Predicting population-level impacts of projected climate heating on a temperate freshwater fish.

Climate heating has the potential to drive changes in ecosystems at multiple levels of biological organization. Temperature directly affects the inherent physiology of plants and animals, resulting in changes in rates of photosynthesis and respiration, and trophic interactions. Predicting temperature-dependent changes in physiological and trophic processes, however, is challenging because environmental conditions and ecosystem structure vary across biogeographical regions of the globe. To realistically predict the effects of projected climate heating on wildlife populations, mechanistic tools are required to incorporate the inherent physiological effects of temperature changes, as well as the associated effects on food availability within and across comparable ecosystems. Here we applied an agent-based bioenergetics model to explore the combined effects of projected temperature increases for 2100 (1.4, 2.7, and 4.4°C), and associated changes in prey availability, on three-spined stickleback (Gasterosteus aculeatus) populations representing latitudes 50, 55, and 60°N. Our results showed a decline in population density after a simulated 1.4°C temperature increase at 50°N. In all other modeled scenarios there was an increase (inflation) in population density and biomass (per unit area) with climate heating, and this inflation increased with increasing latitude. We conclude that agent-based bioenergetics models are valuable tools in discerning the impacts of climate change on wild fish populations, which play important roles in aquatic food webs.

Heterogeneity in biological assemblages and exposure in chemical risk assessment: Exploring capabilities and challenges in methodology with two landscape-scale case studies.

Chemical exposure concentrations and the composition of ecological receptors (e.g., species) vary in space and time, resulting in landscape-scale (e.g. catchment) heterogeneity. Current regulatory, prospective chemical risk assessment frameworks do not directly address this heterogeneity because they assume that reasonably worst-case chemical exposure concentrations co-occur (spatially and temporally) with biological species that are the most sensitive to the chemical's toxicity. Whilst current approaches may parameterise fate models with site-specific data and aim to be protective, a more precise understanding of when and where chemical exposure and species sensitivity co-occur enables risk assessments to be better tailored and applied mitigation more efficient. We use two aquatic case studies covering different spatial and temporal resolution to explore how geo-referenced data and spatial tools might be used to account for landscape heterogeneity of chemical exposure and ecological assemblages in prospective risk assessment. Each case study followed a stepwise approach: i) estimate and establish spatial chemical exposure distributions using local environmental information and environmental fate models; ii) derive toxicity thresholds for different taxonomic groups and determine geo-referenced distributions of exposure-toxicity ratios (i.e., potential risk); iii) overlay risk data with the ecological status of biomonitoring sites to determine if relationships exist. We focus on demonstrating whether the integration of relevant data and potential approaches is feasible rather than making comprehensive and refined risk assessments of specific chemicals. The case studies indicate that geo-referenced predicted environmental concentration estimations can be achieved with available data, models and tools but establishing the distribution of species assemblages is reliant on the availability of a few sources of biomonitoring data and tools. Linking large sets of geo-referenced exposure and biomonitoring data is feasible but assessment of risk will often be limited by the availability of ecotoxicity data. The studies highlight the important influence that choices for aggregating data and for the selection of statistical metrics have on assessing and interpreting risk at different spatial scales and patterns of distribution within the landscape. Finally, we discuss approaches and development needs that could help to address environmental heterogeneity in chemical risk assessment.

The importance of including habitat-specific behaviour in models of butterfly movement.

