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Mollusc assemblages associated with invasive and native Sargassum species
Author’s Accepted Manuscript Mollusc assemblages associated with invasive and native Sargassum species Puri Veiga, Ana Catarina Torres, Celia Besteiro, Marcos Rubal www.elsevier.com/locate/csr
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S0278-4343(18)30002-5 https://doi.org/10.1016/j.csr.2018.04.011 CSR3757
To appear in: Continental Shelf Research Received date: 3 January 2018 Revised date: 16 April 2018 Accepted date: 20 April 2018 Cite this article as: Puri Veiga, Ana Catarina Torres, Celia Besteiro and Marcos Rubal, Mollusc assemblages associated with invasive and native Sargassum species, Continental Shelf Research, https://doi.org/10.1016/j.csr.2018.04.011 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting galley proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
Mollusc assemblages associated with invasive and native Sargassum species
Puri Veiga*1,2, Ana Catarina Torres1,2, Celia Besteiro3, Marcos Rubal1,2
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Laboratory of Coastal Biodiversity, Interdisciplinary Centre of Marine and
Environmental Research (CIIMAR/CIMAR), University of Porto, Terminal de Cruzeiros do Porto de Leixões; Av. General Norton de Matos s/n 4450-208 Matosinhos, Portugal 2
Department of Biology, Faculty of Sciences, University of Porto, Rua do Campo
Alegre s/n 4150-181 Porto, Portugal 3
Estación de Bioloxía Mariña da Graña Universidade de Santiago de Compostela, Spain
*
Corresponding-author. Tel.: +351 223401800; fax: +351 223390608. e-mail:
[email protected]
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ABSTRACT
Molluscs associated with the native macroalga Sargassum flavifolium and the invasive S. muticum were compared. The influence of habitat complexity provided by each macroalga was considered using biomass and fractal measures as proxies of habitat size and architecture, respectively. Results showed that biomass and fractal area of the alga and abundance, specific richness and diversity of the mollusc assemblages were significantly lower in the invasive macroalga and that mollusc assemblage differed significantly between macroalgae. Among species responsible for dissimilarity between macroalgae, microphytobenthos-grazing gastropods were more abundant in the native seaweed whereas two filter-feeding bivalves were more abundant in the invasive. Results also revealed significant correlations between biomass and fractal area with mollusc assemblages. However, the largest correlation coefficients for fractal area suggest more relevance of habitat architecture. Despite being two closely taxonomically macroalgae, of similar morphology, our findings suggest that the function of invasive macroalga as habitat provider differs from the native and induces changes in its associated fauna, which could imply food web modifications.
Graphical abstract
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Keywords: Sargassum flavifolium; Sargassum muticum; mollusc assemblage; diversity; complexity; Atlantic Ocean
1. Introduction Biological invasions are broadly considered as one of the largest component of global change (Galil, 2007; Occhipinti-Ambrogi, 2007; Olenin et al., 2011). Marine systems are particularly vulnerable to invasions because the ocean is an open environment, with species that have naturally broad geographical ranges and potentially high capacities for dispersion (Salvaterra et al., 2013). Despite this, invasions in marine systems have received comparatively less attention to those in terrestrial or freshwater habitats (Grosholz, 2002; Occhipinti-Ambrogi and Savini, 2003; Occhipinti-Ambrogi and Sheppard, 2007). In the European Union, the Marine Strategy Framework Directive (MSFD; EU 2008) recognises invasive marine species as a main threat to biodiversity and ecosystem health. Member States are required to develop strategies that avoid, 3
minimise and mitigate the adverse effects of invaders on biodiversity and ecosystems services (OJEU, 2014). However, this issue requires a deep understanding about the impact of invasive species (Molnar et al., 2008; Galil et al., 2014; Katsanevakis et al., 2014). Marine invasions have been mainly studied to determine their effects on biodiversity (Bax et al., 2003; Galil, 2007), although their economic consequences are also recognized (Perrings, 2002; Nunes and Markandya, 2008; Katsanevakis et al., 2014). However, an issue often ignored in risk assessment of invasions is that many invasive species also act as ecosystem engineers, being able to modify, create or define habitats by changing, directly or indirectly, their physical or chemical properties (Wallentinus and Nyberg, 2007; Katsanevakis et al., 2014). These habitat changes may cause intense alterations in community composition and modify normal ecosystem functioning (Occhipinti-Ambrogi and Sheppard, 2007; Wallentinus and Nyberg, 2007; Queiros et al., 2011). Macroalgae are one of the most notorious invasive species in the marine environment (Sánchez and Fernández, 2005; Irigoyen et al., 2011; Salvaterra et al., 2013). Moreover, macroalgae provide important services in nearshore ecosystems such as habitat, food or nutrient regulation and biogeochemical cycling (Klinger, 2015). This importance is particularly evident in the canopy forming-species (e.g. fucoids or kelps) that function as ecosystem engineers, generating three-dimensional habitat and increasing the habitat complexity (Wallentinus and Nyberg, 2007). By providing habitat, macroalgae can enhance the biodiversity of their associated assemblages and thus influence the functioning of food webs (Irigoyen et al., 2011; Salvaterra et al., 2013). Although some invasive macroalgae are also canopy forming-species, (e.g. Sargassum muticum, Undaria pinnatifida) their function as providers of habitat usually does not 4
correspond to that of the native canopies (e.g. Raffo et al., 2009; Veiga et al., 2014). Katsanevakis et al. (2014) identified alien marine species that have a high impact on ecosystem services and biodiversity in European seas finding that 45 of the 87 assessed species can be considered as ecosystem engineers. Within these, the brown seaweed Sargassum muticum was among the invasive species that affected the highest number of ecosystem services. This macroalga is native to East Asia around Japan but it was introduced in Europe in early 1970s and today it is present from Norway to Morocco and along the Mediterranean Sea (Sabour et al., 2013). Most studies that have evaluated the effects of S. muticum invasion have focused on its competitive interactions with native seaweeds (e.g. Stæhr et al., 2000; Britton-Simmons, 2004; Sánchez et al., 2005; White and Shurin, 2011). However, the interaction between seaweeds and their associated fauna are of great value in the framework of understanding marine invasion impacts, as associated fauna might increase the biotic resistance of a system to invasions or, alternatively may produce alterations in the composition of faunal assemblages (Engelen et al., 2013). Moreover, fauna associated with seaweeds constitutes essential linkages between higher trophic levels and primary producers such as the host macroalga with its associated periphyton and phytoplankton from the surrounding seawater (in the case of filter feeders). Therefore, modifications in the abundance, richness or composition of these faunal assemblages may have great repercussions on the ecosystems (Engelen et al., 2013). Generalizations about effects of invaders on the associated fauna with canopy-forming macroalgae are complicated by morphological differences between invasive and native seaweeds. Previous studies, comparing the invertebrate assemblages associated with S. muticum with those associated with other complex macroalgae (i.e. Halidrys siliquosa, Bifurcaria bifurcata, Cystoseira spp. and Halopteris scoparia), concluded that the 5
introduction of S. muticum has not caused considerable changes in the composition of faunal assemblages (Wernberg et al., 2004; Buschbaum et al., 2006; Engelen et al., 2013; Veiga et al., 2014). However, when morphologically less complex native macroalgae (Fucus sp. and Chondrus crispus) were considered, the abundance and diversity of invertebrates was higher in the invasive seaweed (Viejo, 1999; Buschbaum et al., 2006; Veiga et al., 2014). Differences in macroalgal complexity are probably one of the main drivers that explain much of the variation in the fauna associated with native and invasive macroalgae (Veiga et al., 2014; Dijkstra et al., 2017). Therefore, differences in the morphology between native and invasive algae can be also an important confounding factor. Despite this, few studies have properly used complexity measures (Gee and Warwick, 1994; Torres et al., 2015; Dijkstra et al., 2017). In the intertidal rocky shores of the south Portuguese coast, the native Sargassum flavifolium shares habitat with the invasive S. muticum. The coexistence of both Sargassum species offers an excellent opportunity to compare faunal assemblages harboured by these two taxonomically close canopy-forming-macroalgae and therefore, with a very similar morphology, one invasive and the other native. The aim of this study is to examine the effects of the invasive canopy-forming species by comparing its abundance, diversity and multivariate structure of mollusc assemblages with those associated with the native canopy-forming alga, considering the potential influence of macroalgal complexity. Molluscs were used as model because they live commonly associated with macroalgae since they provide them refuge from predation, shelter from hydrodynamism or serving as nursery area (Chemello and Milazzo, 2002). Moreover, previous studies have shown that mollusc assemblages are influenced by the complexity of macroalgae (Kelaher, 2003).
