Byron Et Al, 2011_Capacidad de Carga Modelo de Balance de Masa

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Ecological Modelling 222 (2011) 17431755

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Ecological Modelling

j o u r n a l h o m e p a g e : w w w . e l s e v i e r . c o m / l o c a t e / e c o l m o d e l

Calculating ecological carrying capacity of shellfish aquaculture usingmass-balance modeling: Narragansett Bay, Rhode Island

Carrie Byron a,b,, Jason Link c, Barry Costa-Pierce a,d, David Bengtson a

a University of Rhode Island, Department of Fisheries, Animal and Veterinary Sciences, Kingston, RI 02881, USAb Gulf of Maine Research Institute. 350 Commercial Avenue, Portland, ME 04101, USAc National Marine Fisheries Service, Northeast Fisheries Science Center, 166 Water Street, Woods Hole, MA 02543, USAd Rhode Island Sea Grant College Program, University of Rhode Island, Narragansett, RI 02882, USA

a r t i c l e i n f o

Article history:

Received 3 September 2010Received in revised form 3 February 2011Accepted 8 March 2011Available online 31 March 2011

Keywords:

Carrying capacityAquacultureShellfishModelingEcopathNarragansett Bay

a b s t r a c t

Increasing growth in the aquaculture industry demands ecosystem-based techniques for managementif that growth is to be ecologically sustainable and promote equity among users of the ecosystems inwhich it occurs. Models of carrying capacity can be used to responsibly limit the growth of aquaculturein increasingly crowded coastal areas. Narragansett Bay, Rhode Island, USA is one such crowded coastalregionexperiencinga rapid increase in bivalve aquaculture. An ecosystem mass-balancemodelwas usedto calculate the ecological carrying capacity of bivalve aquaculture. Cultured oyster biomass is currentlyat 0.47t km2 and could be increased 625 times without exceeding the ecological carrying capacity of297tkm2. This translates to approximately 38,950 t of harvested cultured oysters annually which is 4times the total estimated annual harvest of finfish. This potential for growth is due to the high primaryproductivityandlargeenergythroughputtodetritusofthisecosystem.Shellfishaquaculturehaspotentialfor continued growth and is unlikely to become food limited due, in part, to the large detritus pool.

2011 Elsevier B.V. All rights reserved.

1. Introduction

Growth of bivalve aquaculture worldwide (Costa-Pierce, 2008a;FAO, 2009) presents new challenges in coastal management. Thisgrowth is happening in both developing and industrial countries innearshore coastal environments where user conflict is high (Costa-Pierce, 2008a; Hamouda et al., 2004). Over 50% of the humanpopulation lives within 100km of the coast and several industriescompete for use of coastal resources (Martnez et al., 2007).

One such bay with increasing aquaculture and high user con-flict is Narragansett Bay,Rhode Island (RI),USA. Approximatelyhalfof Rhode Islands aquaculture takes place in Narragansett Bay. Inthe matter of 6 years(20012007), the industrygrewexponentiallyfrom a $300,000 to a $1,600,000 industry doubling the number of

farms and submerged land under lease (Alves, 2007). Ninety-ninepercent of the aquaculture in Rhode Island is oysters (Crassostreav-irginica). On a global scale, this industry is quite small. However,given that RhodeIsland (RI) isthe smallestand secondmostdenselypopulated state in the United States, the rate of growth is notable.

Corresponding author at: University of Rhode Island, Department of Fisheries,Animal and Veterinary Sciences, Kingston, RI 02881, USA. Tel.: +1 401 874 2668;fax: +1 401 874 7575.

E-mail addresses: [email protected], [email protected] (C. Byron),[email protected] (J. Link), [email protected] (B. Costa-Pierce), [email protected](D. Bengtson).

Over the past decade, bivalve aquaculture has progressedin technological, political, and social sustainability (Costa-Pierce,2008a,b; National Research Council, 2010). Rearing and harvestingtechniques are more efficient (Costa-Pierce, 2008a,b). Technolo-gies and policies aimed to mitigate the spread of disease haveincreased (Bushek et al., 2004; Forrest et al., 2009; Sapkota et al.,2008; Sindermann, 1984). Societys acceptance of bivalve aqua-culture continues to grow in part due to educational campaignsaimed at increasing awareness to the ecosystem services providedby shellfish (Coen et al., 2007). Additionally, bivalve aquacultureis one of the most ecologically sustainable types of aquaculture(Shumway et al., 2003). Bivalve aquaculture has little negativeimpact on the benthos (Crawford et al., 2003; Forrest et al., 2009;Grant et al., 1995). Bivalves act as a benthic-pelagic link making

planktonic nutrients available for benthic deposit feeders and sub-mergedaquaticvegetation(Newell,2004;PetersonandHeck,1999,2001) and improve water quality (Newell et al., 2002). Cages andother gear provide structure and habitat for a suite of other organ-ismsthereby increasingbiodiversity(Dealteris et al.,2004; Tallmanand Forrester, 2007).

As social acceptance of bivalve aquaculture continues toincrease, management strategies that promote sustainable indus-tries become critical. The most important question managers needto ask is; How much aquaculture can the system support? Thisquestion can be addressed by calculating the carrying capacity ofthe system for bivalveaquaculture. Limitingaquaculture within the

0304-3800/$ see front matter 2011 Elsevier B.V. All rights reserved.

doi:10.1016/j.ecolmodel.2011.03.010
http://dx.doi.org/10.1016/j.ecolmodel.2011.03.010http://dx.doi.org/10.1016/j.ecolmodel.2011.03.010http://www.sciencedirect.com/science/journal/03043800http://www.elsevier.com/locate/ecolmodelmailto:[email protected]:[email protected]:[email protected]:[email protected]:[email protected]://dx.doi.org/10.1016/j.ecolmodel.2011.03.010http://dx.doi.org/10.1016/j.ecolmodel.2011.03.010mailto:[email protected]:[email protected]:[email protected]:[email protected]:[email protected]://www.elsevier.com/locate/ecolmodelhttp://www.sciencedirect.com/science/journal/03043800http://dx.doi.org/10.1016/j.ecolmodel.2011.03.010


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1744 C. Byron et al. / Ecological Modelling222 (2011) 17431755

carrying capacity is the most straightforward and obvious way tocontinued sustainability.

If we fail to manage within carrying capacity guidelines, thereis potential to cause degradation of the system function. TracadieBay, PEI, is operatingabove itscarryingcapacity(Waite et al., 2005).Although a further increase in bivalve production may be possible,it would stress the system outside its normal range of variation(Filgueira and Grant, 2009). Similarly, river basins (rias) of the Gali-cian area in northern Spain are operating at carrying capacity withno room for growth of the industry (Duarte et al., 2008; Smaal,2002). Systems such as these threaten the ecosystem sustainabilityfor, not only their own, but other industries as well. Social equityis likely to decline as user-conflict increases with environmentaldegradation.

1.1. Carrying capacity

The definition of carrying capacity has been extended to fourtypes of carrying capacity that can be applied directly to bivalveaquaculture (Inglis et al., 2002).

1. Physicaltotalarea of marine farms that canbe accommodatedin the available physical space

2. Productionthe stocking density of bivalves at which harvestsare maximized

3. Ecologicalthe stocking or farm density which causes unac-ceptable ecological impacts

4. Socialthe levelof farm development that causesunacceptablesocial impacts.

While physical and production carrying capacity are useful ona farm-scale, acknowledging that the farm is only a part of a largerecosystem requires consideration of ecological and social carryingcapacities. In order to take an ecological approach to aquaculture(Soto, 2010), it is helpful to consider ecological carrying capacity.

