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Ecotoxicological Mechanisms and Models in an Impact Analysis Tool forOil SpillsFrederik De Laendera; Gro Harlaug Olsenb; Tone Frostc; Bjørn Einar Grøsvikd; Merete Grunge; BjørnHenrik Hansenf; A. Jan Hendriksg; Morten Hjorthh; Colin R. Janssena; Chris Kloki; Trond Nordtugf;Mathijs Smitc; JoLynn Carrollbj; Lionel Camusbk

a Laboratory of Environmental Toxicology and Aquatic Ecology, Ghent University, Ghent, Belgium b

Akvaplan-niva, Fram—High North Research Centre for Climate and the Environment, Tromsø,Norway c Department of Environment & Climate, Statoil Research Center, Trondheim, Norway d

Institute of Marine Research (IMR), Bergen, Norway e Norwegian Institute for Water Research (NIVA),Oslo, Norway f SINTEF Materials and Chemistry, Marine Environmental Technology, Trondheim,Norway g Department of Environmental Science, Institute for Water and Wetland Research, RadboudUniversity, Nijmegen, Nijmegen, The Netherlands h National Environmental Research Institute (NERI),Aarhus University, Roskilde, Denmark i IMARES, Den Helder, the Netherlands j Department ofGeology, University of Tromsø, Tromsø, Norway k University Center In Svalbard, Longyearbyen,Norway

Online publication date: 09 March 2011

To cite this Article De Laender, Frederik , Olsen, Gro Harlaug , Frost, Tone , Grøsvik, Bjørn Einar , Grung, Merete ,Hansen, Bjørn Henrik , Hendriks, A. Jan , Hjorth, Morten , Janssen, Colin R. , Klok, Chris , Nordtug, Trond , Smit,Mathijs , Carroll, JoLynn and Camus, Lionel(2011) 'Ecotoxicological Mechanisms and Models in an Impact Analysis Toolfor Oil Spills', Journal of Toxicology and Environmental Health, Part A, 74: 7, 605 — 619To link to this Article: DOI: 10.1080/15287394.2011.550567URL: http://dx.doi.org/10.1080/15287394.2011.550567

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Journal of Toxicology and Environmental Health, Part A, 74:605–619, 2011Copyright © Taylor & Francis Group, LLCISSN: 1528-7394 print / 1087-2620 onlineDOI: 10.1080/15287394.2011.550567

ECOTOXICOLOGICAL MECHANISMS AND MODELS IN AN IMPACT ANALYSISTOOL FOR OIL SPILLS

Frederik De Laender1, Gro Harlaug Olsen2, Tone Frost3, Bjørn Einar Grøsvik4, Merete Grung5,Bjørn Henrik Hansen6, A. Jan Hendriks7, Morten Hjorth8, Colin R. Janssen1, Chris Klok9,Trond Nordtug6, Mathijs Smit3, JoLynn Carroll2,10, Lionel Camus2,11

1Laboratory of Environmental Toxicology and Aquatic Ecology, Ghent University, Ghent, Belgium2Akvaplan-niva, Fram—High North Research Centre for Climate and the Environment, Tromsø,Norway3Department of Environment & Climate, Statoil Research Center, Trondheim, Norway4Institute of Marine Research (IMR), Bergen, Norway5Norwegian Institute for Water Research (NIVA), Oslo, Norway6SINTEF Materials and Chemistry, Marine Environmental Technology, Trondheim, Norway7Department of Environmental Science, Institute for Water and Wetland Research, RadboudUniversity, Nijmegen, Nijmegen, The Netherlands8National Environmental Research Institute (NERI), Aarhus University, Roskilde, Denmark9IMARES, Den Helder, the Netherlands10Department of Geology, University of Tromsø, Tromsø, Norway11University Center In Svalbard, Longyearbyen, Norway

In an international collaborative effort, an impact analysis tool is being developed to pre-dict the effect of accidental oil spills on recruitment and production of Atlantic cod (Gadusmorhua) in the Barents Sea. The tool consisted of three coupled ecological models thatdescribe (1) plankton biomass dynamics, (2) cod larvae growth, and (3) fish stock dynamics.The discussions from a series of workshops are presented in which variables and parametersof the first two ecological models were listed that may be affected by oil-related compounds.In addition, ecotoxicological algorithms are suggested that may be used to quantify sucheffects and what the challenges and opportunities are for algorithm parameterization. Basedon model exercises described in the literature, survival and individual growth of cod larvae,survival and reproduction of zooplankton, and phytoplankton population growth are denotedas variables and parameters from the ecological models that might be affected in case of anoil spill. Because toxicity databases mostly (67%) contain data for freshwater species in tem-perate environments, parameterization of the ecotoxicological algorithms describing effectson these endpoints in the subarctic marine environment is not straightforward. Therefore,it is proposed that metadata analyses be used to estimate the sensitivity of subarctic marinespecies from available databases. To perform such analyses and reduce associated uncertaintyand variability, mechanistic models of varying complexity, possibly aided by new experimen-tal data, are proposed. Lastly, examples are given of how seasonality in ecosystems mayinfluence chemical effects, in particular in the subarctic environment. Food availability andlength of day were identified as important characteristics as these determine nutritional statusand phototoxicity, respectively.

