swashed away? storm impacts on sandy beach macrofaunal communities

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Estuarine, Coastal and Shelf Science 94 (2011) 210e221

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Estuarine, Coastal and Shelf Science

journal homepage: www.elsevier .com/locate/ecss

Swashed away? Storm impacts on sandy beach macrofaunal communities

Linda Harris a,b,*, Ronel Nel a, Malcolm Smale a,c, David Schoeman a,b,d

aDepartment of Zoology, P.O. Box 77000, Nelson Mandela Metropolitan University, Port Elizabeth 6031, South Africab School of Biological and Conservation Sciences, University of KwaZulu-Natal, Private Bag X54001, Durban 4000, South Africac Port Elizabeth Museum at Bayworld, P.O. Box 13147, Humewood 6013, South Africad School of Environmental Science, University of Ulster, Ulster, Ireland

a r t i c l e i n f o

Article history:Received 16 August 2010Accepted 18 June 2011Available online 24 June 2011

Keywords:coastal erosionecosystem resiliencedynamic responseburrowing organismsclimatic changesbounding co-ordinates: (N): 34� 010 50.9900 S(S): 34� 020 25.6700 S(W): 25� 290 14.0500 E(E): 25� 300 21.9400 E

Regional index terms:South AfricaEastern CapePort ElizabethSardinia Bay

* Corresponding author. Department of Zoology, P.OMetropolitan University, Port Elizabeth 6031, South A

E-mail addresses: harris.linda.r@gmail.com (L. H(R. Nel),msmale@bayworld.co.za (M. Smale), d.schoema

0272-7714/$ e see front matter � 2011 Elsevier Ltd.doi:10.1016/j.ecss.2011.06.013

a b s t r a c t

Storms can have a large impact on sandy shores, with powerful waves eroding large volumes of sand offthe beach. Resulting damage to the physical environment has been well-studied but the ecologicalimplications of these natural phenomena are less known. Since climate change predictions suggest anincrease in storminess in the near future, understanding these ecological implications is vital if sandyshores are to be proactively managed for resilience. Here, we report on an opportunistic experiment thattests the a priori expectation that storms impact beach macrofaunal communities by modifying naturalpatterns of beach morphodynamics. Two sites at Sardinia Bay, South Africa, were sampled for macro-fauna and physical descriptors following standard sampling methods. This sampling took place five timesat three- to four-month intervals between April 2008 and August 2009. The second and last samplingevents were undertaken after unusually large storms, the first of which was sufficiently large to trans-form one site from a sandy beach into a mixed shore for the first time in living memory. A range ofunivariate (linear mixed-effects models) and multivariate (e.g. non-metric multidimensional scaling,PERMANOVA) methods were employed to describe trends in the time series, and to explore the likeli-hood of possible explanatory mechanisms. Macrofaunal communities at the dune-backed beach (Site 2)withstood the effects of the first storm but were altered significantly by the second storm. In contrast,macrofauna communities at Site 1, where the supralittoral had been anthropogenically modified so thatexchange of sediments with the beach was limited, were strongly affected by the first storm and showedlittle recovery over the study period. In line with predictions from ecological theory, beach morphody-namics was found to be a strong driver of temporal patterns in the macrofaunal community structure,with the storm events also identified as a significant factor, likely because of their direct effects on beachmorphodynamics. Our results also support those of other studies suggesting that developed shores aremore impacted by storms than are undeveloped shores. Whilst recognising we cannot generalise too farbeyond our limited study, our results contribute to the growing body of evidence that interactionsbetween sea-level rise, increasing storminess and the expansion of anthropogenic modifications to theshoreline will place functional beach ecosystems under severe pressure over the forthcoming decadesand we therefore encourage further, formal testing of these concepts.

� 2011 Elsevier Ltd. All rights reserved.

1. Introduction

Many natural processes and phenomena contribute to thedynamic nature of unconsolidated coasts, of which storms canhave the most wide-scale effects. Although storms are a naturaldriver of sandy shores, the magnitude of their contemporaryimpact is heightened because of the historical (and current)

. Box 77000, Nelson Mandelafrica.arris), Ronel.Nel@nmmu.ac.zan@ulster.ac.uk (D. Schoeman).

All rights reserved.

mismanagement of beach ecosystems (Schlacher et al., 2007;Dugan et al., 2010). Armoured shorelines and/or altered sedi-ment budgets both starve the coast of sand, compromising theresilience of beaches to pulse disturbance events. Under theseconditions, erosion and impacts to the biota following large stormsare magnified (Lucrezi et al., 2010). Superimposed on these trendsof coastal urbanisation is a predicted increased intensity andfrequency of extreme storms due to global climate change (IPCC,2007). Consequently, these natural phenomena (storms) are nowamong the greatest threats to beaches (Brown and McLachlan,2002; Schlacher et al., 2006, 2007; Defeo et al., 2009). Theunpredictable nature of extreme weather events in both space and

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time generally precludes a rigorous experimental design thatspecifically tests for their impacts. Despite this, given the pre-dicted heightened severity of storms in the near future, under-standing how these pulse disturbance events affect shorelineecology is of great importance.

The impact of large storms on the physical coastal environ-ment has been well-studied. Storm surges cause powerful wavesto disturb greater sediment depths than normal (Gómez-Pujolet al., 2011), resulting in severe erosion of the beach, dunes anddune vegetation and accelerated transport of the eroded sand instrong coastal currents (Jaffe et al., 1997; Miller, 1999). Theseeffects may be transient or persistent (Anderson et al., 2010), withbeaches consequently taking weeks (List et al., 2006), years(Leadon, 1999) or even decades (Zhang et al., 2002) to return totheir pre-storm morphology. In some instances, full recovery doesnot occur (Morton et al., 1994; Hill et al., 2004; Costas et al.,2005), in spite of management interventions such as beachnourishment (Anderson et al., 2010). Developed (urbanised)shores tend to sustain greater impacts from storms compared tounarmoured shores and recover much slower (Morton, 1988; Hillet al., 2004; Castelle et al., 2008; Lucrezi et al., 2010). Conse-quently, cases of sustained storm impacts to beaches tend to beassociated with developed coastlines. Thus it seems that sandybeach ecosystems with intact littoral-active zones are moreresilient to large storms than beaches with disrupted littoral-active zones.

