MARINE ECOLOGY PROGRESS SERIESMar Ecol Prog Ser

Vol. 253: 25–38, 2003 Published May 15

INTRODUCTION

Marine protected areas are riding the wave ofocean governance reform (Kelleher 1997, Allison etal. 1998, Lauck et al. 1998, Boersma & Parrish 1999,Carleton Ray 1999, Costanza et al. 1999, Agardi 2000,Roberts et al. 2003). In an environment that has tradi-tionally been regarded as open-access commons,resource allocation via formalised and integratedmanagement arrangements is emerging as a keycomponent for the ecologically sustainable use ofmarine resources. Where such reform processes aregrounded in space, whether it be in delineating areassuitable for aquaculture or areas of high conservation

value, a decision theory framework and mathematicalalgorithms can provide repeatable, systematic andefficient solutions to spatial allocation problems(Possingham et al. 2001).

The application of mathematical algorithms to con-servation planning and reserve system design is wellknown in terrestrial systems (Cocks & Baird 1989,Pressey & Nicholls 1989, Pressey et al. 1995, Margules& Pressey 2000, Possingham et al. 2000). These meth-ods provide decision support for efficient reserve sys-tem design through their capacity to include ecologi-cal, spatial and socio-economic information and theirflexibility to respond quickly to a variety of treat-ments and scenarios set by planners, stakeholders and

© Inter-Research 2003 · www.int-res.com*Email: rstewart@zen.uq.edu.au

Opportunity cost of ad hoc marine reserve designdecisions: an example from South Australia

R. R. Stewart1,*, T. Noyce2, H. P. Possingham1

1The Ecology Centre, Department of Zoology and Entomology, The University of Queensland, St Lucia, Queensland 4072, Australia2Environment and Geographic Information Division, Department for Environment and Heritage, PO Box 550, Marleston,

South Australia 5033, Australia

ABSTRACT: Like many states and territories, South Australia has a legacy of marine reserves con-sidered to be inadequate to meet current conservation objectives. In this paper we configuredexploratory marine reserve systems, using the software MARXAN, to examine how efficiently SouthAustralia’s existing marine reserves contribute to quantitative biodiversity conservation targets. Ouraim was to compare marine reserve systems that retain South Australia’s existing marine reserveswith reserve systems that are free to either ignore or incorporate them. We devised a new interpreta-tion of irreplaceability to identify planning units selected more than could be expected from chancealone. This is measured by comparing the observed selection frequency for an individual planningunit with a predicted selection frequency distribution. Knowing which sites make a valuable contri-bution to efficient marine reserve system design allows us to determine how well South Australia’sexisting reserves contribute to reservation goals when representation targets are set at 5, 10, 15, 20,30 and 50% of conservation features. Existing marine reserves that fail to contribute to efficientmarine reserve systems constitute ‘opportunity costs’. We found that despite spanning less than 4%of South Australian state waters, locking in the existing ad hoc marine reserves presented consider-able opportunity costs. Even with representation targets set at 50%, more than half of South Aus-tralia’s existing marine reserves were selected randomly or less in efficient marine reserve systems.Hence, ad hoc marine reserve systems are likely to be inefficient and may compromise effective con-servation of marine biodiversity.

KEY WORDS: Marine reserves · Reserve selection · Efficiency · Decision theory · Representationtargets · Irreplaceability · Biodiversity conservation · South Australia

Resale or republication not permitted without written consent of the publisher

Mar Ecol Prog Ser 253: 25–38, 2003

the community (Margules & Nicholls 1988, Pressey &Nicholls 1989, Vane-Wright et al. 1991, Nicholls &Margules 1993, Williams et al. 1996, Rodrigues et al.1999, Pressey & Cowling 2001, McDonnell et al. 2002).Reserve selection algorithms hold significant potentialas tools for systematic marine reserve design and areespecially suited for the design of reserve systems(rather than constructing single reserves), where theinformation required on any one site needs to incorpo-rate spatially explicit data and concepts such as adja-cent land and sea uses, boundary lengths, connectivityand minimum reserve size. Yet it is only recentlythat reserve selection algorithms have been applied tomarine reserve planning (Vanderklift et al. 1998, Wardet al. 1999, Ardron et al. 2001, Beck & Odaya 2001,Leslie et al. 2003).

In this paper, we extend on the work of Ward et al.(1999) and Leslie et al. (2003) to apply mathematicalmethods for the design of marine reserve systems withdifferent representation targets. In doing so, we ex-amine how well an existing marine reserve systemachieves conservation goals. For our case study, weconsidered the state waters of South Australia, locatedon the southern temperate coast of Australia and fea-turing some of the highest levels of marine biodiversityand endemism in Australia and the world (Edyvane1999a). Creswell & Thomas (1997) identified 15 marineprotected areas (reserves) in South Australia amount-ing to almost 60 000 ha. This figure represents 0.9% ofSouth Australian state waters and <0.2% of Australia’snational total. More recently, the establishment of theGreat Australian Bight Marine Park has increasedthe percentage of state waters protected in marinereserves to 3.1% (Edyvane 1999a).

Where marine reserve selection has occurred inSouth Australia, it has largely been ad hoc, ratherthan the outcome of a state-wide strategy to representmarine biodiversity, and is thought to be inadequate tomeet current conservation objectives (Government ofSouth Australia 1998, ANZECC 1999). Recognizing theshortfall of the existing marine reserve system, theSouth Australian government recently announcedits intention to establish a system of marine reservesaround the state (Government of South Australia2001). We propose 2 planning scenarios for the designand selection of marine reserves in South Australia.The first scenario is free to either ignore or incorporatethe existing marine reserves, whilst the second sce-nario must retain South Australia’s existing marinereserve system.

