A comparison of sediment reworking rates by the surface ... · A comparison of sediment reworking rates by the surface deposit-feeding bivalve Abra ovata during summertime and wintertime,

gy and Ecology 343 (2007) 21–36www.elsevier.com/locate/jembe

Journal of Experimental Marine Biolo

A comparison of sediment reworking rates by the surfacedeposit-feeding bivalve Abra ovata during summertime

and wintertime, with a comparison betweentwo models of sediment reworking

O. Maire a,⁎, J.C. Duchêne a, A. Grémare a, V.S. Malyuga b, F.J.R. Meysman b

a CNRS, UMR7621, F-66650 Banyuls-sur-Mer, Franceb The Netherlands Institute of Ecology (NIOO–KNAW), Centre for Estuarine and Marine Ecology,

Korringaweg 7, 4401 NT Yerseke, The Netherlands

Received 8 June 2006; received in revised form 26 August 2006; accepted 28 October 2006

Abstract

Sediment reworking rates by the surface deposit-feeding bivalve Abra ovata were assessed in thin aquaria using an automatedimage analysis procedure for luminophore tracer particles. Experiments were carried in winter (10 °C) and summer (20 °C), andthree food treatments were tested: no food addition (CF), low food addition (LF) and high food addition (HF). The rate of sedimentreworking was characterized in four different ways: (1) the maximum penetration depth of luminophores (MPD), (2) the proportionof reworked sediment surface (PRS) from which individual rates of surface area reworking (IRSAR) were derived, (3) the fitting ofthe standard biodiffusion model resulting in a biodiffusion coefficient Db, and (4) the application of a new non-local bioturbationmodel that produced an alternative mixing intensity Db

NL. In winter, sediment reworking was low and was not affected by foodavailability. In contrast, during summer, reworking activity was very high and significantly affected by food availability. Thissuggests that temperature and not food availability controls sediment reworking during wintertime. Although the biodiffusive andnon-local models produced similar values for mixing intensities after 48 h, the non-local model gave markedly better fits during theinitial stage of the experiment. This agrees nicely with theoretical predictions: over short-time scales the non-local model shouldprovide a more accurate description of bioturbation, but as the number of bioturbation events increases, the non-local model shouldconverge to the biodiffusion one. Yet, an additional advantage of the non-local model is that it allows constraining two crucialparameters characterizing A. ovata bioturbation: the average distance over which particles are displaced (2.1 mm) and the waitingtime between two bioturbation events (5.39 h). Accordingly, reworking is characterized by highly frequent and small-scale particledisplacement, which makes that A. ovata can be classified as a true biodiffuser.© 2007 Elsevier B.V. All rights reserved.

Keywords: Abra ovata; Bioturbation modelling; Food availability; Image analysis; Sediment reworking; Temperature

⁎ Corresponding author. Laboratoire d'Océanographie Biologiquede Banyuls, UMR7621, BP44, F-66650 Banyuls-sur-Mer, France.Tel.: +33 4 68 88 73 94; fax: +33 4 68 88 73 95.

E-mail address: [email protected] (O. Maire).

0022-0981/$ - see front matter © 2007 Elsevier B.V. All rights reserved.doi:10.1016/j.jembe.2006.10.052

1. Introduction

Sediment reworking results from various activities ofbenthic infauna (i.e. burrowing, feeding and locomotion),

mailto:[email protected]://dx.doi.org/10.1016/j.jembe.2006.10.052

22 O. Maire et al. / Journal of Experimental Marine Biology and Ecology 343 (2007) 21–36

and strongly affects the physical, chemical and biologicalcharacteristics of marine sediments (Rhoads, 1974; Aller,1982; Meadows and Meadows, 1991; Hall, 1994;Rowden et al., 1998). Sediment reworking stronglyinfluences organic matter mineralization (Kristensenet al., 1992; Sun et al., 1999; Ingalls et al., 2000; Greenet al., 2004), the structure and porosity of the sedimentmatrix (Meysman et al., 2005), the release of nutrientsfrom the sediment to the water column (Biles et al., 2002;Mermillod-Blondin et al., 2005), and the sequestration ofpollutants and contaminants (Lee and Swartz, 1980;Thompson and Riddle, 2005; Bradshaw et al., 2006). Therate of sediment reworking directly depends on thecharacteristics of the dominant macrobenthic species(i.e., size, density and feeding ethology), and on theintensity of infaunal activity, which depends on environ-mental parameters such as food availability and temper-ature (Gérino et al., 1998; Sandnes et al., 2000; Bileset al., 2002; Mermillod-Blondin et al., 2005).

Seasonal changes in environmental parameters canbe significant in temperate areas. They are even morepronounced in shallow semi-enclosed environmentssuch as Mediterranean lagoons where the yearly am-plitude of the temperature can reach 25 °C (Wilke andBoutière, 2000). Such changes clearly affect sedimentreworking rates as recently suggested for the ThauLagoon (Lecroart et al., 2005). The influence of envi-ronmental parameters on sediment reworking has beenassessed via: (1) in situ measurements carried out atdifferent periods of the year (Kudenov, 1982; Sunet al., 1991; Wolfrath, 1992; Gérino et al., 1998;Mugnai et al., 2003; Lecroart et al., 2005), and (2)dedicated laboratory experiments with selected organ-isms. These experimental approaches includes both:(1) experiments carried out at a single date and in-volving the manipulation of a single factor (Benderand Davis, 1984; White et al., 1987; Rowden et al.,1998; Hollertz and Duchene, 2001; Ouellette et al.,2004), and (2) comparative experiments carried out atdifferent periods of the year (Coulon and Jangoux,1993; Retraubun et al., 1996; Berkenbusch andRowden, 1999). These studies have documented im-portant temporal changes in sediment reworkingrates of natural communities. Moreover, they suggestthat changes in the sediment reworking rate of theentire community largely result from changes in thesediment reworking rate of a few dominant spe-cies (Mugnai et al., 2003; Lecroart et al., 2005).These results underline the crucial importance of con-sidering temporal changes in environmental para-meters when up-scaling sediment reworking rates toa longer period of time than the one over which actual

measurements were carried out (Berkenbusch andRowden, 1999).

To our knowledge, there has been no attempt toassess the temporal changes in sediment reworkingrates of individual species in environments with strongseasonal changes in temperature, such as Mediterra-nean lagoons (Wilke and Boutière, 2000). The deposit-feeding bivalve Abra ovata is a dominant species inMediterranean lagoons, where densities can reach up toseveral thousand individuals per square meter (Gue-lorget and Mayere, 1981; Reizopoulou et al., 1996).This bivalve is an efficient sediment reworker andcontributes for a large proportion to sediment rework-ing rate of the total community (Maire et al., 2006).The aim of the present study is to quantify and comparesediment reworking rates induced by A. ovata duringboth summer and winter periods. Sediment reworkingrates were estimated using a new method based onautomated image analysis of luminophore tracerparticle profiles (Maire et al., 2006). Luminophoreprofiles were then analysed with two differentbioturbation models, the standard biodiffusion modeland a newly developed, more sophisticated non-localbioturbation model (Meysman et al., submitted forpublication).

