Mechanistic effect modeling for ecological risk assessment: where to go from here?

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

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.

Reproductive toxicity in birds predicted by physiologically-based kinetics and bioenergetics modelling

Effects on the growth and reproduction of birds are important endpoints in the environmental risk assessment (ERA) of pesticides. Toxicokinetic-toxicodynamic models based on dynamic energy budget theory (DEB) are promising tools to predict these effects mechanistically and make extrapolations relevant to ERA. However, before DEB-TKTD models are accepted as part of ERA for birds, ecotoxicological case studies are required so that stakeholders can assess their capabilities. We present such a case-study, modelling the effects of the fluopyram metabolite benzamide on the northern bobwhite quail (Colinus virginianus). We parametrised a DEB-TKTD model for the embryo stage on the basis of an egg injection study, designed to provide data for model development. We found that information on various endpoints, such as survival, growth, and yolk utilisation were needed to clearly distinguish between the performance of model variants with different TKTD assumptions. The calibration data were best explained when it was assumed that chemical uptake occurs via the yolk and that benzamide places stress on energy assimilation and mobilisation. To be able to bridge from the in vitro tests to real-life exposure, we developed a physiologically-based toxicokinetic (PBK) model for the quail and used it to predict benzamide exposure inside the eggs based on dietary exposure in a standard reproductive toxicity study. We then combined the standard DEB model with the TKTD module calibrated to the egg injection studies and used it to predict effects on hatchling and 14-day chick weight based on the exposure predicted by the PBK model. Observed weight reductions, relative to controls, were accurately predicted. Thus, we demonstrate that DEB-TKTD models, in combination with suitable experimental data and, if necessary, with an exposure model, can be used in bird ERA to predict chemical effects on reproduction.

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.

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.

Interactive Metal Mixture Toxicity to Daphnia magna Populations as an Emergent Property in a Dynamic Energy Budget Individual-Based Model

Environmental risk assessment of metal mixtures is challenging due to the large number of possible mixtures and interactions. Mixture toxicity data cannot realistically be generated for all relevant scenarios. Therefore, methods for prediction of mixture toxicity from single-metal toxicity data are needed. We tested how well toxicity of Cu-Ni-Zn mixtures to Daphnia magna populations can be predicted based on the Dynamic Energy Budget theory with an individual-based model (DEB-IBM), assuming non-interactivity of metals on the physiological level. We exposed D. magna populations to Cu, Ni, and Zn and their mixture at a fixed concentration ratio. We calibrated the DEB-IBM with single-metal data and generated blind predictions of mixture toxicity (population size over time), with account for uncertainty. We compared the predictive performance of the DEB-IBM with respect to mixture effects on population density and population growth rates with that of two reference models applied on the population level, independent action and concentration addition. Our inferred physiological modes of action (pMoA) differed from literature-reported pMoAs, raising the question of whether this is a result of different model selection approaches, intraspecific variability, or whether different pMoAs might actually drive toxicity in a population context. Observed mixture effects were concentration- and endpoint-dependent. The independent action was overall more accurate than the concentration addition but concentration addition-predicted effects on population growth rate were slightly better. The DEB-IBM most accurately predicted effects on 6-week density, including antagonistic effects at high concentrations, which emerged from non-interactivity at the physiological level. Mixture effects on initial population growth rate appear to be more difficult to predict. To explain why model accuracy is endpoint-dependent, relationships between individual-level and population-level endpoints should be illuminated. Environ Toxicol Chem 2021;40:3034-3048. © 2021 SETAC.

