# Artificial reef effectiveness changes among types as revealed by underwater hyperspectral imagery

Elisabeth Riera, Cédric Hubas, Mischa Ungermann, Guy Rigot, Alexis Pey, Patrice Francour, Francesca Rossi

► To cite this version:

Elisabeth Riera, Cédric Hubas, Mischa Ungermann, Guy Rigot, Alexis Pey, et al.. Artificial reef effectiveness changes among types as revealed by underwater hyperspectral imagery. Restoration Ecology, 2023, 31 (8), pp.e13978. 10.1111/rec.13978. hal-04170878

HAL Id: hal-04170878

https://hal.science/hal-04170878

Submitted on 29 May 2024

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<figure><img src="image_1.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
# Artificial reef effectiveness changes among types as revealed by underwater hyperspectral imagery

Elisabeth Riera, Cédric Hubas, Mischa Ungermann, Guy Rigot, Alexis Pey,

Patrice Francour, Francesca Rossi

► To cite this version:

Elisabeth Riera, Cédric Hubas, Mischa Ungermann, Guy Rigot, Alexis Pey, et al.. Artificial reef effectiveness changes among types as revealed by underwater hyperspectral imagery. Restoration Ecology, 2023, 31 (8), pp.e13978. 10.1111/rec.13978. hal-04170878

HAL Id: hal-04170878

https://hal.science/hal-04170878v1

Submitted on 29 May 2024

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_1.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
PRACTICE AND TECHNICAL ARTICLE

# Artificial reef effectiveness changes among types as revealed by underwater hyperspectral imagery

Elisabeth Riera<fnref n="1" />,<fnref n="2" />,<fnref n="3" /> ID, Cédric Hubas<fnref n="2" />, Mischa Ungermann<fnref n="4" />, Guy Rigot<fnref n="4" />, Alexis Pey<fnref n="5" />, Patrice Francour<fnref n="1" />, Francesca Rossi<fnref n="1" />,<fnref n="6" />

Artificial reefs (ARs) are designed to mimic natural habitats and promote marine life. Their effectiveness is however debatable and can depend on factors such as structural complexity and construction material. Old artificial reefs (OARs) were made of concrete mold of simple geometric shapes, limiting their ability to mimic the complexity of natural reefs. Recent advancements in three-dimensional (3D)-printing technology have enabled the creation of 3D-printed artificial reefs (3DRs) with biocompatible material and complex structures that can better simulate the natural habitats. We employed underwater hyperspectral technology to estimate the performance of these reefs and compare the benthic photosynthetic signal of natural reefs (NATs) with those of ARs (OARs and 3DRs) in coastal area of the north-western Mediterranean (France and Monaco Principality). We expected differences in reflectance signals between OARs and NATs, and signals closer to NATs in 3DRs than OARs. Underwater hyperspectral technology was able to detect higher chlorophyll-a derived signals on NATs than OARs. Moreover, the magnitude of differences between 3DRs and NATs was smaller than that between OARs and NATs. Although ARs were not capable of mimicking natural reefs, the use of 3D-printed ARs might ameliorate their effectiveness for coastal reconciliation.

**Key words:** 3D-printing, artificial reefs, ecological reconciliation, hyperspectral signal, photosynthetic benthic community

## Implications for Practice

* Monitoring of artificial reefs should be done by comparing their assemblages to those of natural habitats in order to assess their effectiveness for reconciliation.

* The type of substrate and structural complexity of the artificial reef should be considered for improving effectiveness.

* Underwater hyperspectral imaging is a promising approach for nondestructive method of marine benthic communities monitoring.

## Introduction

Artificial reefs (ARs), which are placed on the seabed to mimic some characteristics of natural reefs (NATs) (Pickering et al. 1998), are nowadays used to ameliorate the ecological conditions of key habitats (Coloul et al. 2020). They should thus be constructed based on the ecological knowledge of the natural system, respecting natural habitat complexity and using substrates made of inert materials (UNEP 2009). So far, when used as “natural surrogates,” old-generation of ARs, mainly built of concrete casting of simple geometric shape, have shown to support less diversity than adjacent natural habitats (Ferrario et al. 2016; Hill et al. 2021). The three-dimensional (3D)-printing technology can contribute to improve this use of ARs, by enhancing design complexity and supporting the use of biocompatible materials, as recently observed for coral reefs (Levy et al. 2022). Such a structure may be more effective in

facilitating the settlement of communities that better mimic the characteristics of natural assemblages than the old generation of ARs.

The effectiveness of ARs in enhancing marine life has been mainly evaluated using fish monitoring (Paxton et al. 2020). Monitoring benthos is, however, fundamental since ARs are “fouling objects” and their benthic assemblages are expected, over time, to converge toward natural communities (Svane & Petersen 2001). Benthic communities are sampled by scraping or using photoquadrat, which are destructive and/or time-consuming methods. Recent advances in underwater

Author contributions: ER, CH, PF, FR conceived and designed the research; ER, MU, GR analyzed the data; ER, AP, RG performed the sampling field work; ER, CH, AP, MU, GR, FR wrote and edited the manuscript.

<footnote n="1">¹Université Côte d'Azur, UMR 7035 ECOSEAS, CNRS, Parc Valrose, 06108 Nice Cedex 02, France</footnote>

<footnote n="2">²Muséum National d'Histoire Naturelle de Paris, UMR 8062 BOREA, Sorbonne Université, CNRS, UCN, UA, IRD, Station de Biologie Marine, Concarneau, France</footnote>

<footnote n="3">³Address correspondence to E. Riera, email riera.elisabeth13@gmail.com</footnote>

<footnote n="4">⁴planblue GmbH, BITZ—Bremen Innovation and Technology Center, Fahrenheitstrasse 1, 28359 Bremen, Germany</footnote>

<footnote n="5">⁵THALASSA Marine Research & Environmental Awareness, 286 F route d'Aspremont, 06690 Tourrette-Levens, France</footnote>

<footnote n="6">⁶Department of Integrative Marine Ecology (EMI), Genoa Marine Centre (GMC), Stazione Zoologica Anton Dohrm—National Institute of Marine Biology, Ecology and Biotechnology, Villa del Principe, Piazza del Principe 4, 16126 Genoa, Italy</footnote>

<footnote>© 2023 The Authors. Restoration Ecology published by Wiley Periodicals LLC on behalf of Society for Ecological Restoration.
This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.</footnote>

<footnote>doi: 10.1111/rec.13978</footnote>

<footnote>Supporting information at:</footnote>

<footnote>http://onlinelibrary.wiley.com/doi/10.1111/rec.13978/suppinfo</footnote>
PRACTICE AND TECHNICAL ARTICLE

# Artificial reef effectiveness changes among types as revealed by underwater hyperspectral imagery

Elisabeth Riera<fnref n="1" />,<fnref n="2" />,<fnref n="3" /> ID, Cédric Hubas<fnref n="2" />, Mischa Ungermann<fnref n="4" />, Guy Rigot<fnref n="4" />, Alexis Pey<fnref n="5" />, Patrice Francour<fnref n="1" />, Francesca Rossi<fnref n="1" />,<fnref n="6" />

Artificial reefs (ARs) are designed to mimic natural habitats and promote marine life. Their effectiveness is however debatable and can depend on factors such as structural complexity and construction material. Old artificial reefs (OARs) were made of concrete mold of simple geometric shapes, limiting their ability to mimic the complexity of natural reefs. Recent advancements in three-dimensional (3D)-printing technology have enabled the creation of 3D-printed artificial reefs (3DRs) with biocompatible material and complex structures that can better simulate the natural habitats. We employed underwater hyperspectral technology to estimate the performance of these reefs and compare the benthic photosynthetic signal of natural reefs (NATs) with those of ARs (OARs and 3DRs) in coastal area of the north-western Mediterranean (France and Monaco Principality). We expected differences in reflectance signals between OARs and NATs, and signals closer to NATs in 3DRs than OARs. Underwater hyperspectral technology was able to detect higher chlorophyll-a derived signals on NATs than OARs. Moreover, the magnitude of differences between 3DRs and NATs was smaller than that between OARs and NATs. Although ARs were not capable of mimicking natural reefs, the use of 3D-printed ARs might ameliorate their effectiveness for coastal reconciliation.

**Key words:** 3D-printing, artificial reefs, ecological reconciliation, hyperspectral signal, photosynthetic benthic community

## Implications for Practice

* Monitoring of artificial reefs should be done by comparing their assemblages to those of natural habitats in order to assess their effectiveness for reconciliation.

* The type of substrate and structural complexity of the artificial reef should be considered for improving effectiveness.

* Underwater hyperspectral imaging is a promising approach for nondestructive method of marine benthic communities monitoring.

## Introduction

Artificial reefs (ARs), which are placed on the seabed to mimic some characteristics of natural reefs (NATs) (Pickering et al. 1998), are nowadays used to ameliorate the ecological conditions of key habitats (Coloul et al. 2020). They should thus be constructed based on the ecological knowledge of the natural system, respecting natural habitat complexity and using substrates made of inert materials (UNEP 2009). So far, when used as “natural surrogates,” old-generation of ARs, mainly built of concrete casting of simple geometric shape, have shown to support less diversity than adjacent natural habitats (Ferrario et al. 2016; Hill et al. 2021). The three-dimensional (3D)-printing technology can contribute to improve this use of ARs, by enhancing design complexity and supporting the use of biocompatible materials, as recently observed for coral reefs (Levy et al. 2022). Such a structure may be more effective in

facilitating the settlement of communities that better mimic the characteristics of natural assemblages than the old generation of ARs.

