Part IV Analysis of Effects In the analys is of effects, assessors characteriz e the nature and magni tude of e ffects of chemi cals or other agen ts as func tions of exp osure. Effect s may be estimated by perfor ming tests, 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 assessment endp oint, and then rean alyzed and summ arized a s ap propria te to make them useful for risk charact erization. Two issues must be consider ed. First, what form of each avail able measure of effect best ap proximates the assessment endp 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 n after 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, given the spati al and tempor al patte rn of exp osure level s. Therefor e, the relev ant sp atial and tempor al dimension s of e ffects must be defined and used in the expression of effects. For exampl e, if the expo sure is to a mate rial such as unl eaded gasoline that persists at toxic levels only brief ly in soil, effe cts that are induced in that time period must be extra cted from the effects 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 long- term responses . The de gree of de tail an d conserva tism in the analysis of effects de pends on the tier of the asses 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 the approp 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 s and life stage s of con cern, extra polatio n models are need ed to esti mate effe cts on attr ibutes of organis ms (C hapter 26), populatio ns (Chapter 27), or ecosystems (Chap ter 28). Bec ause nearly all testing determines organism-level responses, extrapolations to organism-level ß 2006 by Taylor & Francis Group, LLC. endpoint attributes use simple assumptions or statistical models. In contrast, the extrapolation to population and ecosystem-level attributes requires an extrapolation across levels of organ- ization that typically requires mathe matical simulations. ß 2006 by Taylor & Francis Group, LLC. 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 must determ ine the relationshi p betw een the dose level and the toxic respon se. Mo re generally, to asses s the risk posed by any agent, it is ne cessary to determ ine the relationshi p betw een exposure 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 e specie s r is extirpate d. Hence, we may more gen erally state that we wish to estimat e the expecte d response r given a specif ied exposure e , E( r je ). Expos ure–res ponse relationshi ps serve a t least three pur poses. Estimat ion : If an e xposure–r esponse model is availab le, an appropri ate estimate of expos- ure can be used with that mo del to estimat e the respo nse. Suc h estimat es can be use d in risk asses sments to charact erize risks from future contam ination or in ecologi cal epidemio logy to determ 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 EC 20 or a Benchmark Dose Limit (these are test endp oints; Box 23.1), that value can be used to separat 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 other means (Chapter 29). Commu nicat ion: Stakehol ders an d de cision makers are often unfami liar with the ways in which effects change in response to change s in exposure level s. Rat her, they tend to think in terms 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, optimal exposure 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 toxicity test or other expe rimental study (Ch apter 24) or in observation al studies (C hapter 25). In either case, the importance of the an alysis comes from the assum ption that by quantitatively modeling the association of exposure and response one can generalize to other cases in which that cause and the affected entity are associated. That is, if a chemical’s 96 h LC 50 for fathead minnows is determined from a laboratory test to be 2 mg=L, one would expect a fish kill to occur if that chemical occurred at 2 mg=L for at least 96 h in a stream with similar water ß 2006 by Taylor & Francis Group, LLC. chemi stry. Hence, when develop ing expo sure–re sponse relationshi ps, we must answ er the que 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 cal respon ses to nut rients, heat, and other nont oxic agents is the ind ividual organ ism. What prop ortion die, what is the average grow th, etc. ? How ever, respo nses of other entities such 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 s sensi tivity distribut ions (Posthum a et al. 2001). How ever, since they are most commonl y though 