Geostatistical approach to investigate spatial patterns of the endangered fan mussel Pinna nobilis (Linnaeus, 1758)

Regional Studies in Marine Science 32 (2019) 100884

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Geostatistical approach to investigate spatial patterns of the endangered fan mussel Pinna nobilis (Linnaeus, 1758) ∗

Marco Secci a , , Cecilia Biancacci b , Angelica Giglioli a , Daniela Loddo a , Viviana Pasquini a , Antonio Pusceddu a , Piero Addis a a b

Department of Environmental and Life Science, University of Cagliari, Via Fiorelli 1 – 09126 Cagliari, Italy The Scottish Association for Marine Science (SAMS), Scottish Marine Institute, Oban, Argyll, PA37 1QA, Scotland, UK

article

info

Article history: Received 11 June 2019 Received in revised form 27 September 2019 Accepted 13 October 2019 Available online 17 October 2019 Keywords: Pinna nobilis Population structure Spatial modelling Mediterranean sea

a b s t r a c t The manta tow survey technique combined with the geostatistical approach was used to identify the reliability of these tools to identify the position of the banks of P. nobilis and evaluate the spatial distribution and abundance of its population. A large spatial scale study was carried out through a geostatistical approach to identify the spatial distribution and abundance of the endangered fan mussel Pinna nobilis populations in the Sant’Antioco Island, Sardinia, Tyrrhenian Sea. A smaller spatial scale approach, which was carried out by using the standard method of quadrates by scuba diving, was used to study the size distribution and to supply correction factors for the estimates obtained by geostatistics. Kriging mapping revealed the presence of a very dense population with values of abundance as high as up to 70 individuals 100 m−2 , corresponding to a total number upscaling to 3.9 million individuals over the whole area. The small spatial scale study showed that the fan mussel population had a spatially heterogeneous size structure, with each location characterized by a different size/age structure mirroring the observed patterns of spatial autocorrelation. The comparison between the density of specimens directly observed by scuba diving and those estimated by the geostatistical technique showed that estimated value was overestimated when the density was low and underestimated when the density was high. We therefore conclude that the technique proposed in this study is a less time-consuming technique for prospection studies for the assessment of the presence/absence of P. nobilis population and their abundances in shallow waters, which are the first step for the development of conservation and management strategies of this endangered species. © 2019 Elsevier B.V. All rights reserved.

1. Introduction Geostatistics is a branch of applied statistics, which focuses on detecting, modelling, and estimating spatial patterns in georeferenced data (Rossi et al., 1992). Geostatistical techniques were initially developed for use in terrestrial applications, and, during the last 20 years, have also been increasingly applied in marine systems (Petitgas, 2001) to solve fishery related problems (Simard et al., 1993), planning resource management and conservation (Castilla and Defeo, 2001; Stelzenmüller et al., 2004), reducing the risks of overfishing (Addis et al., 2009a, 2012), and quantifying relationships between abiotic variables and species distributions over large spatial scales (Maravelias et al., 1996). Moreover, the use of geostatistics has allowed optimizing the effectiveness of sampling designs and maximizing the level of information acquired from a relatively small number of samples (Defeo and Rueda, 2002; Petitgas, 1993). This represents an advantage of ∗ Corresponding author. E-mail address: [email protected] (M. Secci). https://doi.org/10.1016/j.rsma.2019.100884 2352-4855/© 2019 Elsevier B.V. All rights reserved.

geostatistics over other spatial patterns reconstruction tools, as it produces also error maps, which allow providing more realistic and reliable information on the spatial distribution of targeted objects (Petitgas, 2001). In this context, geostatistics could represent an extremely useful tool to study the spatial distribution of sessile benthic marine organisms, as, these organisms, remaining anchored to the sea bottom even after death, represent an ideal target for distributional investigations. The fan mussel Pinna nobilis Linnaeus, 1758 (Mollusca, Bivalvia) is one of the largest bivalves worldwide, reaching a maximum size of 120 cm in length (Galinou-Mitsoudi et al., 2006; Katsanevakis et al., 2008). It is a long-living mollusc (Butler et al., 1993) and the older specimens (45–50 years old) have been observed in the Port-Cros National Park (Rouanet et al., 2015). P. nobilis, which is endemic in the Mediterranean Sea (GarcíaMarch et al., 2007a), occurs at depths between 0.5 and 60 m (Richardson et al., 1999; García-March et al., 2007a) on soft bottoms characterized by meadows of the seagrasses Posidonia

