Comparison between the dead and living benthic foraminiferal assemblages in Aveiro Lagoon (Portugal)

Palaeogeography, Palaeoclimatology, Palaeoecology 455 (2016) 16–32

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Comparison between the dead and living benthic foraminiferal assemblages in Aveiro Lagoon (Portugal) Maria Virgínia Alves Martins a,b,⁎, Johann Hohenegger c, Fabrizio Frontalini d, Paulo Miranda b, Maria Antonieta da Conceição Rodrigues a, João Manuel Alveirinho Dias e a Universidade do Estado do Rio de Janeiro, Faculdade de Geologia, Departamento de Estratigrafia e Paleontologia, Av. São Francisco Xavier, 524, sala 2020A, Maracanã, 20550–013 Rio de Janeiro, RJ, Brazil b Universidade de Aveiro, Dpto. Geociências, GeoBioTec, CESAM, Campus de Santiago, 3810–193 Aveiro, Portugal c Department of Palaeontology, Geozentrum, Althanstrasse 14, A–1090 Vienna, Austria d Università degli Studi di Urbino “Carlo Bo”, Dipartimento di Scienze Pure e Applicate (DiSPeA) Urbino, Italy e CIMA (Centro de Investigação Marinha e Ambiental), Universidade do Algarve, Campus de Gambelas, Faro, Portugal

a r t i c l e

i n f o

Article history: Received 4 December 2015 Received in revised form 15 April 2016 Accepted 4 May 2016 Available online 06 May 2016 Keywords: Dead and living Intertidal and subtidal foraminifera Ecology Multivariate statistical analysis

a b s t r a c t This work provides a comparison between living (LAs) and dead (DAs) benthic foraminiferal assemblages in 53 sampled sites located in intertidal and subtidal areas of Ria de Aveiro (Portugal). The results of multivariate statistical analysis provide evidence for the main factors that control the distribution of DAs and LAs, which seems to correspond to different gradients of marine/continental influence, conditioned by differences in the hydrodynamics of tidal currents. In addition to these factors, the LAs are clearly influenced by the total organic carbon, biopolymers and pollutants. The main factor that however drives the high level of agreement or the degree of disorder between the DAs and the LAs seems to be the hydrodynamics. The highest level of agreement between the DAs and the LAs is found in sheltered inner lagoon areas, out of the tidal currents strongest influence. This study shows an interesting application of advanced statistical analysis to study the similarity between LAs and DAs aiming to identify the zones or conditions of a coastal system in which the DAs can potentially be a better record of Las. Through similar studies conducted in coastal systems it is possible to select the most suitable sites to collect cores in view of the development of paleoenvironmental studies. © 2016 Elsevier B.V. All rights reserved.

1. Introduction Benthic foraminifera, single-cell organisms, have been largely applied in environmental and paleoenvironmental reconstructions (e.g., Holbourn et al., 2001a, 2001b; Reolid et al., 2008). These organisms are quite abundant in marine and transitional marine settings and are highly sensitive to change in the environmental conditions (Sen Gupta, 1999; Murray, 2006). They are commonly investigated in terms of assemblage characteristics such as species compositions, abundance and diversity (Frontalini et al., 2009; Bouchet et al., 2012). Despite plenty of ecological studies considering living (LAs) or dead (DAs) benthic foraminiferal assemblages over time having been published, only a few have concurrently considered and compared the two assemblages. Accordingly, there is a demand for further investigations in a range of

⁎ Corresponding author at: Universidade do Estado do Rio de Janeiro, Faculdade de Geologia, Departamento de Estratigrafia e Paleontologia, Av. São Francisco Xavier, 524, sala 2020A, Maracanã, 20550–013 Rio de Janeiro, RJ, Brazil. E-mail addresses: [email protected] (M.V.A. Martins), [email protected] (J. Hohenegger), [email protected] (F. Frontalini), [email protected] (P. Miranda), [email protected] (M.A. da Conceição Rodrigues), [email protected] (J.M.A. Dias).

http://dx.doi.org/10.1016/j.palaeo.2016.05.003 0031-0182/© 2016 Elsevier B.V. All rights reserved.

modern environments to provide baseline data that can be used for the improvement of benthic foraminifera as ecological proxies. The understanding of the relationships between the living and dead benthic foraminifera and all the factors that rule their distribution should be a key to interpret paleoenvironmental changes (Goineau et al., 2015), at least during the Quaternary or in the most recent periods of the Cenozoic, in which the majority of the benthic foraminiferal species can be found living and can be studied for their ecology. The comparison between LAs and DAs in surficial sediments has been addressed in intertidal (e.g., Berkeley et al., 2007), shallow-water (e.g., Murray, 1976, 1992; Lutze, 1980; Alve and Murray, 1997; Jorissen and Wittling, 1999; de Stigter et al., 1999; Horton and Murray, 2006; Schumacher et al., 2007; Diz and Francés, 2009; Mendes et al., 2013; Goineau et al., 2015; Dimiza et al., 2016a) and some deep-sea environments (e.g., Lutze and Coulboum, 1984; Mackensen et al., 1985, 1990; Mackensen and Douglas, 1989; Loubere and Gary, 1990; Loubere et al., 1993; Gooday and Hughes, 2002; Licari and Mackensen, 2005; Duros et al., 2014). Significant differences between LAs and DAs can be observed, as LAs only represent the moment of sampling, whereas the DAs represent the tests' accumulation of many generations over a longer period of time (Murray, 1991). The abundance and composition of LAs vary over short time periods throughout

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the year in response to both specific-season reproduction and environmental parameters (Murray, 1991). Population dynamics and dissimilarities of biological nature (differences in turnover rate and seasonal alterations in standing stock) can cause significant divergences between the LAs and DAs of benthic foraminifera, as well as the taphonomic process (e.g., de Stigter et al., 1999; Jorissen and Wittling, 1999; Duros et al., 2014). Rathburn and Miao (1995) related these differences to post-depositional processes and Gooday and Hughes (2002) to local processes. Duros et al. (2014) observed significant divergences between LAs and DAs along the Cap–Ferret Canyon (NE Atlantic) caused not only by biological effects (i.e., population dynamic), but also by taphonomic processes (i.e., tests' destruction and transport). The disintegration of agglutinated specimens can cause a strong reduction in the abundance of this group of foraminifera in the DAs compared with the LAs (Mackensen et al., 1985, 1990; de Stigter et al., 1999; Gooday and Hughes, 2002). Calcium carbonate tests may be dissolved in undersaturated deep ocean waters or in the corrosive pore-water sediments of areas with high organic matter accumulation (e.g., Mackensen and Douglas, 1989; de Stigter et al., 1999; Licari and Mackensen, 2005; Murray, 2006). The intense bioturbation and predation can also cause divergences between the living and dead fauna (e.g., Mackensen and Douglas, 1989). In shallow waters, the assemblage differences between live and dead benthic foraminifera have been attributed, for instance by Horton (1999), to a series of environmental variables such as river flooding frequency, pH, salinity and vegetation coverture (Dimiza et al., 2016a). However, according to Murray (2006), transport of tests is one of the main post-mortem processes that can bias the similarity between LAs and DAs of benthic foraminifera. Transport and deposition of allochthonous individuals or the ablation and exportation of autochthonous taxa can introduce great changes in the composition of the DAs (Duros et al., 2014). This effect might be related to hydrodynamic conditions. The main goal of this study is to examine to what extent the DAs along the intertidal areas of the main channels of the Ria de Aveiro with variable hydrodynamical conditions are representative of the original living counterpart (Rose Bengal stained organisms). This work also aims to identify areas where the DAs are similar to the LAs. Previous studies based on LAs preformed in the Ria de Aveiro essentially aimed to evaluate the ecological and environmental quality of the lagoon (Martins et al., 2010, 2011, 2013, 2015a). However, no studies have yet compared the similarity between the LAs and DAs to understand the preservation of foraminiferal tests in this system. Accordingly, this study also aims to investigate similarities and discrepancies between LAs and DAs in surface sediments and to analyze possible factors for those discrepancies. 1.1. Study area The Ria de Aveiro is a shallow water mesotidal lagoon located on the northwest coast of Portugal (40°38′N, 8°44′W) and is connected with the Atlantic Ocean by an artificial inlet stabilized with two breakwaters (Fig. 1). The lagoon, which covers an area of 83 km2 and 66 km2 at high tide (spring tide) and low tide respectively, has a very irregular geomorphology (Dias and Lopes, 2006). It is composed of five main channels, each of them receiving fresh water contribution from a river: the Vouga River in the Espinheiro Channel, the Antuã River in the Murtosa Channel, the Caster River in the Ovar Channel, the Boco River in the Ílhavo Channel and in the Mira Channel from some artificial streams (Trancoso et al., 2005) (Fig. 1). The central lagoonal body has an intricate network of small channels, intercepted by small islands. The channels are bordered by extensive areas of intertidal mud flats and salt marshes. Considering the mean sea level, the average depth of the lagoon is about 3 m (Picado et al., 2010). The deeper channels (reaching about 30 m depth) are located near the lagoon mouth (Vaz and Dias, 2008).