While the impact from a single storm may extend for hundredsof kilometres along the shore away from the storm centre (Wanget al., 2006; Smith et al., 2007a,b), the localised effects can beextremely heterogeneous. This is because there are numerouscoastal features that can intensify or ameliorate the magnitude ofsite-specific impacts, e.g. by enhancing wave set-up (Battjes andStive, 1985; Héquette et al., 2001), or causing wave focussing,generation of rip currents, mega-cusps or erosion hotspots (Listet al., 2006 and references therein; Thornton et al., 2007; Smithet al., 2010). These coastal features include: the nature of thebackshore (Leatherman, 1979; Morton, 1988; Morton et al., 1994;Castelle et al., 2008; Lucrezi et al., 2010; Revell et al., in press);shoreline morphology (Héquette et al., 2001; Backstrom et al.,2008); nearshore bathymetry (Maa and Hobbs, 1998; Smith et al.,2010); coastal orientation (Storlazzi et al., 2000; Cooper et al.,2004; Hill et al., 2004; Wang et al., 2006; Backstrom et al., 2008);beach morphodynamic state (Backstrom et al., 2008; Qi et al.,2010); geological material (Benumof et al., 2000); depth of near-shore sediment layers (Backstrom et al., 2007), and wind strength,wind direction and wind-generated currents (Xu andWright, 1988;Cooper et al., 2004; Backstrom et al., 2008). In addition, the natureof the storm (intensity, duration and return period) can also affectthe extent of impact. While large storms indeed represent salientdisturbance events, numerous smaller storms may cause a cumu-lative impact of similar or possibly even greater magnitude(Moeller et al., 1993).

In contrast to our good understanding of storm impacts on thephysical aspects of sandy beaches, very little is known about theirecological implications. While previous studies have shown stormsto be unequivocally detrimental to large, mobile charismaticspecies, such as turtles (Ragotzkie, 1959; Kraemer and Bell, 1980;Martin, 1996; Ross, 2005), seabirds (White et al., 1976) and dunemice (Pries et al., 2009), the conclusions for similar studies onmacrofaunal communities seem to be less consistent. Sandy beachmacrofaunal communities are known to be dynamic even undercalm conditions (Saloman and Naughton, 1977; Jaramillo et al.,1996; Schoeman et al., 2000), and thus separating storm impactsfrom natural variability can be difficult (Hughes et al., 2009).Manypapers have concluded that storms have a minimal impact on

macrofauna, in spite of the changes to their habitat (Crocker, 1968;Saloman and Naughton, 1977; Alves and Pezzuto, 2009). However,there are also records of significant negative (e.g. Jaramillo et al.,1987) and positive (Hughes et al., 2009) impacts to macrofaunalcommunities following large-scale erosion events. Yet anotherstudy showed that, following the passage of two cold fronts in lessthan a week, the first storm had a significant impact on the mac-rofaunal community, but the second disturbance was less marked(Gallucci and Netto, 2004). This seems to suggest that stormimpacts on macrofaunal communities is a complex problem todisentangle, further complicated by our limited understanding ofkey community ecological processes on beaches, such as (meta)population connectivity.

However, if beach ecosystems are to be managed for resilienceagainst a background of accelerated climate change, understandingthe impacts of storms on macrofaunal communities is important.The aim of this paper is therefore to use an opportunistic fieldexperiment at two sites to investigate whether storms are amongthe environmental variables that structure macrofaunal commu-nities. Specifically we sought to identify which environmentalvariables might be significant drivers of the macrofaunal commu-nities during the passage of a storm and to identify trends in beachmacrofaunal community structure in space and through time,thereby assessing the extent of potential ecological impactsresulting from storms.

Our a priori prediction was that the macrofaunal communitiesat the two sites would be different initially, as reflected by theirmorphodynamic state, and that the trends in these communitieswould differ through time: the dune-backed site should becomparatively more resilient to the storm impacts and thus havea more stable community over time. Further, we hypothesizedthat the underlying mechanism for ecological responses tostorms is that the macrofauna are pelagic-like within the sedi-ment body, responding to the three-dimensional, dynamicnature of the medium (e.g. Ellers, 1995a,b). During periods ofacute beach erosion, macrofauna seem to be physically movedwith the sand into the surf zone. The macrofauna (being active)should return to the beach quicker than the sand (that ispassive), but these animals will probably reorganise themselvesalongshore according to the availability of sand and the range ofbeach morphodynamic types available to them (as per well-established beach ecological theory). We recognise that wecannot test these hypotheses unequivocally, especially byexamining only two local sites over short temporal scales.However, given that processes like storms, which act at suchlarge scales (in both space and in time), are not amenable tomanipulative experimental designs, it is appropriate to advanceunderstanding by matching field observations to a prioriconceptual predictions and hypotheses.

2. Materials and methods

2.1. Study site

This study took place in the Sardinia Bay Marine Protected Areain Port Elizabeth, South Africa (Fig. 1). Site 1 (34� 020 5.1600 S; 25� 300

7.9800 E) is at the edge of the beach, bordered by a rocky headland. Itis backed by a car park and a building that is used as a surf life-saving clubhouse, and is also the only access point onto thispopular beach. Site 2 (34� 010 59.6100 S; 25� 290 31.8000 E) is 1 kmwest of Site 1, and is partially protected by small patches of near-shore rocky reef. On the landward side, Site 2 is backed by mobiledunes that are formed fromwind-blown sand. The dominant swelland wind direction is south west.

Fig. 1. (a) Insert of South Africa, with the small black box showing the location of Sardinia Bay. (b) The study sites and (c) salient site features at Sardinia Bay are highlighted onSPOT5 satellite imagery. Note: the prevailing swell direction is south west.

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2.2. Data collection

The two sites were sampled for macrofauna (on 4 April 2008 forSite 1, and 7 May 2008 for Site 2), broadly following standard beachsampling methods (Schlacher et al., 2008). At each site we tooka 0.1 m2 core of sand to a depth of 0.3 m at 10 equidistant across-shore levels, along three shore-normal transects arranged at 10-m intervals. The samples were sieved through a 1-mm mesh toreduce the volume of sand in the sample. Macrofauna wereelutriated out of the samples by agitating the retained sand ina bucket of water, and pouring off the supernatant containingsuspended macrofauna through a sieve. This was repeateda minimum of five times per sample or until two successiveelutriations yielded no fauna (a pilot study indicated that thisprocess successfully removes all macrofauna more than 98% of thetime). These animals were preserved in 10% formalin and identifiedin the laboratory. Dry, shell-free biomass was determined by oven-drying the animals at 60 �C for 24 h and then weighing them. Inmost cases, the macrofauna were too small to be weighed indi-vidually. Biomass was thus determined by pooling all individuals ofthe same species, per site, per session. Species abundance wasconverted into number of individuals per running metre (mean ofthe three transects � standard error), and biomass values wereconverted into grams per running metre.

