Thomas G. Preuss

General Unified Threshold Model of Survival - a Toxicokinetic- Toxicodynamic Framework for Ecotoxicology

Toxicokinetic-toxicodynamic models (TKTD models) simulate the time-course of processes leading to toxic effects on organisms. Even for an apparently simple endpoint as survival, a large number of very different TKTD approaches exist. These differ in their underlying hypotheses and assumptions, although often the assumptions are not explicitly stated. Thus, our first objective was to illuminate the underlying assumptions (individual tolerance or stochastic death, speed of toxicodynamic damage recovery, threshold distribution) of various existing modeling approaches for survival and show how they relate to each other (e.g., critical body residue, critical target occupation, damage assessment, DEBtox survival, threshold damage). Our second objective was to develop a general unified threshold model for survival (GUTS), from which a large range of existing models can be derived as special cases. Specific assumptions to arrive at these special cases are made and explained. Finally, we illustrate how special cases of GUTS can be fitted to survival data. We envision that GUTS will help increase the application of TKTD models in ecotoxicological research as well as environmental risk assessment of chemicals. It unifies a wide range of previously unrelated approaches, clarifies their underlying assumptions, and facilitates further improvement in the modeling of survival under chemical stress.

Framework for traits‐based assessment in ecotoxicology

A key challenge in ecotoxicology is to assess the potential risks of chemicals to the wide range of species in the environment on the basis of laboratory toxicity data derived from a limited number of species. These species are then assumed to be suitable surrogates for a wider class of related taxa. For example, Daphnia spp. are used as the indicator species for freshwater aquatic invertebrates. Extrapolation from these datasets to natural communities poses a challenge because the extent to which test species are representative of their various taxonomic groups is often largely unknown, and different taxonomic groups and chemicals are variously represented in the available datasets. Moreover, it has been recognized that physiological and ecological factors can each be powerful determinants of vulnerability to chemical stress, thus differentially influencing toxicant effects at the population and community level. Recently it was proposed that detailed study of species traits might eventually permit better understanding, and thus prediction, of the potential for adverse effects of chemicals to a wider range of organisms than those amenable for study in the laboratory. This line of inquiry stems in part from the ecology literature, in which species traits are being used for improved understanding of how communities are constructed, as well as how communities might respond to, and recover from, disturbance (see other articles in this issue). In the present work, we develop a framework for the application of traits-based assessment. The framework is based on the population vulnerability conceptual model of Van Straalen in which vulnerability is determined by traits that can be grouped into 3 major categories, i.e., external exposure, intrinsic sensitivity, and population sustainability. Within each of these major categories, we evaluate specific traits as well as how they could contribute to the assessment of the potential effects of a toxicant on an organism. We then develop an example considering bioavailability to explore how traits could be used mechanistically to estimate vulnerability. A preliminary inventory of traits for use in ecotoxicology is included; this also identifies the availability of data to quantify those traits, in addition to an indication of the strength of linkage between the trait and the affected process. Finally, we propose a way forward for the further development of traits-based approaches in ecotoxicology.

CREAM: a European project on mechanistic effect models for ecological risk assessment of chemicals.

Current risk assessments are mainly based on ecotoxicological endpoints at the level of individual organisms, but according to the EU directives, the protection goal aims at achieving sustainable populations (European Commission 2002a, b; Forbes et al. 2009; Preuss et al. 2009a; Thorbek et al. 2009). Population-level effects depend not only on exposure and toxicity, but also on important ecological factors that are impossible to fully address empirically. At present, a number of testing approaches exist that provide endpoints on the community and the population level, respectively (nontarget arthropod and earthworm field tests, aquatic and terrestrial model ecosystem tests). However, not all fields and regulatory questions can be covered by these approaches. To fill these gaps and to enhance the scientific quality of ecological risk assessments, we suggest implementing mechanistic effect models (MEMs), as these also

Toxicokinetic‐toxicodynamic modeling of quantal and graded sublethal endpoints: A brief discussion of concepts

We report on the advantages and problems of using toxicokinetic-toxicodynamic (TKTD) models for the analysis, understanding, and simulation of sublethal effects. Only a few toxicodynamic approaches for sublethal effects are available. These differ in their effect mechanism and emphasis on linkages between endpoints. We discuss how the distinction between quantal and graded endpoints and the type of linkage between endpoints can guide model design and selection. Strengths and limitations of two main approaches and possible ways forward are outlined.

