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Marine Environmental Research 97 (2014) 15e29

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Marine Environmental Research

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

Macrofaunal recovery following the intertidal recharge of dredgedmaterial: A comparison of structural and functional approaches

S.G. Bolam*

Centre for Environment, Fisheries and Aquaculture Science, Pakefield Road, Lowestoft, Suffolk NR33 0HT, UK

a r t i c l e i n f o

Article history:Received 26 November 2013Received in revised form22 January 2014Accepted 27 January 2014

Keywords:Sediment rechargeMacrofaunaFunctionTraitsStructureIntertidal

* Tel.: þ44 1502 524513; fax: þ44 1502 513856.E-mail address: Stefan.Bolam@cefas.co.uk.

http://dx.doi.org/10.1016/j.marenvres.2014.01.0080141-1136/Crown Copyright � 2014 Published by Else

a b s t r a c t

There is a growing need to understand the functional implications of anthropogenic pressures, such asthose following coastal disposal of dredged material. Current assessments, based on taxonomic structureof benthic organisms, only provide a limited capacity to determine functional impacts or recovery. Thisstudy assesses recovery of two intertidal dredged material recharge schemes, comparing results obtainedbased on taxonomic structure (univariate and multivariate approaches) and function (biological traitcomposition, functional diversity, secondary production) of the benthic assemblages.

The assemblages recolonising both schemes were consistently less speciose, less densely-populatedand exhibited multivariate community structures that differed from those of the reference areas.However, for both schemes metrics of functionality converged to those of reference areas, although somedifferences in trait composition persisted for up to 3 years.

These data support the proposition that impacts of, and recovery from, anthropogenic disturbanceshould be assessed using a combination of both functional and taxonomic structural approaches.

Crown Copyright � 2014 Published by Elsevier Ltd. All rights reserved.

1. Introduction

Following the cessation of disposal of industrial waste andsewage sludge at sea by most countries, there has been a greaterfocus on the environmental impacts associated with the disposal ofsedimentary material dredged from riverbeds, estuaries and ports(Vogt and Walls, 1991; Bolam and Whomersley, 2003). Dredging isnecessary to maintain the safety and accessibility of navigationchannels. In the UK, like many other countries (e.g. the USA,Australia), such attention has resulted in a greater emphasis on therelocation of dredged material in such a way as to derive environ-mental benefits (Bolam and Whomersley, 2003). As a result, anumber of intertidal recharge options have developed, whereby thematerial is regarded as a potential resource and used to recharge orrecreate intertidal habitats. In the USA, for example, dredged ma-terial has been used successfully to create newmudflats (Ray, 2000)and saltmarshes (Posey et al., 1997; Streever, 2000) which ulti-mately function like natural systems.

The placement of large quantities of dredged material onintertidal habitats represents a significant (and acute) impact, oftensmothering the resident faunal assemblages (Bolam and

vier Ltd. All rights reserved.

Whomersley, 2003, 2005; Bolam, 2011). There is, consequently, arequirement to understand the rate which faunal assemblages canrespond to, and recover from, such disturbance events. A largenumber of field manipulation experiments and large-scale naturalexperiments have indicated that intertidal invertebrate assem-blages demonstrate a great capacity to respond to physical distur-bance, with large numbers of early colonists appearing within daysor weeks following impact, although rates and mechanisms ofrecolonisation have been shown to depend on the scale of distur-bance and season (Zajac andWhitlach, 1982; Hall et al., 1993; Evanset al., 1998; Beukema et al., 1999; Bolam and Fernandes, 2002;Bolam et al., 2002, 2004; Lewis et al., 2003). Moreover, a highdensity of macrofauna (Bolam andWhomersley, 2003, 2005; Bolamet al., 2010a) and meiofauna (Schratzberger et al., 2006; Bolamet al., 2006) has been observed three months after the rechargeof muddy dredged material onto intertidal habitats. Nevertheless,the increased density of early colonists does not imply recovery;the ‘success’ of an intertidal recharge scheme should be gauged byenvironmental managers as its perceived return to a naturalreference condition. In the UK, monitoring faunal recovery andassessing the success of intertidal habitat restoration schemes havetraditionally been undertakenwith a focus on the structural aspectsof invertebrate benthic assemblages (Evans et al., 1998; Bolam andWhomersley, 2003, 2005; Bolam et al., 2006, 2010a; Garbutt et al.,2006). One may argue, however, that functional recovery of the

S.G. Bolam / Marine Environmental Research 97 (2014) 15e2916

benthic assemblage is of equal or greater importance than struc-tural recovery, and should be assessed in addition to the structuralaspects of the assemblage. In view of the tidal height alterationsresulting from the dredged material recharge community taxo-nomic structure is likely to remain different from that of pre-recharge conditions (Bolam et al., 2010a).

Intertidal mudflats are very productive habitats and macrofaunalinvertebrates form a very important component of the diet of manybirdand juvenilefishspecies (Evansetal.,1998;HiscockandMarshall,2006) and their bioturbative activities have been shown to play amajor role inaugmentingnutrientandcarbonfluxes in thesecohesivesediments (Boudreau, 1998; Bolam et al., 2002; Biles et al., 2002).Certainly, a focus on functional recovery is more aligned with thephilosophy which underpins contemporary marine managementapproaches (e.g. the ecosystem approach (CEC (2008)); the MarineStrategy Framework Directive (MSFD, EU Directive 2008/56/EC)) andakin to that adopted for terrestrial habitat restoration schemeswhererecovery of ecosystem function, structure, and composition areassessed in combination (MacKay et al., 2011). As Diaz and Cabido(2001) pointed out, species composition might be inadequate toinvestigate processes that sustain ecological systems, and such pro-cesses are determined by the functional characteristics of the organ-isms involved rather than by their taxonomic identity (Grime, 1997).

Ecological function of anthropogenically-modified marine hab-itats has been demonstrated to recover independently (i.e. acrossdifferent temporal trajectories) from that of structural recovery(Bolam and Whomersley, 2003; Cooper et al., 2008; Barrio Frojánet al., 2011; Bolam, 2012). Furthermore, Munari (2013) recentlydemonstrated that benthic assemblages characterising sandy bea-ches displayed a greater functional persistence (as depicted by theirbiological traits) in response to management measures comparedwith their taxonomic composition. Similarly, Marchini et al. (2008)observed that the functional structure (i.e. biological trait compo-sition) of benthic assemblages of eight Italian lagoonswas relativelyindependent of each lagoon’s ecological quality. While measuringecological function directly remains time-consuming and meth-odologically and logistically difficult, the recent development of anumber of functional diversity indices has allowed functional di-versity to be quantified and its recovery compared with trends intraditional indices of community structure (Cooper et al., 2008;Barrio Froján et al., 2011; Wan Hussin et al., 2012). Specifically,recent studies applying Biological Traits Analysis (BTA) haveresulted in a better understanding of benthic function to a numberof anthropogenic pressures (e.g. Bremner et al., 2003; Tillin et al.,2006; Papageorgiou et al., 2009; Frid, 2011; Wan Hussin et al.,2012; Dimitriadis et al., 2012; Oug et al., 2012; Munari, 2013;Bolam et al., 2014).

