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Cite This: Environ. Sci. Technol. 2019, 53, 9818−9825
Mechanistic Effect Modeling Approach for the Extrapolation of Species Sensitivity Andre ́ Gergs,*,†,‡ Kim J. Rakel,† Dino Liesy,§ Armin Zenker,∥ and Silke Classen† †
Research Institute for Ecosystem Analysis and Assessment (gaiac), Kackertstrasse 10, 52072 Aachen, Germany Institute for Environmental Sciences, University of Koblenz-Landau, Fortstraße 7, 76829 Landau, Germany ∥ Institute for Ecopreneurship, School of Life Sciences, University of Applied Sciences and Arts Northwestern Switzerland, Hofackerstrasse 30, 4132 Muttenz, Switzerland Downloaded via 46.161.59.31 on August 30, 2019 at 04:55:35 (UTC). See https://pubs.acs.org/sharingguidelines for options on how to legitimately share published articles.
§
S Supporting Information *
ABSTRACT: In the higher-tier environmental risk assessment of chemicals, species sensitivity distributions (SSDs) are used to statistically describe differences in sensitivity between species and derive community level endpoints. SSDs are usually based on the results from short-term laboratory experiments performed under constant environmental conditions. However, different species may be kept at different “optimal” temperatures, which influence their apparent sensitivity and thus the derivation of endpoints. Also, the extrapolation capacity of SSDs is largely limited to the tested species and conditions. Time-variable exposures and effects at higher levels of biological organization, including biological interactions, are not considered. The quantitative effect prediction at higher tiers would ultimately require the extrapolation of toxicokinetics and toxicodynamics to untested species and the involvement of population and community modeling. In this regard, we tested a toxicokinetic-toxicodynamic modeling approach to mechanistically consider and correct endpoints for ambient temperature and demonstrate the significance for SSDs. We explored correlations in toxicokinetic-toxicodynamic model parameters which would allow for the extrapolation of sensitivities to untested species. Finally, we illustrate the applicability of the approach for higher level effect predictions using an individual-based model. Our results suggest that mechanistic effect modeling approaches can reduce the uncertainties in higher tier effect assessments related to knowledge gaps.
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INTRODUCTION The environmental risk assessment of chemicals aims at preventing any harmful effects on the environment, including populations and communities of biological species. The underlying effect assessments are based on summary statistics, such as the ECx (representing x% of an affected population), or the no-observed-effect-concentrations (NOECs) which are usually derived from acute or chronic toxicity tests with standard species under constant environmental conditions and exposure. When effect endpoints are available for multiple species from a taxonomic group, species sensitivity distributions (SSD) are used in higher tier assessments to describe and quantify differences in apparent toxicity. In turn, SSDs are used to extract ‘safe’ community effect thresholds in the form of a HC5, i.e., the concentration at which 5% of the species are affected. In current risk assessment practice, the ratio of effect measures and exposure, such as the maximum predicted exposure concentration or the time weighted average, is being employed to characterize risk. This common practice, however, largely ignores the fact that environmental conditions as well as exposure concentrations can be variable in space and time. Also, ecological interactions such as competition1,2 and predation,3,4 which may influence the chemical effect at higher biological levels, as well as ecological recovery,5 are not accounted for. In current risk assessment schemes, safety factors are used to account for the uncertainties related to © 2019 American Chemical Society
extrapolations, which are largely arising from the lack of information. The significance of temperature for the physiology of organisms and temporal dynamics of environmental systems has been well recognized in ecology, e.g., refs 6−9. In ecotoxicology, the temperature dependency of effects has been demonstrated for various aquatic10−14 and terrestrial organisms.15,16 In general, toxicity is positively correlated with temperature,12,17,18 but the opposite has also been reported, e.g., for pyrethroid insecticides.10,11 Therefore, explicitly accounting for environmental conditions, such as the ambient temperature, may reduce uncertainty in the implementation of risk assessments. Species sensitivity toward a certain compound may vary across several orders of magnitude,19 which adds to the uncertainty in risk assessment. Currently, the most efficient way to derive apical endpoints for an untested species might be to perform another toxicity test.20 Testing thousands of biological species is, however, not an option, and thus, in silico methods that exploit existing data are needed. For instance, interspecies correlation estimation models, which are regresReceived: Revised: Accepted: Published: 9818
March 20, 2019 July 17, 2019 July 29, 2019 July 29, 2019 DOI: 10.1021/acs.est.9b01690 Environ. Sci. Technol. 2019, 53, 9818−9825
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Environmental Science & Technology