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Semidiurnal tides (tidal amplitude at the inlet ranges from 0.6 m in neap tides to 3.2 m in spring tides — average range of 2 m) constitute the major hydrodynamic force in the lagoon (Dias et al., 2000), except during periods of peak river flow, which coincide with heavy winter rainfall (Barrosa, 1985). Tidal currents are stronger than 2 m/s at the inlet and some deeper channels near the lagoon mouth (Dias et al., 2000; Vaz et al., 2009). The speed values are much higher in the S. Jacinto and Espinheiro channels (Dias et al., 2003). Depth reduction in the internal lagoonal areas and on tidal flat zones contributes to the dissipation of tidal currents (Vaz et al., 2005). At extreme spring and neap tides, the estimated tidal prism at the lagoon's mouth is 136.7 × 106 and 34.9 × 106 m3, respectively (Dias, 2001), whereas the total estimated freshwater input to the lagoon is about 1.8 × 106 m3 during a complete tidal cycle (Moreira et al., 1993). This means that the fresh water contribution is smaller than the marine, but it may have a long-term influence on the residual transport (Dias et al., 2003) and therefore some channels may have characteristics of a partially mixed estuary, depending on the freshwater input, which is higher during winter (Vaz et al., 2005). The activity of the tidal currents decreases in internal areas of the lagoon. These confined areas are frequently associated with rivers and small fresh water streams that introduce into the lagoon both high concentrations of metals and organic materials of continental origin (Martins et al., 2015a, 2015b). In these zones, high available concentrations of toxic metals associated with several sedimentological phases have also been observed (Martins et al., 2010, 2015a, 2015b). 2. Materials and methods 2.1. Sampling Fifty-three stations were sampled in intertidal areas (between ~ 0.5 m and ~ 2 m water depth), located along several main channels and at the main lagoonal body of the Ria de Aveiro in July 2011 (Fig. 1; Appendix A). Sampling methodology, measurement of environmental parameters, grain-size and biogeochemical analyses are described in Martins et al. (2015a, 2015b). One aliquot of the uppermost first centimeter of sediment (about 50 ml) was stored in buffered ethanol stained with Rose Bengal (2 g of Rose Bengal in 1000 ml alcohol) to differentiate living from dead foraminiferal specimens. 2.2. Abiotic data Abiotic variables used in this work were documented in Martins et al. (2015a, 2015b), such as: salinity, pH, sand and fine fractions of sedimentary content, the enrichment factor of toxic metals, the pollution load index (PLI; based on Tomlinson et al., 1980), total organic carbon (TOC), total biopolymers concentrations (TBC), as well as carbohydrates (CHO), lipids (LIP) and proteins (PTN) concentrations. 2.3. Biotic variables Biotic data in this work were obtained in sediment samples collected in the same sampling campaigns realized by Martins et al. (2015a, 2015b). The abiotic data are therefore contemporary of living benthic foraminiferal assemblages. This work analyses new biotic data obtained from different sub-samples obtained in sampling campaigns and by using a different methodology of analysis. In Martins et al. (2015a, 2015b) only living foraminifera were analyzed in the sediment fraction 63–500 μm. In this work we study both living and dead specimens found in the sediment fraction 125–500 μm, according to the mandatory recommendation of Schönfeld et al. (2012). Thus living and dead benthic foraminiferal specimens were picked, identified and counted in the same sediment split (from the 125–500 μm sediment fraction). Therefore, the number of living and dead individuals of each species in

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CR x

GR

P

FR

OC

AR

Aveiro

VR

SJ

MC

IC

OC BR

VR

SJC MiC EC

S.Jacinto

Aveiro Harbours

Fig. 1. The study area (indicated with an X) in Portugal (P), Iberian Peninsula and the studied sites in a general map of the Ria de Aveiro and detailed images in the following zones (sites are numbered A1–A53): 1. Ovar Channel (OC) at the northern extremity of the lagoon. 2. Aveiro City canals; 3. Murtosa Channel (MC); 4. Mira Channel (MiC); 5. Central area of the lagoon including the São Jacinto Channel (SJC) and Espinheiro Channel (EC); 6. The harbor area (HA) as well as the villages of Barra and S. Jacinto (SJ) near the lagoon mouth and Aveiro City are also indicated. Rivers are also indicated: CR — Caster River, GR — Gonde River; FR — Fontela River; VR — Vouga River; BR — Boco River connecting with Ílhavo Channel (IC). The black bar corresponds to 200 m and the white bar to 1500 m. Adapted image from the Google Earth.

each sample can be compared. The absolute abundance of specimens per species and per sample is given for 10 mg of sediment. 2.4. Statistical analysis Rarefaction analysis (e.g., Krebs, 1989) measuring diversity used the complete dataset because it was based on the occurrence of all species represented in the sample. Twenty-one species occurring simultaneously in both DAs and LAs have been used for statistical investigation. For classification analyses, species abundance – both living and dead – was normalized to unit vectors due to differing sample size. This

allows the calculation of Euclidean distances between sites used as dissimilarity measures in cluster and ordination analyses (e.g., principal coordinate analysis, nonmetric multidimensional scaling) being independent of sample size (e.g., Orloci, 1967). Using hierarchical classification analyses, a two-step procedure is introduced. First, continuity between samples checking for inhomogeneities was proven by single linkage analysis (Sneath, 1957) characterized by chaining effects (Jardine and Sibson, 1971). This method is useful for differentiating groups separated by inhomogeneities (natural clusters) from a completely homogeneous distribution. Several chains in single linkage analysis argue for true clusters, while an unseparated

