Halo effects of tourists’ destination image on domestic product perceptions

Marine Pollution Bulletin 72 (2013) 47–54

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Marine Pollution Bulletin journal homepage: www.elsevier.com/locate/marpolbul

Drivers of estuarine benthic species distribution patterns following a restoration of a seagrass bed: A functional trait analyses Marina Dolbeth a,⇑, Patrícia Cardoso b, Tiago Grilo a, Dave Raffaelli c, Miguel Ângelo Pardal a a

CEF – Centre for Functional Ecology, Department of Life Sciences, University of Coimbra, P.O. Box 3046, 3001-401 Coimbra, Portugal IMAR – CMA Marine and Environmental Research Centre, Department of Life Sciences, University of Coimbra, P.O. Box 3046, 3001-401 Coimbra, Portugal c Environment Department, University of York, Heslington, York YO10 5DD, UK b

a r t i c l e

i n f o

Keywords: Functional diversity Partitioning diversity Community assembly Estuarine intertidal benthos Seagrass

a b s t r a c t We integrate information on functional diversity (FD) patterns from estuarine intertidal benthic communities from different habitats and along a temporal disturbance gradient, to understand the drivers of species coexistence patterns. Species and traits’ biomass levels seemed to be first determined by habitat filtering, selecting those traits better adapted to the biologically challenging estuarine environment. Within that subset of traits and within each habitat, biotic interactions were probably high, as evidenced by high a-diversity and community weighted mean differences. The former patterns hold for the disturbance/recovery scenario considered. However, as the estuary recovered, biomass became more distributed among different trait categories, consistent with increases in FD when the seagrass started to increase. Policy towards the restoration of seagrass bed and other biogenic structures, and improving the connectivity within adjacent systems were confirmed and suggested, as this would imply higher FD and potentially higher resilience to disturbance within the estuarine intertidal system. Ó 2013 Elsevier Ltd. All rights reserved.

1. Introduction What drives species distribution patterns and community assembly remains an essential question in ecology, the answer to which would be extremely useful for defining conservation priorities and management purposes (e.g., Mouillot, 2007). Various mechanisms have been proposed to explain community assembly (for review and definitions see Leibold et al., 2004; Chase and Bengtsson, 2010; Logue et al., 2011), and these can be broadly categorised as those based on: (1) stochastic processes, where species are rare or abundant because of stochastic drift and where species are viewed as competitively equivalent, i.e. neutrality (Hubbell, 2005); (2) non-random processes, resulting from differential responses of species to heterogeneous environments and built on traditional niche-differentiation theory, i.e. a species sorting perspective; or (3) a combination of stochastic and non-random processes, assuming that species respond differently to the environment and may have differential stochastic dispersal, colonisation and extinction rates, i.e. a mass effect perspective (Leibold et al., 2004) or stochastic niche theory (Tilman, 2004). Both species sorting and mass effects perspectives assume that niche differentiation occurs among localities within a region, but that diversity is maintained by different processes (Logue et al., 2011), implying a

⇑ Corresponding author. E-mail address: [email protected] (M. Dolbeth). 0025-326X/$ - see front matter Ó 2013 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.marpolbul.2013.05.001

need for different management and conservation strategies (Mouillot, 2007). There has been considerable debate as to which mechanism acts on regulating species patterns in ecological systems and under what conditions (e.g. Bell, 2001; Dornelas et al., 2006; Leibold and McPeek, 2006). A key point is that these hypotheses are not mutually exclusive and should be considered together to explore patterns of species coexistence and diversity (Mouillot, 2007; Chase and Bengtsson, 2010; Mouchet et al., 2010). Over the past 10 years, functional diversity (FD) has emerged as an important tool for exploring species coexistence and effects of biodiversity on ecosystem functioning (Bremner, 2008; Cadotte et al., 2011). FD refers to the functional component of biodiversity, usually measured through species traits (Violle et al., 2007), quantified by various indices (reviewed in Petchey and Gaston, 2006; Mouchet et al., 2010; Schleuter et al., 2010), and is considered a good predictor of ecosystem functioning (Cadotte et al., 2011). Among FD indices, the Rao coefficient and the communityweighted mean trait value (CWM) have been widely used (Ricotta and Moretti, 2011). Rao has been applied to analyse patterns of trait convergence or divergence, after partitioning FD among its different components a, b and c (e.g. de Bello et al., 2009, 2010; Meynard et al., 2011) and by comparing the observed FD to random expectations by null models (de Bello, 2012) and this way, depict on the importance of neutrality and niche-differentiation in regulating community assembly (de Bello et al., 2009; de Bello, 2012). CWM allows the visualisation of the shifts of traits within

