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Estuarine, Coastal and Shelf Science 110 (2012) 202e209

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Estuarine, Coastal and Shelf Science

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Functional diversity in three Mediterranean transitional water ecosystems

Kalliopi Sigala a,b,*, Sofia Reizopoulou a, Alberto Basset c, Artemis Nicolaidou b

aHellenic Centre for Marine Research, Institute of Oceanography, PO Box, 712, 190 13 Anavyssos, Attiki, GreecebDepartment of Zoology and Marine Biology, School of Biology, University of Athens, 15784 Panepistimiopoli, Athens, GreececDiSTeBA, University of Salento, SP Lecce Monteroni, 73100 Lecce, Italy

a r t i c l e i n f o

Article history:Received 4 November 2011Accepted 14 June 2012Available online 23 June 2012

Keywords:functional analysisbiological trait analysismacrobenthoscoastal lagoonsMediterranean

* Corresponding author. Hellenic Centre for MOceanography, PO Box 712, 190 13 Anavyssos, Attiki,

E-mail addresses: sigalakal@hcmr.gr, Sigalakal@gm

0272-7714/$ e see front matter � 2012 Elsevier Ltd.http://dx.doi.org/10.1016/j.ecss.2012.06.002

a b s t r a c t

Biological trait analysis (BTA) is a method that describes ecological functioning of species assemblagesincorporating information on species’ distributions and their biological characteristics. In the presentstudy, soft bottom communities of three Mediterranean coastal lagoons with different degree of salinityrange were analyzed for seven biological traits (mobility, habitat, feeding type, habitat modification, bodyform, body size and feeding apparatus) in order to investigate the differences in communities’ structureand functional diversity across a scale of natural stress. In more variable environments semi-mobile andepibenthic organisms prevailed, while predators were found under more stable conditions. Multivariateanalyses using biological traits gave similar results with those obtained by the traditional analyses usingspecies abundances; however, the distinction among lagoons was less evident, indicating that in tran-sitional waters species can be different but their biological traits similar. This supports the idea of speciesredundancy in transitional water ecosystems.

� 2012 Elsevier Ltd. All rights reserved.

1. Introduction

In recent years, scientific community searches for the bestmethods (in terms of accuracy and quickness) first to describe andunderstand transitional water ecosystems and then to evaluatetheir ecosystem health (Dauvin, 2007). Investigations till now havemostly been based on taxonomic composition and relative abun-dance of benthic taxa (Gibson et al., 2000), which are valuable andeasy to comprehend.

However, recent studies have questioned the use of only speciesnumber and other basic parameters to describe the ecosystem andit’s functioning (Cao et al., 1996; Drobner et al., 1998; Mouillot et al.,2006;Warwick and Somerfield, 2008), since it is not the species perse that are important, at least as far as the ecosystem is concerned.Instead, the use of functional diversity has been proposed as a moresuitable method to study ecosystems, their function and the impactof natural and anthropogenic factors on them (Fano et al., 2003;Bady et al., 2005). Functional diversity, i.e. the diversity and range offunctional traits possessed by the biota of an ecosystem (Wrightet al., 2006), is likely to be the component of biodiversity mostrelevant to the functioning of ecosystems (Hooper et al., 2005).

arine Research, Institute ofGreece.ail.com (K. Sigala).

All rights reserved.

There is neither a unique and most suitable method to measurefunctional diversity nor adequate information concerning thespecies traits that should be used for functional diversity charac-terization (Petchey and Gaston, 2006). In the present study, fromthe various methods that exist, Biological Trait Analysis (BTA) wasselected to be applied.

Ecosystem functions are not depended only upon the diversityof species and their traits, but also upon the environmental contextand how species interact with each other (Bulling et al., 2008,2010). Although the effects of variability in environmental condi-tions on species composition in benthic ecosystems are wellestablished (Millet and Guelorget,1994;McLusky and Elliott, 2004),relatively little is known on how environmental variability relatesto functional diversity. Species are in continuous interaction withthe environment, which means that species are adapted to thephysical and chemical environment, while the environment isconstantly modified by species’ biological activities.