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2. Material and Methods 2.1 Sampling and sample processing Samples of the invasive and native macroalga (i.e. S. muticum and S. flavifolium) were collected in August 2014 on rocky shores of the south Portuguese coast to determine their structural complexity and for identification and quantification of their associated molluscs. A total of 12 replicates, haphazardly selected, of each macroalgal species were collected during low tide at midshore from 4 rocky shores: Oliveirinha (37º53’28.23’’N; 8º47’50.49’’W), Queimado (37º49’19.83’’N; 8º47’34.99’’W), Almograve (37º39’11.58’’N; 8º48’9.60’’W) and Amado (37º9’34.69’’N; 8º54’29.35’’W) (Fig. S1). Each individual macroalga was placed carefully in a plastic bag positioned over the thallus, which was closed before removal from the substrate to prevent small mobile organisms associated with the host macroalga from escaping. The thallus was then detached from the substratum and preserved with 4% formalin and rose Bengal. In the laboratory, individual thallus were washed in freshwater and shaken vigorously several times to remove the epifauna. Water was then sieved (0.5-mm mesh size) to retain the macrofauna and mollusc specimens were later identified to the lowest possible taxon (usually species level) and counted. 2.2 Macroalgal complexity Following Veiga et al. (2014) and Torres et al. (2015), biomass of macroalgae (Bi) was used as proxy for the size of available habitat and fractal dimensions as proxy of the habitat architecture. In order to calculate the fractal dimensions, each replicate macroalga was first slightly smashed and photographed with a Nikon Coolpix S2700 digital camera. Macroalgae were then dried at 60 ºC for 48 h to calculate their constant dry weight, which was measured to the nearest milligram. Fractal dimensions were
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calculated following the procedures described by McAbendroth et al. (2005). Each resulting image was transferred to greyscale and threshold to produce a binary that was used to quantify the fractal dimensions of both area (Da) and perimeter (Dp), by using the ImageJ software (Rasband, 1997). 2.3 Data analyses An analysis of variance (ANOVA) was used to test for differences in the complexity (i.e. Bi, Da and Dp) between macroalgae. These analyses were based on one-way model, including macroalgal species as fixed factor with two levels (S. muticum and S. flavifolium) and 12 replicates each. Cochran's C tests were conducted to examine homogeneity of variances and, when significant (p < 0.05), data were log-transformed to remove heterogeneity of variances. When this was not possible, untransformed data were analysed and results were considered robust if significant (at p < 0.01), to compensate for the increased probability of type I error (Underwood, 1997). In biodiversity studies, researchers usually estimate the number of species from a limited number of samples. However, Smith and van Belle (1984) proved that this methodology could underestimate the real biodiversity. To estimate the true species richness, the non-parametric Chao1 and Chao2 methods (Colwell and Coddington, 1994) were used with 999 random rearrangements of the sample ordering, to compare mollusc diversity harboured by the target macroalgae. Chao1 considers the abundance of each taxon whereas Chao2 is based on species presence/absence, avoiding the possible confounding effect of larger abundances of species in certain samples. Additionally, to compare the mollusc diversity among both macroalgae, the total number of species, the number of species restricted to a single macroalga (unique) and species represented by a single individual (singletons) were calculated (Colwell and Coddington, 1994). 8
In order to consider differences in the amount of habitat provided by both Sargassum species (i.e. Bi) in the total number of individuals (N), taxon richness (S), Shannon's diversity index (H′) and the structure of mollusc assemblages, we used a general linear model, using permutations with a Type I (sequential) sum of squares to calculate the p values by means of PERMANOVA (Anderson, 2001a), where macroalgal species was a fixed factor with two levels and Bi was used as covariate. Interaction between the categorical variables (macroalga) and continuous predictor variable (Bi) was included in the analyses. Macroalgal biomass were log(x+1) transformed to avoid the skewness of data. Analyses were done on the basis of Euclidean similarity matrices for N, S and H’ and Bray-Curtis similarity matrix for multivariate data, which were used untransformed. To visualise multivariate patterns in mollusc assemblages between invasive and native Sargassum, non-metric multi-dimensional scaling (nMDS) was used as an ordination method. In order to test whether differences of mollusc assemblages between macroalgae were due to different multivariate dispersion between groups, the PERMDISP procedure was used (Anderson, 2006). A SIMPER procedure (Clarke, 1993) was used to determine the percentage of contribution (δi%) of each taxon to the Bray-Curtis dissimilarity between mollusc assemblage associated with each Sargassum species (δi). A taxon was considered important if its contribution to total dissimilarity percentage was ≥ 3%. The ratio δi/SD(δi) was used to quantify the consistency of the contribution of a particular taxon to the average dissimilarity in the comparison between macroalgae. In order to explore the relationship between univariate response variables (i.e. N, S and H’) and the macroalgal complexity (i.e. Bi, Da and Dp), rank correlation analyses were utilised. Due to the non-normal distribution of the data, Spearman's rank correlation was used. Moreover, the relationship between the multivariate structure of mollusc 9
assemblage and macroalgal complexity was analysed using nonparametric multivariate multiple regression (McArdle and Anderson, 2001). Complexity variables were subjected to a stepwise forward-selection procedure to develop a model of the multivariate structure of mollusc assemblage. Analyses were done on the basis of BrayCurtis similarity matrix, which were used on untransformed data. P-values were determined using 999 permutations of residuals under the reduced model (Anderson, 2001b). All non-parametric multivariate multiple regressions were conducted using the computer program DISTLM (Anderson, 2002). Draftsman plots were done previously to check the skewness of complexity variables and data were log(x+1) transformed. Constrained ordination, a distance-based redundancy analysis (dbRDA, Legendre and Anderson, 1999), was done to explicitly investigate the relationship between macroalgal complexity and multivariate structure of mollusc assemblages.
