Correlation between textural characteristics of marine sediments and benthic foraminifera in highly anthropogenically-altered coastal areas

Marine Geology 315–318 (2012) 143–161

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Correlation between textural characteristics of marine sediments and benthic foraminifera in highly anthropogenically-altered coastal areas Maria Celia Magno ⁎, Luisa Bergamin, Maria Grazia Finoia, Giancarlo Pierfranceschi, Francesco Venti, Elena Romano ISPRA, Italian National Institute for Environmental Protection and Research, Via di Castel Romano, 100‐00128, Rome, Italy

a r t i c l e

i n f o

Article history: Received 28 September 2011 Received in revised form 11 April 2012 Accepted 15 April 2012 Available online 24 April 2012 Communicated by J.T. Wells Keywords: Grain size Benthic foraminifera Marine coastal areas Anthropogenic impact

a b s t r a c t The coastal zone is the most variable area in the marine system in terms of environmental parameters and it is characterised by the highest granulometric variability. Considering that the distribution of benthic foraminifera is controlled by several biotic and abiotic factors, including sediment texture, this may be considered one of the main factors influencing foraminiferal assemblage composition. The role of sediment grain size on foraminiferal species distribution has been recognised by several studies, but only in a few cases it has been considered from a quantitative viewpoint. On this rationale, 137 sediment samples collected in three different Italian National Relevance Sites (Bagnoli, Baia and Augusta), affected by different type and degree of pollution, were analysed for grain size and benthic foraminifera. Although the influence of pollution on foraminifera distribution in these areas had been recognised in earlier studies, the present research applied statistical analysis (Pearson Correlation and Co-Inertia Analysis) in order to highlight the correlation between sediment fractions and foraminiferal species. The correlation found in all sites between the most abundant species and specific sediment fractions indicated that sediment grain size is a primary factor controlling species distribution. The effect of sediment type conditions the distribution of previously recognised pollution tolerant species, which show preference for silty and/or clayey sediments. Because muddy sediments favour contaminant accumulation, such species are more exposed to polluted habitats and consequently their tolerance to anthropogenically-altered conditions may be considered an adaptive strategy. © 2012 Elsevier B.V. All rights reserved.

1. Introduction In the last decades, the study of benthic foraminifera has been increasingly used as a reliable tool in environmental characterisation of polluted sites, as well as in pollution monitoring of lagoonal and marine areas (see Nigam et al., 2006 for a review). Changes in foraminiferal density and modifications of assemblage composition and structure (species diversity, dominance) may be related to increasing pollution levels (Yanko et al., 1999). The distribution of benthic foraminifera in unpolluted environments is controlled by several abiotic factors such as grain size, sea-bottom oxygenation, nutrient availability, salinity, temperature, and biotic factors such as predation, competition and reproduction (Murray, 1991, 2006). Alve (1999) found that the colonisation of new habitats by foraminifera after a strong human disturbance is controlled by physical and/or biological processes, depending on which is the dominating process. The preference of species for selected substrates was recognised previously and it is well documented (Blanc-Vernet, 1969; Sgarrella and Moncharmont-Zei, 1993; Debenay et al., 2001; Diz et al., 2004;

⁎ Corresponding author. Tel.: + 39 0650073262. E-mail address: [email protected] (M. Celia Magno). 0025-3227/$ – see front matter © 2012 Elsevier B.V. All rights reserved. doi:10.1016/j.margeo.2012.04.002

Frezza and Carboni, 2009, among the others). Hyams-Kaphzan et al. (2008) found distinct foraminiferal biofacies corresponding to different substrates and they recognised sediment type as the main factor influencing species distribution. Armynot du Châtelet et al. (2009) found that both density and species richness are clearly linked with sediment grain size, which was considered, with organic carbon, the main factor limiting species distribution. Nevertheless, few authors have faced this topic from a quantitative viewpoint. Alve and Murray (1999) recognised a significant correlation between the relative abundance of selected species against the mud percentage. Mendes et al. (2004) plotted the abundance of some species into the granulometric ternary diagrams of Folk (1954), pointing out the preference of some species for selected sediment types. Statistical analysis may be a very helpful tool to highlight correlations between foraminifera and single sediment fractions. Hayward et al. (1996), applying the Canonical Correspondence Analysis, found that the percentage of mud is one of the main factors that determined the faunal distribution in a tidal inlet in the New Zealand. Abu Zied et al. (2007) used Redundancy Analysis in order to find the correlation between foraminiferal species and environmental parameters such as grain size, total organic carbon, carbonate content and total dissolved solids in the Qarun salt lake (Egypt). Donnici and Serandrei Barbero (2002) pointed out, in samples collected on the Northern

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Adriatic continental shelf, a linear correlation between the abundance of 43 species and environmental parameters like water depth, total organic carbon, total organic nitrogen, sand and mud. In Italy, the study of foraminifera has been included as experimental research within the environmental characterisation of marine areas of some National Relevance Sites (Bergamin et al., 2003, 2005). In this context, three marine coastal areas, Bagnoli and Baia, in the Pozzuoli Gulf (Naples), and Augusta, in the Eastern coast of Sicily, offered a good opportunity to study the relationship between foraminifera distribution and sediment grain size in the coastal zone, where the highest sediment variability is active. This research highlights the preference of single species for selected sediment fractions by means of statistical analysis and recognises some general patterns in correlations between foraminifera and grain size distribution. The three studied areas are affected by high anthropogenic impact. Discussion centers on whether sediment maintains its primary influence on foraminifera distribution, widely demonstrated for marine coastal areas, under anthropogenically-altered conditions. Data concerning foraminifera distribution and sediment grain size are considered and grain-size influence is compared to previously studied effects of pollution on the foraminiferal assemblages (Bergamin et al., 2009; Romano et al., 2009a, 2009b). The objective is to recognise, on the whole, the dynamics which regulate the distribution of foraminifera in polluted sites. 2. Regional setting The selected marine areas have the common feature of being located in the coastal zone of highly disturbed regions, which have been affected by specific industrial and/or harbour activities. However, the sampling plan also includes wide areas with sectors which may be considered relatively unpolluted with respect to the primary source of local contamination, but affected by the basic pollution of densely inhabited areas. 2.1. Bagnoli and Baia areas The industrial site of Bagnoli and the Baia coastal area are located in the eastern and western sector of the Pozzuoli Gulf (Naples), respectively (Figs. 1, 2). They are included in the active Campi Flegrei volcano-tectonic district and have long been subjected to subsidence (Russo et al., 1998). The main environmental issue of the Pozzuoli Gulf is represented by the Bagnoli industrial site, which was active nearly the whole 20th century with an important steel plant. Due to the high contamination, the industrial site and the neighbouring marine area were included in the National Relevance Site named “Coroglio Bagnoli”. The marine water circulation in the study area is characterised by a high-energy cell in the northern sector, to the north of Bagnoli, which determines the deposition of coarser sediment far from the coast. Conversely, lower energy characterises the sector in front of the industrial site, where the deposition of fine sediment is favoured by the presence of industrial structures (De Pippo et al., 2002; Romano et al., 2009a). Romano et al. (2004) found very high concentrations of contaminants, mainly Fe (up to 59.7%), Pb (up to 896 mg/kg), Zn (up to 2313 mg/kg), and PAHs (up to 2888 mg/kg) in some stations, considered also for the present study, in front of the industrial site. Romano et al. (2009a) recognised the northernmost and southernmost stations of the present study as relatively unpolluted, although a slight contamination by PAHs, typical of densely inhabited zones, was present. Baia is the most important archaeological site in the Pozzuoli Gulf and the overlooking marine area is included in the protected area named “Baia Underwater Park”, created in 2000 by the Italian Ministry of Environment. Only pleasure boating is currently practiced in the area and a shipyard is located on the headland within the bay. In past decades, a harbour devoted to commercial activities was operative. The