Changes on the functional components of the communitiesrepresent the adaptations of the organisms to the environment andtheir response to stress (De Juan et al., 2007). Some traits are bettersuited to particular environmental conditions; therefore onlyspecies carrying these traits are likely to succeed under thoseconditions. For example, in areas with muddy sediments, suspen-sion feeders are not favored due to the clogging effect of particles insuspension and the destabilizing effect of deposit feeders that areusually abundant in those environments (Rhoads and Young, 1970).

K. Sigala et al. / Estuarine, Coastal and Shelf Science 110 (2012) 202e209 203

Whilst some ecosystem functions can be undertaken by a variety ofdifferent organisms, it is generally believed that a greater diversityof species increases the stability and resilience of an ecosystem’scapacity to perform its various functions (Cardinale et al., 2002).Hence, it is critical to understand the connection between envi-ronmental variability, species diversity and functional diversity.

Many studies investigate the relationship between speciesdiversity, functional diversity and ecosystem function (Petchey andGaston, 2002, 2006; Bremner, 2008). Furthermore, there arestudies that examine how biotic and abiotic components affect thetemporal and spatial variability in functional diversity (Emmersonet al., 2001; Raffaelli et al., 2003; Waldbusser et al., 2004; Ienoet al., 2006; Bell, 2007; Norling et al., 2007; Pacheco et al., 2010)indicating that species and functional diversity are affected ina similar way (Bremner et al., 2003; Micheli and Halpern, 2005;Hewitt et al., 2008). However most of these studies deal with plantand freshwater ecology and some recently with marine waters,whereas few studies regard lagoon benthic ecosystems (Marchiniet al., 2008).

Coastal lagoons are considered as functional ecotones (Bassetet al., in press). They are naturally enriched areas (Orfanidis et al.,2005) and they are characterized by marked spatial and temporal(daily and seasonal) fluctuations in environmental conditions, dueto their confinement from the open sea and to their shallowness(Reizopoulou and Nicolaidou, 2004; Nicolaidou et al., 2005;Orfanidis et al., 2005). Furthermore, transitional environments arecritical areas for the decomposition of organic matter and nutrient

Fig. 1. Map of the study sites

cycling (Levin et al., 2001). In that sense they are considered asnaturally stressed environments with low number of species andtaxonomic diversity (Dauvin, 2007) and their benthic fauna ismainly composed of species tolerant to the naturally disturbedconditions (Elliott and Quintino, 2007). Despite their low speciesrichness, these environments are highly resilient (Nilsson et al.,2012). Because biotic diversity in lagoon sediments is inherentlylow, whereas their functional significance is great, shifts in diversityare likely to be particularly important. Thus, since coastal lagoonenvironments are different both from marine and freshwaterdomains, functional analyses of benthic invertebrates may providedifferent results.

The aim of the present work is to explore the relationshipsbetween environmental variability and biological traits of thebenthic communities in three Mediterranean lagoons. Differencesin environmental conditions and biological traits of the benthicecosystems among the study areas have been investigated.

2. Materials and methods

Sampling was carried out in three Mediterranean coastallagoons (Fig. 1) with a variable degree of enclosure and marineinfluence: GradoeMarano, Logarou and Margherita di Savoia.GradoeMarano, situated in the North Adriatic Sea, has highermarine influence and freshwater input and it is affected by chem-ical and organic pollution (Ianni et al., 2008; Ponti et al., 2008).Logarou is formed on the Northern coast of the Amvrakikos Gulf,

and sampling stations.

Table 1Biological traits, their categories and their codes.

Trait Category Category code

Mobility Semi-mobile smMobile mSessile ss

Habitat Epifauna epSurface infauna iSubsurface infauna si

Feeding type Suspension feeder SFDeposit feeder DFPredator PHerbivore HScavenger SOmnivore O

Habitat modification Tube builder tSimple mounds hNo n

Body form Shell sVermiform vGlobulose g

Body size <10 mm 1<50 mm 2<100 mm 3<200 mm 4>200 mm 5

Feeding apparatus Radula rJawed jTentaculated tProboscoidae p

K. Sigala et al. / Estuarine, Coastal and Shelf Science 110 (2012) 202e209204

Western Greece fromwhich it is separated by a narrow barrier withopenings allowing limited water exchange with the marine envi-ronment. Freshwater input in this lagoon is seasonal. Margherita diSavoia, located along the Adriatic coast in Southern Italy, presentsa high degree of enclosure. The study areas are well reflectingdifferences in salinity stress. All the stations (Fig. 1) were charac-terized by bare sediments except for the stations 6 and 7 inGradoeMarano, where macroalgae were present.