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continuous chain hints to a single homogeneous group. Furthermore, outliers from the continuous distributions are marked by the amalgamation nodes, which represent distances. Second, Ward's method (Ward, 1963), constrained to create distinct clusters, was used to find characteristic regions in multidimensional frequency distributions although within continuous distributions. The latter are then called ‘pseudoclusters’. Identification of groups obtained by cluster analyses was done using indicator values (Dufrêne and Legendre, 1997) IndValij ¼ Aij ∙Bij ∙100 where Aij is specificity, i.e. the proportion of individuals of species i that are in class j and Bij is fidelity, i.e. the proportion of sites in class j that contain species i. Ordination analyses were performed on original (not normalized) frequencies combining frequencies of living and dead species into a single matrix. Abiotic factors influencing the distribution of species are related to living individuals because they were measured during collection time, thus influencing the living fauna. Detrended correspondence analysis (DCA; Hill and Gauch, 1980) instead of canonical correspondence analysis was used for preventing the ‘horseshoe effect’ that causes strong distortion at the distribution edges. Additionally, correlation of abiotic factors with the three important DCA axes was pictured in the axis system, similar to canonical correspondence analysis, but preventing distortion. Correlations between the DCA axes and abiotic factors can be used to interpret the influence of abiotic factors on the species (Table 1). Diversities were measured on both LAs and DAs for every sample site using rarefaction analysis. Comparison between sites was done by calculating the parameters a and b of the Bertalanffy growth function y = a(1 − be−cx) fitting the rarefaction curves. Parameter a determines the maximum number of species expected in the sample and parameter b the rate of increase. They are more significant than parameters of the Michaelis–Menten function used by Colwell and Coddington (1994) for fitting rarefaction curves. Comparison of rarefaction curves between living and dead specimens was done using parameters a and b of the Bertalanffy function characterizing the single sample with four parameters. After standardizing these parameters by their means and standard deviations, a principal component analysis PCA (Hotelling, 1933) was used to represent similarities in rarefaction curves by factor scores and the importance

Table 1 Correlations of abiotic factors and DCA axes with probabilities of non-correlation. Shaded cells and bold numbers indicate the grade of significance in correlations. Abiotic variables Depth in m Salinity pH Sand fraction (arcsine rooot) Fine fraction (arcsine root) Pollution load index TOC (arcsine root) CHO mg/g PTN mg/g LIP mg/g TBP mg/g

Correlation Probability Correlation Probability Correlation Probability Correlation Probability Correlation Probability Correlation Probability Correlation Probability Correlation Probability Correlation Probability Correlation Probability Correlation Probability

Axis 1

Axis 2

0.1834 0.1886 0.7262 7.506 E–10 –0.2168 0.1187 0.1896 0.1738 –0.1912 0.1702 –0.3912 0.0037 –0.2880 0.0364 –0.2606 0.0593 –0.3203 0.0193 –0.1830 0.1897 –0.31643 0.0210

–0.0275 0.8449 –0.1205 0.3900 0.3319 0.0152 –0.3103 0.0237 0.3101 0.0238 0.2599 0.0602 0.2332 0.0928 0.3022 0.0279 0.2326 0.0938 0.2202 0.1131 0.2913 0.0343

Axis 3 –0.1786 0.2007 –0.2838 0.0394 –0.0145 0.9180 –0.3985 0.00312 0.4003 0.0030 0.3920 0.00370 0.3367 0.0137 0.4273 0.0014 0.2900 0.0353 0.3091 0.0243 0.3946 0.003458

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of parameters by factor loadings. Due to standardized characters, PCA based on covariance or correlation obtained identical results. In order to estimate intensities of disorder between LAs and DAs (possibly indicating transport), differences between frequencies of living and dead species were calculated for each site. First, proportions of living and dead species were determined. Then species were ordered within every site according to the abundance of living individuals, while species represented just by empty test were connected in order of the ranked dead species. Rank correlation between living and dead tests was calculated, on the one hand restricted to species with both living and dead individuals and on the other hand including species where only empty tests are present. Due to the different sample size influencing the values of correlation coefficients (here Kendall's Tau, e.g., Gibbons, 1976), their size-independent probabilities of non-correlation between living and dead specimens were used to scale intensities of disorder. Since probabilities are nonlinear quantities and thus cannot be compared as linear measures, they were transformed to normal distributions with mean = 0 and standard deviation =1. Significant correlation and weak disorder between living and dead species is certified by values below −1.6449 with 5% error probability and less than −2.3263 with 1% error probability. All values above − 1.6449 mark significant distortion in species abundance between living and empty tests, thus marking intensities of distortion by increasing values. All complex analyses were done using the statistical program packages IBM SPSS Statistics 22 and PAST 3.02 (Hammer et al., 2001), while the remaining calculations were performed in Excel (Microsoft Office 2013). 3. Results 3.1. Abiotic data The sedimentological, physiochemical and biogeochemical data are included in Appendix A. These data provide a general environmental pattern of the lagoon. Salinity is relatively high (up to 33.7) near the lagoon mouth where the oceanic influence is greater. In this zone, the tidal currents are stronger and thus coarse-grained and aerated sediments can be found (as also observed by Martins et al., 2014). In the outer sector of the lagoon, near the connection with the ocean, sediments commonly have relatively high pH values (7.1–7.3); the lowest pH values (4.2–6.3) are found in sites located near the rivers mouths, in the inner part of the lagoon, where the sediments are enriched in organic matter (TOC values up to 7.4%). However, in some inner sites, the release of urban pollutants into the lagoon also results in an increase in the pH of surface sediments such as in the northern area of Ovar Channel (pH = 8.9). In the most confined sites fine–grained sediments (fine fraction up to 89%) and high content of organic matter (TOC up to 7.9% in station A48) are deposited. Relatively high PLI values in the most confined and internal parts of the lagoon channels (up to 43.6) are recorded. According to Martins et al. (2015a, 2015b), the areas with the highest values of PLI also display highest concentrations of available metals associated with different sedimentary phases. The TOC increase in internal and confined sites coincides with enhanced total biopolymers (TBP up to 10.5 mg/g), namely carbohydrates (CHO; up to 3.2 mg/g), proteins (PTN; up to 5.6 mg/g) and lipids (LIP; up to 3.6 mg/g) concentrations. Some differences are, however, observed in the distribution of these biopolymers. 3.2. Benthic foraminiferal distribution A total of 114 species are identified in the study area (Appendix B), 37 species are only identified in the DAs and 77 species are represented in both LAs and DAs. The 21 most represented living species contained in 10 mg sediment (Appendix C) are, in decreasing order of abundance: Ammonia tepida and Haynesina germanica occurring in all sites (53),

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Fig. 2. Hierarchical cluster analyses based on the frequencies of living and dead species normalized to unit vectors. A. Single linkage analysis showing chain effects that hint to continuous distributions. Only a single natural cluster (A1, A2, A3) can be identified. B. Ward's method based on the minimum variance criterion creating distinct clusters.