presence for many years of five abandoned sunken vessels, at two different locations in the northern and southern part of the study area, determined environmental degradation. At the time of the study, only one of these vessels was still in place, while the other ones were removed in 2005. Due to this environmental concern the Baia coastal zone was included in the National Relevance Site named “Litorale Domitio Flegreo ed Agro Aversano”. The bay is characterised by no significant freshwater runoff and, consequently, by a low salinity variability (37.72–38.03), along the water column (Bergamin et al., 2009). The central part of the bay, between 2 and 10 m water depth, is characterised by the presence on the sea-bottom of a wide Caulerpa prolifera meadow. Bergamin et al. (2009) recognised Pb, Zn, PAHs (up to 4361 mg kg − 1, 421 mg kg − 1, and 23.541 mg kg− 1, respectively) and, above all, PCBs (up to 495.97 ng g− 1) as main pollutants of sediments close to the wreck areas and a lower degree of contamination, mainly due to PAHs and PCBs in the central and outermost area. 2.2. Augusta area The Augusta harbour is located in a natural bay, with a mean water-depth of 14.9 m, delimited in the northern sector by the town of Augusta and closed to the South and to the East by artificial dams (Fig. 3). Two inlets allow the connection with the open sea. The mainland at the back of the Augusta harbour is part of the Hyblean plateau, which is constituted by carbonatic sequences from Cretaceous to Quaternary Age, with volcanoclastic and volcanic interbedded horizons and muddy sediments and calcarenites outcropping in the hinterland of the study area (Nigro and Renda, 2000; Tortorici, 2000). Three small streams outflow in the western part of the harbour, but their contributions, with regards to freshwater and sediments, are generally minimal. In the southernmost part of the harbour patchy areas of emerging rocky substrate are present nearshore (Anonymous, 1992, 1995). The Augusta harbour hosts one of the most important Italian petrochemical centres. Although since the 1970s some industries have been closed, several oil-refineries and petrochemical industries are still active, with heavy consequences on the terrestrial and marine environment (Ausili et al., 2008). All the activities are responsible for heavy environmental impact and this is the reason for including the Augusta harbour in the National Relevance Site named “Priolo Gargallo”. Environmental degradation of this marine area is characterized by very high concentrations of Hg, PAHs and PCBs (up to 322 mg kg − 1, 19.468 mg kg − 1 and 3754 ng g − 1 respectively) found in the southern sector of the harbour, close to a former chlor-alkali plant. Considerably lower concentrations of Hg, PAHs and PCBs were detected in the other areas (Romano et al., 2009b). 3. Materials and methods A total of 64, 36 and 37 marine sediment samples were collected respectively from soft-sediment bottoms of the Bagnoli industrial area (Fig. 1), the Baia Bay (Fig. 2) and the Augusta Harbour (Fig. 3). Samples were collected using a van Veen grab and sub-samples of about 50 cm 3 were taken from the upper 2-cm layer for grain size and foraminifera analyses. The location of all sampling stations was determined by Differential Global Positioning System (DGPS). Samples for foraminifera analysis were stained by using a Rose Bengal/ ethanol solution (1 g L − 1). 3.1. Grain size Each sample was treated twice with a hydrogen peroxide (30%) and distilled water solution in proportion 1:3 for 24–48 hours at room temperature, and then washed twice with natural water. Then, samples were wet-separated into two grain size fractions (>63 μm and b63 μm) which were heat-dried and finally weighed. The coarser fraction (>63 μm) was dry-sieved by means of ASTM series sieves with meshes

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Fig. 1. Bagnoli: sampling stations. Label of stations marked only by numbers in the map are preceded by “BA01/” in text, tables and appendices.

ranging from −1 to +4 ϕ, and intervals of 0.5 ϕ. The weight of the whole coarse fraction was calculated through the sum of weights of the fractions withheld by each sieve. The fraction b63 μm, oven dried at 40 °C, was split into sub-samples and put in suspension in a solution of distilled water and sodium hexametaphosphate (0.05%) at the rate of 2.5 g of sample for 80 ml of solution, subjected to bath of ultrasounds for 2 minutes and then analysed by means of a X-ray sedigraph (Micromeritics Sedigraph 5100®). In this study, the limits of sediment fractions according to Wenthworth (1922) were considered and sediment types were determined according to the classification of Shepard (1954). Due to a considerable amount of gravel in sediment collected at Baia, the ternary diagram of Shepard was modified putting gravel, sand and mud as main components. Mud was considered as the sum of silt and clay.

3.2. Foraminifera Samples were washed over a 63 μm sieve in order to eliminate mud particles and staining solution, and then oven dried at 40 °C. The quantitative analysis was carried out by hand-picking on aliquots of dry sediment obtained by splitting the residue into subsamples containing at least 300 specimens (Blanc-Vernet, 1969). Quantitative data from the total assemblage were considered for this study. Scott and Medioli (1980) assessed the validity of using the total assemblage in ecological studies because they found that the high seasonal variability of the living assemblages may be attributed to seasonal meteorological changes and does not represent changes in the prevailing marine environment. Morvan et al. (2006) found that isolated sampling of living assemblages may provide different or even contradictory results, while only the total assemblage is useful to obtain integrated

information over a given period of time, unless repeated sampling of living assemblage during the year is carried out (Debenay et al., 2006). In the present study, due to the very low number of stained tests (not exceeding 5% of the total assemblage), living foraminifera may be considered not significant from a statistical viewpoint and the total assemblage is highly similar to the dead one (Murray, 2000). Because the problem of using the total assemblage is that the dead one, contrary to the living one, may be affected by post-mortem changes, we paid particular attention to recognise possible evidence of test transport, such as mechanical damage. Evidences of dissolution was not found. According to Leorri and Cearreta (2009), the dead assemblage represents a time-averaged accumulation of foraminiferal tests and is not influenced by seasonal variability of environmental parameters. Consequently, the influence of sediment characteristics, which normally are not conditioned by short-term environmental changes, may be more recognisable in the dead foraminiferal assemblage than in the living ones. In this study, only well-preserved tests were picked, counted and classified. The generic classification was made according to Loeblich and Tappan (1987) and most species were determined by comparison with those identified by Jorissen (1988), Cimerman and Langer (1991) and Sgarrella and Moncharmont-Zei (1993).

3.3. Data processing Results from grain-size and foraminifera analysis were processed by means of bivariate and multivariate statistical analysis using respectively the SPSS (version 12.0) and R (version 2.0.1) software. With regards to foraminifera, only commonly occurring species (i.e.

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Fig. 2. Baia: sampling stations. The dotted rectangles delimit the northern and southern “wreck area”. The light dotted line delimits the Caulerpa meadow. Labels of stations are preceded by “DF02/” in text, tables and appendices.

more abundant than 5% in at least one sample) were considered (Fishbein and Patterson, 1993). Data from grain-size analysis and foraminifera analysis were organised into a single matrix in order to find correlations between pairs of variables. The Bivariate Correlation (BC) was calculated by the Pearson's index. This coefficient is considered significant only when a linear correlation between two variables exists and, in this study, it was used to demonstrate the correlation between relative abundance of single species and sediment fractions (Debenay et al., 2001). The Co-Inertia Analysis (CIA) was applied to two distinct matrices including respectively grain size and foraminiferal data, in order to compare two distinct factorial analyses. The main function of this data analysis is the matching of two data tables: a “sites x environmental variables” table and a “sites x species” table, in order to study the relationships between species and environmental data (Dray et al., 2003). The Monte Carlo Test was applied to the CIA in order to verify the goodness of the matching of the two data tables (Heo and Gabriel, 1997).

Distributional maps were drawn by means of Surfer (8.0), a gridbased mapping program that interpolates irregularly spaced XYZ data into a regularly spaced grid, applying the kriging geostatistical method. 4. Results: Bagnoli area 4.1. Grain-size distribution The analysis of grain size (Table 1) shows sediments mainly constituted by sand and silty sand. Sand prevails with the highest value of 99.2% and a mean of 83.5%. Among sandy fractions, fine sand and very fine sand are more abundant (mean 31.9% and 30.4%, respectively). Sand is generally more abundant nearshore, with lower percentages between the two piers, in front of the plant (Fig. 4). The fine fractions, silt and clay, are rather scarce (mean 10.1% and 1.7%, respectively), with silt always prevailing over clay. They may be considered on the whole as mud, which has an opposite trend with respect to sand and reaches the highest values in the offshore stations and

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Fig. 3. Augusta: sampling stations. Labels of stations are preceded by “PR01/” in text, tables and appendices.

high values between the two piers. Gravel is generally scarce (mean 4.4%), but has some peak values (up to 45.4%) at the North of the plant and around the Nisida Island.

subrotunda is more prevalent in the offshore area, with significant percentages also in front of the plant. 4.3. Relationship between foraminifera and grain size

