# Deterministic and stochastic processes drive sessile cryptobenthic community composition on coral reefs at different spatial scales

Baptiste Frattini, Henrich Bruggemann, Eric Goberville, Marion Couëdel, Fleur Bruggemann, Mireille Guillaume

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## To cite this version:

Baptiste Frattini, Henrich Bruggemann, Eric Goberville, Marion Couëdel, Fleur Bruggemann, et al. Deterministic and stochastic processes drive sessile cryptobenthic community composition on coral reefs at different spatial scales. Marine Ecology Progress Series, 2025, 761, pp.21-34. 10.3354/meps14852. hal-05286996

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Submitted on 23 Jan 2026

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# Deterministic and stochastic processes drive sessile cryptobenthic community composition on coral reefs at different spatial scales

Baptiste Frattini<fnref n="1" />,<fnref n="2" />,*, J. Henrich Bruggemann<fnref n="2" />, Eric Goberville<fnref n="1" />, Marion Couëdel<fnref n="2" />, Fleur Bruggemann<fnref n="2" />, Mireille M. M. Guillaume<fnref n="1" />

**ABSTRACT:** Coral reefs host immense biodiversity. The species that live concealed within the reef matrix, the cryptobiome, represent a major component of this diversity and are essential to reef functioning. However, the drivers of their composition and distribution remain incompletely understood. We deployed 27 autonomous reef monitoring structures (ARMS) for 2 yr at 9 outer slope sites on the fringing reefs along the west and southwest coasts of Reunion Island (south-west Indian Ocean). Photo-analysis of the sessile cryptobenthic communities (SCCs) on ARMS plate faces revealed that Rhodophyta, Annelida, Bryozoa, Porifera, Foraminifera, and Ascidiacea were the most abundant taxa. Oceanic distance, habitat characteristics, and environmental parameters were analysed to assess factors controlling the SCCs at various spatial scales. Presence–absence and abundance-based dissimilarity metrics revealed the relative contributions of deterministic and stochastic processes driving SCC beta diversity. At the ARMS unit scale, SCC composition was strongly linked to plate face orientation and exposure to the environment. Along the Reunion coral reef coast, community dissimilarity was associated with discontinuities of reef habitat, grouping SCCs into 4 geographical zones based on environmental factors (sea surface temperature, wave height, water flow velocity). Our results suggest that the SCC membership of taxa may be primarily driven by stochastic processes such as random larval dispersal and settlement on each reef, while their relative abundance within the community may be influenced by deterministic niche-based processes. These findings improve our understanding of the drivers of coral reef biodiversity in support of the conservation of these ecosystems.

**KEY WORDS:** Sessile cryptobiome · Community ecology · Beta diversity · Niche-based processes · Artificial reefs · Autonomous reef monitoring structure · ARMS · Ascidiacea · Bryozoa

## 1. INTRODUCTION

Tropical coral reefs boast extraordinary biodiversity, potentially hosting up to one-third of known marine species (Reaka 2005, Knowlton et al. 2010, Fisher et al. 2015). Most of these species inhabit the intricate reef framework and constitute the ‘cryptobiome’, which encompasses a myriad of motile and sessile organisms. Sessile cryptobenthic communities (SCCs) within this

cryptobiome comprise Rhodophyta, Porifera, Annelida, Bryozoa, Foraminifera, and Ascidiacea (Pearman et al. 2016, Ransome et al. 2017, Casey et al. 2021, Ip et al. 2023). Despite thriving in oligotrophic waters, coral reef ecosystems exhibit remarkable productivity, a phenomenon often referred to as Darwin’s Paradox. This paradoxical productivity can be partly attributed to the large populations, short generation times, and efficient nutrient cycling capacities within the crypto-

<footnote n="1">¹Laboratoire de Biologie des Organismes et des Écosystèmes Aquatiques-BOREA, Muséum national d'Histoire naturelle (MNHN), SU, CNRS, IRD, UA, 75005 Paris, France</footnote>

<footnote n="2">²UMR 9220 ENTROPIE, Université de La Réunion, IRD, IFREMER, Université de Nouvelle-Calédonie, 97444 Saint-Denis, La Réunion, France</footnote>

<footnote>*Corresponding author: baptiste.frattini@edu.mnhn.fr</footnote>
biome (Kramer et al. 2012, de Goeij et al. 2013). Consequently, the cryptobiome exerts bottom-up control over interactions between invertebrates and invertivores, and fisheries production (Rogers et al. 2014, Brandl et al. 2019). Due to their pivotal role in ecosystem functioning, studies aiming to understand the spatiotemporal patterns of cryptobiomes have gained traction (Ransome et al. 2017, Carvalho et al. 2019, Pearman et al. 2020, Villalobos et al. 2024).

Two theoretical models are commonly used in community ecology to explain spatial diversity patterns. The deterministic model, rooted in niche theory (Hutchinson 1973), posits that environmental filtering and biotic interactions drive community composition (i.e. defined as taxa membership and/or abundance in a given community), either by favouring or restricting the growth of certain species. In contrast, the stochastic model states that community variations are mainly due to random changes in species' relative abundance (i.e. ecological drift) and dispersal (i.e. colonisation by propagules). Beta diversity, the variation in the composition of taxa among sites, can be used to study the relative influence of determinist and stochastic processes on community composition at different spatial scales (Legendre et al. 2005).

