Approaches for Establishing Predicted-No-Effect ... - ACS Publications

The establishment of rational frameworks for population- level ecological risk assessment (PLERA) in the context of chemical substances management is ...
0 downloads 0 Views 280KB Size
Download PDF
Environ. Sci. Technol. 2005, 39, 4833-4840

Approaches for Establishing Predicted-No-Effect Concentrations for Population-Level Ecological Risk Assessment in the Context of Chemical Substances Management BIN-LE LIN,* AKIHIRO TOKAI, AND JUNKO NAKANISHI Research Center for Chemical Risk Management, National Institute of Advanced Industrial Science and Technology, 16-1 Onogawa, Tsukuba, Ibaraki Prefecture 305-8569, Japan

The establishment of rational frameworks for populationlevel ecological risk assessment (PLERA) in the context of chemical substances management is an important issue. We illustrate two feasible approaches for establishing predicted-no-effect concentrations (PNECs) for PLERA through a case study of 4-nonylphenol (4-NP) using life-cycle toxicity data for medaka (Oryzias latipes). We first quantified the potential impacts of 4-NP on medaka in terms of reduction of population growth rate (λ). An age-classified population matrix model (daily time-step) was developed and used to combine life-cycle survivorship and fecundity data obtained from individual-level responses of medaka exposed to 4-NP into population-level responses defined by the parameter λ. Thereafter, from the resulting λs, two approaches for establishing population-level PNEC values were proposed and examined. We then derived the PNEC values for population-level impacts, based on (a) the threshold concentration, defined as the chemical concentration at which λ ) 1 as a value with a 95% confidence interval, and (b) the no-observed-effect concentration (NOEC) and the maximum-acceptable-toxic concentration (MATC). The results suggest that PNEC values of 4-NP ranging between 0.82 and 2.10 µg/L affect medaka population growth. Although these approaches have their limitations, current knowledge indicates that they are reasonable and practical for evaluating populationlevel impacts of chemicals, thereby serving as a case study for establishing PNEC values for PLERA in the context of chemical substances management and decision-making.

Introduction The question of how to perform rational ecological risk assessments of chemicals in the context of chemical substances management has drawn much attention in recent years (1, 2). Current methods of ecological risk assessment for chemical substances management are based mainly on the toxicological response at an individual level, including acute and chronic responses, such as LC50 (median lethal concentration), LOEC (lowest-observed-effect concentration), and NOEC (no-observed-effect concentration). Although these conventional concentration-based endpoints * Corresponding author phone: +81(298)61-8844; fax: +81(298)61-8904; e-mail: [email protected]. 10.1021/es0489893 CCC: $30.25 Published on Web 05/28/2005

 2005 American Chemical Society

FIGURE 1. Conceptual diagram of effects transmission system of chemicals to ecosystem. generated from the responses of individuals are designed to indicate the potential of chemical stressors to elicit biological effects in ecosystems, they do not consider basic ecological information and the life cycles of organisms, such as the organisms’ genesis and growth processes (3). The scientific knowledge acquired thus far indicates that, unlike human health risk assessment, ecological risk assessments would be better based on endpoints that either are directly calculated from impact assessment at a population level or are indirectly related to the vitality and sustainability of populations (4-9). This is illustrated by the conceptual diagram (Figure 1), which depicts the mechanism of transmission of the effects of chemicals released into ecosystems and the environment: depending on their density, duration, and magnitude, these effects accumulate gradually from lower to higher hierarchical biological levels. When chemicals are released into the environment, they first affect biochemical responses at both the enzyme and cellular levels of organisms. For example (Figure 1), estrogen-like chemicals can affect vitellogenin and enzyme synthesis in fish. If the degree of these effects is severe, then the effects on biochemical responses accumulate and are transmitted to the fish’s tissues, behaviors, and morphology. As a result, the fish develops tissular or morphological abnormalities such as testis-ova. If the effects of these tissular abnormalities are further accumulated and transmitted, they will influence the fish’s survival, growth, and reproduction processes. Conventional impact assessment parameters, such as LC50, NOEC, and hazard quotient, usually precisely follow this hierarchical assessment. As more and more individuals show individual-level impacts, the effects are gradually accumulated and transmitted from the conventional ecotoxicological sphere to a higher ecological sphere that includes the population level, community level, and finally ecosystem level (Figure 1). Generally, effects at the lower hierarchical levels of biology (such as I and II in Figure 1) will not necessarily be transmitted to higher levels (higher than IV) as ecological impacts. For example, the death of one individual from the effects of chemical toxicity does not necessarily influence the function and structure of the entire ecosystem. By contrast, at a population level we have a pool of individuals of the same species living within a specific area, and the population acts as an entity with ecological functions. If this population is wiped out by the effects of VOL. 39, NO. 13, 2005 / ENVIRONMENTAL SCIENCE & TECHNOLOGY

