Meiofaunal assemblages associated with native and ...

Continental Shelf Research 123 (2016) 1–8

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Meiofaunal assemblages associated with native and non-indigenous macroalgae Puri Veiga a,b,n, Isabel Sousa-Pinto a,b, Marcos Rubal a,b a Laboratory of Coastal Biodiversity, Interdisciplinary Centre of Marine and Environmental Research (CIIMAR/CIMAR), University of Porto, Rua dos Bragas 289, P 4050-123 Porto, Portugal b Department of Biology, Faculty of Sciences, University of Porto, Rua do Campo Alegre s/n, 4150-181 Porto, Portugal

art ic l e i nf o

a b s t r a c t

Article history: Received 18 September 2015 Received in revised form 16 March 2016 Accepted 11 April 2016 Available online 13 April 2016

Meiofauna is a useful tool to detect effects of different disturbances; however, its relevance in the frame of biological invasions has been almost fully neglected. Meiofaunal assemblages associated with the invasive macroalga Sargassum muticum were studied and compared with those associated with two native macroalgae (Bifurcaria bifurcata and Chondrus crispus). We used a linear mixed model to determine the influence of habitat size (i.e. macroalgal biomass) in shaping meiofaunal assemblages. Results showed that habitat size (i.e. macroalgal biomass) shaped meiofaunal assemblages influencing its abundance, richness and structure. However, the identity of macroalga (i.e. species) appears also to play a significant role, particularly the differences of complexity among the studied species may shape their meiofaunal assemblages. Finally, the invasive macroalga appears to influence positively species richness. Our results highlight the need of including different faunal components to achieve a comprehensive knowledge on effects of invasive macroalgae and that meiofaunal assemblages may be a valuable tool to examine them. & 2016 Elsevier Ltd. All rights reserved.

Keywords: Meiofauna Macroalgae Non-indigenous species Sargassum muticum Habitat size Complexity

1. Introduction Meiofauna is a ubiquitous component of benthic assemblages from the supralittoral to the deepest bottoms of the ocean (Giere, 2009). Moreover, meiofauna plays a key role in the function of ecosystems (Piot et al., 2013). Meiofauna is always more abundant than macrofauna, whereas macrofauna generally surpasses meiofauna in terms of biomass (Gibbons and Griffiths, 1986). Nevertheless, the faster turnover rates of meiofauna suggests that it can be as important as the macrofauna in terms of secondary production (Koop and Griffiths, 1982). Meiofauna also represents an important food resource for many fish species and invertebrates (Huff and Jarett, 2007; Giere, 2009). Additionally, meiofauna is essential for maintaining the bacteria in a continued state of growth by means of its grazing activity and nutrient cycling (Gibbons and Griffiths, 1986), making detritus available to macroconsumers either through its enhancement of microbial activity or by ingestion of the meiofauna themselves (Coull, 1988; Huff and Jarett, 2007). In rocky shores, meiofauna density exceeds that of macrofauna n Corresponding author at: Laboratory of Coastal Biodiversity, Interdisciplinary Centre of Marine and Environmental Research (CIIMAR/CIMAR), University of Porto, Rua dos Bragas 289, P 4050-123 Porto, Portugal. E-mail address: [email protected] (P. Veiga).

http://dx.doi.org/10.1016/j.csr.2016.04.007 0278-4343/& 2016 Elsevier Ltd. All rights reserved.

and it represents up to 25% of total secondary production (Gibbons and Griffiths, 1986). Despite its ecological relevance, meiofauna has been understudied, particularly in intertidal rocky shores (Frame et al., 2007). Meiofauna in rocky shores is found in a variety of habitats such as bare rock, rock crevices or sessile macrofauna but it has been more commonly reported in association with macroalgae (Gibbons, 1988, 1991; Norderhaug et al., 2007). The high abundance of meiofauna harboured by rocky macroalgal belts has been frequently reported (Danovaro and Fraschetti, 2002; Frame et al., 2007). For instance, phytal meiofauna may reach a million individuals per m2 of macroalga, which in terms of biomass may correspond to 10% of the macrofauna (Giere, 2009). Although some species of phytal meiofauna show very distinct habitat preferences (Hicks, 1977; Trotter and Webster, 1984), most of them are distributed over a wide range of macroalgae (Frame et al., 2007). However, abundance and diversity of meiofaunal assemblages differs among macroalgae. The macroalgal complexity has been identified as the most powerful parameter that shapes meiofaunal assemblages (Gibbons, 1988; Gee and Warwick, 1994a, 1994b) that in turn, is modified by a set of biotic and abiotic conditions, such as water depth or wave exposure (Gibbons, 1988; Giere, 2009). Macroalgae with a more complex morphology usually offer a large number of habitats for colonisation of meiofauna (Gibbons, 1991). Moreover, complex macroalgae provide a higher variety of food resources (Hicks, 1980) and a better