we corrected the GUTS rate constants for ambient temperature by multiplication with an exponential temperature function,7 while the threshold parameter, which in this model has the unit of the external concentration, is assumed independent of temperature. We analyzed linear correlations among the tree major GUTS parameters for the different species in a first step; the background hazard rate was assumed to be independent of the chemical exposure and thus excluded from the analysis. Temperature corrected parameter values for the dominant rate and the killing rate were used in the correlation analysis. To account for the different life stages or sizes used for the different test species, we scaled the dominant rate constant with the relative surface-to-volume ratio of species before its use in the cross-species parameter correlation approach. In a second step, we evaluated the correlation of the GUTS threshold parameter with the volume-specific somatic maintenance rate pM, also referred to as the metabolic rates of the species, as suggested by Baas and Kooijman.4 Parameter values were log transformed before being processed in linear regression analyses. See the Supporting Information for further details. GUTS assumptions and parameter values were subsequently applied in a population level setting. For the prediction of population level effects from acute toxicity testing, we used an individual based model (IBM) based on dynamic energy budget (DEB) theory.7 The model is freely available online (https://github.com/gaiac-eco/DEB_IBM_Daphnia_magna). The full IBM description, the parametrization for D. magna, and testing with independent control data are available from Gergs et al.;2 thorough testing of the integration of the IBM with GUTS has been previously published.30 In brief, life histories of individual organisms are modeled based on the standard DEB model.31 In the IBM, individuals assimilate food, and energy is allocated to structure, the reproductive system (maturation and reproduction), and maintenance costs via a reserve compartment. Starvation, aging, and chemical exposure may reduce the individual survival probability. Individuals sense their environment and may adapt to crowding at high population densities and conditions of low food availability, e.g., as a result of competition. In the IBM, population dynamics emerge from these interactions and individual life history processes which are driven by ambient temperature, food availability, and chemical exposure. IBM simulations were carried out to mimic population experiments, as described below. This step was performed to test the predictive capability of the regression analysis for the GUTS parameters. Model equations as well as details on parameter estimation, statistical analyses, and model simulations are provided in the Supporting Information. Toxicity Data. Acute toxicity tests were carried out for a total of seven invertebrate species. The organophosphate pesticide chlorpyrifos was chosen for this case study. The same test design was used for different species, with the exception of the concentration range (see legends of Figures S2−S11) and the ambient temperatures which were chosen to account for sensitivities and ecological preferences, respectively. Test species for this study were the water flea Daphnia magna (20 °C), the waterlouse Asellus aquaticus (20 °C), the phantom midge Chaoborus crystallinus (20 °C), the mayfly species Cloeon dipterum (20 °C), Epeorus assimilis (12 °C), Rhithrogena semicolorata (12 °C), and the mudsnail Potamo-
sion models based on acute toxicity summary statistics, allow the extrapolation of toxicity to untested species from the known toxicity of surrogate species.21,22 As the summary statistics themselves, these models, however, only use parts of the available toxicity information and have limited predictive capacities, e.g., for time-variable exposure or when dealing with delayed effects23,24 or increased apparent sensitivity over time.25,26 For these purposes, the underlying processes have to be taken into account, and attempts to predict toxicokinetictoxicodynamic (TK-TD) model25−27 parameters are more likely to succeed as these models make most of the available data and the parameters have a biological meaning.20 Here, we test a TK-TD modeling approach to mechanistically consider and correct model parameters and calculated endpoints for ambient temperature using Daphnia magna as a case study. We applied this approach to demonstrate the significance of ambient temperature for the derivation of endpoints from an SSD, based on toxicity tests with invertebrates that were kept at different optimal temperatures. In the next step, we explored correlations in TK-TD model parameters that will allow for the extrapolation of sensitivities to untested species. Finally, we applied the parameter correlation approach to predict population level effects for an unknown species using an individual-based model. As we had more data available for daphnids, which could be used for validation of the approach, than for any other species, in this step, we removed the D. magna data from the correlation analysis and pretended this to be the “unknown species”. The data available for daphnids was subsequently used for model testing. The three topics or rather TK-TD modeling steps, i.e., (1) temperature and SSD, (2) cross-species parameter correlations, and (3) population level extrapolation, are separately addressed in the Results and Discussion section below. Our overall aim was to explore extrapolation approaches for TK-TD model parameters and, in this regard, illustrate the ability of mechanistic effect models to reduce the uncertainties in higher tier effect assessments, by providing valuable information.
