Impact of the Costa Concordia shipwreck on a Posidonia oceanica meadow: a multi-scale assessment from a population to a landscape level

Marine Pollution Bulletin 148 (2019) 168–181

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Marine Pollution Bulletin journal homepage: www.elsevier.com/locate/marpolbul

Impact of the Costa Concordia shipwreck on a Posidonia oceanica meadow: a multi-scale assessment from a population to a landscape level

T

G. Mancinia,*, E. Casolia, D. Venturaa, G. Jona-Lasiniob, A. Criscolia, A. Belluscioa, G.D. Ardizzonea a b

Sapienza University of Rome, Department of Environmental Biology, Piazzale Aldo Moro, 5, Rome 00185, Italy Sapienza University of Rome, Department of Statistical Sciences, Piazzale Aldo Moro, 5, Rome 00185, Italy

A R T I C LE I N FO

A B S T R A C T

Keywords: Posidonia oceanica Costa Concordia shipwreck Human impact Regression Seascape metrics Structural descriptors

The Costa Concordia shipwreck permitted to assess how multiple disturbances affected marine biota at different spatial and temporal scales, evaluating the effects of mechanical and physical disturbances on Posidonia oceanica (L.) Delile, an endemic seagrass species of the Mediterranean Sea. To assess the impacts of the shipwreck and its salvaging from 2012 to 2017 at a population and a landscape level, a diversified approach was applied based on the application of a geographical information system coupled with seascape metrics and structural descriptors. Benthic habitat maps and seascape metrics highlighted cenotic transitions, as well as fragmentation and erosion phenomena, resulting in 9952 m2 of seagrass area impacted. Regression of the meadow was unveiled by both multivariate and interpolation analysis, revealing a clear spatio-temporal gradient of impacts based on distances from the wreck. Results highlighted the effectiveness of the descriptors involved that permitted to reveal temporal changes at both fine and large scales.

1. Introduction 1.1. P. oceanica features Posidonia oceanica (L.) Delile, 1813 is the most widespread and important endemic seagrass species in the Mediterranean Sea, being capable of forming extensive meadows from the surface up to 40–45 m depth, in relation to water turbidity and light penetration (Dennison, 1987; Duarte, 1991). It constitutes a coastal ecosystem ranked among the most valuable on Earth in terms of goods and services for its ecological, physical, economic and bio-indicator roles (Harmelin-Vivien, 1983; Pergent-Martini et al., 1994; Francour, 1997; Terrados and Borum, 2004; Boudouresque et al., 2012; Salomidi et al., 2012; Vassallo et al., 2013). P. oceanica meadows are steeply declining at alarming rates mainly due to human activities (e.g. coastal urban development, illegal trawling, aquaculture, sewage discharge and pollution, boats anchoring and moorings; Ruiz and Romero, 2003; Ardizzone et al., 2006; Leriche et al., 2006; Montefalcone et al., 2007; Boudouresque et al., 2012; Telesca et al., 2015) climate changes (e.g. global warming, ocean acidification; Diaz-Almela et al., 2007; Halpern Benjamin S.

et al., 2008; Marbà and Duarte, 2010; Jordà et al., 2012) and alien species invasion (e.g. Caulerpa cylindracea (Sonder) Verlaque, Huisman et Boudouresque 2003, and C. taxifolia (M.Vahl) C. Agardh, 1817; De Villele and Verlaque, 1995; Ceccherelli et al., 2000; OcchipintiAmbrogi and Savini, 2003)). Due to its wide distribution, its value and susceptibility to changing environmental conditions, P. oceanica is protected globally and regionally both at species and at habitat level, by the EC Habitat Directive (92/43/CEE), the Water Framework Directive (WFD) (2000/60/EC) (Gobert et al., 2005), the Marine Strategy Framework Directive (MSFD) (2008/56/EC) (EC, 2008), the Barcelona Convention and the Bern Convention. 1.2. The shipwreck and salvage operations On 13 January 2012 the cruise liner Costa Concordia collided with the rocky formations of “Le Scole”, south of Giglio Porto (northern Tyrrhenian Sea, Italy) and sunk on its starboard side close to the small promontory of “La Gabbianara”. After recovering the diesel fuel, an environmental characterization study (baseline survey) was immediately carried out to i) investigate the morphological and biological

*

Corresponding author. E-mail addresses: [email protected] (G. Mancini), [email protected] (E. Casoli), [email protected] (D. Ventura), [email protected] (G. Jona-Lasinio), [email protected] (A. Criscoli), [email protected] (A. Belluscio), [email protected] (G.D. Ardizzone). https://doi.org/10.1016/j.marpolbul.2019.07.044 Received 8 February 2019; Received in revised form 17 July 2019; Accepted 19 July 2019 Available online 16 August 2019 0025-326X/ © 2019 Elsevier Ltd. All rights reserved.