Comparative investigations have revealed variations in SCCs (1) among sea regions, (2) within sea regions (10–30 km), and (3) among micro-habitats (David et al. 2019). Around the Arabian Peninsula, both dispersal limitation and niche-based processes contribute to the structuring of cryptic coral reef communities (Villalobos et al. 2024). SCC composition also responds to various environmental attributes, including sea surface temperature (SST; Pearman et al. 2020, Steyaert et al. 2022), pH, dissolved oxygen concentration, water flow velocity, wave height (Steyaert et al. 2022), particulate organic matter (Carvalho et al. 2019), and photosynthetically active radiation (Pearman et al. 2020).

In this study, we deployed autonomous reef monitoring structures (ARMS) to sample SCCs. These artificial reefs, originally developed to assess marine biodiversity during the Census of Marine Life (Knowlton et al. 2010), are passive, non-destructive, and standardised sampling devices designed to mimic the structural complexity of coral reefs. Most studies using ARMS for exploring cryptic diversity have employed barcoding (Vicente et al. 2022, Couëdel et al. 2024) or metabarcoding methods (Leray & Knowlton 2015, Pearman et al. 2016, 2018, Ransome et al. 2017, Ip et al. 2022). Here we used photo-analysis of ARMS plate faces. Despite a lower taxonomic comprehensiveness compared to metabarcoding, photo-analysis offers several advantages, including the (1)

accurate estimation of taxa abundance in SCCs; (2) opportunity for analysing variation in SCCs among micro-habitats within ARMS; (3) visual prospection of a compartment of reef biota, thus overcoming the limitations of barcoding (e.g. limited universality of primers) for certain groups (Wangensteen et al. 2018); and (4) enhanced statistical power, as each ARMS provided a set of 16 photographs, whereas metabarcoding usually processes the sessile fraction collected across all plates in bulk. David et al. (2019) suggested that appropriate experimental design and fine taxonomic resolution could enhance the power of ARMS plate photo-analysis, considerations adopted in our study design. Using morpho-species (MSP)-level taxonomic resolution allowed us to analyse SCC dissimilarities, while a balanced set of distances between sampling sites enabled detailed beta-diversity spatial analyses.

Here, we investigated variations in SCC composition across multiple spatial scales, from micro-habitats within ARMS to a north-south gradient along the coral reef coast of Reunion Island. We estimated the cover of sessile cryptobenthic taxa on ARMS plate faces using point-count analysis and characterised the benthic habitat and environmental parameters at each sampling site. We calculated beta diversity descriptors to examine spatial variations in SCCs, focusing on MSPs that drive these variations. This protocol enabled us to explore how deterministic and stochastic processes influence beta diversity patterns of SCC composition on the coral reef slopes of Reunion Island.

## 2. MATERIALS AND METHODS

### 2.1. ARMS units and their deployment, retrieval, and processing

Each ARMS unit consisted of 9 superposed PVC plates (22.5 × 22.5 cm) separated by 1 cm high spacers or cross bars, creating alternating open and closed spaces that mimic the structural complexity of reef habitat and which provide different levels of exposure to light and water flow (Fig. 1). Each ARMS unit had a volume of just over 4.5 l and a colonisable area of 0.81 m².

A total of 27 ARMS were deployed at 9 fore-reef slope sites along the western and southwestern coasts of Reunion Island, south-west Indian Ocean (RUNA1 to RUNA9; Fig. 2a; Appendix S1 in Couëdel et al. 2023). At each site, 3 replicate ARMS units (Fig. 2b) were affixed to the substrate on top of spurs at a depth
<figure><img src="image_5.png" /><figcaption>Fig. 1. Layout of an autonomous reef monitoring structure (ARMS) showing examples of the closed and open plate faces colonised by sessile cryptobenthic communities after 2 yr of deployment off Reunion Island (see Fig. 2 for site locations)</figcaption></figure>

<figure><img src="image_6.png" /><figcaption>Fig. 2. Experimental design. (a) Site locations on the fringing reefs along the western and southwestern coasts of Reunion Island. Coral reefs are represented in blue. (b) Three ARMS replicates (A, B, and C) were deployed at each site. (c) Oceanic distance modalities between sites</figcaption></figure>
of 10–12 m. Site selection warranted a balanced
spatial distribution of sampling units, with incremen-
tal distances varying from 3 m (among replicates at
each site) to 52 km (between the 2 most distant sites;
Fig. 2a,c). To mitigate effects of season and immer-
sion duration (Couëdel et al. 2024), all ARMS units
were deployed (17 December 2018 to 25 January
2019) and retrieved (14 December 2020 to 1 January
2021) during hot seasons, ensuring a consistent im-
mersion duration of 2 yr at all sites.

Before retrieval, ARMS units were covered with a
crate lined with 48 µm mesh to prevent the escape of
motile fauna and were subsequently transported in
aerated seawater. In the laboratory, the plates were
dismantled, gently brushed, and photographed (up-
ward and downward plate faces) using a standardised
camera setup. Individual MSPs were photographed
alive (Fig. 1) before collection, preserved in 90% eth-
anol, stored at 4°C, and vouchered with linked data-
base entries. Taxonomic identification was performed
by specialists when possible.

