food web interactions and modeling 2 element · 2020. 10. 15. · food web interactions and...

Food Web Interactions and Modeling 103

Food WebInteractions and Modeling

elem

ent

2IntroductionThe Chesapeake Bay food web hasexperienced significant historicalalterations due to overfishing,anthropogenic stress, and naturaldisturbances. Although conventionalsingle-species managementapproaches do not typically addresspredator–prey dynamics, thesedynamics form the heart ofinteractions among species affectingabundance and production. Suchinteractions have dramatic andsubstantial effects on communitystructure, ultimately affecting fisheriesyields in the Bay, and must beconsidered when developing oramending ecosystem-based fisherymanagement plans (FMPs).

Fishing mortality—an importantfraction of total mortality for mostexploited species—represents humanpredation on fishery resources.Multispecies fisheries managementincorporates not only fishing mortal-ity information but also key predator–prey linkages and their contributionsto natural mortality. Understandingsuch food web dynamics allows quanti-fication of the energy and biomasstransfers in the food web that dictatesustainable levels of fishery exploita-tion. Food web relationships are notindependent of habitat and waterquality issues; they may vary with

changes in the productive capacity ofthe environment, the abundance ofplanktonic and benthic prey, and thestructure of food webs that supportfisheries.

Researchers have a good understand-ing of some food web relationships inthe Chesapeake Bay. Atlantic menha-den Brevoortia tyrannus, Atlanticcroaker Micropogonias undulatus, bayanchovy Anchoa mitchilli, blue crabCallinectes sapidus, and spotLeiostomus xanthurus are integrallinks between and within benthic andplanktonic components of the Bayfood web. Heavily exploited, predatoryfish consume forage species, such asmenhaden and bay anchovy; thesepredators may also rely on juvenileblue crabs as part of their diet and mayultimately affect the abundance ofrecruiting crabs.

We must expand our understanding offood web interactions, quantify theireffects, develop new food web models,and implement existing models toprovide the requisite information thatwill permit managers to define sus-tainable catch levels and estimatefishing mortality rates of species in thewebs. In this fisheries ecosystem plan(FEP), we have included preliminarydiagrammed food webs of managedspecies, indicating strong and weak

Fisheries Ecosystem Planning for Chesapeake Bay104

interactions between predator andprey. These webs can guide managersas they explore policy options todevelop ecosystem-based regula-tions—allowing high yields of pis-civorous fish, for example, whileconserving forage fish resources andimportant predator–prey relation-ships. Managers can now use funda-mental knowledge of food webstructure and relationships in aprecautionary manner, but majorresearch is needed to ensure effectivemultispecies fisheries management inthe Bay.

This FEP element addresses theimportance and limitations ofdeveloping food web models for theChesapeake, considers the degree ofconnectivity between particularspecies (or trophic groups) and theirpredators and prey, and describessubwebs of the Bay’s economicallyvaluable species. In addition, theelement describes and discusses theutility of several recognizedmultispecies and ecosystem modelsthat managers could adopt forecosystem-based fisheriesmanagement in the Bay.

Food Web Dynamics

Importance

Sustainable use of exploited specieswill depend, at least in part, uponinclusion of multispecies fisheriesmanagement approaches based largelyon food web dynamics (Christensen1996; Daan 1997; Christensen andPauly 1998; Pauly et al. 1998). Manag-ers have not yet applied a multispeciesapproach to fisheries management inthe Chesapeake, despite its potentialutility (Houde et al. 1998) as well asthe availability of a food web modelfor the mesohaline (middle) portion ofthe Bay (Baird and Ulanowicz 1989),which could provide a framework foradditional modeling focused on man-agement needs.

Fishing affects ecosystems by remov-ing biomass from the complex ofspecies that feed upon each other inthe web (Pauly et al. 2000). It alsoshifts the relative abundance ofexploited species at different trophiclevels. Such changes—from fishing,other anthropogenic stresses (e.g.,habitat alteration and pollution), orenvironmental change—may lead toshifts in the productivity and sustain-able yields of species. These shifts, inturn, may affect the value of fisheries,species biodiversity, or the structuralintegrity of the ecosystem(Winemiller and Polis 1996; Pauly etal. 2000; Jackson et al. 2001; Link2002a). For instance, researchers havepostulated that changes in the abun-dance of key fishery species, such asthe oyster and blue crab, may havealtered community structure andpathways of production in the Bay(Jackson et al. 2001; Silliman and

A food web is defined as a “network of consumer-resource interactions among a group of organisms,populations, or aggregate trophic units” (Winemillerand Polis 1996).


Bertness 2002). Massive fishery-induced reductions in the abundanceof eastern oyster Crassostrea virginica,a suspension feeder on phytoplank-ton, have contributed to abnormallyhigh phytoplankton production,eutrophication, and seasonal hypoxiathat reduce secondary production andspecies diversity (Jackson et al. 2001).In coastal salt marshes, declines inblue crab abundance (Lipcius andStockhausen 2002), due partly toheavy fishing pressure, may haveallowed marsh periwinkle Littorariairrorata to become more abundantand overconsume salt marsh grasses—a process which ultimately could leadto the destruction of salt marshesimportant for blue crab production(Silliman and Bertness 2002).

An ecosystem’s carrying capacity,production potential, and totalsustainable yield to fisheries cannotsimply be calculated as the sums ofyields for individual componentspecies (Link 2002a) using traditional,single-species stock assessmenttechniques. Rather, fisheriesproduction of an ecosystem dependssignificantly on food web dynamics(Pauly et al. 2000; Link 2002a). Toevaluate the impact of a species’fishing mortality upon food webinteractions and ecosystem processes,therefore, the ecosystem’s chief foodweb interactions must be defined andquantified (Pauly et al. 2000).Similarly, researchers must considerthe effects of other controllingfactors, such as habitat quality andenvironmental conditions (seeHabitat Requirements andExternalities elements), within thecontext of ecosystem-based

management.

Predation is key in determining theabundance and size structure ofpopulations, as well as theorganization and functioning ofcommunities in the Chesapeake andother ecosystems (Lipcius and Hines1986; Hines et al. 1990; Seitz et al.2001). Predation affects all life stagesof marine organisms and constitutesthe primary source of naturalmortality for fish in well-studiedmarine ecosystems (Bax 1991, 1998),even for those species with highfishing mortality during theirexploitable life stages.

The relative importance of predationand fishing mortality varies amongspecies, but is typically skewed towardspredation for younger (and smaller)individuals and towards fishingmortality for older individuals. Forinstance, predation largely accounts forthe mortality of young juvenile bluecrabs whereas fishing becomesresponsible for 80% of the mortality ofolder juveniles and adults. Predationmay also play a major role incontrolling food web dynamics inmarine ecosystems, altering the effectsof reductions or increases in fishingmortality of species (Andersen andUrsin 1977; Laevastu and Favorite1988; Bax 1991, 1998; Christensen1996; Trites et al. 1999; Pauly et al.2000; Link 2002a).

With a heavily fished population atlow abundance, predation may limitpopulation recovery despite potentiallyhigh recruitment of incoming yearclasses (Sissenwine 1984; Bax 1991,1998; Christensen 1996; Link 2002a).In such cases, the predator may have


Fishery

a

b

Fishery

Top Predator

Top Predator

Target Species

Target Species

IntermediatePredator

remained at high population levels orit may be a fished species that hasrecovered after management-inducedreductions in fishing mortality. Forexample, Lipcius and Stockhausen(2002) hypothesized that predationpressure by Atlantic croaker (at highabundance in Chesapeake Bay fornearly a decade) or by striped bassMorone saxatilis (which dramaticallyresurged during the last decadefollowing rigorous managementmeasures) may be responsible for thelack of recovery of the depressed bluecrab population in the Bay. Similarly,restoration of the Bay’s native oysterpopulation may be hampered bydisease in older juveniles and adults

or by blue crab predation; either ofthese forces could prevent oysterrecovery given that overfishing,habitat degradation, and disease havedriven the population to extremelylow levels (Rothschild et al. 1994).

For some species, natural mortalitythrough predation—especially onyoung stages—may prove more signifi-cant in controlling population abun-dance than fishing mortality onrecruited stages. Such species may besubject to little or no fishing pressure,but serve as forage species for a spec-trum of natural predators (Overholtzet al. 2000). Historically, watermenhave fished some forage species (e.g.Atlantic menhaden), which form amajor component of the Chesapeakefisheries ecosystem. During the past50 years, when overfishing has causeddeclines of top predator species,fisheries have increasingly targetedspecies at lower trophic levels (Paulyet al. 1998). Significant reductions inthe abundance of these species maycause fundamental changes in commu-nity structure and the ecosystem(Pauly et al. 1998; Jackson et al.2001). This process, referred to as“fishing down food webs,” may disruptnatural predator–prey relationships.The effects of such fishing includeshifts in trophic-level structure, alongwith changes in population abundanceand age structure of target species.Selective fishing on forage species canprecipitate indirect impacts on otherspecies in the food web as predatorstransfer their emphasis to alternativeprey.

In some cases, predation may not playa major role in controlling abundance,

Figure 1. Hypothetical simple (a) and moderately complex (b)food webs with top and intermediate predators that are notfished, a target fishery species, and human fishing at a trophiclevel equivalent to that of the top predator (adapted from Yodzis2001).


as in shoaling pelagic species(Overholtz et al. 1991, 2000; Jenningsand Kaiser 1998), which fluctuate inresponse to variable ocean conditions.Such variability in controlling mecha-nisms accentuates the need to definethe major food web interactions in anecosystem before we can understandthe relative impacts of natural andfishing mortality upon food webdynamics and ecosystem processes.

Limitations

Despite the growing awareness thatfisheries management requires amultispecies approach, considerabledebate remains over the reliability ofpredictions of changes in targetspecies abundance derived frommultispecies approaches. Yodzis (2001)provides an illuminating example ofthe problems associated with predic-tions of fishery-induced alterations infood webs, examining a situation inwhich fisheries cull a top predator toincrease production of a target speciesby reducing the natural predation onthis species. In this case, fisheriescatch a target species consumed by thetop predator (Figure 1a). If the cullsignificantly reduces the population ofthe top predator, then the abundanceand yield of the target fishery speciesshould increase.