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features of the site, ii) highlight the presence of priority habitats such as P. oceanica meadows and coralligenous reefs and iii) develop a monitoring plan, based on both biota and physico-chemical parameters, to detect any disturb affecting the local benthic communities and marine environment. According to the request of the Ministry for the Environment, which asked to remove the whole ship without cutting it, the largest naval removal operation in history (“The parbuckling project”) was launched. Salvage operations were structured in nine subsequent work packages (named “WP”), whose aims and details are reported in Tab. S1 (Supplementary materials). At the end of July 2014, two and half years after the shipwreck, the cruise liner was refloated and towed to Genoa harbor where it was dismantled. After the removal of the wreck, it was possible to assess the damage even on the seabed first covered by the right side of the ship. In January 2015, few months later the wreck towing, the “site remediation phase” took place and lasted until mid 2018, aiming to remove the steel structures (such as retaining turrets, sustaining platforms, metal sheets and strands), clump weights, grout bags, debris and sediments produced from the accident and subsequent activities. Since 2012, salvage activities were carried out with the support of several barges and vessels. Among these, we mention, due to their huge size and their long-term presence, the ASV Pioneer, an accommodation support vessel coming from UK and recruited from late 2012 to early 2014, Micoperi-61 (M-61) barge, a selfelevating platform involved from 2013 to 2015 and Micoperi-30 (M-30) barge, on site from 2012 to 2018. 1.3. Monitoring activities During the six-years monitoring plan, from June 2012 to June 2018, attention was paid to detect any alteration in the marine environment. The main issue concerned the possibility of spillage of pollutant substances from the wreck (oils, nitrate, sulphates, heavy metals, etc.). Water and biota analysis excluded serious contamination events or consistent increases of environmental pollution, although some episodic spill with reversible effects were detected (Regoli et al., 2014). The seabed and associated benthic communities were affected by the salvage operations in different ways. The arrival of ASV-Pioneer led to a massive ingression of the alien species Mytilus edulis and the following regression of the Posidonia meadow below due to the fall of dead mussel shells (Casoli et al., 2016). Fine sediment dispersion and debris diffusion led to a reduction in quality of coralligenous assemblages with deterioration of the structure and function of deep habitats (Casoli et al., 2017; Penna et al., 2018). The shadow of the wreck and the presence of barges also caused the alteration in P. oceanica secondary metabolites and the deterioration of the meadow was proportional to the degree of light reduction (Toniolo et al., 2018).

Fig. 1. Study area map in 2013. Sub-areas locations are identified by the black dotted polygons and roman numbers (I–VIII); coastline is represented by dashed black line. Posidonia oceanica meadow extension is depicted in dark gray. Shape of Costa Concordia wreck, salvage vessels and anthropic structures are marked in continuous black lines. Black squares represent clump weights.

avoid any touristic or recreational activity (i.e. diving, boating, fishing) interference. For this reason, we can ascribe the impact detected only to the presence of the wreck and subsequent salvage activities and not due to other sources of human disturbances. Study area was divided into eight sub-areas, named from I to VIII (Fig. 1), identified by different geographical location and related to 4 distinct types of disturbance magnitudes (none, low, medium and high) (Table 1), defined according to the types of disturbance (physical, mechanical or combined). Subarea I was defined as an undisturbed site for subsequent analysis due to its long distance from the shipwreck work site and the lack of any working interferences.

1.4. Aim of the study This investigation intends to evaluate the impact of the Costa Concordia shipwreck and its salvage operations on a P. oceanica meadow, from a population to a landscape level, through the application of its spatial and structural descriptors, in particular in terms of distribution pattern, seascape metrics and fine scale changes of structural descriptors, such as shoot density and bottom coverage. 2. Materials and methods 2.1. Study area

2.2. Spatial data

Investigations were carried out on a P. oceanica meadow located in a northern section of Giglio Porto (Giglio Island, Tuscany, Tyrrhenian Sea) at a latitude comprised between 42°2141N and 42°2210N and a longitude comprised between 10°5502E and 10°5518E (Fig. 1). The study area corresponds to the restricted area established by the Coast Guard during the Costa Concordia salvage and remediation phases to

2.2.1. Geophysical data acquisition Geophysical surveys were performed in 2012, 2013, 2014, 2015 and 2017 over a 0.5 km 2 area. Bathymetric and backscatter data were derived from a hull-mounted RESON Seabat 7125 SV2. This system works at the frequency of 400 kHz emitting up to 512 beams across an angular coverage of 140°–165° wide swath. Vessel positioning during MBES 169

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Table 1 Sub-areas features: ID, disturbance type (physical and mechanical), effects on Posidonia meadow, and disturbance magnitude (based on disturbance types). ID sub-areas

Disturbance Types

Effects on Posidonia meadow

Disturbance magnitude

-

None

Physical

Mechanical

I

-

-

II

-

Steel-strand adjustment

Abrasion

Low

III

-

Clump weights displacement, steel-strand adjustment, debris and working scraps diffusion

Abrasion

Medium

IV

Concordia wreck shadowing and fine sediment dispersion

Direct impact of wreck hull, debris diffusion, clump weights and grout bags displacement

Abrasion, shadowing and suffocation

High

V

Concordia wreck shadowing and fine sediment dispersion

Direct impact of wreck hull, debris diffusion, clump weights and grout bags displacement

Abrasion, shadowing and suffocation

High

VI

ASV-Pioneer barge shadowing and fine sediment dispersion

Diffusion of debris and working scraps, clump weights displacement

Abrasion, shadowing and suffocation

High

VII

-

Clump weights displacement, steel-strand adjustment, debris and working scraps diffusion

Abrasion

Medium

VIII

-

Clump weights displacement

Abrasion

Low

3. P. oceanica on matte, dead matte and isolated shoots (Pm-Dm-is): fixed regressive status including 20–80 % of dead matte presence; 4. Dead P. oceanica area (DP): unvegetated surface recently forming; 90–100 % of dead matte; 5. Dead Matte (DM): 100% of unvegetated area formerly existing before shipwreck baseline survey.

surveys were provided by a dual frequency GNSS system (Leica 1200) using RTK corrections received by a temporary GPS base station located on land at Giglio Porto (baseline < 2 km) leading to sub-centimeter level of accuracy both horizontally (10 mm + 1 ppm) and vertically (20 mm + 1 ppm). Survey track lines were mostly run latitudinally and parallel to the isobaths, overlapping at 20% to guarantee the full coverage of the seafloor in the bathymetric range encompassed, went from the coastline (or at the minimum distance for safety of navigation) to 100 m of depth. Bathymetry was obtained through PDS2000 swath editor tool, providing together with backscatter data a high-quality digital terrain model (DTM, 20 × 20 cm grid size) imported in ArcGIS 10.2.2 (ESRI, 2014) geographical software. Georeferenced track lines were merged together in GIS in order to create a unique high resolution (0.20 cm) sonogram.

