# Unveiling hidden connections using photogrammetry: Multi-scale relationships between benthic communities and rocky subtidal seascapes

Quentin Ternon, Frédéric Ysnel, Antoine Collin, Pierre D Thiriet, Mireille Guillaume, Valentin Danet, Éric Feunteun

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

## ► To cite this version:

Quentin Ternon, Frédéric Ysnel, Antoine Collin, Pierre D Thiriet, Mireille Guillaume, et al. Unveiling hidden connections using photogrammetry: Multi-scale relationships between benthic communities and rocky subtidal seascapes. Regional Studies in Marine Science, 2025, 82, pp.104010. 10.1016/j.rsma.2025.104010. hal-04923178

HAL Id: hal-04923178

https://hal.science/hal-04923178v1

Submitted on 7 Feb 2025

HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published 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_2.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
<figure><img src="image_3.png" /></figure>

# Unveiling hidden connections using photogrammetry: Multi-scale relationships between benthic communities and rocky subtidal seascapes

Q. Ternon<sup>a,b</sup>, F. Ysnel<sup>c,*</sup>, A. Collin<sup>d</sup>, P. Thiriet<sup>e</sup>, M. Guillaume<sup>a</sup>, V. Danet<sup>a</sup>, E. Feunteun<sup>a,b,d</sup>

## ARTICLE INFO

**Keywords:**

Photogrammetry

Temperate rocky reefs

Benthic community

Infralittoral

Morphotype

Geomorphology

Substrate typology

Multi-scale

Seascape

## ABSTRACT

The objective of the present study is to unravel the multi-scale processes involved in structuring of infralittoral rocky biocenoses habitat characteristics. A photogrammetric methodology (supervised classification) was developed in three rocky reefs of Saint-Malo bay (~ 250 km², France) to describe the biotope and the associated benthic community (photo-quadrat and morphotypic approach) along 120-m² transects. The methodology captured more than 70 % of morphotypic richness, and successfully estimated the morphotype proportions. The results confirmed the importance of the broad-scale (> 120 m²) processes as a driver of benthic communities (24 % of variance explained). Interestingly, the photogrammetric metrics revealed that the benthic morphotypic composition was also shaped by fine-scale (< 120 m²) habitat characteristics (18 % and 37 % of variance explained by geomorphology and substrate composition, respectively). Substrate typological patches and geomorphological descriptors also structured the benthic community composition. This study provides tools for characterizing and predicting benthic community characteristics from multi-scale habitat descriptions in temperate rocky reefs.

## 1. Introduction

Temperate rocky reefs are highly productive and support an important biodiversity sheltering a broad range of algae and benthic invertebrates (Bravo et al., 2020). In a context where human activities are spreading in the marine environment (Halpern et al., 2008; Crain et al., 2009; Mcquatters-Gollop et al., 2022), there is a strong need to study the relationships between benthic organisms and their habitat for monitoring purposes. Studying the key environmental drivers of benthic communities changes would indeed help to detect and understand anthropogenic impacts (Henriques et al., 2014; Lamy et al., 2018). For instance, temperature (Gallon et al., 2014; Schiel, Steinbeck and Foster, 2014), salinity (Menge and Olson, 1990; Wahl, 2009), turbidity (Castric-Fey et al., 1973; Moore, 1973; Schramm and Nienhuis, 1996), eutrophication (Schramm and Nienhuis, 1996), exposure to waves (Duckworth, 2016; Pita et al., 2018), the substrate nature (Guichard and Bourget, 1998; Duckworth, 2016), topography, bathymetry and

consequently changes in the light spectrum (Castric-Fey et al., 1973; Sebens, 2011; Bravo et al., 2020) are commonly described as important drivers of the presence and abundance of some benthic organisms, (in particular primary producers, sciophilous organisms, filter-feeders and supensivores) in temperate rocky reefs.

This study aims to relate shallow subtidal substate types to benthic communities at different scales through the use of an innovative photogrammetric methodology.

Previous studies have broadly described the habitats from geomorphological point of view by discriminating walls and flats (see for instance Castric-Fey et al., (1973) or the review by Wood (1987), scoring the degree of substrate complexity (i.e. the substrate rugosity and the vertical relief; Luckhurst and Luckhurst, 1978), or annotating substrate typological classes (Guichard and Bourget, 1998). More recently, Brandl and Bellwood (2016) highlighted the effect of microtopography by studying microrefuges and their effects on algae and grazers. However, the previous available methods have not been able to describe the whole

<footnote><sup>a</sup> Museum National d'Histoire Naturelle (MNHN), Station Marine de Dinard, Dinard 35800, France</footnote>

<footnote><sup>b</sup> Laboratoire Biologie des Organismes et Écosystèmes Aquatiques (UMR 8067 BOREA), MNHN, CNRS, Sorbonne Université, Université de Caen Normandie, Université des Antilles, IRD, Paris Cedex 75231, France</footnote>

<footnote><sup>c</sup> Université de Rennes, UMR 8067 BOREA, MNHN, Rennes Cedex 35042, France</footnote>

<footnote><sup>d</sup> Centre de Géoécologie Littorale (CGEL), Ecole Pratique des Hautes Études (EPHE), PSL Université, CGEL, Dinard 35800, France</footnote>

<footnote><sup>e</sup> UAR Patrimoine Naturel, OFB, CNRS, MNHN, CRESCO, Dinard 35800, France</footnote>

<footnote>* Corresponding author.</footnote>

<footnote>E-mail address: frederic.ysnel@univ-rennes.fr (F. Ysnel).</footnote>
dimensions of geomorphology and habitat patches variability at the local scale (below 100 m²). This was due to limitations in fine-resolution and continuous acquisition of substrate and associated benthic community data. In others words, the protocols implemented in those previous studies did not highlight the turnover of benthic species, especially along a horizontal gradient of habitats at a local scale.

To achieve this goal, we developed a robust and rapid method of inventorying biodiversity associated with different types of subtidal rocky substrate. The description of the benthic community has been largely carried out through *in-situ* identification (see Munro, 2005) but this method is intended for biological experts and could be time consuming in a subtidal environment. Quite recently, the use of morphotypic approaches like the CATAMI classification has shown good performances in comparative studies of community structure across different habitats (Taormina, 2020). This morphotypic approach simplifies sampling and be geared for non-experts, while remaining relevant to highlight ecological processes. Optical imagery methodologies like photo quadrats can provide standardized, more complete and quantitative information to describe benthic communities. They can characterize the presence, the abundance, the dominance and surface cover of organisms and their organization in a specific area (Trygonis and Sini, 2012). In parallel, this fine-scale understanding of benthos and habitat relationships contextualized at a larger scale should be of great importance to enhance multi-scale approaches for studying benthic communities and habitat relationships. These multi-scale approaches improve benthic habitat mapping and help for ecological management (Wilson et al., 2007a; Porskamp et al., 2018; Swanborn et al., 2022). It is currently very difficult to precisely and finely match fine substrate characteristics with related benthic communities at scales below 100 m².

Recently, photogrammetry is becoming widely used in subtidal environments, and allows describing geomorphology (through 3D modelling) and the substrate composition (through classification mapping (Menna et al., 2022; Ternon et al., 2022)) very fine (< 1 cm² resolution) on a 100-m² scale. In parallel, benthic communities can now be described by photogrammetry, but they are only limited to easily recognizable dominant species (Robert et al., 2017; Kanki et al., 2021) or to a broad description of benthic organisms including algae, sponges and corals (Burns et al., 2016; Anelli et al., 2017; Ferrari et al., 2021; Menna et al., 2022).