## 2.2. Assessment of SCCs on ARMS plate faces

SCCs were analysed from photographs of ARMS
plate faces using the image annotation software Coral
Point Count with Excel extension (CPCe; Kohler &
Gill 2006) via the CoralNet website (https://coralnet.
ucsd.edu/). For each plate face, we employed a strat-
ified random sampling (10 grid cells, 5 points per cell)
to determine the relative abundance of each MSP.
CPCe facilitated manual association of each pro-
jected point with a label, generating a table from
which the relative abundance of each label was calcu-
lated for each plate face. MSPs were distinguished
within Porifera, Ascidiacea, Bryozoa, and Foraminif-
era (Table S1 in the Supplement at www.int-res.com/
articles/suppl/m761p021_supp.pdf). MSP distinctions
were conducted by a single observer to ensure con-
sistency, and regular taxonomic conciliation among
team members was done to maintain accuracy.

We classified polychaetes into 2 categories: 'soft
worm tubes' and 'calcareous worm tubes'. Macroalgae
were categorised based on appearance and growth
form: 'encrusting Chlorophyta', 'erect Chlorophyta',
'crustose coralline algae', 'erect Rhodophyta', and
'erect Phaeophyceae'. Bivalves were separated into
2 groups: 'Streptopinna sp.' and 'other bivalves'. Ag-
laopheniidae (Cnidaria, Hydrozoa) and barnacles
(Arthropoda) could not be identified more precisely.
We grouped prokaryotic biota as 'Cyanobacteria',
'red biofilms', and 'green biofilms'. We recorded

points on bare plates and sediment, and we excluded
those targeting motile fauna or non-colonisable areas.
A plate—species abundance matrix (normalised to
100%) was generated, comprising 21 600 identified
points from 432 plate faces. The analysis excluded
the upward faces of the ARMS top plates, since they
do not represent conditions of a cryptic habitat.

## 2.3. Habitat characteristics and environmental parameters

A habitat complexity score (0 to 5) was assigned to
each sampling site based on the classification pro-
vided by Polunin & Roberts (1993). The community
composition of the benthic habitat at each site was
assessed using 3 photo-transects (10 m, comprising
20 photographs of 0.50 × 0.33 m). Grid-based image
analysis of each photograph was performed with
CPCe, with 36 points distributed across 6 cells. Thus,
the mean abundance of the main hard coral genera
(Scleractinia and *Millepora*) as well as the abundance
of macroalgae and sediment was quantified based on
~2160 identified points per site.

We obtained SST at 10 m depth from the Coperni-
cus model 'Global Ocean Physics Analysis and Fore-
cast' (European Union-Copernicus Marine Service
2016) for the 9 sites. First, daily values of SST were
obtained for each site. To smooth out extreme values,
a 7-day rolling mean was applied to the data from
each site. We then calculated a daily mean tempera-
ture by averaging SST across all sites. Deviations for
each site were computed by subtracting this overall
mean temperature from the individual site tempera-
ture for each day.

We extracted both eastward and northward water
flow velocity from the same model. Daily water flow
velocity was computed as the square root of the sum
of the squared eastward and northward components,
thus providing a single value. Daily wave height was
obtained from the Global Ocean Waves Analysis and
Forecast (European Union-Copernicus Marine Ser-
vice 2018). Deviations from the means for water flow
velocity and wave height were calculated as for SST
and were used for further analysis. We extracted
Copernicus data for 2021 using the R package 'ncdf4'
(Pierce 2024), assuming relatively constant spatial
trends in environmental conditions across consecu-
tive years. Average deviations from the means of SST,
wave height, and water flow were used as single rep-
resentative values in partial Mantel tests and random
forest models. A list of all environmental and benthic
habitat variables is available in Table S2.
## 2.4. Data analysis

### 2.4.1. Data pre-processing and beta diversity descriptors

Our raw data set contained few very abundant and many rare MSPs. To minimise the influence of the most abundant species on the analysis of beta diversity, we applied the Hellinger transformation (Borcard et al. 2011), which uses the square root of the relative abundance of MSPs on each plate face. Furthermore, to avoid potential analytical problems associated with the numerous rare species, such as 'double-zero observations' (i.e. instances where a species is absent from 2 sites, which artificially inflates similarity between sites based on absences rather than shared presences; Legendre & Gallagher 2001, Borcard et al. 2011, Brasil et al. 2020), the site—species matrix was partitioned following Souissi et al. (2001). MSPs occurring on less than 1% of the 432 analysed plates were considered rare. This removed the 30 rarest MSPs and 26.6% of the zeros, resulting in a dominant-MSPs abundance matrix. From this matrix, various dissimilarity matrices were calculated based on beta-diversity descriptors. The Bray-Curtis (BC) index provides a measure of dissimilarity based on relative abundance of taxa in communities, whereas the Jaccard index ($J_{JAC}$) provides a measure of dissimilarity in terms of community membership (presence—absence) of taxa. Two components of $J_{JAC}$ were analysed: one describing the proportion of community membership dissimilarity attributable to species replacement across sites (i.e. turnover, $J_{TUR}$), the other describing the proportion of dissimilarity attributable to differences in richness across sites (i.e. nestedness, $J_{NES}$). We used the 'betapart' package (Baselga et al. 2023) to compute $J_{JAC}$, $J_{TUR}$, and $J_{NES}$ indices.