This study compares structural recovery (using traditional taxo-nomic diversity indices andmetrics) with functional recovery of twointertidal sediment-recharge schemes situated in southeast England.Benthic function is estimated using BTA and its derivative, theFunctionalDiversity (FD) coefficient (Botta-Dukát, 2005). In addition,total secondary production of the faunal assemblages is estimated toenable assessments of the recovery of this important ecosystemfunction. The ultimate aim of this investigation is to determinewhether conclusions regarding the time taken for recovery of suchintertidal schemes differs depending upon whether structural orfunctional approaches are adopted as the assessment tool.

2. Methods

2.1. Study sites

Two intertidal recharge schemes have been investigated:Westwick Marina (hereafter termed WW) (51� 38.69200 N; 00�

39.6100 E) and Titchmarsh Marina (hereafter termed TM) (51�

51.76300 N, 001� 15.13300 E) in southeast England (Fig. 1).WW marina, located within a protected inlet on the north bank

of the Crouch Estuary, requires maintenance dredging of silts on aregular basis to ensure viability of the marina. This material isregularly disposed of subtidally within the Crouch, but in 2001 anearby area of marsh was selected for recharge and enclosed bywoven fences; the material being used to recharge the muddychannels as opposed to covering the bordering saltmarsh. Wovenfences have been shown to successfully retain fine-grained dredgedmaterial and allow natural de-watering during periods of tidalemersion. Sediment was dredged using a suction dredger andpumped along a floating pipeline to the recharge area (a total dis-tance of 50m), the complete operation taking place over a period ofone month.

TM is located within Hamford Water, a semi-enclosed embay-ment bordered by sand and shingle spits which provide protectionagainst wave action from the open sea. These natural features havebeen eroding and migrating landwards and, consequently, protec-tion and creation of intertidal mudflats and saltmarshes are keymanagement practices for the region. TM is subject to significanttidal deposition of fine sediment and this requires frequentdredging. While this dredged material is routinely placed at anearby subtidal disposal site, a specific licence was granted for theone-off intertidal placement of the material. Sediment was dredgedusing a grab dredger, placed into a hopper and suspended into aslurry using a water jet. The slurry was pumped 100 m along afloating pipeline to the recharge area which was enclosed by claybunds to retain the sediment component of the rechargedmaterial.

The primary management objectives of the two schemes were(a) to allow the sediment to be retained within the estuarine sys-tem (i.e. to minimise sediment budget alterations resulting fromdredge-disposal operations), and (b) to improve the biologicalcharacteristics of the muddy areas being recharged. While therecharge schemes should inherently achieve the former, theassessment of the latter objective was the goal of the samplingprogramme presented here. The sediments of both recharge areasreceived 60e80 cm (vertical overburden) of dredged material uponcompletion. As the resulting tidal height of each recharge area wasbelow the threshold for saltmarsh plant colonisation (i.e. meanhigh water neaps), high-level mudflats were chosen as the mostappropriate reference habitats with which to assess benthic faunalrecovery (Bolam and Whomersley, 2003, 2005; Bolam et al., 2006).Reference sites were selected as near as possible to the rechargearea without being impacted by the recharge process itself. Thecomparison of faunal metrics of the assemblage of the rechargearea with that of the reference area is used here to assess ‘recovery’of the scheme. However, as the recharge area initially representedlow-level mudflat and higher level mudflat (equivalent to theheight following recharge) is used for the reference, one may arguethe study assesses macrofaunal ‘convergence’ of a low-levelmudflat to a high-level mudflat. However, the term ‘recovery’ willbe used to describe this process here.

2.2. Sampling and sample processing

At both WW and TM, three sampling stations were assignedwithin the recharge area and three stations within the referencearea. At each station, three sediment samples were taken using a0.01 m2 perspex corer to a depth of 15 cm. Samples were fixed in10% buffered formalin with 0.01% Rose Bengal stain. These werelater washed over a 500 mm mesh sieve in the laboratory to sepa-rate the infauna from the sediment. The retained invertebrateswere sorted into major taxonomic groups under a dissecting mi-croscope, identified to the highest possible taxonomic resolution,

Fig. 1. Locations of the recharge (black symbols) and reference (white symbols) stations at the two study sites, Westwick Marina (WW) and Titchmarsh Marina (TM), southeastEngland.

S.G. Bolam / Marine Environmental Research 97 (2014) 15e29 17

counted and weighed. Sampling was initially performed 3 monthsafter the cessation of dredged material recharge, then 6, 12, 18, 24,30, 36, 42 and 48 months post-recharge. On three occasions (3, 12and 24 months) it was only possible to sample two of the threereference stations at TM.

A number of structural and functional metrics were derivedfrom the faunal data (see Table 1, Sections 2.3 & 2.4) to allow theassessment of recovery of the assemblages of the recharge area(hereafter termed ‘recharge assemblages’) to those of the referencearea (termed ‘reference assemblages’).

The top 3 cm of the sediment at each station was sampled forsediment analyses. These samples were frozen prior to the deter-mination of water content (weight loss on drying at 80 �C for sevendays), carbon content (Leeman CE440 element analyser) and par-ticle size distribution (Coulter LS-130 laser diffraction) analyses.Redox potential values (at 2 and 4 cm sediment depth) weremeasured at each station using a calibrated Russell RL100 redoxmetre with a calomel probe following the methods outlined byPearson and Stanley (1979).

2.3. Assemblage structure

The faunal data were analysed mainly at the species level oftaxonomic resolution, although higher taxonomic levels were usedfor some groups (e.g. NEMERTEA, OSTRACODA).

For each recharge and reference area, mean number of species,individuals, Pielou’s evenness andwet biomass were calculated. Foreach sampling event, differences in values of these metrics weretested between recharge and reference areas using one-wayANOVA with Tukey multiple comparison tests (using Minitab�

V10.1) following a suitable data transformation (if necessary) tocomply with parametric assumptions.

Multivariate analyses were carried out to assess the degree ofsimilarity in taxonomic structure between recharge and referenceassemblages at each sampling event. All multivariate analyses wereperformed using the PRIMER package, version 6.15 (Clarke andGorley, 2006). Non-metric Multidimensional Scaling (MDS) wascarried out on BrayeCurtis similarity values calculated from log-transformed abundance data to produce an ordination plot. Totest for differences between the assemblages of the recharge andreference areas, one-way ANOSIM tests were performed. PRIMERpresents both the ANOSIM test statistic (R) and the p-value for thistest. However, the significance level is very dependent on thenumber of replicates and Clarke and Gorley (2006) propose that theR value is the most useful criterion to aid interpretation as it is not afunction of the number of replicates. ANOSIM R-values >0.5 indi-cate clear differences between groups with some degree of overlap(Clarke and Gorley, 2006); thus values p � 0.5 are those which aredeemed to depict statistical significance for the present study. Todetermine the taxa most responsible for discriminating between

Table 1Summary of structural and functional metrics used in the present study and implications of their recovery between recharge and reference assemblages.

