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ava i l ab l e a t www.sc i enced i rec t . com

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Ecological food web analysis for chemical risk assessment

Damian V. Preziosia,⁎, Robert A. Pastorokb

aIntegral Consulting Inc., 4D Bay Street, Berlin, Maryland 21811, USAbIntegral Consulting Inc., USA

A R T I C L E I N F O

⁎ Corresponding author. Tel.: +1 410 629 1301E-mail address: dpreziosi@integral-corp.coURL: http://www.integral-corp.com (D.V. P

0048-9697/$ – see front matter © 2008 Elsevidoi:10.1016/j.scitotenv.2008.06.063

A B S T R A C T

Article history:Received 27 June 2008Accepted 27 June 2008Available online 13 August 2008

Food web analysis can be a critical component of ecological risk assessment, yet it hasreceived relatively little attention among risk assessors. Food web data are currently used inmodeling bioaccumulation of toxic chemicals and, to a limited extent, in the determinationof the ecological significance of risks. Achieving more realism in ecological risk assessmentsrequires new analysis tools and models that incorporate accurate information on keyreceptors in a food web paradigm. Application of food web analysis in risk assessmentsdemands consideration of: 1) different kinds of food webs; 2) definition of trophic guilds; 3)variation in food webs with habitat, space, and time; and 4) issues for basic sampling designand collection of dietary data. The different kinds of food webs include connectance webs,materials flow webs, and functional (or interaction) webs. These three kinds of webs playdifferent roles throughout various phases of an ecological risk assessment, but riskassessors have failed to distinguish among web types. When modeling food webs, choicesmust be made regarding the level of complexity for the web, assignment of species totrophic guilds, selection of representative species for guilds, use of average diets, thecharacterization of variation among individuals or guild members within a web, and thespatial and temporal scales/dynamics of webs. Integrating exposure and effects data inecological models for risk assessment of toxic chemicals relies on coupling food webanalysis with bioaccumulation models (e.g., Gobas-type models for fish and their foodwebs), wildlife exposure models, dose–response models, and population dynamics models.

© 2008 Elsevier B.V. All rights reserved.

Keywords:Food webEcological risk assessmentEcosystemTrophicBioaccumulationToxic chemical

1. Introduction

Analysis of ecological food webs can provide importantinsights on both severe acute and subtle chronic effects ofchemicals and other stressors in the environment, yet it hasreceived relatively superficial treatment by risk assessors. Forexample, ecological risk assessment practices in Europe andNorth America are commonly based on simplistic assump-tions about food web structure and assignment of key speciesto trophic guilds to illustrate pathways of flow of chemicalcontaminants to valued species at higher trophic levels (Fig. 1).Information used to support food web analysis is typicallyobtained from the literature and interpreted in the context of

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simple linear relationships under the presumption of steady-

^state equilibrium (e.g., the application of food chain multi-pliers as described by USEPA, 1995). Little attention is given tosite-specific considerations, such as the often complex rela-tions among species and variation over time and space in dietsof species within a given food web (Schoenly and Cohen, 1991;Scharler et al., 2005).

Nevertheless, simple food web relationships are specifiedto model bioaccumulation of toxic chemicals and to someextent population or community dynamics in an attempt todetermine the ecological significance of chemical risks(Pastorok et al., 1996). Such simplifications do not accountfor inherent complexities in food web structure and are

: +1 410 629 1303.

.

Fig. 1 –Example of simple food web diagram used in a riskassessment to show possible pathways of trophic transfer oftoxic chemicals.

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unlikely to accurately predict possible dynamic and nonlinearprocesses.

Supporting information for food web analysis is providedby available compilations of trophic relationships in theecological literature, as well as direct observations of foragingbehavior and dietary composition for many species (Schoenlyand Cohen, 1991; Layman et al., 2005; including the EcologicalSociety of America data archive), community network andecosystem models, (Pimm, 2002; USEPA, 2004), analysis ofstable isotopes in organism tissue to estimate trophic position(Post, 2002), and experiments to elucidate interaction webs,such as predator removal studies (Paine, 1980; Power et al.,1996). In this paper, we call attention to the need for greaterincorporation of food web analysis to improve ecological riskassessments. Our objectives are to: 1) illustrate the importanceof knowledge of food webs in environmental management, 2)define the role of food web analysis in ecological riskassessment, and 3) identify issues in constructing andmodeling food webs.

2. The importance of food webs forenvironmental management

A food web is a representation of the aggregation of individualfood chains in a community (Pimm, 2002). Food webs extendthe food chain construct of a simple linear energy andmaterial transfer along a single pathway in a community toamore complex visual representation of multiple pathways ina network. Since the early days of recorded natural history,ecologists have been fascinated with trophic interactionsamong species.