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M. Secci, C. Biancacci, A. Giglioli et al. / Regional Studies in Marine Science 32 (2019) 100884

oceanica and Cymodocea nodosa or in open sandy areas (Katsanevakis, 2006, 2007). P. nobilis buries a part of the shell into sandy or muddy bottoms leaving its wider posterior end exposed and uses its byssus to secure the shell to the sea bottom (García-March et al., 2007a). Usually, fan mussels have a patchy distribution (Richardson et al., 2004; Katsanevakis, 2006) that depends on optimal habitat availability, recruitment success and pollution (García-March et al., 2007a,b; Katsanevakis and Thessalou-Legaki, 2009; Rabaoui et al., 2010). Higher density values were registered only in particular habitats such as coastal lagoon, estuaries or protected inlets (De Gaulejac and Vicente, 1990; Galinou-Mitsoudi et al., 2006; Katsanevakis, 2006; Rabaoui et al., 2008, 2010; Russo, 2012). These habitats are characterized by easy human accessibility, which increases the vulnerability of the species to anthropogenic impacts (Richardson et al., 1999, 2004; García-March et al., 2007a,b; Hendriks et al., 2013; Deudero et al., 2015). P. nobilis is listed as ‘‘endangered’’ under the Barcelona Convention (ANNEX II), which strictly forbids collection, capture, killing, commerce, transportation, and disturbance, especially during reproduction, and it is under strict protection according to the Habitats Directive 92/43/ECC (ANNEX IV). Although the directive has laudable aims, the effectiveness of current protection measures is questionable and current practices need to be urgently reviewed (Basso et al., 2015). Moreover, the EU legislations are not applied in southern Mediterranean countries, where, in addition, people is not aware of the status of endangered marine species (Rabaoui et al., 2010, 2008). This rank though not fully supported by robust information about P. nobilis distribution, abundance and population dynamics at the basin scale (Basso et al., 2015), would soon become worst. In fact, the populations of this mussel are currently experiencing a mass mortality event (MME) on a basin scale because of an alien disease (caused by a pathogenic microorganism) spread during the last three years over the entire Mediterranean Sea (Vázquez-Luis et al., 2017). The possibility to explore the reasons for the disease spreading, to model eventual future patterns of diffusion and to foresee the fate of P. nobilis in the Mediterranean Sea all depend upon the availability of information about its spatial distribution over different spatial scales before, during and after the MME. To provide a contribution to this issue, we applied the geostatistical techniques on data obtained from remote and discrete sampling investigating abundance and distribution patterns of P. nobilis around the S. Antioco Island (Sardinia, Italy, Tyrrhenian Sea). The overall aim of our study was to test the following (null) hypotheses: (1) P. nobilis are uniformly distributed around the S. Antioco Island, and (2) the size structure of P. nobilis population is invariant within the considered spatial scale. A comparison between the direct observation of density and the density estimated by the geostatistical analyses was carried out to assess the bias of the estimation and, thus, assess the reliability of the geostatistical approach. 2. Material and methods 2.1. Study area The study was conducted in July 2010 around the S. Antioco Island, located close to the SW coast of Sardinia (central Mediterranean), in three adjoining zones: Calasetta (CL), S. Antioco North (SN) and S. Antioco South (SS) (Fig. 1). CL (10.2 km2 ) is an open sheltered bay that is exposed to NW winds. It is characterized by sandy, gently sloping bottoms, and is occupied by continuous and patchy meadows of P. oceanica, which provide a suitable habitat for the fan mussel.

