Interspecies Correlation Estimates Predict Protective Environmental

Mar 28, 2006 - To help overcome the cost of multiple toxicity tests, we explored the potential of the U.S. EPA's Interspecies Correlation Estimation (...
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Environ. Sci. Technol. 2006, 40, 3102-3111

Interspecies Correlation Estimates Predict Protective Environmental Concentrations S C O T T D . D Y E R , * ,† DONALD J. VERSTEEG,† SCOTT E. BELANGER,† JOEL G. CHANEY,† AND FOSTER L. MAYER‡ The Procter and Gamble Company, 11810 East Miami River Road, Cincinnati, Ohio 45253-8707 and ToxGap, 8069 Constitution Road, Las Cruces, New Mexico 88007-8982

Environmental risk assessments often use multiple single species toxicity test results and species sensitivity distributions (SSDs) to derive a predicted no-effect concentration in the environment, typically the 5th percentile of the SSD, termed the HC5. The shape and location of the distribution are best known when populated with numerous toxicity values. To help overcome the cost of multiple toxicity tests, we explored the potential of the U.S. EPA’s Interspecies Correlation Estimation (ICE) program to predict single species toxicity values from a single known toxicity value. ICE uses the initial toxicity estimate for one species to produce correlation toxicity values for multiple species, which can be used to develop SSD and HC5. To test this approach to deriving HC5, we generated toxicity values based on measured toxicity values for three surrogate species Pimephales promelas (Fathead minnow), Onchorynchus mykiss (Rainbow trout), and Daphnia magna (water flea). Algal taxa were not used due to the paucity of high quality algal-aquatic invertebrate and algal-fish correlations. The compounds used (dodecyl linear alkylbenzenesulfonate (LAS), nonylphenol, fenvalerate, atrazine, and copper) have multiple measured toxicity values and diverse modes of action and toxicities. Distribution parameters and HC5 values from the measured toxicity values were compared with ICE predicted distributions and HC5 values. While distributional parameters (scale and intercept) differed between measured and predicted distributions, in general, the ICE-based SSDs had HC5 values that were within an order of magnitude of the measured HC5 values. Examination of species placements within the SSDs indicated that the most sensitive species were coldwater species (e.g., salmonids and Gammarus pseudolimnaeus). These results raise the potential of using quantitative structure activity models to estimate HC5s.

Introduction Environmental risk characterizations typically compare a concentration expected to protect most or all species with an exposure concentration. Concentrations protective of * Corresponding author telephone: (513)627-1163; fax: (513)6271208; e-mail: [email protected]. † The Procter and Gamble Company, Cincinnati. ‡ ToxGap, Las Cruces, New Mexico. 3102

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most species are typically extrapolated from acute and, less frequently, chronic toxicity values on a limited number of test species (e.g., Daphnia magna and Pimephales promelas (Fathead minnow)). Over the past 30 years, thousands of tests with aquatic biota have been conducted to assess the potential risk of chemicals as a consequence of environmental exposure. Since it is impossible to test all organisms with any one compound, test species are considered surrogates for the species found in ecosystems. Uncertainty or assessment factors (AFs) and/or species sensitivity distributions (SSDs) are then used to derive a protection level for all species within an ecological assemblage (or within an ecosystem) (1, 2). SSDs are statistical distributions that describe the variation among a set of species in toxicity of a particular chemical or mixture (3). Many aquatic quality criteria are based on SSDs using the 5th percentile cutoff (HC5) concentration. The U.S. EPA has used the log-triangular distribution for establishing water quality criteria (4), whereas other experts and regulators use other distributions such as log-logistic (5- 7). The choice of the shape of the distribution has been discussed extensively elsewhere (8, 9). At present, and soon, there is a need to ensure the protection of a wide variety of species for all of the chemicals that will reach the aquatic environment. Regulatory activities occurring around the globe are attempting to address risk assessment needs for thousands of chemicals (e.g., EU REACh legislation or Registration, Evaluation, and Authorization of Chemicals (10; http://europa.eu.int/comm/environment/ chemicals/reach.htm), categorization of the Canadian Domestic Substance List (11), and the ICCA HPV Chemicals Challenge program with OECD (http://www.icca-chem.org/)). Difficulties arise as there is increased pressure to reduce fish testing in some regulatory contexts, and the direct evaluation of threatened or endangered species is necessarily restricted (e.g., U.S. Endangered Species Act of 1973, amended 2003; 12). Hence, the need exists to better understand how to use limited toxicity data to protect all species including those that cannot be tested. Recently, the U.S. EPA developed the Interspecies Correlation Estimation (ICE) program to predict acute effects to 143 aquatic and terrestrial species based on toxicity results from surrogate species (13, 14). Two-thousand-eighty-four species to species regression models (Model II least squares) based on 4472 LC/EC50 values from 120 fish and invertebrate species were compiled and developed, making it the most comprehensive analysis and tool for interspecies comparisons available. Among the most frequently tested freshwater species were D. magna, P. promelas (Fathead minnow), and Onchorhynchus mykiss (Rainbow trout). ICE allows the prediction of acute toxicity values for a wide variety of species based on the input of a single known, or estimated, acute toxicity value. In this study, ICE was seeded with a single toxicity value and used to estimate acute toxicity values to a diverse set of aquatic species. The estimated toxicity values were expressed in log-logistic-based species sensitivity distributions (SSDs) for comparison. SSDs have been used with chronic toxicity data to develop predicted no-effect concentrations (PNECs) in the environment (7), although Versteeg et al. (15) have suggested the use of acute SSDs in risk assessments. The purposes of this study were to (i) determine the relationship of starting toxicity values and surrogate species choice on ICE-based SSDs; (ii) compare SSDs derived using all possible species versus a robust dataset that uses only significant regressions generated from multiple pairwise comparisons; and (iii) determine if there is a pattern of species sensitivities across a wide array of toxicant modes 10.1021/es051738p CCC: $33.50

