D R Naiara Rodriguez

J K Pearman

G Chust

E Aylagas

E Villarino

J R Watson

A Borja

A E Cahill

L Carugati

R Danovaro

R David

I Mendibil

N Rodríguez-Ezpeleta

M C Uyarra

Susana Carvalho
email: susana.carvalho@kaust.edu.sa

King Abdullah

Pan-regional marine benthic cryptobiome biodiversity patterns revealed by metabarcoding Autonomous Reef Monitoring Structures

Keywords: biodiversity, ecosystem connectivity, climate change, metapopulation, dispersal limitation, ecological niche, ecological monitoring, mitochondrial cytochrome oxidase I (COI)

de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

Oceanic and coastal areas are essential sources of goods and services for human well-being [START_REF] Barbier | Estuarine and coastal ecosystems and their services[END_REF][START_REF] Costanza | The value of the world's ecosystem services and natural capital[END_REF], but are also affected by human pressures [START_REF] Halpern | A global map of human impact on marine ecosystems[END_REF][START_REF] Korpinen | A global review of cumulative pressure and impact assessments in marine environments[END_REF][START_REF] Lotze | Public perceptions of marine threats and protection from around the world[END_REF]. These pressures highlight the need for scientifically informed conservation and management efforts [START_REF] Costello | Predicting the number of known and unknown species in European seas using rates of description[END_REF]. Understanding what shapes biodiversity is vital so that changes in the status of biological communities can be anticipated and managed [START_REF] Andersen | Baltic Sea biodiversity status vs. cumulative human pressures[END_REF][START_REF] Costello | Predicting the number of known and unknown species in European seas using rates of description[END_REF][START_REF] Elliott | Integrated marine science and management: wading through the morass[END_REF][START_REF] Micheli | Cumulative human impacts on Mediterranean and Black Sea marine ecosystems: assessing current pressures and opportunities[END_REF], for example through the protection of species on the brink of extinction [START_REF] Costello | Predicting the number of known and unknown species in European seas using rates of description[END_REF]. Contrary to terrestrial domains, biogeographic barriers are relatively limited in the oceans making marine ecosystems particularly vulnerable to the effects of local disasters, for example oil spills and fisheries overharvesting, expanding over large distances [START_REF] Cordes | Environmental Impacts of the Deep-Water Oil and Gas Industry: A Review to Guide Management Strategies[END_REF][START_REF] Sammarco | Distribution and concentrations of petroleum hydrocarbons associated with the BP/Deepwater Horizon Oil Spill, Gulf of Mexico[END_REF]. Thus, the development of data-driven and standardized environmental monitoring tools, to maintain natural levels of biodiversity within nearshore ecosystems, is of paramount importance [START_REF] Danovaro | Implementing and Innovating Marine Monitoring Approaches for Assessing Marine Environmental Status[END_REF].

Traditional monitoring techniques in hard-bottom marine environments have mainly been based on visual census and morphological identification of the most conspicuous organisms present (e.g. macroalgae, corals, sponges, fish) along transects (that vary in length, width and also on the method -e.g. photo-transects versus line intercept method) [START_REF] Danovaro | Implementing and Innovating Marine Monitoring Approaches for Assessing Marine Environmental Status[END_REF]. However, a high proportion of the benthic biodiversity in these systems comprises small sessile, encrusting or mobile organisms [START_REF] Enochs | Environmental determinants of motile cryptofauna on an eastern Pacific coral reef[END_REF]. These organisms are considered to be part of the cryptobiome (Carvalho et al., 2019)

as they inhabit cavities

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This article is protected by copyright. All rights reserved (cryptic habitats) within the rocky architecture for temporary shelter (e.g. nocturnal species) or as a source of food, and are often neglected during traditional surveys [START_REF] Pearman | Please mind the gap -Visual census and cryptic biodiversity assessment at central Red Sea coral reefs[END_REF][START_REF] Reaka-Kudla | The Global Biodiversity of Coral Reefs: A comparison with rain forests[END_REF]. The cryptobiome encompasses a diverse selection of ecologically important groups such as suspension feeders [START_REF] Richter | Endoscopic exploration of Red Sea coral reefs reveals dense populations of cavity-dwelling sponges[END_REF][START_REF] Scheffers | The cave-profilier: a simple tool to describe the 3-D structure of inaccessible coral reef cavities[END_REF], predators [START_REF] Reaka | Adult-juvenile interactions in benthic reef crustaceans[END_REF], herbivores [START_REF] Coen | Herbivory by Caribbean majid crabs: feeding ecology and plant susceptibility[END_REF] and detritivores (Rothans & Miller, 1991). Because of their diverse ecological roles, small size and fast generation times [START_REF] Finlay | Global dispersal of free-living microbial eukaryote species[END_REF], the responses of the cryptobiome to environmental stressors may differ from those of the larger macro-organisms usually studied.

Despite their importance in benthic ecosystems, the cryptic nature of being small and difficult to spot, as well as the diversity of the phyla represented, requires specialized taxonomists for identification, creating a bottleneck which can limit both the temporal and spatial scales at which studies can be undertaken.

Recognizing the urgency for standardized methods to comprehensively assess hard bottom benthic biodiversity across different habitats and regions, the Coral Reef Ecosystem Division (CRED) of the United States National Oceanic and Atmospheric Administration (NOAA) developed the

Autonomous

Reef Monitoring Structures (ARMS; https://www.pifsc.noaa.gov/cred/survey_methods/arms/overview.php). The alternating open and obstructed format in the gaps between plates comprising the ARMS was designed to mimic the structural complexity of hard-bottom substrata allowing the colonization of a variety of organisms with different niche preferences (Zimmerman & Martin, 2004). ARMS-associated communities can be analyzed either morphologically [START_REF] David | Lessons from photo analyses of Autonomous Reef Monitoring Structures as tools to Accepted Article This article is protected by copyright. All rights reserved detect (bio-)geographical, spatial, and environmental effects[END_REF] or through DNA metabarcoding (e.g.

targeting a short DNA fragment of the mitochondrial cytochrome oxidase I (COI) gene) to identify the whole spectrum of their biodiversity and community composition of sessile and mobile organisms [START_REF] Leray | DNA barcoding and metabarcoding of standardized samples reveal patterns of marine benthic diversity[END_REF]. In addition, with the deployment of ARMS across large spatial scales and the use of standardized sampling protocols, large scale biodiversity patterns can be obtained. So far this has been undertaken using molecular approaches along the length of the Red Sea (Carvalho et al., 2019;[START_REF] Pearman | Disentangling the complex microbial community of coral reefs using standardized Autonomous Reef Monitoring Structures (ARMS)[END_REF] with morphological assessments of benthic substrates being undertaken in European waters and the Red Sea [START_REF] David | Lessons from photo analyses of Autonomous Reef Monitoring Structures as tools to Accepted Article This article is protected by copyright. All rights reserved detect (bio-)geographical, spatial, and environmental effects[END_REF]. ARMS have been deployed globally (https://www.oceanarms.org/deployments/search, accessed 07/07/2020) opening the possibility for global studies. Comparisons between ARMS and coral dead head communities show the same average similarity as comparisons between dead head communities

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This article is protected by copyright. All rights reserved [START_REF] Plaisance | The diversity of coral reefs: What are we missing?[END_REF] while similar microphytobenthos communities have been observed on ARMS as found in natural substrates [START_REF] Pennesi | Assessing marine environmental status through microphytobenthos assemblages colonizing the Autonomous Reef Monitoring Structures (ARMS) and their potential in coastal marine restoration[END_REF]. ARMS have thus been increasingly applied over the last decade to research coral habitats in different regions (e.g. east coast of the USA [START_REF] Leray | DNA barcoding and metabarcoding of standardized samples reveal patterns of marine benthic diversity[END_REF]; Red Sea (Carvalho et al., 2019;[START_REF] Pearman | Please mind the gap -Visual census and cryptic biodiversity assessment at central Red Sea coral reefs[END_REF][START_REF] Pearman | Disentangling the complex microbial community of coral reefs using standardized Autonomous Reef Monitoring Structures (ARMS)[END_REF]French Polynesia (Ransome et al., 2017); Gulf of Aqaba [START_REF] Al-Rshaidat | Deep COI sequencing of standardized benthic samples unveils overlooked diversity of Jordanian coral reefs in the northern Red Sea[END_REF], Indonesia [START_REF] Hazeri | Latitudinal species diversity and density of cryptic crustacean (Brachyura and Anomura) in micro-habitat Autonomous Reef Monitoring Structures across Kepulauan Seribu, Indonesia[END_REF] and European waters [START_REF] David | Lessons from photo analyses of Autonomous Reef Monitoring Structures as tools to Accepted Article This article is protected by copyright. All rights reserved detect (bio-)geographical, spatial, and environmental effects[END_REF][START_REF] Pennesi | Assessing marine environmental status through microphytobenthos assemblages colonizing the Autonomous Reef Monitoring Structures (ARMS) and their potential in coastal marine restoration[END_REF]). The combination of a standardized tool for sampling biodiversity (i.e. ARMS)

and metabarcoding-based characterization of the benthic biota has great potential to allow for assessments of biodiversity to be more spatially and temporally comprehensive, allowing to tackle questions at both local and global scales.