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communities and clarifies which traits are exerting a dominant effect on the ecosystem processes (Lepš et al., 2011; Ricotta and Moretti, 2011). With respect to niche-differentiation, two main processes govern ecological communities: habitat filtering and species biotic interactions (Grime, 2006; Leibold and McPeek, 2006; de Bello, 2012). Habitat filtering should reduce the spread of ecological strategies resulting in trait-convergence and in decreased ‘‘within community’’ trait differentiation, i.e. a FD (de Bello et al., 2009, 2010; de Bello, 2012). Biotic interactions within communities should maximise the differentiation between coexisting species, resulting in trait-divergence (Grime, 2006; de Bello, 2012) and increased a FD (de Bello et al., 2009). In theory, for non-random patterns the decrease (trait-convergence) or increase (traitdivergence) of observed FD compared with null models is due to the extent of habitat filtering versus limiting similarity due to biotic interactions (de Bello, 2012). These ideas have generated much debate (Grime, 2006; Szabó and Meszéna, 2006) and have been tested mainly for terrestrial plant communities (e.g., de Bello et al., 2009; Cornwell and Ackerly, 2009; Lepš et al., 2011), with fewer tests involving other trophic groups and environments (e.g., Pavoine and Dolédec, 2005; Mouillot et al., 2007; Meynard et al., 2011). In the present study, we explore FD patterns using data from intertidal benthic communities in different habitats within the Mondego estuarine system (Portugal), in order to understand the drivers of species distribution and coexistence patterns, by partitioning taxonomical diversity (TD) and FD, comparing it with null models and estimating CWM. We predict that the species coexistence spatial patterns will be governed essentially by niche-differentiation processes, especially habitat filtering (as seen by Mouillot et al. (2007)), because the study system is an estuary and naturally biologically challenging for its resident fauna, due to the high physico-chemical variability (Elliott and Whitfield, 2011). These environmental filters will probably constrain coexisting species to share similar traits. The estuary has suffered eutrophication, resulting in the decline of its seagrass bed, followed by a restoration plan implemented to restore environmental quality. In addition to trying to understand what drives species distributions within the system, we also explore the mechanisms behind species coexistence and how these might change over time with disturbance impact, to propose strategies for conservation. We predict that the temporal changes associated with the recovery will be characterised by an increase in FD together with an increase in the importance of biotic interactions (trait-divergence).

2. Material and methods 2.1. Case study – the Mondego estuary All data were collected in the south arm of the Mondego estuary (area of 8.6 km2), located in a warm temperate region of Portugal (40°080 N, 8°500 S), characterised by large intertidal flats (75% of the area) with several marshes and a seagrass bed. In the recent past, anthropogenic activities triggered a eutrophication process in the south arm, which, together with an increase of water residence time, led to a strong decline of the seagrass bed in terms of biomass and spatial extent, from 16 ha in 1986 to 0.02 ha in 1997 (Patrício et al., 2009; Dolbeth et al., 2011; Lillebø et al., 2011). By 1998, mitigation measures were in place to reduce nutrient loading and enhance the environmental quality of the south arm (Cardoso et al., 2010; Lillebø et al., 2011), and the system showed signs of recovery manifested by an absence of macroalgal blooms, increased seagrass biomass and spatial cover (Fig. 1).