Both the ecological and social carrying capacities are defined bythe acceptability of change and, therefore, depend on social val-

ues (Mckindsey et al., 2006). Mckindsey et al. (2006) explainedthat society defines the variables of interest and how much thosevariables can change. Therefore, society has a part in definingacceptability. Society can determinethe acceptabilityof alterationsto sustained ecological function, species biomasses and energyflows between trophic levels. This information can be used todetermine ecological carrying capacity using mass-balance mod-eling (Jiang and Gibbs, 2005; Mckindsey et al., 2006). Stakeholdersin RI wanted to calculate ecological carrying capacity for currentconditions in Narragansett Bay and were therefore, unwilling toaccept any change in ecosystem function, biomasses, or energyflows.

1.2. Modeling

Ecopath is static, mass-balance, ecosystem-based modelingsoftware that focuses on energy transfer between trophic levelsand is widely used in fisheries management (www.ecopath.org).Ecopath has been used for modeling a wide range of systems andmanagement scenarios (Christensen, 1995; Christensen and Pauly,1993; Monaco and Ulanowicz, 1997; Vasconcellos et al., 1997)including the carrying capacity of bivalve aquaculture (Jiang andGibbs, 2005). It differs from other modeling approaches because itencompassesthefulltrophicspectrum,whichiswhatmakesittrulyan ecosystem model appropriate for determining ecological carry-ing capacity. Most other shellfish carrying capacity models are attheproduction or farm scale (Bacheret al., 1998; Carverand Mallet,1990; Nunes et al., 2003; Raillard and Mnesguen, 1994) which

fails to incorporate all trophic levels equal to and higher than the

bivalves. This approach is useful on a farm scale but is shortsightedfor ecosystem management where several user groups depend onthe stability and sustainability of other trophic levels across theentire ecosystem. Furthermore, Ecopath provides a methodologyto standardize model outputs thereby making it easy to compareacross systems.

Since Ecopath is a foodweb-based model, special emphasis isplaced on predatorprey interactions and they are handled as theywould be in a foraging arena (Walters et al., 1997). Overall, Ecopathis a good balance between simplicity and the complexity of otherecosystem models. Some applications of shellfish carrying capac-ity models only consider nutrients, plankton, detritus, and bivalves(Bacheret al.,1998; Hawkins, 2007; Raillardand Mnesguen,1994;Smaal et al., 1998) which limit the scope of the model. Ecosystemmodelsare more appropriate in scope,but canhave unrealistic datademands and require advanced computer programming skills tooperate (Plagnyi, 2007). Ecopath provides a structured, yet flexi-ble, framework for ecosystem modeling.

Ecopath, like anymodel, has shortfalls andlimitations (Plagnyiand Butterworth, 2004). Most shortcomings are attributed to usererror such as uncritical use of Ecopath default settings. It is up tothe modeler to change default settings so that they are appropriatefor each functional group. Failure to do so treats all groups equally

which can lead to erroneous conclusions (Plagnyi, 2007). Perhapsthemostunavoidableshortfall of anyecosystem model is thequan-tityandqualityofdataavailabletofeedthemodel.Weattemptedtominimizethisshortfallbyusingdatacollectedatthemodellocationto calculate input parameters and by employing a series of diag-nostic tests to evaluate data parameterization and identify areas ofdata weakness that may need further investigation prior to modelbalancing (Link, 2010).

An ecosystem model of Narragansett Bay, consisting of 14functional groups, has been previously defined by Monaco andUlanowicz (1997). It was originally designed and used to comparetrophic structure and sustainability of three major Atlantic bays;Narragansett Bay, Chesapeake Bay, Delaware Bay. These originalmodels included no fisheries or aquaculture. Including both activi-

ties in themodelare essential to fully understand thedynamicsandfunction of the system. In order to aid the development of a long-term plan for aquaculture in Rhode Island, a working group of thestate aquaculture regulatory agency recommended thatthe ecolog-icalcarryingcapacityofNarragansettBay(andothercoastalwaters)for oyster aquaculture be determined. The purpose of this studywas to update the Ecopath model of Narragansett Bay developedby Monaco and Ulanowicz (1997) and to use the updated modelto calculate that ecological carrying capacity by increasing farmedoysterbiomass until themodelbecame unbalanced. A similar mod-elingeffortwasconductedforRhodeIslandscoastallagoons( Byronetal.,2011a). Anoteworthyaspectoftheseeffortsistheinclusionofa wide variety of stakeholders in the development and applicationof the models (Byron et al., 2011b).

2. Methods

2.1. Study area

Narragansett Bay (355km2) in Rhode Island, USA (W7120

N4035) is an eutrophic, well-mixed estuary with relatively lit-tle fresh water input (Saarman et al., 2008), residence timeof 26days, an average depth of 9 meters (Boothroyd andAugust, 2008; Nowicki and Nixon, 1985a,b) and average yearlytemperature of 11.24 C (Oviatt et al., 2002; Pilson, 2008;http://www.narrbay.org/physical data.htm). Narragansett Bay hasbeen well studied andmodeled over thepast 3 decades (Desbonnet

and Costa-Pierce, 2008; Kremer and Nixon, 1978; Monaco and
http://www.narrbay.org/physical_data.htmhttp://www.narrbay.org/physical_data.htm


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C. Byron et al. / Ecological Modelling222 (2011) 17431755 1745

Ulanowicz, 1997). Recently (2007), 50 hectares of Narragansett Baywere leased for oyster aquaculture which is a sharp increase from3.2 hectares in 1995 and exponentially less than the 8436 hectaresleased in 1911 when peak production biomass was approximately144,562 t (Alves, 2007; Beutel, 2009; Pietros andRice, 2003; RhodeIsland Commissioners of Shellfisheries, 1912). Several anthro-pogenic and environmental factors contributed to the decline inaquaculture in the 1920s1950s (increased raw sewage inputs,cumulative effects of continued soil erosion, increased metal fin-ishing effluents, hurricane of 1938, labor shortages during WWII,and several state socio-political changes) and continued lack ofgrowth in the 1950s1990s (lingering pollution, rise of tourism,suburbanizationofcoastalzone,robustcapturefisheries,andfearofreturning to a mill town social system) (Rice, 2006). Wild harvestof clams (Mercenariamercenaria), wild capture of finfish, tourism,and recreational fishing are additional industries vying for spaceand resources in Narragansett Bay. The environmental impact ofincreased aquaculture is onlyone of several concerns forstakehold-ersincludingnutrientcontrolandperiodicbloom-initiatedhypoxiaeventsoccurringinpatchesoftheupperBay(Desbonnetand Costa-Pierce, 2008).

2.2. Input parameters

Calculating ecological carrying capacityusing a static mass-balance ecosystem model is an iterative process that was donethrough several steps to ensure the validity and quality of theoutput. In order to calculate carrying capacity, a cultured shell-fish group and fisheries removals were added to Monaco andUlanowicz (1997) model. The biomass parameters were updatedand improved to reflect current conditions in Narragansett Bay aschanges in theecology have been documented over thepast decade(Collie et al., 2008; Nixon et al., 2008; Oviatt et al., 2003). The orig-inal Monaco and Ulanowicz (1997) diet matrix (Table 1a), with theaddition of cultured oysters, was used forthis study.All parametersreflectatime-lapsedsnapshotoftheecosystemsothatseasonalandtemporalvariabilityareincluded,butexpressedasayearlyaverage.