 2006 American Chemical Society Published on Web 03/28/2006

FIGURE 1. Cumulative distribution of 4772 LC/EC50 values in the U.S. EPA’s Interspecies Correlation Estimation (ICE) program. of action. Finally, ICE-based SSDs were compared to published SSDs employing acute toxicity data to evaluate the use of acute toxicity data in environmental risk assessments.

Materials and Methods ICE Dataset. The aquatic data set was a compilation of Mayer (16), Mayer and Ellerseick (17), AQUIRE (18), and the U.S. EPA’s Office of Pesticide Programs aquatic database. Data approximated standardized acute toxicity test conditions (19) using criteria described by Buckler et al. (20). Briefly, tests retained in the database include the following characteristics: 96 h LC50 for fish; 48 h EC/LC50 data for most invertebrates (e.g., Daphnia); fish weighing 0.2-0.5 g; fish were less than 3 months old or less than 50 mm in length; test chemicals were identified as technical grade or >90% active ingredient; water hardness was between 30 and 60 mg CaCO3/L; pH 6.8-7.8; and temperatures were appropriate per species (e.g., rainbow trout 10-15 °C; Daphnia magna ∼20 °C). Standardization resulted in a data set with 4772 tests comprising 247 aquatic species and 660 chemicals. The number of species within groups corresponded to six amphibian; three bryozoan; 82 fish; 146 invertebrates; and 10 plants (nine algae). Because of the availability of few correlations between algae and animal taxa, we focused our work on only invertebrate and fish species. The dataset used in this study is characterized by EC/LC50 values ranging from 0.0017 to 45 × 106 µg/L with a median of 490 µg/L and interquartile range of 36-4800 µg/L (Figure 1). The mean and standard deviation EC/LC50 values were 46 216 and 847 084 µg/L, respectively. ICE Regressions. Interspecies correlations were determined via Model II least squares regressions where both independent and dependent variables are random (21). Slopes and intercepts were derived from the equation

log X2 ) a + b(log X1)

(1)

where X1 refers to the surrogate species (e.g., P. promelas, O.