The 18th century naturalist, Richard Bradley (1718, Part3:60–61) recognized the concept of a food chain when hewrotethat “Insects which prey upon others are not without someothers of lesser Rank to feed upon them likewise, and so toInfinity.” Charles Elton's (1927) text on Animal Ecology is often

cited as a source of the earliest food web diagram, having anillustration of the “food cycle” of Bear Island in the Artic.However, Pierce et al. (1912), Shelford (1913), and possiblyothers offered similar illustrative diagrams of other foodwebs.Lindeman (1942) developed the concept of materials andenergy flow in a food web, serving as the basis for ecosystemecology and current developments in ecological modeling andrisk assessment.

Environmental management demands an understandingof food web structure and dynamics (Scharler et al., 2005). Forexample, fishery scientists and managers increasingly rely ontheuse of foodwebs tomodel andpredict changes inharvestedfish relative to energy flows between predators, prey, andprimary energy sources in marine ecosystems (Beddingtonand May, 1977). Agronomists and agricultural managers haveincorporated food web considerations to understand andmanage the health of soil communities and their possiblerelationship to crop yields (Brussaard et al., 1988; Moore, 1994).Similar application of food webs has occurred for the purposesof forestry and rangeland ecosystem management as well asfor the protection and management of endangered andthreatened species (McNaughton et al., 1989; Mills et al., 1993).

The incorporation of food web analysis into chemical riskassessment can similarly be used to effectively manage thepotential impacts of toxic chemicals and other stressors in theenvironment. One of the earliest examples of how food webanalysis was used to support management decisions was thebanning of DDTproduction and use in the 1970s and 1980s afterit was understood that DDT was accumulating in aquatic foodwebs and resulting in toxic exposures in piscivorous birds.

Other formal recognition of the importance of food webs inassessing chemical exposures and ecological risk in the late1980s/early 1990s was the publication by USEPA of its firstecological risk assessment guidance (USEPA, 1992). Asdescribedin guidance documents, in the initial planning stage of anecological risk assessment (e.g., often referred to as the“problem formulation” stage) assessment endpoints are identi-fied and defined. Assessment endpoints are operationallydefined as ecological entities and their attributes and are usedto represent measurable ecosystem characteristics that ade-quately represent management goals. Ecosystem “integrity,”“structure,”and “function,”are summoned into theprocess, anddespite the importance of foodwebanalysis in elucidating thesefeatures, it is not more formally integrated into the process.

Although many of the assumptions and methods ofecological risk assessment have undergone refinement withrespect to the importance of food web constructs (USEPA,1993; Pastorok et al., 1996; Sample et al., 1997), to date there isno formalized process for the direct integration of food webanalysis. By proxy, some consideration of food webs isinherent in the traditional risk assessment process, particu-larly for receptor species at upper trophic levels. However, byaddressing one or only a few uptake exposure pathways in afood web, traditional risk assessment results preclude a morecomplete understanding of interactions at other levels in foodweb networks that could potentially have effects on thestructure and function of communities and ecosystems.Whilethe inclusion of multiple receptors to address various mea-sures of effects is not uncommon, assessors are often left withan understanding of risk for unconnected individual

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components, as opposed to a holistic portrayal of communityor ecosystem-level impacts or risks.

The need for more formal integration of a food^web

perspective in traditional ecological risk assessment hasbecome increasingly salient in the last decade (Pastorok et al.,2002; Peterson et al., 2003). In the case of assessing potential riskassociated with a chemical stressor or groups of chemicalstressors, risk assessors and managers have been increasinglycharged with communicating risks beyond single receptorspecies to address community and ecosystem-

^level impacts.

For example, a host of mesocosm- and microcosm-basedapproaches have been used in Europe and North America forthe assessment of pesticide and other chemical impacts at theecosystem level (Campbell et al., 1999). At the regulatory level inthe United States, the endorsement of these approaches hasbeen varied. European scientists and managers have beensuccessful at using mesocosms experiments to assess risks ofpesticides in aquatic systems. Though other large-scaleapproaches have examined potential exposures and risksthroughout a watershed or ecosystem, much of the analyseshave been based on evaluations conducted at the individualspecies level using more traditional food chain analysis.

The importance of integrating food webs in risk assess-ment to understand chemical exposures and ecologicalconsequences has also gained recent attention with respectto the evaluation of ongoing impacts nearly twenty years afterthe 1989 Exxon Valdez Oil Spill in Prince William Sound,Alaska. Peterson et al. (2003) based their interpretation ofongoing oil spill effects on delayed population reductions andcascades of indirect effects in the Prince William Soundmarine food web. The authors emphasize the need for insighton ecosystem-level toxicology and risk assessment and care-ful consideration of the role of food web interactions. To date,such perspectives have not been applied to the methods bywhich injury and recovery of natural resources under NaturalResource Damage Assessment (NRDA) are determined in theUnited States.