The effectiveness of ARs in enhancing marine life has been mainly evaluated using fish monitoring (Paxton et al. 2020). Monitoring benthos is, however, fundamental since ARs are “fouling objects” and their benthic assemblages are expected, over time, to converge toward natural communities (Svane & Petersen 2001). Benthic communities are sampled by scraping or using photoquadrat, which are destructive and/or time-consuming methods. Recent advances in underwater

Author contributions: ER, CH, PF, FR conceived and designed the research; ER, MU, GR analyzed the data; ER, AP, RG performed the sampling field work; ER, CH, AP, MU, GR, FR wrote and edited the manuscript.

<footnote n="1">¹Université Côte d'Azur, UMR 7035 ECOSEAS, CNRS, Parc Valrose, 06108 Nice Cedex 02, France</footnote>

<footnote n="2">²Muséum National d'Histoire Naturelle de Paris, UMR 8062 BOREA, Sorbonne Université, CNRS, UCN, UA, IRD, Station de Biologie Marine, Concarneau, France</footnote>

<footnote n="3">³Address correspondence to E. Riera, email riera.elisabeth13@gmail.com</footnote>

<footnote n="4">⁴planblue GmbH, BITZ—Bremen Innovation and Technology Center, Fahrenheitstrasse 1, 28359 Bremen, Germany</footnote>

<footnote n="5">⁵THALASSA Marine Research & Environmental Awareness, 286 F route d'Aspremont, 06690 Tourrette-Levens, France</footnote>

<footnote n="6">⁶Department of Integrative Marine Ecology (EMI), Genoa Marine Centre (GMC), Stazione Zoologica Anton Dohrm—National Institute of Marine Biology, Ecology and Biotechnology, Villa del Principe, Piazza del Principe 4, 16126 Genoa, Italy</footnote>

<footnote>© 2023 The Authors. Restoration Ecology published by Wiley Periodicals LLC on behalf of Society for Ecological Restoration.
This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.</footnote>

<footnote>doi: 10.1111/rec.13978</footnote>

<footnote>Supporting information at:</footnote>

<footnote>http://onlinelibrary.wiley.com/doi/10.1111/rec.13978/suppinfo</footnote>
hyperspectral (HP) imaging could be valuable for greatly improving fast data collection nondestructively. HP imaging allows integrating spatial information of an object with its spectral characteristics, by combining imaging and spectrometry (Liu et al. 2020). It has been widely used for monitoring terrestrial and shallow coastal habitats since the early 2000s (Khan et al. 2018) and, more recently, underwater, as an emerging tool for coral reefs (Chennu et al. 2017) or deep-sea benthic megafauna (Dumke et al. 2018).

This case study investigates how photosynthetic benthic assemblages differ between natural and artificial reefs types using underwater HP imagery to detect these putative differences. We expected that underwater HP imagery would detect the spectral signature of photosynthetic communities and discriminate between natural and artificial reefs and between AR types (old-generation and 3D-printed reefs). We also expect that the magnitude of differences between natural reefs and artificial reefs will be reduced using 3D-printed reefs.

## Methods

### HP Camera

We used the DiveRay camera, a dive operator prototype version of a commercial hardware solution provided by planblue GmbH (https://www.planblue.com/) to scan the seafloor. It includes red-green-blue (RGB, resolution: 2448 × 2048 pixels) and HP (resolution: 680 pixels across track resolution, Field of view of approximately 30°, spectral resolution: approximately 2.9 nm) cameras, equipped with two modular lights (intensity: 4215 lm each). Distance to the seabed is derived from stereo camera measurements. Depth, position, and velocity are derived from a combination of sensors, including a Doppler velocity log measurement, a magnetometer, and an inertial measurement unit. This information is available on the display screen of the DiveRay in addition to the view of the scanned habitat (in HP or RGB format as required). The DiveRay is neutrally buoyant underwater, making it extremely flexible to handle, allowing the camera to be oriented in any direction the diver wishes. A reference board (a gray-level plastic board that reflects 55% of the light) must be placed at the beginning and end of each transect for data processing afterwards. When recorded at the beginning and end of the transect, and at similar distances under consistent artificial lighting conditions, it serves to compensate for variations in natural light penetration into the water column for each transect scanned. After each dive, data are transferred from the DiveRay to a computer. The data are composed of RGB, HP, and stereo (3D) video files, and geo-positional data.

### Study Location and Sampling Design

The sampling took place during the last 2 weeks of June 2020 along the northern-western Mediterranean coastline. We sampled within urban marine protected area (MPA) of the Principality of Monaco (MON, in Fig. 1) managed by the Monegasque Association for Nature Protection (Debernardi &

Allemand 1993), and within the MPA of Roquebrune Cap Martin (RCM, in Fig. 1) managed by "Service de l'ingénierie environnementale du Département des Alpes Maritimes." ARs have been immersed for several years in these MPA for reconciliation measures and fishing support (Allemand et al. 2000; Tessier et al. 2015). We sampled natural reefs (NATs) as control, old artificial reefs (OARs), and the 3D-printed artificial reefs (3DRs). The latter was present only in MON area and therefore was sampled (Fig. 1). The ARs were at comparable depths (30 m), and orientations, in similar conditions with regard to northern Mediterranean current as the natural reefs, and at maximum 2 km apart (see details on sampling effort and reef details in Fig. 1).

Scuba divers recorded the vertical surfaces of the ARs because they were oriented similarly to the natural reefs. Measurement were taken with the DiveRay camera at a constant distance, maintained by the diver, about 80 cm from the reef scanned. Recorded transects were approximately 42-cm wide and 6–20 m long as a function of the circumference of the reef unit, and we ran 6 m linear transects on the natural reefs (Fig. 1).

### HP Data Processing

To generate HP data's true- and false-color representations, we used a program delivered by planblue. We then annotated regions of interest (ROIs) for each scanned transect: including the two reference boards recorded at the beginning and end of each transect, and ROIs of benthic community. We were careful to avoid void spaces of the structure. All annotations are stored in a database collecting spectral, spatial, and environmental data. These recorded spectra of each ROI are standardized to unit range and divided by the average spectrum of the reference board recorded at the beginning and end of each transect. That way, the effects of light conditions on different recordings can be accounted for. Among the 302 annotated ROIs (all types of reef), we randomly selected 30 ROIs for each reef type to standardize our sampling effort. We standardized their size by 12 pixels in width and 444 pixels in length, corresponding to the reference board size (25 × 25 cm).

### Wavelength Selection

In our study, we used the second derivative ($R''$) of the reflectance signal to assess the presence of photosynthetic pigments. We employed two methods: (1) converting positive and negative $R''$ values into a binary presence/absence as a cover proxy and (2) using the positive values of $R''$ as a semiquantitative proxy for pigment intensity. The second derivative spectrum was computed from the reflectance spectrum for each pixel in each ROI with the Savitzky-Golay algorithm (Savitzky & Golay 1964).

Using wavelengths (WLs) correspondence from the literature, we characterized the overall phototrophic signal by the chlorophylls *a*, *b*, *c*, and accessory photosynthetic pigments (fucoxanthin [Fuco], phycoerythrin [PhycoE], and phycocyanin [PhycoC]) to distinguish green, brown, and red algae (Table 1; Egeland et al. 2011; Mogstad & Johnsen 2017).

The different steps of the HP data process were done on Python (3.6.15) and are summarized in a diagram (Fig. S1).
hyperspectral (HP) imaging could be valuable for greatly improving fast data collection nondestructively. HP imaging allows integrating spatial information of an object with its spectral characteristics, by combining imaging and spectrometry (Liu et al. 2020). It has been widely used for monitoring terrestrial and shallow coastal habitats since the early 2000s (Khan et al. 2018) and, more recently, underwater, as an emerging tool for coral reefs (Chennu et al. 2017) or deep-sea benthic megafauna (Dumke et al. 2018).

This case study investigates how photosynthetic benthic assemblages differ between natural and artificial reefs types using underwater HP imagery to detect these putative differences. We expected that underwater HP imagery would detect the spectral signature of photosynthetic communities and discriminate between natural and artificial reefs and between AR types (old-generation and 3D-printed reefs). We also expect that the magnitude of differences between natural reefs and artificial reefs will be reduced using 3D-printed reefs.

# Methods

## HP Camera

We used the DiveRay camera, a dive operator prototype version of a commercial hardware solution provided by planblue GmbH (https://www.planblue.com/) to scan the seafloor. It includes red-green-blue (RGB, resolution: 2448 × 2048 pixels) and HP (resolution: 680 pixels across track resolution, Field of view of approximately 30°, spectral resolution: approximately 2.9 nm) cameras, equipped with two modular lights (intensity: 4215 lm each). Distance to the seabed is derived from stereo camera measurements. Depth, position, and velocity are derived from a combination of sensors, including a Doppler velocity log measurement, a magnetometer, and an inertial measurement unit. This information is available on the display screen of the DiveRay in addition to the view of the scanned habitat (in HP or RGB format as required). The DiveRay is neutrally buoyant underwater, making it extremely flexible to handle, allowing the camera to be oriented in any direction the diver wishes. A reference board (a gray-level plastic board that reflects 55% of the light) must be placed at the beginning and end of each transect for data processing afterwards. When recorded at the beginning and end of the transect, and at similar distances under consistent artificial lighting conditions, it serves to compensate for variations in natural light penetration into the water column for each transect scanned. After each dive, data are transferred from the DiveRay to a computer. The data are composed of RGB, HP, and stereo (3D) video files, and geo-positional data.