2. Materials and methods

2.1. Bivalve collection and maintenance

Abra ovata specimens were hand collected inshallow areas of the Lapalme Lagoon (North WestMediterranean). A first batch was collected in July 2004(20 °C, 6 salinity). Bivalves were kept in natural sedi-ment and well aerated lagoon water during 15 days atthe Observatoire Océanologique de Banyuls sur Mer,France. Salinity was progressively increased to 20.Subsequently, bivalves were packed in sealed refriger-ated boxes filled with O2 saturated water, and brought tothe Kristineberg Marine Research Station (Sweden),where they were kept in tanks with natural sediment andflow through seawater (20 °C, 24 salinity). A secondbatch was collected in December 2004 (10 °C, 22 sali-nity). These bivalves were kept in Lapalme lagoonsediment during 1 week at the Observatoire Océanolo-gique de Banyuls sur Mer, France, with flow through seawater (10 °C, 24 salinity). Bivalves of the two batcheswere fed every other day with crushed Tetramin® fishfood. Before each experiment, specimens were mea-sured to the nearest mm. The total shell lengths of thebivalves used during the two experiments were between12 and 13 mm.

23O. Maire et al. / Journal of Experimental Marine Biology and Ecology 343 (2007) 21–36

2.2. Sediment reworking experiments

Thirty four thin aquaria (33×17×1.2 cm) were filledwith a layer of 15 cm of natural lagoon sediment (mediandiameter: 103 μm, organic carbon: 4.55% DW andnitrogen: 0.56% DW), which was first sieved on a 1 mmmesh to remove macrofauna. All aquaria were kept in athermo-regulated room in tanks filled with flow throughsea water (20 °C, 24 salinity and 10 °C, 24 salinity,corresponding to summer and winter experimentsrespectively) for a few days. Sediment reworking rateswere quantified using luminophores (i.e., natural sedi-ment particles colored with a yellow fluorescent paint)(Mahaut and Graf, 1987). The luminophores used duringthe present study were between 100 and 160 μm in sizeand had a density of 2.5 g cm−3. Preliminary observa-tions showed that Abra ovata was able to ingest lumi-nophores similar to natural sediment particles (Maire,personal observation).

Twenty-four hours before the beginning of the experi-ments, 3 bivalves were gently deposited on the sedimentsurface. They usually buried within a few minutes, butwere replaced if they did not do so within 1 h. After 24 hof acclimation, 3 g of dry luminophores was homoge-neously and gently spread on the sediment surface of eachaquariumwith a Pasteur pipette. Subsequently 2.86mg Cm−2 of phytodetritus (Tetraselmis 3600 Premium Fresh,Reed marine culture) was added to a first batch of 5aquaria (Low Food Treatment). Ten times this dose(28.6mgCm−2 of the same phytodetritus) was added to asecond batch of five aquaria (High Food Treatment). Fiveaquaria did not receive any food addition (Control FoodTreatment). Immediately after the luminophore input, theaquaria were placed in a stand in front of a digital camera(Olympus® Camedia E10) and the two sides weresequentially photographed under UV light. This opera-tion was repeated after 3, 6, 12, 24 and 48 h. The aquariawere connected to flow-through seawater after 3 h andkept in darkness during the whole experiments. Summerexperiments were carried out at 20 °C corresponding tothe mean lagoon water temperature between April andSeptember (Wilke and Boutière, 2000). Winter experi-ments were carried out at 10 °C corresponding to themean lagoon water temperature between October andMarsh (Wilke and Boutière, 2000). In addition we ran 4controls without bivalves and without food addition (2 at10 °C and 2 at 20 °C).

2.3. Image analysis

Image analysis was carried out with the CVABimagesoftware developed at the Laboratoire d'Océanologie

Biologique de Banyuls (Duchêne and Nozais, 1994;Duchêne et al., 2000). At each separate image, thewater–sediment interface was manually drawn. Thisline represented the initial reference used to calculatethe sediment depth. The sediment–water interface wasthen “flattened” by vertically translating each pixelcolumn. After this, the pixel y-positions correspondeddirectly to depth within the sediment. Images were thenthresholded and transformed to a binary matrix whereluminophore pixels were assigned a value of 1 andsediment pixels a value of 0. Luminophore pixels werefinally summed for each pixel row, which then produceda vertical profile of luminophore concentration withdepth.

2.4. Quantification of sediment reworking

Two indices of sediment reworking activity weredirectly calculated by the CVABimage software: (1) themaximum penetration depth (MPD) of luminophores,measured as the distance between the sediment surfaceand the deepest luminophore pixel, and (2) the pro-portion of the reworked sediment surface (PRS), rep-resenting the percentage of surface area that had beenreworked by the bivalves. The PRS was defined as thefraction of the first pixel row that was devoid ofluminophores. When the reworking of the surface area isthought of as a stochastic Poisson process (each unit ofsurface area has an equal chance of being reworked perunit of time), one would expect the PRS to evolve withtime t as:

PRS ¼ 100 1−exp − ttPRS

� �� ð1Þ

The characteristic time scale of areal reworking tPRScan be determined from the initial slope of the PRSversus time curve.

The individual rate of surface area reworking (IRSAR)is determined as the surface area of the aquarium dividedby the characteristic time scale of areal reworking and theorganism density within the aquarium.

IRSAR ¼ Aaquariumn� tPRS ð2Þ

A third way to quantify the rate of sediment rework-ing is to derive a biodiffusion coefficient from the ver-tical luminophore profile. Two different models wereemployed to estimate such biodiffusion coefficients: (1)a standard biodiffusion model (Boudreau, 1986) and (2)a new non-local transport model (Meysman et al.,submitted for publication).


2.5. Biodiffusion model

Assuming that particles are mixed infinitely fastover infinitely small distances, bioturbation can berepresented as a diffusive process (Boudreau, 1986;Meysman et al., 2003), and the governing conservationequation for the luminophore concentration C thusbecomes

A2CAt

¼ Db A2CAz2

ð3Þ

where Db is the biodiffusion coefficient (which re-mains constant in time), and z represents the sedimentdepth (measured downwards from the sediment–waterinterface).

Initially, the luminophores are located in a very nar-row layer at the water–sediment interface, which isidealized by the “pulse” condition

Cðx; 0Þ ¼ dðxÞ ð4Þwhere δ(x) represents the Dirac delta function. Over theinfinite sediment domain, the initial-value problem (1),(2) has the solution (Crank, 1975)

Cðz; tÞ ¼ NA

ffiffiffiffiffiffiffiffiffiffikDbt

p exp −z2

4Dbt

� �ð5Þ

whereN is the number of luminophores originally spreadat the sediment surface, and A represents the surface areaof the aquarium. The parameters N and A are constant:we assume that no luminophores are lost, and thatreworking does not alter the area of the sediment–waterinterface.

2.6. Non-local transport model

The classical biodiffusion model has the idealizingassumption that particle displacement occurs infinitelyfrequent and over infinitely small length scales (Meys-man et al., 2003). Yet, in real bioturbation activity,particles are displaced over finite distances, with finitetime periods between displacements. To account forthis, a new stochastic model of bioturbation wasrecently proposed (Meysman et al., submitted forpublication), which describes bioturbation as a se-quence of random bioturbation events. Within a givenbioturbation event (e.g. a worm passing by, or theinfilling of a burrow structure), a reshuffle of thesediment occurs, and particles are dislocated to a newposition. Thereafter, the particle remains at that locationuntil it is subject to another bioturbation event. In thisview, a wandering particle shows two types of

“behavior”: either waiting at a given location, or step-ping to a new location.