As good reserve design should be based on estab-lished principles of conservation planning, we beginwith a summary of key reserve planning principles. Wethen formulate the problem of reserve system design inmathematical terms and briefly introduce MARXAN

(Ball & Possingham 20001), an optimisation algorithmfor marine reserve system selection. Our aim was tocompare the performance of alternative marine re-serve systems for the 2 planning scenarios when repre-sentation targets were set at 5, 10, 15, 20, 30 and 50%of conservation features. Because MARXAN can be setto ensure that all conservation targets will be met, weconsider a range of measures that describe the perfor-mance of alternative reserve system solutions in termsthat reflect secondary goals that the design processis attempting to capture. Pressey & Nicholls (1989) de-scribed efficiency as one such measure. It recognisesthat the area available for reservation is often limitedand so highly efficient marine reserve systems aremore desirable. We evaluated the best marine re-serve systems identified using measures of efficiency(Pressey & Nicholls 1989), compactness (Possingham etal. 2000) and counts of the number of spatially sepa-rated reserves.

Extending on the work of Pressey et al. (1994), Fer-rier et al. (2000) and Ball & Possingham (2001), wedevised a way of interpreting summed irreplaceabilityto assess the relative contribution of an individual siteto reserve system goals. This approach uses selectionfrequency counts to determine whether sites wereincluded in the reserve system more or less than bychance alone. We introduce the notion of ‘opportunitycost’ to quantify the inefficiencies of the existing sys-tem, defined as that portion of the existing marinereserves that would be selected by random or less, inthe design of an efficient marine reserve system.

Reserve planning principles

We present some general reserve design principlesderived from existing scientific theory and expert opin-ion. These principles serve to inform the reserve selec-tion process by providing a clear statement about whatreserve systems aim to achieve. They aim to circum-vent the practice of creating ineffective and inefficientmarine reserves that ultimately displaces the designa-tion of more representative areas as competition forspace becomes more intense (Pressey & Nicholls 1989,Pressey et al. 1993, Pressey & Tully 1994, Ballantine1997, Hockey & Branch 1997, Roberts et al. 2003).

Complementarity. Sites complement each other wellif the species or habitats they contain are quite differ-ent, so their selection provides a combination of sitesthat together achieve the ultimate goal of comprehen-siveness in the most efficient way. Consequently, plan-

26

1Ball IR, Possingham HP (2000) MARXAN v1.2: mari-ne reserve design using spatially explicit annealing. www.ecology.uq.edu.au/marxan.htm

Stewart et al.: Opportunity cost of ad hoc marine reserves

ning for marine reserve systems should be informed bywhat is already contained within the existing reserves(Kirkpatrick 1983, Vane-Wright et al. 1991, Pressey etal. 1993). This means that the conservation value of anarea is dynamic and will change as the reserve systemis established (Margules & Pressey 2000).

Comprehensiveness and representativeness. Thenotion of comprehensiveness implies sampling the fullrange of biodiversity within the marine reserve system.It should take into consideration biodiversity composi-tion (species and genetic diversity), structure (physicalorganisation, e.g. habitat, patches) and function (eco-logical and evolutionary processes, e.g. reproduction,recruitment), providing for shifts in habitat preferencesof marine species at different life stages (Noss 1990).Sampling should target both typical and atypical ex-amples of the biodiversity features they are intended torepresent.

Adequacy and self-sustainability. Persistence andlong-term viability rely upon the degree of connectiv-ity between reserves which, to borrow from the con-cepts of metapopulation dynamics, is a function ofpatch size, patch quality, recruitment, mortality anddispersal (e.g. local oceanography) (Crowder et al.2000). Typically, systems of marine reserves should beconfigured so that sites interact in a positive fashion.Reserves located at source populations will ideallyretain sufficient recruits to sustain local populations,with surplus larvae exported to other areas. Reserveslocated at sink populations are likely to depend uponreplenishment from elsewhere, thereby diminishingprospects for long-term viability (Pulliam & Danielson1991, Roberts 1998).

Systems, replication and spatial cohesion. Becausedifferent ecological processes operate across severalscales, marine populations are best supported by well-designed reserve systems in which the whole is morethan the sum of its parts. A systems-based approach isconsidered superior to the creation of isolated indi-vidual reserves for it can provide meaningful spatialrelationships amongst sites for the maintenance of eco-system linkages and connectivity, as well as offsettingeffects from localized catastrophes (Ballantine 1997,Allison et al. 2003, Roberts et al. 2003). Furthermore, itis generally desirable for reserves to be both compactand contiguous, with some level of replication (Pressey& Nicholls 1989, Ballantine 1997, Possingham et al.2000).

Flexibility. The principle of flexibility arises from thenon-unique occurrences of many biodiversity features.This has important implications for selection proce-dures for it means that conservation goals can bemet in different ways. The more systems that can beappraised, the more likely the planner will find onewhich not only satisfies the conservation objectives,

but also contributes to secondary goals such as designand costs. Having a variety of possible configurationsgives scope for sensible resolutions of resource useconflicts (Kelleher & Kenchington 1992). Equally im-portant is the implication that we lose flexibility whensites with unique features are lost.

METHODS

The reserve system design problem

Reserve selection algorithms differ from scoringmethods in how they define the reserve selection prob-lem. At the core of the problem is the goal of minimis-ing the area of the reserve system whilst meeting con-servation targets (Bedward et al. 1991, Pressey et al.1993, 1997, Leslie et al. 2003). This was first describedby Kirkpatrick (1983) as the minimum representationproblem. It is derived from the idea that while biodi-versity conservation objectives may wish to maximisethe area within the reserve system, they must competeagainst social, economic and management constraints(Possingham et al. 2000). This is formulated by McDon-nell et al. (2002) as a mathematical programming prob-lem:

Minimise the objective function

(1)

subject to the constraints:

for all j = 1...N, (2)

xi ∈ {0,1} for all i = 1...M,

where xi is the control variable such that if xi = 1 thensite i is selected in the reserve system, and if xi = 0then site i is not in the reserve system. The parameterci is the ‘cost’ of site i, li is the perimeter or boundarylength of site i, bik is the common boundary length ofsites i and k, and BLM is a boundary length modifierthat converts the reserve system cost and its boundarylength into a common currency. The parameter N is thenumber of conservation features and M is the numberof sites.