Metric used Implication of recovery assemblage to reference condition

Structural Number of species (No./core) At the scale of the 0.01 m�2 core, the assemblage of the recharge area is as speciose as that of thereference area. The identity of the species may vary between the two areas, thus they may functiondifferently

Number of individuals (No. m�2) Total infaunal density of the assemblage of the recharge area is the same as that of the referencearea. The identity of the species may vary between the two areas, thus they may function differently

Evenness (Pielou’s (J)) Although the number of species may or may not be the same, the numerical distribution ofindividuals across the species is the same, i.e., equal numerical dominance across species. Theidentity of the species may vary between the two areas, thus they may function differently

Biomass (g m�2) The living biomass of the sampledmacrofauna of the assemblage of the recharge area is equal that ofthe reference area. The taxonomic identity of the species may be different between the two areas,thus the energetic properties may vary

Assemblage structure The taxonomic identity and relative abundance of the assemblages of the recharge areamatches thatof the reference area. Biomass may or may not be the same between the two areas

Functional Biological traits composition The numerical composition of the assemblage of the recharge area is similar to that of the referencearea with respect to life history, behavioural and morphological traits. Implies that the twoassemblages are functionally similar. The biomass distribution of these traits may vary

Functional diversity (Rao’s FDQ) The value and range of those species and organismal traits that influence ecosystem functioning.Recovery implies that the numerical distribution of trait characteristics of the assemblage of therecharge area is the same as that of the reference area.

Total secondary production (kJ m�2 y�1) The total amount of energy generated (per year) by the assemblage of the recharge area equals thatof the reference area. Estimated based on sampled fauna, does not imply the same taxa contribute tototal production

Proportional contribution to production (%) Total production of the assemblage of the recharge area is proportionally contributed by taxa in thesame way as that for the total production of the reference area assemblages. Does not imply thattotal production is the same between the two areas. Equitable proportional contribution wouldimply (assuming the same total production) prey availability to predators is recovered

S.G. Bolam / Marine Environmental Research 97 (2014) 15e2918

recharge and reference assemblages where significant differencesexisted, the SIMPER routine (PRIMER; Clarke and Gorley, 2006) wasused.

2.4. Assemblage function

2.4.1. Biological traits compositionTo estimate assemblage function, 10 biological traits were cho-

sen to describe the life history, morphological and behaviouralcharacteristics of the constituent taxa (Table 2). There is currentlyno accepted methodology for selecting the most appropriate traitsfor a given study (Marchini et al., 2008; Bolam, 2013) and often thefinal selection is guided by the limited biological informationavailable for benthic invertebrate taxa (Bremner, 2008; Marchiniet al., 2008; Tyler et al., 2012; Munari, 2013; Bolam and Eggleton,2014). As the aim of the use of traits in this study was to estimateassemblage function, an attempt to focus on ‘functional effects’ traitsas opposed to ‘response’ traits was made. Functional effects traitsare those which affect ecosystem properties while response traitsare those which affect a species’ response to changes in the envi-ronment such as disturbance (Lavorel and Garnier, 2002; Hooperet al., 2004). While focussing on effects traits may seem logicalfor this study, there is currently a limited understanding of whichtraits may be regarded as functional effects traits or have directrelevance to a particular ecological function (Pakeman, 2011).

The approach used here is fundamentally similar to that of anumber of relevant studies (e.g. Munari, 2013; Bolam et al., 2014;Bolam and Eggleton, 2014). Each of the 10 traits was subdividedinto various categories chosen to encompass the range of possibleattributes of all the taxa; 45 categories were identified in total(Table 2). Some of the traits referred to measurable characteristics(e.g. maximum size, longevity) whose categories presented a ‘hi-erarchical’ organisation, while others (e.g. mobility) were whollyqualitative characteristics whose categories represented discreteclasses.

Information regarding all 10 biological traits for all 67 taxa wascollected from a variety of sources, principally published journalpapers and books and websites of various scientific institutions

(e.g., http://marlin.ac.uk/biotic/). While it was possible to accessreliable information for many taxa from these sources regardingcertain traits (e.g. larval development location, morphology), pub-lished information describing other traits (e.g. longevity) was notavailable for some taxa. In such cases, rather than assigning a scoreof zero to all categories for a trait (Chevenet et al., 1994), thecategory entries for the most closely-related taxa were used as abasis for entering missing information (Bolam et al., 2014). It isassumed that this approach resulted in a reasonably accuratecompletion of categories where the category entries across closelyrelated taxa were consistent, but not where traits were known tovary across closely-related taxa. In the latter situation, it wasnecessary to spread the category scores across a wider number ofcategories (see below).

Taxa can display multi-faceted behaviour depending upon thespecific conditions and resources available. Therefore, each taxonwas assessed using a “fuzzy coding” approach (Chevenet et al.,1994) on the basis of the extent to which each displayed the cate-gories of any one trait. Fuzzy coding allows taxa to exhibit cate-gories to different degrees, avoiding the obligate assignment of ataxon to a single category which can lead to an inaccurate repre-sentation of the biological information (Usseglio-Polatera et al.,2000). The resulting taxon-by-trait matrix was combined withthe taxon abundance-by-station (Nom�2) matrix to create the finalstation-by-trait matrix on which all subsequent trait analyses werebased (Marchini et al., 2008; Munari, 2013). The analysescomparing the relative trait composition of recharge and referenceassemblages were analogous to those performed on assemblagestructure data (see Section 2.3).

2.4.2. Functional diversity (FD)Functional Diversity (FD) indices are increasingly being used in

ecological research as they are thought to provide improved pre-dictions of important ecosystem functions (e.g. productivity, sta-bility) than those possible from the use of species diversity indices(Tilman, 2001). That is, FD indices measure the distribution andrange of what organisms do, taking into account the complemen-tarity and redundancy of co-occurring species (Schleuter et al.,

Table 2Description of traits and categories used in the biological traits analysis.

Trait Category Description

Maximum size <10 Maximum size (length or height) of adult (mm)10e2021e100101e200>200

Morphology Soft External tissue soft and not covered by any form of protective casingTunic Body covered by a protective outer tissue made up of, for example, cellulose, e.g., tunicatesExoskeleton Body covered or encased in either a thin chitinous layer or calcium carbonate shell

Longevity <1 The maximum lifespan of the adult stage (y)1e33e10>10

Larval development location Pelagic Planktotrophic Larvae feed and grow in the water columnPelagic Lecithotrophic Larvae feed on yolk reservesBenthic (direct) Larval stage missing (eggs develop into juvenile forms) or larvae are limited to the bed

Egg development location Asexual / budding Species can reproduce asexually, either by fragmentation, budding, epitoky, etc. Often this is inaddition to some form of sexual reproduction

Sexual-shed eggs (pelagic) Eggs are released into the water columnSexual-shed eggs (benthic) Eggs are released onto/into the bed, either free or maintained on bed by mucous or other meansSexual-brood eggs Eggs are maintained by adult for protection, either within parental tube or within body cavity

Living habit Tube-dwelling Tube may be lined with sand, mucus or calcium carbonateBurrow-dwelling Lives within a permanent or temporary burrowFree-living Not limited to any restrictive structure at any time. Able to move freely within and/or on the

sedimentsCrevice/hole/under stones Adults are typically cryptic, predominantly found inhabiting spaces made available by coarse/rock

substrate and/or tubes made by biogenic species or algal holdfastsEpi/endo zoic/phytic Live on other organismsAttached to substratum Attached to larger, stable boulders or rock