This simple view of food web dynam-ics is based on the assumption thatremoving a top predator from thesystem will increase the abundance ofprey it would have consumed, whichthen becomes available to the fishery.If, however, the addition of an inter-mediate predator complicates the foodweb (Figure 1b), the potential for

indirect effects confounds the abilityto determine either the direction ofthe system response to the removal ofthe top predator or its magnitude(Yodzis 2001). In this scenario, thereduced top predator population willeat less of the target species, which

should result in an increase in itsabundance. The top predator will alsoeat fewer of the intermediate preda-tors, however, which should decreasetarget species abundance. In thiscircumstance, the net result of reduc-ing the top predator upon the targetfishery species shown in this relativelysimple food web remains uncertain.Ultimately, the abundance of thetarget species might increase, decrease,or be unaffected, depending on thestrengths of the various predator–preylinks (Punt and Butterworth 1995;Abrams et al. 1996; Yodzis 2001).

Another complication arising from thecomplexity of food web dynamics isthe possibility that ecosystems havealternative stable states (Sheffer et al.,2001; Carpenter 2002). Given that theChesapeake Bay food web hasundergone dramatic, historicalalterations due to anthropogenicchanges—such as overfishing (Jacksonet al. 2001) and eutrophication(Boesch 2000)—and naturaldisturbances (R. N. Lipcius and R. D.

Stability is not limited to pristine systems; it is also afeature of disturbed systems (Sheffer et al. 2001;Carpenter 2002) and contributes to the difficulty inrestoring disturbed ecosystems such as ChesapeakeBay.


Seitz, unpublished manuscript),restoring the food web to its“pristine” state may proveimpossible. Even if managers agreeon the preferred food web, itscomposition, and its biomassstructure, such a food web may beunattainable due to the stability ofthe degraded ecosystem characterizedby the distorted food web (Sheffer etal. 2001; Carpenter 2002; Petersonand Lipcius 2003). Stability refers to asituation in which a disturbed ordegraded ecosystem is in an“alternative stable state” (Sheffer etal. 2001; Carpenter 2002), which willnot easily shift back to theundisturbed state due to feedbackmechanisms maintaining thestructure of the disturbed stablestate. Stability is not limited topristine systems; it is also a feature ofdisturbed systems (Sheffer et al.2001; Carpenter 2002) andcontributes to the difficulty inrestoring disturbed ecosystems suchas Chesapeake Bay.

Management, therefore, shouldconsider the possibility that somedesired food web configurations maynot be achievable (Peterson andLipcius 2003), at least in the shortterm, without massive intervention(Carpenter 2002). For instance, theseaside lagoons of the Eastern Shoreharbored extensive seagrass bedsthat supported a lucrative Bay scallopfishery until the Storm Kinghurricane of 1933 devastated theecosystem. The resultant turbidconditions not only precludedrestoration of the seagrass beds, butalso hindered the reestablishment of

a productive scallop fishery in theseaside lagoons for over 6 decades (R.N. Lipcius and R. D. Seitz, unpublishedmanuscript).

Three basic approaches exist for theanalysis of food webs (Paine 1966;Winemiller and Polis 1996). One istopological, providing a static descrip-tion of predator and prey links be-tween species or trophic groups. Inthe following section, we offer a basictopological analysis of the connectivityof species and trophic groups in theChesapeake Bay food web. A secondapproach uses quantitative analysis ofenergy and matter flow through thefood web via predation (e.g., Ecopathwith Ecosim, Pauly et al. 2000), amodeling approach that researchershave started using in the Bay. Thefinal approach is functional, identify-ing the species and trophic links thatdetermine community structure. Thefunctional approach typically dependson field experiments that deal withspecific links between importantconsumers (predators) and resources(prey) in the food web (see Sillimanand Bertness 2002).

Topological and energy flow analysesare instructive (Pauly et al. 2000; Link2002b), but not always capable ofexplaining the dynamics ofpopulations and communities. Thedynamic influence of a particularspecies or trophic group is notnecessarily proportional to the energyflow between trophic links (see reviewof the three analysis types inWinemiller and Polis 1996). Forinstance, keystone species may initiatetrophic cascades (i.e., significanteffects of changes in one species upon


others in the food web without directlinks), forming a key force instructuring marine, aquatic, andterrestrial communities. Yet, theirinfluence often appearsdisproportionately high relative totheir biomass (Power et al. 1996).

Recent research suggests that the bluecrab is a keystone species in theChesapeake. First, the blue crabenhances salt marsh grass productionand the associated marsh communityby feeding upon marsh periwinkles,which at high densities can reduce saltmarsh productivity (Silliman andBertness 2002). Second, the blue crab

may strongly influence seagrassproduction and community structurethrough consumption of seagrassgrazers (e.g., amphipods and isopods),which increase seagrass productivity bygrazing upon seagrass epiphytes (M.Harris, E. Duffy, and R. N. Lipcius,unpublished manuscript). Theinfluence of these complexmechanisms on community structurein Bay habitats indicate that anexperimental, functional approach tofood web analysis is needed to identifythe major controlling factors of foodweb dynamics. Unfortunately, theexperimental field manipulationstypically required to evaluate the

10.Sea

nettle

28.Whiteperch

18.Crust. dep.

feeders

2.Bac-teria

3.Bac-teria

1.Phytoplankton

34.DOC

5.Free

Bacteria

6.Hetero.

Microflag.

7.Microzoo-plankton

7.Zoo-

plankton

4.Benthicdiatoms

36.POC

17.Meiofauna

14.Other

polychaetes

15.Nereis

11.Other susp.

feeders

12.Mya

13.Oyster

16.Macoma

spp.

19.Blue crab

24.Shad

23.Menhaden

22.Bay

anchovy

21.Alewife/Blue

Herring

20.Fish

larvae

25.Croaker

30.Bluefish

31.Weakfish

32.Summerflounder

33.Stripedbass

29.Catfish

27.Spot

26.Hogchoker

9.Ctenophore

35.POC

Suspended

Sediment

43229.222774 14639.2

447

6157

154.8

2293.1

7555 579.8

10716.2

3439

21986

292.2

24062.9

286.9107.5

293

352

967

237211711

198

194.760.3

510.5

219847 30209137437

5013

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30209

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48.1

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22.6

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22.3

14

14 50.3

4.9

2.5

0.32.1 7.5

0.90.9

2.4

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113.8

78.8

Figure 2. Food web components of middle (mesohaline) Chesapeake Bay, indicating composite cycling ofcarbon. This web is generally representative of the major food web components used in previous network(energy flow) analyses of the Chesapeake food web (Baird and Ulanowicz 1989; Monaco and Ulanowicz 1997;Hagy 2002) (adapted from Baird and Ulanowicz 1989).


dynamics and functional roles ofspecies in a food web often provelogistically intractable at spatial andtemporal scales that capture the fulldynamics of an ecosystem. In suchcases, topological analyses ormodeling may become the onlyoptions. A balanced inclusion of thethree approaches to food webanalysis may best address the foodweb dynamics of large ecosystems.Moreover, collective uncertainties infood web investigations demandcaution in the application of foodweb analyses to fisheriesmanagement.

Chesapeake Bay Food Web

General Features

The Chesapeake Bay food web (Figure2) contains several features typical ofmost estuarine food webs:

1) Predominance of generalist feed-ers, both benthic and pelagic, thattypically consume prey in propor-tion to their availability (Baird andUlanowicz 1989; Monaco andUlanowicz 1997; Hagy 2002);

2) Moderately interconnected trophicpathways between predators andprey (Monaco and Ulanowicz1997; Dunne et al. 2002);

3) Modified food web structure duelargely to anthropogenic alter-ations (Monaco and Ulanowicz1997; Jackson et al. 2001; Hagy,2002);

4) High fisheries production (Nixon1982); and

5) High phytoplankton primaryproduction, much of which is notconsumed and is transformed to

detritus, particularly in the middleBay (Monaco and Ulanowicz 1997;Hagy 2002).

Although the ratio of primary produc-tion in the water column to that inthe benthos is 6:1, production in theBay relies heavily on inputs fromdetritus and the microbial loop (i.e.,organic matter cycles through bacteriato protozoan consumers with subse-quent grazing by microzooplankton)to fuel secondary production at highertrophic levels, including most fisheryspecies (Monaco and Ulanowicz 1997).Production of predatory fish in theBay may depend significantly onbenthic deposit feeders, detritus, andthe microbial loop, in addition topelagic primary production (Baird andUlanowicz 1989; Monaco andUlanowicz 1997; Hagy 2002), as inmany marine ecosystems characterizedby high bacterial biomass (Pomeroy2001). Benthic suspension feeders usephytoplankton production,allochthonous inputs (e.g., externalnutrient sources from freshwaterinflows), and benthic production(Monaco and Ulanowicz 1997). Conse-quently, benthic suspension feedersand deposit feeders form criticalconduits between the pelagic andbenthic components of the food web,since deposit and suspension feeders(such as worms and clams) are eventu-ally consumed by predatory demersalfish and benthic invertebrates. Thesepredators subsequently become preyfor larger, pelagic, predatory fish.