4.2. Foraminifera In total, 42 commonly occurring species were found in 64 analysed samples (Appendix A). Among these, 1 belongs to Textulariina, 18 belong to Miliolina and 23 belong to Rotaliina. Considering the whole area, the most abundant species are Quinqueloculina lata, with 1421 specimens, and Quinqueloculina stelligera, with 1390 specimens. Secondly, Elphidium advena, with 950 specimens, and Miliolinella subrotunda, with 695 specimens, are very common. These four species reach the highest relative abundance in a sample with 31%, 31%, 39% and 13%, respectively. The two Quinqueloculina species have a very similar distribution, with the highest percentages in the nearshore area, mainly in front of the plant (Fig. 5). Elphidium advena reaches high percentages in a small, localised area near the southern pier. Miliolinella

Several significant correlations between foraminiferal species and grain-size fractions have been found (Table 2). A group of 17 species show a positive correlation with one or more sandy fractions. Among these, some species such as Asterigerinata mamilla, Elphidium crispum, Lobatula lobatula and Textularia truncata have positive correlation also with gravel. In addition, most species show positive correlation with specific sandy fractions and negative correlations with other fractions. Particularly, Ammonia inflata, Elphidium aculeatum, Lobatula lobatula, Siphonaperta aspera and Textularia truncata have positive correlation with coarse or medium sediment fractions and negative correlation with finer ones, while Buccella granulata, Elphidium advena, Quinqueloculina lata and Quinqueloculina stelligera show the opposite behaviour. A smaller group including 7 species show positive correlation with the muddy fractions. Among these Bolivina

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Table 1 Bagnoli: percentages of grain-size fractions and sediment nomenclature according to Shepard (1954). Sand is subdivided into five classes. Station label

Gravel

Very coarse sand

Coarse sand

Medium sand

Fine sand

Very fine sand

Silt

Clay

Shepard nomenclature (modified)

BE16 BE2 BE23 BE25 BE31 BE35 BE39 BES15 BES3 BES6 BES9 F1 F3 G4 H4 R1 SF1 SF2 SF3 SF4 SF5 SF6 SF7 SF8 SF9 T1 T3 BA01/4 BA01/9 BA01/11 BA01/13 BA01/26 BA01/28 BA01/29 BA01/31 BA01/37 BA01/39 BA01/42 BA01/47 BA01/49 BA01/56 BA01/62 BA01/67 BA01/74 BA01/77 BA01/78 BA01/82 BA01/84 BA01/85 BA01/86 BA01/88 BA01/90 BA01/91 BA01/94 BA01/95 BA01/96 BA01/99 BA01/102 BA01/110 BA01/111 BA01/112 BA01/114 BA01/116 BA01/122

0.0 0.1 0.0 0.0 0.0 0.0 0.6 0.2 2.0 17.8 1.3 0.0 0.0 0.0 0.0 0.0 0.8 0.8 0.8 0.0 9.2 1.0 4.3 38.0 0.1 1.3 0.0 0.2 0.2 2.9 4.2 0.0 0.0 0.6 3.6 0.5 0.0 1.1 0.0 7.8 0.0 0.0 0.7 4.8 0.2 0.0 0.0 0.4 7.9 15.4 0.0 3.5 3.5 21.2 22.4 5.9 1.1 15.6 0.1 0.1 45.4 1.2 12.0 0.3

0.0 0.2 0.4 0.1 0.2 0.1 0.3 0.6 1.2 11.0 2.2 0.8 0.6 0.3 0.0 0.2 0.4 0.5 0.2 0.0 9.7 5.3 11.4 34.5 0.3 2.0 0.8 0.4 0.3 2.2 2.7 0.3 0.4 0.4 2.3 0.2 0.6 1.0 0.3 5.5 0.2 0.1 0.9 1.7 0.3 0.7 0.6 2.2 25.8 4.1 1.4 9.6 12.8 32.9 32.9 5.8 0.8 8.7 0.4 0.5 18.9 0.8 2.3 0.9

0.2 1.2 0.7 0.6 0.2 1.0 0.7 1.6 1.8 11.0 2.6 1.4 1.6 1.0 0.9 0.6 1.8 1.2 1.3 0.1 34.2 23.3 51.0 25.5 0.7 6.1 1.4 0.8 0.6 2.6 3.7 0.4 0.5 0.5 2.5 0.3 0.5 1.4 0.8 8.1 0.8 0.2 1.7 4.0 0.8 6.5 1.5 6.9 17.1 3.3 10.4 42.9 34.3 36.6 34.2 6.2 2.4 23.8 4.4 2.2 7.3 1.0 2.5 0.7

2.4 9.0 2.6 3.8 0.4 8.6 3.4 10.8 8.1 10.9 5.1 5.0 8.1 6.6 6.0 3.2 10.8 5.3 13.4 0.8 40.1 53.3 31.9 1.2 5.5 21.0 5.0 3.9 6.4 7.0 7.5 1.9 2.8 2.9 4.9 0.9 1.6 2.8 3.6 18.7 3.0 0.8 4.4 17.1 6.4 21.1 9.3 21.9 25.8 5.1 41.9 33.9 13.3 8.4 8.3 6.3 7.0 36.0 26.7 11.6 3.1 2.2 3.4 1.4

35.0 62.8 32.0 29.6 15.4 50.0 30.3 57.5 51.7 23.5 35.0 55.9 43.5 52.6 31.4 50.3 60.5 31.2 56.8 34.9 6.4 16.2 1.2 0.2 53.5 51.9 55.9 32.7 48.6 27.0 30.7 21.8 29.5 30.8 15.7 15.5 26.0 14.5 29.4 36.7 45.2 35.8 27.5 40.4 50.3 59.4 54.8 55.1 18.0 12.2 35.0 6.3 9.1 0.5 0.3 21.2 47.3 14.5 52.8 46.9 5.6 6.6 9.3 4.3

49.1 25.1 47.9 44.8 63.7 36.1 45.3 25.5 29.1 18.2 41.0 35.1 35.5 32.7 45.3 41.0 21.5 41.6 25.7 53.1 0.3 0.8 0.2 0.1 37.3 17.1 35.0 55.7 40.7 36.1 42.5 48.9 50.2 49.2 39.8 51.0 52.9 43.6 51.4 18.1 45.3 56.4 49.4 25.3 38.1 10.9 28.0 13.1 1.9 35.8 7.4 1.5 12.7 0.2 0.2 34.9 39.6 0.7 14.7 35.9 11.4 20.0 19.0 16.3

11.8 1.6 15.3 19.2 16.7 4.2 17.1 3.8 6.1 7.6 11.1 1.1 9.4 6.8 14.8 4.7 4.2 16.3 1.8 9.9 0.1 0.1 0.0 0.5 2.6 0.5 1.9 5.4 3.2 19.7 7.6 24.5 15.3 14.0 27.4 28.8 16.2 30.6 13.7 3.8 4.5 5.5 13.0 5.5 3.9 1.4 4.7 0.4 3.5 18.0 3.9 2.3 10.1 0.2 1.7 14.0 1.8 0.7 0.9 2.8 6.8 54.4 40.1 58.5

1.5 0.0 1.1 1.9 3.4 0.0 2.3 0.0 0.0 0.0 1.7 0.0 1.3 0.0 1.6 0.0 0.0 3.1 0.0 1.2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.9 0.0 2.5 1.1 2.2 1.3 1.6 3.8 2.8 2.2 5.0 0.8 1.3 1.0 1.2 2.4 1.2 0.0 0.0 1.1 0.0 0.0 6.1 0.0 0.0 4.2 0.0 0.0 5.7 0.0 0.0 0.0 0.0 1.5 13.8 11.4 17.6

sand sand sand sand sand sand sand sand sand gravelly sand sand sand sand sand sand sand sand sand sand sand sand sand sand gravelly sand sand sand sand sand sand muddy sand sand muddy sand sand sand muddy sand muddy sand sand muddy sand sand sand sand sand sand sand sand sand sand sand sand muddy sand sand sand sand sand sand muddy sand sand sand sand sand gravelly sand sandy mud sandy mud sandy mud

variabilis, Bulimina aculeata, Bulimina elongata, Miliolinella subrotunda, Nonionella atlantica, and Rectuvigerina phlegeri are correlated both with silt and clay, while only Quinqueloculina parvula is positively correlated with very fine sand and silt. A peculiar behaviour that warrants further research in the future is shown by Gavelinopsis praegeri, which is positively correlated with gravel, coarse sand, very fine sand, but also with clay.