Elphidium excavatum (recognized in DAs and LAs in 47 and 50 stations, respectively) and Trochammina inflata (identified in DAs and LAs in 47 and 36 stations, respectively) and Miliammina fusca, Elphidium margaritaceum, Gavelinopsis praegeri, Quinqueloculina seminula, Planorbulina mediterranensis, Entzia macrescens, Cibicides ungerianus, Arenoparrella mexicana, Elphidium williamsoni, Lobatula lobatula, Bulimina gibba, Elphidium gerthi, Globocassidulina crassa rossensis, Scherochorella moniliforme, Lepidodeuterammina ochracea, Aubignyna hamblensis and Discorbis globularis are found at some places. The grouping of sample sites based on frequencies of living and dead species normalized to unit vectors was tested. Single linkage analysis resulted in a chain confirming a single continuous distribution. Only sample sites A1, A2 and A3 seem to represent a natural cluster (Fig. 2A) set apart from the main chain. Furthermore, the extreme position of sample sites A39 and A38 marks them as outliers. Samples grouping by Ward's method resulted in seven clusters, where clusters three and four are divided into subgroups (Fig. 2B). Outliers A38 and A39 as marked by single linkage analyses are positioned at opposite cluster groups, possibly indicating outliers at both ends of a continuous distribution. Except for group 2, all groups must be regarded as ‘pseudoclusters’. The DCA confirms the continuous distribution of pseudoclusters along axis 1 that represents 63.5% of explained variance (Fig. 3). Outliers

A38 and A39 are located at opposite ends of the first axis. The separation of cluster two is indicated in axis 2 representing 19.5% of explained variance, while pseudocluster five marks the opposite position along axis 2. Three tendencies from the extremes group 1 to group 7 recognizable as pseudoclusters in the continuous distribution can be identified (Fig. 3). The first connection close to natural cluster two runs through: route 1: group 1 — group 3a — group 3b — group 4c — group 7. The second route connects the extremes by group 4 in order of route 2: group 1 — group 4a — group 4b — group 4c — group 7, while the third connection runs over route 3: group 1 — group 5 — group 6 — group 7. The following description of groups is based on indicator values given in brackets. Independent from the ranking in frequencies, this values mark important species characterized by the abundance in the cluster in relation to the abundance summed over all clusters. For example, A. tepida or H. germanica are the most frequent species in groups 2 to 5, thus they cannot be regarded as indicator species. Only species more or less restricted to specific groups or showing higher abundance within the groups compared to other groups characterize the cluster. The complete list of indicator values is given in Appendix D. Group 1: A. mexicana (12.4) and M. fusca (6.2) characterize the LAs. The order of indicator species in the DAs with lower values is led by A. mexicana (5.1) followed by S. moniliforme (3.4) and M. fusca (3.3).

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Fig. 3. Detrended correspondence analysis: sample distribution based on the LAs and DAs of foraminiferal species.

The outlier sample A38, most similar to group 1, is differentiated in its LA by E. macrescens (2.9). Just opposite to this indicator species in the LA, M. fusca (12.5) totally dominates the DA simultaneously becoming the primary indicator species. Group 2: This natural group is indicated in both LAs and DAs by A. hamblensis (11.5 versus 6.4) followed by E. excavatum (2.9 versus 3.1). Group 3: A. tepida is the dominant species in both LAs and DAs while H. germanica is less dominant. In both subgroups 3a and 3b no excellent indicator species can be found, but are better represented in subgroup 3a by M. fusca (3.4 versus 1.8) and S. moniliforme (2.1 versus 2.4) than in subgroup 3b with more or less slightly decreasing indicator values starting with C. ungerianus (1.5) in living and A. tepida (1.3) in DAs (Appendix D). Group 4: The three subgroups 4a, 4b and 4c differ in LAs by their indicator species. While L. ochracea shows extreme values (30.2) in subgroup 4a followed by S. moniliforme (3.5), A. hamblensis (3.2) and E. williamsoni (2.1), subgroup 4b shows much lower indicator values starting with A. mexicana (2.6) followed by E. williamsoni (2.1). The LAs of subgroup 4c are characterized by A. mexicana (4.4), B. gibba (3.2) and A. hamblensis (2.5). The three subgroups 4a, 4b and 4c do not show good indicator species in DAs starting with the highest indicator values ≤2.0 (Appendix D). Group 5: The most characteristic species in LAs is A. mexicana (5.6) followed by E. macrescens (3.5), M. fusca (3.4) and T. inflata (2.9). The DAs are characterized by S. moniliforme (4.0), followed by T. inflata (2.2), A. mexicana (2.1) and E. macrescens (2.0) leading the slightly decreasing order in indicator values (Appendix D). Group 6: The extreme indicator value of D. globularis (100) is caused by the exclusive presence of living specimens in this group. Beside this most indicative species, Bulimina gibba (11.0) is also extremely indicative, while E. gerthi (2.7) and E. margaritaceum (2.4) show much lower values. A slow and continuous decrease starting with E. margaritaceum (2.1) and E. williamsoni (2.1) characterizes the DAs (Appendix D). Group 7: This group is distinguished by the influence of shallow subtidal species. The LAs are characterized by high indicative L. ochracea (19.8), followed by less but still high indicative C. ungerianus (6.2), G. crassa rossensis (4.0), G. praegeri (3.8), P. mediterranensis (3.1,) E. margaritaceum (2.8) and L. lobatula (2.8). Indicator species starts in the DAs at lower values with D. globularis (3.7),

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followed by G. crassa rossensis (3.0), L. lobatula (2.9), G. praegeri (2.4), E. williamsoni (2.3) and B. gibba (2.3). The extreme sample A39 shows the strongest subtidal influence. G. praegeri (14.4), P. mediterranensis (12.1) and G. crassa rossensis (12.0) are the most indicative for the LAs, followed in lower importance by B. gibba (9.7), L. lobatula (9.0), E. gerthi (7.9), and C. ungerianus (6.0). D. globularis (6.9) is the most indicative species for the DAs, followed by G. praegeri (4.3), G. crassa rossensis (4.3), P. mediterranensis (3.5), L. lobatula (2.7), B. gibba (2.5) and E. gerthi (2.3). The distributions of living and dead species within the seven groups are shown in Fig. 4, where the averaged frequencies are based on unit vectors. Tendencies along the three routes of the characteristic species (Fig. 3) are described below. A. tepida, together with H. germanica, is the most abundant living species in the outlier sample A38. Frequent in group 1, A. tepida strongly increases at route 1 until group 4c, then decreases from group 7 to A39. The same tendencies are pictured in DAs. At route 2, the increase of living and dead species until group 4c is strong but weaker compared with route 1. Living and dead species decrease at route 3, attaining higher abundance with group 7. H. germanica is the second most important species in the intertidal groups and shows an opposite trend to A. tepida. After a slight increase at route 1 (group 3a), it shows minimum abundance in group 3b, slightly increasing in group 4c, becoming more abundant with the end members group 7 and A39. At route 2 the increase after initial group 1 is stronger in group 4a, slightly decreasing towards group 4c. The third route is distinguished by high abundance in group 5, obtaining the highest values within group 6. Tendencies in routes are parallel between LAs and DAs. T. inflata is mostly abundant in group 1 and group 5 (route 3) in both LAs and DAs. It is not frequent at route 1 and slightly decreases at route 2. Abundance between LAs and DAs is very similar. E. excavatum is the most abundant taxon in LAs and DAs in groups 1 and 2 and exhibits interesting parallel tendencies in LAs and DAs along the routes. Route 1 shows parabolic tendencies with a minimum in group 3a, while at route 2, after the strong initial decrease in group 4b, frequencies increase constantly until group 7. Similar behavior can be detected at route 3. E. macrescens shows similar tendencies at routes 1 and 2, which, after the maximum abundance in group 1, are followed by rare specimens until group 7. Group 5 in route 3 shows the highest abundance of E. macrescens in both LAs and DAs. M. fusca is an intertidal species showing great differences in sample A38, where no living individuals are detected, but dead individuals are the most abundant by far. After the highest abundance in group 1, M. fusca suddenly decreases at all routes in LAs and DAs. Q. seminula is the last important taxon in the deeper intertidal and subtidal areas and shows at all routes an abundance increase from group 1 to group 7 in both LAs and DAs. 3.3. Abiotic factors influencing the distribution of living benthic foraminifera In Fig. 5 the first DCA axis including correlations between the DCA axes and abiotic factors (Table 1) shows the dominant importance of salinity combined with the negative correlation, to some extent, with the PLI (Fig. 5A). This is coupled with a negative influence of TOC, CHO, LIP and PTN. Fine fraction proportion, pH, CHO, LIP and PTN are positively correlated with the second DCA axis, while the sand fraction proportion shows a negative correlation opposite to the fine fraction. The influence of pollution by metals, indicated by the PLI, is in combination with TOC and biopolymers (CHO, LIP and PTN) both in the first (Fig. 5A) and in the second DCA (Fig. 5B). The DCA factor loadings of the species allow us to interpret the influence and intensities of abiotic factors. These interpretations are strengthened by calculating means and standard deviations of abiotic variables

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Fig. 4. Frequencies of living and dead species according to groups obtained by cluster analysis.