Sampling was performed in autumn of 2004 and spring/earlysummer of 2005. Temperature, salinity and dissolved oxygen weremeasured just above the bottom using temperature/salinity andoxygen probes. Salinity was measured using the Practical SalinityScale. Grain-size measurement of the sediment was undertakenusing a Sedigraph 5100 system after the separation of the sandfraction (>63 mm) bywet sieving, for both seasons and all the studysites except for Margherita di Savoia in the fall.

Five replicates of quantitative benthic samples were taken from8 stations in each lagoon, by means of a box corer (0.03 m2). Thesamples were sieved through a 0.5 mm mesh, fixed in neutralizedformalin (4%) and stained with Rose Bengal. In the laboratory, themacrofauna was sorted, identified to species level where possible,and counted.

As far as BTA is concerned, selection of biological traits is basedon a trade-off between their efficacy for describing variability inecological functioning and the time and effort required to gatherinformation on the biological characteristics of the taxa studied(Bremner et al., 2006). Seven biological traits were chosen for theBTA, reflecting morphology (body form, body size, feeding appa-ratus) and behavior (mobility, habitat, feeding type, habitat modi-fication). Each trait was subdivided into several categories (Table 1)(Papageorgiou et al., 2009). The category of all the traits for eachtaxon is gained from literature sources, except for body formwhichwe were able to get directly from observing the species.

Traits for each taxon were derived from information gatheredfrom diverse and scattered literature sources, such as papers anddatabases (e.g. Pearson and Rosenberg, 1978; Fauchald and Jumars,1979; Bonsdorff and Pearson, 1999; Rouse and Pleijel, 2001;Macdonald et al., 2010; http://www.marlin.ac.uk/species.php).

Individual taxa were coded for the extent to which they displayeach category using a fuzzy-coding procedure (Chevenet et al.,1994), which allows assessment of affinity of a taxon to multiplecategories, using discrete scores from 0 (no affinity) to 3 (totalaffinity). For example, if a species is a suspension feeder, but undercertain circumstances can feed also by deposit feeding, then thatspecies is scored with 2 for suspension feeding and with 1 fordeposit feeding. If it is only a suspension feeder, then it is scoredwith 3 for suspension feeding and 0 for the other trait categories.Trait category scores for each taxon presented at a station wereweighted (multiplied) by their abundance at that station. Theseabundance-weighted trait category scores were then summed overall taxa presenting that code, at the station, to provide a measure ofthe frequency of occurrence of trait categories over the wholeassemblage (Charvet et al., 1998). This weighting procedure wasrepeated for each station in the dataset, resulting in a station bytrait table. This matrix was subjected to multivariate analysis. BTAuses multivariate ordination to describe patterns of biological traitcomposition over entire assemblages. Several ordination tools areavailable for this purpose. The choice of analytical tool is a balancebetween the powers of the tool to describe changes in traitcomposition and the ease with which results can be interpreted(Bremner et al., 2006). Fourth root transformation, BrayeCurtissimilarities and NMDS (Clarke, 1993) were applied to the table oftrait frequencies. Twomore NMDSswere constructed, one based onspecies abundances and one based on presenceeabsence of bio-logical trait categories. This method allows a description of

similarities between stations in terms of their biological traitcomposition. The ANOSIM test was applied to the resemblancematrices based on species abundance, biological traits andpresenceeabsence data, testing the null hypothesis that there wereno significant differences between study areas and samplingoccasions. All statistical procedures and analysis were performedusing PRIMER 6 software (Clarke and Gorley, 2006).