The consideration of food webs also appears to be a logicalprogression in the assessment of risks from multiple stressors(i.e., chemical, biological, and physical stressors) at regionalscales. Landis andWiegers (2005) present a formalized approachto assess relative risks from multiple stressors in a modelapplied to regional risk assessments. The approach for themodel consists of the identification of stressors in a region andthen the ranking of those stressors based on the magnitude oftheir effects and geographical extent. The ecological relevanceof the model's predictions has been confirmed using thetoxicological benchmark paradigm (Landis and Wiegers, 2005).The integration of food web measures to quantify stressormagnitude assumptions would be another useful method tovalidate model predictions and in so doing add increasedecological relevance to those predictions.

Despite such needs, the acceptance and use of informationon food web interactions in ecological risk assessment lags.Such a delay is unlikely attributable to any absence in theavailability of suitable models, given that a host of modelscurrently exist (Pastorok et al., 2002; USEPA, 2004). Data on foodweb composition, structure, and dynamics is often a limitingfactor in assessments of toxic chemicals. In silvacultural andagriculturalmanagement, analysis of foodwebs has had amore

prominent role, especially in regard to supporting the develop-ment of biological control methods (Van Veen et al., 2006).

The limited use of detailed food web analysis in ecologicalrisk assessmentmaybe attributible to the lack of data availableto parameterize such models. For complex models, extensiveamounts of data are needed to achieve a good understandingof food web structure, keystone species, and interactionsamong species. Winemiller and Layman (2005) pointed outthe difficulty of obtaining species inventories and developinginformation on species interactions, especially on the magni-tude of feeding relationships and the strength of functionalrelationships. While it is not necessary to implement complexmodels when the available data do not support such models,we believe it is useful to develop good conceptual models offood web structure and species interactions.

3. Types of food web models in use for eco-logical risk assessment

A number of currently available numerical models exist whichintegrate individual components or complete representationsof food webs for inclusion in ecological risk assessment.

Themost widely known and relatively simple form of theseincludes bioaccumulation models derived from the fugacity-basedmultimedia boxmodels described byMackay (1991). Thebasic Mackay model assumptions have been complementedwith bioaccumulationmodules to address contaminant fluxesin basic food web structures. The modules are user definedand are patterned after the food web structure defined in theconceptual model stage of the analysis. These bioaccumula-tion models notably include those developed for hydrophobicorganic chemicals by Thomann et al. (1992) and Gobas (1993),as well as their subsequent modifications.

Other models that integrate simple food web constructsinclude fish and wildlife exposure models, such as thosedeveloped for traditional chemical ecological risk assessment(USEPA, 1993; Sample et al., 1997). Such models are used forthe quantification of chemical exposure through single (e.g.,via diet) or multiple exposure pathways (via diet, incidentalingestion, inhalation, dermal exposure, etc.). Dietary expo-sure for upper

^trophic-level consumers is addressed by first

defining basic food web structure during the conceptualmodel stage of the analysis. Bioaccumulation models andwildlife exposure models hold in common their dependenceupon the characterization of food web linkages and chemicaltransfer pathways developed in the conceptual modelanalysis stage.

Typically, however, these linkages are defined simplisti-cally andmay not reflect the complex relations among speciesacross space and time.

Ecosystem-levelmodels tend tomove away from simplisticcharacterizations of food webs and examine interactionsacross and amongst species and their habitats. Such modelsare distinct in their description of functional relationships anddynamics of species or trophic guilds. Thus, ecosystem-levelmodels depend on not just characterizing trophic linkagesamong species but also knowledge of the nature of competitiveand predator–prey processes and the intensity of thoseinteractions, as well as functional webs.

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One example of an ecosystem model is USEPA's Aquatoxmodel (USEPA, 2004). The model consists of both ecologicaleffects and environmental fate considerations, with bioener-getics and nutrient cycling playing sentinel roles in themodel. Achiefadvantageof thismodel andsimilarmodels is that itaffordsthe user the ability to setup and examine complex food webs.Resultantmodelpredictionscanbebasedoneither riskquotientsor fluxes in biological (e.g., species abundance) and physical (e.g.,nutrient levels) parameters. In general, ecosystem-level modelsare distinct in their description of functional relationships anddynamics of species or trophic guilds (De Ruiter et al., 1996).

Food web models of varying levels of sophistication andcomplexity suggest that there isamodel formostneedsalong theriskassessmentspectrum.Despite theexistenceandaccessibilityof these models, they are used infrequently for chemical riskassessments and have not achieved widespread regulatoryacceptance. One reason for this may be the need for relativelylarge amounts of data to parameterize food web models.