Eq. (1) is the objective function which seeks to mini-mise a linear combination of site costs and reserve sys-tem boundary length. Eq. (2) is the set of constraintsthat ensure the target for each conservation feature ismet, where aij is the abundance of feature type j in sitei and tj sets the target fraction for each feature type.The target fraction can be modified to suit differentscenarios (for example we may wish to have higherrepresentation of certain habitat types). Our objectiveis to identify a number of reasonably good marine

a x t aiji

M

i j iji

M

= =∑ ∑>

1 1

c x x l x x bi

i

M

i ii

M

i ii

M

kk

M

ik= = = =∑ ∑ ∑ ∑+ −

1 1 1 1

0 5BLM .

27

Mar Ecol Prog Ser 253: 25–38, 2003

reserve systems that come close to minimising theobjective function. A feasible solution is one that con-tains a set of sites (depicted by the control variable xi)such that all the constraints are met.

The reserve system design algorithm, MARXAN.Because our reserve design problem is large (with 23119

possible solutions), it is impossible to find an optimalsolution in reasonable time. We used the reserve designsoftware MARXAN, created by Ball & Possingham(20001) for systematic marine reserve design. MARXANuses a variety of optimisation methods to produce solu-tions to the reserve system design problem. We usedthe simulated annealing feature which consistentlyout-performs simpler iterative or heuristic algorithms,such as the greedy and rarity-based selection algo-rithms, particularly for complex spatial problems(Pressey & Nicholls 1989, Pressey et al. 1997, Ball 2000,McDonnell et al. 2002). The method is fast, allowingone to find a number of good solutions to the problem.Each solution is scored against the objective function(Eq. 1) according to how well it meets the prescribedtargets and design constraints.

Spatial design requirements can be incorporatedinto the method in several ways. We use the boundarylength modifier (BLM), which determines the relativeimportance placed on minimising the boundary lengthrelative to minimising cost. Leslie et al. (2003) foundthis was an effective way to exert control over the spa-tial arrangement of a marine reserve network, as in-creasing the BLM gives preference to the inclusion ofsites which minimise the overall perimeter, therebyclustering the sites and generating a well connectedreserve system.

Planning units. The study area was defined by the3 nautical mile legal limits to South Australian statewaters. This area was divided into 3119 planning units(sites), with each planning unit forming a 5 × 5 km cell.Due to the study area’s irregular shape, a number ofplanning units were truncated at the coastline and off-shore islands, providing some variation in planningunit size (area) and boundary length. Information onthe amount of conservation features contained withineach planning unit was compiled.

Conservation features and feature targets. Regard-less of the reserve selection methodology, fulfilmentof biodiversity conservation objectives through in situconservation is largely premised on the representationof biodiversity surrogates at appropriate levels; yet theinformation available for marine planning is limited,with data coverage often biased towards special inter-est sites (such as existing marine reserves) or charis-matic species. We selected datasets that provided con-sistent quality and coverage across the state waters ofSouth Australia. Data was sourced from state govern-ment agencies, with the chosen conservation features

derived from 6 biophysical data layers (Appendix 1).The amount of each conservation feature j, in eachplanning unit i, formed the basic data matrix aij. Thisequated to 102 conservation features distributed across3119 sites, producing approximately 17 000 records.The distribution of conservation features across theplanning units is shown in Fig. 1. For this study, repre-sentation targets t, were set at values between 5, 10,15, 20, 30 and 50% of the total amount of each conser-vation feature type.

Marine reserve planning scenarios

We formulated the reserve design problem for 2alternative planning scenarios: the ‘No Reserves’ sce-nario and the ‘Reserves Fixed’ scenario.

The No Reserves scenario follows the problem de-fined by Eqs. (1) & (2). Ignoring the status of South Aus-tralia’s existing marine reserves, our control variable xi

can assume a value of either 0 or 1 for all 3119 planningunits. If xi = 1, then that planning unit forms part of thereserve system, and if xi = 0, then planning unit i is ex-cluded from the reserve system. We set the cost vari-able ci to 1, which means that every planning unit hasequal cost. The parameter li is the boundary length inkilometres of planning unit i, and bik is the commonboundary length in kilometres of planning units i and k.This serves to minimise the cost of planning units thatshare a common boundary. The boundary length modi-fier (BLM) was fixed at 0.5 to minimise fragmentationand to produce both reasonably compact and efficientreserve systems (Stewart & Possingham in press).Lastly, the abundance of the conservation feature type jin planning unit i is depicted by the variable aij.

The Reserves Fixed scenario generates marine reservesystems using South Australia’s existing marine reserves

28

Fig. 1. Distribution of conservation features for the South Aus-tralian dataset. No. of conservation features per planningunit. A full description of features is provided in Appendix 1

Stewart et al.: Opportunity cost of ad hoc marine reserves

as the seed for all solutions. Constructing the bestreserve system we can with the existing system locked inand comparing this with the best reserve systems of theNo Reserves scenario allows us to evaluate the perfor-mance of South Australia’s existing marine reserves. Weadopt the matrix defined for the No Reserves scenariowith minor amendment, since fewer planning units arenow available for selection due to the existing reservesbeing locked in. We revisit Eqs. (1) & (2) to formulate theproblem as before but with xi = 1 for all planning unitscontained within an existing marine reserve. The controlvariable xi now assumes a value of either 0 or 1 for only2831 planning units. The marine reserve system designproblem is to make additions to the 288 sites that containSouth Australia’s existing marine reserves until conser-vation targets are met.