The standard suite of physical variables on the beach (Schlacheret al., 2008) was also measured as follows. Beach slope was deter-mined by across-shore profiling of the central transect. Sandsamples were taken at the high-, mid- and low shore, washedthrough a 63-mm mesh sieve, oven-dried at 60 �C for 24 h andsieved through a half-phi stack of sieves. The fraction of sandretained in each of the sieves was recorded, and the data wereanalysed statistically using GRADISTAT Version 4.0 (Blott and Pye,

2001) to determine mean sand grain size, sorting and skewnessusing the Geometric (modified) Folk and Ward graphical measures(Folk andWard, 1957). Wave height was calculated as an average of10 visual estimates (by the same person throughout the study toensure consistency, and ground-truthed to ensure accuracy) of thesize of waves between the trough and crest. The period of a singlewave and swash were each calculated by averaging 10 replicates ofthe time taken for fivewaves/swashes to pass a set point in the surf/swash zone. All wave climate data were collected as the spring lowtide began to turn. The physical variables were used to calculate thefollowing indices that describe beachmorphodynamic type: Dean’sparameter (U, Wright and Short, 1984); relative tide range (RTR;Masselink and Short, 1993); beach state index (BSI; Defeo andMcLachlan, 2005); beach index (BI; McLachlan and Dorvlo, 2005),and beach deposit index (BDI; Soares, 2003).

On 1 September 2008, a big-wave pulse resulting from a largestorm at sea hit the South African south coast. Even the mostsheltered bays experienced very large waves that significantlyeroded the beaches and caused structural damage to touristamenities. In the city of Port Elizabeth, waves broke over roads andrailway lines behind beaches, to the point that the normally well-protected N2 national highway was temporarily closed. Thisstorm surge also impacted local estuarine systems (Riddin andAdams, 2010). The two Sardinia Bay sites were re-sampled on 17October 2008. The delay to sampling after the storm was becausethis experiment was conceived retrospectively, and we also had towait for a spring tide. We continued monitoring both sites in 2009to account for natural variation in the macrofaunal communities,sampling on 29 January and again on 27 April. A second stormfollowed on the evening of 16 June 2009, when torrential rainflooded roads and caused the Port Elizabeth airport to be tempo-rarily closed. We sampled on the first spring tide following this

L. Harris et al. / Estuarine, Coastal and Shelf Science 94 (2011) 210e221 213

storm (25 June 2009). These five consecutive sampling events arehereafter referred to as sessions. The two sessions associated withstorms are called the storm sessions, and the other three arereferred to as the calm sessions. It must also be noted that thesetwo storm events both resulted from strong mid-latitude cyclones,which are typical meteorological phenomena along the SouthAfrican south coast. Evidently, both storms approached from thesame direction, with 20 knot and 30 knot south westerly windsassociated with the respective storms.

2.3. Data analysis

In order to identify the factors that might structure macrofaunalcommunities through time, we evaluated the environmental vari-ables against an ordination of the biotic variables. First, a series ofnon-metric multidimensional scaling (nMDS) plots were con-structed from resemblance matrices of BrayeCurtis similarities in Rversion 2.12.0 (R Development Core Team, 2010) using the met-aMDS function in the R package vegan (Oksanen et al., 2010). Theoptimal (lowest-stress) two-dimensional ordination was selectedafter repeating the nMDS routine on untransformed, square- andfourth-root transformed data, using both the full set of species anda reduced set that excluded the rare species (those occurring infewer than 10% of the transects). The envfit function in the Rpackage vegan (Oksanen et al., 2010) was then used to fit envi-ronmental gradients (abiotic vectors and factors) across the surfaceof this optimal nMDS plot. The abiotic variables used include thestandard physical descriptors of sandy beaches (Table 1, excludingtide range because it was identical for all samples), as well as thefactors storm (binary: storm vs calm sessions), site, and session.Interpretation of results was assisted by assessing potential colin-earity among environmental predictors using simple scatter or box-plots, as appropriate. For comparison, the bioenv function in the Rpackage vegan (Oksanen et al., 2010) was then used to matchordinations of subsets of the same environmental variables againstthe biotic ordination (in this case, though, factors in the envfitanalysis had to be considered as continuous variables).

We used linear mixed-effects models (package lme4 (Bates et al.,2011) in R 2.12.0; R Development Core Team, 2010) to test hypoth-eses regarding changes in univariate measures of communitystructure (IST and species richness) through time and space

Table 1The physical characteristics of Site 1 and Site 2 during the calm (clear) and storm (grey) saC1 and C2 ¼ first (January 2009) and second (April 2009) calm samples, respectively; an

Physical variablesa Site 1

C0 S1 C1 C2

Width (m) 84.60 85.50 123.00 95.00Elevation (m) 2.10 3.72 2.92 2.851/Slope 40.30 23.00 42.10 33.30Hb (cm) 70.50 100.00 195.70 97.20Tw (s) 7.00 12.00 16.48 12.60Ts (s) 18.00 10.00 11.90 15.10Mz (mm) 256.05 246.06 272.43 269.40Sorting 1.33 1.29 1.34 1.27Skewness �0.17 �0.02 0.03 �0.09Kurtosis 0.99 0.87 0.92 0.97Tide (m) 1.70 1.70 1.70 1.70Ws (cm s�1) 3.13 2.96 3.40 3.35RTR 2.41 1.70 0.87 1.75U 3.22 2.82 3.49 2.30BI 2.27 2.04 2.28 2.18BSI 5.06 4.42 5.48 3.62BDI 162.31 96.39 159.36 127.47

a Width ¼ Beach Intertidal Width; Elevation ¼ Beach Intertidal Elevation; Hb ¼ BreaTide¼Mean Tide Range;Ws¼ Sediment Fall Velocity; RTR¼ Relative Tide Range;U¼ Dea

following the methods of Pinheiro and Bates (2000) and Zuur et al.(2007). Here, session was considered to be a fixed factor and sitewas considered random (i.e., we considered sites in the same senseas we would have treated subjects in a traditional repeated-measures design, thus controlling for potential pseudoreplication).Corresponding tests of multivariate community structure wereconducted using Monte Carlo PERMANOVA (PRIMER 6.1.13 withPERMANOVAþ 1.0.3, Clarke and Warwick, 2001).