mykiss, and D. magna) toxicity value and X2 is the predicted species toxicity value. When more than one toxicity value was present per X1 and X2 pair, the geometric mean was used. Generation of ICE-Based SSDs. ICE was used to generate acute toxicity values on numerous species after seeding with a single toxicity value from Rainbow trout, Fathead minnows, or D. magna. These species were selected as they are among the most commonly tested species in the ICE database. A robust data subset was created from each prediction since many of the interspecies correlations in ICE are not significant. This robust data subset was developed by including only significant correlations (pe0.05) with at least 10 observations per species pair to building the robust ICEbased SSD. Fish and invertebrates, not algae, were represented in the robust dataset. Using each of the starting species (i.e., Rainbow trout, Fathead minnows, and D. magna) as surrogates, regressions for 31, 23, and 15 fish and invertebrate species were selected. That is, for example, toxicity values for 31 species could be obtained assuming a starting value for Rainbow trout, 23 for Fathead minnows, and 15 for D. magna. It should also be pointed out that some pairwise comparisons to surrogate species are unique for the initial species value (i.e., not all species are represented in the rainbow trout comparison that may be found in the D. magna comparison and vice versa). Predicted log values were expressed as a logistic cumulative probability to estimate the species sensitivity distributions. Published Toxicity Data. Species sensitivity distributions for dodecyl linear alkylbenzene sulfonate (LAS), nonylphenol (NP), fenvalerate, atrazine, and copper were developed from published sources. LAS data were obtained from Versteeg et al. (5), NP data from Staples et al. (22), and from the U.S. EPA (23). Fenvalerate and atrazine data were gathered from the Pesticide Action Network (PAN) North America (http:// www.pesticideinfo.org/). Ecotoxicity data found in the PAN datasets were originally obtained from the U.S. EPA’s AQUIRE database but provided in a user-friendly framework. Copper data were provided from the U.S. EPA water quality criteria VOL. 40, NO. 9, 2006 / ENVIRONMENTAL SCIENCE & TECHNOLOGY

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document (24). Where more than one acute LC50 or EC50 value was available, geometric species mean acute toxicity values were calculated and used to seed ICE. Typically, the endpoint reported at 48 and 96 h was used for invertebrates and fish, respectively. These data were used in the measured distributions and Fathead minnow, Rainbow trout, and D. magna toxicity values from these data sets were used as surrogates for ICE-based SSD generation. Statistics. Several hypotheses were tested using species toxicity values predicted from ICE using Rainbow trout, Fathead minnow, and D. magna as surrogate species. Since ICE does not factor in the mode or mechanism of action, species sensitivity distributions were created from surrogate species using toxicity values as initial values of 0.0001, 0.001, 1.0, and 100 mg/L. SSDs were developed for all invertebrate and fish species in ICE (all dataset) as well as the robust dataset (g10 species pair observations, p e 0.05). To describe a SSD, the following two-parameter loglogistic model was fit via maximum likelihood estimation:

Y ) 1/(1 + exp((A - X)/B))

(2)

where X is the independent variable (i.e., log EC/LC50); Y is the cumulative probability (i.e., percentage of affected species); A is the parameter representing the location (i.e., the intercept parameter); and B is the parameter representing the slope of the curve (i.e., the scale parameter). Models with parameters fit via maximum likelihood can be compared using likelihood ratio tests, provided that one of the models is a reduced form of the other (i.e., one or more parameters have been deleted). Under this scenario, a likelihood ratio test (LRT) statistic is formed from the ratio of the likelihoods associated with the two models. It can be shown that -2(log(LR/LF)), where LF is the maximum likelihood of the full model and LR is the maximum likelihood of the reduced model, is distributed as a chi-squared value with degrees of freedom equal to the difference in the number of parameters of the two models. The SSD models are compared in terms of their slopes and locations. First, an overall test is conducted by fitting a full model that includes separate slope and location parameter estimates for each SSD and a reduced model that includes only one slope parameter and only one location parameter for all of the data (all SSDs). If this test is significant, the SSDs differ significantly. To determine exactly how the SSDs differ, two additional LRTs are conducted. The first test compares a full model that includes separate location (but not slope) parameter estimates for each SSD to the same reduced model as stated previously. The second test compares the full model used in the overall test (with separate parameters for both location and slope for each SSD) to the reduced model that includes separate location (but not slope) parameter estimates for each SSD. The results of the first test indicate whether the location differs significantly among SSDs, and the results of the second test indicate whether the slope differs significantly among SSDs. The significance was always inferred at R ) 0.05. Model fitting, graph creation, prediction, and statistical testing were performed using S-Plus 6.2 (Insightful Corporation, Seattle, WA). The effect of surrogate species on the overall SSD model, scale, and location parameters were compared for each surrogate species start value using the LRT described previously. The effect of the dataset (all vs robust) per surrogate species start value was also determined. To examine the potential that some species may be consistently more sensitive across the surrogate species choice, predicted species from the Fathead minnow-based SSD were ranked based on toxicity values from the robust dataset. Ranks were imputed for the same species found in Rainbow trout and 3104

