Molluscs community as a keystone group for assessing the impact of urban sprawl at intertidal ecosystems

Vol.:(0123456789) 1 3 Urban Ecosystems (2021) 25:819–834 https://doi.org/10.1007/s11252-021-01192-6 Molluscs community asakeystone group forassessing theimpact ofurban sprawl atintertidal ecosystems E.Ortega‑Jiménez1 · F.Sedano1· F.Espinosa1 Accepted: 28 November 2021 / Published online: 8 January 2022 © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022, corrected publication 2022 Abstract Mollusc communities are getting endangered in the aftermath of urban sprawl because artificial structures do not surrogate natural substrates. In this study, we compared the diversity, community and trophic arrangements of molluscs among different models of artificial substrate and their adjacent natural rock, to detect relationships between some abiotic variables and the mollusc communities. Complexity, chemical composition and age were tested as potential drivers of the community. Diversity, community and trophic structure differed between natural and artificial substrates. Complexity at the scale of cm was detected as the most important factor driving the community structure. In addition, a chemical composition based on silica and/or scarce calcium carbonates seems to be relevant for molluscs, as well as for the secondary substrate where they inhabit. However, age did not seem to be a driving factor. Among the different artificial structures, macroscale complexity was detected as the main factor diverging a drastically poor community at seawall from other artificial structures.In this context, macro and microscale complexity, chemical composition and mineral type are variables to consider in future designs of artificial substrates. Keywords Artificial substrates· Urban sprawl· Molluscs· Complexity· Chemical composition· Age· Trophic groups Introduction Biodiversity on natural coastal habitats is under threat by many causes, mainly: coastal artificialization, exploitation of renewable (fisheries) and non-renewable (mineral and energy extraction) resources, pollutant discharge and marine debris (Dulvy etal. 2003; Jacob etal. 2018). In the Bay of Sydney (Australia), 50% of the natural coastline is replaced by artificial substrates (Chapman2006; Dafforn etal.2015) and around 22.000 Km2 of European coasts are covered with concrete or asphalt (European Environment Agency Report2006; Airoldi and Beck2007). This coastal transformation, the so-called ‘urban sprawl’ (Firthet al.2016), is being boosted by shore erosion due to more frequent stormy events and the sea-level rise (Bouma etal. 2014; Bulleri and Chapman2010), altogether, threatening intertidal ecosystems. Intertidal communities are diverse and complex due to the broad range of biotic and abiotic interactions that occur on intertidal natural substrates (Chapman2013). For example, wave and tide action (Southward and Orton1954), desiccation or top-down processes (predation, competition, grazing, etc.) modulate both the sessile and vagile biota, promoting the development of rich and ecologically important communities. Molluscs are one of the most abundant taxa in the intertidal zone, providing important ecosystem services (see Table2in Firth etal.2016). They are considered early colonizers of substrates (Underwood and Chapman2013) and play important roles in C and Si cycles (Meysman and Montserrat2017). Sessile filter molluscs can contribute to clean water and improve nutrient uptake for algae (Eriksson etal. 2017) and together with other sessile organisms, they serve as engineers (Melero etal. 2017;Commito etal. 2018) setting up a secondary substrate for many different species. Also, mobile grazers can feed on macrophytes, cleaning areas for subsequent colonization of many species (Firth etal. 2016). Previous studies have reported a negative impact of artificial substrates on intertidal molluscs. For example, Moreira etal. (2006) suggested that seawall do not sustain viable populations of limpets. Furthermore, complexity/heterogeneity (e.g. micro-roughness) can affect * E. Ortega-Jiménez elena.orteg[email protected] 1 Laboratorio de Biología Marina, Departamento de Zoología, Facultad de Biología, Universidad de Sevilla, Avenida de la Reina Mercedes 6, 41012Sevilla, Spain 820 Urban Ecosystems (2021) 25:819–834 1 3 the abundance of chitons (Moreira etal. 2007) or limpets (Rivera-Ingraham etal. 2011) on artificial substrates. In fact, substrate complexity is one of the biggest drivers of intertidal biodiversity. Concrete-made artificial substrates usually lack microhabitats (crevices, rock