3. Results

3.1. General descriptions and observations

3.1.1. Description of the physical characteristics of the sitesThe two sites sampled in this study are fairly wide (by microtidal

standards) and flat intermediate beaches, generally with smallwaves (Table 1). The fine- tomedium-grained sand iswell sorted (sG:1.27e1.41) to very well sorted (sG: <1.27), symmetrical (SkG: �0.1to þ0.1) to fine-skewed (SkG: �0.1 to �0.3), and with kurtosisranging from platykurtic (KG: 0.67e0.90) to leptokurtic (KG: 1.11e1.5)(Folk and Ward, 1957; Blott and Pye, 2001). Field observations sug-gested that the first storm impacted Site 1more intensely than Site 2,because the former site wasmore greatly eroded than the latter. Thissite (1), which had been a sandy beach for the past few decades, waseroded in parts to bedrock by the first storm. The transformation ofSite 1 from a sandy beach into a mixed shore has persisted and,almost two years later, the beach had still not recovered to its pre-storm morphology and condition. In addition, there was a lot ofrubble scattered on the high shore at this site following the firststorm because of the damage to the clubhouse and concrete seawallsthat functioned to stabilize sections of the backshore at the accesspoint. The second storm appeared to impact Site 1 to a lesser extent,although more bedrock was exposed following the storm than waspreviously visible. Impacts to the beach at Site 2 were less marked,with some erosion and scarping of the dune toe, which appeared tobe greater following the second storm compared to the first. In spiteof the variability in the physical descriptors through time, bothbeaches remained in an intermediate morphodynamic state (sensuWright and Short, 1983) throughout the sampling period. Note,however, that beaches were steeper following storms, largely asa consequence of a greater difference in beach elevation (height

mples. C0 ¼ initial calm sample (April 2008); S1 ¼ first storm sample (October 2008);d S2 ¼ second storm sample (July 2009).

Site 2

S2 C0 S1 C1 C2 S2

95.00 81.90 83.70 83.50 72.00 58.503.47 4.23 4.60 3.20 2.44 3.7027.40 19.40 18.20 26.10 29.50 15.80243.00 221.00 108.50 262.00 104.50 212.0015.03 15.00 15.00 16.63 25.25 15.8317.93 5.00 13.00 16.03 8.95 23.50278.64 256.32 253.16 253.10 241.27 252.111.27 1.26 1.27 1.26 1.29 1.25�0.08 �0.13 �0.11 �0.05 0.04 �0.011.19 0.78 0.81 0.75 0.92 0.741.70 1.70 1.70 1.70 1.70 1.703.51 3.13 3.08 3.08 2.88 3.060.70 0.77 1.57 0.65 1.63 0.804.61 4.71 2.35 5.12 1.44 4.382.09 2.01 1.98 2.14 2.20 1.927.24 8.33 4.16 9.07 2.55 7.75101.41 78.05 74.14 106.34 126.09 64.63

ker Height; Tw ¼ Wave Period; Ts ¼ Swash Period; Mz ¼ Mean Sand Grain Size;n’s Parameter;BI¼ Beach Index; BSI¼ Beach State Index; BDI¼ Beach Deposit Index.

Table 2Macrofaunal community descriptors and diversity. Abundance (per site, session and species) values are presented as the mean number of individuals m�1 of the three transects, � standard error. Biomass (in grams per runningmetre) and species richness (excluding insects) are given per site, per session. If a species was absent from the site, it is indicated with a dash (�). My ¼ Mysidacea, Mo ¼ Mollusca, In ¼ Insecta. C0 ¼ initial calm session (April2008); S1 ¼ first storm session (October 2008); C1 and C2 ¼ first (January 2009) and second (April 2009) calm sessions, respectively; S2 ¼ second storm session (July 2009). Note: storm sessions are highlighted in grey.

Species Site 1 Site 2

C0 S1 C1 C2 S2 C0 S1 C1 C2 S2

Overall abundance (individuals m�1) 582.8 � 52.3 218.5 � 50.3 464.6 � 36.2 316.6 � 79.7 116.1 � 21.1 400.4 � 55.4 418.4 � 112.8 436.0 � 75.9 464.0 � 40.0 253.5 � 39.0Biomass (g m�1) 10.1 9.7 12.3 25.6 20.1 41.6 28.2 11.1 14.6 8.5Richness (excluding insects) 7 8 9 12 5 8 10 6 7 7Polychaeta Armandia sp. 197.4 � 43.1 9.5 � 9.5 e e e e e e e e

Glycera sp. e e e 10.6 � 10.6 e e e e e e

Lumbrineris sp. e e e 10.6 � 10.6 e e e e e e

Neridae e e 13.7 � 13.7 e e e e e e e

Polychaete sp. A 28.2 � 16.3 e e e e e e e e e

Polychaete sp. C e e 13.7 � 13.7 e e e e e e e

Polychaete sp. E e e e 10.6 � 10.6 e e e e e e

Polychaete sp. F e e e 10.6 � 10.6 e e e e e e

Scolelepis squamata e 28.5 � 28.5 27.3 � 13.7 31.7 � 18.3 e e 9.3 � 9.3 e e 13.0 � 6.5Sigalion capense e e 27.3 � 13.7 10.6 � 10.6 e e e e e e

Amphipoda Amphipoda sp. A e e e e e e e e 16.0 � 8.0 e

Amphipoda sp. B e e e e e 9.1 � 9.1 e e e e

Hyale sp. e e e e e e e e 8.0 � 8.0 e

Talitridae e e e e e e 9.3 � 9.3 e e e

Talorchestia capensis e e e e e 18.2 � 18.2 e e e e

Urothoe coxalis 9.4 � 9.4 e e e e 9.1 � 9.1 111.6 � 83.7 e e e

Urothoe serrulidactylus e 19.0 � 9.5 41.0 � 23.7 42.2 � 27.9 42.2 � 10.6 e 27.9 � 27.9 37.1 � 9.3 e 32.5 � 23.4Urothoe sp. e 9.5 � 9.5 e e e e 9.3 � 9.3 e e 13.0 � 6.5