M.V.A. Martins et al. / Palaeogeography, Palaeoclimatology, Palaeoecology 455 (2016) 16–32

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Fig. 5. Detrended correspondence analysis based on living species, including correlations to abiotic factors. A) Plot of the axis 1 against the axis 2; B) Plot of the axis 2 against the axis 3.

for every species (Fig. 6), weighted by their abundance at each site (e.g., Ferschl, 1978). It must be mentioned that the dependence of species on abiotic factors are related to the investigation area, but must fall into the broader general distribution limits of the mentioned species.

3.4. Assessing the relationship between LAs and DAs Relationships between LAs and DAs can be measured in different ways. First, differences in species diversities between the time—instantaneous

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Fig. 6. Means and standard deviations of abiotic variables weighed by individual numbers in the samples. Limits are given by x  s including 66% of specimens.

M.V.A. Martins et al. / Palaeogeography, Palaeoclimatology, Palaeoecology 455 (2016) 16–32

25

Fig. 7. Principal component analyses based on parameters a and b of the Bertalanffy growth function fitting rarefaction curves of living and dead individuals. Groups I to V based on Ward's method. Rarefaction functions of selected stations typical for each group are added.

LAs and the time—averaged DAs can be checked. This allows us to estimate the grade of concordance between LAs and DAs and to look for factors explaining discrepancies.

Species diversities of LAs and DAs were calculated for every sample using rarefaction analyses. For parametrical comparison within (living versus dead) and between samples, rarefaction curves have been fitted

Standard deviation

Group

Mean

Standard deviation

Group

I

7.05

1.81

I

8.38

0.99

I

1.33

0.006

II

5.72

1.22

II

12.30

1.55

II

6.58

2.75E–07

III

11.96

1.06

III

15.92

1.04

III

3.96

4.39E–05

IV

8.79

1.03

IV

15.70

1.47

IV

6.91

1.65E–08

V

5.59

1.13

V

16.92

0.67

V

11.33

1.19E–05

Group

Mean

Standard deviation

Group

Mean

Standard deviation

Group

Difference dead–living

p(T)

I

0.821

0.040

I

0.817

0.025

I

–0.004

0.406

II

0.751

0.047

II

0.870

0.024

II

0.119

1.67E–06

III

0.873

0.035

III

0.899

0.019

III

0.026

0.036

IV

0.862

0.034

IV

0.897

0.021

IV

0.035

6.270E–05

V

0.820

0.039

V

0.883

0.007

V

0.063

0.005

Parameter b

Parameter a

Mean

Dead parameter a

Group

Dead parameter b

Living parameter b

Living parameter a

Table 2 Distribution parameters of Bertalanffy functions for LAs and DAs according to groups I to V. Tests of concordance between LAs and DAs checked by paired T-test.

Difference dead–living

p(T)

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Fig. 8. Comparing means of Bertalanffy parameters a and b in groups I to V between LAs and DAs.

by Bertalanffy growth functions, resulting in parameter a approximating the maximum species number and parameter b determining the rate of increase for both living and dead species (Appendix E). After standardizing parameter values, samples were arranged in a two-dimensional axis system using factor scores obtained by principal component analysis (Fig. 7). Grouping within the axis system is based on cluster analysis using Ward's method (not pictured). According to factor loadings, the first axis marks increasing intensities in all growth parameters, e.g., from low parameter values a and b to high parameter values in both LAs and DAs, while the second axis shows increasing differences in growth parameters between living and dead individuals, e.g., from weak (high axis values) to strong differences (low axis values; Fig. 7). Groups are characterized as follows by means and standard deviations of parameters a and b both in LAs and DAs (Table 2, Fig. 8): Group I. The average species number limit (parameter a) is low and does not differ strongly between LAs and DAs, as does the grade of increase (parameter b). Group II. In LAs the averaged species number limit is low compared with group I, but much higher in DAs. Indeed, the increase rate (parameter b) is weaker in LAs than DAs. Group III. The averaged species number limit is the highest for LAs and slightly higher for DAs, while the rate of increase weakly differs. Group IV. Compared with group III, the DAs possess similar averaged species number limits, but LAs differ strongly by much lower number limits. The increase rate is similar to group III. Group V. This is the most interesting group, with the lowest averaged species number limit in LAs but the highest numbers in DAs

(Table 2). The increase rate is quite similar to the former groups III and IV in both LAs and DAs. Significant differences between groups in the four parameters were tested by analysis of variance (ANOVA) with subsequent Tukey-HSD tests for significant differences (Table 3). The grade of disorder in abundance of species between LAs and DAs was used as a second measure for comparing them. Kendall's Tau correlation coefficient was used for comparison of ranks based on species proportions (Appendix F). As explained above, two calculations were done, first calculating rank correlation between living species and their dead counterparts, then calculating rank correlation including dead species without living counterparts. Probabilities of non-correlation scaled to normal distributions were used as measures of concordance or disorder. Significant correlation indicating weak disorder between living and dead species is certified by values below − 1.6449, while significant disorder is characterized by values above this threshold (Fig. 9). Comparing rank correlation of living species and their dead counterparts with the rank correlation comparing all living and dead species leads to different groupings (areas A to D in Fig. 9). Area A: This group shows approximately identical ranks in both investigations (only living species and their dead counterparts on the abscissa and all species on the ordinate). The subdivision into groups A1 to A4 depends on the position between 1% and 5% error probabilities, either being a little bit disordered in all species (A2) or disordered in living species plus dead counterparts (A3) or in both (A4). Area B: The few sites in this group are characterized by significant disorder regarding all species, while they are identical (B1) or slightly but insignificantly disordered (B2) in

Table 3 Pairwise comparison of parameters a and b between groups I to V in the LAs and DAs using Tukey HSD statistics as ‘a posteriori’ tests following ANOVA. Homogeneities in variance checked by Levene-Test. Bold numbers indicate significant differences.