Evaluating reserve system performance. Marine re-serve systems were generated to evaluate the effect of 2variable factors: the planning scenario (No Reserves andReserves Fixed) and the representation target (set at 5,10, 15, 20, 30, and 50%). This presents 12 different plan-ning problems, with 1000 independent runs of the algo-rithm performed for each. With many alternative reservesystems generated, our task was to identify the combi-nation of planning units that made up the ‘best’ marinereserve system. As we also wished to consider the con-tribution of individual planning units to the reserve sys-tem goals, we were interested in the number of times aplanning unit was chosen out of the total number of runs,otherwise referred to as ‘summed irreplaceability’ (Ball& Possingham 2001, Leslie et al. 2003).

Performance measures for the best marine reservesystems: We examined the performance of marine re-serve systems using the summary data generated byMARXAN. Summary data included measures of the ob-jective function score, the number of planning units inthe marine reserve system and the overall boundarylength. The best marine reserve system was identified asthe solution with the lowest objective function score,then mapped in a geographical information system (GIS)to calculate the total area, boundary length (perimeter)and number of individual reserves in the reserve sys-tem. Individual reserves were identified as a cluster ofadjacent planning units that share a common boundary.This process was repeated for the best marine reservesystem generated for each planning problem.

Because the solution with the lowest objective func-tion may not always be the most desirable (or practical)solution, we sought additional measures to evaluatealternative marine reserve system configurations andquantify trade-offs that may arise when different con-straints are considered. We used Pressey’s measure ofefficiency (Pressey & Nicholls 1989) to describe howefficiently representation targets are met. It variesfrom 0 to 1, with 1 being the most efficient solution.

E = 1 – X/T (3)

where E is efficiency, X is the number of planningunits needed to meet the constraints, and T is the totalnumber of planning units. As we were also interestedin the spatial configuration of the reserve system, weused compactness to assess the degree of spatial clus-tering in the reserve systems. This is defined in Eq. (4)as the ratio between the perimeter of the marinereserve system and the circumference of a circle of thesame area (the theoretical minimum) (Possingham etal. 2000):

(4)

Values approaching 1 resemble the shape of a circleand are highly clustered. In calculating both compact-ness and efficiency, we used a measure for boundarylength derived through GIS processing.

Summed irreplaceability: Irreplaceability was firstdescribed by Pressey et al. (1993, 1994) as the likeli-hood of a site being required as part of a represen-tative conservation system (Ferrier et al. 2000). Itprovides a measure of the contribution of individualplanning units to the marine reserve system goals byindicating how frequently a site is amongst solutionsthat meet all biodiversity targets. Summed irreplace-ability is slightly different from irreplaceability and isreported as the frequency of all the reserve systems towhich a planning unit belongs out of the total numberof systems generated (Ball & Possingham 2001). Be-cause the selection of planning units incorporates aspatial configuration component, Ball & Possingham(2001) explain how a planning unit might appear fre-quently, partly because of what it contains but alsobecause of its spatial relationship with other sites.Here, we used selection frequency probability distrib-utions to devise a new technique that allows one toassign meaningful measures of summed irreplace-ability to individual planning units. This enabled us tocompare planning units that perform no better thanrandom (or, as Pressey & Tulley [1994] described, aread hoc) with those that are selected more often thancould be expected from chance alone.

We compared selection frequencies for planning unitsin a given planning problem with selection frequenciesfor a randomly sampled population of given combina-tion size. The combination size was taken as the aver-age number of planning units contained in a marinereserve system for a given planning problem, calcu-lated from 1000 repeat runs. The probability that aplanning unit could be expected to be selected bychance alone was defined according to the formula:

P = (C – R)�(T – R) (5)

RatioBoundary length

2 Area=

×π

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Mar Ecol Prog Ser 253: 25–38, 2003

where C is the combination size and R is the number ofplanning units not available for selection, which underthe Reserves Fixed scenario was the number of plan-ning units that make up South Australia’s existingmarine reserves. The parameter T is the total numberof planning units (n = 3119). Predicted selection fre-quency distributions were then derived and Tukey’s95% confidence interval determined. By comparingthe selection frequencies observed for the planningscenarios with the predicted selection frequenciesderived from our parameters, we could assert that aplanning unit selected more often than the upper 95%confidence limit of the predicted selection frequencywas included in the reserve system more than could beexpected from chance alone and could thus be con-sidered to be irreplaceable.

Opportunity costs: We applied this new interpreta-tion of summed irreplaceability to evaluate the contri-bution of South Australia’s existing marine reserves toreservation goals and objectives. Knowing which plan-ning units are irreplaceable means we can also identifyplanning units that are ‘replaceable’ (i.e. selected nomore than could be expected by chance alone). Ouraim was to determine the inefficiencies (opportunitycost) of South Australia’s existing marine reserves byidentifying the portion of replaceable planning unit’sthat make up South Australia’s existing marine reserves.