Sediment position Surface Found on or just above the seabedShallow infauna (0e5 cm) Species whose bodies are found almost exclusively below sediment surface between 0 and 5 cm

sediment depthMid-depth infauna (5e10 cm depth) Species whose bodies are partly or exclusively found below sediment surface at a depth generally

between 5 and 10 cm sediment depthDeep-infauna (>10 cm) Species whose bodies are partly or exclusively found below sediment surface at a depth greater than

10 cm sediment depthFeeding mode Suspension The removal of particulate food taken from the water column, generally via filter-feeding

Surface deposit Active removal of detrital material from the sediment surface. This class includes species whichscrape and/or graze algal matter from surfaces

Sub-surface deposit Removal of detrital material from within the sediment matrixScavenger / opportunist Species which feed upon dead animalsPredator Species which actively predate upon animals (including the predation on smaller zooplankton)

Mobility Sessile Species in which the adults have no, or very limited, mobility either because they are attached or arelimited to a (semi-) permanent tube or burrow

Swim Species in which the adults actively swim in thewater column (many usually return to the bedwhennot feeding)

Crawl/creep/climb Capable of some, generally limited, movement along the sediment surface or rocky substrataBurrowers Infaunal species in which adults are capable of active movement within the sediment

Bioturbation Diffusive mixing Vertical and/or horizontal movement of sediment and/or particulatesSurface deposition Deposition of particles at the sediment surface resulting from e.g. defecation or egestion

(pseudofaeces) by, for example, filter and surface deposit feeding organismsUpward conveyor Translocation of sediment and/or particulates from depth within the sediment to the surface during

subsurface deposit feeding or burrow excavationDownward conveyor The subduction of particles from the surface to some depth by feeding or defecationNone Do not perform any of the above and/or not considered as contributing to any bioturbative capacity

S.G. Bolam / Marine Environmental Research 97 (2014) 15e29 19

2013). For the present study, Rao’s Q (hereafter termed FDQ) wascalculated for the assemblage sampled at each station (Botta-Dukát, 2005). This index performs well compared with manyother FD indices (Schleuter et al., 2013) and has gained notablecredibility as a useful functional diversity index (Petchey andGaston, 2002; Mason et al., 2003; Ricotta, 2005). FDQ utilises in-formation on a number of traits and their relative representationwithin the assemblage, improving its performance compared withthose that rely solely on trait presence/absence. However, one mustbe aware of a counterintuitive property of FDQ, its value maydecrease if species richness increases (Botta-Dukát, 2005).

The FDQ of an assemblage was derived using the two-stepapproach described by Van der Linden et al. (2012), calculatingFDQ separately for each of the 10 traits, followed by averaging theseacross each assemblage. All calculations were performed using a

freely-available, purpose-built Excel macro created by Lep�s et al.(2006).

2.4.3. Secondary productionSecondary production estimates (kJ m�2 y�1) were derived in a

stepwise approach from the raw abundance and biomass datafollowing themethods described by Bolam et al. (2010b) and Bolam(2012). Firstly, the biomass data were converted to energy valuesusing published conversion factors. Energy values were then con-verted to production values using a spreadsheet freely available onthe Internet http://www.thomasbrey.de/science/virtualhandbook/navlog/index.html (Brey, 2001). This method unifies habitat andtaxonomic information which affect productivity of individualsfrom their size and biomass into a multiple regression modelestimating annual production of macrobenthos. The Brey model

Table 3Mean values (�95% CI) of measured sediment characteristics at WW and TM throughout the sampling period.

Months(post-recharge)

Area WW TM

Watercontent (%)

Silt/claycontent (%)

Carboncontent (%)

2 cm redox(mV)

4 cm redox(mV)

Watercontent (%)

Silt/claycontent (%)

Carboncontent (%)

2 cm redox(mV)

4 cm redox(mV)

3 Rech 63.2 � 7.2 92.2 � 6.7 2.3 � 0.2 �68.1 � 38.1 �111.6 � 22.1 62.3 � 2.9 97.4 � 2.0 1.6 � 0.1 �92.7 � 31.2 �103.7 � 34.6Ref 60.0 � 4.0 92.9 � 3.7 2.2 � 0.3 �84.4 � 24.2 �139.1 � 12.6 63.8 � 5.9 86.9 � 1.1 2.5 � 0.5 �86.1 � 16.3 �133.7 � 14.2

6 Rech 60.3 � 3.9 92.2 � 3.9 2.5 � 0.3 �52.6 � 43.2 �81.9 � 34.2 58.8 � 1.2 95.9 � 1.3 1.6 � 0.1 �47.4 � 15.2 �59.8 � 14.5Ref 59.2 � 1.2 95.3 � 2.3 2.1 � 0.2 �49.3 � 11.7 �84.9 � 18.9 63.8 � 2.9 90.8 � 2.6 2.7 � 0.4 �62.8 � 31.2 �100.1 � 24.4

12 Rech 66.5 � 7.3 89.5 � 6.3 2.7 � 0.5 �128.1 � 43.1 �165.9 � 22.9 60.6 � 0.6 88.2 � 1.9 1.6 � 0.1 �130.4 � 14.3 �149.6 � 6.2Ref 62.1 � 4.5 91.1 � 0.3 2.0 � 0.2 �145.7 � 16.7 �163.0 � 6.9 49.6 � 1.2 66.5 � 32.2 1.9 � 0.9 �110.0 � 62.7 �117.1 � 62.2

18 Rech 63.1 � 5.1 87.4 � 1.3 2.5 � 0.4 45.7 � 34.4 �107.2 � 33.0 62.1 � 3.8 95.3 � 1.1 1.3 � 0.1 �87.0 � 17.5 �132.9 � 18.2Ref 64.2 � 4.0 96.1 � 2.8 2.3 � 0.1 �16.0 � 46.1 �98.8 � 47.5 67.9 � 1.0 88.6 � 3.8 2.5 � 0.3 �28.5 � 21.1 �122.7 � 43.5

24 Rech 62.2 � 4.1 87.6 � 8.8 2.5 � 0.2 �77 � 33.1 �114.7 � 26.6 57.8 � 4.6 89.8 � 2.1 1.3 � 0.1 �99.2 � 29.2 �108.9 � 26.3Ref 66.1 � 3.9 85.8 � 2.1 2.3 � 0.1 �152.3 � 10.9 �161.4 � 20.3 51.6 � 3.6 58.2 � 28.3 1.8 � 0.9 �104.8 � 68.1 �125.5 � 79.7

30 Rech 67.1 � 2.5 83.7 � 6.3 2.4 � 0.4 e e 59.5 � 1.4 92.5 � 0.9 1.3 � 0.1 �11.6 � 5.6 �74.1 � 16.9Ref 59.4 � 14.1 88.8 � 3.1 2.1 � 0.3 e e 65.9 � 5.1 86.2 � 3.4 2.3 � 0.2 �79.5 � 41.6 �145.0 � 24.0