Frederik de Laender is a postdoctoral research fellow from the Research Foundation–Flanders (FWO). The authors thank Statoil forfinancially supporting the workshops during which the concepts and ideas presented in this paper were formulated. The authors alsothank the scientific partners that are involved in other aspects of the ongoing project, for constructive feedback and interesting discussionsduring these workshops. In particular, we wish to acknowledge Akvaplan-niva for leading the development of this impact analysis tool.The two reviewers are thanked for their constructive remarks and suggestions.

Address correspondence to Frederik De Laender, Laboratory of Environmental Toxicology and Aquatic Ecology, Ghent University,Plateaustraat 22 9000 Ghent, Belgium. E-mail: frederik.delaender@ugent.be

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606 F. DE LAENDER ET AL.

The Barents Sea is an ecologically and eco-nomically important region because it is thelargest among the pan-arctic shelf seas and (1)accounts for nearly 50% of the regional pri-mary production (Sakshaug, 2004), (2) is essen-tial to arctic ocean biogeochemical cycling(Carmack & Wassmann, 2006), and (3) sup-ports one of the world’s largest fish stocksand fisheries (Norwegian Ministry of Fisheriesand Coastal Affairs, 2009). Atlantic cod (Gadusmorhua) is a top predator in the Barents Seaecosystem and an important commercial fishspecies in the North Atlantic. The status ofits stock therefore determines ecosystem sta-bility and sustainability of cod fisheries. Asfor any species, recruitment through reproduc-tion is a key process in maintaining the stockstatus of cod. Spawning and nursery groundssuch as the Lofoten archipelago are essentialin the reproduction cycle of this species. Inthis area hatched cod eggs develop into lar-vae, which feed on rich plankton fields, and aretransported by ocean currents to the BarentsSea.

In an international collaborative effort inte-grating the fields of ecological modeling andecotoxicology, an impact analysis tool is beingdeveloped to predict possible ecological effectsof an accidental oil spill in the vicinity of codspawning areas in the Norwegian and BarentsSea. The goal is to examine to what extenteffects on developing cod larvae result in sig-nificant effects on Atlantic cod recruitment andstock size. The impact analysis tool follows anecosystem approach because it considers indi-rect effects of an oil spill that originate fromecological interactions. An example of such anindirect effect may be food shortage for fish lar-vae due to oil-induced plankton mortality. Toachieve these goals, the impact analysis toollinks three ecological models. A first model(model 1) describes the growth of plankton, themain food item for cod larvae, near the codspawning areas. A second model (model 2) usesestimates of food (i.e., plankton) productionto predict growth and survival of fish larvae,again near the cod spawning areas. In addi-tion, model 2 describes dispersion from thespawning grounds in the Barents Sea. Finally,

a third model (model 3) uses estimates of lar-val development, growth, and dispersion topredict stock sizes and dynamics of adult fishacross the Barents Sea. Model 1 (plankton)and model 2 (larvae) contain ecotoxicologicalalgorithms to simulate possible effects of oil-associated compounds on plankton and fishlarvae fitness. Initially, oil toxicity will not beconsidered in model 3 (fish). Indeed, an oil spilloccurring near the spawning grounds, wherefish larvae reside, is likely to affect fish lar-vae and locally available food (plankton) butless likely to directly affect adult fish stockswhose habitat extends throughout the Barents.Instead, effects of the oil spill on stocks of adultfish will be solely indirect, i.e., originating fromdirect oil toxicity on fish larvae and their food(plankton), resulting in smaller recruitment forfuture cod generations.

Based on a series of workshops heldbetween autumn 2008 and summer 2010, thisstudy lists and discusses parameters and vari-ables of the plankton and larvae models thatmay be affected by the toxicity of oil-relatedcompounds, together with the algorithms thatcan be used to simulate these effects. In addi-tion, data that are needed to parameterizethese algorithms are reviewed. The informationgaps are also identified in existing data sets andoffer alternatives to deal with these limitationsin data availability. Lastly, possible implicationsof seasonal changes in length of day and foodavailability are discussed in relation to the riskoil spills pose to subarctic ecosystems. Becauseoil toxicity on adult fish is not included in theimpact assessment tool, the effects that oil-related compounds may have on adult fishare not discussed but may well be included infuture research.