3. Results

The ranges of abiotic variables for each lagoon over the samplingoccasions are shown in Table 2. GradoeMarano is bigger than theother two lagoons and presents a higher tidal range; the temper-ature was higher in Logarou and the oxygen concentrations lowerin Margherita di Savoia. Smaller salinity ranges were observed inGradoeMarano and Logarou in comparison toMargherita di Savoia,which presented wider salinity fluctuations due to its lower degreeof communication with the sea.

Lagoon sediments were mainly muddy. In GradoeMarano thesediment was mostly muddy sand (varying amount of sand8.2e31.1%) with high silt and clay content (varying amount of silt60.7e82.2% and clay 6.4e10.0%), with the exception of stationsGM6 and GM7 where the sediment was sandy mud. Muddy sandsediments were also found in Logarou (varying amount of sand0.6e42.3%), but with a higher percentage of silt and especially claycontent (varying amount of silt 20.7e74.7% and clay 12.3e74.9%) incomparison to the other two lagoons. In Margherita di Savoiamuddy sand was also found (varying amount of sand 12.0e47.5%)with high silt and clay content (varying amount of silt 42.0e81.5%and clay 6.5e22.5%), except for stations M7 and M10 wheresandy mud was found.

The sediment grain-size did not change considerably for the twosampling occasions in GradoeMarano and Logarou. ANOSIM testapplied on sediment data of GradoeMarano and Logarou showedthat the lagoons did not differ seasonally (p > 0.05).

The ANOSIM test applied to the spring sediment data of all thestudy areas indicated a differentiation of Logarou in comparison to

Table

2Su

rface,

tidal

range

,dep

than

drange

sof

abioticparam

etersforea

chlago

onov

erthesamplin

goc

casion

s.

Lago

onSu

rface(km

2)

Tidal

range

(m)

Dep

th(m

)Sa

linity

Temperature

(�C)

Oxy

gen(m

gL�

1)

Season

Fall

Spring

Fall

Spring

Fall

Spring

Ran

geMea

n(SD)

Ran

geMea

n(SD)

Ran

geMea

n(SD)

Ran

geMea

n(SD)

Ran

geMea

n(SD)

Ran

geMea

n(SD)

Gradoe

Maran

o14

2.0

0.7

1.2

23e30

26.5

(3.3)

17.4e27

.222

.3(4.6)

5.3e

7.7

6.5(1.0)

19.9e21

.920

.9(0.7)

9.7e

10.6

10.2

(0.4)

6.3e

7.9

7.1(0.7)

Loga

rou

24.2

0.4

0.9

36.3e38

.837

.5(0.8)

26.4e30

.928

.7(1.7)

22.2e23

.522

.9(0.4)

25.5e29

.127

.3(1.4)

5.1e

5.6

5.4(0.2)

8.2e

9.5

8.85

(0.8)

Margh

erita

diS

avoia

12.0

0.1

0.4

37.1e52

44.6

(5.4)

26.2e48

.637

.4(3.2)

8.2e

14.2

11.2

(2.9)

19.1e23

.521

.3(1.9)

3.7e

8.4

6.1(2.0)

5.3e

76.15

(0.7)

K. Sigala et al. / Estuarine, Coastal and Shelf Science 110 (2012) 202e209 205

GradoeMarano (p< 0.05) andMargherita di Savoia (p< 0.001), dueto the higher clay content in the sediments of Logarou.

The differences of the three lagoons regarding the environ-mental parameters are illustrated in the PCA (Fig.2), where acrossPC1 axis there is a succession of stations with lower salinity at theleft part of the graph toward the high salinity stations of Margheritadi Savoia, which are grouped to the right part of the plot. Across PC2axis there is a succession of the fall stations toward the springstations at the upper part of the graph.

In total 103 macrofaunal species were identified, among them,common lagoonal species such as Abra segmentum, Cerastodermaglaucum, Hediste diversicolor, Nephtys hombergii. The lower numberof species (31) was found in the most confined lagoon, Margheritadi Savoia, dominated by insect larvae, Ventrosia ventrosa andCorophium species. In Logarou 46 species were found and thedominant species were Corophium acherusicum, Iphinoe serrata, N.hombergii and Microdeutopus gryllotalpa. In GradoeMarano,a higher number of taxa (67) were found in comparison to theother two lagoons, due to the higher degree of marine influence inthis lagoon, where the dominant taxa were Cirratulus sp., Gam-maridae, Corophium sp. and Oligochaeta.