36 Rech 58.7 � 3.1 76.9 � 6.0 2.4 � 0.5 �127.5 � 10.9 139.3 � 8.5 63.2 � 1.8 82.9 � 0.9 1.6 � 0.2 �125.3 � 10.3 �144.7 � 5.0Ref 65.0 � 2.7 79.0 � 3.3 2.3 � 0.3 �102.2 � 26.8 �112 � 13.5 67.9 � 3.6 79.9 � 2.0 2.2 � 0.2 �233.7 � 7.3 �246.7 � 9.5

42 Rech 62.2 � 3.8 75.7 � 4.3 2.5 � 0.3 23.7 � 30.0 10.9 � 33.6 62.1 � 3.6 82.1 � 3.7 1.3 � 0.3 �26.6 � 41.5 �55.2 � 54.1Ref 70.5 � 1.3 80.3 � 2.2 2.1 � 0.3 �81.7 � 28.0 �114.0 � 25.1 75.2 � 0.3 67.4 � 3.7 3.0 � 0.3 70.0 � 10.4 22.6 � 6.5

48 Rech 62.7 � 8.3 73.3 � 6.3 2.2 � 0.3 �131.1 � 17.1 �148.0 � 9.2 56.9 � 6.5 77.3 � 3.1 1.4 � 0.3 �125.3 � 17.9 �144.7 � 17.4Ref 62.3 � 2.2 78.3 � 0.9 1.8 � 0.1 �141.2 � 15.4 �140.5 � 11.9 65.8 � 9.2 65.5 � 12.3 2.5 � 1.1 �233.8 � 27.7 �246.7 � 20.7

Redox potentials were not measured at WW 30m post-recharge.

S.G. Bolam / Marine Environmental Research 97 (2014) 15e2920

was found to be one of the most reliable and robust models avail-able during a critical appraisal of such methods (Cusson andBourget, 2005; Dolbeth et al., 2005). However, one shouldremember that while these indirect methods have obvious appli-cability to ecological studies, the estimates obtained are not asaccurate as those values obtained using more involved, directmethods of secondary production measurement. Additionally, asthe biomass data used here are those obtained at the time ofsampling as opposed to annual means for the community (therequirement for the Brey model), the accuracy of these estimates islikely to be reduced.

Differences in total secondary production between assemblagesfrom recharge and reference areas at each sampling event weretested for significance using one-way ANOVA (following trans-formation, where necessary). Finally, a multivariate assessment ofdifferences in the relative contribution to production of thedifferent taxa was conducted using PRIMER. This approachresembled that described above (Section 2.3), but was based on theproportion of the assemblage’s total production by each taxonrather than by its abundance. As such, this approach allowed anassessment of the functional difference, as opposed to taxonomicdifference, between recharge and reference assemblages.

3. Results

3.1. Sediment characteristics

Characteristics of the sediments at both the recharge andreference areas were measured to allow an assessment of the de-gree of physical (dis)similarity between the two areas over time.These data are used here to offer the potential of ascertainingwhether any observed lack of faunal recovery for either site mayhave resulted from sediment differences. In general, sediments ofthe reference and recharge areas of both schemes displayed similarcharacteristics.

The dredged material recharged at WW rapidly dewatered andconsolidated to possess a water content equivalent to that of thereference sediments three months after recharge. Sediment watercontent of both recharge and reference areas remained consistentthroughout the sampling period (between 60 and 70%), as did silt/clay content (approximately 90%, although this slightly declined

over time) and carbon content (generally between 2 and 2.5%)(Table 3). The measured 2 and 4 cm sediment depth redox potentialvalues for both areas were similar throughout the sampling period.

The material recharged at TM dewatered rapidly to create sed-iments with similar water contents to those at the reference sta-tions after three months (between 62 and 64% water; Table 3).However, unlike at WW, the recharged sediments at TM consis-tently displayed a higher silt/clay content and a lower carboncontent than those of the reference area. Finally, although the redoxpotential values varied between recharge and reference areas,these differences showed no consistent trend.

3.2. Fauna

3.2.1. Assemblage structureThe recharge assemblage atWWcomprised equivalent numbers

of species to that of the reference assemblage three monthsfollowing recharge (Fig. 2a). However, this situation did not persistand the recharge assemblage became significantly less speciosethan that at the reference area (hereafter termed ‘referenceassemblage’) 12 months post-recharge and remained so until theend of the sampling programme. Total macrofaunal density of therecharge assemblages were significantly less densely populatedthan those of the reference assemblages, particularly between 24and 42 months post-recharge (Fig. 2b). The distribution of in-dividuals amongst species was more equal in the recharge assem-blages than in those of the reference assemblages (Fig. 2c). Wetbiomass showed a high degree of variation between the rechargeand reference assemblages with little overall trend. For example,recharge assemblage biomass was lower than that of the referenceassemblage after 3 months, higher after 12 months, and remainedmarkedly lower than that of the reference assemblage from 30mpost-recharge (Fig. 2d).

The recharge assemblage at TM was consistently less specioseand less densely populated throughout the study period relative tothe reference assemblage (Fig. 3a and b). The assemblages of thesetwo areas, however, showed no difference in their evenness andgenerally exhibited similar wet biomass values for most samplingevents.

The main species characterising recharge and reference as-semblages are listed in Table 4. While some taxa are found to be

02468

101214161820

3m 6m 12m 18m 24m 30m 36m 42m 48m

Mea

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o. s

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(+/-9

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I) (a) Species

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3m 6m 12m 18m 24m 30m 36m 42m 48mB

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3m 6m 12m 18m 24m 30m 36m 42m 48m

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(+/-

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CI)

(c) Evenness

** ****

** **

** **

**

****

** **

**

**

***

*

*

*

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Fig. 2. (aed). Indices of community structure and biomass (mean � 95% CI) for the recharge (grey bars) and reference (open bars) areas for each sampling period at WW. Asterisksdenote statistical difference between recharge and reference areas (* ¼ p < 0.05; ** ¼ p < 0.01; *** ¼ p < 0.001, one-way ANOVA).

0

5

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3m 6m 12m 18m 24m 30m 36m 42m 48m

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3m 6m 12m 18m 24m 30m 36m 42m 48m

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3m 6m 12m 18m 24m 30m 36m 42m 48m

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0123456789

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3m 6m 12m 18m 24m 30m 36m 42m 48m

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I) (d) Biomass

**

**** **

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Fig. 3. (aed). Indices of community structure and biomass (mean � 95% CI) for the recharge (grey bars) and reference (open bars) areas for each sampling period at TM. Asterisksdenote statistical difference between recharge and reference areas (* ¼ p < 0.05; ** ¼ p < 0.01; one-way ANOVA).

S.G. Bolam / Marine Environmental Research 97 (2014) 15e29 21

common between recharge and reference assemblages at WW (e.g.Tubificoides benedii, Streblospio shrubsolii), other taxa were morecharacteristic of one assemblage type. Recharge communities weredominated by the annelids Hediste diversicolor and T. benedii, thelatter also being numerical dominant of the reference assemblage,together with S. shrubsolii. At TM, the species characterising the two

areas were more divergent than at WW, the gastropod molluscHydrobia ulvae being dominant in the recharge assemblages andthe annelids T. benedii and Capitella capitata being the two mainspecies responsible for characterising the reference assemblages.