Ortophotos provided by Regione Toscana and Unmanned aircraft system (UAS) surveys were merged together to gain high resolution imagery of the shallow bottoms (less than 10 m of depth) (Ventura et al., 2017, 2016). Geographical layers were stored in the geographical software and updated via field activities in 2012, 2013, 2014, 2016 and 2017. Cartography allowed us to identify and delineate the extension of P. oceanica meadow over the years. Spatial metrics commonly used in landscape ecology (Gustafson, 1998; Pittman et al., 2011; Wedding et al., 2011; Abadie et al., 2015; Abadie et al., 2018) were applied on Posidonia bionomic categories in order to assess and quantify seascape changes and to highlight potential disappearance, erosion, fragmentation and regression processes (Fig. 2). Disappearance refers to the complete loss of Posidonia canopy; fragmentation is defined as the process during which a large area of healthy Posidonia is transformed into a number of smaller phanerogam patches, isolated from each other by a matrix of habitat unlike the original (Wilcove et al., 1986); erosion is the opposite of the latter aforementioned case, i.e. the formation of small dead Posidonia patches inside a healthy meadow; lastly, regression is the process during which a homogeneous meadow starts to lose its structural integrity, leaving on the substrate a faded canopy. An ArcGIS tool developed by Rempel et al. (2012) named “Patch Analyst” was implemented in the geographical software to carry out the analysis according the following procedures: i) a hexagonal grid of 20 m side was created (we chose hexagons because they are the closest packing shape to a circle, thus decreasing the effects of artificial edges; see Jackson et al., 2005); ii) the grid was joined with sub-areas shapefile and iii) intersected with benthic habitat polygon features to populate the attribute tables with their specific features. The following composition and spatial configuration metrics were chosen to describe seascape structure in 2012, 2013, 2014, 2016 and 2017: surface extension or class area (CA), number of patches (NumP) and mean patch size (MPS). CA represents the surface area of each bionomic category expressed in m2, NumP and MPS respectively counts the number of bionomic categories portions within each hexagon and measures their average dimension in (m2). A comprehensive dataset composed by the 3

2.2.2. Ground-truth data Ground truth georeferenced data on biological communities were obtained by digital videos and photographs with the ausilium of an Ultra-short Base Line (USBL, MicroNav) tracking system mounted on the top of each respective frame. Towed camera and remote operated vehicle (ROV) (Peirano and Bianchi, 1997; Piazzi et al., 2000) produced 54 transects, runned perpendicularly to the coastline, designed to homogeneously cover the Multibeam area. Still photographs were taken at random intervals during specific SCUBA diving inspection of uncertain areas (Leriche et al., 2006; Rovere et al., 2010). 2.2.3. Benthic habitat map production A manual classification process was adopted to overlay biological and substrate ground-truth information to acoustic data. The video of each transect was classified into segments of common substrate and biota and the resulting information was superimposed on the sonogram in the geographical software; still data were similarly arranged. Benthic habitat maps represented the synthesis and integration of all the biological and acoustic data and were produced in accordance with Pérès and Picard (1964) and Bianchi et al. (2004) codifications in the same aforementioned geographical software. P. oceanica was divided into 5 different bionomic categories according to dead matte covering percentage: 1. P. oceanica meadow on matte and/or sand (Pm-s): stable meadow with natural variations in its descriptors; 0–20 % of dead matte; 2. P. oceanica meadow on rock (Pm-r); 170

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et al., 2015). Percentage of bottom covered by P. oceanica was visually estimated in a 5 m radius circle area during every sampling station (Buia et al., 2003). Data were transferred to ArcGIS after each dive in order to create a points shapefile dataset including: date of survey (2012, 2013, 2014, 2016 and 2017), depth (m), shoot density (n/m2), Posidonia bed covering (%), geographical coordinates (decimal degrees with WGS 1984 Datum and UTM cartographic projection), sub-area ID (I–VIII) and disturbance types and magnitudes (types: physical and mechanical; magnitude: none, low, medium and high). 2.4. Statistical analysis Seascape metrics (CA, NumP and MPS) values were investigated through a multifactorial analysis of variance (ANOVA) to examine the temporal variations at ecological (bionomic categories, 3 levels: Pm-s/r, Pm-Dm-is and DP) and spatial (sub-areas, 8 levels) scales. Dependent variables were square root transformed and tested for normality and homoscedasticity by Shapiro and Levene's tests, respectively. Structural descriptors were firstly explored through a principal component analysis (PCA) (Pearson, 1901; Hotelling, 1933) to perform an ordination of samplings, based on quantitative variables – i.e. shoot density, bottom covering, depth, years and geographical coordinates – and to highlight possible relations among the variables. Inverse distance weighting (IDW) spatial interpolation technique was then used over the entire study period (2012, 2013, 2014, 2016 and 2017) to create a continuous surface for shoot density, starting from values measured at georeferenced sampling points. From these measures, IDW predicted shoot density values at unsampled points within an elliptical search neighborhood (20 m × 25 m semi axis extent), with the major axis approximately parallel to the shoreline (Criscoli et al., 2017). IDW produces surfaces from measured points based on the assumption that things that are closer to one another are more alike than those farther apart (Johnston et al., 2001). Hence, during prediction, each measured point has a local influence that decreases with distance. The selection of the best model is assessed according to root mean square (RMS) statistic from a cross-validation test: the lower the RMS value the better the model. Interpolations were performed on ArcGIS 10.2.2. A recap surface was lastly produced through the difference of 2012 and 2017 shoot density interpolations in order to obtain a synoptic map of the meadow density evolution. To assess changes of shoot density according to spatial and temporal scales, a generalized least squares regression (GLS) was carried out on square root-transformed data (Pinheiro and Bates, 2000). The independent factors were sub-area (8 levels), year (5 levels) and bathymetric class (3 levels). This weighted linear regression was chosen because of the presence of heteroschedastic residuals. The issue was solved by adding to the model a combination of constant variance structure with different spread per stratum (sub-area: 8 levels, year: 5 levels, bathymetric class: 3 levels), that is, each level of the independent variables is allowed to have different variance (Zuur et al., 2009). Models were chosen by the Akaike's information criterion (AIC), an entropy-based measure of goodness of fit of statistical models: the smaller the resulting value the better the model (Akaike, 1974). Pearson correlation coefficient between observed and predicted values was used as further goodness of fit measure. To assess the response on percentage covering, a multifactorial analysis of variance (ANOVA) was performed on non-transformed data; independent factors were the same of GLS model. For ANOVA and the GLS-derived linear model, a significance level of 0.05 (P-value < 0.05) was chosen. All the statistical analysis were performed through R platform (version 3.4.3; R Core Team, 2017).