The ARMS monitoring system provides a standardized methodology to disentangle the relative importance of dispersal limitation (Wilson & MacArthur, 1967) and niche [START_REF] Hutchinson | Concluding Remarks, Cold Spring Harbor Symposium[END_REF] adaptive processes to the assembly of biological communities (Burgess, Baskett, Grosberg, Morgan, & Strathmann, 2016;[START_REF] Chave | Neutral theory and community ecology[END_REF]. Dispersal plays a key role in connecting populations, in maintaining species coexistence, and promoting regional biodiversity [START_REF] Mouquet | Community patterns in source-sink metacommunities[END_REF]. However, the dispersal strategies of many marine species are still unknown due to the difficulty of tracking the trajectory and fate of propagules [START_REF] Selkoe | Marine connectivity: a new look at pelagic larval duration and genetic metrics of dispersal[END_REF]Weersing & Toonen, 2009). According to the neutral theory of biodiversity, dispersal limitation of communities can be inferred from the logarithmic decline of species composition similarity among sites (i.e., the opposite of beta diversity) with increasing geographical distance (termed "distance decay"), when migration rate is low [START_REF] Hubbell | A unified theory of biogeography and relative species abundance and its application to tropical rain forests and coral reefs[END_REF][START_REF] Hubbell | The Unified Neutral Theory of Biodiversity and Biogeography[END_REF]. In agreement with this view, previous studies have shown that dispersal-related traits of species (e.g. sessile vs mobile, pelagic larval duration, size) affect their biogeographic distribution [START_REF] Kinlan | Propagule Dispersal in Marine and Terrestrial Environments: A Community Perspective[END_REF], and this distribution can be indirectly estimated at community level [START_REF] Chust | Dispersal similarly shapes both population genetics and community patterns in the marine realm[END_REF]Villarino et al., 2018). In contrast, the ecological niche theory (Pocheville, 2015) assumes that differences in species composition among communities is caused by heterogeneity in the environment or limiting resources, and by environmental filtering of species according to their environmental requirements, such as climate, oceanographic and coastal conditions, and competition for resources such as nutrients for marine algae. Moreover, historical events such as glacial periods have been shown to affect taxonomic patterns over large spatial scales (Bestová, Munoz, Svoboda, Škaloud, & Violle, 2018;[START_REF] Normand | Postglacial migration supplements climate in determining plant species ranges in Accepted Article This article is protected by copyright[END_REF]). The regional distribution of species arises from

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This article is protected by copyright. All rights reserved limitations to dispersal or niche adaptive processes and has been a long-standing and challenging debate in ecology as space is often correlated with environment [START_REF] Hubbell | The Unified Neutral Theory of Biodiversity and Biogeography[END_REF][START_REF] Legendre | Chapter 1 -Complex ecological data sets[END_REF]. The relative importance of the two processes shaping the community distribution and species turnover can differ according to the community, scale of study, or habitat (terrestrial or marine); yet, such studies in hard-bottom communities are scarce.

Here we investigate the composition and spatial species turnover (beta diversity) of the cryptobiome, comprising sessile and mobile organisms (<2 mm) which inhabit hard-bottoms. We used standardized methods to address those communities, specifically ARMS combined with a metabarcoding approach targeting the mitochondrial cytochrome oxidase I (COI) gene. The study was undertaken on a pan-regional scale ranging from the Baltic Sea (~56°N) to the Red Sea (~21°N) and from the Bay of Biscay (NE Atlantic, ~2°W) to the Black Sea (~28°E), including also Western Mediterranean and Adriatic Seas. We tested whether environmental conditions are more important than dispersal limitation in explaining community composition, and whether the relative contribution of both components (dispersal and environmental) is group (sessile and mobile) dependent. The pan-regional domain of this study enables a unique opportunity to investigate benthic biodiversity patterns across a broad range of environmental gradients and hierarchical spatial scales using a standardized approach, and to test which fundamental ecological processes are responsible for setting observed patterns in biodiversity of an overlooked biological component of hard bottom environments.

Methods

ARMS

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This article is protected by copyright. All rights reserved of 26 months. Storms in the Adriatic caused the loss of the deployed ARMS and thus another set were deployed the following year. ARMS were deployed and retrieved by scuba. During retrieval the ARMS unit was covered so that mobile organisms within the ARMS could not escape. Upon retrieval the ARMS were placed in a large container filled with 106 μm filtered seawater (from the site) for transport back to the laboratory. ARMS were processed by representative institutes within each region and processed following the protocol described in [START_REF] Leray | DNA barcoding and metabarcoding of standardized samples reveal patterns of marine benthic diversity[END_REF].

Visual analysis of all the ARMS showed them to be extensively colonized, the extent of which has been described for a subset of these ARMS by [START_REF] David | Lessons from photo analyses of Autonomous Reef Monitoring Structures as tools to Accepted Article This article is protected by copyright. All rights reserved detect (bio-)geographical, spatial, and environmental effects[END_REF]. Briefly, the ARMS were dissembled within water obtained from the sampling site and the plates submerged in 0.2 μm filtered seawater. The plates were gently brushed to remove any mobile organisms. These mobile organisms were retained to be sieved. Sessile organisms growing on the plates were scraped into a tray and homogenized using a blender with approximately 40 g of tissue, being preserved in DMSO buffer. The water containing the ARMS was filtered through a stack of sieves (2mm, 500 μm and 106 μm). The largest fraction was individually photographed and stored in 95% ethanol, but is not considered in this study. The small fractions were bulk preserved in 95% ethanol before being separated from sediments by decantation. The organic fraction was crushed using a mortar and pestle. Environmental DNA was extracted from 10 g of the sessile fraction (comprising the homogenized bulk sample scraped from the plates) and from two size fractions of crushed bulk mobile organisms (106-500 μm and 500-2000 μm) using the Powermax Soil DNA kit (MO BIO), as per the manufacturer's instructions with the exception of the bead-beating step, which was replaced by shaking incubation overnight at 56 °C with the addition of Proteinase K (0.4 mg/mL).

DNA was purified using the Powerclean DNA Clean-up kit (MO BIO) and quantified with a Quibit Fluorometer (Invitrogen For each sequencing run sequences were automatically demultiplexed on the MiSeq and raw reads were quality checked using FastQC [START_REF] Andrews | FastQC: a quality control tool for high throughput sequence data[END_REF]. Primers were removed from the sequences using cutadapt [START_REF] Martin | Cutadapt removes adapter sequences from high-throughput sequencing reads[END_REF] with a single mismatch allowed (parameters: -e 0.05 --discarduntrimmed). The reads were subsequently processed using the DADA2 package (Callahan et al., [START_REF] Team | R: A language and environment for statistical computing[END_REF]. Reads were truncated (165 and 160 bp for forward and reverse reads respectively) and filtered with a maximum allowable number of "expected errors" (maxEE)

2016) within R (R
of four (forward reads) and six (reverse reads) with the exception of the Red Sea where sequence quality allowed a more stringent criteria of two and four for the forward and reverse reads respectively. A parametric error matrix was constructed using the first 10 8 bp of the sequences from each sequencing run. Sequences were dereplicated and sequence variants inferred based on this error matrix with singletons being discarded. The remaining paired-end reads were merged with a minimum overlap of 10 bp and a maximum mismatch of 0 bp. At this point, all sequencing runs were combined and chimeric sequences were removed using the removeBimeraDenovo script within DADA2. Pseudogenes were detected and removed using the methods described in [START_REF] Leray | DNA barcoding and metabarcoding of standardized samples reveal patterns of marine benthic diversity[END_REF]. Sequences were translated and aligned against a subset of the MIDORI database [START_REF] Machida | Metazoan mitochondrial gene sequence reference datasets for taxonomic assignment of environmental samples[END_REF] using Multiple Alignment of Coding Sequences (MACSE; (Ranwez, Harispe, Delsuc, & Douzert, 2011)). The translations were first made using the invertebrate code and then the vertebrate code. Any sequences with a stop codon or possessing greater than two frame shifts were considered as pseudogenes and removed from further analysis. The number of reads removed at each step is depicted in Supplementary Table 2. The scripts used for processing the reads can be found at:

https://github.com/jkpearmanbioinf/DEVOTES-Bioinformatics

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Reference sequences of the ASVs were taxonomically classified using the method implemented by the Ribosomal Database Project (rdp) as described in (Wang, Garrity, Tiedje, & Cole, 2007) against a redundant BOLD database (Ratnasingham & Hebert, 2007) supplemented with COI sequences from NCBI. The threshold for taxonomic assignment was 0.51 and ASVs that were assigned at the kingdom level to eukaryotes kept. For comparisons between samples and fractions all samples were subsampled to an even depth of 10000 sequences (meaning that a total ARMS sample comprised 30000 sequences over the three fractions). For bioinformatics analysis, the fractions of the ARMS (sessile; mobile: 106 -500 μm and 500 -2000 μm) were combined in two different ways. Firstly, the total ARMS was considered and this comprised all three fractions being merged together. If a fraction did not meet the threshold of subsampling then this whole ARMS was removed from the analysis. A second grouping of fractions separated the sessile from the mobile assemblages. The mobile organisms (106 -500 μm and 500 -2000 μm fractions) were merged to comprise a single mobile fraction (combined mobile samples comprised 20000 sequences per ARMS). Similarly, fractions with lower values were not considered further.