pools, etc.), preventing refuge from stressful conditions such as desiccation or predation, and are largely responsible for the biodiversity deficit of artificial substrates compared with the natural rocky shore (Firth etal. 2016 and references therein). Therefore, eco-engineering actions that added habitat complexity at different scales have been related with a higher number of taxa (Kefi etal. 2015; Strain etal. 2018) and enhanced recruitment and survival of sessile and mobile macrofauna (Atilla and Finelli2005). Substrate composition, like minerals and elements, is also known to be an important factor affecting communities developing on artificial structures (Coombes etal. 2015; Sempere-Valverde etal. 2018). For example, acidic siliceous quartz from sandstone may cause oxidative stress and hold less diverse and mature community when compared to limestone (Bavestrello etal. 2000; Cattaneo-Vietti etal. 2005). The mineralogical composition usually varies from artificial substrates (normally made from concrete) to natural substrates (Ido and Shimrit2015; Ponti etal. 2015). Concrete may liberate toxic metals and carbonates that enhance alkalinity (pH ~ 13) producing stress on individuals (Ido and Shimrit2015). In the case of molluscs, higher saturation of aragonite can facilitate a higher occurrence of burrowing bivalves (Mos etal. 2019) and alkaline concrete surfaces may increase oysters’ recruitment (Anderson1996). Although ecological succession may not occur in a parallel manner on artificial and natural substrates (Burt etal. 2011), the age of substrates has been considered as an important factor explaining the differences between artificial and natural substrates (Glasby and Connell1999a, b). Some authors have estimated that it takes from 5 to 20years for artificial structures to reach climax communities (Coombes2011; Hawkins etal. 1983; Pinn etal. 2005), while others suggest that communities on low crested structures never reach climax (Gacia etal. 2007) or take more than 100years (Perkol-Finkel etal. 2005). Consequently, we decided to consider the date of substrates deployment in the present study, together with substrate composition and complexity, to study mollusc diversity associated with artificial substrates. Furthermore, changes in the community structure of epifaunal organisms associated with artificial substrates can cause trophic shifts (Sedano etal. 2020a). Artificial substrates are known to affect prey resources (Munsch etal. 2015), limiting the diet of some mollusc species (Burgos-Rubio etal. 2015) and ultimately restricting the diversity of trophic strategies. For example, the reduced primary productivity on seawalls has been related to the scarcity of herbivore grazers (Lai etal. 2018). These effects, among others, call for an ecological evaluation of coastal artificial substrates to prevent the decline of intertidal habitats (Dafforn etal. 2015; Firth etal. 2016) and promote other ecological services (García-Gómez etal.2014; Dearborn and Kark2010). Taking into account that molluscs are diverse and contribute highly to this habitat (Ricciardi etal.1997), we decided to study the community of molluscs as a model to detect relationships between the abiotic features of the man-made intertidal substrate and the associated fauna. We focused on habitat complexity, substrates composition and age intending to identify which factors are driving the differences in molluscs taxonomic and trophic structure between artificial substrates and natural substrates. In this regard, we hypothesized that: 1. Substrate complexity and composition would be the main drivers differentiating artificial from natural substrates, given the differences in complexity and composition between artificial and natural substrates in our study area. Additionally, we hypothesized that the mollusc community at rip-raps (an artificial substrate made from natural rock) would be the most similar to natural substrates. 2. Trophic community structure would vary among different artificial substrates and between artificial and natural substrates. 