In a previous study (Ternon et al., 2022), we demonstrated the utility of photogrammetric methods to map the habitat changes along different gradients and scales of rocky infralittoral seascapes (distance to the coast, variation in turbidity, and hydrodynamic conditions). Based on that innovative photogrammetric method we aim to demonstrate i) how a new analysis coupling a stratified photo-quadrat sampling (using substrate typological classes) to a morphotypic approach could be efficient to characterize benthic communities at a high resolution (0.1 m²) across a local scale (100 m²) ii) In the specific context of the study area, how the local geomorphology of the rocky reefs – especially its complexity at a fine scale – drives the morphotypic benthic diversity iii) how a finer description of the relief can reveal the importance of local refuges in the larger (broad scale > 100 m²) ecological process.

First, we tested the completeness of the morphotypic sampling by virtual quadrat projection. Second, we studied the variance of benthic community descriptors (morphotype frequencies and diversity indices) explained by various scales, from a broad scale to a very fine scale and related to geomorphology and substrate typological composition. Finally, we investigated the resulting key substrate descriptors that drive

<figure><img src="image_4.png" /><figcaption>**Fig. 1.** Locations of the three sampling sites (Vieux Banc, Buharats and Bizeux) in the rocky substrate distribution of the Emerald Coast. The isobaths are displayed with a 5-m step.</figcaption></figure>
morphotypic benthic communities.

## 2. Materials and methods

### 2.1. Broad-scale sampling area

The study was carried out on three distinct infralittoral rocky reefs (Fig. 1) of the English Channel in Saint-Malo bay (Brittany, France) by scuba-diving. They are located at different distances from the coast. The reef of Vieux Banc (48°42′04.19″ N, 2°09′43.24″ W; WGS84) is located 7 km offshore, and subject to marine influence (natural tide and swell). The islet of Bizeux (48°37′40.95″ N, 2°01′34.96″ W; WGS84) is located 0.5 km from the coast; it is additionally under the influence of the hydroelectric dam of the Rance river, with turbid and very strong bidirectional current episodes (Gallon et al., 2013). The last reef - Buharats (48°40′24.08″ N, 2°07′12.88″ W; WGS84) - has an intermediate location 3 km off the coast and is mostly under marine influence.

### 2.2. Imagery acquisition

For each reef, two 120-m² transects (30 m long and 4 m wide) were sampled at depths of 8 and 13 ± 1 m (0 corresponding to the lowest astronomical tide level). Between 1580 and 2152 pictures were taken for each transect (Fig. 2, step 1) by two divers following the methodology used by Ternon et al. (2022). A 30-m tape was positioned to mark the transect area. Six quadrats (50 cm×50 cm) and a 2-m rule were placed on the whole area for a post calibration. Special photogrammetric markers were fixed on quadrats (8 markers) and rules (6 markers) for the 3D model construction. The distances of the different markers fixed to

the 2 m rule and the quadrats where precisely measured (to the nearest millimeter). Those distances were used to calibrate (post calibration) the scale of the photogrammetric models and Root Mean Square value (RMS) can be generate to control the good scale calibration. The two divers were positioned ± 1 m (estimated distance) from the substrate and took pictures on half of the area. They modulated their speed to ensure a 60 % overlay between pictures. Each quadrat was georeferenced underwater with a dive computer (Suunto Vyper©, 10 cm precision) for depth and with a submarine geolocation system (UWIS© system; 1-m precision) for latitude and longitude.

### 2.3. Photogrammetric model construction and substrate typological classification

The whole process of model construction and substrate typological mapping was carried out following the methodology developed by Ternon et al. (2022). Computing was performed using Agisoft Meta-shape software (version 1.6.1) for 3D modeling (Fig. 2, steps 2 and 3) and ENVI Classic software (version 5.3) for substrate typological mapping (Fig. 2, step 4). Details on the parametrization, model error estimation and overall accuracy of the classification are available in Tables S1 and S2 and Fig. 2 (step 4). The mean scale error was 0.21 m and the mean overall accuracy of the classification was 84.86 %. Nine typological classes were classified (bedrock, big/medium/small boulder, cobble, pebble, sand, artificial, and field material) and were used to map the substrate typological composition of the 12 transects.

<figure><img src="image_5.png" /><figcaption>Fig. 2. Sampling process from photograph acquisition (step 1) to morphotypic photo-identification (step 8).</figcaption></figure>
## 2.4. Benthic community sampling: virtual quadrat projection

Benthic communities were sampled based on a photo-quadrat methodology adapted to the photogrammetric tool. Samplings were grouped in different substrate typological classes. A class of interest was selected from the classification map and imported in QGIS software (version 3.10). The shapefile was pre-processed to remove small substrate patches (< 0.09 m²; i.e., the quadrat sampling size) and identify areas of substrate typological patches suitable for the quadrat projection (i.e., areas that ensured quadrat projections fully included in the patch without overlapping with other classes; see Figs. 2 and 3). Then, seven virtual quadrats, which ensure completeness of over 90 % for all typological class combined (see figure S14), were randomly generated as 0.09 m² buffer zones on the treated patches (Fig. 2, step 5). This led to a total surface sample of 0.63 m² per substrate typological class, more than the 0.52 m² recommended by Taormina et al. (2020) for natural rocky substrate. Points were spatially stratified and randomly plotted inside these quadrats (Fig. 2, step 6). This stratified and random projection was the best point projection method to describe the community inside each quadrat, so as to better capture diversity, estimate the coverage of organisms (Taormina et al., 2020) and illustrate their dominance. Points were exported from the GIS and imported in Metashape software (Fig. 2, step 7). The original photographs that supported the photogrammetric model were retrieved to identify benthic organisms (Fig. 2, step 8). Photo identification is known to limit the precision of identification. Yet, as presented in the introduction, morphotypic (CATAMI) approaches have been used in several studies to simplify identification and limit identification errors while keeping community description ecologically relevant. In order to improve the ecological relevance of the present study, the CATAMI classification (Hill, 2014) was adapted according to recent studies on algae and sponges (Figures S1 and S2). Information on algae color (green, brown or red) was upgraded to the first node of the classification for this taxon, and sponge morphology was expanded to more categories. This process of substrate typological class selection,

carried out only for bedrock and big/medium/small boulders because of the limitation of photograph quality (poor brightness and focus). Some organisms remained unidentified (annotated as unidentified (UI) morphotypes). Consequently, 160–250 points were projected to reach a threshold of 150 well identified morphotypes.

## 2.5. Benthic community characterization

For each quadrat, several diversity indexes were calculated using Hill numbers parametrized with q values of 0, 1 and 2 to obtain morphotypic richness, Shannon diversity and the inverse Simpson index, respectively (Hill, 1973). The proportions of the different morphotypes were also calculated as the number of points targeting a morphotype over the total number of projected points in the quadrat.

## 2.6. Associated geomorphological characterization

In addition to information about the substrate typological classes of the quadrats (Fig. 3, top right) resulting from the stratified projection, geomorphological variables were calculated from the digital surface model (DSM) inside the specific quadrat areas (Fig. 3, top left). For each quadrat, the mean slope, the slope of the slope, the plan and profile curvature, northness and eastness, roughness (see Pittman et al., 2009 for accurate definitions) can be derived from the DSM to various scales to optimize model performance (Quentin et al., 2022). The DSM ranges were calculated with 0.5, 1, 2 and 5 cm pixel resolution (Table 1, see Wilson et al., 2007a and Pittman et al., 2009 for calculation details). The substrate typological composition and geomorphological information were also calculated at the transect level. For the whole transect area, the same geomorphological variables were calculated with 1, 10 and 100 cm pixel resolution (Fig. 3 middle left). For substrate typological composition, the diversity (richness and Shannon index), the proportion of the different classes, their mean size patches and the distances

<figure><img src="image_6.png" /><figcaption>**Fig. 3.** Design of the reef sampling with the two 120-m² (30 m x 4 m) transects per target depth (8 and 13 m). For each transect, representations of the geomorphological (left) and substrate typological composition (right) measurements are given, i.e., the slope (1-cm resolution) and substrate mapping (supervised classification process), respectively. Representations of the measurements are also given at the quadrat scale, with several non-exhaustive geomorphological variables calculated with 0.5-cm resolution (left) and substrate typological classes (right): bed rock (homogeneous cover of rock), big (x > 1 m), medium (1 m > x > 50 cm) and small (50 cm > x > 25 cm) boulder, cobble (25 cm > x > 6 cm), pebble (6 cm > x > 4 mm), sand (4 mm > x).</figcaption></figure>
<caption>**Table 1**</caption>

Environmental variables used to describe the geomorphology and the nature of the substrate using photogrammetry.