From the CIA it was possible to highlight some significant correlations (Fig. 6). Particularly, gravel, very coarse sand and coarse sand are correlated with Elphidium crispum and Lobatula lobatula, mainly in samples located in the northern sector of the study area, such as SF8, BA01/94 and BA01/95 (Fig. 1). Medium sand is correlated with Siphonaperta aspera, mainly in samples from the southern sector (stations BA01/110, BA01/78 and T1). Quinqueloculina stelligera and

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149

Fig. 4. Distribution of gravel, sand and mud percentages in the Bagnoli area. Due to the low percentage of clay, the sum of silt and clay (mud) was represented in the map.

Quinqueloculina lata are distinctly correlated with very fine sand mainly in samples located in front of the plant (stations BA01/37 and BA01/62). Finally, Miliolinella subrotunda and Bulimina aculeata appear correlated with silt, mainly in samples BA01/116 and BA01/ 122 from the offshore area. No one species is correlated with fine sand.

(mean 23.8%), with silt always prevailing over clay (mean 17.9% and 6%, respectively). The highest concentration of sand is recorded nearshore, in the northern sector, and offshore, while mud is more abundant in the central and southern sector close to the coast (Fig. 7). Gravel percentages are generally low (mean 9.1%), with the highest values, up to 33.2%, recorded mainly far from the coast, in correspondence of the Caulerpa meadow.

5. Results: Baia area 5.2. Foraminifera 5.1. Grain size distribution Sediments collected in the Baia marine area are classified as sand or silty sand. The results (Table 3) demonstrate that sand is always the prevailing fraction (up to 97.6%, mean 68.3%) and, among sandy fractions, fine and very fine sand are the most abundant, with means of 21.6% and 29.2%, respectively. Mud is also rather abundant

Among the 25 commonly occurring species found in the Baia area, 7 belong to Miliolina and 18 to Rotaliina (Appendix B). Lobatula lobatula, Rosalina bradyi and Ammonia tepida are the most abundant species, with a total abundance of respectively 810, 759 and 595 specimens counted and classified, and the highest relative abundance in a sample of respectively 23%, 21% and 17%. The first two species show a

Fig. 5. Distribution of the most abundant foraminiferal species percentages in Bagnoli area.

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Table 2 Bagnoli: Pearson correlation between pairs of species and sediment fractions. Black bold: correlation is significant at the 0.01 level. Grey bold: correlation is significant at the 0.05 level.

Gravel Ammonia inflata Asterigerinata mamilla Bolivina variabilis Buccella granulata Bulimina aculeata Bulimina elongata Elphidium aculeatum Elphidium advena Elphidium crispum Gavelinopsis praegeri Lobatula lobatula Miliolinella semicostata Miliolinella subrotunda Neoconorbina terquami Nonionella atlantica Quinqueloculina auberiana Quiqueloculina bosciana Quinqueloculina lata Quinqueloculina parvula Quinqueloculina stelligera Rectuvigerina phlegeri Rosalina bradyi Siphonaperta aspera Textularia truncata

Medium sand

Fine sand

Very fine

Silt

Clay

0.215 0.383

Very coarse sand 0.536 0.475

Coarse sand 0.336 0.285

0.070 −0.019

−0.274 −0.293

−0.285 −0.195

−0.076 −0.134

−0.038 0.002

−0.021

0.111

0.052

−0.159

−0.245

−0.024

0.327

0.352

−0.285

−0.263

−0.267

−0.198

0.355

0.319

−0.145

−0.183

−0.073

−0.142

−0.198

−0.246

−0.256

0.101

0.620

0.624

0.067

−0.053

− 0.131

−0.240

−0.312

0.147

0.455

0.354

0.178

0.042

0.171

0.382

−0.039

−0.263

−0.163

−0.121

−0.237

−0.275

−0.293

−0.270

0.257

0.393

−0.072

−0.115

0.504

0.815

0.543

0.103

−0.398

−0.464

−0.217

−0.146

0.263

0.237

0.285

0.069

−0.416

−0.239

0.200

0.269

0.536

0.472

0.245

0.077

−0.242

−0.293

−0.199

−0.062

−0.067

−0.005

0.139

0.428

−0.047

−0.181

−0.126

−0.090

0.164

−0.128

−0.219

−0.095

−0.207

0.031

0.503

0.433

0.118

0.146

0.155

0.288

−0.156

−0.175

−0.113

−0.111

−0.167

−0.164

−0.142

−0.014

0.053

−0.075

0.311

0.332

0.085

0.134

0.171

0.334

−0.136

−0.194

−0.126

−0.097

−0.214

−0.234

−0.282

−0.211

0.108

0.235

0.234

0.132

−0.265

−0.371

−0.364

−0.303

0.095

0.515

0.227

0.011

−0.112

−0.291

−0.273

−0.096

−0.060

0.275

0.339

0.118

−0.255

−0.309

−0.365

−0.401

0.011

0.568

0.246

0.083

0.111

0.007

−0.032

−0.150

−0.321

−0.089

0.496

0.638

0.266 0.119

0.422 −0.082

0.288 0.056

0.130 0.469

−0.210 0.236

−0.231 −0.288

−0.261 −0.243

−0.136 −0.194

0.275

0.281

0.260

0.330

−0.252

−0.328

−0.089

−0.024

comparable distribution, with high percentages in the central part of the bay and nearshore in the southern sector (Fig. 8). Nevertheless, Rosalina bradyi mainly increases in the central study area corresponding to the Caulerpa meadow, while Lobatula lobatula shows the highest percentages in the southern bay. On the contrary, Ammonia tepida is more abundant in the nearshore stations of the northern sector. 5.3. Relationship between foraminifera and grain size The BC showed a positive correlation between 18 species and one or more sandy fractions (Table 4). Among these, some species such as Ammonia beccarii, Elphidium crispum, Lobatula lobatula and Rosalina bradyi show positive correlation with coarse sandy fractions and gravel, and negative correlation with the fine sandy fraction. Conversely, Ammonia parkinsoniana, Elphidium pulvereum, Haynesina depressula and Quinqueloculina lata show negative correlation with coarse sandy fractions and positive correlation with the fine ones. Peneroplis pertusus shows positive correlation with sandy fractions and negative correlation with the muddy ones. Other species such as

Ammonia tepida, Buccella granulata and Gavelinopsis praegeri show only the positive correlation with very fine sand. Few species have positive correlation with muddy fractions. Among these, Bulimina elongata and Haynesina germanica have positive correlation both with silt and clay, while Bolivina variabilis is positively correlated with silt and Miliolinella subrotunda with clay. The CIA highlighted some significant correlations (Fig. 9). Species with negative values for the first axis, such as Lobatula lobatula, Rosalina bradyi, Rosalina floridana and Ammonia beccarii, are mainly correlated with gravel, very coarse and coarse sand in samples mostly located in the central area and covered by the Caulerpa meadow (stations DF02/24, DF02/36, DF02/37 and DF02/38, Fig. 2). Conversely, a large group of species with positive values for the first axis (Ammonia tepida, Ammonia parkinsoniana, Buccella granulata, Quinqueloculina lata, Quinqueloculina stelligera, Elphidium advena, Elphidium pulvereum, Haynesina depressula) is correlated with very fine sand, mainly in samples located in the northern coastal area named “wreck area” (station DF02/02, DF02/08, DF02/10 and DF02/11). Haynesina germanica and Bulimina elongata, which have negative values for the second axis, are

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Fig. 6. Bagnoli: Co-Inertia Analysis. a) PCA applied to grain size: plot of factors loading on the Co-Inertia plane; b) PCA applied to foraminiferal data: plot of factors loading on the Co-Inertia plane; c) plot of factor scores on the Co-Inertia plane; d) histogram of Eigenvalues. The Monte Carlo Test applied to the Co-Inertia Analysis demonstrated the significant association between the two data sets (r = 0.44; p = 0.001).

correlated with silt, in samples DF02/26, DF02/30 and DF02/31 mainly, collected close to the headland. Similar to the Bagnoli area, no one species is correlated with fine sand.

absent or very scarce and it is only locally present with significant percentages (up to 29.4%). 6.2. Foraminifera