### 2.4.2. Within-ARMS variations in SCCs

Differences among micro-habitats within ARMS were examined conducting permutational multivariate analysis of variance (PERMANOVA) tests using both BC and $J_{JAC}$ dissimilarity values. We categorised micro-habitats based on water flow (open/closed), plate orientation (upward/downward), and height above the substrate (bottom/middle/top) (Fig. 1). Permutations were constrained to account for the 3 replicate ARMS units per site using the R package 'vegan' ('adonis2' function; Oksanen et al. 2024). Sediment cover was compared between plate faces (open/upward, open/downward, closed/upward, and closed/

downward) using Kruskal-Wallis and pairwise Wilcoxon tests, with Bonferroni correction for multiple comparisons. We employed constrained analysis of principal coordinates (CAPscale) analysis using the BC dissimilarity matrix to assess the effect of within-ARMS factors on the distribution of each MSP. ANOVA with adjusted R² was used to test the significance of each axis. Each within-ARMS factor was displayed on the CAPscale plot.

### 2.4.3. Between-ARMS spatial patterns in SCCs

We performed cluster analysis using the BC dissimilarity matrix to identify distinct groups within the SCCs. We determined the most suitable algorithm by maximising fidelity to the initial distance matrix using the cophenetic correlation (Sokal & Rohlf 1962) and the '2-norm' index (Mérigot et al. 2010). We then conducted multiscale bootstrap analysis to compute a reliability score for each cluster node (Suzuki & Shimodaira 2004). The optimal number of clusters was determined using the 'kgs' function (Kelley et al. 1996). MSP abundances were transformed into discrete variables for visualisation on a heatmap alongside the cluster dendrogram. A percentage similarity analysis was used to measure the contribution of MSPs, and only those with a p-value below 0.01 were retained in the heatmap for their strong contribution to the differentiation between clusters ('simper' function; Oksanen et al. 2024). This partitioning was used to compute the relative contribution of each cluster to beta diversity, called 'local contribution to beta diversity' (LCBD), along with p-values. This index represents the 'degree of uniqueness of the sampling units in terms of community composition' (Legendre & De Cáceres 2013, p. 953).

The difference between the average $J_{TUR}$ within-site comparisons and the average $J_{TUR}$ among-site comparisons was assessed using a Student's t-test. Mantel tests with permutations were conducted to explore correlations between SCC dissimilarity measures (BC, $J_{JAC}$, $J_{TUR}$, $J_{NES}$) and oceanic distance (i.e. distance measured by following the coastline). $J_{JAC}$, $J_{TUR}$, and $J_{NES}$ for multi-site comparisons were also computed, multi-site comparisons not being the mean of pairwise comparisons (see Baselga 2010).

### 2.4.4. Contributions of habitat and environmental conditions to SCC variations

To investigate relative importance of oceanic distance, environmental distance, and habitat dissimilar-
ity in explaining SCC dissimilarity (both BC and $J_{JAC}$), we computed partial Mantel tests using the 'vegan' package in R (Oksanen et al. 2024). Scaled environmental variables constituted the Euclidean environmental distance matrix and relative abundance of benthic habitat categories were used to compute a BC dissimilarity matrix.

Random forest modelling was performed to investigate the contribution of specific habitat and environmental variables in explaining abundance variations of each cryptobenthic category. Random forest is a machine learning method well-suited for complex ecological data. The algorithm combines multiple decision trees to enhance accuracy and reduce overfitting by averaging their predictions. We explored collinearity using principal component analysis (PCA) and the R package 'rfUtilities' (Evans & Murphy 2018) but kept all variables, as random forest is robust to collinearity. To predict the distribution of each cryptobenthic category, we built random forest models with a hyperparameter tuning strategy, using the 'h2o' R package (Fryda et al. 2024). The number of (1) trees, (2) variables randomly sampled at each split, (3) samples used for training, (4) samples within terminal nodes, and (5) terminal nodes (Probst et al. 2019) were optimised using a random discrete search with a 20 min time limit per model as a stopping point. Model performance was evaluated using out-of-bag root mean squared error (OOB-RMSE). Variable importance, adjusted R-squared, sample rate (rate of subsampling in the original data for bootstrap), and the number of trees used (number of decision trees in the model) were calculated and tabulated for each model. Analyses were performed using R v.4.2.2 (R Core Team 2022).

# 3. RESULTS

## 3.1. Relative abundance of sessile cryptobenthic taxa

We classified the 92 labels (90 MSPs, and 2 for sediment and bare plate) into 14 categories (Fig. 3). Dominant taxa included crustose coralline algae (mean ± SD: 19.38 ± 5.33%), annelids, primarily represented by polychaetes (12.86 ± 2.48%), bryozoans (11.94 ± 8.04%), and sponges (7.92 ± 3.96%). Certain categories, such as annelids, bivalves, and biofilms, showed relatively uniform abundance across ARMS units (Fig. 3). In contrast, variability in abundance existed for categories such as bryozoans, ranging from absent (RUNA2C) to 37.63 ± 27.27% (RUNA8B), and ascidians, occupying between 0.86 ± 2.19%

(RUNA2B) and 15.59 ± 12.27% (RUNA7C) of ARMS surfaces.