Isopoda Eurydice kensleyi 272.6 � 47.0 95.0 � 9.5 205.0 � 41.0 63.3 � 18.3 190.0 � 36.6 200.2 � 18.2 167.4 � 85.2 222.7 � 57.9 232.0 � 32.0 91.0 � 17.2Excirolana latipes 18.8 � 9.4 19.0 � 9.5 e 21.1 � 10.6 e 36.4 � 9.1 9.3 � 9.3 27.8 � 16.1 24.0 � 13.9 e

Excirolana natalensis e e 13.7 � 13.7 21.1 � 10.6 10.6 � 10.6 27.3 � 15.8 9.3 � 9.3 55.7 � 27.8 72.0 � 36.7 84.5 � 6.5Exosphaeroma sp. e 9.5 � 9.5 e e e e e e e e

My Gastrosaccus psammodytes 28.2 � 16.3 e 109.3 � 49.3 21.1 � 10.6 21.1 � 21.1 36.4 � 24.1 9.3 � 9.3 55.7 � 16.1 32.0 � 8.0 6.5 � 6.5Mo Donax serra e 9.5 � 9.5 e e e e e e e e

Bullia rhodostoma 9.4 � 9.4 e 13.7 � 13.7 31.7 � 0.0 31.7 � 18.3 27.3 � 15.8 46.5 � 24.6 27.8 � 27.8 16.0 � 16.0 6.5 � 6.5In Coleoptera e e e 10.6 � 10.6 e 9.1 � 9.1 9.3 � 9.3 e e e

Diptera 9.4 � 9.4 9.5 � 9.5 e 21.1 � 10.6 10.6 � 10.6 e e 9.3 � 9.3 64 � 21.2 6.5 � 6.5Insect larva 9.4 � 9.4 9.5 � 9.5 e e e 27.3 � 27.3 e e e e

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Fig. 2. Graphic representation of the linear mixed-effects modelling. Plots are ofmeans � standard error, with Site 1 shown in white and Site 2 in black.

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between the spring low water mark and the driftline) during stormsessions compared to calm sessions.

3.1.2. Description of the macrofaunal communitiesThe twositeswereeach sampledfive times,with species richness

per site ranging between 6e14 at Site 1 and 7e10 at Site 2 (Table 2).The isopods, mysids and molluscs were the most consistentcomponents in the communities at both sites. The polychaetes andamphipods were the two most diverse taxa represented at SardiniaBay, but Site 1 supportedmore polychaetes (10 species, compared to1 species at Site 2), and Site 2 supportedmore amphipods (8 species,compared to3 species at Site 1).Macrofaunal abundanceper sitewaslowest at Site1during the stormsessions (655.5 individualsm�2 and918.3 individuals m�1), and highest in the initial calm session(1748.4 individuals m�1). Thus, the total macrofaunal abundance atSite 1 was reduced to almost a third following the first storm andtherehas beenonlyaminor recovery since. Thebiomass remained atfairly consistent (10e12 g m�1) during the first three sessions, butapproximately doubled thereafter. This is because large-bodiedBullia were present in the latter two sessions. The macrofaunalabundance at Site 2 remained relatively constant throughout thestudyperiod (1200e1400 individualsm�1), peakingduring the third(C2) calm session (1392.0 individualsm�1) and decreasingmarkedlyafter the second storm (to 760.5 individuals m�1). Biomass at Site 2was also greatly affected by the occasional presence of large-bodiedBullia in the samples. On the basis of the very large disparity in bodysize/mass among individuals of different species in themacrofaunalcommunities at Sardinia Bay (such that much greater biomassreflectsonly thepresenceofbivalvesand/orgastropods),wedecidedto exclude biomass from the analyses.

3.2. Changes in macrofaunal community structure through time

3.2.1. Univariate patterns in macrofaunal community structureAlthough linear mixed-effects models of species richness found

no significant trends, corresponding models of macrofaunal abun-dance (Table 3, Fig. 2) formalise the trends described above.Specifically, they show that the community at Site 1 was

Table 3Simultaneous tests for the general linear hypotheses associated with linear mixed-effects models of species richness and macrofaunal abundance modelled as a func-tion of Session (fixed) and Site (random). Models fitted in R using lme4, and outputswere provided via cftest from the package multcomp (Hothorn et al., 2011). Esti-mates provided here were used to plot Fig. 2.

Hypothesis Estimate Std error z-value Pr(>jzj)Species richnessIntercept ¼ 0 5.33 1.28 4.171 3.03E-05Site 2 ¼ 0 0.33 1.81 0.184 0.8538Session 2 ¼ 0 �0.67 1.46 �0.456 0.6481Session 3 ¼ 0 �0.00 1.46 0.000 1.0000Session 4 ¼ 0 2.67 1.46 1.826 0.0679Session 5 ¼ 0 �1.67 1.46 �1.141 0.2538Site 2:session 2 ¼ 0 0.00 2.07 0.000 1.0000Site 2:session 3 ¼ 0 �0.67 2.07 �0.323 0.7469Site 2:session 4 ¼ 0 �2.67 2.07 �1.291 0.1967Site 2:session 5 ¼ 0 1.00 2.07 0.484 0.6283Macrofaunal abundance (IST)Intercept ¼ 0 582.80 76.35 7.633 2.29E-14Site 2 ¼ 0 �182.40 107.97 �1.689 0.0912Session 2 ¼ 0 �364.30 87.22 �4.177 2.96E-05Session 3 ¼ 0 �118.13 87.22 �1.354 0.1756Session 4 ¼ 0 �266.13 87.22 �3.051 0.0023Session 5 ¼ 0 �276.69 87.22 �3.172 0.0015Site 2:session 2 ¼ 0 382.40 123.35 3.100 0.0019Site 2:session 3 ¼ 0 153.79 123.35 1.247 0.2125Site 2:session 4 ¼ 0 329.73 123.35 2.673 0.0075Site 2:session 5 ¼ 0 129.79 123.35 1.052 0.2927

considerably more variable in time than was Site 2, which wasstable until Session 5, when macrofauna abundance was greatlyreduced relative to previous sessions. By contrast, macrofaunalabundance at Site 1 started comparatively high, declining sharplyafter the first storm before exhibiting an unstable recovery. Thispattern is consistent with the hypothesis that Site 2, with its intactdunes, was more resilient to the first storm. However, the sharpdecline in macrofaunal abundance after the second storm suggeststhat this site may have been more vulnerable to cumulativeimpacts.