## Study Location and Sampling Design

The sampling took place during the last 2 weeks of June 2020 along the northern-western Mediterranean coastline. We sampled within urban marine protected area (MPA) of the Principality of Monaco (MON, in Fig. 1) managed by the Monegasque Association for Nature Protection (Debernardi &

Allemand 1993), and within the MPA of Roquebrune Cap Martin (RCM, in Fig. 1) managed by "Service de l'ingénierie environnementale du Département des Alpes Maritimes." ARs have been immersed for several years in these MPA for reconciliation measures and fishing support (Allemand et al. 2000; Tessier et al. 2015). We sampled natural reefs (NATs) as control, old artificial reefs (OARs), and the 3D-printed artificial reefs (3DRs). The latter was present only in MON area and therefore was sampled (Fig. 1). The ARs were at comparable depths (30 m), and orientations, in similar conditions with regard to northern Mediterranean current as the natural reefs, and at maximum 2 km apart (see details on sampling effort and reef details in Fig. 1).

Scuba divers recorded the vertical surfaces of the ARs because they were oriented similarly to the natural reefs. Measurement were taken with the DiveRay camera at a constant distance, maintained by the diver, about 80 cm from the reef scanned. Recorded transects were approximately 42-cm wide and 6–20 m long as a function of the circumference of the reef unit, and we ran 6 m linear transects on the natural reefs (Fig. 1).

## HP Data Processing

To generate HP data's true- and false-color representations, we used a program delivered by planblue. We then annotated regions of interest (ROIs) for each scanned transect: including the two reference boards recorded at the beginning and end of each transect, and ROIs of benthic community. We were careful to avoid void spaces of the structure. All annotations are stored in a database collecting spectral, spatial, and environmental data. These recorded spectra of each ROI are standardized to unit range and divided by the average spectrum of the reference board recorded at the beginning and end of each transect. That way, the effects of light conditions on different recordings can be accounted for. Among the 302 annotated ROIs (all types of reef), we randomly selected 30 ROIs for each reef type to standardize our sampling effort. We standardized their size by 12 pixels in width and 444 pixels in length, corresponding to the reference board size (25 × 25 cm).

## Wavelength Selection

In our study, we used the second derivative ($R''$) of the reflectance signal to assess the presence of photosynthetic pigments. We employed two methods: (1) converting positive and negative $R''$ values into a binary presence/absence as a cover proxy and (2) using the positive values of $R''$ as a semiquantitative proxy for pigment intensity. The second derivative spectrum was computed from the reflectance spectrum for each pixel in each ROI with the Savitzky-Golay algorithm (Savitzky & Golay 1964).

Using wavelengths (WLs) correspondence from the literature, we characterized the overall phototrophic signal by the chlorophylls *a*, *b*, *c*, and accessory photosynthetic pigments (fucoxanthin [Fuco], phycoerythrin [PhycoE], and phycocyanin [PhycoC]) to distinguish green, brown, and red algae (Table 1; Egeland et al. 2011; Mogstad & Johnsen 2017).

The different steps of the HP data process were done on Python (3.6.15) and are summarized in a diagram (Fig. S1).
<figure><img src="image_2.png" /></figure>

<table><caption>Figure 1. Sampling scheme and table description of the scanned habitats. NAT: natural reef, OAR: old artificial reef, 3DR: 3D-printed artificial reef, MON: Monaco Principality, RCM: Roquebrune Cap Martin.</caption><thead><tr><th>Habitats</th><th>Location</th><th>Immersion</th><th>Description</th><th>Design</th><th>Materials</th><th>Replication</th></tr></thead><tbody><tr><td>OAR_MON</td><td>43.74559°N,<br>7.439978°E<br>(MON)</td><td>In 1979<br>At 30 m deep<br>On sandy bottom</td><td>6 unit reefs<br>made of assembled<br>breeze block<br>(2 × 2 × 1 m, 6 m<br>circumference)</td><td>simple<br>geometric<br>design<br>displaying flat<br>surfaces</td><td>ordinary<br>concrete<br>(pH 13)</td><td>6<br>(1 transect/unit)<br>total 36 m scanned</td></tr><tr><td>OAR_RCM</td><td>43.75158°N,<br>7.449783°E<br>(RCM)</td><td>In 1991<br>At 30 m deep<br>On sandy bottom</td><td>4 unit reefs<br>made of an aggregate<br>of 36 cube<br>(1 × 1 × 1 m, 20 m<br>circumference)</td><td></td><td></td><td>4<br>(1 transect/unit)<br>total 80 m scanned</td></tr><tr><td>3DR_MON</td><td>43.74582°N,<br>7.440709°E<br>(MON)</td><td>In 2017<br>At 30 m deep<br>On sandy bottom</td><td>6 unit reefs<br>made by 3D-printing<br>(2 × 2 × 1 m, 6 m<br>circumference)</td><td>mimicking the<br>topography of<br>the adjacent<br>natural habitat</td><td>dolomite sand,<br>white<br>concrete,<br>volcanic ashes<br>(pH 8.3)</td><td>6<br>(1 transect/unit)<br>total 36 m scanned</td></tr><tr><td>NAT_MON</td><td>43.748664°N,<br>7.485529°E<br>(MON)</td><td rowspan="2">At 30 m deep</td><td rowspan="2">Coralligenous rims: a biogenic hard substratum of calcareous encrusting red algae (genus Mesophyllum and Pseudolithophyllum) and animal builders (bryozoans, serpulid polychaetes, cnidarians, molluscs, sponges, crustaceans and foraminifera) growing in dim light conditions (Ballesteros 2006)</td><td rowspan="2"></td><td rowspan="2"></td><td>6<br>(6 transects/rims)<br>total 36 m scanned</td></tr><tr><td>NAT_RCM</td><td>43.737778°N,<br>7.429833°E<br>(RCM)</td><td></td><td>6<br>(6 transects/rims)<br>total 36 m scanned</td></tr></tbody></table>

Data and Python script to process it are respectively available on Zenodo (https://doi.org/10.5281/zenodo.7054415) and GitHub (https://github.com/ELI-RIERA/HYPER3D).

False-Positive Photosynthetic Signal

To ensure the accuracy of data processing and WL identification, we visually inspected all HP images in conjunction with
<figure><img src="image_2.png" /></figure>

<table><caption>Figure 1. Sampling scheme and table description of the scanned habitats. NAT: natural reef, OAR: old artificial reef, 3DR: 3D-printed artificial reef, MON: Monaco Principality, RCM: Roquebrune Cap Martin.</caption><thead><tr><th>Habitats</th><th>Location</th><th>Immersion</th><th>Description</th><th>Design</th><th>Materials</th><th>Replication</th></tr></thead><tbody><tr><td>OAR_MON</td><td>43.74559°N,<br>7.439978°E<br>(MON)</td><td>In 1979<br>At 30 m deep<br>On sandy bottom</td><td>6 unit reefs<br>made of assembled<br>breeze block<br>(2 × 2 × 1 m, 6 m<br>circumference)</td><td>simple<br>geometric<br>design<br>displaying flat<br>surfaces</td><td>ordinary<br>concrete<br>(pH 13)</td><td>6<br>(1 transect/unit)<br>total 36 m scanned</td></tr><tr><td>OAR_RCM</td><td>43.75158°N,<br>7.449783°E<br>(RCM)</td><td>In 1991<br>At 30 m deep<br>On sandy bottom</td><td>4 unit reefs<br>made of an aggregate<br>of 36 cube<br>(1 × 1 × 1 m, 20 m<br>circumference)</td><td></td><td></td><td>4<br>(1 transect/unit)<br>total 80 m scanned</td></tr><tr><td>3DR_MON</td><td>43.74582°N,<br>7.440709°E<br>(MON)</td><td>In 2017<br>At 30 m deep<br>On sandy bottom</td><td>6 unit reefs<br>made by 3D-printing<br>(2 × 2 × 1 m, 6 m<br>circumference)</td><td>mimicking the<br>topography of<br>the adjacent<br>natural habitat</td><td>dolomite sand,<br>white<br>concrete,<br>volcanic ashes<br>(pH 8.3)</td><td>6<br>(1 transect/unit)<br>total 36 m scanned</td></tr><tr><td>NAT_MON</td><td>43.748664°N,<br>7.485529°E<br>(MON)</td><td rowspan="2">At 30 m deep</td><td rowspan="2">Coralligenous rims: a biogenic hard substratum of calcareous encrusting red algae (genus Mesophyllum and Pseudolithophyllum) and animal builders (bryozoans, serpulid polychaetes, cnidarians, molluscs, sponges, crustaceans and foraminiferans) growing in dim light conditions (Ballesteros 2006)</td><td rowspan="2"></td><td rowspan="2"></td><td>6<br>(6 transects/rims)<br>total 36 m scanned</td></tr><tr><td>NAT_RCM</td><td>43.737778°N,<br>7.429833°E<br>(RCM)</td><td>6<br>(6 transects/rims)<br>total 36 m scanned</td></tr></tbody></table>

Data and Python script to process it are respectively available on Zenodo (https://doi.org/10.5281/zenodo.7054415) and GitHub (https://github.com/ELI-RIERA/HYPER3D).