PROBLEM DEFINITION

The goal of this study was to list algo-rithms that quantify direct chemical effects ona number of endpoints of plankton and fishlarvae. In general, ecotoxicological algorithmsuse exposure concentration as a parameter anddefine an effect concentration, i.e., a biologicalthreshold above which effects are to be

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EFFECT ASSESSMENT OF OIL SPILLS ON STOCK SIZE OF COD 607

expected. This implies that exposure and effectconcentrations are expressed using one “com-mon currency.” In a water-based approach,exposure and effect concentrations are quan-tified as concentrations in the water that sur-rounds the organism (e.g., has units such as µgchemical/L). In a tissue-based approach, bothconcentrations are expressed as concentrationsin organ tissue (e.g., liver tissue) or in a phaseof the organism (e.g., the lipid phase) and willtherefore have units such as ng chemical/glipid. In the context of oil-associated com-pounds, a tissue-based approach was preferredover a water-based approach for the followingreasons. First, most oil-associated compounds(aromatic hydrocarbons and (cyclo)alkanes) pri-marily act through narcosis, making lipid mem-branes the primary site of toxic action. Second,studies demonstrated that tissue-based effectconcentrations for narcotics show less vari-ability among species and chemicals thanwater-based effect concentrations (Vanwezel& Opperhuizen, 1995; Escher & Hermens,2004). Further, tissue concentrations offer atime-integrated measure of chemical exposure.

Chemical fate models calculate the timecourse of concentrations of the main petroleumcompounds in three spatial dimensions ofthe marine subarctic environment after a dis-charge event (Reed et al., 2001). These aque-ous chemical concentrations are then usedto derive concentrations in lipid tissues ofcod larvae and plankton. In order to do so,bioaccumulation models are used that con-sider chemical uptake through water and food.Various contemporary bioaccumulation modelsthat are based on fugacity theory and only dif-fer in their mathematical formulation (Gobas,1993; Campfens & MacKay, 1997; Hendrikset al., 2001) have been successfully appliedin many freshwater and marine environments(Borga & Di Guardo, 2005; Veltman et al.,2006; Nfon & Cousins, 2007; De Laenderet al., 2010a). The sensitivity of model predic-tions to changes in parameters is well known(Linkov et al., 2001; MacLeod et al., 2002;Lin et al., 2004; Schenker et al., 2009), whichmakes their predictions more readily inter-pretable and allows one to focus uncertainty

analyses on those parameters for which themodel is most sensitive. Because some parame-ters of these models (e.g., ventilation rates) aredifficult to measure, Hendriks et al. (2001) pro-posed to incorporate allometric relationships infugacity based bioaccumulation models. Thisapproach demonstrated its value in extensivecalibration and validation exercises (Hendriks& Heikens, 2001; Hendriks et al., 2001) andallows one to use well-known species char-acteristics (e.g., weight and lipid content) toapproximate poorly known parameters such asrespiration and ingestion rates.

Sensitivity analyses with mechanistic bioac-cumulation models demonstrate that chemicaluptake through food is especially of concernfor substances with an octanol–water partition-ing coefficient (Kow) > 105 (Smitkova et al.,2005; De Laender et al., 2010b). For chemicalsthat are less hydrophobic, data indicate thatinternal concentrations can be described suf-ficiently well by considering uptake via wateronly. Hence, when chemicals (log Kow val-ues < 5) are introduced into the environ-ment during an oil spill, bioaccumulations aredescribed by fugacity-based models that ignoredietary uptake. For more hydrophobic sub-stances, bioaccumulation models are neededthat consider dietary uptake of chemicals.

EFFECT ASSESSMENT

The mechanisms that are incorporated inthe existing ecological models for plankton(model 1) and fish larvae (model 2) were usedas a basis for a preliminary endpoint selec-tion. This approach is supported by the factthat the incorporated processes appear to resultin acceptable simulations of biological rele-vant phenomena. The resulting ecotoxicolog-ical endpoints are listed in Table 1 and themotivation for their selection is described inwhat follows. However, it should be noted thatthis selection may be extended or restricted,depending on additional research that will becarried out as the impact analysis tool is beingdeveloped. In this context, it might be useful toperform a series of sensitivity analyses with the

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608 F. DE LAENDER ET AL.

TABLE 1. Overview of the Two Ecological Models in Which Effects of Oil-Associated Compounds Will Be Incorporated,Their Key Equations, the Variables or Parameters That Will Be Affected in These Equations, and the CorrespondingEcotoxicological Endpoints

Ecologicalmodel Key equations

Affected variable(s)and/or parameter(s)

Correspondingecotoxicologicalendpoint(s)

Plankton

∂Phy∂t

+ AD(Phy) = Phy[fPhy − μPhy − SPhy

− aDOCPhy fPhy]

fPhy = f(nutrients, light)

fPhy

(d−1)population growth

∂Zoo∂t

+ AD(Zoo) = Zoo[aZooIZoo − EZoo]

− μZooZoo2

μZooZoo2

(mmol N m−3 d−1mortality

∂Zooa∂t + dZooa

dw = birth − mortality mortalitybirth (numbers m−2)

mortalityreproduction

Larvae Ai,t = (Li, wi, xi, yi, zi, Psi) Psi mortalityLi = f(Gi) Gi growthwi = f(Gi)

