EFFECT oF TAxoNoMIC AggREgATIoN IN MACRoALgAE

EFFECT oF TAxoNoMIC AggREgATIoN IN MACRoALgAE ASSEMBLAgES IN A RoCkyShoRE oF MAR DEL PLATA, ARgENTINA,

SouThwEST ATLANTIC oCEAN

(1) laboratorio de bioindicadores bentónicos, Facultad de cs. exactas y naturales, Universidad nacional de mar del Plata, dean Funes 3350. mar del Plata, buenos aires, argentina. [email protected], [email protected]

(2) laboratorio de Producción Primaria, instituto nacional de investigación y desarrollo Pesquero, Paseo victoria ocampo n°1 escollera norte. mar del Plata, buenos aires, argentina. [email protected]

(*) corresponding author: e-mail address: [email protected]. Phone number: (00549) (223) 537 1930

Thalassas, 30(1) · January 2014: 9-20An International Journal of Marine Sciences

Palabras clave: ambiente marino, aguas costeras, algas, agregación, mar del Plata, argentina.

m. e. becherUcci(1*), h. benavides(2), e. a. vallarino(1)

9Thalassas, 30(1) · January 2014

ABSTRACT

The variation of the composition of macroalgae assemblages at different taxonomic aggregation was assessed at two different wave exposure conditions on the coast of Mar del Plata, Argentina, at the Southwest Atlantic Ocean. The macroalgae assemblages attached in two sites were compared: an inside Harbor, a sheltered site from the waves, and a wave-exposed site in Playa Grande, a nearby open-beach. The species composition was significantly different between sites. Ulva lactuca, Ulva spp., Corallina officinalis and Gigartinaceae sp.1 were the most conspicuous species in the wave-sheltered site, while Hildenbrandia sp., Ulva lactuca, Ahnfeltiopsis sp. and Ceramium uruguayense were the most abundant species in the wave-exposed site. This spatial pattern was similar throughout the year. The sites were different regarding to species, genera, order and phyla levels. The different assemblage composition founded could be a result of the variation of both waves exposure and water pollution level in the two sites.

RESUMEN

Se analizó la composición de la comunidad de macroalgas a diferentes categorías taxonómicas, en dos sitios con diferente exposición al oleaje en la costa de Mar del Plata, Argentina, Atlántico Sudoccidental. Se compararon las asociaciones de algas en dos sitios de estudio: un sitio dentro del Puerto reparado de las olas, y otro en Playa Grande, una playa cercana expuesta al oleaje. La composición de especies varió significativamente entre los sitios de estudio. Ulva lactuca, Ulva spp., Corallina officinalis y Gigartinaceae sp.1 fueron las especies más conspicuas en el sitio reparado, mientras que Hildenbrandia sp., Ulva lactuca, Ahnfeltiopsis sp. y Ceramium uruguayense fueron las especies más abundantes en el sitio expuesto al oleaje. Este patrón se observó durante todo el año. Los sitios de estudio difirieron en la composición de especies, géneros, ordenes y phyla de macroalgas. Las diferencias entre las asociaciones pueden ser el resultado de la distinta exposición al oleaje y/o el grado de polución de las aguas entre los sitios de estudio.

key words: marine environment; coastal water; algae; aggregation; mar del Plata; argentina

M. E. BEchErucci, h. BENAvidEs & E. A. vAllAriNo

INTRoDUCTIoN

The development of macroalgae species at intertidal zones of rocky shores shows a vertical variation (from high to low tide levels), chiefly due to biological interactions and physical stress (Underwood, 1981). Also the macroalgae community variation along the coastline (horizontal variation) reflects the environmental gradients in a certain area. The factors that may have an effect on the horizontal variation of algae are salinity, waves action, substratum, and to a lesser extent the slope and texture of the substratum (Lobban et al., 1985). Also, macroalgae communities have generally resulted in changes in both community structure and diversity in response to toxic substances (Say, 1990; Villares, 2001). Nitrogen supply near sewage outfalls tends to support the typical macrophytes of early successional stages (O`Shanahan Roca, 2003; Martinetto et al., 2009; Santiago, 2009).

Pollution of the marine coastal water is a matter of global concern. European countries adopted in 2000 the Water Framework Directive focused at the protection and management of European water bodies. In this context, a systematic assessment of benthic communities, including macroalgae, is required to properly evaluate the grade of pollution of the water bodies (Ballesteros et al., 2007; Wells et al., 2007). Accordingly, studies on the relations between both diversity and richness of macroalgae communities and the environmental factors have been widely documented in several coasts of Europe (Krausen-

Jensen et al., 2007; Ballesteros et al., 2007; Wells et al., 2007; Puente & Juanes 2008). All these studies focused on the use of biological indicators of water quality to provide a suitable statistical tool for the assessment of the ecological status of the coast. Several indexes have been developed based on the evaluation of the relative abundance of macroalgae indicator species, as the Ecological Evaluation Index (EEI) (Orfanidis et al., 2001), Reduced Species List (RSL) (Wells et al., 2007), Littoral Cartography Methodology (CARLT) (Ballesteros et al., 2007), Quality of Rocky Bottoms Index (CFR) (Juanes et al., 2008) and the Marine Macroalgae Assessment tool (MarMat) (Neto et al., 2012).

The use of macroalgae as biological indicators implies the assessment of their abundance, richness, diversity and community structure according to different environmental gradients as pollution, depth or waves exposure. However, the assessment of macroalgae communities demands a high sampling effort and laboratory processing when measuring their biomass and also when the list of species is not updated; thus, resulting in a highly time consuming task. The complete quantification of algae assemblages at coarser taxonomic resolution than species is a novel method that was recently proposed to reduce sampling efforts and processing time of samples (Puente & Juanes, 2008; Smale et al., 2010). A strong relationship must exist between species level patterns and the coarser taxonomic level patterns to choose the method as a tool for ecological monitoring.