In a one-dimensional model description, the motionof a particle is then governed by two variables: (1) thestep length L, i.e. the direction and distance a particletravels along the z-axis, and (2) the waiting time T, i.e.the time a particle waits until the next event. When theinterplay between particles and biological activity issufficiently erratic, both step length and waiting timecan be modeled as stochastic variables, respectivelyrepresented by a step length and waiting time probabil-ity distribution functions (PDF).

WT ðsÞds ¼ PrfsbTbsþ dsg ð6Þ

WLðkÞdk ¼ PrfkbLbkþ dkg ð7Þ

Then given a certain initial tracer profile C0(z), theevolution of the tracer profile with time is given by inte-gral equation (Meysman et al., submitted for publication)

Cðz; tÞ ¼ C0ðzÞ 1−Z t0

WT ðsÞds� �

þZ t0

Z−l

l

WT ðsÞWLðkÞCðz−k; t−sÞdkds

ð8Þ

Given appropriate forms forΨT andΨL, this equationpredicts how a surficial layer of luminophores will bedown-mixed under the influence of bioturbation. How-ever, we do not have any prior knowledge on the shapeof these PDFs for the specific case of Abra ovata. Toresolve this, we can implement some standard PDFforms, and assume that the bivalve reworking is gov-erned by an exponential waiting time distribution and aGaussian step length distribution, i.e.,

WT ðsÞ ¼ 1sc expð−s=scÞ ð9Þ

WLðkÞ ¼ 1r

ffiffiffiffiffiffi2k

p exp − k2

2r2

� �ð10Þ

Expressions (9) and (10) exemplify the fundamentaldifference with the “infinite” biodiffusion model andthe “finite” non-local model. In the waiting time distri-bution (9), the average waiting time τc denotes the fi-nite time scale in between particle displacements.Similarly, in the step length distribution (10), thesquare root variance σ represents finite length scaleover which particles are displaced. These are two


fundamental parameters that characterize particle dis-placement due to A. ovata reworking.

Although the non-local model (8) looks verydifferent from the biodiffusion model (3), both modelsare in fact tightly related. A well-known theorem fromrandom walk theory asserts that if the statistics τc and σare finite quantities, then for long times t≫τc, thebehavior of the stochastic model (8) always approx-imates that of the classical diffusion Eq. (5) (Hughes,1995; Meysman et al., submitted for publication). Theconnection between the parameters in the stochasticmodel (8) and the mixing intensity is given by

DNLb ¼r2

2scð11Þ

In other words, once the average waiting time τc andthe characteristic step length σ are known, we can usethe decomposition (11) to construct a non-local biodif-fusion coefficientDb

NL. To this end, we first calculate thesolution of the non-local model (8) for the exponentialwaiting time PDF (9) and the Gaussian step length PDF(10). As initial conditions C0(z) we used a uniform layerof luminophores with a finite thickness (the averagethickness of the initial luminophore layer as derivedfrom the image analysis). The solution to Eq. (8) wasthen obtained numerically using a Fortran program. Weadjusted the two free parameters τc and σ so that thesolution fitted the luminophore data profile best. For the

Fig. 1. Example of two images recorded after 48 h during a summer (A) and aare green. Note the occurrence of well-defined conical structures characteristicthe summer experiment. These structures are much smaller during the winterlegend, the reader is referred to the web version of this article.)

optimal τc and σ values, we then used Eq. (11) to derivethe non-local biodiffusion coefficient Db

NL, whichrepresents the intensity of mixing in the non-localmodel. Theory predicts that after a sufficient number ofbioturbation events, the values of Db and Db

NL shouldconverge (Hughes, 1995; Meysman et al., submitted forpublication). However, over short experimental timescales, differences may arise. This is because the as-sumption of infinitely frequent mixing becomes inap-propriate, and so when still applying the biodiffusionmodel, it may produce biased values for the mixingintensity.

2.7. Data fitting and statistical analysis

Db and DbNL values were estimated by convergent

iterations and weighted least-squares regression of ob-served luminophores profiles on predicted tracer con-centration. The difference between fitted solution anddata (“fitting error”) was expressed via the Root-Mean-Square (RMS) error.

RMS ¼ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi1n

Xni¼1

ðobsi−prediÞ2s

ð12Þ

For a particular combination of aquarium and incu-bation duration, Db and Db

NL values were averaged forboth sides of the aquarium.

winter (B) experiment. The sediment matrix is dark and luminophoresof sediment reworking by individual Abra ovata (white arrows) duringexperiment. (For interpretation of the references to color in this figure

Fig. 2. Temporal changes in Maximum Penetration Depth (MPD)during summer and winter experiments for 3 different food treatments.A: Control Food treatment, B: Low Food treatment, C: High Foodtreatment. Vertical bars represent standard deviations.


Differences in MPDs, PRSs,Db's andDbNL's between

experimental durations, food treatments and seasonswere tested using 3-way ANOVAs. Differences inIRSAR between food treatments and seasons were test-ed using 2-way ANOVAs. ANOVASwere carried out onsquare-root-transformed data to homogenize variances.Whenever appropriate, a posteriori Least SignificantDifference (LSD) tests were used to assess differencesbetween experimental durations, food treatments, andseasons.

3. Results

Visual observations carried out throughout the ex-periments showed no displacements of luminophoresin the control aquaria. The thickness of the initiallydeposited luminophore layer remained constant overtime (i.e., close to 500 μm), and there was no signi-ficant resuspension of luminophores linked to physicaldisturbance. Therefore, any displacement of lumino-phores recorded in the aquaria with organisms cor-responded to sediment reworking by Abra ovata. Foreach food treatment, sharp differences in sedimentreworking were visually observed between summer andwinter experiments. During the summer experiments,there were clear signs of intense sediment reworking.Feeding activity resulted in conspicuous biogenic struc-tures: zones of intense luminophore mixing with atypical inverted conical shape (Fig. 1a). These struc-tures coincided with the network of siphonal channelscreated by A. ovata when feeding. The tip of the conewas located right above the subsurface chamber wherethe bivalve's body remains stationary positioned. Thebase of the cone corresponded to the section of thesediment–water interface that is explored by the inha-lant siphon when feeding. During the winter experi-ments, there were only marginal signs of sedimentreworking, and conical biogenic structures were onlyoccasionally visible (Fig. 1b).

3.1. Maximum penetration depth of luminophores

MPDs were significantly affected by experimentduration, food treatment and season (3-way ANOVA,pb0.001 in all cases) with a significant interactionbetween experiment duration and season (pb0.001)(Fig. 2). MPDs were significantly higher during thesummer than during the winter experiments (LSD testpb0.05) (Table 1). MPDs showed a characteristic in-crease with experiment duration during summer ex-periments as expected from the down-mixing of atracer pulse, but remained almost constant during win-

Table 1Average values of the different sediment reworking indices recordedduring both summer and winter experiments

Summertime (20 °C) Wintertime (10 °C)

Food treatments CF LF HF CF LF HF

MPD (cm) 3.25 4.04 3.17 1.13 1.62 0.81tPRS (h) 12.22 15.14 22.56 675.10 851.37 428.96IRSAR (cm2 h−1) 0.64 0.50 0.32 0.02 0.03 0.03Db (cm

2 yr−1) 30.68 51.63 30.14 0.17 0.25 0.23DbNL (cm2 yr−1) 32.60 45.37 28.15 1.01 1.03 0.95

τc (h) 7.61 5.39 7.73 21.30 18.25 24.33σ (cm) 0.21 0.21 0.21 0.07 0.07 0.07

Values are provided for the whole 48 h experiment and the all 3 testedfood treatments (Control Food, Low Food, High Food).