Note. Symbols in the larvae model denote attributes of individual i on time t (Ai,t), i.e., its length (Li; mm), weight (wi;mg DW), position (xi, yi, zi), survival probability (Psi), and individual growth (Gi; s−1); f( ) represents functional relationshipsthat are not specified here but can be found in the original papers that describe these ecological model. Symbols in thephytoplankton equations represent density (Phy; mmol N m−3), advection and diffusion (AD; mmol N m−3 d−1), net growthrate (fPhy; d−1), mortality (μPhy; d−1), sedimentation (SPhy; d−1), and the fraction of net phytoplankton production that isexcreted to dissolved organic carbon (aDOC

Phy; —). Symbols in the zooplankton equation are density (Zoo; mmol N m−3),advection and diffusion (AD; mmol N m−3 d−1), assimilation efficiency (aZoo; —), ingestion rate (IZoo; d−1), excretion rate(EZoo; d−1), and mortality rate (μZoo; m6 mmol N−2 d−1). A stage-structured population model can be included to modeldensities (Zooa; mmol N m−3) of different zooplankton life stages, where w is the weight of the given life stage.

plankton and larvae models to evaluate whichparameters contribute most to model simula-tions and for which parameters model outputis insensitive.

Endpoints for PlanktonThe density of phytoplankton and zoo-

plankton populations is simulated by theSINMOD model (model 1), which predictsspatiotemporal patterns of phytoplankton anddifferent life stages of zooplankton (Wassmannet al., 2006; Slagstad & Tande, 2007). Inrecent years, implementation of algorithmsthat describe chemical effects on growth-determining processes in ecosystem modelswith very similar key equations as SINMODhas produced promising results. However, thephysiological processes that are assumed to beaffected by chemical stress differ among stud-ies. Traas et al. (2004) implemented effectson mortality and assimilation rate in a food

web model to predict effects of the insec-ticide chlorpyrifos in freshwater microcosms.Bartell et al. (1999) use a “toxic effects fac-tor” approach that is based on the assump-tion that a chemical has an equal impacton every physiological process. Naito et al.(2003) showed that this approach estimatesecosystem-level no-observed-effect concentra-tions (NOEC) that are correlated to observedecosystem-level NOEC. For 7 organic chemi-cals evaluated in 11 mesocosm and enclosurestudies, De Laender et al. (2008b) found thatimplementing chemical effects on zooplank-ton mortality and phytoplankton growth ratein an ecosystem model resulted in ecosystem-level NOEC that were lower than the observedecosystem-level NOEC, i.e., protective, in 85%of the cases. In another study, De Laenderet al. (2008a) found that the additionalconsideration of effects on feeding rate (sub-lethal endpoints) in the same ecosystem model

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EFFECT ASSESSMENT OF OIL SPILLS ON STOCK SIZE OF COD 609

did not result in more accurate predictionsthan when only effects on mortality wereimplemented.

Although there are differences betweenthe various models in the processes, and it isassumed to be affected by chemical stress, zoo-plankton mortality rate is always included andtherefore seems a reasonable starting point.For phytoplankton, population growth rate wasselected because it corresponds to the end-point that is most often measured in toxicitytests and because it was involved in previoussuccessful modelling exercises.

The zooplankton species that is typical forthe spawning region of Atlantic cod, Calanusfinmarchicus, and that is thus incorporated inthe ecological model SINMOD, has a life cycleof 1 yr. This is longer than the life cycle of thezooplankton species in ecosystem models thathave been used in the earlier effect assessmentexercises mentioned earlier. The importance ofthe heterogeneity in developmental stage inproducing accurate predictions of Calanus pop-ulation dynamics is recognized by the stage-based structure of SINMOD. For this reason,zooplankton reproduction was added as a pos-sible endpoint.

Endpoints for LarvaeThe existing ecological model that will be

included in the impact assessment tool that isto be developed to describe dispersion andgrowth of cod larvae (model 2) is an individual-based model. Individuals are described interms of length, weight, position, and survivalprobability (Vikebo et al., 2007). No exampleswere found in the literature where this typeof ecological model was extended with algo-rithms that describe effects of toxic compounds(but see Pecquerie et al., 2009, for an appli-cation on the impact of environmental condi-tions on growth and development in anchovy).Because of the apparent usefulness of mortalityas an endpoint in zooplankton models, mortal-ity was considered a reliable starting point forcod larvae as well. Because individual growthdetermines larval length and weight, individ-ual growth rate was also added as a possible

endpoint of concern. A distinction was madebetween effects that occur immediately follow-ing an accidental oil spill (instantaneous effect)and delayed effects, i.e., effects that appearlong after the oil spill event has occurred.Both survival and growth were found to beaffected in early life stages of fish that wereexposed to oil associated compounds as lar-vae or eggs. Heintz et al. (2000) showed thatearly life stages of pink salmon had a reducedgrowth and a lower survival probability whenthey were exposed to crude oil as larvae.Recently, cardiac arrhythmia (Incardona et al.,2009) and resulting morphological abnormal-ities (Incardona et al., 2004) were suggestedas mechanisms underlying delayed effects ofoil associated compounds. Together with asuite of biomarkers that can be measured inthe embryo stage (Carls et al., 2005), heartfunctioning and development during the larvalphase have been suggested as possible proxiesfor delayed effects on survival and growth ofearly life stages of fish.