6. Results: Augusta area 6.1. Grain size distribution Sediments collected in Augusta Harbour cover a wide range of granulometric classes, comprised between sand and silty clay (Table 5). Silt and clay are the prevailing fractions, with the highest values (up to 55.2% and 53.5%, respectively) in the northern sector where they appear rather homogeneously distributed, with means of 34.6% and 33.9%, respectively. The total percentage of sand (mean 30.1%) reaches high values (up to 88%) nearshore, in the central sector, and near to the southern inlet (Fig. 10). Among sandy fractions, fine and very fine sand (mean 10.1% and 11.3%, respectively) are the most common. Gravel is generally

Among the 34 commonly occurring species recognised in Augusta Harbour, 7 belong to Miliolina and 27 to Rotaliina (Appendix C). Rosalina bradyi and Ammonia tepida are the most abundant species, with 657 and 539 specimens counted and classified. They reach the highest percentages in a sample of 19% and 28%, respectively. Secondly, Miliolinella subrotunda and Lobatula lobatula are also very common, with a total of 458 and 445 specimens, and have highest percentages in a sample of 16% and 22% respectively. The distribution of Rosalina bradyi and Ammonia tepida appears to be complementary in nearshore stations because the first is more abundant in the southern sector, while the second in the northern sector (Fig. 11). Particularly, Ammonia tepida shows the highest abundance in correspondence of small river outflows. Lobatula lobatula covers partly the same area of Rosalina

152

M. Celia Magno et al. / Marine Geology 315–318 (2012) 143–161

Table 3 Baia: percentages of grain size-fractions and sediment nomenclature according to Shepard (1954), modified taking into account the gravelly fraction (see text). Sand is subdivided into five classes. Station label

Gravel

Very coarse sand

Coarse sand

Medium sand

Fine sand

Very fine sand

Silt

Clay

Shepard nomenclature (modified)

DF02/1 DF02/2 DF02/3 DF02/4 DF02/5 DF02/6 DF02/7 DF02/8 DF02/9 DF02/10 DF02/11 DF02/12 DF02/13 DF02/14 DF02/15 DF02/16 DF02/17 DF02/18 DF02/19 DF02/20 DF02/21 DF02/22 DF02/23 DF02/24 DF02/25 DF02/26 DF02/27 DF02/28 DF02/29 DF02/30 DF02/31 DF02/32 DF02/33 DF02/36 DF02/37 DF02/38

0.1 0.0 3.1 0.0 0.0 6.8 1.2 0.9 4.5 0.0 1.3 14.9 8.7 2.7 5.5 23.0 19.2 9.1 8.7 11.0 18.1 11.5 20.8 31.5 0.0 0.0 1.8 16.1 7.1 17.0 0.0 2.2 4.5 33.2 9.7 19.8

0.2 0.2 1.8 0.0 0.0 2.9 0.8 0.8 2.6 0.1 1.0 4.1 5.4 5.4 6.2 4.7 4.5 4.9 4.8 4.4 12.7 2.8 10.8 14.0 0.5 0.0 3.9 6.7 6.4 4.3 0.5 1.4 1.1 11.0 6.6 7.6

0.8 0.3 3.9 0.1 1.3 3.6 1.1 0.8 3.3 0.2 0.6 4.5 5.7 5.5 4.8 5.8 4.0 5.2 5.9 5.5 24.1 1.9 11.6 13.2 1.0 0.2 14.5 9.1 5.3 3.3 0.5 3.7 1.6 11.9 7.5 8.2

6.4 1.5 19.4 1.0 7.2 10.5 7.2 2.0 9.7 1.0 1.3 5.0 8.6 6.7 5.6 10.5 5.2 5.9 17.4 8.5 29.8 2.5 9.9 11.6 2.9 1.1 40.9 14.0 6.5 3.7 1.3 20.8 6.8 12.1 8.2 8.1

48.1 33.5 54.8 36.3 31.5 42.2 46.1 24.0 24.6 31.9 16.8 7.8 20.2 17.9 10.1 12.6 10.2 11.4 27.4 14.4 7.2 10.5 8.6 7.9 29.1 7.8 33.6 19.2 9.5 6.7 11.8 31.2 28.1 9.2 10.1 10.1

42.1 55.0 16.2 58.0 42.2 26.5 40.4 49.0 20.3 60.9 50.3 14.9 28.2 29.6 18.3 17.5 19.7 20.5 19.2 27.8 4.4 41.9 17.5 8.0 58.5 36.3 3.6 20.8 19.9 18.2 53.2 15.2 32.8 9.4 20.2 18.4

2.3 7.2 0.8 4.6 14.0 5.3 3.2 17.9 25.5 5.3 21.1 36.1 15.4 23.4 33.3 18.3 26.6 31.1 13.2 22.5 3.7 23.4 16.1 11.2 6.4 43.6 1.7 10.9 32.0 35.8 25.2 19.6 19.2 9.6 27.1 20.4

0.0 2.3 0.0 0.0 3.8 2.2 0.0 4.6 9.5 0.6 7.6 12.7 7.8 8.8 16.2 7.6 10.6 12.0 3.4 5.9 0.0 5.5 4.7 2.6 1.6 11.0 0.0 3.2 13.3 11.0 7.5 5.9 5.9 3.6 10.6 7.4

sand sand sand sand sand sand sand sand muddy sand sand muddy sand sandy mud muddy sand muddy sand sandy mud gravel sand mud muddy sand muddy sand muddy sand muddy sand sand muddy sand gravel sand mud gravelly sand sand muddy sand sand muddy sand muddy sand sandy mud muddy sand muddy sand muddy sand gravelly sand muddy sand muddy sand

bradyi, while Miliolinella subrotunda is more abundant in the offshore stations, mainly in correspondence to the inlets. 6.3. Relationship between foraminifera and grain size A group of 10 species show a positive correlation with one or more sandy fractions (Table 6). Among these, Ammonia parkinsoniana, Asterigerinata planorbis, Nonion fabum and Triloculina plicata have

positive correlation only with fine sand. Other species, such as Asterigerinata mamilla, Cassidulina carinata, Elphidim crispum, Lobatula lobatula and Rosalina bradyi, show positive correlation with more classes of sand. Most species having positive correlation with sandy fractions have also negative correlations with muddy fractions. A group of 11 species display positive correlation with muddy fractions. Among these, only Sigmoilinita costata is positively correlated both to silt and clay, while Ammonia tepida, Bolivina seminuda,

Fig. 7. Distribution of gravel, sand and mud percentages in the Baia area. Due to the low percentage of clay, the sum of silt and clay (mud) was represented in the map.

M. Celia Magno et al. / Marine Geology 315–318 (2012) 143–161

153

Fig. 8. Distribution of the most abundant foraminiferal species percentages in the Baia area.

Haynesina depressula and Hopkinsina pacifica are correlated to silt, and Bolivina pseudoplicata, Bulimina elongata, Nonionella atlantica and Rectuvigerina phlegeri are correlated to clay.

The CIA (Fig. 12) confirms the correlation of fine sand with Ammonia parkinsoniana, Asterigerinata planorbis, Nonion fabum and Triloculina plicata, which show negative values for the first axis and positive values

Table 4 Baia: Pearson correlation between pairs of species and sediment fractions. Black bold: correlation is significant at the 0.01 level. Grey bold: correlation is significant at the 0.05 level.