Because the existing marine reserves are locked into every marine system in the Reserves Fixed scenario,these planning units are reported as 100% irreplace-able. However, this result is purely a construct of theconstraints of the reserve design problem and doesn’ttell us much about the planning unit’s contribution toan efficient and representative marine reserve system.One way of examining the contribution of fixed plan-ning units to the reservation goals is to generate multi-ple solutions, as though the existing reserve systemdoesn’t exist. These are the conditions of the NoReserves scenario. Our analysis examined the subsetof planning units representing South Australia’smarine reserves and recorded the number of timesunits appeared in marine reserve systems generatedunder the No Reserves scenario. Planning unitsselected less than or equal to what would be expectedfrom random sampling were identified using the rule:

If Xr ≤ x0.975, then planning unit r is replaceable (6)where Xr ∈ {0…1000} for all existing reserves r = 1...288

where Xr is the observed selection frequency for plan-ning unit r in the No Reserves scenario and r is thesubset of planning units locked in under the ReservesFixed scenario. F(x) = 0.975 where x is the predictedselection frequency at the upper 95% confidence limit.The opportunity cost can be reported for the existingreserve system in its entirety, or at the level of individ-

ual reserves. In either case, it is the area of the subsetof planning units found to be replaceable divided bythe total area of the existing reserves. Areas were cal-culated using the area of the existing marine reservesdataset, where this varied from the area encompassedby the planning units.

RESULTS AND DISCUSSION

Alternative marine reserve system solutions forSouth Australia

Examples of the best marine reserve systems for dif-ferent planning problems are shown in Fig. 2. Here,we illustrate the requirement for much larger reservesystems as representation targets are increased from5% (Fig. 2A) to 50% (Fig. 2B). Included also are thealternative marine reserve systems for the No Reservesand Reserves Fixed scenarios. The effect of the plan-ning scenario on marine reserve system solutions atequivalent representation targets is not obvious fromFig. 2, so marine reserve systems generated from theseand other planning problems are described using per-formance measures reported in Table 1.

Although all marine reserve systems identified weresuccessful in achieving their goal, how well they didwas largely a function of the constraints of the reservedesign problem. The most efficient marine reserve sys-tem identified was generated in the No Reserves sce-nario at 5% representation targets (Table 1). The effi-ciency of marine reserve systems then decreased astargets increased. This is due to efficiency being adirect measure of efficiency of sampling (Pressey &Nicholls 1989), so marine reserve systems requiringfewer planning units will always be more efficient.Notwithstanding, we found measures of efficiency tobe useful for comparisons of the No Reserves andReserves Fixed reserve systems at the equivalent rep-resentation targets, with the Reserves Fixed scenarioproducing less efficient marine reserve systems inevery case. The inefficiency of Reserves Fixed systemsis best illustrated at 5% representation targets with402 planning units (288 of which make up South Aus-tralia’s existing marine reserve system) required toachieve the same targets that the No Reserves scenariomet with just 169 planning units.

Intuitively, we would expect the performance of theReserves Fixed marine reserve systems to improve asrepresentation targets increase. Although this was ob-served to some degree, the No Reserves systems werealways more efficient than the Reserves Fixed scenarioat an equivalent representation target. Single-factoranalysis of variance confirmed that the reserve systemcombination size (the average number of planning

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Stewart et al.: Opportunity cost of ad hoc marine reserves 31

Fig. 2. Alternative marine reserve systems for the No Reserves and Reserves Fixed scenarios with representation targets set(A) at 5% and (B) at 50%. The locations of South Australia’s existing marine reserves are shown in red (individual marine reservesare identified in Appendix 1). These areas are locked in to every marine reserve system generated in the Reserves Fixed scenario.Under the No Reserves scenario, the existing reserves can be either ignored or incorporated. For summary data see Table 1

A

B

No Reserves 5% Targets

Reserves Fixed 5% Targets

Existing marine reserves

South Australia land

South Australia state waters

No Reserves 50% Targets

Reserves Fixed 50% Targets

Existing marine reserves

South Australia land

South Australia state waters

0 50 100 200 km

0 50 100 200 km

Mar Ecol Prog Ser 253: 25–38, 2003

units in 1000 marine reserve systems) was significantlydifferent (p < 0.05) between the 2 scenarios at the equi-valent representation target. From these results, weconclude that certain features must be contained withinthe existing marine reserve system at levels exceedingthe required target, even when targets are as high as50%. As representation of features surplus to the pre-scribed target goes unrewarded, this translates intolarger and less efficient marine reserve systems.

A second way of evaluating the performance ofmarine reserve systems is to measure their compact-ness (Table 1). The No Reserves systems were alwaysmore compact than the Reserves Fixed systems, irre-spective of the target. Even when the Reserves Fixedsystems comprised fewer individual reserves than theNo Reserves scenario, as reported at 5, 10, 15 and 30%targets, their configuration was more fragmented. Thisresult is perhaps counter-intuitive, as one would nor-mally presume that fewer reserves would deliver amore compact solution. However, there are 2 parts to areserve system’s compactness, the number of reservesand the shape. The less compact Reserves Fixed sys-tems most likely resulted from the inclusion of existingreserves such as the Coorong and the Great AustralianBight Marine Park (GABMP) which are characterisedby a high perimeter-area ratio.

Next, we examined the selection frequencies forindividual planning units to assess their importance forefficient marine reserve system design. We applied ournew interpretation of irreplaceability to identify plan-ning units selected more than could be expected fromchance alone, with the top 100 irreplaceable planningunits identified from these. The example in Fig. 3 illus-trates the distribution of irreplaceable areas for bothscenarios when representation targets were set at

20%. We considered the top 100 irreplaceable plan-ning units to be key locations for conservation plan-ning, with several locations common to both planningscenarios. With only 1 planning unit in the ReservesFixed scenario identified as 100% irreplaceable, thissuggests that the distribution of our chosen biodiver-sity features in South Australia affords some degree offlexibility for marine reserve system design.

