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Data Availability Statement: The stickleback and trout population model code and a log of changes from the original versions are available in the Supporting Information. The FOCUS output files from Toxic Substances in Surface Waters (TOXSWA) are also available, with details of how these were used in the population models. Supporting Information is available online at https://setac.onlinelibrary.wiley.com/doi/10.1002/etc.5640#support-information-section . Copyright © 2023 The Authors. 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. BASF SE; UK Research and Innovation. Grant Number: MR/V025570/1.

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.

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., Dispersal is often predicted using correlations with species traits. Here, we present new data that evaluate the success of the trait approach for predicting the movement behavior of four species of butterfly. We show that wingspan correlates well with flight speeds and turning angles but that to achieve good prediction of displacement over 10 min the model must also include the proportion of time spent flying.

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 BEEHAVE

Decision makers must have sufficient confidence in models if they are to influence their decisions. We propose three screening questions to critically evaluate models with respect to their purpose, organization, and evidence. They enable a more transparent, robust, and secure use of model outputs.

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.

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.

The impact on pollination of supplementing wild pollinators with commercially reared Osmia bicornis in commercial orchards growing the self-fertile sweet cherry variety “Stella” was investigated in...

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.

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), Key Points In this paper we consider how the European Commission's hazard‐based Plant Protection Product Regulation can be operationalized to distinguish between substances with adverse population‐level effects and those for which observed adverse effects do not translate into adverse population‐level effects.Our approach is to use population models within the adverse outcome pathways framework to link the nonlinear relationship between adverse effects at the individual and population levels.Only if the population threshold is exceeded should the active substance be classified as an endocrine disruptor with population‐relevant adversity, unless this classification is challenged by other lines of evidence (e.g., from field studies) within a weight‐of‐evidence approach.We believe this approach is scientifically robust and addresses the political and legal requirement for a hazard‐based assessment.

International audience; The acceptance and usefulness of simulation models are often limited by the efficiency, transparency, reproducibility, and reliability of the modelling process. We address these issues by suggesting that modellers (1) "trace" the iterative modelling process by keeping a modelling notebook corresponding to the laboratory notebooks used by empirical researchers, (2) use a standardized notebook structure and terminology based on the existing TRACE documentation framework, and (3) use their notebooks to compile TRACE documents that supplement publications and reports. These practices have benefits for model developers, users, and stakeholders: improved and efficient model design, analysis, testing, and application; increased model acceptance and reuse; and replicability and reproducibility of the model and the simulation experiments. Using TRACE terminology and structure in modelling notebooks facilitates production of TRACE documents. We explain the rationale of TRACE, provide example TRACE documents, and suggest strategies for keeping "TRACE Modelling Notebooks."Highlights:• The quality and reproducibility of modelling would be increased by keeping modelling notebooks.• Simulation modellers have not yet developed a strong culture of keeping notebooks.• We propose a standard format for notebooks based on the TRACE document framework.• We make recommendations about the type of information to be included in the notebooks.• We describe existing tools to keep notebooks.

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.

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. Electronic supplementary material The online version of this article (10.1007/s00442-020-04638-4) contains supplementary material, which is available to authorized users.

The effects of toxicant exposure on individuals captured in standard environmental risk assessments (ERA) do not necessarily translate proportionally into effects at the population-level. Population models can incorporate population resilience, physiological susceptibility, and likelihood of exposure, and can therefore be employed to extrapolate from individual- to population-level effects in ERA. Here, we present the development of an individual-based model (IBM) for the three-spined stickleback (Gasterosteus aculeatus) and its application in assessing population-level effects of disrupted male breeding behaviour after exposure to the anti-androgenic pesticide, fenitrothion. The stickleback is abundant in marine, brackish, and freshwater systems throughout Europe and their complex breeding strategy makes wild populations potentially vulnerable to the effects of endocrine disrupting chemicals (EDCs). Modelled population dynamics matched those of a UK field population and the IBM is therefore considered to be representative of a natural population. Literature derived dose-response relationships of fenitrothion-induced disruption of male breeding behaviours were applied in the IBM to assess population-level impacts. The modelled population was exposed to fenitrothion under both continuous (worst-case) and intermittent (realistic) exposure patterns and population recovery was assessed. The results suggest that disruption of male breeding behaviours at the individual-level cause impacts on population abundance under both fenitrothion exposure regimes; however, density-dependent processes can compensate for some of these effects, particularly for an intermittent exposure scenario. Our findings further demonstrate the importance of understanding life-history traits, including reproductive strategies and behaviours, and their density-dependence, when assessing the potential population-level risks of EDCs.