Predicting Population Dynamics from the Properties of Individuals: A Cross-Level Test of Dynamic Energy Budget Theory

Individual-based models (IBMs) are increasingly used to link the dynamics of individuals to higher levels of biological organization. Still, many IBMs are data hungry, species specific, and time-consuming to develop and analyze. Many of these issues would be resolved by using general theories of individual dynamics as the basis for IBMs. While such theories have frequently been examined at the individual level, few cross-level tests exist that also try to predict population dynamics. Here we performed a cross-level test of dynamic energy budget (DEB) theory by parameterizing an individual-based model using individual-level data of the water flea, Daphnia magna, and comparing the emerging population dynamics to independent data from population experiments. We found that DEB theory successfully predicted population growth rates and peak densities but failed to capture the decline phase. Further assumptions on food-dependent mortality of juveniles were needed to capture the population dynamics after the initial population peak. The resulting model then predicted, without further calibration, characteristic switches between small- and large-amplitude cycles, which have been observed for Daphnia. We conclude that cross-level tests help detect gaps in current individual-level theories and ultimately will lead to theory development and the establishment of a generic basis for individual-based models and ecology.

Extrapolating ecotoxicological effects from individuals to populations: a generic approach based on Dynamic Energy Budget theory and individual-based modeling.

Individual-based models (IBMs) predict how dynamics at higher levels of biological organization emerge from individual-level processes. This makes them a particularly useful tool for ecotoxicology, where the effects of toxicants are measured at the individual level but protection goals are often aimed at the population level or higher. However, one drawback of IBMs is that they require significant effort and data to design for each species. A solution would be to develop IBMs for chemical risk assessment that are based on generic individual-level models and theory. Here we show how one generic theory, Dynamic Energy Budget (DEB) theory, can be used to extrapolate the effect of toxicants measured at the individual level to effects on population dynamics. DEB is based on first principles in bioenergetics and uses a common model structure to model all species. Parameterization for a certain species is done at the individual level and allows to predict population-level effects of toxicants for a wide range of environmental conditions and toxicant concentrations. We present the general approach, which in principle can be used for all animal species, and give an example using Daphnia magna exposed to 3,4-dichloroaniline. We conclude that our generic approach holds great potential for standardized ecological risk assessment based on ecological models. Currently, available data from standard tests can directly be used for parameterization under certain circumstances, but with limited extra effort standard tests at the individual would deliver data that could considerably improve the applicability and precision of extrapolation to the population level. Specifically, the measurement of a toxicant’s effect on growth in addition to reproduction, and presenting data over time as opposed to reporting a single EC50 or dose response curve at one time point.

Chemical and natural stressors combined: from cryptic effects to population extinction.

In addition to natural stressors, populations are increasingly exposed to chemical pollutants released into the environment. We experimentally demonstrate the loss of resilience for Daphnia magna populations that are exposed to a combination of natural and chemical stressors even though effects on population size of a single stressor were cryptic, i.e. hard to detect statistically. Data on Daphnia population demography and along with model-based exploration of our predator-prey system revealed that direct trophic interactions changed the population size-structure and thereby increased population vulnerability to the toxicant which acts in a size selective manner. Moreover, population vulnerability to the toxicant increases with predator size and predation intensity whereas indirect trait-mediated interactions via predator kairomones may buffer chemical effects to a certain extent. Our study demonstrates that population size can be a poor endpoint for risk assessments of chemicals and that ignoring disturbance interactions can lead to severe underestimation of extinction risk.