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P. Veiga et al. / Continental Shelf Research 123 (2016) 1–8

protection from predators (Coull and Wells, 1983), desiccation and wave action (Gibbons, 1988; Hull, 1997; Hooper and Davenport, 2006; Norderhaug et al., 2014); facilitating the trapping of sediment and detritus which adhere to the exudations and the biofilms of the macroalgae (Giere, 2009). Therefore, meiofauna associated with complex macroalgae usually shows higher abundance and diversity than the associated with those less complex (e.g. Hicks, 1980; Gee and Warwick, 1994a, 1994b; Hooper and Davenport, 2006; Frame et al., 2007). Experiments using artificial substrates have also confirmed the positive relationship between complexity and meiofaunal density (Atilla et al., 2005). However, complexity includes two main components of habitat: the morphology and the size (Gee and Warwick, 1994a, 1994b; Veiga et al., 2014). Regarding the effect of macroalgal size, some studies found that larger macroalgae offer a large surface area for the attachment of meiofaunal individuals (Gunnill, 1982a, 1982b). Nevertheless, Arroyo et al. (2006) reported that the relationship between macroalgal size (i.e. biomass) and the meiofaunal abundance and diversity on Laminaria ochroleuca was dependent on the considered part of the macroalga (i.e. frond versus holdfast) and Norderhaug et al. (2007) showed that habitat size influenced the abundance of macrofauna associated with Laminaria hyperborea but not that of meiofauna. More recently, Richardson and Stephens (2014) found that correlation between biomass and meiofaunal abundance differ among macroalgal species. Therefore, the role of macroalgal size in shaping meiofaunal assemblages is not yet clear and different works have provided contradictory results. Nowadays, invasive species are considered one of the greatest threats to biodiversity and ecosystem functioning (Pejchar and Mooney, 2009; Salvaterra et al., 2013). It is estimated that the ecological impacts of only about 6% of exotic seaweeds have yet been studied, with most studies concentrated on a small list of notorious species (Smith et al., 2014), including Sargassum muticum (Yendo) Fensholt. This macroalga was introduced in Europe in the early 1970s and nowadays it is distributed from Norway to Morocco as well as in the Mediterranean Sea (Sabour et al., 2013). Meiofaunal assemblages have been widely used as tool to detect effects of pollution (Austen et al., 1994; Rubal et al., 2009; Veiga et al., 2009, 2010; Baguley et al., 2015), even at higher taxonomic levels (Herman and Heip, 1988). However, few studies have yet explored potential effects of invasive macroalgae on the structure of meiofaunal assemblages. Moreover, most of them have focused on the effects of Caulerpa spp. in meiofauna from sedimentary environments showing that the invasive increases the abundance of meiofauna but decreases the diversity of some meiobenthic taxa (Carriglio et al., 2003; Sandulli et al., 2004; Travizi and Zavodnik, 2004). Up to the moment, only two studies have explored the effects of invasive macroalgae on meiofauna from rocky shores (i.e. Smith et al., 2014; Richardson and Stephens, 2014). For instance, Richardson and Stephens (2014) showed that S. muticum harbours a different meiofaunal assemblage to that of the studied native species and that meiofaunal abundance is poorly correlated with biomass of S. muticum. On the contrary, Smith et al. (2014) pointed that the invasive turf Caulacanthus ustulatus (Mertens ex Turner) Kützing seems to facilitate a more diverse meiofaunal assemblage. Most of the studies about meiofauna associated with macroalgae have been focused on specific taxa such as harpacticoid copepods (e.g. Hicks, 1980; Steinarsdóttir et al., 2003; Arroyo et al., 2006; Song et al., 2010), nematodes (e.g. Trotter and Webster, 1984; Da Rocha et al., 2006), turbellarians (Boaden, 1996) and ostracods (Hull, 1997; Frame et al., 2007). However, quantitative ecological data of the whole meiobenthic assemblage are still scarce (but see Arroyo et al. (2004)). In this context, the present study aims to investigate the meiofaunal assemblages associated with the invasive macroalga S. muticum in intertidal rock pools;