2.3. Data

Multiple data sources were available for several of the func-tional groups in the model. Due to the extent of these sources,only the most recent and accurate sources are mentioned in thisreport. Further details on datasources and model parameterizationwerereported by Byron(2010). Notall functional groupshave beenmeasured with equal intensity and precision, despite centuriesof surveys and examination in Narragansett Bay (Desbonnet andCosta-Pierce, 2008). Top trophic levels were measured more oftenand more accurately than lower trophic level consumers. On-goingsurveys and monitoring efforts were targeted as current sourcesof data for all model input parameters. Only when recent (2003)

data within Narragansett Bay were not available were older datasets considered.Noneofthevariousdatasourcesusedforparameterizationwere

collected for the sole purpose of constructing this model. Otherprojects that measured or surveyed the biota used different unitsof measures at different spatial and temporal scales. To unify thesediffering units, data were converted into standard units (gramsdry weight per square meter per year; g DW m2) and then re-evaluated before they were considered appropriate for this model.Dry weight was calculated from wet weight assuming 0.20 con-version coefficient for most species (Baird and Ulanowicz, 1989).Benthic invertebrate species biomasses were converted to dryweights according to Mckinney et al. (2004). Algae were convertedto dry weight assuming 87% moisture (Thornber and Guidone,

unpublished data). Carbon was converted to dry weight assumingT

able

1

ParameterInputs.(a)Inputdietmatrix.Alldietmatrixparametersrepresentpercent(%)compositio

nofdietandweretakenfromtheoriginalMonac

oandUlanowicz(1997)modelwiththeexception

ofculturedoysters.Numbers

inparenthesiswereestimatedbyEcopathduringtheauto-balancingroutine.(b)Mass-balanced

inputparameters.ValuessolvedbyEcopathare

inbold.Trophiclevel(TL),biomass(gDWm2)(B),production/biomass(P/B)

(gDWm2),consumption/biomass(C/B),ecotroph

icefficiency(EE),production/consumption(P/C)

.(c)Fisherieslandingsunitsaregramsdryweigh

tpersquaremeterperyear(gDWm2).Finfishw

erecaughtbytrawl(T),wild

clamswereharvested(H),lobsterswerecaughtu

singpots(P),andoysterswereextractedfromfarms(F).SeeTable2forprey/predatorgroupabbreviations.

Prey/Pred.

(a)Dietmatrix

(b)Inputparam

eters

(c)F

isherieslandings

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

TL

B

P/B

C/B

EE

P/C

T

H

P

F

1CF

8

3.7

1

0.385a

1.495

3.87

0.4

2

0.3

86

0.12

2PF

21

10(11)

3.1

9

1.24b

3.81

12.4

0.9

34

0.3

07

1.3

3BIC

6

6(7)

3.3

3.91ac(3.17)

1.94

12.4(7.6)

0.2

93

0.2

55

0.05

4BDF

37

8

42(34)

3

10

2

2.3

1

2.29d(2

.9)

6.1(7.3)

9.4(15.8)

0.9

09

0.4

64

5BSF

2

5(6)

2.3

9

5.32d

46.92

103.6

0.0

06

0.4

53

0.17

6Par

25

21

23(26)

2.4

2

2.77c

13.998

40(36)

0.2

55

0.3

89

7IC

1

3

2

3.6

6

2.03ae

38.4

114.05

0.0

63

0.3

37

8Mes

44

20

50

10

4

2.5

5

7.55f

66.02

210.06

0.6

04

0.3

14

9Mic

7

20

3

10

12

20

5

2.9

5

2.26f

330.1

965.3

0.8

35

0.3

42

10PB

2

8

20

62

20

16

2

5.25g

373.8

800

0.8

55

0.4

67

11POC

13

11

20

40

2

15h

595.2

1246

0.0

26

0.4

78

12CO

2.7

9

0.00949

i

46.92

103.6

0.0

08

0.4

53

0.0035

13BA

5

10

13

5

1

3.63c

282

0.0

76

14Phy

9

6

67

30

2

60

3

20

9

1

246.8

j

81.58

0.0

71

15D

8

14(16)

60

40

13

100

100

20

1

12.89k

0.6

53

GPP/Import

100

100

17

aRIDEM20062008unpublisheddata;bRIDEM2

0032006unpublisheddata;cThornberandGuid

oneunpublisheddata;dSchult,2010;eKremer,1

975;1976;fMonacoandUlanowicz,1997;gStaro

scikandSmith,2005;hMann,

2000;iBeutel,2010;jNOAANarragansettBayLab

unpublisheddata;kcalculatedusingPaulyetal.,

1993.


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1gdryweight=0.4gC(Bairdand Ulanowicz, 1989; Jrgensen et al.,1991; Parsons et al., 1984).

Ecopath requires input of atleastthreeparameters (biomass (B),production/biomass (P/B), consumption/biomass (C/B)) for everydefined functional group in the system (Christensen et al., 2005).From these three parameters the fourth main parameter requiredfor balancing, ecotrophic efficiency (EE), which is a measure of theamount of production used in the system, can be calculated auto-matically by Ecopath. The final two input components, which mustbe entered into the model for every functional group, are diet com-position and fisheries removals (i.e. aquaculture harvest or wildcatch).

Biomass estimates for fish and invertebrate carnivores groupsoriginated from 2003 to 2006 Rhode Island Department of Envi-ronmental Management (RIDEM) trawl survey of Narragansett Bay(Longval, 2009) (Table 1b). Finfish species caught were categorizedinto carnivorous fish or planktivorous fish. Invertebrate carnivoresare primarily comprised of squid and ctenophores (Monaco, 1995).

Benthic invertebrates were comprised of several groups eachmeasured using different techniques. Benthic invertebrate carni-vores were primarily comprised of lobsters and crabs (Monaco,1995). The primary sources of data for biomass of benthic inverte-bratecarnivoreswerefrombottomtrawlsurveysbyRIDEM,bottom

trawl surveys from the University of Rhode Islands GraduateSchoolofOceanography(GSO),andsubtidalsamplinginGreenwichBay (Thornber and Guidone, unpublished data). Infauna (benthicdeposit feeders and benthic suspension feeders groups) were sam-pled from four stations throughout Narragansett Bay on the sameday inJuly 2006(Table 1b). Parabenthos were primarily comprisedofCrangon and Palaemonetes shrimp (Monaco, 1995). Shrimp werecounted in June 2006August 2008 in the subtidal region of Green-wich Bay in Narragansett Bay (Thornber and Guidone, unpublisheddata) (Table 1b). The biomass of cultured oysters was estimatedusing planting and harvest estimations from the farmers and usingallometric conversions derived from a 2008 survey conducted oncultured oysters in RI coastal lagoons (Markey, 2009). Length andwet weight measurements were taken on 647 individual oysters

between 35 and 100 mm and used to allometrically infer a biomassfrom counts.

