Nature-based solutions for coastal protection in sheltered and exposed coastal waters: integrated monitoring program for baseline ecological structure and functioning assessment

Vol.: (0123456789) 1 3 Environ Monit Assess (2024) 196:316 https://doi.org/10.1007/s10661-024-12480-x RESEARCH Nature‑based solutions forcoastal protection insheltered andexposed coastal waters: integrated monitoring program forbaseline ecological structure andfunctioning assessment ArnaudBoulenger· PabloLanza‑Arroyo· KobusLangedock· AlexiaSemeraro· GertVanHoey Received: 28 November 2023 / Accepted: 17 February 2024 / Published online: 28 February 2024 © The Author(s) 2024 Abstract Nature-based solutions, such as shellfish reefs, can support natural coastal defence and be a potential solution for climate-resilient shorelines in the future. In the Belgian Part of the North Sea, the “Coastbusters” projects aim to develop nature-based coastal protection by favouring subtidal mussel bed establishment on the seafloor through typical longline aquaculture techniques. Mussel beds are dependent on environmental conditions, and both influence the physical and biogeochemical features in a soft-sediment environment. Therefore, a comprehensive ecological monitoring program is essential to assess the success of future mussel bed development and its influence on the surrounding ecosystem. For establishing a monitoring baseline of the two experimental areas, a combination of conventional benthic assessment methods (grab sampling and granulometry) and non-invasive techniques (sediment profile imaging and transect diving video surveys) were utilised. Although mussel reefs did not yet develop by the time of this study, clear differences in ecological and sedimentological characteristics were found between two experimental areas (sheltered and exposed), subjected to slightly different hydrodynamic conditions. The one sheltered by coastal sandbanks was dominated by fine-muddy sand, higher species richness, biomass, and higher biological activity (burrows, fauna, and biological beds) as observed by all methods in one or another way. Moreover, functional diversity indices revealed a higher partitioning of the total available resources, suggesting more complex ecological processes in the sheltered area. Conversely, the area more exposed to the open sea was dominated by more sandy sediments, and fewer organisms were found. The combination of those different monitoring tools provides an integrated, complementary view, from different perspectives, on the biological, physical and functional characteristics of the study areas. Keywords Biogenic reefs· Mytilus edulis· Benthic monitoring· Ecosystem functioning· Biotic indices Arnaud Boulenger and Pablo Lanza-Arroyo contributed equally to this work. A.Boulenger(*) Laboratory ofOceanology, MARE Centre, UR FOCUS, University ofLiège, 11 allée du six août, 4000Liège, Belgium e-mail: arnaud.bouleng[email protected] A.Boulenger· P.Lanza-Arroyo IMBRSea, Marine Biology Research Group, Ghent University, Krijgslaan 281/S8, 9000Ghent, Belgium P.Lanza-Arroyo Laboratorio de Biología Marina, Departamento de Zoologia, Universidad de Sevilla, E-41012Seville, Spain K.Langedock Flanders Marine Institute (VLIZ), Jacobsenstraat 1, 8400Ostend, Belgium A.Semeraro· G.VanHoey Flanders Research Institute forAgriculture, Fisheries andFood (ILVO), Jacobsenstraat 1, 8400Ostend, Belgium Environ Monit Assess (2024) 196:316 1 3 316 Page 2 of 21 Vol:. (1234567890) Introduction Over the last century, coastal countries have implemented hard engineering structures to maintain beach levels and resist exposure to severe climate events (Williams etal., 2018). Typically, groynes and seawalls are combined with beach nourishment schemes, and the association of both is the dominating form of coastal defence used worldwide (Spalding etal., 2014). However, while they have shown to be effective in some cases, they are also responsible for important financial costs due to maintenance and the necessity of sand replenishment every few years (Morris et al., 2018; Speybroeck etal., 2006; Temmerman etal., 2013; Williams etal., 2018). Nature-based solutions are defined by the European Commission as ‘actions that address environmental, social and economic challenges simultaneously by maximising the benefits provided by nature (…) inspired by, supported by, or copied from nature’ (European Commission, 2015). They have gained increasing attention for coastal management strategies, as restoring or implementing natural coastal ecosystems could replace or complement artificial structures for coastal protection (Spalding et al., 2014; Temmerman et al., 2013). These ecosystems provide coastal erosion and flood defence services through ecological processes such as increased bottom-shear stress, localised water shallowing, sediment deposition and seabed stabilisation (Gracia et al., 2018; Speybroeck et al., 2006). Mangroves and coral reefs are very efficient at reducing coastal erosion, but they cannot be implemented in temperate regions. On the Atlantic European coastline, one example of an effective ecosystem to reduce wave height and protect the shoreline is salt marshes, but their distribution is limited as they have been drained since the Middle Ages for land reclamation or dikes’ construction (Cattrijsse & Hampel, 2006; Narayan etal., 2016). Seagrass meadows are also another efficient marine ecosystem for coastal protection and are present on the Atlantic European coastline (such as Zostera marina and Zostera noltei), but they are not present in Belgian waters (Ondiviela et al., 2014; Tullrot, 2009). Other solutions must therefore be considered to protect the sandy coastline of the Belgian coast. In the nearshore area, aggregations of the tube-dwelling polychaetes Lanice conchilega are forming aggregations on soft sediments (Degraer etal., 2008; Rabaut etal., 2009). Moreover, the reef-building blue mussel Mytilus edulis is also found in the Belgian part of the North Sea (thereafter named BPNS), most of the time colonising artificial hard substrates (Degraer etal., 2019). Although no established subtidal mussel beds on soft sediment have been found occurring in the area, its natural presence in Belgian waters suggests that the environmental conditions present in the BPNS are appropriate for mussel bed development. The “Coastbusters” projects are a Belgian publicprivate partnership borne out of this need to develop sustainable and more environmentally friendly alternatives in coastal protection systems by using nature-based solutions. The first field pilot project (2017–2020) was set-up in the BPNS to test the feasibility of using reef-initiating structures for three types of bio-builder organisms (seaweed, blue mussel, L. conchilega) as