<table><thead><tr><th>Habitat component group</th><th>Variable (unit)</th><th>Definition</th></tr></thead><tbody><tr><td rowspan="10">Geomorphology</td><td>Slope (deg)</td><td>Maximum rate of change in slope between a given cell and 8 surrounding cells</td></tr><tr><td>Roughness (m)</td><td>Difference between the maximum and the minimum value of a given cell and 8 surrounding cells</td></tr><tr><td>slope of the slope (deg²)</td><td>Maximum rate of maximum slope change between a given cell and 8 surrounding cells</td></tr><tr><td>plan curvature</td><td>Horizontal rate of change in curvature across the surface</td></tr><tr><td>profile curvature</td><td>Vertical rate of change in curvature across the surface</td></tr><tr><td>northness</td><td>Degree of exposure to the north</td></tr><tr><td>eastness</td><td>Degree of exposure to the east</td></tr><tr><td>patch size (m²)</td><td>Mean size of patches of a given typology</td></tr><tr><td>patch distance (m)</td><td>Mean distance between patches of a given typology</td></tr><tr><td>patch number</td><td></td></tr><tr><td rowspan="3">Substrate typological composition</td><td>patch number</td><td></td></tr><tr><td>patch distance (m)</td><td></td></tr><tr><td>patch size (m²)</td><td></td></tr><tr><td rowspan="2">typological proportion (%)</td><td>Total number of patches of a given typology</td><td></td></tr><tr><td>patch number</td><td></td></tr><tr><td>Surface ratio of a given typology among all typologies</td><td>typological richness</td><td></td></tr><tr><td>typological diversity</td><td>Total number of different typologies</td><td></td></tr><tr><td>Shannon index of the typologies using proportion values</td><td></td><td></td></tr></tbody></table>

between patches were calculated based on the substrate typological map (Fig. 3 middle right; Table 1).

## 2.7. Statistical analysis

### 2.7.1. Sampling effort testing at a small scale

In order to test the completeness of the sampling procedure, a rarefaction curve was first built for each photo-quadrat (sampling effort = number of points) and for each substrate typological class, based on morphotype richness, with 100 randomizations of samples (Figure S3). This procedure was applied to 0–150 points, with 10-point steps. For each rarefaction curve, the estimated morphotypic richness was calculated with the abundance-based coverage estimator (ACE) (Hortal et al., 2006). Then, the completeness index was calculated by the ratio (Eq. 1):

$$IC = \frac{Richness_{obs.}}{Richness_{est.}} \times 100 \qquad (1)$$

With IC the completeness index in %, Richnessobs. the morphotypic richness observed for a given sampling effort (i.e., the number of points) and Richnessest. the morphotypic richness estimated by the ACE. The evolution of the IC (number of points identified and number of quadrats sampled per class) was checked throughout the sampling effort to ensure sampling completeness. A minimum completeness of 70 % was expected and considered good; sampling exhaustiveness was not sought for (Soberon et al., 2007; Gallon et al., 2013).

### 2.7.2. Estimation of occurrence frequencies

In order to test the completeness of the morphotype list according to

the sampling effort, the frequencies of the morphotype occurrences were calculated. Quadrats were first randomly selected per substrate typological class (bootstrap selection with 100 randomizations). Second, for each quadrat a given and cumulative number of points was randomly selected (25–150 with 25-point steps, and bootstrap selection with 100 randomizations), and the morphotype frequencies were calculated as the number of points related to each morphotype over the total number of selected points. An error rate of the occurrence frequency was calculated as follows (Eq. 2):

$$ER = \frac{|Freq_{obs.} - Freq_{real}|}{Freq_{real}} \qquad (2)$$

With ER the error rate (unitless), Freqobs. the occurrence frequency observed for a given sampling effort, and Freqreal the actual occurrence frequency, i.e., the frequency obtained with the maximum sampling effort for each quadrat. The ERs were compared with the numbers of sampled points to check that the estimations of the frequencies of morphotype occurrences inside the quadrats were accurate. An ER close to zero was expected to highlight a reliable occurrence frequency close to real life, and a low dispersion of morphotypes was expected in order to obtain a reliable estimation of occurrence frequencies whatever the morphotype.

### 2.7.3. Effect of geomorphology on the benthic community structure

A matrix was built based on the occurrence frequencies of the different morphotypes, in relation with environmental variables to assess the response of the benthic community to intra- and inter-site environmental variability. The effect of geomorphology on the benthic community structure was studied by variance partitioning.

Environmental variables (Table 1) were selected for each group of habitat components (i.e., geomorphology and substrate typological composition at the transect and quadrat scales taken independently). A Spearman correlation process was chosen to highlight correlated variables. A correlation between two variables was considered when the coefficient was higher than 0.7 (with p. values > 0.05). In parallel, a random forest analysis was computed to model the contribution of environmental variables to the benthic community structure. At the community level, a gradient forest analysis (a multivariate alternative to the random forest analysis) was applied to determine the importance of environmental variables on the proportion of morphotypes. Finally, the most important uncorrelated variables (those with the highest value) were retained for each family of habitat components. If two or more variables were correlated (> 0.7), the highest one was retained.

A variance partitioning analysis was carried out to highlight (1) which scale of environmental variability (site, transect or quadrat) and (2) which habitat component (geomorphology or substrate typological composition) played the most important role in benthic community structuring. Moreover, the analysis quantified the explained variance shared by these scale and habitat components or exclusive of one of them. This process was based on a multiple redundancy analysis (RDA). The ‘varpart’ function of the vegan package was applied for each benthic community descriptor grouping environmental variables by scale and then by information type. The process provided the percentage of variance of the benthic community variables explained by each group, and distinguished the parts exclusively and jointly explained by the various groups. The RDA was implemented to refine the relationships (high/low and positive/negative correlations) between benthic community metrics and habitat descriptors. This analysis was focused on the sole part of the variance explained by transect and quadrat habitat descriptors (substrate typological composition and geomorphology). In this way, the RDAs were conditional on ‘site’ and ‘depth’ in order to erase their effects. Nevertheless, unconditioned RDA was also available (Figure S5). A forward stepwise selection of environmental variables was conducted to only keep and print relevant variables revealed by the RDA.
The whole statistical process was conducted using RStudio software
(version 1.4.1103; R version 4.0.4).

3. Results

3.1. Substrate typological seascape of the sampled areas

The substrate typological mapping carried out through the classifi-
cation process described the reef seascapes over a 120-m² area
(Figure S6) and revealed two major categories: (1) uniform bedrock flats
(and cliffs), and (2) heterogenous boulders, cobbles, and pebble mosaics.
The sites of Vieux Banc (8- and 13-m depths) and Buharats (8-m depth)
exhibited transects dominated by bedrock. Transect T2 of Buharats at
8 m depth and transect T2 of Vieux banc at 13 m depth showed sand and
cobble deposits, respectively. On the other hand, Bizeux (8- and 13-m
depths) and Buharats (13 m) displayed seafloors composed of a mix of
boulders (big, medium and small) and cobble and several soft-substrate
typological classes (pebble and sand). The transects of Bizeux at 8 m
depth showed a mix of medium and small boulders and cobble, whereas
the transects at 13 m depth showed a mix of big, medium and small
boulders sparsely resting on a cobble and pebble mixture. The transects
of Buharats at 13 m depth had bedrock patches in contact with small
boulder, cobble, pebble and a sand basin. Transect T2 of Bizeux at 8 m
depth was the only one with artificial substrate. These results reveal
seascape heterogeneity in Saint-Malo bay depending both on site and
depth.