Mesozooplankton and microzooplankton biomass remainunchanged from Monaco and Ulanowiczs 1997 model (Table 1b).The two dominant mesozooplankton species are Acartiahudson-ica and A. tonsa (Durbin and Durbin, 1981; Durbin et al., 1983;Hulsizer, 1976; Kremer and Nixon, 1976; Monaco and Ulanowicz,1997; Smayda and Borkman, 2008). The dominant microzooplank-tonare ciliates andmicroflagellates (Monaco and Ulanowicz, 1997;Smayda and Borkman, 2008). Monaco and Ulanowicz (1997) zoo-plankton estimates were primarily based on work done in the late1970s (Durbin and Durbin, 1981; Durbin et al., 1983; Hulsizer,1976; Kremer and Nixon, 1976). Zooplankton work from this timeperiodremainsthemostcomprehensiveandprecisetodate.Recent

estimates of mesozooplankton were considered, but proved tobe an order of magnitude too low during the diagnostics andwere confirmed by E.G. Durbin (personal communication). Micro-zooplankton has not been given much attention in NarragansettBay (Durbin and Durbin, 1998) and no current estimates areavailable.

Pelagic bacteria in Narragansett Bay were measured weeklyfrom September 1999 to June 2000 at two stations (Staroscik andSmith, 2004) (Table 1b). Bacteria particulate organic carbon (POC)in the sediment has not been directly measured in NarragansettBay. Typical benthic bacteria abundances in estuarine systemsrange from 5 to 50g DW m2 (Mann, 2000) (Table 1b). Detrituswas estimated using an empirical relationship that relates detritusbiomass to primary productivity and euphotic depth (Pauly et al.,

1993) (Table 1b).

Benthic macroalgae were surveyed monthly between Jan-uary 2006 and December 2007 intertidally and between June2006 and August 2008 subtidally in Greenwich Baya smallbay located within Narragansett Bay (Thornber and Guidone,unpublished data) (Table 1b). Chlorophyll fluorescence was mea-sured monthly (20 surveys) from December 2006 to October2007 using a sampler that undulates through the water columnwhile being towed along a transect through East and West Pas-sages, Rhode Island Sound, Mount Hope Bay, and the ProvidenceRiver (NOAA Narragansett Bay, RI Lab). Survey methods and datawere available (as of December 2010) at http://www.narrbay.org/d projects/nushuttle/shuttletree.htm(Table1b).Eachmonthlysur-vey takes over 100,000 measurements which were averaged from20 surveys to get a current biomass estimate of phytoplankton.

Vital rates (P/B and C/B) for all species groups originated fromthe Monaco and Ulanowicz (1997) model. The same P/B and C/Bvalues used for the filter feeders group were also used for thecultured oysters group, 46.92 and 103.64, respectively (Table 1b).Respiration was calculated for each functional group as 65% ofassimilated consumption (Link et al., 2006). Respiration, althoughnot a required Ecopath input, is a useful parameter for diagnostics.

Major species landed from fisheries in the bay includeStenotomuschrysops, Paralichthysdentatus, Brevoortiatyrannus,

Homarusamericanus, Mercenariamercenaria, Myaarenaria, Spisu-lasolidissima (SAFISStandard Atlantic Fisheries Information Sys-tem, queried December 2009). Commercial and recreational catchwere based on the NOAA Fisheries Vessel Trip Reports (VTRs), theRhode Island Department of Fish and Wildlife Harvester Catch andEffort Logbook and allocations set by the RIDEM. The harvest loca-tion of theclamswas preciselyrecordedwhereasfinfish andlobstercatch in NarragansettBaywere deduced from harvest ofa largearea(statistical area 539) both in and out of the Bay (Table 1c).

2.4. Diagnostics

Prior to balancing the model, additional non-Ecopath deriveddiagnostics were performed to evaluate the validity of the input

parameters. Diagnosticscheck andaid in balancingthe model inde-pendent of Ecopath assumptions prior to the mandatory Ecopathautomated balancing routine. Diagnostic tests allow evaluation ofthe cohesiveness of the data despite the natural discrepancies thatoccur when using a multitudeof data sources collectedacross vary-ing temporalandspatial scales. Pre-balancing diagnostics allowthemodeler to look at the system holistically instead of piecemeal inthe way the individual data sources were collected.

Diagnostic tests were performed on: biomass, the ratio ofbiomass to primary production, thevitalrates (production (P), con-sumption (C), respiration(R)), the ratios of vital rates (e.g. P/C) andthe total consumptive removals. For detailed description on diag-nostic testing see Link (2010). In brief, each functional group wasplotted alongthex-axis in order of decreasing trophic level to allow

easy visualization of trophic relationships (Link, 2010).

Biomass and vital rates should decrease with trophic level. Ratios of biomass to primary production should remain less than

one. Vital rate ratios should be within ecologically acceptable limits

(i.e. P/C


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Fig. 1. Pre-balancing diagnostics. See Tables 1 and 2 for abbreviations. In some cases, groups were summed; all primary producer groups (PP), all zooplankton groups (Zoo).Biomass, P/B, and C/B values depicted were used as initial Ecopath input parameters. (a) Trophic decline in biomass across species groups. (b) Biomass and production ratiosto total primary production showing all levels remained at or below one. (c) Vital rates showing relative decline with trophic level and higher consumption than productionand respiration. (d) Vital rate ratios. (e) P/C remaining less than P/R rates. (f) Total consumptive removals which are less than production and consumption.

2, 3, 10, or 25 until the parameter conformed to the expectationsof the diagnostic test (Fig. 1). Performing diagnostic tests alloweda manual check of the validity of data sources and greater controlover the mass-balancing of the model by manually bringing theparameters closer to mass-balance instead of relying solely on theautomated Ecopath mass-balance routine.

2.5. Model outputs

In addition to diagnostic tests, Ecopath is equipped with toolsthat can be used to address uncertainty in the data, thereby furtherimproving the quality of the parameter inputs through the mass-balancing routine. The Pedigree routine allows entry of a range foreach parameterinputwhich evaluates statistical uncertainty. Pedi-gree allows the user tomarkthe sources of datafor each parameterand has a ranking scheme for the goodness of those data givingeach parameter confidence intervals. These confidence intervalswere then used by the Ecoranger module to give a probabilitydistribution for each parameter using a Monte-Carlo parametric(Christensen et al., 2005). Additionally, a sensitivity analysis wasperformedto evaluatethe effectof each ofthe entered inputparam-eters on all of the missing basic parameters for each group inthe system by varying each input parameter from 50% to +50%

(Christensen et al., 2005). Data were not always available for every

parameter and the sensitivity analysis was a way to identify thoseparameters and, subsequently, functional groups that were mostlikely to be impacted by slight modeling perturbations.

Mixed Trophic Impact Analysis was used to evaluate whichfunctional groups were most likely to be impacted by slight per-turbations by measuring the impact of an infinitesimally smallincrease in group biomass on other groups (Christensen et al.,2005). Mixed Trophic Impact is the measure of direct or indirectinfluenceagroup(ontheleftofthematrix)hasonanothergroup(at

the top of the matrix) based on food web characteristics. Ecopathproduces a matrix that reports the measured direct and indirectincrease or decrease in every groups biomass parameter caused byan infinitesimally small increase in every other groups biomass.

Ecopath Summary Statistics provide informative systems mea-sures such as throughput, cycling index, and pathways that can beusedtocharacterizethesystem.Totalsystemthroughputisthesumof allflows in a system: consumption,export, respiration,and flowsto detritus (Christensen et al., 2005). It represents the size of thesystem expressed as a flow (Ulanowicz, 1986). Cycling index is thepercentage of energy throughput in the system that is recycled andcorrelates to system maturity, resilience and stability (Christensenet al., 2005; Finn, 1976; Odum, 1969). The number of pathways isindicative of the redundancy and stability of the system.