nature-based coastal protection elements to stabilise the shoreline (Goedefroo etal., 2022; Sterckx et al., 2019). The resilience, survivability and reef-building capacity of those three taxa were explored. Preliminary results demonstrate a high potential for M. edulis beds, since a mussel bed developed during summer and the beginning of fall of 2019. Unfortunately, they mostly disappeared each winter season, probably due to predation (sea stars) and winter storms (Goedefroo etal., 2022). Following these promising results, a follow-up project “Coastbusters 2.0” (2020–2023) is focused on optimisation of monitoring techniques of the mussel bed development area and on potential ecological responses to the newly created biogenic reef. In particular, to understand the impact of abiotic conditions on reef development, this project focuses on two areas with different hydrodynamics (sheltered and exposed). To fully capture the ecological structure of the area, an integrated ecological monitoring program was indispensable. Therefore, traditional benthic assessment methodologies (grab sampling and granulometry) and 2 non-invasive benthic assessment techniques (sediment profile imaging and transect diving video surveys) were used for the baseline monitoring of the areas prior to the installation of the aquaculture set-up for facilitating mussel bed development. The sediment profile imaging (SPI) Environ Monit Assess (2024) 196:316 1 3 Page 3 of 21 316 Vol.: (0123456789) consists of a camera delivering undisturbed images of the water-sediment interface and the presence of biological structures (i.e. burrows, tubes) (Van Hoey etal., 2014). Past studies have proven the usefulness of the combined use of benthic grabs and SPI to provide an accurate assessment of the biodiversity and functioning of benthic systems (Birchenough etal., 2012; Van Hoey etal., 2014; Wilson etal., 2009). Additionally, video surveys have been widely used in the past as a tool for characterisation, impact assessment and monitoring of the seafloor (Fields etal., 2019; Karatayev etal., 2018; Sheehan etal., 2010). In this sense, most of the studies mentioned rely on towed sledges attached to cameras for filming the areas. However, irregular topography and variable visibility of the studied area make diving surveys a more suitable option for this study area, since the diver can adapt easily to challenging conditions while filming (Kendall et al., 2005). Furthermore, diving surveys are usually less destructive than some towed sledges that may cause disturbances to the seabed. All these techniques are not new, but using them in an integrated way is seldomly executed and should deliver an integrated view on the biological (species density, richness, diversity and biomass), biogeochemical (redox potential sediment layers) and functional (functional indices) characteristics of the soft-bottom ecosystems. This study is a baseline survey of the area where a biogenic reef (M. edulis) will be induced. This work allows us to assess the suitability, redundancy and added value of the combination of different monitoring methodologies and parameters obtained from them. The research questions for this study are as follows: (1) Can a comprehensive understanding of the studied area’s habitat be achieved by combining quantitative and qualitative data from video surveys, SPI and Van Veen grabs? (2) Are variations in hydrodynamic conditions among the study areas reflected in the sedimentological and biological structural characteristics (density and diversity) of the benthic habitats? (3) Is this observed difference also reflected in the biogeochemical state and measures of functional diversity? The main objective of this study is to get an integrated perspective on the biological, physical and functional characteristics of the area. This integrated approach would serve as a robust foundation for the future longer-term monitoring of newly established biogenic reefs. Materials andmethods Study area and experimental design The BPNS is a small shallow area within the Southern Bight of the North Sea covering around 3600 km2, with an average depth of 20 m. Given the presence of multiple sandbanks, the area is characterised by a complex topography (Verfaillie etal., 2006) and highly variable benthic sedimentary habitats in terms of grain size (Breine etal., 2018; Van Hoey etal., 2004). Within the BPNS, the “Coastbusters 2.0” experimental site is located in a subtidal area in front of the town of De Panne (Fig.1). This study focuses on two separated areas under different hydrodynamic conditions: a sheltered (51° 07′ 19.2″ N, 2°35′ 16.8″ E) and an exposed area (51° 07′ 22.2″ N, 2° 33′ 28.5″ E). They lie respectively 2 and 5 km from the shore. The sheltered area is located south of the Broers sandbank, relatively sheltered from the waves and currents, and the exposed area is located north of the Trapegeer sandbank, facing the open sea (Fig.9) (Langedock etal., 2020). The mean depth of both areas is around 5 m (lowest astronomical tide) (Fig. 4). A sea surface and bottom current estimate for both areas were made based on data from 01 June 2017 until 01 June 2020 from the open source COHERENS V1 (v8.7) model based on the harmonic tides and the wind forecast (Fig.10) (Langedock etal., 2020). Based on those information, we considered one as a sheltered area because of the lower bottom currents and position on the slope of the bank (coastward). Exposed is the area with slightly higher bottom currents and positioned on the seaward side of the bank. Along each area, two types of sampling localities were selected (“mussel” and “control” locality) (Fig. 1). The underneath and nearby (10 m2) areas of the ‘mussel’ locality potentially correspond to the future aquaculture longline and dropper line installation, which will support the creation of the mussel bed. A control locality is 500 m westwards from the installation, away from the dominant residual current direction and therefore out of the influence sphere (Fig.1). Five replicates of SPI and Van Veen grabs were taken within each locality and area. Sampling was conducted along 2 campaigns in June and September 2020 (Table1). Environ Monit Assess (2024) 196:316 1 3 316 Page 4 of 21 Vol:. (1234567890) This design provides an effective basis for the future use of a before/after–control/impact (BACI) design (Underwood, 1992). However, we expect no current mussel/control (locality) or before/after effects in our results, since the sampling was conducted only prior to the installation of the aquaculture set-up. Still, this experimental design encompasses 3 factors. ‘Area’ is a fixed factor with 2 levels, and it seeks to examine differences between sheltered and exposed hydrodynamic conditions. ‘Locality’ factor is fixed, and