Dispersal is a key process affecting population persistence and major factors affecting dispersal rates are the amounts, connectedness and properties of habitats in landscapes. We present new data on the butterfly Maniola jurtina in flower-rich and flower-poor habitats that demonstrates how movement and behaviour differ between sexes and habitat types, and how this effects consequent dispersal rates. Females had higher flight speeds than males, but their total time in flight was four times less. The effect of habitat type was strong for both sexes, flight speeds were ~ 2.5 × and ~ 1.7 × faster on resource-poor habitats for males and females, respectively, and flights were approximately 50% longer. With few exceptions females oviposited in the mown grass habitat, likely because growing grass offers better food for emerging caterpillars, but they foraged in the resource-rich habitat. It seems that females faced a trade-off between ovipositing without foraging in the mown grass or foraging without ovipositing where flowers were abundant. We show that taking account of habitat-dependent differences in activity, here categorised as flight or non-flight, is crucial to obtaining good fits of an individual-based model to observed movement. An important implication of this finding is that incorporating habitat-specific activity budgets is likely necessary for predicting longer-term dispersal in heterogeneous habitats, as habitat-specific behaviour substantially influences the mean (> 30% difference) and kurtosis (1.4 × difference) of dispersal kernels. The presented IBMs provide a simple method to explicitly incorporate known activity and movement rates when predicting dispersal in changing and heterogeneous landscapes.

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.

Capturing ecology in modeling approaches applied to environmental risk assessment of endocrine active chemicals in fish.

Endocrine active chemicals (EACs) are widespread in freshwater environments and both laboratory and field based studies have shown reproductive effects in fish at environmentally relevant exposures. Environmental risk assessment (ERA) seeks to protect wildlife populations and prospective assessments rely on extrapolation from individual-level effects established for laboratory fish species to populations of wild fish using arbitrary safety factors. Population susceptibility to chemical effects, however, depends on exposure risk, physiological susceptibility, and population resilience, each of which can differ widely between fish species. Population models have significant potential to address these shortfalls and to include individual variability relating to life-history traits, demographic and density-dependent vital rates, and behaviors which arise from inter-organism and organism-environment interactions. Confidence in population models has recently resulted in the EU Commission stating that results derived from reliable models may be considered when assessing the relevance of adverse effects of EACs at the population level. This review critically assesses the potential risks posed by EACs for fish populations, considers the ecological factors influencing these risks and explores the benefits and challenges of applying population modeling (including individual-based modeling) in ERA for EACs in fish. We conclude that population modeling offers a way forward for incorporating greater environmental relevance in assessing the risks of EACs for fishes and for identifying key risk factors through sensitivity analysis. Individual-based models (IBMs) allow for the incorporation of physiological and behavioral endpoints relevant to EAC exposure effects, thus capturing both direct and indirect population-level effects.

Modeling Effects of Honeybee Behaviors on the Distribution of Pesticide in Nectar within a Hive and Resultant in-Hive Exposure.

Recently, the causes of honeybee colony losses have been intensely studied, showing that there are multiple stressors implicated in colony declines, one stressor being the exposure to pesticides. Measuring exposure of individual bees within a hive to pesticide is at least as difficult as assessing the potential exposure of foraging bees to pesticide. We present a model to explore how heterogeneity of pesticide distribution on a comb in the hive can be driven by worker behaviors. The model contains simplified behaviors to capture the extremes of possible heterogeneity of pesticide location/deposition within the hive to compare with exposure levels estimated by averaging values across the comb. When adults feed on nectar containing the average concentration of all pesticide brought into the hive on that particular day, it is likely representative of the worst-case exposure scenario. However, for larvae, clustering of pesticide in the comb can lead to higher exposure levels than taking an average concentration in some circumstances. The potential for extrapolating the model to risk assessment is discussed.

Predicting Honeybee Colony Failure: Using the BEEHAVE Model to Simulate Colony Responses to Pesticides.

To simulate effects of pesticides on different honeybee (Apis mellifera L.) life stages, we used the BEEHAVE model to explore how increased mortalities of larvae, in-hive workers, and foragers, as well as reduced egg-laying rate, could impact colony dynamics over multiple years. Stresses were applied for 30 days, both as multiples of the modeled control mortality and as set percentage daily mortalities to assess the sensitivity of the modeled colony both to small fluctuations in mortality and periods of low to very high daily mortality. These stresses simulate stylized exposure of the different life stages to nectar and pollen contaminated with pesticide for 30 days. Increasing adult bee mortality had a much greater impact on colony survival than mortality of bee larvae or reduction in egg laying rate. Importantly, the seasonal timing of the imposed mortality affected the magnitude of the impact at colony level. In line with the LD50, we propose a new index of "lethal imposed stress": the LIS50 which indicates the level of stress on individuals that results in 50% colony mortality. This (or any LISx) is a comparative index for exploring the effects of different stressors at colony level in model simulations. While colony failure is not an acceptable protection goal, this index could be used to inform the setting of future regulatory protection goals.