3.2. *Sampling completeness*

Except for the medium and big boulder class of transect 2 of Buharats at 13 m depth (median IC value 50.6), the IC appeared higher than 70 % of the replicates for a sampling effort of 150 points identified in seven distinct quadrats (Fig. 4). The 70 % threshold was reached from 30 points on average for the bedrock class, only at Buharats and Vieux Banc (no bedrock substrate was present at Bizeux; Figure S6). Different observations were made for the boulder classes of Bizeux and Buharats (no boulder was present at Vieux Banc; Figure S6). For big boulders, the 70 % threshold was reached from 15 points on average at Bizeux, whereas 60 points were necessary at Buharats to obtain the same completeness. The same observation was made for medium boulders, with 25 and 90 points for Bizeux and Buharats, respectively. The 70 % completeness threshold for the small boulders of Bizeux and Buharats was reached from 50 and 20 points, respectively. As expected, the rate of completeness varied among substrate types and sites, but it globally reached a great completeness (> 70 %) from 80 points projected in the quadrats across all classes and sites.

3.3. *Estimation of occurrence frequencies*

The ERs of occurrence frequencies decreased progressively with the
sampling point incrementation from a median of 0.38 for 25 sampled
points down to 0.07 for 150 points (Fig. 5). However, this latter value
was not significantly different from the median of 0.11 obtained for 125
points. The margin between quartiles also decreased with the number of
projected points, e.g., from 0.11 to 0.03 for 25 and 150 points, respec-
tively. The results obtained for 150 points were the only ones that
showed a 75 % quartile below 0.1 of the ER. Finally, the sampling of 150
points per 0.09 m² quadrat appeared to best estimate the coverage of
benthic invertebrates.

3.4. *The morphotypic benthic communities of Saint Malo bay*

The broad description of the communities shows that red algae,
hydroids and crust-forming sponges, ascidians and erected bryozoans
were the subdominant groups in Bizeux (Figure S7 and table S3). A
difference was observed between the two depths (8 and 13 m) with

fewer red algae and more crusted sponges, hydroids, erected bryozoans and barnacles at 13 m depth. In Buharats and Vieux Banc, the dominant morphotypes (> 20 %) were brown and red algae, hydroids, erected bryozoans and encrusted sponges, and massive sponges for Vieux Banc (Figures S8 and S9). No clear difference appeared between the two depths for these two sites. Hill's number 0 (morphotype richness) was lower at Bizeux than at the other two sites (Figure S10) with 7.5, 8.6 and 9.3 morphotypes on average at Bizeux, Buharats and Vieux Banc, respectively. Hill's number 1 (the Shannon index – common morphotype diversity) was similar at Bizeux and Buharats with mean values of 4.45 and 4.53, respectively, and higher at Vieux Banc (5.56) (Fig. 6). Bizeux and Buharats exhibited similar Hill's numbers 2 (the Simpson index – dominant morphotype diversity), with means of 3.62 and 3.49, respectively, compared to 4.39 for Vieux Banc (Figure S10).

Comparing the Hill values belonging to the same typological class within sites, morphotype diversity (Hill 0, 1 and 2) tended to increase with depth (Fig. 6). The 'bedrock' typological class exhibited greater diversity (Hill 0, 1 and 2) at Buharats, whereas the small boulders of Bizeux sheltered a greater morphotype diversity than big and medium boulders did (Figure S6). No comparison between typological classes was available for Vieux Banc as only bedrock was available at 8- and 13-m depths.

3.5. *Selection of environmental variables*

The correlation and random forest analyses led to the selection of the
most significant habitat variables for each habitat component. The mean
profile curvature (10- and 100-cm resolution), the plan curvature (1, 10
and 100 cm) and northness (100 cm resolution) were retained for the
geomorphological description at the transect scale (Figure S10). The
mean size of the bedrock, cobble and pebble patches, the number of
bedrock and cobble patches and the proportion of artificial substrate
were retained for the substrate typological composition at the transect
scale (Figure S11). The depth range (5 cm resolution), northness and
eastness (2 cm resolution), the profile and plan curvatures (0.5, 1, 2 and
5 cm resolution) and the slope of the slope (1, 2 and 5 cm resolution)
were retained for the geomorphological description at the quadrat scale
(Figure S12). Finally, bedrock, boulders (big, medium and small) and
artificial substrate were retained for the substrate typological composi-
tion at the quadrat scale (Figure S13).

3.6. *Multi-scale processes involved in benthic community structuring*

The variance partitioning analysis showed higher variance explained for the morphotype frequencies and Hill 0 (41 and 35 %, respectively). The analysis showed that benthic community variability was mostly explained by environmental variability at the transect and photo-quadrat scales for all benthic community variables (highest total percentage of variance explained; Fig. 7). At the transect scale, geomorphological variables better explained the variability of morphotype frequencies. No distinction between the substrate typological compositions clearly appeared for Hill numbers 0, 1 and 2. At the photo-quadrat scale, benthic community variability was similarly explained by geomorphology and the substrate class. Site-scale information did not bring any information explaining the variance depicted at the lower scales (Fig. 7). Because the redundancy analysis (RDA, Fig. 8) was performed based on sites and depths to focus on the effect of local substrate characteristics, the explained percentages were low: 11.8 % explained for RDAs related to substrate typological composition and geomorphology at the transect level (Fig. 8.A and B), and 4.6 and 6.0 % explained for substrate typological composition and geomorphology at the quadrat level, respectively (Fig. 8.C and D). The RDAs related to the transect scale (Fig. 8.A and B) first highlighted a community dominated by hydrarians, social unstalked ascidians, sheet-like and filamentous red algae positively correlated with big cobble patches (10- and 100-cm pixel resolution). On the other hand, a community dominated by
<figure><img src="image_7.png" /><figcaption>**Fig. 4.** Completeness of the number of points for 7 sampled quadrats for Bizeux (top), Buharats (middle) and Vieux Banc (bottom). For each site, the different depths and transects are distinguished: top, 8 m depth; bottom, 13 m depth; left, transect 1; right, transect 2. Colors, sampling classes (bedrock, big/medium/small boulders, artificial substrate). Boxplots, median and 25 % and 75 % quartiles; points, outliers. Continuous red line, 70 % completeness, commonly considered as sufficient to accurately describe benthic communities.</figcaption></figure>
<figure><img src="image_8.png" /><figcaption>**Fig. 5.** Error rate (ER) of the calculation of occurrence frequencies divided by the number of points sampled in each quadrat. Boxplots, medians and 25 % and 75 % quartiles of the replicates (sites, depths, transects and quadrats), classes and morphotypes. Different letters indicate a significant difference (pairwise Dunn test, p-value < 0.05). The specific ERs of each morphotype are available in Fig. S4.</figcaption></figure>

brown sheetlike macroalgae, flexible erected bryozoans, fine branched red algae, encrusted sponges and high diversity (Hill 0, 1 and 2) were positively associated to the size of bedrock and pebble patches, the number of bedrock and cobble patches, northness (100-cm pixel resolution), the profile curvature (100-cm pixel resolution) and the plan curvature (1-cm pixel resolution). At the quadrat scale, hydrarians were associated with small boulders, whereas encrusted sponges, fine branched red algae, sheet-like brown algae, barnacles and a great diversity were observed for the bedrock typological class, and filamentous and sheet-like red algae and erected bryozoans were associated to artificial substrate. As for geomorphology, sheet-like red algae and hydrarians were positively correlated to the plan curvature (2-cm pixel

resolution), communities dominated by filamentous red algae and sheet-like brown algae were linked to the profile curvature (2-cm pixel resolution), massive lobed sponges were related to a high depth range (5-cm pixel resolution) and diversified (Hill numbers 0, 1 and 2) communities dominated by flexible erected bryozoans, and encrusted sponges were associated to northness and eastness (2-cm pixel resolution). Globally, different morphotypes responded similarly to descriptors of the habitat structure, and can be divided in three groups: (1) sheet-like red algae and hydroids, (2) filamentous red algae, and (3) sheet-like brown algae, fine branched red algae, encrusted sponges, massive lobed sponges, barnacles and flexible erected bryozoans. They were mainly related to (a) the structure of the bedrock and cobble patches, (b) the presence of small boulders, bedrock or artificial substrate, and (c) concavity, convexity and exposure (at different resolution levels and scales) and the depth range at the quadrat scale.