3. Results 3.1 Macroalgal complexity Biomass (Bi) and fractal area (Da) were significantly higher in the native Sargassum species as compared to the invasive Sargassum (ANOVA Table 1; Fig. 1A-B). However, fractal perimeter (Dp) was found to be similar in both seaweed species (ANOVA Table 1, Fig. 1C). 3.2 Mollusc assemblage A total of 4,293 molluscs belonging to 37 taxa were identified, 23 in the invasive macroalga and 31 in the native. The species accumulation plot using the non-parametric estimator Chao1, considering all samples, reached a maximum value of 43.25 species for the native Sargassum and a lower value (31.17) for the invasive (Fig. 2A). The
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species accumulation plot using the non-parametric estimator Chao2, followed a similar pattern with a maximum value of 61.25 species for the native macroalga and only 26.5 species for the invasive (Fig. 2B). In addition, the native macroalga harboured 14 unique species whereas the invasive Sargassum harboured only six. Concerning singletons, both macroalgae harboured seven singleton species. PERMANOVA revealed that N, S and H’ of molluscs was significantly higher in the native Sargassum species (Table 2; Fig. 3A-C). Moreover, a significant effect of biomass in the N, S and H’ was found (Table 2). PERMANOVA also showed that the structure of mollusc assemblages differed significantly between Sargassum species (Table 3). Additionally, Bi showed a significant interaction with macroalgal species. Therefore, Bi significantly influenced the structure of mollusc assemblages but its effect was different between macroalgae (Table 3). The documented multivariate pattern was visualized as a clear separation between both macroalgae in the nMDS ordination (Fig. 4). The PERMDISP analysis (F=10.52, p < 0.05) indicated that the dispersion of samples provided a significant contribution to the detected differences, with higher dispersion for the invasive macroalga than for the native (Fig. 4). However, despite this dispersion, replicates of mollusc assemblage constitute two groups clearly separated in function of macroalgal species (Fig. 4). SIMPER analysis identified eight species as the main responsible for differences between invasive and native macroalgae, which showed an average dissimilarity of 86%. Collectively, these eight species contributed more than 90% to the total dissimilarity and the individual contribution of each one of them was ≥ 3% (Table 4). Noticeably, the abundance of these species was larger in the native Sargassum
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compared to the invasive, except for two bivalve species (i.e. Mytilus galloprovincialis and Musculus costulatus), which reported higher abundance in the invasive macroalga (Table 4). 3.3 Relationship between macroalgal complexity and mollusc assemblages Spearman's rank correlations showed that N and S significantly increased with the Bi of macroalgae (Fig. 5A-B). However, the correlation between Bi and H’ was not significant (Fig. 5C). Moreover, Spearman's rank correlations showed that N, S and H’ significantly increased with the Da of macroalgae, reaching relatively large correlation coefficients, ranging between 0.524 for H’ and 0.741 for N (Fig. 5D-F). Finally, correlations between Dp of macroalgae and N, S and H’ were not significant (Fig. 5GI). Results of DISTLM showed that Bi and Da together explained 50.32% of the variance in the multivariate structure of mollusc assemblages (Table 5). The variable explaining the greatest amount of variation was Da (40.38%). Once Da was fitted, Bi only added another 9.93% to the explained variation in the multivariate structure of macrobenthic assemblages but it was statistically significant. However, after fitting these two variables, the p-value associated to include Dp in the model was not statistically significant (p > 0.05) (Table 5). The first two dbRDA axes explained 97% of the fitted variation, which is about 51.2% of the total variation in the multivariate structure of the mollusc assemblage (Figure 6). All dbRDA axes together explain 100% of the fitted variation and 53% of the total variation.
4. Discussion
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Despite being two species of the same genus and appearing to have similar morphology, our results showed that the invasive and native Sargassum significantly differed in their complexity (i.e. Bi and Da). The invasive macroalga provided faunal communities with a lower habitat size of less complex architecture than that provided by the native species. These differences could be partially explained because in the sampling locality the invasive Sargassum is close to its southern boundary (i.e. Morocco, Sabour et al., 2013). Biomass and fractal measures of Sargassum muticum in the sampling locality (South Portugal) were also lower when comparing to those reported for the same species in the North Portuguese coast, far from its boundary, in an earlier work by the authors (Fig. S2; Veiga et al., 2014). Similarly, previous studies have pointed out that Fucus serratus, F. vesiculosus and F. gardneri in marginal populations show an obvious reduction in their size, with changes in their morphology in comparison with populations of the same species but in their centre of distribution (Sideman and Mathieson, 1983, Wright et al., 2004, Tatarenkov et al., 2005, Viejo et al., 2011). The invasive macroalga harboured a lower biodiversity, than the associated with the native macroalga, which may indicate a simplification of the food web (O’Gorman et al., 2012). Moreover, the structure of the mollusc assemblage was significantly different between macroalgae. However, these results, contrast with previous studies that have compared faunal assemblages on S. muticum to those associated with other complex native canopy species. Most of these studies consider unlikely that the introduction of S. muticum had produced substantial modifications in the composition of faunal assemblages (e.g. loss of diversity) suggesting only a weak impact upon native faunal diversity (Viejo, 1999; Wernberg et al., 2004; Buschbaum et al., 2006; Gestoso et al., 2010; Engelen et al., 2013). As biomass of S. muticum shows seasonal variability, Engelen et al. (2013) suggest that faunal shifts will be particularly obvious during the 13
growth season (spring-summer), when S. muticum is much larger than the native macroalgae, and the dominance of this invasive species may positively contribute to the trophic links within the ecosystem during this period. Our sampling of Sargassum individuals was done in summer, but the results did not support this. Moreover, our data showed that the invasive Sargassum reported less than half of unique species than the associated with the native seaweed contrasting with previous studies that did not find species exclusively associated with S. muticum or different native macroalgae (Gestoso et al., 2010; Engelen et al., 2013). Despite both the native and invasive Sargassum being canopy-forming, ecosystem engineer species, our data suggest that the function of the invasive macroalga as habitat provider does not match with that of the native Sargassum. Since S. muticum tolerates a wide range of environmental conditions (e.g. salinity or temperature) (Norton, 1976), it may be a better competitor for space, light, nutrients or oxygen, which may limit the distribution of native macroalgae (Critchley et al., 1986; Stæhr et al., 2000; Sánchez and Fernández, 2005). In the hypothetical case that this invasive seaweed supplants the native species, our results provide evidence of a strongly negative influence on mollusc biodiversity with consequences that may extend to higher trophic levels. In concordance, Salvaterra et al. (2013) suggest that S. muticum directly impedes the native macroalgal assemblages and that its effect outspreads indirectly to the native fauna, producing major alterations all over the ecosystem. They found that S. muticum decreased primary production and tended to reduce the species richness and diversity of fauna. Moreover, they found that S. muticum modifies food webs by altering the proportion of intermediate and top species, including the arrival of generalist species associated with S. muticum. Results of SIMPER analysis in our study pointed out a dissimilarity of 86% between the structure of mollusc assemblages associated with S. 14
muticum and S. flavifolium. Species that most contributed to this dissimilarity, were microphytobenthos-grazing gastropods (Gofas et al., 2011) which were more abundant in the native Sargassum. However, two filter-feeding species of bivalves (Gofas et al., 2011) were more abundant in the invasive seaweed, probably due to differences in the complexity between both macroalgae; bivalves usually are more associated with the holdfast of macroalga whereas gastropods are present in the secondary ramifications of the macroalga, which were more abundant in the native Sargassum (higher values of Da). Thus, our results suggest a potential change of the food webs; however, future research should explore this issue with more detail. The mollusc assemblage associated with the invasive Sargassum showed more variability than that on the native (Fig. 4 as indicated by dispersion of replicates). Therefore, assemblages associated with the invasive macroalga seem to be less stable and may be more influenced by the environment. This could be explained by the seasonality of the invasive Sargassum, its decreasing in biomass during autumn-winter may lead to a scarcity of habitat for fauna in this period and consequently generate an habitat less stable (Wernberg et al., 2004; Pedersen et al., 2005; Engelen et al., 2013). In contrast, mollusc assemblage associated with the native macroalga seem to be more dependent on seaweed itself. Our results also indicated a significant effect of biomass highlighting the significant role of habitat size in shaping the abundance, diversity and structure of mollusc assemblages. Salvaterra et al. (2013) explain the reduction in richness and diversity due to the invasion of S. muticum because this invasive species provides less canopy cover than the native seaweeds, thus offering less habitat for invertebrates. However, values of correlation coefficients for Da were higher than those for habitat size (as biomass), in all the response variables. Da reveals the way in which area of macroalga is divided up in
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space describing the gap structure. Thus, higher values of Da indicate high divisions and a smaller mean gap size (McAbendroth et al., 2005). Therefore, our results suggest that habitat architecture seem to be more relevant than habitat size in shaping mollusc assemblages associated with the studied macroalgae. Chemello and Milazzo (2002) also attribute differences in the molluscan assemblages associated with six Mediterranean algae to their architecture, which in turn, could be related to variations in food supply and predation rate experimented by mollusc assemblage. Complexity of macroalgae has been considered as an important driver of their associated faunal assemblages (Veiga et al., 2014; Torres et al., 2015). If complexity of macroalgae differs among areas (Fig. S2), to achieve a good understanding of the effects of invasive macroalgae on their associated faunal assemblages, it is crucial that future studies include different geographical areas. Our study provides evidence that S. muticum is able to induce significant changes in native mollusc assemblages where it becomes established in the south boundary of its range. If these changes in the composition of assemblages result in functional feeding differences, this may lead to modifications in the consumer-resource interactions with consequences to the whole ecosystem. Acknowledgement This research was partially supported by the Strategic Funding UID/Multi/04423/2013 through national funds provided by FCT – Foundation for Science and Technology and European Regional Development Fund (ERDF), in the framework of the programme PT2020. During this study, P. Veiga (SFRH/BPD/81582/2011) and M. Rubal (SFRH/BDP/104225/2014) were supported by postdoctoral grants and A.C. Torres (SFRH/BD/114935/2016) was supported by PhD grant awarded by Fundacão para a
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Figure caption Figure 1. Mean values (+SE) of biomass in gr (A), fractal dimensions based on area (B) and fractal dimensions based on perimeter (C) for Sargassum muticum and Sargassum flavifolium. Asterisk indicates significant differences (Pairwise test, **: p < 0.01; ***: p < 0.001); ns: not significant. Figure 2. Species accumulation curve obtained from the Chao1 (A) and Chao2 estimators (B) for Sargassum muticum and Sargassum flavifolium. Figure 3. Mean values (+SE) of total number of individuals (A), taxon richness (B) and Shannon diversity index of molluscs associated with Sargassum muticum and Sargassum flavifolium. Asterisk indicates significant differences (Pairwise test, *: p < 0.05; **: p < 0.01). Figure 4. nMDS ordination of mollusc assemblage based on Bray-Curtis similarities associated with Sargassum muticum and Sargassum flavifolium. Figure 5. Spearman's rank correlations between biomass, fractal dimensions based on area and fractal dimensions based on perimeter of macroalgae with the total number of individuals (A, B, C), taxon richness (D, E, F) and Shannon's diversity index (G, H, I) of molluscs. Figure 6. Distance-based redundancy (dbRDA) plot illustrating the DISTLM model based on the mollusc assemblage associated with Sargassum muticum and Sargassum flavifolium and the fitted complexity variables as vectors using DISTLM analysis in Table 5.