Robust Likelihood-Based Approach for Automated Optimization and Uncertainty Analysis of Toxicokinetic-Toxicodynamic Models

Toxicokinetic-toxicodynamic (TKTD) models offer a mechanistic understanding of individual-level toxicity over time and allow for meaningful extrapolations from laboratory tests to exposure conditions in the field. Thereby, they hold great potential for ecotoxicological studies, both in a regulatory context as well as for basic research. In contrast to mechanistic effect models at higher levels of biological organization, TKTD models can be, and generally are, parameterized by fitting them to data (results from toxicity tests). Fitting models comes with a range of statistical and numerical challenges, which may hamper the application of TKTD models in a practical setting. Especially in the context of environmental risk assessment, there is a need for robust and user-friendly software tools to automatically extract the best-fitting model parameters and quantify their uncertainty from any data set. The study presents a general outline for TKTD model analysis, rooted in likelihood-based ("frequentist") inference. The general outline is followed by a presentation of the specific algorithm that has been implemented into software for the robust and automated analysis of toxicity data for survival. However, the presented approach is more broadly applicable to low-dimensional problems. Integr Environ Assess Manag 2021;17:388-397. © 2020 SETAC.

Herbicide risk assessments of non-target terrestrial plant communities: A graphical user interface for the plant community model IBC-grass

Plants located adjacent to agricultural fields are important for maintaining biodiversity in semi-natural landscapes. To avoid undesired impacts on these plants due to herbicide application on the arable fields, regulatory risk assessments are conducted prior to registration to ensure proposed uses of plant protection products do not present an unacceptable risk. The current risk assessment approach for these non-target terrestrial plants (NTTPs) examines impacts at the individual-level as a surrogate approach for protecting the plant community due to the inherent difficulties of directly assessing population or community level impacts. However, modelling approaches are suitable higher tier tools to upscale individual-level effects to community level. IBC-grass is a sophisticated plant community model, which has already been applied in several studies. However, as it is a console application software, it was not deemed sufficiently user-friendly for risk managers and assessors to be conveniently operated without prior expertise in ecological models. Here, we present a user-friendly and open source graphical user interface (GUI) for the application of IBC-grass in regulatory herbicide risk assessment. It facilitates the use of the plant community model for predicting long-term impacts of herbicide applications on NTTP communities. The GUI offers two options to integrate herbicide impacts: (1) dose responses based on current standard experiments (acc. to testing guidelines) and (2) based on specific effect intensities. Both options represent suitable higher tier options for future risk assessments of NTTPs as well as for research on the ecological relevance of effects.

Similar individual-level responses to stressors have different population-level consequences among closely related species of trout

In this paper, we applied an individual-based model to study the population-level impacts of sub-lethal stressors affecting the metabolic pathways of three closely related trout species: Oncorhynchus mykiss (rainbow trout, RT), Salmo trutta (brown trout, BT) and Oncorhynchus calrki stomias (greenback cutthroat trout, GCT). Both RT and BT are well-studied species, and the former is widely used as a standard cold-water test species. These species are known to outcompete GCT, which is listed as threatened under the US Endangered Species Act. Our goal was to understand the extent to which stressor effects, which are often measured at the individual level, on taxonomically-related (i.e., surrogate) species can be informative of impacts on population dynamics in species that cannot be tested (e.g., listed species). When comparing stressor effects among species, we found that individual-level responses to each stressor were qualitatively comparable. Individual lengths and number of eggs decreased by similar percentages with respect to baseline, even if small quantitative differences were present depending on the physiological mode of action of the stressor. Individual-level effects in GCT were slightly greater when ingestion efficiency decreased, whereas effects in GCT and RT were greater when maintenance costs increased, and effects in BT were slightly greater when costs of growth increased. In contrast, results at the population level differed markedly among species with GCT the most impacted by sub-lethal stress effects on individual metabolism. Our findings suggest that using non-listed species to assess the risks of stressors to listed species populations may be misleading, even if the species are closely related and show similar individual-level responses. Mechanistic population models that incorporate species life history and ecology can improve inter-species extrapolation of stressor effects.

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.