False-Positive Photosynthetic Signal

To ensure the accuracy of data processing and WL identification, we visually inspected all HP images in conjunction with
<table><caption>**Table 1.** Characterization of green, brown, red algae, and cyanobacteria according to their pigment content related to their associated wavelength in our hyperspectral system (+ means the presence of the pigment, while — means its absence).</caption><thead><tr><th></th><th>666 nm<br>(chlorophyll-a)</th><th>648 nm<br>(chlorophyll-b)</th><th>635 nm<br>(chlorophyll-c)</th><th>546 nm<br>(fucoxanthin)</th><th>620 nm<br>(phycocyanin)</th><th>567 nm<br>(phycoerythrin)</th></tr></thead><tbody><tr><td>Green algae</td><td>+</td><td>+</td><td>—</td><td>—</td><td>—</td><td>—</td></tr><tr><td>Brown algae</td><td>+</td><td>—</td><td>+</td><td>+</td><td>—</td><td>—</td></tr><tr><td>Red algae</td><td>+</td><td>—</td><td>+</td><td>—</td><td>+</td><td>+</td></tr><tr><td>Cyanobacteria</td><td>+</td><td>—</td><td>—</td><td>—</td><td>+</td><td>+</td></tr></tbody></table>

the RGB images from ROIs. We also checked if the WLs we associate with pigments of photoautotrophs could be linked to other possible signals in the spectral data. The 567-nm WL that was associated with the presence of PhycoE pigment occured not only in red algae but also in the sponge *Crambe crambe* (Fig. S2). Moreover, both PhycoE and PhycoC can be present in microbial cyanobacteria biofilm that can cover every organic or nonorganic surface (Callow & Callow 2006; Salta et al. 2013). Therefore, we were cautious with our interpretation regarding these WLs and used ratio of Chl-a/PhycoE and Chl-c/PhycoE to cross check out results. An example of this cross-check evaluation is available in Figure S2.

## Statistical Analysis

We ran all analyses using R v3.4.1. We fitted generalized linear models (GLMs) on the mean signal intensity of pixels for each selected WL in each selected ROI. To quantify the differences between reef types (old artificial and natural reefs; hereafter OARs and NATs), we combined reef types and areas (MON and RCM) in a new factor, which we named “Habitat”. We did this because we observed large variability between these areas, which could mask differences between AR types (see results in Figs. 2 & 3). The factor “transect” was nested in “Habitat.” We expected significant differences between OAR and NAT situated both in MON and RCM. To estimate the

<figure><img src="image_3.png" /><figcaption>**Figure 2.** Mean second derivative of the reflectance signal (R'') of photosynthetic pigments at both areas (MON, Principality of Monaco; RCM, Roquebrune Cap Martin) for each reef type (3DR, 3D printed reefs; NAT, natural reefs; OAR, old artificial reefs). The gray shades represent minimum and maximum values of the 150 ROIs extracted from each transect scanned. The dotted lines indicate the WLs related to photopigment signals (546 nm: fucoxanthin, 567 nm: phycoerythrin, 620 nm: phycocyanin, 635 nm: chlorophyll-c, 648 nm: chlorophyll-b, 666 nm: chlorophyll-a).</figcaption></figure>
<table><caption>**Table 1.** Characterization of green, brown, red algae, and cyanobacteria according to their pigment content related to their associated wavelength in our hyperspectral system (+ means the presence of the pigment, while – means its absence).</caption><thead><tr><th></th><th>666 nm<br>(chlorophyll-a)</th><th>648 nm<br>(chlorophyll-b)</th><th>635 nm<br>(chlorophyll-c)</th><th>546 nm<br>(fucoxanthin)</th><th>620 nm<br>(phycocyanin)</th><th>567 nm<br>(phycoerythrin)</th></tr></thead><tbody><tr><td>Green algae</td><td>+</td><td>+</td><td>—</td><td>—</td><td>—</td><td>—</td></tr><tr><td>Brown algae</td><td>+</td><td>—</td><td>+</td><td>+</td><td>—</td><td>—</td></tr><tr><td>Red algae</td><td>+</td><td>—</td><td>+</td><td>—</td><td>+</td><td>+</td></tr><tr><td>Cyanobacteria</td><td>+</td><td>—</td><td>—</td><td>—</td><td>+</td><td>+</td></tr></tbody></table>

the RGB images from ROIs. We also checked if the WLs we associate with pigments of photoautotrophs could be linked to other possible signals in the spectral data. The 567-nm WL that was associated with the presence of PhycoE pigment occured not only in red algae but also in the sponge *Crambe crambe* (Fig. S2). Moreover, both PhycoE and PhycoC can be present in microbial cyanobacteria biofilm that can cover every organic or nonorganic surface (Callow & Callow 2006; Salta et al. 2013). Therefore, we were cautious with our interpretation regarding these WLs and used ratio of Chl-a/PhycoE and Chl-c/PhycoE to cross check out results. An example of this cross-check evaluation is available in Figure S2.

## Statistical Analysis

We ran all analyses using R v3.4.1. We fitted generalized linear models (GLMs) on the mean signal intensity of pixels for each selected WL in each selected ROI. To quantify the differences between reef types (old artificial and natural reefs; hereafter OARs and NATs), we combined reef types and areas (MON and RCM) in a new factor, which we named “Habitat”. We did this because we observed large variability between these areas, which could mask differences between AR types (see results in Figs. 2 & 3). The factor “transect” was nested in “Habitat.” We expected significant differences between OAR and NAT situated both in MON and RCM. To estimate the

<figure><img src="image_3.png" /><figcaption>**Figure 2.** Mean second derivative of the reflectance signal (R") of photosynthetic pigments at both areas (MON, Principality of Monaco; RCM, Roquebrune Cap Martin) for each reef type (3DR, 3D printed reefs; NAT, natural reefs; OAR, old artificial reefs). The gray shades represent minimum and maximum values of the 150 ROIs extracted from each transect scanned. The dotted lines indicate the WLs related to photopigment signals (546 nm: fucoxanthin, 567 nm: phycoerythrin, 620 nm: phycocyanin, 635 nm: chlorophyll-c, 648 nm: chlorophyll-b, 666 nm: chlorophyll-a).</figcaption></figure>
<figure><img src="image_4.png" /><figcaption>Figure 3. Nonmetric multidimensional scaling (nMDS) based on the triangular matrices calculated using the Bray–Curtis dissimilarity index on the multivariate data matrices for second derivative of the reflectance signal (R") of photosynthetic pigments on 3D printed reefs (3DR), natural reefs (NAT), and old artificial reefs (OAR) scanned at Monaco Principality (MON); and on NAT and OAR at Roquebrune Cap Martin (RCM). Bray–Curtis dissimilarities between factors: MON_3DR vs. MON_NAT: 25%, MON_3DR vs. MON_OAR: 26%, MON_OAR vs. MON_NAT: 36%, RCM_OAR vs. RCM_NAT: 16%; RCM_NAT vs. MON_NAT: 12%; RCM_OAR vs. MON_OAR: 37%. Both ordinations are on the same axes. The convex hulls of the reef type were drawn and calculated based on the ordination results.</figcaption></figure>

differences between the 3D-printed reefs (3DRs), the OARs and the NATs on MON, we used the factor “Reef type” (NATs, OARs, and 3DRs) and the factor “transect” nested in “Reef type.” The factor transect was random, except for multivariate data as it was not implemented in the package.

Multivariate data (mean R'' for each WL) were analyzed after square-root transformation using the function manylm from the “mvabund” package (Wang et al. 2020). These data were

represented by an nMDS to visualize the dissimilarity of the HP signal using Bray–Curtis distance with the function vegdist from the “vegan” package (Oksasen et al. 2022). Percentage dis-similarity between factors is also shown in the Results section.

For the univariate data, we used the glmmTMB function from the “glmmTMB” package (Brooks 2022). The family distribu-tion was selected after visually examining the scaled residual using the simulateResiduals function from the package

<table><caption>**Table 2.** Mean and standard deviation of the percentage cover of the WL related to chlorophyll-a (Chl-a), phycoerythrin (PhycoE), chlorophyll-c (Chl-c), phycocyanin (PhycoC), fucoxanthin (Fuco), and chlorophyll-b (Chl-b), on the different reefs samples (see Fig. 1 for the legend).</caption><thead><tr><th rowspan="2">Habitats</th><th colspan="2">666 nm<br>(Chl-a cov.)</th><th colspan="2">567 nm<br>(PhycoE cov.)</th><th colspan="2">635 nm<br>(Chl-c cov.)</th><th colspan="2">620 nm<br>(PhycoC cov.)</th><th colspan="2">546 nm<br>(Fuco cov.)</th><th colspan="2">648 nm<br>(Chl-b cov.)</th></tr><tr><th>Mean</th><th>SD</th><th>Mean</th><th>SD</th><th>Mean</th><th>SD</th><th>Mean</th><th>SD</th><th>Mean</th><th>SD</th><th>Mean</th><th>SD</th></tr></thead><tbody><tr><td>RCM_NAT</td><td>99.59</td><td>0.63</td><td>94.94</td><td>4.79</td><td>85.60</td><td>6.96</td><td>86.41</td><td>4.86</td><td>70.77</td><td>5.75</td><td>3.71</td><td>4.04</td></tr><tr><td>RCM_OAR</td><td>96.34</td><td>5.10</td><td>88.83</td><td>9.88</td><td>87.87</td><td>9.97</td><td>79.14</td><td>10.25</td><td>77.41</td><td>6.20</td><td>22.95</td><td>23.66</td></tr><tr><td>MON_OAR</td><td>90.09</td><td>8.25</td><td>88.40</td><td>9.13</td><td>57.27</td><td>18.34</td><td>62.02</td><td>16.60</td><td>80.68</td><td>8.82</td><td>40.48</td><td>24.73</td></tr><tr><td>MON_NAT</td><td>98.55</td><td>1.92</td><td>93.48</td><td>4.39</td><td>70.53</td><td>13.90</td><td>78.94</td><td>9.07</td><td>69.78</td><td>1.06</td><td>10.45</td><td>12.26</td></tr><tr><td>MON_3DR</td><td>97.76</td><td>3.15</td><td>63.26</td><td>13.98</td><td>78.38</td><td>6.89</td><td>57.18</td><td>13.97</td><td>74.61</td><td>7.63</td><td>36.16</td><td>17.88</td></tr></tbody></table>
<figure><img src="image_4.png" /><figcaption>Figure 3. Nonmetric multidimensional scaling (nMDS) based on the triangular matrices calculated using the Bray–Curtis dissimilarity index on the multivariate data matrices for second derivative of the reflectance signal (R") of photosynthetic pigments on 3D printed reefs (3DR), natural reefs (NAT), and old artificial reefs (OAR) scanned at Monaco Principality (MON); and on NAT and OAR at Roquebrune Cap Martin (RCM). Bray–Curtis dissimilarities between factors: MON_3DR vs. MON_NAT: 25%, MON_3DR vs. MON_OAR: 26%, MON_OAR vs. MON_NAT: 36%, RCM_OAR vs. RCM_NAT: 16%; RCM_NAT vs. MON_NAT: 12%; RCM_OAR vs. MON_OAR: 37%. Both ordinations are on the same axes. The convex hulls of the reef type were drawn and calculated based on the ordination results.</figcaption></figure>