24
Table 1. Summary of ANOVAs for biomass (Bi), fractal dimensions based on area (Da) and fractal dimensions based on perimeter (Dp) of macroalgae. s: significant; ns: not significant; **: p < 0.01; ***: p < 0.001. Significant differences indicated in bold.
Source of variation Macroalga Residual Total Cochran's test
df 1 22 23
Bi MS 2.0035 0.2299
Da F 8.71**
C= ns 0.559 Ln (x+1)
Transformation
MS 0.1166 0.0004
F 264.10***
C= 0.802
s
Dp MS F 0.0096 4.05 0.0024 C= ns 0.631 none
none
Table 2. Summary of PERMANOVAs for total number of individuals (N), taxon richness (S) and Shannon index (H’) of molluscs including biomass as covariate. *: p < 0.05; **: p < 0.01. Significant differences indicated in bold.
Sourc e of variat ion Biom ass (Bi) Macr oalga (Ma) Bi x Ma
d f
1
1
1
MS 3.3 3 105 1.1 4 105 106 06
N Pse udoF
S Pse udoF
Per ms
MS
Per ms
54.9 8**
99 7
217 .66
39.2 9**
99 6
18.7 2**
99 4
47. 25
8.53 **
99 7
1.75
99 8
2.2 4
0.40
99 4
M S 0. 46
2 0
606 1.2
5.5 4
2 3
25
99 4
8.69 **
99 6
5. 17 10
6.02 10-2
99 6
8. 60 10 -2
Total
*
Per ms
0. 75
-3
Resid ual
H’ Pse udoF 5.38
Table 3. Summary of PERMANOVA on multivariate structure of mollusc assemblages present on each macroalgal species, including biomass as covariate. **: p < 0.01. Significant differences indicated in bold.
Source of variation
df
Biomass (Bi) Macroalga (Ma) Bi x Ma Residual Total
1 1 1 20 23
MS 13556 21642 5442.6 1223.5
Assemblage Pseudo-F 11.08** 17.69** 4.45**
Perms 999 999 999
Table 4. Contribution (δi) of individual taxa to the average Bray-Curtis dissimilarity between macroalgae.
Species Eatonia fulgida Tricolia pullus Barleeia unifasciata Mytilus galloprovincialis Rissoa parva Skeneopsis planorbis Musculus costulatus Gibbula pennanti
Average Abundance S. flavifolium S. muticum 129.17 0.08 41.25 0.00 45.17 0.58 26.50 35.50 11.67 2.25 9.58 0.17 9.67 9.83 11.92 0.42
δi
δi%
δi/SD(δi)
34.44 13.15 10.40 10.16 4.02 2.96 2.90 2.80
39.64 15.13 11.97 11.69 4.63 3.41 3.34 3.22
2.64 1.57 1.21 1.19 0.61 1.52 1.00 0.79
Table 5. Results of DISTLM carried out to ascertain the role of macroalgal complexity (Bi, Da and Dp), considering each variable taken individually (ignoring other, Marginal tests) and forward-selection of variables, where amount explained by each variable added to model is conditional on variables already included in the model on the multivariate structure of mollusc assemblages (Sequential tests). %Var: percentage of 26
variance in species data explained by that set of variables; Cum. %: cumulative percentage of variance explained. Variables significantly related to multivariate structure of mollusc assemblages indicated in bold. **: p < 0.01
%Var Pseudo-F Cum (%) Marginal tests Da Dp Bi
40.38 8.39 19.25
14.90** 2.02 5.25**
Sequential tests Da Bi Dp
40.38 9.93 2.81
14.90** 4.20** 1.20
40.38 50.32 53.12
Figure 1 8
A
**
Bi
6
**
4
2
0 S. muticum 2.0
B
***
S. flavifolium
***
Da
1.5
1.0
0.5
0.0 S. muticum 1.4 1.2
C
ns
S. flavifolium
ns
Dp
1.0 0.8 0.6 0.4 0.2 0.0 S. muticum
S. flavifolium
27
Figure 2
Figure 3 400
A **
N
300
200
100
**
0 S. muticum 14
S. flavifolium
**
B
12 10
**
S
8 6 4 2 0
S. muticum 1.8
C
*
1.6 1.4
H'
1.2
S. flavifolium
*
1.0 0.8 0.6 0.4 0.2 0.0 S. muticum
S. flavifolium
Figure 4
28
Figure 5
29
N
600
600
A
500
500
500
400
400
400
300
300
300
200
200
200
100
100
100
0 0
S
2
4
6
r = 0.641; p < 0.001
0
8
1.65 1.70 1.75 1.80 1.85 1.90 1.95
10
12
14
B
18
18
r = 0.741; p < 0.001
E
18 16
14
14
14
12
12
12
10
10
10
8
8
8
6
6
6
4
4
0
2
4
6
8
10
12
14
C
2.0
r = 0.588; p < 0.01
2 1.65 1.70 1.75 1.80 1.85 1.90 1.95 2.0
F
2.0 1.8
1.6
1.6
1.6
1.4
1.4
1.4
1.2
1.2
1.2
1.0
1.0
1.0
0.8
0.8
0.8
0.6
0.6
r = 0.524; p < 0.01 0.6
0
2
4
6
8
Bi
10
12
14
0.4 1.65 1.70 1.75 1.80 1.85 1.90 1.95
Da
r = -0.0793; p > 0.05
2 1.05 1.10 1.15 1.20 1.25 1.30 1.35
1.8
r = 0.258; p > 0.05
H
4
1.8
0.4
r = -0.254; p > 0.05
1.05 1.10 1.15 1.20 1.25 1.30 1.35
16
r = 0.571; p < 0.01
G
0
16
2
H'
600
D
I
r = -0.341; p > 0.05
0.4 1.05 1.10 1.15 1.20 1.25 1.30 1.35
Dp
Figure 6
30
31
Highlights
Mollusc assemblages associated with native and invasive Sargassum.
Invasive Sargassum showed lower biomass and fractal area than the native one.
Invasive Sargassum showed lower abundance and diversity than the native one.