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Table 2

Ecopath Model Outputs. (a) Pedigree Index. Higher numbers signify greater confidence. See Table 1 for abbreviations. (b) Information analysis of balanced model outputs.

Pedigree index Group name Information analysis

B P/B Q/B Diet Catch Ascendency Overhead Capacity Info. ThroughputgDWm2y1*b its gDWm2y1*bits gDWm2y1*bits bits gDWm2y1

4 2 2 1 4 Carnivorous Fish (CF) 2.566 23.407 25.973 0 1.494 2 2 1 4 Planktivorous Fish (PF) 41.56 178.083 219.644 0 15.3934 2 2 1 4 Benthic Invertebrate Carnivores (BIC) 43.287 271.198 314.485 0.001 24.0014 2 2 1 Benthic Deposit Feeders (BDF) 172.115 429.003 601.118 0.002 45.768

4 2 2 1 4 Benthic Suspension Feeders (BSF) 557.899 3971.221 4529.121 0.007 551.1394 2 2 1 Parabenthos (Par) 167.577 949.081 1116.658 0.002 99.724 2 2 1 Invertebrate Carnivores (IC) 279.254 1955.309 2234.563 0.003 231.751 2 2 1 Mesozooplankton (Mes) 1946.625 9883.553 11830.18 0.023 1585.9721 2 2 1 Microzooplankton (Mic) 2415.679 13636.42 16052.1 0.029 2181.584 2 2 1 Pelagic Bacteria (PB) 7118.605 18955.1 26073.7 0.084 42002 2 2 1 Bacteria Sediment POC (POC) 17705.37 41618.1 59323.46 0.21 186905 1 1 0 5 Cultured Oysters (CO) 3.169 12.982 16.152 0 0.9844 2 Benthic Algae (BA) 1333.879 5606.105 6939.984 0.016 1022.8145 2 Phytoplankton (Phy) 22923.92 27921.23 50845.14 0.272 20130.68Ecopath Pedigree index: 0.3 Detritus (D) 43392.32 52818.09 96210.41 0.514 35583.44Number of living groups: 14 Total 98103.83 178228.9 276332.7 1.163 84364.73Measure of fit: 1.09 Percent of Total (%) 35.502 64.498 100

2.6. Model considerations

All modeling was done using EwE5 software package (avail-able at www.ecopath.com). Ecopath mass-balances the model byslightlyadjustingtheinputparameterswithintheirconfidencelim-its according to two master equations (Equation 12) so that theenergyinputandoutputareequalforeachgroup(Christensenetal.,2005).

Production = predation+ catches+ net migration

+accumulated biomass+ other mortality. (1)

Consumption= production+ respiration+ unassimilated food.(2)

Energy between groups is linked through the diet matrix.

Ecopath provided a range of setup options forthe automatedmass-balancing routine. Five thousand (5000) iterations per run werechosen and the EE goal was forced to 0.95. The confidence intervalsweresetbythePedigreewithalowerlimit10%oftheoriginalvalue.For perturbation method, neighborhood perturbations of 10% B,20% DC (diet composition), 10% P/B and 10% C/B were chosen. Theinitial conditions for each run were set to continue with B and DCvalues at end of last run. Finally, both reduce sum of excess EEand reduce max EE were selected for the iteration design logic.The model was considered balanced when all EEs were below 1.0,all P/C were below 0.5, and there was no negative respiration.

2.7. Carrying capacity calculations

Ecological and production carrying capacities were calculatedfollowing the methods ofJiang and Gibbs (2005). Cultured oysterbiomass and proportional cultured oyster harvest were increasedin consecutive models until the system became unbalanced andno longer represented its present condition. The point just priorto any change in the system was the ecological carrying capacity.No parameters other than cultured oyster biomass and catch weremanually altered while calculating ecological carrying capacity.

Zooplankton are major competitors with oysters for food (Jiangand Gibbs, 2005). Removing zooplankton from the system wouldallow greater food source for oyster thereby maximizing oys-ter production. Production carrying capacity was calculated byremoving all zooplankton groups from the models, rebalancing themodel using current levels of cultured oyster biomass and har-

vest, and then iteratively increasing cultured shellfish biomass and

proportionalculturedoysterharvestuntilthemodelbecameunbal-anced.The biomass value just prior to causing thezooplankton-lessmodel to become unbalanced was the production carrying capac-ity.Because the two zooplankton groups were removed from thediet matrix, effectively that meant theproportion of each prey itemconsumed by zooplankton predators needed to be redistributed sothat all prey items consumed by a given predator would still addto 100% of their diet. The percentage of zooplankton in each preda-tors diet was redistributed evenly among other prey groups priorto re-balancing.

2.8. Carrying capacity analysis

Robustness of biomass parameters while the system was atecological carrying capacity was examined by changing individual

biomass values of each group by factors of 0.01, 0.5, 2, 10 and 100of their original value when the model was balanced at ecologi-cal carrying capacity. Only one value was changed at a time whileall other biomass values remained constant. The degree to whichthe biomass value could vary while the model remained balancedwas an indicatorof the robustnessof the biomass of that functionalgroup at carrying capacity against perturbations. Additionally, bal-anced biomass parameters at ecological carrying capacity werethen rebalanced using EE as primary constraint (


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Fig. 2. Sensitivity analysis of balanced model. Biomass (B), production/biomass (P/B), ecotrophic efficiency (EE), ecological carrying capacity (ECC).

information was available from Narragansett Bay or when the pre-balancing diagnostics varied greatly from the data-derived inputvalue.

At current levels of cultured biomass, the Sensitivity Analysisshowed that the greatest impact of altering an input parameterwas that of biomass and C/B of microzooplankton on EE of bacte-ria sediment POC (Fig. 2). However, at ecological carrying capacity,the Sensitivity Analysis showed that the greatest impact of alter-ing an input parameter was that of biomass and C/B of benthic

suspension feeders group on the C/B of benthic algae (Fig. 2). Cul-tured oysters were not sensitive to changes in input parametersfrom any group other than itself. For instance, altering the biomass

and C/B of cultured oysters impacted the EE of cultured oysters(Fig. 2). Cultured oysters also did not alter input parameters ofany group other than itself under current conditions. However,at ecological carrying capacity, altering the cultured oyster inputparameters did have an impact on other groups. Specifically, thebiomass input parameter of the cultured oysters group impactedthe EE of the microzooplankton, pelagic bacteria,bacteria sedimentPOC, and cultured oysters groups and the C/B of the phytoplanktongroup. Similarly, altering the C/B input parameter of cultured oys-

ters altered the EE of the microzooplankton, pelagic bacteria, andbacteria sediment POC groups and the C/B of the phytoplanktongroup.

Fig. 3. Mixed trophic impact index of balanced model. An infinitesimally small increase in biomass of the groups on the left column impact the groups across the top row. Abar extending downwards symbolizes a negative impact and an upwards rising bar symbolizes a positive impact.Catch is noted for finfish (trawl), wild clams (harvesters),

lobsters (pots), and oysters (farmers).


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Table 3

Changes in Narragansett Bay Ecopath model while estimating carrying capacity using the automated mass-balancing routine.The bold-type numbers are the calculatedecological carrying capacity and production carrying capacity biomass values for cultured oysters. Any biomass value below the carrying capacity did not affect the balancedmodel.Any biomass value above thecarryingcapacity unbalanced themodel by unrealistically increasing theecotrophic efficiency(EE) of another group to one(1) or highersignifying that the entire group was consumed.

