Ecological Risk Assessment - Part 4 ppsx

tests, assessors often can do little m ore t han present qualitative rela tionships, f or example,the r eduction in abundance of aquatic i ns ects will r educe growth a nd fecundity of f

Part IV

Analysis of Effects

In the analys is of effects, assessors characteriz e the nature and magni tude of e ffects ofchemi cals or other agen ts as func tions of exp osure Effect s may be estimated by perfor mingtests, by observi ng effects in the field, or by mathe matical ly simulat ing effe cts In the an alysis

of effe cts, effects data must be evaluated to de termine which are relev ant to each assessmentendp oint, and then rean alyzed and summ arized a s ap propria te to make them useful for riskcharact erization Two issues must be consider ed

First, what form of each avail able measure of effect best ap proximates the assessmentendp oint? Thi s issue should ha ve been consider ed during the problem form ulation (Chapter

18) However, the a vailabil ity of una nticipated data and better unde rsta nding of the situatio nafter data collection often requ ire reconsi deratio n of this issue

Secon d, is the exp ression of the effects data consis tent wi th the expression s of exposure?Integr ation of exp osure and effe cts defines the nature and magnitud e of effe cts, giventhe spati al and tempor al patte rn of exp osure level s Therefor e, the relev ant sp atial andtempor al dimension s of e ffects must be defined and used in the expression of effects Forexampl e, if the expo sure is to a mate rial such as unl eaded gasoline that persists at toxic levelsonly brief ly in soil, effe cts that are induced in that time period must be extra cted from theeffects data for the chemi cals of concern, and the analys is of field-der ived data should focus

on biologi cal respon ses such as mass mort alities that co uld oc cur rapidl y rather than term responses

long-The de gree of de tail an d conserva tism in the analysis of effects de pends on the tier of theasses sment (Sect ion 3.3) Screening asses sments typic ally define the exposure–r espo nse rela-tions hip in term s of a bench mark v alue, a concen tration or dose that is conserva tively define d

to be a thres hold for toxic effects (Chap ter 31) Defi nitive asses sment s should define theapprop riate exp osure–r esponse relat ionshi p Typical ly, this req uires performing tests (i.e.,control led exposures to the agent of co ncern; Chapter 24) or field studi es (Chapter 25)associ ating exp osures and effe cts (Ch apter 23) Bec ause tests typic ally do not include all specie sand life stage s of con cern, extra polatio n models are need ed to esti mate effe cts on attr ibutes oforganis ms (C hapter 26), populatio ns (Chapter 27), or ecosystems (Chap ter 28) Bec ausenearly all testing determines organism-level responses, extrapolations to organism-level

endpoint attributes use simple assumptions or statistical models In contrast, the extrapolation

to population and ecosystem-level attributes requires an extrapolation across levels of ization that typically requires mathematical simulations

organ-23 Exposure–Response Relationships

What is there that is not poison ?All thin gs are poison , and nothing is withou t poison Solely the dose deter min es that a thing is n ot a poison

Paracel sus, trans lation by Deic hmann et al (1986)Paracel sus’s famous insight that the dos e makes the poiso n implies that toxic ologists mustdeterm ine the relationshi p betw een the dose level and the toxic respon se Mo re generally, toasses s the risk posed by any agent, it is ne cessary to determ ine the relationshi p betw eenexposure and respon se Expos ure–res ponse relationshi ps are, in general , quantitati ve models

of the form r ¼ f( e ), wher e r and e are response and exposure metrics, respect ively However,they may be qua litative relat ionshi ps such as: where introdu ced specie s e is pr esent, nativ especie s r is extirpate d Hence, we may more gen erally state that we wish to estimat e theexpecte d response r given a specif ied exposure e , E( r je ) Expos ure–res ponse relationshi psserve a t least three pur poses

Estimat ion : If an e xposure–r esponse model is availab le, an appropri ate estimate of ure can be used with that mo del to estimat e the respo nse Suc h estimat es can be use d in riskasses sments to charact erize risks from future contam ination or in ecologi cal epidemio logy todeterm ine whet her observed levels of exp osure are credibl e causes of observed impai rments.Bench marks : If an ex posure–r esponse relationshi p is reduced to a poi nt such as an EC20 or

expos-a Benchmexpos-ark Dose Limit (these expos-are test endp oints; Box 23.1), that value can be used toseparat e accepta ble from unacceptabl e exp osure level s They are used as regula tory standar ds

or as screenin g benc hmarks , either directly or after ad justment wi th safety factors or othermeans (Chapter 29)

Commu nicat ion: Stakehol ders an d de cision makers are often unfami liar with the ways inwhich effects change in response to change s in exposure level s Rat her, they tend to think interms of dicho tomies such as safe or uns afe Hence, it is often impor tant to pr esent exp osure–response relationships, particularly when complexities such as time to response, optimalexposure levels, or thresholds are involved

Exposure–response relationships are expressions of the observation that effects are caused

by associations of affected entities with causal agents The associations may occur in a toxicitytest or other expe rimental study (Ch apter 24) or in observation al studies (C hapter 25) Ineither case, the importance of the analysis comes from the assumption that by quantitativelymodeling the association of exposure and response one can generalize to other cases in whichthat cause and the affected entity are associated That is, if a chemical’s 96 h LC50for fatheadminnows is determined from a laboratory test to be 2 mg=L, one would expect a fish kill tooccur if that chemical occurred at 2 mg=L for at least 96 h in a stream with similar water

chemi stry Hence, when develop ing expo sure–re sponse relationshi ps, we must answ er theque stion, what express ion of the observed associ ation be tween the causal agen t and the effect

of interest will allow us to make the most useful pred ictions of futur e effects?

The responding uni t in ne arly all toxicity tests and in many studies of biologi calrespon ses to nut rients, heat, and other nont oxic agents is the ind ividual organ ism Whatprop ortion die, what is the average grow th, etc ? How ever, respo nses of other entitiessuch as experi menta l populati ons (e.g , algal test s), experi menta l communi ties (e.g., micro-cosms ), and field popul ations and c ommuni ties may be related to their levels of exposure.The most common respon ding uni t in ecologi cal risk asses sment , afte r organ isms, is specie s

Mo dels relating the responses of indivi dual specie s to exposure levels are term ed specie ssensi tivity distribut ions (Posthum a et al 2001) How ever, since they are most commonl ythough t of as models to extra polate from specie s to commun ities, they are discus sed in

Secti on 26.2.3

Depen ding on the assessment prob lem, it may be useful to de fine an exp osure–r esponserelation ship wi th respect to any numb er of the dimens ions descri bed in Sectio n 6.3: space,time, intens ity, severity, pro portion respo nding, an d type of respon se Expos ure–res ponserelation ships are often express ed as poi nts, such as LC50 s or no observed effect con centrations(NOE Cs), but the most useful of the commonl y available relationshi ps is a line in two-dimens ional space define d by one ex posure metric (usual ly co ncentra tion or dos e) and onerespon se metr ic (usually severi ty or proportio n responding ) (Figur e 23.1) Thes e relationshi ps

BOX 23.1

Termin ology fo r Test Endpoint s

Analyses of exposure–response relationships should aim to develop models of how responseschange as exposure changes However, results of tests or observations are commonly reduced to apoint that is thought to provide a threshold or to summarize the results The terminology for thesevalues is inconsistent in practice and may be confusing In this explanation, the intensity of agents

is defined as concentration (C), since it is the most common unit in ecological risk assessment.However, one can substitute dose (D), time (T) or, more generally, level (L) for C in any of theterms

If regression analysis has been used to develop a model that relates responses to exposureestimates, inverse estimation can derive an exposure level corresponding to a specified effects level(Figure 23.1) For quantal variables, those that are proportions of subjects displaying a dichot-omous trait such as survival=death or presence=absence, these are termed ECp, the concentrationcausing the effect in proportion p The median lethal concentration (LC50) is a particular ECp.For continuous variables such as weight or eggs per female, these values are known as ICp, theconcentrations inhibiting the response by proportion p Other terms are used in particularcircumstances For example, the term infective dose (IDp) is used for tests of pathogens Forsimplicity, ECp may be used for all of these values

In human health risk assessment and some wildlife risk assessments, the term equivalent to ECp

is the benchmark dose (BMD) (Crump 1984) A lower confidence limit on the BMD is termed aBMDL

If hypothesis-testing statistics are used, two test endpoints are derived The first is the lowestconcentration causing an effect that is statistically significantly different from control or reference,the lowest observed effect concentration (LOEC) The second is the highest concentration that isbelow the LOEC, which is the no observed effect concentration (NOEC) If adverse effects aredistinguished from those that are not considered adverse, LOAEC and NOAEC terminology isused

are typically sigmoid The slope or spread of the curve depends on the variance in sensitivityamong the exposed units Tests of very similar organisms, such as inbred laboratory rats,yield very steep curves, while dissimilar units, such as stream communities in a field study,yield much broader curves with respect to a particular range of exposure Surfaces in threedimensions should be used much more often than they are, because we often want to knowabout responses to both the concentration and duration of exposure (Figure 23.2) Similarly,for a fish population model, we may need to know the relation to concentration of both thereduction in fecundity and the proportion of females exhibiting a given level of reduction,because the implications of a relatively uniform reduction in fecundity may be different fromthe same average effect due to sterility of part of the population and no effect on the rest Thenext logical step is volume in four dimensions (e.g., concentration, duration, severity, and

FIGURE 23.1 Exposure–response relationship with inverse regression The benchmark dose (BMD) isderived from the benchmark response (BMR) Generated by the Benchmark Dose software

0.94 0.00 0.25 0.50

0.75 1.00

19

FIGURE 23.2 Toxic effects as a function of concentration, duration, and proportion responding

proportion) or even five (add the distribution in space) More information is always desirable,but data become limiting We can gather data on concentration, duration, severity, andproportion from a conventional toxicity test, but the conventional number of replicateswould seldom be sufficient to statistically fit a four-dimensional model However, such datamay be displayed without fitting a function (Figure 23.3).

Often, risk assessors must settle for whatever standard or nonstandard expressions of theexposure–response relationship are available This chapter discusses alternative approachesand associated issues so that assessors understand the types of relationships that they may berequired to use and in the hope that they will have the opportunity to derive relationshipsfrom available data or even direct the generation of new data

50

N E T

N E T

100 3rd day

50 100 4th day

50 100 5th day

50 100 6th day

50 100% 7th day

AN AC D

AN AC D

N E T AN AC D

N E T AN AC D

N E T AN AC D

Five test methods to assess chemical toxicity

FIGURE 23.3 The relationship of concentration, duration, severity of response, and proportion displayingthe response, shown as a set of severity vs proportion responding relationships, arrayed onconcentration and time axes The responses are N¼ normal; E ¼ eyespot; T ¼ tetratopthalmic; AN ¼anopthalmic; AC¼ acephalic; and D ¼ death (FromYosioka, Y., Ose, Y., and Sato, T., Ecotoxicol.Environ Saf., 12, 15, 1986 With permission.)