We used data on the macrobenthic communities from three distinct habitats within the system (Fig. 1): a seagrass bed of muddy sediments covered with Zostera noltii, a mudflat of muddy sediments with some Z. noltii rhizomes, and an eutrophic sandflat which lacked rooted macrophytes and which was regularly covered seasonally by green opportunist macroalgal blooms before 1998 (Cardoso et al., 2010; Dolbeth et al., 2011). Data on the seagrass spatial cover were determined through direct observation in 1986, 1993, 1997 and with GPS in 2002 and 2008 and subsequent mapping in ArcView software vs 9 (expanded maps on Fig. 1, Dolbeth et al., 2011; Lillebø et al., 2011). Measurements of the benthic communities, Z. noltii biomass and Ulva sp. biomass for the three areas only took place from 1993 to 1996 and, after the mitigation measures, from 1999 to 2008. As such, we used a temporal scale defined according to seagrass bed dynamics in the estuary and the available benthic communities and plant biomass dynamics data: (a) 1993 – a measured reference phase, with the highest Z. noltii biomass measured in the seagrass bed before the mitigation measures and macroalgal blooms in the sandflat; (b) 1999 – a decline phase, with the lowest Z. noltii biomass measured during the study period; (c) 2002 – an initial recovery phase, with the seagrass area attaining similar Z. noltii spatial cover as in 1993, but still with considerably lower annual biomass; and (d) 2008 – a recovery phase, with Z. noltii’ biomass increases in all three habitats, including the sandflat, which had not supported rooted macrophytes for more than 20 years. For further details, see Dolbeth et al. (2011) and Grilo et al. (2011). Studies on the macrobenthic fauna in these habitats have revealed a similar pattern of change, with declining diversity and production levels up to 1999 and a recovery trend from 2002 onwards, with a slight increase in community complexity and production of K-strategists, especially in the sandflat area (Dolbeth et al., 2011). 2.2. Selection of traits We considered four traits that best represent those likely to influence the resistance and resilience of benthic invertebrates to eutrophication and consequent changes in seagrass bed dynamics and sediment redox potential (Table 1). Data on traits were obtained from established databases, including BIOTIC – Biological Traits Information Catalogue (www.marlin.ac.uk/biotic/) and WoRMS – world register of marine species (www.marinespecies.org), and supplemented with data on specific traits from other published sources (e.g., Fauchald and Jumars, 1979, Bremner, 2005, Baeta et al., 2009) as well as field data measurements from the Mondego (mean body mass). For trophic traits, we used feeding guilds, where we categorised species into (1) strictly deposit feeders, encompassing both surface and sub-surface deposit feeders (mostly small polychaetes); (2) deposit/grazers, where species can behave both as deposit feeders or grazers (e.g., Hydrobia ulvae, Baeta et al., 2009); (3) suspension/deposit feeders, those species which behave primarily as suspension feeders, but that can also deposit feed depending on the environment (e.g., bivalve Scrobicularia plana, Baeta et al., 2009); and (4) omnivores, here defined as those species that are able to feed in all the above ways (Table 1). Those 25 species that account for more than 98% of density, biomass and secondary production from the intertidal flats of the Mondego estuary were scored for traits. 2.3. Data analysis All calculations were performed on species biomass data, using mean values for 1993 (for which data on replicates are not

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8º 50' W

Figueira da Foz

R

1Km ATLANTIC OCEAN

RIV E

Seagrass

ND EG

O

Mudflat Sandflat

MO

40º 08' N

NORTH ARM

SOUTH ARM

PORTUGAL

PRANTO RIVER Intertidal areas Saltmarshes

1993 1.6 ha

1986 15 ha

Sluice

1997 0.02 ha

2002 1.6 ha

2008 6 ha

Zostera noltii Fig. 1. Location of the Mondego estuary showing sampling stations. Expanded maps show the evolution of the Zostera noltii bed’s area extension, since 1986 until 2008 (Lillebø et al., 2011).

Table 1 Body traits and categories selected for study, with indication of the rational for the selection of the traits. Strictly deposit feeders encompass surface and sub-surface deposit feeders; deposit/grazers are surface deposit feeders that can behave as deposit or grazers; suspension/deposit feeders, suspension feeders that can also behave as deposit feeders depending on the environment. Body traits Categories

Rationale for body traits selection

Body mass Very small Small Medium Large

Defines and correlates with other life history traits, mediates other structuring interactions (Mouillot et al., 2006; Webb et al., 2009). Small-bodied invertebrates may characterise environments with high instability, consequence of environmental/ anthropogenic pressures imposed on the organisms (Mouillot et al., 2006)

Life span

<2 years 2–5 years >5 years

Previous studies showed shifts from large slowing growing species, with longer life spans, to less vulnerable faster growing ones, facing disturbance