Opportunity costs of ad hoc marine reserve design

Knowing that the inefficiencies of marine reserve sys-tems identified under the Reserves Fixed marine reservesystems were due to the inclusion of South Australia’sexisting marine reserves, we sought to determine thecause of these inefficiencies. Our analyses were per-formed on the subset of planning units corresponding toSouth Australia’s existing marine reserves to determinethe opportunity cost for the whole of the existing reservesystem and then for each individual reserve at differentrepresentation targets. These results are presented inFig. 4 with the opportunity cost calculated as the re-placeable fraction of South Australia’s existing reserves.At 5% targets this amounted to an opportunity cost of64% that reduced to 51% when representation targetswere increased to 50%. Therefore, at best, more thanhalf the area of South Australia’s existing reserves re-sulted in an opportunity cost to efficient marine reserveselection, demonstrating the inefficiency of ad hocmarine reserve selection. Increasing the representationtargets failed to greatly improve the performance of theexisting marine reserve system.

Next, we examined whether any reserve in particu-lar accounted for the inefficiencies of the existing

32

Table 1. Summary data for the best marine reserve systems generated for 2 planning scenarios at different representation targets.Marine reserve systems identified under the No Reserves scenario were always more efficient than systems identified under theReserves Fixed scenario at an equivalent representation target. For measures of compactness, a value of 1 is the ideal (mostcompact) configuration. Measures of efficiency are bound between 0 and 1, with 1 being the most efficient system. The combina-tion size is the no. of planning units in a reserve system (calculated from 1000 runs of the algorithm); SD = standard deviation

Scenario Target Score No. of planning No. of Perimeter Area Compactness Efficiency Combination(%) units reserves (km) (km2) size ± SD

No Reserves 5 996 169 58 1695 3743 7.81 0.95 168 ± 3.8710 1419 315 49 2233 7394 7.33 0.90 314 ± 5.1715 1811 461 44 2723 10936 7.35 0.85 457 ± 6.6020 2178 598 44 3165 14264 7.48 0.81 602 ± 7.5130 2866 896 45 3931 21234 7.61 0.71 890 ± 8.7650 4152 14720 35 5240 35135 7.89 0.53 1476 ± 12.51

Reserves Fixed 5 2107 402 46 3308 8020 10.42 0.87 404 ± 4.4310 2496 531 47 3820 11311 10.13 0.83 527 ± 6.0115 2795 652 43 4147 14233 9.81 0.79 651 ± 7.1120 3086 771 45 5504 17061 7.93 0.75 774 ± 8.1730 3596 10200 37 4996 23064 9.28 0.67 1031 ± 8.83050 4753 15810 38 6127 36427 9.06 0.49 1580 ± 13.15

Stewart et al.: Opportunity cost of ad hoc marine reserves 33

Fig. 3. Selection frequency counts are shown for planning units contained within efficient marine reserve systems under the (A)No Reserves and (B) Reserves Fixed scenarios when representation targets are set at 20%. Irreplaceable planning units aredefined as those contained within marine reserve systems more times than could be expected from chance alone. The top 100irreplaceable planning units are regarded as core areas for conservation planning, some of which are common to both planningscenarios and may be considered key locations for efficient marine reserve design. Planning units selected as often as could be

expected from random sampling alone are categorised as Random

165–215 (random)

216–539 (irreplaceable sites)

540–999 (top 100 irreplaceable sites)

1000 (100% irreplaceable)

South Australia land

South Australia state waters

146–194 (random)

195–585 (irreplaceable sites)

586–999 (top 100 irreplaceable sites)

1000 (100% irreplaceable)

Existing marine reserves

South Australia land

South Australia state waters

0 50 100 200 km

0 50 100 200 km

ALegend

No Reserves 20% target

Selection frequency

BLegend

No Reserves 20% target

Selection frequency

Mar Ecol Prog Ser 253: 25–38, 2003

marine reserve system (Fig. 4). Although the entirearea of several existing marine reserves representeda 100% opportunity cost, i.e. no part of their areaselected more than by chance at any representationtarget (Point Labatt, Troubridge Hill and AmericanRiver), their combined size amounted to <1% of theexisting marine reserve system. Therefore, their in-dividual performance did not correspond to a highopportunity cost to the existing marine reserve systemas a whole. By comparison, the Great Australian BightMarine Park (GABMP) makes up almost 60% of SouthAustralia’s existing marine reserve system, so the op-portunity cost of the system was particularly sensitiveto the performance of this individual reserve. Indeed,the individual reserves identified in Fig. 4 are the onlymarine reserves in South Australia to exceed 100 km2.The relatively greater size of these reserves ensuresthat their performance was more likely to impact onthe performance of the system. Even for reserves thatwe considered to make an efficient contribution toreservation goals due to more than 50% of their areabeing irreplaceable (Sir Joseph Banks Group), theirrelatively larger size means that any portion of re-placeable areas was more likely to result in a higheropportunity cost to the system.

Of the larger reserves, the GABMP was especiallycostly. It contributed to the reservation goals most effi-ciently at 15, 20 and 50% targets, resulting in an

opportunity cost of 61% of the reserve itself and 37%of South Australia’s existing marine reserve system.This is equivalent to >950 km2 from the GABMP alonethat was never selected more than by chance in effi-cient marine reserve systems. The distribution of re-placeable planning units for the GABMP was mapped(Fig. 5) at representation targets of 5, 20 and 50%. Ahigh degree of nestedness occurred for replaceableareas as targets were varied, such that planning unitsthat were replaceable with 50% representation targetstended to remain replaceable at lower targets. Thisapproach can similarly be applied to identify thoseparts of existing marine reserves that make an efficientcontribution to biodiversity conservation objectives.

Conclusions

We have shown how a properly posed marine re-serve system design problem and mathematical algo-rithms can be used to investigate the consequences ofalternative representation targets and planning sce-narios. We highlighted the need for systematic andefficient selection methods through our assessment ofthe contribution of South Australia’s existing marinereserve system to meeting conservation targets.