9

4833

chemical toxicity, then the functions and structures of the ecosystem will be affected. Therefore, to protect an ecosystem from chemical effects, chemical effects at a population level rather than at an individual level should be the basis of ecological risk assessments. However, because each ecosystem consists of diverse organisms that have roles as producers, consumers, and decomposers, it is technically and practically impossible to assess chemical effects on all species at a population level (10). Therefore, to perform an ecological risk assessment for a certain chemical, it is realistic to estimate its impacts on the ecosystem by assessing its effects on the populations of certain individual species that are most representative of the ecosystem (2), while taking into consideration the characteristics of the target ecosystem; the characteristics of each constituent organism; the organisms’ ecological function and susceptibility to chemical substances; and the available toxicity data and other scientific information. Various definitions of ecological risk assessments have been proposed so far (1, 2). If we define ecological risk as the probability of unfavorable events happening in an ecosystem, we can then define ecological risk assessment as the analytical process by which the probabilistic occurrence of this ecological risk is determined. Population-level ecological risk assessment (PLERA) can therefore be defined as the same analytical process, used to determine the probability of adverse impacts on a population, in which selection of the appropriate assessment endpoints (such as what kinds of things are unfavorable to the population) becomes the first crucial issue (1, 2, 10, 11). Assessment endpoints are explicit expressions of the crucial characteristics of the actual ecosystem that is to be protected, operationally defined by an ecological entity and its attributes (1). According to this definition, a variety of population-level attributes for a representative organism, such as population extinction rate (e.g., ref 12), population growth rate (λ) (e.g., ref 7), biomass fluctuation rate (e.g., ref 13), and population recovery rate (e.g., ref 14), are potential candidates for the selection of an assessment endpoint. Suter and Barnthouse (10) propose five selection criteria: societal relevance, biological relevance, unambiguous operational definition, accessibility to prediction and measurement, and susceptibility to hazardous agents. In reality, the choice of a population-level attribute as the assessment endpoint for the target ecosystem usually depends on the available toxicity data and the related scientific information. We previously proposed an assessment framework for PLERA that assigned the adverse ecological effects of chemicals to a metric of increased extinction probability (∆P) of the population of a representative organism in the ecosystem, which is specifically explained by three indices, 1/∆T, ∆T/T0, and log ∆T, by calculating the reduction in the mean time to extinction (∆T) when exposed to a chemical (12, 15-18). Equation 1 is an example of the relationship and its concept. In this assessment framework, three parameters for the population of the representative organismsenvironmental carrying capacity (K), environmental fluctuation coefficient (), and intrinsic growth rate (r0)sin a situation without chemical exposure are indispensable to the calculation. It is obvious that to derive a value for each of these three parameters we need baseline research in the form of field studies of the habitat and population dynamics of the target organism. Because there is a dearth of such information, we instead focused here on the use of λ of a representative organism as the assessment endpoint for our PLERA

∆P = 1/∆T ∆T ) T0 - Tx ) f{(r0 - rx),K,} 4834

9

(1)

ENVIRONMENTAL SCIENCE & TECHNOLOGY / VOL. 39, NO. 13, 2005

where ∆P is the change in extinction probability as a result of exposure to a chemical, ∆T is the difference in times to extinction with and without chemical exposure, T0 is the mean time to extinction without chemical exposure, Tx is the mean time to extinction upon exposure to a chemical with a concentration x, r0 is the intrinsic growth rate without chemical exposure, rx is the intrinsic growth rate upon exposure to a chemical with a concentration x (can be derived from available toxicity test data), K is the environmental carrying capacity, and  is the environmental fluctuation coefficient. Until now, ecotoxicologists have studied population-level assessment methods that use knowledge obtained from population ecology. Such methods integrate individual-level toxicological information with population ecology to provide useful assessments of the population-level effects of toxic contaminants (e.g., refs 19-24). However, there have been no reports of a rational framework for incorporating these scientific perspectives on PLERA of chemicals into the context of chemical substances management. In particular, we still have not formulated feasible assessment endpoints or methods of conducting such assessments to provide guidance, e.g., a target value for ecological impacts, usually defined as predicted-no-effect concentration (PNEC), for environmental policy from a population-level viewpoint. To develop a practical and rational framework for PLERA that can be applied to chemical management policies, we will illustrate two feasible population-level approaches through a case study of 4-nonylphenol (4-NP) using life-cycle toxicity data for medaka (Oryzias latipes); the study also defines a range of population-level PNEC values for the effect of 4-NP on the growth of medaka population.

Materials and Methods Test Organism. Medaka (Oryzias latipes) is a small freshwater fish that is native to Japan and is distributed in many catchments. Because medaka has some important biological merits, including a short life cycle, ease of maintenance in the laboratory, daily spawning of 10-40 eggs, and sensitivity to chemicals, it is an ideal test species, as recommended by the Organisation for Economic Co-operation and Development (OECD) and the United States Environmental Protection Agency (U.S. EPA), and it has been widely used for studies of the effects of chemicals and endocrine-disrupting substances (25-28). Full Life-Cycle Toxicity Data for Medaka. A laboratory full life-cycle toxicity test of 4-NP (analytical grade 4-NP; 97.4% purity as a mixture of isomers) in medaka was performed by the Chemicals Evaluation and Research Institute, an authorized good-laboratory-practice (GLP) laboratory in Japan, under a grant from the Japanese Ministry of the Environment. There were five treatment groups (4-NP concentration 4.2, 8.2, 17.7, 51.5, or 183 µg/L) and two control treatments (tap water alone [control] and dechlorinated tap water with ethanol 100 µL/L [solvent control]) under flowthrough conditions. Exposure was initiated at less than 24 h after fertilization and terminated at 60 days after hatching in the F1 larval-juvenile phase. Each of the five chemical treatments and the control treatments (15 embryos per test chamber) was performed in quadruplicate in the embryo phase and the larval-juvenile phase. In the reproductive phase of the study (71-103 days after hatching), groups of six mating pairs of the F1 generation were exposed to each of the two lower treatment doses (4.2 or 8.2 µg/L); groups of six were also used as controls. Details of the exposure design, biological protocols, and test results for the full life-cycle test can be referred to in ref 29. As has been reported by Yokota et al. (29), the highest dose-rate experiment (183 µg/L) was terminated in the embryological phase because of the low hatching rate. During

eigenvalue of the projection matrix A, a series of λ values for each pair at each treatment dose (including controls) was therefore calculated from each of the projection matrices by using MATLAB 6.5 (Cybernet System Inc., Tokyo, Japan)