Burial depth

Epifauna Up to 2 cm depth 2–5 cm depth >5 cm depth

Important to determine vulnerability to hydrodynamics stress at sediment–water interface (Van Colen et al., 2010), risk of predation, influencing biogenic mixing depth and sediment redox potential. Deeper living species are potentially less subjected to hydrodynamics stress, but are more vulnerable to macroalgae blooms impacts, hypoxia and anoxia events

Feeding guilds

Strictly deposit feeders Deposit/grazers Suspension/deposit Strictly herbivores Omnivores

Reflects the trophic structure, distribution of resources and how organisms adapt to the habitat (Bremner, 2008; Webb et al., 2009). Omnivores were included, because encompass all possible feeding guilds and represent higher behavioural plasticity

available) and biomass values for each replicate (5 or 6) for all other years. TD and FD were analysed for their a, b and c components. Partitioning of diversity assumes an additive relationship (c = a + b, Lande, 1996), where total c-diversity comprises a-diversity (within-community), and b-diversity (degree of change in species diversity along an environmental gradient) (Whittaker, 1972). For TD,

Simpson’s index was used and for FD Rao’s coefficient with Jost correction (Jost, 2007) following the recommendation of de Bello et al. (2009, 2010). Rao’s coefficient expresses the average dissimilarity (Gower’s distance) between species pairs in a community and allows the decomposition of diversity into c, a and b components (Mouchet et al., 2010; de Bello et al., 2010). The Jost correction is based on the ‘‘equivalent communities’’ concept (to provide

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independent a and b, Jost, 2007) and is recommended when comparing TD and FD, otherwise b could be inappropriately low and biologically meaningless (de Bello et al., 2010). The upper limit for the Jost-corrected a-Simpson is the total number of species for the region (=estuary in the present study) (de Bello et al., 2010). Partitioned diversity estimates were compared with a and b components of diversity derived from null species distributions (499 randomizations applied to species  plot matrix) (de Bello et al., 2009, 2010; Mouchet et al., 2010), using the commsimulator function implemented in the vegan library (after de Bello, 2012) and by applying the trialswap method (Miklós and Podani, 2004) to maintain species occurrence frequency and sample species richness. This procedure could not be applied to the 1993 data because of low sample size. The degree of trait divergence was expressed as the standardised effect size index (SES, Gotelli and McCabe, 2002) where SES = (observed FD  mean of simulated FD)/standard deviation of simulated FD). An SES index > 0 implies divergence and < 0 convergence (de Bello, 2012). Functional redundancy was estimated as the difference between TD and FD (Redundancy = Simpson’s index  Rao index), after de Bello et al. (2007). If redundancy is zero, all the species are functionally identical, maximum redundancy is when it is equal to Simpson’s index (de Bello et al., 2007). We also computed community weighted means (CWMs) of traits which express the trait mean per sample weighted by species relative biomass (Lepš et al., 2011), in order to better see which traits contribute most to the patterns in the data (Lepš et al., 2011; Ricotta and Moretti, 2011). All indices were computed within the statistical package R, using the routine FD for the estimation of CWM and the function ‘‘Rao’’ by de Bello et al. (2010), which provides a, b and c components for TD and FD. CWM results were further explored using Principal Coordinates Analyses (PCO) using PRIMERv6 and PERMANOVA + routines (Anderson et al., 2008). Additionally, we overlaid vectors based on Spearman correlations onto the PCO plot in order to clarify patterns of change (Anderson et al., 2008). To ensure comparable terminology we related studies on traits and FD. Here each sample is equivalent to a microsite, each sampling area or habitat is equivalent to a locality and the estuary is equivalent to a region sensu Leibold et al. (2004) and used by Mouillot (2007).

3. Results

Fig. 2. Partitioning of diversity of species taxonomical diversity versus functional diversity into a (within community) and b (among communities) components of c (regional diversity, bar length) over the three locations in study and for each year, with indication of redundancy (TD–FD). All values were computed with Jost correction.

