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Estuarine, Coastal and Shelf Science 81 (2009) 267–278

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Estuarine, Coastal and Shelf Science

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

Management strategies to optimise sustainable clam (Tapes philippinarum)harvests in Barbamarco Lagoon, Italy

C.M. Spillman a,*, D.P. Hamilton b, J. Imberger a

a Centre for Water Research, University of Western Australia, Stirling Hwy, Perth 6009, Australiab Centre for Biodiversity and Ecology Research, University of Waikato, Private Bag 3105, Hamilton 3240, New Zealand

a r t i c l e i n f o

Article history:Received 23 July 2008Accepted 10 November 2008Available online 21 November 2008

Keywords:Tapes philippinarumcarrying capacityphytoplanktonnutrientscoupled hydrodynamic–ecological modelNorthern Adriatic Sea

* Corresponding author. Present address: CentreClimate Research (CAWCR), A partnership betweeMeteorology and CSIRO, Bureau of Meteorology, GP3001, Australia.

E-mail address: c.spillman@bom.gov.au (C.M. Spill

0272-7714/$ – see front matter � 2008 Elsevier Ltd.doi:10.1016/j.ecss.2008.11.003

a b s t r a c t

Barbamarco Lagoon is a small lagoon adjoining the Northern Adriatic Sea and is the site of a commer-cially valuable clam (Tapes philippinarum) fishery. A three-dimensional (3D) coupled hydrodynamic–ecological model was applied to the lagoon with the objective of assessing impacts on clam food supply,commercial harvests and water quality of different clam rearing strategies, lagoon morphologies andflow regimes. Harvest and net growth to seeding ratios, total harvest value, clearance efficiencies andclam satiety were used to quantify the commercial success of different management strategies, whilebottom dissolved oxygen concentrations were used as an indicator of ecosystem health. Increasingexchange with the Northern Adriatic Sea or increasing freshwater inputs into the lagoon improved clamfood supply and increased both harvest production and ecosystem health in model simulations of thesystem. Results indicated that the high spatial and temporal variability of clam production and waterquality responses must be considered for a holistic assessment of the outcomes of strategies in thecontext of ecological and production carrying capacity.

� 2008 Elsevier Ltd. All rights reserved.

1. Introduction

Shellfish aquaculture is a rapidly growing global industry thatsupplements supply from natural fisheries and supports localeconomies, e.g., Sanggou Bay in China (Nunes et al., 2003), Mar-ennes-Oleron Bay in France (Bacher et al., 1998), Carlingford Loughin Ireland (Ferreira et al., 1998). There is growing profit-drivenpressure to expand areas, improve yields and increase carryingcapacity of shellfish culture sites. Many coastal lagoons have highrates of biological productivity, and thus aquaculture is regarded asa desirable form of exploitation of these environments (Macintosh,1994).

Production carrying capacity is defined as the stocking densityof bivalves at which harvest is maximised, before growth decreasesor mortality increases beyond commercially acceptable targetlevels (Smaal et al., 1998; Inglis et al., 2000). It is determinedpredominantly by primary production and water exchange withadjacent systems at an ecosystem scale, and physical constraintssuch as substrate and food supply at a local scale (Heip et al., 1995;

for Australian Weather andn the Australian Bureau ofO Box 1289, Melbourne, VIC

man).

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Smaal et al., 1998). Modifications to the morphology of coastallagoons, via dredging, inflow regulation and barrier construction,are often used to improve flushing and food availability and thusincrease fishery production (Grant, 1996; Pastres et al., 2001; Zal-divar et al., 2003). Various management strategies, such as differentharvest techniques and seasonal patterns of harvesting and seed-ing, are also used in an attempt to maximise the productioncarrying capacity and hence commercial value of these fisheries.

However, high densities of filter-feeding bivalves can havesignificant impacts on the trophic status of coastal ecosystems,affecting oxygen and nutrient dynamics, altering the structure ofthe phytoplankton community and stimulating macroalgal growthsuch as Ulva, often to the detriment of the bivalve populationsthemselves (Prins et al., 1998; Smaal et al., 1998; Sorokin et al.,1999; Bartoli et al., 2001). The stocking density of bivalves thata system can support sustainably without significant impacts onecosystem health is referred to as the ecological carrying capacity(Smaal et al., 1998; Gibbs, 2007), and is influenced by rates of waterrenewal, primary production and nutrient turnover, and clearancetimes and biodeposition rates of filter feeders (Dame and Prins,1998; Gangnery et al., 2001). It is therefore essential to quantify theenvironmental impacts of strategies to improve harvests, such asmanipulation of seeding and harvesting regimes, as well asmorphological modifications to regulate flushing and relativecontributions of marine and freshwater. These considerations arecritical not only from a commercial fishery perspective but also for

C.M. Spillman et al. / Estuarine, Coastal and Shelf Science 81 (2009) 267–278268

assessments of environmental impacts, to ensure a more holisticand sustainable approach to fisheries management (Nunes et al.,2003).

Several different models have been used to examine bivalveproduction and estimate carrying capacity (e.g., Solidoro et al.,2000; Gangnery et al., 2001; Zaldivar et al., 2003; Melia et al., 2004;Grant et al., 2007; Ferreira et al., 2008). Existing models tend to bemono-specific and neglect bivalve population structure, andtherefore cannot be applied to test environmental variability orpotential management strategies such as engineering modifica-tions (Nunes et al., 2003). Other common limitations include anunlimited food supply and absence of anoxia, lack of spatial reso-lution (e.g., Cranford et al., 2007), fixed phytoplankton concentra-tions, omission of survivorship and the assumption of a well-mixedwater column which can neglect stratification effects on food andoxygen supply to bottom dwelling bivalve populations (e.g., Duarteet al., 2007). Dekshenieks et al. (2000) performed a 3D modellingstudy of carrying capacity and the effects of changes in freshwaterflow, temperature, seston concentrations and salinity, but does notinclude an ecological model and so cannot resolve impacts onecosystem health. Marinov et al. (2007) also uses a 3D model toinvestigate the effects of macroalgal blooms and climate variabilityon clam production in a small lagoon though feedbacks via clamgrazing and waste production are not considered. A 3D coupledhydrodynamic–ecological model is valuable in estimating systemresponses to pressures or modifications and hence productioncapacity and ecosystem health indicators over a range of space andtime scales.