it incorporates the potential mussel bed site against a control. ‘Campaign’ is fixed and refers to different sampling periods. Locality and campaign are factors intended to test the effect of the mussel bed formation in the future. However, some effects from the previous mussel bed formation in 2019 (only in the mussel-sheltered area) and seasonal effects may be expected (Goedefroo etal., 2022). All factors are orthogonal to each other. Sampling techniques and data analysis Van Veen grabs A total of 40 granulometry and macrobenthos samples were collected by means of a Van Veen grab (sampling surface of 0.1 m2). Before rinsing the Van Veen samples over the sieve, sediment samples for Fig. 1 Bathymetry map and location of sampling sites. MUSSEL (mussel development area, area where mussel-growing longline system will be installed; 5 replicates); CTRL (control area; 5 replicates) Table 1 Van Veen grab, SPI and video surveys sampling campaigns Sampling campaign SPI Van Veen grab Video survey T0 16th June 2020 15th June 2020 15th and 26th June 2020 T1 10th September 2020 7th September 2020 7th and 8th September 2020 Environ Monit Assess (2024) 196:316 1 3 Page 5 of 21 316 Vol.: (0123456789) granulometric analyses were collected using a PVC tube (core of 5 cm diameter), stored and further processed in the lab. The grab samples were washed onboard over a 1-mm mesh sieve and fixed in an 8% formaldehyde-seawater solution with eosine for easier visual detection of the benthic organisms. The sediment samples were analysed by laser diffraction (Malvern Mastersizer 2000) for sediment composition (the percentages of clay, silt, sand and gravel) and median grain size. The coarse fraction (> 1600 μm) was sieved off before laser diffraction. The determination of the percentage of total organic carbon (TOC) in the sediment samples was carried out using a modified Walkley-Black titration method (Gaudette etal., 1974). Benthic organisms were identified to the lowest possible taxonomical level and counted. Taxa were weighed as wet weight (WW) biomass to the nearest 0.00001 g. Density and biomass were standardised to m−2. Abundance and biomass data from one sample in the mussel-sheltered site during the T0 sampling campaign was discarded from the analyses because of bad conservation of the organisms. Statistical analyses were performed on macrobenthic diversity, functional parameters and structure of the community. Macrobenthic communities were described by means of biotic and diversity indices (density (N), biomass (B), species richness (S), Shannon index (H′(loge)) and Simpson index (1−λ′)). The functional characterisation was done by means of functional indices calculation: functional richness (FRic), functional evenness (FEve), functional divergence (FDiv), Rao’s quadratic entropy (RaoQ) and the community bioturbation potential (BPc) (Festjens etal., 2023). Those four indices were selected as they are considered complementary measurements of functional diversity attributes for different environments and assemblages (Gusmao et al., 2016). BPc was calculated according to the methodology proposed by Solan etal. (2004) and Queirós etal. (2013). In order to allow the comparison with previous research conducted in the BPNS, abundance and biomass data for BPc were used as count per sample (0.1 m2) and not standardised value per square metre (Breine etal., 2018). When the required information on sediment reworking (Ri) and mobility (Mi) of species was not defined, values from taxonomically close species were used. Ri and Mi values were obtained from Breine etal. (2018) and Queirós etal. (2013). To determine whether area, locality and their interaction have an effect on the benthic characteristics and functional diversity indices, linear mixed models with area (two levels, exposed and sheltered) and locality (three levels, control and mussel) were chosen as categorical fixed effects and sampling campaign as a categorical random effect (two levels, T0 and T1). The normality and linearity of the residuals were tested by visual inspection of the residuals versus fitted values plot and with a Shapiro-Wilks test, and the homogeneity of variances was checked using Levene’s test. If these assumptions were not satisfied, data were transformed or the Kruskal-Wallis non-par- ametric test was used when no transformation managed to meet the assumptions (Table 5). Moreover, non-metric dimensional scaling (nMDS) built from a Bray-Curtis dissimilarity matrix based on abundance data was used to visualise the different macrobenthic community structures. A nMDS is an ordination method attempting to represent as closely as possible the pairwise dissimilarities between objects in a low-dimensional space. The stress value provides information on the quality of the ordination plot. A PERMANOVA was performed on the Bray-Curtis similarity matrix and followed by a permutational multivariate analysis of dispersion (PERMDISP) to check for multivariate spread among factors. A similarity of percentages (SIMPER) analysis was used to identify the most contributing taxa to the dissimilarities between exposed and sheltered areas. Analyses on community structure were calculated in PRIMER v6 software, while all the other analyses were performed with R Studio (version 1.4.1106) with the ‘FD’ package in R (Laliberte & Legendre, 2010). Sediment profile imagery A SPI device was used to obtain pictures of the watersediment interface to provide biological, chemical and physical features of the localities for seafloor assessment (Birchenough etal., 2006, 2012). The device is composed of a frame and a core with a camera (Nikon D7200). When the SPI reaches the sea bottom, a lever activates the camera, and two pictures per sample station were taken: a first picture10 s after activation of the lever and a second picture15 s later, but only 1 of them will be analysed. In total, 40 pictures were analysed helped by SpiArcBase (Romero-Ramirez etal., 2013) and ImageJ (Schneider etal., 2012) softwares. Environ Monit Assess (2024) 196:316 1 3 316 Page 6 of 21 Vol:. (1234567890) Data was obtained for a set of 16 variables (Table2). Median grain size class was estimated from a standardised set of pictures corresponding to a measured grain size that will be compared with grain size data obtained from laser diffraction of sediment samples (see the “Van Veen grabs” section). Based on some parameters obtained from the SPI pictures, the Benthic Habitat Quality (BHQ) index was calculated. This index was primarily developed for Scandinavian fjords (Nilsson & Rosenberg, 1997), but an adapted version was used in this study due to important differences in environmental conditions between Scandinavian fjords and the BPNS. In this adapted BHQ, the percentage of anoxic