Comparing Sensitivity of Different Bee Species to Pesticides: A TKTD modeling approach.

Risk assessment for bees is mainly based on data for honey bees; however, risk assessment is intended to protect all bee species. This raises the question of whether data for honey bees are a good proxy for other bee species. This issue is not new and has resulted in several publications in which the sensitivity of bee species is compared based on the values of the 48-h median lethal dose (LD50) from acute test results. When this approach is used, observed differences in sensitivity may result both from differences in kinetics and from inherent differences in species sensitivity. In addition, the physiology of the bee, like its overall size, the size of the honey stomach (for acute oral tests), and the physical appearance (for acute contact tests) also influences the sensitivity of the bee. The recently introduced Toxicokinetic-Toxicodynamic (TKTD) model that was developed for the interpretation of honey bee tests (Bee General Uniform Threshold Model for Survival [BeeGUTS]) could integrate the results of acute oral tests, acute contact tests, and chronic tests within one consistent framework. We show that the BeeGUTS model can be calibrated and validated for other bee species and also that the honey bee is among the more sensitive bee species. In addition, we found that differences in sensitivity between species are smaller than previously published comparisons based on 48-h LD50 values. The time-dependency of the LD50 and the specifics of the bee physiology are the main causes of the wider variation found in the published literature. Environ Toxicol Chem 2024;43:1431-1441. © 2024 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.

Environmental Risk Assessment with Energy Budget Models: A Comparison Between Two Models of Different Complexity.

The extrapolation of effects from controlled standard laboratory tests to real environmental conditions is a major challenge facing ecological risk assessment (ERA) of chemicals. Toxicokinetic-toxicodynamic (TKTD) models, such as those based on dynamic energy budget (DEB) theory, can play an important role in filling this gap. Through the years, different practical TKTD models have been derived from DEB theory, ranging from the full "standard" DEB animal model to simplified "DEBtox" models. It is currently unclear what impact a different level of model complexity can have on the regulatory risk assessment. In the present study, we compare the performance of two DEB-TKTD models with different levels of complexity, focusing on model calibration on standard test data and on forward predictions for untested time-variable exposure profiles. The first model is based on the standard DEB model with primary parameters, whereas the second is a reduced version with compound parameters, based on DEBkiss. After harmonization of the modeling choices, we demonstrate that these two models can achieve very similar performances both in the calibration step and in the forward prediction step. With the data presented in the present study, selection of the most suitable TKTD model for ERA therefore cannot be based alone on goodness-of-fit or on the precision of model predictions (within current ERA procedures for pesticides) but would likely be based on the trade-off between ease of use and model flexibility. We also stress the importance of modeling choices, such as how to fill gaps in the information content of experimental toxicity data and how to accommodate differences in growth and reproduction between different data sets for the same chemical-species combination. Environ Toxicol Chem 2024;43:440-449. © 2023 ibacon GmbH. Bayer AG and The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.

Development of a mechanistic model for analyzing avian reproduction data for pesticide risk assessment.

Mechanistic effect models are increasingly recommended as tools for refining evaluations of risk from exposure to pesticides. In the context of bird and mammal risk assessments, DEB-TKTD models have been recommended for characterizing sublethal effects at lower tiers. However, there are currently no such models. Currently, chronic, multi-generational studies are performed to characterize potential effects of pesticides on avian reproduction, but it is has not been established to what extent results from these studies can inform effect models. Here, a standard Dynamic Energy Budget (DEB) model was extended to account for the avian toxicity endpoints observed in regulatory studies. We linked this new implementation to a toxicological module to capture observed pesticide effects on reproduction via a decreased efficiency of egg production. We analysed ten reproduction studies with five different pesticides conducted with the mallard (Anas platyrhynchos) and the northern bobwhite (Colinus virginianus). The new model implementation accurately distinguished between effects on egg production from direct mechanism of toxicity and from food avoidance. Due to the specific nature of regulatory studies, model applicability for risk refinement is currently limited. We provide suggestions for next steps in model development.

Energetic basis for bird ontogeny and egg-laying applied to the bobwhite quail.