Gravel Ammonia beccarii Ammonia parkinsoniana Ammonia tepida Bolivina variabilis Buccella granulata Bulimina elongata Cornuspira involvens Elphidium aculeatum Elphidium advena Elphidium crispum Elphidium pulvereum Gavelinopsis praegeri Haynesina depressula Haynesina germanica Lobatula lobatula Miliolinella subrotunda Peneroplis pertusus Quinqueloculina lata Quinqueloculina stelligera Rosalina bradyi Rosalina floridana Rosalina obtusa

Fine sand

Very fine sand

0.476

Very coarse sand 0.559

Coarse sand 0.398

Medium sand 0.235

−0.362

−0.495

0.142

0.205

−0.293

−0.419

−0.437

−0.434

0.002

0.532

0.137

−0.044

−0.189

−0.287

−0.305

−0.240

0.168

0.338

−0.071

−0.140

−0.163

−0.169

−0.203

−0.370

−0.278

0.304

0.342

0.247

−0.186

−0.275

−0.251

−0.269

0.198

0.462

−0.239

−0.283

0.039

−0.034

−0.148

−0.252

−0.332

−0.017

0.442

0.529

−0.283

−0.279

−0.312

−0.339

−0.078

0.268

0.309

0.268

0.058

0.089

0.172

0.422

−0.082

−0.393

0.140

0.158

−0.138

−0.187

−0.157

−0.272

0.022

0.499

−0.204

−0.254

0.370

0.448

0.468

0.345

−0.349

−0.526

0.169

0.239

−0.237

−0.316

−0.452

−0.549

−0.065

0.491

0.215

0.138

−0.069

−0.203

−0.230

−0.312

−0.089

0.346

0.047

0.032

−0.295

−0.405

−0.408

−0.421

−0.161

0.538

0.246

0.082

−0.035

−0.156

−0.270

−0.411

−0.252

0.136

0.470

0.345

0.383

0.501

0.420

0.328

−0.336

−0.486

0.111

0.212

0.031

0.131

0.095

−0.027

−0.213

−0.138

0.229

0.333

0.063

0.300

0.575

0.522

0.063

−0.175

−0.434

−0.456

−0.301

−0.358

−0.338

−0.303

0.213

0.423

−0.071

−0.142

−0.199

−0.188

−0.189

−0.351

−0.107

0.508

−0.018

−0.060

0.577 0.319

0.613 0.455

0.534 0.445

0.318 0.351

−0.427 −0.310

−0.532 −0.419

0.087 0.048

0.113 0.146

0.519

0.553

0.461

0.333

−0.212

−0.129

−0.124

−0.433

Silt

Clay

154

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Fig. 9. Baia: Co-Inertia Analysis. a) PCA applied to grain size: plot of factors loading on the Co-Inertia plane; b) PCA applied to foraminiferal data: plot of factors loading on the Co-Inertia plane; c) plot of factor scores on the Co-Inertia plane; d) histogram of Eigenvalues. The Monte Carlo Test applied to the Co-Inertia Analysis demonstrated the significant association between the two data sets (r = 0.41; p = 0.001).

for the second. This correlation occurs mainly in samples located nearshore in the central part of the harbour (samples PR01/44 and PR01/ 67). The correlation of species with the highest values for the first axis, such as Ammonia tepida and Bulimina marginata, with silt and clay is also shown. It occurs mainly in samples located in the northern and central area of the harbour (stations PR01/75, PR01/254 and PR01/265). Finally, Rosalina bradyi mainly shows a correlation with medium sand in sample PR01/200, collected in the southernmost sector (Fig. 3). 7. Discussion Several abiotic parameters, such as temperature, salinity, nutrient availability, pH and dissolved oxygen are known to condition the distribution of foraminifera (Murray, 1991, 2006). In addition, the character of the substrate is one of the most important variables because of the selection of species having different behaviour (i.e. mode and position of life and feeding strategy). The integrated use in this research of Pearson's index and CIA to search correlations between species and single sediment fractions offers a double control over the

validity of correlations. Moreover, the CIA allows recognition of which stations show existence of correlations. This is particularly useful because it can highlight if sediment texture plays a role in foraminifera distribution in marine areas with different degrees of pollution, ranging from heavily polluted to relatively unpolluted. Although a single statistical correlation is not sufficient to demonstrate a direct causal effect of sediment on the abundance of foraminiferal species, the recurrence of similar correlations in different areas, under different environmental conditions, may be considered strongly indicative of such relationships. The high number of correlations found in all the sites indicates that type of sediment is, in general, a very important factor controlling species distribution. The groups of species correlated with sandy or muddy sediments are well-separated, while a well-distinct group preferring gravel was not recognised. Species correlated with sandy fractions are not correlated with muddy ones and only a few species correlated with very fine sand show also a correlation with silt. This may depend on the different feeding strategy and on the different microhabitat selected by the two groups, respectively herbivore epifaunal and detritivore infaunal (Murray, 1991; Mendes et al., 2004; Murray, 2006). Indeed, species

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Table 5 Augusta: percentages of grain-size fractions and sediment nomenclature according to Shepard (1954). Sand is subdivided into five classes. Station label

Gravel

Very coarse sand

Coarse sand

Medium sand

Fine sand

Very fine sand

Silt

Clay

Shepard nomenclature

PR01/29 PR01/44 PR01/63 PR01/67 PR01/75 PR01/101 PR01/118 PR01/150 PR01/159 PR01/167 PR01/169 PR01/170 PR01/174 PR01/175 PR01/177 PR01/181 PR01/198 PR01/200 PR01/204 PR01/206 PR01/209 PR01/226 PR01/230 PR01/231 PR01/223 PR01/254 PR01/262 PR01/265 PR01/268 PR01/281 PR01/283 PR01/289 PR01/300 PR01/306 PR01/308 PR01/313 PR01/314

0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.6 0.0 0.0 0.0 0.0 4.2 0.0 0.0 0.0 5.6 0.0 29.4 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 9.0 1.0 0.0

0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.1 0.0 1.9 0.1 0.3 0.7 3.3 1.6 0.2 1.5 5.1 2.7 0.0 7.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.3 0.0 0.0 0.0 2.4 0.6 5.0

0.0 0.9 0.2 0.9 0.0 0.4 0.0 0.6 0.0 3.8 0.5 1.2 4.4 3.2 3.3 0.7 4.3 19.4 5.7 0.0 7.7 0.0 0.0 0.0 0.0 0.0 0.0 1.4 0.0 1.2 1.2 0.8 0.0 0.0 4.4 1.7 4.4

2.6 17.6 1.0 17.4 1.6 1.8 1.1 1.9 1.3 18.1 1.0 3.1 16.0 3.4 4.6 1.0 11.9 39.1 7.4 1.7 8.7 0.0 0.0 4.2 5.6 1.2 3.8 2.3 2.5 3.3 2.5 2.2 5.4 1.3 8.7 6.4 7.0

4.4 54.9 3.5 44.6 4.2 6.9 3.3 9.1 4.0 19.7 2.9 8.5 23.6 7.7 4.4 5.4 10.2 11.9 5.5 7.5 5.3 6.2 20.3 17.0 6.5 1.7 4.3 2.2 4.8 4.9 3.2 3.7 8.1 1.7 14.9 18.1 9.1

6.7 14.6 15.3 19.8 9.9 10.9 5.7 11.4 8.9 6.5 5.9 10.6 15.6 29.9 4.7 14.6 29.3 6.5 11.2 27.6 6.0 11.9 19.5 21.6 5.3 2.3 3.4 1.6 8.5 7.9 3.3 5.3 8.1 2.9 15.8 18.7 8.6

52.2 6.4 49.9 10.7 45.3 37.1 43.9 36.7 39.4 21.4 36.1 41.2 21.3 25.1 31.8 39.6 30.9 9.6 34.2 37.5 21.9 40.5 30.3 33.2 37.4 47.6 36.2 44.0 44.2 41.8 43.3 43.6 43.3 45.0 20.4 30.2 27.3

34.1 5.6 30.1 6.6 39.0 42.9 46.0 40.2 46.4 27.0 53.5 35.1 18.4 27.4 45.4 38.5 11.9 8.4 27.7 25.7 13.9 41.4 29.9 24.0 45.2 47.2 52.3 48.5 40.0 40.9 46.2 44.4 35.1 49.1 24.4 23.3 38.6

clayey silt sand sand silt clay sand clayey silt sand silt clay clayey silt sand silt clay silty clay sand silt clay silty clay sand silt clay silty sand sand silt clay silty clay sand silt clay silty sand sand sand silt clay sand silt clay silty sand silty clay sand silt clay sand silt clay silty clay clayey silt silty clay silty clay clayey silt clayey silt silty clay silty clay sand silt clay clayey silt sand silt clay sand silt clay sand silt clay

positively correlated with the fine sediment fraction such as Bolivina variabilis, Bulimina elongata, Nonionella atlantica and Rectuvigerina phlegeri are detritivore infaunal taxa. Conversely, species that show positive correlation to the sandy fraction, such as Elphidium aculeatum, Elphidium crispum, Quinqueloculina lata and Quinqueloculina stelligera choose epifaunal microhabitat and are herbivore species. Elphidium crispum was observed while grazing to collect algal cells (Goldstein, 1999; Murray, 2003, 2006). It is very common that species correlated with coarser

sandy fractions are also correlated with gravel. This feature is shown by typically epiphytic species such as Asterigerinata mamilla, Lobatula lobatula and Rosalina bradyi (Langer, 1993), which may live on vegetated bottoms but also attached to coarse particles (Hayward et al., 1994; Frezza and Carboni, 2009). The strong influence of grain-size on foraminifera distribution may be widely attributable to two correlated environmental parameters, nutrient availability and oxygen levels, which are linked to sediment

Fig. 10. Distribution of sand, silt and clay percentages in the Augusta area.