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D. magna SSDs, and then all ranked species were placed into quartiles for comparison across all three surrogate species. ICE-based SSDs using measured mean values for Fathead minnow, Rainbow trout, and D. magna as surrogate species for LAS, NP, fenvalerate, atrazine, and copper were compared (model, scale, and location) with SSDs generated from published acute toxicity values using the LRT. The hazardous concentration protective of 95% of species (HC5) for all SSDs was also determined by the methods of Aldenberg and Jaworska (25).

Results The effect on species sensitivity distributions (SSDs) of using all species available within ICE or only those with a highly significant interspecies correlation and a minimum of 10 data points in the regression (robust dataset) was examined. Significant differences were observed, however, for Fathead minnow and Rainbow trout for the scale parameter and the overall regression models from the 0.0001, 0.01, and 1.0 mg/L start values (Table 1). Where the differences in the scale parameter were significant, the scale parameter from the all data sets were greater (more spread in the data) than for the robust data sets. This increased scale and similar location parameters produced a flatter distribution resulting in a decreased HC5 value for distributions calculated with the all datasets (Table 1 and Figure 2). No significant differences were observed within the D. magna datasets. A qualitative comparison of species placement among the various SSDs showed that some species frequently occurred in the sensitive or tolerant portions of the curve. This led us to rank species placements from Rainbow trout and D. magna-based SSDs relative to Fathead minnow-based SSDs (Figure 3). Table 2 illustrates the relative rankings of all predicted species in the three robust datasets, expressed within quartiles. Twenty-one, 27, and 11 species were ranked from the Rainbow trout, Fathead minnow, and D. magna datasets, respectively. Each of the three datasets showed Gammarus pseudolimnaeus as the most sensitive species. Species common within the first quartile (most sensitive) of the Fathead minnow and Rainbow trout datasets included Salmo trutta, Salvelinus fontinalis, and Salmo salar. Chironomus plumosus was common within the second quartile of all three datasets. Two other species (Micropterus salmoides and Oncorhynchus clarki) were in the second quartile of the Fathead minnow and Rainbow trout datasets. Moving to the most tolerant end of the SSDs, the fourth quartile, two species (Ictalurus punctatus and Carassius auratus) were common to all three datasets. Lepomis cyanellus and Ameiurus melas were the other two species within the fourth quartile in the Fathead minnow and Rainbow trout datasets. While there was a considerable agreement of predicted species ranks from the three datasets, there were exceptions. For instance, the stoneflies Pteronarcella badia and Pteronarcella californica were observed in the third quartile of the Fathead minnow dataset but were in the most sensitive quartile of the Rainbow trout dataset. Similarly, D. magna was in the most tolerant (fourth) quartile of the Fathead minnow dataset, yet was found in the second quartile of the Rainbow trout dataset. In contrast, Daphnia pulex and Gammarus fasciatus were observed in the fourth quartile of the Rainbow trout dataset and in the first and second quartiles of the D. magna dataset. Species sensitivity distributions were generated from ICE (robust dataset) seeded with a Rainbow trout, Fathead minnow, or D. magna acute toxicity values for LAS, nonylphenol, fenvalerate, atrazine, and copper. These SSDs were compared to SSDs generated with measured and published toxicity values for these same compounds (Table 3). For LAS, no significant differences in scale parameters were observed

TABLE 1. Descriptors of Log-Logistic Species Sensitivity Distribution Models Derived from ICE Using Fathead Minnow, Rainbow Trout, and D. magna with Four Different Start Valuesa surrogate species Fathead minnow Fathead minnow Fathead minnow Fathead minnow Fathead minnow Fathead minnow Fathead minnow Fathead minnow Rainbow trout Rainbow trout Rainbow trout Rainbow trout Rainbow trout Rainbow trout Rainbow trout Rainbow trout D. magna D. magna D. magna D. magna D. magna D. magna D. magna D. magna

starting value (mg/L) 0.0001 0.0001 0.01 0.01 1.0 1.0 100 100 0.0001 0.0001 0.01 0.01 1.0 1.0 100 100 0.0001 0.0001 0.01 0.01 1.0 1.0 100 100

dataset

scale

location

overall model comparison

all robust all robust all robust all robust all robust all robust all robust all robust all robust all robust all robust all robust