How do we take the pulse of an aquatic ecosystem? Current and historical approaches to measuring ecosystem integrity

Global environmental monitoring has indicated that the structure and function of some aquatic ecosystems has been significantly altered by human activities. There are many potential causes for these changes; however, one major concern is the increasing release of anthropogenic contaminants into aquatic environments. Although toxicological responses of individual organisms are typically well characterized, few studies have focused on characterizing toxicity at the ecosystem level. In fact, because of their scale and complexity, changes in ecosystem integrity are rarely considered in assessments of risks to ecosystems. This work attempts to move the conversation forward by defining integrity of ecosystems, reviewing current and historical approaches to measuring ecosystem integrity status (e.g., structural and functional measurements), and highlighting methods that could significantly contribute to the field of ecosystem toxicology (e.g., keystone species, environmental energetics, ecotoxicological modeling, and adverse outcome pathways [AOPs]). Through a critical analysis of current and historical methodologies, the present study offers a comprehensive, conceptual framework for the assessment of risks of contaminant exposure for whole ecosystems and proposes steps to facilitate better diagnoses of the integrity of aquatic systems. Environ Toxicol Chem 2019;38:289-301. © 2018 SETAC.

Predicting impacts of chemicals from organisms to ecosystem service delivery: A case study of endocrine disruptor effects on trout

We demonstrate how mechanistic modeling can be used to predict whether and how biological responses to chemicals at (sub)organismal levels in model species (i.e., what we typically measure) translate into impacts on ecosystem service delivery (i.e., what we care about). We consider a hypothetical case study of two species of trout, brown trout (Salmo trutta; BT) and greenback cutthroat trout (Oncorhynchus clarkii stomias; GCT). These hypothetical populations live in a high-altitude river system and are exposed to human-derived estrogen (17α‑ethinyl estradiol, EE2), which is the bioactive estrogen in many contraceptives. We use the individual-based model inSTREAM to explore how seasonally varying concentrations of EE2 could influence male spawning and sperm quality. Resulting impacts on trout recruitment and the consequences of such for anglers and for the continued viability of populations of GCT (the state fish of Colorado) are explored. inSTREAM incorporates seasonally varying river flow and temperature, fishing pressure, the influence of EE2 on species-specific demography, and inter-specific competition. The model facilitates quantitative exploration of the relative importance of endocrine disruption and inter-species competition on trout population dynamics. Simulations predicted constant EE2 loading to have more impacts on GCT than BT. However, increasing removal of BT by anglers can enhance the persistence of GCT and offset some of the negative effects of EE2. We demonstrate how models that quantitatively link impacts of chemicals and other stressors on individual survival, growth, and reproduction to consequences for populations and ecosystem service delivery, can be coupled with ecosystem service valuation. The approach facilitates interpretation of toxicity data in an ecological context and gives beneficiaries of ecosystem services a more explicit role in management decisions. Although challenges remain, this type of approach may be particularly helpful for site-specific risk assessments and those in which tradeoffs and synergies among ecosystem services need to be considered.

An agent-based model of cattle grazing toxic Geyer's larkspur

By killing cattle and otherwise complicating management, the many species of larkspur (Delphinium spp.) present a serious, intractable, and complex challenge to livestock grazing management in the western United States. Among the many obstacles to improving our understanding of cattle-larkspur dynamics has been the difficulty of testing different grazing management strategies in the field, as the risk of dead animals is too great. Agent-based models (ABMs) provide an effective method of testing alternate management strategies without risk to livestock. ABMs are especially useful for modeling complex systems such as livestock grazing management, and allow for realistic bottom-up encoding of cattle behavior. Here, we introduce a spatially-explicit, behavior-based ABM of cattle grazing in a pasture with a dangerous amount of Geyer's larkspur (D. geyeri). This model tests the role of herd cohesion and stocking density in larkspur intake, finds that both are key drivers of larkspur-induced toxicosis, and indicates that alteration of these factors within realistic bounds can mitigate risk. Crucially, the model points to herd cohesion, which has received little attention in the discipline, as playing an important role in lethal acute toxicosis. As the first ABM to model grazing behavior at realistic scales, this study also demonstrates the tremendous potential of ABMs to illuminate grazing management dynamics, including fundamental aspects of livestock behavior amidst ecological heterogeneity.