SN (3.76 km2 ) is a shallow coastal lagoon (max depth = 2 m) located between the gently sloping coast of the island of S. Antioco and the mainland of Sardinia. It is connected to the open sea by one large inlet (1 km) in the north (bordering on the CL zone), and a narrow inlet (60 m) in the south (bordering on the SS zone). The bottoms are characterized by mud and sand, and are occupied by mixed meadows of P. oceanica and C. nodosa interspersed with the green algae Caulerpa prolifera. SS (18 km2 ) is a large sandy bay exposed to winds from the south and occupied mainly by C. nodosa and C. prolifera, with small patchy spots of P. oceanica. 2.2. Sampling This study has been conducted in full accordance with institutional, national and international guidelines concerning the use of animals in research and/or the sampling of endangered species. The manta tow survey technique (Hill and Wilkinson, 2004) was used to identify the presence of P. nobilis and its abundance in three zones. The technique was carried out by a snorkel diver and an assistant on a boat. The snorkel diver was held on to a ‘‘manta board’’, which was provided with a digital camera, attached to a small boat by an about 10–15 m rope. The video transect was georeferenced by the assistant recording the starting and the ending points of each transect using a portable GPS (WGS84 geodetic referenced system). The GPS was set to record the boat track automatically. The sampling unit was a video belt-transect, with a length of 30 m. A total of 350 belt-transects, spread among the three zones (113 in CL, 155 in SN, and 80 in SS) were investigated across an area of 32 km2 comprised between 0.5- and the 5-m bathymetric contours (Fig. 1). The depth limit for visual inspections was chosen because it was the depth limit to assess the presence of P. nobilis considering the underwater visibility of the area studied. The depth lower than 0.5 m was not considered because it was not possible to troll the video-towed diver by boat. More specifically, from each transect the footages were obtained by a high-resolution digital camera (mvx10i: Sony Corporation, Tokyo, Japan) protected by a NiMAR housing (NiMAR, Magreta, Italy). The footages were then analysed from a computer screen to assess the abundance of alive and dead P. nobilis individuals. The area (A) covered by each transect was calculated considering the camera angle (60◦ ), the distance covered (in m) and the depth (in m) using the following equation:

√

A (m2 ) = distance × ((depth × 2)/ 3) 2.3. Image analysis The images were transferred from the video-camera to a computer in a non-compressed AVI format. Every frame had 72 dpi, providing a resolution equivalent to 1440 × 960 pixels. Subsequently, the images were recorded onto a DVD, which became the backup copy in addition to the raw images available on the MINI DV video tape. Image analysis was conducted using the program VLC media player (VideoLAN non-profit organization, Paris, France) on slow motion moving images (0.90×) by two viewers. The number of alive and dead specimens observed by the viewers in each transect was averaged and standardized to 100 m2 . Only flipped pen shells were enumerated as dead specimens. For the purposes of the geostatistical analysis, the mean abundance from each transect was assigned to the coordinates of the corresponding starting point.

M. Secci, C. Biancacci, A. Giglioli et al. / Regional Studies in Marine Science 32 (2019) 100884

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Fig. 1. Map of the study area in S. Antioco Island (Sardinia, Italy, Mediterranean Sea). The area under scrutiny (31.96 km2 ) was divided considering three zones, namely: Calasetta (CL), S. Antioco North (SN) and S. Antioco South (SS). For each zone were reported the starting point of each transect (+), the 5 m depth limit (dotted lines) and the presence of site of community importance (dash–dot line and relative code). Latitude and Longitude were expressed in WGS 84 (geodetic referenced system).

2.4. Population abundance Differences in the abundance of alive specimens of P. nobilis among the three zones were assessed using a non-parametric permutational analysis of variance with unrestricted permutations of raw data on a Euclidean distance matrix of fourth-root transformed data. The test, which allows testing hypotheses with unbalanced numbers of replicates among levels of the tested factors (Anderson, 2001), was carried out using the PERMANOVA routine included in the Primer 6+ software (Clarke and Gorley, 2006). 2.5. Geostatistical analyses The geostatistical analysis on the geo-referenced abundances of living and dead specimen was performed assuming that the habitat on each zone was homogeneous. Geo-referenced abundance was analysed using geostatistical tools in order to assess whether the P. nobilis population in the area under scrutiny is distributed uniformly. If the geo-referenced data are not uniformly distributed they will be autocorrelated. The degree of autocorrelation among adjacent sampling points was calculated by a non-directional experimental semivariogram γ (h) using the following equation (Matheron, 1965):

γ (h) =

1 2N(h)

N(h) ∑

(RSS), and the proportion of the explained variance (C/(C0 + C) (Cressie, 1991). We undertook the following estimations for each experimental semivariogram: the nugget effect (C0 ), which is the value of γ (h) at the distance 0 m, is attributable to measurement error, micro-scale variability, or small-scale spatial structure; the sill (C + C0 ), which is the maximum value of γ (h), can be defined as the maximum variability point beyond which the semivariance values become asymptotic; the range (A0 ), which is expressed in metres, represents the distance within which the data remain autocorrelated (Maynou, 1998). Two-dimensional density maps and maps of standard deviation, which contains the standard error of the estimates, were created employing the spatial estimation technique known as ‘‘ordinary point kriging’’. Kriging is a gridding technique that estimates the value of density Z(x) at each node (n) through a linear combination of the samples and the factors (λi ), which depends on the combination of the relative position of the sampling points, the theoretical semivariogram, and the Z(xi ) values at the sampling points (Matheron, 1965). The estimated density values Z(x) were given by: Z (x) =

