Early Succession Patterns of Benthic Assemblages on Artificial Reefs in the Oligotrophic Eastern Mediterranean Basin

Citation: Rallis, I.; Chatzigeorgiou, G.; Florido, M.; Sedano, F.; Procopiou, A.; Chertz-Bynichaki, M.; Vernadou, E.; Plaiti, W.; Koulouri, P.; Dounas, C.; et al. Early Succession Patterns of Benthic Assemblages on Artificial Reefs in the Oligotrophic Eastern Mediterranean Basin. J. Mar. Sci. Eng. 2022,10, 620. https://doi.org/ 10.3390/jmse10050620 Academic Editor: Azizur Rahman Received: 9 March 2022 Accepted: 26 April 2022 Published: 2 May 2022 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. Copyright: © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). Journal of Marine Science and Engineering Article Early Succession Patterns of Benthic Assemblages on Artificial Reefs in the Oligotrophic Eastern Mediterranean Basin Ioannis Rallis 1,2 , Giorgos Chatzigeorgiou 1, Marta Florido 3, Francisco Sedano 3, Avgi Procopiou 4, Melina Chertz-Bynichaki 5, Emmanouela Vernadou 1, Wanda Plaiti 1, Panayota Koulouri 1, Costas Dounas 1, Vasilis Gerovasileiou 1,6,* and Thanos Dailianis 1,* 1Hellenic Centre for Marine Research (HCMR), Institute of Marine Biology, Biotechnology and Aquaculture (IMBBC), 71500 Heraklion, Greece; [email protected] (I.R.); [email protected] (G.C.); [email protected] (E.V.); [email protected] (W.P.); [email protected] (P.K.); [email protected] (C.D.) 2Department of Biology, School of Sciences & Engineering, University of Crete, 71500 Heraklion, Greece 3Laboratorio de Biología Marina, Departamento de Zoología, Facultad de Biología, Universidad de Sevilla, 41012 Seville, Spain; [email protected] (M.F.); [email protected] (F.S.) 4School of Sciences, European University Cyprus, Nicosia 1516, Cyprus; avgi_pr[email protected] 5Department of Marine Sciences, University of the Aegean, 81100 Mytilene, Greece; [email protected] 6Department of Environment, Ionian University, 29100 Zakynthos, Greece *Correspondence: [email protected] (V.G.); [email protected] (T.D.) Abstract: The colonization of artificial structures by benthic organisms in the marine realm is known to be affected by the general trophic patterns of the biogeographical zone and the prevailing environmental traits at the local scale. The present work aims to present quantitative data on the early settlement progress of macrofaunal benthic assemblages developing on artificial reefs (ARs) deployed at the Underwater Biotechnological Park of Crete (UBPC) in the oligotrophic Eastern Mediterranean. Visual census and subsequent image analysis combined with scraped quadrats were used to describe the establishment of the communities and their development over three consecutive campaigns, spanning 5 years post-deployment. Macroalgae consistently dominated in terms of coverage, while sessile invertebrates displayed different patterns over the years. Polychaeta and Bryozoa were gradually replaced by Cnidaria, while Porifera and Mollusca displayed an increasing trend over the years. Motile benthos was mainly represented by Mollusca, while the abundance of Polychaeta increased in contrast to that of Crustacea. For both sessile and motile assemblages, significant differences were observed among the years. The results of this study indicate that ecological succession is still ongoing, and further improvement in the monitoring methodology can assist towards a more accurate assessment of the community composition in complex AR structures. Keywords: artificial habitats; sessile benthos; motile benthos; invertebrates; macroalgae; colonization; visual census; photoquadrats; scientific diving; Aegean Sea 1. Introduction An ongoing anthropogenic modification and enhancement of European coastal marine ecosystems has taken place since the pioneering deployments of artificial reefs (ARs) in the 1960s [ 1 , 2 ], and most interventions are located in the Mediterranean Sea [ 3 ]. In Greece, the first AR initiative was established in 1999 in the North Aegean Sea [ 4 ], and