10 Thalassas, 30(1) · January 2014

Figure 1: Study site, showing the southeast coast of Buenos Aires province in Argentina.

EffEct of tAxoNoMic AggrEgAtioN iN MAcroAlgAE AssEMBlAgEs iN A rockyshorE of MAr dEl PlAtA, ArgENtiNA, southwEst AtlANtic ocEAN

In the South-Western Atlantic, several studies has been conducted to evaluate different types of coastal impacts on the littoral communities, mainly the sewage impact (Lopez-Gappa et al., 1990; Vallarino et al., 2002; Elías et al., 2001, 2004; Muniz et al., 2005; Martinetto et al., 2009; Muniz et al., 2011) and harbor impact (Rivero et al., 2005). The majority of these studies were focused on the macrofauna; however, the assessment of coastal impacts on marine flora has not been fully achieved yet, probably due to the lack of specialists in macroalgae taxonomy (Díaz et al., 2002; Santiago, 2009). Studies on macroalgae have not been updated in the Buenos Aires Province coast, where most studies have been conducted around the 80 s (Sar et al., 1984; Parma et al., 1987).

Along the coast of Mar del Plata, the lack of rivers and the hard quartzite substratum allow the settlement of a high variety of marine vegetation unlike any other coastal sector of the province (Boraso de Zaixo, 2007; Parma et al., 1984). Sheltered areas are scarce along the shores. The inner zone of the city s harbor represents a sheltered area. However, it is well known that harbor areas have water in poor conditions due to the input of waste waters, urban runoff, chemicals and dredged material, and the harbor of Mar del Plata is not an exception (Rivero et al., 2005 and references there in).

This research focused on three goals: 1) The compari-son of macroalgae assemblage pattern in two sites with different wave-exposure, 2) the identification of the main macroalgae species which contribute to the detection of a wave-exposed horizontal variation, and 3) the effect of the taxonomic aggregation on the spatial patterns of the macroalgae assemblages.

MATERIAL AND METHoDS

Study area

Mar del Plata (38ºS, 57º 33’W) is located in the south-east of Buenos Aires Province, Argentina (Fig1). Along the city shoreline there are sandy open beaches which alternate with an extended intertidal quartzite rocky shore. In addition, breakwaters built with cement and quartzite rocks to prevent erosion, offer an additional substratum to seaweeds in sandy beaches areas. The tidal regime is semidiurnal with a tidal amplitude range of approximately 0.8 m, and 1.6 m during exceptional tides (SHN, 2011). Sea surface temperature shows a seasonal variation (9.3° C in winter and 20º C in summer) (Guerrero & Piola, 1997).

The abundance and biomass of macroalgae species were assessed in a wave-exposed breakwater, 2.6 km

distant from the harbor, (site WE hereafter), and in a wave-sheltered site in a breakwater inside the city harbor (site WS hereafter) (Fig 1).

Despite the water pollution inside the harbor (Bastida et al., 1980; Rivero et al., 2005), this site was selected because it provided one of the few sheltered area with a similar type of substrata, slope and it has an easy access to its eulittoral zone.

Sampling design

At each sampling site three vertical transects placed ca. 2 m apart in different rocks were sampled. Three 0.25 x 0.25 m sampling units were taken 0.5 m apart within each transect to represent upper, mid and low levels of the entire eulittoral zone (Raffaelli & Hawkins, 1999). The macroalgae abundance (percent coverage) was assessed monthly from October 2008 to September 2009. Sampling was completed during extraordinary low tides in spring (in September, October and December), summer (in January and February), autumn (in March and May) and winter (in July and August). The species were identified to the lowest possible taxonomical level. The nomenclature given in Algae database was followed (Guiry & Dhonnchan, 2012). The macroalgae specimens which could not be identified in situ were collected and identified in the laboratory.

The macroalgae biomass was sampled on October and December 2008, February, May and August 2009. As for macroalgae abundance, we followed the above mention protocol though; we used a sampling unit of 0.125 x 0.125 m in order to have the minimum impact over the benthic community. Samples were immediately frozen and the dry weight of each macroalgae species was determined in the laboratory.

Temperature (° C) and pH of the sea water were measured in situ by using a U-10 Horiba. The slope of each sampling site was measured according to the horizontal line method (see Alveal & Romo, 1995).

Statistical analysis

Species richness (S), the Shannon diversity index (H´) (Shannon & Weaver 1963) and evenness (J´) (Pielou, 1969) were calculated from abundance data for each sampling unit. Three-way ANOVA was used to test the effect of season, sampling site and level on the mean of these parameters (S , H´ and J´). Comparisons among means were performed using a posteriori Tukey test (Zar, 1999).



Independent t-test was used to evaluate the effect of sampling site on the mean abundance and biomass of macroalgae phyla.

Independent t-test was used to evaluate the effect of sampling site on the mean temperature and pH of the sea water.

To run the ANOVA and t-test, macroalgae abundance and biomass, and the parameters (S , H´ and J´) data were transformed using fourth root (x) to accomplish assumptions of normality and variance homocedasticity (Zar, 1999). Statistical analysis of the ANOVA and t-test data was performed using R software, Version 2.5.1. (R Development Core Team, 2004).

Non- parametric multivariate analyses were performer using the PRIMER v 6.0 software package (Clarke & Warwick, 2001). A Bray Curtis similarity matrix (Bray & Curtis, 1957) was used to generate 2-dimensional plots with non-metric multi-dimensional scaling (nMDS) based on species abundance and biomass. Two test of One-way ANOSIM was used to test no differences in macroalgae assemblage between sampling sites according height level and according season.

The SIMPER routine was applied to assess average dissimilarities between sampling sites in each eulittoral level as well as to identify the species responsible for differences between groups.