Fig. 2. Posidonia oceanica dynamics. Rows refer to each dynamic, i.e. I) disappearance, II) fragmentation, III) erosion and IV) regression, whereas colors indicate different bionomic categories (healthy Posidonia meadow in dark green, dead Posidonia area in beige and regressive Posidonia meadow in light green and brown stripes). (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

metrics (CA, NumP, MPS), reference year, bionomic categories, ID of each sub-area and hexagonal polygon codification was created. The P. oceanica meadow on matte and/or sand (Pm-s) and P. oceanica meadow on rock (P-r) categories were analyzed together (Pm-s/r) whereas the Dead matte category (DM) was excluded from the analysis due to no variations over the years.

2.3. Structural data Non-destructive techniques, such as shoot density counts and bottom cover estimation, were carried out on P. oceanica meadow on matte and/or sand (Pm-s) to assess temporal dynamics of the investigated meadow and highlight possible regression phenomena, i.e. the diminution of both leaf bundles number and canopy coverage. Descriptors were achieved on June 2012, 2013, 2014, 2016 and 2017 through SCUBA diving surveys. Sampling design was conducted on bathymetric gradient transects, from the upper to the lower limit. Depth was classified in 3 bathymetric levels: shallow (0–10 m of depth) (SHW), intermediate (10–20 m) (INT) and deep (20–40 m) (DEP). Subarea I did not show any record in the deeper stratum due to the bathymetric position of its limit (17 m depth), whereas – for the same reason – sub-areas V and VI did not present any record in the shallower stratum (shallow edge from 11 m depth). Starting point of each transect was predetermined with a handled GPS receiver (Montefalcone et al., 2006), whereas swimming directions were kept constant with an underwater compass. Each sampling station, consisted of two transects of 25 m length parallel to the shore, was determined according to the seabed slope and depth (3 m intervals in gently sloping bed and 0.5 m in steeply one) (Short et al., 2001). Density, meant as the number of shoots per square meters (m2), was obtained at each sampling station by scientific divers via 6-9 counts of the number of shoots within a 40 cm × 40 cm frame (Panayotidis et al., 1981), randomly thrown in the meadow from a height of 1 m from the bottom (Pergent-Martini et al., 1994; Buia et al., 2003), highlighting a considerable error reduction when counts were performed in at least five quadrats (Bacci

3. Results 3.1. Spatial data Multibeam surveys allowed to produce several morphologic maps of 171

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Fig. 3. Benthic habitat maps of the investigated area. From top to bottom panel: a) June 2012 (5 months after the shipwreck), b) June 2013 (17 months after the shipwreck), c) June 2017 (a little further than 5 years from the shipwreck). P. oceanica bionomic categories are represented with green and brown colors (“Posidonia on matte and/sand”: Pm-s; “Posidonia on rock”: P-r; “Posidonia on matte, dead matte and isolated shoots”: Pm-Dm-is; “Dead Posidonia area”: DP; “Dead matte”: DM); gray shade refers to other biological categories. Sub-areas are represented by black polygons and identified by roman numbers. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