4. Planning for food web analysis in ecologicalrisk assessment

Given the utility of food web models in characterizing risksand elucidating effective management actions, and the wideavailability of a diversity of models, we recommend thatfood web analysis be conducted routinely as part of riskassessments and that it be considered early in the planningstage. We describe a three-phase process for determiningthe need for and complexity of food web models, the types offood web model diagrams, and some basic steps forconstruction of food web models in the context of ecologicalrisk assessment.

4.1. Phase 1— defining modeling objectives relative to riskassessment objectives

The necessity for using food web modeling in the context ofecological risk assessment is predicated in large part by theneeds and objectives collectively identified by scientists,managers and regulators to fulfill the risk managementobjectives and goals of interest. If the goals are broadlydefined and can be reached using simplistic and conservativeassumptions regarding ecosystem structure and interactions,then traditional screening-

^level ecological risk assessment

techniques are likely sufficient. Nevertheless, food webdiagrams should be included within conceptual models toallow a qualitative evaluation of broader community orecosystem-level impacts that may be associated, for example,with elevated risk quotients for organism-level endpoints(see below, “Phase 2 — deciding on which food web type”).

In instances where more complex assessment endpointsare defined to fulfillmanagement goals, then quantitative foodwebmodeling is likely necessary. Foodwebmodelingwould beappropriate, for example, in instances where subtle and lessreadily transparent toxicological effects arehypothesized. Thismight include a hypothetical assessment of changes in energydynamics in a biological community, or, the indirect (e.g.,cascading) effects of stressors across trophic levels. For suchassessments food webmodels could be used to predict certain

population or community metrics, such as abundances of keyspecies, over time.

Once the need for performing food web modeling isestablished, assessors and risk managers must determinethe level of detail required in the model. This too is predicatedon the objectives and goals under risk management, but isadditionally a function of the complexity of the site, behaviorof the chemical contaminants, and the difficulty of ade-quately describing exposure, toxicity, and other properties ofthe chemicals (Pastorok et al., 2002). In general, themore basicbioaccumulation and ecosystem-level models, which inte-grate food web information, provide results as tissue con-centrations and risk quotients. More complex modelsintegrating data on food web components and structure aregenerally better suited to answering specific questions onpotential alterations in the structure of food web and com-munity dynamics.

Other considerations in performing food web modeling arepractical limitations in cost, timing, and the ability toeffectively communicate the findings to the intended audi-ence. Although the insight provided by food web modeling issufficiently greater than that provided by traditional techni-ques, such models can represent a significant expenditure inresources. In the case of communication to an intendedaudience, the need for and level of complexity of food webmodeling is in part influenced by whether the audienceconsists of the lay public, action groups, regulators, policymakers, or academicians.

4.2. Phase 2 — deciding on which food web type

Consideration of trophic relationships is an integral part offood web modeling in ecological risk assessment. Trophicrelationships are used to describe the arrangement andinteractions of species within a community and are a keycomponent towards quantifying energy transfer and chemi-cal uptake in a community or ecosystem. The kind ofinformation, both in terms of level of complexity andspecificity, about trophic interactions differs among phasesof an assessment.

Food web diagrams can be used in both the planning andanalysis stages of ecological risk assessment to visuallyrepresent trophic interactions in a fashion that best meetsthe needs and scope of the risk assessment objectives. Threetypes of food web diagrams are available to describe trophicinteractions (Paine, 1980):

• Connectedness web• Materials (and energy) flow web• Functional web

Each of these three web diagrams is described in terms oftheir use during the planning and analysis stages in ecologicalrisk assessment.

4.2.1. Connectedness webThe connectedness web is simply a diagram of trophiclinkages among species in a biological community, indicatinginteractions between grazers and plants, predators and prey,

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and decomposers and the rest of the community. Theconnectedness web may be useful for initially identifyingand characterizing an ecosystem in an ecological riskassessment as part of the problem formulation phase. Site-specific data on connectedness webs are typically notavailable for an ecological risk assessment and the riskassessor must rely on knowledge of habitat at a site, generalecological theory, and literature compilations of food webs.Full connectedness webs are rarely developed for an ecolo-gical risk assessment since they are not important in theanalysis stage of an assessment.

4.2.2. Flow webThematerials (and energy) flow web illustrates the pathwaysof flow for nutrients, toxic chemicals, and energy in thecommunity. Knowledge of the materials flow web forreceptors selected in the ecological risk assessment is criticalfor chemical uptake and exposure analysis. In particular,certain bioaccumulative chemicals, such as polychlorinatedbiphenyls (PCBs), chlorinated pesticides (e.g., DDTs, chlor-dane), dioxins/furans, and methyl mercury are passedthrough food chains and may biomagnify in the food web,resulting in the highest tissue concentrations in top pre-dators. The materials flow web is important in estimatingrisks as part of risk characterization, but may also be the

Fig. 2 –Overview of food web analysis

underpinnings of estimates of toxicity thresholds or remedialgoals.