## 3.2. Within-ARMS variations in SCCs

At the ARMS unit scale, SCCs showed strong spatial structuring (Fig. S1). BC-based PERMANOVA tests revealed highly significant differences in community composition based on water flow (open/closed: $F_1 = 32.66, p < 0.001$) and plate face orientation (upward/downward: $F_1 = 38.69, p < 0.001$). The interaction between these factors was also highly sig-

<figure><img src="image_7.png" /><figcaption>Fig. 3. Relative abundance of sessile cryptobenthic categories by ARMS unit (RUNA1 to RUNA9, replicates A,B,C) deployed at 9 fore-reef sites along the western and southwestern coasts of Reunion Island. Each coloured bar represents the mean percentage cover of each category across all plate faces of the ARMS units. Categories are sorted based on their total sum across the 27 ARMS units</figcaption></figure>
nificant ($F_1$ = 11.205, p < 0.001). The height above the substrate was not significant (bottom/middle: $F_1$ = 4.28, p = 1.0; bottom/top: $F_1$ = 3.81, p = 1.0; middle/top: $F_1$ = 2.59, p = 1.0; Tables S4–S7). $J_{AC}$-based PERMANOVA revealed the same trend (open/closed: $F_1$ = 18.68, p = 0.001; upward/downward: $F_1$ = 20.87, p = 0.001; bottom/middle: $F_1$ = 4.71, p = 1.0; middle/top: $F_1$ = 4.00, p = 1.0; bottom/top: $F_1$ = 2.35, p = 1.0; Tables S4–S7).

The variations observed in community composition within ARMS reflect the influence of environmental conditions that shape the composition of SCCs. Closed and open upward-oriented faces exhibited significantly higher abundance of sediment (22.43 ± 12.86 and 18.06 ± 12.70%, respectively) compared to closed and open downward-oriented faces (0.90 ± 1.89 and 3.13 ± 5.07%, respectively; pairwise Wilcoxon tests, p < 0.001; Fig. S2, Tables S8 & S9). The CAPscale chart (Fig. 4) shows that crustose coralline algae and cyanobacteria covered more surface area on upward-oriented faces, regardless of water flow. Polychaetes, particularly those constructing calcareous tubes, were more abundant on plates with reduced water flow (closed) and facing upward. Most ascidians (especially Didemnidae) and hydrozoans (Aglaopheniidae)

occupied a greater surface on closed downward-oriented faces. Ascidian MSP15 and a species of the genus *Aplidium* preferred upward-oriented faces, possibly due to an adaptation to sedimentation. Bryozoans, especially MSP5, *Disporella novaehollandiae*, and juvenile foraminifers of the genus *Miniacina* favoured open downward-oriented faces. Sponges showed no clear preference, with MSP12 and MSP20 favouring open upward-oriented faces, and MSP4 and MSP15 being more abundant on closed and open downward-oriented faces, respectively. Red and green biofilms were more abundant on downward-oriented faces.

### 3.3. Patterns of SCC composition along the coral reef coast of Reunion Island

Clustering revealed a spatial structuring of SCCs corresponding to geography, with 4 clusters each grouping ARMS units belonging to the same area (Figs. 2a & 5). The first cluster gathered ARMS units from the northernmost site RUNA1, except for RUNA1C, which was replaced by RUNA3C. The second cluster grouped ARMS from sites RUNA2 and

<figure><img src="image_8.png" /><figcaption>Fig. 4. Distance-based CAPscale projection using the Bray-Curtis dissimilarity matrix. Red dots represent the scores of the morpho-species (MSPs) contributing most to variations, while blue triangles represent factors significantly influencing the sessile cryptobenthic community composition within ARMS. Green triangles denote centroids of the modes of these crossed factors. All ARMS plate faces are represented by points. Each axis of the CAPscale analysis is significant ($R^2_{adj}$ = 0.091, df$_{CAP1}$ = 1, $F_{CAP1}$ = 34.64, $p_{CAP1}$ < 0.001; df$_{CAP2}$ = 1, $F_{CAP2}$ = 12.39, $p_{CAP2}$ < 0.001; Table S10). MSP prefixes indicate taxa: Ascc = colonial Asciidiacea, Ascs = solitary Asciidiacea, Bry = Bryozoa, Cni = Cnidaria, For = Foraminifera, Por = Porifera. To enhance readability, only MSPs with a score exceeding 0.05 or falling below −0.05 on at least 1 of the 2 axes are displayed</figcaption></figure>
<figure><img src="image_9.png" /><figcaption>Fig. 5. Heatmap showing mean relative abundance (blue scale) of 25 morpho-species (MSPs) contributing strongly to the differentiation between clusters grouping the 9 reef sites off Reunion Island. Columns display the dendrogram of the 27 ARMS, while rows represent MSP labels. The numbers on the dendrogram nodes represent associated unbiased bootstrap p-values. MSP prefixes denote taxa (see Fig. 4)</figcaption></figure>

RUNA3 at La Saline reef, except for the ARMS units RUNA1C and RUNA5B. The third cluster grouped ARMS from Saint-Leu reef sites (RUNA4–6), while the last cluster regrouped ARMS from all sites from Etang-Salé to Grande Anse (RUNA7–9). Among the 4 clusters, only the last one presented a significant LCBD (0.400; *p* < 0.001).

The first node of the dendrogram separated the SCCs of the southwestern sites RUNA7–9 from the rest (Fig. 5). MSPs contributing to this distinction included the ascidians *Polysyncraton rostrum*, *Styela canopus*, MSP9, MSP15, and Didemnidae sp.1, along with

cirripeds. Site RUNA1 stood out due to a greater mean cover of *Crisia elongata*, a lower mean cover of *Miniacina* sp., and the presence of *Streptopinna* sp. The cluster comprising mainly ARMS from La Saline displayed relatively undifferentiated communities, with erect Phaeophyceae being abundant. ARMS from the Saint-Leu cluster exhibited a higher abundance of polychaetes, cyanobacteria, and bryozoans (particularly *Schizomavella* sp.), and individuals of the genus *Streptopinna*.