A toxicokinetic model for thiamethoxam in rats: implications for higher-tier risk assessment.

Risk assessment for mammals is currently based on external exposure measurements, but effects of toxicants are better correlated with the systemically available dose than with the external administered dose. So for risk assessment of pesticides, toxicokinetics should be interpreted in the context of potential exposure in the field taking account of the timescale of exposure and individual patterns of feeding. Internal concentration is the net result of absorption, distribution, metabolism and excretion (ADME). We present a case study for thiamethoxam to show how data from ADME study on rats can be used to parameterize a body burden model which predicts body residue levels after exposures to LD50 dose either as a bolus or eaten at different feeding rates. Kinetic parameters were determined in male and female rats after an intravenous and oral administration of (14)C labelled by fitting one-compartment models to measured pesticide concentrations in blood for each individual separately. The concentration of thiamethoxam in blood over time correlated closely with concentrations in other tissues and so was considered representative of pesticide concentration in the whole body. Body burden model simulations showed that maximum body weight-normalized doses of thiamethoxam were lower if the same external dose was ingested normally than if it was force fed in a single bolus dose. This indicates lower risk to rats through dietary exposure than would be estimated from the bolus LD50. The importance of key questions that should be answered before using the body burden approach in risk assessment, data requirements and assumptions made in this study are discussed in detail.

Stakeholders' perspective on ecological modeling in environmental risk assessment of pesticides: challenges and opportunities.

The article closely examines the role of mechanistic effect models (e.g., population models) in the European environmental risk assessment (ERA) of pesticides. We studied perspectives of three stakeholder groups on population modeling in ERA of pesticides. Forty-three in-depth, semi-structured interviews were conducted with stakeholders from regulatory authorities, industry, and academia all over Europe. The key informant approach was employed in recruiting our participants. They were first identified as key stakeholders in the field and then sampled by means of a purposive sampling, where each stakeholder identified as important by others was interviewed and asked to suggest another potential participant for our study. Our results show that participants, although having different institutional backgrounds often presented similar perspectives and concerns about modeling. Analysis of repeating ideas and keywords revealed that all stakeholders had very high and often contradicting expectations from models. Still, all three groups expected effect models to become integrated in future ERA of pesticides. Main hopes associated with effect models were to reduce the amount of expensive and complex testing and field monitoring, both at the product development stage, and as an aid to develop mitigation measures. Our analysis suggests that, although the needs of stakeholders often overlapped, subtle differences and lack of trust hinder the process of introducing mechanistic effect models into ERA.

Harnessing Modeling for Assessing the Population Relevance of Exposure to Endocrine-Active Chemicals.

The presence of endocrine-active chemicals (EACs) in the environment continues to cause concern for wildlife given their potential for adverse effects on organisms. However, there is a significant lack of understanding about the potential effects of EACs on populations. This has real-world limitations for EAC management and regulation, where the aim in environmental risk assessment is to protect populations. We propose a methodological approach for the application of modeling in addressing the population relevance of EAC exposure in fish. We provide a case study with the fungicide prochloraz to illustrate how this approach could be applied. We used two population models, one for brown trout (Salmo trutta; inSTREAM) and the other for three-spined stickleback (Gasterosteus aculeatus) that met regulatory requirements for development and validation. Effects data extracted from the literature were combined with environmentally realistic exposure profiles generated with the FOCUS SW software. Population-level effects for prochloraz were observed in some modeling scenarios (hazard-threshold [HT]) but not others (dose-response), demonstrating the repercussions of making different decisions on implementation of exposure and effects. The population responses, defined through changes in abundance and biomass, of both trout and stickleback exposed to prochloraz were similar, indicating that the use of conservative effects/exposure decisions in model parameterization may be of greater significance in determining population-level adverse effects to EAC exposure than life-history characteristics. Our study supports the use of models as an effective approach to evaluate the adverse effects of EACs on fish populations. In particular, our HT parameterization is proposed for the use of population modeling in a regulatory context in accordance with Commission Regulation (EU) 2018/605. Environ Toxicol Chem 2023;42:1624-1640. © 2023 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.