In general, concerning biological traits, the most frequent modeof animals observed in all the studied areas was that of semi-mobile(up to 100% in the outer stations in Margherita di Savoia and in theinner station in GradoeMarano in Spring), small body size(<10 mm) (up to 100% in the outer stations in GradoeMarano inSpring), vermiform (up to 100% in the outer stations in Margheritadi Savoia in Fall), living in the upper centimeters in the sediment(up to 100% in one of the outer stations in GradoeMarano inSpring). The communities in all the study sites were dominated bydeposit feeders (up to 100% in the outer stations in Margherita diSavoia in Fall), followed by suspension feeders (up to 47% in one ofthe inner stations in Logarou in Spring), while the number ofpredators generally was low (up to 33% in one of the outer stationsin GradoeMarano in Spring).

More specifically in Margherita di Savoia most species wereepibenthic (e.g. Ventrosia ventrosa) (up to 100% in the outer stations),whereas GradoeMarano and Logarou were dominated by surface infaunal organisms (up to 100% in GradoeMarano and up to 92% inLogarou). In GradoeMarano no sessile organisms were registeredand most species belonged to the semi-mobile (up to 100% in theinner station in spring) infauna (e.g. Corophium sp.). Logarou pre-sented a higher percentage of mobile species (up to 92%), while theanimal size was smaller, in comparison to the other two lagoons.

No significant correlations between abiotic parameters andbiological traits composition were observed.

The NMDS plots based on species abundances, BTA andpresenceeabsence of trait categories are shown in Fig. 3. In the

Fig. 2. PCA ordination on environmental parameters (salinity, temperature andoxygen) (GM ¼ GradoeMarano, LO ¼ Logarou, M ¼ Margherita di Savoia, F ¼ Fall,S ¼ Spring).

Fig. 3. NMDS plots based on species abundance, BTA and presenceeabsence of biological traits (GM ¼ GradoeMarano, LO ¼ Logarou, M ¼Margherita di Savoia; F ¼ Fall, S ¼ Spring).

Table 3ANOSIM results: global R and significance level for each sampling occasion based onthe three data matrices.

Global tests Fall Spring

R-value Significancelevel

R-value Significancelevel

Species abundance 0.906 0.001 0.697 0.001BTA, quantitative 0.370 0.001 0.381 0.001BTA, presenceeabsence 0.361 0.001 0.194 0.001

K. Sigala et al. / Estuarine, Coastal and Shelf Science 110 (2012) 202e209206

NMDS ordination plot based on species abundances there is a cleardiscrimination of the lagoons. In the NMDS ordination plot basedon biological traits composition most stations are grouped togetherexcept for a part of stations of Margherita di Savoia which are sit-uated at the right of the diagram.

Table 3 shows the results of the ANOSIM test separately foreach sampling occasion for the abundance, the BTA and thepresenceeabsence traits data. In both seasons for each datasetthe null hypothesis was rejected at a significance level p ¼ 0.001.The R-value was the lowest for the BTA presence/absence matrix

Table

4ANOSIM

resu

lts:

pair-wiseRan

dsign

ificance

leve

lforea

chsamplin

goc

casion

basedon

thedifferentmatrices.

