Environ. Sci. Technol. 2003, 37, 1609-1616
Estimating the Effects of 17r-Ethinylestradiol on Populations of the Fathead Minnow Pimephales promelas: Are Conventional Toxicological Endpoints Adequate? E R I C P . M . G R I S T , * ,† N . C L A I R E W E L L S , † PAUL WHITEHOUSE,‡ GEOFF BRIGHTY,§ AND MARK CRANE† School of Biological Sciences, Royal Holloway, University of London, Egham, Surrey, TW20 0EX, United Kingdom, WRc-NSF, Henley Road, Medmenham, Marlow, Buckinghamshire, SL7 2HD, United Kingdom, and Environment Agency, National Centre for Ecotoxicology and Hazardous Substances, Howbery Park, Wallingford, Oxfordshire OX10 8BD, United Kingdom
Environmental benchmarks have recently been proposed for several steroids including the synthetic steroid, 17Rethinylestradiol (EE2). These benchmarks are based on extrapolation from studies involving long-term exposure of various fish species to EE2. One of the critical studies was a complete life-cycle experiment performed with the fathead minnow Pimephales promelas over a 289 day exposure period (2). The lowest observed effect concentration (LOEC) and the no observed effect concentration (NOEC) for gonad histology were 4 and 1 ng L-1, respectively. This was because no testicular tissue could be found in any fish exposed to 4 ng L-1. In the present paper, the survival and reproduction data from that study are reanalyzed to determine the effects of EE2 on the intrinsic rate of population growth (r ) ln (λ)), a parameter of demographic importance. We estimate critical threshold concentrations with respect to r and compare these with those previously derived from conventional toxicity test summaries. Further, we assess the influence of individual variability on threshold estimates using a combination of bootstrap and regression approaches, together with a suite of perturbation analyses. These yield ErC100 values (the concentration estimated to reduce intrinsic growth rate to zero) of 3.11 ng L-1 (linear model) and 3.41 ng L-1 (quadratic model), comparable with a maximum acceptable toxicant concentration (MATC) of 2 ng L-1 for feminization of exposed fish calculated by Laenge et al. (2). Our results indicate that reduction in population growth rate with increasing concentration occurred more through EE2 acting to reduce fertility than survival rates. The significance of these summary statistics when deriving environmental benchmarks for steroid estrogens is discussed in the context
* Corresponding author email:
[email protected]. † University of London. ‡ WRc-NSF. § Environment Agency, National Centre for Ecotoxicology and Hazardous Substances. 10.1021/es020086r CCC: $25.00 Published on Web 03/11/2003
2003 American Chemical Society
of affording protection to populations following long-term exposure.
Introduction In recent years, several man-made and naturally occurring compounds have come under scrutiny for their suspected estrogenic effects. Prominent among these are the steroid estrogens because they have been implicated in sex reversal of fish in waters receiving sewage effluents containing these substances (1-5). The compounds of interest are the naturally occurring steroids 17β-estradiol and estrone and the synthetic steroid used in the female contraceptive pill, 17R-ethinylestradiol (EE2). In response to the publication of several studies into their effects on fish reproduction, environmental benchmarks for steroid estrogens are being developed in the UK (1). A threshold of 0.1 ng L-1 for the most potent steroid, EE2, has been proposed, based on extrapolation from long-term studies in which various fish species were exposed to this substance. A critical study yielding one of the lowest credible effects concentrations involved a full life cycle exposure of fathead minnow (Pimephales promelas) over a period of 289 days to a range of concentrations of EE2 (2). From these data, a LOEC and NOEC for feminization of exposed fish of 4 and 1 ng L-1, respectively, were estimated. Although these conventional endpoints describe feminization effects in individual experimental fish, they incorporate no information on effects on recruitment and survival. There is thus a question as to the adequacy of the proposed benchmarks when extrapolating to the population level. No testicular tissue was found in any fish exposed to 4 ng L-1, which would clearly lead to extinction of this population. However, it is unclear whether subtle effects on survival and reproduction at lower concentrations of EE2 could lead to changes in population viability. This paper describes an alternate analysis of the data generated by Laenge et al. (2) in which the age-specific survival and reproduction records are utilized to obtain summary demographic parameters (e.g. refs 6 and 7), most notably the intrinsic rate of population increase (denoted by λ). The sensitivity of λ to perturbations in survival and fertility for different exposure concentrations of EE2 is also determined. Finally, a decomposition analysis is performed to estimate chemical effects from a set of life table response experiments (LTREs) (refs 8 and 9) and to assess the risk that a population will no longer remain viable under a prevailing chemical exposure.