since then, several AR installation initiatives and studies have followed [1,3,5]. ARs are usually deployed to serve the protection, restoration, and enrichment of marine ecosystems by: (a) acting as nurseries for certain marine taxa, (b) increasing the heterogeneity and variety of the seabed, (c) facilitating recreational fisheries, (d) supporting aquaculture, (e) enhancing recreational diving spots, and (f) supporting experimental marine research [1,6,7]. The Cretan Sea (NE Mediterranean), as part of the South Aegean ecoregion, has been characterized as strongly oligotrophic with limited primary productivity [ 8 , 9 ], high salinity J. Mar. Sci. Eng. 2022,10, 620. https://doi.org/10.3390/jmse10050620 https://www.mdpi.com/journal/jmse J. Mar. Sci. Eng. 2022,10, 620 2 of 15 (often exceeding 39 PSU), and temperatures ranging from 14 to 28 ◦ C throughout the year [ 10 , 11 ]. Artificial habitats with several heterogenous microhabitats and structural refugia can attract various benthic and benthopelagic organisms [ 12 ]. Hence, ARs have become an important tool for marine life and biodiversity enrichment [ 13 ]. It is therefore interesting to study the long-term colonization and transformation of the macrobenthic assemblages that are established on ARs over the years, especially when monitoring is initiated concurrently or quickly after their installation. However, while AR-associated macrobenthic assemblages have been extensively studied in the Central and Western Mediterranean, similar assessments are limited in the oligotrophic regime of the Eastern Basin [1,7,14,15]. Different methods are used for sessile and motile benthic assemblages to study their ecological succession on ARs. For sessile communities, non-destructive methods (visual census monitoring with photoquadrats) have been proposed and are widely applied [16–19] . Motile communities, on the other hand, require sampling of the substrate surface with exhaustive methods by scraping and collecting the surface biotic layer, usually using diving. Therefore, Chatzigeorgiou et al. [ 20 ] proposed a hand-operated suction sampler (MANOSS) based on a slurp gun design, which is inexpensive, easy to construct, and allows the collection of successive samples through interchangeable collection mesh bags. The two approaches are proposed in a complementary manner for the characterization of benthic assemblages [21]. This study constitutes a long-term assessment of the structure and succession patterns of benthic communities developed on recently deployed ARs. Both non-destructive and scraping methods have been applied to evaluate the biodiversity patterns on sessile and motile components of benthic assemblages colonizing artificial structures over a 5-year time frame. The environment of their installation is in an experimental underwater facility located in the Southeastern Mediterranean. 2. Materials and Methods 2.1. Study Area The Underwater Biotechnological Park of Crete (UBPC) is an experimental underwater research infrastructure located at 1 nm from the northern Cretan coast, close to the land premises of the Hellenic Centre for Marine Research (HCMR) in Crete (http://ubpcrete. hcmr.gr/, accessed on 11 April 2022). It comprises a seafloor area of 2.5 ha at a depth increasing from 18 to 22 m along the south–north direction (Figure 1) [ 12 , 22 ]. The seawater temperature at the area of the UBPC ranges from 14 to 28 ◦ C over the year, while the salinity is relatively stable at 39 PSU, except for occasional dropdowns during the rainy season (November–April); the main current has a west to east direction due to prevailing NNW winds [ 11 ]. The seafloor area is characterized by a rather flat relief, intermixing coarse sand flats, and dead Posidonia oceanica matte, with a macroalgal cover mainly of Caulerpa prolifera. Patchy seagrass meadows (P. oceanica) are common along the area, while some natural small, flat, rocky reefs of less than a couple square meters of surface area are scarcely distributed. In 2015, twelve units of ARs made of concrete, with base diameters between 1.2 and 2.3 m and 1.4 to 2.5 m high, were deployed at a depth of ca. 20 m at UBPC. The purpose of the ARs project was to test the concept of a “recreational diving oasis” in order to attract diving tourism in overexploited and oligotrophic coastal ecosystems by mimicking natural rocky reefs [12,22] (Figure 2). 