Underwater photogrammetry reveals new links between coral reefscape traits and fishes that ensure key functions

Urbina-Barreto, Isabel; Simon, Elise; Guilhaumon, François; Bruggemann, J. Henrich; Pinel, Romain; Kulbicki, Michel; Vigliola, Laurent; Mou-Tham, Gérard; Mahamadaly, Vincent; Facon, Mathilde; Bureau, Sophie; Peignon, Christophe; Dutrieux, Éric; Garnier, Rémi; Penin, Lucie; Adjeroud, Mehdi

doi:10.1002/ecs2.3934

Cited by 9 publications

(12 citation statements)

References 116 publications

(203 reference statements)

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“…Consequently, the reduction of substrate rugosity, notably after storms, cyclones, or gleaning activities, is associated with reef degradation [ 132 – 134 ]. The outcomes of our survey thus reinforce the positive link between rugosity and the diversity and abundance of reef invertebrates and fishes previously recorded [ 132 , 133 , 135 – 137 ]. However, because the substrate rugosity was visually assessed in the present study, it should be interesting to examine the relationship between rugosity and the structure and dynamics of coral assemblages with the recently developed underwater 3D photogrammetry approach, which allows a much more precise measurement of the structural complexity of reef habitats [ 130 , 138 , 139 ].…”

Section: Discussionsupporting

confidence: 85%

A contemporary baseline of Madagascar’s coral assemblages: Reefs with high coral diversity, abundance, and function associated with marine protected areas

Randrianarivo

Guilhaumon²,

Tsilavonarivo³

et al. 2022

PLoS ONE

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Madagascar is a major hotspot of biodiversity in the Western Indian Ocean, but, as in many other regions, coral reefs surrounding the island confront large-scale disturbances and human-induced local stressors. Conservation actions have been implemented with encouraging results for fisheries, though their benefit on coral assemblages has never been rigorously addressed. In this context, we analyzed the multiscale spatial variation of the composition, generic richness, abundance, life history strategies, and cover of coral assemblages among 18 stations placed at three regions around the island. The potential influences of marine protected areas (MPAs), algal cover, substrate rugosity, herbivorous fish biomass, and geographic location were also analyzed. Our results highlight the marked spatial variability, with variation at either or both regional and local scales for all coral descriptors. The northeast coastal region of Masoala was characterized by the high abundance of coral colonies, most notably of the competitive Acropora and Pocillopora genera and stress-tolerant taxa at several stations. The southwest station of Salary Nord was distinguished by lower abundances, with depauperate populations of competitive taxa. On the northwest coast, Nosy-Be was characterized by higher diversity and abundance as well as by high coral cover (~42–70%) recorded at unfished stations. Results clearly underline the positive effects of MPAs on all but one of the coral descriptors, particularly at Nosy-Be where the highest contrast between fished and unfished stations was observed. Biomass of herbivorous fishes, crustose coralline algae cover, and substrate rugosity were also positively related to several coral descriptors. The occurrence of reefs with high diversity, abundance, and cover of corals, including the competitive Acropora, is a major finding of this study. Our results strongly support the implementation of locally managed marine areas with strong involvement by primary users, particularly to assist in management in countries with reduced logistic and human resources such as Madagascar.

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Section: Discussionsupporting

confidence: 85%

A contemporary baseline of Madagascar’s coral assemblages: Reefs with high coral diversity, abundance, and function associated with marine protected areas

Randrianarivo

Guilhaumon²,

Tsilavonarivo³

et al. 2022

PLoS ONE

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“…SCUBA diver‐based photogrammetry using the ‘Structure‐from‐Motion’ ( SfM ) technique, has been widely adopted in the past decade to monitor shallow biogenic structures due to its ability to account for the three‐dimensional (3D) framework provided by scleractinian corals (Burns et al., 2015; Ferrari et al., 2016; Figueira et al., 2015). Applications of SfM has shown great potential for providing novel insights into seascape ecology, as demonstrated by extensive research on structural metrics (Bongaerts et al., 2021; Lazarus & Belmaker, 2021; Lepczyk et al., 2021; Urbina‐Barreto et al., 2022). Successful protocols have been developed for acquiring structural complexity metrics (e.g., surface complexity, slope, curvatures and fractal dimension) via SfM (Table 1) at multiple spatial scales (several m 2 and between sites) (Bayley & Mogg, 2020; Fukunaga et al., 2019; Lange et al., 2022; Lange & Perry, 2020; Lechene et al., 2019; Young et al., 2017).…”

Section: Introductionmentioning

confidence: 99%

“…Successful protocols have been developed for acquiring structural complexity metrics (e.g., surface complexity, slope, curvatures and fractal dimension) via SfM (Table 1) at multiple spatial scales (several m 2 and between sites) (Bayley & Mogg, 2020; Fukunaga et al., 2019; Lange et al., 2022; Lange & Perry, 2020; Lechene et al., 2019; Young et al., 2017). These protocols rely on the placement of auxiliary reference objects of known dimensions, such as ground control point units (Fukunaga, Burns, et al., 2020) and geodesical networks (Nocerino et al., 2020), permitting the integration of structural metrics in long‐term observations of benthic communities, such as coral growth (Lange et al., 2022; Rossi et al., 2019), effects of natural disturbances (Pascoe et al., 2021), and describe fish functional diversity (Fukunaga, Burns, et al., 2020; Urbina‐Barreto et al., 2022). SfM has been successfully applied via human‐diver surveys of Antarctic coastal benthic ecosystems which have also identified the possible applications in long‐term monitoring campaigns (Menna et al., 2022; Piazza et al., 2018, 2019); however, these studies did not address structural complexity.…”