1. Healthy soils are crucial for sustainable food production, but tillage limits the biological regulation of essential ecosystem services. Better understanding of the mechanisms driving management effects on soil ecosystem engineers is needed to support sustainable management under environmental change. \ud 2. This paper presents the EEEworm (Energy–Environment–Earthworm) model, a mechanistic individual-based model (IBM) of Lumbricus terrestris populations. L. terrestris is a dominant earthworm species in undisturbed habitats and is closely associated with numerous ecosystem services such as water flow regulation, soil structure and crop production. In reduced tillage agriculture a decline in mechanical disturbance allows for L. terrestris proliferation, whilst the activities of L. terrestris can replace many of the soil functions provided by tillage.\ud 3. Extensive EEEworm validation with eight published studies (average R2 = 0.84) demonstrates a mechanistic approach which can extrapolate between diverse soil,\ud management and weather conditions. EEEworm simulation experiments elucidate that a combination of direct and indirect tillage effects lead to population declines in\ud tilled fields, with litter removal from the soil surface being the main driver.\ud 4. We investigate the effects of different tillage intensities under historical and projected\ud soil warming conditions, and find that future warmer and drier soils in our simulation exacerbate the effects of deep ploughing on L. terrestris population declines. These effects result from warmer and drier soil conditions increasing individual metabolic rates and tillage reducing food availability to meet energy demands.\ud 5. Synthesis and applications.\ud Pre-emptive strategies to mitigate climate change impacts on soil health in agroecosystems should focus on decreasing tillage intensity and retention of crop residues following tillage. EEEworm has the potential to benefit land managers, policy makers, risk assessors and regulators by providing a tool to forecast how soil systems respond to combinations of land management and\ud environmental change. To allow better cost-benefit analysis of contrasting land management systems a future aim of mechanistic models like EEEworm is to incorporate the links between earthworm populations, soil functions and ecosystem services.

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.

The potential environmental impacts of chemical exposures on wildlife are of growing concern. Freshwater ecosystems are vulnerable to chemical effects and wildlife populations, including fish, can be exposed to concentrations known to cause adverse effects at the individual level. Wild fish populations are also often subjected to numerous other stressors simultaneously which in temperate climates often include sustained periods of food limitation. The potential interactive effects of chemical exposures and food limitation on fish populations are however difficult to establish in the field. Mechanistic modelling approaches can be employed to help predict how the physiological effects of chemicals and food limitation on individuals may translate to population-level effects. Here an energy budget-individual-based model was developed and the control (no chemical) model was validated for the three-spined stickleback. Findings from two endocrine active chemical (EAC) case studies, (ethinyloestradiol and trenbolone) were then used to investigate how effects on individual fecundity translated into predicted population-level effects for environmentally relevant exposures. The cumulative effects of chemical exposure and food limitation were included in these analyses. Results show that effects of each EAC on the population were dependent on energy availability, and effects on population abundance were exacerbated by food limitation. Findings suggest that chemical effects and density dependent food competition interact to determine population responses to chemical exposures. Our study illustrates how mechanistic modelling approaches might usefully be applied to account for specific chemical effects, energy budgets and density-dependent competition, to provide a more integrated evaluation of population outcomes in chemical risk assessments.

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. Keywords: Movement movement-ecology random-walk behaviour Lepidoptera

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. Electronic supplementary material The online version of this article (10.1186/s40462-019-0171-7) contains supplementary material, which is available to authorized users.

