Modeling Full Life-Cycle Effects of Copper on Brook Trout (Salvelinus fontinalis) Populations.

Population models are increasingly used to predict population-level effects of chemicals. For trout, most toxicity data are available on early-life stages, but this may cause population models to miss true population-level effects. We predicted population-level effects of copper (Cu) on a brook trout (Salvelinus fontinalis) population based on individual-level effects observed in either a life-cycle study or an early-life stage study. We assessed the effect of Cu on predicted trout densities (both total and different age classes) and the importance of accounting for effects on the full life cycle compared with only early-life stage effects. Additionally, uncertainty about the death mechanism and growth effects was evaluated by comparing the effect of different implementation methods: individual tolerance (IT) versus stochastic death (SD) and continuous versus temporary growth effects. For the life-cycle study, the same population-level no-observed-effect concentration (NOECpop) was predicted as the lowest reported individual-level NOEC (NOECind; 9.5 µg/L) using IT. For SD, the NOECpop was predicted to be lower than the NOECind for young-of-the-year and 1-year-old trout (3.4 µg/L), but similar for older trout (9.5 µg/L). The implementation method for growth effects did not affect the NOECpop of the life-cycle study. Simulations based solely on the early-life stage effects within the life-cycle study predicted unbounded NOECpop values (≥32.5 µg/L), that is, >3.4 times higher than the NOECpop based on all life-cycle effects. For the early-life stage study, the NOECpop for both IT and SD were predicted to be >2.6 times higher than the lowest reported NOECind. Overall, we demonstrate that effects on trout populations can be underestimated if predictions are solely based on toxicity data with early-life stages. Environ Toxicol Chem 2024;43:1662-1676. © 2024 SETAC.

Extrapolation of Metal Toxicity Data for the Rotifer Brachionus calyciflorus Using an Individual-Based Population Model.

Ecological risk assessment (ERA) of metals typically starts from standardized toxicity tests, the data from which are then extrapolated to derive safe concentrations for the envisioned protection goals. Because such extrapolation in conventional ERA lacks ecological realism, ecological modeling is considered as a promising new approach for extrapolation. Many published population models are complex, that is, they include many processes and parameters, and thus require an extensive dataset to calibrate. In the present study, we investigated how individual-based models based on a reduced version of the Dynamic Energy Budget theory (DEBkiss IBM) could be applied for metal effects on the rotifer Brachionus calyciflorus. Data on survival over time and reproduction at different temperatures and food conditions were used to calibrate and evaluate the model for copper effects. While population growth and decline were well predicted, the underprediction of population density and the mismatch in the onset of copper effects were attributed to the simplicity of the approach. The DEBkiss IBM was applied to toxicity datasets for copper, nickel, and zinc. Predicted effect concentrations for these metals based on the maximum population growth rate were between 0.7 and 3 times higher in all but one case (10 times higher) than effect concentrations based on the toxicity data. The size of the difference depended on certain characteristics of the toxicity data: both the steepness of the concentration-effect curve and the relative sensitivity of lethal and sublethal effects played a role. Overall, the present study is an example of how a population model with reduced complexity can be useful for metal ERA. Environ Toxicol Chem 2024;43:324-337. © 2023 SETAC.

Assessment of the contribution of surfactants to mixture toxicity in French surface waters.

Surfactants are widely used 'down-the-drain' chemicals with the potential to occur at high concentrations in local water bodies and to be part of unintentional environmental mixtures. Recently, increased regulatory focus has been placed on the impacts of complex mixtures in aquatic environments and the substances that are likely to drive mixture risk. This study assessed the contribution of surfactants to the total mixture pressure in freshwater ecosystems. Environmental concentrations, collated from existing French monitoring data, were combined with estimated ecotoxicological thresholds to calculate hazard quotients (HQ) for each substance, and hazard indices (HI) for each mixture. Two scenarios were investigated to correct for concentrations below the limit of quantification (LOQ) in the dataset. The first (best-case) scenario assumed all values

Predicting Combined Effects of Chemical Stressors: Population-Level Effects of Organic Chemical Mixtures with a Dynamic Energy Budget Individual-Based Model.