Parameter a of living individuals Group

II

I

0.109

II

0.000

0.000

III

0.009

0.000

0.000

IV

0.232

1.000

0.000

III

IV

Parameter a of dead individuals V

0.000

Group

II

I

0.000

II

0.000

0.000

III

0.000

0.000

0.994

IV

0.000

0.000

0.653

Parameter b of living individuals Group

II

I

0.001

II

0.039

0.000

III

0.059

0.000

0.966

IV

1.000

0.016

0.136

III

IV

III

IV

V

0.353

Parameter b of dead individuals V

0.234

Group

II

I

0.000

II

0.000

0.042

III

0.000

0.021

0.999

IV

0.000

0.806

0.695

III

IV

V

0.737

M.V.A. Martins et al. / Palaeogeography, Palaeoclimatology, Palaeoecology 455 (2016) 16–32

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Fig. 9. Normalized probabilities based on Kendall's Tau rank correlation coefficient. Low values indicate identical ranks, high values mark disorder. Increasing values characterize incremental disorder. Significance thresholds of 5% and 1% error probabilities are given; samples with characteristic distributions for groups A to D added (bar diagrams of every site with species indications in Appendix F).

living species plus dead counterparts. Area C: These sites behave the opposite to area B, being identical (C1) or slightly but insignificantly disordered (C2) in all species, while they are significantly disordered in living species plus dead counterparts. Area D: Sites of this group are significantly disordered in living species plus dead counterparts, as well as in all species. Here the outstanding sample site A38 is remarkable with its completely differing distributions of living and dead species (Fig. 9). These “areas” of Fig. 9 do not correspond to specific regions of the Aveiro Lagoon but group stations where the average values of abiotic variables reveal distinct characteristics. For example stations of area A display the finest sediments but with relatively low TOC and TBP content. Stations of area B also have finer grained sediments but have relatively high TOC and TPB content with the highest CHO concentrations. Stations of area C present relatively coarse grained sediment but with the highest TBP content, namely PTN and LIP. Stations of area C present the lowest pH values and the highest TOC content enriched in PTN. 4. Discussion 4.1. Abiotic parameters and living benthic foraminiferal relationships According to the DCA results based on living species (Fig. 5a) and the means and standard deviations of abiotic variables (Fig. 6), it is possible to infer the main relationships among species that compose the LAs of the Ria de Aveiro and the analyzed abiotic parameters, as follows.

A. tepida can be found at the intertidal zone, in the transition from fine sand to silt, adapted to reduced salinity (~ 25‰), high pH (~ 7.0). H. germanica, as the second most important intertidal taxon, somewhat less abundant than A. tepida, behaves similarly in its dependence on abiotic factors. A. tepida has been considered as a “resistant species” to pollution in several environments (Yanko et al., 1994; Samir and El Din, 2001; Kfouri et al., 2005; Ferraro et al., 2006; Dimiza et al., 2016b). H. germanica is also recognized by its tolerance to several kinds of pollutants (Debenay et al., 2001; Armynot du Châtelet et al., 2004; Bergamin et al., 2003; Frontalini and Coccioni, 2008, 2011). However, according to DCA (axes 2 and 3 in Fig. 5), H. germanica seems to be more tolerant to pollution, TOC and increase in biopolymers than A. tepida. E. excavatum is the only species of the genus Elphidium that is abundant in the investigation area as a typical intertidal form. With reduced salinity (~24‰), normal (high) pH (~7.1) and distribution at the transition fine sand-silt, it behaves similarly to A. tepida, similar also in its tolerance to pollution, TOC and biopolymers. E. excavatum is able to live in polluted, near-shore environments (Bates and Spencer, 1979; Setty and Nigam, 1984; Alve, 1991; Schafer et al., 1991). According to Sharifi et al. (1991), this species is one of the most tolerant species to heavy metal pollution, followed by H. germanica and A. tepida in this order. However, the strong decrease in salinity can deeply condition the distribution of E. excavatum (Armynot du Châtelet et al., 2004). E. macrescens behaves similarly to A. tepida and E. excavatum in its dependence on sediment, reduced salinity (~22‰), somewhat reduced

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pH of 6.9, tolerance of pollution and high content of biopolymers. It differs from the previous species in lower TOC dependence. However, this species is known to thrive in association with marsh plants but also to occur outside marshes (Alve and Murray, 1999). A. mexicana is restricted to the sand fraction with low salinity (~24‰) and neutral pH (~7.1). It is not represented in polluted areas and avoids areas with high biopolymer content. A. hamblensis shows the opposite behavior to A. mexicana by preferring fine sediments, lowered salinity (~ 24‰) and relatively high pH (7.4). It is adapted to higher TOC and tolerates pollution as well as high amounts of biopolymers. Lepideuterammina ochracea is rare in the transition zone from intertidal to subtidal, living on fine–sand/silty sediments, where salinity (~ 27‰) and pH (~ 6.9) are a little lower. The tolerance of pollution and biopolymers is similar to H. germanica, while the content of TOC is low compared with the former species. M. fusca lives in the intertidal zone of the lagoon, in fine-sand/silt sediments. It is distinguished by the lowest salinity of all species recorded in the investigation area (~ 21‰), together with normal pH (~7.1). The tolerance of this species to pollution and biopolymers is slightly higher than A. tepida and H. germanica and is similar in the dependence on TOC. M. fusca is a fairly common species in worldwide paralic environments (Debenay and Guillou, 2002) and is the most ubiquitous agglutinated shallow-water species (Murray and Alve, 1999). In Portugal, it was found as a dominant species, associated with calcareous species, in sparsely vegetated low-marsh and tidal-flat zones of the lower to middle estuary of the Guadiana River (Camacho et al., 2015). T. inflata, as the third important intertidal species, shows a similar dependence on abiotic factors as M. fusca. While its tolerance is somewhat higher to low salinity (~23‰) and pH (~6.9) and relatively high TOC, its tolerance to pollution and biopolymers increasing is slightly lower than M. fusca. These species can dominate in the LAs of estuarine salt-marshes (González et al., 2000; Ruiz et al., 2005; Leorri et al., 2010). S. moniliforme exhibits a similar trend to M. fusca in salinity, pH and sediment preference. However it shows a preference to lowered TOC and is less tolerant to pollution and biopolymer enrichment. This taxon is common in brackish waters (Alve and Murray, 1999) and in sub-tidal to low-tidal channels in the outer parts of slightly brackish estuaries elsewhere in New Zealand (Hayward et al., 1999, 2004). D. globularis as an uppermost subtidal form is adapted to silt and fine sand with normal marine salinity (~33‰) and high pH (~7.2). Contrary to A. tepida, but with similar dependence on abiotic factors, it is an excellent indicator for pollution, TOC and biopolymers. D. globularis is a widely spread species of neritic marine environments in boreal and tropical zones (Mikhalevich, 2008). The three lowest intertidal/shallow subtidal species, E. gerthi, E. margaritaceum and E. williamsoni, behave similarly in the investigation area due to normal salinity (~29‰) and low pH (~6.6). They are common in European littoral settings (Alve and Murray, 1999; Polovodova et al., 2011) and transitional environments (Cearreta et al., 2002; Camacho et al., 2015). The order from E. gerthi to E. margaritaceum and to E. williamsoni is related to sediment grain size decreasing from sand to fine-sand/silt and increasing tolerance (from weak to medium) to pollution by metals and TOC, including biopolymers. E. williamsoni and some other species such as S. moniliforme (as Reophax moniliformis) and H. germanica are frequently green-colored, which indicates the presence of pigments. These pigments indicate that they live in symbiotic association with diatoms or their chloroplasts (Knight and Mantoura, 1985) or consume green food. B. gibba, restricted to fine sediments, prefers normal marine salinity (~32‰) and neutral pH (~7.0). It is an excellent indicator of pollution, high TOC and all biopolymers. C. ungerianus prefers sandy sediments, normal salinity (~ 30‰), normal marine pH (~ 7.0) and clean water characterized by low PLI, weak TOC and low biopolymer content.