Although population models are recognized as necessary tools in the ecological risk assessment of pesticides, particularly for species listed under the Endangered Species Act, their application in this context is currently limited to very few cases. The authors developed a detailed, individual-based population model for a threatened plant species, the decurrent false aster (Boltonia decurrens), for application in pesticide risk assessment. Floods and competition with other plant species are known factors that drive the species' population dynamics and were included in the model approach. The authors use the model to compare the population-level effects of 5 toxicity surrogates applied to B. decurrens under varying environmental conditions. The model results suggest that the environmental conditions under which herbicide applications occur may have a higher impact on populations than organism-level sensitivities to an herbicide within a realistic range. Indirect effects may be as important as the direct effects of herbicide applications by shifting competition strength if competing species have different sensitivities to the herbicide. The model approach provides a case study for population-level risk assessments of listed species. Population-level effects of herbicides can be assessed in a realistic and species-specific context, and uncertainties can be addressed explicitly. The authors discuss how their approach can inform the future development and application of modeling for population-level risk assessments of listed species, and ecological risk assessment in general. Environ Toxicol Chem 2017;36:480-491. © 2016 SETAC.

Losses of honeybee colonies are intensely debated and although honeybees suffer multiple stressors, the main focus has been on pesticides. As a result, the European Food Safety Authority (EFSA) revised the guidance for pesticide risk assessment for honeybees. The European Food Safety Authority reported a protection goal of negligible effect at 7% of colony size and then used the Khoury honeybee colony model to set trigger values for forager losses. However, the Khoury model is very simplistic and simulates colonies in an idealized state. In the present study, the authors demonstrate how a more realistic published honeybee model, BEEHAVE, with a few simple changes, can be used to explore pesticide risks. The results show that forage availability interacts with pesticide-induced worker losses, and colony resilience increases with forage quality. Adding alternative unexposed forage to the landscape also substantially mitigates the effects of pesticide exposure. The results indicate that EFSA's reported protection goal of 7% of colony size and triggers for daily worker losses are overly conservative. The authors conclude that forage availability is critical for colony resilience and that with adequate forage the colonies are resilient to even high levels of worker losses. However, the authors recommend setting protection goals using suboptimal forage conditions to ensure conservatism and for such suboptimal forage, a total of 20% reduction in colony size was safe. Environ Toxicol Chem 2017;36:254-264. © 2016 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals, Inc. on behalf of SETAC.

United States legislation requires the US Environmental Protection Agency to ensure that pesticide use does not cause unreasonable adverse effects on the environment, including species listed under the Endangered Species Act (ESA; hereafter referred to as listed species). Despite a long history of population models used in conservation biology and resource management and a 2013 report from the US National Research Council recommending their use, application of population models for pesticide risk assessments under the ESA has been minimal. The pertinent literature published from 2004 to 2014 was reviewed to explore the availability of population models and their frequency of use in listed species risk assessments. The models were categorized in terms of structure, taxonomic coverage, purpose, inputs and outputs, and whether the models included density dependence, stochasticity, or risk estimates, or were spatially explicit. Despite the widespread availability of models and an extensive literature documenting their use in other management contexts, only 2 of the approximately 400 studies reviewed used population models to assess the risks of pesticides to listed species. This result suggests that there is an untapped potential to adapt existing models for pesticide risk assessments under the ESA, but also that there are some challenges to do so for listed species. Key conclusions from the analysis are summarized, and priorities are recommended for future work to increase the usefulness of population models as tools for pesticide risk assessments. Environ Toxicol Chem 2016;35:1904-1913. © 2016 SETAC.