Environmental indicators are selected key statistics thatrepresent or summarise a significant aspect of the state of theenvironment, natural resource or effects of human activities(Vandermeulen, 1998). Indicators of incorporating water residencetime, primary production rates and bivalve clearance times can beused to gauge the success of bivalve management (Gibbs, 2007).Population indicators, such as rates of mortality or exploitation, oraverage bivalve length, are the most practical indicators becausetheir definition is clear and the expected effect of fishing on themis well understood (Rochet and Trenkel, 2003). Generally, a singleindicator is inadequate to describe the carrying capacity ofa system and so multiple indicators need to be used (Granzottoet al., 2004). Dissolved oxygen (DO) concentrations or sedimentoxygen demand (SOD) can also be used as ecosystem healthindicators to assess the impacts of bivalve cultivation (McKindseyet al., 2006).

This study is one of the first comprehensive attempts to quantifythe effects of management strategies, variations in adjacentsystems and engineering modifications on clam harvest yields, andsubsequent feedbacks on ecosystem health, using a dynamicmodelling approach over a range of space and time scales. In thiswork, a coupled 3D hydrodynamic–ecological model was used toassess the impacts of change to the hydrodynamic regime onresident clam populations in Barbamarco Lagoon, in addition toclam feedbacks on water quality. Impacts on food supply to lagoonclam populations due to varying Po River discharge and nutrientloadings to the Northern Adriatic Sea were also tested, providinginsight into the coupling of a large marine system with a smallcoastal lagoon. The interdisciplinary nature of this study, incorpo-rating an understanding of hydrodynamics, primary production,nutrient cycling, bivalve ecology and cultivation practices, allowedfor an integrated assessment of the suitability and success ofa range of management strategies from both economic andecological perspectives. Environmental indicators were used in thisstudy to summarise both impacts on clam production andecosystem health of different scenarios, and to test for practical andapplied solutions for sustainable increases in shellfish yields inNorthern Adriatic lagoons.

2. Methods

2.1. Site description

Barbamarco Lagoon is a small (7 km2), shallow (maximumdepth 2 m), coastal lagoon in the Po River Delta, Italy (Fig. 1). Thehydrodynamic regime of the lagoon is a complex interplay of waterexchange with the adjacent Northern Adriatic Sea via the lagoonmouths Bocca Sud and Bocca Nord, and freshwater inputs from thePo River via the mouths and inflow channels from Pila di Tra-montana and Pila di Maistra. The lagoon contains several smallislands that also affect the circulation regime. Po River dischargepeaks in spring and late autumn (Spillman et al., 2007). Strong tidalflushing results in an average lagoon residence time on the order of1–2 days for March–October, with a maximum hydraulic residencetime of approximately 6 days in mid-summer due to increasedstratification (Spillman et al., 2008).

The lagoon is the site of a commercial clam (Tapes philippinarum)fishery. This introduced clam species is a small, filter-feedingbivalve that lives in sandy sediments of shallow, well-flushed areasof lagoons and estuaries (Breber, 1992). Clams are generally seededin Barbamarco Lagoon during spring and summer, when high watertemperatures and phytoplankton biomass support rapid growth(Soudant et al., 2004). Primary production in the Northern AdriaticSea is the main source of phytoplankton biomass to BarbamarcoLagoon and thus clam populations (Spillman et al., 2008), and isprimarily driven by Po River discharge and associated nutrientloadings (Spillman et al., 2007).

2.2. Physical–ecological and clam model description

The Estuary and Lake Computer Model (ELCOM) is a 3D hydro-dynamic model that simulates velocity, temperature and salinitydistributions in natural water bodies (Hodges et al., 2000; Laval et al.,2003). ELCOM is based on the unsteady Reynolds-averaged, hydro-static, Boussinesq, Navier–Stokes and scalar transport equations,incorporating a mixing model to directly compute vertical turbulenttransport (Laval et al., 2003). The model includes representations ofthe dynamics of stratified water bodies with external environmentalforcing, such as tides, wind stresses and surface thermodynamics, aswell as inflows and outflows. ELCOM can be dynamically coupled tothe computational aquatic ecosystem dynamics model (CAEDYM), tosimulate key elemental cycles (C, N, P and Si). CAEDYM consists ofa series of partial differential equations to simulate time-varyingconcentrations of biogeochemical variables, accounting for processessuch as primary production, nutrient cycling, oxygen dynamics andsediment–water interactions. Spillman et al. (2007, 2008) givea detailed description of CAEDYM that includes bivalve parameter-isations and the equations used to model phytoplankton, carbon,nitrogen, phosphorus and dissolved oxygen. Extensive model vali-dation of hydrodynamic conditions and water quality for BarbamarcoLagoon is also presented in Spillman et al. (2008).

A new generic sub-model to simulate clam growth, productionand population dynamics has been incorporated into CAEDYM(Spillman et al., 2008). Clam biomass (g flesh C) and numbers aresimulated concurrently, with the population separated into threesize classes with size-specific parameters, accounting for decreasesin growth rate with size. Changes in clam biomass (Bj

C) are due tograzing of phytoplankton (GA) and particulate organic carbon (POC;GPOC), respiration (RDIC), excretion (EDOC), egestion and mortality(EPOC), harvesting (HC), seeding (Sd) and recruitment (Tr):

vBCj

vt¼ GA

�Bj�þ GPOC

�Bj�� RDIC

�Bj�� EPOC

�Bj�� EDOC

�Bj�

� HC�Bj�þ Sd

�Bj�� Tr

�Bj�þ Tr

�Bj�1

�

Fig. 1. Barbamarco Lagoon (A) bathymetry and (B) corresponding ELCOM bathymetry. R1, R2 and R3 are clam cultivation areas.

C.M. Spillman et al. / Estuarine, Coastal and Shelf Science 81 (2009) 267–278 269

where j¼ clam size class (1, 2, 3). Clam nitrogen and phosphorusconcentrations are modelled in a similar way, with the exception ofrespiration (affecting only carbon), based on a fixed C:N:P stoi-chiometric ratio for clams.

Grazing rate (G) is a function of filtration rate, which isinfluenced by temperature and clam size, and seston concentra-tion. Grazing rates in the model are reduced under high foodconcentrations due to clam ingestion limitations and satietythresholds or in the presence of detrimentally high inorganicloads. Assimilation ratios are used to derive the fraction offiltered food which is egested or excreted (E). A user-definedfraction of ingested food is egested as particulate organic matterin the model, with excretion then calculated dynamically toensure nutrient homeostasis within the clam. Mortality increaseswith both temperature extremes and low salinity or DO. Respi-ration (R) is a function of clam size and temperature, with oxygenconsumed via respiration calculated by applying a stoichiometricfactor associated with respired carbon. Harvest (H) is calculatedin the model as a set percentage of the biomass for size classesgreater than a prescribed market size.

