A modelling study of the role of marine protected areas in...

A modelling study of the role of marine protected areas inmetapopulation genetic connectivity in Delaware Bay oysters

DAPHNE M. MUNROEa,*, JOHN M. KLINCKb, EILEEN E. HOFMANNb and ERIC N. POWELLc

aHaskin Shellfish Research Laboratory, Rutgers University, Port Norris, NJ, USAbCenter for Coastal Physical Oceanography, Department of Ocean, Earth and Atmospheric Sciences, Old Dominion University

cGulf Coast Research Laboratory, University of Southern Mississippi

ABSTRACT

1. Management decisions concerning location and extent of marine protected areas (MPAs) both for exploitedand unexploited resources rely on understanding how populations are interconnected.

2. The potential effects of MPA location and external fishing pressure on genetic connectivity of eastern oyster(Crassostrea virginica) populations in Delaware Bay were examined.

3. An individual-based metapopulation model that includes post-settlement population dynamics, larvaldispersal, and genetic structure was used to simulate four oyster populations for two periods (1970s and 2000s)with distinct population and environmental conditions. Sensitivity analysis examined the influence of larvaldispersal, and simulations included combinations of MPA location (which population was protected) and threefishing mortality rates for non-MPA populations: low (4%), medium (8%) and high (30%); no fishing was allowedin the MPAs.

4. Results showed (i) salinity-driven changes in larval dispersal led to relatively small, population-specificconnectivity changes, (ii) MPAs can enhance the frequency of genotypes originating within protected populationsin unprotected populations when fishing rates are high (30%), and (iii) demographic shifts can impose temporalvariability on the influence of MPAs on connectivity.

5. These results suggest that genetic consequences of sitingMPAsmust be considered in terms of present populationand environmental conditions, as well as allowing for changes in population and genetic connectivity that may shiftfundamentally over time.

6. Simulation results indicate that siting protected areas for oyster restoration in low salinity (<12ppt) regionsmay interact with development of disease resistance in the metapopulation by altering genotype transfer fromprotected to unprotected downestuary populations.Copyright # 2013 John Wiley & Sons, Ltd.

Received 25March 2013; Revised 28 July 2013; Accepted 18 August 2013

KEY WORDS: population connectivity; oyster; Crassostrea virginica; marine protected area; genetic connectivity; source sinkdynamics

*Correspondence to: D. M. Munroe, Haskin Shellfish Research Laboratory, Rutgers University, Port Norris, NJ 08349, USA. Email:[email protected]

Copyright # 2013 John Wiley & Sons, Ltd.

AQUATIC CONSERVATION: MARINE AND FRESHWATER ECOSYSTEMS

Aquatic Conserv: Mar. Freshw. Ecosyst. 24: 645–666 (2014)

Published online 20 September 2013 in Wiley Online Library(wileyonlinelibrary.com). DOI: 10.1002/aqc.2400

INTRODUCTION

Marine protected areas (MPAs) are increasingly beingused in management of marine resources (Murawskiet al., 2000; Botsford et al., 2009; Gaines et al., 2010;Frid and Paramor, 2012; Rassweiler et al., 2012) as ameans to either enhance conservation or mitigate theeffects of fishing. Marine protected areas establishedwith a conservation goal are designed to protectrare genotypes and maintain genetic variation(Pérez-Ruzafa et al., 2006; Law, 2007; Arrieta et al.,2010; Miethe et al., 2010), preserve fragile habitats(Azimi, 2001; Hamilton et al., 2010), or supportbiodiversity (Engel and Kvitek, 1998; Fernandeset al., 2005; Jones et al., 2007). Those establishedwith fishery goals are anticipated to increase fisheryyield or economic gain (McClanahan and Mangi,2000; Milon, 2000; Murawski et al., 2000; Pan et al.,2001; Hamilton et al., 2010). In both cases, thedegree of benefit from the protected area for theunprotected portion of the resource is determinedby the interconnectivity of the protected andunprotected areas, which occurs by immigration (e.g.adults moving into the MPA) or emigration (e.g.larval dispersal out of the MPA) of individuals(McClanahan and Mangi, 2000; Walters, 2000; Gelland Roberts, 2003; Pelc et al., 2010). Thus, decisionson the location and size of MPAs for exploited andunexploited marine resources require an understandingof population connectivity (Palumbi, 2003; Dawsonet al., 2006; Cowen and Sponaugle, 2009).

Field-based estimates of genetic connectivityhave progressed enormously in recent years, butnevertheless often provide ambiguous results (Piggottet al., 2008; White et al., 2010). Larval dispersal isthe critical mechanism facilitating population geneticconnectivity, particularly in sessile species such asoysters. Similar to the challenges in empirical studyof genetic connectivity, quantitative evaluation ofthe role of larval connectivity is lacking because ofthe difficulty of monitoring and tracking larvaein the field (Arnold et al., 2005; Hare et al., 2006;Hitchcock et al., 2008). Furthermore, quantitativeevaluation of MPA functioning and efficacy islimited by lack of replicate studies and baseline datafor comparisons (Crowder et al., 2000; McClanahanand Mangi, 2000; Pelc et al., 2010; but see Schroederand Love, 2002; Halpern et al., 2004), and by the

difficulty in implementing redesign modificationsonce management measures are established (Powellet al., 2004; Mann and Powell, 2007). As aconsequence of the lack of experimental andobservational data, spatially explicit ecologicalmodels are being used to investigate populationconnectivity (Epperson et al., 2010; Galindo et al.,2010; Pujolar et al., 2011), MPA functioning andeffectiveness, and to evaluate their design (Botsfordet al., 2003; Gerber et al., 2003; Fogarty andBotsford, 2007; Gaines et al., 2010; Hamilton et al.,2010; Rassweiler et al., 2012). A novel modellingapproach was used that integrates populationdynamics, dispersal, and genetics, allowing collectiveexamination of marine protected area strategies,genetic connectivity and metapopulation processeswithin an individual-based model framework.

Most MPA studies focus on their characteristics,such as location, spacing, and extent, and on theireffects on target species, fishery productivity,biomass, or social or economic benefit. Fewerstudies focus on the effect of MPAs on large-scalemetapopulation processes. For sessile or sedentarypopulations, larval dispersal provides geneticconnectivity for the metapopulation, but the extentto which genetic exchange occurs can be mediatedby the demographics of the post-settlementpopulation (Figueira, 2009; Munroe et al., 2012).Eastern oyster (Crassostrea virginica) populations inDelaware Bay (Figure 1) are distributed along asalinity gradient that decreases with increasingdistance from the bay mouth (see discussion in Fordet al., 2012). The along-estuary gradients in salinity,food availability, and disease pressure (Fordand Tripp, 1996; Bushek et al., 2012) result invariability in growth and mortality rates of post-settlement oysters, which produce demographicallyheterogeneous metapopulations. Some of thesepopulations in Delaware Bay have geneticallydistinct characteristics, such as disease resistanceand susceptibility (He et al., 2012).