3. Age will be a driver structuring intertidal molluscs’ community on artificial substrates. Methods Study area Our study area was located in the Algeciras Bay (Cadiz, Spain), which achieves 400m in depth and occupies 73 Km2 of area. This deep bay is found next to one of the most relevant marine regions in the world, the Strait of Gibraltar. It is a marine area with high biodiversity due to its location and structure, which is placed between Africa and Europe and between two water bodies, the Atlantic Ocean and the Mediterranean Sea (Usero etal. 2016). Algeciras Bay contains five different substrates (four artificial and their nearest natural rocky shore), very close to each other and under very similar environmental conditions. We selected four nearby artificial substrates (acropods, cubes, rip-raps and seawall) and compared the molluscan assemblages and trophic structure among them and with the nearest natural substrate. Given the difficulty to find different artificial substrates next to each other, we limited our study area to this single Bay (Fig.1). 821Urban Ecosystems (2021) 25:819–834 1 3 Abiotic analysis To identify possible drivers of the differences between substrates, we measured the physicochemical features of each substrate. The variables included macro and microscale complexity, elemental composition, minerals, crystallinity, calcination percentage (C.P) and age. Complexity measures were divided into macroscale complexity (m) and microscale complexity (cm). In both cases (macro and microscale), substrate roughness was calculated as in Rivera-Ingraham etal. (2011) using the equation by Blanchard and Bourget (1999): Roughness or topographical heterogeneity index (THI) = Tr / Ts, where Tr is the “effective” distance between two points “A-B” (measuring the contour between A-B) and Ts is the linear distance between A-B. Macroscale roughness was calculated over 15m length transects. Three transects were selected at each substrate and a flexible meter was laid directly over it, trying to conform as closely as possible to all contours of the bare substrate. Regarding microscale roughness, three 15cm profile gauges with 0.5mm pins were pushed onto the bare rock to record the surface of each substrate (Frost etal. 2005). The resulting profiles were photographed, and the images were digitally processed with Adobe Photoshop to obtain two coloured images. The length of the contour of the profile was obtained with ImageJ software. The elemental composition and calcination percentage, mineralogical absorption spectra, crystallinity and lithology composition of each sample were obtained from Sedano etal.(2019). All chemical composition was characterized using three powdered fragments of each substrate. Age of the substrate was based on the date of construction and resulting from the difficulty of dating age of the natural substrate, the oldest possible date in the same order of magnitude compared to the oldest artificial substrate was used instead. Also, wave exposure was quantified at each substrate using a combination of the maximum fetch and the modified effective fetch (Fe) index developed by (Howes etal. 1994): Fe = [∑ (cos ɵi) X Fi/∑cos ɵi], where ɵi is the angle between the shore-normal, and the directions 0º, 45º left and 45º right and Fi is the fetch distance in Km along the relevant vector. To determine if substrates differed physico-chemically and to detect the most relevant abiotic components that separate the substrates, we performed a Principal Component Analyses (PCA) using macro, microscale complexity, elemental composition (calcium, silicon and magnesium), crystallinity and age. Data were normalized before analyses. Biotic analysis Community and trophic structure, as well as biodiversity indices (richness, Pielou’s Evenness and Shannon’s diversity), were compared among artificial and natural substrates. Three different sites were randomly selected within each of the five substrates (natural, cubes, acropods, rip-raps and seawall). At each site, three replicate quadrats of 20 × 20cm were scraped (3 sites × 3 replicates × 5 substrates = 45 samples). The samples were collected during low tide and within the lower intertidal zone (5–30cm over the lowest tidal level). We scraped the biotic substrate (secondary substrate) and the associated fauna and preserved it in 96% ethanol until laboratory analyses. At the laboratory, associated molluscs were sorted out from the rest of sessile and vagile biota, identified down to species level whenever possible and quantified in terms of their abundance. Since the secondary substrate (sessile biota developing on the hard primary substrate) can influence the associated fauna (Chapman etal. 2005), all sessile fauna and flora that conform the secondary substrate were volumetrically quantified at each replicated site to control this variable (used as a covariate in the analyses). Besides, percentages of the most abundant species of the secondary substrate were recorded as well, to detect possible differences between substrates. To identify possible trophic shifts, we grouped the