Section: Introductionmentioning

confidence: 99%

Remote sensing of Antarctic polychaete reefs (Serpula narconensis): reproducible workflows for quantifying benthic structural complexity with action cameras, remotely operated vehicles and structure‐from‐motion photogrammetry

Montes-Herrera

Hill

Cummings

et al. 2023

Remote Sens Ecol Conserv

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Quantifying the structural complexity provided by biogenic habitat structures is important in ecology, conservation and management, and yet remains a challenging task, particularly in deep sea and polar environments, that current photogrammetry tools can alleviate. In this study, we demonstrate how small remotely operated vehicles and compact underwater GoPro® action cameras can be easily integrated into coastal Antarctic surveys to quantify structural complexity of under‐ice benthos via underwater photogrammetry. Forty‐four pairs of 1 m2 quadrats at 1 cm resolution, each comprising an orthomosaic and three‐dimensional reconstructions, were analyzed to describe relationships between benthic cover and structural complexity metrics. The study case provided insights into a unique biogenic habitat, highlighting the role of integrating structural complexity metrics in Antarctic benthic surveys. Although no clear relationships between structural complexity and biodiversity were found, high cover of live reef‐building polychaetes was associated with higher levels of structural complexity, particularly fractal dimension (D). Further, broken biogenic structures, product of disturbance events retain habitat structural complexity known to be associated with larvae settlement and biogenic reef growth. This suggests that D can be used as a metric for detecting subtle changes in biogenic structural complexity. We build from available open‐source code, a reproducible scientific workflow that is expected to facilitate the acquisition and analysis of structural complexity metrics. The workflow presented aims to encourage and accelerate the use of photogrammetry tools for benthic studies aiming to quantify biogenic structural complexity across depths and latitudes.

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“…Structure from Motion (SfM) is a relatively new branch of photogrammetry that can be applied to geospatially reconstruct seabed habitats whilst providing detailed descriptors regarding coral reef conditions and microhabitats (Ferrari et al, 2017;Pizarro et al, 2017;Conti, Lim, and Wheeler 2019;Price et al, 2019Price et al, , 2021Calders et al, 2020;Lim et al, 2020c;Urbina-barreto et al, 2021;Urbina-Barreto et al, 2022). In contrast to traditional photogrammetry, SfM uses algorithms such as Scale Invariant Feature Transform (SIFT) (Lowe 1999) to identify matching features in a set of overlapping images whilst calculating the variations in camera orientation and position of the matched features (Carrivick, Smith, and Quincey 2016).…”

Section: Introductionmentioning

confidence: 99%

High-resolution 3D mapping of cold-water coral reefs using machine learning

Oliveira

Lim

Conti

et al. 2022

Front. Environ. Sci.

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Structure-from-Motion (SfM) photogrammetry is a time and cost-effective method for high-resolution 3D mapping of cold-water corals (CWC) reefs and deep-water environments. The accurate classification and analysis of marine habitats in 3D provide valuable information for the development of management strategies for large areas at various spatial and temporal scales. Given the amount of data derived from SfM data sources such as Remotely-Operated Vehicles (ROV), there is an increasing need to advance towards automatic and semiautomatic classification approaches. However, the lack of training data, benchmark datasets for CWC environments and processing resources are a bottleneck for the development of classification frameworks. In this study, machine learning (ML) methods and SfM-derived 3D data were combined to develop a novel multiclass classification workflow for CWC reefs in deep-water environments. The Piddington Mound area, southwest of Ireland, was selected for 3D reconstruction from high-definition video data acquired with an ROV. Six ML algorithms, namely: Support Vector Machines, Random Forests, Gradient Boosting Trees, k-Nearest Neighbours, Logistic Regression and Multilayer Perceptron, were trained in two datasets of different sizes (1,000 samples and 10,000 samples) in order to evaluate accuracy variation between approaches in relation to the number of samples. The Piddington Mound was classified into four classes: live coral framework, dead coral framework, coral rubble and sediment and dropstones. Parameter optimisation was performed with grid search and cross-validation. Run times were measured to evaluate the trade-off between processing time and accuracy. In total, eighteen variations of ML algorithms were created and tested. The results show that four algorithms yielded f1-scores >90% and were able to discern between the four classes, especially those with usually similar characteristics, e.g., coral rubble and dead coral. The accuracy variation among them was 3.6% which suggests that they can be used interchangeably depending on the classification task. Furthermore, results on sample size variations show that certain algorithms benefit more from larger datasets whilst others showed discrete accuracy variations (<5%) when trained in datasets of different sizes.

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Underwater photogrammetry reveals new links between coral reefscape traits and fishes that ensure key functions

Cited by 9 publications

References 116 publications

A contemporary baseline of Madagascar’s coral assemblages: Reefs with high coral diversity, abundance, and function associated with marine protected areas

A contemporary baseline of Madagascar’s coral assemblages: Reefs with high coral diversity, abundance, and function associated with marine protected areas

Remote sensing of Antarctic polychaete reefs (Serpula narconensis): reproducible workflows for quantifying benthic structural complexity with action cameras, remotely operated vehicles and structure‐from‐motion photogrammetry

High-resolution 3D mapping of cold-water coral reefs using machine learning

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