such assemblage will be compared with those harboured by the native species Bifurcaria bifurcata R. Ross and Chondrus crispus Stackhouse present in the same habitat. We hypothesized that: i) habitat size provided by macroalgae will play a significant role shaping the abundance, taxon richness and structure of meiofaunal assemblages; ii) the structure of meiofaunal assemblages will differ among macroalgae of different complexity even when these provide an equal habitat size, and iii) the abundance, richness and structure of meiofaunal assemblages associated with the invasive macroalga will differ from those associated with native ones.

2. Material and methods 2.1. Sampling and sample processing This study was carried out between February and November 2012 at two rocky shores in the North of Portugal, located along about 2.5 km of coast north of Viana do Castelo (between 41°43′ 0.3″N and 41°41′36.36″N; 8°51′10.52″W) and Âncora (between 41°48′58.64″N and 41°50′33.44″N; 8°52′28.67″W) (Fig. 1). A more detailed description of the study area can be found in Veiga et al. (2014). Macroalgae in the study area experience spatial and temporal variability (Rubal et al., 2011; Veiga et al., 2013). Samples of the three target species (i.e. C. crispus, B. bifurcata and the invasive S. muticum) were collected on four dates (February, May, August and November 2012) at two rocky shores to identify and quantify their meiofauna. Submerged macroalgae in tidal pools were collected during low tide at midshore. To ensure the independence of the samples, at each date, different areas of the shores were sampled and each replicate was collected from different pools. In all, 64 individuals of each macroalga (i.e. 8 replicates per macroalga at each date and shore), haphazardly selected, were collected. Each

ÂNCORA

VIANA

PORTO

ATLANTIC OCEAN

10 km

N

Fig. 1. Map of the Portuguese coast indicating the location of the 2 sampled shores.


individual was placed carefully in a plastic bag and preserved with 4% formalin stained with rose Bengal. In the laboratory, individual tallus were washed in freshwater and shaken vigorously several times to remove meiofauna, which was extracted by decantation and sieved through 0.5 mm and 45–mm sieves (Pfannkuche and Thiel, 1988). This procedure was repeated six times for each sample. All meiobenthic organisms were later counted and identified to higher taxa under a stereomicroscope. 2.2. Macroalgal morphology Each macroalga replicate was dried at 60 °C for at least 48 h to constant biomass, to ensure the elimination of water, and was determined to the nearest milligram. Macroalgal biomass was used as proxy of the habitat size. Macroalgal complexity can be numerically estimated using fractal dimensions, which have been previously used with meiofaunal assemblages (Gee and Warwick, 1994a, 1994b; Hooper and Davenport, 2006). Fractal dimensions were used as proxy of the habitat architecture and were calculated on two dates (i.e. 8 replicates 3 macroalgal species 2 dates 2 shores). In order to calculate the fractal dimensions, a branch of each macroalga was photographed with a Nikon Coolpix S2700 digital camera. Fractal dimensions were calculated following procedures described by McAbendroth et al. (2005). Due to the small size of our target organisms (i.e. meiofauna), complexity parameters of a branch of macroalga were considered an appropriate scale (Attrill et al., 2000). Each resulting TIFF image was transferred to greyscale and threshold to produce a binary image that was used to quantify the fractal dimensions of both area and perimeter, by using the ImageJ software (Rasband, 1997). Fractal based on area and perimeter provides subtle differences in the information about the nature of complexity associated with each macroalga. Fractal area is an estimate of area occupancy indicating how the perception of surface area might change with scale whereas fractal perimeter is an estimate of edge complexity, relating the nature of the gaps between the macroalgal parts (McAbendroth et al., 2005). 2.3. Data analysis Analyses of variance (ANOVA) were done to test for differences in the biomass, fractal area and fractal perimeter among macroalgae. These analyses were based on a one-way model, including macroalga as fixed factor with three levels (C. crispus, B. bifurcata and S. muticum) with 64 and 32 replicates each, respectively for biomass and fractal measures, because the later were calculated only at two dates (see above macroalgal morphology section). Cochrans's C tests were previously done to check for homogeneity of variances and, when test was significant (p o0.05) data were transformed to remove heterogeneity of variances. When this was not possible, untransformed data were analysed and results were considered robust if significant at p o 0.01 to compensate for the increased probability of type I error (Underwood, 1997). Whenever ANOVA showed significant differences (p o0.05), a post hoc Student-Newman-Keuls (SNK) test (Underwood, 1997) was done to explore differences among all pairs of levels of the selected factor (i.e. macroalga). In order to determine the influence of habitat size (i.e. biomass) in the abundance, taxon richness and the structure of meiofaunal assemblages, a mixed lineal model, using permutations with a Type I (sequential) sum of squares was used to calculate the p-values using Permutational multivariate analysis of variance (PERMANOVA; Anderson, 2001), where macroalga (three levels: C. crispus, B. bifurcata and S. muticum) was a fixed factor with 64 replicates and biomass was the covariate. Interactions between the