sediment is considered instead of the apparent redox potential discontinuity (aRPD) depth, scoring up to 5 points starting with 0%, then 1–25%, 25–50%, 50–75% and 75–100%. Presence of surface fauna, infauna and faecal pellets scores 1 point each. The number of tube worms and the number of burrows (any aRPD anomaly caused by a burrowing organism) account for up to 3 points each. BHQ consists of a 0–15 range index obtained from summing these assigned points. BHQ values greater than or equal to 6 indicate a “good” quality of habitat (Rosenberg etal., 2004). Univariate statistical analyses were conducted on all studied variables. Additionally, a factor analysis of mixed data (FAMD) was performed based on a mixed multivariate matrix including some of the variables assessed from SPI (Table2). This multivariate analysis allows us to assess the proportion of the variance explained by each variable and the similarity between observations. FAMD was performed in R Studio with “FactoMineR” and “factoextra” packages. In order to handle missing data, an imputation method was performed through a PCA method (“missMDA” package), so missing values were filled in all variables. Seafloor characterisation Diving surveys were recorded to monitor seafloor characterisation as in Goedefroo et al. (2022). The start and end filming points by the diver were georeferenced. The diver slowly moved forward and recorded through a 50-m (100 m in case of T0 mussel localities) transect following a guided rope Table 2 Overview of parameters obtained from the SPI technique, acronyms, method or software used and type of variable (N numerical, C categorical) In bold: variables used in the mixed multivariate matrix for FDMA analysis *1AA% = anoxic sediment area/total sediment area*100 *2rRPD = |mRPD| − RPD*2 Parameter type Parameters Acronym Obtention method Type Overall sediment features Mean penetration depth (of the SPI device plate into de sediment); linked to sediment compactness (Rhoads & Germano, 1982) MPD SpiArcBase N Sediment‑water interface (SWI) length SWI ImageJ Median grain size MGS Observation C Biogeochemical features aRPD (mm) RPD SpiArcBase N Maximum depth aRPD (mm) mRPD SpiArcBase N SPI width W SpiArcBase N Percentage of anoxic sediment AA% Calculation*1N aRPD range rRPD Calculation*2N Length of the aRPD lRPD ImageJ N Biological features Presence (P.) of biogenic beds (mainly L. conchilega) BIOB Observation C P. of surface fauna SF Observation C P. of faecal Pellets FP Observation C P. of feeding mounds or pits FMP Observation C No. infaunal organisms INF Observation N No. Burrows BN Observation N No. Surface fauna (per species) SF_n Observation N Environ Monit Assess (2024) 196:316 1 3 Page 7 of 21 316 Vol.: (0123456789) marked with a black dot every metre. In total, 16 transects were recorded: 8 for each sampling campaign (Table1), in which 4 corresponded to each area (sheltered and exposed), 2 transects in each locality (mussel and control). Each metre from the video survey footage was documented through screenshots (this was done manually by pausing the video and using the marked rope as guidance). Due to poor visibility and suboptimal image quality, modifications to colour, contrast and brightness were applied using the “Auto enhancement 1” setting in ImBatch software version 7.3.0. The images were subdivided into 8 equal quadrats. Subsequently, every quadrat was assessed through six categories (Table3), and the number of quadrats of each category was used to categorise each image into classes (Table 4). Finally, the rest of the classified screenshots were georeferenced by first interpolating the coordinates along the transect line between the starting point and the endpoint and then applying a 0.5 m perpendicular offset to the appropriate side of the line. The resulting matrixes were imported into Geographic Information System, ArcGIS® 10.4.1., ESRI as point data with the objective of visualising the seafloor features on a map. Results Biotic parameters A total of 93 taxa were identified from the Van Veen grabs for the two sampling campaigns with 6 different taxonomic groups: 32 taxa of Malacostraca, 42 taxa of annelids, 14 taxa of molluscs, 3 taxa of echinoderms, 1 taxon of Anthozoa and 1 taxon of Sipuncula. Except for the Shannon-Wiener index, all the structural indices were significantly different between exposed and sheltered areas (Table5), while no significant differences could be found between mussel and control localities. Abundance (8288.42 ± 1529.84 ind.m−2), species richness (26.74 ± 2.27 spp.sample−1) and biomass (508.51 ± 142.52 g.m−2) had higher values in the sheltered area compared to the exposed area (3299.00 ± 792.05 ind.m−2; 17.05 ± 0.77 spp.sample−1; 100.37 ± 32.62 g.m−2). Only the Simpson index (1-D) had higher values in the exposed area compared to the sheltered area (Table5). Table 3 Categories for quadrat assessment *1Confidence level was used as a subjective categorical variable assessing the quality of the image and therefore the confidence in identifying structures in them. Quadrats with low confidence levels were considered unknown Category Unit Mussel cover No. of quadrats (maximum of 8) L. conchilega cover No. of quadrats (maximum of 8) Shell’s cover No. of quadrats (maximum of 8) Sand cover No. of quadrats (maximum of 8) Unknown quadrats No. of quadrats (maximum of 8) Confidence level*1High, medium, low (depending on quality of the footage) Table 4 Decision table for image classification When conditions for no. of quadrats (in bold) were met, the picture was categorised as such. “Unknown” class corresponds to the impossibility of seabed feature’s identification (more than 4/8 unknown quadrats) Class Sand cover Shells cover L. conchilega cover Mussels cover Unknown Unknown (UN) Irrelevant Irrelevant Irrelevant Irrelevant >= 4 quadrats Bare sand (BS) ≥ 6 quadrats Not used None None Irrelevant Bare sand with shells (BSS) > 6 quadrats None None Irrelevant Shell dominated sand (SDS) Irrelevant ≥ 6 quadrats None None Irrelevant Lanice Sparse (LS) Irrelevant Irrelevant ≤ 2 quadrats None Irrelevant Lanice Patchy (LP) Irrelevant Irrelevant 2–6 quadrats None Irrelevant Lanice Dominated (LD) Irrelevant Irrelevant ≥ 6 quadrats None Irrelevant Mussel Sparse (MS) Irrelevant Irrelevant Irrelevant ≤ 2 quadrats Irrelevant Mussels Patchy (M) Irrelevant Irrelevant Irrelevant 2–6 quadrats Irrelevant Mussel Dense bed (MD) Irrelevant Irrelevant Irrelevant ≥ 6 quadrats Irrelevant Environ Monit Assess (2024) 196:316 1 3 316 Page 8 of 21 Vol:. (1234567890) The nMDS plot (Fig.2) represents the macrobenthic community structure and shows a clear difference between exposed and sheltered areas, as well as between the two sampling campaigns (T0, T1). However, no differences could be seen between