differences between the 3D-printed reefs (3DRs), the OARs and the NATs on MON, we used the factor “Reef type” (NATs, OARs, and 3DRs) and the factor “transect” nested in “Reef type.” The factor transect was random, except for multivariate data as it was not implemented in the package.

Multivariate data (mean R'' for each WL) were analyzed after square-root transformation using the function manylm from the “mvabund” package (Wang et al. 2020). These data were

represented by an nMDS to visualize the dissimilarity of the HP signal using Bray–Curtis distance with the function vegdist from the “vegan” package (Oksasen et al. 2022). Percentage dis-similarity between factors is also shown in the Results section.

For the univariate data, we used the glmmTMB function from the “glmmTMB” package (Brooks 2022). The family distribu-tion was selected after visually examining the scaled residual using the simulateResiduals function from the package

<table><caption>**Table 2.** Mean and standard deviation of the percentage cover of the WL related to chlorophyll-a (Chl-a), phycoerythrin (PhycoE), chlorophyll-c (Chl-c), phycocyanin (PhycoC), fucoxanthin (Fuco), and chlorophyll-b (Chl-b), on the different reefs samples (see Fig. 1 for the legend).</caption><thead><tr><th rowspan="2">Habitats</th><th colspan="2">666 nm<br>(Chl-a cov.)</th><th colspan="2">567 nm<br>(PhycoE cov.)</th><th colspan="2">635 nm<br>(Chl-c cov.)</th><th colspan="2">620 nm<br>(PhycoC cov.)</th><th colspan="2">546 nm<br>(Fuco cov.)</th><th colspan="2">648 nm<br>(Chl-b cov.)</th></tr><tr><th>Mean</th><th>SD</th><th>Mean</th><th>SD</th><th>Mean</th><th>SD</th><th>Mean</th><th>SD</th><th>Mean</th><th>SD</th><th>Mean</th><th>SD</th></tr></thead><tbody><tr><td>RCM_NAT</td><td>99.59</td><td>0.63</td><td>94.94</td><td>4.79</td><td>85.60</td><td>6.96</td><td>86.41</td><td>4.86</td><td>70.77</td><td>5.75</td><td>3.71</td><td>4.04</td></tr><tr><td>RCM_OAR</td><td>96.34</td><td>5.10</td><td>88.83</td><td>9.88</td><td>87.87</td><td>9.97</td><td>79.14</td><td>10.25</td><td>77.41</td><td>6.20</td><td>22.95</td><td>23.66</td></tr><tr><td>MON_OAR</td><td>90.09</td><td>8.25</td><td>88.40</td><td>9.13</td><td>57.27</td><td>18.34</td><td>62.02</td><td>16.60</td><td>80.68</td><td>8.82</td><td>40.48</td><td>24.73</td></tr><tr><td>MON_NAT</td><td>98.55</td><td>1.92</td><td>93.48</td><td>4.39</td><td>70.53</td><td>13.90</td><td>78.94</td><td>9.07</td><td>69.78</td><td>1.06</td><td>10.45</td><td>12.26</td></tr><tr><td>MON_3DR</td><td>97.76</td><td>3.15</td><td>63.26</td><td>13.98</td><td>78.38</td><td>6.89</td><td>57.18</td><td>13.97</td><td>74.61</td><td>7.63</td><td>36.16</td><td>17.88</td></tr></tbody></table>
<table><caption>**Table 3.** Summary of results of the analyses of deviance (ANOVA type II Wald $\chi^2$ tests) on the fixed factor "Habitat" for the selected GLM(M) models (see details in Table S1) for each WL for second derivative of the reflectance signal ($R''$) of photosynthetic pigments data using Gaussian family distribution.</caption><thead><tr><th rowspan="2">Variable</th><th colspan="2">Habitat</th></tr><tr><th>Chisq</th><th>Pr (&gt;Chisq)</th></tr></thead><tbody><tr><td>Chl-a R''</td><td>186.81</td><td>&lt;2.2E-016***</td></tr><tr><td>Chl-b R''</td><td>5.90</td><td>1.17E-01 ns</td></tr><tr><td>Chl-c R''</td><td>37.36</td><td>3.86E-08***</td></tr><tr><td>Fuco R''</td><td>16.64</td><td>8.39E-04***</td></tr><tr><td>PhycoC R''</td><td>33.53</td><td>2.50E-07***</td></tr><tr><td>PhycoE R''</td><td>127.62</td><td>&lt;2.2E-016***</td></tr></tbody></table>

"dharma" (Hartig 2022). We used likelihood ratio tests to test the most parsimonious model; e.g. ANOVA function analyses with chi-squared test from package "Car" (Fox & Weisberg 2022) and tested for significant factors for the most parsimonious model. Post hoc analyses were done using pairwise *t* tests from the R "stats" packages. Information on family choice and model parameters are available in Tables S1 and S3.

## Results

### Wavelengths Selection and Global Signal

Analysis on each ROI showed that the selected WLs typical of photosynthetic pigments had in average positive signals, except for 648 nm related to Chl-b that showed negative signal on the

<figure><img src="image_5.png" /><figcaption>**Figure 4.** Box plot showing the signal intensity of the wavelengths associated with the pigments 666 nm (chlorophyll-a), 648 nm (chlorophyll-b), 635 nm (chlorophyll-c), 546 nm (fucoxanthin), 567 nm (phycoerythrin), and 620 nm (phycocyanin) (expressed as the mean second derivative per pixel per ROI) for each reef type (3DR, 3D printed reef; NAT, natural reef; OAR, old artificial reef) at each location (MON, Monaco Principality; RCM, Roquebrune Cap Martin).</figcaption></figure>

<footnote>*** <0.001; ** <0.01; * <0.05; 'n' >0.05.</footnote>
<table><caption>**Table 3.** Summary of results of the analyses of deviance (ANOVA type II Wald $\chi^2$ tests) on the fixed factor "Habitat" for the selected GLM(M) models (see details in Table S1) for each WL for second derivative of the reflectance signal ($R''$) of photosynthetic pigments data using Gaussian family distribution.</caption><thead><tr><th rowspan="2">Variable</th><th colspan="2">Habitat</th></tr><tr><th>Chisq</th><th>Pr (&gt;Chisq)</th></tr></thead><tbody><tr><td>Chl-a R''</td><td>186.81</td><td>&lt;2.2E-016***</td></tr><tr><td>Chl-b R''</td><td>5.90</td><td>1.17E-01 ns</td></tr><tr><td>Chl-c R''</td><td>37.36</td><td>3.86E-08***</td></tr><tr><td>Fuco R''</td><td>16.64</td><td>8.39E-04***</td></tr><tr><td>PhycoC R''</td><td>33.53</td><td>2.50E-07***</td></tr><tr><td>PhycoE R''</td><td>127.62</td><td>&lt;2.2E-016***</td></tr></tbody></table>

"dharma" (Hartig 2022). We used likelihood ratio tests to test the most parsimonious model; e.g. ANOVA function analyses with chi-squared test from package "Car" (Fox & Weisberg 2022) and tested for significant factors for the most parsimonious model. Post hoc analyses were done using pairwise *t* tests from the R "stats" packages. Information on family choice and model parameters are available in Tables S1 and S3.

## Results

### Wavelengths Selection and Global Signal

Analysis on each ROI showed that the selected WLs typical of photosynthetic pigments had in average positive signals, except for 648 nm related to Chl-b that showed negative signal on the

<figure><img src="image_5.png" /><figcaption>**Figure 4.** Box plot showing the signal intensity of the wavelengths associated with the pigments 666 nm (chlorophyll-a), 648 nm (chlorophyll-b), 635 nm (chlorophyll-c), 546 nm (fucoxanthin), 567 nm (phycoerythrin), and 620 nm (phycocyanin) (expressed as the mean second derivative per pixel per ROI) for each reef type (3DR, 3D printed reef; NAT, natural reef; OAR, old artificial reef) at each location (MON, Monaco Principality; RCM, Roquebrune Cap Martin).</figcaption></figure>

<footnote>*** <0.001; ** <0.01; * <0.05; 'n' >0.05.</footnote>
majority of ROIs (Fig. 2). On all reef type scanned the signal of 666 nm for Chl-a was the highest, followed by 567 nm for PhycoE, 635 nm for Chl-c, 620 nm for PhycoC, 546 nm for Fuco and 648 nm for Chl-b (Fig. 2).