2.2. Sampling and Image Processing Sampling was implemented with scuba diving in 2017 (June), 2019 (October), and 2020 (October). Seawater temperatures for the months of sampling were 23 ◦ C for June 2017 and 25 ◦ C for October 2019 and 2020. For the study of sessile benthos, five ARs, randomly selected in each period, were sampled with 25 × 25 cm photoquadrats ( Figure S1 ). Five replicate quadrats per sampling event were randomly positioned over flat, exposed areas of each AR and photographed, resulting in a total of 75 photos (25 in each sampling J. Mar. Sci. Eng. 2022,10, 620 3 of 15 event). For this purpose, a digital mirrorless camera (Olympus OM-D E-M5) was used, paired with two external underwater strobes to ensure consistent illumination. The analysis of photoquadrats is a non-destructive and cost-efficient sampling method that is widely used for the studying and monitoring of sessile communities in both natural and artificial substrates [ 16 – 18 , 21 ]. Additionally, qualitative samples of several taxa were collected for taxonomic identification in the lab or photographed in situ. J. Mar. Sci. Eng. 2022, 10, x FOR PEER REVIEW 3 of 15 Figure 1. (a) Map of the Eastern Mediterranean Sea. (b) Location of the Underwater Biotechnological Park of Crete (UBPC) (red arrow), almost 2 km offshore from the northern coast of Crete in Greece. Figure 1. ( a ) Map of the Eastern Mediterranean Sea. ( b ) Location of the Underwater Biotechnological Park of Crete (UBPC) (red arrow), almost 2 km offshore from the northern coast of Crete in Greece. J. Mar. Sci. Eng. 2022,10, 620 4 of 15 J. Mar. Sci. Eng. 2022, 10, x FOR PEER REVIEW 4 of 15 Figure 2. Concrete artificial reef installed at the Underwater Biotechnological Park of Crete (UBPC), photographed 4 years post-deployment (Photo credit: Thanos Dailianis). 2.2. Sampling and Image Processing Sampling was implemented with scuba diving in 2017 (June), 2019 (October), and 2020 (October). Seawater temperatures for the months of sampling were 23 °C for June 2017 and 25 °C for October 2019 and 2020. For the study of sessile benthos, five ARs, randomly selected in each period, were sampled with 25 × 25 cm photoquadrats (Figure S1). Five replicate quadrats per sampling event were randomly positioned over flat, exposed areas of each AR and photographed, resulting in a total of 75 photos (25 in each sampling event). For this purpose, a digital mirrorless camera (Olympus OM-D E-M5) was used, paired with two external underwater strobes to ensure consistent illumination. The analysis of photoquadrats is a non-destructive and cost-efficient sampling method that is Figure 2. Concrete artificial reef installed at the Underwater Biotechnological Park of Crete (UBPC), photographed 4 years post-deployment (Photo credit: Thanos Dailianis). The cover percentages of taxonomic categories from photoquadrat data were calculated with an advanced image analysis using PhotoQuad [ 23 ] which is a free software specialized for underwater ecological applications in integrating various methods and tools for the accurate calculation of species coverage. In each photo sample, 100 random points J. Mar. Sci. Eng. 2022,10, 620 5 of 15 were spawned within the photo frame. The points over living benthic species were assigned to a specific taxon or morpho-functional group (e.g., Mucilaginous algae or Encrusting Bryozoa). Every single point falling on a biogenic substrate that was impossible to be identified through visual census was designated as an “Unidentified Biogenic Substrate” (UBS) [ 17 , 24 ]. UBS describes the substrates covered by