Oyster larvae disperse throughout Delaware Bay,but some reef areas, such as those in themid-reaches of the bay, contribute a largerpercentage of the larvae that survive and eithersettle in the same area (self-recruitment) or disperseto other areas (Narváez et al., 2012a, b). Thus, thecharacteristics of the post-settlement population

D. M. MUNROE ET AL.646

Copyright # 2013 John Wiley & Sons, Ltd. Aquatic Conserv: Mar. Freshw. Ecosyst. 24: 645–666 (2014)

may be an important determinant of geneticconnectivity through controls on larvae production(Munroe et al., 2012). Fishing provides aconfounding effect. Oyster populations in DelawareBay have been commercially harvested for decades(Ford, 1997; Powell et al., 2008, 2009a), and muchof this fishing pressure is focused on the reefs thatprovide the majority of the larvae (HSRL, 2011).

Measurements of the demographic characteristicsof the Delaware Bay oyster stock have beencollected as part of the annual stock assessmentsince the early 1950s (Ford, 1997; Powell et al.,2009a, b). These data show that the oysterpopulations in the 1970s were distinctly differentfrom those in the 2000s. During the 1970s,abundance was high and mortality low over theentire range of the stock (Powell et al., 2008).During the 2000s, an up-bay/down-bay gradientin mortality and abundance of oysters existedsuch that abundance increased and naturalmortality decreased up-bay (Figure 1 showslocations of up-bay/down-bay populations). Theimportance of demographic characteristics of

fished populations on the efficacy of MPAs hasbeen demonstrated (White et al., 2012). The twocontrasting conditions evident in the time series fromoysters in Delaware Bay (Powell et al., 2009a, b)provide a basis for testing the relative effects of thecharacteristics of the post-settlement populations(abundance, mortality) and larval dispersal ongenetic connectivity of metapopulations and thepotential role of MPAs in either enhancing orminimizing this connectivity through modificationsto fishing pressure.

The objective of this study was to investigatethe effects of MPA location and fishing pressureon genetic connectivity of Delaware Bayoyster populations. This objective is addressedusing a modelling framework that includes anindividual-based model that simulates growth ofeastern oysters at particular locations in DelawareBay (Kraeuter et al., 2007; Powell et al., 2013),oyster larval dispersal obtained from a coupledcirculation-larval model (Narváez et al., 2012a),individual genetics derived from parental matingand exchanged by larval connectivity, and habitatheterogeneity-driven population demographics(Munroe et al., 2012). The model is parameterizedusing the long-term data from fishery assessments ofthe oyster metapopulation in Delaware Bay.Simulations were designed to investigate the effectof MPA placement on metapopulation geneticconnectivity within a network of demographicallydistinct oyster populations under varying fishingpressures. Additional simulations tested thesensitivity of metapopulation genetic connectivity toa range of larval dispersal.

MATERIALS AND METHODS

The model

The Dynamic Population Genetics Engine(DyPoGEn) (Powell et al., 2011a, b; Munroeet al., 2012) is a numerical model that simulatesthe genetic structure and population dynamics fora metapopulation. The model was parameterizedto simulate a metapopulation containing fourconnected populations of eastern oysters(Crassostrea virginica) in Delaware Bay (Figure 1)

Figure 1. Locations of oyster populations in Delaware Bay used in thesimulations. A detailed map of the reefs found within each of theseregions is provided in Figure 1 of Powell et al. (2008). Inset shows the

location of Delaware Bay on the Atlantic Coast of the USA.

MODELLING MPAS AND GENETIC CONNECTIVITY 647


for the decade of the 1970s and the decade of the2000s. Each simulated population is composed ofmultiple cohorts of oysters, and populationsinteract via larval dispersal. Larvae are createdfrom parent pairs via independent assortment ofparental genotypes to simulate meiosis andrandom egg fertilization. Larvae produced ineach population can remain within the sourcepopulation (self-recruitment) or disperse to anyof the other populations.

DyPoGEn has three basic components, (i) apost-settlement population dynamics submodel thatcontains parameterizations for growth, mortality,and reproduction; (ii) a larval submodel thatcontains parameterizations for larval mortality,larval exchange, and early juvenile survival, and(iii) a gene submodel that describes each individualin terms of its genetic structure. Additional detailsof the single population model structure andformulation, on which the metapopulation modelis based, are given in Powell et al. (2011a, b). Themodel processes relevant to the neutral allelebehaviour used in this study, encompassingspecifications for the processes of growth,reproduction, and mortality, are described below(see also Munroe et al., 2012) and shownschematically in Figure 2.

In the population dynamics submodel, theprobability of natural mortality (Pmort) is derivedfrom the age of the animal (Age, in years) as:

Pmort ¼ 0:5 1þ tanhAge� AveAgeMortAveSpreadMort

� �� (1)

where Pmort increases nonlinearly with age such thatthe rate of increase is small at young and old age,and is greatest at the average age of mortality(AveAgeMort). The range of ages over whichmortality probability transitions from 0 to 1 andhow steeply the mortality probability approaches 1is controlled by the denominator of Equation (1)(AveSpreadMort) (see also Figure 2 in Powellet al., 2011c). Juvenile mortality is specifiedseparately as a specific rate applied to recruitedanimals of age 0.

Fishing mortality is applied to all adults in thepopulation that are larger than the specified lowerfishing limit. For these simulations, minimumfishery size limits were set at 63.5 mm (2.5 inches),consistent with the fishery limit and size frequencyof observed landings from the Delaware Bay oysterfishery (Powell et al., 2005). Fishing mortality isspecified by the probability of capture, which wasset at one of three rates (4%, 8%, and 30%). Eachindividual larger than the fishing size limit wasassigned a random value from a uniformdistribution with a range from 0 to 1 obtained froma pseudo-random number generator (ran3 describedby Press et al., 1986). If the random value is lessthan the fraction of the population stipulated forremoval by the fishery, the individual is removedfrom the population; otherwise, the individualremains in the population.

The sex of new recruits is determined by themale-protandric allele system described by Guoet al. (1998). In this system, animals that contain amale and a protandric allele (MF) are permanentmales; those with two protandric alleles (FF) beginlife as protandric males. In each generation, aprotandric male is given the chance to convert to afunctional female. A conversion probability wasobtained from empirical data from Delaware Bay(Powell et al., 2013) using age–length relationshipsdeveloped by Kraeuter et al. (2007). Powell et al.(2013) found that the relationship between the

Figure 2. Model schematic of processes executed in a single time step(one year). Numbered circles below each process indicate theequations invoked in that process (adapted from Munroe et al., 2012).



fraction of the population that is female, Ff, and agecould be modelled as a Gompertz curve:

Ff ¼ αeβe γ�Ageð Þ (2)

where α and β are population-specific parameters(Table 1). The first derivative of Equation (2) givesthe rate at which any animal can change frommale to female (Df) as:

Df ¼ dFfdAge

¼ αβγe γ�Ageð Þþ βe γ�Ageð Þð Þð Þ (3)

where γ is a population-specific parameter (Table 1).The probability of conversion (PsexΔ) is calculatedas:

PsexΔ ¼ min 1;Df

1� Ff

� �(4)

Owing to the age dependency of the probability ofsex change, all long-lived protandric individualseventually become functional females. As all oystersthat are protandric begin life as male, all recruitsare male. However, some recruits convert to femalebefore first spawning, as appears to be the case inpopulations fromDelaware Bay (Powell et al., 2013).