Materials and Methods Experimental Methods. The impact of EE2 on the life cycle of the fathead minnow (Pimephales promelas) was investigated using five experimental concentrations (0.2, 1.0, 4.0, 16.0, 64.0 ng L-1) and a control treatment (dilution water only) delivered by a continuous flow-through system. Measured concentrations in this experiment ranged from 58 to 84% of nominal values, which Laenge et al. (2) suggest are acceptable for such long-term studies performed at such low nominal concentrations. A full description is given in Laenge et al. (2). A brief summary is provided here. Four aquaria were set up per treatment. Each aquarium held two cups, each containing approximately 25 eggs. Eggs were examined daily, and the number of dead eggs and hatched fry was recorded. On the fourth day, when sufficient VOL. 37, NO. 8, 2003 / ENVIRONMENTAL SCIENCE & TECHNOLOGY
9
1609
eggs had hatched, the cups in each aquarium were merged, resulting in the release of 25 fry into the aquarium, randomly and equally selected from each cup. These fry were monitored daily for mortalities. On the 60th day (in week 9) the contents of two aquaria in each treatment were merged, resulting in release of 25 fry into an “adult” aquarium, selected equally from each aquarium on the basis of health. Selection of the healthiest fish will have added bias to the results, the effects of which are considered in the discussion. Mortality was monitored until day 176. However, the 64 ng L-1 treatment was terminated on day 162 as the fish had not reached maturity and exhibited severe abnormalities. On day 176 (in week 26) eight fish were selected from each control, 0.2 and 1.0 ng L-1 aquarium, split into breeding pairs and placed in breeding chambers. Spawning tiles were added to the 4.0 and 16 ng L-1 treatments but fish were not paired. Mortality and egg production were monitored daily until the experiment was terminated on days 239 (in week 35, 16 ng L-1), 289 (in week 42, 4 ng L-1), and 305 (in week 44, control, 0.2 and 1 ng L-1). Egg production was stimulated periodically each day by the onset of photoperiod at 6 a.m.; egg removal took place at 11 a.m. The 16 ng L-1 exposure was terminated prematurely as it was clear that the 4 ng L-1 was an effect concentration and so the 16 ng L-1 treatment was no longer required. On day 289, fish in the 4 ng L-1 exposure were placed in dilution water to depurate. New males were also placed in the same tanks. It was found that eggs were eventually produced (after 14 days), but the viability of these eggs was not tested. Hence the results were inconclusive in determining whether depuration would ultimately lead to normal sexual reproduction, differentiation, or growth. Data Analysis. Life tables of survivorship and egg production between daily observations up to the point of experiment termination were constructed for each treatment. Because of zero fertility and low survival for 16 and 64 ng L-1 treatments (hence the reason for their early termination) neither intrinsic growth rate nor perturbation analyses could be meaningfully computed for these two highest exposures. Data for the other treatments (control, 0.2, 1.0, and 4.0) were grouped into weekly age classes resulting in improved power for further analyses and smoothing. The table entries were used to derive standard functions of survivorship l(x) (giving the probability of survival from birth to age x) and maternity m(x) (giving the mean number of offspring produced by an individual at age x). From these, the probability (Pi) of surviving up to age class i and the mean number of offspring (Fi) produced by an individual in the age class were computed. This was done for each of the treatments t ) 1-4, and the corresponding Leslie Matrix A(t) was parametrized with these values. Because egg deposition occurred periodically each day immediately after the photoperiodic stimulus, the birth-pulse postbreeding formulas (9) were used
Pi )
l(i) l(i - 1)
Fi ) Pimi
(1) (2)
for each age class i. At those time points where individuals were removed from the experiment, data were subject to censorship. For these transitions the proportion that would have survived to the next time point was therefore unknown, and corresponding survival parameters were estimated using the product limit estimator (e.g. equation 4.4 in ref 10). The dominant eigenvalue λ of each treatment matrix was computed as an estimate of the growth rate of a population in exposure to that treatment. Perturbation analyses to take into account the effects of variations in the data on population growth rate were then performed. For reasons of math1610
