 ; Siddiqui et al, 2018 ; Knausgård et al, 2022 ), and identification of diverse benthic fauna ( Abad-Uribarren et al, 2022 ; Song et al, 2022 ; Liu & Wang, 2022 ).…”

Section: Introductionmentioning

confidence: 99%

High-resolution density assessment assisted by deep learning of Dendrophyllia cornigera (Lamarck, 1816) and Phakellia ventilabrum (Linnaeus, 1767) in rocky circalittoral shelf of Bay of Biscay

Gayá-Vilar,

Cobo,

Abad-Uribarren

et al. 2024

PeerJ

Self Cite

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This study presents a novel approach to high-resolution density distribution mapping of two key species of the 1170 “Reefs” habitat, Dendrophyllia cornigera and Phakellia ventilabrum, in the Bay of Biscay using deep learning models. The main objective of this study was to establish a pipeline based on deep learning models to extract species density data from raw images obtained by a remotely operated towed vehicle (ROTV). Different object detection models were evaluated and compared in various shelf zones at the head of submarine canyon systems using metrics such as precision, recall, and F1 score. The best-performing model, YOLOv8, was selected for generating density maps of the two species at a high spatial resolution. The study also generated synthetic images to augment the training data and assess the generalization capacity of the models. The proposed approach provides a cost-effective and non-invasive method for monitoring and assessing the status of these important reef-building species and their habitats. The results have important implications for the management and protection of the 1170 habitat in Spain and other marine ecosystems worldwide. These results highlight the potential of deep learning to improve efficiency and accuracy in monitoring vulnerable marine ecosystems, allowing informed decisions to be made that can have a positive impact on marine conservation.

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“…To solve the difficulties of massive image data processing, deep learning algorithms are proving to be a suitable solution [8] . Deep learning algorithms have been proposed as a powerful tool for monitoring different underwater habitats from recorded images or videos, including shallow and turbid waters [9] , or deep benthic communities [10] .…”

Section: Introductionmentioning

confidence: 99%

Density estimation of the main structuring sessile species in underwater marine caves with a deep learning approach

Sierra,

Prado,

Rodríguez-Cobo

et al. 2023

JGC

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Monitoring marine biodiversity is a challenge in some vulnerable and difficult-to-access habitats, such as underwater caves. Underwater caves are a great focus of biodiversity, concentrating a large number of species in their environment. However, most of the sessile species that live on the rocky walls are very vulnerable, and they are often threatened by different pressures. The use of these spaces as a destination for recreational divers can cause different impacts on the benthic habitat. In this work, we propose a methodology based on video recordings of cave walls and image analysis with deep learning algorithms to estimate the spatial density of structuring species in a study area. We propose a combination of automatic frame overlap detection, estimation of the actual extent of surface cover, and semantic segmentation of the main 10 species of corals and sponges to obtain species density maps. These maps can be the data source for monitoring biodiversity over time. In this paper, we analyzed the performance of three different semantic segmentation algorithms and backbones for this task and found that the Mask R-CNN model with the Xception101 backbone achieves the best accuracy, with an average segmentation accuracy of 82%.

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Deep learning-assisted high resolution mapping of vulnerable habitats within the Capbreton Canyon System, Bay of Biscay

Cited by 12 publications

References 73 publications

Effect of environmental and anthropogenic factors on the distribution and co-occurrence of cold-water corals

Effect of environmental and anthropogenic factors on the distribution and co-occurrence of cold-water corals

High-resolution density assessment assisted by deep learning of Dendrophyllia cornigera (Lamarck, 1816) and Phakellia ventilabrum (Linnaeus, 1767) in rocky circalittoral shelf of Bay of Biscay

Density estimation of the main structuring sessile species in underwater marine caves with a deep learning approach

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