different species into trophic categories and compared the trophic structure among substrates. Species were assigned and grouped according to their trophic strategies Fig.1 Study area with 5 substrates (1 = Cubes, 2 = Acropods, 3 = Rip-Raps, 4 = Seawall, 5 = Natural) 822 Urban Ecosystems (2021) 25:819–834 1 3 (see Table1 in Donnarumma etal. 2018), with slight modifications to better represent the feeding strategy of the species in our study. We assigned them into different groups depending on what they feed on, and the way to obtain the food (trophic guilds) (Table1). Despite being known to feed on larvae of animals and detritus (Burgos- Rubio etal. 2015), limpets were considered herbivores. We also considered omnivores all animals that feed on suspended organic-matter or by filtering particles in the water column. After species identification, three biodiversity indexes were measured (Badalamenti etal. 2002): species richness, Shannon–Wiener diversity and Pielou evenness for each replicated site in all substrates. To test for biodiversity differences among substrates, we performed a nested ANOVA for each biodiversity index using GMAV5 (Underwood etal. 2002). A Student Newman Keuls (SNK) test was also conducted to elucidate differences among pairs of substrates. The statistic design had two factors: “Substrate” (fixed), with five levels: Natural, Cubes, Acropods, Rip-raps and Seawall, and “Sites” (random), which was nested in the substrate and had three levels (1,2,3). Cochran’s test was performed to confirm the homoscedasticity of biotic data. Metric multidimensional scaling (MDS) based on the similarity of Bray Curtis matrix was performed on the community and trophic structure (see Table1 with six categories). We made an additional CLUSTER and SIMPROF test to group the substrates depending on their dissimilarity. Furthermore, a permutational multivariate analysis of variance (PERMANOVA) was also carried out to test if the community and trophic structure of molluscs varied significantly among substrates and sites. Previously, data were square-root transformed and the analysis was carried out on a Bray Curtis triangular matrix. When a significant source of variance was detected, a pair-wise test between pairs of substrates was also computed to obtain the corresponding p-values. Correlation analyses Correlation tests between the community and abiotic matrix were made to explore potential relationships between community structure and the abiotic variables. Multicollinearity among abiotic factors was previously tested with a Draftsman’s plot based on Pearson correlations and only one abiotic factor was used when there were high pairs of correlation (Pearson correlation limit was set a 0.80) (See Fig.5in Sedano etal. 2019). Variance inflance factor (VIF) was also perfomed to avoid multicollinearity. Furthermore, a distance-based redundancy analysis (dbRDA) was computed using a fourth root transformed biotic matrix paired with a normalized abiotic matrix, to give similar weight to variables measured on different units. DbRDA was portrayed into a bidimensional representation. A BIOENV routine (Clarke and Ainsworth1993) was done to detect the best set of variables that better suit the response data. This method calculates correlation coefficients between response variables (community matrix) and predictor variables (abiotic matrix) (Balkenhol etal. 2009). RELATE routine (Clarke and Warwick2001) was carried out to detect the correlation coefficient between both, community and abiotic dissimilarity matrix. All multivariant and correlation analyses were carried out with PRIMER + PERMANOVA 6 using 9999 permutations (Anderson etal. 2008). Results Abiotic analyses The results of the fetch index indicate that all the substrates belong to a semi-exposed wave exposure class, whereas the age of origin was different for each substrate (Table2). Regarding substrate complexity, microscale complexity was higher at natural substrate and cubes comparing with the Table 1 Abiotic variables measured of each substrate. Average ± standard deviation n.a not applicable, Macro Macroscale complexity, Micro Microscale complexity, C.P calcination percentage Abiotic variables Natural Cubes Acropod Rip-Rap Seawall Latitude 36° 0.634.1''N 36°07′ 12.1''N 36°07′03.2''N 36°07′ 01.2''N 36°07′00.5''N Longitude 5° 25′55.4''W 5°26′ 07.6 ''W 5° 36′07.4''W 5° 26 ′07.4'' W 5°25′ 02.4''W Date of deployment n.a 1955 1997 1997 2008 Distance form natural rock n.a 1.19km 0.96km 0.90km 1.50km Effective fetch 95.79km 74.66km 116.52km 116.52km 43.15km Wave exposure class Semi-exposed