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 esponse relation 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 ponse relation ships are often express ed as poi nts, such as LC 50 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 one respon 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 responses change as exposure changes. However, results of tests or observations are commonly reduced to a point that is thought to provide a threshold or to summarize the results. The terminology for these values 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 the terms. If regression analysis has been used to develop a model that relates responses to exposure estimates, 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 concentration causing the effect in proportion p. The median lethal concentration (LC 50 ) is a particular ECp. For continuous variables such as weight or eggs per female, these values are known as ICp, the concentrations inhibiting the response by proportion p. Other terms are used in particular circumstances. For example, the term infective dose (IDp) is used for tests of pathogens. For simplicity, 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 a BMDL. If hypothesis-testing statistics are used, two test endpoints are derived. The first is the lowest concentration 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 is below the LOEC, which is the no observed effect concentration (NOEC). If adverse effects are distinguished from those that are not considered adverse, LOAEC and NOAEC terminology is used. ß 2006 by Taylor & Francis Group, LLC. are typically sigmoid. The slope or spread of the curve depends on the variance in sensitivity among the exposed units. Tests of very similar organisms, such as inbred laborat ory 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 three dimensions should be used much more often than they are, because we often want to know about 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 the reduction 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 from the same average effect due to sterility of part of the population and no effect on the rest. The next logical step is volume in four dimensions (e.g., concentration, duration, severity, and 0.2 0.4 0.6 0.8 1 0 p(0) 50 100 150 200 Proportion responding Dose BMD(10) BMR FIGURE 23.1 Exposure–response relationship with inverse regression. The benchmark dose (BMD) is derived from the benchmark response (BMR). Generated by the Benchmark Dose software. 0.94 0.00 0.25 0.50 Proportional mortality 0.75 1.00 0.49 Log concentration ( μg/L) 0.04 −0.41 1 7 13 Days 19 FIGURE 23.2 Toxic effects as a function of concentration, duration, and proportion responding. ß 2006 by Taylor & Francis Group, LLC. 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, and proportion from a conventional toxicity test, but the conventional number of replicates would seldom be sufficient to statistically fit a four-dimensi onal model. However, such data may be displayed without fitting a function (Figure 23.3). Often, risk assessors must settle for whatever standard or nonstandard expressions of the exposure–response relationship are available. This chapter discusses alternative approaches and associated issues so that assessors understand the types of relationships that they may be required to use and in the hope that they will have the opportunity to derive relationships from 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 160 mg/L 80 mg/L 40 mg/L 20 mg/L 0 mg/L (blank) Nitrobenzene Five test methods to assess chemical toxicity FIGURE 23.3 The relationshipofconcentration, duration,severity ofresponse, andproportion displaying the response, shown as a set of severity vs. proportion responding relationships, arrayed on concentration and time axes. The responses are N ¼ normal; E ¼ eyespot; T ¼ tetratopthalmic; AN ¼ anopthalmic; AC ¼ acephalic; and D ¼ death. (FromYosioka, Y., Ose, Y., and Sato, T., Ecotoxicol. Environ. Saf., 12, 15, 1986. With permission.) ß 2006 by Taylor & Francis Group, LLC. 23.1 APPROACHES TO EXPOSURE–RESPONSE Expos ure–res ponse relat ionshi ps can be de rived in various ways dep ending on the amount and quality of da ta and backgro und informat ion that are avail able. Ideally, mechani sms are unde rstood and can be us ed to model responses to expo sures. At the other extre me, one may not be able to do more than report that a pa rticular respon se occu rred at a parti cular exposure level . Currentl y, the best available guidan ce for ecotoxico logical expo sure–re sponse analys 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). 