Fig. 3. Temporal changes in the proportion of the reworked sedimentsurface (PRS) during the summer and thewinter experiments and for the3 tested food treatments. A: Control Food treatment, B: Low Foodtreatment, C: High Food treatment. Vertical bars are standard deviations.Average IRSAR and standard deviation (cm2 h−1) are also shown.Dotted lines correspond to the best fit using Eq. (1) (see text for details).


ter experiments, indicating a low reworking activity.The difference in MPDs between summer and winterexperiments was higher for the LF treatment (4.04 cmin summer vs. 1.62 cm in winter after 48 h) than for CFtreatment (3.25 cm vs. 1.13 cm) and HF treatment(3.17 vs. 0.81 cm).

3.2. Proportions of reworked sediment surface

PRS values were significantly affected by experi-ment duration, food treatment and season (3-wayANOVA, pb0.001 in all cases) with significantinteractions between all three combinations of these 3factors (pb 0.001 in all cases). PRSs were significantlyhigher during the summer than during the winterexperiments, confirming the strong difference inreworking activity between seasons (LSD test pb0.05)(Table 1). PRS values increased with experimentduration (LSD test pb0.05) both during the summerand the winter experiments (Fig. 3). This indicates thatthe surface sediment layer was not uniformly reworked,but that the deposit-feeding bivalves gradually exploreddifferent areas. Differences in PRS between foodtreatments were only observed in the initial phase ofthe summer experiments (i.e., first 12 h). There, PRSwere higher for CF treatment, intermediate for LFtreatment and lower for HF treatment. All 3 foodtreatments resulted in similar PRS values after 48 h (i.e.,around 93% in summer). This means that after two daysin summer the whole surface of the sediment was“explored” at the experimental density of bivalves (1470bivalves m−2). During winter experiments, PRS valueswere similar between food treatments, reaching only16% after 48 h.

IRSARs were also significantly affected by foodtreatment and season (2-way ANOVA, p=0.036 andpb0.001, respectively), with significant interaction be-

Fig. 4. Evolution of the vertical concentration profiles of luminophore over time, including fits of both the biodiffusive model and the non localtransport models.


tween these two factors (p=0.027) (Fig. 3). IRSARswere significantly higher during summer than duringwinter experiment (LSD test pb0.05). IRSARs were

significantly lower for HF treatment (LSD test pb0.05)but did not significantly differ between CF and LFtreatment.

Fig. 5. Temporal changes in the fitting error (RMS) of the biodiffusionand non-local models. Data for all food treatments have been pooledsince this did not affect the fitting error. Vertical bars are standarddeviations.


3.3. Modelling

An example simulation of the biodiffusive and non-local transport models for a single aquarium (summerexperiment, CF treatment) is presented in Fig. 4.Luminophore data profiles are given at 6 differenttimes (0, 3, 6, 2, 24, 48 h), and the corresponding bestfits of both models are shown. This simulationprocedure was carried out for (3 food treatments)× (2seasons)× (5 replicates), resulting in a total of 30simulated profiles. The simulation depicted in Fig. 4 isrepresentative for the model responses in this set. Atthe beginning of the simulations (0–3 h), the shapes ofthe two model solutions differed slightly. Thisdifference resulted from different initial conditions.The analytical solution of the biodiffusion model (5)initially assumes an infinitely “thin” layer of lumino-phores at the surface (Dirac pulse). Conversely, thenumerical solution of the non-local model uses a finitelayer of luminophores derived from the initial exper-imental image. After that (3–6 h), the solutions of bothmodels start to deviate, and this difference is maximalafter 12 h. This difference between the biodiffusive andnon-local model profiles is principally because bothmodels incorporate a fundamentally different treatmentof the frequency of bioturbation events. The biodiffu-sion model assumes that all particles are synchronouslymoved right from the start of the experiment (i.e., aninfinitely short waiting time between bioturbationevents). In contrast, the non-local model assumes thatparticles have a finite probability of being displaced perunit of time. As a result, some particles in the initiallydeposited layer may remain unmoved for some time.This is clearly seen in the non-local concentrationprofiles at 3 h–6 h–9 h in Fig. 4, which actually consistof two “zones”: an upper “blocky” layer containingparticles that have not yet been moved, and a lower“mixed” layer of particles that have been moved. Inother words, unlike the biodiffusion model, the non-local model explicitly accounts for the fact that thewhole sediment–water interface is not reworked atonce, but that Abra ovata may bioturbate the surfacearea patch after patch. In the later stages of the experi-ment, the difference between biodiffusive and non-local model profiles becomes smaller. This responsenicely confirms the predictions of random walk theoryas discussed earlier: after a sufficient amount of time(i.e. a sufficient number of bioturbation events) thesolution of the stochastic non-local model shouldcoincide with that of the deterministic biodiffusionmodel (Hughes, 1995; Meysman et al., submitted forpublication).

The fitting errors of luminophore profiles recordedduring the summer experiments were significantlyaffected by experiment duration and model (3-wayANOVA, pb0.001 in both cases) but not by foodtreatment (p=0.143). When pooling the results for allfood treatments, the temporal changes in fitting errorwere dependent on experimental durations and models.The fitting error was similar for the two models at thebeginning and the end of the experiments (Fig. 5).However, in the intermediate period, the non-local modelgave a significantly better fit to the data as compared tothe biodiffusive model. This confirms again the aboveresults: over short experimental time intervals, the“finite” character of the length and time scales of bio-turbation becomes relevant (e.g. due to differential re-working of the sediment–water interface), and this isonly accounted for in the non-local model.

3.4. Biodiffusion coefficients

Overall Db's andDbNL's correlated positively (Fig. 6).

However, this correlation was low during the first 12 h ofthe experiment, but drastically increased after 24 h(r2 =0.224 after 3 and r2 =0.678 after 48 h; n=15 inboth cases). The values of Db and Db

NL became verysimilar after 48 h, confirming the theoretical predictionthat mixing intensities should converge given sufficienttime. The optimized value for the characteristic steplength σ was 0.21 cm in summer and 0.07 cm in winter.The corresponding values for the average waiting timeτc were between 1 and 10 h and between 10 and 30 h, forsummer and winter experiments respectively.

Fig. 6. Correlation plots between DbNL's and Db's for various sampling times.


Db's (Fig. 7) were significantly affected by foodtreatment and season (3-way ANOVA, pb0.001 in bothcases) but not by experiment duration (3-way ANOVA,

p=0.082). There were significant interactions between:(1) experimental duration and season, and (2) foodtreatment and season. Db's were significantly higher

Fig. 7. Temporal changes in Db's during the summer and the winterexperiments for the 3 food treatments. A: Control Food treatment,B: Low Food treatment, C: High Food treatment. Vertical bars arestandard deviations.


during the summer than during the winter experiments(LSD test, pb0.05). The difference between summerand winter Db's was highest for the LF treatment(51.63 cm2 yr−1 during summer vs. 0.25 cm2 yr−1 inwinter after 48 h), intermediate for the CF treatment(30.68 cm2 yr−1 vs. 0.17 cm2 yr−1) and lowest for the HFtreatment (30.14 cm2 yr− 1 vs. 0.23 cm2 yr− 1).Differences between food treatments were large duringsummer experiments and small during winter experi-ments (during which reworking rates were overall low).Moreover, temporal changes in Db's differed betweensummer and winter experiments. Db's tended to increasewith experiment duration during summer experiments,whereas they tended to decrease with experimentduration during winter experiments.