n ∑

λi Z (xi )

i

[Z (xi + h) −Z (xi )]2

i=1

where Z(xi ) represents the density of specimens at sampling site xi , Z(xi + h) is the density of specimens at a sampling site separated from xi by a distance h (in m), and N(h) is the number of pairs of observations separated by h. The model that best explained the spatial structure was selected according to the coefficient of determination (r 2 ), the reduced sum of squares

The predictable number of specimens was calculated by the kriging maps, scaling the surface of each countering layer (different colour for each density category) with its average density, including the confidence limits (standard deviation) by using the kriging map of standard deviations. The calculation of semivariograms was done using the software Gs+ ver.7 (Gamma Design Software, Plainwell, MI, USA) and kriging maps were produced using Surfer 12 (Golden Software Inc., Golden, CO, USA).

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M. Secci, C. Biancacci, A. Giglioli et al. / Regional Studies in Marine Science 32 (2019) 100884 Table 1 Summary statistics of P. nobilis abundance (ind. 100 m−2 ) in Calasetta (CL), S. Antioco North (SN) and S. Antioco South (SS). Sd = standard deviation. Zone

Surface (m2 )

N

Min

Max

Mean

Sd

Variance

Skewness

Kurtosis

CL SS SN

10 106 6 928 5 093

113 80 155

0 0 0

132.7 21.9 63.7

12.5 2.3 3.5

20.4 4.5 10.5

417.5 20.9 109.5

3.4 2.6 4.3

14.1 6.5 19.4

Table 2 Summary of relative descriptors of the semivariogram models of alive specimens in Calasetta (CL), S. Antioco North (SN) and S. Antioco South (SS). Data for SN are not reported because of missing autocorrelation (n.a. = not available). C0 = nugget effect, expressed as value of γ at 0 m distance; C0 + C = sill, expressed as value of γ at range (A0 ) distance; A0 = range, expressed in metres (m); RSS = reduced sum of squares, expressed as number; r2 = coefficient of determination, expressed as number; C /(C0 + C ) = proportion of the explained variance, expressed as percentage (%). Zone

Model

C 0 (γ )

C0 + C (γ )

A0 (m)

RSS

r2

C /(C0 + C ) (%)

CL SS SN

Gaussian Spherical na

175 5.1 na

577 26.87 na

1730 264 na

0.176 4.92 na

1 0.934 na

69.7 80.9 na

2.6. Population size structure To provide an attempt to assess the reliability of the geostatistical approach, a detailed analysis of the banks identified in the CL zone was carried out by scuba diving in three different locations (namely: CLa, CLb, CLc) framed within the CL zone. We chose to carry out this assessment only in CL as this zone was the one where, after the survey for collecting data for the geostatistical approach, P. nobilis abundance was visually more homogeneously distributed. The shell width of alive and dead specimens was investigated in each location by two scuba divers using 10 × 10 m quadrat as the sampling units (n = 12). Each quadrat was geo-referenced (WGS84 geodetic referenced system). In each location, alive and dead specimens were counted and photographed (Powershot G10 - 14.7 megapixel, sensor 1/1.7′′ optical zoom 6x: Canon, Tokyo, Japan). The size of each specimen was calculated using a graduated ruler placed on the shell to provide a scale for each picture, which was taken orthogonally to the shell. Photos were analysed using the TpsDig2 software (Rohlf, 2009) to calculate the maximum shell width. The size-frequency distributions in each location were visualized as histograms. The null hypothesis that the size structure of P. nobilis population was invariant among locations was verified by the Kolmogorov– Smirnov (K–S) test. The density observed by scuba diving on each quadrat and the estimated density on that position by geostatistics were plotted on a scatter plot and the Passing Bablock test was performed to compare the two methods (Passing and Bablok, 1983). The Kolmogorov–Smirnov (K–S) test was carried out by the software Statgraphics centurion XVI (Statpoint technologies Inc, United States) whereas the Passing Bablock test was carried out by the XLSTAT-Base (Addinsoft, France).