The species and trophic groups com-prising the Bay’s food web have beenassigned to specific trophic levels(Table 1) and their importance identi-


Table 1. Trophiclevels ofChesapeake Bayfood webcomponents afterHagy (2002),Baird andUlanowicz (1989),and Monaco andUlanowicz (1997).Numbers refer tothe averagetrophic level ofeach group,standardized to1.0 for primaryproducers andsources of organiccarbon (DOC andPOC), 2.0 forprimaryconsumers, 3.0 forsecondaryconsumers, andso on.

puorGcihporT ygaH zciwonalU&driaB&ocanoM

zciwonalU

COP,COD 0.1 0.1 0.1

notknalpotyhP 0.1 0.1 0.1

notknalpociP 0.1 0.1 0.1

VAS 0.1 0.1 0.1

sohtnebotyhporciM 0.1 0.1 0.1

airetcaB 0.2 0.2 0.2

sredeefnoisnepsuS 1.2 1.2 2.2

retsyonretsaE 1.2 1.2 2.2

nedahnemcitnaltA 1.2 8.2 7.2

sohtneboieM 3.2 7.2 -

srefitoR 4.2 2.2 -

sredeeftisopeD 4.2 0.3 8.2

notknalpoozorciM - 2.2 6.2

notknalpoozoseM 5.2 2.2 2.2

setailiC 6.2 8.2 -

setallegalforeteH 0.3 0.3 -

barceulB 1.3 5.3 2.3

topS 1.3 0.4 -

rekaorccitnaltA 1.3 0.4 -

rekohcgoH 1.3 9.3 -

leenaciremA 1.3 - -

)seicepsyaB(hsiftaC 2.3 0.4 -

hcrepetihW 4.3 0.4 -

yvohcnayaB 5.3 8.2 7.2

erohponetcetaboL 5.3 0.3 2.3

gnirrehkcabeulB/efiwelA 6.3 2.3 -

sdahS 6.3 2.3 -

ssabdepirtS 6.3 9.3 8.3

hsifkaeW 0.4 8.3 8.3

hsifeulB 1.4 6.4 8.3

elttenaeS 6.4 4.3 -

rednuolfremmuS - 0.4 8.3


fied through network analysis ofenergy flow (Baird and Ulanowicz1989; Monaco and Ulanowicz 1997;Hagy 2002). Baird and Ulanowicz(1989) and Monaco and Ulanowicz(1997) categorized the principalconsumers and trophic groups interms of energy flow and cycling inthe Bay food web. More recently,Hagy (2002) extended the earlieranalyses and reached fundamentallysimilar conclusions. Although therelative importance of a particularspecies or trophic group as a con-sumer may differ based on occurrencein the upper, middle, or lower Bay(Hagy 2002), generalities do exist inthe Bay’s food web. The followingconclusions are summarized from theextensive investigations of Baird andUlanowicz (1989), Monaco andUlanowicz (1997), and Hagy (2002).Some species may be categorizedpoorly, however, due to incompletediet data or an inadequate under-

standing of ontogenetic diet shifts.Consequently, the assumed trophicposition of individual species shouldbe examined carefully during FMPdevelopment or in food web modelingand investigations.

Food Web Generalities for the Bay.The following list cites some of thegeneralities that apply to the Chesa-peake Bay food web.

1) Of the pelagic consumers, bayanchovy and menhaden transferthe most production from planktonto predatory fish and have thehighest secondary fish production.

2) The lobate ctenophore (e.g., combjelly) is a major consumer ofmesozooplankton (larger zooplank-ton such as copepods and cladocer-ans) and microzooplankton (smallerzooplankton such as nauplii androtifers) particularly in the middleBay. This consumption divertsproduction from forage fish and,

Figure 3. Number of links from the species or trophic group listed on the X-axis to prey of that species ortrophic group. For example, striped bass adults prey on 11 species or trophic groups; blue crab adults prey onnine species or trophic groups, including blue crab juveniles.

Trophic Group

Birds

Nu

mb

er o

f L

inks

as P

red

ato

r

Croak

er

Stripe

d ba

ss a

d.

12

0

2

4

6

8

10

Blue cr

ab a

d.

Bluefis

h ad

.

Blue cr

ab ju

v.

Summ

er flo

unde

r

Whit

e pe

rch

ad.

Blue ca

tfish

Wea

kfish

ad.

Reef d

emer

sal fi

sh

Seatro

ut

Infa

una/

epifa

una

Ducks

Bluefis

h juv

.

Black d

rum

Cowno

se ra

y

Sandb

ar sh

ark

Turtle

s

Catfis

h

Demer

sal fi

sh

Sea n

ettle

sSpo

t.

Stripe

d ba

ss ju

v.

Wea

kfish

juv.

Men

hade

n ad

.

Men

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n juv

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Forag

e fis

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Amer

ican

eel

Bay a

ncho

vy.

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Yellow

per

ch

Hard

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Suspe

nsion

feed

ers

Oyste

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Seatro

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v.

Alewife

/her

ring

Amer

ican

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d sh

ad

Mes

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ton

Cteno

phor

es

Phyto

plank

ton

Horse

shoe

crab

.

Flatfis

h

Bad p

hyto

plank

ton

Hogch

oker

Micr

ozoo

plank

tonSAV

Benth

ic alg

ae

Highly connectedpredators


ultimately, fisheries yield. Cteno-phores also prey on bay anchovyeggs and larvae, reducing thepotential for fish production. Innetwork analyses, sea nettle (the

medusa Chrysaora quinquecirrha)predation on ctenophores appearsto compensate for the negativeeffect of ctenophore predation onbay anchovy. Sea nettles can be

Birds

Croak

er

Stripe

d ba

ss a

d.

Blue cr

ab a

d.

Bluefis

h ad

.

Blue cr

ab ju

v.

Summ

er flo

unde

r

Whit

e pe

rch

ad.

Blue ca

tfish

Wea

kfish

ad.

Reef d

emer

sal fi

sh

Seatro

u ad

.t

Infa

una/

epifa

una

Ducks

Bluefis

h juv

.

Black d

rum

Cowno

se ra

y

Sandb

ar sh

ark

Turtle

s

Catfis

h

Demer

sal fi

sh

Sea n

ettle

sSpo

t

Stripe

d ba

ss ju

v.

Wea

kfish

juv.

Men

hade

n ad

.

Men

hade

n juv

.

Forag

e fis

h

Amer

ican

eel

Bay a

ncho

vy

Whit

e pe

rch

juv.

Yellow

per

ch

Hard

clam

Suspe

nsion

feed

ers

Oyste

r

Soft c

lam

Seatro

ut ju

v.

Alewife

/her

ring

Amer

ican

shad

Gizzar

d sh

ad

Mes

ozoo

plank

ton

Cteno

phor

es

Phyto

plank

ton

Horse

shoe

crab

Flatfis

h

Bad p

hyto

plank

ton

Hogch

oker

Micr

ozoo

plank

ton

SAV

Benth

ic alg

ae0

5

10

15

20

25Highly connectedprey

Nu

mb

er o

f L

inks

as P

rey

Trophic Group

Nu

mb

er o

f L

inks

as P

red

ato

r an

d P

rey

Highly connectedtrophic groups

Trophic Group

Birds

30

0

5

10

15

20

25

Croak

er

Stripe

d ba

ss a

d.

Blue cr

ab a

d.

Bluefis

h ad

.

Blue cr

ab ju

v.

Summ

er flo

unde

r

Whit

e pe

rch

ad.

Blue ca

tfish

Wea

kfish

ad.

Reef d

emer

sal fi

sh

Seatro

ut a

d.

Infa

una/

epifa

una

Ducks

Bluefis

h juv

.

Black d

rum

Cowno

se ra

y

Sandb

ar sh

ark

Turtle

s

Catfis

h

Demer

sal fi

sh

Sea n

ettle

sSpo

t

Stripe

d ba

ss ju

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juv.

Men

hade

n ad

.

Men

hade

n juv

.

Forag

e fis

h

Amer

ican

eel

Bay a

ncho

vy

Whit

e pe

rch

juv.

Yellow

per

ch

Hard

clam

Suspe

nsion

feed

ers

Oyste

r

Soft c

lam

Seatro

ut ju

v.

Alewife

/her

ring

Amer

ican

shad

Gizzar

d sh

ad

Mes

ozoo

plank

ton

Cteno

phor

es

Phyto

plank

ton

Horse

shoe

crab

Flatfis

h

Bad p

hyto

plank

ton

Hogch

oker

Micr

ozoo

plank

ton

SAV

Benth

ic alg

ae

Figure 4. Number of links from the species or trophic group listed on the X-axis to predators of that species ortrophic group. For example, 23 species or trophic groups prey on mesoplankton and 11 on bay anchovy. Dataare derived from the diet matrix (August 2002) of the Chesapeake Bay Ecopath model.

Figure 5. Number of links from the species or trophic group listed on the X-axis to predators and prey of thatspecies or trophic group. For example, 25 links occur from mesoplankton to species or trophic groups that areeither predators or prey of mesoplankton along with 17 links from blue crab juveniles to either predators orprey. Data are derived from the diet matrix (August 2002) of the Chesapeake Bay Ecopath model.


viewed positively in terms ofenergy flow to fish productionwithin the web, therefore, despitetheir moderate predation on bayanchovy. Other forage fish speciesthat form important links fromthe plankton to the benthos andpredatory fish include thealosines—alewife Alosapseudoharengus, blueback herringA. aestivalis, and American andhickory shads A. sapidissima andA. mediocris.

3) Benthic suspension feeders anddeposit feeders, particularlybivalves (e.g., Atlantic rangiaRangia cuneata, Baltic macomaMacoma balthica, northernquahog Mercenaria mercenaria)and various polychaetes (e.g.,deep-burrowing Chaetopterusvariopedatus), consume much ofthe Bay’s detrital, planktonic, andmicrobial loop output. In themiddle Bay, seasonal hypoxiacauses low benthic production(Hagy 2002). Network analyses,however, indicate that productionremains sufficient to satisfy thedemands of demersal (i.e.,epibenthic) and benthic (i.e.,infaunal) predators. Demersal fish(hogchoker Trinectes maculatus,spot, Atlantic croaker) and bluecrab are among the chiefconsumers of the benthos,collectively consuming nearly 90%of benthic production. The mostproductive of the piscivoresinclude weakfish Cynoscion regalis,striped bass, bluefish Pomatomussaltatrix, channel catfish Ictalurusfurcatus, Atlantic croaker, andspot, in no order of importance.