Most regulatory ecological risk-assessment frameworks largely disregard discrepancies between the laboratory, where effects of single substances are assessed on individual organisms, and the real environment, where organisms live together in populations and are often exposed to multiple simultaneously occurring substances. We assessed the capability of individual-based models (IBMs) with a foundation in the dynamic energy budget (DEB) theory to predict combined effects of chemical mixtures on populations when they are calibrated on toxicity data of single substances at the individual level only. We calibrated a DEB-IBM for Daphnia magna for four compounds (pyrene, dicofol, α-hexachlorocyclohexane, and endosulfan), covering different physiological modes of action. We then performed a 17-week population experiment with D. magna (designed using the DEB-IBM), in which we tested mixture combinations of these chemicals at relevant concentrations, in a constant exposure phase (7-week exposure and recovery), followed by a pulsed exposure phase (3-day pulse exposure and recovery). The DEB-IBM was validated by comparing blind predictions of mixture toxicity effects with the population data. The DEB-IBM accurately predicted mixture toxicity effects on population abundance in both phases when assuming independent action at the effect mechanism level. The population recovery after the constant exposure was well predicted, but recovery after the pulse was not. The latter could be related to insufficient consideration of stochasticity in experimental design, model implementation, or both. Importantly, the mechanistic DEB-IBM performed better than conventional statistical mixture assessment methods. We conclude that the DEB-IBM, calibrated using only single-substance individual-level toxicity data, produces accurate predictions of population-level mixture effects and can therefore provide meaningful contributions to ecological risk assessment of environmentally realistic mixture exposure scenarios. Environ Toxicol Chem 2022;41:2240-2258. © 2022 SETAC.

Integrating Bioavailability of Metals in Fish Population Models.

Population models are increasingly being used to extrapolate individual-level effects of chemicals, including metals, to population-level effects. For metals, it is also important to take into account their bioavailability to correctly predict metal toxicity in natural waters. However, to our knowledge, no models exist that integrate metal bioavailability into population modeling. Therefore, our main aims were to 1) incorporate the bioavailability of copper (Cu) and zinc (Zn) into an individual-based model (IBM) of rainbow trout (Oncorhynchus mykiss), and 2) predict how survival-time concentration data translate to population-level effects. For each test water, reduced versions of the general unified threshold model of survival (GUTS-RED) were calibrated using the complete survival-time concentration data. The GUTS-RED individual tolerance (IT) showed the best fit in the different test waters. Little variation between the different test waters was found for 2 GUTS-RED-IT parameters. The GUTS-RED-IT parameter "median of distribution of thresholds" (mw ) showed a strong positive relation with the Ca2+ , Mg2+ , Na+ , and H+ ion activities. Therefore, mw formed the base of the calibrated GUTS bioavailability model (GUTS-BLM), which predicted 30-d x% lethal concentration (LCx) values within a 2-fold error. The GUTS-BLM was combined with an IBM, inSTREAM-Gen, into a GUTS-BLM-IBM. Assuming that juvenile survival was the only effect of Cu and Zn exposure, population-level effect concentrations were predicted to be 1.3 to 6.2 times higher than 30-d laboratory LCx values, with the larger differences being associated with higher interindividual variation of metal sensitivity. The proposed GUTS-BLM-IBM model can provide insight into metal bioavailability and effects at the population level and could be further improved by incorporating sublethal effects of Cu and Zn. Environ Toxicol Chem 2021;40:2764-2780. © 2021 SETAC.

Development and Validation of a Mixture Toxicity Implementation in the Dynamic Energy Budget-Individual-Based Model: Effects of Copper and Zinc on Daphnia magna Populations.