The fraction of the population parenting eachgeneration (FrParaents) is derived from a predefinedfraction of parents reproducing each mating season

(FracParents, set at 0.05% annually), based onestimates of effective population number foroysters in Delaware Bay (Hedgecock et al., 1992;Hedgecock, 1994):

FrParents ¼ FracParents �10 N�FracParentsVarð Þ (5)

where the coefficient FracParentsVar permitsvariability to exist in the fraction of parentsreproducing. The number of parental pairs(nParents) is determined as:

nParents ¼ max 0:5�FrParents�LastAnimal;minParentð Þ(6)

where LastAnimal is the count of adult animals inthe population. A minimal number of parents,minParents, are allowed to reproduce, thusguaranteeing some, albeit low, level of reproductionwhen abundance becomes low. Over all simulationsperformed here, the average number of spawningadults ranged from 224 to 820, with an average of512 (approximately 0.05% of the population).

Potential parents are drawn randomly, withoutreplacement, from a list of all animals greater than1 year of age (Kennedy, 1983; Powell et al., 2013)until enough males and females accrue to providenParents, or until the list of animals is exhausted.Each pair of parents, taken randomly, without

Table 1. Base Case population characteristics for each of the four populations during two different decades, 2000–2010 and 1970–1980 and larvaltransfer rates among populations. The 2000s conditions and larval transfer rates were used to parameterize the Present Conditions simulation; the1970s conditions were used to parameterize the 1970s simulation. Unk, no data available. Larval transfer rates are based on 2001 conditions(Narváez et al., 2012a), see Table 2 for other larval transfer rates

Population 1 Population 2 Population 3 Population 4

Population characteristics of Delaware Bay oysters for the 2000sAbundance (millions of oysters)* 492 395 868 197Average adult non-fishing mortality rate* 8% 10% 16% 26%Juvenile mortality rate* Unk.*** 8% 23% 47%Von Bertalanffy growth parameters (k/L∞)** Unk.*** 0.175 / 110 0.26 / 125 0.23 / 140

Population characteristics of Delaware Bay oysters for the 1970sAbundance (millions of oysters)* 3270 2066 4428 4758Average adult non-fishing mortality rate* 11% 11% 11% 11%Juvenile mortality rate* Unk.*** 8% 23% 47%Von Bertalanffy growth parameters (k/L∞)** Unk.*** 0.175 / 110 0.26 / 125 0.23 / 140

Larval transfer rates among populations****Population 1 to: 11% 54% 27% 8%Population 2 to: 6% 56% 29% 9%Population 3 to: 3% 40% 29% 28%Population 4 to: 3% 19% 14% 64%

*From Powell et al. 2011b; L∞ in mm, k in yr-1.**From Kraeuter et al. 2007.***Used approximated L∞ from stock assessment data and same juvenile mortality and von Bertalanffy k as Population 2.****From Narváez et al., 2012a.



replacement, from the parents’ list, produces anumber of offspring up to a maximum number,which represents a typical larval settlement at thebeginning of the simulation. The number ofoffspring produced is dependent upon parental agethrough a weight-based relationship that is describedby a von Bertalanffy equation (Fabens, 1965; Jensen,1997) that relates size and fecundity to age as:

W ¼ W∞ 1� e�k Age�Ageoð Þ� �b

(7)

where W∞ is the maximum weight, and k and Ageoare population-specific von Bertalanffy parameters(Table 1). The value of W∞ is obtained from theadult maximum length, L∞, using an allometricequation that relates weight and length as:

W ¼ aLb (8)

with a = 0.0003 and b = 2. Note that for oysters,weight scales more nearly with the square oflength rather than the cube (Yoo and Yoo, 1972;Powell and Stanton, 1985).

Equation (7) is applied to fecundity by assumingthat oyster spawn is a standard fraction of biomass(Hofmann et al., 1992, 1994). The number ofoffspring (nOff) produced by a female of a givenage and weight is estimated as:

nOff ¼ W∞

W 761� e�k Age�Ageoð Þ

� �bMaxOff (9)

where W76 is the weight of a 76-mm oyster (notethat 76-mm shell length is a commonplacedefinition of a ‘market-size’ oyster) and MaxOff isthe fecundity of a 76-mm oyster, which can be asmuch as 60 million eggs per female (Davis andChanley, 1955). For the simulations used in thisstudy, the value of MaxOff was set at 100,000eggs per female to reduce computation time.Simulations show that results are little influencedby higher simulated fecundities (Powell et al.,2011c). Genotypes of the offspring are determinedby random combinations of haploid genotypesfrom each parent after meiosis. Crossing over ofgenetic information is permitted during meiosis.

In the larval submodel, all offspring are transferredamong the populations in the metapopulation usinga transfer probability obtained from Lagrangianparticle simulations that used an individual-based

model of oyster larval growth and behaviour thatwas coupled to a Delaware Bay circulation model(Narváez et al., 2012a). The individual-based larvalmodel is based on the growth and behaviour modelsdeveloped for eastern oyster larvae, which aredescribed in Dekshenieks et al. (1993, 1996). Thelarval growth model estimates larval growth as afunction of temperature, salinity, food supply, andturbidity and was parameterized using laboratoryand observational studies (Dekshenieks et al., 1993).Larval vertical migratory behaviour depends onsalinity (controls time swimming), temperature(controls swimming speed) and larval size (controlsswimming and sinking speed) (Dekshenieks et al.,1996). The circulation model is based on animplementation of the Regional Ocean ModelingSystem (ROMS – Haidvogel et al., 2000;Shchepetkin and McWilliams, 2005) for DelawareBay (Narváez et al., 2012a; Wang et al., 2012). TheDelaware Bay circulation model has a horizontalresolution that ranges from 0.2–2.1 km and avertical resolution that ranges from 0.03–6.2 m.Details of the configuration and calibration of theimplementation for Delaware Bay are given inWang et al. (2012).