Connectivity ofPredators and PreyTopological analysis is a usefulinstrument to determine thestructure of particular trophic groupsin the food web, illustrating theconnectivity between a particularspecies or trophic group and itspredators and prey (Winemiller andPolis 1996). The connectivity (i.e.,number of linkages between trophicgroups) of the Bay’s food web ismoderate relative to other terrestrial,aquatic, and marine food webs (Dunneet al. 2002; Link 2002b), suggestingthat it is reasonably resilient tomodest perturbations such as the lossof a few species (Dunne et al. 2002).Such a loss to the integrity of the foodweb becomes most pronounced whenhighly connected species (i.e., speciespossessing multiple linkages to otherpredators and prey) are removed fromthe web (Dunne et al. 2002) and mayhave severe impacts on food webintegrity, carbon cycling, and resilienceto environmental perturbations(Dunne et al. 2002). Consequently,scientists and fishery managers mustrecognize those predators and preyhaving the highest degree ofconnectivity with other trophic groupsin the food web.

To evaluate the degree of connectivityof the various species and trophicgroups of the Bay food web, the NOAAChesapeake Bay Office (NCBO)EcoPath Working Group used theAugust 2002 diet matrix of the Chesa-peake Bay Ecopath model to definethe number of links between speciesand trophic groups (Figures 3–5). The


most highly connected predators (top12%; 6 of 50) were piscivorous birds(e.g., American osprey), striped bassadults, Atlantic croaker, blue crabadults, bluefish adults, and blue crabjuveniles (Figure 3). Each had links to8 to 13 species or trophic groups uponwhich they prey. Of the prey, themost highly connected trophic groups(top 12%; 6 of 50) were those near thebase of the food web, includingbenthic deposit and suspensionfeeders, invertebrate grazers,mesozooplankton, microzooplankton,littoral forage fish (e.g., silversides),and bay anchovy (Figure 4). Each ofthese groups had 10 to 25 links topredators. When considering alltrophic links, both to predators andprey, the most connected trophicgroups (top 12%; 6 of 50) were againthose near the base of the food web,specifically infaunal and epifaunaldeposit and suspension feeders,invertebrate grazers,mesozooplankton, microzooplankton,blue crab juveniles, littoral forage fish,and bay anchovy (Figure 5). Thesegroups had 14 to 31 links to predatorsand prey.

The results bear strong similarity tothose of energy flow analyses (Figure2), indicating that relatively fewspecies and trophic groups driveenergy flow and connectivity in theChesapeake food web. Some speciesthat may be critically important infood webs were not highly connected(e.g., menhaden, ctenophores) due tonarrow dietary preferences. The lowto moderate connectivity of plank-tonic consumers, such as menhadenand ctenophores, may result from the

aggregation of species as trophicgroups serving as their prey. If oneconsiders ontogeny and increase in sizeat progressive life stages of speciessuch as menhaden and lobate cteno-phores, then their connectivity mayincrease. For example, larvae and earlyjuveniles of menhaden primarilyconsume zooplankton, while cteno-phores in the larval stage may have abroader diet than larger individuals.Such patterns may hold for manyspecies, but the connectivity analysesmay not fully account for them.

Managers should value all speciescategorized as important in either theenergy flow or the connectivity analy-

Cownose ray

Phytoplankton

Black seabass

Blue crab (ad.)Croaker

Humans

Blue crab (juv.)Disease Rapa whelk

Softshell clam Oyster Hard clam

Microzooplankton

Figure 6. Subweb of the hard clam, soft-shell clam, and nativeoyster.


ses. Additionally, the connectivitybetween certain species might not beclear due to reduced abundance of thespecies. The most notable example—the native oyster—historically playeda dominant role as a consumer ofphytoplankton.

The Chesapeake Bay food web hasundergone substantial historical

alterations, due in large part tooverfishing (Jackson et al. 2001) (seeExternalities Element). Most likely,the current major role of detritus introphic dynamics results fromconsiderable degradation of the Bayfood web and the accompanying shiftfrom an ecosystem that functionedlargely through benthic algal andseagrass production to one heavilydependent on the microbial loop,phytoplankton production, anddetritus (Jackson et al. 2001).Recognizing that the Bay’s food webhas endured dramatic change due toanthropogenic stress, we must nowaccept the possibility that certaincomponents of the food web may notbe easily restored.

Subwebs of Fishery SpeciesSubwebs of each of the economicallyvaluable species in the Bay can guidethe identification of the species’important predators and prey. Thesubwebs do not portray the relativeimportance of links between predators

Alewife/Herring

Humans

Mesozooplankton

Birds

Blue catfish Yellow perch

Microzooplankton

Figure 7. Subweb of the alewife/herring complex.

Atlantic croaker

Black seabass, Cobia,Sandbar Shark

Blue crab (juv.)

Bivalves Softshell clam MesozooplanktonBenthos

Bluefish (ad.),Striped Bass (ad.)

BirdsHumans

White perch (juv.),Forage fish, Anchovy

Figure 8a. Subweb of the Atlantic croaker.


Blue crab (ad.)

BivalvesBenthos

Cobia

Humans

Striped Bass (ad.)

Blue crab (juv.)

American eelSummer flounder

Yellow perch

OysterSoftshell clam

Spotted seatrout (ad.)

Sandbar SharkBlack seabass

Red drumCroaker

Hard clam

Benthos

Cobia

Humans

Striped Bass (ad.)

Spot

Spotted seatrout (ad.)

Sandbar SharkBlack seabass

Mesozooplankton

Bluefish (ad.)

Reef fish

Summer flounder

Birds

Figure 8b. Subweb of the blue crab.

Figure 8c. Subweb of the spot.


BirdsBlue catfish

Humans

American shad

Mesozooplankton

Microzooplankton

Humans

Blue crab (juv.)

American eel

Benthos Bivalves

Hard clam

Humans

Black Drum

Benthos Softshell clamBivalves

Humans

Bluefish (juv.) Bluefish (ad.)

Menhaden (ad.)

Benthos

Forage fish SpotAnchovy Menhaden (juv.)

Microzooplankton

Mesozooplankton

Figure 9a. Subweb of the American shad. Figure 9b. Subweb of the American eel.

Figure 9c. Subweb of the black drum.

Figure 9d. Subweb of the bluefish.


Microzooplankton

Humans

Spot

Bluecrab (juv.)

AnchovyForage fish

Menhaden (juv.)

Spottedseatrout (ad.)

Spottedseatrout (juv.)

Mesozooplankton

Figure 9e. Subweb of the spotted seatrout.

Figure 9f. Subweb of the striped bass.

Humans

Striped Bass (ad.)

Striped Bass (juv.)

Benthos

SpotAnchovyWhiteperch (juv.)

Croaker

Bluecrab (juv.)

Menhaden (ad.)

Gizzard shad

Forage fishMenhaden (juv.)

Microzooplankton

Mesozooplankton


Humans

Spot

SummerFlounder

Benthos

AnchovyBlue crab (juv.)

Forage fishMenhaden (juv.)

Humans

Benthos

Menhaden (ad.)Forage fishMenhaden (juv.)

Weakfish (ad.)

Weakfish (juv.) Anchovy

Microzooplankton

Mesozooplankton

Figure 9g. Subweb of the summer flounder.

Figure 9h. Subweb of the weakfish.


Humans

White perch (ad.)

White perch (juv.)

Benthos

Anchovy

Croaker

Striped bass (ad.) Blue catfish Birds

Channel catfish Black seabass

Forage fish Striped bass (juv.)

Microzooplankton

Mesozooplankton

Humans

Benthos

Alewife/herring

Yellow perch

Birds

Blue crab (juv.)

Figure 9i. Subweb of the white perch.

Figure 9j. Subweb of the yellow perch.


and prey. Additional factors andprocesses, external to traditionalpredator–prey relationships, mayprove critical in the dynamics of thespecies, such as disease in the case ofthe native Eastern oyster. Thesefactors should be considered in thecomprehensive analysis of all sourcesof mortality affecting a species and indevelopment of ecosystem-basedmanagement plans.

The diet matrix of the NOAAChesapeake Bay Office EcoPathWorking Group provided theinformation to create the subwebs.The first subweb, shown in Figure 6,consists of benthic suspension-feedingbivalves near the base of the food web(Table 1) that support major fisheries,including the northern quahog,softshell (also known as softshellclam) Mya arenaria, as well as theeastern oyster.

The next subweb (Figure 7) includesfinfish near the base of the food web,specifically the alewife-bluebackherring complex. Following is thesuite of subwebs that includes most ofthe intermediate trophic links, andthose with a high degree of connectiv-ity, such as Atlantic croaker, blue crab,and spot (Figures 8a, 8b, and 8c).Finally, we detail the food webs ofhigher-level predators, including mostof the top predatory fishes, specificallyAmerican shad, American eel Anguillarostrata, black drum Pogonias cromis,bluefish, spotted seatrout Cynoscionnebulosus, striped bass, summerflounder Paralichthys dentatus, weak-fish, white perch Morone americana,and yellow perch Perca flavescens(Figures 9a–9j).

For all subwebs illustrated above, theNCBO EcoPath Working Group ismodifying the predator and preylinkages as new information is gath-ered and incorporated into the currentEcopath model. Updated subwebs maybe downloaded athttp:noaa.chesapeakebay.net/fepworkshop/netfep.htm. Furtherdetails on the prey and predators are

Figure 10. Simplified life cycle diagram of the Eastern oyster.highlighting its key life stages.

Figure 11. Adult stage of the Eastern oyster with major sourcesof mortality (disease, fishing, blue crab predation), prey(plankton), and habitat needs (substrate).

Larvae

Life Cycle Diagramof the Oyster

Juveniles Adults

Disease

Predator-prey Interactionsand Sources of Mortality

Fishing

Blue CrabAdults

AdultAdultOystersOysters

Substrate

Phytoplankton

Zooplankton


available on the web site.

Incorporation of Food WebDynamics into FMPsThe following list provides generalguidelines for incorporating food webdynamics into FMPS.

1) Develop and define the life cyclediagram of the target species.

The life cycle diagram explicitlyrecognizes the critical life stagesand sources of mortality. Forexample, Figure 10 diagrams asimple life cycle diagram for theEastern oyster. Although simplistic,elaboration of such diagrams interms of habitat requirements andsources of mortality allows one todiscern if key processes or sourcesof mortality have been ignored in

the analysis of the food web and itspredator–prey relationships(Caswell 2000).