Clam numbers (BjNum) within each size class j, are adjusted for

mortality (M), HC, Sd and Tr:

vBNumj

vt¼ �MC

j BNumj � HNum

j BNumj þ SdNum

j � Tr�

BNumi

�

þ Tr�

BNumj�1

�

Initially, sizes of individuals in a class are distributed nor-mally about the initial mean value of carbon mass per clamusing a user-defined standard deviation. Transfers between sizeclasses are calculated based on the mean class populationdistribution and net growth per class over each time step.Spillman et al. (2008) give a detailed description of the equa-tions and parameter values used in CAEDYM to model clamgrowth and population dynamics; identical values were used inthis study.

2.3. Model inputs and setup

To simulate Barbamarco Lagoon, a variable rectangular grid,composed of horizontal grid cells ranging from 20 m to 250 m, wascreated using detailed maps of the lagoon (Fig. 1B). Two lagoonopenings to the Northern Adriatic Sea, Bocca Nord and Bocca Sud,and two branches of the Po River connected to the lagoon via smallinlets, Pila di Maistra and Pila di Tramontana, were incorporated.Bocca Nord, Bocca Sud and the mouths of Pila di Maistra and Pila diTramontana were configured as open boundaries and forced withvertically and temporally varying conditions generated fromsimulations of the Northern Adriatic Sea (Spillman et al., 2007).Hourly solar radiation, air temperature, relative humidity, airpressure, wind speed and wind direction data, measured at PortoTolle near Barbamarco Lagoon (ENEL, Italy), were applied to themodel domain. Model simulations were run for the period 1March–1 October 2002. The model was run with a set of prelimi-nary parameters and initial conditions, results compared to fielddata, parameters tuned within a certain literature range to improvegoodness of fit over a variety of quantities and the model run again.Once model output showed good agreement with available fielddata, the parameters were set and the model rerun for scenarios.Further details of model setup, parameter values, initialisation,forcing and validation data are given in Spillman et al. (2008).

Clam size classes were categorised as small (0–3 cm), medium(3–3.6 cm) and large (3.6–4.5 cm) as per harvesting definitions forBarbamarco Lagoon. Small clams were seeded in April and July inRegions 1, 2 and 3 only (R1, R2 and R3; Fig. 1). Actual seeding ratesfor 2003 were considered representative of seeding rates for 2002in the absence of measured data for this year and given a similarstrategy between years. A comprehensive sensitivity analysis wasperformed to determine initial populations and harvest rates asa function of clam biomass in the lagoon, as this information wasnot available. Repeated simulations were run with different valuesof initial clam biomass together with different harvesting rates.Simulated and measured monthly harvest yields were compared to

C.M. Spillman et al. / Estuarine, Coastal and Shelf Science 81 (2009) 267–278270

determine the most realistic initial populations and base modelharvest rates, i.e., 30% and 20% of the available biomass harvestedper month for medium- and large-size clams (Table 1), for use insubsequent simulations (Spillman et al., 2008). Medium- and large-size clams were harvested throughout the entire simulation periodfrom all grids cells containing clams in each scenario. Harvest yieldsfor different seeding/harvesting scenarios, lagoon morphologiesand conditions in the adjacent Northern Adriatic Sea were simu-lated. To calculate the value of harvests, a piecewise linear functionthat relates clam size to value was applied:

pðLÞ ¼ 0:1L for 25 � L < 35 mm ðmedium-size clamsÞ

pðLÞ ¼ 3:5 for L � 35 mm ðlarge-size clamsÞ

where p is in units of V kg�1 (Melia and Gatto, 2005). For medium-size clams, L was approximated as 32.5 mm.

2.4. Model scenarios

Simulations with the validated model examined the impacts ofdifferent clam rearing strategies, variations in Northern Adriaticnutrient loadings based on scenarios presented in Spillman et al.(2007) and bathymetric modifications such as dredging of thelagoon. A brief description of each scenario is given in Table 1. Thescenarios were selected to highlight the physical and ecologicalinterconnections between Barbamarco Lagoon, the NorthernAdriatic Sea and the Po River and subsequent impacts on clampopulations, as well as the impact of feedbacks from clam grazingand waste production on lagoon health.

2.5. Indicators

Indicators used to describe the production carrying capacity andecological health of a system were adapted from Gibbs (2007) andapplied in this study. Clearance time (CT) is defined as the timetaken for clam populations to filter the entire water column andwas calculated by dividing volume of the lagoon by total volume ofwater that the clam population in the lagoon filters in one day.Filtration rate was calculated by adjusting maximum individualclam filtration rates by temperature, salinity and suspended solidsconcentrations (Spillman et al., 2008), and scaling up for the total

Table 1Description of different scenarios applied to Barbamarco Lagoon for March–October 200

Scenario Description

0 Base case Current field conditions (clams pof 30% and 20% of available biom

Management scenarios1 All IC Clams present in all model botto

Harvest occurs in all model botto2 Seed� 2 Double base amount of clams see3 Harvest� 2 Double base harvest rates to 40%

medium- and large-size clams, re4 Seed� 2þHarvest� 2 Double base seeding and harvest

Adriatic scenarios5 High Po 50% increase in Po River discharg6 Low Po 50% decrease in Po River discharg7 Flood 100% increase in Po River dischar8 Less TP 50% decrease in total phosphorus

Engineering modifications9 No Po No Po River inflow channels10 Widen Po Widen Po River inflow channels11 No BNord Close Bocca Nord12 Widen BSud Widen Bocca Sud by 100% (80 m13 Dredge Dredge Bocca Sud and surrounds14 Widen and dredge Widen Bocca Sud by 100% (80 m15 No islands Remove islands in lagoon

clam biomass to give an upper limit of CT assuming a well-mixedwater column and neglecting refiltration. Clearance efficiency (CE)is defined as the ratio of lagoon residence time to clam clearancetime (CT). Lagoon water residence time was estimated with ELCOMas the length of time water remains in the model domain, withwater ‘age’ effectively treated as a tracer and aged by the modeltime step at each grid cell. Bottom CE was calculated by dividingresidence time of the bottom model grid cells adjacent to thesediment (depth w30 cm) by the time taken by clams to filter thislayer. The residence time of the bottom model grid cells was esti-mated by releasing a passive tracer into the bottom model layereach week and calculating the time for the mass of tracer to bereduced by 95% from the original level in the layer. The effect ofclam grazing on chlorophyll a concentration was also assessedspatially, following the depletion footprint concept presented byGibbs (2007).