## 4. Discussion

Our study proposes a photogrammetric method to describe benthic sessile rocky reef communities at scales that are usually covered by scientific scuba divers during a sampling session (ca. 100–200 m²), with high spatial resolution relevant for describing sessile species (ca. < 1 cm²). We provide innovative pathways for solving a well-known paradox, namely providing high-resolution (within < 1-m² quadrats) together with large-scale descriptions (i.e., transects) of rocky reef communities because dives are limited to a few tens of minutes due to diving security constraints. We developed a non-destructive method (no extractive sampling), which reduced our ability to properly identify most species based on microscopic details that cannot be seen during a dive. Instead, we chose to identify morphospecies adapted from the CATAMI classification.

The presently described method was inspired from the photo-quadrat methodology, in order to quantitatively link the substrate characteristics (geomorphology and substrate typological composition) to the characteristics of the morphospecies communities. The present study also explores the importance of scale on the morphospecies composition and the abundance of benthic communities. Most of the variability of the community was explained by local habitat characteristics (from 0.09 to 120 m²) rather than by physicochemical variability along the inshore-offshore gradient of Saint-Malo bay. This work is

<figure><img src="image_9.png" /><figcaption>**Fig. 6.** Boxplots representing α-diversity based on Hill numbers for the three sites and the two different depths.</figcaption></figure>
<figure><img src="image_10.png" /><figcaption>**Fig. 7.** Venn diagrams of variance partitioning of different benthic community descriptors (morphotype frequencies here called assemblages, Hill numbers 0, 1 and 2). Top, variance partitioning of site-, transect- and quadrat-scale environmental variation. Bottom, transect- and quadrat-scale variation as related to the variation of the geomorphological and typological composition of the substrate. The values in the diagrams indicate the percentages of variance exclusively explained by one group (for non-intersected zones) or commonly explained by two or more groups (for intersected zones). The color gradient is proportional to the values.</figcaption></figure>

based on the study of Ternon et al. (2022) where the results of the classification process used to map the substrate typological classes are thoroughly discussed. The morphotypic approach detects differences in the benthic community at various scales (probably in link with coastal environmental gradients). Interestingly, our stratification sampling design highlighted significant effects of very small scales (< 0.1 m²) on benthic communities.

# 4.1. Methodological perspectives

## 4.1.1. Sampling completeness

Our digital approach proposes to describe the structure and variability of the benthic sessile community by randomly sampling a number of points to be projected onto photo quadrats of the photogrammetric models. The present work estimates the completeness of the morphospecies list according to the digital sampling effort per habitat type. We targeted a minimum completeness of 70 % that is enough to detect the variability of the dominant CATAMI groups and propose indicators of environmental conditions. The 70 % threshold was reached from 80 projected points for most of the quadrats. This value corroborates the rare studies on benthic communities sampled by the photo-quadrat technique (van Rein et al., 2011; Trygonis and Sini, 2012; Taormina, 2020).

The morphotypic approach and the methodological design seemed to properly capture biological diversity across scales from the quadrat (0.09 m²) to the transect (120 m²) scales. Satisfactory completeness (> 70 %) was reached for most of the transects. However, the 70 %

threshold was not reached for the medium and big boulders of Buharats (13 m deep, T2), maybe due to high biological diversity. The ACE estimator is sensitive to rare species (O'Hara, 2005; Canning-Clode et al., 2008), and high diversity can generate a very high estimated richness value, which is too penalizing and generates a low completeness index. Nevertheless, the use of ACE fulfilled the objectives of this study, i.e., assessing a minimum performance of the photogrammetric methodology to capture biological diversity through a morphotypic approach.

## 4.1.2. Estimation of occurrence frequencies

The stratified random-point projection methodology was chosen to analyze the occurrence frequencies of the morphotypes within the photo quadrats. As highlighted by Taormina et al. (2020), this methodology is more robust than morphotype segmentation across the whole image, which leads to an excessive time needed to digitize a training set of morphotypes. The sampling of 150 points provided a good appraisal of occurrence frequencies with an overall error (generalized to all morphotypes) of 10 % compared to the observed value. Moreover, the methodology proved adapted to characterize dominant organisms (> 5 %, Dumas et al., 2009; Deter et al., 2012; Perkins et al., 2016; Taormina et al., 2020), which was the goal of our research work.

The point projection methodology employed in this study can be deemed as a proxy of the coverage of morphotypes: it does not distinguish individuals or colonies, and should induce a bias in counting the number of specimens per morphotype, especially large ones. Only the presence of specimens in different quadrats can reflect this complementary metric. In our sampling design, the coupling of point and
<figure><img src="image_11.png" /><figcaption>**Fig. 8.** Redundancy analysis (RDA), conditional on site and depth, of the relationship between substrate characteristics and benthic community descriptors. The substrate typological composition (A) and geomorphology (B) at the transect scale are distinguished by substrate typology (C) and geomorphology (D) at the quadrat scale. Points, replicates; arrow sizes are proportional to the significance of the environmental variables (red, substrate typological composition; blue, geomorphology) and to the univariate benthic descriptors (black, Hill numbers 0, 1 and 2).</figcaption></figure>
quadrat projections reduced a potential bias consisting in over-
estimating morphotype occurrences.

## 4.2. Multi-scale processes involved in benthic community structuring

### 4.2.1. Inter-site structuring of morphotypes

The morphotypes – adapted from CATAMI’s classification – formed
communities that varied significantly according to environmental gra-
dients at the scale of the bay and to depths. Different compositions and
dominances were observed between sites positioned along a coast-to-
offshore water mass and bathymetric levels. The offshore sites were
generally dominated by brown and red algae (depending on depth), in
contrast with the coastal site (Bizeux), dominated by suspensive feeders
such as hydroids and sponges, as well as red algae (again depending on
depth). We can hypothesize than this difference has to be related to the
fluvial influence of the Rance river, which supplies freshwater and
suspended matter. These observations were also underlined by Der-
rien-Courtel et al. (2013) in Brittany. The differences in bathymetry
observed at Bizeux followed a classic logic of settlement succession, with
red algae in the shallower depths replaced by suspensive feeders at
greater depths, in link with light availability (Castric-Fey et al., 1973,
2001; Earll and Erwin, 1983; Wood, 1987; Collignon, 1991).

The morphotypic approach allowed us to calculate several diversity
indexes (Hill 0, 1 and 2, associated with richness, the Shannon index and
the Simpson index, respectively) currently used in community studies.
Hill 0 highlighted variation in biological richness, but no difference was
highlighted by Hill 1 and Hill 2. As the morphotypic approach tends to
merge several species in a common morphotypic group, it fails to detect
the overabundance of a given species in a given group, and smooths the
Hill 1 and Hill 2 values. Despite this lack of sensitivity of morphotypes
for detecting variability in diversity indexes, the results show that the
morphotypic approach is relevant to study the relationships between
benthic communities and the environmental drivers tested.