Based on the presence/absence signals for each pixel of the selected ROIs, both natural and artificial reefs were almost entirely covered of Chl-a (90–100%), whereas the other pigments showed a variable cover. PhycoE covered between 63 and 95% of the surface, Chl-c 57 and 88%, PhycoC 57 and 86%, Fuco 70 and 81%, and Chl-b 4 and 41% (Table 2).

## HP Detection of Old-generation ARs' Effectiveness

The HP assemblage signals were significantly different among habitats (analyses of deviance: $F_{[3,116]} = 216.81, p = 0.002$), but there was also variability among transects ($F_{[4,112]} = 52.17, p = 0.002$). Pairwise a posteriori tests on habitats showed differences between natural (NATs) and artificial (OARs) reefs in both areas (RCM_NAT vs. RCM_OAR: $F_{[1,58]} = 86.63, p = 0.002$; MON_NAT vs. MON_OAR: $F_{[1,58]} = 374.1, p = 0.002$). The nonmetric multidimensional scaling (nMDS) ordination also showed more dissimilarity within reef type in MON than in RCM (Fig. 3). In addition, the dissimilarity between the two NATs was smaller than that between the two OARs (Fig. 3).

These differences were also found in univariate analyses of the HP signal. The GLM(M) models showed significantly different signals among habitats, except for the Chl-b signal (Table 3). Pairwise a posteriori tests showed the lowest signal of Chl-a, Chl-c, Fuco, PhycoC, and PhycoE on the OAR immersed nearby the MON coastline (Table S2; Fig. 4). In addition, although this OAR showed the lowest signal associated to Chl-b (666 nm), the 567 nm signal was comparatively high and visual observation showed numerous sponges of the species *Crambe crambe*. The NATs in MON had higher signal of PhycoE but lower signal of Chl-c than the NATs situated in RCM area, where OARs showed lower PhycoE signal than the respective NATs (Table S2; Fig. 4).

## HP Detection of 3D-Printed ARs' Effectiveness

Multivariate HP signal data in MON showed significant differences among the three reef types (NATs, 3DRs, and OARs; analyses of deviance: $F_{[2,87]} = 274.45, p = 0.002$) and transects ($F_{[3,84]} = 54.08, p = 0.002$). Pairwise a posteriori tests showed that the three types differed from each other (3DRs vs. OARs: $F_{[1,58]} = 159.9, p = 0.002$; 3DRs vs. NATs: $F_{[1,58]} = 310.1, p = 0.002$; OAR vs. NATs: $F_{[1,58]} = 374.1, p = 0.002$), as also highlighted by the nMDS ordination (Fig. 3). The nMDS also showed that the dissimilarity between 3DRs and NATs was 25% whereas the one between OARs and NATs was 36% (Fig. 3).

Each response variable differed significantly among reefs, except Chl-b (Table 4). Pairwise a posteriori tests showed that the OARs had less Chl-c and Fuco signals than both NATs and 3DRs, whose values were similar (Table S4; Fig. 4). Finally, the 3DRs showed intermediate values between the OARs and NATs of Chl-a, PhycoC, and PhycoE signals (Table S4; Fig. 4).

<table><caption>**Table 4.** Summary of results of the analyses of deviance (ANOVA type II Wald $\chi^2$ tests) on the fixed factors “Reef type” for the selected GLM(M) models (see details in Table S3) for each WL for second derivative of the reflectance signal ($R''$) of photosynthetic pigments data using Gaussian family distribution.</caption><thead><tr><th rowspan="2">Variable</th><th colspan="2">Reef type</th></tr><tr><th>Chisq</th><th>Pr (&gt;Chisq)</th></tr></thead><tbody><tr><td>Chl-a R''</td><td>49.24</td><td>2.03E-11***</td></tr><tr><td>Chl-b R''</td><td>3.34</td><td>1.88E-01 ns</td></tr><tr><td>Chl-c R''</td><td>67.05</td><td>2.76E-15***</td></tr><tr><td>Fuco R''</td><td>12.94</td><td>1.55E-03**</td></tr><tr><td>PhycoC R''</td><td>13.91</td><td>9.55E-04***</td></tr><tr><td>PhycoE R''</td><td>236.76</td><td>2.20E-16***</td></tr></tbody></table>

*** <0.001; ** <0.01; * <0.05; 'n' >0.05.

## Discussion

The photosynthetic assemblages detected using underwater HP imaging showed a signal of red and brown macroalgae, followed by green algae, as typical in coralligenous habitats (Ballesteros 2006). Visual cross-identification was difficult due to low light conditions and the small size of some of the organisms. Improved camera resolution and light input would allow automatic species identification at a finer scale using morphological features observed in the RGB images associated with each HP image as it has been done in other study using artificial intelligence for identification (Chennu et al. 2017; Dumke et al. 2018). In addition, further calibration using ground truth sampling would distinguish signal spectra typical of nonphotosynthetic organisms. For these reasons, we acknowledge the limitations of our method in revealing spectral signatures to quantify and identify the photosynthetic community in detail. Nevertheless, our results highlighted relative differences in the signal of the photosynthetic community on different habitats and reduce human and time effort required for underwater observations. In our study, one diver could sample 24 transects on five different sites at 30-m depth over 3 days. We believe that our pioneering study will encourage further development of this approach.

The results of our comparative study, based on HP signals and their relative differences, distinguished different uni- and multivariate signals between natural and artificial reefs (NAT vs. OAR). Within each study area, the dissimilarities among OARs were greater than those among NATs, suggesting greater variability among ARs. In particular, the old artificial reef in Monaco Principality showed a high signal at 567 nm, which could not be solely attributed to the presence of red algae, as it exhibited the lowest signal associated with Chl-a at 666 nm. Visual observations of this AR revealed significant coverage by the sponge *Crambe crambe*, which shows a signal at 567 nm. In contrast, in the RCM sampling area, the OAR had signals typical of coralligenous reefs and showed some similarities to the signals measured on the natural reef. However, the OARs in RCM had a weaker signal for red algae compared with the natural reef. Thus, our analysis not only confirmed the differences between natural and artificial reefs, in line with previous research (Perkol-Finkel & Benayahu 2005; Carvalho et al. 2013; Ferrario et al. 2016), but also highlighted that the
majority of ROIs (Fig. 2). On all reef type scanned the signal of 666 nm for Chl-a was the highest, followed by 567 nm for PhycoE, 635 nm for Chl-c, 620 nm for PhycoC, 546 nm for Fuco and 648 nm for Chl-b (Fig. 2).

Based on the presence/absence signals for each pixel of the selected ROIs, both natural and artificial reefs were almost entirely covered of Chl-a (90–100%), whereas the other pigments showed a variable cover. PhycoE covered between 63 and 95% of the surface, Chl-c 57 and 88%, PhycoC 57 and 86%, Fuco 70 and 81%, and Chl-b 4 and 41% (Table 2).

## HP Detection of Old-generation ARs' Effectiveness

The HP assemblage signals were significantly different among habitats (analyses of deviance: $F_{[3,116]} = 216.81, p = 0.002$), but there was also variability among transects ($F_{[4,112]} = 52.17, p = 0.002$). Pairwise a posteriori tests on habitats showed differences between natural (NATs) and artificial (OARs) reefs in both areas (RCM_NAT vs. RCM_OAR: $F_{[1,58]} = 86.63, p = 0.002$; MON_NAT vs. MON_OAR: $F_{[1,58]} = 374.1, p = 0.002$). The nonmetric multidimensional scaling (nMDS) ordination also showed more dissimilarity within reef type in MON than in RCM (Fig. 3). In addition, the dissimilarity between the two NATs was smaller than that between the two OARs (Fig. 3).

These differences were also found in univariate analyses of the HP signal. The GLM(M) models showed significantly different signals among habitats, except for the Chl-b signal (Table 3). Pairwise a posteriori tests showed the lowest signal of Chl-a, Chl-c, Fuco, PhycoC, and PhycoE on the OAR immersed nearby the MON coastline (Table S2; Fig. 4). In addition, although this OAR showed the lowest signal associated to Chl-b (666 nm), the 567 nm signal was comparatively high and visual observation showed numerous sponges of the species *Crambe crambe*. The NATs in MON had higher signal of PhycoE but lower signal of Chl-c than the NATs situated in RCM area, where OARs showed lower PhycoE signal than the respective NATs (Table S2; Fig. 4).

## HP Detection of 3D-Printed ARs' Effectiveness

Multivariate HP signal data in MON showed significant differences among the three reef types (NATs, 3DRs, and OARs; analyses of deviance: $F_{[2,87]} = 274.45, p = 0.002$) and transects ($F_{[3,84]} = 54.08, p = 0.002$). Pairwise a posteriori tests showed that the three types differed from each other (3DRs vs. OARs: $F_{[1,58]} = 159.9, p = 0.002$; 3DRs vs. NATs: $F_{[1,58]} = 310.1, p = 0.002$; OAR vs. NATs: $F_{[1,58]} = 374.1, p = 0.002$), as also highlighted by the nMDS ordination (Fig. 3). The nMDS also showed that the dissimilarity between 3DRs and NATs was 25% whereas the one between OARs and NATs was 36% (Fig. 3).

Each response variable differed significantly among reefs, except Chl-b (Table 4). Pairwise a posteriori tests showed that the OARs had less Chl-c and Fuco signals than both NATs and 3DRs, whose values were similar (Table S4; Fig. 4). Finally, the 3DRs showed intermediate values between the OARs and NATs of Chl-a, PhycoC, and PhycoE signals (Table S4; Fig. 4).