Table 3 Results of the K–S test used to compare the frequency distributions of living specimens in Calasetta locations (namely CLa, CLb, CLc). DN = maximum distance between the cumulative distributions of the two samples. DN K–S statistic P value

CLa vs. CLb

CLa vs. CLc

CLb vs. CLc

0.261 0.912 0.394

0.283 1.726 0.005

0.243 0.834 0.500

cases, the models provided the lowest RSS, the highest r2 values, and the highest proportions of explained variance C /(C0 + C ) (Table 2). The distance of spatial influence, range A0 , was 1750 m and 264 m in CL and SS, respectively. The nugget effect in CL was much larger than that in SS (Table 2). 3.3. Kriging maps Density maps and distribution of standard deviations for CL and SS were shown in Fig. 3. In CL the distribution of P. nobilis abundance was characterized by a high-density spot (up to 70 ind. 100 m−2 ) located just in front of the opening of the lagoon (SN). The distribution of standard deviations in the CL zone showed that the minimum variability occurs at the smallest scale (i.e. among groups of neighbour transects). In SS were observed only two spots with an abundance >10 ind. 100 m−2 (Fig. 3). Using the mean density in each countering layer of the kriging maps and the surface covered by each layer, we estimated a total of 3982683 ± 1262871 and 2096 ± 1266 individuals of P. nobilis in CL and SS, respectively. 3.4. Population size structure

A total of 1816, 380 and 162 alive specimens were counted by manta tow survey technique in CL, SN and SS, respectively. The mean density (± SD) of alive specimens (Table 1) varied significantly among zones (PERMANOVA; Pseudo-F = 46.585; P < 0.001). Dead specimens represented 17%, 5%, and 13% of total abundance, in CL, SS, and SN, respectively.

In CL, overall, the shell width of living P. nobilis specimens ranged from 5 to 25.5 cm (Fig. 4). The mean width (± SD) of the 34 dead and 74 alive specimens encountered in the CLa location was 11.20 ± 4.05 cm, with a few specimens showing a width >15 cm. In the CLb location the 5 dead 14 alive specimens showed a mean width (± SD) of 9.38 ± 2.94 cm. The 78 dead and 410 alive specimens encountered in the CLc location showed a mean width (± SD) of 14.11 ± 3.81 cm. The frequency distribution of alive specimens in CLa was significantly different from that in CLc whereas no significant differences were found between CLb vs. CLc and CLb vs. CLa (Table 3).

3.2. Density autocorrelation

3.5. Comparison of methods

The density of alive specimens showed autocorrelation in CL and SS, but not in SN (Table 2). The best explanatory models in CL and SS were the Gaussian and the spherical model (Fig. 2). In both

The density of alive specimens observed by scuba diving on each quadrat and the density estimated by the geostatistical technique were linearly related (y = 0.9337x – 0.8286; p = 0.057)

3. Results 3.1. P. nobilis abundance

M. Secci, C. Biancacci, A. Giglioli et al. / Regional Studies in Marine Science 32 (2019) 100884

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Fig. 2. Experimental semivariograms of density of alive specimens estimated for CL and SS zones (on the left and the right, respectively). Model parameters are listed in Table 2. The semivariogram of the SN zone is not reported because any experimental semivariogram has been found to explain the spatial variability in this zone.

Fig. 3. Kriging maps (top of the picture) and standard error maps (down of the picture) of P. nobilis population in the CL and SS zones based on geostatistical interpolation of densities. The colour scale at the right of the maps identify the mean density and relative standard deviations with different colours.

(Fig. 5). The plot of residuals (Fig. 6) showed that geostatistics overestimates the density when the observed one is <10 ind. m−2 , whereas when the observed density exceeds 40 ind. m−2 , geostatistics underestimates the density. 4. Discussion The manta tow survey technique combined with the geostatistical approach was a reliable tool to identify the position of the banks of P. nobilis and evaluate the spatial distribution and abundance of the population when it is uniformly distributed. The main advantage of this approach is that permits to cover wide surfaces in comparison to the traditional methods used in the previous studies which ranged from 0.0001 to 14.7 km2 (Katsanevakis, 2007; Katsanevakis and Thessalou-Legaki, 2009; Rabaoui et al., 2010, 2008; Vázquez-Luis et al., 2014). We identified that the variographic analyses indicated that the Gaussian and Spherical models provided the best spatial structure of P. nobilis in two of the three investigated zones (namely CL and SS) confirming that these functions fit the empirical biological data of marine species as already observed in other studies (Stelzenmüller et al., 2004; Castrejón et al., 2005; Addis et al.,