3.1430b

-7.163b

c

1.8398 2.2322b 1.2383 1.3865b 0.7576 0.8293 0.6471 3.1403b 1.6503 2.2112b 1.1613 1.3792b 0.8243 1.0321 0.7840b 2.9108 2.4587 2.0322 1.8729 1.2575 1.3126 1.0479 0.8168

-8.731 -3.467b -4.555 0.214b -0.499 3.939 3.567 -6.493b -6.472 -2.702b -2.841 1.112b 0.785 5.142 4.619 -1.159 -3.165 1.069 -0.363 3.324 2.455 5.844 5.358

c c

c c c

HC5 (mg/L) 7.4 × 10-08 7.2 × 10-07 4.4 × 10-05 0.00027 0.0209 0.0652 4.47 5.27 1.5 × 10-07 1.2 × 10-05 1 × 10-04 0.00191 0.0525 0.194 8.20 10.1 6 × 10-05 3 × 10-05 0.00735 0.00280 0.685 0.244 15.8 19.1

a All datasets pertain to all species correlation pairs found in ICE; robust refers to correlations that were significant (p < 0.05) and had a minimum of 10 species pair observations. HC5 is the hazard concentration for the 5th percentile of species (i.e., protective for 95% of species). b Significant difference between all and robust scale or location parameters of the log-logistic regression, chi-squared test (p e 0.05). c Significant difference in the overall model between all and robust log-logistic regressions chi-squared test (p e 0.05).

between the ICE-based and the measured SSDs. The location parameter of the ICE-based SSD using the Fathead minnow as the surrogate species was significantly less than the location parameter for the measured dataset, and the overall models were significantly different. For D. magna, the location parameter of the ICE-based SSD was significantly greater than for the measured dataset, and the overall models were significantly different (Figure 4A). The resulting HC5 values were 2- and 4-fold lower for the fish-based SSDs and 5.5-fold greater than the measured LAS HC5. For nonylphenol, there was a significant difference in the scale and intercept parameters between the distributions based on ICE and those from the measured data for all surrogate species. The resulting ICE-based HC5 values for nonylphenol were 1.4-12-fold less than the measured HC5 values. For fenvalerate, the scale and intercept parameters for ICE-based distributions seeded with Rainbow trout and D. magna toxicity values were significantly different from the scale and intercept parameters for the measured distribution. The HC5 value from the measured fenvalerate distribution was similar to the ICEbased distributions seeded with Fathead minnow toxicity data (3.7 × 10-5 mg/L vs 3.99 × 10-5 mg/L); however, the ICE-based HC5 values from the Rainbow trout and D. magna datasets were approximately 14- and 6-fold greater than the measured HC5 value. The scale factors based on measured atrazine data and ICE-based predictions seeded with the Fathead minnow were not significantly different; however, scale factors were significantly different for Rainbow trout and D. magna-based distributions. Location parameters from each of the three surrogate species-based SSDs were significantly different than the measured SSD. Even so, the HC5 values from the Fathead minnow and Rainbow trout SSDs were similar to the measured values, 0.809, 0.276, and 0.684, respectively. The HC5 from the D. magna distribution was 6.4-fold greater than the measured HC5. For copper, the distributions based on measured toxicity data and the Fathead minnow and Rainbow trout ICE-based predictions were not significantly different, although the location pa-

rameter for the ICE-based prediction for trout differed from the measured location parameter (p < 0.05). The similarity of the models from the Fathead minnow-based SSD versus the measured distribution is shown in Figure 4B. The ICEbased distribution seeded with a D. magna value of 0.00496 mg/L creating a distribution that was significantly different than the measured model (scale and location) still provided an HC5 value that was within a factor of 2 of the measured HC5 value. Similarly, the Fathead minnow and Rainbow trout HC5 values were within factors of 2.2 of the measured HC5 value.