The BC dissimilarity in SCC composition between ARMS pairs displayed a strong positive correlation with oceanic distance (Mantel *R* = 0.5, Fig. 6a). This rela-
<figure><img src="image_10.png" /></figure>

Fig. 6. Mantel correlation plots depicting the relationship between oceanic distance and ecological dissimilarity of sessile cryptobenthic communities in Reunion Island, calculated based on the indices of (a) Bray-Curtis, (b) Jaccard, (c) Jaccard-based nestedness component, and (d) Jaccard-based turnover component. Each dot represents a site-by-site comparison of sessile cryptobenthic communities in ARMS units. Dots turn black when grey dots overlap. The red curves illustrate the generalised additive model (GAM) computed between these 2 metrics, the red band delineates the 95% confidence interval of the mean estimation made by the GAM. R represents the value of the Mantel correlation coefficient. NS: not significant. A segmented regression is calculated when correlations are significant; dashed lines represent the breakpoints, while the grey zone illustrates the confidence interval

tionship reached a plateau at ~30 km distance between sites. *J*<sub>JAC</sub> dissimilarity between pairs of ARMS showed a weaker (Mantel R = 0.167, Fig. 6b), but still significant, correlation with oceanic distance, with an inflexion point at a shorter oceanic distance (~10 km). *J*<sub>NES</sub> and *J*<sub>TUR</sub> were not significantly correlated with oceanic distance (Fig. 6c,d). However, spatial variation in community membership was mostly due to MSP replacement along sites rather than richness differences.

Within each of the 9 sites, the average *J*<sub>TUR</sub> values between the 3 replicate ARMS units (mean ± SD: 0.28 ± 0.08) were significantly lower than those observed between sites (0.36 ± 0.08; Student's *t*-test, *t*<sub>700</sub> = 6.73, *p* < 0.001). *J*<sub>TUR</sub> contributed most to the multiple-site beta diversity (multiple-site *J*<sub>TUR</sub> = 0.85, multiple-site *J*<sub>NES</sub> = 0.03), indicating a high replacement of MSPs among SCCs along the western and southwestern coasts.
### 3.4. Contributions of habitat and environmental conditions to SCC variability

Habitat complexity varied across sites, with the lowest scores recorded at RUNA1 and RUNA7 (1/5 and 2/5, respectively), while RUNA6 and RUNA9 exhibited the highest complexity (5/5 and 4/5, respectively). RUNA1 was dominated by algal turfs and sand, with the lowest cover of scleractinian corals and genus diversity (2.0 ± 0.0). *Pocillopora* dominated in the second and third clusters, except for RUNA6 where *Porites* prevailed. Scleractinian coral cover was highest in Saint-Leu. In the southwest cluster, *Pocillopora* dominated at RUNA7, *Acropora* and *Porites* were prominent at RUNA8, and *Acropora* prevailed at RUNA9. Sites with the highest diversity of scleractinians were RUNA4, RUNA5, and RUNA7, with 30, 27, and 27 species, respectively (Fig. S3).

Spatial environmental trends indicated increasing wave height along a north-south gradient, with maximal water flow velocities at RUNA7 and RUNA8, concomitant with a decrease in SST (Fig. S4).

The partial Mantel tests showed that abundance-based SCC dissimilarity (BC) was better explained by oceanic distance than by environmental distance or habitat dissimilarity. While SCC membership similarity ($J_{JAC}$) was also better explained by oceanic distance than by environmental distance, benthic habitat dissimilarity appeared to contribute more to determining SCC membership than oceanic distance (Table 1).

The random forest model explained 86% of the variation in ascidian abundance, with water flow velocity and wave height deviation emerging as the most influential explanatory variables (37 and 36%, respectively). Wave height and SST deviations explained abundance variation in bryozoans (41.9 and 13.9%, respectively). Massive coral genera (*Astreopora* and *Porites*) in the surrounding habitat significantly contributed to the variance in crustose coralline algae

cover (78%) and other macroalgae on ARMS plate faces (55%). Diversity of Scleractinia and the cover of branching Scleractinia, such as *Acropora* and *Pocillopora*, influenced annelid abundance (55%). Wave height and SST deviations jointly played a crucial role in explaining variations in cirriped and biofilm abundances (37 and 15%, and 39.7 and 28.2%, respectively). Other SCC categories showed weaker associations with the explanatory variables. To better visualise the correlation between cryptobenthic communities and abiotic or benthic categories, correlation graphs and more details regarding random forest models are available in Table S3 and Fig. S5.

### 4. DISCUSSION

This study showed that both community membership and the relative abundance of sessile cryptic communities at the micro-habitat scale within ARMS are primarily driven by deterministic processes, including exposure to light, sedimentation, and water flow. This is supported by significant PERMANOVA test results for both Jaccard and Bray-Curtis dissimilarity metrics. At the larger scale of the Reunion coral reef coast, we found that community dissimilarity correlated with reef discontinuities along the coastline, leading to the regrouping of SCCs into 4 geographical zones. Notably, the southernmost zone significantly contributed to beta diversity, making it a unique area in terms of MSP composition. Furthermore, the Mantel tests revealed that community membership of MSPs was more strongly influenced by processes operating at smaller scales (10 km), whereas their relative abundance was primarily driven by processes at larger scales (30 km). The partial Mantel tests indicated that geographic distance had a greater influence on community composition than benthic habitat and environmental conditions, while random forest models showed that these latter variables sig-