23.1 APPROACHES TO EXPOSURE–RESPONSE

Expos ure–res ponse relat ionshi ps can be de rived in various ways dep ending on the amountand quality of da ta and backgro und informat ion that are avail able Ideally, mechani sms areunde rstood and can be us ed to model responses to expo sures At the other extre me, one maynot be able to do more than report that a pa rticular respon se occu rred at a parti cularexposure level Currentl y, the best available guidan ce for ecotoxico logical expo sure–re sponseanalys is is provided by Environme nt Canada (2005) , but other organiz ations provide differ-ent guidance (ASTM 1996; OECD 1998, 2004; Crane and Go dolphin 2000; Klemm et al.1994; IP CS 2004)

If the mechani sms by whi ch an exposure causes a respo nse are unde rstood, a mathe maticalmodel that repres ents that relationshi p may be developed These include toxicod ynami cmodels of organis mal responses (Sect ion 23.3), popula tion dy namic mod els (Ch apter 27),and ecosystem mod els (C hapter 28) One advantag e of these models is that their functi onalform is defensi ble on bases other than con vention or goodness of fit Anothe r advantag e ofmechani stic mode ls is their flexibil ity If mechani sms are wel l unde rstood, a mech anisticmodel may be used to simu late cond itions outsi de the range of test s or observation s Ifmechani sms are fully specif ied, responses could be modeled from basic knowl edge withoutany testing or observat ion, as in the app lication of phy sical laws Howev er, models inecologi cal risk are almost ne ver purely mechani stic They usuall y rely on empir ical ap-proache s to esti mate parame ter values , and , in the simplest case, they are eq uivalent tobiologi cally plausi ble regres sion models Hence, their range of app licability must be carefullyconsider ed (Chapter 9) Example s of mech anistic exposu re–respo nse models for organ ismsinclude the Dynam ic Ener gy Budge t mod el (DEB tox) (Kooi jman a nd Bedau x 1996) an dconven tional toxic odynami c models (Sect ion 23.3)

If data are available for respon ses at mult iple exp osure levels, the best general appro ach toexposure–r espo nse modelin g is stat istical regres sion analys is The general ly preferred methodfor regres sion analysis is maximu m likelihood estimation (Envir onment Canada 2005), butleast-s quares regression is usu ally effecti ve Either method provides confide nce bounds , unlessthe error dist ribution is unclear , in which case boot strap e stimates should be used (Sh aw-Allen and Sut er 2005) One may eithe r choose a single functi on and fit it to the data or fitmultiple plausi ble functi ons an d cho ose the one that provides the be st fit Functions may bechosen be cause they are the standar d functi on for a particular use, be cause the form isapprop riate for the data, or bec ause it ha s an app ropriate biologi cal interp retation Themost commonl y used functi on in eco toxicology is the log prob it (the linearized log-nor maldistribut ion), which is used to relat e quan tal data (e.g , prop ortional mort ality) to theindepen dent expo sure varia te (Figur e 23.4) Ther e are no standar d models for co ntinuousdata; approp riate functi ons shou ld be cho sen, fitted to the data, and compared Wh en modelsare comp ared, their relat ive likelihood s are the app ropriate metr ics unless they diff er in thenumber of fitted parame ters , in which case Akaike’s informat ion criteri on should be used(Sect ion 5.4.6) In additio n, plots of the data and fitted mod el should be inspect ed for theirplausibility and for outliers Finally, residuals should be plotted and inspected for patternsthat suggest a systematic lack of fit or heterogeneity of variance

Methods for fitting of exposure–response distributions to toxicity data are discussed byKerr and Meador (1996), Moore and Caux (1997), Bailer and Oris (1997), and Environment

Canada (2005) Software for regression analys is that can be us ed to generat e expo sure–respon se models can be foun d in any of the large statistica l pa ckages such as SAS, SPSS,and Sþ , a nd in R libr aries Commerci al soft ware packages specifical ly for a nalyzing toxic itytest data include CETIS, TOXSTA T, an d TOXC ALC Finally, governm ent agen cies havedevelop ed softwar e that may be recomm ended for pa rticular regula tory asses sment s The USEPA has developed bench mark dose softwar e that is pa rticular ly go od for comparin galte rnative functi ons an d calcul ating co nfidenc e bounds (http: == www.epa gov =nc ea =

for human health risk asses sments, it is also useful for eco logical risk assessment s (Linder

et al 2004)

23.1.3 S TATISTICAL SIGNIFICANCE

The traditional toxicity test endpoints for chronic tests, NOECs and LOECs derived bystatist ical hy pothesi s testing (B ox 5.1) , have low utilit y for ec otoxico logy or eco logical riskassessment (Hoekstra and van Ewijk 1993; Laskowski 1995; Suter 1996a; OECD 1998;Environment Canada 2005) Because they are based on statistical significance, these end-points do not indicate whether the effect is, for example, a large increase in mortality or asmall decrease in growth The level of effect at an NOEC or LOEC is an artifact of thereplication and dosing regime employed As a result, NOECs and LOECs correspond tohighly variable types and levels of effects (Suter et al 1987; Crane and Newman 2000) They

do not indicate how effects increase with increasing exposure, so the effects of slightlyexceeding an NOEC or LOEC are not qualitatively or quantitatively distinguishable fromthose of greatly exceeding it To estimate risks, it is necessary to estimate the nature and

1 2 10 30 50

70 90 98

Concentration (mg/L)

3 4 5 6 7

FIGURE 23.4 Results of an acute lethality test plotted as probits of response against the log tration The LC50¼ 5.6 mg=L and the 95% confidence bound are plotted (From Environment Canada,Guidance document on application and interpretation of single-species tests in environmental toxicol-ogy, EPS 1=RM=34, Ottawa, Ontario, 1999 With permission.)

concen-magni tude of effec ts that are occurri ng or co uld occur at the estimat ed exp osure levels an dassoci ated unc ertainti es Such esti mates are sup plied by the other a pproaches.

When data are not adequ ate for statist ical fitti ng of a model, linear inter polatio n may

be employ ed (Kl emm et al 1994) Hoekst ra and van Ewijk (1993) recomm ended usinglinea r interp olation down from an observed effect of approxim ately 25%, because they feltthat fitted functi ons are not reliable at low levels This method is most accurat e for approxi-mate ly linea r segme nts of exposure–r esponse data and relat ively small intervals betweenexposure level s In mo st cases, log conversio n of the exposure metr ic wi ll increa se thelinea rity The US EPA standar d method and pro gram for linear interpolat ion are avail able

in Norber g-King (1993)

23.1.5 EFFECT L EVEL AND CONFIDENCE

In some cases, the best that can be done wi th expo sure–resp onse informat ion is to rep ort theexposure level and associ ated effe cts level If there is replicati on, geomet ric means andconfide nce limits sho uld be c alculated This a pproach is ap propria te when a test of a singl eexposure level an d con trol is perfor med, as in tests of undilut ed effluent or of a contam inate dmedium at a particu lar location It may also be use d when data do not permi t regres sion, aswhen one treatment level produces partial mort ality and all others cause 100% mortali ty, an done is reluct ant to assum e linearit y for interpo lation

23.2 ISSUES IN EXPOSURE–RESPONSE

The modelin g of exposure–r espo nse is a highly complex topic both because of the co mplexityand he terogen eity of causal relationshi ps in ecology, an d because the statist ics is unsettled.The foll owing issue s are particular ly important for ecologi cal risk assessment

For regula tory standar ds or screenin g bench marks , it is de sirable to define points on theexposure dist ribution that are thres holds for signifi cant effe cts; signifi cant in this case meansthat, if the threshold is exceeded, some action should be taken Thresholds for statisticalsignificance are inappropriate for that purpose Rather, one must choose a level of effects (p)that has legal, policy, or societal significance, but how? LC50s have traditionally beenreported, because values in the middle of the curves are estimated with greatest precision(Figur e 23.4) Fifty pe rcent mortali ty is clearly not a thres hold effe ct Ho wever, if the curve issufficiently steep, so that there is little variance in the effective concentration relative to othersources of variance, the LC50may be reasonably representative of partially lethal concentra-tions However, a low effects level is generally desired for benchmarks Because of concern forprecision of the estimate, Environment Canada (2005) recommended that values less than

EC10 not be used and that p not be within the range expected for control effects OECD(1998) recommended that values from EC5 by increments of 5 up to EC25 be determinedroutinely, and, if a mechanistic model is used, an EC0 should be reported This approachprovides the decision maker with information to select a threshold effect based on policy andcircumstances (e.g., the presence of important species) The approach would be enhanced byreporting confidence limits on each value

If the effect in controls or reference areas is zero (or can be assumed to be zero plus error)and the exposure–response relationship has a lower threshold, the estimated intercept of the

x axis (EC0) is an estimate of the biological threshold Van Straalen (2002b) recommendedusing the HC0from species sensitivity distributions as community no-effect concentrations,using the uniform, triangular, exponential, or Weibull distribution More conventionaldistributions with infinite tails (i.e., the normal and logistic) can be used if the number oforganisms in the endpoint population or species in the endpoint community is specified(Kooijman 1987) If there are 100 species in a community, concentrations below the HC01(the first percentile of the SSD) are estimated to protect them all.

More commonly, the effects data display a nonzero threshold, which can be incorporated

in the exposure–response model Exposures up to some level produce responses equal tobackground (i.e., control treatments or reference sites), and higher levels produce increasingresponses Such cases may be fitted by a hockey-stick model, and the threshold is the exposurelevel at which the two segments meet (Figure 23.5) That is

dEffect ¼ Background for C < CTdEffect ¼ Background þ b(C  CT) for C > CT

(23:1)

where CTis the threshold concentration and b is the slope Examples of hockey-stick models

in ecotoxicology include Beyers et al (1994) and Horness et al (1998) Beyers et al (1994)found that hockey-stick thresholds were a factor of 2 to 4 lower than NOECs

1 0.0

0.1 0.2

0.3

0.4 0.5 0.6

0.7

Threshold = 620 ppb CI: 300–1000 ppb

23.2.2 TIME ASEXPOSURE ANDRESPONSE

Despite its importance, time is relatively neglected in exposure–response analyses Variation

in time may be more important than concentration; we may be concerned about fishswimming through a toxic plume or exposed to an episodic effluent (Brooks and Seegert1977) In analyses of short-term toxicity tests, effects are conventionally reported for the point

at which the test is terminated (e.g., a 96 h LC50) For longer-term ‘‘chronic’’ tests, it istypically assumed that equilibrium exposure and maximum effects have been achieved, soduration is irrelevant For many exposures, neither approach is adequate One solution is totreat duration as the exposure metric Figure 23.6 shows how revealing such relationships canbe; BDE had no effect on egg production until day 10, when treated fish stopped spawning.The same functions may be used as for concentration or dose (e.g., fit the probit function tomortality vs time data) (Environment Canada 2005) However, the associated confidencebounds are not accurate, because the same organisms are repeatedly observed over time, andtherefore the observations are not independent replicates More properly, time–event model-ing approaches can be used These techniques are not in common use, but appropriateprocedures are available in software packages (e.g., LIFEREG in SAS, recommended byNewman and Aplin 1992) and guidance is available (Crane and Godolphin 2000; Crane et al.2002)

These approaches treat time as duration, that is, an exposure continues for a certaindiscrete interval of time However, if concentration or another measure of intensity is variable

in time or if exposure episodes recur without sufficient time for recovery, duration isinsufficient Rather, exposure must be dynamically modeled In ecotoxicology, toxicokinetic

Treatment day

0 1,000 2,000 3,000 4,000 5,000 6,000 7,000 8,000 9,000 10,000 11,000 12,000

BDE-47 Exposed Control

FIGURE 23.6 Cumulative egg production by fathead minnows consuming food treated with tetrabromodiphenyl ether (open circles) and controls (solid circles) (From Muirhead, E., Skillman,A.D., Hook, S.E., and Schultz, I.R., Environ Sci Technol., 40, 523, 2006 With permission.)

2,2,3,3-models may be used to estimat e inter nal concentra tions that are then used in inter nalexpo sure–re sponse models (Sect ion 23.3).