4.2.3. Functional webThe functional web indicates dominant interspecific interac-tions that control community structure (Paine, 1980; Poweret al., 1996). For example, interactions between a keystonepredator and its prey would be an important component of afunctional web. The functional web is often absent in currentecological risk assessments, sometimes because of lack ofspecific information on functional interactions that controlstructure of a particular community. However, this kind offood web can provide critical information for selectingreceptors and interpreting the ecological significance of riskestimates in the context of possible disruptions in communitystructure. For example, knowledge that a keystone predator isat risk due to chemical or other stressors would lead to moreconcern and a higher rating of the ecological significance ofrisk than when a non-keystone predator is at risk.

4.3. Phase 3 — construction and other considerations

We offer some basic conceptual insights on the constructionof food web models and some important considerations ondata and model testing.

for an ecological risk assessment.

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4.3.1. Steps in food web analysis and modelingBasic steps in analysis and modeling of ecological food websinclude the following:

• Define food web modeling objectives relative to risk assess-ment objectives

• Prepare a graphical diagram of the food web model• Define spatial–temporal scale and resolution for the foodweb model

• Characterize dietary composition in space and time• Characterize chemical uptake and trophic transfer processes• Construct, calibrate, and validate the food web model

Fig. 2 illustrates these basic steps in the context of theoverall risk assessment framework. The figure additionallypresents some of the key inputs andwhere they apply to stepsin food webmodeling in general. These include the identifica-tion of ecosystems, habitats, receptors, and stressors relevantto defining the objectives of the model.

Additional inputs include trophic guilds, keystone species,and functional relationships needed to develop a food webmodel diagram in concert with the objectives. Dietary datawhich has undergone statistical analysis for characterization ofdiet of species in time and space is also included, as are uptakeinputs (e.g., tissue-media regressions; bioconcentration factors(BCFs), bioaccumulation factors (BAFs), and biomagnificationfactors (BMFs); and chemical transfer models) needed tocharacterize chemical uptake and trophic transfer processes.

We provide further discussion of key components of theoverall construction process, specifically developing food webmodel diagrams, the characterization of dietary composition,the characterization of chemical uptake and calibration, andvalidation of food web models.

4.3.2. Developing food web model diagramsFood web model diagrams serve an important role inorganizing and communicating information about trophicrelationships in biological communities. To build a food web

Fig. 3 –Alternative functional webs for the

diagram, one must make choices about the particular speciesor trophic guilds to show, representative linkages betweenspecies, and the kind of web (connectedness, materials flow,or functional web). In ecological risk assessment, a materialsflow web for bioaccumulative chemicals is often the food webchosen for exposure analysis (Fig. 1).

But other kinds of webs can play an important rolethroughout an ecological risk assessment, as discussedabove. Typically, the species or trophic guilds shown areimportant receptors (or are their prey items) in the ecologicalrisk assessment.

Even after choosing the species or trophic guilds to show ina web diagram, various approaches are possible, such as:

• Depicting all trophic levels but limiting the selection ofspecies to a few representative guilds important in theecological risk assessment to illustrate trophic transfer ofchemicals (Fig. 1)

• Depicting selected trophic levels important for the ecologi-cal risk assessment with only the prey sampled for tissueanalysis of COPCs and their predators (see Pastorok et al.,1996). Additionally including a functional web important inanalyzing the ecological significance of estimated risks

• Including a mix of microhabitat affinities and documentedand inferred trophic relationships arranged by line diagramsand topological position (e.g., in the Hudson River examplediscussed further below)

• Depicting alternative webs; Fig. 3 showing the three func-tional webs developed by Power et al. (1996) for the SouthFork Eel River, California.

The alternative functional webs developed by Power et al.(1996) illustrate inter-annual variation in food webs driven byhydrological factors. Data to develop these webs wereobtained from field experiments using predator exclusioncages to elucidate the role of key species in controllingcommunity structure in the Eel River. In each of the threewebs (Fig. 3), the strongly interacting species that control

South Fork of the Eel River, California.

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community structure are indicated in bold font, with func-tionally significant food chains shown as solid arrows.

The choice of and level of detail in food web diagramsdepend on the objectives and phase of the assessment, andultimately the purposes for showing a food web. More detail isuseful for technical analysts in the problem formulationphase, but when communicating the results of a given foodweb analysis, only key aspects of the webmay be important toportray.