<table><caption>**Table 4.** Summary of results of the analyses of deviance (ANOVA type II Wald $\chi^2$ tests) on the fixed factors “Reef type” for the selected GLM(M) models (see details in Table S3) for each WL for second derivative of the reflectance signal ($R''$) of photosynthetic pigments data using Gaussian family distribution.</caption><thead><tr><th rowspan="2">Variable</th><th colspan="2">Reef type</th></tr><tr><th>Chisq</th><th>Pr (&gt;Chisq)</th></tr></thead><tbody><tr><td>Chl-a R''</td><td>49.24</td><td>2.03E-11***</td></tr><tr><td>Chl-b R''</td><td>3.34</td><td>1.88E-01 ns</td></tr><tr><td>Chl-c R''</td><td>67.05</td><td>2.76E-15***</td></tr><tr><td>Fuco R''</td><td>12.94</td><td>1.55E-03**</td></tr><tr><td>PhycoC R''</td><td>13.91</td><td>9.55E-04***</td></tr><tr><td>PhycoE R''</td><td>236.76</td><td>2.20E-16***</td></tr></tbody></table>

*** <0.001; ** <0.01; * <0.05; 'n' >0.05.

## Discussion

The photosynthetic assemblages detected using underwater HP imaging showed a signal of red and brown macroalgae, followed by green algae, as typical in coralligenous habitats (Ballesteros 2006). Visual cross-identification was difficult due to low light conditions and the small size of some of the organisms. Improved camera resolution and light input would allow automatic species identification at a finer scale using morphological features observed in the RGB images associated with each HP image as it has been done in other study using artificial intelligence for identification (Chennu et al. 2017; Dumke et al. 2018). In addition, further calibration using ground truth sampling would distinguish signal spectra typical of nonphotosynthetic organisms. For these reasons, we acknowledge the limitations of our method in revealing spectral signatures to quantify and identify the photosynthetic community in detail. Nevertheless, our results highlighted relative differences in the signal of the photosynthetic community on different habitats and reduce human and time effort required for underwater observations. In our study, one diver could sample 24 transects on five different sites at 30-m depth over 3 days. We believe that our pioneering study will encourage further development of this approach.

The results of our comparative study, based on HP signals and their relative differences, distinguished different uni- and multivariate signals between natural and artificial reefs (NAT vs. OAR). Within each study area, the dissimilarities among OARs were greater than those among NATs, suggesting greater variability among ARs. In particular, the old artificial reef in Monaco Principality showed a high signal at 567 nm, which could not be solely attributed to the presence of red algae, as it exhibited the lowest signal associated with Chl-a at 666 nm. Visual observations of this AR revealed significant coverage by the sponge *Crambe crambe*, which shows a signal at 567 nm. In contrast, in the RCM sampling area, the OAR had signals typical of coralligenous reefs and showed some similarities to the signals measured on the natural reef. However, the OARs in RCM had a weaker signal for red algae compared with the natural reef. Thus, our analysis not only confirmed the differences between natural and artificial reefs, in line with previous research (Perkol-Finkel & Benayahu 2005; Carvalho et al. 2013; Ferrario et al. 2016), but also highlighted that the
magnitude of these differences varied depending on the
shoreline.

It is known that the complexity of the structure and the texture of
the material used to construct ARs can influence colonization pat-
terns (Bohnsack 1991; Baine 2001; Riera et al. 2018), and that
ARs are considered mature after 5 years of immersion (D'Anna
et al. 2000). The studied old artificial reefs were similar in design
and material, and were both immersed for over 30 years. During
the field campaign, we carefully sampled habitats that were no
more than 4 km apart, at comparable depths (30 m), and with sim-
ilar orientations to the light and the Mediterranean Northern Cur-
rent. However, the coastal area of the MON has undergone
extensive development, with only 11% of the original natural
coastline remaining (Bottin et al. 2022). Waters close to urban areas
with elevated coastal development receive high inputs of organic
matter (Pooters 2013), which have been shown to promote a shift
from the dominance of macroalgae to that of filamentous or other
ephemeral algae, and filter feeders (Airoldi & Beck 2007). The pat-
tern observed in our study could therefore indicate an effect of
increased particulate organic matter, on macroalgal community,
related to coastal development and urbanization. However, the lack
of replicated coastline conditions prevents us from inferring general
patterns, and further studies replicating coastlines with comparable
ARs are needed.

Regarding the effect of reef type, the 3DRs were not able to
fully mimic the signal of the photosynthetic benthic assem-
bages of the natural adjacent rocky reefs (NATs). However,
the HP signal of 3DRs was closer to that of NATs than OARs.
It showed higher signal of photosynthetic community related
to brown algae (666, 635, and 546 nm) compared to the OARs.
However, there was still a low signal of red algae (567 nm)
compared to NATs which can be explained by a slow growth
of coralligenous algae (Ballesteros 2006). Research on the use
of 3D-printed ARs for enhancing marine life is still in its
infancy, but our results suggested a slight positive increase in
ARs' effectiveness compared to old artificial reefs. Monitoring
over time is however necessary to determine whether the benthic
community they support will be able to converge toward the nat-
ural ones. However, our results are promising and in agreement
with the studies on fish community (Pérez-Pagán & Mercado-
Molina 2018; Ruhl & Dixson 2019) or on substrate comparison
for the settlement of oyster, coral, or primary biofilm (Riera
et al. 2018; Ly et al. 2021; Matus et al. 2021), suggesting that
this approach may help to facilitate the colonization of benthic
communities typical of natural reefs on ARs, to improve the suc-
cess of reconciliation measures.

In conclusion, this pioneering study in the use of HP technol-
ogy on underwater photosynthetic communities of natural and
artificial reefs shows that, after decades of immersion, ARs are
still not able to mimic adjacent natural habitats. In addition,
the use of reefs (such as 3D-printed ones) that can mimic the
complexity of natural reefs and made out of biocompatible
materials appears promising to improve the global success of
reconciliation measures. Nevertheless, this study is an important
step toward developing HP imaging as a useful tool for mapping
underwater photosynthetic communities and reducing the
human and time resources required for such efforts.

Acknowledgments

We dedicate this work to the deceased Prof. P. Francour who
passed away on 13 October 2019, who gave the inspiration and
impulse to the use of more natural artificial reefs by initiating this
project. We are indebted to J. Gautier-Debernardi, Director of
AMPN (Monegasque Association for Nature Protection—
Manager of MPAs of Monaco), for providing facilities and volun-
teers for the field work. We thank C. Serre, manager of the MPA of
Roquebrune Cap Martin (managed by “Service de l’ingénierie
environnementale du Département des Alpes Maritimes”), for giv-
ing us access to the artificial reefs within the MPA. We thank Pro-
fessor D. Allemand for providing historical data on the MPAs of
Monaco Principality. We thank planblue for delivering open access
data. This work was supported by the Prince Albert II of Monaco
Foundation, through the funding of the project HYPER3D; and
by the AMPN, who funded E.R.’s Postdoc.

LITERATURE CITED

Airoldi L, Beck MW (2007) Loss, status and trends for coastal marine habitats of Europe. *Oceanography and Marine Biology: An Annual Review* 45:345-405. https://doi.org/10.4236/nr.2018.912025

Allemand D, Debernardi E, William Seaman J (2000) Artificial reefs in the Principality of Monaco: protection and enhancement of coastal zones. Pages 151–166. In: Jensen AC, Collins KJ, Lockwood APM (eds) Artificial reefs in European seas. Springer Science+Business Media, Dordrecht, The Netherlands

Baine M (2001) Artificial reefs: a review of their design, application, management and performance. *Ocean & Coastal Management* 44:241–259. https://doi.org/10.1016/S0964-5691(01)00048-5

Ballesteros E (2006) Mediterranean coralligenous assemblages: a synthesis of present knowledge. *Oceanography and Marine Biology: An Annual review* 44:123–195. https://doi.org/10.1201/9781420006391

Bohnsack JA (1991) Habitat structure and the design of artificial reefs. Pages 412–426. In: Habitat structure: the physical arrangement of objects in space 1991. Springer Netherlands, Dordrecht

Bottin L, Meinesz A, Garcia D, Boulinguez R (2022) Medam 2020–2021. https://www.medamsigonline.org/data/_archives/rapports/RAPPORT_MEDAM_2020_2021.pdf (accessed 07 Jul 2023)

Brooks M (2022) Package ‘glmmTMB’. Generalized linear mixed models using template model builder. https://github.com/glmmTMB/glmmTMB (accessed 07 Jul 2023)

Callow JA, Callow ME (2006) Antifouling Compounds. Marine Molecular Bio-
technology. Pages 141–169. In: Fusetani N, Clare AS (eds) Biofilms.
Pages. Vol 42. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-
540-30016-3_6

Carvalho S, Moura A, Cúrdia J, da Fonseca LC, Santos MN (2013) How comple-
mentary are epibenthic assemblages in artificial and nearby natural rocky
reefs? Marine Environmental Research 92:170–177. https://doi.org/10.
1016/j.marenvres.2013.09.013

Chennu A, Färber P, De'ath G, de BD, Fabricius KE (2017) A diver-operated hyperspectral imaging and topographic surveying system for automated mapping of benthic habitats. *Science Reports* 7:1–12. https://doi.org/10.1038/s41598-017-07337-y

Coloul B, Pouvreau S, Di Poi C, Pouil S, Merk V, Peter C, Boersma M, Pogoda B (2020) Addressing critical limitations of oyster (*Ostrea edulis*) restoration: identification of nature-based substrates for hatchery production and recruitment in the field. *Aquatic Conservation: Marine and Freshwater Ecosystems* 30:2101–2115. https://doi.org/10.1002/aqc.3454

D'Anna G, Badalamenti F, Riggio S (2000) Artificial reefs in north-west Sicily: comparisons and conclusions. Pages 97–112. In: Jensen AC, Collins KJ, Lockwood APM (eds) Artificial reefs in European seas. Springer Science +Business Media, Dordrecht, The Netherlands
magnitude of these differences varied depending on the
shoreline.