Time is a dimens ion of respo nse as wel l as expo sure The durati on of effects is seldomcon sidered beca use the emphasi s of regula tion and risk asses sment ha s been on determini ngwhet her an effe ct will oc cur However, with increa sing requir ement s for net benefi t and co st–ben efit a nalyses (Chap ter 33 and Chapt er 38 ), the tim e to recover y and other aspect s of effe ctsdurati on are increa singl y impor tant In the sim plest c ase, effe cts reach an asympt ote duringthe exp osure and cease shortl y after exposure en ds (Figur e 23.7a) This is the impl icit modelbeh ind the concept of chron ic toxic ity How ever, even if the ind uction of effects ends whenexpo sure ceases, recover y may be slow, so the durati on of effects may be much longer (Figur e23.7b) In addition , effects may continue or even increa se afte r exposure ceases beca use oftime lags in the inducti on of overt effects; because body residu es are remob ilized at a latertime due to metab olism of fat reser ves or mobil ization of bone during migrati on, hibe rnation,star vation, lactati on, or the pro duction of yo ung; or be cause e ffects are express ed only duringcerta in poin ts in the life cycle (Figur e 23.7c) Dela yed effects are routin ely report ed only insingl e-dose wildli fe toxicity tests, in which the need to wai t for effects foll owing exposure isobv ious Finally, effects may end before expo sure due to accli mation or adaptat ion (Figur e23.7d) Use of the dur ation of exposure to estimat e the durati on of effe cts woul d be reason-able in case (a) but would underest imate effe cts in cases (b) and (c) and woul d overest imateeffe cts in c ase (d)

Effect s increa se with exposure concen tration or duration , so these two dimens ions of expo ure are somew hat interch angeable The simp lest exp ression of this relationshi p is Hab er’srule:

s-Ct ¼ k (23 :2)wher e C is concentra tion, t, tim e, a nd k , a constant expo sure metric that is associa ted with aparti cular effect such as 50% mort ality (log C an d log t may also be used) This equati on isquite hand y in that it allows an asses sor to apply test data for one durati on to exposures ofano ther duratio n For exampl e, if the fathe ad minno w 9 6 h LC50 is 10 mg =L, the con centra-tion need ed to kill the median minnow in 48 h is 20 mg =L Haber’ s rule also allows an assessor

to create an exp osure metric, the produ ct of concentra tion and time, which can be used tomodel response to data from exp osures that vary in both concen tration and tim e (Figur e 6.3)(Newco mbe and MacDo nald 1991) Haber’ s rule does not app ly to all chemi cals or mate rials

or to all effects, and should be rest ricted to relative ly smal l tempor al extrap olations Forrevie ws of these issue s see Gayl or (2000) , Bunce and Rem illard (2003) , an d SAB (1998).When sufficient data are available for responses at different times and concentrations, it isadvisable to determine whether a nonlinear concentration–time relationship fits the databetter than Haber’s rule For a fixed effect (e.g., the LC50), Miller et al (2000) recommend

a simple power law:

where g equals b=a Even better, a surface can be fit to data for variable concentration, time,and response (either pro portion responding or severity) (Figure 23.2) (Sun et al 1995;Newcombe and Jensen 1996) Unfortunately, few reports of toxicity tests contain data forresponses at any time other than the end of the test

(c) (b) (a)

FIGURE 23.7 Toxic effects as a function of duration of effects (solid lines) contrasted with duration ofexposure (dashed line) (a) Effects rapidly decline following cessation of exposure (b) Induction ofeffects ceases after cessation of exposure, but recovery requires significant time (c) Effects are inducedafter cessation of exposure due to lagging responses (thick line) or delayed responses (thin line) (d) Thesystem adapts to the exposure before its cessation

are often toxic c ontaminants are a lso micronutri ents, which may be deficient at sufficientlylow c oncentrations (Cu, Cr, I , Co, Mo, Se, and Zn) In addition, some nonchemical agentssuch as precipitat ion are ef fectively ecosystem nutrients The approach to assessment ofeffects of t hese elements on humans a nd other o rganisms is relati vely straightforward(IPCS 2002) The goal i s to m ai ntain i nt ake in a region between de ficiency and toxicity

in which organisms of the endpoi nt s pe cies may m aintain a desired l evel of nutrition,ther eby maximizing some measure of performance suc h as growth (F igure 7.2) T he i ssuebecomes m ore difficult when community or ecosystem endpoints are considered An excess

of nutrient e lement s in a quatic ecosystems r es ults in eutrophi c ation, whi c h i s unestheticand c auses loss of f ish and other animals due to anoxia O ligotrophy is also generallyundesirable due to low produc t ivity of f ish o r other aquatic r esources Hence, one mightassume that mesotrophy is the g oal, analogous to intermediate levels of nutrient elements

in an organism H owever, there are exceptions; many alpine lakes and other ecosystemsthat are naturally oligot rop hic are appreciat ed for t he clarity of their waters, a nd theircommunities are a dapted to low nutrient level s H ence, t he division of an e xposure rangeinto be neficial and adverse segments depends on the prior adaptations of the system andthe goals of the e nvironmental m anagers

Interm ediate dist urbance : M any agents that physically disturb ecosystems are often ficial at intermediate levels, but deleterious at high or low levels Examples include fire,flooding, wind, and freezing temperatures Thes e agents are effectively e quivalent t o nutri-ents, but their direct effects, even at low levels, are deleterious Their beneficial aspectresults f rom t he stress adaptation of the ecosystems i nvolved For e xample, w hen fire issuppressed, prairies may be replaced by woodl and or shrubland However, sufficiently highfrequencies of fire diminish the diversity and productivity of a prairie and r educe i ts rate

bene-of recovery

Hormesis: Radiation and some chemicals appear to have a stimulatory or protective effect

on organisms at low exposure levels even though they are not nutrients (Calabrese andBaldwin 2000) Hormesis is thought to result from the organism’s overcompensation fortoxic effects This results in J-shaped functions, because mortality or other adverse effectsdimi nish with increa sing dose before increa sing (Figur e 23.8) Altho ugh test results thatsuggest hormesis are common (Calabrese and Baldwin 2001), the reality and mechanism ofthe phenomenon are controversial For example, apparent hormesis in fish toxicity tests may

be due to reduced aggression resulting from toxicity

Hormone-like chemicals: Because hormones are signaling agents involved in back mechanisms that maintain homeostasis, it is possible for a low level of an endo-crine-disrupting chemical (EDC) to have greater effects than a higher level (Welshons

feed-et al 2003)

Because they are inherently qualitative, categorical data present problems for quantitativeexposure–response modeling Some dimensions may be expressed categorically becausequantitative data are unavailable For example, a quick survey of stream macroinvertebratesmay simply classify the streams as having high, moderate, or low species richness Othercategorical dimensions such as acute and chronic durations are simply traditional Somedimensions are categorical to combine disparate data, particularly when different studies arecombined, or to compare chemicals with different reported effects (Teuschler et al 1999) Forexample, a severity scale of (a) no observed effects, (b) no observed adverse effects, (c) adverseeffects, and (d) frank effects may be used to place effects on various organs, species, or

ecosyst ems on a common scale Finally, the type of effect (e.g , survi val, grow th, fecun dity,and beh avior) is unav oidably categor ical

The mo st common asses sment pr oblem invo lving categor ical data is de termining howcategor ical effects (i.e., types of respo nse, categor ical pro portion , or categor ical severity)change with exp osure This may be done sim ply by plotting the various types of responsesusing diff erent symbol s and then draw ing bounda ries betw een the types by eye (Figur e23.9) How ever, a techn ique called categor ical regression qua ntitati vely relat es categoricalresponses to exp osure levels (Dourson et al 1997; Hab er et al 2001) By assi gning scores tothe categor ies, the probab ility of each categor y can be modeled as a functio n of an exposure,

or a prescr ibed probab ility of each respon se (Figur e 23.10) Softw are for categorical sion is ava ilable from the EPA (2005a )

regres-Finall y, one may treat a categor ical scale of respon ses a s a num erical varia ble, an d regres s itagains t exposure The most pro minent eco logical exampl e is the scale of 1 4 response typesused in revie ws of suspen ded sedim ent effects on fish (New combe and M acDonal d 1991;Newco mbe and Jensen 1996) The utility of this appro ach depen ds on de fining the categor ies

in such a way that they form a linea r scale that is wel l correl ated wi th expo sure Newco mbeand Jensen (1996) g enerated respon se planes (seve rity score vs log sed iment concentra tionand log duratio n) with r 2 in the range of 0.6 to 0.7

23.2.6 EXPOSURE –RESPONSE FROM FIELD DATA

Measurem ents of biologic al effects in the field can be us ed to generat e expo sure–resp onsemodels The advantage of these models is that they are based on exposure–response relation-ships from the real world The disadvantages stem from the fact that exposure is not

Maximum response (averages 130% −160% of control)

Distance to NOAEL (averages fivefold)

Hormetic zone (averages 10- to 20-fold)

controlled in the real world, so it is often poorly defined, it may not include the desired range

of exposure levels or conditions, and the ecosystem is exposed to various anthropogenic andnatural agents simultaneously If the ecosystems being studied are sufficiently isolated, onecan be reasonably certain that only one agent is significantly affecting the system and the

and growth effects

3 ) 1,000 1,000

Lethality (2) Category (s)

AE (1) P(S ≥ 1) = 0.1 P(S ≥ 2) = 0.1

same expo sure–resp onse functi ons that are used with toxicity tests may be emp loyed ample s might include a cid mine drainag e to a stre am in a fores ted watershe d or an illegalwaste dum p in a natural area If a few agen ts are affectin g the system an d they are allmeasur ed, mu ltiple regres sion may be app ropria te.

Ex-The associ ation of bird kills wi th pesti cide app lications is a type of case in which othercauses may be neglect ed Mineau (2002) used logisti c regres sion to model the pro bability

of occ urrence of bird kills in fields treat ed with cholines terase-i nhibiting pesticides Heused data compil ed from 181 studi es of 35 pe sticides To make a single model for all ofthe pesti cides, he created exposure pa rameters that reflect the expo sure normal ized to thepotenti al routes of exposure The prim ary predictive varia ble was the oral toxic potentia l,which is the fifth percent ile of the SSDs for a vian oral LD50 s pe r squa re mete r in theapplic ation This varia ble is equival ent to toxic units (Chap ter 8), but express es toxicityper unit area rather than per unit concentra tion or dose Othe r contrib uting varia bleswere the dermal toxicity ind ex an d, for inhala tion, Henry’ s law constant This app roachprovided remark ably good models for field cro ps, forests, and pa stures, given the range

of chemi cals, specie s, applic ation methods , an d cond itions From the mult ivariate logisti cmodels , Mineau calcul ated ap plication rates that woul d result in a 10% prob ability of abird kill for each of the 3 5 pesticides

Griffi th et al (2004) dev eloped models that estimate be nthic macroinv ertebrate communi tycharact eristic s from meta l concentra tions in wat er or sedim ent (Figur e 23.11) They redu cedthe mult iple meta l concen trations to a singl e dimens ion by using the sum of toxic units(Chap ter 8), with toxic ity exp ressed as the ambien t wat er quality crit eria or sedimen t thres h-old effe cts level They dea lt with thresh old effects by using segme nted regression, whi ch isequival ent to hockey-s tick regression but wi th the slope of the lower segme nt not constr ained

to zero The break poi nt was set to zero on the log scale , whi ch is eq uivalent to a ha zardquotient of 1, the exp ected thres hold

Becau se of the complex ity of factors infl uencing communi ties in the field an d theirinherent varia bility amo ng sites, it may be de sirable to isolate the toxic response bytesting c ontaminated media from the field Smith et al (2003) an d Field et al (2002,2005) have used logis tic regres sion to model the prob ability of amphipo d toxic ity of fieldsedim ents, given concentra tions of multiple chemi cals They ha ve explore d various ways

to deal with the multiple chemi cals includin g stepwis e multiple regres sion and (becaus e ofmultiple collin earity) combined varia bles de rived by princip le co mponents analys is an dhazard quotient s Curr ently , they recomm end simply mod eling the probabil ity of toxicityfor all chemic als individ ually using appropri ate da ta from all of No rth Ame rica and , topredict toxicity at a site, using the mo del that estimat es the highest probab ility (Fiel d

et al 200 5) This ap proach seems to imply that one ch emical at each site dominat essedim ent toxicity , but it may be that one chemi cal is usu ally a bette r represe ntative oftoxic ity than a linear co mbination of chemicals or the a verage of probabil ities acrosschemi cals

If num erous a gents are contrib uting to the impai rment of organis ms, popul ations, orcommuni ties, co nventio nal regres sion analys is will model the average effects of indepen dentvaria bles plus all other agen ts, but we often wish to estimat e the effe cts of an agent acti ngalone (Figur e 23.12) If we plo t a biological response variable agains t levels of an agen t ofinterest measur ed at numerous fiel d sites, we will typic ally see a cloud of points, wi th aroughly linear uppe r edge for toxicants or a hump ed e dge for nutri ents or other agen ts with anoptim um exposure level (Figur e 23.13) The upper bounda ry repres ents the maxi mum valueachieved by the biological response variable given the level of the agent Points below theboundary are assumed to be reduced by co-occurring stressors The upper boundary, whichmay be thought of as the response when the independent variable is the limiting factor, is

Loge(S concentration/chronic AWQC)

−3 −2 −1 0 1 2 3 4 5

0 5 10 15 20 25 30

Habitat suitability index

Potential density

FIGURE 23.12 Relationships between population density and a habitat suitability index The line fitted

by linear regression estimates the typical density at a particular habitat suitability The upper line, fitted

by eye, estimates the maximum density given that the population is limited only by habitat suitability.(From Kapustka, L.A Hum Ecol Risk Assess., 9, 1425, 2003 With permission.)

termed the limit ing function It is estimat ed by quantile regres sion, fit ting a regression linefor a high qua ntile of the varia nce of the response with respect to the level of exp osure(e.g., 90%) This can be done by asymm etrically weigh ting the posit ive and negative devi-ations in least-s quares regression The techni que was de veloped in eco nomics (Koenker 2005)but has recently become popular in ecology (Cade and Noon 2003).