Invasive Sargassum harboured distinct mollusc assemblages to native species.
Correlations of biomass and fractal area with mollusc assemblages were significant.
32
PII: DOI: Reference:
S0278-4343(18)30002-5 https://doi.org/10.1016/j.csr.2018.04.011 CSR3757
To appear in: Continental Shelf Research Received date: 3 January 2018 Revised date: 16 April 2018 Accepted date: 20 April 2018 Cite this article as: Puri Veiga, Ana Catarina Torres, Celia Besteiro and Marcos Rubal, Mollusc assemblages associated with invasive and native Sargassum species, Continental Shelf Research, https://doi.org/10.1016/j.csr.2018.04.011 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting galley proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
Mollusc assemblages associated with invasive and native Sargassum species
Puri Veiga*1,2, Ana Catarina Torres1,2, Celia Besteiro3, Marcos Rubal1,2
1
Laboratory of Coastal Biodiversity, Interdisciplinary Centre of Marine and
Environmental Research (CIIMAR/CIMAR), University of Porto, Terminal de Cruzeiros do Porto de Leixões; Av. General Norton de Matos s/n 4450-208 Matosinhos, Portugal 2
Department of Biology, Faculty of Sciences, University of Porto, Rua do Campo
Alegre s/n 4150-181 Porto, Portugal 3
Estación de Bioloxía Mariña da Graña Universidade de Santiago de Compostela, Spain
*
Corresponding-author. Tel.: +351 223401800; fax: +351 223390608. e-mail:
[email protected]
1
ABSTRACT
Molluscs associated with the native macroalga Sargassum flavifolium and the invasive S. muticum were compared. The influence of habitat complexity provided by each macroalga was considered using biomass and fractal measures as proxies of habitat size and architecture, respectively. Results showed that biomass and fractal area of the alga and abundance, specific richness and diversity of the mollusc assemblages were significantly lower in the invasive macroalga and that mollusc assemblage differed significantly between macroalgae. Among species responsible for dissimilarity between macroalgae, microphytobenthos-grazing gastropods were more abundant in the native seaweed whereas two filter-feeding bivalves were more abundant in the invasive. Results also revealed significant correlations between biomass and fractal area with mollusc assemblages. However, the largest correlation coefficients for fractal area suggest more relevance of habitat architecture. Despite being two closely taxonomically macroalgae, of similar morphology, our findings suggest that the function of invasive macroalga as habitat provider differs from the native and induces changes in its associated fauna, which could imply food web modifications.
Graphical abstract
2
Keywords: Sargassum flavifolium; Sargassum muticum; mollusc assemblage; diversity; complexity; Atlantic Ocean
1. Introduction Biological invasions are broadly considered as one of the largest component of global change (Galil, 2007; Occhipinti-Ambrogi, 2007; Olenin et al., 2011). Marine systems are particularly vulnerable to invasions because the ocean is an open environment, with species that have naturally broad geographical ranges and potentially high capacities for dispersion (Salvaterra et al., 2013). Despite this, invasions in marine systems have received comparatively less attention to those in terrestrial or freshwater habitats (Grosholz, 2002; Occhipinti-Ambrogi and Savini, 2003; Occhipinti-Ambrogi and Sheppard, 2007). In the European Union, the Marine Strategy Framework Directive (MSFD; EU 2008) recognises invasive marine species as a main threat to biodiversity and ecosystem health. Member States are required to develop strategies that avoid, 3
minimise and mitigate the adverse effects of invaders on biodiversity and ecosystems services (OJEU, 2014). However, this issue requires a deep understanding about the impact of invasive species (Molnar et al., 2008; Galil et al., 2014; Katsanevakis et al., 2014). Marine invasions have been mainly studied to determine their effects on biodiversity (Bax et al., 2003; Galil, 2007), although their economic consequences are also recognized (Perrings, 2002; Nunes and Markandya, 2008; Katsanevakis et al., 2014). However, an issue often ignored in risk assessment of invasions is that many invasive species also act as ecosystem engineers, being able to modify, create or define habitats by changing, directly or indirectly, their physical or chemical properties (Wallentinus and Nyberg, 2007; Katsanevakis et al., 2014). These habitat changes may cause intense alterations in community composition and modify normal ecosystem functioning (Occhipinti-Ambrogi and Sheppard, 2007; Wallentinus and Nyberg, 2007; Queiros et al., 2011). Macroalgae are one of the most notorious invasive species in the marine environment (Sánchez and Fernández, 2005; Irigoyen et al., 2011; Salvaterra et al., 2013). Moreover, macroalgae provide important services in nearshore ecosystems such as habitat, food or nutrient regulation and biogeochemical cycling (Klinger, 2015). This importance is particularly evident in the canopy forming-species (e.g. fucoids or kelps) that function as ecosystem engineers, generating three-dimensional habitat and increasing the habitat complexity (Wallentinus and Nyberg, 2007). By providing habitat, macroalgae can enhance the biodiversity of their associated assemblages and thus influence the functioning of food webs (Irigoyen et al., 2011; Salvaterra et al., 2013). Although some invasive macroalgae are also canopy forming-species, (e.g. Sargassum muticum, Undaria pinnatifida) their function as providers of habitat usually does not 4
correspond to that of the native canopies (e.g. Raffo et al., 2009; Veiga et al., 2014). Katsanevakis et al. (2014) identified alien marine species that have a high impact on ecosystem services and biodiversity in European seas finding that 45 of the 87 assessed species can be considered as ecosystem engineers. Within these, the brown seaweed Sargassum muticum was among the invasive species that affected the highest number of ecosystem services. This macroalga is native to East Asia around Japan but it was introduced in Europe in early 1970s and today it is present from Norway to Morocco and along the Mediterranean Sea (Sabour et al., 2013). Most studies that have evaluated the effects of S. muticum invasion have focused on its competitive interactions with native seaweeds (e.g. Stæhr et al., 2000; Britton-Simmons, 2004; Sánchez et al., 2005; White and Shurin, 2011). However, the interaction between seaweeds and their associated fauna are of great value in the framework of understanding marine invasion impacts, as associated fauna might increase the biotic resistance of a system to invasions or, alternatively may produce alterations in the composition of faunal assemblages (Engelen et al., 2013). Moreover, fauna associated with seaweeds constitutes essential linkages between higher trophic levels and primary producers such as the host macroalga with its associated periphyton and phytoplankton from the surrounding seawater (in the case of filter feeders). Therefore, modifications in the abundance, richness or composition of these faunal assemblages may have great repercussions on the ecosystems (Engelen et al., 2013). Generalizations about effects of invaders on the associated fauna with canopy-forming macroalgae are complicated by morphological differences between invasive and native seaweeds. Previous studies, comparing the invertebrate assemblages associated with S. muticum with those associated with other complex macroalgae (i.e. Halidrys siliquosa, Bifurcaria bifurcata, Cystoseira spp. and Halopteris scoparia), concluded that the 5
introduction of S. muticum has not caused considerable changes in the composition of faunal assemblages (Wernberg et al., 2004; Buschbaum et al., 2006; Engelen et al., 2013; Veiga et al., 2014). However, when morphologically less complex native macroalgae (Fucus sp. and Chondrus crispus) were considered, the abundance and diversity of invertebrates was higher in the invasive seaweed (Viejo, 1999; Buschbaum et al., 2006; Veiga et al., 2014). Differences in macroalgal complexity are probably one of the main drivers that explain much of the variation in the fauna associated with native and invasive macroalgae (Veiga et al., 2014; Dijkstra et al., 2017). Therefore, differences in the morphology between native and invasive algae can be also an important confounding factor. Despite this, few studies have properly used complexity measures (Gee and Warwick, 1994; Torres et al., 2015; Dijkstra et al., 2017). In the intertidal rocky shores of the south Portuguese coast, the native Sargassum flavifolium shares habitat with the invasive S. muticum. The coexistence of both Sargassum species offers an excellent opportunity to compare faunal assemblages harboured by these two taxonomically close canopy-forming-macroalgae and therefore, with a very similar morphology, one invasive and the other native. The aim of this study is to examine the effects of the invasive canopy-forming species by comparing its abundance, diversity and multivariate structure of mollusc assemblages with those associated with the native canopy-forming alga, considering the potential influence of macroalgal complexity. Molluscs were used as model because they live commonly associated with macroalgae since they provide them refuge from predation, shelter from hydrodynamism or serving as nursery area (Chemello and Milazzo, 2002). Moreover, previous studies have shown that mollusc assemblages are influenced by the complexity of macroalgae (Kelaher, 2003).