Alpha diversity statistics were calculated within phyloseq [START_REF] Mcmurdie | phyloseq: An R package for reproducible interactive analysis and graphics of microbiome census data[END_REF] and tested for significant differences (One way Analysis of Variance (ANOVA) on sqrt transformed data) for the factor region. Tests for normality (shapiro.test) and homogeneity of variance (leveneTest; package car [START_REF] Fox | An R Companion to Applied Regression[END_REF]) were undertaken prior to running the ANOVA. Post-hoc tests were undertaken using the Tukey HSD method. The ASV richness was correlated against environmental variables (see below for how they were selected) using Spearman's Rank correlations. To assess the taxonomic composition of the communities in each region, ASVs were combined at the phylum level using phyloseq in R. The composition was depicted via donut plots with ggplot (Wickham, 2016) and on bathymetric maps produced with marmap [START_REF] Pante | marmap: a package for importing, plotting and analyzing bathymetric and topographic data in R[END_REF]. PCoA plots were produced using square root transformed community data with distances matrices constructed using both Bray Curtis and Jaccard dissimilarity functions and Euclidean distances for the environmental variables. The PCoA ordination was undertaken in phyloseq and plotted in ggplot. Statistical differences were tested using PERMANOVA undertaken in the adonis function of the R package vegan [START_REF] Oksanen | The vegan package[END_REF] on the square root transformed data (one factor: Region; 6 levels:). Pairwise permutation tests were undertaken with RVAidememoire [START_REF] Hervé | RVAideMemoire: Diverse Basic Statistical and Graphical Functions (Version 0.9-66[END_REF]. Local Contributions to Beta Diversity (LCBD) as described by [START_REF] Legendre | Beta diversity as the variance of community data: dissimilarity coefficients and partitioning[END_REF], is the degree of uniqueness of the sites

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This article is protected by copyright. All rights reserved and was calculated in adespatial [START_REF] Dray | adespatial: Multivariate Multiscale Spatial Analysis[END_REF]. Total diversity was calculated on a Hellinger transformed community matrix. Beta diversity was partitioned computing the sums of squares of a sampling unit as a proportion of the total diversity [START_REF] Legendre | Beta diversity as the variance of community data: dissimilarity coefficients and partitioning[END_REF]. Richness and LCBD were compared to see if there was a correlation between richness and the contribution of each site to beta diversity.

To disentangle whether ecological niche factors or dispersal processes are more important explaining species composition across seas, we related beta diversity indices with environmental factors and geographic distance among seas, and among sites and replicates. To address this, similarity/distance matrices were created by investigating pair-wise comparisons of all replicates, sites and seas for three categories: biotic similarity, environmental distance, and geographic distance. For the biotic similarity matrix, we calculated pairwise community similarities using the Jaccard dissimilarity index across sites [START_REF] Legendre | Chapter 1 -Complex ecological data sets[END_REF] with ASV presence/absence data at each station. Environmental distance was computed pair-wise among replicates, sites and seas based on Euclidean distance using five environmental variables obtained from the Bio-oracle database [START_REF] Assis | Bio-ORACLE v2.0: Extending marine data layers for bioclimatic modelling[END_REF]Tyberghein et al., 2012) Available Radiation (PAR) (Supplementary Table 3). Environmental variables selected represent limiting factors, resources, or disturbances (natural or anthropogenic) causally linked to the marine benthic species and its habitat [START_REF] Reiss | Benthos distribution modelling and its relevance for marine ecosystem management[END_REF]; salinity provides information on freshwater sources and it is known to be low in the Black Sea and Baltic Sea compared to other European seas; chlorophyll-a concentration characterize productive seas and anthropogenic eutrophic areas; sea temperature is one of the main influencing factor to marine species; sea temperature range is a proxy to characterize seas with thermal extremes; diffusion attenuation coefficient is a proxy of water transparency which limits primary producers; and PAR is used to measure the spectral range of light that is available in the water column for use by algae for photosynthesis. The selection of those variables was also based on various criteria: availability in a standardized way (i.e. in Biooracle), avoiding other variables correlated with those selected, previous knowledge of reliable variables (most of them combine satellite and in situ data, hence are better estimated than those that cannot be obtained from satellite data such as nutrients, for instance, that can have important local spatial variability), and knowledge of influence on benthic species. The geographic distance

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This article is protected by copyright. All rights reserved matrix was calculated as the shortest oceanic path between two sampling sites (km), avoiding land. To do so, we used the marmap [START_REF] Pante | marmap: a package for importing, plotting and analyzing bathymetric and topographic data in R[END_REF] package in R.

Prior to the analysis relating biotic and environmental variables, we tested for collinearity between explanatory variables by calculating variance inflation factors (VIF) with the ade package in R (Zuur, Ieno, Walker, Saveliev, & Smith, 2009). We excluded any variable that had a VIF > 5, and then recalculated VIF for the remaining variables. We iterated this process until all variables had a VIF < 5. Mantel and partial Mantel correlations [START_REF] Legendre | Chapter 1 -Complex ecological data sets[END_REF] were calculated between ASV similarity and both environmental and geographic distances to analyze the relative contribution of niche and space descriptors to benthos community structure, respectively. Mantel tests were calculated using the vegan package. The slope of the line between assemblage similarity and either spatial or environmental distance was calculated with linear regression and used as an indicator for the rate of spatial species turnover among assemblage composition (distance-decay).

Steeper negative slopes indicate faster spatial turnover, whereas slope = 0 indicates no turnover over space. We also calculated the halving-distance metric that identifies the distance at which community similarity halves, and provides relevant information regarding the spatial scale of community variation [START_REF] Soininen | The distance decay of similarity in ecological communities[END_REF]. Halving-distance for each community (sessile vs mobile) were calculated following Villarino et al., (2018).

Results

Local diversity patterns

Average ASV richness per ARMS varied significantly across the six regions (ANOVA: F value = 47.62; p < 0.001, df=5; residual df =30), ranging between 360 and 1615 ASVs in the Black and Red Seas, representing 1.5% and 6.8% of the gamma diversity (23922 ASVs including only those ARMS that had all fractions, 27473 ASVs total), respectively (Supplementary Table 4). Red Sea ASV richness was significantly (p < 0.001) higher than in all the other regions except for the Mediterranean Sea (p = 0.68). The Baltic and Adriatic Seas presented a similar number of taxa to the Bay of Biscay (p = 0.34 and p = 0.41 respectively), with the Black Sea having significant differences to the other regions (p < 0.001) (Figure 1). There was a significant difference in the Shannon diversity across seas (Kruskal-Wallis chi-squared = 24.44, df = 5, p-value < 0.002). The Baltic and Black Sea had a significantly (all comparisons p < 0.03) lower Shannon diversity than the other seas.

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No clear latitudinal or longitudinal gradient was apparent from our dataset in terms of ASV richness. However, a negative Spearman's rank relationship with the range of SST (Spearman's Rank rho = -1, p = 0.003) and Chl-a (Spearman's Rank rho = -0.886, p = 0.033), and a positive relationship with SSS (Spearman's Rank rho = 0.886, p = 0.033) were observed. For Shannon diversity we observed a negative correlation with the range of SST (Spearman's Rank rho = -0.942, p = 0.016) and Chl-a (Spearman's Rank rho = -0.942, p = 0.016). The two regions with the lowest number of observed ASVs also had the lowest local contribution to beta diversity (Figure 2A). There was a general positive correlation (Linear regression: Total: R 2 = 0.4513; p <0.001, Sessile: R 2 = 0.2852; p = <0.001, Mobile: R 2 = 0.4666; p <0.001) between the number of observed ASVs and LCBD across regions (Figure 2B).

Approximately 17% of the ASVs were shared between the sessile and mobile groups. No ASVs were present in all the regions. Indeed, the number of ASVs present in the Red Sea and in at least one of the other regions was generally low despite the Red Sea having the highest richness (Supplementary Table 5). Taxonomically these shared ASVs were within the groups Sessilia ( 2ASVs) and Primates (7 ASVs) as well as unclassified ASVs (Supplementary Table 6). The ASVs belonging to Primates, were attributed to humans and are likely to be contamination and are not considered further. The highest number of shared ASVs was observed between the Western Mediterranean and the Bay of Biscay (covering 12 classes) and the Adriatic Sea (covering 13 classes). The two regions with low salinities (Baltic and Black Sea) also had a relatively high number of shared ASVs with the majority of these being related to the barnacle genus Amphibalanus (Supplementary Table 6).

The taxonomic composition of the benthic communities changed across the regional seas. The Western Mediterranean Sea and Bay of Biscay had the highest number of phyla (19) whereas the lowest number was observed in Baltic Sea (16 phyla) (Supplementary Table 4). Unique genera were investigated for each region. In the Red Sea three of the most abundant genera were groups of reef fish (Chromis, Pseudochromis and Eviota) (Supplementary Table 7). In the Bay of Biscay, the three most abundant genera are Crustacea, including two Decapoda (Pisidia and Eurynome), and one Cirripede (Verruca). In the Adriatic Sea, the most abundant unique genera belong to Bryozoa (Schizoporella) and to Mollusca, including Bivalvia (Polititapes) and Gastropoda

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This article is protected by copyright. All rights reserved (Hexaplex) (Supplementary Table 7). Several of the unique genera in the Western Mediterranean belonged to the red algae (class: Florideophyceae) including Womersleyella, Peyssonnelia, Kallymenia and Lithophyllum (Supplementary Table 7).