These exampl es serve to illustr ate the complex ity of modelin g exposure–r espon se in thefield where multiple co ntaminan ts and other habita t varia bles all influenc e responses as well

as some of the divers ity of approa ches that have been employ ed No ne of these app roacheshave been suffici ently tested in real assessment s to know which provides the best predictions

In ad dition to the obvious pro blem of multiple natural and anthropo genic causes and the lack

of control of exposure, inherent problem s of sampl ing artifact s must be co nsidered (vonStackel berg an d M enzie 2002) In large part, the cho ice of modeli ng approach depen ds on theasses sor’s conce ptualizat ion of the system For exampl e, the segmen ted regres sion appro ach

of Griffith et al (2004) is based on the assump tions that the toxic ity of meta ls is concen tion-ad ditive and has a thresho ld, but that in those meta l-cont aminated stre ams, othercontam inants or hab itat v ariables have negligible effects How ever, exami nation of the data(Figur e 23.11) could also su pport the assum ption that other age nts are acting and that there is

tra-no thres hold, so quan tile regres sion co uld be used Statist ics alone cantra-not resolve this choice.Rath er, the choice of modeli ng appro ach should be ba sed on general scientif ic unde rsta ndingand knowl edge of the parti cular syst em be ing add ressed

23.2.7 R ESIDUE–RESPONSE R ELATIONSHIPS

Single ch emical toxicity tests may be used to de velop exp osure–r esponse relationshi ps ba sed

on internal exposure measures (residues, also called body burdens) rather than external

FIGURE 23.13 Quantile regression of the 90th percentile of the number of intolerant invertebrate taxaagainst percent fine sediment for Minnesota streams in two sets of ecoregions The figure illustrates thevariance among regions in the effects of siltation (Courtesy of Michael Griffith With permission.)

expo sures (medi a c oncentra tions or admini stered doses) In theory, this approach offer scon siderable advantag es Chem ical s cause toxic effe cts in the or ganism, so measur es ofinter nal exp osure sho uld be more predict ive of effects than measur es of e xternal exposures(McC arty a nd Mack ay 1993; Escher an d Herm ens 2004) Estimati on of effe cts from resi duespotenti ally by passes most of the varian ce among sit es, specie s, and ind ividuals associ ated withthe physica l, ch emical, physiol ogical, and beh aviora l proce sses that control intake , uptake,and retent ion of ch emicals Thi s ap proach may be particular ly relev ant to chemicals that may

be signifi cantly accumul ated by aq uatic biota through food intake as well as direct exposure

to the ch emical in wat er

Inter nal exposure–r espon se function s c an be derive d for body burdens like those forexter nal expo sures For example, the test endpo int equivalen t to the LC50 is termed themedia n lethal resi due (L R50 ) How ever, it is common ly assum ed that the varia nce amongorganis ms and even specie s is relat ively small and eq uilibrium condition s are achieve d, so asingl e thres hold value suffices Thes e are usuall y term ed the critical body resi due (CBR).Since residue– response relationshi ps are not availab le for most ch emicals, the approa ch hasbeen ex tended by assum ing eq uivalen t potency for chemi cals with the same mech anism ofacti on That is, all chemi cals actin g by the same mechani sm of actio n should be effectiv e atapp roximatel y the same molar co ncentra tion at the sit e of acti on (Escher and Herm ens 2004)

If all internal co mpartmen ts (e.g , muscle, fat, and bloo d plasm a) are in eq uilibrium and haverough ly the same relative size across individu als and specie s, the ab solute or adjust ed whole-body effecti ve co ncentra tion woul d be the same for all chemic als with the same mech anism ofacti on Fin ally, if all indivi dual molec ules of chemi cals with the same mech anism of acti onhave the same potency, effecti ve molar con centra tions shou ld be co nstant These assum ptionsunde rlie the compil ation of esti mated CBR s for eight grou ps of ch emicals in fish present ed in

hydro-carbon (P AH) whole-bod y resi dues were found to be effe ctively equal at LC50 s for multiplespecie s (DiTo ro and McGra th 2000) Hence, these thres holds may be used a s a fir st app roxi-matio n to estimate wheth er measur ed resi dues of organic che micals with known mech anisms

of action are likely to be associated with acute or chro nic effe cts

Like all toxicity benchmarks, these s hould be used w ith c aution, and t he original sourcesconsulted before using these values to estim ate r isks The CB R values m ay be applied t ofield data for most chem icals and species in l ong-term e xposures, because the measuredbody residues m ay be assumed to reflect equ i librium with the environment However, that isnot the c ase for brief or episodic exposures or for c hemicals with very slow kinetics Forexampl e, CBR s for 2 ,3,7, 8- TCD D varied 122 -fold when m easured at t he time of death i nfathead minnows (Adams 1986) T his variation was apparently due to nonequilibriumtoxicoki netics in laboratory t ests of different durat ions S im il arly , D iToro and McGrath(2000) fou nd t hat their generalization about equal C BRs for LC50s did not apply t o N OELs,which m ay be due to problems i nherent in that type of test endpoint or to toxicodynamicissues (see Section 23.3)

It is unlikel y that all chemi cals wi th a common mechani sm of action have exactly the samepotenc y, but relative poten cies are seldom known Relati ve toxic ities of doses or e xternalexpo sure con centrations do not esti mate potency because of a ll the kineti c fact ors discus sedabo ve W hen relative potency fact ors are avail able, as they are for the dioxin- like chemicals(Sect ion 8.1.2), they can be used to estimate the effe ctive intern al con centra tion of chemi cals

If the mechanism of action is unknown or not included in Table 23.1, one may assume that

an organic chemical’s toxicity is at least as great as for chemicals acting by baseline narcosis(Ch apter 7) Since all or ganic chemi cals have at least that level of toxic ity, body resid ues ofany organic chemical of 0.8 mmol=kg (the upper limit for chronic narcosis; Table 23.1) orgreater are clearly indicative of chronic toxicity in fish However, since chemicals may have

TABLE 23.1

Summary of Modes of Toxic Action and Associated Estimates of Critical

Body Residue in Fisha

Acute (fenvalerate, permethrin, cypermethrin) 0.002 to 0.017

Acute (fenvalerate, permethrin, cypermethrin) 0.000048 to 0.0013

Source: Reprinted from McCarty, L.S and Mackay, D., Environ Sci Technol., 27, 1719, 1993 With permission.

a The rainbow trout used in this study weighed 600–1000 g; the other data presented are largely for small fish, sometimes

in early life stages, that typically weighed less than 1 g Most estimates were converted from mass-based data.

b The two values represent residues estimated by two different methods.

c

Includes three subgroups characterized by strychnine; fenvalerate and cypermethrin; endosulfan and endrin.

more power ful specif ic mod es of acti on, co ncentra tions less than 0 2 mmol =kg (the low er lim itfor chronic narcosis; Table 23.1) canno t be assum ed to be nont oxic.

Inter pretation of residu es of metals is more pro blematic Bec ause of the nut rient role ofmany meta ls and the numero us process es that co ntrol meta l uptake, depu ration, distribu-tion, and sequest ratio n, effe ctive co ncentra tions are highly varia ble (McCart y and M ackay1993; Bergman and Dorw ard-King 1997) In parti cular, organis ms have evo lved a variety ofmechan isms for regu lating exp osure by sequest ering meta ls in granule s or insolub le precipi-tate s, in inact ive tis sues su ch as hair an d exoskel etons, an d bound to regula tory pro teins(e.g , metallot hioneins an d phy tochel atins) Hence , intern al as well as extern al con centra-tions of meta ls include fractions that are not bioavai lable The bioti c liga nd model (BLM)(dis cussed in Se ction 23.3) address es these issue s in a limit ed context by assum ing that deathoccu rs in a specie s at a partic ular concentra tion of meta l–ligand co mplexes on the surfa ce ofgills, term ed the median level of accumu lation (LA50 ) (Meyer e t al 1999 ; EPA 20 03a).How ever, effects of metals oc cur at diff erent intern al concentra tions for dieta ry vs aq ueousexpo sures and effec ts of dieta ry meta ls on the g ut may occur wi thout any bioaccum ulati on(Meyer et al 2005)

In ad dition to the summ ary values in Table 23.1, residu es associated with effects inindivi dual aquatic toxic ity tests may be found in the literat ure, but the end points are notstandar dized A revie w of such da ta is present ed in Jarvinen and Ankle y (1999) Effectiveresi dues for a varie ty of chemi cals in sedim ents are presented in the Environme ntal Resi due–Effect Data base (http: == www.w es.army mil =el =ered =index html)

The use of chemi cal con centra tions in plant tissu es to estimat e effects may be advan geou s Measurem ent of tissue concentra tions permi ts the asses sor to bypass the very largedifferen ces in bioavailabi lity of ch emicals in different soils as wel l as interspeci es differences inuptake For exampl e, phy totoxicity of meta ls in soils of low organic matt er is not a go odpredict or of the toxicity of metals in sludge- amended soils Chang et al (1992) de velopedempir ical models relating con centrations of copper, nickel , an d zinc in crop foli age to growthretar dation

ta-Alth ough resid ue–effects data are usuall y obt ained from the literature, it is also possible togenerat e them from field data collec ted for assessment of con taminate d sit es As part ofbiologi cal surveys , anima ls or plan ts may be collected, exami ned for signs of toxic effects, andsubject ed to chemi cal analys is A functio n relat ing resid ues to the severi ty or frequen cy ofobserved effects may be developed, or a maxi mum residu e associ ated with no observabl eeffe cts may be establis hed This approach is potenti ally more reliable than the us e of resi due–effe ct relationshi ps from the literatu re, but must be used with care For mobil e specie s, thetime that the collected indivi duals ha ve spent on the contam inated sit e must be con sidered Inadd ition, it must be realized that the most sensi tive indivi duals a nd specie s may ha ve beeneliminat ed from the site by toxic effects, leavin g only resi stant organis ms Thes e two phe-nomen a may interact That is, the loss of indivi duals to toxicity may result in immigra tion ofrelative ly unc ontamin ated indivi duals and eventual ly to the evo lution of resistant localpopulations

An assessment of the Seal Beach Naval Weapons Station used residues in a somewhatunconventional manner that could be applied elsewhere Because of the concern that persist-ent organic chemicals were reducing tern reproduction, the assessors collected tern eggs thatfailed to hatch and analyzed them for the chemicals of concern (Ohlendorf 1998) If thosechemicals were responsible for reproductive failure, concentrations would be elevated relative

to reference populations, and they would be similar to those found in controlled studies thatdemonstrated reproductive effects In this case, the analysis of biological materials was used

to invest igate the cause of apparen t effe cts (C hapter 4) rather than to estimat e the exposure ofthe population

