A User-Friendly Kinetic Model Incorporating Regression Models for Estimating Pesticide Accumulation in Diverse Earthworm Species Across Varied Soils.

Existing models for estimating pesticide bioconcentration in earthworms exhibit limited applicability across different chemicals, soils and species which restricts their potential as an alternative, intermediate tier for risk assessment. We used experimental data from uptake and elimination studies using three earthworm species (Lumbricus terrestris, Aporrectodea caliginosa, Eisenia fetida), five pesticides (log Kow 1.69-6.63) and five soils (organic matter content = 0.972-39.9 wt %) to produce a first-order kinetic accumulation model. Model applicability was evaluated against a data set of 402 internal earthworm concentrations reported from the literature including chemical and soil properties outside the data range used to produce the model. Our models accurately predict body load using either porewater or bulk soil concentrations, with at least 93.5 and 84.3% of body load predictions within a factor of 10 and 5 of corresponding observed values, respectively. This suggests that there is no need to distinguish between porewater and soil exposure routes or to consider different uptake and elimination pathways when predicting earthworm bioconcentration. Our new model not only outperformed existing models in characterizing earthworm exposure to pesticides in soil, but it could also be integrated with models that account for earthworm movement and fluctuating soil pesticide concentrations due to degradation and transport.

Evaluation of models to estimate the bioaccumulation of organic chemicals in earthworms.

Modelling approaches to estimate the bioaccumulation of organic chemicals by earthworms are important for improving the realism in risk assessment of chemicals. However, the applicability of existing models is uncertain, partly due to the lack of independent datasets to test them. This study therefore conducted a comprehensive literature review on existing empirical and kinetic models that estimate the bioaccumulation of organic chemicals in earthworms and gathered two independent datasets from published literature to evaluate the predictive performance of these models. The Belfroid et al. (1995a) model is the best-performing empirical model, with 91.2% of earthworm body residue simulations within an order of magnitude of observation. However, this model is limited to the more hydrophobic pesticides and to the earthworm species Eisenia fetida or Eisenia andrei. The kinetic model proposed by Jager et al. (2003b) which out-performs that of Armitage and Gobas (2007), predicted uptake of PCB 153 in the earthworm E. andrei to within a factor of 10. However, the applicability of Jager et al.'s model to other organic compounds and other earthworm species is unknown due to the limited evaluation dataset. The model needs to be parameterised for different chemical, soil, and species types prior to use, which restricts its applicability to risk assessment on a broad scale. Both the empirical and kinetic models leave room for improvement in their ability to reliably predict bioaccumulation in earthworms. Whether they are fit for purpose in environmental risk assessment needs careful consideration on a case by case basis.

Three perspectives on the prediction of chemical effects in ecosystems.

The increasing production, use and emission of synthetic chemicals into the environment represents a major driver of global change. The large number of synthetic chemicals, limited knowledge on exposure patterns and effects in organisms and their interaction with other global change drivers hamper the prediction of effects in ecosystems. However, recent advances in biomolecular and computational methods are promising to improve our capacity for prediction. We delineate three idealised perspectives for the prediction of chemical effects: the suborganismal, organismal and ecological perspective, which are currently largely separated. Each of the outlined perspectives includes essential and complementary theories and tools for prediction but captures only part of the phenomenon of chemical effects. Links between the perspectives may foster predictive modelling of chemical effects in ecosystems and extrapolation between species. A major challenge for the linkage is the lack of data sets simultaneously covering different levels of biological organisation (here referred to as biological levels) as well as varying temporal and spatial scales. Synthesising the three perspectives, some central aspects and associated types of data seem particularly necessary to improve prediction. First, suborganism- and organism-level responses to chemicals need to be recorded and tested for relationships with chemical groups and organism traits. Second, metrics that are measurable at many biological levels, such as energy, need to be scrutinised for their potential to integrate across levels. Third, experimental data on the simultaneous response over multiple biological levels and spatiotemporal scales are required. These could be collected in nested and interconnected micro- and mesocosm experiments. Lastly, prioritisation of processes involved in the prediction framework needs to find a balance between simplification and capturing the essential complexity of a system. For example, in some cases, eco-evolutionary dynamics and interactions may need stronger consideration. Prediction needs to move from a static to a real-world eco-evolutionary view.

Modelling the effects of the pyrethroid insecticide cypermethrin on the life cycle of the soil dwelling annelid Enchytraeus crypticus, an original experimental design to calibrate a DEB-TKTD model.