Discussion Environmental effect assessments are usually conducted in a tiered process generating PNECs for surface waters, sediments, and soil compartments. The goal of the effects assessment is to estimate the concentration of a chemical that will have a low or no effect in the ecosystem. The PNEC is usually derived by dividing laboratory generated toxicity data on a few species by an appropriate assessment factor to account for uncertainties in extrapolating from the laboratory to the ecosystem, short-term to long-term exposure, few to many species, etc. Naturally, as the test systems become more environmentally realistic, the magnitude of the assessment factor is reduced. Assessment factors have been and are currently used in effects assessments (e.g., refs 1 and 2). Importantly, there is technical support for the factors applied (26, 27). Issues with assessment factors include a lack of knowledge about the true extrapolation from a few single species data points to the entire ecosystem or even to the many species in the community and an inability to evaluate the protection of threatened and endangered species. Use of distributions of chronic toxicity data and assumptions about the shape of the distribution, and the ability of laboratory species to protect species found in the environment, allows the estimation of concentrations protective of most or all species (4, 28, 29). This single species distribution approach has been used in a variety of effect VOL. 40, NO. 9, 2006 / ENVIRONMENTAL SCIENCE & TECHNOLOGY

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FIGURE 2. (Top graph) Species sensitivity distributions (SSDs) from ICE using Fathead minnow as the surrogate species and a start value of 0.01 mg/L. Red is the species from the robust dataset, and black is from all species in the ICE dataset. (Bottom graph) SSDs from ICE using Rainbow trout as the surrogate species with a start value of 0.0001 mg/L. Red refers to species from the robust dataset, and black is from all species in the ICE dataset. Dashed line illustrates the 95th percentile confidence interval. assessments and will continue to be applied to environmental risk assessment (2, 15, 30-33). In a distributional approach, the greater the number of single species toxicity values, the lower the uncertainty about the distributional parameters and the shape of the distribution (34). Uncertainty may also be decreased by examining SSDs corresponding to their ecological context, such as freshwater versus marine, benthic versus pelagic, invertebrate versus fish, etc. (8). Our study used an unweighted, Biblio approach where the proportions of the data were based on the number of compounds and aquatic animal taxa used to generate ICE (9). Given the cost 3106

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of multiple toxicity tests, ICE provides the opportunity to generate multiple toxicity values with limited costs. Holding surrogate species toxicity values constant, but evaluating at different levels within the robust dataset, allowed us to examine potential issues regarding surrogate choices on SSDs and predicted species ranks. For all effect start values, the Fathead minnow-based SSDs provided the lowest HC5 values. This is consistent with our evaluation of LAS, nonylphenol, and fenvalerate. Averaging species ranks, across all start values (0.0001, 0.01, 1.0, and 100 mg/L), from each of the three surrogate species-based ICE predictions

FIGURE 3. Species sensitivity distributions (SSD) from the robust dataset within ICE using a start value of 1 mg/L with each surrogate species: Fathead minnow, Rainbow trout, and D. magna. Species values refer to ranks based on the Fathead minnow SSD. Nonnumbered species in the Rainbow trout and D. magna SSDs were not common with species used in the Fathead minnow SSD.

TABLE 2. Species Ranks, Expressed within Quartile Bins, from ICE-Based SSDs Using the Robust Dataset with Rainbow Trout, Fathead Minnow, and D. magna as Surrogate Speciesa first quartile

second quartile

third quartile

fourth quartile

a

Rainbow trout

Fathead minnow

D. magna

Gammarus pseudolimnaeus Salvelinus fontinalis Salmo trutta Pteronarcys californica Pteronarcella badia Salmo salar Chironomus plumosus O. kisutch M. salmoides Claassenia sabulosa D. magna Lepomis macrochirus O. clarki Asellus brevicaudus Gammarus lacustris Salvelinus namaycush P. flavescens Cypridopsis vidula Pimephales promelas D. pulex G. fasciatus Ictalurus punctatus Carassius auratus Lepomis cyanellus Cyprinus carpio Ameiurus melas Pseudacris triseriata

G. pseudolimnaeus S. trutta S. fontinalis Salmo salar Oncorhynchus kisutch

G. pseudolimnaeus D. pulex

C. plumosus Micropterus salmoides Oncorhynchus clarki Perca flavescens Onchorhynchus mykiss

Simocephalus serrulatus C. plumosus Gammarus fasciatus

P. badia Pt. californica Asellus brevicaudus Lepomis macrochirus Cyprinus carpio

P. californica O. mykiss Lepomis macrochirus

D. magna L. cyanellus C. auratus Cypridopsis vidua I. punctatus A. melas

P. promelas I. punctatus C. auratus

The top of the table (i.e., first quartile) represents the species with the lowest toxicity values (most sensitive).