Recent guidance identified toxicokinetic-toxicodynamic (TK-TD) modeling as a relevant approach for risk assessment refinement. Yet, its added value compared to other refinement options is not detailed, and how to conduct the modeling appropriately is not explained. This case study addresses these issues through 2 examples of individual-level risk assessment for 2 hypothetical plant protection products: 1) evaluating the risk for small granivorous birds and small omnivorous mammals of a single application, as a seed treatment in winter cereals, and 2) evaluating the risk for fish after a pulsed treatment in the edge-of-field zone. Using acute test data, we conducted the first tier risk assessment as defined in the European Food Safety Authority (EFSA) guidance. When first tier risk assessment highlighted a concern, refinement options were discussed. Cases where the use of models should be preferred over other existing refinement approaches were highlighted. We then practically conducted the risk assessment refinement by using 2 different models as examples. In example 1, a TK model accounting for toxicokinetics and relevant feeding patterns in the skylark and in the wood mouse was used to predict internal doses of the hypothetical active ingredient in individuals, based on relevant feeding patterns in an in-crop situation, and identify the residue levels leading to mortality. In example 2, a TK-TD model accounting for toxicokinetics, toxicodynamics, and relevant exposure patterns in the fathead minnow was used to predict the time-course of fish survival for relevant FOCUS SW exposure scenarios and identify which scenarios might lead to mortality. Models were calibrated using available standard data and implemented to simulate the time-course of internal dose of active ingredient or survival for different exposure scenarios. Simulation results were discussed and used to derive the risk assessment refinement endpoints used for decision. Finally, we compared the “classical” risk assessment approach with the model-based approach. These comparisons showed that TK and TK-TD models can bring more realism to the risk assessment through the possibility to study realistic exposure scenarios and to simulate relevant mechanisms of effects (including delayed toxicity and recovery). Noticeably, using TK-TD models is currently the most relevant way to directly connect realistic exposure patterns to effects. We conclude with recommendations on how to properly use TK and TK-TD model in acute risk assessment for vertebrates. Integr Environ Assess Manag 2016;12:32–45. © 2015 SETAC

This brief communication reports on the main findings and recommendations from the 2014 Science Forum organized by CropLife America. The aim of the Forum was to gain a better understanding of the current status of population models and how they could be used in ecological risk assessments for threatened and endangered species potentially exposed to pesticides in the United States. The Forum panelists' recommendations are intended to assist the relevant government agencies with implementation of population modeling in future endangered species risk assessments for pesticides. The Forum included keynote presentations that provided an overview of current practices, highlighted the findings of a recent National Academy of Sciences report and its implications, reviewed the main categories of existing population models and the types of risk expressions that can be produced as model outputs, and provided examples of how population models are currently being used in different legislative contexts. The panel concluded that models developed for listed species assessments should provide quantitative risk estimates, incorporate realistic variability in environmental and demographic factors, integrate complex patterns of exposure and effects, and use baseline conditions that include present factors that have caused the species to be listed (e.g., habitat loss, invasive species) or have resulted in positive management action. Furthermore, the panel advocates for the formation of a multipartite advisory committee to provide best available knowledge and guidance related to model implementation and use, to address such needs as more systematic collection, digitization, and dissemination of data for listed species; consideration of the newest developments in good modeling practice; comprehensive review of existing population models and their applicability for listed species assessments; and development of case studies using a few well-tested models for particular species to demonstrate proof of concept. To advance our common goals, the panel recommends the following as important areas for further research and development: quantitative analysis of the causes of species listings to guide model development; systematic assessment of the relative role of toxicity versus other factors in driving pesticide risk; additional study of how interactions between density dependence and pesticides influence risk; and development of pragmatic approaches to assessing indirect effects of pesticides on listed species. Integr Environ Assess Manag 2015;11:348–354. © 2015 SETAC

For the last couple of years, European environmental risk assessment (ERA) regulations have undergone significant changes. The new 1107/2009 directive which came into effect in 2011 has triggered an on-going debate on defining specific protection goals for ERA. During this period, we conducted a study on policy change among the most influential ERA stakeholders from Europe. We interviewed 43, purposively sampled, participants from the European safety authorities, plant protection product industry and academia. Transcribed interviews underwent thematic analysis conducted separately by two coders. As we followed the advocacy coalition framework, our findings focus on stakeholders’ processes, interrelations and values behind the ERA policy change. The main challenges emerging from our analysis turned out to be the slow uptake of scientific developments into ERA and very broadly defined protection goals. The use of safety factors and cut-off criteria left risk assessors with many uncertainties. With ERA in its current form it turned out to be impossible to determine whether the current scheme is over- or under-protective. Still, the study shows that the problem of over- or under-protectiveness lies deep in the perception of stakeholders and depends greatly on their priorities. Academics strive for better ecological relevance as a priority. They have concerns that ERA is oversimplified. Regulators worry that ERA relies too much on risk mitigation and is possibly not protective enough, but at the same time, the majority believes that the assessment is well established and straightforward to follow. Industry representatives would like to see ERA based more on probabilistic risk assessment. Recent changes, according to risk assessment and management practitioners have led to an inevitable increase in complexity, which is not perceived as a positive thing, and does not necessarily translate into better risk assessment.