The multivariate structure of the recharge assemblage at WWwas consistently different from that of the reference assemblage

S.G. Bolam / Marine Environmental Research 97 (2014) 15e2922

(Fig. 4a). The 2d MDS plot provides a fairly unreliable representa-tion of the relative assemblage similarities (stress value ¼ 0.18) andindicates that not all recharge and reference assemblages werediscernible; there is some overlap and close association but ingeneral the assemblages of the two areas are separate. The plotsuggests that there is little indication that the assemblages of thesetwo areas converge over time, i.e. the left-right separation of therecharge-reference assemblages in the plot is maintained. One-wayANOSIM test results support these conclusions (Table 5); the hightest statistic (i.e. R > 0.5) indicates a significant difference inassemblage structure between the two areas. The increased simi-larity between the two assemblage types at 42m post-recharge inthe plot is supported by the lower R value (R ¼ 0.42).

The recharge and reference assemblages at TM also appeardistinct with respect to their taxonomic structure. Although theMDS plot only provides a moderate representation of the multidi-mensional situation in two dimensions (stress ¼ 0.13) it is evidentthat the recharge assemblages appear separate from those of two ofthe reference stations at all sampling events (Fig. 4b). However,some convergence is apparent between the assemblages of thesetwo areas over time: the assemblage from the recharge area pro-gresses from the left towards the right across successive samplingevents. Additionally, the ordination plot reveals a large variation inthe taxonomic structure of the reference assemblages: one refer-ence station displays a divergent assemblage structure to that fromthe other two reference stations while more similar to those of therecharge area. To note, this reference station occasionally displayeddifferent sediment characteristics (e.g. elevated carbon content)from those of the other two stations.

The high ANOSIM statistic values in the earlier period post-recharge supports the recharge-reference assemblage differences,while the lower R values that emerge over time substantiate thenotion that the two assemblage types were not significantlydistinguishable after 36 months post recharge (Table 5).

Table 4The ten taxa (with mean density m�2) most influential in discriminating betweenrecharge and reference assemblages for both WW and TM.

Recharge Reference

Taxon Meandensity m-2

Taxon Meandensity m-2

WW Hediste diversicolor 2332 Tubificoides benedii 22,921Tubificoides benedii 2872 Streblospio shrubsolii 9564Streblospio shrubsolii 2043 Hydrobia ulvae 2704Hydrobia ulvae 1584 Hediste diversicolor 1369Corophium volutator 1857 Tellinidae 894Tubificoidespseudogaster

806 Tubificoidespseudogaster

817

Abra tenuis 187 Manayunkiaaestuarina

1024

Tellinidae 207 Abra tenuis 372Paranais litoralis 462 Capitella capitata 1024Enchytraidae 127 Eteone longa 278

TM Hydrobia ulvae 8702 Tubificoides benedii 1544Hediste diversicolor 268 Capitella capitata 1576Tellinidae 357 Streblospio shrubsolii 676Abra tenuis 268 Edwardsiidae 640Tubificoidespseudogaster

228 Manayunkiaaestuarina

620

Streblospio shrubsolii 222 Hydrobia ulvae 595Tubificoides benedii 144 Pygospio elegans 497NEMERTEA 102 Tubificoides

pseudogaster404

Hypereteone foliosa 38 Tellinidae 416Paranais litoralis 62 Heterochaeta costata 376

Influence of each taxa was determined by a (one-way) SIMPER programme onPRIMER (Clarke and Gorley, 2006) using square-root transformed abundance data.Data averaged across all sampling events.

3.2.2. Traits compositionThe benthic assemblage of the recharge area at WW showed

noticeable changes in trait composition over the 48 month sam-pling period. The trait composition within the first 6m post-recharge was different from those sampled later (stationssampled subsequently tend to be found towards the top of the MDSplot (Fig. 5a)). Furthermore, trait composition of recharge assem-blages generally appears distinct from those of the reference sta-tions, although there is evidence of greater similarity in traitcomposition of recharge and reference assemblages towards theend of the sampling period. The ANOSIM test results (Table 6)corroborate the recharge-reference assemblage trait compositionaldifferences, and that such differences are far less evident at 18m,30m and after 36m post-recharge.

The trait composition of the recharge assemblage at TM alsoappears different from that of the reference area (Fig. 5b). However,the reference stations on the MDS plot appear separated: whiletrait composition of two stations of the reference area is distinctfrom those of the recharge area one reference station displays atrait compositionmore equitablewith those of the recharge station.The assemblage of this station also showed a different multivariatecommunity structure from those of the other two reference sta-tions (Fig. 4b). Furthermore, there is an indication that the traitcomposition of the assemblages of the recharge area is progressing(left to right on the MDS plot) towards those of the reference sta-tions area (although one station at 48 m opposes this trend). Thedifferences in trait composition between assemblages of therecharge and reference stations are supported by the ANOSIM test(Table 6).

3.2.3. Functional diversity (FD)Rao’s quadratic entropy coefficient (FDQ) was calculated for each

assemblage based on all 10 traits as opposed to one based on eachtrait individually. As such, mean values in Fig. 6a and b representoverall trait functional diversity and may, therefore, obscurerecharge-reference differences in FDQ for individual traits. Never-theless, functional diversity of the assemblages of the rechargestations at WW was generally higher than those of the referencestations. This elevated functional diversity, based on 10 traits, im-plies that assemblages of the recharge stations display a greaterspread of individuals across all the trait categories, the corollary ofthis being that the abundance of individuals within reference sta-tion assemblages tend to display a less even spread across thevarious trait categories.

Functional diversity of the assemblages of the recharge stationsat TM was consistently similar to that of the reference station as-semblages throughout the sampling programme. High variabilityaround the mean was observed for both recharge and referencestation assemblages.

3.2.4. Total secondary productionThere was no difference between total production of the as-

semblages of the recharge and reference areas at WW (Fig. 7a).Total secondary production estimates are evidently highly spatiallyvariable for the assemblages at WW. At TM, mean total productionestimates of reference station assemblages were consistentlyhigher than those of the recharge stations (Fig. 7b) although esti-mates were highly variable between the three reference stations.

3.2.5. Taxonomic contribution to total productionThe relative similarity in the contribution of each taxon to total

production for each assemblage was explored using a multivariateapproach. The resulting MDS plot forWW indicates that while totalproduction did not vary significantly between the assemblages ofthe recharge and reference areas (i.e. Fig. 7a), the identity of the

Fig. 4. Non-metric Multi-Dimensional Scaling (nMDS) plots showing relative differences in assemblage structure between recharge (black symbols) and reference (open symbols)stations at each sampling period for (a) WW and (b) TM. MDS based on BrayeCurtis similarity matrix of log-transformed abundance data.

S.G. Bolam / Marine Environmental Research 97 (2014) 15e29 23

taxa contributing to total production varied between the two areas(Fig. 8a). These taxonomic differences in proportional contributionto total production are statistically significant at certain samplingevents, after 6m and particularly at the end of the sampling pro-gramme (i.e. 36e48m post-recharge; Table 7). The significant dif-ferences at these times resulted from total production beinggoverned primarily by the polychaete H. diversicolor and the crus-tacean Corophium volutator at the recharge stations while primarilyby oligochaetes (Tubificoides sp.), the bivalve Scrobicularia plana andthe polychaete S. shrubsolii at the reference site.