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side of the plot (i.e. shoot density and bottom coverage) and those arranged on the left side of the plane (X coordinate and depth term), while Y coordinate was placed in the lower part of the plane. Structural descriptors were positively correlated with each other, i.e. shoot density increased with bottom coverage values, and counter correlated with depth and X coordinate (the latter positively influenced each other, i.e. eastern moving implied higher depth), meaning that higher structural features values were associated with shallow bottoms. Similar variables distribution was found on the factorial plane outlined by 1st and 2nd components (Fig. S6a), whereas no clear patterns were observed on the plane given by the 2nd and 3rd components (Fig. S6b). Variation ellipses were used to highlight groups in the factorial plane according to magnitude of disturbance as grouping factor. Even if all ellipsoids were overlapped, a slight divergence was noticed on the distribution of groups along the latitudinal gradient (Fig. 5b). Ellipse barycenters of the high magnitude group, characterized by the lowest structural features values, were arranged on the middle-left side of the plot, in the “core” of the study area. Opposite cases were the none and the low magnitudes, arranged on the lower/upper-right side of the plot (higher structural features values), in the boundaries of the study area; in between was placed the intermediate magnitude ellipse. Five different scenarios were produced by shoot density IDW method and a synoptic surface was obtained by the difference between 2012 and 2017 interpolations (Fig. 6). The representation showed, through greens and reds tonalities, that the decrease in P. oceanica shoot density mainly occurred inside the sub-areas close to the wreck. Marked diminutions were noticed inside sub-areas IV, V and VI (reds tonality); lighter losses were observed in “buffer” sub-areas such as III and VII (orange-yellow hues), whereas stability or soft increase and weak decrease were detected in sub-areas I, II and VIII (yellow-green tones). These evidences were clearer in descriptive analysis of shoot density, that highlighted consistent variations – over years and bathymetric classes – in descriptor values of each sub-area, revealing regression phenomena (Fig. 7). Density kept stable or slightly increased in sub-areas I, II and VIII (even if a slight decrease was reported in the two latter sub-areas). Sub-areas III and VII were characterized by similar shoots variations: a negative trend was present in the shallower and deeper portion of the meadow, whereas a stable pattern characterized the middle bathymetric class (2012–2017 average decrease ± standard error of 29 ± 13 %). Sub-areas IV and VI highlighted a conspicuous decrease over the years from the upper to the lower limits of the meadow, revealing regression; sub-area V totally extinguished the presence of Posidonia from the seabed from 2013 (2012–2017 average decrease ± standard error of 65 ± 17 %). Mean coverage percentage showed the same patterns described through shoot density (Fig. S7). The ANOVA assessed at which level coverage percentages of the meadow evolved in relation to years, sub-areas and bathymetric classes (Table 3). All terms included in the analysis were highly significant. The generalized least squares model (GLS) measured how square root-transformed shoot density of the study area changed according to years, sub-areas and bathymetric classes (AIC: 6768.13; r: 0.84702). The effects plot, representing the estimates for each effect in the model with their 95% confidence interval (Fig. 8). The intercept (corner point) was significant and described by the undisturbed sub-area (I), first sampling year (2012) and shallow depth range (SHW). Significant variations were observed at both temporal and spatial scale. Shoot density values from 2013 to 2017 were significantly lower compared to 2012 and all sub-areas showed values significantly smaller than the intercept (sub-area I). At bathymetric scale, the descriptor naturally registered significant diminution in both INT and DEP depth range respect to SHW. The combination between sub-areas and years provided different results: subareas II and VIII shoot density values were not significantly different in any time from the intercept; shoot density of sub-area III and VII varied significantly in 2016 and 2017 years only, while sub-area IV values in 2014, 2016 and 2017 were different from the intercept. Sub-areas V and VI yielded significant P-values for each of the considered years.

the study area; we report three maps, respectively representing the seabed morphology in 2012 (Fig. S1), 2013 (Fig. S2) and 2017 (Fig. S3). UAS, ROV and SCUBA diving inspections were used to gain biological information and create benthic habitat maps in 2012 (Fig. 3a), 2013 b, 2014 (Fig. S4), 2016 (Fig. S5) and 2017 (Fig. 3c). Multibeam map of 2012 (Fig. S1) showed the seabed morphology 5 months after the shipwreck, representing surfaces without any information due to the presence of Concordia that prevented the acoustic waves to propagate. In 2013 (Fig. S2), the sea-bottom was characterized by the presence of artificial structures involved during the wreck removal. The seabed conditions of 2017, after the Concordia was towed away, were reported in Fig. S3, highlighting the removal of all the artificial structures. Seabed morphology was restored but still visible were the trenching on the rocky bottoms, channels and holes inside the sandy bed. Benthic habitat maps were characterized in June 2012 (Fig. 3a) by an almost continuous belt of P. oceanica meadow (Pm-s/r) extending from the harbor to the undisturbed sub-area, from few meters under the surface up to 35 m depth. Impacted areas (Pm-Dm-is regressive bionomic category) were already present under the wreck and along the meadow limits of sub-areas III, VI–VII (upper edges) and sub-areas VI–VII (lower edges). From 2013 (Fig. 3b), the meadow had undergone bionomic shifts, such as the DP, localized from the bow to the stern (sub-areas IV–V), and patches of Pm-Dm-is extending from the wreck. 2014 benthic map was similar to the one of the previous year without the wreck and without ASV-Pioneer barge (Fig. S4). Cartography of 2016 (Fig. S5) and 2017 (Fig. 3c) showed the study area without any structures involved during the Concordia removal. The meadow was subjected to further bionomic shifts and so characterized by a higher presence of DP and Pm-Dm-is portions, mainly gathered in the central subareas (III–VII). Quantitative analysis on Posidonia dynamics were assessed through the seascape metrics that showed temporal variations at both an ecological and a spatial scale, highlighting disappearance, fragmentation and erosion phenomena. Common significant changes were noticed in metrics values per bionomic categories, years and subareas (Fig. 4). Both surface extension (CA) and number of patches (NumP) of Pm-s/r decreased over the years in sub-areas II, III, VI, VII and VIII. Inside sub-areas II, III and VII, Pm-Dm-is showed an opposite trend, increasing in CA and NumP values over the years. CA and NumP of Pm-Dm-is strongly reduced in sub-area IV, whereas in sub-area V they were wiped out, in both cases shifting to DP (not shown in the plot). Mean patch size (MPS) marked diminutions over the years in Pms/r values of sub-areas II, III, VI, VIII and increased in sub-area VII. PmDm-is values raised in sub-areas II and IV, diminished in VI and VII whereas they were zero in sub-area V. Summarizing, from 2012 to 2017 the amount of P. oceanica seabed lost was 8427 m2 (Pm-s/r and Pm-Dmis shifted to DP bionomic category) while the sea-bottom interested by regression (Pm-s/r shifted to Pm-Dm-is category) was 1525 m2. Seascape metrics variations were assessed by the analysis of variance (ANOVA) (Table 2) and the combination between years, bionomic categories and sub-areas revealed a significant interaction in all metrics. 3.2. Structural data The multidimensional factorial plane produced through the principal component analysis (PCA) showed three principal dimensions. The first component was characterized by the 43.19% of explained variance, the second one by 17.65% while the third one incorporated 16.71% of inertia. Variables included in the analysis differently characterized the factorial space. Depth, X coordinate, shoot density and bottom coverage were mostly correlated to the first component (respectively with −0.54, −0.55, 0.54, 0.30 column normed scores); year and coverage terms were associated to the second one (−0.88, 0.41 scores), while Y coordinate was represented on the third component (−0.95 score). The factorial plane defined by the 1st and 3rd components (Fig. 5a), selected to outline the distribution of considered variables, depicted a clear separation between variables spread on the right 173

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Fig. 4. Barplots of seascape metrics evolution for bionomic categories. Mean ± standard deviation of Surface extension (CA*103), Number of patches (NumP) and Mean patch size (MPS) are reported; years are represented in grayscale. Bionomic categories abbreviations refer to “Posidonia on matte and/sand” and “Posidonia on rock”: Pm-s/r; “Posidonia on matte, dead matte and isolated shoots”: Pm-Dm-is.