G. praegeri of the uppermost subtidal prefers normal salinity (~31‰) and seems to support slightly lower pH (~6.7). As an epifaunal species, it is found in the investigation area at the transition from fine-sand to silt with low TOC and possesses weak tolerance for pollution and biopolymer enrichment. G. crassa rossensis behaves similarly to G. praegeri in abiotic factors. Exceptions are the preference for sandy sediments with low TOC and the stronger intolerance of biopolymers. L. lobatula is bound to sandy sediments in the subtidal zone with normal salinity (~30‰) and pH (~6.9). It shows the highest intolerance to pollution and to TOC and biopolymer enrichment. P. mediterranensis, possibly attached to sea grass, can be found in the subtidal zone in finesand/silt sediments with normal salinity (~ 31‰) and somewhat lowered pH (~ 6.7). While TOC is relatively high, pollution by metals and biopolymers are low in these sediments. Q. seminula, abundant in the deeper intertidal and subtidal zones, prefers the transition from sand to silt with somewhat lowered salinity (~27‰) and normal pH (~6.9). As an epifaunal species it depends less on TOC, with low tolerance for pollution and biopolymers. B. gibba, C. ungerianus, G. praegeri, G. crassa rossensis, L. lobatula, P. mediterranensis and Q. seminula are common in the nearby continental shelf environments (Martins et al., 2012). However, whereas Q. seminula is a fairly common species in the Ria de Aveiro, its abundance declines in the inner areas, while the other species have their distribution restricted to the lagoon entrance area. L. lobatula, P. mediterranensis and Q. seminula are commonly epiphytic species. From these species, Q. seminula is much common in the Aveiro Lagoon, followed by L. lobatula. Both species reached highest densities in the LAs than the DAs in most of the stations where they were found. P. mediterranensis is more common in the DAs. However, in station 39, located in a protected area near the lagoon mouth, this species reached the highest density in the LAs. These results evidence two main factors. A loss of empty tests due to taphonomic effects, probably related to hydrodynamic factors and due to input of sediment material supplied from oceanic areas, and its deposition in areas where tidal currents lose their transport capacity.

4.2. General overview of continental/marine influence General trends of living and dead species distribution are clearly indicated by the stations grouped by hierarchical cluster analyses (Fig. 2) as disposed along the three roots established by the DCA (Fig. 3). The station A38 located near the Vouga River mouth (see zone 5 of Fig.1) is at the beginning (one extremity) of these routes. This station was identified by the hierarchical cluster analyses as an outlier, since it has a different assemblage of benthic foraminifera in relation to the other stations. The foraminiferal assemblages of A38 present divergence between the living and the dead assemblage of benthic foraminifera that are of small dimension (Fig. 4; Appendices B and F). The main indicator species of the living assemblage are E. macrescens, A. tepida, E. excavatum, H. germanica, and of the dead assemblage are M. fusca, E. macrescens and T. inflata (Appendix D). These species are common in transitional environments characterized by significant changes in salinity (Coccioni et al., 2009; Frontalini et al., 2009, 2010, 2013; Kemp et al., 2009; Culver et al., 2015) and the agglutinated species are typical from saltmarsh assemblages (Leorri et al., 2010; Camacho et al., 2015; Milker et al., 2015). These assemblages should be influenced by salinity variations bringing saline water by tidal currents and fresh water by the outflow from the Vouga River, which is the main source of continental waters to the lagoon (Moreira et al., 1993; Dias et al., 1999). Station A38 is the one that experiences the greatest influence of continental waters. Significant differences in the dead assemblage characteristics relative to the living one may be related to the high seasonality of environmental conditions in that area alternating with phases of flood or drought of Vouga River or with periods of greater or lesser amount of salt water brought in by tides. But the sampling period was under

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dry weather conditions, thus the discharge of the Vouga River was reduced at that time explaining the dominance of hyaline species in LAs. According to the DCA of Fig. 3, station A39 is positioned at the extremity of the three routes, just opposite to A38. It was also identified by hierarchical cluster analyses as an outlier. Station A39 is located close to the lagoon entrance (Fig.1, Area 6) in the area of highest marine influence in the Ria de Aveiro (Génio et al., 2008; Martins et al., 2014). The foraminiferal assemblage (Fig. 4, Appendix F) is diversified and include significant number of living specimens of calcareous subtidal species. The main indicator species of this station in the living assemblage are, by decreasing order of importance, G. praegeri, G. crassa rossensis, P. mediterranensis, L. lobatula, B. gibba, Elphidium gerthi, C. ungerianus and E. margaritaceum. In the dead assemblage the main indicator species of this station are, by decreasing order of importance D. globularis, G. praegeri, G. crassa rossensis, P. mediterranensis, L. lobatula, B. gibba, Elphidium gerthi, C. ungerianus, A. hamblensis and Q. seminula. The order of significance of the indicator species is different in both assemblages, having the dead assemblage a greater number of indicator species than live one. However, most part of these species are typical of marine environments, such as G. crassa rossensis, G. praegeri, P. mediterranensis, C. ungerianus, B. gibba and L. lobatula (Martins et al., 2012, 2015c; Dessandier et al., 2015). In this station, the number of specimens is much higher in all species in the dead assemblage than in the living one. The absence of juveniles at this site suggests that these species are introduced into the lagoon by tidal currents and live there for some time. The calm currents inside this harbor favor the accumulation of empty tests. The benthic foraminiferal assemblages of the groups of stations of the DCA, based on living and dead species (Fig. 3), placed along route 2 (beginning in A38 and following to groups 1 ⇨ 4a ⇨ 4b ⇨ 4c ⇨ towards group 7) suggest a transition from the highest continental influence to the progressive increase of a marine one. This tendency is also inferred for route 1 (beginning in group 1 and evolving along the sequence group 3a ⇨ group 3b ⇨ group 4c and ⇨ group 7) and for route 3 (defined by the sequence group 1 ⇨ group 5 ⇨ group 6 ⇨ group 7). These inferences are supported in group 1 by the importance of the main indicator species of the living assemblage such as A. mexicana, M. fusca, S. moniliforme, T. inflata, E. macrescens and E. excavatum (similar to that found in dead assemblage), which are known for their high tolerance to the variability of physicochemical parameters such as salinity and temperature (Horton et al., 2003; Rossi et al., 2011; Camacho et al., 2015). In group 7, the marine influence is highlighted by the most important indicator species such as L. ochracea, C. ungerianus, G. crassa rossensis, G. praegeri, P. mediterranensis, E. margaritaceum and L. lobatula associated to waters with relatively high salinities (in the study area; also demonstrated by the DCA of Fig. 5). These species also include the list of indicator species of the dead assemblage but with low importance and with a different order of importance (Appendix D). They are common in the nearby continental shelf environments (Martins et al., 2012). These species are extremely rare in the three routes as living individuals, but they display increasing abundance in dead assemblages from group 1 to group 7 at all these routes. Therefore, the three analyzed routes represent gradients of continental versus oceanic influence in the lagoon. The sequence of stations located along the route 3 (A9 → A35 → A31 → A32 → A33 → A45 → A41) evolve from a lagoonal high confinement (A9 station located in an inner area of the lagoon) to a greater oceanic influence (A45 and A41 stations). This transition is marked by the increase of grain size and the decrease in TOC and TBP indicating a progressive effect of faster tidal currents. The stations of group 5 (A9 → A35 → A31) includes mostly lagoonal species, such as H. germanica, A. tepida, T. inflata, E. excavatum, E. macrescens and M. fusca. Still stations of group 7 at the end of Route 3 (A33 → A45 → A41) include a number of species such as P. mediterranensis, L. lobatula, E. gerthi, G. crassa rossensis and L. ochracea, which are common in the nearby continental shelf environments