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factors and the covariate were included in the analyses. By using a type I sum of squares, the model calculates the significance of the main factor by subtracting the effect of the covariate (i.e. biomass), allowing us to test the effect of macroalgal species independently of the covariate. Analyses were done on the basis of Euclidean similarity matrices for abundance and richness and Bray-Curtis similarity matrices for multivariate data which were fourth-root transformed to downweight the influence of numerically dominant taxa. Moreover, 16 replicates of each macroalgal species, with similar biomass (i.e. 3 gr), were randomly selected from the total data set, to test if significant differences detected on the structure of meiofaunal assemblages among macroalgae were caused by the macroalgal identity or by the covariate effect (i.e. biomass). These data were also analysed with PERMANOVA including macroalga with three levels (C. crispus, B. bifurcata and S. muticum) as fixed factor with 16 replicates each. When PERMANOVA showed significant differences (p o 0.05), a pair-wise comparison (999 permutations) was done to explore differences among all pairs of levels of the selected factor (i.e. macroalga). Biomass was also included as covariate for these pairwise comparisons, where necessary. To visualise multivariate patterns in meiofaunal assemblages across the three macroalgae, non-metric multi-dimensional scaling (nMDS) was used as an ordination method. A separate nMDS plot was done for the full data set, using biomass as bubbles, and for the partial data set, considering macroalgae of equal biomass. In order to test whether differences of meiofaunal assemblages between macroalgae were due to different multivariate dispersion between groups rather than in the location of samples, the PERMDISP procedure was done (Anderson, 2006).

3. Results 3.1. Biomass and complexity of macroalgae Values of biomass and fractal measures were variable among the studied macroalgal species (Fig. 2). Results of ANOVA revealed significant differences among macroalgae for biomass, fractal area and fractal perimeter (Table, 1). Post-hoc comparisons indicated that C. crispus showed significantly lower biomass than B. bifurcata and S. muticum (Fig. 2A). Moreover, significant differences were found among all macroalgae for fractal area and fractal perimeter. Chondrus crispus showed the highest values of fractal area followed by S. muticum and B. bifurcata (Fig. 2B). In contrast, S. muticum displayed the highest values of fractal perimeter followed by B. bifurcata and C. crispus (Fig. 2C). 3.2. Taxonomic composition A total of 336,432 individuals of meiofauna were identified, of which 60,794 were associated with C. crispus, 125,925 with S. muticum and 149,713 with B. bifurcata. The total number of taxa ranged between 21 on C. crispus and 24 on S. muticum (Table, 2). The numerically dominant meiofaunal groups were Copepoda, Nauplii and Nematoda constituting 48.73%, 39.43% and 6.31% of the individuals, respectively. 3.3. Abundance and richness of meiofauna Results of PERMANOVA on the values of abundance and richness showed a significant effect of habitat size and macroalgal species (Table 3). Pair-wise comparisons indicated that the abundance on C. crispus was significantly lower than in B. bifurcata and S. muticum but no significant differences were detected between B.