Abundance macroalgae species matrix was aggregated to genus, order and phyla levels. Consequently, a total of 4 matrices were obtained. The similarities among samples were calculated for each matrix by means of the Bray-Curtis similarity index (Bray & Curtis, 1957). Resemblances among all similarity matrices were determined by a Spearman rank correlation coefficient (see Puente & Juanes, 2008) (Clarke & Warwick, 2001). Further, individual nMDS plots were generated for each matrix.

RESULTS

Description of the macroalgae community and environmental variables

A total of 26 species were recorded representing 13 orders and 3 Phyla of macroalgae: Rhodophyta, Chlorophyta and Heterokontophyta (Table 1).

No significant interaction between season, levels


Figure 2: MDS plots of samples obtained from the abundance and biomass data of

macroalgae species distinguishing sampling site (a and b) and eulitoral levels (c and d).

24

Figure 2

Stress: 0.16SpeciesBiomass

Stress: 0.16SpeciesBiomass

Stress: 0.17SpeciesAbundance

Stress: 0.17SpeciesAbundance

a b

c dsite WE site WS Upper level Mid level Low level


and sampling site for richness (ANOVA F6, 162 =1.234, p=0.292), diversity (ANOVA F6, 162 =1.535, p=0.171) and evenness (ANOVA F6, 162 =0.356, p=0.905) was found. Still, a significant univariate differences between levels was found: diversity (ANOVA F2, 162 =22.269, p<<0.01) and richness (ANOVA F2, 162 = 7.819, p<0.01). Both macroalgae community parameters (H´ and S) were higher in low than in mid and upper levels, and in mid than upper

level (Tukey post hoc comparisons all p<0.01). While, evenness (ANOVA F2, 162 =7.54 p<0.01) was significant higher in low than in upper level (Tukey post hoc comparisons p=0.0004) (Table 2).

The abundance of Rhodophyta was similar in both sampling site (t-test T160, 162 =-0.310, p=0.757). However, biomass was significantly higher in site WS than in site WE (T90, 92 =5.240, p<<0.001). Abundance and biomass of Chlorophyta were significantly higher in site WS than site WE (t-test T160, 162 =4.184, p<<0.001 and T90, 92 =4.185, p<<0.001 respectively). The Phaeophyceae were not evaluated due to the low abundance of them in this area.

According to the relative percentage of macroalgae abundance, Hildenbrandia sp. (58.45%) for upper level, Ulva lactuca (28.49%) and Ahnfeltiopsis sp (24.96%) for mid level, and Ceramium uruguayense (23.06%) for low level dominated in site WE. Likewise, Ulva spp (39.16%) for upper level, Ulva lactuca (34.20%) for mid level and Corallina officinalis (38.47%) for low level dominated in site WS.

The spatial and temporal pattern has a great similarity according both biomass and abundance data. However, it should be indicated that the biomass of Gigartinaceae sp. 1 at low level of the site WS represented the 31% of overall biomass.

The water temperature and pH data showed similar values in both sampling sites (t-test T6, 8= - 0.386, p=0.712 and T6,8 =0.017, p=0.992 respectively). Values of pH were quite stable during all year (7.7 - 7.9). Seasonal temperature range varied between (17.8 – 19.1° C) in spring, (20.0 - 21.0° C) in summer, (19.2 - 19.6° C) in autumn and 10.8° C in winter at both site. The slope of the substrate was 50 % at both sites.

Pattern of macroalgae assemblages

Analysis of similarity (ANOSIM) showed that macroalgae assemblages abundance differed significantly between both sampling sites (global R = 0.41, p= 0.001). The difference was observed between each eulittoral levels: upper (R=0.171, p=0.001), mid (R=0.214, p=0.001) and low (R=0.608, p=0.001), and between each season (all p <0.003).

The MDS plot, derived from the abundance and biomass of macroalgae species data set, showed a clear partitioning between both sampling sites (Fig2a y 2b), and enabled to differentiate the three eulittoral level (upper, mid and low) (Fig2c y 2d). However, the ANOSIM based


Figure 3:

MDS plots of samples obtained from the abundance data of macroalgae

Genus (a), Order (b) and Phyla (c) distinguishing sampling site

25

Figure 3

Stress: 0.17a

Stress: 0.15b

Stress: 0.14c

site WS site WE


14 Thalassas, 30(1) · January 2014 22

Table 1

Site Phylum Order Species Abundance Mean±SD

Biomass Mean±SD

Wave-Exposure Rhodophyta Bangiales Porphyra sp. 14.33±17.59 66.96±115.09 Corallinales Corallina officinalis 5.00±0 62.40±59.18 Jania sp. 28.75±35.44 136.22±137.92 Hildenbrandiales Hildenbrandia sp. 29.45±26.26 0 Gigartinales Gigartinaceae sp. 1 10.00±0 0 Ahnfeltiopsis sp. 23.88±19.01 532.70±626.97 Gymnogongrus sp. 17.00±16.27 91.24±121.21 Gelidiales Gelidium sp. 16.67±4.08 142.50±177.66 Ceramiales Ceramium uruguayense 34.31±24.76 314.99±658.10 Ceramium sp. 24.29±20.90 44.65±40.99 Callithamnion sp. 19.63±12.47 291.61±275.65 Chondria sp. 20.00±0 0 Pterosiphonia sp. 0 1.28±0 Polysiphonia sp 1 24.58±20.30 149.69±276.00 Polysiphonia sp 2 15.00±0 0 Polysiphonia sp 3 7.00±0 438.84±611.47 Rhodymeniales Gastroclonium trichodes 0 0 Chlorophyta Ulvales Ulva lactuca 23.41±19.33 357.56±453.99 Ulva spp. 17.20±21.53 360.75±404.20 Cladophorales Chaetomorpha sp. 35.00±39.05 38.46±52.58 Bryopsidales Bryopsis plumosa 12.71±8.58 64.17±56.79 Codium fragile 7.00±0 96.79±0 Heterokontophyta Ectocarpales Scytosiphon sp. 0 0 Petalonia sp. 13.33±14.43 0 Dictyotales Dictyota sp. 0 3.63±0.37 Rafsiales Ralfsia sp. 34.89±29.06 0 Wave-Sheltered Rhodophyta Bangiales Porphyra sp. 21.13±22.21 416.03±452.22 Corallinales Corallina officinalis 49.12±30.51 2942.53±4762.43 Jania sp. 0 0 Hildenbrandiales Hildenbrandia sp. 23.07±24.53 0 Gigartinales Gigartinaceae sp 1 21.92±18.50 2425.48±3963.33 Ahnfeltiopsis sp. 5.00±0 12.18±9.07 Gymnogongrus sp. 22.33±24.01 0 Gelidiales Gelidium crinale 55.00±27.84 2.56±0 Ceramiales Ceramium uruguayense 12.67±7.97 62.22±68.52 Ceramium sp. 0 0 Callithamnion sp. 12.00±0 19.07±17.68 Chondria sp. 0 18.59±0 Pterosiphonia sp. 0 0 Polysiphonia sp 1 7.00±0 177.24±224.37 Polysiphonia sp 2 25.00±22.91 1681.84±1716.57 Polysiphonia sp 3 0 18.59±6.35 Rhodymeniales Gastroclonium trichodes 38.00±13.04 227.99±217.78 Chlorophyta Ulvales Ulva lactuca 29.45±26.83 1798.99±4800.79 Ulva spp. 52.00±33.40 151.57±216.98 Cladophorales Chaetomorpha sp. 15.20±8.59 267.31±589.74 Bryopsidales Bryopsis plumosa 29.38±26.97 909.62±1480.25 Codium fragile 0 0 Heterokontophyta Ectocarpales Scytosiphon sp. 13.50±11.39 0 Petalonia sp. 9.25±1.50 152.14±62.20 Dictyotales Dictyota sp. 0 0 Ralfsiales Ralfsia sp. 30.00±7.07 0

Table 2 S H´ J´

Table 1:

Mean (± SD) abundance and biomass (g m-2) values of the macroalgae species in wave-exposure site and wave-sheltered site.

The orders and phyla which the species belong to are shown.



on biomass data showed that the macroalgae assemblages differ significantly between both sites in mid (R=0.282, p=0.001) and low (R=0.747, p=0.001) levels during the hole year (all p<0.003), but not between both upper eullittoral levels (R=0.043, p=0.276).

The percentages of dissimilarity between both sampling sites were 82%, 84% and 90% for upper, mid and low levels respectively (SIMPER analysis). The macroalgae species that contributed >10% to each sampling site dissimilarity are showed in Table 3.

According to biomass data, Gigartinaceae sp. 1 contributed ca. 12% to both sampling sites dissimilarity at the low eulittoral level.

Taxonomic aggregation

According to abundance data, the MDS ordination of genera, order and phyla indicated a clear partitioning between both sampling sites, based on the macroalgae assemblages structure (Fig3).

The Spearman rank correlations obtained between the species similarity matrices and the higher taxonomic level were significant (p= 0.001) in all cases. The correlations between the species level and genus, order and phyla level matrices had a Spearman rank correlation coefficient (r) of 0.86, 0.85 and 0.42 respectively.

DISCUSSIoN

Description of the macroalgae community

The algae assemblages presented a significant variation between the wave exposed and the sheltered sampling sites. A horizontal variation of species composition between both sampling sites, and a vertical variation of species abundance, richness, diversity and evenness between the upper, mid and low levels of a eulittoral zone at both sites were observed. The abundance, diversity, richness and evenness increased toward the low level, near the sublittoral fringe. The spatial distribution of macroalgae communities at intertidal rocky shores shows

a vertical variation mainly produced by the biological interactions and physical stress (Underwood, 1981), according to species biology and environmental tolerance. Therefore, the vertical variability of assemblages must be considered in order to prevent some underestimation of the horizontal variability between different sampling sites (Araújo et al., 2005).

Diversity and richness of macroalgae communities change according to disturbance and size of the analyzed area (Patricio et al., 2007; Wells et al., 2007). Araújo et al. (2005) found less macroalgae species in wave-exposed areas compared to a close (ca. 4 km ) wave-sheltered area at the same intertidal levels in the coast of Portugal. Considering the similarities observed in the abundance, richness, diversity and evenness of macroalgae species between both sampled sites, wave-exposure was not an important factor in determining its variability.

The lack of horizontal variation in the ecological parameters and environmental variables may be caused by the proximity between sampling sites (2.6 km) or the existence of other factors (i.e. water turbidity, nutrient load, biological interactions, etc., Underwood, 1981; Arévalo, et al., 2007).

Pattern of macroalgae assemblages

Only the macroalgae species composition differed between both sampling sites. The differences observed between the species assemblages within the same level were largely due to a few species. At WS sampling site, Ulva spp. was dominant in the upper level, Ulva lactuca in the mid level and Corallina officinalis in the low level; and in WE site Hildenbrandia sp., Ahnfeltiopsis sp. and Ceramium uruguayense were dominant in the upper, mid and low levels respectively. Several studies indicated that small-scale substratum heterogeneity is an important factor in determining assemblage species composition within any particular intertidal height (Lapointe & Bourget, 1999). As macroalgae assemblages developed on similar type of substratum (quartzite) and rocks size at both sampling sites, the variation of species composition could be a consequence of different wave-exposure and