Semi-exposed Semi-exposed Semi-exposed Semi-exposed Mayor component Quartz Quartz-Calcite Dolomite Calcite Magnesium calcite Macro (average ± SD) 1.05 ± 0.03 1.43 ± 0.06 1.47 ± 0.24 1.26 ± 0.09 1.00 ± 0.01 Micro (average ± SD) 1.62 ± 0.22 1.62 ± 0.31 1.20 ± 0.07 1.30 ± 0.04 1.00 ± 0.01 C.P 1.17 ± 0.41 29.10 ± 5.20 40.48 ± 1.40 41.56 ± 1.10 1.28 ± 1.60 823Urban Ecosystems (2021) 25:819–834 1 3 rest of substrates, being very low at seawall and acropods. In contrast, macroscale complexity was higher at acropods and cubes than rip-raps and natural substrate (Table2). From a chemical point of view, elemental composition differed between natural and artificial substrates and among all substrates. Silica (SiO2) concentration was higher at natural substrate compared to artificial substrates, which were characterized by a higher concentration of calcium oxide (CaO) at all samples (Table3). According to the mineralogical composition, natural substrate was very different from the artificial ones, and differences were also found within the artificial substrates. The natural substrate was composed of high percentages of quartz, while cubes and rip-raps were mostly composed by quartz and CaO in a carbonated form, calcite (CaCO3). Acropods presented high levels of magnesium oxide (MgO) and their mineralogical composition was based on dolomite (CaMg(CO3)2) (Table3) (full mineralogical composition in supplementary files of Sedano etal. 2019). Figure2 represent these values bidimensionally. In addition, crystallinity was positively correlated with silica and negatively correlated with calcium oxide and calcination percentage. Age causes the separation of cubes and natural samples from the rest of substrate samples, correlating with microscale complexity. Biotic analyses A total of 3198 molluscan specimens were identified during this study, corresponding to 3 classes, 20 orders, 35 families, 41 genera and 46 species (supplementary material Table1). A total of 1855 specimens were collected from 4 artificial substrates (4 × 9 = 36 samples) and 1343 from 1 natural substrate (9 samples). Abundance was drastically lower at seawall (28) and moderately lower at cubes (447), acropods (705) and rip-raps (675). Ellisolandia elongata was the dominant secondary substrate at natural substrate (83.5%) and rip-raps (85.4%); Perforatus perforatus predominated at cubes (66.9%) and acropods (47.5%) and Mytillus galloprovincialis predominated at seawall (Fig.3). Regarding trophic groups, natural substrate contained all the groups measured, and scavenger was exclusive for this substrate. This group was formed by a single species,Tritia tingitana, and its abundance was 2. On the other hand, mostly all artificial substrates lacked the group detritus feeder except for seawall.Shared species per substrate are shown in Fig.4. The differences in the percentages of groups between substrates were also remarkable. In terms of percentages, more macro and micro grazers appeared at artificial substrates compared to natural substrate. In contrast, predators and detritus feeders appeared in higher percentage at natural substrate, with the exception Table 2 Feeding Guilds and Trophic groups of mollusc’s community with Code and Description Trophic groups Feeding guilds Decription Code Omnivores Filter feeder Filter over organic matter suspended in the ocean FF Suspension feeder Suspensivore of organic matter suspended in the ocean SF Herbivores Deposit feeder Feeding over unicelular algae, diatoms, hormogonies from cyanophites or detritus over algae DF Macro/Micro grazer Macro–Micro grazer over tapizant and incrustant macroalgae MG Carnivores Scavenger Feeding over dead animals SC Predator Predator or grazer of cnidarians, sponges, molluscs and other animals PR Table 3 Mayor elements and minerals of each substrate. Average ± standard deviation SiO2 Silica, CaO Calcium oxide, MgO Magnesium oxide, SO3 Sulphur trioxide Elements Natural Cube Acropod Rip-Rap Seawall SiO295.63 ± 1.25 31.96 ± 11.28 6.51 ± 1.76 3.88 ± 2.71 11.34 ± 1.25 CaO 0.22 ± 0.03 33.74 ± 7.91 33.14 ± 1.89 52.66 ± 2.23 45.83 ± 1.85 MgO 0.24 ± 0.10 2.48 ± 0.76 14.27 ± 3.06 0.50 ± 0.19 1.84 ± 0.72 SO30.00 ± 0.00 1.06 ± 0.76 1.33 ± 0.28 0.00 ± 0.00 1.28 ± 0.20 Minerals Quartz 0.96 ± 0.01 0.57 ± 0.16 0.01 ± 0.00 0.08 ± 0.04 0.18 ± 0.03 Calcite 0.00 ± 0.00 0.34 ± 0.07 0.01 ± 0.13 0.92 ± 0.04 0.00 ± 0.00 Dolomite 0.00 ± 0.00 0.00 ± 0.00 0.80 ± 0.12 0.00 ± 0.00 0.00 ± 0.00 Mg-Calcite 0.00 ± 0.00 0.07 ± 0.12 0.00 ± 0.00 0.00 ± 0.00 0.78 ± 0.04 824 Urban Ecosystems (2021) 25:819–834 1 3 of seawall, where the percentage