Defence mechanisms: the role of physiology in current and future environmental protection paradigms

Ecological risk assessments principally rely on simplified metrics of organismal sensitivity that do not consider mechanism or biological traits. As such, they are unable to adequately extrapolate from standard laboratory tests to real-world settings, and largely fail to account for the diversity of organisms and environmental variables that occur in natural environments. However, an understanding of how stressors influence organism health can compensate for these limitations. Mechanistic knowledge can be used to account for species differences in basal biological function and variability in environmental factors, including spatial and temporal changes in the chemical, physical and biological milieu. Consequently, physiological understanding of biological function, and how this is altered by stressor exposure, can facilitate proactive, predictive risk assessment. In this perspective article, existing frameworks that utilize physiological knowledge (e.g. biotic ligand models, adverse outcomes pathways and mechanistic effect models), are outlined, and specific examples of how mechanistic understanding has been used to predict risk are highlighted. Future research approaches and data needs for extending the incorporation of physiological information into ecological risk assessments are discussed. Although the review focuses on chemical toxicants in aquatic systems, physical and biological stressors and terrestrial environments are also briefly considered.

The pros and cons of ecological risk assessment based on data from different levels of biological organization

Ecological risk assessment (ERA) is the process used to evaluate the safety of manufactured chemicals to the environment. Here we review the pros and cons of ERA across levels of biological organization, including suborganismal (e.g., biomarkers), individual, population, community, ecosystem and landscapes levels. Our review revealed that level of biological organization is often related negatively with ease at assessing cause-effect relationships, ease of high-throughput screening of large numbers of chemicals (it is especially easier for suborganismal endpoints), and uncertainty of the ERA because low levels of biological organization tend to have a large distance between their measurement (what is quantified) and assessment endpoints (what is to be protected). In contrast, level of biological organization is often related positively with sensitivity to important negative and positive feedbacks and context dependencies within biological systems, and ease at capturing recovery from adverse contaminant effects. Some endpoints did not show obvious trends across levels of biological organization, such as the use of vertebrate animals in chemical testing and ease at screening large numbers of species, and other factors lacked sufficient data across levels of biological organization, such as repeatability, variability, cost per study and cost per species of effects assessment, the latter of which might be a more defensible way to compare costs of ERAs than cost per study. To compensate for weaknesses of ERA at any particular level of biological organization, we also review mathematical modeling approaches commonly used to extrapolate effects across levels of organization. Finally, we provide recommendations for next generation ERA, submitting that if there is an ideal level of biological organization to conduct ERA, it will only emerge if ERA is approached simultaneously from the bottom of biological organization up as well as from the top down, all while employing mathematical modeling approaches where possible to enhance ERA. Because top-down ERA is unconventional, we also offer some suggestions for how it might be implemented efficaciously. We hope this review helps researchers in the field of ERA fill key information gaps and helps risk assessors identify the best levels of biological organization to conduct ERAs with differing goals.

Next-generation ecological risk assessment: Predicting risk from molecular initiation to ecosystem service delivery

Ecological risk assessment is the process of evaluating how likely it is that the environment may be impacted as the result of exposure to one or more chemicals and/or other stressors. It is not playing as large a role in environmental management decisions as it should be. A core challenge is that risk assessments often do not relate directly or transparently to protection goals. There have been exciting developments in in vitro testing and high-throughput systems that measure responses to chemicals at molecular and biochemical levels of organization, but the linkage between such responses and impacts of regulatory significance - whole organisms, populations, communities, and ecosystems - are not easily predictable. This article describes some recent developments that are directed at bridging this gap and providing more predictive models that can make robust links between what we typically measure in risk assessments and what we aim to protect.