The larval transfer rates used in this study wereobtained from connectivity matrices calculated byNarváez et al. (2012a). Connectivity between oysterreefs in Delaware Bay was determined by thepercentage of particles released in an area thatsettled in the release region (i.e. self-recruitment) oranother region. Populations 1–4 used in this study(Figure 1) correspond to the Hope Creek (HOP),Arnolds (ARN), Shell Rock (SHR), and Bennies(BEN) oyster beds, respectively, used by Narváezet al. (2012a) to calculate larval exchanges for 2001(see Figure 7 in Narváez et al., 2012a). For thisstudy, only larvae released in these four areas thatsettled in the four areas were used to calculatetransfer rates (Tables 1 and 2). Four larvalconnectivity matrices calculated by Narváez et al.(2012a) for the years 1984, 1985, 2000, and 2001were used to perform sensitivity analyses examiningchanges in larval connectivity. These four yearsinclude two high river discharge or low salinityyears (1984 and 2000) and two low river dischargeor high salinity years (1985 and 2001).Simulations testing sensitivity to larval



connectivity were performed with realized fishingmortality rates for Delaware Bay; 2% (Pop 1,2,3);4% (Pop 4) (Powell et al., 2008). Results showedthat changes in larval dispersal resulted in relativelysmall, inconsistent, and population-specific changesin genetic connectivity (Figure 3 and discussed

further in results), and were in agreement withMunroe et al. (2012) who demonstrated thatpost-settlement factors such as relative mortalityand abundance among populations exert a greaterinfluence on genetic connectivity in this system.Therefore, to examine the specific influence of the

Table 2. Larval transfer rates based on connectivity matrices calculated by Narváez et al. (2012a) for the years 1984, 1985, 2000, and 2001. These fouryears include two high river discharge or low salinity years (1984 and 2000) and two low river discharge or high salinity years (1985 and 2001)

High riverflow / Low salinity


1984Population 1 to: 7% 64% 24% 5%Population 2 to: 4% 64% 23% 9%Population 3 to: 3% 54% 21% 22%Population 4 to: 2% 29% 14% 55%


Low riverflow / High salinity



2001/Base CasePopulation 1 to: 11% 54% 27% 8%Population 2 to: 6% 56% 29% 9%Population 3 to: 3% 40% 29% 28%Population 4 to: 3% 19% 14% 64%

Figure 3. Simulated metapopulation allele frequencies (the frequency of the neutral B allele originally fixed in a given population, but absent from theremaining) for each of the four allele markers. Figures left to right show allele markers originally fixed in populations 1–4 respectively. Metapopulationallele frequency is shown for the duration of each simulation (100 generations). Left panels in each plot show allele frequencies for the 1970ssimulations; right panels show 2000s allele frequencies. The four lines represent each of the larval dispersal patterns based on larval connectivitymatrices calculated by Narváez et al. (2012a) for the years 1984, 1985, 2000, and 2001. These four years include two high river discharge or lowsalinity years (1984 and 2000, grey dotted and solid lines respectively) and two low river discharge or high salinity years (1985 and 2001, black

dotted and solid lines respectively). Note: the 2001 larval dispersal (solid black line) was used as the Base Case for all MPA simulations.



mortality gradients created among populations byMPAs and fishing on genetic connectivity, thelarval connectivity matrix was kept constant acrosssimulations; the 2001 larval dispersal matrix wasused for all Base Case and MPA simulations.

Larval recruits were randomly assigned to one ofthe four areas based on the calculated exchangeprobabilities with a survival probability in the areagiven by:

LarvSurv ¼ 0:5þ 1:5Rð Þ KnOff nParents

(10)

where R is a uniform random number, nParents isthe number of parents in the population and K isthe local carrying capacity, which regulates thenumber of animals in the population. Thiscompensatory relationship maintains the oysterpopulation at an abundance that is near itscarrying capacity in the unfished state. The abilityof oysters to filter water more rapidly than food isre-supplied, thereby generating a food limitation(Wilson-Ormond et al., 1997; Powell et al., 2013),and cannibalistic feeding by the adults on thelarvae (Tamburri and Zimmer-Faust, 1996),provide a theoretical basis for the expectation ofcompensatory population dynamics. Theprobability of death (P) for each individual larva is:

P ¼ 1� LarvSurv (11)

If a random draw R < P, then the larva dies. Ifthe larva survives to recruit into the destinationpopulation, it is given an identifying number, abirth date and an age of zero.

Simulations

An individual genotype is defined by a complementof 10 chromosome pairs with four genes perchromosome. Each gene is defined by two alleles,A and B. Gene transfer among the populationswas observed by initializing the model with 100%of the individuals in one population homozygousBB at a particular locus, while the initialindividuals in the remaining three populationswere all homozygous AA at the same locus. Thisallows tracking of allele frequencies of the B alleleto follow the movement of neutral alleles from onepopulation through the metapopulation over time.

The Base Case simulations were parameterizedto allow the four simulated populations to havecharacteristics of the Delaware Bay populations fortwo time periods; the decades of the 1970s and2000s (Table 1). These two periods had distinctiveoyster population dynamics in Delaware Bay,separated by a demographic shift c. 1985 (Powellet al., 2009a). Data from annual stock assessmentsof oysters in Delaware Bay (Powell et al., 2009a, b)document the differential in local populationabundances and discrete annual mortality rates forthe four simulated populations during the 1970scompared with the 2000s. Both the abundances andmortality rates of the four populations wererelatively equivalent among all four populationsduring a period from c. 1970 to 1985 in contrastto the strong up-estuary–down-estuary gradientin mortality and biased abundance favouringPopulation 3 in the 2000s (Table 1). Larval transferrates among populations, von Bertalanffy growthrates, probabilities of juvenile and adult mortality,and carrying capacity are specified for eachpopulation independently (Table 1). Populationabundances were maintained sufficiently high tominimize the influence of drift (Powell et al., 2011c)that might otherwise influence the results fromsimulations of genetic connectivity (Gandon andNuismer, 2009; see Powell et al., 2011c for anexample in a DyPoGEn implementation), but werescaled down in the 1970s simulations to keep thetotal number of animals in the metapopulationto approximately 1 million for efficiency ofcomputation. The gradient in natural mortality inthe 2000s (Table 1) was produced by Dermodisease (caused by the parasite Perkinsus marinus,Ford and Tripp, 1996) that increases in severitywith increasing salinity. Disease mortality wasinconsequential in the 1970s. The analogousgradient in juvenile mortality results from thedown-bay increase in juvenile oyster predators.Note also the differential growth rates that reflectslower growth and longer lifespan at lower salinities(Kraeuter et al., 2007). Thus, these four populationsdiverge in important attributes of populationdynamics.

A series of simulations was performed for bothtime periods (1970s and 2000s) in which each ofthe four populations was used as a no-take MPA



and each of three fishing rates was allowed in theremaining populations, fully crossed for a total of12 simulations in addition to the Base Case foreach time period. A summary of simulation namesand details are provided in Table 3. The threefishing rates used in the MPA simulations, 4%,8%, and 30% of the fishable stock per year, wereintended to span a range of published estimates offishing mortality rates for oyster populations inUS Mid-Atlantic estuaries (Rothschild et al., 1994;Jordan and Coakley, 2004; Powell et al., 2008).The low rate (4%) is reflective of the currentapparent oyster fishing rates in Delaware Bay(Powell et al., 2008). This rate is slightly higherthan the realized fishing rate used in the larvaldispersal sensitivity analysis that reflects onlyoysters removed by the fishery because theapparent fishing rate conservatively includesoysters caught by the fishery and those collectedand moved to other regions of the bay intransplant programmes (Powell et al., 2008). Thehigh rate (30%) reflects rates reported for areas ofChesapeake Bay (Rothschild et al., 1994; Jordan

et al., 2002). The intermediate rate (8%) providesan elevated rate that conservatively reflects areallocation of the low fishing effort into theremainder of the metapopulation, effectivelycompensating for loss of fishing effort overallowing to creation of the MPA (sensu fisherysqueeze – Pelc et al., 2010). Fishing was applied tooysters that were 63.5 mm and larger.