The morphotype communities varied at small scales (< 120 m²). The
variance of morphotypic diversity (Hill 0, 1 and 2) was better explained
(up to 24 % of Hill 0 variability) by local habitat features like geo-
morphology and typology than by inter-site or inter-bathymetric vari-
ability. This result highlights the existence of local processes
conditioned by environmental characteristics at a larger scale. Hydro-
dynamic conditions – in particular swell, fetch and tidal current – are
major key factors that can shape the seascape at a larger scale (Sekund
and Pittman, 2017). Hydrodynamic conditions are known to play a
direct role in the structuring of benthic communities (Earll and Erwin,
1983; Wood, 1987; Ordines et al., 2011). In our study environmental
characteristics at the site scale, such as different hydrodynamic condi-
tions, can structure benthic communities directly, indirectly, or in a
complementary way. The outcome of all these processes remains to be
clarified by specifically studying this variability. Particular tools such as
self-organizing maps can spatially highlight different groups of sampling
stations based on their similar characteristics (Bacao et al., 2004; Russo
et al., 2016; Mendoza-Carranza et al., 2018). Applying this tool to the
development of a sampling strategy would highlight the best spatial
division in positioning the stations, in order to effectively study multiple
environmental effects on the structure of the subtidal community.

## 4.3. Local structuring of morphotypic communities

Local processes at the transect (120 m²) and virtual quadrat
(0.09 m²) scales appeared to be linked to the typological characteristics
of the substrate and to the geomorphological characteristics. The effects
associated with the typological characteristics underline different pro-
cesses. Boulders and their arrangement within the underwater seascape
induce a particular hydrodynamic system locally and participate in the
structuring of benthic communities (Guichard and Bourget, 1998). The
mobility characteristic of the rocky substrate (from immobile bedrock to
very mobile small boulders) can also be considered, where the features

most prone to movement due to swell and currents would be mostly
colonized by pioneer organisms with annual dynamics (filamentous red
algae, hydroids, barnacles, tube worms, encrusting bryozoans). Looking
at the RDAs, filamentous red algae and hydroids were mainly found on
seascapes with patches of pebbles and on quadrats sampled on small
boulders (25–50 cm), in line with previous studies. Our work offers the
possibility to directly link this observation to geomorphological infor-
mation. It reveals that much of the variability of benthic communities is
commonly explained by information on geomorphology and substrate
typologies. This denotes the possible indirect effects of the substrate
typology, which could act as a structuring factor. For example, a pile of
boulders is more geomorphologically complex than a rocky flat. This
geomorphological complexity has already been indicated as an impor-
tant factor affecting the composition of the benthic community (Maia
et al., 2018). On the other hand, the supervised classification of sub-
strate typologies uses information related to the geomorphological
characteristics of the substrate and could partly explain this result.
Consequently, the respective shares of geomorphological and typolog-
ical characteristics in explaining the structuring of the benthic com-
munity of the substrate at the transect scale remain to be clarified. The
descriptors of the local characteristics of the substrate used in this study
appeared relevant. More precisely, patches of bedrock, small boulder,
pebble and artificial substrate were meaningful to be described by
photogrammetry, and so were geomorphological metrics describing
concavity, convexity, aspect and the depth range (a proxy of substrate
verticality).

We did not detect any effect of geomorphological complexity within
the virtual quadrats. Lapointe and Bourget (1999) showed the effects of
this factor on the structure of temperate benthic stands during field
experiments (panels immersed in water). In particular, they showed that
the complexity captured at millimeter resolution influenced on the
abundance of organisms and that it rather influenced the benthic com-
munity diversity at centimeter resolution. The morphotypic approach
used in this study may not be precise and sensitive enough to detect
variations related to geomorphological characteristics at the quadrat
scale. In addition, it is important to note that the relief was described by
a DSM that considered the morphology of all entities present – both
abiotic and biotic ones. Due to the non-destructive dimension of the
sampling procedure, the descriptive metrics of geomorphology such as
complexity were rather related to benthic organisms than to colonized
substrate, as demonstrated by Wilson et al. (2007b). A ‘pre- and post–
scrapping’ parsimonious description of all the benthic fauna and flora
applied locally would enable studying the effect of geomorphological
complexity on colonization by benthic species more precisely. We did
not go down to millimeter resolution. Achieving such resolution requires
adapting the method by getting closer to the substrate, but would have
the effect of reducing the sampled surface. All these methodological
points might explain why the effect of complexity at the quadrat scale
was not observed.

## 5. Conclusion

Describing the fine-scale relationships between morphotypes and the
substrate *a priori* represents a difficult challenge. The method deployed
in the present study is of great interest for seascape ecology purposes and
has proved effective in characterizing benthic communities by capturing
a significant part of the morphotypic biological diversity in relation with
the architectural properties of rocky substrates at different scales. The
use of the different categories of morphotypes has proved relevant for
characterizing intra- and inter-site differences. Thus, the effective mor-
photypes described here could be considered as good proxies for future
monitoring studies. However, this study suggests that standardized *in situ*
visual surveys of morphotypes could allow large-scale monitoring
according to the typological classes described by photogrammetry. The
routine application of the photogrammetric methodology still requires
improvement. The logistical constraints of operator time required for the
steps of supervised classification of the substrate typology as well as identification from images still remain too time-consuming and not operable as such for regular monitoring. Innovations around machine-(including deep) learning are very encouraging and are already performing remarkably in automated recognition of entities (e.g., organisms, substrate types, objects) based on images and videos. Deep learning will probably tend to automate all the steps from the creation of photogrammetric models to the characterization of benthic communities. Finally, the new insights provided by this study are a strong basis for constructing multi-scale explanatory and predictive models of benthic biodiversity and developing precise and reliable maps of marine habitats essential for better conservation and management of the marine environment. The morphotypic approach could be an interesting input for the development of a unique habitat typology applicable at least at a European scale and operable by people who are not experts in taxonomy.

## CRediT authorship contribution statement

**Pierre Thiriet:** Supervision, Conceptualization. **Antoine Collin:** Supervision. **Frederic Ysnel:** Writing - review & editing, Supervision, Methodology. **Quentin Ternon:** Writing - original draft, Methodology, Investigation, Formal analysis. **E. Feunteun:** Writing - review & editing, Supervision, Conceptualization. **V. Danet:** Methodology, Investigation. **M. Guillaume:** Formal analysis.

## Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

## Acknowledgements

Huge thanks to the technical staff of the CRESCO-MNHN, in particular Julien Guillaudeau, Sébastien Aubin and Christophe Boinet, who allowed for the success of the sampling campaigns safely and with custom material. Thanks too to Dorothée James, from the EPHE, for teaching us the first steps with ENVI software. The authors are also grateful to Pertti Arvonen, from UWIS Co. for his technical support for the geolocation system. This work was funded by MARINEFF Project, aimed at developing marine infrastructure improving the ecological state of coastal water along the English Channel (Programme 2019-2022 INTERREG V-A France (Manche) - Angleterre, co-financed by the FEDER).

## Appendix A. Supporting information

Supplementary data associated with this article can be found in the online version at doi:10.1016/j.rsma.2025.104010.

## Data availability

Data will be made available on request.

## References

Anelli, M., Julitta, T., Fallati, L., Galli, P., Rossini, M., Colombo, R., 2017. Towards new applications of underwater photogrammetry for investigating coral reef morphology and habitat complexity in the Myeik Archipelago, Myanmar. Geocart Int.: 1-14. https://doi.org/10.1080/10106049.2017.1408703.