small-sized organisms (e.g., bacterial colonies, padded algae in their early stages of development, encrusting assemblages with no clear characteristics, etc.). To a lesser extent, UBS may refer to blurry and shady areas of biogenic cover in the photo samples, due to substrate rugosity. For the study of motile associated fauna, six replicate padded quadrats of 25 × 25 cm were randomly exhaustively scraped and collected from three random ARs each year with a MANOSS suction sampler (Figure S2) [ 20 ]. The motile macrofauna taxa were collected after sieving with a mesh size of 0.5 mm; preserved in 97% ethanol; sorted; and identified to the family/species level for Polychaeta, Crustacea, and Mollusca. Preserved samples were deposited in the collection of the Biodiversity Laboratory of IMBBC-HCMR. 2.3. Statistical Analysis Statistical analysis was performed on species abundances with the PRIMER (ver. 6) software package [ 25 ]. To mitigate the contribution of the most abundant species, data were transformed under the fourth root formula, and a triangular similarity matrix was created based on the Bray–Curtis similarity index [ 26 ]. In order to investigate spatial patterns in the community structure, a non-metric multidimensional scaling (MDS) was performed separately for the sessile and motile communities. To identify the taxa that contributed most to the multivariate patterns of the sessile and motile communities each year, the similarity percentages (SIMPER) analysis was used. The significance of the multivariate results was assessed using a one-way analysis of similarity (ANOSIM) test on the transformed data. The null hypothesis of the ANOSIM test was that there are no differences in biodiversity patterns among the sampling periods. 3. Results 3.1. Community Structure 3.1.1. Sessile Community A total of 37 sessile taxa were identified in the 75 analyzed photoquadrats, belonging to six major taxonomic groups (12 Macroalgae, 8 Porifera, 1 Hydrozoa, 3 Polychaeta, 6 Mollusca, 3 Bryozoa, and 4 Tunicata). Macroalgae dominated in terms of the surface cover consistently over the studied period, with 82.3% coverage in 2017, 86.8% in 2019, and 86.3% in 2020 (Table S1). In the earliest year after deployment (2017), the turf-forming algae coverage (37.0%) was much lower than the other sampling periods (80.6% in 2019 and 73.7% in 2020), while Mucilaginous algae (27.8%) and Dictyota implexa (13.3%), which covered a significant amount of the surface, were completely absent in the following years. The encrusting macroalgae comprised mostly Peyssonnelia species, while the turf-forming algae were characterized mainly by Rhodophyta such as Jania sp. and Laurencia sp. Sessile invertebrates contributed with 17.0% coverage in 2017, which was considered the initial stage of colonization. Polychaeta and Bryozoa dominated (7.7% and 6.9%, respectively) (Figure 3and Table S1), with Serpulidae being the most abundant polychaete family. Thereafter, Cnidaria, represented only by Hydrozoa (1.7%), and Tunicata (0.7%), followed. Porifera and Mollusca were completely absent from the photoquadrats of 2017. Interestingly, in the following years (2019 and 2020), the sessile invertebrate coverage decreased (13.2% and 13.7%, respectively). Polychaeta (2.3% and 1.7%) and Bryozoa (1.9% and 1.9%) showed a decreasing trend in favor of Cnidaria (6.5% and 6.7%). Porifera (1.6% and 2.1%) and Mollusca (0.3% and 0.6%) showed a slight increasing trend, while Tunicata (0.5% and 0.8%) remained stable throughout the sampling periods. A considerable percentage of the AR surface was classified as UBS, as it represented a biogenic substrate that could not be safely assigned to any specific taxonomic category (19.7% in 2017, 47.6% J. Mar. Sci. Eng. 2022,10, 620 6 of 15 in 2019, and 43.6% in 2020). This category was excluded from the statistical analysis, as it could not be assigned to a more specific morpho-functional group. In all sampling periods, the substrate was almost completely covered