A clear taxonomic difference in proportional contribution tototal production between recharge and reference communities isalso evident for TM (Fig. 8b), although it appears that the produc-tion of one of the reference stations is due to different taxa fromother two reference stations (i.e., those points at the top left of the

MDS plot). The ANOSIM test revealed that these differences wereonly significant during the first 12 months post-recharge; propor-tional contribution to total production was not significantlydifferent between recharge and reference assemblages after 12month post-recharge (Table 7). The significant difference in pro-portional contribution within the first 12 months resulted from aproduction-dominance by the gastropod mollusc H. ulvae withinrecharge assemblages while production of the reference assem-blages was largely provided by Tubificoides sp. and S. plana.

4. Discussion

The presence of high macrofaunal densities after 3m post-recharge (the earliest sampling event) observed at both WW andTM supports the findings of a number of experimental and

Table 5Test statistic (R) values following one-way ANOSIM tests between benthicassemblages inhabiting recharge and reference areas based on faunal (nu-merical) composition.

Month WW TM

3m 0.96 0.836m 1.0 0.5612m 1.0 0.7518m 0.70 0.6324m 1.0 0.5830m 0.82 0.5936m 0.85 0.3042m 0.41 0.3748m 0.82 0.11

R values >0.5 are indicated in bold.

3m 3m

3m

6m

6m

6m 6

12m

12m

12m

11

12m

18m18m

18m18m18m

24m24m

30m30m

30m

30m36m36m

36m

36m42m42m

42m42m

48m

48m

48m

48m

3m

3m

3m

3m

6m

6m

6m

6m

12m

12m

12m

12m

18m18m

18m

18m

24m

24m

24m

24m

24m30m

30m

30m36m36m

36m

36m

42m42m

42m48m

48m

48m

48m

(a)

(b)

Fig. 5. Non-metric Multi-Dimensional Scaling (nMDS) plots showing relative differences istations at each sampling period for (a) WW and (b) TM. MDS based on BrayeCurtis simila

S.G. Bolam / Marine Environmental Research 97 (2014) 15e2924

observational studies (Savidge and Taghon, 1988; Smith andBrumsickle, 1989; Hall et al., 1993; Bolam and Fernandes, 2002;Bolam et al., 2004); macrofaunal recolonisation of intertidal habi-tats can be rapid. However, the present study implied that thesuccess of the two schemes, or the time taken for recovery,depended on whether structural or functional approaches areadopted as the management criterion. The recharge assemblages ofboth schemes were consistently less speciose and less-denselypopulated compared with those of the reference assemblages andtheir structural characteristics remained significantly differentthroughout the 48m sampling period (although there were signs ofrecovery at TM towards the end of this period). Functionally,however, the two schemes could be regarded as recovered, evenafter a short period, although this depended upon the functionalmetric used. For example, average functional diversity of recharge

3m

3m

3m

m6m

6m

2m2m

18m

24m24m

24m

24m

30m

30m36m

36m

42m

42m

48m

48m

2D Stress: 0.1

3m6m

6m

12m18m

18m24m30m

30m

36m36m

42m42m

42m 48m48m

2D Stress: 0.06

n trait composition between recharge (black symbols) and reference (open symbols)rity matrix of log-transformed proportional traits data.

Table 6Test statistic (R) values following one-way ANOSIM tests between rechargeand reference assemblages based on trait composition.

Month WW TM

3m 0.70 0.676m 1.0 0.1912m 0.89 0.6718m 0.30 0.6024m 0.52 0.7530m 0.41 0.7436m 0.82 0.2642m 0.44 0.2648m 0.42 0.04

R values >0.5 are indicated in bold.

S.G. Bolam / Marine Environmental Research 97 (2014) 15e29 25

assemblages was equal that of reference assemblages as early as 3months post-recharge for both WW and TM (even enhanced atWW) and there was indication that total production had recovered.High spatial variability was observed for the latter functionalmetric, especially at TM. However, trait composition of these twoschemes may be regarded as not recovered during the sampling

Fig. 6. Functional diversity (Rao’s quadratic entropy Q) (mean � 95% CI) of recharge (greyFunctional diversity calculated using all 10 biological traits. Asterisks denote statistical difANOVA).

programme and the taxa contributing to secondary productionestimates were significantly different between recharge andreference assemblages at WW.

Habitat restoration and/or improvement schemes require ameasure of success (Thayer et al., 2003; MacKay et al., 2011); failureto achieve the agreed reference condition in the relevant metricwould normally trigger management intervention and/orcompensatory measures. Regarding intertidal recharge schemesusing fine-grained dredged material such as those at WW and TM,macrofaunal recovery is traditionally gauged by comparison of thebenthic structure of the modified (or recharge) area with that of asuitable reference area (Evans et al., 1998; Ray, 2000; Bolam andWhomersley, 2003, 2005; Bolam et al., 2006, 2010a; Garbuttet al., 2006; Schratzberger et al., 2006). However, Bolam andWhomersley (2005) and Bolam et al. (2010a) pointed out that, inview of the tidal height alterations resulting from the recharge, thetaxonomic structure of the establishing communities is inherentlygoing to remain different from reference communities. Theyconcluded that intertidal recharge schemes are likely to be regar-ded as ‘unsuccessful’ as a result and that multivariate structure istoo restrictive to be deemed suitable for assessing recovery in such

bars) and reference (open bars) areas at each sampling event for (a) WW and (b) TM.ference between recharge and reference areas (* ¼ p < 0.05; ** ¼ p < 0.01, one-way

Fig. 7. Mean total production (in kJ m�2 y�1; �95% CI) estimates for assemblages of recharge and reference stations at (a) WW and (b) TM. Asterisks denote statistical differencebetween recharge and reference areas (p < 0.05, one-way ANOVA).

S.G. Bolam / Marine Environmental Research 97 (2014) 15e2926

instances. In this study, the capacity to determine recovery ofintertidal dredged material recharge schemes using functionalmetrics of benthic invertebrates is assessed, and the results arecompared with those from using univariate and multivariatestructural approaches. Elliott and Quintino (2007) advocated thatassessing anthropogenic change should be based on functionalcharacteristics in addition to (or instead of) structural ones. Indeed,structural and taxonomic characteristics of communities varyindependently and thus decisions regarding which measures arechosen for recovery assessment are inherently important. Forexample, Cooper et al. (2008) found that macrofaunal structuraland functional recovery times varied following the cessation ofaggregate extraction in the English Channel, and Bolam (2012)observed, following an assessment of 14 dredged materialdisposal sites across England and Wales, that there was no clearrelationship between multivariate structural changes and taxo-nomic contribution to total production, indicating that a lack ofchange in the former does not always signify a lack of a significantfunctional impact. Finally, Munari (2013) demonstrated thatbenthic communities displayed a greater functional persistence inresponse to management measures compared to their taxonomiccomposition.