3.2.2. Multivariate patterns in macrofaunal community structureInitial ordinations using both all and a subset of the data (the

latter excluding species that occurred in 10% of the transects orfewer, leaving 14 species in total) and various transformationsindicated that the lowest stress value resulted from the full,untransformed data set. We therefore selected this configurationfor all further analyses. The nMDS ordination of this full, untrans-formed data set (Fig. 3) reflects the results of the linear, mixed-effects modelling in that samples from Site 2 are (with fewexceptions) clustered fairly tightly (on the second ordination axis)and arranged coherently according to the sequence of sessions. Bycontrast, samples from Site 1 are spread along both ordination axesand are not arranged in a strict sequence. Interestingly, most of thesamples taken following the storms are ordinated to the bottomright of the plot, whereas samples from calm periods are ordinatedtoward the top left, suggesting a strong influence of storms on thecommunity structure of the beaches.

The Monte Carlo PERMANOVA using Site as a random effect andSession as a fixed effect reveals a significant Site � Session inter-action term, implying that the temporal trends in the macrofaunalcommunities differ between the two Sites (Table 4). Post-hoc testson this interaction term confirm the temporal stability of thecommunity structure at Site 2, with significant differences incommunity structure detected only between Session 5 andSessions 1 and 4. At Site 1, the community structure was substan-tially more variable through time, with significant differences incommunity structure detected between Session 1 and all otherSessions; the community structure also differed between Sessions2 and 3. Together, these results suggest that the macrofauna

Fig. 3. nMDS biplot of BrayeCurtis similarity matrix based on the full, untransformedspecies matrix. Samples from Site 1 plotted in white and from Site 2 in grey. Circlesrepresent samples from calm conditions, squares represent samples after storms.Numeralswithin the symbols reflect session sequence. Stress for this ordination is 0.179.

Table 4Results of a Monte Carlo PERMANOVA with site as a random effect and session asa fixed effect.

Source df SS MS Pseudo-F P(perm) Perms P(MC)

Site 1 2876.8 2876.8 2.5546 0.017 998 0.036Session 4 11908.0 2977.1 0.96793 0.501 965 0.540Site � session 4 12303.0 3075.8 2.7313 0.002 998 0.001Residuals 20 22523.0 1126.1Total 29 49611.0

Table 5Results of the envfit procedure, indicating which of the abiotic variables (vectors and factoof the biotic data.

Abiotic variables NMDS1 NM

Vectors BI �0.58301 �0Sorting �0.77672 �0BDI �0.64593 �0Beach slope �0.60399 �0Elevation 0.557353 0Wave period 0.277418 �0Skewness 0.999903 0Swash period 0.96886 �0RTR �0.94187 0Kurtosis �0.91529 �0Beach width �0.68806 �0Mean sandgrain size

0.434839 �0

Settling velocity 0.434847 �0Wave height 0.595013 �0Dean’s parameter �0.48307 0BSI �0.38296 0

Factors Storm 0 (calm) �0.1196 �0Storm 1 (storm) 0.1794 0Session 1 �0.4704 �0Session 2 0.084 0Session 3 �0.0707 �0Session 4 0.1823 �0Session 5 0.2747 0Site 1 �0.042 0Site 2 0.042 �0

a p < 0.05. P values based on 9999 permutations.

L. Harris et al. / Estuarine, Coastal and Shelf Science 94 (2011) 210e221216

community at Site 1 changed after the first storm and did not returnto its initial state at any point during the study period, whereas thatat Site 2 was fairly stable until after the second storm.

3.2.3. Fitting individual environmental variables to the bioticordination

Of all the abiotic variables considered in the envfit analysis,Sorting, BI and BDI are identified as significant continuous predic-tors of the biotic ordination, and Storm and Session are identified assignificant categorical predictors (at a ¼ 0.05) (Table 5). Since bothStorm and Session are selected, it is likely that the latter is partlyreflecting the storm impact on the communities. The bioenv anal-ysis demonstrates that a Euclidean-distance ordination of Elevationand RTR correlates well with the BrayeCurtis biotic ordination, butthat the addition of Storm and Session improves the correlationslightly (Table 6). This suggests that the biotic ordination is drivenprimarily by the physical characteristics of the beaches, but thatthese probably differ to some degree by Storm and Sessione in thatorder.

When inspecting the relationships among variables identifiedby bioenv and envfit as potential significant drivers of the bioticordination, it is clear that BI and BDI are strongly collinear, and thatElevation and Sorting are also collinear with BI and BDI, althoughless strongly related to each other (Fig. 4). Relationships among BI,BDI and Elevation are expected, since these all reflect aspects ofbeach morphodynamics. Sorting is not usually given much atten-tion in sandy beach ecology, but it is interesting to note that thevalues track the patterns in macrofaunal abundance through time(compare Table 1 with Fig. 2). The trend suggests that the bettersorted the sand, the fewer the macrofauna, although this is notnecessarily indicative of a cause and effect relationship. The a priorihypothesis suggests that storms erode both sediment and macro-fauna off the beach, thus both sorting and faunal abundance mayrather be showing matching responses to storm impacts. Bycontrast, RTR is poorly correlated with all other variables identified.Since the tide range was equal for all samples, RTR in this case is

rs, abbreviations as per Table 1) are significantly correlated with an nMDS ordination

DS2 r2 Pr(>r) Significancea

.81247 0.2458 0.0229 *

.62985 0.212 0.0405 *

.7634 0.2048 0.0427 *

.79699 0.1784 0.0715

.830276 0.1335 0.1448

.96075 0.0992 0.2515

.013901 0.0607 0.4320

.24761 0.0581 0.4461

.33598 0.0476 0.5213

.4028 0.03 0.6620

.72565 0.0252 0.7102

.90051 0.0191 0.7678

.9005 0.0191 0.7679

.80372 0.0166 0.8021

.87558 0.0028 0.9639

.923765 0.0003 0.9965

.108 0.1088 0.0403 *

.1621

.0834 0.2563 0.0482 *

.2773

.1503

.0904

.0468

.0832 0.0242 0.5042

.0832

Table 6Results of the bioenv test, showing which groups of abiotic variables are correlatedwith the nMDS ordination of the macrofaunal communities.