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2. Material and Methods 2.1 Sampling and sample processing Samples of the invasive and native macroalga (i.e. S. muticum and S. flavifolium) were collected in August 2014 on rocky shores of the south Portuguese coast to determine their structural complexity and for identification and quantification of their associated molluscs. A total of 12 replicates, haphazardly selected, of each macroalgal species were collected during low tide at midshore from 4 rocky shores: Oliveirinha (37º53’28.23’’N; 8º47’50.49’’W), Queimado (37º49’19.83’’N; 8º47’34.99’’W), Almograve (37º39’11.58’’N; 8º48’9.60’’W) and Amado (37º9’34.69’’N; 8º54’29.35’’W) (Fig. S1). Each individual macroalga was placed carefully in a plastic bag positioned over the thallus, which was closed before removal from the substrate to prevent small mobile organisms associated with the host macroalga from escaping. The thallus was then detached from the substratum and preserved with 4% formalin and rose Bengal. In the laboratory, individual thallus were washed in freshwater and shaken vigorously several times to remove the epifauna. Water was then sieved (0.5-mm mesh size) to retain the macrofauna and mollusc specimens were later identified to the lowest possible taxon (usually species level) and counted. 2.2 Macroalgal complexity Following Veiga et al. (2014) and Torres et al. (2015), biomass of macroalgae (Bi) was used as proxy for the size of available habitat and fractal dimensions as proxy of the habitat architecture. In order to calculate the fractal dimensions, each replicate macroalga was first slightly smashed and photographed with a Nikon Coolpix S2700 digital camera. Macroalgae were then dried at 60 ºC for 48 h to calculate their constant dry weight, which was measured to the nearest milligram. Fractal dimensions were
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calculated following the procedures described by McAbendroth et al. (2005). Each resulting image was transferred to greyscale and threshold to produce a binary that was used to quantify the fractal dimensions of both area (Da) and perimeter (Dp), by using the ImageJ software (Rasband, 1997). 2.3 Data analyses An analysis of variance (ANOVA) was used to test for differences in the complexity (i.e. Bi, Da and Dp) between macroalgae. These analyses were based on one-way model, including macroalgal species as fixed factor with two levels (S. muticum and S. flavifolium) and 12 replicates each. Cochran's C tests were conducted to examine homogeneity of variances and, when significant (p < 0.05), data were log-transformed to remove heterogeneity of variances. When this was not possible, untransformed data were analysed and results were considered robust if significant (at p < 0.01), to compensate for the increased probability of type I error (Underwood, 1997). In biodiversity studies, researchers usually estimate the number of species from a limited number of samples. However, Smith and van Belle (1984) proved that this methodology could underestimate the real biodiversity. To estimate the true species richness, the non-parametric Chao1 and Chao2 methods (Colwell and Coddington, 1994) were used with 999 random rearrangements of the sample ordering, to compare mollusc diversity harboured by the target macroalgae. Chao1 considers the abundance of each taxon whereas Chao2 is based on species presence/absence, avoiding the possible confounding effect of larger abundances of species in certain samples. Additionally, to compare the mollusc diversity among both macroalgae, the total number of species, the number of species restricted to a single macroalga (unique) and species represented by a single individual (singletons) were calculated (Colwell and Coddington, 1994). 8
In order to consider differences in the amount of habitat provided by both Sargassum species (i.e. Bi) in the total number of individuals (N), taxon richness (S), Shannon's diversity index (H′) and the structure of mollusc assemblages, we used a general linear model, using permutations with a Type I (sequential) sum of squares to calculate the p values by means of PERMANOVA (Anderson, 2001a), where macroalgal species was a fixed factor with two levels and Bi was used as covariate. Interaction between the categorical variables (macroalga) and continuous predictor variable (Bi) was included in the analyses. Macroalgal biomass were log(x+1) transformed to avoid the skewness of data. Analyses were done on the basis of Euclidean similarity matrices for N, S and H’ and Bray-Curtis similarity matrix for multivariate data, which were used untransformed. To visualise multivariate patterns in mollusc assemblages between invasive and native Sargassum, non-metric multi-dimensional scaling (nMDS) was used as an ordination method. In order to test whether differences of mollusc assemblages between macroalgae were due to different multivariate dispersion between groups, the PERMDISP procedure was used (Anderson, 2006). A SIMPER procedure (Clarke, 1993) was used to determine the percentage of contribution (δi%) of each taxon to the Bray-Curtis dissimilarity between mollusc assemblage associated with each Sargassum species (δi). A taxon was considered important if its contribution to total dissimilarity percentage was ≥ 3%. The ratio δi/SD(δi) was used to quantify the consistency of the contribution of a particular taxon to the average dissimilarity in the comparison between macroalgae. In order to explore the relationship between univariate response variables (i.e. N, S and H’) and the macroalgal complexity (i.e. Bi, Da and Dp), rank correlation analyses were utilised. Due to the non-normal distribution of the data, Spearman's rank correlation was used. Moreover, the relationship between the multivariate structure of mollusc 9
assemblage and macroalgal complexity was analysed using nonparametric multivariate multiple regression (McArdle and Anderson, 2001). Complexity variables were subjected to a stepwise forward-selection procedure to develop a model of the multivariate structure of mollusc assemblage. Analyses were done on the basis of BrayCurtis similarity matrix, which were used on untransformed data. P-values were determined using 999 permutations of residuals under the reduced model (Anderson, 2001b). All non-parametric multivariate multiple regressions were conducted using the computer program DISTLM (Anderson, 2002). Draftsman plots were done previously to check the skewness of complexity variables and data were log(x+1) transformed. Constrained ordination, a distance-based redundancy analysis (dbRDA, Legendre and Anderson, 1999), was done to explicitly investigate the relationship between macroalgal complexity and multivariate structure of mollusc assemblages.