23.1.1 MECHANISTIC MODELS If the mechani sms by whi ch an exposure causes a respo nse are unde rstood, a mathe matical model that repres ents that relationshi p may be developed. These include toxicod ynami c models 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 onal form is defensi ble on bases other than con vention or goodness of fit. Anothe r advantag e of mechani stic mode ls is their flexibil ity. If mechani sms are wel l unde rstood, a mech anistic model may be used to simu late cond itions outsi de the range of test s or observation s. If mechani sms are fully specif ied, responses could be modeled from basic knowl edge without any testing or observat ion, as in the app lication of phy sical laws. Howev er, models in ecologi 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 to biologi cally plausi ble regres sion models . Hence, their range of app licability must be carefully consider ed (Chapter 9). Example s of mech anistic exposu re–respo nse models for organ isms include the Dynam ic Ener gy Budge t mod el (DEB tox) (Kooi jman a nd Bedau x 1996) an d conven tional toxic odynami c models (Sect ion 23.3). 23.1.2 R EGRESSION MODELS If data are available for respon ses at mult iple exp osure levels, the best general appro ach to exposure–r espo nse modelin g is stat istical regres sion analys is. The general ly preferred method for regres sion analysis is maximu m likelihood estimation (Envir onment Canada 2005), but least-s quares regression is usu ally effecti ve. Either method provides confide nce bounds , unless the 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 fit multiple plausi ble functi ons an d cho ose the one that provides the be st fit. Functions may be chosen be cause they are the standar d functi on for a particular use, be cause the form is approp riate for the data, or bec ause it ha s an app ropriate biologi cal interp retation. The most commonl y used functi on in eco toxicology is the log prob it (the linearized log-nor mal distribut ion), which is used to relat e quan tal data (e.g ., prop ortional mort ality) to the indepen dent expo sure varia te (Figur e 23.4). Ther e are no standar d models for co ntinuous data; approp riate functi ons shou ld be cho sen, fitted to the data, and compared. Wh en models are comp ared, their relat ive likelihood s are the app ropriate metr ics unless they diff er in the number 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 their plausibility and for outliers. Finally, residuals shou ld be plotted and inspected for patte rns that suggest a systematic lack of fit or heterogeneit y of variance. Methods for fitting of exposure–response distributions to toxicity data are discussed by Kerr and Meador (1996), Moore an d Caux (1997), Bailer and Oris (1997), and Environment ß 2006 by Taylor & Francis Group, LLC. 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 ity test data include CETIS, TOXSTA T, an d TOXC ALC. Finally, governm ent agen cies have develop ed softwar e that may be recomm ended for pa rticular regula tory asses sment s. The US EPA has developed bench mark dose softwar e that is pa rticular ly go od for comparin g alte rnative functi ons an d calcul ating co nfidenc e bounds (http: == www.epa. gov =nc ea = bmdd s.htm). Alt hough it was developed specif ically to calcul ate benchmark dos e values 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 by statist ical hy pothesi s testing (B ox 5.1) , have low utilit y for ec otoxico logy or eco logical risk assessment (Hoekstra and van Ewijk 1993; Laskowski 1995; Suter 1996a; OECD 1998; Environment Canada 2005). Because they are based on statistical significan ce, these end- points do not indicate whether the effect is, for example, a large increase in mortality or a small decrease in growth. The level of effect at an NOEC or LOEC is an artifact of the replication and dosing regime employed. As a resul t, NOECs and LOECs correspond to highly variable types and levels of effects (Suter et al. 1987; Crane and Newman 2000). They do not indicate how effects increase with increa sing exposure, so the effects of slightly exceeding an NOEC or LOEC are not qualitatively or quantitatively distinguishable from those of greatly exceeding it. To estimate risks, it is necessary to estimate the nature and 1 2 10 30 50 Percentage mortality Mortality (probits) 70 90 98 246 Concentration (mg/L) 10 20 40 3 4 5 6 7 FIGURE 23.4 Results of an acute lethality test plotted as probits of response against the log concen- tration. The LC 50 ¼ 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.) ß 2006 by Taylor & Francis Group, LLC. magni tude of effec ts that are occurri ng or co uld occur at the estimat ed exp osure levels an d associ ated unc ertainti es. Such esti mates are sup plied by the other a pproaches. 