Multiplier Biomass (g DW m2) Catch (37% of biomass) Mass-balance changes in model

1 (current conditions) 0.00949 0.00353

Ecological Carrying Capacity2 0.0189 0.00706 Balances

10 0.0949 0.0353 Balances100 0.949 0.353 Balances500 4.745 1.765 Balances600 5.694 2.118 Balances624 5.92176 2.20272 Balances625 5.93125 2.20625 Balances626 5.94074 2.20978 Microzooplanton EE = 1630 5.9787 2.2239 Microzooplanton EE = 1.001

1,000 9.49 3.53 Microzooplanton EE = 1.0995000 47.45 17.65 Microzooplanton EE = 2.153, Pelagic Bacteria EE = 1.357

Production Carrying Capacity6000 56.94 21.18 Balances7000 66.43 24.71 Balances7300 69.277 25.769 Balances7330 69.5617 25.8749 Balances7336 69.61864 25.89608 Balances7337 69.62813 25.89961 Balances7338 69.63762 25.90314 Pelagic Bacteria EE = 17340 69.6566 25.9102 Pelagic Bacteria EE = 19000 85.41 31.77 Pelagic Bacteria EE = 1.028

10000 94.9 35.3 Benthic Deposit Feeders EE = 1.058, Pelagic Bacteria EE = 1.334

3.2. Ecopath summary statistics

As Sensitivity Analysis measures the impacts of parameters,Mixed Trophic Impact Analysis measures the impacts of groups.Mixed Trophic Impact is the measure of direct or indirect influencea group (on the left of the matrix) has on another group (at thetop of the matrix) based on food web characteristics (Fig. 3). Thelargest positive impact of any group is that of benthic suspensionfeeders on wild clam harvesters (harvesters). Similarly, benthic

invertebrates have positive impacton crab andlobster potharvests(pots). The second largest impact is that of planktivorous fish ondetritus. Detritus has a large positive impact on pelagic bacteriaand a slightly smaller positive impact on benthic sediment POCand benthic deposit feeders. The largest negative impact is thatof benthic sediment POC on detritus. Parabenthos has a negativeimpact on benthic deposit feeders (Fig. 3). The biomass of culturedoysters is low compared to its harvest (farmers) relative to othergroups(Table 1, Fig.3). As such, the impact of changes in biomassofcultured oysters on every other group is negligible (Fig. 3).

Carnivorous fish were at the top of the food web (TL 3.71) fol-lowed by invertebrate carnivores (TL 3.66), benthic invertebratecarnivores (TL 3.33), and planktivorous fish (TL 3.19). There were173 relatively short (3.99) pathways that linked these high trophic

level groups to lower trophic levels. This systemalso has 232 cycleswhich are linked pathways starting from a group and returningto it (Christensen et al., 2005). The cycling index was low at 27%and the throughput was high at 84,364 g DW m2 y1 (Table 2b).The complete suite of Ecopath outputs were reported by Byron(2010).


Cultured oyster biomass is currently at 0.0095g DW m2 andcould be increased 625 times to 5.93 g DW m2 without exceedingthe ecological carrying capacity (Table 3). Assuming a conversionof 2% live to dry weight based on measurements made on oystersfarmed in Rhode Island (Rheault, unpublished), that translates to

a live weight of 0.47 t km2

currently and 297tkm2

at ecological

carrying capacity.Given thetotalarea of NarragansettBay, thetotalpotential ecological carrying capacity for bivalve aquaculture was105,279 t. Initial harvest of cultured oysters was 0.18 t km2 whichis 37% of the total biomass. Maintaining this proportion, harvestof cultured oysters could be 110.1t km2 or 38,953 total t at eco-logical carrying capacity. This potential shellfish harvest is morethan 4 times that of reported finfish harvest in Narragansett Bay(ecological carrying capacity of oysters/trawled biomass of finfish;Table 1c).

Production carrying capacity estimated maximum productionwithout zooplankton in the ecosystem and irrespective of the sta-bility of the system and is typically thought about on a farm scale.The production carrying capacity was calculated to be 69.63g DWm2 or 3481tkm2 which equates to 1,235,897 t in NarragansettBay. Cultured oyster biomass at production carrying capacity was7337 times that of its current biomass. If farming at this high pro-duction biomass (3481 t km2) was restricted to only 9% of surfacearea, the Bay on average would still be operating below ecologicalcarrying capacity for aquaculture.

When cultured oysters surpassed ecological carrying capacityin Narragansett Bay, microzooplankton were overgrazed, result-ing in an EE equal to or greater than one (Table 3). Ecotrophicefficiency was the constraining parameter indicating model imbal-

ance. Microzooplankton and benthic deposit feeders were limitingfactors when Narragansett Bay was at ecological carrying capacityfor cultured oysters. This means that a slight change in biomassof either microzooplankton or benthic deposit feeders unbalancedthe model indicating unacceptable change to the system. Otherbiomass parameters in the model at ecological carrying capacitywere more robust. The model remained balanced when any oneof 11 functional groups where halved or any one of 7 groups werereduced 10-fold (Table4). The model also remained balanced whenany one of 6 groups was doubled or any one of 3 producer groupswas increased 10-fold (Table 4). Keeping in mind that there area total of 15 groups, most groups in the model could withstand aperturbationin biomass without influencing thebalance of thesys-temwhileatecologicalcarryingcapacity.Benthicalgaeanddetrituswere the most robust groups at ecological carrying capacity.


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Table

4

Robustnessofbiomass(B)valuesatecologicalcarryingcapacityandproductioncarryingcapacity.Biomassvaluesofeachgroupweremultipliedb

yfactorsof0.01,0.1,0.5,2,10and100.Onlyonevaluewaschangedatatime

whileallotherbiomassvaluesremainedconstan

t.Valuesinitalicsindicatethefactoratwhichthemodelbecameunbalancedandrepresentslow

robustness.Valuesinboldinshadedboxessignifythatthemodelremained

balancedrepresentinghighrobustnessinthosegroupsatcarryingcapacity.

Ecologicalcarryingcapacity

Groupname

Productioncarryingcapacity

=0.01*B

=0.1*B

=0.5*B

Biomass(gD

W/m2)

=2*B

=10*B

=100*B

=0.01*B

=0.1*B

=0.5*B

Biomass(gDW/m2)