3. Results 3.1 Macroalgal complexity Biomass (Bi) and fractal area (Da) were significantly higher in the native Sargassum species as compared to the invasive Sargassum (ANOVA Table 1; Fig. 1A-B). However, fractal perimeter (Dp) was found to be similar in both seaweed species (ANOVA Table 1, Fig. 1C). 3.2 Mollusc assemblage A total of 4,293 molluscs belonging to 37 taxa were identified, 23 in the invasive macroalga and 31 in the native. The species accumulation plot using the non-parametric estimator Chao1, considering all samples, reached a maximum value of 43.25 species for the native Sargassum and a lower value (31.17) for the invasive (Fig. 2A). The
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species accumulation plot using the non-parametric estimator Chao2, followed a similar pattern with a maximum value of 61.25 species for the native macroalga and only 26.5 species for the invasive (Fig. 2B). In addition, the native macroalga harboured 14 unique species whereas the invasive Sargassum harboured only six. Concerning singletons, both macroalgae harboured seven singleton species. PERMANOVA revealed that N, S and H’ of molluscs was significantly higher in the native Sargassum species (Table 2; Fig. 3A-C). Moreover, a significant effect of biomass in the N, S and H’ was found (Table 2). PERMANOVA also showed that the structure of mollusc assemblages differed significantly between Sargassum species (Table 3). Additionally, Bi showed a significant interaction with macroalgal species. Therefore, Bi significantly influenced the structure of mollusc assemblages but its effect was different between macroalgae (Table 3). The documented multivariate pattern was visualized as a clear separation between both macroalgae in the nMDS ordination (Fig. 4). The PERMDISP analysis (F=10.52, p < 0.05) indicated that the dispersion of samples provided a significant contribution to the detected differences, with higher dispersion for the invasive macroalga than for the native (Fig. 4). However, despite this dispersion, replicates of mollusc assemblage constitute two groups clearly separated in function of macroalgal species (Fig. 4). SIMPER analysis identified eight species as the main responsible for differences between invasive and native macroalgae, which showed an average dissimilarity of 86%. Collectively, these eight species contributed more than 90% to the total dissimilarity and the individual contribution of each one of them was ≥ 3% (Table 4). Noticeably, the abundance of these species was larger in the native Sargassum
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compared to the invasive, except for two bivalve species (i.e. Mytilus galloprovincialis and Musculus costulatus), which reported higher abundance in the invasive macroalga (Table 4). 3.3 Relationship between macroalgal complexity and mollusc assemblages Spearman's rank correlations showed that N and S significantly increased with the Bi of macroalgae (Fig. 5A-B). However, the correlation between Bi and H’ was not significant (Fig. 5C). Moreover, Spearman's rank correlations showed that N, S and H’ significantly increased with the Da of macroalgae, reaching relatively large correlation coefficients, ranging between 0.524 for H’ and 0.741 for N (Fig. 5D-F). Finally, correlations between Dp of macroalgae and N, S and H’ were not significant (Fig. 5GI). Results of DISTLM showed that Bi and Da together explained 50.32% of the variance in the multivariate structure of mollusc assemblages (Table 5). The variable explaining the greatest amount of variation was Da (40.38%). Once Da was fitted, Bi only added another 9.93% to the explained variation in the multivariate structure of macrobenthic assemblages but it was statistically significant. However, after fitting these two variables, the p-value associated to include Dp in the model was not statistically significant (p > 0.05) (Table 5). The first two dbRDA axes explained 97% of the fitted variation, which is about 51.2% of the total variation in the multivariate structure of the mollusc assemblage (Figure 6). All dbRDA axes together explain 100% of the fitted variation and 53% of the total variation.
4. Discussion
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Despite being two species of the same genus and appearing to have similar morphology, our results showed that the invasive and native Sargassum significantly differed in their complexity (i.e. Bi and Da). The invasive macroalga provided faunal communities with a lower habitat size of less complex architecture than that provided by the native species. These differences could be partially explained because in the sampling locality the invasive Sargassum is close to its southern boundary (i.e. Morocco, Sabour et al., 2013). Biomass and fractal measures of Sargassum muticum in the sampling locality (South Portugal) were also lower when comparing to those reported for the same species in the North Portuguese coast, far from its boundary, in an earlier work by the authors (Fig. S2; Veiga et al., 2014). Similarly, previous studies have pointed out that Fucus serratus, F. vesiculosus and F. gardneri in marginal populations show an obvious reduction in their size, with changes in their morphology in comparison with populations of the same species but in their centre of distribution (Sideman and Mathieson, 1983, Wright et al., 2004, Tatarenkov et al., 2005, Viejo et al., 2011). The invasive macroalga harboured a lower biodiversity, than the associated with the native macroalga, which may indicate a simplification of the food web (O’Gorman et al., 2012). Moreover, the structure of the mollusc assemblage was significantly different between macroalgae. However, these results, contrast with previous studies that have compared faunal assemblages on S. muticum to those associated with other complex native canopy species. Most of these studies consider unlikely that the introduction of S. muticum had produced substantial modifications in the composition of faunal assemblages (e.g. loss of diversity) suggesting only a weak impact upon native faunal diversity (Viejo, 1999; Wernberg et al., 2004; Buschbaum et al., 2006; Gestoso et al., 2010; Engelen et al., 2013). As biomass of S. muticum shows seasonal variability, Engelen et al. (2013) suggest that faunal shifts will be particularly obvious during the 13
growth season (spring-summer), when S. muticum is much larger than the native macroalgae, and the dominance of this invasive species may positively contribute to the trophic links within the ecosystem during this period. Our sampling of Sargassum individuals was done in summer, but the results did not support this. Moreover, our data showed that the invasive Sargassum reported less than half of unique species than the associated with the native seaweed contrasting with previous studies that did not find species exclusively associated with S. muticum or different native macroalgae (Gestoso et al., 2010; Engelen et al., 2013). Despite both the native and invasive Sargassum being canopy-forming, ecosystem engineer species, our data suggest that the function of the invasive macroalga as habitat provider does not match with that of the native Sargassum. Since S. muticum tolerates a wide range of environmental conditions (e.g. salinity or temperature) (Norton, 1976), it may be a better competitor for space, light, nutrients or oxygen, which may limit the distribution of native macroalgae (Critchley et al., 1986; Stæhr et al., 2000; Sánchez and Fernández, 2005). In the hypothetical case that this invasive seaweed supplants the native species, our results provide evidence of a strongly negative influence on mollusc biodiversity with consequences that may extend to higher trophic levels. In concordance, Salvaterra et al. (2013) suggest that S. muticum directly impedes the native macroalgal assemblages and that its effect outspreads indirectly to the native fauna, producing major alterations all over the ecosystem. They found that S. muticum decreased primary production and tended to reduce the species richness and diversity of fauna. Moreover, they found that S. muticum modifies food webs by altering the proportion of intermediate and top species, including the arrival of generalist species associated with S. muticum. Results of SIMPER analysis in our study pointed out a dissimilarity of 86% between the structure of mollusc assemblages associated with S. 14
muticum and S. flavifolium. Species that most contributed to this dissimilarity, were microphytobenthos-grazing gastropods (Gofas et al., 2011) which were more abundant in the native Sargassum. However, two filter-feeding species of bivalves (Gofas et al., 2011) were more abundant in the invasive seaweed, probably due to differences in the complexity between both macroalgae; bivalves usually are more associated with the holdfast of macroalga whereas gastropods are present in the secondary ramifications of the macroalga, which were more abundant in the native Sargassum (higher values of Da). Thus, our results suggest a potential change of the food webs; however, future research should explore this issue with more detail. The mollusc assemblage associated with the invasive Sargassum showed more variability than that on the native (Fig. 4 as indicated by dispersion of replicates). Therefore, assemblages associated with the invasive macroalga seem to be less stable and may be more influenced by the environment. This could be explained by the seasonality of the invasive Sargassum, its decreasing in biomass during autumn-winter may lead to a scarcity of habitat for fauna in this period and consequently generate an habitat less stable (Wernberg et al., 2004; Pedersen et al., 2005; Engelen et al., 2013). In contrast, mollusc assemblage associated with the native macroalga seem to be more dependent on seaweed itself. Our results also indicated a significant effect of biomass highlighting the significant role of habitat size in shaping the abundance, diversity and structure of mollusc assemblages. Salvaterra et al. (2013) explain the reduction in richness and diversity due to the invasion of S. muticum because this invasive species provides less canopy cover than the native seaweeds, thus offering less habitat for invertebrates. However, values of correlation coefficients for Da were higher than those for habitat size (as biomass), in all the response variables. Da reveals the way in which area of macroalga is divided up in
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space describing the gap structure. Thus, higher values of Da indicate high divisions and a smaller mean gap size (McAbendroth et al., 2005). Therefore, our results suggest that habitat architecture seem to be more relevant than habitat size in shaping mollusc assemblages associated with the studied macroalgae. Chemello and Milazzo (2002) also attribute differences in the molluscan assemblages associated with six Mediterranean algae to their architecture, which in turn, could be related to variations in food supply and predation rate experimented by mollusc assemblage. Complexity of macroalgae has been considered as an important driver of their associated faunal assemblages (Veiga et al., 2014; Torres et al., 2015). If complexity of macroalgae differs among areas (Fig. S2), to achieve a good understanding of the effects of invasive macroalgae on their associated faunal assemblages, it is crucial that future studies include different geographical areas. Our study provides evidence that S. muticum is able to induce significant changes in native mollusc assemblages where it becomes established in the south boundary of its range. If these changes in the composition of assemblages result in functional feeding differences, this may lead to modifications in the consumer-resource interactions with consequences to the whole ecosystem. Acknowledgement This research was partially supported by the Strategic Funding UID/Multi/04423/2013 through national funds provided by FCT – Foundation for Science and Technology and European Regional Development Fund (ERDF), in the framework of the programme PT2020. During this study, P. Veiga (SFRH/BPD/81582/2011) and M. Rubal (SFRH/BDP/104225/2014) were supported by postdoctoral grants and A.C. Torres (SFRH/BD/114935/2016) was supported by PhD grant awarded by Fundacão para a
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Figure caption Figure 1. Mean values (+SE) of biomass in gr (A), fractal dimensions based on area (B) and fractal dimensions based on perimeter (C) for Sargassum muticum and Sargassum flavifolium. Asterisk indicates significant differences (Pairwise test, **: p < 0.01; ***: p < 0.001); ns: not significant. Figure 2. Species accumulation curve obtained from the Chao1 (A) and Chao2 estimators (B) for Sargassum muticum and Sargassum flavifolium. Figure 3. Mean values (+SE) of total number of individuals (A), taxon richness (B) and Shannon diversity index of molluscs associated with Sargassum muticum and Sargassum flavifolium. Asterisk indicates significant differences (Pairwise test, *: p < 0.05; **: p < 0.01). Figure 4. nMDS ordination of mollusc assemblage based on Bray-Curtis similarities associated with Sargassum muticum and Sargassum flavifolium. Figure 5. Spearman's rank correlations between biomass, fractal dimensions based on area and fractal dimensions based on perimeter of macroalgae with the total number of individuals (A, B, C), taxon richness (D, E, F) and Shannon's diversity index (G, H, I) of molluscs. Figure 6. Distance-based redundancy (dbRDA) plot illustrating the DISTLM model based on the mollusc assemblage associated with Sargassum muticum and Sargassum flavifolium and the fitted complexity variables as vectors using DISTLM analysis in Table 5.