Measures of efficiency (Pressey & Nicholls 1989),compactness (Possingham et al. 2000), and the numberof individual reserves were examples used here to de-scribe the performance of alternative marine systemsolutions. We encountered limitations when using effi-ciency to compare between marine reserve systems atdifferent representation targets, as it failed to accountfor the additional area required if higher targets are tobe met. One way of standardising measures of effi-ciency may be to compare the reserve systems gener-ated with marine reserve systems identified whenthere are no spatial constraints (BLM = 0). Reservesystems configured under these conditions place thegreatest importance on minimising area, thus deliver-ing marine reserve systems with the least number ofplanning units, and could be considered the near-minimum solution for a particular planning problem(Possingham et al. 2000, Stewart & Possingham inpress).

A new application of summed irreplaceability thatidentifies areas that make an efficient contribution torepresentative conservation goals was presented. Thisserves to address the practical problem of how andwhere reserve selection should proceed through theidentification of irreplaceable or priority areas. Be-cause reserve planning is iterative and the irreplace-ability value of an individual site is dynamic, havingthe tools to explore the relative contribution of an indi-vidual site can support decision-making for sequential

34

Fig. 4. The opportunity cost of South Australia’s existingmarine reserve system at different representation targets. In-dividual marine reserves representing the greatest opportu-nity cost to the existing system are highlighted and allexceed 100 km2 in size. Reserves defined as ‘Other’ (PointLabatt, Whyalla-Cowley, Blanche Harbour, Yatala Harbour,Zanoni, Barker Inlet/Chapman Creek, Port Noarlunga, Al-dinga Reef, Troubridge Hill, Goose Island, American River,Seal Bay/Bales Beach, The Pages, West Island) represent the smallest opportunity cost to the existing system overall

Stewart et al.: Opportunity cost of ad hoc marine reserves

reserve selection. In addition, it provides a means toevaluate the impact of decisions when sites are lost orchanges to the reserve system are made. Similar opti-misation methods to those used for marine reservesystem selection could be used to guide decisions forsiting of commercial and recreational activities, pro-viding the flexibility to negotiate arrangements thatachieve both conservation and socio-economic objec-tives.

A second application for irreplaceability relates tothe principle of complementarity and examines the op-portunity cost that arises when marine reserve systemsare built around marine reserves selected in an ad hocmanner. Because conservation planning is informed bywhat is contained within the existing marine reserves,how well these areas contribute to the system targetsand goals affects the performance of the system as awhole. Given the ad hoc manner in which South Aus-tralian marine reserves have been chosen in the past, itis not surprising to find that less than half of the exist-ing reserve system made an efficient contribution even

when feature targets were as high as50%. We regard their poor perfor-mance to be an opportunity cost, forthey may constrain effective conserva-tion of marine biodiversity by compro-mising the ability to select more suit-able sites. Furthermore, the inclusion ofthese areas in many cases may displacecommercial effort to non-reservedareas, some of which may have irre-placeable value for conservation plan-ning.

Whilst we don’t expect reserve plan-ners to abandon existing reserves onthe basis of the results shown here, wedo want to highlight that past decisionsyield great impact on the design ofmarine reserve systems. We also recog-nise that part of the existing reservesystem does make an efficient contribu-tion to achieving biodiversity conserva-tion targets. Although this contributionmight be incidental, knowing whichareas are key or irreplaceable toachieving conservation goals providesvaluable information to support man-agement decisions and to determinewhere effort should be targeted. Withseveral of South Australia’s marinereserves being merely extensions ofterrestrial reserves and without formalmanagement arrangements that ex-plicitly incorporate marine biodiversityconservation objectives, a practical

application of this work may be to utilise this informa-tion to review current management arrangements andensure that areas are protected and managed appro-priately.

When defining the problem of marine reserve systemdesign it is important to recognise that there are 2sources of uncertainty. The first (epistemic uncertainty)arises from incomplete knowledge or limitations of dataand, in so far as it relates to marine biodiversity and re-serve design, will be present in all selection methodolo-gies. Although we present a practical case study, wehave not concerned ourselves with this type of uncer-tainty, for our principal goal was to evaluate a method-ology and suite of tools. Confidence in the accuracy ofour findings would be improved by accounting for thiskind of uncertainty through the inclusion of additionaldatasets, improving data quality by evaluating the po-tential effects of error propagation and consideration ofecological processes (Flather et al. 1997, Freitag et al.1998). The second type of uncertainty is called vague-ness and is attributed to the imprecise nature of lan-

35

Fig. 5. Distribution of the replaceable and irreplaceable planning units across theGABMP reserve. Replaceable areas are identified as a measure of the opportu-nity cost, for their inclusion results in the identification of inefficient and lesscompact marine reserve systems. At representation targets of 5, 20 and 50% theopportunity cost of the GABMP was 64, 61 and 62% of the total reserve area.Increasing representation targets did not improve the performance of the re-serve, which is considered to make a poor contribution to efficient marine reserve

design (more than half of its area reported as an opportunity cost)

Opportunity cost for theGreat Australian Bight MarinePark (GABMP)

guage, which can lead to ambiguities and misunder-standings (Regan et al. 2000). We are encouraged bythe capacity of mathematical methods to reduce vague-

ness and subjectivity by relating to clearly articulatedgoals and proceeding in an efficient and systematicmanner. Even more important perhaps is their capacity

Biogeographic regions (meso-scale: 100 to 1000s km)Biogeographic regions have been identified at the nationallevel using a marine ecosystem-based classification scheme,known as the Interim Marine and Coastal Regionalisation ofAustralia (IMCRA). The scheme was derived using a combi-nation of expert ecological knowledge from the field andinterpretation of existing regionalisations (IMCRA Techni-cal Group 1997), which include sea floor topography, seafloor sediments, physical oceanographic water column,coastal zone geomorphology and pelagic and demersal fishregionalisations. There are 60 bioregions delineated for theAustralian coastal and offshore waters and 8 of these occurwholly or partly within South Australian state waters.