Birds build up their reproductive system and undergo major tissue remodeling for each reproductive season. Energetic specifics of this process are still not completely clear, despite the increasing interest. We focused on the bobwhite quail - one of the most intensely studied species due to commercial and conservation interest - to elucidate the energy fluxes associated with reproduction, including the fate of the extra assimilates ingested prior to and during reproduction. We used the standard Dynamic Energy Budget model, which is a mechanistic process-based model capable of fully specifying and predicting the life cycle of the bobwhite quail: its growth, maturation and reproduction. We expanded the standard model with an explicit egg-laying module and formulated and tested two hypotheses for energy allocation of extra assimilates associated with reproduction: Hypothesis 1, that the energy and nutrients are used directly for egg production; and Hypothesis 2, that the energy is mostly spent fueling the increased metabolic costs incurred by building up and maintaining the reproductive system and, subsequently, by egg-laying itself. Our results suggest that Hypothesis 2 is the more likely energy pathway. Model predictions capture well the whole ontogeny of a generalized northern bobwhite quail and are able to reproduce most of the data variability via variability in (i) egg size, (ii) egg-laying rate and (iii) inter-individual physiological variability modeled via the zoom factor, i.e. assimilation potential. Reliable models with a capacity to predict physiological responses of individuals are relevant not only for experimental setups studying effects of various natural and anthropogenic pressures on the quail as a bird model organism, but also for wild quail management and conservation. The model is, with minor modifications, applicable to other species of interest, making it a most valuable tool in the emerging field of conservation physiology.

The BEEHAVEecotox Model-Integrating a Mechanistic Effect Module into the Honeybee Colony Model.

Mechanistic effect models are powerful tools for extrapolating from laboratory studies to field conditions. For bees, several good models are available that can simulate colony dynamics. Controlled and reliable experimental systems are also available to estimate the inherent toxicity of pesticides to individuals. However, there is currently no systematic and mechanistic way of linking the output of experimental ecotoxicological testing to bee models for bee risk assessment. We introduce an ecotoxicological module that mechanistically links exposure with the hazard profile of a pesticide for individual honeybees so that colony effects emerge. This mechanistic link allows the translation of results from standard laboratory studies to relevant parameters and processes for simulating bee colony dynamics. The module was integrated into the state-of-the-art honeybee model BEEHAVE. For the integration, BEEHAVE was adapted to mechanistically link the exposure and effects on different cohorts to colony dynamics. The BEEHAVEecotox model was tested against semifield (tunnel) studies, which were deemed the best study type to test whether BEEHAVEecotox predicted realistic effect sizes under controlled conditions. Two pesticides used as toxic standards were chosen for this validation to represent two different modes of action: acute mortality of foragers and chronic brood effects. The ecotoxicological module was able to predict effect sizes in the tunnel studies based on information from standard laboratory tests. In conclusion, the BEEHAVEecotox model is an excellent tool to be used for honeybee risk assessment, interpretation of field and semifield studies, and exploring the efficiency of different mitigation measures. The principles for exposure and effect modules are portable and could be used for any well-constructed honeybee model. Environ Toxicol Chem 2022;41:2870-2882. © 2022 Bayer AG & Sygenta, et al. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.

BeeGUTS-A Toxicokinetic-Toxicodynamic Model for the Interpretation and Integration of Acute and Chronic Honey Bee Tests.

Understanding the survival of honey bees after pesticide exposure is key for environmental risk assessment. Currently, effects on adult honey bees are assessed by Organisation for Economic Co-operation and Development standardized guidelines, such as the acute and chronic oral exposure and acute contact exposure tests. The three different tests are interpreted individually, without consideration that the same compound is investigated in the same species, which should allow for an integrative assessment. In the present study we developed, calibrated, and validated a toxicokinetic-toxicodynamic model with 17 existing data sets on acute and chronic effects for honey bees. The model is based on the generalized unified threshold model for survival (GUTS), which is able to integrate the different exposure regimes, taking into account the physiology of the honey bee: the BeeGUTS model. The model is able to accurately describe the effects over time for all three exposure routes combined within one consistent framework. The model can also be used as a validity check for toxicity values used in honey bee risk assessment and to conduct effect assessments for real-life exposure scenarios. This new integrative approach, moving from single-point estimates of toxicity and exposure to a holistic link between exposure and effect, will allow for a higher confidence of honey bee toxicity assessment in the future. Environ Toxicol Chem 2022;41:2193-2201. © 2022 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.