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Table 1. Summary of ANOVAs for biomass (Bi), fractal dimensions based on area (Da) and fractal dimensions based on perimeter (Dp) of macroalgae. s: significant; ns: not significant; **: p < 0.01; ***: p < 0.001. Significant differences indicated in bold.
Source of variation Macroalga Residual Total Cochran's test
df 1 22 23
Bi MS 2.0035 0.2299
Da F 8.71**
C= ns 0.559 Ln (x+1)
Transformation
MS 0.1166 0.0004
F 264.10***
C= 0.802
s
Dp MS F 0.0096 4.05 0.0024 C= ns 0.631 none
none
Table 2. Summary of PERMANOVAs for total number of individuals (N), taxon richness (S) and Shannon index (H’) of molluscs including biomass as covariate. *: p < 0.05; **: p < 0.01. Significant differences indicated in bold.
Sourc e of variat ion Biom ass (Bi) Macr oalga (Ma) Bi x Ma
d f
1
1
1
MS 3.3 3 105 1.1 4 105 106 06
N Pse udoF
S Pse udoF
Per ms
MS
Per ms
54.9 8**
99 7
217 .66
39.2 9**
99 6
18.7 2**
99 4
47. 25
8.53 **
99 7
1.75
99 8
2.2 4
0.40
99 4
M S 0. 46
2 0
606 1.2
5.5 4
2 3
25
99 4
8.69 **
99 6
5. 17 10
6.02 10-2
99 6
8. 60 10 -2
Total
*
Per ms
0. 75
-3
Resid ual
H’ Pse udoF 5.38
Table 3. Summary of PERMANOVA on multivariate structure of mollusc assemblages present on each macroalgal species, including biomass as covariate. **: p < 0.01. Significant differences indicated in bold.
Source of variation
df
Biomass (Bi) Macroalga (Ma) Bi x Ma Residual Total
1 1 1 20 23
MS 13556 21642 5442.6 1223.5
Assemblage Pseudo-F 11.08** 17.69** 4.45**
Perms 999 999 999
Table 4. Contribution (δi) of individual taxa to the average Bray-Curtis dissimilarity between macroalgae.
Species Eatonia fulgida Tricolia pullus Barleeia unifasciata Mytilus galloprovincialis Rissoa parva Skeneopsis planorbis Musculus costulatus Gibbula pennanti
Average Abundance S. flavifolium S. muticum 129.17 0.08 41.25 0.00 45.17 0.58 26.50 35.50 11.67 2.25 9.58 0.17 9.67 9.83 11.92 0.42
δi
δi%
δi/SD(δi)
34.44 13.15 10.40 10.16 4.02 2.96 2.90 2.80
39.64 15.13 11.97 11.69 4.63 3.41 3.34 3.22
2.64 1.57 1.21 1.19 0.61 1.52 1.00 0.79
Table 5. Results of DISTLM carried out to ascertain the role of macroalgal complexity (Bi, Da and Dp), considering each variable taken individually (ignoring other, Marginal tests) and forward-selection of variables, where amount explained by each variable added to model is conditional on variables already included in the model on the multivariate structure of mollusc assemblages (Sequential tests). %Var: percentage of 26
variance in species data explained by that set of variables; Cum. %: cumulative percentage of variance explained. Variables significantly related to multivariate structure of mollusc assemblages indicated in bold. **: p < 0.01
%Var Pseudo-F Cum (%) Marginal tests Da Dp Bi
40.38 8.39 19.25
14.90** 2.02 5.25**
Sequential tests Da Bi Dp
40.38 9.93 2.81
14.90** 4.20** 1.20
40.38 50.32 53.12
Figure 1 8
A
**
Bi
6
**
4
2
0 S. muticum 2.0
B
***
S. flavifolium
***
Da
1.5
1.0
0.5
0.0 S. muticum 1.4 1.2
C
ns
S. flavifolium
ns
Dp
1.0 0.8 0.6 0.4 0.2 0.0 S. muticum
S. flavifolium
27
Figure 2
Figure 3 400
A **
N
300
200
100
**
0 S. muticum 14
S. flavifolium
**
B
12 10
**
S
8 6 4 2 0
S. muticum 1.8
C
*
1.6 1.4
H'
1.2
S. flavifolium
*
1.0 0.8 0.6 0.4 0.2 0.0 S. muticum
S. flavifolium
Figure 4
28
Figure 5
29
N
600
600
A
500
500
500
400
400
400
300
300
300
200
200
200
100
100
100
0 0
S
2
4
6
r = 0.641; p < 0.001
0
8
1.65 1.70 1.75 1.80 1.85 1.90 1.95
10
12
14
B
18
18
r = 0.741; p < 0.001
E
18 16
14
14
14
12
12
12
10
10
10
8
8
8
6
6
6
4
4
0
2
4
6
8
10
12
14
C
2.0
r = 0.588; p < 0.01
2 1.65 1.70 1.75 1.80 1.85 1.90 1.95 2.0
F
2.0 1.8
1.6
1.6
1.6
1.4
1.4
1.4
1.2
1.2
1.2
1.0
1.0
1.0
0.8
0.8
0.8
0.6
0.6
r = 0.524; p < 0.01 0.6
0
2
4
6
8
Bi
10
12
14
0.4 1.65 1.70 1.75 1.80 1.85 1.90 1.95
Da
r = -0.0793; p > 0.05
2 1.05 1.10 1.15 1.20 1.25 1.30 1.35
1.8
r = 0.258; p > 0.05
H
4
1.8
0.4
r = -0.254; p > 0.05
1.05 1.10 1.15 1.20 1.25 1.30 1.35
16
r = 0.571; p < 0.01
G
0
16
2
H'
600
D
I
r = -0.341; p > 0.05
0.4 1.05 1.10 1.15 1.20 1.25 1.30 1.35
Dp
Figure 6
30
31
Highlights
Mollusc assemblages associated with native and invasive Sargassum.
Invasive Sargassum showed lower biomass and fractal area than the native one.
Invasive Sargassum showed lower abundance and diversity than the native one.
Invasive Sargassum harboured distinct mollusc assemblages to native species.
Correlations of biomass and fractal area with mollusc assemblages were significant.
32