2009b, 2012). Among the two zones, CL was characterized by very high densities of alive specimens (up to 70 ind. 100 m−2 ), which are, so far, among the highest ever reported in the literature (Table 4). P. nobilis shows in general sparse and small populations, although in Greece (Souda Bay) and in Spain (Alfacs Bay) large populations with up to 90 000–130 000 specimens have been observed (Table 4). In this regard, it is noticeable that the total abundance of P. nobilis around the Sant’Antioco Island has been estimated as ca. 3 900 000 individuals, which, as far as we know, represents the highest value ever reported so far (Table 4). Although this number was undoubtedly the real mean estimate of abundances between the shoreline and the contour depth of five metres a certain bias has been evaluated in this estimated value. Indeed, the comparison of methods between densities estimated by geostatistics and the direct observation of alive specimens showed that geostatistics overestimates the values at values <10 ind. m−2 , whereas it underestimates the values when the observed density exceeds 40 ind. m−2 . This bias could be caused by the underestimates of small specimens, due to the canopy heights especially in the P. oceanica meadows (Hendriks et al., 2012), and the overestimates of living specimens, due to specimens stilling straight up the bottom could also be

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M. Secci, C. Biancacci, A. Giglioli et al. / Regional Studies in Marine Science 32 (2019) 100884

Fig. 4. Size distribution of alive specimens of P. nobilis observed by SCUBA diving in CLa (N = 108), CLb (N = 19) and CLc (N = 488).

dead. The much lower densities observed in the SS zone can be ascribed to the presence of only small and very sparse seagrass beds, which generally represent the most suitable habitat for the fan mussel (García-March, 2005). On the other hand, the very low values of P. nobilis mean density in SN, in spite of the presence of a few very small hot spots of abundance (with up to 64 ind. 100 m−2 ), suggest that this species does not fit well with the environmental characteristics of this transitional aquatic environment, where seagrasses are absent or present with sparse and inconsistent beds. In CL and SS zones, the high values of semivariance indicate the presence of a large variability in fan mussel density at the scale of zone. Moreover, in both those zones the density of live specimens showed a very ample distance of persistent autocorrelation (range 264–1750 m in SS and CL, respectively), whereas no spatial structure or autocorrelation was found for dead specimens in any of the investigated zones or for alive specimens in the (SN) lagoon zone. Altogether these results indicate that the populations of P. nobilis in the two zones are characterized by either a patchy distribution or very dense hot spots. This result

Fig. 5. Observed values by scuba diving on each quadrat versus estimated densities on that position by geostatistics (n = 36) in the CLa, CLb and CLc.

M. Secci, C. Biancacci, A. Giglioli et al. / Regional Studies in Marine Science 32 (2019) 100884

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Table 4 Summary table of the published literature on P. nobilis populations in the Mediterranean Sea. Country (location)

Surface investigated (km2 )

Year

Depth range (m)

Total population (no. ind.)

Mean density (ind./100 m2 )

Max density (ind./100 m2 )

Habitat

Source

Croatia (Adriatic Sea) Croatia (Island of Mljet) Croatia (Mljet National Park) France (Dina Bay) France (Port Cros) France (Port Cros) France (Port-Cros) Greece (Golf of Geras) Greece (Lake Vouliagmeni) Greece (Lake Vouliagmeni) Greece (Souda Bay)

na

na

10–20

na

9

na

Cymodocea sp.

0.0004

1998–2001

0–15

na

17

90

Cymodocea sp.

0.002

1998–2000

3–15

182

10

20

C. nodosa

na

1986

0.5–1

na

400

600

na

na

na

na

na

1.93

na

Na

na

na

10–12

na

1

na

na

na

1969–1979

na

na

1

na

P. oceanica

na

1986–1987

na

na

na

500

na

1.500

2004

1–23

8501a

0.57

17

mud

1.504

2004

0–30

8393a

0.45

10

mud

Zavodnik et al. (1991) Peharda et al. (2002) Šiletić and Peharda (2003) De Gaulejac and Vicente (1990) Vicente et al. (1980) Combelles et al. (1986) Vicente and Moreteau (1991) Catsiki and Catsilieri (1992) Katsanevakis (2006) Katsanevakis (2007) Katsanevakis and Thessalou-Legaki (2009) Galinou-Mitsoudi et al. (2006)