of predators and detritus feeders was higher than that of natural substrate. There were also differences between artificial substrates because the percentage of filter feeders was higher at ripraps and cubes compared to natural substrate and acropods, while more macro and micro grazers appeared at acropods compared to the rest of substrates. Finally, seawall had a very heterogeneous trophic structure among samples (Fig.5, supplementary material Table1). Shannon’s diversity and richness varied significantly among substrates (p < 0,001) (Table4). According to SNK test, Shannon’s diversity was greater on the natural substrate compared to the artificial substrate. Among artificial substrates, acropods showed higher Shannon’s diversity values than seawall, cubes and rip-raps. In contrast, cubes, rip-raps and seawall did not differ significantly on Shannon’s diversity. Similarly, SNK test showed that natural substrate had higher species richness than the artificial substrates. Among Fig. 2 Principal Component Analyses (PCA) portraying the differences among substrates according to the abiotic data measured Fig. 3 Total volume of secondary substrate (ml) in the Y axis and substrates in the X axis. Percentages of volume of the most abundant species from secondary substrate (species ml/total species ml) at each substrate.Percentages < 1% are not included 825Urban Ecosystems (2021) 25:819–834 1 3 artificial substrates, acropods were richer than cubes, ripraps (p < 0,05) and seawall (p < 0,01). cubes and rip-raps did not differ between them but both were richer than Seawall (p < 0,05). Finally, Pielou’s evenness did not differ among substrates (Fig.6). MDS analysis for the community structure showed three groups: 1 = natural, 2 = seawall and 3 = acropods, rip-raps and cubes. These groups were statistically supported by the SIMPROF test (p < 0.05). The natural group appeared homogeneous and different from the rest of the substrates. Seawall group was heterogeneous, but it also appeared clearly segregated. The third group, formed by acropods, cubes and rip-raps was homogeneous but distinct from natural substrate and seawall groups (Fig.7). MDS for the trophic structure revealed three groups: 1 = seawall, 2 = seawall and 3 = natural, acropods, cubes, rip-raps and seawall. These groups were statistically supported by the SIMPROF test (p < 0.05). Seawall was the most heterogeneous substrate diverging into three groups, while acropods, rip-raps, natural substrate and cubes were more similar. However natural substrate was homogeneous and was significantly different from the rest at a level of similarity of 80% (Fig.8). PERMANOVA test indicated significant differences in community structure between substrates and among sites. According to the pair-wise tests, the community at natural substrate differed from the rest. When comparing among artificial substrates, seawall differed from rip-raps and acropods (p < 0,01), but not from cubes. Cubes, acropods and rip-raps seemed to have a similar community structure (Table5). PERMANOVA test indicated differences in trophic structure among substrates but not among sites. The pair-wise test revealed that trophic structure on the natural substrate was different from the artificial substrates. Among artificial substrates, seawall differed from rip-raps (< 0,05) and acropods (p < 0,01) but not from cubes. Also, acropods, rip-raps and cubes had a similar trophic community (Table6). The volume of the secondary substrate using as a covariable was significant for taxonomical PERMANOVA but not for the trophic one. Fig. 4 Venn diagram with the percentage of shared species of associated fauna of molluscs among the five substrates 826 Urban Ecosystems (2021) 25:819–834 1 3 Correlation analyses A dbRDA analysis revealed a relationship between the physical–chemical composition and the molluscan community. The associated community of molluscs at natural substrate was highly correlated with a low CaO concentration and carbonated nature, a high microscale complexity and older age. On the other hand, the community at artificial substrates was correlated with a high CaO concentration and carbonated nature, low microscale complexity and younger age. Because SiO2 appeared negatively collineated with CaO and C.P, these parameters were not included in the analyses. Among artificial substrates, the seawall community was also correlated with a low macroscale complexity, and therefore they have clustered apart from the more heterogeneous artificial substrates (acropods, cubes and rip-raps) (Fig.9). The BIOENV analyses showed that