Bacao, F., Lobo, V., Painho, M., 2004. Geo-self-organizing map (Geo-SOM) for building and exploring homogeneous regions. Lect. Notes Comput. Sci. (Incl. Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinforma.) 3234 (ember), 22-37. https://doi.org/10.1007/978-3-540-30231-5-2.

Brandl, S.J., Bellwood, D.R., 2016. Microtopographic refuges shape consumer-producer dynamics by mediating consumer functional diversity. Oecologia 182 (1), 203-217. https://doi.org/10.1007/s00442-016-3643-0.

Bravo, G., Livore, J.P., Bigatti, G., 2020. The importance of surface orientation in biodiversity monitoring protocols: the case of patagonian rocky reefs. Front. Mar. Sci. 7, 1-12. https://doi.org/10.3389/fmar.2020.578595.

Burns, J.H.R., Delparte, D., Kapono, L., Belt, M., Gates, R.D., Takabayashi, M., 2016. Assessing the impact of acute disturbances on the structure and composition of a coral community using innovative 3D reconstruction techniques. Methods Oceanogr. 15-16 49-59. https://doi.org/10.1016/j.jmio.2016.04.001.

Canning-Clode, J., Valdivia, N., Molis, M., Thomason, J.C., Wahl, M., 2008. Estimation of regional richness in marine benthic communities: quantifying the error. Linnol. Oceanogr.: Methods 6, 580-590. https://doi.org/10.4319/lo.m.2008.6.580.

Castric-Fey, A., Girard-Descatoire, A., Lafargue, F.L., Hardy-Halos, M.-T., 1973. Etagement des algues et des invertébrés sessiles dans l'Archipel de Glénan. Helgol. Wiss. Meeresunters. 24, 490-509.

Collignon, J., 1991. Ecologie et biologie marines: introduction à l'halieutique. Masson, Paris, p. 298.

Crain, C.M., Halpern, B.S., Beck, M.W., Kappel, C.V., 2009. Understanding and managing human threats to the coastal marine environment. Ann. N. Y. Acad. Sci. 1162, 39-62. https://doi.org/10.1111/j.1749-6632.2009.04496.x.

Derrien-Courtel, S., Le Gal, A., Grall, J., 2013. Regional-scale analysis of subtidal rocky shore community. Helgol. Mar. Res. 67 (4), 697-712. https://doi.org/10.1007/s10152-013-0355-2.

Deter, J., Descamp, P., Ballesta, L., Boissery, P., Holon, F., 2012. 2012. A preliminary study toward an index based on coralligenous assemblages for the ecological status assessment of Mediterranean French coastal waters. Ecol. Indic. 20, 345-352. https://doi.org/10.1016/j.ecolind.2012.03.001.

Duckworth, A.R., 2016. Substrate type affects the abundance and size of a coral-reef sponge between depths. Mar. Freshw. Res. 67 (2), 246-255. https://doi.org/10.1071/MF14308.

Dumas, P., Bertaud, A., Peignon, C., Léopold, M., Pelletier, D., 2009. A "quick and clean" photographic method for the description of coral reef habitats. J. Exp. Mar. Biol. Ecol. 368 (2), 161-168. https://doi.org/10.1016/j.jembe.2008.10.002.

Earll, R., Erwin, D.G., 1983. Sublittoral ecology. The ecology of the shallow sublittoral benthos. New York: Oxford University Press.

Ferrari, R., Lachs, L., Pygas, D.R., Humanes, A., Sommer, B., Figueira, W.F., Edwards, A. J., Bythell, J.C., Guest, J.R., 2021. Photogrammetry as a tool to improve ecosystem restoration. Trends Ecol. Evol. 36 (12), 1093-1101. https://doi.org/10.1016/j.tree.2021.07.004.

Gallon, R.K., Ysnel, F., Feunteun, E., 2013. Optimization of an "in situ" subtidal rocky-shore sampling strategy for monitoring purposes. Mar. Pollut. Bull. 74 (1), 253-263. https://doi.org/10.1016/j.marpolbul.2013.06.049.

Gallon, R.K., Robuchon, M., Leroy, B., Le Gall, L., Valero, M., Feunteun, E., 2014. Twenty years of observed and predicted changes in subtidal red seaweed assemblages along a biogeographical transition zone: Inferring potential causes from environmental data. J. Biogeogr. 41 (12), 2293-2306. https://doi.org/10.1111/jbi.12380.

Guichard, F., Bourget, E., 1998. Topographic heterogeneity, hydrodynamics, and benthic community structure: a scale-dependent cascade. Mar. Ecol. Prog. Ser. 171, 59-70. https://doi.org/10.3354/meps171059.

Halpern, B.S., Walbridge, S., Selkoe, K.A., Kappel, C.V., Micheli, F., D.Agrosa, C., Bruno, J.F., Casey, K.S., Ebert, C., Fox, H.E., Fujita, R., Heinemann, D., Lenihan, H.S., Madin, E.M.P., Perry, M.T., Selig, E.R., Spalding, M., Steneck, R., Watson, R., 2008. A global map of human impact on marine ecosystems. Science 319 (5865), 948-952. https://doi.org/10.1126/science.1149345.

Henriques, S., Pais, M.P., Vasconcelos, R.P., Murta, A., Azevedo, M., Costa, M.J., Cabral, H.N., 2014. Structural and functional trends indicate fishing pressure on marine fish assemblages. J. Appl. Ecol. 51 (3), 623-631. https://doi.org/10.1111/j.1365-2664.12235.

Hill, M.O., 1973. Diversity and evenness: a unifying notation and its consequences. Ecology 54 (2), 427-432. https://doi.org/10.2307/1934352.

Hill, N., Althaus, F., Rees, T., Jordan, A., Industries, P., Colquhoun, J., Sch, C., Case, M., Scott, F., Edgar, G., Legorreta, R.F., Fromont, J., Museum, W.A., Przeslawski, R., Hayes, K., Marzinelli, Z., Friedman, A., Toohey, L., Hibberd, T., Hovey, R., Kendrick, G., Wyatt, M., Edwards, L., Centre, P.S., 2014. CATAMI classification scheme for scoring marine biota and substrata in underwater imagery: 1-24.

Hortal, J., Borges, P.A.V., Gaspar, C., 2006. Evaluating the performance of species richness estimators: Sensitivity to sample grain size. J. Anim. Ecol. 75 (1), 274-287. https://doi.org/10.1111/j.1365-2656.2006.01048.x.

Kanki, T., Nakamoto, K., Hayakawa, J., Kitagawa, T., Kawamura, T., 2021. A new method for investigating relationships between distribution of sessile organisms and multiple terrain variables by photogrammetry of subtidal bedrocks. Front. Mar. Sci. 8 (March), 1-16. https://doi.org/10.3389/fmars.2021.654950.

Lamy, T., Reed, D.C., Rassweiler, A., Siegel, D.A., Kui, L., Bell, T.W., Simons, R.D., Miller, R.J., 2018. Scale-specific drivers of kelp forest communities. Oecologia 186 (1), 217-233. https://doi.org/10.1007/s00442-017-3994-1.

Lapointe, L., Bourget, E., 1999. Influence of substratum heterogeneity scales and complexity on a temperate epibenthic marine community. Mar. Ecol. Prog. Ser. 189, 159-170. https://doi.org/10.1002/asna.200310104.

Luckhurst, B.E., Luckhurst, K., 1978. Analysis of the influence of substrate variables on coral reef fish communities. Mar. Biol. 49 (4), 317-323. https://doi.org/10.1007/BF00455026.