Abiotic variables No.variables

Correlation(r2)

Elevation 1 0.1985Elevation þ RTR 2 0.2039Elevation þ RTR þ Storm 3 0.1897Elevation þ RTR þ Storm þ Session 4 0.2060Elevation þ RTR þ Storm þ Session þ Tw 5 0.1794Elevation þ RTR þ Storm þ Session þ BI þ Slope 6 0.1760

L. Harris et al. / Estuarine, Coastal and Shelf Science 94 (2011) 210e221 217

essentially measuring trends in wave height. While wave heightplays a role in beach morphodynamics, it is highly variable on timescales much shorter than the other variables. This may explain whythe relationships between RTR and the other variables are weak,particularly since the storm surge would have likely dissipated bythe time the storm sampling sessions took place. Nevertheless,given the close relationships among the above variables, it is verylikely that they are all selected by the bioenv and envfit analysesbecause they reflect the same ecological process. It is also verylikely that this is the process that is structuring the macrofaunalcommunities.

Evidently there are strong patterns in most of the environ-mental variables by Site and Storm. Site 2 is more reflective than

Fig. 4. Matrix of analyses exploring the relationships among the significant variables identi(abbreviations according to Table 1). Below the diagonal are scatter plots with fitted LOESS smwith RTR). Above the diagonal, the corresponding Pearson correlation coefficient (r) and it

Site 1 (lower BI and BDI scores), and storms made both sites morereflective compared to their beach morphodynamic state duringcalm conditions; this is reflected by Sorting and also by inversepatterns in Elevation (Fig. 5). These observations correspond wellwith the patterns in the biotic ordination (Fig. 3). It thus appearsthat storms impact beach morphodynamic state, making it morereflective, and that this is the process (identified in the envfitanalysis above) that is in turn structuring the macrofaunalcommunities. The only explanatory variable that does not fit thispattern is RTR. As mentioned above, RTR in this case is essentiallyreflecting wave height trends, which are incredibly variable on veryshort time scales. Therefore, it is not surprising that RTR does notshow a difference between calm and storm sessions, as samplingoccurred some time after the storms and likely after the stormsurge had passed. Nonetheless, RTR (wave height) is likelycontributing to the changes in beach morphodynamic state as well,since the larger waves during storms are what drives the erosionand thus drives the trends in Elevation and Sorting.

4. Discussion

4.1. Storm impacts to the macrofauna at Sardinia Bay

Broadly speaking, the sandy beach macrofauna at Sardinia Baypersisted through both storms, with neither community

fied by envfit and bioenv routines. Elements of the diagonal indicate selected variablesoothers to emphasise non-linearities, where they exist (they are associated exclusively

s significance (p) are provided.

1.26

1.28

1.30

1.32

1.34

So

rtin

g

Site 1 Calm Site 2 Calm Site 1 Storm Site 2 Storm

1.952.002.052.102.152.202.25

BI

Site 1 Calm Site 2 Calm Site 1 Storm Site 2 Storm

80100120140160

BD

I

Site 1 Calm Site 2 Calm Site 1 Storm Site 2 Storm

2.02.53.03.54.04.5

Elevatio

n (m

)

Site 1 Calm Site 2 Calm Site 1 Storm Site 2 Storm

1.0

1.5

2.0

RT

R

Site 1 Calm Site 2 Calm Site 1 Storm Site 2 Storm

Fig. 5. Box-plots by Site and Storm of the significant continuous predictors (sorting, BI and BDI), revealed by the envfit analysis and Elevation and RTR identified by bioenv.

L. Harris et al. / Estuarine, Coastal and Shelf Science 94 (2011) 210e221218

irreparably damaged. However, the communities at each siteresponded very differently to the two storms. Site 1 was stronglyimpacted by the first storm, which caused enough erosion toexpose bedrock, and transform the beach into a mixed shore for theremainder of the sampling period, and beyond. Although therewere pulses of sand that accreted on (and eroded back off) theshore, with associated temporary recovery in the macrofaunalcommunity, the habitat itself was unstable. The second stormconsequently had a lesser impact on the already disrupted, desta-bilized community. Site 2, in comparison, proved to be resilient inthe face of the first storm and the macrofaunal communityappeared reasonably stable throughout the sampling period.However, it did not withstand the impact from a second severestorm and the macrofauna were strongly affected after this eventtoward the end of the sampling period. There are several possiblefactors that may have contributed to the contrasting responses bymacrofauna at the two sites; we discuss these below in the light ofavailable evidence.

The greatest difference between these two sites is the nature ofthe coast behind the beach. Site 1 has an interrupted littoral-active

zone because concrete retaining walls, a car park and a surf life-saving clubhouse have been constructed in place of the primarydune, whereas Site 2 is backed by relatively undisturbed sanddunes. Our result that dune-backed Site 2 had a more stable mac-rofaunal community and was more resilient to storm impacts (atleast initially) thus supports the findings of similar studies(Leatherman, 1979; Morton, 1988; Morton et al., 1994; Castelleet al., 2008; Revell et al. in press), including those that havespecifically tested storm impacts to coasts with different backshoretypes (Lucrezi et al., 2010). Our results also support the propositionthat coastal morphology can play a role in the varied impact ofstorms to beaches (Héquette et al., 2001; Backstrom et al., 2008).Site 1 is adjacent to a rocky headland, and consequently would havebeen subjected to terminal scour that Site 2 would not haveexperienced. Similarly, nearshore bathymetry may have playeda role (as suggested by Maa and Hobbs, 1998; and Smith et al.,2010), because Site 2 is protected to some degree by nearshorerocky reefs, and Site 1 is not. Not only would the nearshore reefshave dissipated the wave-energy reaching the shore at Site 2, but itmay also have trapped the eroded sediment and prevented it from

L. Harris et al. / Estuarine, Coastal and Shelf Science 94 (2011) 210e221 219

being washed too far offshore, thus facilitating its faster return tothe beach after the storm. Finally, the two sites are of a slightlydifferent coastal orientation, which may have affected the intensityof the storm impact (Storlazzi et al., 2000; Cooper et al., 2004; Hillet al., 2004; Wang et al., 2006; Backstrom et al., 2008). Irrespectiveof these differences between sites, and the resultant effects onstorm impacts, our data suggest that Site 1 was more prone tochronic storm erosion thanwas Site 2. Although we cannot link thevulnerability of Site 1 to anthropogenic modification of the littoral-active zone specifically, it is likely that the removal and/or stabili-sation of dunes that could otherwise have released sand onto thebeach in response to erosion of this Site were at least contributoryfactors. By comparison Site 2, with its intact littoral-active zone,was able to re-establish a stable beach face reasonably quickly afterthe first storm.