A review of the tissue residue approach for organic and organometallic compounds in aquatic organisms

This paper reviews the tissue residue approach (TRA) for toxicity assessment as it applies to organic chemicals and some organometallic compounds (Sn, Hg, and Pb) in aquatic organisms. Specific emphasis was placed on evaluating key factors that influence interpretation of critical body residue (CBR) toxicity metrics including data quality issues, lipid dynamics, choice of endpoints, processes that alter toxicokinetics and toxicodynamics, phototoxicity, species- and life stage-specific sensitivities, and biotransformation. The vast majority of data available on TRA is derived from laboratory studies of acute lethal responses to organic toxicants exhibiting baseline toxicity. Application of the TRA to various baseline toxicants as well as substances with specific modes of action via receptor-mediated processes, such as chlorinated aromatic hydrocarbons, pesticides, and organometallics is discussed, as is application of TRA concepts in field assessments of tissue residues. In contrast to media-based toxicity relationships, CBR values tend to be less variable and less influenced by factors that control bioavailability and bioaccumulation, and TRA can be used to infer mechanisms of toxic action, evaluate the toxicity of mixtures, and interpret field data on bioaccumulated toxicants. If residue-effects data are not available, body residues can be estimated, as has been done using the target lipid model for baseline toxicants, to derive critical values for risk assessment. One of the primary unresolved issues complicating TRA for organic chemicals is biotransformation. Further work on the influence of biotransformation, a better understanding of contaminant lipid interactions, and an explicit understanding of the time dependency of CBRs and receptor-mediated toxicity are all required to advance this field. Additional residue-effects data on sublethal endpoints, early life stages, and a wider range of legacy and emergent contaminants will be needed to improve the ability to use TRA for organic and organometallic compounds.

Life stage- dependent bioconcentration of a nonylphenol isomer in Daphnia magna.

Bioaccumulation is an important aspect for the fate and effects of xenobiotics in the environment. In this study we used a radiolabeled nonylphenol isomer to investigate the bioconcentration in Daphnia magna at different ages. Apart from the total radioactivity we measured the metabolism of p353-NP in D. magna, to calculate the amount of p353-NP compared to total radioactivity found within the daphnids. Bioconcentration factors, based on wet weight, calculated from the rate constants for total radioactivity in neonates and adults were 4271 kg/l and 760 kg/l respectively, leading to a 5.6 deviance in bioconcentration. This deviance was even more pronounced, nearly one order of magnitude, for the p353-NP concentration with bioconcentration factors of 302 kg/l for neonates and 31 kg/l for adults. We were able to describe the bioconcentration for all daphnids by a weight-dependent one- compartment model. These results pointed out that it is not possible to compare bioconcentration experiments conducted with different substances and different sized daphnids. Additionally it was shown that it is not possible to describe the bioconcentration by measuring the total radioactivity. Metabolism of nonylphenol occurs at a very fast rate and bioconcentration is not triggered by the partition between two phases, but by metabolism. Discrimination between the two mechanisms was achieved using radiolabeled substances and a pseudo two-compartment model to describe metabolism and elimination by two rate constants which afterwards can be compared between different substances.

The minimum detectable difference (MDD) and the interpretation of treatment-related effects of pesticides in experimental ecosystems

In the European registration procedure for pesticides, microcosm and mesocosm studies are the highest aquatic experimental tier to assess their environmental effects. Evaluations of microcosm/mesocosm studies rely heavily on no observed effect concentrations (NOECs) calculated for different population-level endpoints. Ideally, a power analysis should be reported for the concentration–response relationships underlying these NOECs, as well as for measurement endpoints for which significant effects cannot be demonstrated. An indication of this statistical power can be provided a posteriori by calculated minimum detectable differences (MDDs). The MDD defines the difference between the means of a treatment and the control that must exist to detect a statistically significant effect. The aim of this paper is to expand on the Aquatic Guidance Document recently published by the European Food Safety Authority (EFSA) and to propose a procedure to report and evaluate NOECs and related MDDs in a harmonised way. In addition, decision schemes are provided on how MDDs can be used to assess the reliability of microcosm/mesocosm studies and for the derivation of effect classes used to derive regulatory acceptable concentrations. Furthermore, examples are presented to show how MDDs can be reduced by optimising experimental design and sampling techniques.