Overall, Arthropoda dominated the cryptobiome in all regions investigated (Figure 1 and Supplementary Table 4) although there were statistical differences between the regions (ANOVA: F = 14.99; p < 0.001) with the Baltic Sea having a higher relative abundance of reads (p < 0.001) than the other seas. Differences were observed in the contribution of the other main phyla with Porifera (14%; ANOVA: F = 14.51; p < 0.001) having a higher abundance in the Red Sea (p < 0.01) especially in the sessile assemblage. Annelida (61%) dominated the sessile benthos in the Adriatic Sea, while Bryozoa were predominant in the Black Sea (sessile: 42%; mobile: 5%).

Community composition in the Bay of Biscay and Western Mediterranean Sea was highly comparable, with Rhodophyta, Annelida and Arthropoda being the major constituents. No statistical difference was observed for Annelida and Arthropoda (p = 0.52 and p = 0.99 respectively) while Rhodophyta was slightly higher in the Bay of Biscay (p < 0.01). In general, the contribution of unclassified ASVs to the gamma diversity was substantial (range: 25% -41%) in the ARMS as a whole, whilst it was lower in terms of sequence abundance (range: 15% -28%), especially in the sessile biota of the Adriatic Sea (4.6%) and Baltic Sea (3.5%).

Cross-regional biodiversity

Multivariate analysis performed using Jaccard (presence/absence) dissimilarity showed significant spatial differences for both sessile (PERMANOVA: F=4.026; p < 0.001) and mobile (PERMANOVA: F = 3.5266; p < 0.001) assemblages (Figure 3). Pairwise permutational tests indicated that the communities in all seas were significantly different for both the sessile and mobile assemblages. Our results also show that differences between communities, can be well mirrored in the environmental PCoA analysis, where environmental variables clustered in welldefined groups according to the different environmental characteristics of regional seas across Europe (Figure 3).

Spatial patterns of benthos beta-diversity are slightly more determined by the oceanic distance among pair sites relative to environmental differences as seen in the partial mantel correlations between community similarity, oceanic distance, and environmental distance (Table1). Higher

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This article is protected by copyright. All rights reserved associations were observed between community assembly and oceanic distance than to environment are also observed for each taxonomic group, with Chordata, Annelida and

Echinodermata showing the highest partial correlations (Table 2, Figure 4). Thus, for these latter groups, dispersal limitation is particularly important in driving spatial distribution (Table 2, Figure 4). Community similarities decreases with oceanic distance showing a similar pattern (turnover rate, slope of distance-decay) in both mobile and sessile fractions (Table 3, Figure 5). Community similarity decay against environmental differences is also similar for both groups (slopes = -0.1986 and -0.1936 for the sessile and mobile respectively) (Figure 5). Indeed, the differences in environmental distances correlates well with oceanic distance (r 2 =0.55). Mantel tests reveal that community similarities are significantly correlated with all environmental descriptors, especially SST, SST-range and PAR (Figure 6). The Mantel correlation for each of the environmental variable, as well as the sum of all of them, is slightly higher in the mobile group compared to the sessile one. A similar environmental decay response is observed for both the sessile and mobile groups (Figure S1) with temperature (mean and range), having steeper slopes than with other set environmental variables.

Discussion

Our metabarcoding-based pan-regional study provides a framework for the assessment of the ARMS biodiversity associated with rocky-bottoms and their patterns, including those of the often neglected "hidden majority" of small organisms contributing to the cryptic community [START_REF] Pearman | Cross-shelf investigation of coral reef cryptic benthic organisms reveals diversity patterns of the hidden majority[END_REF]Ransome et al., 2017). Here, we reveal pan-regional patterns in diversity of benthic organisms with the marginal seas (i.e. Baltic, Black and Red Seas) having a distinct composition, similar to that found using other methods (Cahill et al., 2018;[START_REF] David | Lessons from photo analyses of Autonomous Reef Monitoring Structures as tools to Accepted Article This article is protected by copyright. All rights reserved detect (bio-)geographical, spatial, and environmental effects[END_REF].

Pan-regional biodiversity patterns

No clear trends in richness or Shannon diversity were observed with latitude or longitude with the highest values being observed in the Red Sea and the lowest ones in the Black Sea. However, there were significant trends with environmental variables. Negative associations were observed between richness and diversity and the range of SST and chlorophyll a. Further SSS showed a positive relationship with richness and diversity. Temperature has often been suggested as having an impact on diversity primarily due to the energy hypothesis where increased metabolic rates promote higher speciation (Tittensor et al., 2010). However, environmental stability, as indicated

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This article is protected by copyright. All rights reserved here by lower SST ranges has also been previously shown to have negative associations with richness [START_REF] Rombouts | Global latitudinal variations in marine copepod diversity and environmental factors[END_REF]Tittensor et al., 2010). The Red Sea and Western Mediterranean both had high values of ASV richness. ARMS were deployed on coral reef and photophilic algae biocenosis habitats respectively in the Red and Western Mediterranean Seas, respectively and it is likely that these habitats have higher heterogeneity than other temperate hard bottomed communities [START_REF] Loreau | Biodiversity and ecosystem functioning: recent theoretical advances[END_REF]. The higher structural complexity would provide more ecological niches, attenuating the effects of competition and promoting the co-existence of more species at the scale of the reef [START_REF] Gratwicke | The relationship between fish species richness, abundance and habitat complexity in a range of shallow tropical marine habitats[END_REF].

In general, the contribution of each site to beta diversity (LCBD) was positively correlated with richness. This is especially noticeable for the Red Sea which, for both the sessile and mobile groups, has comparatively high values for both richness and LCBD compared with other regions.

This suggests that the Red Sea has a distinct community relative to the other seas considered here, which is backed up with the identification of a limited number of ASVs that are shared between the Red Sea and the other seas and is a similar trend to that observed by Cahill et al. ( 2018) using artificial substrate units (ASUs). This is likely a reflection of the predominant Indo-Pacific origin of the Red Sea fauna [START_REF] Sherman | Large Marine Ecosystems of the Indian Ocean: Assessment, Sustainability and Management[END_REF] with only the artificial Suez Canal, built between 1859 and 1869, connecting the Red Sea with the Mediterranean Sea. In the current study, the order Sessilia (2 ASVs) and 9 ASVs unidentified could be identified that were shared between the Red Sea and the other seas. The first group was in the order Sessilia, the acorn barnacles, which was observed in both the Red Sea and the Adriatic. While it was not possible to identify the ASVs below the order Sessilia, species in this order have been observed in both the Adriatic and the Red Sea [START_REF] Igić | Cirripedia of Adriatic[END_REF].

Interestingly, a relatively high number of ASVs were shared between the Baltic and Black Seas and this may account for the low values of LCBD in these regions as they were also the poorest in terms of richness. The majority of these ASVs were taxonomically attributed to Amphibalanus improvisus. This species is one of the most successful aquatic invaders and is proposed to spread via shipping routes (Wrange et al., 2016). While Amphibalanus improvises has a broad environmental tolerance it performs best at low salinity (Wrange et al., 2014). While it is likely that this species has been transported throughout European waters through shipping routes, the niche preference of this species for low salinity environments could explain why it is

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This article is protected by copyright. All rights reserved predominantly found in the Baltic and Black Seas in the current dataset. The Black Sea shared considerably fewer ASVs with the Adriatic and Western Mediterranean than it did with the Baltic.

The Black Sea is connected to the Mediterranean through the Bosphorus strait. Prior to the Bosphorus connection being formed approximately 7000 years ago, there is evidence of long-term isolation of fauna dating from the Pleistocene [START_REF] Nikula | Phylogeography of Cerastoderma glaucum (Bivalvia: Cardiidae) across Europe: a major break in the Eastern Mediterranean[END_REF][START_REF] Papadopoulos | Phylogeography of the calanoid copepods Calanus helgolandicus and C. euxinus suggests Pleistocene divergences between Atlantic, Mediterranean, and Black Sea populations[END_REF]. About 7000 years ago, connection to the Mediterranean Sea, through the Bosphorus, led to an increase in the salinity of the Black Sea, allowing for the introduction of species from the Mediterranean (Zaitsev & Mamaev, 1997); however, the relatively lower salinity still present, as well as the colder winter temperatures may act as an ecological barrier that differentiates the communities within the two regions.

The highest number of ASVs were shared between the Western Mediterranean and the Adriatic [START_REF] Bahri-Sfar | Fragmentation of sea bass populations in the western and eastern Mediterranean as revealed by microsatellite polymorphism[END_REF]Borsa et al., 1997;[START_REF] Debes | Mitochondrial phylogeography of the European sprat (Sprattus sprattus L., Clupeidae) reveals isolated climatically vulnerable populations in the Mediterranean Sea and range expansion in the northeast Atlantic[END_REF]. A complementary explanation could come from the environmental similarity between Western Mediterranean Sea and Bay of Biscay, since the Adriatic presents different environmental conditions (Supplementary Table 3) such as higher diffuse attenuation values, chlorophyll a concentration and sea temperature range, with hotter summers and colder winters, according to our results and described elsewhere (Borja et al., 2004;Cahill et al., 2018). The fact that only a single ASV (belonging to the genus Oscarella (Porifera)) was found in all three regions in this dataset indicated that a combination of the variables described above was limiting the number of species found in these locations that shared a similar latitudinal position.