Mechanistic population models are gaining considerable interest in ecological risk assessment. The dynamic energy budget approach for toxicity (DEBtox) and the general unified threshold model for survival (GUTS) are well-established theoretical frameworks that describe sublethal and lethal effects of a chemical stressor, respectively. However, there have been limited applications of these models for mixtures of chemicals, especially to predict long-term effects on populations. We used DEBtox and GUTS in an individual-based model (IBM) framework to predict both single and combined effects of copper and zinc on Daphnia magna populations. The model was calibrated based on standard chronic toxicity test results with the single substances. A mixture toxicity implementation based on the general independent action model for mixtures was developed and validated with data from a population experiment with copper and zinc mixtures. Population-level effects of exposure to individual metals were accurately predicted by DEB-IBM. The DEB-IBM framework also allowed us to identify the potential mechanisms underlying these observations. Under independent action the DEB-IBM was able to predict the population dynamics observed in populations exposed to the single metals and their mixtures (R2 > 65% in all treatments). Our modeling shows that it is possible to extrapolate from single-substance effects at the individual level to mixture toxicity effects at the population level, without the need for mixture toxicity data at the individual level from standard mixture toxicity tests. The application of such modeling techniques can increase the ecological realism in risk assessment. Environ Toxicol Chem 2021;40:513-527. © 2020 SETAC.

The Use of Mechanistic Population Models in Metal Risk Assessment: Combined Effects of Copper and Food Source on Lymnaea stagnalis Populations.

Environmental risk assessment (ERA) of chemicals aims to protect populations, communities, and ecosystems. Population models are considered more frequent in ERA because they can bridge the gap between the individual and the population level. Lymnaea stagnalis (the great pond snail) is an organism that is particularly sensitive to various metals, including copper (Cu). In addition, the sensitivity of this species to Cu differs between food sources. The first goal of the present study was to investigate whether we could explain the variability in sensitivity between food sources (lettuce and fish flakes) at the individual level with a dynamic energy budget (DEB) model. By adapting an existing DEB model and calibrating it with Cu toxicity data, thereby combining information from 3 studies and 2 endpoints (growth and reproduction), we put forward inhibition of energy assimilation as the most plausible physiological mode of action (PMoA) of Cu. Furthermore, the variation in Cu sensitivity between both food sources was considerably lower at the PMoA level than at the individual level. Higher Cu sensitivity at individual level under conditions of lower food quality or availability appears to emerge from first DEB principles when inhibition of assimilation is the PMoA. This supports the idea that DEB explained Cu sensitivity variation between food sources. Our second goal was to investigate whether this food source effect propagated to the population level. By incorporating DEB in an individual-based model (IBM), population-level effects were predicted. Based on our simulations, the food source effect was still present at the population level, albeit less prominently. Finally, we compared predicted population-level effect concentration, x% (ECx) values with individual-level ECx values for different studies. Using the DEB-IBM, the range of effect concentrations decreased significantly: at the individual level, the difference in chronic EC10 values between studies was a factor of 70 (1.13-78 µg dissolved Cu/L), whereas at the population level the difference was a factor of 15 (2.9-44.6 µg dissolved Cu/L). To improve interstudy comparability, a bioavailability correction for differences in water chemistry was performed with a biotic ligand model. This further decreased the variation, down to a factor of 7.4. Applying the population model in combination with a bioavailability correction thus significantly decreased the variability of chronic effect concentrations of Cu for L. stagnalis. Overall, the results of the present study illustrate the potential usefulness of transitioning to a more modeling-based environmental risk assessment. Environ Toxicol Chem 2019;00:1-16. © 2019 SETAC.

Time-varying effects of aromatic oil constituents on the survival of aquatic species: Deviations between model estimates and observations.