N(i,j)(t + 1) ) A(i,j)N(i,j)(t) where

FIGURE 2. Details of the full life-cycle toxicity test data available for the investigation in this case study. the test period, life-cycle data such as hatchability and mortality in each of the four chambers were recorded at intervals, and reproductive abilitysincluding fecundity (number of eggs) and fertility (number of fertilized eggs)sfor each of the six pairs was accurately recorded on a daily basis. Figure 2 gives details of the available toxicity data for the full life-cycle toxicity test, as used in this case study; these data were kindly provided by the Japanese Ministry of the Environment. Formulation of a Population Matrix Model. Using the raw data from the full life-cycle toxicity test on medaka, we employed an age-based matrix projection model (30) to investigate the impacts of 4-NP on population growth of medaka for the purpose of exploiting the available daily recorded reproductive toxicity data. We formulated a series of daily time-steps for the projection matrix model expressed in eq 2 (below) for each pair (i) at each treatment dose (including controls), making up each group (j). That is, we derived each of the necessary parameters for each pair of medaka at each treatment dose, including controls, from the raw data of the full life-cycle toxicity test, with the following considerations: (1) Daily mean survival rate (pi,j) for each pair at each treatment dose (including controls) was calculated as the following three representative values for the three different phases: (a) pi,j, daily mean hatching rate in the embryological phase (1-16 days); (b) pi,j, daily mean mortality rate in the larval-juvenile phase (17-70 days); and (c) pi,j, daily mean mortality rate in the reproductive phase (71-103 days), assumed to be 1 because all pairs of medaka lived through the reproductive period. (2) Although there were only three pairs of reproduction data for the 17.7 µg/L dose rate, and one of the three pairs had a very low fertility rate (29, 31), we assumed these data to be valid and used them for the analysis. (3) No pairing test for the reproductive phase was performed (29, 31) at a dose rate of 51.5 µg/L, because the male medaka could not be distinguished by their external secondary sexual characteristics at this dose rate (29, 31). We therefore assumed that the reproductive ability of male medaka at 51.5 µg/L 4-NP treatment was zero; that is, spawned eggs failed to be fertilized, and there was nil fecundity in pairs of medaka subjected to 4-NP at 51.5 µg/L. Given these presumptions for the toxicity test data, the daily mean survival rate (pi,j) for the whole period and the daily mean offspring (number of fertilized eggs) during the reproductive phase (71-103 days after hatching) (fi,j, fertility rate × egg number) for each pair at each treatment dose (including controls) were calculated from the respective raw observational data, as summarized in Table 1. A series of projection matrix models with 103 rows and 103 sequences, shown in eq 2, was therefore formulated by incorporating the full-life cycle text information with the calculated parameters (Table 1). Because λ is defined as the largest

(2)

[

0 ‚‚‚ f71,i,j ‚p71,i,j ‚‚‚ f103,i,j ‚p103,i,j p1,i.j 0 0 ‚‚‚ 0 A(i,j) ) 0 p2,i,j 0 ‚‚‚ l l 0 ‚‚‚ ‚‚‚ l p102,i,j p103,i,j 0 ‚‚‚ 0

]

t is the day of the 103-day test period for the full life-cycle toxicity test, i is the pair of medaka in the reproductive phase, represented as pairs A, B, C, D, E, F in Tables 2 and 3, j is the group, including 4-NP treatment groups and control groups, N(i,j)(t) is the number of female medaka at time t (Only females are considered and males are ignored. The sex ratio was assumed to be 1:1.), N(i,j)(t + 1) is the number of female medaka at time t + 1, A(i.j) is the projection matrix for each pair at each treatment dose (including controls), p1,i,j ‚ ‚ ‚p103,i.j is the mean survival rate of each pair of medaka at each treatment dose (including controls) at time t (t ) 1-103), and f71,i.j ‚ ‚ ‚f103,i,j is the mean number of female offspring (number of fertilized eggs) produced by each pair of medaka at each treatment dose (including controls) at time t (t ) 71-103). Proposed Approaches for Establishing PNEC Values for PLERA. We proposed to set λ of a representative organism as the assessment endpoint for the PLERA (32, 33). Although calculation of λ from laboratory toxicity test data is not always feasible because of the scarcity of full life-cycle toxicity test, Figure 3 shows the concept of the two approaches we proposed through the case study of 4-NP in a medaka population. To derive a PNEC value from a population-level viewpoint of the impacts of this chemical on the medaka population by using λ values calculated for each pair of fish at each treatment dose (including controls), we can use the following two approaches: (1) We can use the theory of population growth: that is, λ > 1 indicates a growing population; λ < 1 indicates a population in decline and possibly headed toward extinction; and λ ) 1 indicates a stable population in which the number of individuals does not fluctuate (34). We therefore proposed to set λ ) 1 as the threshold concentration for population growth impacts. (2) By applying the conventional methods of calculating values for LOEC, NOEC, and maximum-acceptable-toxic concentration (MATC) at the individual level to the population level, we can derive the values of LOEC, NOEC, and MATC for population-level impacts based on the calculated λs. That is, we can test for statistically significant differences in the calculated λs at different treatment doses and then assign the lowest dose rate at which a significant difference is detected as the LOEC and the next lowest as the NOEC. A value for MATC is then calculated as the geometric mean of NOEC and LOEC. With the PNEC values established by the above two proposed approaches, risk-based decision making for chemical substances management to protect against populationlevel impacts is supported by comparing the PNEC values obtained with the predicted environmental concentration (PEC) on both spatial and temporal scales. Statistical Analysis. Statistical analyses were performed with SPSS Base 11.5.1 J for Windows (35). To deal with the control and solvent control in the analysis of variance VOL. 39, NO. 13, 2005 / ENVIRONMENTAL SCIENCE & TECHNOLOGY

9

4835

TABLE 1. Full Life-Cycle Data Available for Formation of Projection Matrix for Medaka (Oryzias latipes) Exposed to 4-Nonylphenola 4-nonylphenol concentration (µg/L) S.C. age (ds)

S

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51 53 55 57 59 61 63 65 67 69 71 73 75 77 79 81 83 85 87 89 91 93 95 97 99 101 103