23

Table 2 S H´ J´

Upper 1.8 0.15 0.71 Mid 3.02 0.33 0.74 Low 4.02 0.45 0.78

Table 3 Groups UWE & UWS Average dissimilarity = 81, 98 Group UWE Group UWS Species Av.Abund Av.Abund Av.Diss Diss/SD Contrib% Cum.% Hildenbrandia sp. 29,32 16,35 30,48 1,16 37,18 37,18 Ulva spp. 0,00 21,81 16,10 0,64 19,64 56,82 Ralfsia sp. 14,88 2,31 15,32 0,72 18,69 75,51 Porphyra sp. 2,60 9,27 10,81 0,65 13,18 88,69 Groups MWE & MWS Average dissimilarity = 83, 78 Group MWE Group MWS Species Av.Abund Av.Abund Av.Diss Diss/SD Contrib% Cum. % Ulva lactuca 21,85 32,22 18,97 1,16 22,64 22,64 Ahnfeltiopsis sp. 19,15 0,19 10,61 0,92 12,66 35,30 Corallina officinalis 0,00 18,59 9,78 0,65 11,68 46,98 Groups LWE & LWS Average dissimilarity = 89, 56 Group LWE Group LWS Species Av.Abund Av.Abund Av.Diss Diss/SD Contrib% Cum. % Corallina officinalis 0,19 41,44 18,87 1,22 21,07 21,07 C. uruguayenese 23,89 2,63 11,38 0,88 12,71 33,78 Ulva lactuca 17,15 14,93 9,36 0,95 10,46 44,23

Table 2:

Mean richness (S), diversity (H’) and evenness (J’) at sampled levels

of the eulittoral zone: Upper, Mid and Low.



water quality conditions: as was previously observed for small and large spatial scale (Araújo et al., 2005; Tuya & Haroun, 2006; Puente & Juanes, 2008).

Also the vertical distribution patterns of the species vary between wave-exposed and wave-sheltered substrates (Araújo et al., 2005; Rico et al., 2005); so these changes could be reflected by the horizontal variability. This was observed for the Ulva species growing in a southern patagonian harbor. Their vertical distribution pattern differed from that observed for the population growing outside the harbor owing to the low waves-exposure condition, their interaction with herbivores and desiccation (Rico et al., 2005).

Corallina officinalis inhabit the sublittoral fringe in the rocky shore of Mar del Plata (Olivier et al., 1966; Sar et al., 1984; Parma et al., 1987). Hence, the canopy effect of Gigartinaceae sp. 1 over C. officinalis could allows the unusual distribution of C. officinalis in site WS and, thus developed in the eulittoral zone. Moreover, Ahnfeltiopsis sp. appear only in WE site, partially in agreement with other authors who cited A. devoniensis as a species of moderate and fully wave-exposure in a Portugal and Greek rocky shore respectively (Diéz et al., 1999; Araújo et al., 2005).

The distribution of macroalgae is also affected by the water quality. Dissolved inorganic nutrients or contaminants load were not measured in this study, but it is well known that harbors enclose quite polluted waters (Darbra et al., 2005). Opportunist species like Ulva lactuca or Ulva spp. are well developed in areas with high dissolved nutrients concentration (López Gappa et al., 1997; Santiago, 2009; Martinetto et al., 2009; O`Shanahan Roca, et al. 2003, Kraufvelin, 2007; Torres & Caille, 2009) or even with trace metals (Say et al., 1990; Fytianos et al., 1999). Ulva lactuca was the most adaptive species, showing high abundances in the mid and lower levels at both sampling sites. The genus was reported to dwell in several argentine harbors (Brankevich et al., 1986; Martín et al., 2000; Rico et al., 2005) including the Mar del Plata city harbor (Bastida et al., 1980).

Corallina officinalis presented the maximum abundance and biomass in site WS, contributing to dissimilarity between both sampling sites. Populations of several Corallina species presented a wide range of adaptation to environmental variations and were considered as especially resistant to disturbance (Diez et al., 1999; Soltan et al., 2001; Pinedo et al., 2007). Corallina was assigned to an intermediate low sensibility to pollution level in the coast of Spain (Ballesteros et

23

Table 2 S H´ J´

Upper 1.8 0.15 0.71 Mid 3.02 0.33 0.74 Low 4.02 0.45 0.78

Table 3 Groups UWE & UWS Average dissimilarity = 81, 98 Group UWE Group UWS Species Av.Abund Av.Abund Av.Diss Diss/SD Contrib% Cum.% Hildenbrandia sp. 29,32 16,35 30,48 1,16 37,18 37,18 Ulva spp. 0,00 21,81 16,10 0,64 19,64 56,82 Ralfsia sp. 14,88 2,31 15,32 0,72 18,69 75,51 Porphyra sp. 2,60 9,27 10,81 0,65 13,18 88,69 Groups MWE & MWS Average dissimilarity = 83, 78 Group MWE Group MWS Species Av.Abund Av.Abund Av.Diss Diss/SD Contrib% Cum. % Ulva lactuca 21,85 32,22 18,97 1,16 22,64 22,64 Ahnfeltiopsis sp. 19,15 0,19 10,61 0,92 12,66 35,30 Corallina officinalis 0,00 18,59 9,78 0,65 11,68 46,98 Groups LWE & LWS Average dissimilarity = 89, 56 Group LWE Group LWS Species Av.Abund Av.Abund Av.Diss Diss/SD Contrib% Cum. % Corallina officinalis 0,19 41,44 18,87 1,22 21,07 21,07 C. uruguayenese 23,89 2,63 11,38 0,88 12,71 33,78 Ulva lactuca 17,15 14,93 9,36 0,95 10,46 44,23

Table 3:

SIMPER result with abundance data. Principal species that contribute to the differentiation of groups upper wave-exposed and upper wave-sheltered

(UWE and UWS), mid wave-exposed and mid wave-sheltered (MWE and MWS), and low wave- exposed and low wave-sheltered (LWE and LWS).



al., 2007). However Orfanidis et al. (2001) considered Corallina as a late-successional species and also as possible indicator of unpolluted areas.