3.1. Partitioning of taxonomic and functional diversity Mean a-diversity was always higher than b-diversity for both TD and FD, and this was consistent across years (Fig. 2), reflecting a moderately low turnover of the most (98%) abundant species and traits. Fig. 2 shows that both TD and FD were higher in 2008 and lower in 1999, for all three components (c, a and b). Compared to null models, a-diversity was always higher than the expected and b-diversity lower than expected for both TD and FD and years, indicative of non-random patterns in the data. Computation of standardised effect size index (SES) confirmed this pattern, being always negative for a-diversity and positive for bdiversity (not shown here), suggesting trait convergence (de Bello, 2012). When each habitat (area) is considered separately, the highest adiversity was found for the sandflat in 2008 for TD and FD, although it had a lower species number when compared to the other habitats and years. In general, the seagrass habitat supported a higher number of species, but had lower a-diversity in all years for TD and FD (1.4–2.7) (Table 2). Pairwise comparisons of b-diversity

(i.e. turnover of species or traits), revealed lower values between the mudflat and sandflat compared to either of these habitats and the seagrass bed (Table 2), with highest values for comparisons of the seagrass bed in 1993 to other areas and years. Comparing TD and FD, b-Simpson was always higher than b-Rao for all the traits considered, meaning that the turnover of species was greater than turnover of traits (Fig. 2). However, c (corresponding to the region/estuary) and the proportion of a- and bcomponents of diversity varied considerably with year and trait. Lowest b-Simpson and b-Rao values occurred in 1999, followed by 1993, with an increase in 2002–2008. These results agree with CWM variation over time, which showed a narrower range in the variation of the different trait categories within the three areas in 1999, especially for life-span (Supplementary Material), also as a result of general lower FD in this year (Fig. 2). For traits, the lowest b-Rao was found for life-span, followed by burial depth, size and feeding guilds (Fig. 2). Redundancy was always lower than or equal to 0.6 and varied within similar values for each trait (Fig. 2). The lowest values were

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Table 2 Taxonomical diversity (TD) measured by species number (no. sps) and Simpson index, and functional diversity (FD) measured by Rao index for each functional trait in study, after partitioning into a-diversity for each area/year and b-diversity pairwise sample comparison (%), and minimum and maximum values for redundancy, taking into account all traits. For b-diversity, only comparisons between seagrass 1993 community used as the measured reference and remaining sites and years are presented. Habitat/year

No. sps

TD

FD-size

FD-life span

FD-burial depth

FD-feeding guilds

Redundancy range (TD–FD)

a-Diversity (within communities) Seagrass 1993 Seagrass 1999 Seagrass 2002 Seagrass 2008 Mudflat 1993 Mudflat 1999 Mudflat 2002 Mudflat 2008 Sandflat 1993 Sandflat 1999 Sandflat 2002 Sandflat 2008

25 23 19 19 24 21 18 19 21 17 18 18

1.6 2.7 1.9 2.2 2.9 2.6 2.7 2.3 3.3 1.7 1.0 4.2

1.4 2.4 1.9 2.0 2.1 2.0 1.7 2.1 2.0 1.7 1.0 2.6

1.4 2.3 1.8 1.9 2.0 1.9 1.6 2.1 2.0 1.6 1.0 2.1

b-Diversity % (among communities), relative to community of Seagrass 1993 mean Seagrass 1993 – – – Seagrass 1999 18 14 12 Seagrass 2002 2 1 1 Seagrass 2008 3 1 1 Mudflat 1993 26 20 16 Mudflat 1999 44 37 23 Mudflat 2002 38 32 31 Mudflat 2008 34 27 26 Sandflat 1993 20 15 4 Sandflat 1999 46 38 27 Sandflat 2002 45 38 20 Sandflat 2008 35 31 11

obtained in 2002 (0.1–0.2) and the highest in 2008 (0.4–0.6, Fig. 2). When considering each habitat separately redundancy values were slightly greater, but still quite low compared to the maximum potential redundancy, with occasional zero values, indicative of no redundancy (Table 2).

1.4 2.1 1.7 2.0 2.1 1.9 2.1 1.7 2.0 1.6 1.0 2.9 – 19 3 5 20 30 37 34 7 34 28 15

1.5 2.5 1.9 2.2 2.7 1.9 1.6 2.2 3.1 1.7 1.0 2.5

0.1–0.2 0.2–0.6 0–0.2 0–0.3 0.2–0.9 0.6–0.7 0.6–1.1 0.1–0.6 0.1–1.3 0–0.1 0 1.3–2.1

– 16 1 2 22 40 36 30 17 41 41 34

the sandflat supported small-sized deposit feeders and some omnivore species, which could be found up to 5 cm in the sediment (Fig. 3B).