The Western Mediterranean Sea and Bay of Biscay had the highest number of phyla (19) whereas the lowest number was observed in the Baltic Sea (16 phyla). While this study is unlikely to be a comprehensive assessment of the phyla present in the regions, due to limited within region replication, possible primer bias, as well as incomplete reference molecular databases (Danovaro

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This article is protected by copyright. All rights reserved et al., 2016), it confirms the rich phylogenetic composition of the Western Mediterranean, Bay of Biscay, and the Red Sea (18 phyla), which have previously been reported as hotspots of diversity [START_REF] Coll | The Biodiversity of the Mediterranean Sea: Estimates, Patterns, and Threats[END_REF]. Overall, arthropods dominated the cryptobiome across all seas. The phylum Arthropoda, and particularly the class Crustacea, is known to have the greatest species richness in the marine system [START_REF] Narayanaswamy | Synthesis of Knowledge on Marine Biodiversity in European Seas: From Census to Sustainable Management[END_REF]. Changes in the relative contribution of Arthropoda to the overall biodiversity were aligned with the latitudinal extremes, being highest in the Baltic Sea and at a minimum in the Red Sea. Community composition in Bay of Biscay and Western Mediterranean Sea was highly comparable, with Rhodophyta, Annelida and Arthropoda being the major constituents, with these two regions sharing the highest percentage of ASVs with this observation similar to previous non ARMS studies (Borja et al., 2004[START_REF] David | Lessons from photo analyses of Autonomous Reef Monitoring Structures as tools to Accepted Article This article is protected by copyright. All rights reserved detect (bio-)geographical, spatial, and environmental effects[END_REF]Cahill et al., 2018;Wangensteen & Turon, 2015).

The most abundant unique genera in the Red Sea (Chromis, Pseudochromis and Eviota) are Indo-Pacific fish taxa which are commonly found on coral reefs within the Red Sea. In the Adriatic Sea, the unique genera were dominated by Bivalvia and Gastropoda with these groups often dominating the macrobenthic community on artificial substrates this region (Cahill et al., 2018(Cahill et al., , 2018;;[START_REF] Spagnolo | Patterns of colonization and succession of benthic assemblages in two artificial substrates[END_REF]. Species from the genus Polititapes, especially P. aureus, can be described as common bivalve in this basin [START_REF] Milišić | Glavonošci, puževi i školjke Jadrana[END_REF]. Further, Hexaplex is one of the most abundant and widespread genera of muricid gastropods in the Adriatic, and some species, such as H. trunculus, are fished for human consumption and used as fish bait (Benović, 1997). The Bryozoan genera Schizoporella are commonly observed in the Adriatic Sea [START_REF] Hayward | Northern Adriatic Bryozoa from the vicinity of Rovinj, Croatia[END_REF] and is tolerant to high salinity and mostly settle on hard bottoms of various origins (rocks, shells, logs, algae, sea plants etc) [START_REF] Igić | Cirripedia of Adriatic[END_REF]. ARMS were positioned in a habitat where red algae were predominant in the Western Mediterranean so the presence of several unique genera belonging to the class Florideophyceae would be expected with Verlaquea (synonym of Kallymenia) lacerata for example being common in the western parts of the Mediterranean and present in the sampling area, but rare elsewhere in the basin [START_REF] Rodríguez-Prieto | Geographical Distribution, habitat and reproductive phenology of the genus Kallymenia (Gigartinales, Rhodophyta) from Catalonia, Spain[END_REF].

Relative contribution of niche and dispersal processes structuring the cryptobiome

Dispersal limitation and niche descriptors determine community beta-diversity patterns in rocky shore benthos to a similar amount, although values for dispersal limitation are slightly higher both

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This article is protected by copyright. All rights reserved in general and for each one of the five taxonomic groups, in particular if we look at the partial Mantel correlations (Oceanic distance (dispersal): patial r M =0.25-0.43; Environmental (niche): partial r M =0.07-0.26). A previous study in a soft-bottom macroinvertebrate community using traditional taxonomy methods revealed dispersal traits to be more determinant on benthic regional beta-diversity patterns [START_REF] Chust | Dispersal similarly shapes both population genetics and community patterns in the marine realm[END_REF]. The use of metabarcoding in the current study enabled a broader range of taxa to be incorporated into the analysis including those with smaller body sizes and different life histories. While dispersal limitation was observed to be a slightly stronger determinant, a high shared covariation between environmental distance and spatial distance is observed, indicating that both descriptors might contribute to structuring the community, making it difficult to estimate their exact relative importance.

That no differences in the species turnover rate (slope of distance decay) and dispersal scale (halving distance) between biological groups found is also worth mentioning (Table 3). That sessile organisms often have a larval dispersal phase [START_REF] Shanks | Pelagic larval duration and dispersal distance revisited[END_REF] may explain the similarity in these dispersal scales. This might indicate that both sessile and mobile groups have similar dispersal constraints from local to regional scales. Additionally, the portion of ASVs shared between sessile and mobile groups (17%) could partially explain this pattern. In contrast, the correlation between species similarity and environmental variables is slightly higher in the mobile group compared to the sessile one, which is interpreted as the sessile group having a wider niche breath.

While investigating species was beyond the ability of the current study analysis was conducted for the five most ASV rich phyla. For all phyla the contribution of oceanic distance and environmental distance to the distribution was similar with oceanic distance in general being slightly higher. In the case of Chordata, environmental contribution is not significant, indicating that dispersal limitation might strongly constrain their distribution. However, we note the limitations of investigating the response at a phylum level with a variety of responses likely to occur within each phylum depending on the species.

There are some limitations to detecting barriers to dispersal based on relating community similarity with geographical distances. Dispersal of marine organisms through the seascape is mainly determined by the ocean currents and the dispersal capacity of organisms (e.g. Alberto et

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This article is protected by copyright. All rights reserved [START_REF] Martin | Cutadapt removes adapter sequences from high-throughput sequencing reads[END_REF][START_REF] Cowen | Population Connectivity in Marine Systems: An Overview[END_REF]. Therefore, a better estimate of oceanic connectivity than oceanic distance (as calculated by geographic distance circumventing land) would be the surface ocean transit times from site to site, derived for instance from Lagrangian particle simulations [START_REF] Jönsson | The timescales of global surface-ocean connectivity[END_REF]Villarino et al., 2018). However, this approach needs global current models to cover all the assessed seas, and these models are unavailable at the spatial resolution needed in our case, for instance connections through the Strait of Gibraltar, Bosphorus and Suez Canal. Others constraints are as follows: 1) the current ARMS sampling scheme is not comprehensive enough for abrupt transitions to be detected; 2) ARMS sampling does not consider temporal variation; hence, we do not have information on the colonization rates nor the dynamics of dispersal rates; and 3) tracking the trajectory and fate of propagules (e.g. [START_REF] Selkoe | Marine connectivity: a new look at pelagic larval duration and genetic metrics of dispersal[END_REF][START_REF] Shanks | Pelagic larval duration and dispersal distance revisited[END_REF] are methods that enable direct measures to detect dispersal barriers; however, deploying these methodologies to cover the overall biogeographic area we are considering is not feasible.

Limitations of ARMS and molecular methods to assess the hard-bottom benthic environment.

This study represents one of the largest cross-seas metabarcoding surveys of the cryptobiome conducted to date. As a consequence, we were able to advance the understanding of the drivers of benthic cryptobiome community structure. However, we do recognize that there are potential limitations to the methodologies used. Firstly, ARMS are artificial substrates and thus may have a different colonization pattern to that of the natural substrates. Limited comparisons have been undertaken but [START_REF] Plaisance | The diversity of coral reefs: What are we missing?[END_REF] for invertebrates and [START_REF] Pennesi | Assessing marine environmental status through microphytobenthos assemblages colonizing the Autonomous Reef Monitoring Structures (ARMS) and their potential in coastal marine restoration[END_REF] for the microphytobenthos have shown similarities with natural communities. Further comparisons between ARMS and natural substrates in a variety of habitats would be required, especially if ARMS were to be incorporated into long term monitoring systems, to assess truly how representative the ARMS community is compared to the natural habitat. We also recognize that despite using up to date bioinformatic pipelines, the molecular approach used have some clear limitations (Zinger et al., 2019) and, therefore, the results should be considered carefully. Firstly, samples were rarefied at 10000 reads and this may not reveal their full diversity especially in species rich areas. To get a better estimation of diversity further replicates could be taken [START_REF] Lanzén | DNA extraction replicates improve diversity and compositional dissimilarity in metabarcoding of eukaryotes in marine sediments[END_REF]. In the current study, sequencing was undertaken at several facilities which could possibly lead to batch effects due to differences in

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This article is protected by copyright. All rights reserved library preparation. While error matrices were constructed for each run individually the possibility of biases could still exist. While it was not undertaken in this study, the construction of mock communities could highlight differences in the sequencing efficiencies among facilities [START_REF] Deiner | Environmental DNA metabarcoding: Transforming how we survey animal and plant communities[END_REF]. Negative controls were undertaken in this study and were investigated by gel electrophoresis and shown to be negative. However, there is a possibility that contamination could be below the level of detection on a gel and thus, there is a possibility that a low level of diversity within the samples could be due to contamination [START_REF] Deiner | Environmental DNA metabarcoding: Transforming how we survey animal and plant communities[END_REF]. Challenges also persist in estimations of relative abundance due to a mixture of field and laboratory factors [START_REF] Kelly | Making environmental DNA count[END_REF].