=2*B

=10*B

=100*B

0.0

0385

0.0

385

0.1

925

0.3

85

0.7

7

3.8

5

38.5

CarnivorousFish

0.0

0385

0.0

385

0.1

925

0.3

85

0.7

7

3.8

5

38.5

0.0

124

0.1

24

0.6

2

1.2

4

2.4

8

12.4

124

PlanktivorousFish

0.0

124

0.1

24

0.6

2

1.2

4

2.4

8

12.4

124

0.0

3167

0.3

167

1.5

835

3.1

67

6.3

34

31.6

7

316.7

BenthicInvert.Carnivores

0.0

3167

0.3

167

1.5

835

3.1

67

6.3

34

31.6

7

316.7

0.0

2903

0.2

903

1.4

515

2.9

03

5.8

06

29.0

3

290.3

BenthicDepositFeeders

0.0

3193

0.3

193

1.5

965

3.1

93

6.3

86

31.9

3

319.3

0.0

5318

0.5

318

2.6

59

5.3

18

10.6

36

53.1

8

531.8

BenthicSuspension

0.0

5318

0.5

318

2.6

59

5.3

18

10

.636

53.1

8

531.8

0.0

277

0.2

77

1.3

85

2.7

7

5.5

4

27.7

277

Parabenthos

0.0

277

0.2

77

1.3

85

2.7

7

5.5

4

27.7

277

0.0

2032

0.2

032

1.0

16

2.0

32

4.0

64

20.3

2

203.2

InvertebrateCarnivores

0.0

2032

0.2

032

1.0

16

2.0

32

4.0

64

20.3

2

203.2

0.0

755

0.7

55

3.7

75

7.5

5

15.1

75.5

755

Mesozooplankton

0.0

226

0.2

26

1.1

3

2.2

6

4.5

2

22.6

226

Microzooplankton

0.0

525

0.5

25

2.6

25

5.2

5

10.5

52.5

525

PelagicBacteria

0.0

525

0.5

25

2.6

25

5.2

5

10

.5

52.5

525

1.0

5

1.5

7.5

15

30

150

1500

BenthicSedimentPOC

0.1

5

1.5

7.5

15

30

150

150

0.0

5931

0.5

931

2.9

655

5.9

31

11.8

62

59.3

1

593.1

CulturedOysters

0.6

9628

6.9

628

34.8

14

69.6

28

139.2

56

696.2

8

6962.8

0.0

3627

0.3

627

1.8

135

3.6

27

7.2

54

36.2

7

362.7

BenthicAlgae

0.0

3627

0.3

627

1.8

135

3.6

27

7.2

54

36.2

7

362.7

2.4

676

24.6

76

123.3

8

246.76

493.5

2

2467.6

24676

Phytoplankton

2.4

676

24.6

76

123.3

8

246.7

6

49

3.5

2

2467.6

24676

0.1

289

1.2

89

6.4

45

12.8

9

25.7

8

128.9

1289

Detritus

0.1

289

1.2

89

6.4

45

12.8

9

25

.78

128.9

1289

When cultured oyster biomass surpassed the production carry-ing capacity, pelagic bacteria were overgrazed, resulting in an EEgreater than one (Table 3). Planktivorous fish were also sensitive atproduction carrying capacity and were not able to withstand anychange in biomass. However, the model could withstand a halv-ing in biomass of any one of 9 functional groups and a 10-folddecrease in biomass of any one of 6 groups (Table 4). The modelalso remained balanced when any one of 8 groups was doubledor one of 5 groups was increased 10-fold (Table 4). Benthic algaeand detritus were the most robust groups at production carryingcapacity.

Changingtheoysterbiomassresultedinminorchangesinmodeloutputs of other groups. Scenarios of oyster biomass two to threeorders of magnitude above the ecologicalcarryingcapacity resultedin perturbations of less than oneorder of magnitude in other groups(Fig. 4). All groups were affected at the 1000 scenario wherebenthic and zooplankton groups decreased slightly and detritus,primary producers, and fish groups increased slightly. This patternsuggests that cultured oysters have little impact on the system atvarying biomass scenarios.

4. Discussion

4.1. Ecopath summary statistics

Cycling index is the percentage of system throughput that isrecycled and corresponds to system maturity, resilience and stabil-ity (Christensen et al., 2005; Finn, 1976; Odum, 1969). The cyclingindex calculated by this model (27%) is much lower than thatcalculated by the Monaco and Ulanowicz (1997) model (48%). Fur-thermore, the path length calculated by Monaco and Ulanowicz(1997) model was much longer (6.01 compared to 3.99). Thedecrease in cycling and path length in the last decade suggests thatNarragansett Bay is becoming less efficient at recycling energy andretaining material. Increasing the biomass of bivalves may improveefficiencyof energy cycling byincreasingthe benthic pelagic link by

filtering material out of the water column and repackaging it so itis biologically available for plants and benthic consumers (Newell,2004; Peterson and Heck, 1999, 2001).

Narragansett Bay is a large system with high overall activity, asindicated by the high throughput (84,364 g DW m2 y1) (Table 2b& 3). Throughput represents the size of the systems energy flow(Christensen et al., 2005; Ulanowicz, 1986) and was higher thanMonaco and Ulanowicz (1997) estimate of 5,147,600 mgC m2 y1

(equivalent to 12,869 g dry weight m2 y1 using the conversioncarbonto dryorganic matter, 1:2.5). Twentyeight percent (27.79%)of total throughput was cycled (23,019 g DW m2 y1 includingdetritus and 426.97 g DW m2 y1 excluding detritus). Most of theenergy in the system (42%) was diverted to detritus or consumedby predators (32%). Only 3.24% of the total cycled throughput (w/odetritus) constitutes predatory cycling. Low trophic levels con-tribute most to detritus and also had the highest throughput andexport which makes sense since low trophic levels also had themost biomass. The largest flows were through the primary produc-ers (Table 2b). More than half (51.5%) of the primary productionwasconsumedby zooplankton whichwere direct competitors withcultured oysters for food.


Currently, cultured oysters are not a significant part of ecosys-tem, despite rapid increase in the industry. As an exercise, thisNarragansett Bay model was balanced twice; once with the cul-tured oysters group, and once without cultured oysters as it was

in the original Monaco and Ulanowicz (1997) model structure. The


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Fig. 4. Changes in biomass of key groups under different scenarios of cultured oysters.

diagnostics, automated mass-balance routine and the final outputsfor both models with and without cultured oysters were almostidentical. Additionally, the impact analysis showed little to noimpact of cultured oysters on other groups (Fig. 4). Those groupsthat were most impacted, although minimally, by oysters were thelowertrophiclevels, specificallyplanktongroups. Althoughbottomup effects on higher trophic levels are possible (Steele et al., 2007;Steele, 2009), the model results suggest that they are weak. Higher

trophic level groups are not feeding on oysters thereby eliminat-ing a potential mechanism for a direct impact between oysters andhigh trophic levels. Indirect impacts are possible, butunlikely to bedetected, due to the low biomass of oysters. As such, the culturedoysters group is functionallya very small and redundantpart of theecosystem.

At or below the ecological carrying capacity, there are nochanges to the ecosystem in the model (Table 3). One reasonfor Narragansett Bays high carrying capacity potential is its highnutrient loading and primary productivity. The effects of exten-sive terrestrial nutrient inputs are exacerbated by its extensivecoastline and shallow depth (Boothroyd and August, 2008). Sincehalf of primary production is consumed by zooplankton alone, cul-tured oysters and other filter feeders are competing for the otherhalf. The high throughput of energy to detritus suggests a largeunused source of food for oysters. Typically attention to bivalveshellfish nutrition is focused on plankton; however,detritus shouldnot be overlooked. In the Irish Lough system, it is estimated thatcultured shellfish remove four times as much detritus as phyto-plankton (Ferreira et al., 2007).There is no indication that shellfishin Narragansett Bay are currently food limited or will become foodlimited with continued growth of the shellfish aquaculture indus-try. According to the model, high oyster biomass does not havea trickle effect through the ecosystem and on the biomasses ofother species groupsexcept at extremelyhigh biomasses above theecological carrying capacity (i.e. 1001000 scenarios; Fig. 4).