4. Discussion 3.2. Differences across habitats and years In terms of community structure and composition, all areas and years were distinctive, with the sandflat standing out in 2002 due to its higher biomass of S. plana, Cyathura carinata and small polychaetes (Fig. 3A). A higher prevalence of H. ulvae, some Crustacea and other Annelida species was associated with all the other samples, especially those from the seagrass area. Spatial differences were mostly due to the dominance of H. ulvae downstream in the seagrass habitat and to an increasing number of C. carinata from the mudflat to the sandflat (Fig. 3A). In these two latter areas there was also a higher prevalence of small polychaetes. However, the community of mudflat in 2008 resembled that of the seagrass in 1999, associated to other polychaete species, such as Heteromastus filiformis (Fig. 3A). The habitats appear much more distinctive when traits rather than species are considered (Fig. 3b). The seagrass area is distinctive from other areas, dominated by very small epifauna (<0.001 g) that could behave both as surface deposit feeders and grazers on periphyton (Fig. 3 and Supplementary Material). In 1999, the community in the seagrass was similar to those of the mud and sandflat in 1993, which also supported small epifauna with a short life-span, species behaving both as deposit feeders/ grazers, and some strictly herbivores (Fig. 3B, Supplementary Material). The mudflat and sandflat appeared more similar in terms of functional composition, with a few exceptions in 2008. Both areas supported a higher prevalence of long-lived and larger-size individuals that could be found below 5 cm depth, primarily suspension feeders that could also behave as deposit feeders. In 2008,

The analyses described here were designed to explore TD and FD patterns within the benthic communities and how they have evolved in a system which is in the process of recovery from severe eutrophication. We predicted that these patterns would be driven ultimately by niche-differentiation processes, especially habitat filtering that leads to trait-convergence (Mouillot et al., 2007), owing to the physically challenging nature of the estuarine environment, and indeed, this seems to be the case. Below, we expand on these findings, reflecting on the detailed nature of the drivers of species distributions and how these may have changed during the recovery process. Patterns of FD may reveal species coexistence patterns and community assembly rules driven by functional traits, but their relative importance is usually scale dependent (Mouchet et al., 2010). When exploring narrower geographical scales, such as in the present study, mechanisms generally need to specifically address niche differentiation and its processes habitat filtering and biotic interactions (Chase and Bengtsson, 2010, Mouchet et al., 2010), because coexistence requires species differential responses to ecological heterogeneities, usually resulting from trade-offs between their tolerances and the abiotic and biotic environment (Leibold and McPeek, 2006). Habitat filtering implies selecting traits which maximise the ability to acquire limiting resources given local environmental conditions, and in demanding environments like an estuary (Elliott and Whitfield, 2011), this should lead to trait convergence (Grime, 2006; Mason et al., 2011; de Bello, 2012). In contrast, biotic interactions would lead to trait divergence, as the most abundant species would occupy distinct and non-overlapping niches (Szabó and Meszéna, 2006; Mason et al., 2011).

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(A)

(B)

Fig. 3. PCO ordination for (A) macrobenthic community biomass and (B) community trait means for each area (colours) and year (symbols). The length and direction of each vector indicates the strength and sign of the relationship between species or trait category and PCO axes, based on a Spearman correlation (only vector with length > 0.6 were represented).

For the Mondego estuary, the biological importance of niche differentiation among coexisting species was clear, as the observed FD was never within the simulated null models range. There was strong evidence for trait convergence, implying habitat filtering as a driver of species distribution (Leibold and McPeek, 2006; Petchey et al., 2007), as also observed for fish in coastal areas (e.g. Mouillot et al., 2007). Interestingly, FD, taking into the distinctive habitats in study, was considerably higher within-communities than among-communities (mean FD a > b), suggesting limiting similarity among coexisting species traits, a hypothesis that is consistent with trait divergence and implies a degree of biotic interactions (Cornwell and Ackerly, 2009; de Bello et al., 2009). Thus, there were few dominant species that could occur in the different habitats, as expressed by the low b-diversity for both TD and FD, but within each habitat, niche overlap between species was low, as evidenced by the high a-diversity values and CWM spatial patterns. The small differences between TD and FD results suggest that there is little functional redundancy for these assemblages, as also suggested for subtidal benthos (Linden et al., 2012) and for fish in coral reef systems (Micheli and Halpern, 2005; Guillemot et al., 2011). Combined with low levels of biodiversity, low functional redundancy implies that the system could be vulnerable to the removal of key species. On the other hand, some species, such as S. plana and Hediste diversicolor, which are capable of feeding in different ways (expressing intraspecific trait variability) depending on environmental conditions, seem to be highly adaptable whilst