Extrapolating from organism-level endpoints, as generated from standard pesticide toxicity tests, to populations is an important step in threatened and endangered species risk assessments. We apply a population model for a threatened herbaceous plant species, Boltonia decurrens, to estimate the potential population-level impacts of 3 herbicides. We combine conservative exposure scenarios with dose-response relationships for growth and survival of standard test species and apply those in the species-specific model. Exposure profiles applied in the B. decurrens model were estimated using exposure modeling approaches. Spray buffer zones were simulated by using corresponding exposure profiles, and their effectiveness at mitigating simulated effects on the plant populations was assessed with the model. From simulated exposure effects scenarios that affect plant populations, the present results suggest that B. decurrens populations may be more sensitive to exposures from herbicide spray drift affecting vegetative stages than from runoff affecting early seedling survival and growth. Spray application buffer zones were shown to be effective at reducing effects on simulated populations. Our case study demonstrates how species-specific population models can be applied in pesticide risk assessment to bring organism-level endpoints, exposure assumptions, and species characteristics together in an ecologically relevant context. Environ Toxicol Chem 2018;37:1545-1555. © 2018 SETAC.

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.

Population models are used as tools in species management and conservation and are increasingly recognized as important tools in pesticide risk assessments. A wide variety of population model applications and resources on modeling techniques, evaluation and documentation can be found in the literature. In this paper, we add to these resources by introducing a systematic, transparent approach to developing population models. The decision guide that we propose is intended to help model developers systematically address data availability for their purpose and the steps that need to be taken in any model development. The resulting conceptual model includes the necessary complexity to address the model purpose on the basis of current understanding and available data. We provide specific guidance for the development of population models for herbaceous plant species in pesticide risk assessment and demonstrate the approach with an example of a conceptual model developed following the decision guide for herbicide risk assessment of Mead's milkweed (Asclepias meadii), a species listed as threatened under the US Endangered Species Act. The decision guide specific to herbaceous plants demonstrates the details, but the general approach can be adapted for other species groups and management objectives. Population models provide a tool to link population-level dynamics, species and habitat characteristics as well as information about stressors in a single approach. Developing such models in a systematic, transparent way will increase their applicability and credibility, reduce development efforts, and result in models that are readily available for use in species management and risk assessments.

Ecological modelling 364, 77-88 (2017). doi:10.1016/j.ecolmodel.2017.09.019, Published by Elsevier Science, Amsterdam [u.a.]

Simple, deterministic screening-level assessments that are highly conservative by design facilitate a rapid initial screening to determine whether a pesticide active ingredient has the potential to adversely affect threatened or endangered species. If a worst-case estimate of pesticide exposure is below a very conservative effects metric (e.g., the no observed effects concentration of the most sensitive tested surrogate species) then the potential risks are considered de minimis and unlikely to jeopardize the existence of a threatened or endangered species. Thus by design, such compounded layers of conservatism are intended to minimize potential Type II errors (failure to reject a false null hypothesis of de minimus risk), but correspondingly increase Type I errors (falsely reject a null hypothesis of de minimus risk). Because of the conservatism inherent in screening-level risk assessments, higher-tier scientific information and analyses that provide additional environmental realism can be applied in cases where a potential risk has been identified. This information includes community-level effects data, environmental fate and exposure data, monitoring data, geospatial location and proximity data, species biology data, and probabilistic exposure and population models. Given that the definition of “risk” includes likelihood and magnitude of effect, higher-tier risk assessments should use probabilistic techniques that more accurately and realistically characterize risk. Moreover, where possible and appropriate, risk assessments should focus on effects at the population and community levels of organization rather than the more traditional focus on the organism level. This document provides a review of some types of higher-tier data and assessment refinements available to more accurately and realistically evaluate potential risks of pesticide use to threatened and endangered species. Integr Environ Assess Manag 2015;11:102–117. © 2014 SETAC