by sessile benthos (mean coverage: 95.0% in 2017, 97.6% in 2019, and 99.9% in 2020). J. Mar. Sci. Eng. 2022, 10, x FOR PEER REVIEW 6 of 15 followed. Porifera and Mollusca were completely absent from the photoquadrats of 2017. Interestingly, in the following years (2019 and 2020), the sessile invertebrate coverage decreased (13.2% and 13.7%, respectively). Polychaeta (2.3% and 1.7%) and Bryozoa (1.9% and 1.9%) showed a decreasing trend in favor of Cnidaria (6.5% and 6.7%). Porifera (1.6% and 2.1%) and Mollusca (0.3% and 0.6%) showed a slight increasing trend, while Tunicata (0.5% and 0.8%) remained stable throughout the sampling periods. A considerable percentage of the AR surface was classified as UBS, as it represented a biogenic substrate that could not be safely assigned to any specific taxonomic category (19.7% in 2017, 47.6% in 2019, and 43.6% in 2020). This category was excluded from the statistical analysis, as it could not be assigned to a more specific morpho-functional group. In all sampling periods, the substrate was almost completely covered by sessile benthos (mean coverage: 95.0% in 2017, 97.6% in 2019, and 99.9% in 2020). Figure 3. Biotic mean coverage of the major taxonomic groups of sessile invertebrates (%) showing changes over the years. Colors indicate different major taxonomic groups that contributed to the coverage of photoquadrats of the artificial reefs. Additionally, three non-indigenous species (NIS) have been reported in photoquadrats: the brown alga Stypopodium schimperi with increasing coverage from 0.5% in 2017 to 1.3% in 2020 and two NIS of Mollusca, Pinctada radiata with 0.4% in 2019 and Dendostrea cf. folium in 2020 with 0.1%. The MDS analysis revealed a clear differentiation among the three sampling periods regarding sessile communities (Figure 4a). The community of 2017 tended to be more distinctive from subsequent years 2019 and 2020, forming a well-defined cluster. One-way ANOSIM showed a discrimination among the three groups (R = 0.443, p < 0.01). Additional pairwise tests revealed significant differences between 2017 and 2019 (R = 0.516, p < 0.01), between 2017 and 2020 (R = 0.685, p < 0.01), and between 2019 and 2020 (R = 0.167, p < 0.01), supporting the MDS results. Figure 3. Biotic mean coverage of the major taxonomic groups of sessile invertebrates (%) showing changes over the years. Colors indicate different major taxonomic groups that contributed to the coverage of photoquadrats of the artificial reefs. Additionally, three non-indigenous species (NIS) have been reported in photoquadrats: the brown alga Stypopodium schimperi with increasing coverage from 0.5% in 2017 to 1.3% in 2020 and two NIS of Mollusca, Pinctada radiata with 0.4% in 2019 and Dendostrea cf. folium in 2020 with 0.1%. The MDS analysis revealed a clear differentiation among the three sampling periods regarding sessile communities (Figure 4a). The community of 2017 tended to be more distinctive from subsequent years 2019 and 2020, forming a well-defined cluster. One-way ANOSIM showed a discrimination among the three groups (R = 0.443, p< 0.01). Additional pairwise tests revealed significant differences between 2017 and 2019 (R = 0.516, p< 0.01), between 2017 and 2020 (R = 0.685, p< 0.01), and between 2019 and 2020 (R = 0.167, p< 0.01 ), supporting the MDS results. J. Mar. Sci. Eng. 2022,10, 620 7 of 15 J. Mar. Sci. Eng. 2022, 10, x FOR PEER REVIEW 7 of 15 (a) (b) Figure 4. Non-metric multidimensional scaling (MDS) plots showing the similarities among the three sampling periods (indicated by different colors) between: (a) sessile communities and (b) motile communities. A SIMPER analysis between 2017 and the other two years showed that the Bray– Curtis dissimilarity was mainly due to Mucilaginous algae showing 52.37% dissimilarity with 2019 and 54.71% with 2020 (Table 1). Between 2019 and 2020, the dissimilarity was much lower (39.20%), with encrusting macroalgae contributing the most. The similarity of each sampling period