This can be due to differences in the production of eDNA [START_REF] Klymus | Quantification of eDNA shedding rates from invasive bighead carp Hypophthalmichthys nobilis and silver carp Hypophthalmichthys molitrix[END_REF], the production of co-purifying contaminants (e.g. mucopolysaccharides) by taxa [START_REF] Pereira | An Efficient Method for Genomic DNA Extraction from Different Molluscs Species[END_REF], and also through primer biases that skew these estimates [START_REF] Elbrecht | Can DNA-Based Ecosystem Assessments Quantify Species Abundance? Testing Primer Bias and Biomass-Sequence Relationships with an Innovative Metabarcoding Protocol[END_REF]. Indeed, this could explain the relative lack of molluscs in the current dataset. This low abundance was also found by Cahill et al., (2018) in metabarcoding results which contrasted with morphological results undertaken at the same sites as this study but using ASUs instead. Furthermore, a large proportion of the ASVs could not be given reliably phylogenetic assignments even at phylum level. Improvements in taxonomic assignments would enhance the analysis of the biological groups and could allow the analysis to be undertaken at lower phylogenetic levels (e.g. class or family). A multi-gene approach to assessing the communities would likely mitigate primer bias and to a certain extent taxonomic assignment issues and reveal a more complete view of the communities. However, it has previously been shown that while complementary patterns were observed for taxonomic compositions, the spatial patterns observed for the two genes (18S rRNA and COI) were similar [START_REF] Pearman | Cross-shelf investigation of coral reef cryptic benthic organisms reveals diversity patterns of the hidden majority[END_REF].

Although clear differentiations amongst regions were observed and the study covers large spatial scales; we believe further sampling within each region would give a better estimation of the effects of ocean distance and environmental distance across multiple scales and would also improve estimations of connectivity between regions. Lastly, communities are known to change over time and the current study only investigated the cumulative community present on the ARMS over an annual period. Looking at the community changes over multiple temporal scales (e.g. seasonal to interannual) would give a better idea of how communities change in and amongst different regions and can also provide some baseline data to the taxa regularly present in the community.

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Conclusions

By using a standardized sampling and analysis protocol, we show that oceanic distance among sampling sites are slightly more important than niche descriptors in driving ASV distribution.

However, high-shared covariation between environmental distance and oceanic distance is observed, indicating that both descriptors might contribute to structuring the rocky bottom mobile and sessile communities, and making it difficult to estimate their relative importance. While understanding spatial patterns in biodiversity is essential to fully discern anthropogenic impacts on ecosystems, temporal patterns via long-term datasets, are also critical [START_REF] Magurran | Long-term datasets in biodiversity research and monitoring: assessing change in ecological communities through time[END_REF].

However, to avoid bias in assessments over time, which often involve changing monitoring equipment, quantifications and characterizations of biodiversity should be standardized. Here, we have presented a study across multiple institutions spanning different regional seas using standardized ARMS and a metabarcoding approach. We believe that this framework has the potential to improve the monitoring of rocky areas at global scale. For instance, long term insights are crucial to understanding ecological processes [START_REF] Seddon | Looking forward through the past: identification of 50 priority research questions in palaeoecology[END_REF] and the retrieval and replacement of ARMS in the exact same spot has the potential to create comparable long-term datasets. With ARMS units deployed temporally at local, regional and global scales investigations into the changes in the presence and absence of species across time may be feasible, which has the potential to show shifts in the species with global pressures such as climate change. This approach will provide managers and other stakeholders valuable information about how the cryptobiomecomprised of small and highly diverse, yet often neglected organisms -respond to a variety of natural and anthropogenic impacts. This information can only benefit policy making, as it incorporates responses of organisms that have different life histories and functions than the macroorganisms (e.g. algae, macroinvertebrates and fish) that are currently relied on. The regional composition for the sessile (inner ring) and mobile (outer rings) assemblages is

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This article is protected by copyright. All rights reserved depicted with an individual donut and Bottom) alpha diversity (# ASVs -Amplicon Sequence Variants-) per ARMS (line across the boxplot depicts the median with the diamond highlighting the mean; the lower hinge represents the 25% quantile with the upper hinge at 75%; the whiskers indicate the 95% confidence interval). Note, "other" in the taxonomy list defines various phyla with low relative abundances. 2). Blue: oceanic distance; Green: environmental distance; Grey: Shared

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  within the 2000-2014 period and extracted for each biological station: Sea Surface Salinity (SSS), Chlorophyll-a concentration (Chl-a), Sea Surface Temperature (SST), Sea Surface Temperature Range (SST-range), Photosynthetic

(

  116 ASVs) and the Bay of Biscay (115 ASVs). Despite the higher oceanic distance, the Bay of Biscay and Western Mediterranean populations shared similar numbers of ASVs compared with the Western Mediterranean and Adriatic Sea which are geographically closer. This could indicate a physical barrier to dispersal being present, caused by the Sicilian-Tunisian strait that has been implicated in limited gene flow between West and East Mediterranean populations of fish, invertebrates and seagrasses (Arnaud -Haond et al., 2007; Bahri-Sfar, Lemaire, Hassine, &

Figure 1 :

 1 Figure 1: Top) Map indicating the regions investigated with the insert showing the pan-regional composition (sequence abundance) of ARMS for both the sessile and mobile components. Middle)

Figure 2 :

 2 Figure 2: A) Local contribution to beta diversity (LCBD) of each site. The dotted grey line represents the value where all sites would contribute equally. B) Local contribution to beta diversity against the observed number of Amplicon Sequence Variants (ASVs). Linear regression for 2B: Total: R 2 = 0.4513; p <0.001, Sessile: R 2 = 0.2852; p = <0.001, Mobile: R 2 = 0.4666; p <0.001. Light grey shading represents the standard error of the linear regression. Colors for the points represent the different regions.

Figure 3 :

 3 Figure 3: Principal Coordinate Analysis plot depicting the variability in the cryptobiome composition based on Jaccard similarity matrices of the COI gene. Analysis was undertaken on the full ARMS unit as well as the sessile and the mobile groups (combination of 106-500 μm and 500-2000 μm fractions). Points were colored according to region. Principal Coordinate Analysis (PCoA) showing the variability of environmental variables among regional seas based on Euclidean distances. Analysis was undertaken on the full ARMS unit (combination of sessile 106-500 µm and 500-2000 µm fractions). Points were colored according to region. Heatmap showing the similarity (Jaccard) between pairs of regions and within regions.

Figure 4 :

 4 Figure 4: Correlations between community similarity with oceanic and environmental distance. Partial mantel correlations (R 2 ) showing the relative contribution of oceanic and environmental distance shaping the community structure for the different biological groups. The Mantel tests is based on Pearson's product moment correlation using 9999 permutations (see Table2). Blue:

Figure 5 :

 5 Figure 5: Community similarity vs. ocean distance (a) and vs. environmental distance (b) for benthos sessile and mobile assemblages. Community similarity is fitted with a logarithmic decay model. Scatter plots depict data variability at each distance interval.

Figure 6 :

 6 Figure 6: Tile diagram showing the Mantel correlations of the mobile and sessile community with environmental factors. The Mantel r values fall in the range of -1 to +1. Brighter cells with negative values in the plot indicate a negative correlation and redder cells with positive values a strong positive correlation. An r value of 0 indicates no correlation between community similarity and environmental variables. T: sea surface temperature (°C), T_range: SST range (°C), S: salinity, Chl-a: chlorophyll a (μl), PAR: Photosynthetic Available Radiation (E.m -2 .day -1 ).

  ). Samples were normalized and pooled before samples were diluted to 4 nM and denatured as per the Illumina 16S metagenomics library prep manual. A 10-20% PhiX spike was added to the library. Sequencing was undertaken on an Illumina MiSeq at the King Abdullah University of Science and Technology (KAUST) Bioscience Core Laboratory, Centre d'Innovation Génome Québec, McGill University, LGC Genomics GmbH (Berlin, Germany) and SGiker Genotyping and Sequencing Unit (EHU/UPV, ERDF and ESF). Sequences used for this study are deposited in the NCBI short read archive under accession PRJNA557002.