The temporal changes in DbNL's were very similar

to those of Db's (Fig. 8). DbNL's were significantly

affected by experiment duration, food treatment andseason (3-way ANOVA, pb0.001 for food treatment andseason, and p=0.016 for sampling time) with significantinteractions between experiment duration and season,and food treatment and season (pb0.001 in both cases).As forDb's, the values forDb

NL were significantly higherduring summer than during winter experiments (LSDtest, pb0.05). Db

NL's were also significantly higher forthe LF treatment (45.37 during summer vs. 1.03 cm2

yr−1 during winter after 48 h) than for the CF treatment(32.60 cm2 yr−1 vs. 1.01 cm2 yr−1) and the HF treatment(28.15 cm2 yr−1 vs. 0.95 cm2 yr−1) (LSD tests, pb0.05).The interaction between experiment duration and seasonreflected the fact that Db

NL's increased over time duringthe summer experiments and decreased over time duringwinter experiments. The interaction between foodtreatment and season indicates that differences betweenfood treatments were larger during the summer thanduring the winter experiments.

4. Discussion

4.1. Seasonal changes in sediment reworking

There was a clear difference in sediment reworkingby Abra ovata during summer and winter experiments.In winter, MPDs at the end of the experiment remainedalmost the same as in the initial situation (∼1 cm). Thissuggests that MPDs recorded throughout winter experi-ments mostly resulted from the initial sinking of lumi-nophores within the galleries formed by A. ovata, andthat there was hardly any transport of luminophores intodeeper sediment due to biological activity.

PRS, IRSAR, Db and DbNL values (Table 1) were

also significantly higher in summer than in winter. All

Fig. 8. Temporal changes in DbNL's during the summer and the winter

experiments for the 3 food treatments. A: Control Food treatment, B:Low Food treatment, C: High Food treatment. Vertical bars arestandard deviations.


these results clearly pinpoint to high sediment re-working in summer, and the virtual absence thereof inwinter. This observation is fully coherent with the data

reported by Lecroart et al. (2005) for the Mediterre-nean Thau Lagoon. These authors studied seasonalchanges in mixing intensity based on radioisotopemeasurements (234Th and 7Be) and reported signifi-cantly higher Db's during summer (Db=32 cm

2 yr−1)than during winter (Db=1 cm

2 yr−1). Note that also inabsolute terms, the mixing intensities Db and Db

NL

obtained here are strikingly similar to those of Lecroartet al. (2005). Similar results have also been reportedfor an Adriatic Lagoon by Mugnai et al. (2003) basedon in situ luminophore experiments. The strongtemporal difference in sediment reworking ratesobserved here also matches similar results that wereobtained for several benthic invertebrates inhabitingtemperate areas and belonging to different taxa. Mostof these studies were based on the monitoring ofdefecation or egestion of sediment (Kudenov, 1982;Dobbs, 1983; Retraubun et al., 1996; Rowden et al.,1998; Berkenbusch and Rowden, 1999) and not ondirect measurement of Db's using tracers. Testedspecies generally showed a strong decrease in theirsediment reworking rates during wintertime because ofreduced feeding (Kudenov, 1982; Dobbs, 1983; Coulonand Jangoux, 1993; Retraubun et al., 1996), andburrowing and/or ventilation (Wolfrath, 1992) activities.

4.2. Sediment reworking and food availability

We noticed a clear interaction between rate ofsediment reworking in summer and the effect of foodavailability. IRSAR were significantly lower in the HFtreatment. Moreover, the highest value for the MPD, thetime-averaged Db and the time-averaged Db

NL werealways found in the LF treatment, then followed by CFtreatment, while lowest values corresponded to highestfood availability HF. The ratios of Db

NL's between sum-mer and winter experiments were of 20, 40 and 14 in theCF, LF and HF treatments respectively.

Here again, these results are coherent with thoseobtained by Lecroart et al. (2005) for the Thau Lagoon.These authors compared changes at two stations, onelocated in themiddle of the lagoon and the other one in theimmediate vicinity of oyster tables, which tend to enhancefood availability at the water–sediment interface due tobiosedimentation. Their results also showed that thetemporal changes in mixing intensity were much higherunder the oyster tables (ratio of 31) than in the middle ofthe lagoon (ratio of 12). This difference was mostly due tohigher summer Db's under the oyster tables than in themiddle of the lagoon, whereasDb's were almost similar atthe two stations during wintertime. These results areconsistent with the positive effect of the LF treatment on


sediment reworking recorded during our summer experi-ments. Overall, they suggest that during wintertime,sediment reworking in Mediterranean lagoons is limitedby another factor than food availability.

4.3. What controls the sediment reworking rate?

Since food availability does not affect sediment re-working during winter, we suggest that seasonal changesin sediment reworking are related to temperature, whichdiffered by 10 °C during our summer and winterexperiments. The effect of temperature on the mixingintensity of sediments has been investigated for a largevariety of benthic invertebrates (Bender and Davis,1984; Rowden et al., 1998; Hollertz and Duchene, 2001;Ouellette et al., 2004). Temperature may influence themixing intensity in various ways:

(1) Temperature may have a direct effect on metab-olism and enzyme activity. Temperature depen-dence of biological rates is usually describedthrough a Q10 relation. Values of Q10 calculatedfor many organisms are about 2 (Newell andBranch, 1980; Coma et al., 2000; Whiteley et al.,2001). This value is much lower than the ratiosbetween summer and winter Db's (i.e., 20 and 40in CF and LF treatments respectively). This clear-ly indicates that the temperature-dependence ofsediment reworking rate is probably not gradual,but rather involves a threshold phenomenon. Sucha threshold was reported by Bender and Davis(1984) for the feeding activity of another deposit-feeding bivalve Yoldia limatula.

(2) Temperature may also act indirectly since it is oneof the major environmental factors interfering inthe reproduction activity of benthic invertebrates(Kinne, 1963). Seasonal changes in sediment re-working activity may thus reflect changes in thephysiological state of the organisms linked to highenergetic investment in reproduction activity asalready reported for the drastic decline in thefeeding activity of the polychaeteDitrupa arietinaduring its reproductive season (Jordana et al.,2000). This however probably does not explaindifferences in sediment reworking rates betweenour summer and winter experiments. In Abraovata, the reproductive period extends fromMarchto August and the main spawning usually occursduring mid-summer (Denis, 1981). Specimens ofA. ovata used during winter experiments werethus in a resting reproductive stage. Differencesin sediment reworking rates between summer and

winter therefore probably resulted from a directeffect of temperature on feeding activity.Our results thus suggest that, in MediterraneanLagoons and on a seasonal time scale, tempera-ture is the main environmental factor controllingsediment reworking of A. ovata. In cold temper-ate areas, temperature and food availability areusually positively correlated (Coma et al., 2000).This is also the case in Mediterranean lagoons(Guyoneaud et al., 1998; Wilke, 1998). The winterreduction of metabolism and activities in benthicinvertebrates are usually interpreted as a physio-logical acclimation, allowing to reduce energeticcosts when both temperature and food concentra-tions are low (Kinne, 1963; Newell and Bayne,1980; Barnes and Clarke, 1995; Ouellette et al.,2004). Results of the present study suggest thatin A. ovata, the effect of temperature is dominantrelative to the one of food availability in controllingsediment reworking during wintertime. However, itshould be stressed that sediment reworking inA. ovata is also clearly affected by food availabilityduring summertime.