and the taxa that contributed most to the fauna patterns are presented in Table 2. The category of turf-forming algae contributed the most to the similarity Figure 4. Non-metric multidimensional scaling (MDS) plots showing the similarities among the three sampling periods (indicated by different colors) between: ( a ) sessile communities and (b) motile communities. A SIMPER analysis between 2017 and the other two years showed that the Bray–Curtis dissimilarity was mainly due to Mucilaginous algae showing 52.37% dissimilarity with 2019 and 54.71% with 2020 (Table 1). Between 2019 and 2020, the dissimilarity was much lower (39.20%), with encrusting macroalgae contributing the most. The similarity of each sampling period and the taxa that contributed most to the fauna patterns are presented in Table 2. The category of turf-forming algae contributed the most to the similarity of each year, followed by Cnidaria (Hydrozoa) in 2019 and 2020 and, in 2017, by Mucilaginous J. Mar. Sci. Eng. 2022,10, 620 8 of 15 algae, which disappeared the following years. The similarity index increased over the years (56.12% in 2017, 63.99% in 2019, and 64.47% in 2020). Table 1. Pairwise similarity percentage analysis (SIMPER), showing the pairwise average dissimilarity (%) among three sampling periods. Sessile Assemblages Motile Assemblages Sampling Periods Average Dissimilarity Sampling Periods Average Dissimilarity 2017–2019 52.37 2017–2019 61.52 2017–2020 54.71 2017–2020 74.45 2019–2020 39.2 2019–2020 67.83 Table 2. Similarity percentage analysis (SIMPER), showing the contribution of common taxa to the average similarity (%) in each year of sampling on the artificial reefs. Major Taxonomic Group 2017 2019 2020 Average Similarity 56.12 63.99 64.47 Sessile Assemblages Taxonomic Group/Species Macroalgae Turf-forming algae 34.43 49.25 39.23 Encrusting macroalgae 7.31 12.92 16.46 Mucilaginous algae 19.82 - - Acetabularia sp. 5.56 - - Dictyota implexa 3.87 - - Cnidaria Hydrozoa 4.97 16.92 17.36 Polychaeta Other Serpulidae 17.77 16.90 12.71 Protula spp. - - 5.66 Average Similarity 53.87 40.58 33.77 Motile assemblages Family Polychaeta Capitellidae - 6.42 - Chrysopetalidae 6.22 13.74 7.78 Dorvilleidae - 4.13 - Eunicidae 3.28 - - Nereididae 3.39 - 2.57 Paraonidae 4.06 - - Syllidae 4.13 8.65 - Mollusca Cerithiidae 24.92 36.59 73.86 Rissoidae 7.78 9.19 - Trochidae - - - Crustacea Aoridae 9.41 7.89 - Bodotriidae 4.20 - - Caprellidae 7.78 - - Dexaminidae 10.50 - - Lysianassidae 6.09 - - Pasiphaeidae - 4.24 7.14 3.1.2. Motile Community Overall, 1898 individual macroinvertebrates (1130 in 2017, 583 in 2019, and 185 in 2020) were counted and identified at least to the family level in 18 scraped quadrats. These were classified as Polychaeta (14.4%), Mollusca (75.5%), and Crustacea (10.0%). The mean contribution of each taxon is presented in Figure 5and Table S2. The participation of Mollusca was the highest during all the sampling periods, represented by 33 species of 19 families. Bittium latreillii was the most abundant species in all the samples, with a contribution of 63.2–69.1%. Polychaeta were represented by 15 families and showed an increase in their contribution over the years (10.5% in 2017, 17.6% in 2019, and 28.1% in 2020), while Crustacea, represented by 16 families, decreased from 14.2% in 2017 to 3.6 and 4.3 in 2019 and 2020, respectively. J. Mar. Sci. Eng. 2022,10, 620 9 of 15 J. Mar. Sci. Eng. 2022, 10, x FOR PEER REVIEW 9 of 15 Overall, 1898 individual macroinvertebrates (1130 in 2017, 583 in 2019, and 185 in 2020) were counted and identified at least to the family level in 18 scraped quadrats. These were classified as Polychaeta (14.4%), Mollusca (75.5%), and Crustacea (10.0%). The mean contribution of each taxon is presented in Figure 5 and Table S2. The participation of Mollusca was the highest during all the sampling periods, represented by 33 species of 19 families. Bittium latreillii was the most abundant species in all the samples, with a contribution