2) Identify the major predator-preyinteractions and sources ofmortality for each life stage byexpansion of the life cycle diagram(Figure 11).

3) Identify critical habitat relation-ships for each life stage.

Expansion of the life cycle and foodweb diagrams should considerhabitat needs (see Habitat Require-ments Element), including possibleuse of protected or closed areas inmanagement and conservation ofexploited species. Figure 12 showsan example of stage-specific preda-tor-prey interactions with probablemodification in protected areas for

1+Blue Crabs

Protected SAV Beds

Protected Oyster Reefs

Striped BassCroaker, Red Drum

Striped BassCroaker, Red Drum

FisheryExploitation

0+Oysters, Clams

1+Oysters, Clams

0+Blue Crabs

1+Blue Crabs

0+Oysters, Clams

1+Oysters, Clams

0+Blue Crabs

Strong effect

Weak effect

Figure 12. Potential food web interactions in no-take protected (shaded) and open-to-fishing (clear) habitats for a benthic component of the Chesapeake Bay food web thatemphasize predator-prey relationships of striped bass/blue crab and other demersalfishes. This example, while hypothetical, indicates the kind of process that fisheriesmanagers should follow in developing multispecies fisheries management plans thataccount for predator–prey interactions in complex ecosystems such as the Chesapeake.


striped bass/blue crab/bivalvelinkages.

Although such detailed linkagesare difficult to evaluate and fullyunderstand, they do reflect eco-logical reality; therefore, consider-ing subsets of the food web re-mains important in developingand implementing ecosystem-based fisheries management.

4) Compare and contrast analyses offood web dynamics with food web

modeling.

This exercise entails a thorough andcomprehensive comparison of thefindings from food web analyses (i.e.,topological, energy flow, functional)with those from modeling efforts, asdescribed below.

Multispecies ModelingApproachesHistorically, fisheries management hasrelied on single-species models thatignored the effects of biological andtechnical interactions on populationabundance (Figure 13). Traditionalfisheries models focus on the interplaybetween exploitation level andsustainability, generally not consider-ing in detail the biology and ecology ofthe managed species. In recent years,however, researchers have started toovercome this deficiency by consider-ing the feasibility of ecosystem-basedapproaches to fisheries management.One important element of ecosystem-based management is developmentand incorporation of multispeciesmodels into management programs.Like single-species models,multispecies models yield informationabout sustainability but are structuredto do so by more accurately reflectingbiological and ecological reality.

Over the past several years, thenumber and types of multispeciesmodels that provide insight on fisher-ies issues have grown significantly(Hollowed et al. 2000). This growthhas been fueled by the need to betterinform fisheries policymakers andmanagers; however, recent concernsabout fishing effects on the structureof ecosystems has also prompted

Recruitment

Recruitment

Naturalmortality

Naturalmortality

Fishingmortality

Fishingmortality

Stock 2

Growth

Growth

Stock 1

PredationBycatch

Figure 13. A conceptual description of two fish stocks (S1 and

S2) linked through both biological and technical interactions. A

predator-prey relationship is shown in which S1 preys upon the

juveniles of S2. Increased predation by S

1, therefore, leads to

an increase in its growth rate and a decrease in the rate atwhich the juveniles of S

2 are captured incidentally by the fishery

that targets recruitment rate for S2. Thus, an increase in the

fishing mortality rate will again lead to a decrease in therecruitment rate for S

2. Adapted from Miller et al. (1996).


research on multispecies modeling andimplied predator-prey relationships.From a theoretical perspective, basingfisheries stock assessments onmultispecies (rather than single-species) models appears more appro-priate, since multispecies approachesallow explicit modeling of more of theprocesses that govern populationabundance. This increased realism,however, requires additional param-eters (particularly for models thatassess the impact of biological interac-tions), which in turn creates the needfor more types of data. In the absenceof these additional data, or if unreli-able data are used to meet the require-ments of multispecies stock assess-ments, more uncertainty in manage-ment outcomes will undoubtedly arisecompared to single-species stockassessment methods. Consequently,multispecies models are not replace-ments for single-species models, butrather tools that provide additionaltypes of stock assessment insightwhen used in concert with single-species models (National ResearchCouncil 1999).

In recent years, interest has grown inmultispecies fisheries management inthe Chesapeake region, as evidencedby the development of fisheriessteering groups, the convening ofmultispecies technical workshops(Miller et al. 1996; Houde et al. 1998),and the requirement for developmentand implementation of multispeciesfisheries management plans by theChesapeake 2000 agreement (CBP2000). In this section, we describe andevaluate some multispecies modelscommonly applied to understand the

Figure 14. General overview of multispecies fisheries models.Adapted from Hollowed et al. (2000).

Species 1:Prey

Species 2:Predator

Age 1

Age 2Age 2

Age 3

Age 1

Age 3

FM1 M2

FM1

F

M1

FM1

Figure 15. General schematic of two-species MSVPA with arrows indicatinglosses due to fishing (F), predation (M2),and residual (M1) mortality (Jennings et al.2001).

Theory ofMultispecies Modeling

Biological interactionsTechnical interactions

MSYPRMurawski (1984)

MSVPASparre (1991)

Magnusson (1995)

Size-spectraPope et al. (1988)

Duplisea and Bravington (1999)

MS surplus productionRalston and Polovina (1982)

Koslow et al. (1994)

Ecosystem modelsAggregate system: Baird and Unlanwicz (1989)

Christensen and Pauly (1992); Bax (1985, 1991)Dynamic system: Walters et al. (1997)


effects of both biological and techni-cal interactions; these models havepotential for development andapplication in the Bay.

This overview should inform fisher-ies managers and policymakers aboutthe capabilities of more commonlyapplied multispecies modeling tech-niques. For biological interactions, wereview multispecies virtual popula-tion analysis (MSVPA), size spectrumanalysis, and ecosystem models(Ecopath with Ecosim). For technicalinteractions, we review multispeciesyield per recruit (MSYPR) and

multispecies surplus productionmodels (Figure 14).

Biological Interactions

Multispecies Virtual PopulationAnalysis

Single-species virtual populationanalysis (VPA), as developed by Fry(1949) and Gulland (1965), is a stockassessment technique that usescommercial-catch-at-age data tocalculate retrospective stock sizes andfishing mortality rates (F) of recruited,age-based cohorts. This approachoften is referred to as cohort analysis.For a given cohort, the number alivein the previous year is calculated byadding the number caught by thefishery in the current year to theestimated number that died fromnatural causes during that same timeperiod. Inherent in this technique aretwo important features: each cohort istreated separately (i.e., the variablesassociated with a cohort are calculatedindependently of those from othercohorts); and an estimate of thenatural mortality rate (M) is requiredas input for the model. When M is notknown, the traditional approach is toestimate it roughly from life historyparameters or to use an educatedguess.

The dependence of VPA on areasonable estimate of M hasmotivated researchers to focus onnatural mortality estimations.Although natural mortality occursfrom various causes, predation isgenerally believed to be the dominantsource of mortality. This belief, alongwith preliminary quantitative work onfeeding and food consumption of

Inputs

MSVPA-species

Model Outputs

Catch at age in numbers

Terminal F s

Residual mortality

Predator consumption rates

Body weight at age

Predator stomach contents

Minumum abundances in numbers

Consumption rates

Body weight

Stomach contents

Other Predators

Other Prey

Minumum abundances in numbers

Consumption rates

Body weight

Fishing mortality rates

Retrospective stock sizes

Suitability coefficients

Predation mortality rates

Figure 16. Types of data needed to perform an MSVPA basedon the role each species assumes in the analysis. “MSVPA-species” are those predators and prey for which retrospectivestock sizes are reconstructed using the MSVPA model and“other predators” and “other prey” represent species for whichVPA-type results are not desired, but the researchers know orsurmise that these species significantly influence the trophicdynamics of the food web under study. From Latour et al. 2003.


North Sea cod Gadus morhua (Daan1973, 1975), provided the foundationfor the development of models thataccounted for species interactions.Anderson and Ursin (1977) developedan ecosystem model that gave aconceptual framework for modelingpredator-prey interactions. Althoughthis complex model could not adapt toreal-world management applications,it ultimately facilitated the extensionof VPA to multispecies virtualpopulation analysis (MSVPA).

Helgason and Gislason (1979) andPope (1979) independently combinedthe theoretical predation relationshipsof the Anderson and Ursin (1977)model with the VPA methodology ofGulland (1965) to develop MSVPA.The primary feature of the method isthe split of the natural mortality rateinto two components. That is,

M = M1 + M2 (1)

in which M2 represents the predationmortality between and within theexploited species in the ecosystem—asdetermined by suitability parametersthat reflect predator preference for aparticular prey species—and M1

represents the mortality due to allfactors not explicitly included in themodel (Figure 15). For a review of theMSVPA approach, see Sparre (1991),Magnusson (1995), and Jennings etal. (2001).

The data requirements for an MSVPAvary according to the role each speciesassumes in the model and the pre-ferred model output (Figure 16). Ifspecies’ stock sizes are reconstructedusing the MSVPA model, with thesespecies referred to as

“MSVPA-species,” the data require-ments include catch-at-age in num-bers, fishing mortality rates in theterminal year and for the oldest ageclass, residual mortality rates, predatorconsumption rates, body weights atage, and predator stomach contents.

Phytoplankton

Zooplankton

Fish

Largest

Smallest

Body Size

Abundance

Figure 17a. Trophic pyramid relating the abundance of generalspecies groups within an aquatic ecosystem. The width of thepyramid is proportional to abundance; the height is proportionalto body size (Jennings et al. 2001). With biomass used as ametric rather than abundance, the pyramid would be greatlycompressed with little difference among trophic levels (Sheldonet al. 1972; Kerr and Dickie 2001).

Phytoplankton

Zooplankton

Fish

Body Size (log weight)

Ab

un

dan

ce (

log

)

Figure 17b. Plot of normalized biomass (i.e., number density)as a function of body size. The slope of the line is a qualitativerepresentation of the structure of an aquatic ecosystem(Jennings et al. 2001). As with Figure 17a, using biomassinstead of abundance results in a “flat” slope, although a smallnegative slope sometimes occurs.


