Integrating Bioavailability of Metals in Fish Population Models

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Ecological risk assessment (ERA) of metals typically starts from standardized toxicity tests, the data of which is then extrapolated to derive safe concentrations for the envisioned protection goals. As such extrapolation in conventional ERA is lacking ecological realism, ecological modelling is considered as a promising new approach for extrapolation. Many published population models are complex, i.e. include many processes and parameters, and thus require an extensive dataset to calibrate. In this 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 to the rotifer Brachionus calyciflorus. Data on survival over time and reproduction at different temperatures and food conditions was 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 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, this study is an example of how a population model with reduced complexity can be useful for metal ERA.

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Viaene,

De Schamphelaere,

Van Sprang

2023

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“…To implement the IT model, we assumed that the threshold for both adult and juvenile brook trout was reached within 30 days of exposure, which is supported by model fits of Cu toxicity to the related species rainbow trout (Janssen et al, 2021). We implemented the IT model by killing a fixed fraction of randomly selected adults or juveniles after 30 days of exposure, corresponding to the observed relative survival fraction at the end of the test (Supporting Information, Table S1-5).…”

Section: Incorporation Of Cu Toxicity In the Population Modelmentioning

confidence: 99%

“…Because the chosen method of implementation for survival effects (IT or SD) can affect population-level predictions, this led to additional uncertainty in our population-level predictions. In an earlier study, Janssen et al (2021) found that the IT model was the most plausible model to explain Cu mortality to juvenile rainbow trout, based on results obtained with waters that had a wide variety of physicochemical compositions. Because both our present study and that of Janssen et al (2021) are about trout species, one could assume that the NOEC pop values obtained with the IT simulations may be the most realistic ones in our present study.…”

Section: Sd or Itmentioning

confidence: 99%

“…In an earlier study, Janssen et al (2021) found that the IT model was the most plausible model to explain Cu mortality to juvenile rainbow trout, based on results obtained with waters that had a wide variety of physicochemical compositions. Because both our present study and that of Janssen et al (2021) are about trout species, one could assume that the NOEC pop values obtained with the IT simulations may be the most realistic ones in our present study. On the other hand, Gao et al (2017) found the SD model to fit the survival-time concentration data of Cu toxicity to zebra fish larvae the best.…”

Section: Sd or Itmentioning

confidence: 99%

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

Janssen,

Viaene,

Van Sprang

et al. 2024

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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;00:1–15. © 2024 SETAC

Population Modeling in Metal Risk Assessment: Extrapolation of Toxicity Tests to the Population Level

Viaene,

Vlaeminck,

Hansul

et al. 2024

Population models can be a useful tool for ecological risk assessment to increase ecological realism. In the present study, population models were used to extrapolate toxicity test results of four metals (Ag, Cu, Ni, Zn) to the population level. In total, three primary producers, five invertebrate species, and five fish species were covered. The ecological modeling–based laboratory to population effect extrapolation factor (ECOPEX factor), defined as the ratio of the predicted 10% effect concentration (EC10) at the population level and the observed EC10 for the laboratory toxicity test, ranged from 0.7 to 78.6, with a median of 2.8 (n = 27). Population modeling indicated clearly higher effect concentrations in most of the cases (ECOPEX factor >2 in 14 out of 27 cases), but in some cases the opposite was observed (in three out of 27 cases). We identified five main contributors to the variability in ECOPEX factors: (1) uncertainty about the toxicity model, (2) uncertainty about the toxicity mechanism of the metal, (3) uncertainty caused by test design, (4) impact of environmental factors, and (5) impact of population endpoint chosen. Part of the uncertainty results from a lack of proper calibration data. Nonetheless, extrapolation with population models typically reduced the variability in EC10 values between tests. To explore the applicability of population models in a regulatory context, we included population extrapolations in a species sensitivity distribution for Cu, which increased the hazardous concentration for 5% of species by a factor 1.5 to 2. Furthermore, we applied a fish population model in a hypothetical Water Framework Directive case using monitored Zn concentrations. This article includes recommendations for further use of population models in (metal) risk assessment. Environ Toxicol Chem 2024;43:2308–2328. © 2024 SETAC