Because aquatic macroinvertebrates may be exposed regularly to pesticides in edge-of-the-field water bodies, an accurate assessment of potential adverse effects and subsequent population recovery is essential. Standard effect risk assessment tools are not able to fully address the complexities arising from multiple exposure patterns, nor can they properly address the population recovery process. In the present study, we developed an individual-based model of the freshwater amphipod Gammarus pulex to evaluate the consequences of exposure to 4 compounds with different modes of action on individual survival and population recovery. Effects on survival were calculated using concentration-effect relationships and the threshold damage model (TDM), which accounts for detailed processes of toxicokinetics and toxicodynamics. Delayed effects as calculated by the TDM had a significant impact on individual survival and population recovery. We also evaluated the standard assessment of effects after short-term exposures using the 96-h concentration-effect model and the TDM, which was conservative for very short-term exposure. An integration of a TKTD submodel with a population model can be used to explore the ecological relevance of ecotoxicity endpoints in different exposure environments.

Population modelling is increasingly recognised as a useful tool for pesticide risk assessment. For vertebrates that may ingest pesticides with their food, such as woodpigeon (Columba palumbus), population models that simulate foraging behaviour explicitly can help predicting both exposure and population-level impact. Optimal foraging theory is often assumed to explain the individual-level decisions driving distributions of individuals in the field, but it may not adequately predict spatial and temporal characteristics of woodpigeon foraging because of the woodpigeons’ excellent memory, ability to fly long distances, and distinctive flocking behaviour. Here we present an individual-based model (IBM) of the woodpigeon. We used the model to predict distributions of foraging woodpigeons that use one of six alternative foraging strategies: optimal foraging, memory-based foraging and random foraging, each with or without flocking mechanisms. We used pattern-oriented modelling to determine which of the foraging strategies is best able to reproduce observed data patterns. Data used for model evaluation were gathered during a long-term woodpigeon study conducted between 1961 and 2004 and a radiotracking study conducted in 2003 and 2004, both in the UK, and are summarised here as three complex patterns: the distributions of foraging birds between vegetation types during the year, the number of fields visited daily by individuals, and the proportion of fields revisited by them on subsequent days. The model with a memory-based foraging strategy and a flocking mechanism was the only one to reproduce these three data patterns, and the optimal foraging model produced poor matches to all of them. The random foraging strategy reproduced two of the three patterns but was not able to guarantee population persistence. We conclude that with the memory-based foraging strategy including a flocking mechanism our model is realistic enough to estimate the potential exposure of woodpigeons to pesticides. We discuss how exposure can be linked to our model, and how the model could be used for risk assessment of pesticides, for example predicting exposure and effects in heterogeneous landscapes planted seasonally with a variety of crops, while accounting for differences in land use between landscapes.

Ecological risk assessments (ERAs) of toxicants are predominantly based on data from laboratory tests on individuals. However, the protection goal is generally at the population level. Ecological modelling has the potential to link individual-level effects to population-level outcomes. Here we developed an individual-based zebrafish population model to study the possible population-level relevance of toxicant-mediated changes in sex ratio. The model was structured with sub-models based on empirical data (e.g. growth, reproduction, mortality) derived from a combination of our own laboratory and field experiments, the literature and theoretical concepts. The outputs of the default model were validated against size distributions for wild populations of zebrafish sampled in Bangladesh. Sensitivity analysis showed that population abundance was most sensitive to changes in density-dependent survival and the availability of refugia for juveniles. The model was then used to determine the population-level relevance of changes in sex ratio caused by an androgenic (dihydrotestosterone) and oestrogenic (4-tert-octylphenol) substance. Both were investigated under acute (10 day) and chronic (1 year) exposure regimes. Acute exposures to the test chemicals had little effect on population-level endpoints at any of the concentrations tested. Chronic exposures decreased population abundance at higher concentrations for both chemicals and most strongly with DHT. However, these concentrations were far in excess of environmentally realistic levels. Our study demonstrated that ecological models can be applied to link laboratory derived ecotoxicity data at the individual level to impacts at the population level and in our study we found different modes of action and potencies caused different levels of population perturbation. Ecological models can therefore help in assessing the ecological relevance of different organism-level effects of toxicants aiding future environmental protection strategies.