1.000 1.000 0.993 0.993 0.993 0.993 0.993 0.993 0.997 0.997 0.992 0.992 0.992 0.992 0.992 0.985 0.985 0.985 0.985 0.985 0.992 0.992 0.992 0.992 0.992 0.992 0.992 0.992 0.992 0.992 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000

C F

S

7.0 10.5 8.5 8.0 8.0 8.0 9.5 11.0 9.5 9.5 9.0 9.5 8.0 10.5 10.5 9.5 9.5

1.000 1.000 0.980 0.980 0.980 0.980 0.980 0.980 0.999 0.999 0.992 0.992 0.992 0.992 0.992 0.992 0.992 0.992 0.992 0.992 0.992 0.992 0.992 0.992 0.992 0.997 0.997 0.997 0.997 0.997 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000

4.2 F

S

6.5 11.0 9.0 9.0 8.5 10.0 6.5 9.5 8.0 8.0 9.0 8.5 7.5 11.5 9.5 9.5 9.5

1.000 1.000 0.995 0.995 0.995 0.995 0.995 0.995 1.000 1.000 1.000 1.000 1.000 1.000 1.000 0.998 0.998 0.998 0.998 0.998 0.998 0.998 0.998 0.998 0.998 0.996 0.996 0.996 0.996 0.996 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000

4-nonylphenol concentration (µg/L)

8.2 F

S

5.5 10.0 9.5 7.5 8.0 10.0 7.5 8.5 9.5 9.5 8.5 10.0 8.0 10.0 8.0 8.0 8.0

1.000 1.000 0.991 0.991 0.991 0.991 0.991 0.991 1.000 1.000 0.996 0.996 0.996 0.996 0.996 0.994 0.994 0.994 0.994 0.994 0.991 0.991 0.991 0.991 0.991 0.991 0.991 0.991 0.991 0.991 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000

17.7 F

S

7.5 10.0 9.5 7.0 10.0 11.5 8.5 11.5 11.0 10.0 11.5 10.5 12.0 12.0 10.5 10.5 11.0

1.000 1.000 0.989 0.989 0.989 0.989 0.989 0.989 1.000 1.000 0.998 0.998 0.998 0.998 0.998 0.986 0.986 0.986 0.986 0.986 0.981 0.981 0.981 0.981 0.981 0.978 0.978 0.978 0.978 0.978 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000

51.5 F

S

8.0 9.5 10.0 8.0 10.5 9.5 10.0 6.0 6.0 8.0 5.5 6.5 8.5 6.5 7.5 6.5 6.0

1.000 1.000 0.988 0.988 0.988 0.988 0.988 0.988 1.000 1.000 1.000 1.000 1.000 1.000 1.000 0.992 0.992 0.992 0.992 0.992 0.992 0.992 0.992 0.992 0.992 0.992 0.992 0.992 0.992 0.992 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000

S.C. age F (ds)

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40 42 44 46 48 50 52 54 56 58 60 62 64 66 68 70 72 74 76 78 80 82 84 86 88 90 92 94 96 98 100 102

S 1.000 1.000 0.993 0.993 0.993 0.993 0.993 0.997 0.997 0.992 0.992 0.992 0.992 0.992 0.985 0.985 0.985 0.985 0.985 0.992 0.992 0.992 0.992 0.992 0.992 0.992 0.992 0.992 0.992 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000

C F

S

8.5 8.0 9.5 8.0 8.5 11.0 7.0 8.5 10.0 9.5 11.0 12.5 10.5 10.5 11.0 11.5

1.000 1.000 0.980 0.980 0.980 0.980 0.980 0.999 0.999 0.992 0.992 0.992 0.992 0.992 0.992 0.992 0.992 0.992 0.992 0.992 0.992 0.992 0.992 0.992 0.997 0.997 0.997 0.997 0.997 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000

4.2 F

S

7.5 6.5 11.0 6.5 8.5 7.0 8.5 7.5 8.0 8.5 9.0 11.0 8.0 7.5 9.5 8.0

1.000 1.000 0.995 0.995 0.995 0.995 0.995 1.000 1.000 1.000 1.000 1.000 1.000 1.000 0.998 0.998 0.998 0.998 0.998 0.998 0.998 0.998 0.998 0.998 0.996 0.996 0.996 0.996 0.996 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000

8.2 F

S

7.0 10.0 10.5 7.5 7.5 7.0 7.5 7.0 6.5 8.5 8.0 9.5 8.5 10.5 9.0 10.5

1.000 1.000 0.991 0.991 0.991 0.991 0.991 1.000 1.000 0.996 0.996 0.996 0.996 0.996 0.994 0.994 0.994 0.994 0.994 0.991 0.991 0.991 0.991 0.991 0.991 0.991 0.991 0.991 0.991 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000

17.7 F

S

8.5 7.5 11.5 8.5 10.0 10.0 11.0 11.0 9.0 9.0 9.5 12.5 10.5 10.0 11.0 18.5

1.000 1.000 0.989 0.989 0.989 0.989 0.989 1.000 1.000 0.998 0.998 0.998 0.998 0.998 0.986 0.986 0.986 0.986 0.986 0.981 0.981 0.981 0.981 0.981 0.978 0.978 0.978 0.978 0.978 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000

51.5 F

S

F

8.0 7.5 11.5 7.5 7.5 6.0 5.5 5.5 5.5 5.0 5.5 8.0 7.0 6.5 7.0 6.0

1.000 1.000 0.988 0.988 0.988 0.988 0.988 1.000 1.000 1.000 1.000 1.000 1.000 1.000 0.992 0.992 0.992 0.992 0.992 0.992 0.992 0.992 0.992 0.992 0.992 0.992 0.992 0.992 0.992 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

a S represents the daily mean survival rate and F represents the daily mean spawning fertilized eggs from each pair in per treatment measured throughout the 103-d experiment and the 33-d breeding period.