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(Martins et al., 2012, 2015c). These benthic foraminiferal assemblages, in addition to typical lagoonal species, indicates a higher connection with marine waters. The environmental conditions in route 2 evolve under high degree of confinement. These groups of stations have in general finer sediments, with high TOC and biopolymers content and relatively high concentrations of metals. The stations of route 1 are located in open spaces submitted to moderate hydrodynamic currents where sediments accumulate intermediate values of TOC but display high biopolymers concentrations, namely PTN (indicative of fresh organic matter with high quality). Opposite to the stations of route 3, the second axis of the DCA (Fig. 3) separates stations A1–A3 included in the natural cluster 2 identified by hierarchical cluster analyses (Fig. 2). Stations A1–A3 are located at the northern part of the lagoon, in the extremity of the Ovar Channel. They belong to a lagoonal environment where the degree of confinement is high because currents velocities decline, but where the tidal influence and ocean water intake is still felt (Martins et al., 2015a, 2015b). In this area, fine grained sediments with high TOC and biopolymers content and relatively high concentrations of metals prevail. The most important indicator species in the living assemblage are A. hamblensis and E. excavatum. They are typical intertidal species, which prefer to live in calm areas being able to drive in. They are adapted to lowered salinity and high pH, high TOC and biopolymers which tolerates pollution caused by metals. In which way the sequence within the routes combined to environmental conditions is reflected in the relation between dead and living assemblages is analyzed in the following topic. 4.3. Areas where DAs best reflect their living counterpart The rarefaction curves of selected stations typical for each of the five groups of stations and the parameters a and b of the Bertalanffy growth function fitting the rarefaction curves of living and dead individuals separated by the principal component analyses give us information about the agreement between the DAs and LAs of benthic foraminifera (Fig. 7). This analysis highlights that the stations of groups I and III are analogous in their similarity between DAs and LAs, differing in the much higher species number limits in III compared with I. Stations of group I are located in protected and inner areas of several channels, namely the Ovar, Murtosa and Mira channels, while most stations of group III are positioned in sheltered areas located near the lagoon mouth, under the highest marine influence. Thus, the assemblages should be minimally affected by external factors in these groups, weakest in group I. An opposite behavior is defined for groups II and IV, where the limits of species numbers are much higher in DAs compared with LAs, indicating the influence of external factors. The strongest influence of these factors should be responsible for the large difference in species number limits between DAs and LAs in group V. Here, the lowest species number limits in LAs, similar to group II, is combined with the highest number limits of DAs. Considering the characteristics of abiotic parameters, it can be observed that the stations of groups I and III are those with finer sediments, indicating that they are affected by a relatively weaker hydrodynamic regime than the other stations, where this regime is strongest in the stations of group V. Other important abiotic parameters such as salinity, as well as TOC and biopolymer content and metal enrichment, do not have a direct link with the establishment of these groups of stations. Comparing the PCA, similarities between the DAs and LAs (Fig. 7) and the means of Bertalanffy parameters a and b in groups I to V for living and dead assemblages (Fig. 8) with the normalized probabilities based on Kendall's Tau rank correlation coefficient (Fig. 9), stations where dead and living assemblages are in higher agreement can be distinguished. Those stations where the DAs best reflect the living

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counterpart must include simultaneously groups I and III (Fig. 7) and A1 of the PCA based on the Kendall's Tau rank correlation coefficient (Fig. 9). Such stations may be, for instance, A9, A14, A15 and A16, located in protected areas at the northern extremity of the lagoon connected with the Ovar Channel, stations A25 and A28 in an internal area of the Murtosa channel, A50 in the southern extremity of Mira Channel and A47 in an Aveiro City canal. These stations are located in sheltered internal lagoonal areas, and includes benthic foraminifera assemblages typical of lagoonal environments out of the strongest influence of tides. The similarity between living and dead assemblages is much lower near the Vouga River mouth and is reduced in open areas exposed to tidal currents through the main channels and near the lagoon entrance. In this zone are excluded the protected areas by harbors structures, such as station A39 located near the S. Jacinto pier. Whereas abiotic parameters, such as salinity and pH, as well as TOC, biopolymers and pollution enrichment, are important in the distribution of living assemblages and thus of dead assemblages, the agreement between both assemblages seems to be to a certain extent ruled by hydrodynamics. Nevertheless some areas where the DAs best reflect their living counterpart should not be chosen to carry out studies of paleoenvironmental evolution. This is the case of Aveiro City canals which are frequently subject to human intervention to preserve the structure of the canals' walls and to dredging for navigation. Over Mira Channel also dredging and disposal of dredged materials occur. So the most suitable sites to collect sediment cores for paleoenvironmental evolution studies seems to be located in some areas of the northern end of Ria de Aveiro or in the eastern central region connected with the Murtosa Channel. 5. Conclusion On the basis of complex statistical analyses, this work allows us to establish the ecological requirements of the main species living in the Aveiro Lagoon with physicochemical parameters of water and sediment, sediment texture, tolerance degree of eutrophication (enrichment in organic matter and biopolymers) and metals contamination. This information allows the interpretation of environmental characteristics in the channels of the Aveiro Lagoon. Based on the LAs and DAs in the studied samples and cluster analysis, a natural cluster and several pseudo-clusters of stations are identified. The PCA identifies gradients, along routes, where these clusters and pseudo-clusters evolve from a dominance of marine influence to the highest continental influence and high degree of confinement. These routes are differentiated from each other in the degree of exposure to hydrodynamics, type of environment (typically intertidal or subtidal) and degree of confinement. Statistical analysis was applied to compare the similarities between the DAs and LAs and to identify the stations where the DAs best reflect the living counterpart. These stations are located in protected internal lagoonal areas out of the strongest influence of tides, for instance: i) at the northern extremity of Ovar Channel (stations A9, A14, A15 and A16); ii) at the internal area of the Murtosa channel (stations A25 and A28); iii) at southern extremity of Mira Channel (station A50) and; iv) at Aveiro City canals (station A47). The man interventions in some areas however exclude the possibility of study the paleoenvironmental evolution during the Holocene for example in Mira Channel and Aveiro City canals. Tidal currents, near the lagoon entrance, contribute to the establishment of major differences between the LAs and DAs as well as near the Vouga River mouth. The same is observed along the main channels. This study shows an interesting application of statistical analysis to study the similarity between LAs and DAs to identify the zones and conditions of a coastal system in which the DAs can potentially be a better record of LAs and to give support to paleoenvironmental studies. Supplementary data to this article can be found online at http://dx. doi.org/10.1016/j.palaeo.2016.05.003.

Acknowledgments The authors would like to thank you to the Editor of Palaeogeography, Palaeoclimatology, Palaeoecology, Prof. Thierry Corrège, to Maria Triantaphyllou and to an anonymous reviewer for their collaboration in the work improvement. This work was partially supported by Fundação para a Ciência e a Tecnologia (FCT, Portuguese Science and Technology Foundation) grant given to PEst -OE/CTE/ UI4035/2014, UID/GEO/04035/, and by the CNPq (401803/2010–4) from Brazil.

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