Algorithms to Model Effects on theSelected EndpointsPhytoplankton and Zooplankton Quan-

tifying phytoplankton growth at a given con-centration of an oil-associated compound canbe done using the concentration response func-tions that are typically derived in toxicity tests.Indeed, the endpoint that is measured dur-ing such a test (specific population growthrate; d−1) is identical to the parameter that isincluded in contemporary models for phyto-plankton growth (Table 1).

Effects of chemicals on zooplankton spe-cific mortality are also easily derived fromsingle-species toxicity data, although someassumptions and conversions are needed. Oneapproach that has been adopted previously(Traas et al., 2004; De Laender et al., 2008b)is based on the assumption that the incrementin population biomass from B0 to B (i.e., pop-ulation biomass growth) of the organisms in asingle-species toxicity test can be approximatedby an exponential function:

B = B0 · erT (1)

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610 F. DE LAENDER ET AL.

where T is time and r is population growth rate,which is composed of a number of subpro-cesses:

r = food assimilation − respiration

− mortality − excretion(2)

Note that all processes in Eq. (2) are biomassspecific, i.e., they have units of g biomass ·(g biomass)−1 · d−1, or simply d−1. Becauseorganisms are typically not fed during an acutetoxicity test, food assimilation = 0. For cope-pods, maintenance respiration is about 0.01d−1 (Drits et al., 1993). Excretion is between 33and 100% of respiration, i.e., between 0.0033d−1 and 0.01 d−1 (Vezina & Platt, 1988). Atthe (tissue-based) LC50, 50% of the individualsdie during the toxicity test duration t, i.e., theirpopulation growth rate is negative. For exam-ple, in case T = 1 d (which is often the case inacute toxicity tests), B/B0 is 1/2 and accordingto Eq. (2), r = ln(1/2) = −0.69 d−1. For thecase of both respiration and excretion = 0.01,the specific mortality rate at the LC50 can becalculated as:

−0.69 = − 0.01 − mortality − 0.01,

or mortality = 0.67 d−1(3)

This shows that mortality is most likely the dom-inating process during an acute toxicity test andsuggests that respiration and excretion can beassumed negligible. The general form of Eq. (3)is then:

mortalityRate = expT−1·ln(1 + (c

LC50)slope)

(4)

and allows one to calculate the specific mortal-ity rate (d−1) at a given concentration c, basedon information gained from a single speciestoxicity test with a given test duration (exp T;d), slope, and LC50. This specific mortality rate(d−1) can be readily used to estimate mortality(mmol N m−3 d−1) in the plankton model bymultiplication with zooplankton density (mmol

N m−3). In case c changes over time, Eq. (4)can be calculated for every time step.

In a single species reproduction test withzooplankton, one evaluates the number of off-spring that are produced during the test periodand compares this number between chemi-cal treatments and a control (Jensen & Carroll,2010). An ECx with reproduction as an end-point is therefore the concentration at whichthe zooplankton organisms produce x% lessoffspring than in the control. As for algal photo-synthesis, the concentration response functionfor reproduction can therefore be directly usedto estimate how much reproduction is reduced.

Fish Larvae Because mortality of fish lar-vae is modeled as a probability, toxicity testresults have to be interpreted accordingly. Forexample, when larvae are exposed for 1 d toa concentration equal to their 24-h LC50, thesurvival probability (Ps1day) equals 0.5. Whenexposed to the 24-h LC10, this probability isincreased to 0.9, and so on. In general, one canwrite:

Ps1day = 11 + ( c

24hLC50)slope

(5)

where c is the exposure concentration andslope is the slope of the concentration responsefunction, respectively. Clearly, if larvae areexposed for a period >1 d, the value of the24-h LC50 may no longer be valid, as the lethalconcentration depends on the exposure time(Kooijman, 1981):

LC50(t) = LC50(∞)1−e−k.t

(6)

where LC50(∞) and k are the incipient LC50 andthe elimination rate, respectively. This relation-ship can be combined with Eq. (5) to calculatePs in case of an exposure event to a concentra-tion c and a duration of t days:

Pstdays = 11 + ( c

LC50(t) )slope

(7)

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EFFECT ASSESSMENT OF OIL SPILLS ON STOCK SIZE OF COD 611

To account for time-varying concentrationsof the oil related compounds, the continu-ous concentration pattern during a spill willhave to be transformed into a series of timeperiods with time t to which Eq. (7) can beapplied.