Problems encountered in constructing realistic food webdiagrams include the difficulties of identifying the key trophiclinkages for a specific ecological risk assessment, the inabilityto show all important links without creating a tangled web(the “Spaghetti effect”), limited data on dietary composition ofspecies, and variation of webs in space and time. From thetheoretical standpoint, Maurer (1999) offers poignant perspec-tives on the application of a pluralistic rather than thetraditional reductionist approach towards resolving complex-ity in ecological systems. From a more applied standpointrelevant to food webs in particular, Caddy and Sharp (1986),building on the ideas of Cousins (1985), attacked the conceptof a numerical trophic level and proposed alternativemethods for food web diagrams (elongated “parallelograms”in their Fig. 7). They note the difficulty of assigning trophiclevels. For example, the Peruvian anchoveta is both anherbivore and primary carnivore feeding on zooplankton asan adult. However, while they are in the plankton phase, theyfeed on their own eggs, which in one sense, makes them asecondary predator. When young, herbivores as well ashigher predators may have similar positions in the foodweb, but apical predators may be regarded as movingupwards in the food web during ontogenetic development(Caddy and Sharp, 1986).

Following Elton (1927) and others, Caddy and Sharp (1986)propose classifying feeding levels by size of predator andpreferred size of prey as an alternative to the “difficult-to-quantify concept” of trophic level “based on knowing thewhole feeding history of an organism” (Cousins, 1985).However, size classifications are unlikely to totally replacethe more typical food web diagram based on trophic guilds.Abandoning the food web diagram is not necessary when onefocuses on key pathways for transfer of chemicals/energy in abiological community. Moreover, new ways of portraying foodwebs (see below, “Hudson River case study”) avoid somedifficulties encountered in constructing food webs.

4.3.3. Characterizing dietary composition in space and timeDepending on the chemical and receptors of interest, thecharacterization of dietary composition of receptors or preyitems addressed included in a food web model can play acritical role in the determination of exposure and thecharacterization of risk. For many species, dietary composi-tion varies depending on a host of extrinsic factors andintrinsic needs, such as availability of food, movements ofanimals and food sources, and seasonal and ontogenetic shiftsin dietary preferences. Schoenly and Cohen (1991) discuss theimportance of variation in food web structure using examplesfrom 16 empirical webs.

Sugihara et al. (1997) address the effects of variation oftaxonomic and trophic aggregation on food web properties.

Consideration of such variations is critical in food webmodelsfor risk assessment, because these variations may resultin stochastic baseline conditions (i.e., as defined with theabsence of chemical contaminants).

Practical steps can be taken to improve the characterizationof variable dietary composition data for food webs. Cohen et al.(1993) provide recommendations for improving dietary analysisand food web compilations, including: 1) developing methodo-logical standards, 2) being explicit at each step in planning,executing, and reporting fieldwork, 3) defining the setting of thefood web precisely (i.e., geographic area, observation time, andmethods), 4) using specific units of reporting and yield–effortcurves, 5) using primarilymatrix formats for foodweb data, andother topics. Each of these is viewed as not only helpful inproviding greater accuracy in basic data, but also in improvingthe understanding of variation.

A number of current methods are available for collection ofdietary data that can be used to improve the accuracy ofdietary composition data when using food web models in riskassessment. These include the following:

• Basic field observations (Cohen et al., 1993)– Foraging behavior– Diet taxonomic composition

• Gut content analysis (Cohen et al., 1993)– Diet taxonomic composition– Chemical analysis of gut contents

• Isotopeanalysis of organism tissue (Post, 2002; Laymanet al.,2005)

• Predator removal experiments to elucidate interaction webs(Paine, 1980; Power et al., 1996; Layman et al., 2005).

A special issue in characterizing dietary compositionconcerns large-bodied prey. For example, adult predatoryfish such as largemouth bass may eat a wide size range ofprey from zooplankton only a few millimeters long to otherfish and frogs many centimeters long. Typically, the largerthe prey, the rarer it will be in the diet. Numerical counts ofprey items in guts of sampled predators may not adequatelyquantify the importance of large prey in the diet because ofsampling issues. Yet these large prey items may beimportant from the perspective of prey biomass, as well astransfer of energy and chemical contaminants to thepredator. Large prey themselves often eat at higher trophiclevels than small prey and therefore may contribute dis-proportionately to contaminant transfer on a per unit bio-mass basis.

4.3.4. Characterizing chemical uptake and trophic transferprocessesTraditional assumptions used in ecological risk assessmentshould be carefully thought over when implementing chemicaluptake components in food web modeling. Among others,assessors should carefully consider the assumption that uptakeoccurs as a predominantly linear process towards steady-stateequilibrium. Researchers have acknowledged the limitations ofpharmacokineticmodels in determining uptake of hydrophobicorganic chemicals in aquatic environments (Barron, 1990;Landrum et al., 1992). Metals also require careful attention.Williams et al. (2006) demonstrate that in the case of arsenic,

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bioconcentration in aquatic organisms may not follow first-order kinetic assumptions, and suggest an alternative bioaccu-mulation relationship wherein the highest bioaccumulationfactors may occur at low background levels and may decreaseas environmental concentrations increase above the ambientrange.