omnivores are, by definition, capable of expressing multiple feeding traits, and these species and traits increased in later years, consistent with the view that estuarine systems have a high natural resilience (Elliott and Whitfield, 2011). However, other traits (body size, lifespan and, possibly, burrowing depth) may not be so plastic. From a meta-community perspective (communities of different localities linked by dispersion of multiple interacting species, Leibold et al., 2004), both species-sorting and mass effect mechanisms could explain the obtained results, depending whether there are strong competitors or high regional dispersal rates (Mouillot, 2007; Logue et al., 2011). Understanding these processes would be important for defining conservation priorities (Mouillot, 2007). Given that macrophytes are known to enhance habitat heterogeneity (Duffy, 2006), a policy towards the restoration of seagrass bed and other biogenic structures, and improving the connectivity between adjacent systems, might be sensible for engineering greater resilience (e.g. Mouillot, 2007; Godbold et al., 2011). The lowest values for both TD and FD were observed in 1999, when the extent of the seagrass bed, overall benthic diversity and secondary production were low (Grilo et al., 2011; Dolbeth et al., 2011). Traits were more similar among areas, mainly because the community was already extremely impoverished and probably there is a selection for the fittest in those environmental conditions. Nevertheless, there was also a tendency for increasing species size, life-span and for those species living deeper in the sediment in all areas and for an increase of suspension/deposit feeders when compared to 1993. These results are consistent with recovery of the system which is more evident in 2002 for the mudflat and sandflat areas. Such differences may be associated with changes in ecosystem functioning (Cadotte et al., 2011; Lepš et al., 2011): for instance, more energy placed into individual growth representing higher stability (K-strategists), deeper sediment reworking, more intense bioturbation with consequences for nutrient dynamics and sediment oxygen levels, among other processes (Solan et al., 2008). In some occasions, the seagrass area had lower TD (Simpson index) and FD, being dominated by small-sized epibenthic species, with a short life span. One might expect always higher TD and FD for such a highly structured environment (Duffy, 2006; Mouillot, 2007), but previous studies have shown that the gastropod H. ulvae is overwhelmingly dominant by numbers and biomass in this area (Dolbeth et al., 2011; Grilo et al., 2011), contributing disproportionally to ecosystem functioning (Cadotte et al., 2011, Lepš et al., 2011). Previous studies on the Mondego have suggested that the seagrass area was more resistant to the eutrophication effects (Patrício et al., 2009; Dolbeth et al., 2011), but the present analysis suggests lower resilience due to lower FD, and that the seagrass may not be able to recover to its pre-eutrophication state of the early 80s (Patrício et al., 2009). After 10 years of restoration measures in the estuary there is evidence of recovery of the system by 2008, with all areas becoming more similar with regard to habitat heterogeneity, due to the seagrass spread. However, Z. noltii biomass levels in 2008 have yet to fully recover to the biomass levels of 1993 (annual mean biomass was 206.4 gAFDW m2 in 1993 and 126.0 gAFDW m2 in 2008). TD and FD were highest in 2008, with biomass becoming more evenly distributed among different trait categories, especially evident for the sandflat area. Greater FD should result in greater stability through time as multiple functional traits can help to buffer ecosystems against abiotic variation and anthropogenic change (Cadotte et al., 2011).