How to use mechanistic effect models in environmental risk assessment of pesticides: Case studies and recommendations from the SETAC workshop MODELINK

Mechanistic effect models (MEMs) are useful tools for ecological risk assessment of chemicals to complement experimentation. However, currently no recommendations exist for how to use them in risk assessments. Therefore, the Society of Environmental Toxicology and Chemistry (SETAC) MODELINK workshop aimed at providing guidance for when and how to apply MEMs in regulatory risk assessments. The workshop focused on risk assessment of plant protection products under Regulation (EC) No 1107/2009 using MEMs at the organism and population levels. Realistic applications of MEMs were demonstrated in 6 case studies covering assessments for plants, invertebrates, and vertebrates in aquatic and terrestrial habitats. From the case studies and their evaluation, 12 recommendations on the future use of MEMs were formulated, addressing the issues of how to translate specific protection goals into workable questions, how to select species and scenarios to be modeled, and where and how to fit MEMs into current and future risk assessment schemes. The most important recommendations are that protection goals should be made more quantitative; the species to be modeled must be vulnerable not only regarding toxic effects but also regarding their life history and dispersal traits; the models should be as realistic as possible for a specific risk assessment question, and the level of conservatism required for a specific risk assessment should be reached by designing appropriately conservative environmental and exposure scenarios; scenarios should include different regions of the European Union (EU) and different crops; in the long run, generic MEMs covering relevant species based on representative scenarios should be developed, which will require EU-level joint initiatives of all stakeholders involved. The main conclusion from the MODELINK workshop is that the considerable effort required for making MEMs an integral part of environmental risk assessment of pesticides is worthwhile, because it will make risk assessments not only more ecologically relevant and less uncertain but also more comprehensive, coherent, and cost effective.

Predicting environmental risk: A road map for the future

Frameworks for environmental risk assessment (ERA) focus on comparing results from separate exposure and effect assessments. Exposure assessment generally relies on mechanistic fate models, whereas the effects assessment is anchored in standard test protocols and descriptive statistics. This discrepancy prevents a useful link between these two pillars of ERA, and jeopardizes the realism and efficacy of the entire process. Similar to exposure assessment, effects assessment requires a mechanistic approach to translate the output of fate models into predictions for impacts on populations and food webs. The aim of this study was to discuss (1) the central importance of the individual level, (2) different strategies of dealing with biological complexity, and (3) the role that toxicokinetic-toxicodynamic (TKTD) models, energy budgets, and molecular biology play in a mechanistic revision of the ERA framework. Consequently, an outline for a risk assessment paradigm was developed that incorporates a mechanistic effects assessment in a consistent manner, and a "roadmap for the future." Such a roadmap may play a critical role to eventually arrive at a more scientific and efficient ERA process, and needs to be used to shape our long-term research agendas.

Modeling Population-Level Consequences of Polychlorinated Biphenyl Exposure in East Greenland Polar Bears

Polychlorinated biphenyls (PCBs) can cause endocrine disruption, cancer, immunosuppression, or reproductive failure in animals. We used an individual-based model to explore whether and how PCB-associated reproductive failure could affect the dynamics of a hypothetical polar bear (Ursus maritimus) population exposed to PCBs to the same degree as the East Greenland subpopulation. Dose-response data from experimental studies on a surrogate species, the mink (Mustela vision), were used in the absence of similar data for polar bears. Two alternative types of reproductive failure in relation to maternal sum-PCB concentrations were considered: increased abortion rate and increased cub mortality. We found that the quantitative impact of PCB-induced reproductive failure on population growth rate depended largely on the actual type of reproductive failure involved. Critical potencies of the dose-response relationship for decreasing the population growth rate were established for both modeled types of reproductive failure. Comparing the model predictions of the age-dependent trend of sum-PCBs concentrations in females with actual field measurements from East Greenland indicated that it was unlikely that PCB exposure caused a high incidence of abortions in the subpopulation. However, on the basis of this analysis, it could not be excluded that PCB exposure contributes to higher cub mortality. Our results highlight the necessity for further research on the possible influence of PCBs on polar bear reproduction regarding their physiological pathway. This includes determining the exact cause of reproductive failure, i.e., in utero exposure versus lactational exposure of offspring; the timing of offspring death; and establishing the most relevant reference metrics for the dose-response relationship.