Debris diffusion (wreck parts and working materials), due to parbuckling and subsequent wreck refloating, coupled with steel-strand adjusting, were the main pressures of 2014 whereas steel-strand adjusting was the only pressure of 2016 and 2017, leading to fragmentation and erosion processes. An impact gradient of the studied meadow was noticeable, as graphically shown in multivariate analysis and numerically reported in spatial and structural descriptors values, and was based on both distances from the wreck and magnitudes of disturbance undergone by the meadow over the years. Massive bionomic shifts, disappearance and regression phenomena were observed in sub-areas V and, partially, in sub-areas IV and VI (inside the latter, extensive fragmentation process was also observed). In the aforementioned sub-areas, characterized by high disturbance and the proximity to the wreck, mechanical abrasions and physical alterations in column of water (extreme and high shading conditions according to Toniolo et al., 2018) strongly affected the meadow mainly from 2012 to 2013. Cartographic representations of the first two years showed extensive bionomic shifts from stable categories – Pm-s/r – to regressive bionomic categories — such as DP and Pm-Dm-is. Spatial metrics consequently registered the shifts in terms of surfaces extension (CA) and, through the increase of the number of patches and the diminution of their mean size, enlightened the fragmentation process of sub-area VI. In sub-areas IV and VI, structural descriptors recorded strong diminution leading, in some cases, to the disappearance of the phanerogam from the seabed. These phenomena were noticeable in the deeper part of sub-area VI, settled under ASV-Pioneer barge, the entire meadow of sub-area V, placed under the deck and the stern of the wreck, and the southern part of subarea IV, just below the stern of the wreck. The weakened meadow continued to be affected by multiple disturbances over the years, from 2014 to 2017 leading to further bionomic shifts, such as regression and fragmentation phenomena, especially in sub-area VI where the deeper edge dynamic drove to a patchiness morphology. Moderate changes from 2012 to 2017 in bionomic categories, spatial metrics and structural descriptors were detected in sub-areas III and VII, characterized by medium disturbance and farther distance from the wreck. In the

Table 2 Results of analysis of variance (ANOVA) on seascape metrics of the investigated meadow. Significant values are reported in bolt. Metric

Factor

CA (Surface extension)

Year: Category: Sub-area Residuals

NumP (Number of patches)

MPS (Mean patch size)

Year : Category : Sub-area Residuals Year : Category : Sub-area Residuals

df

MS

F-value

P(> F)

15

1829.25

25.797

< 2.2e-16

1360

70.91

15

39.945

11.153

< 2.2e-16

568

0.3582

15

1150.85

15.662

< 2.2e-16

569

73.48

4. Discussion Results highlighted the heterogeneous magnitude and distribution of impacts acting on the P. oceanica meadow due to the shipwreck and its salvage operations from 2012 to 2017. In 2012, the main pressure on Posidonia was related to the presence of the wreck and its mechanical and physical synergic disturbance. The hull impact on the seabed led to a massive eradication of the canopy and the severe shadow negatively affected the surrounding meadow through decreasing photosynthetic radiation (Toniolo et al., 2018), causing strong disappearance and regression phenomena. In 2013 the meadow coped with both mechanical disturbances – due to the working activities – and physical alterations in the column of water – induced by the shadow of the wreck and spotted fine sediment dispersion – related to drilling piles for platforms and anchor-block installation, as reported by Casoli et al. (2017). Furthermore, the mooring of ASV-Pioneer over the meadow led to a compromise of the prairie below its hull, from 15 to 34.5 m of depth: the shadowing consequently reduced the photosynthetic radiation reaching the canopy leading to death (Casoli et al., 2016; Toniolo et al., 2018).

Fig. 5. Principal component analysis plots (left panel) and confident ellipses based on disturbance magnitudes (right panel) made up of axis 1 and 3. PCA was performed in relation to shoot density, bottom coverage, depth, years, and geographical coordinates (latitude Y and longitude X). The first three PCA axes explain 77.56% of the total variance (43.19% axis 1, 17.65 axis 2 and 16.71% axis 3, respectively).

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Fig. 6. P. oceanica shoot density (m2) evolution. Differences from the interpolated surfaces between 2012 and 2017.

experimental shading, even maintaining growth thanks to internal carbon reserves (Ruiz and Romero (2001) in P. oceanica; Collier et al. (2009) in P. sinuosa). Ruiz and Romero (2001) stated that severe shading (10.4% of surface irradiance) reduced P. oceanica shoot density to 80–90 % of initial values after 4 months of shading, comparable to leaf bundles values observed under the wreck in 2012 (sub-area V). Collier et al. (2009) assessed that the complete shoot loss in P. sinuosa will take almost 2 years in heavy shading (4–5 % of surface irradiance), whereas our study highlighted the resistance of P. oceanica up to 5 months and the total extinction in 6–7 months. In the present study the decrease in ppfd was caused by the presence of the Concordia wreck on one side and the Pioneer hull on the other: consequences were the total disappearance and regression of the meadow. The meadow present under the wreck kept on living – even if strongly compromised – until June 2012 to completely die the following months, as confirmed by August 2012 samplings of Toniolo et al. (2018). Light limitation was demonstrated to affect the phanerogam across a broad range of spatial scale (Dalla Via et al., 1998), from leaf response passing through shootscale and meadow structure scale response. In natural condition, where light attenuation mainly occurs along a depth gradient from the upper to the lower limit of the meadow – as the ppfd observed in the undisturbed sub-area and in the shipwreck work site not interested by any disturbance – photo-acclimation led to variations in population structure, morphology and photosynthetic activity. In the lower limit it has been proved that shoot density naturally declines by 72% with respect to the upper edge (Dalla Via et al., 1998; Pergent et al., 1995; Lejeusne et al., 2010). Nevertheless a low respiration rate and carbon demand allow the phanerogam to be well-adapted to light limitation effects in the deeper edges of the meadows (Dalla Via et al., 1998; Olesen et al., 2002; Ralph et al., 2007). Seagrasses regression due to physical