With MSVPA, modeling species as“other predators” or “other prey” ispossible in cases for which the stan-dard VPA results are not desired, butthe researchers know or surmise thatthese species significantly influencethe trophic dynamics of the food webunder study. For “other predators,”data requirements include minimumabundances in numbers, bodyweights, consumption rates, andstomach contents; for “other prey”only minimum abundances in num-bers and body weights are typicallyrequired.

MSVPA in Chesapeake Bay

To date, MSVPA has not been usedfor stock assessments of fish popula-tions in Chesapeake Bay, although itis recognized as a possible approach(Miller et al. 1996; Houde et al.1998). An expanded MSVPA modelfor assessment of the Atlantic men-haden stock in the coastal waters ofthe eastern United States is currentlyunder development (Garrison andLink 2002) to supplement existingsingle-species assessments of theAtlantic menhaden stock and toallow fisheries managers to evaluatepossible alternatives to the currentmanagement scenario. The MSVPAmodel addresses four major topicsthrough evaluation of

1) The nature and magnitude oflinkages among menhaden and itskey predators;

2) The current use of menhaden as adirected fishery, its ecological roleas a forage fish, and sustainabilityof the stock;

3) The possible optimal size (or age)composition of Atlantic menhaden

to support its ecological role as aprey species and the goals of thedirected fishery; and

4) The biological reference points formenhaden recommended formanagement derived from single-species assessments along withdetermination of whetheradjustments are necessary withpredation included in theassessment.

Two major extensions to the basemodel also are being developed. First,a stochastic feeding model is beingincorporated into the MSVPA frame-work to account for the effects ofchanges in menhaden populationabundance on the diets and consump-tion patterns of predators. This modelwill require additional input data onthe relative abundance of alternativeprey species. Second, the growth andpopulation dynamics of predators(striped bass, bluefish, and weakfish)will be modeled more explicitly toexplore and evaluate the effects, ifany, of prey quality. This extensionwill incorporate the effects of preyavailability, diet composition, andfeeding rates.

In recent years, fishing effort onmenhaden has shifted from northerlywaters to southern areas; as a result,the Chesapeake Bay has become acenter of menhaden fishing. Althoughrecent coastwide stock assessmentshave characterized the menhadenstock as healthy, concern exists thatthis characterization does not apply tomenhaden in the Bay. Recruitment ofBay menhaden has declined since the1990s and young-of-year abundancehas been low. Low menhaden abun-


dance may cause nutritional stress inpredators, such as striped bass. Recentstudies suggest that striped bass inthe Bay suffer from poor nutrition,evidenced by an increase in the num-ber of diseased fish exhibiting lesionsfrom mycobacteriosis. The MarylandDepartment of Natural Resources(MD DNR) Pound Net Survey (2002)revealed that 17% of the striped basshad lesions or sores.

The MSVPA analysis of menhadenrepresents one of the firstmultispecies modeling efforts of a fishspecies indigenous to Chesapeake Bay(discussed later is another modelingeffort—the Chesapeake Bay Ecopathwith Ecosim model). The MSVPA willdocument quantitatively thesimultaneous effects of predation andfishing on the menhaden stock.Although best interpreted on acoastwide scale, these results shouldprovide information to betterevaluate the role of menhaden as aforage fish in the Bay. An MSVPAanalysis reflecting Bay-specific inputdata for menhaden, striped bass,bluefish, and weakfish would provideadditional insight into managementof these species.

Size-spectrum AnalysisA fundamental characteristic ofaquatic food webs is conservation ofmass through energy conservation viaproduction, respiration, growth, andpredation (Jennings et al. 2001). Bodysize determines these processes,leading to trophic pyramids with thesmallest species at the bottom andthe largest species on top (Figure 17a).

By turning this pyramid on its side, a

plot results that linearly relates lognumbers (or production) to log bodysize (Figure 17b). Based on theaforementioned law of conservation,perturbations to the ecosystem viaremovals will cause a change in theline slope. In theory, therefore, it ispossible to detect and interpretchanges in the structure of anexploited ecosystem by comparingslopes of biomass or abundance (i.e.,normalized biomass) in relation tobody size. This approach is formallyknown as size-spectrum analysis.

Size–spectrum analysis has since beenadapted and applied in ecologicalstudies ranging from characterizationof marine benthic invertebrateassemblages (Schwinghammer 1981,1983; Saiz-Salinas and Ramos 1999) toharvesting strategies and communitystructure of fish populations (Pope etal. 1988; Macpherson and Gordoa1996; Duplisea and Bravington 1999;Kerr and Dickie 2001). Researchershave also used the results of a size-spectrum model (e.g., quadraticregression equations depicting themajor biomass domes in a particularecosystem [Thiebaux and Dickie 1993])to develop estimates of annualproduction for the taxonomic groupsrepresented by these domes. The studyby Sprules and Goyke (1994)represents a specific example of thisapplication, in which the researcherscomputed an estimate of annualproduction for zooplankton in LakeOntario. Sprules et al. (1991) showthat examining the complete biomasssize spectrum is possible; theydescribed the pelagic biomass sizespectrum including phytoplankton,zooplankton, planktivorous fish, and


piscivorous fish from nine majorregions of Lake Michigan in bothspring and summer and alsoestimated the potential annualproduction of several trophic groups.

Within fisheries, the use of spectralmethods has rarely been appliedwhen developing managementstrategies. One exception is therecent study by Duplisea andBravington (1999) in which theyused spectra models to explore theimplications of different harvestingstrategies on total yields and commu-nity stability and persistence inmarine ecosystems. Their analysisindicated potential for application ofthe method, but counterintuitivefishing strategies emerged for someharvesting questions in a few in-stances. For example, one mightexpect that the best strategy formaximizing total yield would specifythe harvest of fish at lower trophiclevels (i.e., remove biomass from thesystem by fishing before it is lost topredation up the food chain).Duplisea and Bravington (1999)showed, however, that total yieldwould be maximized if larger fishwere exploited preferentially (i.e.,intentionally fishing the larger fish inthe ecosystem), since this strategyreduces predation on smaller fishand, therefore, increases their pro-duction. Although other researcherswithin the ICES community haveexplored the size–spectrum approachas an option for fisheries and ecosys-tem management (Rice and Gislason1996; Gislason and Rice 1998;Jennings et al. 2002), additionalresearch is needed to fully character-ize its potential.

Size–spectra in Chesapeake Bay

Researchers have not yet used size–spectra analyses and models to de-velop management strategies forChesapeake fish. Recently, however,Jung (2002) conducted biomass size–spectrum analyses using midwatertrawl catch data to estimate biomass,production, contribution to predators,and recruitment numbers (young ofyear) for forage fish (primarily bayanchovy). Jung also included analysesof higher trophic-level pelagic andbenthopelagic fishes, some of whichare piscivores (e.g., weakfish) in theBay. Jung’s analysis indicated thatannual, seasonal, and regional differ-ences in size spectra occur in responseto changing environmental conditionsand these environmental conditionsprimarily affected recruitment andyoung of year biomass production. Theresults may prove useful in develop-ment of biomass spectrum modelsthat address fishery managementissues.

Multispecies fisheries management isdesigned, by definition, to incorporateecosystem processes into managementplans. Knowledge of the magnitude ofpredation on bay anchovy and otherforage fish is important, therefore, ifmultispecies plans for these predatorspecies (e.g., bluefish, weakfish,striped bass) are developed.

Ecosystem Models

Ecosystem models form anotherapproach to characterize biologicalinteractions in multispecies fisheries.In effect, these models are mathemati-cal representations of whole ecosys-tems, typically used to elucidate the


effects of fishing pressure on thesystem. Although many of thesemodels are extremely quantitativeand employ sophisticated math-ematical theory, researchers haveused the models primarily for policyexploration and the models haveyielded results best interpretedqualitatively. As such, ecosystemmodels can serve a useful purpose indeveloping management strategies.Using these models for tacticalapplications is difficult, however,because accurate parameterization

depends on the availability of demo-graphic data for ecologically valuablespecies (as opposed to species thatare commercially or recreationallyvaluable); such data are often un-available. This limitation is impor-tant. Nevertheless, in recent yearsecosystem models have receivedincreasing attention from fisheriesresearchers and managers who usedthem successfully to summarizeknowledge and determine propertiesrelated to structure and function ofaquatic ecosystems worldwide.

Table 2. A subset of the potential policy questions motivated by the Chesapeake 2000agreement prioritized into the present (P), the near future (NF), and the longer-termfuture (LTF) based upon the Chesapeake Bay EwE model’s ability to address eachissue.

eussIyciloP P FN FTL

ehteratahwdnadlofnetsretsyokcabgnirbewnacwoH.1?secneuqesnoc X

rotaderphguorhtsbarcfosnoitalupopesaercniewnacwoH.2?snoitcuderyrehsifronoitalupinam

X

emagsi;troppusyaBekaepasehCnacssabdepirtsynamwoH.3?hsifegaroffoskcotswolnevigtnedurpnoitarotserhsif

X

yaBekaepasehCnihsifegaroffostceffeehteratahW.4?scimanydmetsysoce

X

cihporteratahwroyaBekaepasehCyhtlaehasenifedtahW.5?noitcudorpotstimil

X

cihportrepputceffaytivitcudorpyramirpnisegnahcthgimwoH.6?slevel

X

thgimstceffecihporttahwdnaderotserebVASdluocwoH.7?rucco

X

?saeradetcetorp/desolcfossenevitceffeehtssessaewnaC.8 X

enirautseehttceffasecitcarptnemeganamdnalthgimwoH.9?bewdoof

X

retsyoesaercniottupniretawhserfetalupinamewnaC.01?lavivrus

X

metsysocelamitporofnedahnemhsifewdluohswoH.11?noitcnuf

X


One widely used class of ecosystemmodels is that which packagesEcopath with Ecosim (EwE) andEcospace (Christensen et al. 2000).The development of this modelingtechnique stems from early work onthe ecosystem dynamics of a coralreef in Hawaii (Polovina 1984).Application of the EwE approachbegins with the construction of anEcopath model (Christensen andPauly 1992; Pauly et al. 2000), whichcreates a mass-balanced snapshot ofthe resources and interactions in anecosystem represented by trophicallylinked biomass pools. The biomasspools generally consist of either a

single species or a group of speciesthat represent an ecological guild.Researchers can also split them intoontogenetic age or size categories(juvenile, subadult, adult, etc.) ifnecessary. The data requirements forEcopath are fairly simple and oftenobtainable from traditional single-species analytical stock assessmenttechniques (e.g., VPA). Theparameterization of an Ecopath modelrests on the satisfaction of two masterequations. Equation 2 (below)describes how the biomass productionfor each group is allocated within theecosystem over an arbitrary timeperiod term. Using the principle ofconservation of matter within agroup, equation 3 (below) balances theenergy flows of a biomass pool.