The application and limitations of exposure multiplication factors in sublethal effect modelling.

Thanks to growing interest and research in the field, toxicokinetic-toxicodynamic (TKTD) models are close to realising their potential in environmental risk assessment (ERA) of chemicals such as plant protection products. A fundamental application is to find a multiplicative scale factor which-when applied to an exposure profile-results in some specified effect relative to a control. The approach is similar to applying assessment factors to experimental results, common in regulatory frameworks. It also relies on the same core assumption: that increasing the scaling always produces more extreme effects. Unlike experimental approaches, TKTD models offer an opportunity to interrogate this assumption in a mathematically rigorous manner. For four well-known TKTD models we seek to prove that the approach guarantees a unique scale factor for any percentage effect. Somewhat surprisingly, certain model configurations may have multiple scale factors which result in the same percentage effect. These cases require a more cautious regulatory approach and generate open biological and mathematical questions. We provide examples of the violations and suggest how to deal with them. Mathematical proofs provide the strongest possible backing for TKTD modelling approaches in ERA, since the applicability of the models can be determined exactly.

Considerations for using reproduction data in toxicokinetic-toxicodynamic modeling.

Toxicokinetic-toxicodynamic (TKTD) modeling is essential to make sense of the time dependence of toxic effects, and to interpret and predict consequences of time-varying exposure. These advantages have been recognized in the regulatory arena, especially for environmental risk assessment of pesticides, where time-varying exposure is the norm. We critically evaluate the link between the modeled variables in TKTD models and the observations from laboratory ecotoxicity tests. For the endpoint reproduction, this link is far from trivial. The relevant TKTD models for sublethal effects are based on dynamic energy budget (DEB) theory, which specifies a continuous investment flux into reproduction. In contrast, experimental tests score egg or offspring release by the mother. The link between model and data is particularly troublesome when a species reproduces in discrete clutches and, even more so, when eggs are incubated in the mother's brood pouch (and release of neonates is scored in the test). This situation is quite common among aquatic invertebrates (e.g., cladocerans, amphipods, mysids), including many popular test species. In this discussion paper, we treat these and other issues with reproduction data, reflect on their potential impact on DEB-TKTD analysis, and provide preliminary recommendations to correct them. Both modelers and users of model results need to be aware of these complications, as ignoring them could easily lead to unnecessary failure of DEB-TKTD models during calibration, or when validating them against independent data for other exposure scenarios. Integr Environ Assess Manag 2022;18:479-487. © 2021 SETAC.

Bioenergetics modelling to analyse and predict the joint effects of multiple stressors: Meta-analysis and model corroboration.

Understanding the consequences of the combined effects of multiple stressors-including stress from man-made chemicals-is important for conservation management, the ecological risk assessment of chemicals, and many other ecological applications. Our current ability to predict and analyse the joint effects of multiple stressors is insufficient to make the prospective risk assessment of chemicals more ecologically relevant because we lack a full understanding of how organisms respond to stress factors alone and in combination. Here, we describe a Dynamic Energy Budget (DEB) based bioenergetics model that predicts the potential effects of single or multiple natural and chemical stressors on life history traits. We demonstrate the plausibility of the model using a meta-analysis of 128 existing studies on freshwater invertebrates. We then validate our model by comparing its predictions for a combination of three stressors (i.e. chemical, temperature, and food availability) with new, independent experimental data on life history traits in the daphnid Ceriodaphnia dubia. We found that the model predictions are in agreement with observed growth curves and reproductive traits. To the best of our knowledge, this is the first time that the combined effects of three stress factors on life history traits observed in laboratory studies have been predicted successfully in invertebrates. We suggest that a re-analysis of existing studies on multiple stressors within the modelling framework outlined here will provide a robust null model for identifying stressor interactions, and expect that a better understanding of the underlying mechanisms will arise from these new analyses. Bioenergetics modelling could be applied more broadly to support environmental management decision making.