4.4. Potential contribution of Abra ovata to sedimentreworking

When comparing sediment reworking rates, oneshould be cautious, as the mixing intensity depends onthe mode of sediment reworking and on the size and thedensity of organisms. The mixing intensities recordedhere during the summer experiment (32.60 cm2 yr−1 inthe CF treatment and 45.37 cm2 yr−1 in the LF treat-ment; Db

NL after 48 h) are higher than Db's based onluminophores and biodiffusive models from: (1) labo-ratory experiments involving other benthic invertebrates(François et al., 1998; Francois et al., 2002; Mermillod-Blondin et al., 2005), and (2) in situ experiments (Gérinoet al., 1994; Gilbert et al., 2003). This high reworking rateper individual, combined with the high densities of Abraovata in NW Mediterranean lagoons (Guelorget andMayere, 1981), suggests that A. ovata accounts for asignificant part of sediment reworking occurring in theselagoons. This indicates that A. ovata may be consideredan ecosystem engineer sensu Jones and Jago (1993).A. ovata thus likely has a significant effect on the seques-tration and the degradation of the particulate organicmatter within the lagoons, which naturally tends to beexported to the sea (Wilke andBoutière, 2000).Moreover,physical disturbances generated by an intense sedimentreworking alter the cohesive properties of the upperfew centimetres of sediment and thereby enhances


sediment resuspension (Rhoads andYoung, 1970; Nowellet al., 1981; Meadows and Tait, 1989; Orvain et al., 2003)induced by the wind in these shallow areas (Milletand Guelorget, 1994). Since most of the sediments ofthese lagoons are polluted (Bernat et al., 1984; Rigolletet al., 2004), this process may contribute to enhance thespreading of contaminants in the trophic food chainas recently pointed out by Bradshaw et al. (2006) inthe Baltic Sea.

4.5. Modelling and the nature of Abra ovata reworking

The sediment reworking of Abra ovata is generatedby the surface deposit feeding activity of the inhalantsiphons. The bivalves remain stationary at some loca-tion, and using their siphons, they explore the foodresources at that spot. This creates characteristic mixingzones that have an inverted conical shape (Fig. 1). It alsomeans that sediment reworking becomes very patchy:some sediment zone become heavily reworked (the con-ical mixing zones), while other sediment zones remainundisturbed. This patchiness has important implicationsfor one-dimensional tracer studies over relatively shorttime-scales (such as conventional luminophore experi-ments). When creating 1D tracer profiles, the sedimentis laterally averaged, and hence, “mixed” and “unmixed”zones are merged into one single tracer depth profile.Classically, such 1D tracer profiles are then interpretedwith the standard biodiffusion model. However, animplicit assumption in the biodiffusion model is thatall particles are mixed at all times (infinitely frequentbioturbation events). In other words, the biodiffusionmodel assumes that the sediment is uniformly affectedby sediment reworking, and hence, it cannot cope withlateral heterogeneity in bioturbation activity.

Over long time scales, this is no problem: afterexploring a particular spot, the bivalves will move. Soeventually, the whole sediment surface layer will beexplored (see Fig 4. — after 48 h the PRS amounted to93% in summer). However, over short time scales,lateral heterogeneity in bioturbation activity cannot beignored in tracer studies. In terms of modelling thisimplies that 1D bioturbation models should account fordifferential timing in particle displacements: all particlesshould not be displaced synchronously as in the bio-diffusion model. The non-local model employed hereallows for such differential timing. Lateral spatial he-terogeneity in reworking is essentially translated intovertically stochasticity of particle displacement. In otherwords, the tracer layer initially deposited at the sedi-ment–water interface will now be gradually affected andmixed down (rather than that all particles are affected at

once). Because of this feature, the fits of the non-localmodel to the experimental data were clearly better forthe biodiffusive model over the first 24 h of the incu-bations (Fig. 4). After a sufficient time however, thenon-local model profile becomes similar to that of thebiodiffusion model (i.e. after 48 h — when the surfacesediment layer becomes fully explored and the PRSamounts to 100%). At this stage, the more complex non-local model looses its advantage over the much simplerbiodiffusion model. At this stage also, the mixing in-tensity estimated by the biodiffusion model becomesidentical to that from the non-local model.

Accordingly, if one is only interested in the mixingintensity for a particular sediment setting, one should be“patient”, and perform a tracer study that spans a suf-ficiently long period of time. There is however a clearadvantage in performing short-term tracer studies andsubsequently analysing them with the non-local model(8): one can estimate the average time τc between bio-turbation lengths and the characteristic distance σ overwhich particles are displaced. To our knowledge, ourstudy is the first to quantify these two biological para-meters. The average length step (2.1 mm) and the timebetween two consecutive displacements (5.39 h) areboth small for A. ovata. This way, we have now quan-titatively determined that the reworking by A. ovatagenerates small and frequent particle displacements,which supports the qualitative conclusion by Maire et al.(2006) that A. ovata is a biodiffuser (François et al.,1997, 2001).

Acknowledgments

This work was carried out within the framework ofthe French National Program on Coastal Environment(PNEC) and more specifically in the thematic ActionMESO. Olivier Maire was supported by a grant from theFrench Ministry of Education, research and Technology.This work was in partial fulfilment of the doctoral thesisof Olivier Maire at the University Pierre et Marie Curie.Volodymyr Malyuga and Filip Meysman were sup-ported by a PIONIER from the Netherlands Organiza-tion for Scientific Research (NWO, 833.02.2002). Thisis publication 3848 from the Netherlands Institute ofEcology (NIOO–KNAW). [SS]

References

Aller, R.C., 1982. The effects of macrobenthos on chemical propertiesof marine sediment and overlying water. In: McCall, P.L., Tevesz,M.J.S. (Eds.), Animal–Sediment Relations — the BiogenicAlteration of Sediments. Topics in Geobiology. Plenum Press,New York, pp. 53–102.


Barnes, D.K.A., Clarke, A., 1995. Seasonality of feeding activity inAntarctic suspension feeders. Polar Biol. 15, 335–340.

Bender, K., Davis,W.R., 1984. The effect of feeding by Yoldia limatulaon bioturbation. Ophelia 23, 91–100.

Berkenbusch, K., Rowden, A.A., 1999. Factors influencing sedimentturnover by the burrowing ghost shrimp Callianassa filholi(Decapoda: Thalassinidea). J. Exp. Mar. Biol. Ecol. 238, 283–292.

Bernat, M., Cauwet, G., Chassefiere, B., Faguet, D., Gadel, F., Mo-naco, A., Ouakad, M., Thommeret, Y., 1984. Behaviour of metallicand radioactive elements in lagoonal sediments: the example of theCanet–St Nazaire Pond (Mediterranean Coast). Estuar. Coast.Shelf Sci. 18, 557–570.