provided in essence a sensitivity ranking of species for fish and invertebrates tested across a large range of modes of

actions. To our knowledge, this is the first evaluation of its kind where species sensitivity was ranked across chemicals VOL. 40, NO. 9, 2006 / ENVIRONMENTAL SCIENCE & TECHNOLOGY

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TABLE 3. Comparison of Species Sensitivity Distributions (SSDs) for Dodecyl Linear Alkylbenzenesulfonate (LAS), Nonylphenol, and Fenvalerate to ICE-Based SSDs Using Measured Surrogate Toxicity Values for Rainbow Trout (RBT), Fathead Minnow (FHM), and D. magna (Dm) source/surrogate species

compound

measured SSD ICE-FHM ICE-RBT ICE-Dm measured SSD ICE-FHM ICE-RBT ICE-Dm measured SSD ICE-FHM ICE-RBT ICE-Dm measured SSD ICE-FHM ICE-RBT ICE-Dm measured SSD ICE-FHM ICE-RBT ICE-Dm

LAS LAS LAS LAS nonylphenol nonylphenol nonylphenol nonylphenol fenvalerate fenvalerate fenvalerate fenvalerate atrazine atrazine atrazine atrazine copper copper copper copper

ICE surrogate toxicity value (mg/L)a 3.59 2.69 5.03 0.134 0.221 0.127 0.00279 0.00138 0.00152 15.0 11.2 22.6 0.0720 0.0216 0.00496

scale

location

0.5185 0.6819 0.7051 1.0248 0.3178 0.9499b 0.8093b 1.5590b 1.4130 1.4404 1.1875b 2.1962b 1.0291 0.7170 0.7418b 0.9430b 1.0968 1.0205 1.1735 1.9609b

1.550 0.602b 1.494 4.265b -1.615 -2.242b -0.520b 1.188b -6.044 -5.890 -4.052b -1.983b 2.649 1.899b 0.896b 4.247b 3.802 4.118 4.797b 6.117b

model comparison

c c c c c c c c c

c

HC5 (mg/L) 1.02 0.265 0.566 3.48 0.0780 0.00720 0.0533 0.0220 3.70 × 10-05 3.99 × 10-05 0.000527 0.000214 0.684 0.809 0.276 4.35 0.00177 0.00305 0.00382 0.00141

a

Acute toxicity value for fathead minnow, rainbow trout, or D. magna used to initiate ICE predictions. b Significant difference between. c Significant difference in the overall models between measured SSD and ICE-based SSD, logistic regression chi-squared test.

with different modes of action. The result of the ranking clearly illustrated that coldwater species, salmonids, and the invertebrate G. pseudolimnaeus were the most sensitive. Dyer et al. (35) also have illustrated that coldwater fish species were more sensitive than temperate and tropical species for a wide range of chemical modes of action. Tremolada et al. (36) also concluded via interspecies correlations with pesticides that trout were consistently the most sensitive species. The construction of two datasets, robust and all, allowed us to examine the potential of ICE-based SDDs and predicted hazard concentrations. Figure 2 illustrates that the general tendency of the addition of more species to the robust dataset (i.e., all) results in a more shallow scaling factor with more species providing greater toxicity values. That is, with increased numbers of species, there is a greater tendency to add more tolerant species to the distribution rather than more sensitive species. This may have particular ramifications as one considers issues regarding rare and endangered species. Sappington et al. (37) and Hansen et al. (38) have indicated that threatened and endangered species are not uniquely sensitive to toxicants. Even so, ICE may provide the only practical and rapid mechanism to estimate toxicity values for these taxa. Since the scaling factors for all-based SSDs were generally greater for than the robust SSDs, the resulting HC5 values for all species were generally lower than robust species. The phenomenon is a common occurrence when comparing subsets of data versus an entire dataset (3, 39). As a consequence for estimating protective concentrations, the use of all tested species will result in a more conservative HC5 value. Laboratory-derived SSDs have been shown to be predictive of NOECs from well-conducted, comprehensive mesocosm tests (5, 32). Typically, the focus of these types of comparisons is between chronic SSDs and long-term, sensitive mesocosm NOECs (40). Direct comparisons of acute SSDs, such as those derived from ICE, with mesocosm effects may be of particular value when considering pesticides because these exposures are often pulsed and ephemeral (39). Comparisons of a wider array of ICE-based SSDs, real databased SSDs, mesocosm responses, and effects realized in field experiments would be fruitful to understand strengths 3108