There is a need to study the time course of toxic chemical effects on organisms because there might be a time lag between the onset of chemical exposure and the corresponding adverse effects. For aquatic organisms, crude oil and oil constituents originating from either natural seeps or human activities can be relevant case studies. In the present study the authors tested a generic toxicokinetic model to quantify the time-varying effects of various oil constituents on the survival of aquatic organisms. The model is based on key parameters applicable to an array of species and compounds with baseline toxicity reflected by a generic, internal toxicity threshold or critical body burden (CBB). They compared model estimates with experimental data on the effects of 8 aromatic oil constituents on the survival of aquatic species including crustaceans and fish. The average model uncertainty, expressed as the root mean square error, was 0.25 (minimum-maximum, 0.04-0.67) on a scale between 0 and 1. The estimated survival was generally lower than the measured survival right after the onset of oil constituent exposure. In contrast, the model underestimated the maximum mortality for crustaceans and fish observed in the laboratory. Thus, the model based on the CBB concept failed to adequately predict the lethal effects of the oil constituents on crustaceans and fish. Possible explanations for the deviations between model estimates and observations may include incorrect assumptions regarding a constant lethal body burden, the absence of biotransformation products, and the steady state of aromatic hydrocarbon concentrations in organisms. Clearly, a more complex model approach than the generic model used in the present study is needed to predict toxicity dynamics of narcotic chemicals. Environ Toxicol Chem 2017;36:128-136. © 2016 SETAC.

Species interactions and chemical stress: combined effects of intraspecific and interspecific interactions and pyrene on Daphnia magna population dynamics.

Species interactions are often suggested as an important factor when assessing the effects of chemicals on higher levels of biological organization. Nevertheless, the contribution of intraspecific and interspecific interactions to chemical effects on populations is often overlooked. In the present study, Daphnia magna populations were initiated with different levels of intraspecific competition, interspecific competition, and predation and exposed to pyrene pulses. Generalized linear models were used to test which of these factors significantly explained population size and structure at different time points. Pyrene had a negative effect on total population densities, with effects being more pronounced on smaller D. magna individuals. Among all species interactions tested, predation had the largest negative effect on population densities. Predation and high initial intraspecific competition were shown to interact antagonistically with pyrene exposure. This was attributed to differences in population structure before pyrene exposure and pyrene-induced reductions in predation pressure by Chaoborus sp. larvae. The present study provides empirical evidence that species interactions within and between populations can alter the response of aquatic populations to chemical exposure. Therefore, such interactions are important factors to be considered in ecological risk assessments.

Evaluating the contribution of ingested oil droplets to the bioaccumulation of oil components--a modeling approach.

The dietary uptake of oil droplets by aquatic organisms has been suggested as a possible exposure pathway for oil-related chemicals. We confronted two bioaccumulation models, one including and one neglecting oil droplet uptake, with measured polycyclic aromatic hydrocarbon (PAH) body burdens of five marine species. The model without oil droplet uptake was able to predict 75% of the observations within one order of magnitude. Total PAH body burdens were predicted within a factor of five. For most species, inclusion of oil droplet uptake did not improve model accuracy, suggesting a negligible contribution of oil droplet uptake to PAH bioaccumulation. Only for Mytilus edulis, model accuracy improved (up to five times) after the inclusion of oil droplet uptake. Our findings suggest filter feeding as a determinant for the PAH uptake via oil droplets, but more research is needed to test this hypothesis.

Modelling bioaccumulation of oil constituents in aquatic species.

Crude oil poses a risk to marine ecosystems due to its toxicity and tendency to accumulate in biota. The present study evaluated the applicability of the OMEGA model for estimating oil accumulation in aquatic species by comparing model predictions of kinetic rates (absorption and elimination) and bioconcentration factors (BCF) with measured values. The model was a better predictor than the means of the measurements for absorption and elimination rate constants, but did not outperform the mean measured BCF. Model estimates and measurements differed less than one order of magnitude for 91%, 80% and 61% of the absorption and elimination rates and BCFs of all oil constituents, respectively. Of the "potentially modifying" factors: exposure duration, biotransformation, molecular mass, and water temperature, the last two tended to influence the performance of the model. Inclusion of more explanatory variables in the bioaccumulation model, like the molecular mass, is expected to improve model performance.