To date, delayed effects of toxic compo-nents on survival and growth of cod larvaehave never been modeled. However, relation-ships between exposure during emergence andsurvival probability of subsequent life stageshave been derived experimentally, for exam-ple, as for Oncorhynchus gorbuscha exposed tototal polycyclic aromatic hydrocarbons (PAH)by Carls et al. (2005). Based on the datareported by these authors (their Figure 7, upperpanel), one could statistically relate survivalprobability of juvenile fish to the exposure pat-tern they experience as hatched eggs. Delayedeffects on growth may be derived from similarexperiments in which weight and/or length aremeasured over time (Heintz et al., 2000; Carlset al., 2005).

A mechanistic alternative to the use of sta-tistical models for larvae modelling is proposedby dynamic energy budget (DEB) modeling(Kooijman, 2000, 2009). The DEB is basedon the principle that mass and energy areconservative quantities (one of the few hardlaws available in biology), energy conversionprocess leads to dissipation, and mass andenergy transport occur across surfaces. DEBdescribes the energy pathways in individuals.When food is assimilated, energy is transportedfirst to a storage compartment. These reservesintroduce metabolic memory and can explainmany biological phenomena such as repro-duction taking place during starvation. Fromthese reserves, energy is allocated in a fixedratio to maintenance of structural elementsand investment in growth and reproduction.Maintenance of structural elements alwaystakes precedence over growth and reproduc-tion. The DEB model is developed as a non-species-specific metabolic model that allowsdifferences between species to be reduced todifferences in the set of parameter values. TheDEB model is tested for many species anddeviations are rare (Kooijman, 2009).

Algorithm Parameterization: Problemsand OpportunitiesBased on the endpoints listed in Table 1

and the algorithms described earlier, tissue-based effect concentrations are needed thathave phytoplankton population growth, zoo-plankton mortality and reproduction, and(instantaneous) larval mortality as endpoints.Because no algorithms were found that calcu-late delayed effects on mortality of cod larvae,it is unsure which types of data will be neces-sary. Most likely, algorithms will have to be con-structed so that available (and future) experi-mental data sets such as those discussed earliercan be used to obtain survival probability andgrowth for cod larvae.

Availability of Toxicity DataA query (performed on 15/03/10) in

the U.S. Environmental Protection Agency(EPA) database ECOTOX (http://cfpub.epa.gov/ecotox) reveals that of the 2543 effectconcentrations found for animals exposed toPAH, an important group of oil-associated com-pounds, only 864 are for marine species (i.e.,about 33%). Of these 864 marine records, lessthan 1% deal with arctic or subarctic species.For all effect concentrations retrieved, about5% expressed the concentration as a tissueconcentration. The vast majority of the 2543records was expressed as an aqueous concen-tration. Lastly, among endpoints, mortality wasmost frequently inspected (in 25% of the cases).Reproduction was only studied in about 2% ofthe cases (16 out of the 864 marine records).In addition, studies in the database that con-sidered endpoints in the “development” group(U.S. EPA, 2010) are scarce (15 out of the864 marine records). This might be problematicbecause the sensitivity of the latter endpointsand of reproduction is essential to parameterizethe algorithms listed in Table 1.

Clearly, the freely accessible database usedhere is not exhaustive as a good amountof data is most likely contained in unpub-lished reports, in publications that have not(yet) been included in the used database,or in databases that were not considered

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612 F. DE LAENDER ET AL.

here (e.g., the RIVMe-toxBase: http://www.e-toxBase.com). Nevertheless, the exercise per-formed here at least indicates that effectconcentrations for the endpoints included inTable 1 will not be readily available for inclu-sion in the ecotoxicological algorithms dis-cussed previously. To successfully parameterizethese algorithms, it may therefore be necessaryto obtain new information about the possi-ble effects of oil related compounds on theendpoints listed in Table 1. Although a set ofwell-defined experiments can be used—andmay be necessary—to obtain such information,this study focuses on computational techniquesthat try to maximize the amount of informa-tion gained from available data sets. Someof these techniques are straightforward, suchas the conversion from effect concentrationsthat are expressed as aqueous concentrations(ECx,water) to tissue-based effect concentrations(ECx,tissue) using bioaccumulation factors [BAF= ECx,tissue · ECx,water

−1]. Other techniques areless straightforward and may involve formalmetadata analysis techniques.

Using Metadata Analyses to FurtherExploit Toxicity DataDespite ongoing efforts, published meta-

data analyses that compare the sensitivity of(sub)arctic species with the sensitivity of tem-perate species are still scarce. Ecotoxicologicalresearch that addresses differences between(sub)arctic and temperate ecosystems has pri-marily focused on comparing environmentalconcentrations (Law et al., 2001; Sormo et al.,2009) or concentrations in biota from bothregions (Borga et al., 2004). Additionally, thefew reports on this topic do not always pro-duce similar results. De Hoop et al. (2009)found no significant difference between speciessensitivity distributions (SSD; probability dis-tributions containing species sensitivities) thatcontained temperate species and SSD contain-ing polar species. However, this could onlybe established for those substances for whichmore than four effect concentrations for tem-perate and for polar species were available(cadmium chloride, crude oil, and xylene). In

contrast, research carried out by Olsen et al.(2007) showed that benthic communities fromtemperate waters were less sensitive to hydro-carbons than benthic communities from arcticwaters.