Pair-w

isetests

Fall

Spring

Speciesab

undan

ceBTA

,quan

titative

BTA

,presenceeab

sence

Speciesab

undan

ceBTA

,quan

titative

BTA

,presenceeab

sence

R-value

Sign

ificance

leve

lR-value

Sign

ificance

leve

lR-value

Sign

ificance

leve

lR-value

Sign

ificance

leve

lR-value

Sign

ificance

leve

lR-value

Sign

ificance

leve

l

Gradoe

Maran

oLo

garou

0.87

20.00

10.29

60.02

20.17

10.00

70.65

30.00

10.38

30.00

40.18

80.00

6Lo

garou

Margh

erita

diSa

voia

0.95

40.00

10.50

10.00

20.46

20.00

10.85

30.00

20.38

10.00

10.25

10.00

2

Margh

erita

diS

avoia

Gradoe

Maran

o0.91

10.00

10.42

00.00

10.49

60.00

20.60

40.00

10.43

90.00

30.19

70.00

6

K. Sigala et al. / Estuarine, Coastal and Shelf Science 110 (2012) 202e209 207

and the highest for the species abundance matrix in both samplingoccasions. The respective pair-wise comparisons among lagoonsseparately for each sampling occasion gave similar results (Table 4).Indeed, in most cases, the species abundance data presenteda higher differentiation of the study areas, at a significance levelp ¼ 0.001, in comparison to the results from the biological traits.The ANOSIM test was also applied to compare the fall and springdata for each lagoon (Table 5). On the basis of species abundancedata, all the lagoons showed significant differences between thetwo sampling occasions at p < 0.05, while based on the biologicaltraits, only GradoeMarano and Margherita di Savoia presenteda significant seasonal differentiation at p < 0.05. When consideringsolely the presenceeabsence of the biological traits, none of thestudied lagoons presented significant differences between the twosampling moments (Table 5).

4. Discussion

Functional diversity is a term increasingly used in the last 10years. However, its definition is not yet clear and efforts are made tofind the most adequatemethod tomeasure and to assess functionaldiversity performance to characterize an ecosystem. If ecosystemfunctioning matters more than species per se, then functionalanalysis may be more efficient in reflecting the ecosystem’s status.Such approach is more developed in terrestrial, marine and fresh-water domains than in transitional water ecosystems.

In this work, three coastal lagoons were studied, using theclassic taxonomy approach and biological traits analysis (BTA). Thebiological traits used in the present study occur in most benthicorganisms and have important effects on ecosystem processes(nutrient fluxes across the sedimentewater interface, bioturbationand irrigation, habitat creation, secondary production, sedimentstability/transport and carbon sequestration). Mobility, an impor-tant ecological trait affecting the food capture method anddefining the trophic relationships of a benthic community(Marchini et al., 2008), is described as the capacity of the organ-isms to move in and out of the sediment. Semi-mobile organismshave a limited ability to move. Their movements are very slow andperformed only if necessary. Benthic organisms can be found on orinside the sediment e close to the surface or deeper e affectingnutrient fluxes, bioturbation, sediment stability and otherprocesses (Michaud et al., 2006). Feeding has often been consid-ered an important variable of benthic community diversity and hasbeen used in many studies of benthic community structure(Taurusman, 2010; Uwadiae, 2010). Feeding guilds of benthicorganisms combine a variety of different attributes ranging fromsuspension feeding to omnivory and have implications forresource use and energy transfer. Habitat modification trait refersto animals with the ability to build tubes or live in mounds, whichaffects nutrient recycling and substrate stability (Rhoads, 1974).Animal size, characteristic of the life strategy, energy transfer andthe ecology of the species (De Roos et al., 2003), could also bea suitable descriptor of lagoon ecosystem health (Reizopoulou andNicolaidou, 2007; Basset et al., 2008; Basset et al., 2012). Thefundamental concept is that the life history traits are reflected inbody size and may control species composition and relativeabundance.

In the present study the distinction between lagoons wasstronger when analyzing the abundance data than when analyzingthe biological traits data, indicating common patterns in the bio-logical traits among the lagoons, especially when these areanalyzed in terms of presenceeabsence. Most of the speciesinhabiting lagoons are less specialized in food, with shorter lifecycles and high reproductive rates. A weaker association betweenbiological trait composition and environmental conditions than

Table 5ANOSIM results: global R for each sampling occasion in each lagoon based on the different data matrices (GM ¼ GradoeMarano, LO ¼ Logarou, M ¼ Margherita di Savoia).

Global tests Species abundance BTA, quantitative BTA, presenceeabsence

R-value Significancelevel

R-value Significancelevel

R-value Significance level

GM Fall GM Spring 0.206 0.016 0.158 0.014 0.069 0.134LO Fall LO Spring 0.299 0.004 0.114 0.098 0.107 0.057M Fall M Spring 0.272 0.022 0.208 0.036 0.104 0.960

K. Sigala et al. / Estuarine, Coastal and Shelf Science 110 (2012) 202e209208

that shown by relative taxon composition has also been detected inmarine benthic communities (Bremner et al., 2006).