Biles, C.L., Paterson, D.M., Ford, R.B., Solan, M., Raffaelli, D.G.,2002. Bioturbation, ecosystem functioning and community struc-ture. Hydrol. Earth Syst. Sci. 6, 999–1005.

Boudreau, B.P., 1986. Mathematics of tracer mixing in sediments:I Spatially-dependent, diffusive mixing. Am. J. Sci. 286, 161–198.

Bradshaw, C., Kumblad, L., Fagrell, A., 2006. The use of tracers toevaluate the importance of bioturbation in remobilising contami-nants in Baltic sediments. Estuar. Coast. Shelf Sci. 66, 123–134.

Coma, R., Ribes, M., Gili, J.M., Zabala, M., 2000. Seasonality incoastal benthic ecosystems. Trends Ecol. Evol. 15, 448–453.

Coulon, P., Jangoux, M., 1993. Feeding rate and sediment reworkingby the holothuroid Holothuria tubulosa (Echinodermata) in aMediterranean seagrass bed off Ischia Island, Italy. Mar. Ecol.Prog. Ser. 92, 201–204.

Crank, J., 1975. The Mathematics of Diffusion. Oxford UniversityPress, Oxford.

Denis, P., 1981. Length growth, weight growth and reproductionperiod of Abra ovata, Mollusca Pelecypoda, in the eastern area ofthe Golfe du Morbihan. Cah. Biol. Mar. 22, 1–9.

Dobbs, F.C., 1983. Monitoring defecation activity of infaunal depositfeeders. Mar. Ecol. Prog. Ser. 12, 47–50.

Duchêne, J.C., Nozais, C., 1994. Light influence on larval emissionand vertical swimming in the terebellid worm Eupolymnia nebu-losa (Montagu, 1818). Mém. Mus. Natl. Hist. Nat., Sér. A Zool.162, 405–412.

Duchêne, J.C., Jordana, E., Charles, F., Grémare, A., Amouroux, J.M.,2000. Experimental study of filtration activity in Ditrupa arietina(Annelida Polychaeta) using an automated image analysis system.Oceanol. Acta 23, 805–817.

François, F., Poggiale, J.-C., Durbec, J.-P., Stora, G., 1997. A newapproach for the modelling of sediment reworking induced by amacrobenthic community. Acta Biotheor. 45, 295–319.

François, F., Dalegre, K., Gilbert, F., Stora, G., 1998. Specific vari-ability within functional groups: study of the sediment reworkingof two Veneridae bivalves, Ruditapes decussatus and Venerupisaurea. C. R. Acad. Sci., Sér. 3 Sci. Vie 322, 339–345.

François, F., Poggiale, J.-C., Durbec, J.-P., Stora, G., 2001. A newmodel of bioturbation for a functional approach to sedimentreworking resulting from macrobenthic communities. In: Aller,J.Y., Woodin, S.A., Aller, R.C. (Eds.), Organism–Sediment Inter-actions. University of South Carolina Press, Columbia, pp. 75–78.

Francois, F., Gérino, M., Stora, G., Durbec, J.-P., Poggiale, J.-C., 2002.Functional approach to sediment reworking by gallery-formingmacrobenthic organisms: modeling and application with the poly-chaete Nereis diversicolor. Mar. Ecol. Prog. Ser. 229, 127–136.

Gérino, M., Stora, G., Durbec, J.-P., 1994. Quantitative estimation ofbiodiffusive and bioadvective sediment mixing: in situ experi-mental approach. Oceanol. Acta 17, 547–554.

Gérino, M., Aller, R.C., Lee, C., Cochran, J.K., Aller, J.Y., Green, M.A.,Hirschberg, D., 1998. Comparison of different tracers and methods

used to quantify bioturbation during a spring bloom: 234-Thorium,luminophores and chlorophyll a. Estuar. Coast. Shelf Sci. 46,531–547.

Gilbert, F., Hulth, S., Stroemberg,N., Ringdahl, K., Poggiale, J.-C., 2003.2-D optical quantification of particle reworking activities in marinesurface sediments. J. Exp. Mar. Biol. Ecol. 285/286, 251–263.

Green, M.A., Gulnick, J.D., Dowse, N., Chapman, P., 2004. Spatio-temporal patterns of carbon remineralization and bio-irrigationin sediments of Casco Bay Estuary, Gulf of Maine. Limnol.Oceanogr. 49, 396–407.

Guelorget, O., Mayere, C., 1981. Growth, biomass and production ofAbra ovata in a Mediterranean lagoon, the Etang du Prevost atPalavas (Herault, France). J. Rech. Oceanogr. 6, 23–41.

Guyoneaud, R., De Wit, R., Matheron, R., Caumette, P., 1998.Impact of macroalgal dredging on dystrophic crises and photo-trophic bacterial blooms (red waters) in a brackish coastal lagoon.Oceanol. Acta 21, 551–561.

Hall, S.J., 1994. Physical disturbance and marine benthic communi-ties: life in unconsolidated sediments. Oceanogr. Mar. Biol. Ann.Rev. 32, 179–219.

Hollertz, K., Duchene, J-C., 2001. Burrowing behaviour and sedimentreworking in the heart urchin Brissopsis lyrifera Forbes (Spatan-goida). Mar. Biol. 139, 951–957.

Hughes, B.D., 1995. Random walks and Random Environments.Random Walks, vol. 1. Clarendon Press, Oxford.

Ingalls, A.E., Aller, R.C., Lee, C., Sun, M.Y., 2000. The influence ofdeposit-feeding on chlorophyll-a degradation in coastal marinesediments. J. Mar. Res. 58, 631–651.

Jones, S.E., Jago, C.F., 1993. In situ assessment of modification ofsediment properties by burrowing invertebrates. Mar. Biol. 115,133–142.

Jordana, E., Duchêne, J.-C., Charles, F., Grémare, A., Amouroux,J.M., 2000. Experimental study of suspension-feeding activity inthe serpulid polychaete Ditrupa arietina (O.F. Müller). J. Exp.Mar. Biol. Ecol. 252, 57–74.

Kinne, O., 1963. The effects of temperature and salinity on marineand brackish water animals. Oceanogr. Mar. Biol. Ann. Rev. 1,301–340.

Kristensen, E., Andersen, F., Blackburn, T.H., 1992. Effects ofbenthic macrofauna and temperature on degradation of macro-algal detritus: the fate of organic carbon. Limnol. Oceanogr. 37,1404–1419.

Kudenov, J.D., 1982. Rates of seasonal sediment reworking in Ax-iothella rubrocincta (Polychaeta: Maldanidae). Mar. Biol. 70,181–186.

Lecroart, P., Schmidt, S., Jouanneau, J.M., Weber, O., 2005. Be7 andTh234 as tracers of sediment mixing on seasonal time scale at thewater–sediment interface of the Thau Lagoon. Radioprotection 40,661–667.

Lee, H., Swartz, R.C., 1980. Biological Processes Affecting theDistribution of Pollutants in Marine Sediments. Part II: Biodeposi-tion and bioturbation. In: Baker, R.A. (Ed.), Contaminants andSediments. Analysis, Chemistry, Biology. Ann Arbor SciencePublishers, Ann Arbor, MI, pp. 555–606.

Mahaut, M.L., Graf, G., 1987. A luminophore tracer technique forbioturbation studies. Oceanol. Acta 10, 323–328.