the mussel and control localities. The PERMANOVA test revealed that the area (R2 = 0.40121, p = 0.001) and, to a lesser extent, the sampling campaign (R2 = 0.07465, p = 0.001) have a significant effect on the community structure. The R2 values show that the area was the factor having the biggest contribution (40.12%) to the variance. The top five contributing taxa to the dissimilarities between exposed and sheltered areas (EXP and SHL) were Oligochaeta spp., Spiophanes bombyx, Magelona johnstoni, Magelona juveniles and Cirratulidae, while Oligochaeta spp., S. bombyx, Magelona juveniles, M. johnstoni and Lanice spp. were the top 5 taxa contributing to the dissimilarities between sampling campaigns. They contributed 69.8% to the dissimilarity between the two areas, while 67.7% contributed to the dissimilarity between the two sampling campaigns. Concerning biological parameters obtained through SPI, 22.5% of the pictures showed infauna, and 55% showed surface fauna embracing 7 different taxa, mainly L. conchilega but also the sea star Asterias rubens and the decapod Crangon crangon. Infauna and surface fauna, as well as other faunistic Table 5 Structural biological indices, mean values ± standard error for each area (EXP and SHL) and associated p-values *p < 0.05 Variable EXP SHL Statistical test used p-value Abundance (ind.m−2) 3229.00 ± 792.05 8288.42 ± 1529.84 Linear mixed model (log transformation) 0.0005626* Burrows (SPI) 1.4 ± 2.23 6.2 ± 6.07 Kruskal-Wallis 8.60E−06* Species richness (spp.sample−1) 17.05 ± 0.77 26.74 ± 2.27 Linear mixed model (log transformation) 0.0003802* Shannon-Wiener (H′) 1.78 ± 0.069 1.53 ± 0.12 Linear mixed model 0.07834 Simpson index (1-D) 0.73 ± 0.020 0.57 ± 0.043 Linear mixed model (Cube transformation) 0.004731* Biomass (g.m−2) 100.37 ± 32.62 508.51 ± 142.52 Linear mixed model (log transformation) 0.0004217* Fig. 2 Non-metric multidimensional scaling (nMDS, performed on Bray-Curtis dissimilarity matrix) of macrobenthos abundance data separated by localities (MUSSEL, CONTROL), areas (EXP, SHL) and campaign (T0, T1). Stress value: 0.17 Environ Monit Assess (2024) 196:316 1 3 Page 9 of 21 316 Vol.: (0123456789) parameters such as faecal pellets, biogenic beds and feeding pits/mounds, were more frequently observed in the sheltered area (Fig.3). Sedimentology and seafloor characterisation Sedimentological analyses based on the Van Veen grab samples showed a coarser median grain size in the exposed area (275.33 ± 6.75 μm) than in the sheltered area (196.11 ± 6.77 μm) (significant difference, p = 2.973E−07) and were relatively similar among localities in the same area. The median grain size reached its maximum and minimum at T0 in the control locality, respectively, in the exposed area (261.37 ± 4.40 μm) and in the sheltered area (168.56 ± 14.88 μm). Similar results were obtained through SPI assessment, where grain size exhibited a clear dominance of “very fine sand” throughout the sheltered, while the exposed one was homogenously dominated by “fine sand”. In addition, a much higher mud content was found in the sheltered area compared to the exposed area. Mud content has an additional seasonal change, with higher mud content present in T0 compared to T1. The maximum value was reached at T0 in the control locality in the sheltered area (20.27 ± 7.77%), where mud was also detected through SPI. However, no mud content (0%) was found for the two localities at T1 in the exposed area (Fig.4C) (Table6). TOC was significantly higher in the sheltered area compared to the exposed area but was not influenced by the locality (Table6, Fig.11). The compactness of the sediment can be assessed through SPI mean penetration depth, which also varied between areas, reaching significantly deeper layers in the exposed samples (73.72 ± 29.52 cm) compared to sheltered (52.40 ± 27.87 cm). Similar differences were Fig. 3 Cumulative occurrence of biological features found through SPI at each area, along both campaigns and localities. BIOB biogenic Beds, FMP feeding mounds or pits, FP faecal pellets, INF infaunal organisms, SF surface faunal organisms Fig. 4 Sedimentological features at the exposed and sheltered areas for each locality and the two sampling campaigns. A Proportion of the median grain size observed through SPI. B Penetration depth of SPI (cm). C Mud content (0.01 μm < grain size < 63 μm). D Sediment-water interface (SWI) length. E Median grain size (μm). Vertical error bars represent standard errors Environ Monit Assess (2024) 196:316 1 3 316 Page 16 of 21 Vol:. (1234567890) diving video surveys have ensured seafloor characterisation, with detailed information about biogenic reef development. In conclusion, the integration of Van Veen grabs, SPI and diving video surveys into the monitoring protocol of Coastbusters projects has allowed a holistic view of biodiversity and ecosystem functioning in the study area, providing a better understanding of environmental changes during the implementation of nature-based solutions. This study revealed that the assessed biological, biogeochemical and functional features extensively reflect the differences between the sheltered and exposed areas. Each technique brings a different, yet complementary, approach to the assessment of biological, physical and functional characteristics, allowing an integrated understanding of ecosystem processes (Birchenough etal., 2012; Van Hoey etal., 2014). Fig. 9 Cross section of the bathymetry from 7 km offshore at Small Bank (A) to the coastline of De Panne (A′). Depths in metres of lowest astronomical tide. Sandbanks are marked in black, channels are marked in blue and locations of both areas are marked in bold and indicated by a blue arrow. Modified from Langedock etal. (2020) Annexes Environ Monit Assess (2024) 196:316 1 3 Page 17 of 21 316 Vol.: (0123456789) Fig. 10 Surface and bottom currents at exposed (A, C) and sheltered (B, D) areas. Concentric circles indicate the percentage of the time the current runs in a specific direction. The colours of the vectors represent the magnitude. Due to bottom friction, the modelled current magnitude at the bottom (C, D) is approximately half the current magnitude at the surface. While the average modelled NE-going surface currents were stronger in the sheltered area, the average modelled NE-going bottom currents are stronger in the exposed area. Modified from Langedock etal. (2020) Environ Monit Assess (2024) 196:316 1 3 316 Page 18 of 21 Vol:. (1234567890) Acknowledgements Arnaud Boulenger and Pablo Lanza Arroyo were supported by the master program IMBRSea (International Master’s in Marine Biological Resources). All laboratory work and analyses were executed at ILVO (Flanders Research Institute for Agriculture, Fisheries and Food) and VLIZ (Flanders Marine