Assessors also need to consider interactions between toxicchemicals and info-chemical signals that modulate food webinteractions. Scharler et al. (2005) described the role of info-chemicals in foodwebs, suchaschemicals releasedbypredatorsthat induce prey to form defensive structures or take refuge, apredator's use of prey-waste products or pheromones to locateprey, and related changes in functional responses and interac-tion strength in food webs. Interference with normal ecologicalsignaling through info-

^chemical pathways due to the presence

of toxic chemicals is an area in need of research.

4.3.5. Calibration and validation of food web modelsReliance upon food web modeling in exposure analysis, riskassessment, and riskmanagement requires careful evaluationto the predictive capabilities of a given model (Preziosi andDurda, 2003). Performing simple checks on the predictionsprovided by food web models is a critical step in the modelingprocess, but it is one that is often overlooked or considered notfeasible given practical limitations. This can be especiallyproblematic in those instances where faulty model predic-tions are used to establish putative impacts to ecologicalhealth. This in turn, can ultimately lead to risk managementand regulatory decisions that are at best, unscientific, and atworst, erroneous.

Calibration and post-hoc validation may be performed toensure that the model is providing reliable results. However,the confidence in the calibration and validation techniquesshould also be carefully considered (Preziosi and Durda, 2003).Calibration is an iterative process during which both inputsandmodel are “fine tuned” based on a subset of preliminary orcomparable observations. Model validation is a continuationof the calibration process, and involves the comparison ofpredictions to a more complete set of observations, some ofwhich may be obtained post-hoc. The purpose of validation isto ensure that once sufficiently calibrated, the model reliablyrepresents all the variables and conditions that can affectmodel results. Ultimately, the predictive capability of a modelis based on the strength of the calibration and validationtechniques employed, and on the ability of a single set ofinputs to represent observed data within a prescribed range ofstatistical confidence.

Various calibration and validation techniques, many ofwhich are rooted in classical statistics, are available toevaluate the predictive capabilities of models. These generallyinclude examination of predicted and measured data dis-tributions, visual inspection of data, and parametric andnonparametric comparative tests. Numerous examples ofcalibration and validation of foodchain and other environ-mental models are also available in the literature (Beck et al.,1997; Gobas et al., 1998). Use of these techniques can lead tofundamental improvements in the reliability of calibrationand validation approaches by providing a statistical basis forassessing predictive capability and by lending insight inadditional modeling and data needs.

5. Case study — aquatic food web analysis forthe Upper Hudson River

The ecological risk assessment for the Upper Hudson RiverSuperfund site illustrates the context for some solutions tocomplex issues in food web analysis for bioaccumulativechemicals, in this case PCBs.

The Upper Hudson River Superfund site is located alongapproximately 40miles of theUpperHudsonRiver fromHudsonFalls downstream (south) to the federal dam at Troy, NY. PCBsoriginated from two General Electric capacitor-manufacturingplants located in Hudson Falls and Fort Edward, NY. As part ofthe remedial investigation for the site, mechanistic mathema-tical models were developed for the hydrodynamic, sedimenttransport, PCB fate, and bioaccumulation processes importantfor transfer of PCBs through food webs. To characterize the no-action alternative and other remedial options (e.g., sedimentremoval or capping), a model was used to predict futuretemporal trends in PCB concentrations in environmentalmedia and the associated residual risk (TAMS et al., 2000a,b).An important aspect of the model was the ability to estimateaccurately PCB concentrations in fish tissue.

The conceptual model developed for the Hudson Riverecological risk assessment incorporated a simple food webdiagram based on classification of ecological receptors intotrophic levels and guilds. Although a simple diagram ofrelationships among trophic guilds was also used as thebasis for bioaccumulation modeling (TAMS et al., 2000a,b), adetailed analysis of dietary composition of the dominant fishspecies was conducted to identify the important pathways ofPCB exposure for fish (PTI, 1997). Because of their partialseparation from the sediments, vegetation-associated foodwebs may be more responsive than sediment-based webs tochanges in the rate of PCB release to the water column andtherefore may respond relatively rapidly to PCB sourcecontrols. Differences in food web structure and PCB contentof selected fish species among habitats and among areas couldlead to variation in risk among habitats and potentiallydifferent strategies for remediation. Aquatic vegetation bedscover at least 50%

^of the bottom in some areas of the Upper

Hudson River, so bioaccumulation patterns and responses tovariations in PCB inputs characteristic of vegetation-basedfood webs are important to the river system as a whole.

Sampling of benthicmacroinvertebrates (BMI), phytophilusmacroinvertebrates (PMI) (i.e., zooplankton species commonlyfound in the littoral zone plus epiphytic macroinvertebrates),and fish gut contentswas conducted in several locations of theUpper Hudson River in autumn (September), 1998 and spring(May) 1998 (Fig. 4).