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and limitations of these approaches and should be a future area of study. For example, Maltby et al. (33) suggested that a species choice was a less critical factor than geographic and habitat distribution of taxa present in an SSD to allow the SSD to be predictive. In this study, we investigated the use of an interspecies correlation estimation (ICE) dataset to predict species sensitivity distributions (SSDs) based on three surrogate species (Fathead minnow, Rainbow trout, and D. magna) acute toxicity values. These comparisons were robust enough to lead to consideration of how ICE-based predictions relate to chronic SSDs and then to field effects. Five compounds, LAS, nonylphenol, fenvalerate, atrazine, and copper, were chosen to assess the ability of ICE to make acceptable acute toxicity predictions. These compounds were chosen based on their diversity of mode of action, toxicity, and chemical structure. ICE-predicted SSDs were, in most cases, statistically different than the distributions based on measured data. However, despite modes of actions including a neurotoxicant (fenvalerate), two narcotics (LAS and nonylphenol), a herbicide (atrazine), and a heavy metal (copper), ICE-based predicted HC5 values were generally within a factor of 10 of the HC5 value based on measured data, despite that no algae were included in the ICE-based SSDs. Considering D. magna as the surrogate species, the HC5 values from the ICE-predicted distributions differed from the HC5 values from the distribution based on measured data by factors ranging from 1.3 to 6.4. This is surprising given that the ICE-based distribution generated from D. magna was always significantly different from the measured data distribution. For use in risk assessment, the issue is not the similarity in the distributions but is if the uncertainty of the ICE predicted distributions are sufficiently well-understood such that the HC5 values can be used in the risk assessment. This analysis of five compounds, despite their diversity of modes of action and acute toxicity, only begins the evaluation of ICE as a tool to predict acute LC50 and EC50 values for use in risk assessment. Future questions that may be addressed include the following: how can toxicity data from plant taxa (e.g., measured, QSAR, or ICE-based) best be included in ICEbased SSDs; what is the potential impact for the inclusion of correlation variation on resulting ICE-based SSDs; what is the effect of other surrogate species in creating SSDs; is

FIGURE 4. Comparison of species sensitivity distributions (SSDs) generated from measured data and ICE using a surrogate species. Two distributions are shown. (A) Significantly different distributions, measured acute toxicity values (red/right) with ICE-based predicted acute values (black/left) using D. magna as the surrogate species for dodecyl linear alkylbenzenesulfonate (LAS). (B) Not significantly different distributions, measured acute toxicity values (red/right) with ICE-based predicted acute values (black/left) using Fathead minnow as the surrogate species for copper. Dashed line illustrates the 95th percentile confidence interval. there a guidance that can be provided for surrogate choices based on the chemical mode of action; and do predicted species sensitivity ranks maintain their quartile status based on other surrogate species choices? Further, the conversion of mass units to molar units may decrease the uncertainty among species tested against chemicals with similar modes of action (e.g., polar and nonpolar narcosis). Even with these questions, and others that require investigation, our study illustrates that ICE provides a potential option to generate acute toxicity predictions, thereby reducing or eliminating the need for an uncertainty factor that accounts for differ-

ences in species sensitivity, as stated by Zeeman (1) and the EU TGD (2). We have demonstrated the potential utility of ICE to generate HC5 values for use in the risk assessment of chemicals. We believe that another use for ICE may be the generation of SSDs and HC5s based on QSARs (quantitative structure activity relationships). Where chemical structures are within the domain of applicability with good agreement between predicted toxicity values, QSARs can provide surrogate species values for ICE-based SSDs. In essence, the combination of ICE with QSARs provides the potential for VOL. 40, NO. 9, 2006 / ENVIRONMENTAL SCIENCE & TECHNOLOGY

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probabilistic aquatic effects assessments based on chemical structure alone. Given the impending regulatory and subsequent data needs of the European REACH, Canadian DSL, and OECD HPV programs, the combination of QSARs and ICE provides an approach worth further evaluation by risk assessors.

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Received for review September 1, 2005. Revised manuscript received December 22, 2005. Accepted January 30, 2006. ES051738P

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