9
ENVIRONMENTAL SCIENCE & TECHNOLOGY / VOL. 37, NO. 8, 2003
ematical tractability the demographic parameter λ is the most frequently used summary population statistic in the standard analytical literature (e.g. refs 9 and 11). We therefore use it in the following analysis, but choice of λ or r to describe population growth rate is arbitrary, as they are interchangeable through a simple logarithmic transformation [r ) ln (λ)]. A perturbation analysis may be evaluated either in terms of the “sensitivity” or alternatively “elasticity” of λ to perturbations of each matrix parameter aij. These measures are defined by Caswell (9) as the respective gradients ∂λ/∂aij and ∂log λ/∂logaij. Elasticity is therefore equivalent to sensitivity multiplied by the factor aij/λ. Sensitivity can be readily computed by the formula derived by Caswell (9) of
viwj ∂λ ) ∂aij 〈w.v〉
(3)
where w and v are respectively the right and left eigenvectors of A associated with λ, and 〈 . 〉 denotes the scalar product of two vectors. Sensitivity is a measure of the absolute sensitivity of λ to an incremental change in each of the constituent fertility and survival parameters obtained under a given treatment. It is the slope of the partial derivative of λ with respect to the parameter in question, where λ is a function of all the matrix entries (9). Sensitivity analysis therefore describes the effect on λ of small perturbations in each of the constituent fertility and survival parameters aij in the scale of the parameter, under a given treatment. Elasticity analysis describes this as a proportional change in the parameter. The purpose of either is to quantify robustness of population growth rate under a perturbation in any specific survival or fertility parameter. Neither approach is necessarily more informative than the other because the scale of perturbation affecting each parameter aij is an unknown here and may differ by several orders of magnitude. To achieve conciseness, we apply Ockam’s razor and present the perturbation analysis in terms of sensitivity. Because LTRE’s can be decomposed directly in terms of sensitivity (see eq 4) this also leads more naturally to the subsequent decomposition analysis. Decomposition analysis was performed by the first-order approximation (12) for decomposing the effect of a given treatment t in comparison to the control treatment C
λ(t) - λ(C) ≈
∑ (a i, j
(t) ij
- a(C) ij )
∂λ ∂aij
(4)
where the partial derivative in eq 4 may be evaluated at any matrix “located” between the treatment matrix A(t) and the control treatment matrix A(C). Throughout this paper the partial derivative calculation was calculated at the mean matrix (A(t) + A(C))/2. Decomposition analysis shows the effect of each exposure treatment (0.2, 1.0, and 4.0 ng L-1) in comparison with a base treatment (the controls in this study). The overall contribution, which is defined as the reduction in population growth rate under a given treatment, when compared with the control experiment, is decomposed into a component effect observed in each of the model parameters when compared with corresponding parameters in the controls. Each component effect (associated with each parameter change) is referred to as a “contribution” toward the overall reduction in intrinsic growth rate (9). Contributions are, therefore, the differences between each of the parameters of a given treatment compared with corresponding parameters in the control experiment but weighted by the sensitivity of λ to the treatment.
TABLE 1. Effect of EE2 on Population Growth Ratea population growth rate r/week (95% bootstrap confidence interval) reduction from control () 0.204)
control 0.204 (0.165 to 0.224) 0%
test concentration (ng/L) 0.2 1.0 0.194 0.179 (0.174 to 0.206) (0.147 to 0.198) 4.8% 12.1%
4.0 -0.069 (-0.070 to -0.043) 134.0%
a Population growth (r/week) for the control, 0.2, 1.0, and 4.0 ng L-1 exposure concentrations, with corresponding 95% bootstrap percentile confidence intervals computed from B )2000 bootstrap resamples drawn from each test population. The bottom row shows the percentage reduction in growth rate for each treatment, relative to the control growth rate () 0.204).