The metapopulation allele frequency wascalculated as the fraction of animals in all fourpopulations possessing a B allele at a given time.This metric was used because we were interested inconnectivity among populations and possession ofthe neutral marker, even as the heterozygote,indicates transfer of genetic material from thepopulation that is a source of that marker tothe population in which it is observed later. Thechange in allele frequency was calculated as thefrequency in the metapopulation at the end ofthe simulation (100 years) minus the frequency atsimulation year 1. An analysis of variance(ANOVA) was used to assess the influence offishing rate on change in allele frequency. Theaverage abundance and proportional abundanceof oysters in each population were calculated fromyears 20 to 100; these years were used to avoidinclusion of biased values due to model start-upeffects. The relationship of average proportionalabundance of oysters in each population versus thechange in metapopulation allele frequency for theallele (B) initially present in that same populationwas examined for groups of simulations based ontime period and population. Linear regressionswere performed on the proportion ofmetapopulation abundance in each populationversus the change in metapopulation allelefrequency for the allele marker from thatpopulation; separate regressions were performedfor each of the time period and population groups.

RESULTS

Change in metapopulation allele frequency is themetric used here to describe the potential for apopulation to act as a source of alleles to themetapopulation as a whole. When relativepopulation abundances remain constant through

Table 3. Names and characteristics of simulations performed. Each ofthe simulations listed was run for both the 1970s and 2000s timeperiods. Fishing rate is the percentage of the fishable populationremoved each year: the comparable fraction is the probability ofcapture for any oyster in the fishable size class. Minimum fishery sizelimits for all simulations were set at 63.5 mm (2.5 inches), consistentwith the fishery limit and size frequency of observed landings from theDelaware Bay oyster fishery (Powell et al., 2005)

Simulationname

Marineprotected area

Fishing rate (outsideMPA)

Larvaldispersal

Larv1984 None 2% (Pop 1,2,3);4% (Pop 4)

1984

Larv1985 None 2% (Pop 1,2,3);4% (Pop 4)

1985

Larv2000 None 2% (Pop 1,2,3);4% (Pop 4)

2000

Base Case None 2% (Pop 1,2,3);4% (Pop 4)

2001

MPA_Low1 Population 1 4% 2001MPA_Low2 Population 2 4% 2001MPA_Low3 Population 3 4% 2001MPA_Low4 Population 4 4% 2001MPA_Med1 Population 1 8% 2001MPA_Med2 Population 2 8% 2001MPA_Med3 Population 3 8% 2001MPA_Med4 Population 4 8% 2001MPA_High1 Population 1 30% 2001MPA_High2 Population 2 30% 2001MPA_High3 Population 3 30% 2001MPA_High4 Population 4 30% 2001



time (as was the case for the Base Case simulationshere, Figure 4) an increase in the metapopulationallele frequency results from the ability of thepopulation from which the allele originates to exportthat allele to other populations, thus that populationacts like a source for that allele. Conversely, adecrease in the metapopulation allele frequencyresults from the inability of the population fromwhich the allele originates to export that allele toother populations, thus that population acts likea sink. Change in metapopulation allele frequency(Figure 5) ranged from an increase of 0.44(MPA_High1, Population 1 allele, Table 3) to adecrease of –0.26 (MPA_High2, Population 3 allele,Table 3). The change in metapopulation allelefrequency varied depending on the larval dispersalused, the simulated time period (1970s versus 2000s),MPA location, fishing pressure, and the originpopulation for the marker allele. The frequency ofthe neutral allele was similarly dynamic within eachpopulation, with the frequency beginning at 100% inthe source population and 0% in all others, thenreaching an equilibrium level within 15 to 30 years(Figure 6).

The four larval dispersal patterns used inthe larval dispersal sensitivity analyses includedtwo high river discharge or low salinity years (1984and 2000) and two low river discharge or highsalinity years (1985 and 2001) as calculated byNarváez et al. (2012a). These four dispersalpatterns allowed changes in metapopulationgenetic connectivity to be examined using a rangeof possible larval connectivity patterns. Changes inlarval connectivity failed to produce consistent orlarge changes in metapopulation allele frequency(Figure 3). Here, changes in larval dispersal owing toriver discharge led to populations-specific changes inmetapopulation connectivity (Figure 3 and Figure 5,solid white and black bars). For Population 1,lower river discharge (1985 and 2001) leads to aslight increase in change in metapopulation allelefrequency, while in Population 2 the oppositepattern is observed. Further, in Population 3,the change in allele frequency is higher on lowriver discharge in the 1970s simulations, butlower in the 2000s simulations. In addition, themagnitude of the influence of changing larvaldispersal is small relative to that of the MPA

Figure 4. Simulated oyster abundance in each of the four populations for the duration of each Base Case simulation (100 generations). Upper panelshows abundances for the 1970s simulations; lower panel shows 2000s abundances.



simulations to be discussed subsequently.Maximally, the difference between change inallele frequency owing to varied larval dispersalversus the Base Case change in allele frequencyis 0.10 (seen for the Population 4 allele inLarv1984 – 1970s), whereas the maximumdifference in allele frequency owing to differences inMPA strategy versus the Base Case is three timeshigher at 0.30 (for the Population 3 allele inMPA_High3 – 2000s).

Simulations based on each of the two timeperiods showed distinct differences in the changein allele frequency in the metapopulation overtime for the range of simulated conditions(Figure 5, for completeness, a full time seriesof metapopulation allele frequencies for all MPAsimulations are provided in Supplementary

Materials). For the 1970s simulations, allelesinitially present in Populations 1 and 2 decreasedin frequency in the metapopulation (with theexception of cases MPA_High1 and MPA_High2),while alleles initially present in Populations 3 and4 increased in frequency (excepting MPA_High1,MPA_High2 and MPA_High3, Figure 5). The2000s simulations generally showed the oppositepattern with alleles initially present in Populations1 and 2 mostly increasing in frequency inthe metapopulation, and those in Populations3 and 4 decreasing in frequency. Populationswhose marker allele (B) increased in overallmetapopulation frequency (an allele exportingpopulation) during one period shifted to onewhose marker allele decreased in metapopulationfrequency (an allele importing population) in the

Figure 5. Changes in simulated allele frequency in the metapopulation for the marker allele (the neutral B allele originally fixed in a given population,but absent from the remaining three) for each of the four populations over 100 generations. The metapopulation allele frequency was calculated as thefraction of animals in all four populations possessing a B allele at a given time. Black solid bar indicates the Base Case (no MPA) for each time period.Hatched bars indicate simulations for which the MPA was co-located with the neutral marker allele source (all animals in the population BB initially).



other time period. The change in metapopulationallele frequency for the marker allele for Population4 demonstrates this pattern; the allele frequency forthis allele indicates a shift from an allele exportingpopulation in the 1970s to an allele importingpopulation in the 2000s (bottom right panel,Figure 5).