Ratios of harvest (H) and net growth (NG) to seeding (S), inaddition to satiety indexes, clearance times and impacts on DOconcentration, were also assessed in order to quantify the effects ofdifferent simulated scenarios on clam populations and ecosystemhealth (Table 2). The satiety index (SI) indicates the available frac-tion of the daily food ration required for optimal clam growth, i.e.,

SI ¼GA�Bj�þ GPOC

�Bj�

ksatBCj

where ksat is defined as the daily food ration for optimal growth(g C (g C)�1 d�1). When the satiety index has a value of one, clamgrowth is not food-limited. However, when it exceeds a certainthreshold, clam filtration rate is reduced using the satiety functionfFj, which has a value of zero when uptake equals the ration, i.e.,clams are ‘‘full’’ (Spillman et al., 2008). To assess the impacts of clamfeedbacks on the water column, nutrient fluxes and oxygenconcentrations were also examined in model simulation outputs.

3. Results

3.1. Model validation

Simulated physical and water quality variables were comparedwith field data collected from 8 to 16 September 2002. Modelled

2.

resent in R1, R2 and R3 only) with base harvest ratesass per month for medium- and large-size clams, respectively.

m cells for initial conditions. Seeding in April and July occurs in R1, R2 and R3 only.m cells.ded in April and July in R1, R2 and R3 only.and 60% of available biomass per month forspectively, in R1, R2 and R3 only.ing rates in R1, R2 and R3 only.

eege and 50% decrease in total phosphorus (TP) in Po River discharge(TP) in Po River discharge

to Pila di Maistra and Pila di Tramontana by 30% and 130%, respectively.

)(w0.5 m deeper)

) and dredge surrounds (w0.5 m deeper)

Table 2Mean clam growth rates, total net growth, total mortality and total harvest value, in addition to total net growth: total biomass inputs (NG:B), harvest: seeding (H:S) andclearance efficiency (CE) ratios and satiety indexes, for simulations for the period March–October 2002 in Barbamarco Lagoon. Values�110% or�90% of the base case values areindicated by ‘y’ and ‘*’, respectively.

Scenario Mean net growth rates (10�4 g C clam�1 day�1) Total Ratios

Small clams Medium clams Large clams Net growth (kg C) Mortality (kg C) Harvest value (� 103 V) NG:B H:S CE Satiety

0 Base 3.64 8.94 17.42 9708 1078 2454 1.17 1.72 0.381 0.53Management scenarios1 All IC �0.46* �0.96* �0.39* 13,914y 12,512y 16,980y 0.12* 0.78* 3.565y 0.36*2 � 2 Seed 2.10* 5.53* 8.23* 11,507y 2080y 3682y 0.69* 1.26* 0.740y 0.46*3 � 2 harvest 4.37y 10.04y 21.28y 8619* 703* 2649 1.04* 1.80 0.265* 0.564 � 2 Seed and � 2 harvest 2.96* 7.80* 13.85* 12,322y 1353y 4458y 0.74* 1.50* 0.523y 0.50

Adriatic scenarios5 High Po 3.96 9.68 19.34y 10,419 1107 2545 1.26 1.79 0.386 0.546 Low Po 3.19* 7.72* 14.16* 8701* 1060 2334 1.05* 1.63 0.381 0.517 Flood 3.35 8.19 15.40* 9203 1114 2548 1.11 1.67 0.360 0.518 Less TP 3.02* 7.49* 13.47* 8451* 1071 2288 1.02* 1.60 0.381 0.50

Engineering modifications9 No Po 3.49 8.58 16.65 9321 1068 2399 1.12 1.68 0.437y 0.5110 Widen Po 4.11y 9.79 19.81y 10,670 1086 2598 1.29y 1.83 0.325* 0.5511 No BNord 4.23y 10.60y 21.73y 11,380y 1130 2671 1.37y 1.89 0.402 0.5612 Widen BSud 4.08y 10.06y 20.44y 10,658 1096 2565 1.29y 1.80 0.403 0.5513 Dredge 3.52 8.54 16.65 9645 1079 2470 1.16 1.73 0.387 0.5214 Widen and dredge 3.73 9.23 18.03 10,150 1103 2525 1.23 1.77 0.394 0.5415 Islands 4.00y 9.75 19.50y 10,655 1078 2610 1.29y 1.84 0.382 0.55

C.M. Spillman et al. / Estuarine, Coastal and Shelf Science 81 (2009) 267–278 271

current velocities and directions through Bocca Sud correlated wellwith measured values (r2¼ 0.979 and r2¼ 0.888, respectively) andindicate that simulations accurately reproduce the magnitude andtiming of tidal exchange with the Northern Adriatic Sea (Pearsoncorrelation coefficient r¼ 0.84; Fig. 2). Depth-averaged simulatedtemperature and salinity (psu) over 30 sampling stations across thelagoon for 11–14 September 2002 were 22.0 �C and 26.6, respec-tively, compared with measured values of 22.7 �C and 26.4 at thesame locations. For surface salinity and temperature, r¼ 0.46 andr¼ 0.54, respectively. Measured bottom temperature and salinityaveraged across the same stations during the same period were23.2 �C and 29.7, respectively, compared with correspondingsimulated values of 22.8 �C and 27.8. Bottom salinity values wereslightly lower in the model simulations but reproduced generalobserved salinity distributions in the lagoon (Spillman et al., 2008).For bottom temperature and salinity r¼ 0.46 and r¼ 0.27, respec-tively, though r¼ 0.45 for prediction for salinities above 28 orbelow 24.