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MATERIALS AND METHODS Modeling Approaches. TK-TD modeling is based on the reduced general unified threshold model of survival (GUTS27). The “reduced” GUTS refers to the use of the dose metric of the scaled internal concentration and comes with three major parameters. In the case of the toxicodynamic assumption of stochastic death, these are the dominant rate constant kd, the threshold z, and the killing rate kk; in addition, a background hazard rate might be considered. In this model, the temporal change of the dose metric is triggered by a single parameter, the dominant rate constant, which may represent either a toxicokinetic or toxicodynamic process. Here, we focus on the toxicodynamic assumption of stochastic death (SD) to link the dose metric to survival probability: beyond the threshold, the hazard for an individual increases linearly with the dose metric (and the background hazard rate), while the killing rate determines the slope of the hazard rate distribution. Survival probability decreases in an exponential fashion as the hazard increases, and, accordingly, death is stochastic at the level of the individual organism. We extended the original GUTS framework27 by scaling the dominant rate constant with the relative surface-to-volume ratio, which allows differences in body size to be accounted for (see also refs 28−30). Moreover, 9819
DOI: 10.1021/acs.est.9b01690 Environ. Sci. Technol. 2019, 53, 9818−9825
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Figure 1. GUTS parameter values (dots), i.e., dominant rate (kd), killing rate (kk), and threshold (z), for D. magna as a function of temperature. The prediction of the temperature dependency (lines) for the rate constants is based on the respective parameter values for 20 °C and the Arrhenius temperature parameter, as derived from the add-my-pet collection (Table S12), whereas the effect threshold is assumed to be independent of temperature.
Figure 2. SSDs for chlorpyrifos and 8 aquatic arthropod species (black dots) based on LC50 (96 h) values calculated from GUTS. The regular SSD is based on the original test temperatures for the different species ranging from 12 to 25 °C, while in the temperature corrected SSD the LC50 (96 h) values were normalized to 20 °C (see the main text for further explanations). Lines represent the regression and 95% prediction intervals.