Production = catch + predation + netmigration + biomass accumulation+other mortality (2)

Consumption = production +respiration + unassimilated food (3)

In general, an Ecopath model requiresinput of three of the following fourparameters: biomass, total mortality,consumption and biomass ratio, andecotrophic efficiency for each speciesor biomass pool in a model. Theecotrophic efficiency represents theproportion of the production used inthe ecosystem, incorporating allproduction terms apart from “othermortality” (for more details onEcopath, including the equations, seeChristensen and Pauly 1992; Walterset al. 1997; Pauly et al. 2000).

Although Ecopath can describe anecosystem, it cannot project the

Species 3

Fishing Effort

Species 1

Species 2

Cat

ch (

lbs

x 10

)6

5

10

15

20

25

30

35

40MSY3

2MSY

MSY1

Figure 18. A hypothetical graph showing a three-speciesfishery in which all species are caught in the same fishing gear.The productivity of each species differs, leading to differentlevels of maximum sustainable yield (MSY). The fishing effort(i.e., levels of fishing mortality, F) that yields the respectiveMSYs also differs for the three species. To illustrate theimportance of technical interactions, suppose that themultispecies fishery exerts a fishing effort that leads to amortality rate of F3. Fishing at F3 will cause the abundance ofspecies 1 and 2 to decline to a level at which substantial risk ofstock collapse exists. From a fisheries managementperspective, reducing fishing effort to either F1 or F2 to lower therisk of collapse is desirable. This management stratetgy is fairlyobvious given the graph above; however, independentexamination of catch/effort data for each species might notyield as certain a conclusion. Adapted from Houde et al. (1998).


effects of different managementstrategies on the structure andfunction of an ecosystem. OnlyEcosim—a time-dynamic simulationmodule that facilitates policyexploration—can accomplish thesetypes of projections. Ecosim re-expresses the static mass-balancedequations inherent to Ecopath as asystem of coupled differentialequations (Walters et al. 1997). Thissystem of equations represents thespatially aggregated dynamics of entireecosystems and is combined withdelay-difference, age- and size-structured equations to representpopulations with complex ontogeniesand selective harvesting of olderanimals. Summarized, the importantcomputational aspects of Ecosim are

1) Parameter estimation based on themass-balance results from Ecopath;

2) Variable speed-splitting methods tosimulate the dynamics of both fast(e.g., phytoplankton) and slow (e.g.,large predatory fish) biomassgroups;

3) Explicit incorporation of top-down(i.e., predation) vs. bottom-upcontrol (i.e., food limitation); and

4) Flexibility to incorporate age- andsize-structure of biomass groups.

One obvious deficiency of EwE is itsassumption that the resources,interactions, and subsequent dynamicsof an ecosystem are spatiallyhomogeneous. Ecospace—a dynamicspatial version of Ecopath thatincludes all of the key features ofEcosim—overcomes this deficiency.The details of Ecospace are notpresented here, but Walters et al.(1999) and Pauly et al. (2000)

thoroughly describe them. Researchershave formulated several EwE modelsand used them for policy exploration.Trites et al. (1999) developed an EwEmodel of the Bering Sea to examinepossible explanations for the changesthat occurred in the ecosystembetween the 1950s and 1980s. Kitchellet al. (1999) used the EwE approach tostudy the effects of fishing down toppredators in the central Pacific.Shannon et al. (2000) used EwE tocompare the effects of fishing in thesouthern Benguela upwelling systemunder different combinations ofbottom-up and top-down control. Ona larger scale, Stevens et al. (2000)summarized the direct effects offishing on chondrichthyans byexamining global information on theresponses of shark and ray populationsto fisheries. They developed Ecosimmodels of three previously publishedEcopath models to simulate changes inbiomass of all groups in response tothe rapid declines of shark species.

EwE in Chesapeake Bay

In 2001, the NOAA Chesapeake BayOffice (NCBO), in collaboration withthe University of British Columbia(UBC), initiated a workshop seriesto provide the foundation for con-struction of an EwE model of Chesa-peake Bay. Researchers are develop-ing a large-scale model (~50 species/functional groups) to serve as a“base” and “continuously living”model for future fisheries policyexploration (Table 2).

Technical InteractionsAlthough technical interactions arenot inherent to food web dynamics,


they are important and relevant tothe topic of multispecies fisheriesmanagement (Figure 18 provides adetailed description of technicalinteractions).

MultispeciesYield-per-RecruitManagers can use single-species yield-per-recruit (SSYPR) models todetermine fishing mortality ratesthat achieve optimal trade-offbetween the size of the individualsharvested and the number ofindividuals available for capture. Iffishing mortality is too high, thenyield will not be optimal since toomany individuals will be harvestedbefore having a chance to grow.Conversely, if fishing mortality is toolow, the yield will also not be optimalbecause not enough individuals willbe harvested (even though eachindividual will be large whencaptured).

Conducting an SSYPR analysis re-quires an estimate or assumed valuefor the natural mortality rate, infor-mation on growth in weight, anddata on selectivity (i.e., age or size atrecruitment). Such models generallyassume that recruitment—and hencethe age-structure of the population—are constant over time and thatfishing and natural mortality remainconstant once the fish becomevulnerable to fishing gear. Theseassumptions are important and theirviolation may have adverse effects onmodel performance; however, practi-cal application of an SSYPR modelusually characterizes the effects ofdifferent ages at first capture and

varying rates of fishing mortality.

Fishing gear (e.g., trawls, gill nets)used to exploit fish populations aresomewhat indiscriminant. If severalfish species occupy the samegeographic location at a particulartime, these fish may be captured inproportion to their relativeabundance, subject to the selectivityof the gear. The overall fishingmortality rates of the different speciescaptured can then be interpreted as afunction of gear selectivity and cullingpractices. Typically, the highest Fvalues are associated with the targetspecies (and size classes); a gradient ofF values that depends on selectivityand post-capture survival (assuming itis not uniform) is associated withbycatch or discarded species.

Researchers use multispecies yield-per-recruit (MSYPR) models to studysituations in which several stocks aresimultaneously exploited by a singlefishing gear. The calculationsassociated with the approach followthose of single-species models exceptthat the results are summed over allspecies (see Murawski 1984 for adetailed description of the equationsassociated with MSYPR). Given thissummation, researchers can simulatevarious regulatory scenarios thatreflect different levels of total fishingeffort (and thus F values) and theselectivity of different gear types.

Using MSYPR, it is also possible toaccommodate a situation in whichseveral independent fisheries harvestone or more species and stocks (e.g.,exposure to multiple fisheries due toseasonal migrations; the use of differ-


ent gear types). In this instance, thefishing mortality rates for each spe-cies/stock must be adjusted to reflectthe relative contribution by eachfishery (see Murawski 1984 for de-tails).

Multispecies Yield-per-recruit inChesapeake Bay

To date, researchers have not usedMSYPR models to study the effects oftechnical interactions in ChesapeakeBay. Several types of fishing gear havebeen used historically to harvestseveral fish species in the Baysimultaneously—most notably poundnets and haul seines (Chittenden1989). Species typically captured inpound nets and haul seines includestriped bass, Atlantic croaker, Atlanticmenhaden, and weakfish, makingthese species candidates for anMSYPR analysis. The results of anyMSYPR analysis should be interpretedcautiously, however, since a reasonablerisk exists that the assumptionsinherent to the model will be violated.As such, characterizing the potentialfor and effects of assumption violationshould become an importantcomponent in any MSYPR analysis ofChesapeake Bay fisheries.

Multispecies SurplusProductionSingle-species surplus production(SSP) models are typically used toidentify the rates of fishing mortalitythat generate sustainable yields—including the maximum sustainableyield (MSY)—given a population’s rateof growth in terms of changes inbiomass over time. Due to theirsimplicity and relatively modest data

requirements (catch and fishing ef-fort), SSP models generally provide astarting point for fisheries stockassessments. The general surplusproduction model in discrete timetakes the form:

Bt+1 = Bt + f(Bt)–Yt (4)

in which Bt represents the exploitablebiomass of a particular species at timet;f (Bt) is a general function describingsurplus production (often assumed tobe the difference between productionand natural mortality) as a function ofbiomass at time t; and Yt is the yield tothe fishery at time t.

Schaefer (1954) formulated the firstwidely used equation for f (B) fromearlier research by Graham (1935). Anapplication of the classical logisticequation for population growth, theSchaefer model is relatively simple andyields a symmetric relationshipbetween surplus production (dB/dt)and biomass. Pella and Tomlinson(1969) proposed a generalizedextension of the Schaefer model toalleviate the inherent requirement ofhaving a symmetrical relationshipbetween surplus production andbiomass. Studies of surplus productionrelated to stock size have shown thatnon-symmetrical relationships oftenexist, implying that use of thisgeneralized model may prove desirable.

One of two general modeling strategiesis possible when extending theproduction model approach toestimate multiple stocks production ina multispecies fishery. The first—atemporal multispecies production(TMP) model—evaluates productionby applying a single-species model


using the assumption thatmultispecies fisheries behave as asingle-species stock. The term“temporal” is used to define thisapproach because the productionanalysis is usually based on combinedtime series of catch-and-effort datafor all species under consideration(i.e., the parameters of f (B) areestimated from that time-seriesdata). Ralston and Polovina (1982)used this approach to investigate thetotal production of 13 demersal fishspecies from the Hawaiianarchipelago. In general, theyconcluded that the approach couldprove useful for production analysisin a multispecies fishery.