Average bottom DO concentrations measured at field stationsduring the September 2002 experiment were 7.9 g m�3, comparedwith 6.6 g m�3 in model simulations, with the lower simulationvalues possibly due to an overestimate of the model inputparameter sediment oxygen demand (SOD). The average differ-ence between model and simulated bottom DO values is approx-imately 1 g m�3 with r¼ 0.27. The simulated spatial distribution ofchlorophyll a (chl a) was similar to the measured distribution,though bottom simulated concentrations were on average2.6 mg chl a m�3 lower than measured values. If measured valuesbelow 14 g m�3 only are considered, r¼�0.21. This problem is

08/09/02 10/09/02 1

−200

0

200

Tim

Flo

w (m

3s

−1)

Fig. 2. Observed and simulated flow through Bocca Sud fo

most likely due to an underestimate of chl a in the forcingboundary conditions or an overestimate of field concentrationsattributable in part to issues of use of fluorescence to denote chla concentrations. Further model validation can be found in Spill-man et al. (2008)

Total simulated harvest of clam carbon for R1 and R2 was8300 kg C for March–October 2002 (r¼ 0.55), which was similar tothe calculated harvest of 8000 kg C, based on total live weightharvested and a carbon to live weight relationship (Solidoro et al.,2000; Kasai et al., 2004), for the two regions. Simulated harvestedclam numbers were 2.5�107, considerably lower than the esti-mated actual harvest of 4.6�107 (r¼ 0.54). This indicates that themodelled harvest was comprised of fewer larger clams comparedwith the observed harvest, despite the same clam biomass yield. Itis important, however, to note that the actual number of clamsharvested was estimated using measured total harvested liveweight and a carbon to live weight relationship, whilst alsoassuming an average size based on catch rates, which gives noindication of the real distribution of clam sizes. Monthly simulatedand estimated harvest values are presented in Spillman et al.(2008). Maximum simulated clam growth rates were0.044 mm d�1, 0.047 mm d�1 and 0.061 mm d�1 for small, mediumand large clams, respectively, and fall within the literature range of0.0029–0.16 mm d�1 (Breber, 1985; Thompson, 1995; Goulletqueret al., 1999; Solidoro et al., 2000). Average clam biomass, numbers,chlorophyll a and DO concentrations, residence time and clearanceefficiency for the base case for May–October 2002 are presented inFig. 3, to provide a reference for comparison with the differentmanagement scenarios run with the model.

2/09/02 14/09/02 16/09/02e

Observed

r 8–16 September 2002 ($$$ observed, d modelled).

Distan

ce (m

)

500

1000

A1500

0 4 8 12 16

Mean Clam Biomass (gC m−2

)

40 60 80 100 120

Mean Clam Numbers (m−2

)

Distan

ce (m

)

500

1000

1500

0 1.5 3 4.5 6

Mean Chlorophyll a (mg m−3

)

0 2 4 6 8

Mean DO (g m−3

)

Distan

ce (m

)

Distance (m)

2000 4000 6000 8000

500

1000

1500

0 1 2 3 4Mean Bottom Residence Time (days)

Distance (m)

2000 4000 6000 8000

0 1 2 3 4

Mean Bottom Clearance Efficiency

B

C D

E F

Fig. 3. Time-averaged mean (A) clam biomass, (B) clam numbers, (C) chl a and (D) DO concentrations and (E) bottom residence time and (F) CE for the base case for March–October 2002.

C.M. Spillman et al. / Estuarine, Coastal and Shelf Science 81 (2009) 267–278272

3.2. Management scenarios

Daily net growth rates, total net growth (NG), mortality andharvest values for the simulation period, in addition to indicators ofclam growth and ecosystem health, are presented for all scenariosfor March–October 2002 in Table 2. Closing Bocca Nord, removingthe islands from the lagoon and widening the Po River freshwaterinlets or Bocca Sud, all resulted in increases in daily net growth ratesof>10%, most likely as a result of improved flushing and, hence, foodavailability. Widening Bocca Sud plus dredging (Case 14), however,resulted in smaller relative increases in growth rates. Highestgrowth rates were achieved in simulations with harvest ratesdoubled (Case 3), due to decreased competition for food particles.Conversely, increased seeding or harvesting rates, low Po Riverdischarge, or reduced Po River TP loadings all led to decreases of atleast 10% in net growth rates compared with the base case. Total NGwas elevated, however, in scenarios with increased seeding, despitelower mean growth rates, due to higher populations. Initial condi-tions of clams throughout the entire lagoon (Case 1) resulted in thehighest total NG of all scenarios due to much greater clam numbers,but also the highest total mortality. Total NG to total biomass input(seeding plus initial conditions) ratios (NG:B) were highest insimulations with Bocca Nord removed (Case 11), and lowest, only12%, for Case 1, most likely a consequence of severe food limitationdue to higher grazing demands. Average clearance efficiency (CE)was almost 10 times that of the base case when clams were presentthroughout the lagoon (Case 1), whereas values for all otherscenarios were similar to that of the base case.

Commercial values of total harvest for March–October 2002 forall scenarios are presented in Table 2. Total harvest value increased

in all scenarios compared with the base case, with the exceptions ofwhen Po River inflows or nutrient loadings were reduced, thoughonly slight increases were evident when the lagoon was dredged(Case 13). Initialising clams throughout the entire lagoon (Case 1)gave the largest harvest of greatest commercial value, despite thelowest mean growth rates and NG:B ratio, due to greatly increasedinitial clam populations. However, a higher proportion of the yieldin Case 1 was comprised of medium-sized clams, a reflection ofa shorter growing time. Doubling seeding rates, both alone andcombined with a doubling of harvesting rates, also resulted inharvest values at least 10% higher than the base case. Harvest toseeding ratios (H:S) approximate the cost to benefit ratios of thefishing program in Barbamarco Lagoon and show that sealing BoccaNord resulted in the greatest harvest return, proportional toseeding inputs, whereas return was lowest for initialising clamsacross the lagoon (Case 1), i.e., only 78% of the amount of carbonseeded was recovered by harvesting.

Time-averaged residence times (s) for bottom model grid cells(depth w30 cm) as a percentage of the base case are presented inFig. 4 for selected scenarios for the period March–October 2002. Ofall scenarios, removing the Po River inlets (Case 9) had the greatesteffect, with an increase in s of 0.27 days and 2.12 days in the averageand maximum lagoon-wide bottom residence times, respectively,compared with the base case (Fig. 4B). Closing Bocca Nord resultedin a 56% decrease in exchange with the Northern Adriatic Sea whichsubstantially increased s, with increases of at least 25% in areasopposite the sealed lagoon mouth (Fig. 4D) and a mean lagoon s of4.1 days compared to the base case value of 3.4 days. Conversely,widening the southern Po River inlet (Fig. 4C) decreased the bottoms by up to 25% over much of the southern part of the lagoon, leading

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Fig. 4. Time-averaged bottom residence times for March–October 2002 in simulations of Barbamarco Lagoon, represented as a percentage of the base case residence time: (A) PoRiver discharge increased by 50% (Case 5), (B) Po River freshwater inlets removed (Case 9), (C) lagoon freshwater inlets widened (Case 10), (D) Bocca Nord removed (Case 11), (E)Bocca Sud widened (Case 12) and (F) lagoon islands removed (Case 15).