It is known that the complexity of the structure and the texture of
the material used to construct ARs can influence colonization pat-
terns (Bohnsack 1991; Baine 2001; Riera et al. 2018), and that
ARs are considered mature after 5 years of immersion (D'Anna
et al. 2000). The studied old artificial reefs were similar in design
and material, and were both immersed for over 30 years. During
the field campaign, we carefully sampled habitats that were no
more than 4 km apart, at comparable depths (30 m), and with sim-
ilar orientations to the light and the Mediterranean Northern Cur-
rent. However, the coastal area of the MON has undergone
extensive development, with only 11% of the original natural
coastline remaining (Bottin et al. 2022). Waters close to urban areas
with elevated coastal development receive high inputs of organic
matter (Pooters 2013), which have been shown to promote a shift
from the dominance of macroalgae to that of filamentous or other
ephemeral algae, and filter feeders (Airoldi & Beck 2007). The pat-
tern observed in our study could therefore indicate an effect of
increased particulate organic matter, on macroalgal community,
related to coastal development and urbanization. However, the lack
of replicated coastline conditions prevents us from inferring general
patterns, and further studies replicating coastlines with comparable
ARs are needed.

Regarding the effect of reef type, the 3DRs were not able to
fully mimic the signal of the photosynthetic benthic assem-
bages of the natural adjacent rocky reefs (NATs). However,
the HP signal of 3DRs was closer to that of NATs than OARs.
It showed higher signal of photosynthetic community related
to brown algae (666, 635, and 546 nm) compared to the OARs.
However, there was still a low signal of red algae (567 nm)
compared to NATs which can be explained by a slow growth
of coralligenous algae (Ballesteros 2006). Research on the use
of 3D-printed ARs for enhancing marine life is still in its
infancy, but our results suggested a slight positive increase in
ARs' effectiveness compared to old artificial reefs. Monitoring
over time is however necessary to determine whether the benthic
community they support will be able to converge toward the nat-
ural ones. However, our results are promising and in agreement
with the studies on fish community (Pérez-Pagán & Mercado-
Molina 2018; Ruhl & Dixson 2019) or on substrate comparison
for the settlement of oyster, coral, or primary biofilm (Riera
et al. 2018; Ly et al. 2021; Matus et al. 2021), suggesting that
this approach may help to facilitate the colonization of benthic
communities typical of natural reefs on ARs, to improve the suc-
cess of reconciliation measures.

In conclusion, this pioneering study in the use of HP technol-
ogy on underwater photosynthetic communities of natural and
artificial reefs shows that, after decades of immersion, ARs are
still not able to mimic adjacent natural habitats. In addition,
the use of reefs (such as 3D-printed ones) that can mimic the
complexity of natural reefs and made out of biocompatible
materials appears promising to improve the global success of
reconciliation measures. Nevertheless, this study is an important
step toward developing HP imaging as a useful tool for mapping
underwater photosynthetic communities and reducing the
human and time resources required for such efforts.

Acknowledgments

We dedicate this work to the deceased Prof. P. Francour who
passed away on 13 October 2019, who gave the inspiration and
impulse to the use of more natural artificial reefs by initiating this
project. We are indebted to J. Gautier-Debernardi, Director of
AMPN (Monegasque Association for Nature Protection—
Manager of MPAs of Monaco), for providing facilities and volun-
teers for the field work. We thank C. Serre, manager of the MPA of
Roquebrune Cap Martin (managed by “Service de l’ingénierie
environnementale du Département des Alpes Maritimes”), for giv-
ing us access to the artificial reefs within the MPA. We thank Pro-
fessor D. Allemand for providing historical data on the MPAs of
Monaco Principality. We thank planblue for delivering open access
data. This work was supported by the Prince Albert II of Monaco
Foundation, through the funding of the project HYPER3D; and
by the AMPN, who funded E.R.’s Postdoc.

LITERATURE CITED

Airoldi L, Beck MW (2007) Loss, status and trends for coastal marine habitats of Europe. *Oceanography and Marine Biology: An Annual Review* 45:345-405. https://doi.org/10.4236/nr.2018.912025

Allemand D, Debernardi E, William Seaman J (2000) Artificial reefs in the Principality of Monaco: protection and enhancement of coastal zones. Pages 151–166. In: Jensen AC, Collins KJ, Lockwood APM (eds) Artificial reefs in European seas. Springer Science+Business Media, Dordrecht, The Netherlands

Baine M (2001) Artificial reefs: a review of their design, application, manage-
ment and performance. Ocean & Coastal Management 44:241–259.
https://doi.org/10.1016/S0964-5691(01)00048-5

Ballesteros E (2006) Mediterranean coralligenous assemblages: a synthesis of present knowledge. *Oceanography and Marine Biology: An Annual review* 44:123–195. https://doi.org/10.1201/9781420006391

Bohnsack JA (1991) Habitat structure and the design of artificial reefs. Pages 412–426. In: Habitat structure: the physical arrangement of objects in space 1991. Springer Netherlands, Dordrecht

Bottin L, Meinesz A, Garcia D, Boulinguez R (2022) Medam 2020–2021. https://www.medamsigonline.org/data/_archives/rapports/RAPPORT_MEDAM_2020_2021.pdf (accessed 07 Jul 2023)

Brooks M (2022) Package ‘glmmTMB’. Generalized linear mixed models using template model builder. https://github.com/glmmTMB/glmmTMB (accessed 07 Jul 2023)

Callow JA, Callow ME (2006) Antifouling Compounds. Marine Molecular Bio-
technology. Pages 141–169. In: Fusetani N, Clare AS (eds) Biofilms.
Pages. Vol 42. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-
540-30016-3_6

Carvalho S, Moura A, Cúrdia J, da Fonseca LC, Santos MN (2013) How comple-
mentary are epibenthic assemblages in artificial and nearby natural rocky
reefs? Marine Environmental Research 92:170–177. https://doi.org/10.
1016/j.marenvres.2013.09.013

Chennu A, Färber P, De'ath G, de BD, Fabricius KE (2017) A diver-operated hyperspectral imaging and topographic surveying system for automated mapping of benthic habitats. *Science Reports* 7:1–12. https://doi.org/10.1038/s41598-017-07337-y

Coloul B, Pouvreau S, Di Poi C, Pouil S, Merk V, Peter C, Boersma M, Pogoda B (2020) Addressing critical limitations of oyster (*Ostrea edulis*) restoration: identification of nature-based substrates for hatchery production and recruitment in the field. *Aquatic Conservation: Marine and Freshwater Ecosystems* 30:2101–2115. https://doi.org/10.1002/aqc.3454

D'Anna G, Badalamenti F, Riggio S (2000) Artificial reefs in north-west Sicily: comparisons and conclusions. Pages 97–112. In: Jensen AC, Collins KJ, Lockwood APM (eds) Artificial reefs in European seas. Springer Science +Business Media, Dordrecht, The Netherlands
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## Supporting Information

The following information may be found in the online version of this article:

**Figure S1.** Summary diagram from underwater sampling with the DiveRay, followed by annotation of region of interest on planblue software and hyperspectral data process on Python.

**Figure S2.** Second derivative comparison of ROIs: *Crambe crambe* versus coralligenous concretion.

**Table S1.** Summary of the parameters and of the analyses of deviance of the fixed factor "Habitat" of the selected GLM(M) models for each WL second derivative (R") intensity data.

**Table S2.** Pairwise comparison for GLM(M) post hoc analyses with Bonferroni correction (on Habitat fixed factor with RCM, Roquebrune Cap martin; MON, Monaco Principality; NAT, natural reefs; OAR, old artificial reefs).

**Table S3.** Summary of the parameters and of the analyses of deviance of the fixed factor "Reef type" of the selected GLM(M) models for each WL second derivative (R") intensity data.

**Table S4.** Pairwise comparison for GLM(M) post hoc analyses with Bonferroni correction (on Reef type fixed factor: NAT, natural reefs; 3DR, 3D printed reefs; OAR, old artificial reefs).
Debernardi E, Allemand D (1993) Zones protegées sur le littoral de la Principauté de Monaco. Bollettino di Oceanologia Teorica ed. Applicata XI:173-182

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## Supporting Information

The following information may be found in the online version of this article:

**Figure S1.** Summary diagram from underwater sampling with the DiveRay, followed by annotation of region of interest on planblue software and hyperspectral data process on Python.

**Figure S2.** Second derivative comparison of ROIs: *Crambe crambe* versus coralligenous concretion.

**Table S1.** Summary of the parameters and of the analyses of deviance of the fixed factor "Habitat" of the selected GLM(M) models for each WL second derivative (R") intensity data.

**Table S2.** Pairwise comparison for GLM(M) post hoc analyses with Bonferroni correction (on Habitat fixed factor with RCM, Roquebrune Cap martin; MON, Monaco Principality; NAT, natural reefs; OAR, old artificial reefs).

**Table S3.** Summary of the parameters and of the analyses of deviance of the fixed factor "Reef type" of the selected GLM(M) models for each WL second derivative (R") intensity data.

**Table S4.** Pairwise comparison for GLM(M) post hoc analyses with Bonferroni correction (on Reef type fixed factor: NAT, natural reefs; 3DR, 3D printed reefs; OAR, old artificial reefs).