In all simulations except MPA_Low1 (Table 3,allele marker from Population 1 in 1970), creating aMPA co-located with the source of the allele(hatched bars, Figure 5) leads to an increase in theallele frequency within the metapopulation relativeto the Base Case where no MPA exists (solid blackbars, Figure 5). When one population is designatedas an MPA, increasing fishing pressure outside theprotected population tends to increase the potentialfor neutral alleles from within the protected area toincrease in frequency within the metapopulation

(left to right, hatched bars in Figure 5). With theexception of the marker allele for Populations 3 and4 in 2000, a step-wise increase occurs in the changein allele frequency within the metapopulation fora neutral allele originally present only within theprotected area as fishing pressure outside theprotected area increases. In general, higher fishingpressure around the MPA creates a larger increasein the frequency of alleles from the protectedpopulation within the metapopulation whencompared with the no-MPA Base Case (Figure 7).Averaged over all simulations, 4% and 8% fishingrates generate metapopulation allele frequenciesthat differ by 42% and 53%, respectively, from theBase Case, while 30% fishing generates asignificantly higher difference in allele frequency of119% (ANOVA P=0.003, F=6.3, n=96). Withineach time period and for each allele, the largest

Figure 6. Allele frequency within each population over time for the neutral allele marker in the 1970s (bottom row) and 2000s simulations (top row).Columns from left to right show the influence of fishing rate around the MPA on the frequency of the neutral allele initiated in Population 1 to 4 withthe MPA co-located at that population (left to right). Lines show the allele frequency within each individual population (shown in different shades ofgrey; solid lines indicate the Base Case, dashed lines indicate 4% fishing, dotted lines indicate 30% fishing). The shaded areas show the allele frequencyin the metapopulation, overlaid with the darkest color representing 30% fishing, medium indicating 4% fishing, and lightest indicating the Base Case.



increase in metapopulation allele frequency isobserved for the simulation in which the MPA isco-located with the source of the allele and fishingrates outside the protected area are highest.

Oyster abundance in the metapopulationdecreases as fishing pressure is increased. Thistrend holds for abundance within each individualpopulation regardless of location of the MPA(Figure 8); however, the proportion of the totalmetapopulation within each of the protectedpopulations increases as fishing pressure outside theMPA increases. As the proportional contribution tometapopulation abundance by each populationincreases, the change in metapopulation allelefrequency also increases (Figure 9). This trend issignificant for all simulations grouped by timeperiod and allele source, as illustrated by the trendlines in Figure 9 (see Table 4 for adjusted R2, slopesand P-values for test of slope = 0 for eachregression). Three clusters of points, separatedalong the x-axis, are reflective of three distinctbackground mortality rates (Figure 9). The centralcluster includes all 1970s simulations andPopulations 1 and 2 in the 2000s, all of whichexperience non-fishing mortality of 8–10%.

Populations 3 and 4 in the 2000s experienced highernon-fishing mortality and this attribute, along withdifferences in location-specific carrying capacity(high carrying capacity in population 3, lowcarrying capacity in population 4), led to differencesin the proportion of the overall abundance foundwithin each population and thus generated theobserved separation of these two populations fromthe remainder along the x-axis. Despite theirdiscrepancies in proportional abundance, a positivetrend remains for both wherein as proportionalabundance increases, the change in metapopulationallele frequency increases.

DISCUSSION

The influence of larval dispersal

Larval dispersal in a sessile marine populationis the mechanism for gene transfer amongpopulations. The simulations performed heredemonstrate that changes in larval dispersaldriven by changes in salinity and river flow cangenerate changes in apparent genetic connectivityamong populations; however, these changes weresmall and inconsistent among populations(Figure 3) in agreement with model results ofMunroe et al. (2012). In their examination ofsensitivity of genetic connectivity to changes inlarval dispersal versus other post-settlementpopulation demographics, Munroe et al. (2012)found that even full reversal of larval dispersalpatterns led to small changes in observed geneticconnectivity relative to changes in post-settlementmortality or relative population abundances. Asan example of the influence of changes in larvaldispersal, relative to changes in mortality onconnectivity the change in metapopulation allelefrequency in Population 1 in the 2000s, showed overa 4% change in the amount of larvae beingdispersed out of that population (this is true forLarv_1984 and Base Case), versus a 4% change inmortality in the surrounding populations (this istrue for simulations MPA_Low1 and MPA_Med1).A 4% increase in larval export generates a 2%increase in allele export, while a 4% increase insurrounding mortality generates a 5% increase inallele export. This example shows that changes

Figure 7. Percentage change in allele frequency for the marker alleleover 100 generations for all simulations compared with the Base Case(no MPA), grouped by fishing rate in unprotected populations. Errorbars show the 95% confidence interval. The effect of fishing rate onthe change in allele frequency is significant (ANOVA, P=0.003,F=6.3, n=96). Multiple comparison shows that 30% fishing results ina significantly larger change in allele frequency when compared with4% and 8% fishing (Tukey multiple comparison test, P=0.004; 0.017,

respectively).



in mortality generate approximately twice the changein allele export potential as a comparable change inlarval export. The same comparison between thechange in allele export with an approximate 4%change in larval export, versus a 4% change inmortality in the population surrounding the MPAfor each population in each of the time periods,reveals that on average the change in mortalitygenerates nearly a three times greater change inallele export than a change in larval export.

In the sensitivity analyses performed here,the inconsistency among populations in the effectof changing larval dispersal suggests that thecharacteristics of the local population (such asmortality, growth rate, and abundance) moderatethe influence of changes in larval supply. Theseresults agree with others who note that larvaldispersal may act as genetic connections, but thatthese connections can be interrupted whendispersers fail to reproduce (Bohonak, 1999;

Hedgecock et al., 2007; Pineda et al., 2007). Pinedaet al. (2007) argue for consideration of ‘reproductivepopulation connectivity’ such that populationconnectivity by definition must include larvaldispersal, but should not fail to include other factorsthat influence post-recruitment survival and survivalto reproduction. The current sensitivity analyses oflarval dispersal (Figure 3) suggest that this conceptapplies for population genetic connectivity and thatdifferential adult mortality rates among populationsmay be of importance, at least in sessile marinespecies like oysters.