(ANOVA), we performed a prior difference analysis between them by using either a chi-squared test (nonparametric data) or Student’s t-test (parametric data). If no differences were found, these two groups were pooled for the subsequent analyses; otherwise, the control group without solvent was excluded from subsequent analyses. For ANOVA, an exploratory data analysis was performed to examine the assumption of normality (Kolmogorov-Smirnov test and Shapiro-Wilk test) in each group. The assumption of equality of variance across groups was checked by Levene’s test. If both of the assumptions were met, one-way ANOVA was performed, followed by Bonferroni’s and Dunnett’s multiple comparison tests. If only one of the two assumptions was met, nonparametric testing was performed by the Kruskal-Wallis H 4836

9

ENVIRONMENTAL SCIENCE & TECHNOLOGY / VOL. 39, NO. 13, 2005

test followed by the Mann-Whitney test. Differences were considered to be significant at P e 0.05.

Results Table 2 summarizes the estimated λ values for each of the six pairs at each treatment dose (including controls). The mean values of λ for each group are also shown. By using these results, we first examined the proposed approach by determining the threshold concentration for population growth impacts. That is, we performed an optimum function fitting analysis by using the 4-NP treatment doses and their corresponding average λ values shown in Table 2. As a result of fitting analysis by least-squares approximation (R2 ) 0.998), we obtained an optimum function equation with its plotting

TABLE 2. Population Growth Rate (λ) Estimated from Projection Matrix for Medaka (Oryzias latipes) Exposed to 4-Nonylphenol Using 103-d Life-Cycle Toxicity Test Data estimated λ (103-d) nominal concn (µg/L) solvent control control 1.85 5.56 16.7 50

measured concn (µg/L) solvent control control 4.2 8.2 17.7a 51.5b

A

B

C

D

E

F

av

1.070 1.066 1.069 1.072 1.058 0.000

1.067 1.072 1.066 1.069 1.065 0.000

1.064 1.071 1.070 1.061 1.052 0.000

1.060 1.070 1.070 1.065

1.069 1.062 1.068 1.069

1.063 1.066 1.068 1.063

0.000

0.000

0.000

1.066 1.068 1.068 1.067 1.060 0.000

a

b

Only three pairs from 17.7 µg/L treatment were mated for the investigation. No pairs was mated for the investigation of fecundity, and fertility from 51.5 µg/L treatment due to the failure of selecting male ones based on their external sex appearance.

TABLE 3. All Estimated Population Growth Rate (λ) Used for Statistical Analysis to Determine the Values of Lowest-Observed-Effect Concentration (LOEC), No-Observed-Effect Concentration (NOEC), and Maximum-Acceptable-Toxicant Concentration (MATC) for Population-Level Growth measured concn (µg/L) solvent control control 4.2 8.2 17.7

LOEC (µg/L)

NOEC (µg/L)

MATC (µg/L)

8.2 17.7

A

B

1.070 1.066 1.069 1.072 1.058

1.067 1.072 1.066 1.069 1.065

estimated λ (103-d) C D 1.064 1.071 1.070 1.061 1.052

1.060 1.070 1.070 1.065

E

F

1.069 1.062 1.068 1.069

1.063 1.066 1.068 1.063

12.0a a

MATC was calculated as the geometric mean of the LOEC and NOEC.

FIGURE 3. Two types of practical approach proposed for populationlevel ecological risk assessment (PLERA) for chemicals based on the population growth rate (λ).

FIGURE 4. Estimated relationships with a 95% confidence intervals (C.I.) of population growth rate (λ) to 4-nonylphenol concentrations. confidence curves, defined as the quadratic function shown in Figure 4 and described as eq 3

y ) a1x + a2x2 + b

(3)

where y is the value of estimated λ, x is the treatment dose

of 4-NP (µg/L), a1 is the coefficient defined as a value of 0.01012, a2 is the coefficient defined as a value of -0.00059, and b is the constant defined as a value of 1.047841. From eq 3, the 4-NP dose rate at which λ ) 1 is estimated to be 21.0 µg/L, with a 95% confidence interval of 15.7-24.9 µg/L. This means that the threshold concentration of 4-NP for impacts on the medaka population is 21.0 µg/L: when a medaka population is exposed to a water body in which the concentration of 4-NP exceeds 21.0 µg/L, it will start to decrease in abundance, and if the toxic effect continues, there will be an increased possibility of extinction. The calculated threshold concentration of 21.0 µg/L (95% C.I.: 15.7-24.9) for medaka compares favorably to the threshold concentration of 16 µg/L calculated for population-level impacts of 4-NP on the mysid shrimp, Americamysis bahia, using similar methods (23). We also examined the second proposed approach by which we derived values of LOEC, NOEC, and MATC for population-level impacts. Because no differences were found between the control and solvent control, these two groups were pooled for the subsequent ANOVA analyses. Although the assumption of normality in each group was met, the assumption of equality of variance across groups was violated. Therefore, we performed the Kruskal-Wallis H test followed by the Mann-Whitney test (nonparametric test) for significant difference among the groups. Results of the analyses are summarized in Table 3. Statistically significant differences were found in the 17.7 µg/L group, even though there were only three samples in this group. Therefore, by employing the same methods (1) for determining LOEC, NOEC, and MATC for individual-level impacts, we assigned a dose rate of 17.7 µg/L as the LOEC, 8.2 µg/L as the NOEC, and the geometrical mean of LOEC and NOEC (calculated as 12.0 µg/L) as the MATC for medaka population-level impacts. In considering the PNEC values for medaka populationlevel impacts for regulatory science, we used an uncertainty factor of 10 (29, 36, 37) in accordance with the following considerations: (1) each of the λ values was calculated from chronic toxicity data, including survival rates and reproductive ability, collected from medaka full life-cycle toxicity test VOL. 39, NO. 13, 2005 / ENVIRONMENTAL SCIENCE & TECHNOLOGY

9

4837

and (2) the laboratory results would be extrapolated to the field environment. That is, we proposed to set PNEC values for medaka population-level impacts by dividing three valuessthe threshold concentration, NOEC, and MATCsby an adopted uncertainty factor designated as 10. As a result, the three respective possible PNEC values for the medaka population-level impacts of 4-NP were 2.10 µg/L (95% C.I.: 1.57-2.49), 1.20 µg/L, and 0.82 µg/L. This suggests a range of PNEC values between 0.82 and 2.10 µg/L.