156

M. Celia Magno et al. / Marine Geology 315–318 (2012) 143–161

Fig. 11. Distribution of the most abundant foraminiferal species percentages in the Augusta area.

features. The well-recognised affinity of organic matter with fine sediment fractions (Kennedy et al., 2002) was demonstrated in two of the three study areas, Bagnoli and Augusta, by the positive correlation of organic matter (or TOC) with silt/clay, and the negative correlation with sand (Bergamin et al., 2003; Romano et al., 2008, 2009a, 2009b). Due to the higher organic load, eutrophic conditions in muddy seabottoms may be associated with hypoxic or even anoxic environments (Fontanier et al., 2008; Koho et al., 2008). Under these conditions some

well-adapted foraminiferal species are able to survive (Bernhard and Sen Gupta, 1999; Pucci et al., 2009). Consequently, different trophic needs and different tolerance to low-oxygen levels are important factors controlling the selection of sediment by foraminifera. In sandy sediments from Bagnoli and Baia, species correlated with muddy fractions such as Bulimina elongata and Nonionella atlantica are not able to discriminate silt and clay, while at Augusta, where fine sediments are abundant, these species may select a particular

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Table 6 Augusta: Pearson correlation between pairs of species and sediment fractions. Black bold: correlation is significant at the 0.01 level. Grey bold: correlation is significant at the 0.05 level.

Ammonia parkinsoniana Ammonia tepida Asterigerinata mamilla Asterigerinata planorbis Bolivina aenariensis Bolivina pseudoplicata Bolivina seminuda Bulimina elongata Bulimina marginata Cassidulina carinata Elphidium crispum Elphidium granosum Elphidium pauciloculim Haynesina depressula Hopkinsina pacifica Lobatula alobatula Nonion fabum Nonionella atlantica Rectuvigerina phlegeri Rosalina bradyi Sigmoilinita costata Triloculina plicata

Gravel

Very coarse

Coarse sand

Very fine sand

Silt

Clay

−0.041

−0.178

−0.140

0.182

0.549

−0.054

−0.223

−0.271

−0.189

−0.259

−0.266

−0.296

−0.322

−0.240

0.520

0.311

0.361

0.426

0.485

0.499

0.265

0.126

−0.448

−0.521

−0.037

−0.087

−0.051

0.252

0.523

0.200

−0.364

−0.349

0.403

0.366

0.366

0.124

−0.207

−0.106

−0.018

−0.121

0.031

−0.064

−0.196

−0.430

−0.346

−0.037

0.288

0.363

0.053

−0.038

−0.258

−0.415

−0.322

0.088

0.381

0.189

−0.142

−0.094

−0.116

−0.183

−0.301

−0.262

0.319

0.331

−0.029

−0.184

−0.229

−0.317

−0.296

−0.372

0.393

0.401

0.014

−0.128

−0.124

0.065

0.561

0.382

−0.385

−0.353

−0.100

0.324

0.740

0.735

0.115

−0.199

−0.353

−0.306

−0.081

−0.206

−0.244

−0.337

−0.403

−0.253

0.534

0.344

−0.054

0.222

0.129

0.033

−0.031

0.608

−0.137

−0.260

0.160

0.039

0.014

−0.149

−0.335

−0.170

0.398

0.055

−0.079

−0.190

−0.159

−0.186

−0.238

−0.265

0.340

0.264

−0.018

0.192

0.386

0.702

0.704

0.270

−0.726

−0.651

−0.047 −0.116

−0.128 −0.123

−0.097 −0.126

0.195 −0.191

0.436 −0.278

0.075 −0.347

−0.299 0.308

−0.207 0.370

−0.106

−0.145

−0.151

−0.181

−0.231

−0.352

0.261

0.374

0.142

0.400

0.577

0.582

0.269

0.257

−0.543

−0.502

−0.067

−0.154

−0.190

−0.299

−0.334

−0.236

0.380

0.358

0.014

−0.056

−0.027

0.281

0.480

0.120

−0.370

−0.309

muddy fraction. A positive linear correlation between Bulimina elongata and mud, on the whole, was found by Donnici and Serandrei Barbero (2002) in samples from the northern Adriatic Sea. Both Bulimina elongata and Nonionella atlantica are known to prefer infralittoral and circalittoral zones (Pérès and Picard, 1964) with organic matter enriched mud, tolerating also low-oxygen conditions (van der Zwaan and Jorissen, 1991). Furthermore, because fine sand has no correlations with species both in the Bagnoli and Baia areas (with the exception of Buccella granulata and Elphidium advena), it may be inferred that this is the less suitable grain size fraction to foraminiferal life. Thirteen species (Ammonia parkinsoniana, Ammonia tepida, Asterigerinata mamilla, Bolivina variabilis, Buccella granulata, Bulimina elongata, Elphidium aculeatum, Elphidium advena, Haynesina depressula, Nonionella atlantica, Quinqueloculina lata, Quinqueloculina stelligera and Rectuvigerina phlegeri) show significant, identical or similar, correlations with sediment fractions in two of the three studied areas. Bulimina elongata, Elphidium crispum, Lobatula lobatula, and Rosalina bradyi have this common feature in all the three areas (Table 7). This suggests that, mostly for the last four species, the correlation with specific fractions is not due to peculiar local conditions, but is a general pattern. Elphidium crispum, Lobatula lobatula, and Rosalina bradyi show a considerable adaptation to a broad range of sediment fractions as they live in elevated position, attached or free over grains or vegetated substrates (Murray, 1991; Langer, 1993; Murray, 2006). Rosalina bradyi and Lobatula lobatula demonstrate a high adaptation because they are both

Medium sand

Fine sand

very abundant at Baia, on vegetated substrate, and in the Augusta area, on detritic sand. Conversely, some species, such as Quinqueloculina lata and Quinqueloculina stelligera, are very selective with the sediment type because they have negative correlation with fractions ranging from gravel to medium sand and positive correlation with very fine sand. Applying the results obtained from the study of correlations between sediment fractions and foraminiferal species to previous research on the effects of pollution on foraminiferal assemblages from Bagnoli, Baia and Augusta, additional information about factors controlling species distribution is obtained. The results from this study demonstrated, for the Bagnoli area, the strong control of sediment on the distribution of the most abundant species, Quinqueloculina lata and Quinqueloculina stelligera. They were known to occur mainly in the infralittoral zone, in sandy bottoms or also above vegetated covers (Sgarrella and MoncharmontZei, 1993). The present research demonstrated that these taxa select specifically a single class of sand (very fine sand). Stations that highlight this correlation are located in front of the steel plant and are heavily polluted by Fe, Pb, Zn and PAHs. Conversely, the northern unpolluted stations mostly contribute to the correlation of Elphidium crispum and Lobatula lobatula with coarse grain-size fractions. The study of a sediment core collected in the most polluted sector, in front of the plant, revealed sediments with about 90% sand, containing an assemblage with Elphidium crispum and Lobatula lobatula, in old levels referable to pre-industrial time (Romano et al., 2006).

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Fig. 12. Augusta: Co-Inertia analysis. a) PCA applied to grain size: plot of factors loading on the Co-Inertia plane; b) PCA applied to foraminiferal data: plot of factors loading on the Co-Inertia plane; c) plot of factor scores on the Co-Inertia plane; d) histogram of Eigenvalues. The Monte Carlo Test applied to the Co-Inertia Analysis demonstrated the significant association between the two data sets (r = 0.43; p = 0.001).