The potential of ecological models for supporting environmental decision making is increasingly acknowledged. However, it often remains unclear whether a model is realistic and reliable enough. Good practice for developing and testing ecological models has not yet been established. Therefore, TRACE, a general framework for documenting a model's rationale, design, and testing was recently suggested. Originally TRACE was aimed at documenting good modelling practice. However, the word ‘documentation’ does not convey TRACE's urgency. Therefore, we re-define TRACE as a tool for planning, performing, and documenting good modelling practice. TRACE documents should provide convincing evidence that a model was thoughtfully designed, correctly implemented, thoroughly tested, well understood, and appropriately used for its intended purpose. TRACE documents link the science underlying a model to its application, thereby also linking modellers and model users, for example stakeholders, decision makers, and developers of policies. We report on first experiences in producing TRACE documents. We found that the original idea underlying TRACE was valid, but to make its use more coherent and efficient, an update of its structure and more specific guidance for its use are needed. The updated TRACE format follows the recently developed framework of model ‘evaludation’: the entire process of establishing model quality and credibility throughout all stages of model development, analysis, and application. TRACE thus becomes a tool for planning, documenting, and assessing model evaludation, which includes understanding the rationale behind a model and its envisaged use. We introduce the new structure and revised terminology of TRACE and provide examples.

The intensification of agricultural practices throughout the twentieth century has had large detrimental effects on biodiversity and these are likely to increase as the human population rises, with consequent pressure on land. To offset these negative impacts, agri-environment schemes have been widely implemented, offering financial incentives for land-owners to create or maintain favourable habitats that enhance or maintain biodiversity. While some evidence is available on the resulting species richness and abundance for groups such as natural predators, pollinating insects including butterflies and moths, this is costly to obtain and it is difficult to predict the effects of specific habitat designs. To alleviate this problem we here develop an individual-based model (IBM), modelling the detailed movement behaviour, foraging, and energy budget of a grassland butterfly Maniola jurtina Linn. in patches of varying dimensions and quality. The IBM is successfully validated against data on M. jurtina densities, movement behaviour, resource use, fecundity and lifespan in habitats of varying quality. We use the IBM to quantify the benefits for life-history outcomes of M. jurtina of increasing the quantity and the quality of field margins within agricultural landscapes. We find that increasing the quantity of field margin habitat from 1 to 3 ha per 100 ha, as recommended in agri-environment schemes, increases the average number of eggs laid across a two-week period by 60% and adds an extra day to the average lifespan. Similar effects are reported for variation in the quality of field margins. We discuss the implications of the result for modelling butterfly responses to management scenarios.

The aim of the present study was to examine acute and delayed effects of pulse exposure of the pyrethroid pesticide, permethrin, on precopulatory pairs of Hyalella azteca. Pairs of H. azteca were exposed to a single 1h pulse of different nominal concentrations of permethrin: 0, 0.3, 0.9 or 2.7 μg/L. During exposure, pairing behavior was observed, and during a 56 day post-exposure period the treatments were monitored for pairing behavior, survival and reproductive output. All permethrin-exposed pairs separated within minutes during exposure and shortly thereafter became immobile; however they regained mobility after transfer to clean water. The time to re-form pairs was significantly longer in all tested concentrations compared to the control, although all surviving pairs re-formed within the 56 day test period. Nevertheless not all pairs exposed to 0.9 and 2.7 μg/L reproduced. Furthermore the numbers of juveniles produced by pairs exposed to 0.9 and 2.7 μg/L, but not 0.3 μg/L, were lower throughout the entire post-exposure period compared to the control groups, and the total numbers of juveniles produced during 56 days were significantly lower in organisms exposed to 0.9 and 2.7 μg/L, but not 0.3 μg/L, compared to the control groups. The long-term effects of short-term exposure on reproductive behavior of pairs could potentially have consequences for the population dynamics of H. azteca. However, since individual-level responses can both overestimate and underestimate effects at the population level, appropriate population models are needed to reduce the uncertainty in extrapolating between these levels of biological organization.