Adaptation costs to constant and alternating polluted environments.

Some populations quickly adapt to strong and novel selection pressures caused by anthropogenic stressors. However, this short-term evolutionary response to novel and harsh environmental conditions may lead to adaptation costs, and evaluating these costs is important if we want to understand the evolution of resistance to anthropogenic stressors. In this experimental evolution study, we exposed Caenorhabditis elegans populations to uranium (U populations), salt (NaCl populations) and alternating uranium/salt treatments (U/NaCl populations) and to a control environment (C populations), over 22 generations. In parallel, we ran common-garden and reciprocal-transplant experiments to assess the adaptive costs for populations that have evolved in the different environmental conditions. Our results showed rapid evolutionary changes in life history characteristics of populations exposed to the different pollution regimes. Furthermore, adaptive costs depended on the type of pollutant: pollution-adapted populations had lower fitness than C populations, when the populations were returned to their original environment. Fitness in uranium environments was lower for NaCl populations than for U populations. In contrast, fitness in salt environments was similar between U and NaCl populations. Moreover, fitness of U/NaCl populations showed similar or higher fitness in both the uranium and the salt environments compared to populations adapted to constant uranium or salt environments. Our results show that adaptive evolution to a particular stressor can lead to either adaptive costs or benefits once in contact with another stressor. Furthermore, we did not find any evidence that adaptation to alternating stressors was associated with additional adaption costs. This study highlights the need to incorporate adaptive cost assessments when undertaking ecological risk assessments of pollutants.

Integrated presentation of ecological risk from multiple stressors.

Current environmental risk assessments (ERA) do not account explicitly for ecological factors (e.g. species composition, temperature or food availability) and multiple stressors. Assessing mixtures of chemical and ecological stressors is needed as well as accounting for variability in environmental conditions and uncertainty of data and models. Here we propose a novel probabilistic ERA framework to overcome these limitations, which focusses on visualising assessment outcomes by construct-ing and interpreting prevalence plots as a quantitative prediction of risk. Key components include environmental scenarios that integrate exposure and ecology, and ecological modelling of relevant endpoints to assess the effect of a combination of stressors. Our illustrative results demonstrate the importance of regional differences in environmental conditions and the confounding interactions of stressors. Using this framework and prevalence plots provides a risk-based approach that combines risk assessment and risk management in a meaningful way and presents a truly mechanistic alternative to the threshold approach. Even whilst research continues to improve the underlying models and data, regulators and decision makers can already use the framework and prevalence plots. The integration of multiple stressors, environmental conditions and variability makes ERA more relevant and realistic.

Modelling survival: exposure pattern, species sensitivity and uncertainty.

The General Unified Threshold model for Survival (GUTS) integrates previously published toxicokinetic-toxicodynamic models and estimates survival with explicitly defined assumptions. Importantly, GUTS accounts for time-variable exposure to the stressor. We performed three studies to test the ability of GUTS to predict survival of aquatic organisms across different pesticide exposure patterns, time scales and species. Firstly, using synthetic data, we identified experimental data requirements which allow for the estimation of all parameters of the GUTS proper model. Secondly, we assessed how well GUTS, calibrated with short-term survival data of Gammarus pulex exposed to four pesticides, can forecast effects of longer-term pulsed exposures. Thirdly, we tested the ability of GUTS to estimate 14-day median effect concentrations of malathion for a range of species and use these estimates to build species sensitivity distributions for different exposure patterns. We find that GUTS adequately predicts survival across exposure patterns that vary over time. When toxicity is assessed for time-variable concentrations species may differ in their responses depending on the exposure profile. This can result in different species sensitivity rankings and safe levels. The interplay of exposure pattern and species sensitivity deserves systematic investigation in order to better understand how organisms respond to stress, including humans.

Transgenerational Adaptation to Pollution Changes Energy Allocation in Populations of Nematodes.