Linking feeding inhibition with reproductive impairment in Gammarus confirms the ecological relevance of feeding assays in environmental monitoring

The in situ feeding bioassay in Gammarus fossarum is recognized as a reliable tool for monitoring the toxicity of freshwater contamination. However, whether recorded feeding inhibitions can potentially provoke population-level adverse outcomes remains an open question. In the present study, the authors present an experimental study in G. fossarum, which contributes to the quantitative description of the links between feeding inhibitions and impacts on female reproductive performance. The authors studied the impacts of food deprivation on reproductive endpoints (i.e., fecundity, fertility, molt cycle) during 2 successive molting cycles. Among the main results, the authors found that food deprivation triggered a slowdown of the molting process and a reduction in fertility but no alteration to embryonic development. These reproductive impairments appeared for feeding inhibition values usually recorded in monitoring programs of environmental pollution. Using a population model translating Gammarus life-history, the authors predicted that the observed reproductive alterations predict a strong degradation of population dynamics. The present study underlines the importance of feeding inhibition in population-level risk assessment and discusses how establishing upscaling schemes based on quantitative mechanistic links between impacts at different levels of biological organization can be applied in environmental monitoring to propose an ecotoxicological assessment of water quality, which would be sensitive, specific, and ecologically relevant.

Limitations of extrapolating toxic effects on reproduction to the population level

For the ecological risk assessment of toxic chemicals, standardized tests on individuals are often used as proxies for population-level effects. Here, we address the utility of one commonly used metric, reproductive output, as a proxy for population-level effects. Because reproduction integrates the outcome of many interacting processes (e.g., feeding, growth, allocation of energy to reproduction), the observed toxic effects in a reproduction test could be due to stress on one of many processes. Although this makes reproduction a robust endpoint for detecting stress, it may mask important population-level consequences if the different physiological processes stress affects are associated with different feedback mechanisms at the population level. We therefore evaluated how an observed reduction in reproduction found in a standard reproduction test translates to effects at the population level if it is caused by hypothetical toxicants affecting different physiological processes (physiological modes of action; PMoA). For this we used two consumer–resource models: the Yodzis-Innes (YI) model, which is mathematically tractable, but requires strong assumptions of energetic equivalence among individuals as they progress through ontogeny, and an individual-based implementation of dynamic energy budget theory (DEB-IBM), which relaxes these assumptions at the expense of tractability. We identified two important feedback mechanisms controlling the link between individual- and population-level stress in the YI model. These mechanisms turned out to also be important for interpreting some of the individual-based model results; for two PMoAs, they determined the population response to stress in both models. In contrast, others stress types involved more complex feedbacks, because they asymmetrically stressed the production efficiency of reproduction and somatic growth. The feedbacks associated with different PMoAs drastically altered the link between individual- and population-level effects. For example, hypothetical stressors with different PMoAs that had equal effects on reproduction had effects ranging from a negligible decline in biomass to population extinction. Thus, reproduction tests alone are of little use for extrapolating toxicity to the population level, but we showed that the ecological relevance of standard tests could easily be improved if growth is measured along with reproduction.

More ecological ERA: incorporating natural environmental factors and animal behavior

We discuss the importance of selected natural abiotic and biotic factors in ecological risk assessment based on simplistic laboratory bioassays. Although it is impossible to include all possible natural factors in standard lower-tier ecotoxicological testing, neglecting them is not an option. Therefore, we try to identify the most important factors and advocate redesigning standard testing procedures to include theoretically most potent interactions. We also point out a few potentially important factors that have not been studied enough so far. The available data allowed us to identify temperature and O2 depletion as the most critical factors that should be included in ecotoxicity testing as soon as possible. Temporal limitations and fluctuations in food availability also appear important, but at this point more fundamental research in this area is necessary before making decisions on their inclusion in risk assessment procedures. We propose using specific experimental designs, such as Box-Behnken or Central Composite, which allow for simultaneous testing of 3 or more factors for their individual and interactive effects with greater precision and without increasing the effort and costs of tests dramatically. Factorial design can lead to more powerful tests and help to extend the validity of conclusions. Finally, ecological risk assessment procedures should include information on animal behavior, especially feeding patterns. This requires more basic studies, but already at this point adequate mechanistic effect models can be developed for some species.