Considering both abundance and biomass data, Ahnfeltiopsis sp. and Ceramium uruguayense were scarce in site WS. The low abundance and biomass of Ceramium uruguayense was probably associated to water quality. Some macroalgae species require high quality environmental conditions, and the presence of contaminants may decrease their development (Diéz et al., 1999). This is consistent with a previous unpublished study that considered C. uruguayense as an indicator of intertidal non-sewage impacted areas in the Mar del Plata coast (Santiago, 2009). However, Ceramium sp. was mentioned as an opportunistic species that responds to an elevated nutrient concentration in the Cantabrian coast of Spain (Juanes et al., 2008) and as a frequent species in harbor polluted areas (Diéz et al., 1999). The low abundance and biomass of Ahnfeltiopsis sp. was rather associated to the reduced wave-exposure conditions in the harbor, as many Ahnfeltiopsis species prefer highly exposed substrata (Diéz et al., 1999; Araújo et al., 2005).

Chlorophyta were dominant in site WS, as previously reported for disturbed environments, were a higher abundance of green algae species respect to an undisturbed environment was reported (O`Shanahan Roca, 2003; Wells et al., 2007). Thus, site WS could be considered as a disturbed area (Bastida et al., 1980; Rivero et al., 2005).

Taxonomic aggregation

The orders Ulvales and Corallinales were dominant in site WS, while Ceramiales, Gigartinales and Hidenbrandiales were dominant in site WE. Both sampling sites differed in the macroalgae species composition, but also according to genera and orders; supporting the use of higher taxonomic levels for the assessment of macroalgae assemblages in coastal monitoring and management. The spatial patterns of marine benthic community structure at the genus and family levels are often sufficiently consistent with species level patterns to utilize it as a useful method to evaluated broader spatial scales faster than with traditional quantitative sampling procedure (Puente & Juanes, 2008; Smale et al., 2010). Both, genera and orders spatial pattern presented a high similarity to species pattern and showed a significant correlation coefficient (rho= 1); while phyla spatial pattern had a lower rho value (rho= 0.4) resulting in a not clear partitioning in the grouping of MDS. The order level spatial pattern showed the best similarity to the species distribution pattern.

Our observations are in agreement with previous studies conducted in the area (Pujals, 1963; Olivier, 1966; Parma et al., 1987; Sar et al., 1984). However, Ahnfeltiopsis sp. and Gigartinaceae sp. 1 have not been mentioned in previous literature and, thus, may represent exotic species as they appeared approximately 10 years ago in the area (Benavides, pers. obs.). Unfortunately, the marine flora in southeastern Buenos Aires Province has not been deeply studied or monitored during the past 25 years. Moreover, the city supports an important harbor, which represent a suitable introduction vector for exotic marine species (Carlton & Geller, 1993; Grosholz, 2002; Albano, 2012) including, for example, microscopic stages of macroalgae species in ballast water tanks (Flagella et al., 2007). Recently, several alien macroalgae species has been reported for the Mar del Plata area, like Sporochnus pedunculatus (Boraso & Negri, 1997), Anotrichium forcellatum (Boraso & Akselmann, 2005), Schizymenia dubyi (Ramirez et al., 2012) and Undaria pinnatifida (Meretta et al., 2012). It is supposed that a high number of alien macroalgae species has recently taken place in the Mar del Plata rocky shore.

CoNCLUSIoN

The macroalgae assemblage composition varied between sampling sites. Ulva spp. follows a water quality condition; while Ahnfeltiopsis sp. follows a high wave-exposure condition. Further, order level pattern was in accordance with the species pattern, being a good representative taxonomic level to distinguish the sampling sites.

ACKNowLEDGEMENTS

This study was carried out thanks to the support from the Universidad Nacional de Mar del Plata. We want to thank Lic. Juan Pablo Seco Pon for assisting with the writing of an early draft and to Dr. Rodolfo Elías for proof reading the article.

REFERENCES

Albano M (2012) Patrones de distribución y abundancia de invertebrados bentónicos exóticos en áreas naturales y portuarias de la provincia de Buenos Aires, Aregentina. Universidad Nacional de Mar del Plata, Mar del Plata, Argentina.

Alveal K, Romo H (1995). Estudios zonacionales. In: K Alveal, ME Ferrario, EC Oliveira, E Sar, eds, Manual de Métodos Ficológicos. Universidad de Concepción, Concepción, Chile, 624-626.

Araújo R, Bárbara I, Sousa-Pinto I, Quintino V (2005). Spatial variability of intertidal rocky shore assemblages in the



northwest coast of Portugal, Estuarine, Coastal and Shelf Science, 64: 658-670.

Arévalo R, Pinedo S, Ballesteros E (2007). Changes in the composition and structure of Mediterranean rocky-shore communities following a gradient of nutrient enrichment: Descriptive study and test of proponed methods to assess water quality regarding macroalgae, Marine Pollution Bulletin, 55: 104-113.

Ballesteros E, Torras X, Pinedo S, García M, Mangialajo L, de Torres M (2007). A new methodology based on littoral community cartography dominated by macroalgae for the implementation of the European Water Framework Directive, Marine Pollution Bulletin, 55: 172-180.

Bastida R, Trivi M, Lichtschein V, Stupak M (1980). Ecological aspects of Marine Fouling at the Port of Mar del Plata (Argentina). V Congreso Internacional de Corrosión Marina e Incrustaciones. Barcelona, España.

Boraso de Zaixso AL (2007) Macroalgas marinas. In: J I Carreto, C Bremec, eds, El mar Argentino y sus recursos pesqueros, Publicaciones especiales INIDEP, Vol 5, Mar del Plata, Argentina, 71-90.

Boraso AL, Negri R (1997). Presencia de Sporochnus pedunculatus (Sporochnales, Phaeophycofita) en la costa argentina, Phycis, 54: 23-24.

Boraso A, Akselman R (2005). Anotrichium furcellatum (Ceramiaceae, Rhodophyta) en Argentina. Una posible especie invasora, Boletín Sociedad Argentina de Botánica, 40: 207-213.