Biounits (micro-scale: 10 to 100s km)At this micro-scale level, distinct regional and local varia-tions in habitat and biodiversity are classified on the basis oflocal-scale ecological units (i.e. rocky shores, shoals or reefsystems) and information on the spatial extent of these units.South Australia’s micro-scale biounits were defined primar-ily on the basis of coastal physiography, topography andmajor marine physical habitat or seascape features, as wellas habitat distribution (Edyvane 1999b). A total of 35biounits have been identified and comprise 30 coastalbiounits and 5 offshore units (Edyvane 1999b). Seawardboundaries of the coastal biounits were defined using the30 m bathymetric contour. The offshore biounit boundarieswere drawn at the 50 m depth contour.

Marine benthic habitat mapsMarine benthic habitat coverage has been mapped forcoastal and gulf waters of the state. Habitats were identifiedby tracing discernible underwater features on satelliteimages and using aerial photographs for ‘truthing’. Theresulting dataset uses biological data for classification ofseagrass densities and geomorphological descriptions forreefs. As a benthic classification scheme has not yet beendeveloped for South Australia, habitat type is classified at abroad level (i.e. seagrass, platform reef, sand) and does notincorporate information on the dominant species assem-blages. We identified 6 unique conservation features in thisfeature class.

Coastal saltmarsh and mangrove habitatsIntertidal vegetation of the coastal regions of South Aus-tralia has been identified from digitised 1:40 000, 1:15 000and 1:10 000 non-rectified aerial photography. Habitatswere classified and coded using landform, life form and con-dition categories. Classification was based on aerial photointerpretation, survey data, ground truthing and expertknowledge. In total, 65 habitat classes were identified with

11 of these relating to intertidal or tidal areas. These classeswere collapsed into the more generalised categories ofintertidal/tidal marine algae; intertidal and tidal bare sand;intertidal/tidal seagrass, mangroves and saltmarsh. Thisclassification provided a further 5 unique conservationfeatures.

Species occurrenceSpecies occurrence data were incorporated where coverageextended across the study area and occurrence recordswere of sufficient quality. On these grounds, we identified 3conservation features from the data layers available forSouth Australian distributions of seabirds, Australian sealions and New Zealand fur seals.

The seabirds of South Australia dataset describes popula-tion and breeding seasons, including mainland and offshoreisland sites. The dataset is suitable for the identification ofsignificant seabird habitat communities within South Aus-tralia. Australian sea lion (Neophoca cinerea) and New Zea-land fur seal (Artocephalus forsteri) locations within SouthAustralian waters are identified, with population, breedingseason and breeding and haul out sites for both the main-land and island locations.

BathymetryThis dataset is a compilation of water depths for the offshorewaters of South Australia, with depth values representingdepths to the seabed. Data were collected as part of theNational Mapping Bathymetric Program, which was de-signed to provide generalised detail of the seabed of theContinental Shelf. Depth values were delineated into 6 cat-egories; 0 to 10 m, 10 to 20 m; 20 to 30 m; 30 to 40 m; 40 to 50m and 50+ m. We required representation of each depth cat-egory occurring within each of the 8 bioregions. This pro-vided an additional 45 conservation features.

Protected areasThis data layer provides an accurate location for the legalboundaries of both terrestrial and marine reserves and wasused to formulate marine reserve design scenarios. Wegenerated a user-defined marine reserve theme as a subset ofprotected areas derived from the Australian Collaborative Pro-tected Area Database. It includes areas recognised as marinereserves, as well as coastal/offshore island reserves with sig-nificant marine components. In this paper, rock lobster sanc-tuaries and most netting closures were not included. Conse-quently, South Australia’s existing marine reservescomprised 17 marine reserves with a total area of approxi-mately 2600 km2. The location of individual reserves is shownin Fig. 2, with reserves identified below.

Appendix 1. Conservation features were identified for state waters using 6 data layers obtained from South Australian stategovernment agencies. An additional feature class was delineated to represent the status of South Australia’s existing reserves

(1) Great Australian Bight Marine Park(2) Point Labatt(3) Neptune Islands(4) Sir Joseph Banks Group(5) Whyalla-Cowleds Landing(6) Blanche Harbour-Douglas Bank

(7) Yatala Harbour(8) Goose Island(9) Troubridge Hill(10) Zanoni(11) Barker Inlet/Chapman Creek(12) Port Noarlunga/Aldinga Reef

(13) West Island(14) The Pages(15) American River(16) Seal Bay/Bales Beach(17) Coorong

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to support inquiry about what level of uncertainty is ac-ceptable. They do this by being flexible, by providing>1 solution and responding well to complex decisionrules. We believe that mathematical algorithms providea means to (1) consider different levels of representa-tion, replication and spatial constraints; (2) integratemultiple goals within a single planning framework; and(3) inform where reservation should proceed.

Acknowledgements. The authors would like to thank theSouth Australian government and K. S. Edyvane for approvalto access data used in this study. The first author is grateful toThe Ecology Centre for financial support and to A. J. Tyre, I.R. Ball and W. Rochester for helpful discussions during thecourse of this research. Finally, we would like to thank 4anonymous referees for their useful comments on earlierdrafts of this manuscript.

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Editorial responsibility: Otto Kinne (Editor), Oldendorf/Luhe, Germany

Submitted: May 31, 2002; Accepted: January 7, 2003Proofs received from author(s): April 8, 2003