pyrgus antipodarum (20 °C). For D. magna, we tested acute toxicity for three additional ambient temperatures: 11.5, 14.9, and 17.2 °C. Acute toxicity tests were carried out during a period of 96 h, using static chlorpyrifos exposure and 30 individuals (divided into 6 replicates) for each of the test concentrations and controls. Test organisms were checked for mobility at predefined intervals until test termination. Body lengths of tested specimens were measured under magnification, except for D. magna, where siblings from the same broods were measured at the start of the experiment. Further experimental details and a description of the chemical analysis are given in the Supporting Information. Published toxicity data for the water flea Ceriodaphnia dubia32 (25 °C), Gammarus fossarum 33 (12 °C), and fathead minnow Pimephales promelas34 (25.1 °C) have complemented our species list. Population dynamics of D. magna were assessed for a control and two different time-variable exposure scenarios with chlorpyrifos. In the first scenario, populations were exposed to a single two-day pulse of 0.95 μg/L initiated on day 21. In the second scenario, populations were additionally exposed to a concentration of 0.26 μg/L chlorpyrifos for 2 days at the start of the experiment. The population experiments were carried out using 900 mL medium and four replicates, at an ambient temperature of 19 °C, and were initiated with three adult daphnids (21−28 days old) and five neonates (2). Please note that the stepwise regression approach applied in our study does not take into account the propagation or accumulation of uncertainties with each of the steps, which reduces the potential for efficient application in future. Possible ways to overcome this limitation include the following ones: A first obvious remedy would be to assess the correlation of every parameter directly to the metabolic rate, which however requires that this parameter could reliably be estimated. A second option might be to estimate GUTS regression parameters directly from the entire toxicity data set including the different species, rather than fitting the GUTS model individually as it was currently done. This latter option would allow quantifying the uncertainty of regression parameters analogous to the estimation of confidence limits for the GUTS parameters. Population Level Extrapolation. Daphnid abundance rapidly increased initially in the control population experiments and leveled off after about 3 weeks at population sizes of ∼130 individuals, with juvenile daphnids being the most dominant size class; the IBM predicted the dynamics as well as the size distribution in the populations well (Figures S16 and S17). Most of the replicated populations went extinct upon a two-day pulse of 0.95 μg/L chlorpyrifos initiated on day 21, but all of the populations in the double-pulse treatment survived the initial two-day exposure of 0.26 μg/L. In combination with the GUTS model parametrized for D. magna and 20 °C, the IBM overall captures these patterns in population dynamics well but slightly overpredicted the effects, i.e., the predicted population size is lower than observed (Figure 4A). The reason for the higher effect in the model compared to the data is likely due to the fact that the GUTS parametrization is based on neonates, but the populations are size structured (Figures S18 and S21) and larger daphnids should be less sensitive.29 However, most importantly, the combined model is able to describe the continued effect after the termination of the first exposure pulse, which is likely a result of the toxicokinetics; for a discussion see ref 30. These simulations serve as a reference for the alternative approach below. Our aim was to test the cross-species parameter correlation approach for its predictive capability at the population level. We therefore removed the D. magna data from the correlation analysis (Figure S15) and recalculated the GUTS parameters based on the metabolic rate pM for this species (Table S12). The resulting population level predictions are shown in Figure 4B. Overall, the effects are somewhat underestimated by this model. The main difference to the prediction described above is that the correlation provides a value for the scaled dominant rate kd*, and size dependency has been considered in the prediction. As a consequence, the adult fraction of the simulated population is least affected. The high effect on smaller size classes in this simulation has reduced the competition within the population, which subsequently lead to an increased reproduction rate particularly in the double pulse scenario. This is revealed by a comparison of the number of simulated neonates (Figure S21) and overall higher population size (Figure 4B) derived from the IBM using the
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ASSOCIATED CONTENT
S Supporting Information *
The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acs.est.9b01690. GUTS model description; methods for parameter estimation, statistical analyses, and model simulations; details on acute toxicity experiments and analytical methods; experimental raw data; and suppporting GUTS and IBM modeling results (PDF)
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AUTHOR INFORMATION
Corresponding Author
*Phone: +49(0)2173-386465. E-mail:
[email protected]. Corresponding author address: Bayer AG, Alfred-Nobel-Straße 50, 40789 Monheim am Rhein, Germany. ORCID
André Gergs: 0000-0002-1752-1342 Present Address ‡
Bayer AG, Alfred-Nobel-Straße 50, 40789 Monheim am Rhein, Germany. Author Contributions
A.G. conceived the study and drafted the manuscript, A.G. and K.J.R. analyzed the data, D.L. and S.C. did the experiments, and A.Z. did the chemical analytics. All authors contributed to and agreed on the final version of the manuscript. Notes
The authors declare no competing financial interest.
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ACKNOWLEDGMENTS
This study was financially supported by the CEFIC LongRange Research Initiative (project no. ECO28). The authors thank Nils Lippmann and Timm Knautz for laboratory assistance and Tido Strauss for valuable discussions. 9823
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