Study design, sampling and analysismethods, and data areprovided in PTI (1997) and Exponent (1998).

Certain prey taxa found in fish stomachs can be classifiedas either PMI or BMI, thereby linking the fish to the habitats inwhich they forage and the ultimate pathway for exposure toPCBs. Thus, characterizing the ambient macroinvertebratecommunities and the diets of dominant fish species providesthe basis for documenting those food web pathways that aremost important to PCB exposure of upper trophic-levelspecies. However, the complexity of the food web and the

Fig. 4 –Alternative aquatic food webs in the Upper Hudson River depending on habitat type and season.

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Fig. 5 –Variation in breadth of diet in fish with habitat type and location in the Upper Hudson River.

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need to characterize diet composition by macroinvertebratetype (i.e., benthic or phytophilous) for each species of fishmade diagramming the food webs challenging. To resolve thisproblem, a new method of portraying food webs was devel-oped, as shown in Fig. 4. Each panel of this figure shows a foodweb for the dominant fish species of the Upper Hudson Riverfor a particular habitat type (Vallisneria, Trapa, or mixedvegetation including Elodea canadensis, Potamogeton spp., Bra-senia sp., and other species), river location and season (fall1997 or spring 1998).

The differences in community structure of PMI, BMI, andfishes among habitats and locations lead to spatial variationin the dominant species in the aquatic food webs of the UpperHudson River (Fig. 5). The dietary composition of the targetfish species varies substantially with habitat type and riversampling area, as well as with season. Moreover, the complex-ity of the food webs in Vallisneria habitat decreases from northto south in the Upper Hudson River based on the number oftrophic links per fish species (Fig. 5), whether the entire suiteof prey taxa or only the primary taxa are considered. Vallis-neria-based webs (11–13 links between the dominant fish andtheir primary prey) are more complex than Trapa-based webs(6 and 10 links).

Dominant fish species of the Upper Hudson River includelargemouth bass (Micropterus salmoides), brown bullhead(Ameiurus nebulosus), yellow bullhead (A.

^natalis), pumpkinseed

(Lepomis gibbosus), log perch (Percina caprodes), yellow perch(Perca flavescens), and spottail shiner (Notropis hudsonius).Overall, these species of fishes feed mainly on phytophilousand planktonic invertebrates (e.g., Cladocera, Cyclopoida,Ostracoda, Amphipoda, Diptera, Trichoptera) and to a lesserextent on taxa that are almost exclusively benthic (i.e., Hexa-genia and Caecidotea). Cladocera and Diptera (including chir-onomid midge larvae) are primary prey in all of the webs inthis study and typically are themost important components offish diets. Generally, the dominant fishes feedmost heavily on

invertebrate taxa consisting of herbivores, detritivores, andomnivores (or mixed predatory, omnivorous, and herbivorousspecies). Invertebrate taxa that are almost exclusively pre-datory (i.e., cyclopoid copepods, damselflies, and dragonflies)were important prey items only for yellow bullhead in Vallis-neria habitat and yellow perch in Trapa habitat at GriffinIsland. Yellow perch, log perch, spottail shiner, and large-mouth bass show a strong preference for PMI as theirinvertebrate food resource (the ratio of PMI to BMI in the dietexceeded 1). Fish are also a large component of the diet oflargemouth bass. PMI were less important than BMI in thespring because aquatic vegetation beds were not well devel-oped at the time of sampling (May, 1998).

6. Conclusions

Food web analysis is used to model bioaccumulation andtransfer of chemicals through biological communities, wildlifeexposure to chemicals, and ecosystem dynamics in responseto multiple stressors. Food web analyses presently conductedfor ecological risk assessments are often elementary andignore spatial–temporal variation in communities and dietcomposition of key receptors. The decision to use quantitativemodeling for exposure analysis (e.g., wildlife exposure modelsand aquatic food web bioaccumulation models) depends onthe objectives of the assessment relative to risk managementgoals, but the value of such modeling is being realized moreand more. Construction of food web diagrams is a key step inany ecological risk assessment and different kinds of webs(i.e., connectedness, materials flow, and functional) are usefuland may apply to different phases of an assessment. Despitethe difficulty of characterizing functional webs, progress inecological risk assessment will come only from consideringsuch important trophic interactions and the role they play instructuring biological communities (e.g., trophic cascades). A

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valid determination of ecological significance during the riskcharacterization phase of an assessment cannot be madewithout some consideration of the functional context of trophicrelationships among key ecological receptors and their prey.

Acknowledgements

We thank the Netherlands Organisation for ScientificResearch (NWO) for travel funding to attend the 2006 annualSETAC Europe meeting where portions of this paper werepresented. Integral Consulting Inc. provided support fordevelopment of this manuscript.

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