	(Supplementary Table 2
	). A versatile primer set amplifying a 313 bp fragment of the COI GGWACWGGWTGAACWGTWTAYCCYCC; Reverse: TAIACYTCIGGRTGICCRAARAAYCA; (Geller, Meyer, Parker, & Hawk, 2013; Leray et al., gene (Forward: 2013). The PCR profile consisted of an initial 3 min denaturation step at 98 °C, followed by 27 cycles at 98 °C for 10 sec, 46 °C for 45 sec and, 72 °C for 45 sec, with a final extension of 5 min at 72 °C. All PCR reactions were done in triplicate, using 0.4 μl of 10 μM primers, 10 μl of Phusion High Fidelity Mix (2X), 7.2 μl of water and 2 μl (~ 10 ng) of DNA. PCR triplicates were combined and cleaned up using AMPure beads. A second PCR was undertaken following the Illumina 16S metagenomics library prep protocol to attach Nextera XT Illumina multiplex tags or in the case of the Adriatic Sea sample the Ovation Rapid DR Multiplex System (NuGen) Accepted Article
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Acknowledgements

This manuscript is a result of the DEVOTES (DEVelopment Of Innovative Tools for understanding marine biodiversity and assessing good Environmental Status) project, funded by the European Union under the 7 th Framework Programme, "The Ocean of Tomorrow" Theme (grant agreement no. 308392), www.devotes-project.eu. S Carvalho and JK Pearman were funded through the Saudi Aramco-KAUST Center for Marine Environmental Observations (SAKMEO).

MC Uyarra was partially funded through the Spanish Programme for Talen and Employability in

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This article is protected by copyright. All rights reserved R + D + I "Torres Quevedo". We would also like to thank the editor Prof. Creer and three anonymous reviewers for the time and effort they dedicated to reviewing the manuscript.

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This article is protected by copyright. All rights reserved Pocheville, A. (2015). The Ecological Niche: History and Recent Controversies. In T. Heams, P. Huneman, G. Lecointre, & M. Silberstein (Eds.), Handbook of Evolutionary Thinking in the Sciences (pp. 547-586). Dordrecht: Springer Netherlands. doi: 10.1007/978-94-017-9014-7_26 Ransome, E., Geller, J. B., Timmers, M., Leray, M., Mahardini, A., Sembiring, A., … Meyer, C. P. (2017). The importance of standardization for biodiversity comparisons: A case study using autonomous reef monitoring structures (ARMS) and metabarcoding to measure cryptic diversity on Mo'orea coral reefs, French Polynesia. PloS One, 12(4), e0175066. Ranwez, V., Harispe, S., Delsuc, F., & Douzert, E. J. P. (2011). MACSE: Multiple Alignment of Coding SEquences Accounting for Frameshifts and Stop Codons. PLOS ONE. doi: https://doi.org/10.1371/journal.pone.0022594 Ratnasingham, S., & Hebert, P. D. N. ( 2007). BOLD: The Barcode of Life Data System (www.barcodinglife.org). Molecular Ecology Notes, 7, 355-364. doi: 10.1111Notes, 7, 355-364. doi: 10. /j.1471Notes, 7, 355-364. doi: 10. -8286.2006.01678..01678.x

Data Availability

Sequences used for this study are deposited in the NCBI short read archive under accession PRJNA557002 (NCBI 2019). The scripts used for processing the reads can be found at: https://github.com/jkpearmanbioinf/DEVOTES-Bioinformatics. Adapter information can be found in the supplementary information

Author Contributions

JKP, SC, AB, NR-E, LC, RD, ACh, AC, SM organized and led the deployment and retrieval in their respective regions. JKP, AC, LC, IM undertook the lab analysis while JKP, SC, GC, EV, JW undertook the data analysis. JKP, SC, GC, EA wrote the manuscript in discussion with Ach, AC, LC and AB. All authors contributed to various drafts of the manuscript. This article is protected by copyright. All rights reserved

Accepted Article

This article is protected by copyright. All rights reserved


RECURRENT NEURAL NETWORKS FOR COMPRESSIVE VIDEO RECONSTRUCTION

Antonio Lorente Mur, Franc ¸oise Peyrin, Nicolas Ducros Univ Lyon, INSA-Lyon, UCB Lyon 1,CNRS, Inserm, CREATIS UMR 5220, U1206, Lyon, France ABSTRACT Single-pixel imaging allows low cost cameras to be built for imaging modalities where a conventional camera would either be too expensive or too cumbersome. This is very attractive for biomedical imaging applications based on hyperspectral measurements, such as image-guided surgery, which requires the full spectrum of fluorescence.

A single-pixel camera essentially measures the inner product of the scene and a set of patterns. An inverse problem has to be solved to recover the original image from the raw measurement. The challenge in single-pixel imaging is to reconstruct the video sequence in real time from under-sampled data.

Previous approaches have focused on the reconstruction of each frame independently, which fails to exploit the natural temporal redundancy in a video sequence. In this study, we propose a fast deep-learning reconstructor that exploits the spatio-temporal features in a video. In particular, we consider convolutional gated recurrent units that have low memory requirements. Our simulation shows than the proposed recurrent network improves the reconstruction quality compared to static approaches that reconstruct the video frames independently.

Index Terms-Computational optics, video reconstruction, single-pixel camera, recurrent neural networks, imageguided surgery.

INTRODUCTION

Traditional imaging approaches based on arrays of sensors are ill-suited to imaging modalities where each detector is either too expensive or too cumbersome to be arranged as an array. Single-pixel imaging is a computational imaging strategy that relies on only a single point detector. Therefore, unlike conventional cameras, single-pixel cameras (SPCs) are suited to imaging at wavebands where silicon-based detectors are blind [START_REF] Shin | Single-pixel imaging using compressed sensing and wavelength-dependent scattering[END_REF] or to hyperspectral imaging [START_REF] Rousset | Timeresolved multispectral imaging based on an adaptive singlepixel camera[END_REF] where each pixel is a spectrometer. This makes SPCs very attractive for This work was supported by the French National Research Agency (ANR), under Grant ANR-17-CE19-0003 (ARMONI Project). It was performed within the framework of the LABEX PRIMES (ANR-11-LABX-0063) of Université de Lyon, within the programme "Investissements d'Avenir" (ANR-11-IDEX-0007) operated by the ANR. biomedical imaging applications based on hyperspectral measurements. In particular, we are interested in fluorescenceguided neurosurgery where exploitation of the full spectrum of fluorescence allows tumours to be detected that would have otherwise gone undetected [START_REF] Valdés | Quantitative fluorescence using 5aminolevulinic acid-induced protoporphyrin ix biomarker as a[END_REF].

A SPC measures the inner product of the image of a scene with a set of user-defined patterns [START_REF] Duarte | Single-pixel imaging via compressive sampling[END_REF], through a spatial light modulator and a set of lenses. As the measurements are performed sequentially, it is usually necessary to keep the number of patterns small for real-time applications. Therefore, the reconstruction problem in single-pixel imaging is typically under-determined, with more unknowns than measurements.

Traditional reconstruction strategies for single-pixel imaging can be categorized into two groups. The 2 -regularized approaches [START_REF] Rousset | Adaptive Basis Scan by Wavelet Prediction for Single-pixel Imaging[END_REF], and the 1 -(or total variation-) regularized algorithms [START_REF] Duarte | Single-pixel imaging via compressive sampling[END_REF][START_REF] Baraniuk | Compressive video sensing: Algorithms, architectures, and applications[END_REF]. On the one hand, 2 approaches are fast, but they lead to reduced image quality. On the other hand, while 1 regularization leads to improved image quality, the resulting iterative algorithms are too slow to be implemented in real time. Recently, deep neural networks have been used successfully in medical image reconstruction problems, such as computed tomography [START_REF] Gupta | Cnn-based projected gradient descent for consistent ct image reconstruction[END_REF][START_REF] Yong Chun | Bcd-net for low-dose ct reconstruction: Acceleration, convergence, and generalization[END_REF] and magnetic resonance imaging [START_REF] Hammernik | Learning a variational network for reconstruction of accelerated mri data[END_REF]. In [START_REF] Higham | Deep learning for real-time single-pixel video[END_REF], they proposed an auto-encoder network for single-pixel image reconstruction. Although this represents a useful step towards real-time imaging, the network in [START_REF] Higham | Deep learning for real-time single-pixel video[END_REF] reconstructs every frame independently, and cannot exploit the temporal redundancy of video sequences.

In the present study, we propose a fast deep-learning reconstructor that exploits the spatio-temporal features in a video. In particular, we consider a recurrent neural network (RNN) that is suited to handling image sequences through its internal state that memorizes previous inputs. Among recurrent neural networks, the long short-term memory cells are probably the most popular deep-learning variant [START_REF] Hochreiter | Long short-term memory[END_REF]. Here, we consider gated recurrent units (GRUs), which have been shown to have similar performance to long short-term memory cells [START_REF] Chung | Empirical evaluation of gated recurrent neural networks on sequence modeling[END_REF], although they have less memory requirements.

This paper is organized as follows. In section 2, we introduce the mathematical framework of single-pixel imaging alongside the classic single-pixel reconstruction approaches. In Section 3, we present our proposed RNN for solving the single-pixel video problem. Section 4 describes our numerical experiments, and our findings are reported and discussed in Section 5.