23.3 TOXICODYNAMICS—MECHANISTIC INTERNAL

EXPOSURE–RESPONSE

If the respon se to a parti cular inter nal concentra tion is not constant across exposure ations , specie s, or life stages, the inductio n of e ffects mu st be mod eled If the rates ofinducti on of damage or repair are modeled, these are toxic odynami c models (Ramsey an dGehri ng 1980; Lee et al 2002) The basic toxic odyn amic models repres ent revers ible an dirreversi ble bin ding of a chemi cal to a recep tor

dur-Rever sible bin ding is typical of ba seline na rcotics an d other che micals that cause an effectwhen they reach a critical co ncentra tion, but effe cts short of death are reversible Up take an drelea se from the recept or are repres ented by the same first-o rder model used to repres entuptake (Eq uation 22.31) The recep tor may be a spe cific organ or tissue or may sim ply berepres ented by the entir e org anism, in whi ch case the concen tration associated with an effect

is the CBR The CBR for aq uatic letha lity is the pro duct of the incipi ent LC50 (the LC 50 atequilib rium or effe ctively infinit e time) and the biocon centra tion fact or (ku=k e) (Sect ion 22.9).For shorte r exposures , a kinetic form ula is requir ed:

LC50 ( t ) ¼ CBR = [(ku =ke ) (1  e  ket )] (23: 4)wher e t is the exp osure durati on (Lee et al 2002) Thes e CBR models requir e that theexposure con centration and bioava ilability be constant , that organism weigh t be con stant,and that there be negli gible dieta ry uptak e or biotr ansfor mation

Irrev ersible binding is typic al of orga nophosp hate pesticides and oth er chemicals that cause

an effect when they bind a critical pro portio n of the recept or sites Suc h dy namics arerepres ented by the critical target occupati on (C TO) model (L egierse et al 1999) The organo-phosp hates are metabo lized to oxo n analogs that covalent ly bin d to the neurotr ansmi tteracetylch oline, which is thereby inhibi ted Death occurs when a pa rticular prop ortion ofacetylch oline is bound and inhibited, the CTO Since the binding is irre versible, the CTOoccurs at the critical area unde r the react ion curve for the toxican t and recepto r, not a criticalconcen tration Hence, this is also known as the critical area unde r the cu rve (CAU C) model(Verhaar et al 1 999)

These two models (CBR and CTO) ca n be derive d as extreme cases of a more generaldamage– repair model (Lee et al 2002) This mo del combines a fir st-order toxic okineti c modeland a first–or der damage– repair mod el:

wher e A ¼ a ccrued damage (dimensionl ess) ; ka ¼ rate of accrual of damage (kg=mmol h);

R ¼ tissue residue (mmol=kg); and kr ¼ rate of repair (1=h)

Lee et al (2002) found that this model fits lethality data for amphipods exposed to PAHsbetter than the CBR (equivalent to kr ¼ 1) or CTO (equivalent to kr ¼ 0) models It had beencommonly assumed that a constant CBR would apply to PAHs In fact, the CBR continued

to decline after amphipods achieved steady state, apparently because damage continued toaccumulate

These toxicodynamic models are semimechanistic extensions of toxicokinetic models (Section22.9) (Figure 23.14) They are based on assumed mechanisms but are derived in practice

by empirical curve fitting to accumulation and toxicity data Toxicodynamics could, however,

be much more complicated and genuinely mechanistic Models that simulate multistepprocesses of effects induction at the molecular level are being developed for human healthrisk assessments Such models will be particularly useful for chemicals like dioxins or endocrine

disruptors that involve signaling systems Genomics, proteomics, and metabolomics, in bination with computational biology, promise to provide the basis for simulating cells and evenwhole organisms at the molecular level.

com-23.3.1 T OXICODYNAMICS OF METALS ON GILLS

The biotic liga nd model is a toxic odynami c model for effects of meta ls on the gills of aqu aticorganis ms It is atypi cal in that it is not linked to an exter nal exposure mo del rather than atoxic okineti c model The BLM assum es that the site of acti on is certa in enzymes or ionchan nel pro teins (biotic liga nds) on the surfa ce of gills, so the site of extern al exp osure andeffe cts inducti on is the same Thes e bioti c ligands compet e for meta l ions with abioti c liga ndsincludi ng dissolved organ ic matter, hydroxides , ch lorides, sulfides, and carbo nates (Figur e22.1) Hence, the BLM consis ts of a metal speciation model to esti mate free ion con centra-tions of the toxic meta l as well as other ions that co mpete for ligan d site s (primari ly calcium,magnes ium, sodium , and hydrogen) and a gill surfa ce inter action model Althoug h the modeldoe s not dep end on a spe cific mechani sm of acti on (Mo A), it appears that letha lity isassoci ated with loss of sodium or calcium due to loss of ion chan nel function The BLM isdescri bed in Paquin et al (2002) , DiToro et al (2001), an d Niyog i and W ood (2004) BLMsoft ware is avail able (Hydro qual 2 003) and ha s be en used to develop propo sed water qualitycriteri a for cop per (E PA 2003a )

The toxic effects portion of the BLM is an equilibrium model of toxic metal loading of thebiotic ligands Loading is determined by the binding affinities of the competing ions for the

Overt effects

Metabolism/excretion

FIGURE 23.14 A generic conceptual toxicodynamic model

biotic ligand s (the binding con stant—log K ) and the binding sit e densit y ( Bmax ) Thes e valueswere de termined by studies of fish gills in sho rt-term ex posures such as Playle et al (1993) It

is assum ed that the loading associ ated with a parti cular effect is constant In particu lar, an

LC50 has a corresp onding con centration of the metal–l igand complex , the media n letha laccumul ation (LA50 ) When using the BLM soft ware, one pro vides an LC50 for a specie s–meta l combinat ion and the water chemi stry for that test The softwar e then ca lculates an

LA50 for the meta l and specie s This value can then be used to calculate a dissol ved LC 50 forany ambie nt wat er ch emistry

Altho ugh the BLM is a major advance in ecotoxi cologi cal modelin g, it has signifi cantlimit ations In pa rticular , it is cu rrently limited to acute lethal effects of a few metals (Ag, Cd,

Co, Cu, Ni, an d pos sibly Pb) in fres hwater The BLM has not been success fully a pplied tononletha l effe cts from susta ined expo sures, because the more complex toxico kinetics an ddynami cs mak e links be tween residu es and responses elusive (McG eer et a l 2 002) Extensio n

to chronic e xposures wi ll also requ ire determ ining whi ch cases involv e other toxic isms of action For example, lead appears to act on calcium and sodium transport in acutelethality studies, but is neurotoxic in long-term aqueous exposures and causes paralysis ofintesti nal pe ristalsis and other intestin al effec ts in dieta ry exposures (Chapter 7) Differ encesamong species in LA50values may be large and require more species-specific studies (Taylor

mechan-et al 2003) Extension of the BLM to saltwater requires dealing with very different ionchemistry Other complexities to be considered include nonequilibrium uptake kinetics,effects of acclimation, differences in dissolved organic carbon properties, and temporalvariance in water chemistry However, these issues are active areas of research

23.4 INDIRECT EFFECTS

Ecological risk assessments have been consistent with human health risk assessments inemphasizing direct toxic effects of contaminants However, because nonhuman organismsare much more subject to indirect effects such as habitat modification and reductions in theabundance of food species than humans are, indirect effects should be included in moreexposure–response models The term indirect effects refers to effects that result when acontaminant directly affects an entity (population, community, or ecosystem), and that directeffect becomes a hazardous agent with respect to an assessment endpoint entity Hence, theindirect effect is a response to exposure to a direct effect Indirect effects of chemicalcontaminants result from effects on trophic and competitive relationships, such as reducedabundance due to toxic effects on food species In addition, indirect effects due to habitatalteration should be considered For example, the toxicity of chemicals to earthworms in apasture may result in soil compaction, which could inhibit seed germination and result inother adverse effects to plants Decomposition of organic contaminants in soil, surface water,

or sediment could cause depletion of oxygen and reduced availability of nitrogen, adverselyaffecting endpoint species and processes In contrast, after decomposition of petroleum ismostly completed, plant production may actually be greater due to improved soil structure,nitrogen availability, or other factors (McKay and Singleton 1974; Bossert and Bartha 1984).Like direct toxic effects, habitat-mediated effects of contaminants may depend on the mag-nitude of exposure For example, at high exposures, petroleum and other nonaqueous-phaseliquids may fill soil pores that would otherwise be habitat for microorganisms and mesofaunaand that provide gas exchange for plant roots and soil macrofauna

These indirect effects should have been identified in the conceptual model and theirrelationship to exposure should be quantified as far as possible However, because of thecomplexity and heterogeneity of ecosystems, it is difficult to list all potentially importantindirect effects, much less estimate them When effects must be estimated from laboratory

tests, assessors often can do little m ore t han present qualitative rela tionships, f or example,the r eduction in abundance of aquatic i ns ects will r educe growth a nd fecundity of fish.When the results of microcosm, m esocosm, or fi eld tests are available, t hey c an be used toempirically estimate the indirect effects or, for l es s s elective agent s, the combined direct andindirect effects Biological surveys of c ontaminated or disturbed areas can potentially revealindirect effects, but becaus e t he exposures ar e uncontrolled and unreplicated, indirecteffects a re difficult t o di s tinguish from direct ef fects or biologi cal var iability in such studies.Alternatively, simple assumptions can be made such as an x% loss of w etlands will result in

an x% reduction in the abundance of s pecies that depend on that community for any of theirlife stages Finally, ecos ystem models may b e used t o e stimate t he consequences for a llendpoint taxa of toxic effects on all modeled components of t he exposed ecosystem (Chap-

exposure and effects models and measurements are compounded

24 Testing

The toxicological literature is vast and largely trivial

Moriarty (1988)Toxicity tests are studies in which organisms, populations, or ecosystems are exposed to achemical or mixture to determine the nature of effects and the relationship between the degree

of exposure and effects Similar tests may be performed for other agents Examples includepathogenicity tests and tests of responses to physical=chemical conditions such as pH,dissolved oxygen, and suspended sediment Tests resemble experiments in that the degree ofexposure is controlled, exposures are replicated, assignment of replicate test subjects israndomized, and extraneous variance among replicates is minimized They differ from classicscientific experiments in that they are intended to establish a functional exposure–responserelationship A test should determine not only that zinc can kill fish, or even that it kills them

by disrupting ion exchange in the gills, but also how the proportion of fish killed increaseswith concentration or duration of exposure This chapter describes tests of individual chem-icals or materials, tests of contaminated media, and field tests

The treatment of testing in this chapter is somewhat cursory, since the goal is to familiarizeassessors with the types of test data being generated, not to teach them to perform tests.Detailed procedures are published by governments and standards organizations, which arecited in the appropriate sections More detailed reviews can be found in ecotoxicology texts(Calow 1993; Rand 1995; Hoffman et al 2003) or appropriate government documents such asAnderson et al (2003)

24.1 TESTING ISSUES

Conventional toxicity tests determine effects on organisms and are divided into two classes,acute and chronic Acute tests are those that last a small proportion of the life span of theorganism (<10%) and involve a severe effect (usually death) on a substantial proportion ofexposed organisms (conventionally 50%) Acute tests also usually involve well-developedorganisms rather than eggs, larvae, or other early life stages Chronic tests include much orall of the life cycle of the test species and include effects other than death (most often, growthand fecundity) Chronic test endpoints are typically based on statistical significance, so theproportion affected and the magnitude of affect may be large or small In addition, there aremany tests that fall between these two types that are termed subchronic, short-term chronic,etc They typically have short durations but include sublethal responses A prominentexample is the 7 d fathead minnow test, which includes growth as well as death and includesonly a small part of the life cycle, but that part is a portion of the larval stage (Norberg andMount 1985)

In general, tests with longer durations, more life stages, and more responses are more usefulfor risk assessment, because they provide more information and because exposures in the realworld are often sustained However, if exposures are brief, acute or subchronic tests may bepreferred Examples include exposures of transients such as migratory waterfowl or highlymobile species that may use a site in transit or episodic exposures such as overflow of wasteponds, applications of pesticides, effluents generated during treatment failures, blow-down ofcooling water, or flushing of contaminants into surface waters by storms.