5. Conclusion This study confirmed a pattern that was expected for an estuarine system. Habitat filtering was the main driver of species

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distribution and coexistence, selecting for few species with similar traits that are able to cope with the environmental variability of the estuary. Within that subset of traits, biotic interactions were high, suggesting that species did not share the same ecological niche. Low functional redundancy was found, mainly in the eutrophication–disturbance period, suggesting that the system was prone to the removal of key species. However, with the recovery of system and consequent seagrass spread, FD increased (although the low species number) and so the traits associated to higher stability, which reinforces the effectiveness of the measures taken in the past to restore the seagrass bed as for the improvement of the whole estuarine system. Acknowledgments We are grateful for the constructive input of Francesco De Bello on the r functions used in the study. This research was supported by FCT (Portuguese Foundation for Science and Technology), through a grant attributed to M Dolbeth (SFRH/BPD/41117/2007) and BIOCHANGED project (PTDC/MAR/111901/2009), with funds from POPH (Portuguese Operational Human Potential Program), QREN Portugal (Portuguese National Strategic Reference Framework), and MCTES (Portuguese Ministry of Science, Technology, and Higher Education). Appendix A. Supplementary material Supplementary data associated with this article can be found, in the online version, at http://dx.doi.org/10.1016/j.marpolbul.2013. 05.001. References Anderson, M.J., Gorley, R.N., Clarke, K.R., 2008. PERMANOVA+ for PRIMER: Guide to Software and Statistical Methods. Primer-e, Plymouth, UK. Baeta, A., Valiela, I., Rossi, F., Pinto, R., Richard, P., Niquil, N., Marques, J.C., 2009. Eutrophication and trophic structure in response to the presence of the eelgrass Zostera noltii. Mar. Biol. 156, 2107–2120. Bell, G., 2001. Neutral macroecology. Science 293, 2413–2418. Bremner, J., 2008. Species’ traits and ecological functioning in marine conservation and management. J. Exp. Mar. Biol. Ecol. 366, 37–47. Bremner, J., 2005 Assessing ecological functioning in marine benthic communities. PhD Thesis, University of Newcastle upon Tyne, UK. Cadotte, M., Carscadden, K., Mirotchnick, N., 2011. Beyond species: functional diversity and the maintenance of ecological processes and services. J. Appl. Ecol. 48, 1079–1087. Cardoso, P.G., Leston, S., Grilo, T.F., Bordalo, M.D., Crespo, D., Raffaelli, D., Pardal, M.A., 2010. Implications of nutrient decline in the seagrass ecosystem success. Mar. Pollut. Bull. 60, 601–608. Chase, J.M., Bengtsson, J., 2010. Increasing spatio-temporal scales: metacommunity ecology. In: Verhoef, H.A., Morin, P.J (Eds.), Community Ecology. Processes, Models and Applications. Oxford University Press, pp. 57–68. Cornwell, W.K., Ackerly, D.D., 2009. Community assembly and shifts in plant trait distributions across an environmental gradient in coastal California. Ecol. Monogr. 79, 109–126. de Bello, F., 2012. The quest for trait convergence and divergence in community assembly: are null-models the magic wand? Global Ecol. Biogeogr. 21, 312–317. de Bello, F., Lepš, J., Lavorel, S., Moretti, M., 2007. Importance of species abundance for assessment of trait composition: an example based on pollinator communities. Commun. Ecol. 8, 163–170. de Bello, F., Thuiller, W., Lepš, J., Choler, P., Clément, J., Macek, P., Sebastià, M., Lavorel, S., 2009. Partitioning of functional diversity reveals the scale and extent of trait convergence and divergence. J. Veg. Sci. 20, 475–486. de Bello, F., Lavergne, S., Meynard, C., Lepš, J., Thuiller, W., 2010. The partitioning of diversity: showing Theseus a way out of the labyrinth. J. Veg. Sci. 21, 992–1000. Dolbeth, M., Cardoso, P.G., Grilo, T.F., Bordalo, M.D., Raffaelli, D., Pardal, M.A., 2011. Long-term changes in the production by estuarine macrobenthos affected by multiple stressors. Estuar. Coast. Shelf Sci. 92, 10–18. Dornelas, M., Connolly, S., Hughes, T., 2006. Coral reef diversity refutes the neutral theory of biodiversity. Nature 440, 80–82. Duffy, J.E., 2006. Biodiversity and the functioning of seagrass ecosystems. Mar. Biodivers. Ecosyst. Funct.: Empirical Approach Future Res. Needs 311, 233–250. Elliott, M., Whitfield, A., 2011. Challenging paradigms in estuarine ecology and management. Estuar. Coast. Shelf Sci. 94, 306–314.

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