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.

The use of ecological models to assess the effects of a plant protection product on ecosystem services provided by an orchard.

The objective of this case study was to explore the feasibility of using ecological models for applying an ecosystem services-based approach to environmental risk assessment using currently available data and methodologies. For this we used a 5 step approach: 1) selection of environmental scenario, 2) ecosystem service selection, 3) development of logic chains, 4) selection and application of ecological models and 5) detailed ecosystem service assessment. The study system is a European apple orchard managed according to integrated pest management principles. An organophosphate insecticide was used as the case study chemical. Four ecosystem services are included in this case study: soil quality regulation, pest control, pollination and recreation. Logic chains were developed for each ecosystem service and describe the link between toxicant effects on service providing units and ecosystem services delivery. For the soil quality regulation ecosystem service, springtails and earthworms were the service providing units, for the pest control ecosystem service it was ladybirds, for the pollination ecosystem service it was honeybees and for the recreation ecosystem service it was the meadow brown butterfly. All the ecological models addressed the spatio-temporal magnitude of the direct effects of the insecticide on the service providing units and ecological production functions were used to extrapolate these outcomes to the delivery of ecosystem services. For all ecosystem services a decision on the acceptability of the modelled and extrapolated effects on the service providing units could be made using the protection goals as set by the European Food Safety Authority (EFSA). Developing quantitative ecological production functions for extrapolation of ecosystem services delivery from population endpoints remains one of the major challenges. We feel that the use of ecological models can greatly add to this development, although the further development of existing ecological models, and of new models, is needed for this.

Mechanistic Effect Modeling of Earthworms in the Context of Pesticide Risk Assessment: Synthesis of the FORESEE Workshop.

Earthworms are important ecosystem engineers, and assessment of the risk of plant protection products toward them is part of the European environmental risk assessment (ERA). In the current ERA scheme, exposure and effects are represented simplistically and are not well integrated, resulting in uncertainty when the results are applied to ecosystems. Modeling offers a powerful tool to integrate the effects observed in lower tier laboratory studies with the environmental conditions under which exposure is expected in the field. This paper provides a summary of the (In)Field Organism Risk modEling by coupling Soil Exposure and Effect (FORESEE) Workshop held 28-30 January 2020 in Düsseldorf, Germany. This workshop focused on toxicokinetic-toxicodynamic (TKTD) and population modeling of earthworms in the context of ERA. The goal was to bring together scientists from different stakeholder groups to discuss the current state of soil invertebrate modeling and to explore how earthworm modeling could be applied to risk assessments, in particular how the different model outputs can be used in the tiered ERA approach. In support of these goals, the workshop aimed at addressing the requirements and concerns of the different stakeholder groups to support further model development. The modeling approach included 4 submodules to cover the most relevant processes for earthworm risk assessment: environment, behavior (feeding, vertical movement), TKTD, and population. Four workgroups examined different aspects of the model with relevance for risk assessment, earthworm ecology, uptake routes, and cross-species extrapolation and model testing. Here, we present the perspectives of each workgroup and highlight how the collaborative effort of participants from multidisciplinary backgrounds helped to establish common ground. In addition, we provide a list of recommendations for how earthworm TKTD modeling could address some of the uncertainties in current risk assessments for plant protection products. Integr Environ Assess Manag 2021;17:352-363. © 2020 SETAC.