The Dynamic Energy Budget theory (DEB) enables ecotoxicologists to model the effects of chemical stressors on organism life cycles through the coupling of toxicokinetic-toxicodynamic (TK-TD) models. While good progress has been made in the application of DEB-TKTD models for aquatic organisms, applications for soil fauna are scarce, due to the lack of dedicated experimental designs suitable for collecting the required time series effect data. Enchytraeids (Annelida: Clitellata) are model organisms in soil ecology and ecotoxicology. They are recognised as indicators of biological activity in soil, and chemical stress in terrestrial ecosystems. Despite this, the application of DEB-TKTD models to investigate the impact of chemicals has not yet been tested on this family. Here we assessed the impact of the pyrethroid insecticide cypermethrin on the life cycle of Enchytraeus crypticus. We developed an original experimental design to collect the data required for the calibration of a DEB-TKTD model for this species. E. crypticus presented a slow initial growth phase that has been successfully simulated with the addition of a size-dependent food limitation for juveniles in the DEB model. The DEB-TKTD model simulations successfully agreed with the data for all endpoints and treatments over time. The highlighted physiological mode of action (pMoA) for cypermethrin was an increase of the growth energy cost. The threshold for effects on survival was estimated at 73.14 mg kg- 1, and the threshold for effects on energy budget (i.e., sublethal effects) at 19.21 mg kg- 1. This study demonstrates that DEB-TKTD models can be successfully applied to E. crypticus as a representative soil species, and may improve the ecological risk assessment for terrestrial ecosystems, and our mechanistic understanding of chemical effects on non-target species.

Characterization of patterns and variability in the dynamics of outdoor aquatic mesocosms: exploring the capabilities and challenges in data supporting aquatic system models.

Aquatic mesocosms are complex test systems used within regulatory risk assessment of plant protection products. These model ecosystems allow researchers to capture interactions of multiple species under realistic environmental conditions. They enable assessment of direct and indirect effects of stressors at all trophic levels (i.e., from primary producers to secondary consumers) and impacts on ecosystem functions. Due to the limited ability to test the multitude of potential exposure scenarios, cross-linking aquatic mesocosm studies with virtual mesocosms, i.e., aquatic system models (ASMs), can serve to meet the demand for more environmental realism and ecological relevance in risk assessment. In this study, full control data sets from seven aquatic mesocosm studies conducted at a single test facility under GLP were analysed graphically and using descriptive statistics. Thereby, not only a comprehensive data base but also an insight into the species present, their dynamics over time, and variability in unchallenged mesocosms was observed. While consistency in dynamics could be discerned for physical and chemical parameters, variability was evident for several biological endpoints. This variability points to amplification of small differences over time as well as to stochastic processes. The outline of existing gaps and uncertainties in data leads to the estimation of what can be expected to be captured and predicted by ASMs.

Interactive effects of multiple stressors vary with consumer interactions, stressor dynamics and magnitude.

Predicting the impacts of multiple stressors is important for informing ecosystem management but is impeded by a lack of a general framework for predicting whether stressors interact synergistically, additively or antagonistically. Here, we use process-based models to study how interactions generalise across three levels of biological organisation (physiological, population and consumer-resource) for a two-stressor experiment on a seagrass model system. We found that the same underlying processes could result in synergistic, additive or antagonistic interactions, with interaction type depending on initial conditions, experiment duration, stressor dynamics and consumer presence. Our results help explain why meta-analyses of multiple stressor experimental results have struggled to identify predictors of consistently non-additive interactions in the natural environment. Experiments run over extended temporal scales, with treatments across gradients of stressor magnitude, are needed to identify the processes that underpin how stressors interact and provide useful predictions to management.

Modelling the effects of variability in feeding rate on growth - a vital step for DEB-TKTD modelling.

A major limitation of dietary toxicity studies on rodents is that food consumption often differs between treatments. The control treatment serves as a reference of how animals would have grown if not for the toxicant in their diet, but this comparison unavoidably conflates the effects of toxicity and feeding rate on body weight over time. A key advantage of toxicity models based on dynamic energy budget theory (DEB) is that chemical stress and food consumption are separate model inputs, so their effects on growth rate can be separated. To reduce data requirements, DEB convention is to derive a simplified feeding input, f, from food availability; its value ranges from zero (starvation) to one (food available ad libitum). Observed food consumption in dietary toxicity studies shows that, even in the control treatment, rats limit their food consumption, contradicting DEB assumptions regarding feeding rate. Relatively little work has focused on addressing this mismatch, but accurately modelling the effects of food intake on growth rate is essential for the effects of toxicity to be isolated. This can provide greater insight into the results of chronic toxicity studies and allows accurate extrapolation of toxic effects from laboratory data. Here we trial a new method for calculating f, based on the observed relationships between food consumption and body size in laboratory rats. We compare model results with those of the conventional DEB method and a previous effort to calculate f using observed food consumption data. Our results showed that the new method improved model accuracy while modelled reserve dynamics closely followed observed body fat percentage over time. The new method assumes that digestive efficiency increases with body size. Verifying this relationship through data collection would strengthen the basis of DEB theory and support the case for its use in ecological risk assessment.