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to improved food availability to clam populations in seeding areas,and increases in net growth rates of clams (Table 2). Widening thePo River inflow at the northern end of the lagoon had a lesser effect,however, similar to increasing Po River discharge (Fig. 4A).Widening Bocca Sud resulted in a 50% increase in exchange withthe Northern Adriatic Sea, which reduced s by 10–15% in the regionnear Bocca Sud, but increased bottom residence time adjacent toBocca Nord (Fig. 4E). Removal of islands from the lagoon resulted inslight changes in residence time, increasing residence time in thecentre of the lagoon but improving flushing in the vicinity of nearbyR2 (Fig. 4F).

Mean chl a concentrations in bottom waters for March–October2002 are presented as percentages of the base case concentrationsin Fig. 5 to reveal how chl a in different areas is affected by clamgrazing and food availability for selected scenarios. Lagoon-widemean chl a concentrations showed little change from the base case,with the exception of when clams were initialised in all lagoonbottom cells (Case 1) which resulted in a reduction of2.7 mg chl a m�3. For this case there were reductions in mean chla of up to 40% in areas outside of R1–3 compared with the base case.These substantial reductions can be attributed to the presence ofclams in these areas and associated grazing demand, whereas theseregions were uninhabited by clams in the base case. Impacts ofclam grazing were also evident when clam seeding was doubled(Fig. 5B), with reductions of up to 20% in chl a in R1–3. The influenceof phosphorus loading from the Po River was evident as decreasesof approximately 5–10% in phytoplankton biomass across themajority of the lagoon bottom water layer when TP concentrationswere decreased by 50% in the Po River (Case 8; Fig. 5C). Addition-ally, increases in average bottom chl a concentrations of up to 10%occurred, even in water overlying clam regions, when the Po River

inlets were widened (Fig. 5D). Widening Bocca Sud also led to slightincreases in bottom chl a due to increased flushing and inputs fromthe Northern Adriatic Sea (Fig. 5F), as did closing Bocca Nord(Fig. 5E), possibly as a result of increased residence times andgreater in situ primary production. However, when Bocca Sud wasdredged and widened (Case 14), the benefits of increased exchangewere outweighed by reduced vertical mixing and increased bottomresidence times that decreased both food availability to clampopulations and harvest values (Table 2).

Clam water column clearance times, clearance efficiencies forthe bottom water layer and average satiety indexes are shown forMarch–October 2002 in Fig. 6. The presence of clams across theentire lagoon (Case 1) resulted in consistently lower daily clear-ance times than in the base case, with reductions of up to 90% attimes (Fig. 6A), due to the elevated filtration capacity of the greatlyincreased clam population. Doubling seeding rates also resulted inlower clearance times compared with the base case. Conversely,the highest clearance times corresponded to the scenario ofdoubled harvest rates, due to diminished clam populations.Excepting Cases 1, 2 and 4 when seeding was doubled, meanbottom clearance efficiencies were <1.0 for all scenarios, indicatingthat the bottom water layer was replaced before clams couldtheoretically filter its entire volume (Fig. 6B). Values for Case 1exceed 1.0 for the majority of the simulation period, suggestingthat clams were filtering the bottom layer repeatedly before it wasflushed and were therefore strongly regulating phytoplanktonbiomass above the clam beds. Clam satiety indexes were highestduring May–June 2002 (Fig. 6C), implying that levels of phyto-plankton biomass in the bottom layer met clam’s grazingrequirements, despite high calculated clearance efficiencies.Higher seeding rates or a larger initial biomass in Cases 1, 2 and 4

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Fig. 5. Time-averaged bottom chl a concentrations for March–October 2002 in simulations of Barbamarco Lagoon, represented as percentages of the base case bottom chla concentrations: (A) initialising clams throughout the lagoon (Case 1), (B) doubling clam seeding in R1–3 (Case 2), (C) reducing Po TP loading by 50% (Case 8), (D) widening lagoonfreshwater inlets (Case 10), (E) closing Bocca Nord (Case 11) and (F) widening Bocca Sud (Case 12).

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Fig. 6. Averaged daily clam indices: (A) clearance times (CT), (B) bottom layer clearance efficiencies (CE) and (C) satiety indexes for selected simulations in Barbamarco Lagoon forMarch–October 2002.

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resulted in the lowest average satiety indexes, e.g., satiety was only0.36 when clams were present throughout the entire lagoon, dueto strong competition for food particles.

Time-averaged bottom-water DO concentrations for March–October 2002, as a percentage of the base case concentrations, arepresented over the lagoon domain in Fig. 7 for selected scenarios.The greatest reductions occurred when clams were initialisedacross the entire lagoon, with decreases of up to 20% over largeareas of the lagoon (Fig. 7A). This decline in DO contributes to dailymortality rates as high as 0.3% compared with 0.18–0.2% for otherscenarios. Removing Bocca Nord inflows resulted in reductions inDO of greater than 20% in front of the lagoon mouth, though levelsin areas adjacent to Bocca Sud and where clams were locatedincreased by the same amount (Fig. 7D), possibly due to decreasedresidence time in the area (Fig. 4D). Mean bottom DO concentra-tions averaged over the simulation period decreased only slightly inthis scenario to 6.69 g m�3 compared to the base case level of6.73 g m�3. Widening Po River inflows resulted in increases inbottom DO concentrations of approximately 5–8% over much of thelagoon (Fig. 7C). Widening Bocca Sud inflows increased DOconcentrations slightly in the southern parts of the lagoon, but alsocaused lower DO concentrations opposite Bocca Nord (Fig. 7E) dueto increased residence times in this region (Fig. 4E), while removingthe islands increased DO in south-west parts of the lagoon butresulted in reduced DO near Bocca Sud (Fig. 7F). Doubling rates ofclam seeding had little impact on DO (Fig. 7B), suggesting that clamrespiration has little direct effect on DO in the lagoon, and thatlarger variations occur mostly in response to physical measuresthat alter flushing and circulation of the lagoon.