Discussion Currently PLERA is a challenging field with little toxicity test data and a dearth of useful analytical methods. The λ value, which integrates the ability of reproduction and survival of each organism involved in a population, is an ecological index that indicates the ability of a population to persist. Through this case study, we proposed and demonstrated the designation of λ of a representative organism as the assessment endpoint and the establishment of PNEC values for PLERA based on λs that could be calculated from current available laboratory toxicity test data. The results of our case study demonstrate that it has three useful implications for performing PLERA in the context of chemical risk management: (1) the approach shown here is a practical way of establishing a target value for population-level impacts; (2) it is able to exploit currently available toxicity test data; and (3) it provides useful information on how to design future toxicity tests for PLERA. However, our results obviously include the uncertainties and/or limitations discussed below. As a Deterministic Approach. The PNEC values on medaka population-level impacts were established from the calculated λs by a deterministic approach. In calculating λs from the series of laboratory toxicological test data at an individual level, we assume that the environmental conditions under which medaka are held and tested are sufficiently comparable to optimum conditions, such that the observed effects on λ are limited to those due to chemical exposure only. That is, λs were calculated from the laboratory full lifecycle toxicity test data in a situation where the environmental and demographic fluctuations were ignored. This, of course, would be a limitation if we were to extrapolate the laboratory results to field conditions in general. A stochastic simulation approach would be necessary for addressing this issue. Employing an Uncertainty Factor. The use of uncertainty factors in establishing PNEC values for ecological risk assessment is typical in a regulatory sense. On the basis of the current recommendations of OECD (36, 37) we employed an uncertainty factor, defined as 10, to establish the PNEC values for population-level impacts. It is obvious that scientific evidence supporting the use of uncertainty factors still needs further investigation. The three PNEC values for population-level impacts obtained by the two proposed approaches were 2.10 µg/L (95% C.I.: 1.57-2.49), 0.82 µg/L, and 1.20 µg/L. We could therefore conclude that the target value for ecological impacts by 4-NP in medaka is between 0.82 and 2.10 µg/L from a population-level viewpoint. In fact, another target value for the ecological impacts of 4-NP has been proposed as the environmental criterion for 4-NP, at 0.608 µg/L (29, 31); this value was derived by using the development of testis-ova (tissue-level) and vitellogenin induction (serum-level) as the assessment endpoints, primarily from results obtained in a partial life-cycle toxicity test of medaka that was performed in parallel to the full life-cycle toxicity test. As was stated in previous sections (Figure 1), it is more rational to set the assessment endpoints for ecological risk assessments of chemical effects at a population level rather than at an individual level or serum and tissue level. Although comparing our values derived from population-level impacts with those derived from serumand tissue-level impacts is not the focus of this study, all of 4838

9

ENVIRONMENTAL SCIENCE & TECHNOLOGY / VOL. 39, NO. 13, 2005

our values were greater than the criterion value. Forbes and Calow (33) compared and examined 41 existing studies to determine the relationship between the population-level assessment endpoint (intrinsic natural growth rate of population) and individual-level assessment endpoints (survival and reproduction). They concluded that the sensitivity of population-level assessment endpoints is less than, or equal to, that of individual-level assessment endpoints. Quality of the Toxicity Test Data Used. A more precise estimate of the critical threshold concentration would be obtained if the full life-cycle toxicity test data were to include some treatment doses intermediate between 17.7 µg/L and 51.5 µg/L, even though in practice it is usually difficult to design and perform such intermediate treatment toxicity tests (38). In addition to this flaw in experimental design associated with failure to include some intermediate treatment doses, because exposure to a dose rate within the range of 17.7 to 51.5 µg/L often results in unclear secondary sexual characteristics (e.g., ref 29), failure in selecting male medaka for mating pairs because of the unclear external sex appearance at these dose rates is another flaw in this toxicity test. For the purpose of utilizing the full life-cycle toxicity test data, we thereby made two assumptions in estimating the values of λ. First, we used fertility rate data for three pairs of medaka subjected to a dose rate of 17.7 µg/L 4-NP, even though there was no statistically significant difference between them. In addition, we assumed that female reproductive ability (egg production) at 51.5 µg/L 4-NP was zero, owing to the failure of the male in each pair to fertilize the female. Because of these two assumptions, an approximately linear decline between 17.7 µg/L and 51.5 µg/L was observed (Figure 4). Therefore, to perform more reliable PLERA, we recommend that (1) appropriate experimental designsfor example, the inclusion of a sufficient number of test concentrations within the “critical range” to ensure that intermediate values of λ are obtainedsis essential; and (2) it would be preferable to use medaka in which sexual determination can be easily made by body color, such as the d-rR strain, to perform full life-cycle toxicity tests on endocrine disrupters such as 4-NP. Inherent Uncertainty in the Two Approaches. To determine the relationship between 4-NP treatment doses and λs, we adopted a statistically optimum function (quadratic function) as the response of λ to treatment doses. It is conceivable that the choice of mathematical function will affect the estimate of the threshold concentration, and the uncertainty included in the estimate can be quantitatively understood by employing simulation approaches. The plotting of 95% confidence curves (Figure 4) indicates that variances in λ value are conceivable with exposure to 4-NP at certain dose rates. Note that this regression-based approach may be not a perfect fit, because the toxicity data used in this study were directly derived from one of the OECD standard protocols, which currently specify a design appropriate for hypothesis testing but not for regression analysis (38). A conventional approachsdetermining NOEC and/or MATC by hypothesis testingswas simultaneously proposed. Note that because the values of NOEC and/or MATC are determined in accordance with the dose rates used in the toxicity test, the uncertainty inherent in the hypothesis testing approach is obvious. The current trend in ecological risk assessment is to employ the regression-based approach instead of the hypothesis testing approach (38). From the standpoint of exploiting the currently available toxicity test data, the conventional hypothesis testing approach is still valuable. Future Research Needs. Because the framework discussed above for establishing PNEC values for PLERA concerns only survival rates and offspring in the first generation of the full life-cycle toxicity test, the question remains as to whether it is appropriate for the assessment of suspected endocrine