The influence of mortality gradient

In sessile species such as oysters, connectivity amongpopulations operates through larval dispersal, aprocess that is also the mechanism for geneticconnectivity. Conservation and management goalsthat underlie MPA strategies can be driven by

Figure 8. Average oyster abundance for each of the four populations over generations 20–100. Black solid bar indicates the Base Case (no MPA) foreach time period. Hatched bars indicate simulations for which the population was located within the MPA.



concerns about genetic diversity (Pérez-Ruzafa et al.,2006; Arrieta et al., 2010) which rely on connectivityof the MPA with adjacent, unprotected populations(Laurel and Bradbury, 2006; Christie et al., 2010;Miethe et al., 2010). Thus the way that MPAs andfishing practices interact with the overall geneticconnectivity of the protected and unprotectedpopulations should be critical to decisions aboutMPA location and extent. Previous modelling

studies demonstrated the increased importance ofdemographic factors, such as population abundanceand mortality relative to larval dispersal, indetermining the degree of genetic connectivity withina metapopulation (Figueira, 2009; Weesing andToonen, 2009; Munroe et al., 2012). By preventingfishing mortality and allowing exploitation alongthe borders of a population, as is the case when anMPA is created, a manager is effectively altering therelative demographics of adjacent populations andthereby potentially influencing genetic connectivity.

Results of the simulations described here showthat mortality gradients created or modified by theestablishment of an MPA can exert a determiningrole in resultant population connectivity. As thegradient in mortality between the protectedpopulation and adjacent populations increases, sodoes the potential for export of alleles from theprotected population to other populations (Munroeet al., 2012). Ultimately, this gradient can be adeterminant of source and sink dynamics forgenotypes within the metapopulation. This meansthat when exploitation rates are higher outsidereserves, changes to the genetic connectivity in themetapopulation relative to the non-reserve scenariowill be greater.

The influence of the mortality gradient on geneticconnectivity becomes an important aspect ofplanning reserve placement, particularly when‘fishery squeeze’ is considered. Fishery squeeze isthe transfer of fishing effort that previously existedin the MPA to adjacent areas so that overallfishing effort across the entire metapopulationremains constant, in effect causing the fishingefforts in areas around the MPA to increase tocompensate for loss of fishing effort within the

Table 4. Statistical results from linear regressions between the change in metapopulation allele frequency and the percentage of metapopulationabundance contributed by the source population (data and trend lines shown in Figure 9). Simulation results for regressions were grouped by timeperiod and allele source; n=13 for each test

Allele marker Time period Intercept Slope P-value (H0 slope=0) Adjusted R2

Population 1 1970 -1.35 5.23 0.00001 0.86Population 2 1970 -0.78 3.07 0.0001 0.68Population 3 1970 -1.41 5.23 0.001 0.76Population 4 1970 -0.73 3.31 0.0001 0.83Population 1 2000 -1.73 6.85 0.001 0.76Population 2 2000 -2.03 7.93 0.001 0.71Population 3 2000 -2.71 7.03 0.0001 0.76Population 4 2000 -0.16 1.03 0.02 0.13

Figure 9. For each simulation, average proportional metapopulationabundance is plotted for each population versus the change inmetapopulation allele frequency for the allele marker from thatpopulation. Years 20 to 100 of the simulation were used forcalculation of average abundance to avoid inclusion of biased valuesowing to model start-up effects. Solid symbols show 1970s simulationresults; open symbols show 2000s simulation results. Circle symbolsindicate cases in which the allele marker (all animals BB) was initiallypresent in Population 1; square symbols indicate cases in which theallele marker was initially present in Population 2; diamond symbolsindicate cases in which the allele marker was initially present inPopulation 3; triangle symbols indicate cases in which the allelemarker was initially present in Population 4. Trend lines showsimulation results for regressions grouped by time period and allelesource. Results of linear regression tests for significance of trend line

slopes are presented in Table 4.



MPA (Pelc et al., 2010). Fishery squeeze increasesthe mortality gradient. Likewise, fishing mortalitytends to increase around reserve boundaries formobile species because of increased fishery resourcesspilling out from the reserve (Wilcox and Pomeroy,2003; Murawski et al., 2005), again effectivelyincreasing the mortality gradient between protectedand unprotected populations and thereby creatinga greater potential change in genetic connectivity.The influence of an increased mortality gradient isdemonstrated in the simulations presented here bycomparing simulations in which the fishing pressureoutside the MPA increases from 4% to 8%(Figure 3). In all cases, except locating an MPA atPopulation 3 in the 2000s, increasing the fisherypressure around the MPA from 4% to 8% (sensufishery squeeze) increases the overall change in alleleexport from the MPA to the metapopulation(Figure 5 hatched bars). This trend is amplifiedwhen increasing fishing pressure around the MPA to30%, the highest fishing pressure tested (Figure 5hatched bars). Thus, higher fishing pressure inpopulations genetically connected to an MPA willinduce an MPA to act as a stronger genetic exporterpopulation within the metapopulation.

Other empirical (Murawski et al., 2000;Pérez-Ruzafa et al., 2006; Barrett et al., 2007;Botsford et al., 2009) and modelling studies(Gerber et al., 2003; Baskett et al., 2005) foundthat fishing pressure can influence the observedefficacy of protected areas on fish stocks. In thesimulations in the current study, higher fishingrates, that create the largest gradient in mortalityaround the MPA, generated the largest differencein the final allele frequencies within themetapopulation compared with the no-MPA case(Figures 5 and 7). In some simulations, thisdifference was substantial, potentially reflectingimportant shifts in population connectivity. Asan example, in some scenarios, high fishing ratescan transform an allele importing population(Base Case) into an allele exporting population(Population 1 in 1970, Populations 2/3 in 2000).These simulations were specified for oysterpopulations in Delaware Bay, yet this result isplausible more generally considering connectivityin other species and systems. By creating a reservethat has elevated fishing rates around its border, a

potentially large differential in mortality iscreated between nearby and connected populationsthat can lead to reduced reproductive output inthe unprotected population and a greater overallreproductive contribution from the protectedpopulation into the unprotected population.This outcome has important implications formanagers given that the choice of MPAlocation and the fishing rates allowed inbordering areas can drastically alter connectivitywithin the metapopulation.

The influence of population abundance anddemographic shifts

Many studies concerning MPAs focus on the sizeand spatial orientation of the MPA (Crowderet al., 2000; Palumbi, 2003; Kaplan and Botsford,2005). Although this was not explicitly consideredin the current simulations, the various abundancesof the protected populations can be considered asa surrogate for MPA size, at least in the case ofDelaware Bay, because larval dispersal distancesspan the bay and thus MPA size cannot exceed theability of larvae to disperse among populations.Others (Murawski et al., 2000; Fogarty andBotsford, 2007) have predicted that the relativeimpact of an MPA will depend on the fraction ofthe population protected. The simulations in thisstudy support this prediction by demonstratingthat protecting a population tends to increase thepopulation’s proportion of the overall abundance inthe metapopulation (Figure 8), and that increasedproportion correlates significantly with increasedgenetic output of that population (Table 4). Thus,as more of a metapopulation is protected, the MPAwill have a greater genetic contribution to theremaining unprotected populations.