aforementioned sub-areas, mechanical disturbance has led to similar effects observed in sub-area IV-VI, although with less intensity. Subarea III was frequently interested by the abrasion of steel-strand, due to the presence of a mooring spot next to the northern ridge in the middle of the bay. Sub-area VII, instead, “hosted” the M-61 barge inside the sandy area, leading to the weakening of the meadow and to bionomic shifts. The northern and southern sub-areas, I, II and VIII, were characterized by none (I) or low disturbance (II and VIII) magnitudes and placed on the boundaries of the study area. No significant changes were noticed in sub-area I, the undisturbed one. Sub-areas II and VIII (low disturbance) were interested by circumscribed cenotic transitions caused by steel-strand adjusting and 1 m × 1 m clump weights installation, whereas no significant changes were noticed in structural descriptors values. The present work highlighted the vulnerability of the P. oceanica habitats to direct human pressure such as shading, sediment dispersion and mechanical disturbances, confirming that actions aiming at reducing threats should be mandatory especially in coastal engineering activities (Nepote et al., 2017). Long-term shadowing effects on Posidonia physiology after Costa Concordia shipwreck was thoroughly investigated by Toniolo et al. (2018). The authors demonstrated how the reduction in sunlight flow affected the plant secondary metabolism and led to the decline and regression of the meadow proportionate to the degree of the shadow, confirming the pivotal role of irradiance on the seagrass health. P. oceanica minimum quantum requirements (MQR) (%) for survival and growth have been demonstrated to be comprised between 10 and 16% of surface irradiance (Ruiz and Romero, 2001; Lee et al., 2007). When the photosynthetic photon flux density (ppfd) reaching the canopy is less than the MQR, the meadow starts to regress. Posidonia spp. structural descriptors have been proved to be significantly affected from 90 to 100 days after 176

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Fig. 7. Shoot density plots ( ± standard error). X-axis are characterized by bathymetric classes (shallow 0–10 m, intermediate 10–20 m, deep 20–40 m), years are depicted in grayscale. Frames represent the 8 sub-areas, identified by roman numbers.

positioning and led to physical variations in the column of water (Casoli et al., 2017). These events likely contributed to intensify the light attenuation process, damaging part of the meadow not directly affected by the wreck's shadow (such as in sub-areas III and VII) and more deteriorating the one interested by the presence of shade. Sedimentation could have produced a partial and temporary burial of the Posidonia canopy, moreover an increase in turbidity and organic load, as observed in Posidonia leaves epiphytic assemblage by Bacci et al. (2016), negatively affected seagrass growth and meadow lower limit stability. Similar effects were noticed during dredging and artificial beach replenishments on coastal ecosystems that led to complex effects on marine biota (Longstaff and Dennison, 1999; Garcia and Servera, 2003; Ruiz and Romero, 2003; Erftemeijer and Robin Lewis, 2006; GonzálezCorrea et al., 2008; González-Correa et al., 2009). The authors demonstrated that fine sediment dispersion caused the increase in water turbidity and reduction in seagrass photosynthesis, by reflecting and absorbing available light, and intensified the growth of leaves epiphytes biota. Over-sedimentation on Posidonia shoots and rhizomes has been proved to be connected with shoot mortality (Manzanera et al., 2011, 1998); the authors reported a disappearance of ca. 65% shoots with a burial of 4 cm, and reached the 100% with 9 cm burial. Nevertheless, a response capacity to burial, consisting of rhizomes elongation and

Table 3 Results of analysis of variance (ANOVA) on coverage values of the investigated meadow. Significant values are reported in bolt. Factor

df

MS

F-value

P(> F)

Year

4

4364

37.571

< 2.2e-16

Sub-area

7

63438

546.212

< 2.2e-16

Bathymetric class

2

68056

585.972

< 2.2e-16

Year : Sub-area

28

3517

30.281

< 2.2e-16

Residuals

1282

116

variations in column of water was also reported as response to human pressures, e.g. dredging (Badalamenti et al., 2006;Erftemeijer and Robin Lewis, 2006), fish farming (Cancemi et al., 2003; Pergent et al., 1999; Sarà et al., 2004), coastal constructions (Ruiz and Romero, 2003) and other impacts (Peirano et al., 2005). During the shipwreck and its removal, the studied meadow also coped with alteration of flow regimes and turbidity. Fine sediment dispersion, induced by drilling piles for platforms and anchor-block landscaping, together with grout-bag 177

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Fig. 8. Regression coefficient plot. Variations are evaluated with respect to the corner point given by categories combination: first sampling year (Year 2012), undisturbed area (sub-area I) and shallow depth class (Depth SHW). Vertical lines depict the 95% confidence interval and if they do not meet the axis denoting the zero value – depicted by the gray line – the corresponding effect is statistically significant (highlighted in the lower part of the plot through underline).