4.2. Broader ecological implications of storms for beaches

Despite the limitations of this study caused by the unpredict-able and large-scale nature of storms, the following explanation ofstorm impacts to beaches appears to be supported by findings ofthis study and beach ecological theory in the literature. Macro-faunal communities are variable in space and time (Saloman andNaughton, 1977; Jaramillo et al., 1996; Schoeman et al., 2000), andare strongly structured by their physical environment, sensu theautecological (Noy-Meir, 1979) and swash exclusion (McLachlanet al., 1993) hypotheses, which are both well supported in theliterature (e.g. Defeo andMcLachlan, 2005; McLachlan and Dorvlo,2005, and many others). Storms are among the physical factorsthat structure sandy beaches and their influence on local sedi-ment dynamics (slope, depth and grain size) functions to altersandy beach morphodynamics, as demonstrated by our results.Macrofauna subsequently respond to these changes in beachmorphodynamics by rearranging themselves along the shoreaccording to what is available to them. Further, there are localisedfeatures along the shore that serve either to accentuate orameliorate storm impacts, thereby conferring greater or lesserimpacts to macrofaunal communities, respectively. Since thesephysical impacts can be transient or persistent, impacts to mac-rofaunal communities may be correspondingly transient (quickrecovery) or persistent (very slow recovery, if at all). This may alsoexplain the inertia of responses in biological communities (asobserved at Site 1 in this study) where physical impacts carrymomentum, particularly when beach equilibrium profiles aredisrupted sufficiently for erosion to persist even months after thestorm (e.g. Smith et al., 2010). Finally, it appears that developedshorelines are impacted the most and recover the slowest becausethe littoral-active zone has been compromised, supported by thefindings of Lucrezi et al. (2010). If this is indeed true, there aremajor implications for sandy beaches when superimposing pre-dicted climate change impacts on burgeoning coastal urbanisationtrends; these over and above the already established threat ofcoastal squeeze (Schlacher et al., 2006, 2007, 2008; Dugan et al.,2008; Defeo et al., 2009).

Of all resident biota on sandy shores, macrofauna are the largest,and have the greatest relative control of their position in theirenvironment. Consequently, they are likely to be the least vulner-able in the ecosystem. If impacts from storms on this faunalcomponent can be detected, it seems likely that interstitialcommunities must be more susceptible to impacts, particularly byre-suspension from turbulence. Given that depth of disturbanceduring storms has been recorded to be in the region of 24 cm(Gómez-Pujol et al., 2011), and it is the top 50 cm of sand that is themost biologically active (McLachlan et al., 1981), this could implythat half of the interstitial communities, and concomitant

ecosystem services they provide (e.g. nutrient remineralisation),are vulnerable to disturbance from storms.

While storms can have strong negative impacts on sandy beachecosystems, there may be an indirect positive effect in the weeks ormonths to follow. Nutrient inputs to beaches could be raisedthrough washed-up wrack and carrion, or breaching of nearbytemporary open-closed estuaries (Dugan et al., 2003; Schlacher andConnolly, 2009; McKenzie et al., 2011). The extent of these effectswould depend on the local features of the coast. One study, forexample, showed a positive macrofaunal response to a hurricanewhere species diversity and abundance increased over the nearlytwo-year long monitoring period (Hughes et al., 2009). Given thatthe surface deposit-feeding trophic guild nearly trebled asa proportion of the total community (Hughes et al., 2009), it is likelythat this was a response to the increase in food availability, as wasthe case in Brazil (Alves and Pezzuto, 2009), because macro faunarespond positively to increased wrack on the beach (Dugan et al.,2003). Another positive effect of storms could be through theirpotential role in the dispersal and distribution of beach biota. Littleis known about the connectivity of sandy beach populations but theheightened winds, waves and coastal currents associated withstorms (e.g. Jaffe et al., 1997) may play a key role in sandy beachmetapopulation dynamics. Evidently, though, storms may ratherhave the opposite effect by increasing larval mortality, given thatzooplankton have been found to be sensitive to high levels ofturbulence (e.g. Bickel et al., 2011).

While storms may appear to be a threat to sandy beaches, inreality they are one of many abiotic factors that shape sandybeach ecosystems and contribute to their dynamic nature. Theconcern should rather lie in current management practices thatfail to control inappropriate urbanisation of the coast. It is onlyonce the natural, inherent resilience of beaches to storms hasbeen compromised (by disrupting the littoral-active zone), thatstorms and other natural disturbances threaten ecosystemfunctioning. Our results support this notion, but conclusiveevidence will require robust, long-term ecological monitoringprogrammes for replicated beaches across large geographicareas; there are no examples of such programmes anywhere inthe world to the best of our knowledge. In the interim, criteria forstorm-related erosion hotspots could be identified, and managedusing generous setback lines to preclude infrastructural devel-opment in the littoral-active zone. Apart from sandy beachconservation, this would have the concomitant benefit of pro-tecting human populations and valuable coastal infrastructurefrom likely impacts.

5. Conclusions

Despite the necessary limitations of our opportunistic samplingdesign, the results support our a priori hypotheses. In the absenceof contradictory evidence, we therefore tentatively conclude thatmacrofaunal communities are indeed variable and that theyrespond strongly to beachmorphodynamics. Storms appear to havean impact on macrofauna by their effect on beach morphodynamicstate. The magnitude of those effects (and the consequent impactsto macrofauna) probably depends on a range of other local coastalfeatures (such as topography, orientation and nearshore bathym-etry) that may either enhance or reduce the extent of the damage tothe physical habitat.Where storm impacts to the sediment body aresevere, impacts may be chronic and will likely be reflected byresident macrofaunal communities. By implication, managingbeaches to be resilient to storm impacts bymaintaining a functionallittoral-active zonewill most likely confer resilience to macrofaunalcommunities and the ecosystems goods and services they provide.

L. Harris et al. / Estuarine, Coastal and Shelf Science 94 (2011) 210e221220

Acknowledgements

We would like to thank staff at Bayworld, and Zoology studentsat Nelson Mandela Metropolitan University for assistance in thefield, and to Karien Bezuidenhout for assistance with the macro-fauna identification. Two anonymous reviewers provided invalu-able comments and suggestions, which contributed to improvingthe original manuscript. This research was supported with fundingby Marine and Coastal Management of the Department of Envi-ronmental Affairs and Tourism (now Marine and Coastal Manage-ment of the Department of Agriculture, Forestry and Fisheries),coordinated by the National Research Foundation (NRF). Support(for LH) from NRF, German Academic Exchange Service (DAAD),University of KwaZulu-Natal and Nelson Mandela MetropolitanUniversity is gratefully acknowledged.

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