4


10

A

Table 2 Total abundance of each meiofaunal taxon collected in each macroalgal species.

Biomass (g)

b

b

8

Chondrus crispus

Bifurcaria bifurcata

Sargassum muticum

Acari Bivalvia Caprellidae Chironomidae Cnidaria Collembola Copepoda Cumacea Gammaridea Gastropoda Gastrotricha Isopoda Kinorhyncha Nauplii Nematoda Nudibranchia Oligochaeta Ostracoda Polychaeta Polyplacophora Rotifera Sipuncula Tanaidacea Tardigrada Turbellaria Veliger N total S total

860 44 1 11 3 0 27,426 0 77 39 0 7 4 26,595 1995 4 18 627 2091 3 0 0 26 6 956 1 60,794 21

369 1194 0 30 4 0 72,064 1 114 62 1 29 14 64,114 7797 1 213 468 2939 3 1 0 31 13 242 9 149,713 23

1725 355 1 137 14 2 64,449 0 218 69 0 20 11 41,960 11,420 3 53 1150 1947 10 13 1 6 48 2305 8 125,925 24

6

a 4 2 0 C. crispus 2.0

B

a

B. bifurcata S. muticum

c

b

1.5

Da

Taxa

1.0

0.5

0.0 C. crispus 1.4 1.2


C a

c

b

1.0

Table 3 Summary of PERMANOVA results for total abundance (N) and taxon richness (S) of meiofauna including biomass (Bi) as covariate.

Dp

0.8 0.6

Source of variation

0.4

df

N

S

MS

0.2

Biomass (Bi) Macroalga (Ma) Bi Ma Residual Total

0.0 C. crispus


Fig. 2. Mean values ( þ SE) of biomass (A), fractal dimensions based on area (Da) (B) and fractal dimensions based on perimeter (Dp) (C) of Chondrus crispus, Bifurcaria bifurcata, and Sargassum muticum. Different letters indicate significant differences between macroalgae (po 0.01) as detected by pair-wise tests.

*

bifurcata and S. muticum (Fig. 3A). Taxon richness was, however, significantly different among the three macroalgae reaching the lowest values on C. crispus and the highest on S. muticum (Fig. 3B).

**

Pseudo-F

1 2.21 10 8 93.68** 2 6.85 10

6

2.91

*

2 10065.00 4.27 10 186 2.36 10 6 191

3

Perms MS

Pseudo-F Perms

997

102.37 20.81**

996

999

**

999

107.54 21.86

998

1.68 0.34 4.92

999

po 0.05. p o 0.01.

size showed a significant interaction with macroalgal identity. This means that the habitat size significantly influenced the structure of meiofaunal assemblages but its effect was different among macroalgae (Table 4). The documented multivariate pattern did not show a clear separation between meiofaunal assemblages in function of biomass in the nMDS ordination (Fig. 4). Results of PERMANOVA, based on meiofaunal assemblage

3.4. Structure of meiofaunal assemblages Results of PERMANOVA showed that the structure of meiofaunal assemblages differed significantly among macroalgae (Table 4). Pair-wise comparisons indicated significant differences between the three macroalgal species (Table 4). Moreover, habitat

Table 1 Summary of ANOVAs for biomass (Bi) and fractal dimensions of macroalgae based on area (Da) and on perimeter (Dp). Source of variation

Macroalga Residual Total Cochran's test s: significant. ***

p o 0.001.

df

2 189 191

Bi

df

Da

Dp

MS

F

2

MS

F

MS

F

199.439 26.506

7.52***

2 93 95

0.177 0.002

102.47***

0.079 0.002

32.41***

C¼ 0.694

s

C¼ 0.544

s

C¼ 0.573

s


3000

A

12

b

2500

B

c b

10

b

2000

a

8

S

N

1500

a

6

1000

4

500

2

0

5

0 C. crispus


C. crispus


Fig. 3. Mean values ( þSE) of total abundance (N) (A) and taxon richness (S) (B) of meiofauna associated with Chondrus crispus, Bifurcaria bifurcata and Sargassum muticum. Different letters indicate significant differences between macroalgae (po 0.05) as detected by pair-wise tests.