RELATED WORK 2.1. Single-pixel acquisition

We consider here a video sequence (f t ) t∈N ∈ R N×1 , where f t is the t-th frame in the sequence. A single-pixel camera provides access to the measurement sequence (m t ) t∈N ∈ R K×1 , which is given by [START_REF] Duarte | Single-pixel imaging via compressive sampling[END_REF] m t = P t f t ∆t, ∀t,

where (P t ) t∈N ∈ R K×N is the sequence of patterns that are uploaded to the spatial light modulator, and ∆t is the integration time for each single pattern. At each time frame,

P t = (p t,1 , . . . , p t,K ) ∈ R K×N +
is a matrix that contains a sequence of K patterns. Patterns are typically chosen on an orthogonal basis (e.g., Hadamard, Fourier, wavelets [START_REF] Rousset | Adaptive Basis Scan by Wavelet Prediction for Single-pixel Imaging[END_REF]). For simplicity, the same patterns are usually chosen for measuring each frame; i.e., P t = P, ∀t.

We assume that f t is constant for a time period of K∆t, which corresponds to the acquisition of each measurement frame m t .

Single-pixel image reconstruction

Static reconstruction

Static reconstruction recovers f * t ≈ f t by designing an inverse mapping Φ that relies solely on the current measurements m t ; i.e.,

f * t = Φ(m t ), ∀t, (2)
Traditional static approaches solve a sequence of optimization problems of the form

f * t ∈ argmin R(f t ) s.t. P t f t ∆t = m t ( 3
)
where R is typically the 2 norm [START_REF] Rousset | Adaptive Basis Scan by Wavelet Prediction for Single-pixel Imaging[END_REF]), the 1 norm [START_REF] Duarte | Single-pixel imaging via compressive sampling[END_REF], or the total variation norm [START_REF] Li | An efficient algorithm for total variation regularization with applications to the single pixel camera and compressive sensing[END_REF].

In [START_REF] Higham | Deep learning for real-time single-pixel video[END_REF], they proposed the use of an auto-encoder that processes each measurement frame independently

f * t = Φ θ θ θ * (m t ), ∀t, (4)
where θ θ θ * represents the weights of the networks that are computed during a training phase. Although the training phase is time consuming, evaluation of ( 4) is fast. However, this approach fails to exploit the spatio-temporal redundancy within video sequences, as the same network Φ θ θ θ * is used for all of the time frames and it has no feedback mechanism.

Dynamic reconstruction

Dynamic reconstructions exploit temporal features by designing inverse mapping that takes into account the measurements of previous frames (m t ) 0≤t ≤t for the reconstruction of the current frame f t : In particular, sparsity promoting solutions that rely on minimizing a problem of the form

f * t = Φ(m t , . . . , m 0 ) (5
f * t ∈ argmin R(f t , . . . , f 0 ) s.t. P t f t ∆t = m t , 0 ≤ t ≤ t (6)
have been proposed by different authors [START_REF] Baraniuk | Compressive video sensing: Algorithms, architectures, and applications[END_REF]. Despite their elegance, such approaches require iterative schemes that lead to reconstruction times (∼min) that are too long for real-time applications.

PROPOSED RECURRENT NETWORK FOR DYNAMIC RECONSTRUCTION

We propose to reconstruct a video sequence using a RNN that makes use of a hidden memory state. The current frame f * t is estimated from the current measurement vector m t and the previous hidden state h t-1 :

(f * t , h t ) = Ψ θ θ θ * (m t , h t-1 ) (7)
where Ψ represents the RNN and θ θ θ * are the parameters of the RNN obtained after training. Note that the hidden state is also updated so as to maintain long-term dependency. We consider the four-layer network depicted in Fig. 1. The first layer is a fully connected layer that projects the measurement frame m t to the image domain through the statistical completion presented in [START_REF] Ducros | A completion network for compressed acquisition[END_REF]. The next two layers are two ConvGRUs, followed by a (regular) convolutional layer.

The output of a ConvGRU given an input x t is the same as its memory state h t and can be computed as follows [START_REF] Ballas | Delving deeper into convolutional networks for learning video representations[END_REF]:

         z t = σ (W z * x t + U z * h t-1 + b z ) (8a) r t = σ (W r * x t + U r * h t-1 + b r ) (8b) ht = tanh(W h * x t + U h * (r t h t-1 ) + b h ) (8c) h t = (1 -z t ) h t-1 + z t ht (8d)
where σ is the sigmod function, is the Hadamard product of two vectors, * is a convolution operator, and

(W r , W z , W h , U r , U z , U h , b r , b z , b h
) are the parameters of the unit. The update gate z l t defines how much information from the memory state we want to keep, and the reset gate r l t decides if we want to forget our previous memory state.

Given a training set {(f q {1,...,T } , m q {1,...,T } )} 1≤q≤Q , where m q t = Pf q t ∆ according to (1), we trained our network using the following loss function:

θ θ θ * ∈ arg min Q ∑ q=1 T ∑ t=1 f q t -Ψ θ θ θ (m q t , h q t-1 ) 2 2 2QT + λ θ θ θ 2 2 ( 9
)
where λ is the weight decay parameter.

NUMERICAL SIMULATIONS

In our experiments, we chose M 333 Hadamard patterns of size N = 64 × 64. The size of the convolutional kernels and number of feature maps of our RNN were chosen to mimic those in [START_REF] Higham | Deep learning for real-time single-pixel video[END_REF].

Training dataset

We use the UCF-101 [START_REF] Soomro | Ucf101: A dataset of 101 human actions classes from videos in the wild[END_REF] dataset to train test our network. The UCF-101 dataset is an action recognition dataset consisting of 13 320 videos from 101 action categories. Each video has a different number of frames and a different resolution. Therefore we down-sample all of the frames to 64 × 64.

Training procedure

We train our RNN using Pytorch [START_REF] Paszke | Automatic differentiation in pytorch[END_REF]. For training, we consider the ADAM optimiser for 150 epochs. The step size is initialized to 10 -3 and divided by 5 every 40 epochs. At each epoch, we randomly extract 10 consecutive frames from each video. The weight decay regularization parameter λ is set to 10 -6 . The number of learned parameters is 1033601. Note that the fully connected layer is computed beforehand [START_REF] Ducros | A completion network for compressed acquisition[END_REF], so that our network does not need to learn it.

RESULTS AND DISCUSSION

We compare our dynamic reconstruction method with three static approaches. We consider the pseudo inverse solution, the total variation solution, and a deep-learning approach. More precisely, the pseudo inverse and the total variation [START_REF] Higham | Deep learning for real-time single-pixel video[END_REF] 23.63 0.9479 Proposed dynamic network 23.92 0.9508 solutions correspond to choosing R as the 2 -norm and as the total variation in (3), respectively. The deep-learning approach is the network proposed in [START_REF] Higham | Deep learning for real-time single-pixel video[END_REF]. As reported in Table 1, our proposed dynamic reconstruction method yields better average peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) results than the other two static reconstruction methods.

We also compare our proposed method using a real fluorescence-guided neurosurgery video. From the original video, we consider the fluorescence signal from protoporphyrin IX within the tumours. We consider 20 frames that we reshape to 64 × 64 pixels. Fig. 2 shows the fluorescence ground-truth and reconstruction for two frames. Although our network is trained on an action recognition dataset, it provides better reconstruction results than the other three methods, both visually and it terms of PSNR and SSIM.

CONCLUSION AND PERSPECTIVES

We propose a RNN to solve the single-pixel video problem. We use ConvGRUs that enable the spatio-temporal redundancy within natural videos to be exploited. Compared to strategies based on static reconstructions, the reconstruction error is decreased and the reconstruction quality is visually improved. In future, we will evaluate this method on experimental data. Although this study focuses on single-pixel videos, our method can be adapted for similar problems that incompletely sample the Fourier domain, such as magnetic resonance imaging.

Fig. 1 :

 1 Fig.1: Proposed recurrent neural network for single-pixel video reconstruction. The design is inspired by[START_REF] Higham | Deep learning for real-time single-pixel video[END_REF], but the convolutional layers are replaced by convolutional gated recurrent units (ConvGRUs) that maintain the long-term temporal dependency. ConvGRU(K s , F m ) designates a ConvGRU cell with convolutional kernels of size K s × K s and F m output feature maps.

Fig. 2 :

 2 Fig. 2: Reconstruction through different methods of a frame of a fluorecence-guided neurosurgery video. (a) Ground truth frame 10. (b) Proposed recurrent network; peak signal-to-noise ratio (PSNR) = 24.50, structural similarity (SSIM) = 0.86. (c) Static network proposed in [10]; PSNR = 24.06; SSIM = 0.84; (d) Total Variation [13]; PSNR = 24.35, SSIM = 0.85. (e) Ground truth frame 10. (f) Proposed recurrent network; peak signal-to-noise ratio (PSNR) = 24.64; SSIM = 0.87. (g) Static network proposed in [10]; PSNR = 24.35; SSIM = 0.86; (h) Total Variation [13]; PSNR = 24.16, SSIM = 0.85.

  )

	m t	x t 1	FC(M,N)	x t 2	ConvGRU(9, 64)	x t 3	ConvGRU(1, 32)	x t 4	Conv(5,1)	f t
					h t 2		h t 3
	FC = Fully			ConvGRU =			Conv =
	Connected Layer		Convolutional Gated	Convolutional Layer
					Recurrent Unit

Table 1 :

 1 Average peak signal-to-noise ratio (PSNR) and average structural similarity (SSIM) over the UCF-101 test dataset for the three different methods

	Method	PSNR	SSIM
	Pseudo-inverse	22.05 0.9278
	Static network