as proposed by Pergent-Martini et al. (2005). Bionomic cartography together with seascape metrics and structural descriptors revealed – at a fine scale – disappearance, fragmentation, erosion and regression events occurred from 2012 to 2017. P. oceanica shoot density and bottom coverage confirmed their pivotal role as “early-warning” signals (Marbà et al., 2005) giving us a population scale point of view, providing important information about vitality and dynamic of the meadow and rapidly revealing the human influence on the environment through regression. Aforementioned effect reflects at a landscape level throughout bionomic charts and spatial metrics supplying quali-quantitative information on the disappearance, fragmentation and erosion phenomena undergone by the investigated meadow. Very little evidence of large scale Posidonia natural recovery, even after eliminating the cause of impact is reported in literature: Meinesz and Lefevre (1984), after mines and bombs explosion in Bay of Villefrance, reported a recovery time exceeding a century due to the slow rate of new patches forming. González-Correa et al. (2005) estimated almost 100 years to reach the total recuperation of a P. oceanica meadow degraded by trawling. The same authors also highlighted the low vegetative growth rate of a P. oceanica meadow impacted eighteen years before by beach replenishment (González-Correa et al., 2008); consequences of the impact endured in time and led to a decrease in net total rhizomes recruitment rate – in the amount of 45% – furthermore a diminution in starch reserves and leaf production of horizontal rhizomes were observed. Delgado et al. (1999) reported a continuing

branching, was detected. In the present experience, unfortunately, the investigated Posidonia meadow was affected by multiple pressures, physical and mechanical, that amplified the effects and led to the observed effects. Mechanical impacts regarding the wreck collision, anchors, clump weights, grout bags deposition and steel-strand adjusting produced similar circumscribed effects – e.g., dead P. oceanica areas and scars – from boat anchoring and trawling (Francour et al., 1999; Boudouresque, 2003; Milazzo et al., 2004; Ganteaume et al., 2005; González-Correa et al., 2005; Ardizzone et al., 2006; Boudouresque et al., 2012; Abadie et al., 2016). Mechanical pressures led to changes in P. oceanica cover and structure which consequences were developed in 2 phases: i) at first, mechanical impacts weaken phanerogam formations causing scars and dead Posidonia areas resulting in bare matte small patches (erosion process); ii) with the continued and increased magnitude of the phenomenon, loss of canopy and reduction in shoot density and bottom coverage values gradually occurs resulting in cenotic transitions to Posidonia regressive bionomic categories as PmDm-is and DP (fragmentation and regression phenomena). P. oceanica represents the ‘climax’ stage on the ecological succession in a benthic infralittoral community and, for this reason, it is characterized by high resistance and low resilience (Montefalcone et al., 2012). Nevertheless, synergic effect of multiple sources of disturbance, led to the loss of structural integrity. The present work underlines how the descriptors adopted were able to represent a comprehensive fine-scale and midterm assessment tool for the seagrass distribution, status and evolution, 178

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decline in a P. oceanica meadow after 4 years from the elimination of fish farming: differences between areas with and without past farming activity were appreciable in terms of shoot density and size demonstrating that the recovery time needed for the meadow to self-restore was related to the endurance and magnitude of disturbance previously faced. Marín-Guirao et al. (2016) showed the reversibility of short-term heat stress effects also supposing that, if the stress would last longer than the time experience in their study, the damage could be irreversible, causing plant death.

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5. Conclusion The Mediterranean basin has 46,000 km of coastline occupied for more than 1 of its length by P. oceanica meadows in regression status 4 except for some areas (e.g. Corsica, part of Sardinian coastline and Valencia region in Spain, see Telesca et al., 2015). The present work, first cartographic and landscape study in bibliography dealing with a shipwreck and P. oceanica meadow interaction, highlighted how multiple pressures, such as mechanic disturbance and light reduction, affected the meadow, giving information on their temporal effects on a spatial and a structural level. It should be noted that the regression which occurred from 2012 to 2017 described in this work was due to direct pressures on the meadow and no permanent alteration of the environment was produced (e.g., change in currents system or sedimentary cycle). Thus, the next key research step will be to assess the capability of the impacted meadow to restore itself, both naturally and through an experimental transplant. Declaration of Competing Interests All the authors, as scientists of Sapienza University of Rome, were commissioned by the Titan-Micoperi Group to assess the environmental risks of engineering operations and to monitor the conditions of the shipwreck work site in order to protect marine habitats during the salvage of Concordia wreck. The authors' work was controlled and continuously shared with an Italian governmental body (The “Osservatorio”), which fulfilled the public function of verifying the performance of the monitoring. This paper was authorized for publication by Costa Crociere S.p.A. Acknowledgments This research was supported by funding from the Titan-Micoperi Group, a consortium made by two societies, the american "Titan Salvage" and the italian "Micoperi Spa", during the Costa Concordia removal operation. I’m grateful to Max Sharkeology for the underwater assistance and Paul Hammond for the English revision. Thanks to my family, friends and colleagues for providing advices and encouragement. I also acknowledge the three anonymous referees for their comments that improved the quality of the manuscript. Appendix A. Supplementary data Supplementary data to this article can be found online at https:// doi.org/10.1016/j.marpolbul.2019.07.044. References Abadie, A., Gobert, S., Bonacorsi, M., Lejeune, P., Pergent, G., Pergent-Martini, C., 2015. Marine space ecology and seagrasses. Does patch type matter in Posidonia oceanica seascapes? Ecol. Indic. 57, 435–446. https://doi.org/10.1016/j.ecolind.2015.05.020. Abadie, A., Lejeune, P., Pergent, G., Gobert, S., 2016. From mechanical to chemical impact of anchoring in seagrasses: the premises of anthropogenic patch generation in Posidonia oceanica meadows. Mar. Pollut. Bull. 109, 61–71. https://doi.org/10.1016/ j.marpolbul.2016.06.022. Abadie, A., Pace, M., Gobert, S., Borg, J.A., 2018. Seascape ecology in Posidonia oceanica seagrass meadows: linking structure and ecological processes for management. Ecol. Indic. 87, 1–13. https://doi.org/10.1016/j.ecolind.2017.12.029.

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