Margherita di Savoia, which presented the wider range ofsalinity and the lower oxygen concentrations, was characterized byepifaunal organisms. At the same time, the low frequencies ofborrow-builders observed in Margherita di Savoia may reduce theability of these benthic assemblages to process and store chemicalsand waste materials, since burrows promote microbial biomass(Aller and Aller, 1986) and increase organic matter decompositionand nutrient recycling.

The presence of different feeding trait categories in the lagoonsindicate diverse food sources, while the presence of a highproportion of deposit feeders indicates that the main food source isthe organic matter suspended in the water column and depositedin the sediment. The presence of suspension feeders in all thelagoons indicates the importance of seston transport in the watercolumn (Palmer et al., 1993; Uwadiae, 2010). The more feedingtraits are detected, themore diverse pathways of energy andmattercycling are expected. The low number of predators may be attrib-uted to the fact that specialized feeders are more sensitive andthought to be well represented in more stable environments(Uwadiae, 2010). In contrast generalists, such as deposit feeders,which were well represented in this study, have a broader range ofacceptable food materials and thus are more tolerant.

It is very difficult to determine whether it is the environmentalparameters included in the analysis that drive the faunal patternsobserved, or whether other environmental variables not measured,or their combination that may affect the ecological patterns (Clarke,1993). The key factors that affect biodiversity and functionaldiversity in coastal lagoons are many, including salinity. Speciesrichness in coastal lagoons is not dependent on salinity alone, but isthe result of a complex of factors, described by the term “confine-ment”e the time required to renew themarine element (Guelorgetand Perthuisot, 1983; Reizopoulou and Nicolaidou, 2004).Furthermore, many are the anthropogenic stressors, includinghabitat loss and environmental deterioration, eutrophication,sewage, fisheries overexploitation, altered freshwater flows, inva-sive species that also influence benthic communities. For manytransitional water ecosystems, all these factors and stressors actsimultaneously, and their combined effects are not easy to predict.Experimental investigations will be necessary to determine theextent and consequences of these important relationships. Inaddition, in naturally occurring communities, the individualcontribution of species to ecosystem function is a complex productof niche complementarity, species density, sampling and selectioneffects, although the relative contribution of each of these mecha-nisms most likely alters in time and space (Biles et al., 2003).

In the present study, when analyzing the biological traits datano clear groups of the lagoons are formed; however there is a shiftof some stations of Margherita di Savoia, the most confined lagoonwith a stronger salinity gradient. Generally it has been observedthat ecosystem and environmental variability influences the bio-logical traits composition (Bremner et al., 2006; Pacheco et al.,2010).

Environmental managers seek methods, which are not specificto one location or time and which are cost-effective and easy toapply and interpret. Although functional diversity is an importantcomponent of biodiversity, the methods to quantify it are less welldeveloped than those used for taxonomic diversity. The applicationof functional multi-trait analyses to species level in transitionalsystems is still time-consuming, taxonomically difficult and needsfurther refinement (Albert et al., 2010). The major difficulty inperforming BTA involves the amount of time and informationrequired to compile a dataset of species traits. Furthermore, gaps inthe knowledge of species biology may limit the power of thesetypes of analyses.

Transitional water ecosystems differ in their nature both fromthe freshwater ecosystemswhere the functional diversity approachwas initially developed and from the marine environments wheremore studies than in transitional environments have been carriedout. Hence, methodologies devised for freshwater and marineecosystems may not necessarily be the most informative for tran-sitional systems. More effort should be taken to associate functionaldiversity and the assessment of the ecological quality in the lagoonenvironments (Mouillot et al., 2006; Elliott and Quintino, 2007;Reizopoulou and Nicolaidou, 2007).

Acknowledgments

The authors would like to thank Hara Kiriakidi for her contri-bution to the maps of this paper and the referees for theimprovement of the manuscript.

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