This demonstrates that the northern sector is presently characterised by natural environmental conditions. Bergamin et al. (2003) and Romano et al. (2008, 2009a) found that species considered “pollution-tolerant”, because positively correlated to heavy metals (mainly Pb and Zn) or PAHs, were correlated also to muddy fractions. The considerable influence of muddy sediments on the distribution of some of these species, Miliolinella subrotunda, Quinqueloculina parvula and Bulimina sublimbata (Bulimina elongata in this paper), is confirmed by the results of this study. Romano et al. (2008) assessed the importance of pollution, but recognised also the influence of the sediment type on species distribution. The results of this study confirm and strengthen this hypothesis and let us suppose that the sediment type remains an important factor, not only for the distribution of the most abundant species, but also for the distribution of pollution-tolerant species in very contaminated areas. Because for the Bagnoli area it was demonstrated that such species prefer fine sediments even if they are strongly polluted, it may be deduced that they develop an adaptive strategy aimed to survive in hostile anthropogenically-altered environments.

The most abundant species from Baia, Lobatula lobatula, Rosalina bradyi and Ammonia tepida have shown correlation with sediment type. The first two show similar preference for sediment fraction: Lobatula lobatula is positively correlated with sand, from coarse to fine, and negatively correlated with mud; and, Rosalina bradyi is positively correlated with sand, from very coarse to medium and negatively correlated with mud. As a consequence, the spatial distribution of the two species is well comparable. Both are considered typical epiphytic species although they may live also on detritic sand (Sgarrella and Moncharmont-Zei, 1993; Murray, 2006). Stations which mostly contribute to the correlation of the two most abundant species are located in the central sector of the bay showing lower contamination degree and characterised by the presence of Caulerpa meadow. This indicates the strong control of sediment texture on the distribution of epiphytic species in spite of the presence of a vegetated bottom. The similar distribution of Caulerpa meadow (Fig. 2) and gravel fraction (Fig. 7a) determines the correlation between epiphytic taxa and gravelly sediments. On the other hand, the dominance of epiphytic species was also found in some stations of the

M. Celia Magno et al. / Marine Geology 315–318 (2012) 143–161

159

Table 7 List of species showing common or similar correlations in two or three of the study areas. The grey colour indicates negative correlation; the black colour indicates positive correlation.

Gravel Ammonia

Very coarse sand

Coarse sand

Medium sand

Fine sand

Very fine sand

Baia

Baia

Baia

Augusta

Baia

Silt

clay

parkinsoniana Ammonia tepida

Baia

Asterigerinata

Bagnoli

Bagnoli

Bagnoli

mamilla

Augusta

Augusta

Augusta

Bolivina

graulata

Augusta Bagnoli

Baia

variabilis Buccella

Augusta

Bagnoli

Bagnoli

Baia Bagnoli

Bagnoli

Baia

Bulimina elongata

Bagnoli Baia

Elphidium

Bagnoli

Bagnoli

aculeatum

Baia

Baia

Bagnoli

advena Elphidium

Bagnoli

Bagnoli

crispum

Baia

Baia

Haynesina depressula

Bagnoli Bagnoli Baia Augusta

Baia

Baia

Lobatula

Bagnoli

Bagnoli

Baia

lobatula

Baia

Baia

Augusta

Bagnoli

Baia

Bagnoli

Bagnoli

Augusta

Baia

Baia

Baia

Augusta

Baia

Baia

Bagnoli

Augusta

Baia

Nonionella Quinqueloculina lata Quinqueloculina stelligera

Bagnoli Bagnoli

Bagnoli

Bagnoli

Baia

Baia

Bagnoli

Bagnoli

Rosalina bradyi

Bagnoli

Bagnoli

Bagnoli

Baia

Baia

Baia

Augusta

Augusta

Augusta

Bagnoli

Baia

Baia

southern sector without algal growths but characterised by higher content of coarse sediment fractions (Bergamin et al., 2009). These findings indicate that the presence of Caulerpa has a secondary influence on the distribution of these taxa and the weak control of vegetation is probably due to the scarce thickness and patchy distribution of the meadow. With regards to Ammonia tepida, it showed positive correlation with very fine sand. Such correlation is stronger in the northern wreck area, affected by higher contamination by Pb, Zn and PCBs. Alve and Murray (1999) demonstrated that Ammonia beccarii (Ammonia tepida in this study) may be common in sediments with a range of mud content from 0% to 80%. Abu Zied et al. (2007) showed the positive correlation between Ammonia tepida and fine-grained sediment. This species is well-known as typical of lagoonal and estuarine settings associated with muddy sediments (Jorissen, 1988; Almogi Labin et al., 1995) and it is considered as “resistant species” in polluted coastal marine environments, mainly by heavy metals (Sharifi et al., 1991; Yanko et al., 1994; Alve, 1995; Samir and El Din, 2001; Kfouri et al., 2005; Ferraro et al., 2006). Ammonia tepida was found abundant in muddy sediments, under natural as well as stressed conditions due to anthropogenic organic load (Hyams-Kaphzan et al., 2008, 2009). Bergamin et al. (2009) pointed out the statistical correlation between PCBs and Ammonia tepida from the Baia coastal zone. Interpreting these results in light of the above cited research, it may be deduced that pollution and sediment type concur in determining distribution and abundance of the pollutiontolerant Ammonia tepida in the northern sector of Baia coastal zone.

Augusta

Augusta

Augusta Augusta Bagnoli

Augusta Bagnoli Augusta

Baia

Bagnoli

phlegeri

Baia

Bagnoli

Bagnoli

Rectuvigerina

Bagnoli

Baia

Augusta

atlantica

Bagnoli

Bagnoli

Bagnoli

Augusta

Augusta

Bagnoli

Baia

Elphidium

Augusta

Bagnoli

Augusta

Baia

Baia

Bagnoli Bagnoli Augusta

Bagnoli Augusta Augusta

As well as in the other areas in Augusta Harbour the most abundant species showed linear correlation with sediment fractions. Rosalina bradyi is positively correlated with very coarse, coarse and medium sand and negatively correlated with muddy fractions. This correlation is higher in sample PR01/200, where Hg and PCBs had high concentrations of 22 mg kg− 1 and 266 ng g− 1, respectively (Romano et al., 2009b). From this feature it may be deduced that sediment texture remains a main factor controlling species distribution under heavy pollution. Ammonia tepida was found to be positively correlated with the silt and clay fraction, in the central and northern harbour, according to the above cited sediment preference. These sectors, although considerably less polluted then the southern one, are still affected by significant contamination due to Hg, PAHs and PCBs. Romano et al. (2009b) studied the effect of pollution on benthic foraminifera in the Augusta harbour, pointing out the positive correlation of Ammonia tepida, among the others, with PAHs and Hg. Consequently, it may be inferred that the effect of pollution and sediment type concur in influencing abundance and distribution of this species in the heavily polluted Augusta harbour. 8. Conclusions Results regarding the correlation of species with sediment grain size have been obtained by means of the statistical analysis applied to sediment textural data and foraminiferal abundance. Information about the preference of species for particular sediment types has been improved

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by our research. It has been highlighted that the group of species preferring gravelly-sandy sediment is well-separated from the group of species preferring muddy sediment, mainly due to different trophic requirements. The latter seems to be able to better discriminate silt from clay when the muddy fraction is prevailing. In addition, because several species demonstrated a correlation with the same sediment fractions in at least two sites, characterised by different environmental settings, this feature has been considered typical of the species and not due to local site conditions. In each study area both bivariate and multivariate statistical analysis revealed the correlation of most species, and particularly of the most abundant ones, with one or more sediment fractions. The primary role of sediment type on foraminifera distribution, well stated by previous studies, has been confirmed. The grain size has been demonstrated as being a primary factor controlling species distribution not only in moderately polluted sites such as Baia, but also in heavily polluted sites such as Bagnoli, in front of the industrial site, and Augusta, in the southern sector of the harbour. The effects of sediment grain size may be associated with the effect of pollution even in conditioning the distribution of pollution-tolerant species. These species, which may be abundant in spite of high levels of pollution, showed a positive correlation with fine sediment fractions. The high tolerance of these species to pollution could be due to their preference of fine sediments which are the main receptor of contaminants. Our results show that for studies dealing with foraminifera in polluted areas it is important to evaluate the response of foraminifera to polluted conditions taking into account not only the pollutant concentrations, but also possible changes of grain size. Because both the factors influence species distribution, they may concur in influencing composition and structure of foraminiferal assemblages. In this case, the effects due to sediment changes may be erroneously attributed to pollution.

Acknowledgments We are grateful to Éric Armynot du Châtelet and to an anonymous reviewer for their helpful comments.

Appendix A. Supplementary data Supplementary data to this article can be found online at doi:10. 1016/j.margeo.2012.04.002.

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