Institute), wherefore we thank all contributed colleagues and, furthermore, BREVISCO for their precise work at sea with the installation and samplings and the VLIZ Diving Team for diving on the Coastbusters site. Further, we thank all Coastbusters colleagues for their general interest and feedback, as well as the two anonym reviewers for their helpful comments and suggestions. Author contribution Arnaud Boulenger: conceptualisation, methodology, formal analysis, investigation, writing and visualisation. Pablo Lanza-Arroyo: conceptualisation, methodology, formal analysis, investigation, writing and Visualisation. Kobus Langedock: conceptualisation, methodology, investigation, supervision and writing. Alexia Semeraro: conceptualisation, methodology, investigation, supervision, writing and project administration. Gert Van Hoey: conceptualisation, methodology, writing and supervision. Funding This work was funded by the Flanders Innovation and Entrepreneurship agency (VLAIO) (HBC.2019.0037) Coastbusters 2.0: “Development of ecosystem-based protection against coastal erosion.” It is as a public-private innovation partnership (3 companies—Dredging international (DI), Jan de Nul (JDN) and Sioen industries (SIOEN)—and 2 research institutions—Research institute for agriculture, fisheries and food (ILVO) and Flanders Marine Institute (VLIZ)). Data availability The datasets generated during and/or analysed during the current study are available by contacting the project responsible ([email protected]). Declarations Competing interests The authors declare no competing interests. Fig. 11 Mean organic enrichment (TOC) values at the exposed and sheltered areas for each locality and the two sampling campaigns. Vertical error bars correspond to standard error Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. References Biles, C. L., Paterson, D. M., Ford, R. B., Solan, M., & Raffaelli, D. G. (2002). Bioturbation, ecosystem functioning and community structure. Hydrology and Earth System Sciences, 6(6), 999–1005. https:// doi. org/ 10. 5194/ hess-6- 999- 2002 Birchenough, S. N., Boyd, S. E., Coggan, R. A., Limpenny, D. S., Meadows, W. J., & Rees, H. L. (2006). Lights, camera and acoustics: Assessing macrobenthic communities at a dredged material disposal site off the North East coast of the UK. Journal of Marine Systems, 62(3-4), 204–216. https:// doi. org/ 10. 1016/j. jmars ys. 2006. 03. 011 Birchenough, S. N. R., Parker, R. E., McManus, E., & Barry, J. (2012). Combining bioturbation and redox metrics: Environ Monit Assess (2024) 196:316 1 3 Page 19 of 21 316 Vol.: (0123456789) Potential tools for assessing seabed function. Ecological Indicators, 12(1), 8–16. https:// doi. org/ 10. 1016/j. ecoli nd. 2011. 03. 015 Borja, Á., Franco, J., & Pérez, V. (2000). A marine biotic index to establish the ecological quality of soft-bottom benthos within European estuarine and coastal environments. Marine Pollution Bulletin, 40, 1100–1114. https:// doi. org/ 10. 1016/ S0025- 326X(00) 00061-8 Borja, Á., Franco, J., Valencia, V., Bald, J., Muxika, I., & Solaun, O. (2004). Implementation of the European Water Framework Directive from the Basque Country (northern Spain): A methodological approach. Marine Pollution Bulletin, 48(3–4), 209–218. https:// doi. org/ 10. 1016/j. marpo lbul. 2003. 12. 001 Borja, Á., Rodríguez, J. G., Black, K., Bodoy, A., Emblow, C., Fernandes, T. F., Forte, J., Karakassis, I., Muxika, I., Nickell, T. D., Papageorgiou, N., Pranovi, F., Sevastou, K., Tomassetti, P., & Angel, D. (2009). Assessing the suitability of a range of benthic indices in the evaluation of environmental impact of fin and shellfish aquaculture located in sites across Europe. Aquaculture, 293(3–4), 231–240. https:// doi. org/ 10. 1016/j. aquac ulture. 2009. 04. 037 Breine, N. T., De Backer, A., Van Colen, C., Moens, T., Hostens, K., & Van Hoey, G. (2018). Structural and functional diversity of soft-bottom macrobenthic communities in the Southern North Sea. Estuarine, Coastal and Shelf Science, 214, 173–184. https:// doi. org/ 10. 1016/j. ecss. 2018. 09. 012 Bremner, J. (2008). Species’ traits and ecological functioning in marine conservation and management. Journal of Experimental Marine Biology and Ecology, 366(1–2), 37–47. https:// doi. org/ 10. 1016/j. jembe. 2008. 07. 007 Cadotte, M. W., Carscadden, K., & Mirotchnick, N. (2011). Beyond species: Functional diversity and the maintenance of ecological processes and services. Journal of Applied Ecology, 48(5), 1079–1087. https:// doi. org/ 10. 1111/j. 1365- 2664. 2011. 02048.x Cattrijsse, A., & Hampel, H. (2006). European intertidal marshes: A review of their habitat functioning and value for aquatic organisms. Marine Ecology Progress Series, 324, 293–307. https:// doi. org/ 10. 3354/ MEPS3 24293 Degraer, S., Brabant, R., Rumes, B., & Vigin, L. (2019). Environmental impacts of offshore wind farms in the Belgian part of the North Sea: Marking a decade of monitoring, research and innovation (p. 134). Royal Belgian Institute of Natural Sciences, OD Natural Environment, Marine Ecology and Management. Degraer, S., Moerkerke, G., Rabaut, M., Van Hoey, G., Du Four, I., Vincx, M., Henriet, J. P., & Van Lancker, V. (2008). Very-high resolution side-scan sonar mapping of biogenic reefs of the tube-worm Lanice conchilega. Remote Sensing of Environment, 112(8), 3323–3328. https:// doi. org/ 10. 1016/j. rse. 2007. 12. 012 European Commission, Directorate-General for Research and Innovation, (2015). Towards an EU research and innovation policy agenda for nature-based solutions & renaturing cities: Final report of the Horizon 2020 expert group on ‘Nature-based solutions and re-naturing cities’: (Full version), Publications Office. https:// doi. org/ 10. 2777/ 479582 Festjens, F., Buyse, J., de Backer, A., Hostens, K., Lefaible, N., Vanaverbeke, J., & van Hoey, G. (2023). Functional trait responses to different anthropogenic pressures. Ecological Indicators, 146, 109854. https:// doi. org/ 10. 1016/J. ECOLI ND. 2022. 109854 Fields, S., Henkel, S., & Roegner, G. C. (2019). Video sleds effectively survey epibenthic communities at dredged material disposal sites. Environmental Monitoring and Assessment, 191(6). https:// doi. org/ 10. 1007/ s10661- 019- 7348-9 Foulquier, C., Baills, J., Arraud, A., D’Amico, F., Blanchet, H., Rihouey, D., & Bru, N. (2020). Hydrodynamic conditions effects on soft-bottom subtidal nearshore benthic community structure and distribution. Journal of Marine Sciences, 2020, 1–16. https:// doi. org/ 10. 1155/ 2020/ 46745 80 Gaudette, H. E., Flight, W. R., Toner, L., & Folger, D. W. (1974). An inexpensive titration method for the determination of organic carbon in recent sediments. Journal of Sedimentary Research, 44, 249–253. https:// doi. org/ 10. 