Oysteraquaculturehaspotentialtocontinueonitscurrenttrendof increase (Alves, 2007; Beutel, 2009) and is capable of returningto peak historic biomass levels (144,562 t) without altering major

energy flows or ecological structure of Narragansett Bay (Pietrosand Rice, 2003). The potential production biomass calculated inEcopath (3481 t km2) was much higher than current productionbiomass reported by RI farmers of 1121 t km2 (converted fromRheault, 2008). This suggests that farms are currently not operat-ing at maximum potential. Regulatory constraints, limited use ofvertical structures, and reliance by most growers on bottom cul-ture for part of the life cycle all contribute to current levels of lower

production.If growers continue to use current production techniques andmaintain biomass densities at or below 1121 t km2, managerscould theoretically allow expansion of shellfish farming to cover26% of the surface area of the Bay without exceeding the ecologi-cal carrying capacity. In other words, oyster biomass could exist at1121 t km2 in isolated patches totaling 26% of the bay while stillremaining belowthe ecological carrying capacity on average acrossthe entire bay. This 26% is very close to the 24% bay area that wasleased in 1911 when the RI shellfish aquaculture industry was atits peak(Pietros and Rice, 2003). Notably, 1911 oyster farmers alsoused bottom culture and therefore were presumably operating atsimilar biomass densities as todays farmers.

The ecological carrying capacity in Narragansett Bay exceedsthat of some of the major shellfish aquaculture producing loca-tions worldwide. New Zealand is the primary global exporter ofGreenshellTM musselsandharvests97,000tannually(New ZealandMinistry of Fisheries, 2008). The estimated ecological carryingcapacity of Tasman and Golden Bays in New Zealand is 65 t km2

(Jiang and Gibbs, 2005) which is only about one-fifth that of cal-culated for Narragansett Bay. Tasman and Golden Bays are oneof the primary shellfish aquaculture locations in New Zealandand encompass 4500 km2 surface area compared to the relativelysmall Narragansett Bay (355km2) ecosystem. Furthermore, Tas-man and Golden Bays are comparatively oligotrophic and have alower standing stock of primary production.

Although the carrying capacity in Narragansett Bay is high, itis similar to historic oyster biomass levels (Pietros and Rice, 2003)and within the range of bivalve filter feeder biomasses in similar


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Fig. 5. Final biomass input parameters from similar systems with bivalve filter feeders (Byron et al., 2011a; Christian and Luczkovich, 1999; Jiang and Gibbs, 2005; Leloupet al., 2008; Link et al., 2006; Monaco and Ulanowicz, 1997; Rybarczyk and Elkaim, 2003; Rybarczyk et al., 2003; Wolff, 1994; Zajac et al., 2009 ). Models with more than onebivalve groups were combined. Original units were converted to gDWm2 assuming 80% moisture (Mckinney et al., 2004) and 0.4 gC to dry weight (Jrgensen et al., 1991).

systems (Fig. 5). In fact, the ecological carrying capacity of oys-ters in Narragansett Bay is similar to the standing stock of oystersin near-by Chesapeake Bay and Delaware Bay and a fraction ofthe standing stock in Long Island Sound and the Bay of Sommein the Seine Estuary (Fig. 5). All of these estuarine systems sharecommon characteristicsas shallow water estuaries withtidal influ-ence at one end of the bay and riverine-input at the other endof the bay. Narragansett Bay, Long Island Sound, Delaware Bay

and Cheseapeake Bay are all located on the urbanized easterncoast of the USA and are open to the Atlantic Ocean. NarragansettBay, like many estuarine bays, is capable of hosting high bivalvebiomass.

However,aslightincreasebeyondtheecologicalcarryingcapac-ity does have dramatic ecological implications for the biomasses ofother groups as demonstrated by the robustness tests (Table 4).Trophic cascade effects are apparent in that the model will sup-port a doubling in the biomass of planktivorous fish (Table 4), amajor consumer of zooplankton who inturn compete with oystersfor phytoplankton, detritus, and bacteria food sources. The modelbecomes unbalanced if there is a doubling of biomass of most othergroups besides food groups of oysters. Increases in biomass up to100 of food sources of oysters (phytoplankton, detritus, bacteria)

do not unbalance the model.

4.3. Ecology

Using mass-balance models to calculate production carryingcapacity allows for scientific questioning of ecological processessuch as competitive species interactions. Removing zooplanktongroups from the system is comparable to oysters outcompetingzooplankton for food thereby starving them to death. The questionof whether cultured oysters are better competitors than zooplank-ton has not been experimentally tested. However, modeling suchinteractions is a useful academic exercise to simulate possible out-comes should cultured oysters exceed their ecological carrying

capacity.

The role of zooplankton in Narragansett Bay is important andmay contribute to the uniqueness of Narragansett Bay relative toothercoastalsystemssuchastheneighboringlagoons.Zooplanktonwere a major stabilizing species in Narragansett Bay, more so thanin the lagoons. Zooplankton limit phytoplankton (Deason, 1980;Durbin and Durbin, 1981; Durbin et al., 1983) thereby reducing theamount of primary production available for cultured oysters andother species.

Invertebrate carnivores (ctenophores) are major predators inNarragansett Bay. Biomass was lower than what the diagnosticssuggestedso the value at the maximum end of the calculated rangewas used. Kremer (1976, 1979) and Sullivan et al. (2008) showedthat ctenophores have strong impact on controlling zooplanktonin Narragansett Bay. However, this model conversely shows thatzooplankton have more of an effect in stabilizing the system thanctenophores through grazing. Experimentally removing zooplank-ton using modeling gives us the ability to measure the effects ofthese extreme changes in the system.

Based on the model, oyster biomass at or below the ecologi-cal carrying capacity will not change the impact on the ecosystemfrom that of the current biomass. These results have implicationsfor management of aquaculture. High ecological carrying capacity

has strong implications for management and ecosystem models,such as this one, are useful tools towards that end (Hral, 1993). Itshould be stressed that modeled carrying capacity is a theoreticallimit and should be viewed with caution. A suggested precaution-ary approach is to limit aquaculture to half the calculated carryingcapacity value. Half of carrying capacity is the maximum sustain-able yield (MSY) where growth rate is high and is an acceptedmanagement target for many fin fisheries (Mace, 2001). Manag-ing at MSY will also allow for the inherently dynamic variabilityof carrying capacity that is not described by static models. Naturaland anthropogenic perturbations, temporal and spatial scales atwhich populations are measured, and climatechange contribute tothe variability of carrying capacity.Climate change in coastal zones,predicted to be characterized by increase in temperature, possi-

ble decreases in salinity, and decrease in pH (Anthony et al., 2008;


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IPCC, 2007), will greatly influence the vital rates of bivalves (Davis,1958; Miller et al., 2009; Motes et al., 1998) and subsequently theircarrying capacity.

Astheaquacultureindustrycontinuestogrow,carryingcapacitytechniques using mass-balance modeling can be used to guide thedevelopment of the industry in an ecologically sustainable manner.The ecology of Narragansett Bay may be unique, but the methodsapplied here to calculate carrying capacity are easily transferableto other coastal habitats experiencing rapid aquaculture growthand user conflict issues. Understanding the uniqueness of theparticular system is imperative for responsible ecosystem-basedmanagement and mitigating user conflict between industries andstakeholders.

Acknowledgements

Robert Rheault, David Beutel and David Alves provided valu-able assistance. This work was possible through the cooperationand support of all the agencies and labs that shared the datathat were used to parameterize this model. This work wasfunded by the NOAA National Marine Aquaculture Initiative grant#NA08OAR4170838, NSF IGERT grant #DGE-0504103, and a JohnWald Science Grant.

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