the most correlated variables (p < 0.01) were macro, microscale complexity and crystallinity (Rho = 0,669). The RELATE test showed a significant correlation of abiotic with taxonomic matrices (Rho = 0,518; p < 0,01). Discussion The molluscs community structure and diversity seemed to be significantly different between artificial and natural substrates. Among our studied variables, substrate complexity (macro and microscale roughness) and chemical composition appeared to be the main drivers of those differences. Besides, the trophic structure also seemed to be different between artificial and natural substrates. By our results, habitat complexity in terms of the relative abundance of microhabitats such as crevices (Evans etal. 2016), rockpools and macrophytes is considered as one of the most influencing factors on intertidal communities (Warfe etal. 2008). Higher heterogeneity at the scale of centimeters increases recruitment of spores and larvae (Sempere-Valverde etal.2018) due to a higher number of refugees (Kostylev etal.2005; Coombes etal. 2010). This can be particularly important for intertidal molluscs since they can not only find shelter against environmental stress (Meager etal.2011; Harley and Helmuth2003; Loke etal. 2015), but also against predation (Warfe etal. 2008) and competition (Huston1979) by finding crevices that fit their shell size (Loke and Todd2016), determining community structure and diversity. Moreover, algae turfs that cover the Fig. 5 Total percentage of abundance (individuals) of each trophic group per substrate. Groups with < 1% are not included. SF: Suspension feeder, FF: Filter feeder, MG: Macro–micro grazer, DF: Detritus feeder, PR: Predator Table 4 Two-way ANOVA test for the three diversity indices. Substrate (Su), Site (Si) df degrees of freedom, MS Mean square, P p value, Perms permutations,n.snotsignificant *** p < 0,001 Index Richness Source df MS F P Su 4 188,911 27,60 *** Si(Su) 8 6,0667 0,81 0,4543 Residual 30 7,4889 Cochran test n.s SNK test N > (A > ((C = R) > S)) Index Pielou’s Eveness Source df MS F P Su 4 0,0631 1,32 n.s Si(Su) 8 0,0008 0,01 0,3407 Residual 30 0,1162 0,9933 Cochran test n.s SNK test N = A = C = R = S Index Shannon’s Diversity Source df MS F P Su 4 1,9643 34,05 *** Si(Su) 8 0,0649 0,16 0,8552 Residual 30 0,4128 Cochran test n.s SNK test N > (A > (C = R = S)) 827Urban Ecosystems (2021) 25:819–834 1 3 rocky substrate can influence the abundance and biodiversity of associated fauna, playing biogenic roles, similar to sessile animals such as barnacles or annelids who play the role of “ecological engineers”, providing the secondary substrate where many species live (Simboura etal. 1995; Bavestrello etal. 2000). The greater abundance of the calcareous algaeEllisolandia elongataon natural substrate can influence the associated fauna via increasing both the habitat volume and habitat complexity (Guerra-García etal. 2012; Veiga etal.2014; Torres etal. 2015). Moreover, it can decrease desiccation and temperature stress by providing shelter for mobile fauna (Singh etal. 2013; Kefi etal. 2015). In our study, 15 species were exclusive of natural substrate. Natural substrate had high microscale complexity, but they were also highly covered by the calcareous algaeEllisolandia elongata, altogether possibly boosting the higher occurrence of more taxa. Species sensitive to disturbance such as the bivalvesIrus irus,Parvicardium vroomiand gastropods such asSkeneopsis planorbisonly appeared at natural substrate. For example,S. planorbisandP. vroomiare known to be well represented along the Algeciras Bay associated with the algae Halopteris sp. (Sánchez-Moyano etal. 2000), a highly complex algae (as E. elongata) that can support rich associated communities (Navarro-Barranco etal. 2018). In addition, P. vroomi has shown preference for the algaeHalopteris filiscina(Avila2003). Similarly, more abundance of sea snails and bivalves appeared at natural substrate. For example, Cerithiopsis tubercularisis usually restricted to live on algae that are associated with its food (sponges), as it is the case of the branchedEllisolandiaspp. and its association with the spongesHalichondriaandHymeniacidon(Fretter and Manly1977). Given the Fig. 6 Bar graph for mean Pielou’s Evenness, Shannon’s Diversity and Richness. Error bars represent Standard Deviation. a > b > c 834 Urban Ecosystems (2021) 25:819–834 1 3 Exp Mar Biol Ecol 464:96–106. https:// doi. org/ 10. 1016/j. jembe. 2014. 12. 016 Underwood AJ, Chapman MG, Richards SA (2002) GMAV-5 for Windows. 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