Table 4 Summary of PERMANOVA on meiofaunal assemblages present on each macroalgal species, including biomass (Bi) as covariate, and pair-wise comparisons between each pair of macroalgae. Source of variation

df

Assemblage MS

Biomass (Bi) Macroalga (Ma) Bi Ma Residual Total

1 2 2 186 191

13,991.00 7581.80 821.41 444.89

Pair-wise tests Bifurcaria bifurcata vs Chondrus crispus Bifurcaria bifurcata vs Sargassum muticum Chondrus crispus vs Sargassum muticum * **

Table 5 Summary of PERMANOVA on meiofaunal assemblages present on each macroalgal species of equal biomass, and pair-wise comparisons between each pair of macroalgae. Source of variation

Pseudo-F **

31.45 17.04** 1.85*

t

Perms 998 999 999

Perms **

3.68 3.92** 4.38**

df

998 999 999

Assemblage MS

Macroalga (Ma) Residual Total

3491.00 452.28

**

Perms

7.72

998

Pair-wise tests

t

Perms

Bifurcaria bifurcata vs Chondrus crispus Bifurcaria bifurcata vs Sargassum muticum Chondrus crispus vs Sargassum muticum

2.46** 3.04** 2.92**

998 996 999

**

2 33 35

Pseudo-F

p o 0.01.

p o 0.05. po 0.01.

Fig. 4. nMDS ordination of samples of meiofaunal assemblages based on fourthroot transformed abundances and Bray-Curtis similarities using biomass as bubbles.

structure and considering macroalgal species of equal biomass (F2, 33 ¼0.40, p 40.05), showed significant differences between macroalgae (Table 5). Pair-wise comparisons indicated significant differences among all macroalgal species (Table 5). The documented multivariate pattern was visualized as a clear separation between macroalgae in the nMDS ordination (Fig. 5). Moreover, the PERMDISP analysis (F ¼2.85, p¼ 0.132) indicated that the dispersion of samples did not provide a significant contribution to the detected differences.

Fig. 5. nMDS ordination of samples of meiofaunal assemblages based on fourthroot transformed abundances and Bray-Curtis similarities associated with B. bifurcata, C. crispus and S. muticum (circle, diamond and quadrate triangle, respectively) of equal biomass.

4. Discussion Results of the present study showed that the habitat size played a significant role in shaping the abundance, diversity and structure of meiofaunal assemblages because a significant effect of the biomass, when considered as covariate, was found, supporting thus the first hypothesis of our study. The macroalgal species that provided a smaller habitat size (i.e. C. crispus) harboured also fewer meiofaunal abundance and richness. A larger habitat size provides a greater surface to be colonized by meiofauna (Gibbons,