The already-mentioned review indicatesthat efforts need to be continued to betterunderstand if and to what extent (sub)arcticand temperate species differ in sensitivity.Such efforts may include a database develop-ment of effect concentrations for (sub)arcticspecies. Available techniques to examine rela-tionships between sensitivities of (sub)arcticmarine species and sensitivities of temperatefreshwater species could include the inter-species correlation estimation technique (Dyeret al., 2006) as a powerful metadata analysistool. This technique uses a single known toxic-ity value to predict single species toxicity valuesfor species with an unknown sensitivity.

To infer effect concentrations for reproduc-tion from lethal concentrations (LCs), acute tochronic ratios (ACR) have been proposed, i.e.,the ratio between the LCx and an ECx with asublethal endpoint. Literature on this topic isquite extensive, yet predominantly based ondata for temperate freshwater species (Mayeret al., 1994; Brix et al., 2001; Duboudinet al., 2004; Ahlers et al., 2006). Whetheror not the information contained in thesestudies is suitable for impact assessments insub(arctic) environments will depend on thedifferences between (sub)arctic and temperatespecies sensitivity and is therefore subject tofurther research.

The analyses that are proposed in this sec-tion would be based on data sets that con-tain toxicity data for many different speciesand chemicals. Inevitably, these species wouldexhibit many differences in, for example, bodysize or composition. Chemicals for which dataare available will deviate in several ways fromthe oil-related compounds for which data areneeded. Therefore, the relationships that resultfrom the proposed metadata analyses mayexhibit considerable variance, an undesirablecharacteristic if they are to be used in aneffect assessment context. To deal with uncer-tainty and variability, mechanistic models could

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be included in the meta-analyses. By doingso, data on species and chemical characteris-tics could be used to reduce uncertainty andvariability. Further, mechanistic models wouldprovide a theoretically sound platform to esti-mate unknown effects on endpoint y fromknown effects on endpoint x. Indeed, mech-anistic models add information on the under-lying processes of the toxic response to thepredictive framework and therefore allow foreducated extrapolations of effects outside therange of tested toxicant concentrations butalso to other environmental conditions (Jager &Klok, 2010). Mechanistic modeling frameworksdiffer in complexity. More complex modelstypically include more processes and there-fore allow one to study relationships amongmore endpoints than simpler models that onlyincorporate a small number of processes. Anexample of a mechanistic model that inte-grates most of the endpoints that are typicallyincluded in ecotoxicity tests is the dynamicenergy budget model (DEB). The underlyingprinciples of the DEB were already outlined.Examples of less complex models that can beused to estimate unknown effect concentra-tions include routines that are based on speciesand chemical characteristics (Hendriks et al.,2005), as well as techniques that relate chem-ical descriptors to toxic action such as quanti-tative structure activity relationships (Parkerton& Konkel, 2000) or “read-across” approachesthat group chemicals based on their structureand assume comparable toxicity within groups(Patlewicz et al., 2008). Ideally, a metadataanalysis such as the one proposed here wouldinclude multiple mechanistic models of vary-ing complexity. There are several reasons todo so. First, databases typically include a mix-ture of simple (mortality) and more complexendpoints (metabolism). To convert such end-points to those for which data are needed(Table 1), models would be needed with acorresponding degree of complexity. Second,application of multiple modeling approacheswould allow for cross-validation of modelingresults and therefore more robust estimates ofthe effect concentrations needed to parameter-ize the algorithms that were listed earlier.

While the use of mechanistic models mostlikely reduces variance of the estimated effectconcentrations that are needed to parameter-ize the algorithms denoted in Table 1, vari-ability and uncertainty can never be ruledout and will therefore always have to bedealt with. Because risk assessors increasinglyadopt probabilistic approaches (Hope, 2006),a proper quantification of both sources of vari-ance (uncertainty and variability) is necessary.This can be achieved by characterizing all theexamined properties (e.g., ratio of (sub)arcticECx over temperate ECx) as probability den-sity functions (pdfs), rather than as point values.If also pdfs for tissue concentrations are avail-able, a formal effect analysis can be performedby comparing both pdfs (De Laender et al.,2010b). Pdfs of tissue concentrations can beobtained by running bioaccumulation modelsin a Monte Carlo fashion, in which all rele-vant sources of variability and uncertainty arepropagated.