The second modeling strategy evalu-ates production over space ratherthan time. A spatial multispeciesproduction (SMP) model treats thevarious locations within the totalfished area (e.g., islands, reefs) asreplicate fisheries and assumes theproduction from each location is thesame (i.e., the parameters of f (B) areestimated from spatial fisheriesdata). The assumption remains thatthe spatially explicit fisheries are inequilibrium. As with the SSP andTMP models, the results of an SMPanalysis become unreliable if thisassumption is violated.

Koslow et al. (1994) used this ap-proach to study two Caribbean reeffisheries in southern Jamaica andBelize, concluding that SMP modelsshould be used with caution in reeffisheries management due to thehigh probability of assumptionviolation. Specifically, Koslow andothers noted the nonequilibrium

condition of the fisheries, the hetero-geneous mix of species both withinand between Jamaica and Belize, thediversity of fisheries targeting variousspawning, sedentary, and migratoryfish, and the possible differences inproductivity among sites as factorscontributing to the limited success ofthe analysis.

Multispecies Surplus Production inChesapeake Bay

To date, TMP and SMP models havenot been used to developmanagement strategies for Bay fish.The modest data requirements ofthese models relative to othermultispecies modeling approaches,however, imply some fairly immediatepossibilities for development. Sincewatermen have used pound nets andhaul seines to harvest several fishspecies in the Bay, development ofboth TMP and SMP models couldutilize landings data from these gears.Importantly, the MSY of the speciescomplex in a multispecies productionmodel is not simply the sum of theMSYs of the individual species.

Modeling SummaryWe have reviewed five valuableapproaches for investigating variousmultispecies fisheries questions.Specifically, we considered MSVPA,size-spectra, and EwE models toevaluate the effects of biologicalinteractions, as well as MSYPR andmultispecies production models tomake inferences about technicalinteractions. In addition to thetechniques described here, othermodeling techniques have proveduseful in evaluating impacts on marine


communities. Hollowed et al. (2000)reviewed a larger body of multispeciesmodels and described their strengthsand weaknesses (as compared tosingle-species models) in determiningthe causal mechanisms responsible forshifts in marine ecosystemproduction. A brief summary of theirgeneral conclusions follows.

Hollowed et al. (2000) concluded thatmultispecies models have a distinctadvantage over single-species modelssince they depict natural mortality andgrowth rates more realistically. Anexception to this lies in the use ofsingle-species models for short-termpredictions of large fish species, astrends in predation mortality are notoften immediately obvious. Withrespect to their ability to generatereliable long-term predictions,multispecies models are a work inprogress primarily because of theirsometimes strong sensitivity toparameter estimates and assumptionsabout recruitment. Additionally,multispecies models may have thepotential to describe the indirecteffects of fishing on individual species.Until they are more fully tested andvalidated, however, relying on generalrather than specific model predictions(i.e., qualitative rather thanquantitative results) seems moreprudent. Undoubtedly, multispeciesmodels that incorporate biologicalinteractions have improved ourunderstanding of fish populationdynamics. In some cases, lessonslearned from these models have evenled to improvements in the single-species models used to characterize thefishing impact on individual species.

Major Findings

Food Web

The Chesapeake Bay food web hasundergone significant historicalalterations, primarily due toanthropogenic influences such aseutrophication, overfishing, andhabitat degradation over the pastthree centuries. Eutrophication maydrive bottom-up control of food webdynamics, whereas fishing upon toppredators likely dominates top-downcontrol.

Food web interactions—the outcomeof predator–prey relationships—canhave dramatic and substantial effectson the ecosystem’s communitystructure, including productivity ofspecies supporting important fisheries.The form and magnitude of the effectsfrom altering food web interactionsare somewhat unpredictable, both inform and magnitude, due to the highconnectivity within and between thebenthic and planktonic components ofthe Chesapeake Bay food web.Connectivity among the food webcomponents must be betterunderstood to avoid individual andaggregate population collapses orextinctions of Bay species.

Major links between and withinbenthic and planktonic components ofthe Chesapeake food web includeAtlantic menhaden, Atlantic croaker,bay anchovy, blue crab, forage fish(e.g., bay anchovy), spot, and Atlanticcroaker. Researchers have onlyidentified a few keystone (e.g., bluecrab) or dominant (e.g., oyster) speciesin Chesapeake Bay. Further researchmust identify such species as they may


exercise control over communitystructure and productivity out ofproportion to their abundance anddominant species may be the majorcontributors to energy flow andbiomass production in aquaticecosystems.

Some food web interactions that arenot normally regarded as predator–prey or consumer–prey interactionsmay be consequential in the food weband population dynamics of keyspecies (e.g., disease in oyster,bycatch mortality for endangered orthreatened sea turtles and birds ofprey).

Modeling

The five modeling approachesreviewed in this element may proveuseful in addressing manymultispecies fisheries questions.Specifically, MSVPA, size–spectra,and EwE models can evaluate theeffects of biological interactions;MSYPR and multispecies productionmodels can make inferences abouttechnical interactions.

In addition to models described here,other modeling techniques could beused to evaluate the impacts onmarine communities. Hollowed et al.(2000) reviewed a larger body ofmultispecies models and describedtheir strengths and weaknesses (ascompared to single-species models)for determining the causalmechanisms that induce productionshifts in marine ecosystems.

Hollowed et al. (2000) concluded thatmultispecies models have a distinctadvantage over single-species models

in that they depict natural mortalityand growth rates more realistically.

An exception lies in the use of single-species models for short-termpredictions of growth and mortality inlarge fish species in which trends inpredation mortality are not alwaysobvious. Regarding reliable long-termpredictions, multispecies modelsshould be considered works inprogress due to their strong sensitivityto parameter estimates andassumptions about recruitment. Inaddition, multispecies models mayhave the potential to describe theindirect effects of fishing on individualspecies quantitatively. Until thesemodels are more fully tested andvalidated, however, relying onqualitative rather than specific modelpredictions remains prudent.

Multispecies models that incorporatebiological interactions have improvedour understanding of fish populationdynamics. In some cases, the lessonslearned from these models have led toimprovements in the single-speciesmodels used to characterize theimpact of fishing on individual species.

Panel Recommendations

Management

1) Develop life cycle diagrams and foodwebs for target species; use them todefine important food web linkagesand validate food web models.

Identify the major predator–preyinteractions, including all significantsources of food and mortality withoutignoring atypical sources (e.g., disease,bycatch) and noncommercial species.


Document beneficial aspects of foodweb interactions, such as the potentialbenefits of bycatch as food forendangered, threatened, andoverexploited species (e.g., seaturtles). Consider vital prey speciesthat potentially are affected byincreases in the abundance of thetarget species. In examining food webinteractions of the target species,explore the likelihood that food web(energy transfer) approaches may failto identify dynamically importantlinkages (e.g., trophic cascades andkeystone species).

2) Distinguish between anthropogenicand natural processes that affectwater and habitat quality and thustrophic interactions, such as pollu-tion effects on water quality,watershed influences, hydrodynam-ics, and variation in environmentalprocesses.

Both anthropogenic and naturalcauses—or some combination of thetwo—can alter food web dynamics. Itis important to recognize the causesof variability before undertakingmanagement actions intended toeither shift the balance among preda-tor and prey species or promote theproductivity of prey resources byappropriate controls of fishing ontarget species.

3) Use multiple models and varyingdata sources to explore the Chesa-peake Bay ecosystem and the waysin which fishing may affect thefood web dynamics and productionof target species.

Each modeling approach may serve aunique purpose. Run alternative foodweb models (e.g., Ecopath with

Ecosim) when developing FMPs tounderstand linkages between foodwebs, fish habitat, environmentalchanges, and fisheries production. Todetect robust responses to changes intarget species abundance, developseveral models within each class offood web model (e.g., multispeciesstock assessments, ecosystem models),when possible, to facilitate modelcomparison.

Needed Research and Development

4) Conduct field investigations todetermine and quantify majorpredator–prey interactions andsignificant sources of food andmortality.

Predator–prey dynamics are at theheart of interactions among speciesthat affect abundance and production.Such interactions have dramatic andsubstantial effects on communitystructure and may influence the yieldsof Bay fisheries. At present, managerscan use the fundamental knowledge offood web structure in a precautionarymanner, but major research mustproceed to ensure that multispeciesfisheries management in the Bay isconfidently implemented.

5) Conduct modeling and fieldinvestigations to determine foodweb (energy transfer) approaches toidentify and quantify the effects ofdynamically important linkages(e.g., trophic cascades and keystonespecies).

Do trophic cascades and keystonespecies exist in Chesapeake Bay?Evaluate existing Bay-area fishery-independent and fishery-dependentdatabases as sources for inputs to


multispecies models. Integrate thesedatabases to reflect a baywide scale.

6) Investigate the connectivityamong components of the foodweb to better understand andavoid the risk of individual oraggregate extirpation of Chesa-peake Bay species.

The form and magnitude of effects ofalterations in food web interactionsare somewhat unpredictable due tothe high degree of connectivitywithin and between the benthic andplanktonic components of the Chesa-peake Bay food web.

7) Investigate anthropogenic (e.g.,hypoxia) and natural (e.g., climateand weather) processes that affectwater or habitat quality and,therefore, trophic interactions.

Understanding how natural processescontrol or destabilize food webrelationships (in addition to fishingeffects) is important. Research onvariability in food consumption bytarget species, in relation to environ-mental factors and under varyingenvironmental conditions, will helpaddress this issue.

8) Compare and contrast the vari-ous modeling approaches of foodweb dynamics. Several modelingapproaches (e.g., EcoPath andEcoSpace, multispecies virtualpopulation analysis) are availableto address food web dynamics.Researchers should apply andevaluate more than one modelconcurrently.

Testing and comparing modelingapproaches to assess modelperformance and to understand the

causes of differences in model resultsremain important. Thoroughcomparative modeling research willlead to rigorous and robust modelapplications for evaluating food webrelationships.

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