<table><caption>Table 1. Results of the partial Mantel tests. Mantel's R is measured between the dissimilarity matrix of the sessile cryptobenthic communities and the oceanic distance matrix, while removing the effect of the environmental distance matrix (Oceanic d./Envt. d.), and the environmental distance matrix, while removing the effect of the oceanic distance matrix (Envt. d./Oceanic d.), and the oceanic distance matrix, while removing the effect of the habitat matrix (Oceanic d./Habitat), and the habitat matrix, while removing the effect of the oceanic distance matrix (Habitat/Oceanic d.). Tests are computed with both Bray-Curtis and Jaccard sessile cryptobenthic community dissimilarity, and significance is indicated with asterisks: \*p ≤ 0.05, \*\*p < 0.01, \*\*\*p ≤ 0.001</caption><thead><tr><th rowspan="2"></th><th colspan="2">Oceanic d./Envt. d.</th><th colspan="2">Envt. d./Oceanic d.</th><th colspan="2">Oceanic d./Habitat</th><th colspan="2">Habitat/Oceanic d.</th></tr><tr><th>R</th><th>p</th><th>R</th><th>p</th><th>R</th><th>p</th><th>R</th><th>p</th></tr></thead><tbody><tr><td>Bray-Curtis</td><td>0.459</td><td>0.001***</td><td>-0.263</td><td>0.995</td><td>0.474</td><td>0.001***</td><td>-0.094</td><td>0.907</td></tr><tr><td>Jaccard</td><td>0.155</td><td>0.028*</td><td>-0.088</td><td>0.811</td><td>0.088</td><td>0.123</td><td>0.099</td><td>0.076</td></tr></tbody></table>
nificantly influenced the relative abundance of certain dominant cryptobenthic taxa.

At the ARMS unit scale, SCC composition was influenced by deterministic processes, including exposure to light and sedimentation (evidenced by the differences in communities between upward and downward faces) and water flow (evidenced by the differences in communities between open and closed plate faces). For example, sessile polychaetes, particularly Terebellidae, that specialise in feeding on organic components within sediments (Maire et al. 2007), were more abundant on upward-oriented plate faces, likely due to their preference for detrital deposits. Filter-feeding MSPs, like the colonial ascidians Didemnidae, dominated closed downward-oriented plate faces with less sediment accumulation. Sediment build-up on upward faces may clog their filter apparatus, hindering food capture and gas exchange, and forcing them to expend more energy on mucus production (Torre et al. 2014). Hydrozoan Aglaopheniidae favoured closed downward faces, aligning with their preference for low light conditions (Di Camillo et al. 2008). Bryozoans and foraminifers, possessing calcium carbonate skeletons, thrived on open downward faces, likely due to their tolerance of water flow and resistance to predation (Lombardi et al. 2008, Prazeres et al. 2017).

At this micro-habitat scale, biotic interactions such as competition for resources and the release of allelochemicals by some MSPs can lead to the exclusion of others (Chadwick & Morrow 2011). For example, ascidians like *Polysyncraton* utilise antifouling strategies, such as producing secondary metabolites or sloughing off tissue, to deter competitors for space (Wahl & Banaigs 1991). Encrusting bryozoans employ various strategies to overgrow neighbouring colonies, whether conspecific or not (Liow et al. 2016). Over the 2 yr period of ecological succession, these interactions led to the formation of stacked layers of biota on ARMS plates, whereas other surfaces remained devoid of cryptic organisms. This pattern suggests the existence of facilitation processes (Connell & Slatyer 1977), as observed in certain taxa like crustose coral-line algae (Littler & Littler 2013) and biofilms (Zardus et al. 2008), which can create micro-habitats that support subsequent colonisation. However, we did not investigate any positive or negative interactions among the SCC members. They deserve further research to gain valuable insights into the assembly processes of cryptic communities.

At the scale of the reef coast of Reunion, environmental parameters also govern SCC patterns, with hydrodynamics and SST identified as key determinants.

Sites within the southwestern cluster experience southern swell regimes and cooler SSTs, creating conditions conducive to high abundances of certain taxa. Cirripeds thrive in these high-energy conditions (Reustle et al. 2023), as do ascidians (Lambert 2005). The effects of both parameters on SCCs were previously documented in the Chagos Archipelago (Steyaert et al. 2022). Seawater quality strongly influences benthic communities (Duprey et al. 2016). At Saint-Leu, cyanobacterial proliferation on all ARMS upward facing plates suggests enrichment of dissolved organic matter, iron, and phosphorus due to increased terrestrial runoff (Ahern et al. 2006). Indeed, the Saint-Leu reef has a history of recurrent sediment and nutrient 'pulse' type inputs due to watershed urbanisation and episodic heavy rainfall (Scopélitis et al. 2009), impacting corals and potentially cryptic communities as well. Variations in environmental conditions and past disturbances have probably shaped the benthic communities of the different reefs, and this spatio-temporal environmental stochasticity (Chesson & Huntly 1989) may partially explain the community variations along the Reunion reef coast.

Benthic habitat communities also shape SCC patterns indirectly. The southwestern cluster exhibits a significant LCBD, coinciding with a high cover of branching corals (Fig. S5), particularly *Acropora abrotanoides* at site RUNA9. This scleractinian coral, characterised by a complex growth form of superimposed tables enhancing 3D reef complexity (Wallace 1999), increases the local diversity of ecological niches for cryptic species, potentially enriching the larval pool for settlement (Graham & Nash 2013).