Maire, O., Duchêne, J.-C., Rosenberg, R., Braga de Mendonça Jr, J.,Grémare, A., 2006. Effects of food availability on sediment reworkingin Abra ovata and Abra nitida. Mar. Ecol. Prog. Ser. 319, 135–153.

Meadows, P.S., Meadows, A., 1991. The geotechnical and geochem-ical implications of bioturbation in marine sedimentary ecosys-tems. Symp. Zool. Soc. Lond. 63, 157–181.


Meadows, P.S., Tait, J., 1989. Modification of sediment permeabilityand shear strength by two burrowing invertebrates. Mar. Biol. 101,75–82.

Mermillod-Blondin, F., François-Carcaillet, F., Rosenberg, R., 2005.Biodiversity of benthic invertebrates and organic matter processingin shallow marine sediments: an experimental study. J. Exp. Mar.Biol. Ecol. 315, 187–209.

Meysman, F.J.R., Boudreau, B.P., Middelburg, J.J., 2003. Relationsbetween local, non local, discrete and continuous models ofbioturbation. J. Mar. Res. 61, 391–410.

Meysman, F.J.R., Boudreau, B.P., Middelburg, J.J., 2005. Modelingreactive transport in sediments subject to bioturbation andcompaction. Geochim. Cosmochim. Acta 69, 3601–3617.

Meysman, F.J.R., Malyuga, V.S., Boudreau, B.P., Middelburg, J.J.,(submitted for publication). A generalized stochastic approach toparticle dispersal in soils and sediments: the continuous-timerandom walk.

Millet, B., Guelorget, O., 1994. Spatial and seasonal variability in therelationships between benthic communities and physical environ-ment in a lagoon ecosystem. Mar. Ecol. Prog. Ser. 108, 161–174.

Mugnai, C., Gerino, M., Frignani, M., Sauvage, S., Bellucci, L.G.,2003. Bioturbation experiments in the Venice Lagoon. Hydro-biologia 494, 245–250.

Newell, R.I.E., Bayne, B.L., 1980. Seasonal changes in the physio-logy, reproductive condition and carbohydrate content of thecockle Cardium (=Cerastoderma) edule (Bivalvia: Cardiidae).Mar. Biol. 56, 11–19.

Newell, R.C., Branch, G.M., 1980. The influence of temperature onthe maintenance of metabolic energy balance in marine inverte-brates. Adv. Mar. Biol. 17, 239–396.

Nowell, A.R.M., Jumars, P.A., Eckman, J.E., 1981. Effects of bio-logical activity on the entrainment of marine sediments. Mar. Geol.42, 133–153.

Ouellette, D., Desrosiers, G., Gagne, J.P., Gilbert, F., Poggiale, J.-C.,Blier, P.U., Stora, G., 2004. Effects of temperature on in vitrosediment reworking processes by a gallery biodiffusor, thepolychaete Neanthes virens. Mar. Ecol. Prog. Ser. 266, 185–195.

Orvain, F., Le Hir, P., Sauriau, P.G., 2003. A model of fluff layererosion and subsequent bed erosion in the presence of the bio-turbator, Hydrobia ulvae. J. Mar. Res. 61, 823–851.

Reizopoulou, S., Thessalou-Legaki, M., Nicolaidou, A., 1996. Assess-ment of disturbance in Mediterranean lagoons: an evaluation ofmethods. Mar. Biol. 125, 189–197.

Retraubun, A.S.W., Dawson, M., Evans, S.M., 1996. Spatialand temporal factors affecting sediment turnover by the lugwormArenicola marina (L.). J. Exp. Mar. Biol. Ecol. 201, 23–35.

Rhoads, D.C., 1974. Organism–sediment relations on the muddy seafloor. Oceanogr. Mar. Biol. Ann. Rev. 12, 263–300.

Rhoads, D.C., Young, D.K., 1970. The influence of deposit-feedingorganisms on sediment stability and community trophic structure.J. Mar. Res. 28, 150–178.

Rigollet, V., Sfriso, A., Marcomini, A., De Casabianca, M.L., 2004.Seasonal evolution of heavy metal concentrations in the surfacesediments of two Mediterranean Zostera marina L. beds at Thaulagoon (France) and Venice lagoon (Italy). Bioresour. Technol. 95,159–167.

Rowden, A.A., Jago, C.F., Jones, S.E., 1998. Influence of benthicmacrofauna on the geotechnical and geophysical properties ofsurficial sediment, North Sea. Cont. Shelf Res. 18, 1347–1363.

Sandnes, J., Forbes, T., Hansen, R., Sandnes, B., Rygg, B., 2000.Bioturbation and irrigation in natural sediments, described byanimal-community parameters.Mar. Ecol. Prog. Ser. 197, 169–179.

Sun, M.Y., Aller, R.C., Lee, C., 1991. Early diagenesis of chlorophyll-a in Long Island Sound sediments: a measure of carbon flux andparticle reworking. J. Mar. Res. 49, 379–401.

Sun, M.Y., Aller, R.C., Lee, C., Wakeham, S.G., 1999. Enhanceddegradation of algal lipids by benthic macrofaunal activity: effectof Yoldia limatula. J. Mar. Res. 57, 775–804.

Thompson, B.A.W., Riddle, M.J., 2005. Bioturbation behaviour of thespatangoid urchin Abatus ingens in Antarctic marine sediments.Mar. Ecol. Prog. Ser. 290, 135–143.

White, D.S., Klahr, P.C., Robbins, J.A., 1987. Effects of temperatureand density on sediment reworking by Stylodrilus heringianus(Oligochaeta: Lumbriculidae). J. Great Lakes Res. 13, 147–156.

Whiteley, N.M., Robertson, R.F., Meagor, J., El Haj, A.J., Taylor,E.W., 2001. Protein synthesis and specific dynamic action incrustaceans: effects of temperature. Comp. Biochem. Physiol.,A 128, 593–604.

Wilke, M., 1998. Variability of abiotic characteristics in the water of aMediterranean Lagoon, the Etang de Canet (Pyrenées-Orientales,France). Vie Milieu 48, 157–169.

Wilke, M., Boutière, H., 2000. Hydrobiological, physical and chemicalcharacteristics and spatio-temporal dynamics of an oligotrophicMediterranean lagoon: the Etang de Lapalme (France). Vie Milieu50, 101–115.

Wolfrath, B., 1992. Burrowing of the fiddler crab Uca tangeri in theRia Formosa in Portugal and its influence on sediment structure.Mar. Ecol. Prog. Ser. 85, 237–243.

A comparison of sediment reworking rates by the surface deposit-feeding bivalve Abra ovata duri.....IntroductionMaterials and methodsBivalve collection and maintenanceSediment reworking experimentsImage analysisQuantification of sediment reworkingBiodiffusion modelNon-local transport modelData fitting and statistical analysis

ResultsMaximum penetration depth of luminophoresProportions of reworked sediment surfaceModellingBiodiffusion coefficients

DiscussionSeasonal changes in sediment reworkingSediment reworking and food availabilityWhat controls the sediment reworking rate?Potential contribution of Abra ovata to sediment reworkingModelling and the nature of Abra ovata reworking

AcknowledgmentsReferences

Documents

A comparison of sediment reworking rates by the surface ... · A comparison of sediment reworking rates by the surface deposit-feeding bivalve Abra ovata during summertime and wintertime,