The following are general recommendations for selecting tests of chemicals or materials.Other issues specific to tests of particular media or of other agents are addressedsubsequently:

Standardization: In general, choose standard tests Standard test protocols have beendeveloped or recommended by governments (Keddy et al 1995; EPA 1996b), and standardorganizations (APHA 1999; OECD 2000; ASTM 2002) Most extrapolation models forrelating test endpo ints to asses sment en dpoints requir e standar d data (Chap ter 26) Inaddition, results of standard tests are likely to be reliable because methods are well developed,QA=QC procedures are defined, and test laboratories are likely to conduct standard testsroutinely However, nonstandard tests should be used when particular assessment-specificissues cannot be resolved by standard test results Some effects such as effects of estrogenicchemicals on sexual development or the behavioral effects of lead are not observed instandard tests Also, assessments may require tests of important local species or at leastspecies that are relevant to a location In particular, the biota of certain ecosystem types such

as arid ecosystems are not well represented by standard tests (Markwiese et al 2001).Duration: Choose tests with appropriate durations Two factors are relevant The first is theduration of the exposures in the field If exposures are episodic, as is often the case foraqueous contamination, tests should be chosen with durations as great as the longest epi-sodes The second factor is the kinetics of the chemical Some chemicals such as chlorine inwater or low-molecular-weight narcotics are taken up and cause death or immobilization in amatter of minutes or hours Others have very slow kinetics and require months or years tocause some effects such as reproductive decrements For example, tests of effects of poly-chlorinated biphenyls (PCBs) on mink have shown much greater effects on kit production andsurvival in the second year than in the first (Restum et al 1998; Hornshaw et al 1983).Time course: Time to response is a neglected aspect of ecotoxicology and other components

of applied ecology In some cases, particularly episodic or accidental releases, variation in theduration of the exposure is more important to the risk estimate than concentration, whichmay be relatively constant or uncontrollable In such cases, it is highly desirable to use datathat report responses at multiple time points For example, a report of a 96 h acute lethalitytest would be more useful if it reported the proportion surviving at each exposure level at 3, 6,

12, 24, 48, 72, and 96 h

Response: Choose tests with appropriate responses In particular, if an apparent effect ofthe contaminants has been observed in field studies, tests that include that effect as ameasured response should be used More generally, chosen tests should include responsesthat are required to estimate the assessment endpoint The most common response param-eters in toxicity tests are survival, fecundity, and growth, and most ecological effects modelsuse one or more of these responses as parameters Physiological and histological responses aregenerally not useful for estimating risks, because they cannot be related to effects at higherlevels However, if they are characteristic of particular contaminants, they can be useful fordiagno sis (Chapter 4)

Media: Prefer tests conducted in media with physical and chemical properties similar to sitemedia or media characteristic of the exposure scenario For example, if assessing a pesticidefor use on cotton, use tests in soils similar to those used for cotton production

Orga nisms : Prefer taxa that are closel y related taxonom ically to the endpo int spec ies an dlife stage s that are likely to be expo sed If an assessment en dpoint is define d in term s of acommuni ty, one may eithe r choo se tests of specie s that are closel y relat ed to membe rs of thecommuni ty or use all high-qua lity tests in the hope of repres enting the distribut ion ofsensi tivity in the end point communi ty (C hapter 26) Spec ies, life stages, and respo nses shouldalso be cho sen so that the rate of respo nse is a ppropria te to the duratio n of exposure an dkinetics of the chemic al In gen eral, responses of smal l organis ms su ch as zoop lankters an dlarva l fish are more rapid, beca use they achieve a toxic body burden more rapidl y than large rorganis ms Therefor e, if exposures are brief and if those small organ isms are relevan t to theasses sment en dpoint, test s of small organis ms sho uld be prefer red over large r organisms thatare no more relev ant How ever, such tests may not be appropri ate if, for examp le, theendp oint is fish kills Finally, choose taxa and life stage s that are known to be sensi tive tothe agent be ing asses sed.

Mult iple exposur e levels : Stud ies that employ only a singl e concentra tion or dose level plus acontrol are seldom useful If the ex posure cau ses no effect, it may be con sidered a no obs ervedeffects level (NOE L), but no infor mation is obtaine d about level s at whi ch effe cts occur If theexposure causes a signifi cant effe ct, it may indica te that a reducti on in exposu re is requir ed,but the necessa ry magni tude of reductio n cannot be determined Studies in whi ch multipleexposure levels wer e applie d allow an expo sure–respo nse relationshi p to be evaluat ed an dthres holds for effe cts to be determined Conseq uently, studies that applied multiple ex posurelevels are strongly prefer red

Expo sure quantifica tion : To c orrectly interp ret the resul ts of toxicity tests and to apply theseresul ts in risk assessment s, the exposure con centrations or dos es sh ould be clear ly quantified.Ideally, the test chemi cal shou ld be measur ed a t each exposure level; measur ed concen trationsare preferable to nominal con centrations

Chemic al form : Corr ect estimat ion of exp osure requir es that the form of toxic ant used inthe test be clear ly de scribed For example , in tests of lead, the de scription of the dosingprotocol sh ould specif y whet her the dose is expressed in term s of the elem ent (e.g., lead) or theapplie d compou nd (e.g., lead acetat e) Tests of chemi cals in the forms occu rring on the site areprefer red Thi s is pa rticular ly impor tant for chemi cals that may occur unde r ambie nt condi-tions in multiple ioniza tion states or other varia nt forms that have diff ering toxic ities.Statisti cal express ions of results : The traditi onal toxic ity test endpoints for ch ronic tests,NOELs and lowest observed effe ct levels (LOELs) , have been used to establis h be nchmarks

or criteri a (C hapter 29), but they have low utilit y for risk assessment , because they are based

on statistica l signi ficance rather than biologi cal signi ficance (Chapter 23) To fully estimat erisks, it is nece ssary to estimat e the na ture and magni tude of effe cts that are occu rring orcould occu r at the estimated exposure level s To do this, exposu re–response relationshi psshould be developed for chemi cals evaluated in ecologi cal risk asses sments

These criteria may conflict in some cases, be cause the best test data for one criterion maynot be the be st for another Therefor e, asses sors must judge their relative impor tance to theparticu lar asses sment, and apply them acco rdingly

24.2 CHEMICAL OR MATERIAL TESTS

In ecological risk assessment s, effe cts data for singl e ch emicals, organis ms (e.g , an exoti cparasitoid), or materials (e.g., gasoline, silt) may be obtained from tests performed ad hoc(primary data), but are usually obtained from the literature or from databases (secondaryand tertiar y data) One useful tertiar y source is the EPA ECO TOX databas e (http: == ww w.epa.gov =medat wrk =databas es.html) , whi ch co ntains toxicity data for aqu atic biota, wildlife,and terrestrial plants Reviews such as those produced by R Eisler for the US National

Biologi cal Se rvice are also useful tert iary sources (w ww.pwrc usgs.g ov=new =chrback.ht m).Test data gen erated for an asses sment (primary data) are relevant by design, but when dataare taken from the literat ure or from revie ws (seconda ry or tertiar y data) , asses sors mustselec t those da ta that are most relev ant to the asses sment en dpoints and that ca n be used withthe exposure estimates, as discussed in the previous chapter However, because the varianceamong chemicals is greater than the variance among species and life stages, any toxicityinformation concerning the chemicals of interest is potentially useful If no toxicity data areavailable that can be applied to the assessment endpoints (e.g., no data for fish or noreproductive effects data), or if the test results are not applicable to the site because ofdifferences in media characteristics (e.g., pH or water hardness), tests may be conducted

ad hoc If combined toxic effects of multiple contaminants are thought to be significant, and ifappropriate mixtures are not available in currently contaminated media, synthetic mixturesmay be created and tested, or co mbined effe cts models may be applie d (C hapter 8).Test data from the literature have biases that should be understood by ecological riskassessors Assessors must be aware of these biases when test data are used to derive toxicitybenchmarks or exposure–response models for chemicals Potential sources of bias in test datainclude:

. Form: The forms of chemicals used in toxicity tests are likely to be more toxic than thedominant forms in the field For metals the tested forms are usually soluble salts, andorganic chemicals may be kept in aqueous solution by solvents In oral dosing tests,organic chemicals are often dissolved in readily digested oils

. Species: The test species may not be representative of the sensitivity of species native tothe site

. Media: The standard media used in toxicity tests may not be representative of those at aparticular contaminated site For example, aqueous tests typically use water with mod-erate pH and hardness with little suspended or dissolved matter, and soil tests typicallyuse agricultural loam soils or similar artificial soils

. Conditions: Laboratory test conditions are less variable and may not be representative offield conditions (e.g., optimum temperature, sieved soil, or constant moisture)

More test data are available for aquatic biota than any other type of ecological receptors

preferred over static-renewal tests, which renew the water periodically, and those in turnare preferred over static tests, which do not change the water Flow-through tests maintainconstant concentrations, whereas concentrations may decline significantly in static tests oreven static-renewal tests due to evaporation, degradation, sorption, etc However, static testsmay be appropriate for extremely short-duration tests The most abundant type of testendpoint is the 48 or 96 h median lethal concentration (LC50) Life cycle tests that includesurvival, development, and reproduction provide the most generally useful data, but they arelargely restricted to short-generation invertebrates because of the expense of long tests Forfish, early life stage tests of survival and growth are most commonly used, based on thepresumption that early life stages are the most sensitive (McKim 1985) However, reproduc-tion is often the most sensitive life stage (Suter et al 1987), and, even if embryos or larvae arethe most sensitive stage, maternal transfer may be an important route of exposure Theseconcerns may be addressed by short-term fish reproduction tests (Ankley et al 2001), but forsome chemicals, only a full life cycle test will reveal the effects on reproduction of long-termexposures of the adult fish

Dietary exposures may be important contributors to toxicity for bioaccumulative organicchemicals and metals, but are seldom tested This is in part because of the difficulty ofculturing or collecting contaminated food organisms or of realistically contaminating artifi-cial diets It also reflects a lack of general acceptance of the importance of aquatic dietaryexposures Hence, most aquatic dietary toxicity studies have been concerned with demon-strating the reality and nature of the problem rather than generating relevant exposure–response relationships (Meyer et al 2005).

Currently, the most popular freshwater test organisms in the United States are fatheadminnows (Pimephales promelas) and daphnids (Daphnia spp and Ceriodaphnia dubia) Themost common saltwater organisms are sheepshead minnows (Cyprinodon variegatus) and themysid shrimp (Americamysis bahia) Test results for algae (often Selinastrum capricornutum)and aquatic plants (often duckweed, Lemna gibba) are less abundant than for aquaticanimals These tests have short durations (72 to 96 h), but they include multiple generations

of vegetative reproduction Further, plant tests usually report growth (e.g., cell or frondnumber) or production (e.g., carbon fixation rates), which are often applicable to assessment

of risks to ecosystems

Selecting representative sediment tests and test results is complicated by the interactionsamong the multiple phases (i.e., particles, pore water, and overlying water) of the sedimentsystem Sediment tests may be conducted using whole sediment or aquatic tests may be used

to represent one of the aqueous phases Test selection depends on the expected mode of

TABLE 24.1

Examples of Standard Aquatic Toxicity Tests Published by the US EPA or the AmericanSociety for Testing and Materials (ASTM)

EPA=600=4-90=027F EPA=712-C-96-118 ASTM E729-96, -88

EPA=712-C-96-121

EPA=600=4-95=136 EPA=600=4-91=003

ASTM E 1218-97a a

EPA method reports may be obtained by searching www.epa.gov for the listed report number.

ASTM methods may be purchased by standard number from www.astm.org

expo sure, and more than one test type may be appro priate Spiked sedim ent tests consis t ofthe additio n of known qua ntities of the test ch emical or mate rial to a na tural or syntheticsedim ent to which the test organis m is expo sed Spik ed sedim ent tests provide an esti mate ofeffe cts based on all direct mod es of exposure, includin g ingest ion, respi ration , and abso rption.Hence, toxicity to sedim ent ingest ing organisms may be be st approx imated by spiked sedi-ment tests The prim ary disadva ntage is the unc ertain applic ability of the exp osure–r esponseresul ts to any parti cular field sed iment or even to the distribut ion of fiel d sedimen ts Aqueoustests are mo st appropri ate if inter stitia l or overlying water is belie ved to be the pr imaryexpo sure pa thway for the toxic ants and recept ors at a site.