Two communities with similar multivariate taxonomic struc-tures will inherently be similar with respect to their trait compo-sitions, however, the opposite does not necessarily follow. That is,two structurally-dissimilar communities may, theoretically, displaysimilar trait compositions. This may be observed where the specieswhich vary between the two communities possess the samefunctional traits. At both WW and TM, the assemblages of the

recharge areas were less speciose and possessed altered taxonomicstructures from their respective reference assemblages. The resultof these differences was that assemblages of recharge and referenceareas comprised altered trait compositions (in the first 3 years afterrecharge). If trait composition determines function, the resultsimply that the species which varied between recharge and refer-ence assemblages (or the species which failed to recolonise withinthe study period) were functionally different. This finding is con-trary to that observed for comparable studies in which functionalrecovery based on trait composition has been shown to occursooner, or has been observed to bemore persistent, than taxonomicstructure (Barrio Froján et al., 2011; Munari, 2013). The low di-versity of intertidal mudflats relative to subtidal habitats whichwere the focus of the other published studies may be responsiblefor the contrasting observation. That is, there is a greater chance fora taxonomic change in a species-poor assemblage to alter traitcomposition as there are fewer taxa representing each trait cate-gory; these communities may, therefore, be regarded as less dis-playing a lower functional redundancy (Naeem, 1998). The presentdata, therefore, reflecting the situation for a relatively species-poorhabitat, support the notion that functional redundancy is lower inless diverse communities (Wall and Nielsen, 2012).

The relative rapidity of the recovery of functional diversity andtotal production, in the light of the long-term separation of bothtrait and taxonomic composition (and number of species anddensity) between recharge and reference communities at bothWWand TM, may indicate that functional diversity and production aremore resilient, i.e. they return to equilibrium more quickly, inmudflat systems. The data obtained here demonstrated clearly that

Fig. 8. nMDS plots showing proportional production across taxa for assemblages of recharge and reference stations at (a) WW and (b) TM.

S.G. Bolam / Marine Environmental Research 97 (2014) 15e29 27

recovery of trait composition of an assemblage to those of referenceareas is not a pre-requisite for recovery of functional diversity orsecondary production. However, the implications of functional di-versity recovery for benthic function are not straightforward.Equality of functional diversity implies that the numerical distri-bution of individuals across the various trait categories has been re-established, although it does not imply that this applies to the sametrait categories. As the functional diversity metric used here wasbased on all 10 traits, functional diversity of any one biological traitmay have actually shown a lack of (or slower) recovery than thatobserved based on all traits.

Intertidal mudflats are very productive areas (Kenji et al., 2012)and the highly productive macrofaunal organisms form an impor-tant component of the diet of many fish and bird species (Goss-Custard, 1984; Evans et al., 1998; Saint-Béat et al., 2013). In thisrespect, productivity recovery should be a management priority for

intertidal recharge schemes and recovery of structural attributesperhaps a secondary consideration. The recovery of secondaryproduction of the assemblages of the recharge stations at WWafter6 months post-recharge is an important finding of the presentstudy and one which would not have been expected given thelimited recovery of macrofaunal density throughout the whole ofthe sampling programme (Fig. 2b). Thus, although fewer in-vertebrates recolonised the recharge scheme at WW, secondaryproductivity recovered quickly, presumably as those organismswhich did recolonise possessed traits which afforded greater rela-tive productivity than those of the reference assemblage. Indeed,these assemblages maintained significant differences in traitcomposition for the majority of the sampling programme. Theimplication of this is that the assemblage of the dredged materialrecharge scheme at WW was equally capable of supporting theenergetic requirements of predators as that of the reference area.

Table 7Test statistic (R) values following one-way ANOSIM tests between assemblagesof recharge and reference areas based on proportional contribution to totalsecondary production.

Month WW TM

3m 0.30 0.586m 0.67 0.7012m 0.41 0.5818m �0.10 0.4024m 0.48 0.4030m 0.33 0.3736m 0.78 0.1942m 0.70 0.3348m 0.74 0.30

R values >0.5 are indicated in bold.

S.G. Bolam / Marine Environmental Research 97 (2014) 15e2928

However, the taxa contributing to total production in the rechargeassemblages varied from those contributing to production in thereference area, especially towards the end of the sampling pro-gramme; this is likely to have implications for prey availability forcertain predators depending on predation-specificity. In contrast,secondary production of the recharge assemblages at TM of therecharge area was consistently suppressed relative to that of thereference area, despite non-significant differences in the taxonomiccontribution to total production from 12 months post-recharge.Fundamentally, these data infer after 48 months that at WW, therecharge assemblage may provide an equitable food supply but tonon-specific predators, while that of TM provides prey to the samepredator groups as that of the reference assemblage but to areduced energetic capacity.

Recently, BTA has been used successfully to estimate the effec-tiveness of management measures in marine systems (van Kleefet al., 2006; Verissimo et al., 2012; Munari, 2013) and the presentstudy has augmented this to include the recovery of fine-grained,intertidal sediment recharge schemes. However, this approach isstill relatively novel in the marine realm and marine benthic sci-entists are currently attempting to understand the effect of anumber of methodological aspects (e.g. numerical correlation ap-proaches, weighting methods (Bremner et al., 2006; Munari, 2013))on eventual conclusions regarding functionality. Of particularimportance is the number and identity of the traits included withina particular study. There is currently no accepted mechanism todetermine which, and how many, traits should be included(Marchini et al., 2008; Bolam, 2013; Munari, 2013); while moretraits may provide a more informative representation of function,the inclusion of too few traits risks affording a misleading view offunction (Bremner et al., 2006). The implication of this is thatoutcomes regarding functional recovery of intertidal rechargeschemes may inevitably depend onwhich traits are selected as partof the assessment process. Mean values of functional diversity for arange of traits will also be sensitive to the traits included within thestudy. Determining the traits which are predominantly responsiblefor differences between assemblages, and discussing their signifi-cance, is perhaps the most suitable way to use multiple traits data.

In conclusion, assessing the infaunal recovery of intertidaldredged material recharge schemes should be undertaken using acombination of both structural and functional approaches, asadopted for terrestrial schemes (MacKay et al., 2011). This is inagreement with the recommendation proposed by Cooper et al.(2008) regarding improvements to assessing recovery followingdredging. A focus solely on functional recovery without due regardto structural recovery would be undesirable until our under-standing of the relationships between biodiversity and functionimproves. For example, one cannot conclude two assemblages to befunctionally equivalent based on functional diversity or trait

composition unless the number of species upon which these met-rics are based is known to be equivalent for the two assemblages.

Acknowledgements

Data for this study were acquired from a project (currently‘SLAB5’) funded by the Department for the Environment, Food andRural Affairs (Defra), while the traits work was made possible dueto resource from a number of projects such as ME5301 and FP7project BENTHIS (312088). A number of Cefas staff are to bethanked for their help including Dr. PaulWhomersley for assistancein the field, Claire Mason for the analysis of the sediments, and Drs.Keith Cooper and Christopher Barrio Froján for their constructivecomments to earlier versions of this manuscript. Three anonymousreviewers are to be thanked for their constructive comments on anearlier version of this manuscript.

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