1306/ 74D72 9D7- 2B21- 11D7- 86480 00102 C1865D Germano, J. D., Rhoads, D. C., Valente, R. M., Carey, D. A., & Solan, M. (2011). The use of sediment profile imaging (SPI) for environmental impact assessments and monitoring studies: Lessons learned from the past four decades. Oceanography and Marine Biology: An Annual Review, 49, 235–298. Goedefroo, N., Benham, P., Debusschere, E., Deneudt, K., Mascart, T., Semeraro, A., Sterckx, T., & Van Hoey, G. (2022). Nature-based solutions in a sandy foreshore: A biological assessment of a longline mussel aquaculture technique to establish subtidal reefs. Ecological Engineering, 185, 106807. https:// doi. org/ 10. 1016/j. ecole ng. 2022. 106807 Gracia, A., Rangel-Buitrago, N., Oakley, J. A., & Williams, A. T. (2018). Use of ecosystems in coastal erosion management. Ocean and Coastal Management, 156, 277–289. https:// doi. org/ 10. 1016/j. oceco aman. 2017. 07. 009 Grizzle, R. E., & Penniman, C. A. (1991). Effects of organic enrichment on estuarine macrofaunal benthos: A comparison of sediment profile imaging and traditional methods. Marine Ecology Progress Series, 74(2-3), 249–262. https:// doi. org/ 10. 3354/ meps0 74249 Gusmao, J. B., Brauko, K. M., Eriksson, B. K., & Lana, P. C. (2016). Functional diversity of macrobenthic assemblages decreases in response to sewage discharges. Ecological Indicators, 66, 65–75. https:// doi. org/ 10. 1016/j. ecoli nd. 2016. 01. 003 Karatayev, A. Y., Mehler, K., Burlakova, L. E., Hinchey, E. K., & Warren, G. J. (2018). Benthic video image analysis facilitates monitoring of Dreissena populations across spatial scales. Journal of Great Lakes Research, 44(4), 629–638. https:// doi. org/ 10. 1016/j. jglr. 2018. 05. 003 Kendall, M. S., Jensen, O. P., Alexander, C., Field, D., McFall, G., Bohne, R., & Monaco, M. E. (2005). Benthic mapping using sonar, video transects, and an innovative approach to accuracy assessment: A characterization of bottom features in the Georgia Bight. Journal of Coastal Research, 21(6), 1154–1165. https:// doi. org/ 10. 2112/ 03- 0101R.1 Lacson, A. Z., Piló, D., Pereira, F., Carvalho, A. N., Cúrdia, J., Caetano, M., Drago, T., Santos, M. N., & Gaspar, M. B. Environ Monit Assess (2024) 196:316 1 3 316 Page 20 of 21 Vol:. (1234567890) (2019). A multimetric approach to evaluate offshore mussel aquaculture effects on the taxonomical and functional diversity of macrobenthic communities. Marine Environmental Research, 151. https:// doi. org/ 10. 1016/j. maren vres. 2019. 104774 Laliberte, E., & Legendre, P. (2010). A distance-based framework for measuring functional diversity from multiple traits. Ecology, 91(1), 299–305. https:// doi. org/ 10. 1890/ 08- 2244.1 Langedock, K., Johnen, G., Pirlet, H., & Boone, W. (2020). Coastbusters 2.0 factual data report of existing hydrometeo background parameter. Llanos, E. N., Saracho Bottero, M. A., Jaubet, M. L., Elías, R., & Garaffo, G. V. (2020). Functional diversity in the intertidal macrobenthic community at sewage-affected shores from Southwestern Atlantic. Marine Pollution Bulletin, 157, 111365. https:// doi. org/ 10. 1016/j. marpo lbul. 2020. 111365 Marín, S. L., Pino, L., Núñez, R., Farías, D., Iriarte, J. L., Borja, A., & Muxika, I. (2023). Ecological status of benthic communities associated to mussel farms in South of Chile: Can it be predicted using AMBI? Regional Studies in Marine Science, 66. https:// doi. org/ 10. 1016/j. rsma. 2023. 103132 Mason, N. W. H., Mouillot, D., Lee, W. G., & Wilson, J. B. (2005). Functional richness, functional evenness and functional divergence: The primary components of functional diversity. Oikos, 111(1), 112–118. https:// doi. org/ 10. 1111/j. 0030- 1299. 2005. 13886.x Morris, R. L., Konlechner, T. M., Ghisalberti, M., & Swearer, S. E. (2018). From grey to green: Efficacy of eco-engi- neering solutions for nature-based coastal defence. Global Change Biology, 24(5), 1827–1842. https:// doi. org/ 10. 1111/ gcb. 14063 Moser, A., Pheasant, I., MacPherson, W. N., Narayanaswamy, B. E., & Sweetman, A. K. (2021). Sediment profile imaging: Laboratory study into the sediment smearing effect of a penetrating plate. Frontiers in Marine Science, 8, 398. https:// doi. org/ 10. 3389/ fmars. 2021. 582076 Narayan, S., Beck, M. W., Reguero, B. G., Losada, I. J., van Wesenbeeck, B., Pontee, N., Sanchirico, J. N., Ingram, J. C., Lange, G.-M., & Burks-Copes, K. A. (2016). The effectiveness, costs and coastal protection benefits of natural and nature-based defences. PLoS One, 11(5), e0154735. https:// doi. org/ 10. 1371/ journ al. pone. 01547 35 Nilsson, H. C., & Rosenberg, R. (1997). Benthic habitat quality assessment of an oxygen stressed fjord by surface and sediment profile images. Journal of Marine Systems, 11(3–4), 249–264. https:// doi. org/ 10. 1016/ S0924- 7963(96) 00111-X Nilsson, H. C., & Rosenberg, R. (2000). Succession in marine benthic habitats and fauna. Marine Ecology Progress Series, 197, 139–149. https:// doi. org/ 10. 3354/ meps1 97139 Ondiviela, B., Losada, I. J., Lara, J. L., Maza, M., Galván, C., Bouma, T. J., & van Belzen, J. (2014). The role of seagrasses in coastal protection in a changing climate. Coastal Engineering, 87, 158–168. https:// doi. org/ 10. 1016/j. coast aleng. 2013. 11. 005 Queirós, A. M., Birchenough, S. N. R., Bremner, J., Godbold, J. A., Parker, R. E., Romero-Ramirez, A., Reiss, H., Solan, M., Somerfield, P. J., Van Colen, C., Van Hoey, G., & Widdicombe, S. (2013). A bioturbation classification of European marine infaunal invertebrates. Ecology and Evolution, 3(11), 3958–3985. https:// doi. org/ 10. 1002/ ece3. 769 Rabaut, M., Guilini, K., Van Hoey, G., Vincx, M., & Degraer, S. (2007). A bio-engineered soft-bottom environment: The impact of Lanice conchilega on the benthic speciesspecific densities and community structure. Estuarine, Coastal and Shelf Science, 75(2007), 525–536. https:// doi. org/ 10. 1016/j. ecss. 2007. 05. 041 Rabaut, M., Vincx, M., & Degraer, S. (2009). Do Lanice conchilega (sandmason) aggregations classify as reefs? Quantifying habitat modifying effects. Helgoland Marine Research, 63(1), 37–46. https:// doi. org/ 10. 1007/ s10152- 008- 0137-4 Rhoads, D. C., & Germano, J. D. (1982). Characterization of organism-sedimet relations using sediment profile imaging: An efficient method of remote ecological monitoring of the seafloor. Marine Ecology Progress Series, 8, 115–128. Robertson, B. P., Gardner, J. P. A., & Savage, C. (2015). Macrobenthic-mud relations strengthen the foundation for benthic index development: A case study from shallow, temperate New Zealand estuaries. Ecological Indicators, 58, 161–174. https:// doi. org/ 10. 1016/j. ecoli nd. 2015. 05. 