Risk communication, especially to the general public and end users of plant protection products, is an important challenge. Currently, much of the risk communication the general public receives is ...

1. The health of managed and wild honeybee colonies appears to have declined substantially in Europe and the United States over the last decade. Sustainability of honeybee colonies is important not only for honey production, but also for pollination of crops and wild plants alongside other insect pollinators. A combination of causal factors, including parasites, pathogens, land use changes and pesticide usage, are cited as responsible for the increased colony mortality. 2. However, despite detailed knowledge of the behaviour of honeybees and their colonies, there are no suitable tools to explore the resilience mechanisms of this complex system under stress. Empirically testing all combinations of stressors in a systematic fashion is not feasible. We therefore suggest a cross-level systems approach, based on mechanistic modelling, to investigate the impacts of (and interactions between) colony and land management. 3. We review existing honeybee models that are relevant to examining the effects of different stressors on colony growth and survival. Most of these models describe honeybee colony dynamics, foraging behaviour or honeybee - varroa mite - virus interactions. 4. We found that many, but not all, processes within honeybee colonies, epidemiology and foraging are well understood and described in the models, but there is no model that couples in-hive dynamics and pathology with foraging dynamics in realistic landscapes. 5. Synthesis and applications. We describe how a new integrated model could be built to simulate multifactorial impacts on the honeybee colony system, using building blocks from the reviewed models. The development of such a tool would not only highlight empirical research priorities but also provide an important forecasting tool for policy makers and beekeepers, and we list examples of relevant applications to bee disease and landscape management decisions.

The authors present a method to predict fish survival under exposure to fluctuating concentrations and repeated pulses of a chemical stressor. The method is based on toxicokinetic-toxicodynamic modeling using the general unified threshold model of survival (GUTS) and calibrated using raw data from standard fish acute toxicity tests. The model was validated by predicting fry survival in a fish early life stage test. Application of the model was demonstrated by using Forum for Co-ordination of Pesticide Fate Models and Their Use surface water (FOCUS-SW) exposure patterns as model input and predicting the survival of fish over 485 d. Exposure patterns were also multiplied by factors of five and 10 to achieve higher exposure concentrations for fish survival predictions. Furthermore, the authors quantified how far the exposure profiles were below the onset of mortality by finding the corresponding exposure multiplication factor for each scenario. The authors calculated organism recovery times as additional characteristic of toxicity as well as number of peaks, interval length between peaks, and mean duration as additional characteristics of the exposure pattern. The authors also calculated which of the exposure patterns had the smallest and largest inherent potential toxicity. Sensitivity of the model to parameter changes depends on the exposure pattern and differs between GUTS individual tolerance and GUTS stochastic death. Possible uses of the additional information gained from modeling to inform risk assessment are discussed. Environ. Toxicol. Chem. 2013;32:954–965. © 2013 SETAC

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.

The wood mouse is a common and abundant species in agricultural landscape and is a focal species in pesticide risk assessment. Empirical studies on the ecology of the wood mouse have provided sufficient information for the species to be modelled mechanistically. An individual-based model was constructed to explicitly represent the locations and movement patterns of individual mice. This together with the schedule of pesticide application allows prediction of the risk to the population from pesticide exposure. The model included life-history traits of wood mice as well as typical landscape dynamics in agricultural farmland in the UK. The model obtains a good fit to the available population data and is fit for risk assessment purposes. It can help identify spatio-temporal situations with the largest potential risk of exposure and enables extrapolation from individual-level endpoints to population-level effects. Largest risk of exposure to pesticides was found when good crop growth in the “sink” fields coincided with high “source” population densities in the hedgerows.