23.1.4 INTERPOLATION 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 using linea r interp olation down from an observed effect of approxim ately 25%, because they felt that 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 between exposure level s. In mo st cases, log conversio n of the exposure metr ic wi ll increa se the linea 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 the exposure level and associ ated effe cts level . If there is replicati on, geomet ric means and confide nce limits sho uld be c alculated. This a pproach is ap propria te when a test of a singl e exposure level an d con trol is perfor med, as in tests of undilut ed effluent or of a contam inate d medium at a particu lar location . It may also be use d when data do not permi t regres sion, as when one treatment level produces partial mort ality and all others cause 100% mortali ty, an d one 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 mplexity and 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 . 23.2.1 THRESHOLDS AND B ENCHMARKS For regula tory standar ds or screenin g bench marks , it is de sirable to define points on the exposure dist ribution that are thres holds for signifi cant effe cts; signifi cant in this case means that, if the threshold is exceeded, some action should be taken. Thresholds for statistical significance are inappropriate for that purpose. Rather, one must choose a level of effects (p) that has legal, policy, or societal significance, but how? LC 50 s have traditionally been reported, 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 is sufficiently steep, so that there is little variance in the effective concentration relative to other sources of variance, the LC 50 may be reasonably representative of partially letha l concentra- tions. However, a low effects level is generally desired for benchmarks. Because of concern for precision of the estimate, Environment Canada (2005) recommended that values less than EC 10 not be used and that p not be within the range expected for control effects. OECD (1998) recommended that values from EC 5 by increments of 5 up to EC 25 be determined routinely, and, if a mechanistic model is used, an EC 0 should be reported. This approach provides the decision maker with information to select a threshold effect based on policy and circumstances (e.g., the presence of important species). The approach would be enhanced by reporting confidence limits on each value. If the effe ct in controls or refer ence 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 ß 2006 by Taylor & Francis Group, LLC. x axis (EC 0 ) is an estimate of the biological threshold. Van Straalen (2002b) recommended using the HC 0 from species sensitivity distributions as community no-effect concentrations, using the uniform, triangular, exponential, or Weibull distribution. More conventional distributions with infinite tails (i.e., the normal and logistic) can be used if the number of organisms 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 HC 01 (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 to background (i.e., control treatments or refer ence sites), and higher levels produce increasing responses. Such cases may be fitted by a hockey-stick model, and the threshold is the exposure level at which the two segments meet (Figure 23.5). That is dEffect ¼ Background for C < C T dEffect ¼ Background þb(C À C T ) for C > C T (23:1) where C T is 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 Any lesion (prevalence) 0.4 0.5 0.6 0.7 Threshold = 620 ppb CI: 300–1000 ppb 10 100 Total aromatic hydrocarbons (ppb) 1,000 10,000 FIGURE 23.5 Hockey-stick regression of liver lesion prevalence in English sole as a function of total aromatic hydrocarbon concentration in sediment. The 95% confidence interval on the break point is plotted as a gray rectangle. (From Horness, B.H., Lomax, D.P., Johnson, L.L., Myers, M.S., Pierce, S.M., and Collier, T.K., Environ. Toxicol. Chem., 17, 872, 1998. With permission.) ß 2006 by Taylor & Francis Group, LLC. [...]