Clam populations and bottom chl a and DO concentrations,residence time and clearance efficiencies averaged over the periodMarch–October 2002 are presented in Fig. 8 for the scenario when

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Fig. 7. Averaged bottom DO concentrations for simulations for March–October 2002 in Barb(A) initialising clams across the entire lagoon (Case 1), (B) doubling clam seeding in R1–3 (Ca(E) widening Bocca Sud (Case 12) and (F) removing lagoon islands (Case 15).

clams were initialised throughout the whole lagoon (Case 1). Clambiomass was lowest in the shallow periphery of the lagoon (Fig. 8A)where residence time was high (Fig. 8E), chl a concentrations werelow (Fig. 8C) and DO concentrations were reduced (Fig. 8D).Clearance efficiencies were also very high near the lagoonboundary, reflecting elevated residence times (Fig. 8F). Conversely,clam biomass was highest in the deeper, well-flushed, southernparts of the lagoon opposite Bocca Sud, where chl a and DOconcentrations were highest due to close proximity to the lagoonmouth. However, clam numbers were low in this region (Fig. 8B),indicating that these areas were inhabited by fewer larger clams.Reduced food availability in Case 1 was also partly responsible forhigh mortality rates, coupled with low DO concentrations. BottomDO concentrations decreased by 10–20% over much of the lagoon(Fig. 8D), with a 10% drop in average lagoon-wide concentrations,most likely as a result of high rates of clam respiration, in additionto increased mineralisation of clam wastes.

4. Discussion

Results of this study indicate the impact of different clammanagement strategies, nutrient and freshwater loadings, andmorphological changes in habitat on resident bivalve cultures, bothfrom commercial and ecological perspectives. On a system scale,exchange with the adjacent Northern Adriatic Sea is the key factorinfluencing clam growth in lagoon and impacts on water quality,from both lagoon phytoplankton biomass supply and replenish-ment of food- and oxygen-depleted bottom-water perspectives. Ona local scale, the suitability of areas of the lagoon for clam culti-vation is tightly coupled with food availability, clam densities andDO levels, with highly flushed regions promoting high net growth

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amarco Lagoon, expressed as percentages of the base case bottom DO concentrations:se 2), (C) widening lagoon freshwater inlets (Case 10), (D) closing Bocca Nord (Case 11),

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Fig. 8. Time-averaged mean (A) clam biomass, (B) clam numbers, (C) chl a and (D) DO concentrations and (E) bottom residence time and (F) CE for Case 1 when clams were presentthroughout Barbamarco Lagoon for March–October 2002.

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rates, highlighting the importance of considering the spatial aspectof carrying capacity using a spatially resolved model.

Total harvest value is a primary indicator of the productioncarrying capacity of a system, and hence of commercial success ofa fishery. Total harvest value increased in 12 of the 15 scenariosinvestigated, which suggests that there is considerable scope fordevelopment of the fishery and that, at base stocking levels, there ispotential to increase lagoon production carrying capacity.Management scenarios which involved doubling seeding rates and/or harvest rates (Cases 1–4) resulted in four of the top five harvestvalues, with Cases 1, 2 and 4 the only scenarios to producea increase of more than 10% in harvest value. There is someuncertainty in these results due to the limited clam growth dataavailable for verification, though scenario intercomparisonsprovide valuable insights into the relative effects of the differentmanagement strategies. Initialising clams across the entire lagoon(Case 1) produced the largest harvest of greatest commercial value,but with lowest net growth rates and highest mortality losses,indicating harvest value should not used as the sole criterion of thecommercial success of management strategies. Clams are priced ona sliding scale according to size and so their value is therefore notnecessarily proportional to harvest yield; an important consider-ation in determining the cost/benefit of harvesting intensity.Moreover, harvest return is not necessarily proportional toincreased seeding or harvesting effort, due to potential for reducedfood availability arising from increased grazing demands.

Bottom CE values for Cases 1, 2 and 4 are the highest of allscenarios (Table 2), reflecting increased clam populations andassociated filtration capacity. In Case 1 bottom CE values were as

high as 4.0 in shallow areas (Fig. 8F), indicating that the clamsprocessed the volume of the bottom water layer up to four timesbefore it was replenished. These high clearance efficiencies due tolarge clam numbers cause depletion of overlying phytoplanktonbiomass and self limitation of populations (Fig. 5), resulting in thelowest satiety indexes and net growth rates, for Cases 1, 2 and 4 ofall scenarios (Table 2). Average growth rates for Case 1 are allnegative, indicating that the lagoon is merely acting as storage forthe clam biomass as resources are generally not sufficient tosupport growth. Moreover, nearly 45% of clams seeded in thelagoon in Case 1 in the previous year (ICs) died prior to harvest(Table 2), with these rates of loss likely to be uneconomic. However,doubling harvest rates (Case 3) appear to be the exception in thissubset of scenarios, with high growth rates, improved satiety andlow mortality, suggesting that thinning of dense populations viaincreased rates of harvest has benefits for improving food avail-ability and should be considered if stocking rates are increased.

Primary production in the Northern Adriatic Sea is the mainsource of phytoplankton biomass to Barbamarco Lagoon (Spillmanet al., 2008), and is primarily driven by Po River discharge andassociated nutrient loadings (Spillman et al., 2007). The Adriaticscenarios (Cases 5–8) essentially assess the influence on lagoonclam production of the Po River and conditions in the adjacentAdriatic Sea. Of the four scenarios, only when Po River dischargeincreased by 50% (Case 5), did net growth and harvest valuesactually improve on those of the base case. When Po dischargedecreased (Case 6) or nutrient loadings are reduced (Case 8),decreases in net growth and harvest values are actually greaterthan when the Po River inlets to the lagoon are sealed (Case 9).

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Spillman et al. (2007) found that a 50% increase in Po dischargeresulted in an increase of 6% in mean surface chl a concentrations inthe Northern Adriatic Sea, whereas reduced nutrient loadings ora 100% increase in Po discharge (Case 7) produced decreases of 11–18% in mean chl a levels. This suggests that it is the amount ofphytoplankton biomass in the Northern Adriatic, and hence inputto the lagoon, that is of greater importance to lagoon clam pop-ulations than the physical flushing of the lagoon by Po inflows.

Scenarios which modified lagoon bathymetry and exchangewith the Northern Adriatic Sea (Cases 9–15) were used to assess theinfluence of changes in residence time on clam harvest values aswell as on water quality. Improving flushing (Cases 10, 12–14)generally improved food availability, evident in increased bottomchl a concentrations, satiety index values, net growth rates andharvest values. Improved flushing also increased bottom DOconcentrations in these scenarios, which combined with clamproduction increases, suggests that this approach is the most viablefor optimising sustainable harvests in the lagoon. It is important toalso consider the balance between improved flushing and theresulting increases in average bottom velocities (up to 25% for Cases10 and 12) as high bottom currents may prove detrimental tobottom culture due to resuspension or burial of animals. However,when Bocca Sud was dredged and widened (Case 14), the benefitsof increased exchange, as compared to widening Boca Sud only(Case 12), were reduced by diminished vertical mixing andincreased bottom residence times that decreased both food avail-ability and harvest values (Table 2). The use of a 3D model allowsfor the simulation of the effects of such modifications on thehydrodynamic regime within the lagoon and quantification ofimpacts on clam production that may not be easily estimated usinga 1D or box model.