039 Rodil, I. F., Lastra, M., & López, J. (2007). Macroinfauna community structure and biochemical composition of sedimentary organic matter along a gradient of wave exposure in sandy beaches (NW Spain). Hydrobiologia, 579(1), 301–316. https:// doi. org/ 10. 1007/ s10750- 006- 0443-2 Romero-Ramirez, A., Grémare, A., Desmalades, M., & Duchêne, J. C. (2013). Semi-automatic analysis and interpretation of sediment profile images. Environmental Modelling and Software, 47, 42–54. https:// doi. org/ 10. 1016/j. envso ft. 2013. 04. 008 Rosenberg, R., Blomqvist, M., Nilsson, H. C., Cederwall, H., & Dimming, A. (2004). Marine quality assessment by use of benthic species-abundance distributions: A proposed new protocol within the European Union Water Framework Directive. Marine Pollution Bulletin, 49(9–10), 728–739. https:// doi. org/ 10. 1016/j. marpo lbul. 2004. 05. 013 Schneider, C. A., Rasband, W. S., & Eliceiri, K. W. (2012). NIH image to ImageJ: 25 years of image analysis. Nature Methods, 9(7), 671–675. https:// doi. org/ 10. 1038/ nmeth. 2089 Sheehan, E. V., Stevens, T. F., & Attrill, M. J. (2010). A quantitative, non-destructive methodology for habitat characterisation and benthic monitoring at offshore renewable energy developments. PLoS One, 5(12). https:// doi. org/ 10. 1371/ journ al. pone. 00144 61 Solan, M., Cardinale, B. J., Downing, A. L., Engelhardt, K. A. M., Ruesink, J. L., & Srivastava, D. S. (2004). Extinction and ecosystem function in the marine benthos. Science, 306(5699), 1177–1180. https:// doi. org/ 10. 1126/ scien ce. 11039 60 Spalding, M. D., Ruffo, S., Lacambra, C., Meliane, I., Hale, L. Z., Shepard, C. C., & Beck, M. W. (2014). The role of ecosystems in coastal protection: Adapting to climate change and coastal hazards. Ocean and Coastal Management, 90, 50–57. https:// doi. org/ 10. 1016/j. oceco aman. 2013. 09. 007 Environ Monit Assess (2024) 196:316 1 3 Page 21 of 21 316 Vol.: (0123456789) Speybroeck, J., Bonte, D., Courtens, W., Gheskiere, T., Grootaert, P., Maelfait, J. P., Mathys, M., Provoost, S., Sabbe, K., Stienen, E. W. M., Van Lancker, V., Vincx, M., & Degraer, S. (2006). Beach nourishment: An ecologically sound coastal defence alternative? A review. Aquatic Conservation: Marine and Freshwater Ecosystems, 16(4), 419–435. https:// doi. org/ 10. 1002/ aqc. 733 Sterckx, T., Lemey, E., Huygens, M., Fordeyn, J., Groenendaal, B., Delbare, D., Vanagt, T., Pycke, B., Semeraro, A., & Mascart, T. (2019). Coastbusters: Investigation of ecosystem based coastal stabilisation solutions. 22nd World Dredging Congress, WODCON 2019, 813–822. Temmerman, S., Meire, P., Bouma, T. J., Herman, P. M. J., Ysebaert, T., & De Vriend, H. J. (2013). Ecosystembased coastal defence in the face of global change. Nature, 504(7478), 79–83. https:// doi. org/ 10. 1038/ natur e12859 Tonk, L., Bernard, M. S., & Jansen, H. (2019). The use of video-techniques for monitoring and quantification of mobile fauna in marine cultivation systems. https:// doi. org/ 10. 18174/ 471068 Tullrot, A. (2009). Background document for Zostera beds, seagrass beds. OSPAR Commission. Underwood, A. J. (1992). Beyond BACI: The detection of environmental impacts on populations in the real, but variable, world. Journal of Experimental Marine Biology and Ecology, 161(2), 145–178. https:// doi. org/ 10. 1016/ 0022- 0981(92) 90094-Q Van Colen, C., De Backer, A., Meulepas, G., Van Der Wal, D., Vincx, M., Degraer, S., & Ysebaert, T. (2010). Diversity, trait displacements and shifts in assemblage structure of tidal flat deposit feeders along a gradient of hydrodynamic stress. Marine Ecology Progress Series, 406, 79–89. https:// doi. org/ 10. 3354/ meps0 8529 van der Wal, D., Lambert, G. I., Ysebaert, T., Plancke, Y. M. G., & Herman, P. M. J. (2017). Hydrodynamic conditioning of diversity and functional traits in subtidal estuarine macrozoobenthic communities. Estuarine, Coastal and Shelf Science, 197, 80–92. https:// doi. org/ 10. 1016/j. ecss. 2017. 08. 012 Van Hoey, G., Birchenough, S. N. R., & Hostens, K. (2014). Estimating the biological value of soft-bottom sediments with sediment profile imaging and grab sampling. Journal of Sea Research, 86, 1–12. https:// doi. org/ 10. 1016/j. seares. 2013. 10. 010 Van Hoey, G., Cabana Permuy, D., Vandendriessche, S., Vincx, M., Hostens, K., Van Hoey, G., Permuy, D. C., Vandendriessche, S., Vincx, M., & Hostens, K. (2013). An ecological quality status assessment procedure for soft-sedi- ment benthic habitats: Weighing alternative approaches. Ecological Indicators, 25, 266–278. https:// doi. org/ 10. 1016/j. ecoli nd. 2012. 09. 024 Van Hoey, G., Degraer, S., & Vincx, M. (2004). Macrobenthic community structure of soft-bottom sediments at the Belgian Continental Shelf. Estuarine, Coastal and Shelf Science, 59(4), 599–613. https:// doi. org/ 10. 1016/j. ecss. 2003. 11. 005 Van Hoey, G., Guilini, K., Rabaut, M., Vincx, M., & Degraer, S. (2008). Ecological implications of the presence of the tube-building polychaete Lanice conchilega on soft-bot- tom benthic ecosystems. Marine Biology, 154(6), 1009– 1019. https:// doi. org/ 10. 1007/ s00227- 008- 0992-1 Verfaillie, E., Van Lancker, V., & Van Meirvenne, M. (2006). Multivariate geostatistics for the predictive modelling of the surficial sand distribution in shelf seas. Continental Shelf Research, 26(19), 2454–2468. https:// doi. org/ 10. 1016/j. csr. 2006. 07. 028 Volkenborn, N., Polerecky, L., Hedtkamp, S. I. C., Van Beusekom, J. E. E., & De Beer, D. (2007). Bioturbation and bioirrigation extend the open exchange regions in permeable sediments. Limnology and Oceanography, 52(5), 1898–1909. https:// doi. org/ 10. 4319/ lo. 2007. 52.5. 1898 Williams, A. T., Rangel-Buitrago, N., Pranzini, E., & Anfuso, G. (2018). The management of coastal erosion. In Ocean and Coastal Management (Vol. 156, pp. 4–20). Elsevier Ltd.. https:// doi. org/ 10. 1016/j. oceco aman. 2017. 03. 022 Wilson, S. J. K., Fredette, T. J., Germano, J. D., Blake, J. A., Neubert, P. L. A., & Carey, D. A. (2009). Plan-view photos, benthic grabs, and sediment-profile images: Using complementary techniques to assess response to seafloor disturbance. Marine Pollution Bulletin, 59(1–3), 26–37. https:// doi. org/ 10. 1016/j. marpo lbul. 2008. 11. 019 Ysebaert, T., Herman, P. M. J., Meire, P., Craeymeersch, J., Verbeek, H., & Heip, C. H. R. (2003). Large-scale spatial patterns in estuaries: Estuarine macrobenthic communities in the Schelde estuary, NW Europe. Estuarine, Coastal and Shelf Science, 57(1-2), 335–355. https:// doi. org/ 10. 1016/ S0272- 7714(02) 00359-1 Ysebaert, T., Hart, M., & Herman, P. M. J. (2009). Impacts of bottom and suspended cultures of mussels Mytilus spp. on the surrounding sedimentary environment and microbenthic biodiversity. Helgoland Marine Research, 63(1), 59–74. https:// doi. org/ 10. 1007/ s10152- 008- 0136-5 Publisher’s Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.