Results and Discussion Life Table Analysis. Population growth rate r ) ln(λ) in the control, 0.2 and 1.0 ng L-1 exposures achieved a positive value, indicating that population growth would be projected under all these concentrations (Table 1). Across all age classes up to and including an exposure concentration of 1.0 ng L-1, population increase through reproduction therefore exceeded the decrease through mortality. This result was not visibly apparent from inspecting the data. However, in the 4.0 ng L-1 exposure, reproduction was inhibited totally, implying a negative growth rate (r ) -0.069) and thus that population extinction would then be projected. Feminization as an observed effect in fish has been reported in several previous studies (see for example refs 12-15). The Laenge et al. (2) experiment demonstrates that when an exposure concentration of 4 ng L-1 is reached, there is no population recruitment because there is zero fertility. This is a consequence of the reproductive disablement with increasing EE2 concentration exposure of all fish and arguably, the feminization of male fish in particular. Evidence for feminization was provided by the fact that males were ultimately indistinguishable from females at an exposure of 16 ng L-1. Together, these estimates further imply that a zero growth rate (r ) 0) would have been projected for an exposure between 1.0 and 4.0 ng L-1. As well as providing an interpolative tool for estimating that concentration, the effect of any designated concentration on intrinsic growth rate can be estimated (this cannot be calculated from the conventional NOEC or LOEC approaches). We proceed by assuming that r ) f(c) where c is concentration of the substance, and f is a linear or nonlinear function. In the absence of further information, the choice of this model to describe the relationship between population growth rate and exposure concentration must be empirically based. If an appropriate model is regressed to these data, the effect of any designated concentration can be estimated from the curve of best fit. In Figure 1A, the control and treatment population growth rates {r(t)} are plotted versus respective exposure concentrations (solid circles) together with respective “best fit” regression curves (thick curve) for f as the linear function f(x) ) ax + b. In Figure 1B the same plot is shown with f as the quadratic function f(x) ) ax2 + bx + c. It can be seen that an EE2 concentration of 3.11 ng L-1 (linear) or 3.41 (quadratic) would be estimated to reduce intrinsic population growth rate to zero. Throughout this paper, linear regressions were performed by standard least-squares regression. Nonlinear regressions were performed by the Gauss-Newton method with Levenberg-Marquardt modifications for global convergence as specified within the Statistics Toolbox of MATLAB version 5.3 software (17). The concentration required to reduce population growth rate by any designated percentage compared with a “base” concentration can also be estimated. Choosing the base concentration as the control would lead to a convenient ErCx summary metric. We define this as the concentration of test substance estimated to reduce intrinsic growth (as
FIGURE 1. The effect of EE2 on population growth rate. The curve of best fit (thick line) is shown using (a) linear and (b) quadratic regression models. In each frame, 95% pointwise double bootstrap percentile confidence intervals (dotted lines) obtained with B ) 2000 are shown together with 30 double bootstrap curve replicates (thin lines). Normal distribution 95% confidence intervals (dashed) for each curve of best fit are also shown. • ) data, x ) first bootstrap resamples (up to 30 shown). The best fit curve parameters are (a) linear model, a ) - 0.0700, b ) 0.2179, R2 ) 0.9745, and (b) quadratic model, a ) -0.015, b ) -0.0076, c ) 0.2006, R2 ) 0.999 where R2 is the coefficent of determination. represented by the value of r obtained in the control) by x% (18). In particular, at the ErC100 concentration, x ) 100% so that r is reduced to zero. The population would then be at a theoretical equilibrium but in practice would still be expected to become extinct due to the stochasticity of natural systems. It is clear that r must remain positive to ensure that the population would survive. The amount by which r must remain above zero to maintain population sustainability is dependent on the (unknown) amount of stochasticity present. However, a more conservative threshold concentration could VOL. 37, NO. 8, 2003 / ENVIRONMENTAL SCIENCE & TECHNOLOGY
9
1611
TABLE 2. Comparison of ErCx Concentrationsa ErCx (ng/L) linear (R 2 ) 0.97)
quadratic (R 2 ) 0.99)
confidence interval bootstrap reduction in r x % 20 50 100
0.78 1.66 3.11
confidence interval
normal
bootstrap
lower
upper
lower
upper
0.53 1.50 3.03
0.96 1.85 3.45