Ecological models and pesticide risk assessment: current modeling practice.

Ecological risk assessments of pesticides usually focus on risk at the level of individuals, and are carried out by comparing exposure and toxicological endpoints. However, in most cases the protection goal is populations rather than individuals. On the population level, effects of pesticides depend not only on exposure and toxicity, but also on factors such as life history characteristics, population structure, timing of application, presence of refuges in time and space, and landscape structure. Ecological models can integrate such factors and have the potential to become important tools for the prediction of population-level effects of exposure to pesticides, thus allowing extrapolations, for example, from laboratory to field. Indeed, a broad range of ecological models have been applied to chemical risk assessment in the scientific literature, but so far such models have only rarely been used to support regulatory risk assessments of pesticides. To better understand the reasons for this situation, the current modeling practice in this field was assessed in the present study. The scientific literature was searched for relevant models and assessed according to nine characteristics: model type, model complexity, toxicity measure, exposure pattern, other factors, taxonomic group, risk assessment endpoint, parameterization, and model evaluation. The present study found that, although most models were of a high scientific standard, many of them would need modification before they are suitable for regulatory risk assessments. The main shortcomings of currently available models in the context of regulatory pesticide risk assessments were identified. When ecological models are applied to regulatory risk assessments, we recommend reviewing these models according to the nine characteristics evaluated here.

Behavior underpins the predictive power of a trait-based model of butterfly movement.

Dispersal ability is key to species persistence in times of environmental change. Assessing a species' vulnerability and response to anthropogenic changes is often performed using one of two methods: correlative approaches that infer dispersal potential based on traits, such as wingspan or an index of mobility derived from expert opinion, or a mechanistic modeling approach that extrapolates displacement rates from empirical data on short-term movements.Here, we compare and evaluate the success of the correlative and mechanistic approaches using a mechanistic random-walk model of butterfly movement that incorporates relationships between wingspan and sex-specific movement behaviors.The model was parameterized with new data collected on four species of butterfly in the south of England, and we observe how wingspan relates to flight speeds, turning angles, flight durations, and displacement rates.We show that flight speeds and turning angles correlate with wingspan but that to achieve good prediction of displacement even over 10 min the model must also include details of sex- and species-specific movement behaviors.We discuss what factors are likely to differentially motivate the sexes and how these could be included in mechanistic models of dispersal to improve their use in ecological forecasting.

Integrating the influence of weather into mechanistic models of butterfly movement.

BACKGROUND: Understanding the factors influencing movement is essential to forecasting species persistence in a changing environment. Movement is often studied using mechanistic models, extrapolating short-term observations of individuals to longer-term predictions, but the role of weather variables such as air temperature and solar radiation, key determinants of ectotherm activity, are generally neglected. We aim to show how the effects of weather can be incorporated into individual-based models of butterfly movement thus allowing analysis of their effects. METHODS: We constructed a mechanistic movement model and calibrated it with high precision movement data on a widely studied species of butterfly, the meadow brown (Maniola jurtina), collected over a 21-week period at four sites in southern England. Day time temperatures during the study ranged from 14.5 to 31.5 °C and solar radiation from heavy cloud to bright sunshine. The effects of weather are integrated into the individual-based model through weather-dependent scaling of parametric distributions representing key behaviours: the durations of flight and periods of inactivity. RESULTS: Flight speed was unaffected by weather, time between successive flights increased as solar radiation decreased, and flight duration showed a unimodal response to air temperature that peaked between approximately 23 °C and 26 °C. After validation, the model demonstrated that weather alone can produce a more than two-fold difference in predicted weekly displacement. CONCLUSIONS: Individual Based models provide a useful framework for integrating the effect of weather into movement models. By including weather effects we are able to explain a two-fold difference in movement rate of M. jurtina consistent with inter-annual variation in dispersal measured in population studies. Climate change for the studied populations is expected to decrease activity and dispersal rates since these butterflies already operate close to their thermal optimum.