Brankevich G, Flamingo JL, Bastida R (1986). Estudios ecológicos sobre las comunidades incrustantes de la toma de agua de la central eléctrica Necochea (puerto Quequén, Argentina), período 1981-1982, CIDEPINT, 41-100.

Bray JR, Curtis JT (1957). An ordination of the upland forest communities of Southern Wisconsin, Ecological Monographs, 27: 325-349.

Carlton JT, Geller JB (1993). Ecological roulette: The global transport of nonindigenous marine organisms, Science, 261: 78-82.

Clarke KR, Warwick RM (2001). Change in Marine Communities: an Approach to Statistical Analysis and Interpretation, PRIMER-E Ltd, Plymouth, UK.

Darbra RM, Ronza A, Stojanovic TA, Wooldridge C, Casal J (2005). A procedure for identifying significant environmental aspects in sea ports, Marine Pollution Bulletin, 50: 866-874.

Díaz P, López Gappa JJ, Piriz ML (2002). Symptoms of eutrophication in intertidal macroalgae assemblage of Nuevo Gulf (Patagonia, Argentina), Botánica Marina, 45: 267-273.

Díez I, Secilla A, Santolaria A, Gorostiaga JM (1999). Phytobenthic Intertidal Community Structure Along an Environmental Pollution Gradient, Marine Pollution Bulletin, 38: 463-472.

Elías R, Bremec CS, Vallarino EA (2001). Polychaetes from a southwestern shallow shelf area (Argentina, 38°S) affected by sewage discharge, Revista Chilena de Historia Natural, 74: 523-531.

Elías R, Palacios JR, Rivero MS, Vallarino EA (2005). Short-term responses to sewage discharge and storms of subtidal sand-bottom macrozoobenthic assemblages off Mar del Plata City, Argentina (SW Atlantic), Journal of sea research, 53: 231-242.

Flagella MM, Verlaque M, Soria A, Buia MC (2007). Macroalgal survival in ballast water tanks, Marine Pollution Bulletin, 54: 1395-1401.

Fytianos K, Evgnidou E, Zachariadis G (1999). Use of macroalgae as biological indicators of heavy metal pollution in Thermaikos Gulf, Greece, Bulletin Environmental Contamination Toxicology, 62: 630-637.

Grosholz E (2002). Ecological and evolutionary consequences of coastal invasions, Trends in Ecology & Evolution, 17: 22-27.

Guerrero RA, Piola AR (1997). Masas de agua en la plataforma continental. In: EE Boschi, ed, El mar Argentino y sus recursos pesqueros, Publicaciones especiales INIDEP, Vol 1, Mar del Plata, Argentina, 107-118.

Guiry MD, Dhonncha EN (2012). AlgaeBase. WWW Page, www.algaebase.org (accessed 08.01.13).

Juanes JA, Guinda X, Puente A, Revilla JA (2008). Macroalgae, a suitable indicador of the ecological status of coastal rocky communities en the NE Atlantic, Ecological Indicators, 8: 351-359.

Kraufvelin P (2007). Responses to nutrient enrichment, wave action and disturbance in rocky shore communities, Aquatic Botany, 87: 262-274.

Krause-Jensen D, Carstensen J, Dahl K (2007). Total and opportunistic algal cover in relation to environmental variables, Marine Pollution Bulletin, 55: 114-125.

Lapointe L, Bourget E (1999). Influence of substratum heterogeneity scales and complexity on a temperate epibenthic marine community, Marine Ecology Progress Series, 189: 159-170.

Lobban CS, Harrison PJ, Duncan MJ (1985). The physiological ecology of seaweeds. Cambridge University Press, New York, United States of America.

López Gappa JJ, Tablado A, Magaldi NH (1990). Influence of sewage pollution on a rocky intertidal community dominated by the mytilid Brachidontes rodriguezi, Marine Ecology Progress Series, 63: 163-175.

López Gappa JJ, Tablado A, Magaldi NH (1993). Seasonal changes in a intertidal community affected by sewage pollution, Environmental Pollution, 82 : 157-165.

Martin P, Bastida R, Ieno E, Rivero L (2000). Estudio sobre el biofouling de los humedales del estuario del río de la Plata (Argentina), Thalassas, 16: 50-69.

Martinetto P, Daleo P, Escapa M, Alberti J, Isacch JP, Fanjul E, Botto F, Piriz ML, Ponce G, Casas G, Iribarne O (2010). High abundance and diversity of consumers associated with eutrophic areas in a semi-desert macrotidal coastal ecosistema in Patagonia, Argentina, Estuarine, Coastal and Shelf Science, 88: 357-364.

Meretta PE, Matula CV, Casas G (2012). Occurrence of the alien



kelp Undaria pinnatifida (Laminariales, Phaeophyceae) in Mar del Plata, Argentina, Biological invaders, 1: 59-63.

Muniz P, Venturini N, Pires-Vanin AMS, Tommasi LR, Borja A (2005). Testing the applicability of a Marine Biotic Index (AMBI) to assessing the ecological quality of soft-bottom benthic communities, in the South America Atlantic region, Marine Pollution Bulletin, 50: 624-637.

Muniz P, Venturini N, Hutton M, Kandratavicius N, Pita A, Brugnoli E, Burone L, García-Rodríguez F (2011). Ecosystem health of Montevideo coastal zone: A multi approach using some different benthic indicators to improve a ten-year-ago assessment, Journal of Sea Research, 65: 38-50.

Neto J, Gaspar MR, Pereira L, Marques JC (2012). Marine Macroalgae Assessment Tool (MarMAT) for intertidal rocky shores. Quality assessment under the scope of the European Water Framework Directive, Ecological Indicators, 19: 39-47.

O`Shanahan Roca L, Troncoso EV, Sanchez González A (2003). Efectos de un vertido de aguas residuales sobre una comunidad bentónica del litoral de Telde, NE de Gran Canaria (islas Canarias), VIERAEA, 31: 253-266.