disrupters such as 4-NP. Suspected endocrine disrupters may have reproductive effects that accumulate through generations, and assessments based on toxicity tests that span many generations remain a challenge for future studies. Moreover, endocrine disruption influences lead to sexual differentiation abnormalities such as testis-ova appearance, vitellogenin induction, and breeding period disturbance, but they are not necessarily directly related to the survival ability or even the reproductive ability of the exposed generation of the study organism. Therefore, we have not included in our framework these types of measured effects that may first appear in subsequent generations, although there is a real need to do so and the realization of this need is a challenge for the future. The availability of full life-cycle toxicity test data determines the possibility of performing PLERA. However, the acquisition of these data often takes a great amount of time, work, and money. Besides this, only a limited number of organisms with biological meritsssuch as small body mass or a short life cyclesare suitable for such toxicity tests, and, in reality, extremely limited numbers of chemical substances, such as 4-NP, that have high priority for risk assessment have been examined in full life-cycle toxicity tests. Therefore, we need to break through two restricting factorsslimitations on the acquisition of full life-cycle toxicity test data and the shortage of full life-cycle toxicity test data on chemical substancessif we are to perform PLERAs on a more general basis. For these reasons, future studies should be focused on the following: (1) the use of approaches for estimating intrinsic natural growth rates of fish populations by using the extrapolation methods proposed by Barnthouse and Suter et al. (19, 20) and (2) the development of methods based on life history sensitivity analysis (30), which may help to simplify full life-cycle toxicity tests.

Acknowledgments This research was conducted under the Comprehensive Chemical Substances Assessment and Management Program, with the funding of the New Energy and Industrial Technology Development Organization of Japan. Special thanks to the Japanese Ministry of the Environment for providing the valuable full life-cycle toxicity test data for this study. We especially thank Lawrence W. Barnthouse (LWB Environmental Services, Inc.) and Wayne R. Munns (NHEERL, U.S. EPA) for their critical comments and kind suggestions. We also thank the four anonymous reviewers who provided helpful comments on an earlier draft of this manuscript.

Literature Cited (1) U.S. Environmental Protection Agency. Guidelines for Ecological Risk Assessment; Notice; Federal Register: Vol. 63, No. 93, May 14, pp. 26846-26924; United States Environmental Protection Agency: Washington, DC, 1998. (2) Suter, II G. W.; Efroymson, R. A.; Sample, B. E.; Jones, D. S. Ecological risk assessment for contaminated sites; CRC Press: Boca Raton, FL, 2000. (3) Pastorok, R. A.; Bartell, S. M.; Ferson, S.; Ginzburg, L. R.; Lewis Publishers: CRC Press: Boca Raton, FL, 2001. (4) National Research Council. Testing for Effects of Chemicals on Ecosystem; National Academy Press: Washington, DC, 1981. (5) Moriarty, F. Ecotoxicology: The Study of Pollutants in Ecosystems, 2nd ed.; Academic Press: New York, U.S.A., 1988. (6) Barnthouse, L. W. Population-level effects. In Ecological Risk Assessment; Suter, II G. W., Ed.; Lewis Publishers: Chelsea, MI, 1993; pp 247-274. (7) Forbes, V. E.; Calow, P. Is the per capita rate of increase a good measure of population-level effects in ecotoxicology? Environ. Toxicol. Chem. SETAC 1999, 18, 1544-1556. (8) Landis, W. G. Population is the appropriate unit of interest for a species-specific risk assessment. SETAC Globe (Learned Discourse) 2002, 3, 31-32. (9) Munns, Jr. W. R.; Beyer, W. N.; Landis, W. G.; Menzie, C. What is a population? SETAC Globe (Learned Discourse) 2002, 3, 2931.