Greece (Thermaikos Gulf) Italy (Gulf of Aranci) Italy (Gulf of Oristano) Italy (Gulf of Oristano) Italy (Ligurian coast) Italy (Mar Grande of Taranto) Italy (Messina) Italy (Sant’Antioco Island) Italy (Venetia Lagoon) Morocco (Chafarinas Islands) Spain (Alfacs Bay) Spain (Almeria Coast) Spain (Balearic Islands) Spain (Balearic Islands) Spain (Columbretes Island) Spain (Moraira Bay) Tunisia (Ghar El Melh lagoon) Tunisia (Northern and Eastern coasts) Tunisia (South-eastern coast) a

Average estimates.

a

14.72

2007

0–40

130 900

na

20

C. nodosa/C. racemosa

0.0001

2004

2–3

73

104

130

na

na

na

8–11

na

56

0.005

2006–2007

2–7

530

6.3

29

0.067

2007–2009

2–10

1285

2.7

6.7

P. oceanica/C. nodosa/sand/mud P. oceanica/C. nodosa

0.0021

2015

7–25

34

1.8

30

P. oceanica

0.600

2004–2005

3–30

14

0.0023

0.007

P. oceanica/C. nodosa/sand/mud

na

1986

10–20

na

6.89

na

na

31.960

2010

1–5

3 984 779a

6.3

132

P. oceanica/C. nodosa/C. prolifera

na

2006–2012

0-0.5

na

na

1600

0.180

na

7.4–11.2

na

2.9

na

Mud/sand/Zoostera marina P. oceanica/C. nodosa/macroalgae

7.100

2011–2012

0.1–1.3

90303.52a

1.61

20

C. nodosa/sand

0.003

1995–1996

3–17

55

17

30

P. oceanica

0.0059

2007–2010

5–6

356

4.75

10

P. oceanica

0.150

2011–2012

4.2–46

1457

3.81

37.33

0.001

na

20–34

55

1.5

10.3

P. oceanica/detritic/rocky /sandy C. nodosa

0.001

1997–2002

6–13

123

8.15

0.005

na

0.3–0.5

152

3.01

9.6

0.034

2007

0–6

845

2.5

20

0.021

2008–2009

0–6

318

1.5

56

C. prolifera

Porcheddu et al. (1998) Addis et al. (2009b) Coppa et al. (2013) Molinari and Bernat (2016) Centoducati et al. (2007) Giacobbe and Leonardi (1987) Present study

Russo (2012) Guallart and Templado (2010) Prado et al. (2014) Richardson et al. (1999) Hendriks et al. (2013) Vázquez-Luis et al. (2014)

Ruppia spp./N. noltii/C. nodosa/sand P. oceanica/C. nodosa/macroalgae

García-March and Kersting (2006) García-March et al. (2011) Zakhama-Sraieb et al. (2011) Rabaoui et al. (2008)

P. oceanica/C. nodosa/C. prolifera

Rabaoui et al. (2010)

P. oceanica

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M. Secci, C. Biancacci, A. Giglioli et al. / Regional Studies in Marine Science 32 (2019) 100884

Fig. 6. Passing and Bablok regression residuals between the observed values by scuba diving on each quadrat versus estimated densities on that position by the geostatistical model.

is in good agreement with previous studies (Addis et al., 2009b; Katsanevakis and Thessalou-Legaki, 2009) and would support the hypothesis by which larval settlement for this species largely depends upon the availability of a suitable habitat and is less affected by local adult density-dependent mechanisms (Addis et al., 2009b). The biotic and abiotic factors operating at different temporal and spatial scales on P. nobilis populations include, among others recruitment, competition for space, predation, as well as changes in environmental conditions, local site-specific hydrodynamics, pollution, and other human stressors (Vicente, 1990; Zavodnik et al., 1991; Butler et al., 1993; Rodríguez et al., 1993; GarcíaMarch et al., 2007a,b; Cabanellas-Reboredo et al., 2009). A recent study pinpointed that human stressors can influence the spatial distribution of the fan shell to a larger extent than global change processes (Deudero et al., 2015). In the present study, dead individuals of the fan mussel have been observed also outside their preferential habitats. This suggests that areas characterized by very few or missing alive specimens (at times replaced by dead specimens only) likely hosted the P. nobilis preferential habitats which, more or less recently, disappeared. Therefore, we cannot exclude that the patchy distribution of P. nobilis in the coastal areas surrounding the Sant’Antioco Island is also the (possibly cumulative) result of previous anthropogenic impacts. Indeed, P. nobilis has ceased to represent a target for the local fisheries, but other pressures, including ‘‘accidental entangling’’ by artisanal fishery, indiscriminate anchoring on seagrass, and illegal bottom trawling, have threatened the local populations, as observed in other areas of the Mediterranean Sea (Katsanevakis, 2006; Basso et al., 2015; Deudero et al., 2015). The size structure of P. nobilis population in the CL zone was spatially heterogeneous. Results showed that the biggest specimens were found only in the CLa and CLc locations, and different dominant size classes characterized the three locations. More specifically we reported: (i) a remarkable dominance of medium sized specimens of 15–20 cm in CLa; (ii) the lack of specimens higher than 20 cm width class in CLb, and (iii) a dominance of individuals in the 20 cm width class in CLc. These results let us hypothesizing that the population of P. nobilis spread within the CL zone could have not shared the same history. In support to this hypothesis, we report here that the differences in the size structure of P. nobilis populations in the three locations (CLa, CLb and CLc) mirrored the observed patterns of spatial autocorrelation. In fact, for each of the three locations, the mean range of spatial