The nearly opposite allele transfer responseobserved in the simulations of the 1970s comparedwith the 2000s (Figure 5) illustrates that populationand genetic connectivity are dynamic and maychange fundamentally between time periods. Regimeshifts are of recognized importance in marinepopulations (Collie et al., 2004; Rothschild andShannon, 2004) and the demographic shift observedfor oysters in Delaware Bay c. 1985 represents acomparable sudden shift in the population



characteristics of the biological community (Powellet al., 2009a, b). In this case, the 1970s were a periodof high abundance separated from today by anabundance decline in about 1985/1986 by about afactor of five. The model suggests that themechanisms of gene flow and genotype retentionamong the Delaware Bay oyster populations weresubstantively changed by this event. Althoughplanning for changes in MPA functioning over timehas received less attention than spatial planning(Game et al., 2009), the simulations suggest thattemporal variability should be an importantconsideration in reserve location as it has potentialconsequences for the efficacy of MPA functioning inthe metapopulation and allele transfer amongpopulations. Placement of a reserve relative tosources and sinks is an important consideration inMPA location (Crowder et al., 2000). Thesimulations showed differences in allele source andsink characteristics during these two time periods,thereby demonstrating that demographic shifts canalter locations of allele sources and sinks (see alsoMunroe et al., 2012) and thus change the intendedinfluence of an MPA over time, particularly in caseswhere the MPA was planned for protection ofspecific genotypes. The potential for such changes inmetapopulation connectivity should be integratedinto MPA planning, and adaptive strategies shouldbe implemented allowing managers to respond tosuch changes. A temporally dynamic (or adaptivelymanaged) MPA that allows changes in MPAlocation over time is intuitive for mobile resourceslike migratory fish that are likely to change locationover time (Game et al., 2009), but less obvious is theneed for dynamic approaches for sessile species.

In general, these results have two importantimplications for MPA planning and management.Fisheries and demographic shifts influence geneticconnectivity and consequently alter the way thatthe MPA is connected with the unprotectedportion of the metapopulation. The potential forMPAs to significantly influence genetic diversity inthe metapopulation is strongly modulated by themortality gradient, and this gradient is determinedin large measure by the fishing mortality rate inthe unprotected portion of the metapopulation andby the differential in population dynamicsgenerated by demographic shifts. This study has

examined MPAs and genetic connectivity in asingle species only; future consideration ofmultispecies metacommunity dynamics will beimportant (Guichard et al., 2004; Gaines et al.,2007; Hamilton et al., 2010). Nonetheless, theresults suggest that adaptive, temporally dynamicMPA management strategies fully integrated withfisheries management (Gaines et al., 2010) arecritical for planning marine reserves.

The specific case of oyster sanctuaries (MPAs)

Creation of sanctuaries or rehabilitation of oysterbottom permanently closed to fishing is commonlyused in management and restoration of easternoyster populations (Mann, 2000; Paynter et al.,2010). These efforts have most often taken placein the lower-salinity (<12 ppt) portion of the oyster’srange (CBP, Chesapeake Bay Program, 2005;Paynter et al., 2010) because low-salinitypopulations experience higher juvenile and adultsurvival owing to lower predation rates and diseasepressure, from both dominant oyster diseases, MSXand Dermo (Bushek et al., 2012). The simulationspose a possible concern with this strategy. Thesetwo diseases generate an up-estuary–down-estuarymortality gradient (Ford et al., 1999; Bushek et al.,2012), which based on these simulations, underpresent conditions generates a down-bay drift ofgenes from up-bay source populations (see alsoMunroe et al., 2012). Ford et al. (2012) show thatthese up-bay populations function as refuges fordisease-susceptible genotypes. Thus, we hypothesize,based on these simulations that the mortalitygradient generated by disease not only protectssusceptible genotypes up-bay, but also facilitatestheir continual importation into down-baypopulations, thereby restricting the development ofresistance to disease. Creation of MPAs in theselow-salinity areas under present conditions wouldamplify this mortality gradient. An important caveatto consider is that selection does not play a role indetermining the simulated allele frequencies in thesimulations that simulated only neutral alleles. Highdisease pressure on recruits to down-bay populationsshould alter realized genotypes and promoteretention of alleles conferring disease resistance; thusfurther study is necessary to determine the role of



population dynamics in genetic connectivityconcurrent with selection pressure (Sotka andPalumbi, 2006; Galindo et al., 2010). However,both alleles conferring susceptibility and resistanceto disease may operate as neutral alleles oversignificant portions of the estuarine salinity gradientwhere disease mortality and thus selection pressurewould be inconsequential, so that following neutralalleles in this study is consistent with anticipatedconnectivity dynamics. Moreover, the observed ratesof development of disease resistance are consistentwith the premise that down-bay populations areallele importers, not exporters of alleles.

Although no formal sanctuaries are in placecurrently in Delaware Bay, short-term restrictedharvest management areas have been suggested(Kreeger et al., 2011). These simulations showthat protecting up-bay populations with an MPAcould act to counter selection for disease-resistantgenotypes down-bay. This would exacerbate theexisting situation in which the low-mortalityup-bay populations continually export non-resistantoffspring. Successful establishment of oystersanctuaries have been described (Powers, et al., 2009;Schulte et al., 2009; Kennedy et al., 2011), althoughtheir influence on metapopulation genetics remainsunproven. Notwithstanding these encouragingobservations, we suggest that the sanctuary approachimplemented as the restoration of low-mortalityoyster populations in the upper (lower salinity)reaches of estuaries (CBP, 2005) could be acounter-productive approach if the MPA is tofacilitate the long-term adaptation of the oyster todisease. Rather, such siting is likely to supportcontinued high mortality rates down-bay bycircumventing the process of selection for diseaseresistance that can only occur down-bay (Carnegieand Burreson, 2011).

Spatial location of a MPA within a heterogeneousmetapopulation and its influence upon geneticconnectivity (Munroe et al., 2012) is importantboth for restoration of oysters as key ecosystemcomponents and also for long-term sustainability ofoyster (Lipcius et al., 2008) and other reef-dependentfishery resources. These simulations should inspire are-evaluation of restoration strategy, taking intoaccount the probable interaction of the MPA, themortality gradient, and genetic connectivity with the

process of the development of disease resistance(Hofmann et al., 2009). The question of how creationof an MPA within a heterogeneous metapopulationwill influence genetic connectivity is an important one,not only for the restoration of oysters as a keystoneestuarine species, but also for many other marineapplications where the potential for spatial geneticvariability in a resource exists and where the creationof an MPA may generate changes.

ACKNOWLEDGEMENTS

Sincere thanks to all Haskin Shellfish ResearchLaboratory staff, staff of the New JerseyDepartment of Environmental Protection, and theDelaware Bay oystermen who work co-operativelyeach year to provide reliable annual oysterpopulation estimates in Delaware Bay. K.Ashton-Alcox provided historical oyster dataand D. Narváez provided critical insight onlarval dynamics. Funding was provided by theNational Science Foundation (OCE-6022642 andOCE-0622672) and by the Army Corps ofEngineers under their Section 22 fundingauthority, contract #W912BU-11-C-0004, throughthe Seaboard Fisheries Institute, in collaborationwith the Sponsor, the South Jersey PortCorporation, a public agency of the State of NewJersey. Review by three anonymous reviewersprovided valuable improvement to this manuscript.

SUPPORTING INFORMATION

Supporting information can be found in the onlineversion of this article.

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