(10) Suter, II G. W.; Barnthouse, L. W. Assessment Concepts. In Ecological Risk Assessment; Suter, II G. W., Ed.; Lewis Publishers: Chelsea, MI, 1993; pp 21-47. (11) Barnthouse, L. W.; Suter, II G. W. User’s Manual for Ecological Risk Assessment; Environmental Sciences Division Publication No. 2679; 219 pp; United States Environmental Protection Agency: Washington DC, 1986. (12) Tanaka, Y.; Nakanishi, J. Mean extinction time of populations under toxicant stress and ecological risk assessment. Environ. Toxicol. Chem. SETAC 2000, 19 (11), 2856-2862. (13) Lammens, E. H. R. R.; van Nes, E. H.; Meijer, M.-L.; van den Berg, M. S. Effects of commercial fishery on the bream population and the expansion of Chara aspera in Lake Veluwe. Ecol. Mod. 2004, 177 (3-4), 233-244. (14) Barnthouse, L. W. Quantifying population recovery rates for ecological risk assessment. Environ. Toxicol. Chem. SETAC 2004, 23 (2), 500-508. (15) Tanaka, Y.; Nakanishi, J. Model selection and parameterization of the concentration-response function for population-level effects. Environ. Toxicol. Chem. SETAC 2001, 20 (8), 18571865. (16) Hakoyama, H.; Iwasa, Y.; Nakanishi, J. Comparing risk factors for population extinction. J. Theor. Biol. 2000, 204, 327336. (17) Iwasa, Y.; Hakoyama, H.; Nakamaru, M.; Nakanishi, J. Estimate of population extinction risk and its application to ecological risk management. Population Ecol. 2000, 42, 73-80. (18) Nakamaru, M.; Iwasa, Y.; Nakanishi, J. Extinction risk to herring gull populations from DDT exposure. Environ. Toxicol. Chem. SETAC 2002, 21 (1), 195-202. (19) Barnthouse, L. W.; Suter, II G. W.; Rosen, A. E.; Beauchamo, J. J. Estimating responses of fish populations to toxic contaminants. Environ. Toxicol. Chem. SETAC 1987, 6, 811-824. (20) Barnthouse, L. W.; Suter, II G. W.; Rosen, A. E. Risks of toxic contaminants to exploited fish populations: influence of life history, data uncertainty and exploitation intensity. Environ. Toxicol. Chem. SETAC 1990, 9, 297-311. (21) Munns, Jr. W. R.; Black, D. E.; Gleason, T. R.; Salomon, K.; Bengtson, D.; Gutjahr-Gobell, R. Evaluation of the effects of dioxin and PCBs on Fundulus heteroclitus populations using a modeling approach. Environ. Toxicol. Chem. SETAC 1997, 16, 1074-1081. (22) Kuhn, A.; Munns, Jr. W. R.; Poucher, S.; Champlin, D.; Lussier, S. Prediction of population-level response from mysid toxicity test data using population modeling techniques. Environ. Toxicol. Chem. SETAC 2000, 19, 2364-2371. (23) Kuhn, A.; Munns, Jr. W. R.; Champlin, D.; McKinney, R.; Tagliabue, M.; Serbst, J.; Gleason, T. R. Evaluation of the efficacy of extrapolation population modeling to predict the dynamics of Americamysis bahia populations in the laboratory. Environ. Toxicol. Chem. SETAC 2001, 20, 213-221. (24) Kuhn, A.; Munns, Jr. W. R.; Serbst, J.; Edwards, P.; Cantwell, M. G.; Gleason, T. R.; Pelletier, M. C.; Berry, W. Evaluating the ecological significance of laboratory response data to predict population-level effects for the estuarine amphipod Ampelisca abdita. Environ. Toxicol. Chem. SETAC 2002, 21, 865874. (25) Organization for Economic Cooperation and Development. OECD Guidelines for Testing of Chemicals; Section 2; Guideline 203; Fish Acute Toxicity Test; OECD: Paris, France, 1992. (26) Organization for Economic Cooperation and Development. OECD Guidelines for Testing of Chemicals; Section 2; Guideline 204; Fish Chronic Toxicity Test; OECD: Paris, France, 1992. (27) Organization for Economic Cooperation and Development. OECD Guidelines for Testing of Chemicals; Section 2; Guideline 210; Fish Early-life Stage Toxicity Test; OECD: Paris, France, 1992. (28) U.S. Environmental Protection Agency. Guidelines for Conducting Early Life Stage Toxicity Tests with Japanese Medaka (Oryzias latipes); EPA 600/3-01-063; United States Environmental Protection Agency: Duluth, MN, 1991. (29) Yokota, H.; Seki, M.; Maeda, M.; Oshima, Y.; Tadokoro, H. Lifecycle toxicity of 4-nonylphenol to Medaka (Oryzias latipes). Environ. Toxicol. Chem. SETAC 2001, 20 (11), 2552-2560. (30) Caswell, H. Matrix Population Models: Construction, Analysis, and Interpretation, 2nd ed.; Sinauer Associates Inc. Publishers: Sunderland, MA, 2001. (31) Japanese Ministry of the Environment. Report on the Test Results of Endocrine Disrupting Effects of Nonylphenol on Fish. Released on the homepage of the Organization for Economic Cooperation VOL. 39, NO. 13, 2005 / ENVIRONMENTAL SCIENCE & TECHNOLOGY

9

4839

(32)

(33)

(34) (35)

and Development on 23 October 2001: Documents; Report on the Test Results of Endocrine Disrupting Effects of Nonylphenol on Fish: Files 1-3; http://www.oecd.org/searchResult/0,2665,en_2649_201185_1_1_1_1_1.00html (accessed 28 October 2004). Sibly, R. M.; Hone, J. Population growth rate and its determinants: an overview. Philos. Trans. R. Soc. London B 2002, 357, 1153-1170. Forbes, V. E.; Calow, P. Population growth rate as a basis for ecological risk assessment of toxic chemicals. Philos. Trans. R. Soc. London B 2002, 357, 1299-1306. Case, T. J. An Illustrated Guide to Theoretical Ecology; Oxford University Press: Oxford, New York, U.S.A., 2000. SPSS. SPSS Base 11.5.1 J for Windows User’s Guide; SPSS Inc.: Tokyo, Japan, 2002.

4840

9

ENVIRONMENTAL SCIENCE & TECHNOLOGY / VOL. 39, NO. 13, 2005

(36) Organization for Economic Cooperation and Development. Report of the OECD Workshop on the Extrapolation of Laboratory Aquatic Toxicity Data on the Real Environment; OECD Environment Monographs No. 59; OECD: Paris, France, 1992. (37) Organization for Economic Cooperation and Development. Screening Information Data Set (SIDS) Manual Third Revision; OECD Secretariat: Paris, France, July 1997. (38) Moore, D. R. J.; Caux, P.-Y. Estimating low toxic effects. Environ. Toxicol. Chem. SETAC 1997, 16 (4), 794-801.

Received for review July 2, 2004. Revised manuscript received April 4, 2005. Accepted April 14, 2005. ES0489893