Maia, H.A., Morais, R.A., Quimbayo, J.P., Dias, M.S., Sampaio, C.L.S., Horta, P.A., Ferreira, C.E.L., Floeter, S.R., 2018. Spatial patterns and drivers of fish and benthic reef communities at São Tomé Island, Tropical Eastern Atlantic. Mar. Ecol. 39 (6), 1-16. https://doi.org/10.1111/maec.12520.

Mcquatters-gollop, A., Gu, L., Corcoran, E., Dierschke, V., Elliott, S.A.M., Geelhoed, S.C. V., Gilles, A., Gonz, J.M., 2022. Assessing the state of marine biodiversity in the
Northeast Atlantic. Ecol. Indic. 141. https://doi.org/10.1016/j.
ecolind.2022.109148.

Mendoza-Carranza, M., Ejarque, E., Nagelkerke, L.A.J., 2018. Disentangling the complexity of tropical small-scale fisheries dynamics using supervised Self-Organizing Maps. PLoS ONE 13 (5), 1–28. https://doi.org/10.1371/journal. pone.0196991.

Menge, B.A., Olson, A.M., 1990. Role of scale and environmental factors in regulation of community structure. Trends Ecol. Evol. 5 (2), 52–57. https://doi.org/10.1016/ 0169-5347(90)90048-I.

Menna, F., Nocerino, E., Malek, S., Remondino, F., Schiaparelli, S., 2022. A combined approach for long-term monitoring of benthos in antarctica with underwater photogrammetry and image understanding. Int. Arch. Photogramm., Remote Sens. Spat. Inf. Sci. 43 (B2), 935–943. https://doi.org/10.5194/isprs-archives-xliii-b2- 2022-935-2022.

Moore, P.G., 1973. The kelp fauna of northeast britain. II Multivariate classification: turbidity as an ecological factor. J. Exp. Mar. Biol. Ecol. 13, 127–163.

Munro, C., 2005. Diving Sytems. in Methods for the Study of Marine Benthos. Blackwell. Oxford, pp. 112–159.

O.Hara, R.B., 2005. Species richness estimators: How many species can dance on the head of a pin? J. Anim. Ecol. 74 (2), 375–386. https://doi.org/10.1111/j.1365- 2656.2005.00940.x.

Ordines, F., Jordà, G., Quetglas, A., Flexas, M., Moranta, J., Massutí, E., 2011. Connections between hydrodynamics, benthic landscape and associated fauna in the Balearic Islands, western Mediterranean. Cont. Shelf Res. 31 (17), 1835–1844. https://doi.org/10.1016/j.csr.2011.08.007.

Perkins, N.R., Foster, S.D., Hill, N.A., Barrett, N.S., 2016. Image subsampling and point scoring approaches for large-scale marine benthic monitoring programs. Estuar., Coast. Shelf Sci. 176, 36–46. https://doi.org/10.1016/j.ecss.2016.04.005.

Pita, P., Fernández-Márquez, D., Freire, J., 2018. Spatiotemporal variation in the structure of reef fish and macroalgal assemblages in a north-east Atlantic kelp forest ecosystem: Implications for the management of temperate rocky reefs. Mar. Freshw. Res. 69 (4), 525–541. https://doi.org/10.1071/MF17193.

Pittman, S.J., Costa, B.M., Battista, T.A., 2009. Using Lidar Bathymetry and Boosted Regression Trees to Predict the Diversity and Abundance of Fish and Corals. J. Coast. Res. 53, 27–38. https://doi.org/10.2112/si53-004.1.

Porskamp, P., Rattray, A., Young, M., Ierodiazonou, D., 2018. Multiscale and hierarchical classification for benthic habitat mapping. Geosciences 8 (119), 1–24. https://doi. org/10.3390/geosciences8040119.

van Rein, H., Schoeman, D.S., Brown, C.J., Quinn, R., Breen, J., 2011. Development of benthic monitoring methods using photoquadrats and scuba on heterogeneous hard- substrata: a boulder-slope community case study. Aquat. Conserv.: Mar. Freshw. Ecosyst. 21 (7), 676–689. https://doi.org/10.1002/aqc.1224.

Robert, K., Huvenne, V.A.I., Georgiopoulou, A., Jones, D.O.B., Marsh, L., Carter, D.O.G., Chaumillon, L., 2017. New approaches to high-resolution mapping of marine vertical structures. Sci. Rep. 7 (1), 1–14. https://doi.org/10.1038/s41598-017-09382-z.

Russo, T., Carpentieri, P., Fiorentino, F., Arneri, E., Scardi, M., Cioffi, A., Cataudella, S., 2016. Modeling landings profiles of fishing vessels: an application of Self-Organizing Maps to VMS and logbook data. Fish. Res. 181, 34–47 doi:10.1016/j. fishres.2016.04.005.

Schiel, D.R., Steinbeck, J.R., Foster, M.S., 2014. Ten Years of Induced Ocean Warming Causes Comprehensive. Ecology 85 (7), 1833–1839.

Schramm, W., Nienhuis, P.H., 1996. Marine Benthic Vegetation, Recent Changes and the Effects of Eutrophication. Edited by Springer. Berlin, Heidelberg.

Sebens, K.P., 2011. Habitat structure and community dynamics in marine benthic systems. Habitat Struct. 211–234. https://doi.org/10.1007/978-94-011-3076-9_11.

Sekund, L., Pittman, S., 2017. Explaining island-wide geographical patterns of Caribbean fish diversity: a multi-scale seascape ecology approach. Mar. Ecol. 38 (3), 1–19. https://doi.org/10.1111/maec.12434.

Soberón, J., Jiménez, R., Golubov, J., Koleff, P., 2007. Assessing completeness of biodiversity databases at different spatial scales. Ecography 30, 152–16.

Swanborn, D.J.B., Huvenne, V.A.I., Pittman, S.J., Woodall, L.C., 2022. Bringing seascape ecology to the deep seabed: a review and framework for its application. Limnol. Oceanogr. 67 (1), 66–88. https://doi.org/10.1002/lno.11976.

Taormina, B., Marzloff, M.P., Desroy, N., Caisey, X., Dugornay, O., Metral Thiesse, E., Tancray, A., Carlier, A., 2020. Optimizing image-based protocol to monitor macroepibenthic communities colonizing artificial structures. ICES J. Mar. Sci. 77 (2), 835–845. https://doi.org/10.1093/icesjms/fsz249.

Ternon, Q., Danet, V., Thiriet, P., Ysnel, F., Feunteun, E., Collin, A., 2022. Classification of underwater photogrammetry data for temperate benthic rocky reef mapping. Estuar., Coast. Shelf Sci. 270 (107833), 1–11. https://doi.org/10.1016/j. ecs.2022.107833.

Trygonis, V., Sini, M., 2012. PhotoQuad: A dedicated seabed image processing software, and a comparative error analysis of four photoquadrat methods. J. Exp. Mar. Biol. Ecol. 424–425, 99–108. https://doi.org/10.1016/j.jembe.2012.04.018.

Wahl, M., 2009. Marine Hard Bottom Communities: Patterns, Dynamics, Diversity, and Change. Springer-V, Ecological Studies Series 206. Springer-V., Berlin, Heidelberg https://doi.org/10.1007/b76710.

Wilson, M.F.J., O’Connell, B., Brown, C., Guinan, J.C., Grehan, A.J., 2007a. Multiscale terrain analysis of multibeam bathymetry data for habitat mapping on the continental slope. Mar. Geod. 30 (1–2), 3–35. https://doi.org/10.1080/ 01490410701295962.

Wilson, S.K., Graham, N.A.J., Polunin, N.V.C., 2007b. Appraisal of visual assessments of habitat complexity and benthic composition on coral reefs. Mar. Biol. 151 (3), 1069–1076. https://doi.org/10.1007/s00227-006-0538-3.

Wood, E., 1987. Subtidal Ecology, 125. Edward Arnold, London, p. 1.