Metadata analyses and extrapolationsmade by mechanistic models do not guaranteesuccess. In case the information content ofthe assembled data is too low to addressthe most crucial data gaps identified in thisstudy, additional experimental data need tobe generated. While doing so, it would bebeneficial to involve metadata analysis results,insights gained from mechanistic models,and simulated toxicity experiments in thedesign of the real-world experiments. Optimalexperimental design (Whittle, 1973) is a tool todesign experiments to maximize informationretrieval from the generated data set. To attainthis goal, controllable attributes of experimentsare manipulated such as number of replicates,temperature, number of evaluated concentra-tions, test duration, timing of measurements,and many more. Optimal experimental designhas been applied in many fields, e.g., fromneurosciences (Dale, 1999) to the calibration ofwaste water treatment models (Vanrolleghemet al., 1995). In ecotoxicology, its applicationsare scarce, yet promising (Baas et al., 2010).For example, it was shown recently that thetiming of toxicity tests profoundly affects theconclusions one draws regarding the toxicity

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614 F. DE LAENDER ET AL.

of a chemical mixture (De Laender et al.,2009).

Algorithm Parameterization:Particularities of Subarctic EcosystemsApart from possible differences in species

sensitivity, the subarctic ecosystems that char-acterize the spawning grounds of Atlantic coddeviate in several ecological aspects from theecosystems that are considered in risk assess-ments for temperate regions. An importantaspect is seasonal variability in light and tem-perature that is more pronounced in sub-arctic than in temperate regions (Carmack &Wassmann, 2006). Spring and summer arecharacterized by length of day up to 24 hand water temperatures around 10◦C, whileday lengths in winter can be as short as 0 hand winter water temperatures as low as 3◦C.These seasonal changes are the driving forcefor Calanus finmarchicus, the main prey forcod larvae, to descend below the pycnoclineat the end of summer and ascend in earlyspring (Heath et al., 2000). The extent to whichcod prey present near the water surface coin-cides with cod egg hatching will determinefood availability for cod larvae (Brander &Hurley, 1992). A nonexhaustive list of exampleswhere these aspects of subarctic ecosystemsmay result in seasonal variability of oil spilleffects is presented.

Because the nutritional status of organ-isms influences their sensitivity to chemicals(Kooijman & Metz, 1984; Kooijman, 2009),effects of an oil spill on cod larvae might bemore probable or more severe when food (i.e.,Calanus) concentrations are low. A mismatch intiming between Calanus ascendance and codegg hatching may therefore make cod larvaemore sensitive to chemicals (Skarðhamar et al.,2010). Hendriks et al. (2005) and Heugenset al. (2001) showed that multiple low-intensitystress factors affect organisms in an additiveway. However, if one or more of these factorsexerts stress in an extreme way, this is no longervalid (Klok, 2008).

It was demonstrated for many species thatthe toxicity of PAH increases in the presence of

ultraviolet (UV) light (Newsted & Giesy, 1987;El-Alawi et al., 2002). This issue is of particularrelevance for higher latitude regions becauseozone-layer thinning in polar areas has pro-duced an increased incidence of UV light inthese areas (Shindell et al., 1998). In addi-tion, (sub)arctic ecosystems receive up to 24 hof sunlight during late spring, which possiblymakes PAH more toxic during this period thanduring the rest of the year. The breakdown ofPAH into a variety of toxic and nontoxic com-pounds by photo-oxidation is also promoted bylight (Lee, 2003) and may therefore result inseasonal differences in the effects of accidentaloil spills.

The contributions of the ecological fac-tors given here on the ecological effects of anoil spill can be quantified using existing algo-rithms. DEB theory combined with results fromexperimental work (Kooijman & Metz, 1984)provides a basis for quantifying the effect ofnutritional status on species sensitivity. To quan-tify the influence of light level on the speciessensitivity, models have been developed thataccurately reproduce order-of-magnitude dif-ferences in effect concentrations establishedwith and without UV radiation (Grote et al.,2005). It is acknowledged that, apart fromecological differences between subarctic andtemperate ecosystems, also differences in thephysical characteristics of both environmentsmay bring about differences in the fate of theoil following an oil spill. Because the currentstudy focused on ecological effects, this issuewas not addressed in more detail here.

CONCLUSIONS

The effects that oil-related compounds mayexert on larvae from Atlantic cod (Gadusmorhua) and on planktonic community it inter-acts with were identified, and correspondingecotoxicological endpoints were identified anddiscussed. From this list, it became apparentthat the effects data needed to parameterizealgorithms for these endpoints are not readilyavailable. In order to accurately parameterizethese algorithms, a quantitative reevaluation of

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existing data is necessary using meta-analysesthat require a database containing all avail-able information on the sensitivity of (sub)arcticand temperate species. The result of such ananalysis would provide much-needed infor-mation on the sensitivity of subarctic species.A mechanistic modeling framework coveringboth simple and complex models could assistin meta-analyses. Further, meta-analysis resultsand modelling could steer future experimen-tal efforts by optimizing controllable attributesof experiments. Lastly, it may be warranted toexamine the influence of the seasonality thatis typical for subarctic ecosystems in terms oflight regimes and food availability on the effectscaused by an oil spill. This would allow oneto evaluate whether, and to what extent, theseeffects are season specific.

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