Conversely, sealing the Po River inlets to the lagoon (Case 9) andclosing Bocca Nord (Case 11) both produced an increase in residencetimes over large areas of the lagoon (Fig. 4B, D) and consequently,a rise of more than 10% in CE values (Table 2). However, satiety andnet growth rates actually increased in Case 11 and produced thehighest harvest value of all scenarios with base seeding rates,despite reducing exchange with the sea by 56%. One explanationmay be that the northern area of the lagoon acts as a source ofphytoplankton to the southern area, with nutrients from thenorthern Po River inflow and increased residence time combining toboost in situ primary production. Circulation patterns would bealtered by sealing the northern lagoon mouth so that more waterfrom the northern area would pass over the clam regions, providingan additional source of food to clam populations. Residence timesover clam regions also decreased, minimising refiltration andincreasing food supply from the adjacent sea. Closing Bocca Nordappears to be the best strategy to increase clam production atcurrent stocking levels based on harvest and net growth values,however, low DO concentrations (Fig. 7) and high residence times inthe northern area would preclude clam cultivation expansion to thisarea, limiting growth to the southern part of the lagoon. Low bottomDO concentrations can enhance nutrient release from the sedi-ments, encouraging macroalgae growth (e.g., Ulva) and furtherreducing DO levels via increased respiratory demands, whichimpact upon clam population and ecosystem health.

The interacting effects of residence time and clam densitymanipulations on carrying capacity are indicated by clearanceefficiency (CE). However, stratification of Barbamarco Lagoon formuch of the simulation period (Spillman et al., 2008) wouldpreclude clam populations accessing surface water for filtration;calculating CE using the residence time of the whole lagoon istherefore likely to be unrealistic. In this study a bottom CE indexwas applied which gives a more accurate indication of food avail-ability and potential for refiltration for clam populations. Bottom CEvalues for Barbamarco Lagoon are 2–3 times those calculated using

lagoon residence time, indicating that clam filtration has a muchgreater impact on phytoplankton biomass in water overlying thebeds, than suggested using lagoon-wide values. Dame and Prins(1998) estimated system CE values of 1.84 for Ria de Arosa, Spainand 16.6 for South San Francisco Bay, USA. However, Ria de Arosa isstrongly stratified (Figueiras and Pazos, 1991) and South San Fran-cisco Bay is seasonally stratified (Cloern, 1991), suggesting bottomCE values in these systems may be much higher and productioncarrying capacity overestimated for particular seasons.

McKindsey et al. (2006) recommended that models of ecologicalcarrying capacity be made spatially explicit, incorporate temporalvariability and feedback mechanisms between clam populationsand the ecosystem. This study has shown the spatial and temporalresponses of food availability, residence time and DO concentrationswithin a small highly flushed coastal system in response to differentmanagement strategies, and the subsequent impact on residentclam populations. Feedbacks of clam cultivation, both positive andnegative, have also been simulated, evident in phytoplanktondepletion and changes in bottom DO concentrations under a rangeof different flushing and food availability scenarios. Location of clambeds in poorly flushed areas of the lagoon led to localised phyto-plankton depletion, elevated clearance efficiencies and localisedanoxia and contributed to increased mortality, and reduced harvestyields and water quality. Strong coupling with highly seasonal PoRiver discharge and Northern Adriatic conditions also demonstratesthe temporal aspect of carrying capacity, which can be over-simplified by using annual values or a single index. Only a 3Dmodelling approach allows resolution of the interacting effects ofwater column stratification with horizontal distributions of phyto-plankton biomass and clams, to capture time and space scalesrelevant to the carrying capacity of the fishery. However, futuremodelling assessments of carrying capacity and ecological healthshould also increase monitoring and ecological data collection toallow for a more comprehensive verification of model output and tofurther increase the practical value of studies of managed fisheries.

5. Conclusions

This study showed, using a 3D model, that increasing theflushing of Barbamarco Lagoon results in improved food supply toclam populations, increased bottom-water DO concentrations andconsequently, higher satiety indexes, lower clearance efficienciesand mortality, and improved harvest returns. The importance toresident clam populations of phytoplankton inputs to the lagoonvia tidal exchange with the Northern Adriatic Sea is exemplified byhigh clam growth rates close to lagoon mouths and in modelscenarios where flushing is enhanced. The close coupling of theNorthern Adriatic Sea, the Po River and Barbamarco Lagoon isfurther emphasised by impacts on clam harvests due to variationsin Po River discharge regime and TP loadings. Total harvest valueincreased in 80% of scenarios investigated, suggesting that there isconsiderable scope for development of the fishery. Increasingseeding rates improved harvest values, though effects were maxi-mised when combined with increased harvesting rates to thinpopulations and decrease competition for the available food supply.Large decreases in Po River discharge or nutrient loadings, whilepossibly not realistic, decreased clam growth via reduced phyto-plankton biomass in the adjacent sea and provided some insightinto the influence of the Po River on the lagoon. Engineeringscenarios that had the greatest impact on net growth and harvestvalues were those in which food availability was improved by eitherincreasing exchange with the Northern Adriatic, reducing residencetimes in clam regions or promoting in situ primary production(Cases 10–12 and 15). A 3D coupled physical–biological model isnecessary to capture relevant time and space scales of this couplingand the interacting effects of water column stratification with

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phytoplankton production and horizontal distributions of clams.Ecosystem indicators such as bottom DO concentrations must alsoto be considered together with clam production for a complete,integrated assessment of sustainable carrying capacity.

Acknowledgements

The first author was a recipient of an Australian PostgraduateAward and a Centre for Water Research ad hoc scholarship. Theauthors thank Bruno Matticchio and Paolo Peretti (IPROS, Italy),Professor Pietro Traverso (University of Venice, Italy), ProfessorRodolfo Soncini Sessa and Professor Luca Bonomo (Politecnico diMilano, Italy) for assisting with data requests, sourcing invaluableinformation and the collation of field data. Thanks also to exam-iners of the Ph.D. thesis by the first author, on which this manu-script is based. The authors give special thanks to Dr Matt Hipsey(Centre for Water Research) for his modelling assistance andsupport.

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