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1991). Thus, different studies have also reported a significant correlation between habitat size, estimated as biomass or surface area, and the abundance and diversity of meiofaunal assemblages (Hicks, 1980; Gunnill, 1982b; Arroyo et al., 2004). Furthermore, Attrill et al. (2000) pointed out that the size and composition of epifaunal asemblages within a seagrass bed is not determined by the structural complexity of the plants but by the amount of plant available, estimated as biomass; this study was however, only focused on macrofauna. On the other hand, Norderhaug et al. (2007), adding artificial substrates to natural kelps in order to simulate epiphytic algae, found that habitat size influenced the abundance of macrofauna associated with the kelp L. hyperborea but not that of meiofauna. Their results may be explained because meiofauna is more narrowly associated with holdfast than with epiphytes (Arroyo et al., 2004). Moreover, Arroyo et al. (2004) reported that abundance and diversity of meiofauna associated with the frond of the kelp L. ochroleuca and diversity associated with the holdfast were significantly correlated with biomass of each macroalgal fraction. However, no significant correlation was found between the holdfast abundance and its biomass. Our findings also contrast with that of Richardson and Stephens (2014) who found that the abundance of meiofauna is poorly correlated with biomass of S. muticum. The habitat size significantly influenced the structure of meiofaunal assemblages but its effect was dependent on macroalgal identity; this in turn may be attributable to differences of complexity between macroalgae. The univariate model tested was successful in removing the confounding effect of biomass on the abundance and richness of meiofaunal assemblages (i.e. the interaction biomass macroalga was not significant). However, when the whole meiofaunal assemblage structure, including all the species and their abundances, was considered, the model was not able to remove the effect of habitat size (i.e. the interaction biomass x macroalga was significant). This fact seems relevant because usually the meiofaunal abundance is standardized by macroalgal biomass, independently of the macroalgal species (e.g. Beckley, 1982; Gibbons, 1988; Jarvis and Seed, 1996; Ólafsson et al., 2001; Hooper and Davenport, 2006; Frame et al., 2007). If the effect of biomass is dependent on macroalgal identity, as our results have pointed out, probably standardizing by a constant biomass value is not adequate. In fact, Jarvis and Seed (1996) highlighted the impossibility of comparing the meiofaunal abundance standardising by biomass because of the extremely different plant morphologies and other physical features. In this way, the use of biomass as covariate may be more adequate to compare meiofaunal assemblages among macroalgal species. Our results also showed that the abundance and richness of meiofaunal assemblages, once the model had eliminated the confounding effect of biomass, were significantly different between macroalgae. This means that, apart from the effect of habitat size, the macroalgal identity, that in turn may be attributable to differences of complexity between macroalgae, also seems to play a significant role in driving the abundance and richness of meiofauna. The presence of significant differences on the structure of meiofaunal assemblages between macroalgae of equal biomass also confirmed the role of macroalgal identity. Therefore, results also supported the second hypothesis of our study. The effect of macroalgal identity could be attributed to differences in their complexity because the studied macroalgae were significantly different in their fractal dimensions (i.e. fractal area and fractal perimeter). Previous studies have found a good correlation between macroalgal complexity and the structure of meiofaunal assemblages (Gee and Warwick, 1994b; Hooper and Davenport, 2006). Moreover, in contrast to that found for macrofauna (Veiga et al., 2014; Torres et al., 2015), fractal perimeter seems more relevant for meiofaunal assemblages than fractal area.

Chondrus crispus showed the highest values of fractal area harbouring a lower meiofaunal abundance and diversity. Therefore, our results also pointed out the necessity to adopt complexity measures adapted to the target organisms. Macroalgae with different complexity provide different physical environment in terms of desiccation, wave exposure or protection of predation (see introduction section); these factors are likely shaping the structure of meiofaunal assemblages (Frame et al., 2007). The study area here considered is highly exposed to the wave action. Moreover, the current study was done in tidal pools in the middle intertidal, where a variety of meiofaunal predators may be found (e.g. fish and crabs) (Dethier, 1980) and confined to the pool at relatively high densities for long time (Gibbons, 1991). Under such circumstances, algal complexity could be key in relieving predation pressure and protecting from wave exposure, thus contributing to structuring meiofaunal assemblages. Moreover, seaweeds may provide food for the few meiofaunal taxa that are able to feed directly on macroalgal tissues (Gee and Warwick, 1994a; Hull, 1997; Frame et al., 2007; Giere, 2009). Some specialized species grasp and crack epithytic diatoms on the seaweed and others may take up exudates secreted by the seaweeds (Giere, 2009). Nevertheless, the great majority of phytal meiofauna grazes on detritus and microorganisms accumulated on the macroalga. Therefore, similarly to Frame et al. (2007), potential differences in palatability among macroalgae would not explain differences here reported on the structure of meiofaunal assemblages among seaweeds. However, it is possible that the amount of microflora, microorganisms and detritus associated with macroalgae could differ among species thus having a substantial effect on their associated meiofauna (Frame et al., 2007). Alternatively, macroalgae produce antimicrobial compounds that differ between species (e.g. Hornsey and Hide, 1976). Moreover, seasonal and spatial variation in antibiotic production occurs in some marine algae (Hornsey and Hide, 1976; Tanniou et al., 2014). For instance, the antibiotic production of C. crispus is maximum during winter (Hornsey and Hide, 1976), which may deter some harpacticoid copepods from living in this macroalga at that season (Steinarsdóttir et al., 2003). Our results showed that the invasive macroalga S. muticum apparently harboured more meiofaunal taxa than the native ones. In terms of abundance, meiofauna associated with S. muticum was higher than that associated with C. crispus but similar to that found on B. bifurcata. Therefore, our results supported partially the third hypothesis. Invasive macroalgae could noticeably modify the structure and functioning of native assemblages (Levin et al., 2002) by altering the habitat structure, reducing species abundance, richness and the primary production (Salvaterra et al., 2013). Previous studies done with S. muticum have mainly focused on its competitive interactions with native algae (e.g. Stæhr et al., 2000; Britton-Simmons, 2004; Sánchez et al., 2005; White and Shurin, 2011), although some have also investigated its potential influence on higher trophic levels (e.g. Viejo, 1999; Wernberg et al., 2004; Engelen et al., 2013; Salvaterra et al., 2013). Studies that compared the invertebrate assemblages harboured by S. muticum with those associated with other complex macroalgae (e.g. Halidrys siliquosa, B. bifurcata, Cystoseira spp., Stypocaulon scoparium) concluded that it is improbable that its introduction had caused substantial changes in the composition of epifaunal assemblages (e.g. loss of diversity) when compared to that of native species (e.g. Wernberg et al., 2004; Buschbaum et al., 2006; Engelen et al., 2013; Veiga et al., 2014). In contrast, when less morphologically complex native macroalgae (i.e. Fucus sp. and C. crispus) were considered, epifaunal abundance and diversity was higher in the invasive macroalgae (e.g. Viejo, 1999; Buschbaum et al., 2006; Veiga et al., 2014). These previous studies were, however, exclusively focused on macrofauna; therefore, to achieve a better understanding about the effects of invasive macroalgae on