Aqueo us test s are much more common than spiked sedimen t tests, but few aq ueous testsuse ben thic specie s Convent ional aqueou s tests and da ta are used to evaluate aq ueous-phas eexpo sures of benthic specie s, based on data suggest ing that ben thic specie s are not syst emat-ical ly more or less sensi tive than wat er column species (EPA 1993d ) For nonio nic organicchemi cals, aqueous concentra tions and sediment co ncentra tions can be intercon verted byassum ing equilibrium partiti oning between the aqu eous phases and the organic matt er in thesoli d phase (Section 22.3)

An adjustment is also avail able for some sedim ent meta ls, ba sed on the acid volat ile sulfide(AVS) compo nent of sedimen t (Sect ion 22.3) How ever, it doe s not serve to esti mate aq ueouscon centrations or effects

Wh en spiked sediment test s are used, physica l and chemi cal pr operties of the test media areparti cularly impor tant for evaluat ing chemical toxic ity Char acterist ics of the sediment (e.g ,organic carbon c ontent and grain size distribut ion) and of water (e.g., dissolved organiccarbon , hardness , an d pH) can signi ficantly alte r the speciation and bioavai lability of thetested mate rial In sit e asses sments, tests in sedim ents simila r to the sit e media shou ld beprefer red Regressi on models could be derive d to accoun t for confoun ding matrix factors(e.g , grain size or organic carbon content ) (Lamber son e t a l 1992) How ever, such modelsare specie s- and matr ix fact or-spe cific and woul d need to be developed on a c ase-by-cas ebasis The test method also can affe ct exposure For exampl e, chemical co ncentra tions andbioavai labilit y can be a ltered by the ov erlying water turnover rate, the water =sedim ent ratio,and the ox ygenation of the overly ing water (Ginn an d Pastorok 1992) For all of thesereasons , spiked sediment tests are relat ively uncommon

24.2.3 S OIL T ESTS

Ther e are relative ly few standar d soil tests, and the body of publis hed toxic ity da ta is smallrelative to water an d sedim ent tests In particular , few organic chemi cals other than pesticidesare represe nted Standard methods us ing spiked soil or solut ions are availa ble for vascul arplants (mainly crops) and earthworms from the US Environmental Protection Agency (USEPA) (OPPTS 850 test guidelines), American Society for Testing and Materials (ASTM;Committee E47 standards), European Union, and others A variety of additional tests havebeen developed, mostly in Europe (Donkin and Dusenbery 1993; Donker et al 1994; vanGestel and Van Straalen 1994; Kammenga et al 1996; Heiger-Bernays et al 1997; Lokke andvan Gestel 1998) These tests generally treat soil as a medium in which particular species areexposed, rather than as an ecosystem Tests of effects on field soils with associated commu-nities are describ ed in Secti on 24.5.3

Tests in both spiked soil and aqueous solutions may be useful for assessing risks from soilcontaminants The relevance of published tests in soil to the assessment of risks to soilorganisms seems self-evident, but because soil properties are highly variable and greatlyinfluence toxicity, the toxicity in any other soil may be quite different For example, Zelles

et al (1986) found effects of chemicals on microbial processes to be highly dependent on soil

type Unrealist ic extremes of that varia nce should be eliminat ed by excludin g data from tests

in quartz sand , peat, or vermicu lite, unless toxicity of chemi cals mixe d with these mate rials isdemonst rated to be simila r to that in natural soil s Tests co nducted in solut ion have poten-tially more co nsistent resul ts than those c onducted in soil Toxi city obs erved in inorgan ic saltsolut ion may be related to co ncentra tions in soil extra cts, estimated pore wat er concen tra-tions , or contam inated spring s in which wetland plant commun ities are located It has evenbeen pro posed that aquati c toxicity test resul ts cou ld be used to estimat e the effects ofexposure of plants and animals to c ontaminan ts in soil so lution (van de M eent and Toet1992; Lokke 1994), but that practice is not general ly accep ted

Test en dpoints for soil tests are less standar dized than those for aquatic or wi ldlife tests.Plant tests most commonl y include germi nation or grow th Invert ebrate tests most commonl yinclude survi val but somet imes include reprod uction Tests of litter -feeding earthw orms maynot be repres entative of those that ingest soil, an d vice versa Similarl y, althoug h pollut ion-induced communi ty toler ance (PIC T) (Rutgers et al 1998) has been used as a toxicologic alendp oint, it is not alw ays clear that micr obial co mmunitie s that have beco me alte red in theirtoleran ce of contam inants are indica tive of a decreas e in the rate of a valued micr obial process(Efroy mson and Suter 1999)

24.2.4 ORAL AND OTHER WILDLIFE E XPOSURES

Terrest rial and semiaq uatic wi ldlife are expo sed through or al, dermal , and inh alation rou tes,and by intergenerat ional transfers Tes ts exist for each of these routes , but oral test s toestimat e the effects of toxicants in food, water, or other orally consumed materials are mostcommon These tests are e mploye d pr imarily with birds and mammals

For dieta ry test s, test anima ls are allow ed to con sume food or water ad libitum that hasbeen spiked with the test material The amou nt of food consumed shou ld be recorde d da ily sothat the daily dos e can be estimat ed A potenti al prob lem with dietary tests is that an imalsmay not experi ence consistent exposure through out the co urse of the study For example, asanima ls become sick (e.g., due to toxic ity), they are likely to consume less food and water.They may also eat less or refus e to eat if the toxican t impar ts an unpl easant taste to the food

or water or if the toxic effe cts induce aversio n Thes e pro blems are sufficien tly seriou s forcholines terase-i nhibiting pesti cides and so me other chemi cals that the use of dieta ry test s isquesti onable (Mine au e t al 1994)

In oral tests, animals receive periodic (usually dai ly) t ox icant doses by gavage (i.e.,esophageal or stomach t ube) or by c apsules Th e c hemical is usually mixed with a carriersuch as water, mineral oil, or acetone solution to f aci li tate dosing O ral t e st s m ay include asingle dose to simulate an isolated and brief e xposure, or daily doses to simulate a continu-ous or long-term exposure They provide better-defined exposures and c an be representative

of oral exposures other t han f ood or water consumption s uch as incidental s oil ingestion ororal ingestion of oil or ot her m aterials during g rooming

The choice of carrier used for oral or dieta ry tests has been sh own to infl uence uptak e bybinding with the toxicant or otherwise influencing its absorption For example, Stavric andKlassen (1994) reported that the uptake of benzo(a)pyrene by rats is reduced by food or waterbut facilitated by vegetable oil Similarly, uptake of inorganic chemicals varies dramaticallybetween tests with food and water Chemicals are generally taken up more readily from waterthan from food

Results of most dietary toxicity tests are presented as toxicant concentrations (mg=kg) infood or water These data can then be converted into doses (mg agent=kg body weight=d) bymultiplying the concentrations in food or water by food ingestion rates and dividing by bodyweights either report ed in the literat ure or present ed in the study (Sect ion 2 2.8)

Standard methods for avian acute, subacute, and reproductive oral toxicity tests have beendevelop ed (Table 24 2) In gen eral, risks to mamm alian wildlife are assessed using the sametests of laboratory rats and mice that are used in human health risk assessments, but wildmammal tests may be required when the particular issues suggest that those tests may not beadequate.

Dermal and inhalation test results may be found for rodents and sometimes other species inthe mammalian toxicity literature The methods developed for laboratory test species may beadapted for mammalian wildlife (see US EPA guidelines OPPTS 870.3465 and 3250).Methods for testing dermal or inhalation exposures for birds, reptiles, or amphibians must

be developed largely as needed

Effects of developmental toxicants on birds and other oviparous species are readily tested

by egg injection For example, effects of dioxin-like compounds, for which embryo ment is critically sensitive, have been tested by injecting the eggs of chickens and other species(Hoffman et al 1998)

develop-24.3 MICROCOSMS AND MESOCOSMS

Microcosms and mesocosms, together termed model ecosystems, are physical representations

of ecosystems that contain multiple species and usually multiple media and that may bereplicated Microcosms are small enough to be maintained in the laboratory They includeeverything from a mixed microbial culture in a beaker to aquaria and small artificial streams.Mesocosms are larger, more complex, and located out of doors They include artificialstreams and ponds and enclosed areas of terrestrial, wetland, and shoreline ecosystems.Microcosms and mesocosms have similar purposes and overlap in terms of size and complex-ity In general, the purposes for conducting tests in these systems are to (1) provide realisticfate and exposure kinetics by including degradation, sorption, and uptake; (2) include allroutes of exposure; (3) expose a large number and variety of types of organisms; (4) allowsecondary effects due to species interactions; and (5) allow the operation of ecosystemprocesses The biotic components of microcosms may be specified so as to improve replica-tion and understanding of responses (Taub 1969, 1997) More commonly, the microbial andinvertebrate components of these systems are provided by the process of enclosure or byinoculation from a natural ecosystem For example, a pond microcosm or mesocosm might beinoculated with sediment and water from a pond or lake that contains microbes and inver-tebrates Prescribed numbers of fish, amphibians, or other large organisms may then be added

to the inoculated mesocosm or large microcosm

The use of model systems in the assessment and regulation of chemicals has been asource of ongoing controversy Advocates argue that if the goal is to protect ecosystems,one must test ecosystems (Cairns 1983), but they have disagreed about the designs.Opponents argue that these simplified systems do not capture critical properties of realecosystems (Carpenter 1996; Schindler 1998) Even advocates recognize that differentecosystems respond in qualitatively different ways, which are only sometimes reflected inmodel systems (Harrass and Taub 1985) Hence, after decades of development and advo-cacy, these test systems are still rarely used and have had relatively little influence onenvironmental decision making Many issues have contributed to the controversy, some ofwhich are discussed here

Ecosystem definition: Microcosms or mesocosms have been said to represent ecosystems inthe sense that a rat represents mammals and a fathead minnow represents fish That is,ecosystem responses like changes in net production or species number are thought to bereasonably consistent across flasks, ponds, and rivers On the other hand, ecosystems are

TABLE 24.2

Selected Standard Oral Toxicity Methods for Birds

Mallard, Ring-necked pheasant

ASTM E857-87

Eggs laid Egg fertility Egg hatchability Eggshell thickness Weight and survival of young a

much less consistent in terms of their compo nents an d function than classes of or ganisms, andmight not be expecte d to respond as consistentl y to a particu lar exposure.