Assessing the evolutionary responses of long-term exposed populations requires multigeneration ecotoxicity tests. However, the analysis of the data from these tests is not straightforward. Mechanistic models allow the in-depth analysis of the variation of physiological traits over many generations, by quantifying the trend of the physiological and toxicological parameters of the model. In the present study, a bioenergetic mechanistic model has been used to assess the evolution of two populations of the nematode Caenorhabditis elegans in control conditions or exposed to uranium. This evolutionary pressure resulted in a brood size reduction of 60%. We showed an adaptation of individuals of both populations to experimental conditions (increase of maximal length, decrease of growth rate, decrease of brood size, and decrease of the elimination rate). In addition, differential evolution was also highlighted between the two populations once the maternal effects had been diminished after several generations. Thus, individuals that were greater in maximal length, but with apparently a greater sensitivity to uranium were selected in the uranium population. In this study, we showed that this bioenergetics mechanistic modeling approach provided a precise, certain, and powerful analysis of the life strategy of C. elegans populations exposed to heavy metals resulting in an evolutionary pressure across successive generations.

An individual-based model of zebrafish population dynamics accounting for energy dynamics.

Developing population dynamics models for zebrafish is crucial in order to extrapolate from toxicity data measured at the organism level to biological levels relevant to support and enhance ecological risk assessment. To achieve this, a dynamic energy budget for individual zebrafish (DEB model) was coupled to an individual based model of zebrafish population dynamics (IBM model). Next, we fitted the DEB model to new experimental data on zebrafish growth and reproduction thus improving existing models. We further analysed the DEB-model and DEB-IBM using a sensitivity analysis. Finally, the predictions of the DEB-IBM were compared to existing observations on natural zebrafish populations and the predicted population dynamics are realistic. While our zebrafish DEB-IBM model can still be improved by acquiring new experimental data on the most uncertain processes (e.g. survival or feeding), it can already serve to predict the impact of compounds at the population level.

Pollution breaks down the genetic architecture of life history traits in Caenorhabditis elegans.

When pollution occurs in an environment, populations present suffer numerous negative and immediate effects on their life history traits. Their evolutionary potential to live in a highly stressful environment will depend on the selection pressure strengths and on the genetic structure, the trait heritability, and the genetic correlations between them. If expression of this structure changes in a stressful environment, it becomes necessary to quantify these changes to estimate the evolutionary potential of the population in this new environment. We studied the genetic structure for survival, fecundity, and early and late growth in isogenic lines of a Caenorhabditis elegans population subject to three different environments: a control environment, an environment polluted with uranium, and a high salt concentration environment. We found a heritability decrease in the polluted environments for fecundity and early growth, two traits that were the most heritable in the control environment. The genetic structure of the traits was particularly affected in the uranium polluted environment, probably due to generally low heritability in this environment. This could prevent selection from acting on traits despite the strong selection pressures exerted on them. Moreover, phenotypic traits were more strongly affected in the salt than in the uranium environment and the heritabilities were also lower in the latter environment. Consequently the decrease in heritability was not proportional to the population fitness reduction in the polluted environments. Our results suggest that pollution can alter the genetic structure of a C. elegans population, and thus modify its evolutionary potential.

Energy-based modelling to assess effects of chemicals on Caenorhabditis elegans: a case study on uranium.

The ubiquitous free-living nematode Caenorhabditis elegans is a powerful animal model for measuring the evolutionary effects of pollutants which is increasingly used in (eco) toxicological studies. Indeed, toxicity tests with this nematode can provide in a few days data on the whole life cycle. These data can be analysed with mathematical tools such as toxicokinetic-toxicodynamic modelling approaches. In this study, we assessed how a chronic exposure to a radioactive heavy metal (uranium) affects the life-cycle of C. elegans using a mechanistic model. In order to achieve this, we exposed individuals to a range of seven concentrations of uranium. Growth and reproduction were followed daily. These data were analysed with a model for nematodes based on the Dynamic Energy Budget theory, able to handle a wide range of plausible biological parameters values. Parameter estimations were performed using a Bayesian framework. Our results showed that uranium affects the assimilation of energy from food with a no-effect concentration (NEC) of 0.42 mM U which would be the threshold for effects on both growth and reproduction. The sensitivity analysis showed that the main contributors to the model output were parameters linked to the feeding processes and the actual exposure concentration. This confirms that the real exposure concentration should be measured accurately and that the feeding parameters should not be fixed, but need to be reestimated during the parameter estimation process.