... Macroinvertebrates 48 –96 h LC50 Mysid (saltwater crustacean) Life cycle test Daphnia Life cycle test Ceriodaphnia Algae 7 d survival and reproduction 96 h growth a Referencea Type EPA=660= 3-7 5-0 09 EPA=600= 4- 9 0=027F EPA=712-C-9 6-1 18 ASTM E72 9-9 6, -8 8 ASTM E 1 24 1-9 7 EPA=712-C-9 6-1 21 EPA=600= 4- 9 1=002 EPA=600= 4- 9 5=136 EPA=600= 4- 9 1=003 EPA=660= 3-7 5-0 09 ASTM E72 9-9 6, -8 8 EPA=712-C-9 6-1 66 ASTM E 119 1-9 7 EPA=712-C-9 6-1 20... narcosis Acute (summary) Acute (2,3 ,4, 5-tetrachloroaniline) Chronic (summary) Chronic (2 ,4, 5-trichlorophenol) Acute (aniline, phenol, 2-chloroaniline, 2 , 4- dimethylphenol) Respiratory uncoupler Acute (pentachlorophenol) Acute (2 , 4- dinitrophenol) Chronic (pentachlorophenol, 2 , 4- dinitrophenol) Chronic (pentachlorophenol) Chronic (pentachlorophenol) Acute (pentachlorophenol, 2 , 4- dinitrophenol) AChE inhibitor... EPA=712-C-9 6-1 20 ASTM E 119 3-9 7 EPA=600= 4- 9 1=002 EPA=712-C-9 6-1 64 ASTM E 121 8-9 7a 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 Dietary exposures may be important contributors to toxicity for bioaccumulative organic chemicals and metals, but are seldom tested This is in part because of the difficulty... 0.07x1 − 0.25logex2 − 1.20(x1∗logex2) 25 r 2 = 0.18 20 15 10 5 0 0 1 2 3 4 5 −3 20 −2 30 Intolerant taxa richness EPT taxa richness 10 5 1 2 3 4 5 r 2 = 0.22 25 r = 0.17 15 0 y = 16.90 − 2.77x1 + 1.57*logex2 − 3.69*(x1 ∗logex2) * y = 9 .40 − 1 .48 x1 + 0.52logex2 − 1.61 (x1 ∗logex2) 2 −1 20 15 10 5 0 0 −3 −2 −1 0 1 2 3 4 5 −3 −2 −1 0 1 2 3 4 5 Loge(S concentration/chronic AWQC) Loge(S concentration/chronic... acclimation, differences in dissolved organic carbon properties, and temporal variance 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 in emphasizing direct toxic effects of contaminants However, because nonhuman organisms are much more subject to indirect effects such as habitat... some cases, because the best test data for one criterion may not be the best for another Therefore, assessors must judge their relative importance to the particular assessment, and apply them accordingly 24. 2 CHEMICAL OR MATERIAL TESTS In ecological risk assessments, effects data for single chemicals, organisms (e.g., an exotic parasitoid), or materials (e.g., gasoline, silt) may be obtained from tests... or constant moisture) 24. 2.1 AQUATIC TESTS More test data are available for aquatic biota than any other type of ecological receptors (Table 24. 1) In general, flow-through tests, which constantly renew the test water, are preferred over static-renewal tests, which renew the water periodically, and those in turn are preferred over static tests, which do not change the water Flow-through tests maintain... Toxicol Chem., 23, 1786–1795, 20 04 With permission.) Observed population density (animals/ha) 10 8 y = 7.0884x − 0.5571 r 2 = 0. 549 6 6 Potential density 4 2 0 0.00 0.20 0 .40 0.60 0.80 1.00 −2 Habitat suitability index 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... maximum density given that the population is limited only by habitat suitability (From Kapustka, L.A Hum Ecol Risk Assess., 9, 142 5, 2003 With permission.) ß 2006 by Taylor & Francis Group, LLC Ecoregions 46 , 47 , 48 , and 51 Ecoregions 50 and 52 Intolerant taxa richness 20 15 10 5 0 0 10 20 30 40 50 60 70 80 90 100 Percentage fines FIGURE 23.13 Quantile regression of the 90th percentile of the number... the boundary are assumed to be reduced by co-occurring stressors The upper boundary, which may be thought of as the response when the independent variable is the limiting factor, is ß 2006 by Taylor & Francis Group, LLC y = 34. 22 + 0.02x1 − 0.17logex2 − 5 .40 *(x1∗logex2) 60 2 Collector-gatherer richness r = 0.32 Total taxa richness− macroinvertebrates 50 40 30 20 10 0 −3 −2 −1 y = 9.12− 0.07x1 − 0.25logex2 . To estimate risks, it is necessary to estimate the nature and 1 2 10 30 50 Percentage mortality Mortality (probits) 70 90 98 246 Concentration (mg/L) 10 20 40 3 4 5 6 7 FIGURE 23 .4 Results of. Examples of hockey-stick models in ecotoxicology include Beyers et al. (19 94) and Horness et al. (1998). Beyers et al. (19 94) found that hockey-stick thresholds were a factor of 2 to 4 lower than. −1 012 345 Total taxa richness− macroinvertebrates 0 10 20 30 40 50 60 Log e (S concentration/chronic AWQC) −3 −2 −1012 345 Intolerant taxa richness 0 5 10 15 20 −3 −2 −1012 345 Collector-gatherer