Size : Mod el syst ems vary great ly in size Small er systems provide more replicati on at lesscost, but larger syst ems can support more types of orga nisms and pe rhaps better repres entecosyst ems of concern

Com position : No model syst em can sup port the full range of taxa and trophi c level s of a reallake, river , or forest How ever, it is not clear how much simp lification is allowabl e be fore amicr ocosm or mesoco sm fails to be a model of eco systems of con cern

Typ e of respon se : Are model systems best used to reali stically expo se organ isms, eluci datespecie s interactio ns, measur e ecosyste m prop erties, doc ument recove ry, or something else?Mod el syst ems vs system models : Mathem atical models of eco systems (Chapter 28) andphy sical models (microcosm s and mesocos ms) pot entially serve the same purpose in eco-logic al risk asses sment While phy sical models are clear ly more realist ic, mathe matical mod elsare more flex ible in terms of being ab le to repres ent a varie ty of syst ems, state s of the syst ems,and cond itions of exp osure, and they are much less expe nsive to implement Phys ical mod elsrequir e a major assum ption; they rep resent the real ec osystems of co ncern Mathem aticalmodels requir e an eq uivalen t major assum ption (e.g., that the response of the system can berepres ented by a tropho dynami c model) plu s many assum ptions associ ated with their equa-tion forms and parame ter values

Most of these issues are a r esult of t he difficulty in defining ecosystems compared toorganisms When testing f ishes or birds, one does not need to decide how many livers oreyes to include, how big the tested piece should be (use the whole organism), or even whatthe basic responses are (there is a consensus for survival, growth, and reproduction) This isbecause organisms are clearly defined entities, not just representatives of a level of organ-ization Hence, assessors must define assessment communities but not assessment organisms

designs, these fundamental issues have led to problems of interpretation that have severelylimited the utility of these systems for regulation or management In particular, the US EPAdeveloped a standard aquatic mesocosm for tier IV testing of pesticides (Tuart 1988), butdropped the requirement for these tests in 1992 This decision was based on the judgmentthat field tests had not significantly improved the bases for most registration decisions overlaboratory tests, in part because of the ambiguities in interpreting their complex and highlyvariable results Also, it was judged that post-registration field monitoring could serve theneed for field results in most cases (Tuart and Maciorowski 1997) Problems in interpretingresults of microcosms and mesocosms were addressed by a recent workshop (Giddings

et al 2002) The recommendations include the following points that are particularly vant to assessors:

rele-Design: Tests should be designed to provide exposure–response relationships, not just totest a particular predicted exposure level

Types of endpoints: Structural and functional endpoints are generally equally important,but species structure is primary, and functional endpoints alone do not protect biodiversity.Recovery: Initial effects should not be considered unacceptable if population recoveryoccurs in an acceptable time, and they do not cause adverse indirect effects

Models: Ecological models for extrapolation to real ecosystems or to other exposuresshould be developed

Data for extrapolation: Biological and physical conditions of the actual ecosystems to besimulated by the model systems should be determined to aid extrapolation

Scenarios: The agricultural landscape or other landscape context should be used to designreasonable exposure scenarios

Endpoints: Regulatory authorities must develop goals and assessment endpoints that testsystems can be designed to support.

Training: Tests in model systems are difficult to conduct and their responses are complexand difficult to interpret, so guidance, training and tools are required

An important additional recommendation is that users of model systems should state theirassumptions (Clements and Newman 2002) Like mathematical models, physical models aresimplifications of real systems, and those simplifications imply assumptions about what is and

is not important to understanding the response of the real systems being simulated Forexample, an aquatic microcosm may require the assumptions that planktivorous fish are notimportant to understanding effects on plankton and that macrophytes are dominant com-ponents of the ecosystems being assessed

Aquatic model systems range from flasks in the laboratory to artificial ponds and streams(Graney et al 1994, 1995; Kennedy et al 2003) The following are major types of modelecosystems:

Standard aquatic microcosms: This system is assembled in a flask from sterile sand andwater, ten species of algae, five species of zooplankters, and a bacterium plus inadvertentlyintroduced microbes (Taub and Read 1982; OPPTS 1996a; Taub 1997)

Pond microcosms: Pond water and sediment, with associated microbes and invertebrates,are placed in flasks, aquaria, or tanks, sometimes with macrophytes but seldom with fish(Giddings 1986)

Pond mesocosms: Ponds ranging from 0.04 to 0.1 ha are dug and filled with sediment andwater from a real pond or lake Macrophytes and fish may then be added These systems haveoften been used to study the fate and effects of pesticides in the United States and Europe(Tuart and Maciorowski 1997; Campbell et al 2003)

Artificial streams: Indoor or outdoor channels may be treated with once-through orrecycling water Unlike pond mesocosms these systems have not been standardized andthey range from small channels that support algae and invertebrates to in-ground channelswith pools and riffles that are large enough to support fish (Graney et al 1989)

Littoral enclosures: Replicate systems are created by enclosing 50 m2portions of a pond orlake and adjoining shoreline (Brazner et al 1989; Lozano et al 2003)

Limnocorrals: Portions of a littoral ecosystem are enclosed by a large plastic bag or cylindersuspended from a floating platform and anchored to the bottom They vary in volume from

1000 to 100,000 L (Graney et al 1995)

Microcosm tests of the soil community and processes such as decomposition incorporateindirect effects of chemical addition as well as direct toxic effects In the United States,standard test of soil function determines effects on respiration, ammonification, and nitrifi-cation in sieved soil (Suter and Sharples 1984; OPPTS 1996f), or nutrient retention, respir-ation, and plant production in soil cores (Van Voris et al 1985; OPPTS 1996b) Other soilmicrocosms are used to test effects on soil community structure (Parmelee et al 1997).Soil microcosm tests usually focus on changes in the rates of soil microbial processes,which, however, may increase or decrease in response to a chemical exposure Some metalsare nutrients at low concentrations and most organic chemicals are microbial substrates Forexample, the antibiotic streptomycin reduces bacterial abundance but increases overall soilactivity by serving as a carbon and nitrogen source (Suter and Sharples 1984) Further,reductions in some soil processes such as litter decomposition may be desirable or acceptable

in particular ecosystems (Efroymson and Suter 1999) A litter layer is esthetically desirable,reduces erosion, and is important to successful germination of some trees, but introducedearthworm species are destroying litter layers in forests of the northeastern United States.Therefore, if soil processes are assessment endpoints, it is desirable to determine the relevantexposure–response relationship and to understand the ecosystem context of the processes

Mesocosm studies of wildlife are much less common Even more than aquatic mesocosms,they are primarily used to study effects of realistic exposures, secondarily to reveal secondaryeffects, and very little to show population-level effects For example, Dieter et al (1995)placed mallard ducklings in littoral mesocosms to evaluate effects of aerial application of theorganophosphate insecticide phorate on waterfowl in prairie wetlands In another study,Barrett (1968) evaluated the effects of the carbamate insecticide carbaryl on plants, arthro-pods, and small mammals in 1 acre old-field enclosures.

24.4 EFFLUENT TESTS

Standard toxicity tests have been developed for determining the acceptability of aqueouseffluents and are widely used in effluent permitting in the United States Although conventionalacute lethality tests may be used, short-term chronic tests using short-lived species or subchro-nic tests using sensitive life stages are used (Table 24.3) These tests are unique in the extent towhich they have been validated against biosurvey data (Mount et al 1984; Birge et al 1986;Norberg-King and Mount 1986; Dickson et al 1992, 1996) In those studies, the 7 d fatheadminnow and C dubia tests have been found to be predictive of reductions in the species richness

of aquatic communities As a result of this intensive development and validation, these tests arewidely used beyond the regulation of aqueous effluents, and many laboratories are available toconduct them Other species and taxa are used outside the United States (Herkovits et al 1996).Effluent tests may pass or fail, i.e., only the undiluted effluent or only one critical dilution may

be tested to determine acceptability However, it is preferable to test a series of dilutions toestablish the exposure–response relationship (EPA 2002b) Most effluent tests are static-renewal, but static tests may be used if the effluent has little oxygen demand and the toxicconstituents are not lost through volatilization or other processes If tests are performed on site,flow-through effluent tests are possible

Effluent tests may also be used to identify which components of the contaminant mixtureare responsible for effects, a process called Toxicity Identification Evaluation (TIE) (EPA

TABLE 24.3

Standard Procedures Used to Test the Toxicity of Effluents and Ambient Watersa

a Test protocols are found in (EPA 2002b,h,i) and ASTM standards.

b

FW ¼ freshwater; SW ¼ saltwater; FW=SW ¼ protocols are available for species from both media.

c The standard freshwater fish in the United States is the fathead minnow (Pimephales promelas) and the saltwater fish

is the sheepshead minnow (Cyprinidon variegatus) or inland silverside (Menidia beryllina).

1993a,b; Norberg-King et al 2005) In TIE, the toxic constituents of a mixture are identified

by removing components of a mixture and testing the residue, fractionating the mixture andtesting the fractions, adding components of the mixture to background medium and testingthose components, or other techniques (Figure 24.1)

24.5 MEDIA TESTS

The toxicity or other adverse properties of ambient media can be tested in at least three ways

In the least-used technique, contaminated biota are brought into the laboratory and tested.This technique is appropriate if the contaminant is persistent and bioaccumulated, or if it is

Baseline toxicity test

EDTA chelation test

pH adjustment test (pH 3, pH i, pH 11)

Filtration test (pH 3, pH i, pH 11)

Graduated pH tests e.g., pH 6.5, 7.5, 8.5

Aeration test (pH 3, pH i, pH 11)

Oxidant reduction test

C18 solid-phase extraction test (pH 3, pH i, pH 11)

Conduct toxicity test on samples and dilutions Manipulations to samples

Oxidants

Nonpolar organic Ammonia

Total dissolved solids

Surfactants

Results of phase I are then categorized into various types of toxicants

Cationic metals

FIGURE 24.1 A logic diagram for toxicity identification evaluation (TIE) for acutely toxic aqueoussamples (From EPA (US Environmental Protection Agency), Methods for Aquatic Toxicity Identifica-tion Evaluations: Phase I Toxicity Characterization Procedures, 2nd ed., EPA-600=6-91-003, US Envir-onmental Protection Agency, Duluth, MN, 1991a With permission.)

known to cause pe rsistent injur y For exampl e, herri ng eggs from areas exposed to spilled oiland from unexpo sed areas wer e bro ught into the laborato ry, and their ha tching rates andfrequenci es of abnormal ities record ed (Pears on et al 1995) By far the most co mmonapp roach is to bring contam inated a nd reference media into the labo ratory for toxicitytesting This is a v ery acti ve area of ecotoxico logy, and test methods have been de velopedfor a mbient waters, sedim ents, soils, an d biota M ethods for testing aqueous effluents

2005) Methods specifical ly recomm end ed for use at contam inated sites in the United Statesand Canada may be found in Office of Emergency and Rem edial Respons e (1994b) andKeddy et al (1995)

In a ssessment s of contam inate d sites, testing the co ntaminated media from the site hassevera l ad vantage s relat ive to testing individua l chemi cals in laborato ry media :

1 The bioavai labilit y of the contam inants is realist ically repres ented Bec ause of sorption,form ation of comp lexes, an d other process es that reduce the availab ility of a chemica l foruptake by organisms, the toxic effects of a parti cular concen tration of a ch emical may behighly variable In parti cular, standar d single chemi cal toxic ity tests are con ducted undercond itions that tend to maximize bio availabil ity, so toxicity values from the literat uremay be con servative Media toxicity tests can redu ce or eliminat e this source of uncer-tainty by conservi ng the bioavai lability of the con taminants to which or ganisms areexpo sed on the site

2 The forms of the contam inants are realist ic The toxicity of ch emicals depend s on theirform includin g the ioniza tion states and co -ions for meta ls and other ion izable chem-icals Typical ly, the form s of con taminants at a site are unknow n Eve n when know n, thepredo minant form s foun d at the site may not be those for whi ch toxicity da ta areavail able Media toxicity tests can redu ce or eliminat e this source of unc ertainty byconservi ng the forms of the contam inants to whi ch or ganisms are ex posed on the site

3 Com bined toxic effe cts are elicited Few sites a re con taminate d by only one chemi cal,and the toxic inter action s of chemi cals are seldom wel l known In additio n, the inter-actio ns dep end on the form of the chemi cals whi ch is its elf pro blematica l Media toxic itytests can reduce or eliminat e this source of uncerta inty by retaining the combinat ion ofcontam inants in the forms and prop ortions that oc cur at the sit e

4 The effects of contam inants for whi ch few or no relevan t test data are available areincluded Ecoto xicolo gical testing ha s focused on pesticides and metals, not the indus-trial chemi cals found at many co ntaminated sites Even for meta ls an d pe sticides, thetaxa and responses of inter est may not have been test ed Med ia toxic ity tests greatlyreduce or eliminate this source of uncertainty by including all contaminants to whichorganisms are exposed on the site in a test that has been chosen to represent the endpointresponse

5 The type of effects may be determined The specific effects of the mixture may not bepredictable from available knowledge of the effects of the components The test can bedesigned to determine the occurrence of effects that are relevant to the assessmentendpoint

6 The spatial distribut ion of toxic ity can be determined (Figur e 20.3) The ex tent ofthe area to be assessed or remediated and the priority to be assigned to different sources

or receptor ecosystems can be more appropriately determined on the basis of thedistribution of toxicity than from the distribution of individual chemical concentrations

7 Remedial goals may be determined Toxicity can provide a better basis for definingmedia and areas to be remediated than chemical concentrations can