Rapid evolutionary responses of life history traits to different experimentally-induced pollutions in Caenorhabditis elegans.

BACKGROUND: Anthropogenic disturbances can lead to intense selection pressures on traits and very rapid evolutionary changes. Evolutionary responses to environmental changes, in turn, reflect changes in the genetic structure of the traits, accompanied by a reduction of evolutionary potential of the populations under selection. Assessing the effects of pollutants on the evolutionary responses and on the genetic structure of populations is thus important to understanding the mechanisms that entail specialization to novel environmental conditions or resistance to novel stressors. RESULTS: Using an experimental evolution approach we exposed Caenorhabditis elegans populations to uranium, salt and alternating uranium-salt environments over 22 generations. We analyzed the changes in the average values of life history traits and the consequences at the demographic level in these populations. We also estimated the phenotypic and genetic (co)variance structure of these traits at different generations. Compared to populations in salt, populations in uranium showed a reduction of the stability of their trait structure and a higher capacity to respond by acclimation. However, the evolutionary responses of traits were generally lower for uranium compared to salt treatment; and the evolutionary responses to the alternating uranium-salt environment were between those of constant environments. Consequently, at the end of the experiment, the population rate of increase was higher in uranium than in salt and intermediate in the alternating environment. CONCLUSIONS: Our multigenerational experiment confirmed that rapid adaptation to different polluted environments may involve different evolutionary responses resulting in demographic consequences. These changes are partly explained by the effects of the pollutants on the genetic (co)variance structure of traits and the capacity of acclimation to novel conditions. Finally, our results in the alternating environment may confirm the selection of a generalist type in this environment.

Effects of chronic gamma irradiation: a multigenerational study using Caenorhabditis elegans.

The effects of chronic exposure to (137)Cs gamma radiation (dose rate ranging from 6.6 to 42.7 mGy h(-1)) on growth and reproductive ability were carried out over three generations of Caenorhabditis elegans (F0, F1, and F2). Exposure began at the egg stage for the first generation and was stopped at the end of laying of third-generation eggs (F2). At the same time, the two subsequent generations from parental exposure were returned to the control conditions (F1' and F2'). There was no radiation-induced significant effect on growth, hatchability, and cumulative number of larvae within generations. Moreover, no significant differences were found in growth parameters (hatching length, maximal length, and a constant related to growth rate) among the generations. However, a decrease in the cumulative number of larvae across exposed generations was observed between F0 and F2 at the highest dose rate (238.8 ± 15.4 and 171.2 ± 13.1 number of larvae per individual, respectively). Besides, the F1' generation was found to lay significantly fewer eggs than the F1 generation for tested dose rates 6.6, 8.1, 19.4, and 28.1 mGy h(-1). Our results confirmed that reproduction (here, cumulative number of larvae) is the most sensitive endpoint affected by chronic exposure to ionizing radiation. The results obtained revealed transgenerational effects from parental exposure in the second generation, and the second non-exposed generation was indeed more affected than the second exposed generation.

Consequences of a multi-generation exposure to uranium on Caenorhabditis elegans life parameters and sensitivity.

The assessment of toxic effects at biologically and ecologically relevant scales is an important challenge in ecosystem protection. Indeed, stressors may impact populations at much longer term than the usual timescale of toxicity tests. It is therefore important to study the evolutionary response of a population under chronic stress. We performed a 16-generation study to assess the evolution of two populations of the ubiquitous nematode Caenorhabditis elegans in control conditions or exposed to 1.1 mM of uranium. Several generations were selected to assess growth, reproduction, survival, and dose-responses relationships, through exposure to a range of concentrations (from 0 to 1.2 mM U) with all endpoints measured daily. Our experiment showed an adaptation of individuals to experimental conditions (increase of maximal length and decrease of fecundity) for both populations. We also observed an increase of adverse effects (reduction of growth and fertility) as a function of uranium concentration. We pointed out the emergence of population differentiation for reproduction traits. In contrast, no differentiation was observed on growth traits. Our results confirm the importance of assessing environmental risk related to pollutant through multi-generational studies.