Olivier SR, Escofet A, Orensanz JM, Pezzani SE, Turro AM, Turro ME (1966). Contribución al conocimiento de las comunidades bénticas de Mar del Plata, Anales de la Comisión de Investigaciones Científicas Provincia de Buenos Aires, 7 : 185-206.

Orfanidis S, Panayotidis P, Stamatis N (2001). Ecological evaluation of transitional and coastal waters: a marine benthic macrophytes-based model, Mediterranean Marine Science, 2: 45-65.

Orfanidis S, Panayotidis P, Stamatis N (2003). An insight to the ecological evaluation index (EEI), Ecological Indicators, 3: 27-33.

Parma A, Pascual M Sar E (1987). Clave para el reconocimiento de los géneros de algas macrofitas del intermareal rocoso bonarense, Serie aperiódica de la Facultad de Ciencias Naturales y Museo de La Plata, Argentina, 15- 29.

Patrício J, Neto JM, Teixeira H Marques JC (2007). Opportunistic macroalgae metrics for transitional waters. Testing tools to assess ecological quality status in Portugal, Marine Pollution Bulletin, 54: 1887-1896.

Pielou EC (1969). An introduction to mathematical ecology. Wiley-Interscience, New York.

Pinedo S, García M, Satta MP, de Torres M, Ballesteros E (2007). Rocky-Shore communities as indicators of water quality: A case study in Northwestern Mediterranean, Marine Pollution Bulletin, 55: 126-135.

Puente A, Juanes JA (2008). Testing taxonomic resolution, data transformation and selection of species for monitoring macroalgae communities, Estuarine, Coastal and Shelf Science, 78: 327-340.

Pujals C (1963). Catálogo de Rhodophyta citadas para la Argentina, Revista Museo Argentino Ciencias Naturales Botanica, 3: 57-76.

R Development Core Team (2004). R: a language and environment for statistical computing. Vienna, Austria, R Foundation for Statistical Computing. WWW Page, http://www.R-project.org (accessed 04.04.11).

Raffaelli D, Hawkins S (1999). Intertidal ecology. Kluwer Academic Publishers, Dordrecht, Netherlands.

Ramirez ME, Nuñez JD, Ocampo EH, Matula CV, Suzuky M, Hashimoto T Cledón M (2012). Schizymenia dubyi (Rhodophyta, Schizymeniaceae), a new introduced species in Argentina, New Zealand Journal of Botany, 50: 51-58.

Rico A, Lanas P, López Gappa JJ (2005). Colonization potential of the genus Ulva (Chlorophyta, Ulvales) in Comodoro Rivadavia harbor (Chubut, Argentina), Ciencias Marinas, 31: 719-725.

Rivero MS, Elías R, Vallarino EA (2005). First survey of macroinfauna in the Mar del Plata Harbor (Argentina), and the use of polychaetes as pollution indicators, Revista de Biología Marina y Oceanografía, 40: 101-108.

Santiago LI (2009) Distribución espacial y temporal de las macroalgas intermareales en áreas naturales e impactadas de Mar del Plata. Su valor como bioindicadores de contaminación orgánica. Universidad Nacional de Mar del Plata, Argentina.

Sar E, Pascual M, Parma A (1984). Consideraciones ecológicas sobre las algas del litoral rocoso bonaerense, Revista Museo de La Plata (n.s.) Botánica, 13: 143-147.

Say PJ, Burrowst IG, Whitton BA (1990). Enteromorpha as a monitor of heavy metals in estuaries, Hydrobiologia, 195: 119-126.

Shannon CE, Weaver W (1963). The mathematical theory of communication. University Illinois Press, Urbana, Illinois.

Smale DA, Kendrick GA, Wernberg T (2010). Assemblage turnover and taxonomic sufficiency of subtidal macroalgae at multiple spatial scales, Journal of Experimental Marine Biology and Ecology, 384: 76-86.

Soltan D, Verlaquet M, Boudouresque CF, Francour P (2001). Changes in Macroalgal communities in the vicinity of a mediterranean sewage outfall after the setting up of a treatment plant, Marine Pollution Bulletin, 42: 59-70.

Torres A, Caille G (2009). Las comunidades del intermareal rocoso antes y después de la eliminación de un disturbio antropogénico: un caso de estudio en las costas de Puerto Madryn (Patagonia, Argentina), Revista de Biologia Marina y Oceanografia, 44: 517-521.

Tuya F, Haroun RJ (2006). Spatial patterns and response to wave exposure of shallow water algal assemblages across the Canarian Archipelago: a multi-scaled approach, Marine Ecology Progress Series, 311: 15-28.

Underwood AJ (1981). Structure of a rocky intertidal community in New South Wales: patterns of vertical distribution and seasonal changes, Journal of Experimental Marine Biology and Ecology, 51: 57-85.

Vallarino EA, Rivero MS, Gravina MC, Elías R (2002). The community-level response to sewage impact in intertidal



mytilid beds of the Southwestern Atlantic, and the use of the Shannon index to assess pollution, Revista de Biologia Marina y Oceanografia, 37: 25-33.

Villares R, Puente X, Carballeira A (2001). Ulva and Enteromorpha as indicators of heavy metal pollution, Hydrobiología, 462: 221-232.

Wells E, Wilkinson M, Wood P, Scanlan C (2007). The use of

macroalgal species richness and composition on intertidal rocky seashores in the assessment of ecological quality under the European Water Framework Directive, Marine Pollution Bulletin, 55: 151-161.

Zar J (1999). Bioestatistical Analysis, fourth ed. Prentice-Hall Press, Englewood Cliffs, New Jersey.

(Received: February, 17, 2013; Accepted: August, 12, 2013)

Documents

EFFECT oF TAxoNoMIC AggREgATIoN IN MACRoALgAE