Data on the movement behaviour of four species of grassland butterfly.

This Data in Brief article describes data on the movement behaviour of four species of grassland butterflies collected over three years and at four sites in southern England. The datasets consist of the movement tracks of Maniola jurtina, Aricia agestis, Pyronia tithonus, and Melanargia galathea, recorded using standard methods and presented as steps distances and turning angles. Sites consisted of nectar-rich field margins, meadows, and mown short turf grasslands with minimal flowers. In total, 783 unique movement tracks were collected. The data were used for analysing the movement behaviour of the species and for parameterising individual-based movement models.

Predicting impacts of chemicals from organisms to ecosystem service delivery: A case study of insecticide impacts on a freshwater lake.

Assessing and managing risks of anthropogenic activities to ecological systems is necessary to ensure sustained delivery of ecosystem services for future generations. Ecological models provide a means of quantitatively linking measured risk assessment endpoints with protection goals, by integrating potential chemical effects with species life history, ecological interactions, environmental drivers and other potential stressors. Here we demonstrate how an ecosystem modeling approach can be used to quantify insecticide-induced impacts on ecosystem services provided by a lake from toxicity data for organism-level endpoints. We used a publicly available aquatic ecosystem model AQUATOX that integrates environmental fate of chemicals and their impacts on food webs in aquatic environments. By simulating a range of exposure patterns, we illustrated how exposure to a hypothetical insecticide could affect aquatic species populations (e.g., recreational fish abundance) and environmental properties (e.g., water clarity) that would in turn affect delivery of ecosystem services. Different results were observed for different species of fish, thus the decision to manage the use of the insecticide for ecosystem services derived by anglers depends upon the favored species of fish. In our hypothetical shallow reservoir, water clarity was mostly driven by changes in food web dynamics, specifically the presence of zooplankton. In contrast to the complex response by fishing value, water clarity increased with reduced insecticide use, which produced a monotonic increase in value by waders and swimmers. Our study clearly showed the importance of considering nonlinear ecosystem feedbacks where the presence of insecticide changed the modeled food-web dynamics in unexpected ways. Our study highlights one of the main advantages of using ecological models for risk assessment, namely the ability to generalize to meaningful levels of organization and to facilitate quantitative comparisons among alternative scenarios and associated trade-offs among them while explicitly accounting for different groups of beneficiaries.

Assessing the population relevance of endocrine-disrupting effects for nontarget vertebrates exposed to plant protection products.

The European Commission intends to protect vertebrate wildlife populations by regulating plant protection product (PPP) active substances that have endocrine-disrupting properties with a hazard-based approach. In this paper we consider how the Commission's hazard-based regulation and accompanying guidance can be operationalized to ensure that a technically robust process is used to distinguish between substances with adverse population-level effects and those for which it can be demonstrated that adverse effects observed (typically in the laboratory) do not translate into adverse effects at the population level. Our approach is to use population models within the adverse outcome pathway framework to link the nonlinear relationship between adverse effects at the individual and population levels in the following way: (1) use specific protection goals for focal wildlife populations within an ecosystem services framework; (2) model the effects of changes in population-related inputs on focal species populations with individual-based population models to determine thresholds between negligible and nonnegligible (i.e., adverse) population-level effects; (3) compare these thresholds with the relevant endpoints from laboratory toxicity tests to determine whether they are likely to be exceeded at hazard-based limits or the maximum tolerated dose/concentration from the experimental studies. If the population threshold is not exceeded, then the substance should not be classified as an endocrine disruptor with population-relevant adversity unless there are other lines of evidence within a weight-of-evidence approach to challenge this. We believe this approach is scientifically robust and still addresses the political and legal requirement for a hazard-based assessment. Integr Environ Assess Manag 2019;15:278-291. © 2018 The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals, Inc. on behalf of Society of Environmental Toxicology & Chemistry (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 (17α­ethinyl 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.