influence (A0 = 1750 m) was smaller than the actual distance between the three locations (range 1800–1900 m). This result suggests that the size structure of P. nobilis populations can vary considerably even at a relatively small spatial scale, i.e. within a few km. Moreover, the presence of three apparently segregated sub-populations in CL supports the hypothesis that in this zone the fan mussels could have been exposed in time to different pressures or the three sites have been subjected to different recruitment events. Indeed, the large variability in the population structure of P. nobilis populations at a relatively small spatial scale observed in this study is plausibly also the result of the patchy distribution of suitable habitats for recruits, which, in turn, could be the result of a complex combination of anthropogenic pressures and biological processes. Nonetheless, the hypothesis of the presence of three populations exposed to different pressures must be considered with caution as our study was conducted well in advance to the recruitment period (in late summer according to Cabanellas-Reboredo et al., 2009) and we could not be able to observe juvenile specimens during the small-scale study. In spite of the presence of a potentially large array of stressors acting even synergistically on the P. nobilis populations in the study area, we notice here that the extension and the abundances of P. nobilis around the S. Antioco Island, for the first time ever estimated using a geostatistical approach, are evidence for the uniqueness of the investigated area. Unfortunately, the lack of previous information does not allow us ascertaining definitely whether the current conditions of the P. nobilis population in the investigated area represent a regression or an expansion phase. We therefore conclude that the technique proposed in this study can represent a valuable tool to for the rapid assessment of the presence/absence and abundances of this endangered species on a Mediterranean Basin scale. The high surfaces covered by this technique with a relative low monitoring effort would make possible long-term studies, which are essential to identify the trends of the species, and resolve the rates of decline, which are currently affecting the whole Mediterranean Sea. Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Acknowledgements We thank Ms. Chiara Vigo, Andrea Manunza and Stefano Corrias for help during the field work. This work was supported by Sardinian Regional Agency for Coastal Conservation (Italy) in the framework of the project ABISSO; CAMP-Italy ICZM program, by the European Union’s Horizon 2020 research and innovation programme in the framework of the project MERCES (grant agreement No. 689518), and by the University of Cagliari, Italy. References Addis, P., Secci, M., Angioni, A., Cau, A., 2012. Spatial distribution patterns and population structure of the sea urchin Paracentrotus lividus (Echinodermata: Echinoidea), in the coastal fishery of western Sardinia: a geostatistical analysis. Sci. Mar. 76, 733–740. http://dx.doi.org/10.3989/scimar.03602.26B. Addis, P., Secci, M., Brundu, G., Manunza, A., Corrias, S., Cau, A., 2009b. Density, size structure, shell orientation and epibiontic colonization of the fan mussel Pinna nobilis L. 1758 (Mollusca: Bivalvia) in three contrasting habitats in an estuarine area of Sardinia (W Mediterranean). Sci. Mar. 73, 143–152. http://dx.doi.org/10.3989/scimar.2009.73n1143. Addis, P., Secci, M., Manunza, A., Corrias, S., Niffoi, A., Cau, A., 2009a. A geostatistical approach for the stock assessment of the edible sea urchin, Paracentrotus lividus, in four coastal zones of Southern and West Sardinia (SW Italy, Mediterranean Sea). Fish. Res. 100, 215–221. http://dx.doi.org/10. 1016/j.fishres.2009.07.008.

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