The partial Mantel tests showed that SCC dissimilarity is better explained by oceanic distance than by environmental conditions or benthic habitat. This suggests that dispersal limitation, a stochastic process, may also contribute to shaping SCC composition on the coral reefs along western and southwestern coasts of Reunion. The discontinuous nature of these fringing reefs (Fig. 2a), fragmented by intermittently or continuously flowing rivers, may restrict propagule dispersal (Rocha 2003). This hypothesis is supported by the geographic clustering of SCCs (Fig. 5), which mirrors the discontinuous reefs, as well as the inflection point in $J_{JAC}$ dissimilarity at ~10 km, which corresponds to the average length of these fringing reefs. Significant genetic differentiation between western and southwestern populations of the scleractinian *Poecilopora damicornis* type beta exists along the coral reef coast of Reunion (Gélin et al. 2017), suggesting dispersal barriers for this coral species. In contrast, the holothurian *Stichopus chloronotus* showed no genetic
structuring on the reef coast, indicating the absence of reproductive barriers, possibly due to its long larval lifespan (Pirog et al. 2019). These examples highlight the different dispersal abilities among taxa.

The high contribution of $J_{TUR}$ to multi-site beta diversity as well as the high proportion of $J_{TUR}$ between ARMS replicates may result from certain stochastic processes, such as demographic stochasticity (Engen et al. 2017) or random dispersion and settlement (Pearman et al. 2018, Carvalho et al. 2019). In coastal marine ecosystems, and particularly in coral reefs, the regional species pool is large and consists of few dominant species and many rare species (Leray & Knowlton 2015, Carvalho et al. 2019). Since each ARMS unit samples only a small fraction of this diversity, a high beta diversity occurs between samples, mainly due to the numerous rare species that settle randomly (Chase & Myers 2011). Using a selection of dominant species to explore dissimilarity patterns allowed us to mitigate this 'lottery effect'.

Both deterministic and stochastic processes jointly shape communities (Chase & Myers 2011), representing opposing ends of a continuum ranging from competitive to stochastic exclusion (Gravel et al. 2006). For instance, at the scale of the Arabian Peninsula, Villalobos et al. (2024) found that oceanic distance (corresponding to the stochastic argument) was correlated more strongly with dissimilarities of cryptobenthic communities than environmental distance (corresponding to the niche-based deterministic argument). However, environmental distance had a strong effect at smaller spatial scales. The difference in the oceanic distances of the inflection points of membership-based versus abundance-based dissimilarities in SCC composition (10 vs. 30 km) likely reflects the scale-dependent influence of various processes on community composition. Community membership might be primarily driven by random larval dispersal and settlement (stochastic processes), while relative abundance of taxa could be more influenced by deterministic niche-based processes such as environmental forcing, as highlighted by the results of random forest modelling.

on the deterministic and stochastic processes shaping SCCs across multiple spatial scales. Disentangling the relative significance of each process presents challenges, as dispersal limitations often coincide with environmental changes (Pearman et al. 2020). This limitation is commonly encountered in community ecology (Chase & Myers 2011, Engen et al. 2017). Here, we showed that the relative contributions of deterministic and stochastic processes vary depending on whether a large or small spatial scale is considered but also whether membership or the abundance of species in communities is taken into account. We propose that sampling SCCs using ARMS photo-analysis can complement traditional survey methods, which provide a limited view of biodiversity (Knowlton et al. 2010), and thus aid in the identification of ecologically significant areas (McClanahan et al. 2024). This information is particularly valuable for guiding conservation and restoration efforts (Voolstra et al. 2023).

*Acknowledgements.* This study was supported by the research program 'Fonds européen de développement régional' (FEDER) 20171591-0002633 CALIBIOME 2017–2022 (Part 4) granted to J.H.B. and M.M.M.G. ARMS deployments were conducted under permit nos. 2018-61 DEAL/SEB/UBIO and 2020-09-DEAL/SEB/UBIO of the Direction de l'environnement, de l'aménagement et du logement de La Réunion, and permit nos. 2019-083 and 2020-054 of the Direction de la mer Sud océan Indien. We are grateful to the AAP BOrEA_Sud in 2023 and 2024, and to Nathalie Niquil for her constant support of our research. B.F. benefited from a PhD fellowship provided by ED 227 MNHN-SU. We extend our gratitude to Michel Fontaine and Philippe Faconnier for granting us access to the facilities of the Nautical Base of the Municipalité of Saint-Pierre. We acknowledge the assistance of MSc students Gwennaïs Fustemberg and Auriane Serval in processing the ARMS. We thank Matthieu Leray (Smithsonian Tropical Research Institute, Panama) for early discussions that contributed to the sampling design of this study and Alice Guenot for exchanges about data analyses. Special thanks to Sophie Bureau, Lionel Bigot, Erwan Meyer, and Simon Elise for their support during fieldwork, and to Françoise Monniot, Jean-Loup d'Hondt, Annachiara Bartolini, and David Ory for their expertise in taxonomic identifications. We are grateful to Christine Carrau, librarian of the Bibliothèque Théodore Monod at MNHN, for facilitating access to old literature. Finally, we thank the reviewers for constructive comments on an earlier version of the paper.

## 5. CONCLUSIONS

Previous research (David et al. 2019, Steyaert et al. 2022) has supported ARMS photo-analysis for detecting variations in SCCs across broad spatial scales and at different taxonomic resolutions. By combining this method with a fine taxonomic resolution and a balanced set of distances between sampling sites, we shed light

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