native fauna, studies should also include meiofauna (Thomsen et al., 2013) because meiofauna, due to its particular biological characteristics, may respond in a different way than macrofauna to invasions. Results from our study contrast with those found for macrofauna associated with the same macroalgal species because S. muticum harboured lower richness and exclusive macrofaunal taxa than B. bifurcata (Veiga et al., 2014). Similar results were reported by Smith et al. (2014) for the invasive turf C. ustulatus; this alga favoured a more diverse meiofauna in the upper zone of the rocky shore by increasing the habitat complexity in this area where turfs are scarce but, in turn, affected negatively to macroinvertebrates. On soft bottoms, the invasive Caulerpa racemosa seems to enhance the abundance of meiofauna, however it caused also a decrease in the diversity of some meiobenthic crustaceans (Carriglio et al., 2003; Sandulli et al., 2004; Travizi and Zavodnik, 2004). Similar results were achieved by Bohórquez et al. (2013), who found that green macroalgal blooms in sediments, increased meiofaunal abundance but at the same time their effects differed between taxonomic groups. Therefore, the effects on the fauna seem to depend on the identity of the invasive species and the habitat where it occurs. Summarizing, the present study showed that habitat size influenced the abundance, richness and structure of meiofauna. However, the identity of macroalga seems to play also a significant role. Future experimental work will be needed to test the role of complexity shaping meiofaunal assemblages. Moreover, the invasive macroalga S. muticum seems to affect positively the richness of meiofaunal assemblages. Our results and those by Smith et al. (2014) emphasize the necessity of including different faunal components (i.e. meio- and macrofauna) to achieve a better knowledge about ecological effects of invasive macroalgae on higher trophic levels.

Acknowledgments Authors would like to thank Dr. Juan Moreira for reviewing a previous draft of this paper. We are also grateful to two anonymous referees for all the helpful comments and suggestions, which greatly improved this paper. Financial support was partially provided by the European Regional Development Fund (ERDF) through the programme POFC-COMPETE, ‘Quadro de Referência Estratégico Nacional (QREN), and the Portuguese Fundação para a Ciência e a Tecnologia (FCT) through the project PEst-C/MAR/ LA0015/2011. During this study, P. Veiga (SFRH/BPD/81582/2011) and M. Rubal (SFRH/BDP/104225/2014) were supported by postdoctoral grants awarded by Fundacão para a Ciência e Tecnologia (FCT, Portugal).

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