of 63.2–69.1%. Polychaeta were represented by 15 families and showed an increase in their contribution over the years (10.5% in 2017, 17.6% in 2019, and 28.1% in 2020), while Crustacea, represented by 16 families, decreased from 14.2% in 2017 to 3.6 and 4.3 in 2019 and 2020, respectively. Figure 5. Relative abundance (%) of the major motile invertebrate taxonomic groups per year. Colors indicate different major taxonomic groups of the invertebrates identified from the scrapes of the MANOSS sampler. The MDS analysis showed a clear distinction between the communities of 2017 and 2020, while 2019 stood as a discrete cluster between the two other sampling years (Figure 4b). Additionally, one-way ANOSIM showed a discrimination among the sampling periods (R = 0.417, p < 0.01). Additional pairwise tests revealed significant differences between 2017 and 2019 (R = 0.389, p < 0.01), 2017 and 2020 (R = 0.717, p < 0.01), and 2019 and 2020 (R = 0.217, p < 0.01). It should be noted that the motile community of 2017 showed a more accumulated distribution, contrary to the pattern of depiction from the sessile data. The SIMPER analysis on the motile community showed great dissimilarity among the sampling periods compared to the sessile SIMPER results (Table 1). The Bray–Curtis dissimilarity index between 2017 and 2020 was 74.45%, with Dexaminidae (Crustacea) contributing the most. The dissimilarly percentages between 2019 and 2020 were 67.83%, with Syllidae (Polychaeta) contributing the most, and between 2017 and 2019 was 61.52%, with Caprellidae (Crustacea) contributing the most. The similarity of each sampling year and the families that contributed most to the similarity pattern are presented in Table 2 Specifically, the family Cerithidea (Mollusca) was the main driver for the assessed dissimilarity between the sampling years due to the high abundance of the species B. latreillii. Figure 5. Relative abundance (%) of the major motile invertebrate taxonomic groups per year. Colors indicate different major taxonomic groups of the invertebrates identified from the scrapes of the MANOSS sampler. The MDS analysis showed a clear distinction between the communities of 2017 and 2020, while 2019 stood as a discrete cluster between the two other sampling years (Figure 4b). Additionally, one-way ANOSIM showed a discrimination among the sampling periods ( R = 0.417 ,p< 0.01). Additional pairwise tests revealed significant differences between 2017 and 2019 (R = 0.389, p< 0.01), 2017 and 2020 (R = 0.717, p< 0.01), and 2019 and 2020 (R = 0.217, p< 0.01). It should be noted that the motile community of 2017 showed a more accumulated distribution, contrary to the pattern of depiction from the sessile data. The SIMPER analysis on the motile community showed great dissimilarity among the sampling periods compared to the sessile SIMPER results (Table 1). The Bray–Curtis dissimilarity index between 2017 and 2020 was 74.45%, with Dexaminidae (Crustacea) contributing the most. The dissimilarly percentages between 2019 and 2020 were 67.83%, with Syllidae (Polychaeta) contributing the most, and between 2017 and 2019 was 61.52%, with Caprellidae (Crustacea) contributing the most. The similarity of each sampling year and the families that contributed most to the similarity pattern are presented in Table 2Specifically, the family Cerithidea (Mollusca) was the main driver for the assessed dissimilarity between the sampling years due to the high abundance of the species B. latreillii . The result of the similarity index complied with the findings of the MDS analysis, showing a much higher similarity within the 2017 replicates (53.87%) than the other two periods (40.58% in 2019 and 33.77% in 2020). They were, however, in contrast with the patterns observed for the sessile community analysis. 4. Discussion Significant shifts in the structure of benthic assemblages on the ARs were observed during the three sampling periods over a five-year survey following the initial deployment.