Predicting Mixture Effects over Time with Toxicokinetic-Toxicodynamic Models (GUTS): Assumptions, Experimental Testing, and Predictive Power.

Current methods to assess the impact of chemical mixtures on organisms ignore the temporal dimension. The General Unified Threshold model for Survival (GUTS) provides a framework for deriving toxicokinetic-toxicodynamic (TKTD) models, which account for effects of toxicant exposure on survival in time. Starting from the classic assumptions of independent action and concentration addition, we derive equations for the GUTS reduced (GUTS-RED) model corresponding to these mixture toxicity concepts and go on to demonstrate their application. Using experimental binary mixture studies with Enchytraeus crypticus and previously published data for Daphnia magna and Apis mellifera, we assessed the predictive power of the extended GUTS-RED framework for mixture assessment. The extended models accurately predicted the mixture effect. The GUTS parameters on single exposure data, mixture model calibration, and predictive power analyses on mixture exposure data offer novel diagnostic tools to inform on the chemical mode of action, specifically whether a similar or dissimilar form of damage is caused by mixture components. Finally, observed deviations from model predictions can identify interactions, e.g., synergism or antagonism, between chemicals in the mixture, which are not accounted for by the models. TKTD models, such as GUTS-RED, thus offer a framework to implement new mechanistic knowledge in mixture hazard assessments.

A knowledge-based approach to designing control strategies for agricultural pests.

Chemical control of insect pests remains vital to agricultural productivity, but limited mechanistic understanding of the interactions between crop, pest and chemical control agent have restricted our capacity to respond to challenges such as the emergence of resistance and demands for tighter environmental regulation. Formulating effective control strategies that integrate chemical and non-chemical management for soil-dwelling pests is particularly problematic owing to the complexity of the soil-root-pest system and the variability that occurs between sites and between seasons. Here, we present a new concept, termed COMPASS, that integrates ecological knowledge on pest development and behaviour together with crop physiology and mechanistic understanding of chemical distribution and toxic action within the rhizosphere. The concept is tested using a two-dimensional systems model (COMPASS-Rootworm) that simulates root damage in maize from the corn rootworm Diabrotica spp. We evaluate COMPASS-Rootworm using 119 field trials that investigated the efficacy of insecticidal products and placement strategies at four sites in the USA over a period of ten years. Simulated root damage is consistent with measurements for 109 field trials. Moreover, we disentangle factors influencing root damage and pest control, including pest pressure, weather, insecticide distribution, and temporality between the emergence of crop roots and pests. The model can inform integrated pest management, optimize pest control strategies to reduce environmental burdens from pesticides, and improve the efficiency of insecticide development.

Toxicokinetic-Toxicodynamic Modeling of the Effects of Pesticides on Growth of Rattus norvegicus.

Ecological risk assessment is carried out for chemicals such as pesticides before they are released into the environment. Such risk assessment currently relies on summary statistics gathered in standardized laboratory studies. However, these statistics extract only limited information and depend on duration of exposure. Their extrapolation to realistic ecological scenarios is inherently limited. Mechanistic effect models simulate the processes underlying toxicity and so have the potential to overcome these issues. Toxicokinetic-toxicodynamic (TK-TD) models operate at the individual level, predicting the internal concentration of a chemical over time and the stress it places on an organism. TK-TD models are particularly suited to addressing the difference in exposure patterns between laboratory (constant) and field (variable) scenarios. So far, few studies have sought to predict sublethal effects of pesticide exposure to wild mammals in the field, even though such effects are of particular interest with respect to longer term exposure. We developed a TK-TD model based on the dynamic energy budget (DEB) theory, which can be parametrized and tested solely using standard regulatory studies. We demonstrate that this approach can be used effectively to predict toxic effects on the body weight of rats over time. Model predictions separate the impacts of feeding avoidance and toxic action, highlighting which was the primary driver of effects on growth. Such information is relevant to the ecological risk posed by a compound because in the environment alternative food sources may or may not be available to focal species. While this study focused on a single end point, growth, this approach could be expanded to include reproductive output. The framework developed is simple to use and could be of great utility for ecological and toxicological research as well as to risk assessors in industry and regulatory agencies.