Bio-QSARs 2.0: Unlocking a new level of predictive power for machine learning-based ecotoxicity predictions by exploiting chemical and biological information.

Practical, legal, and ethical reasons necessitate the development of methods to replace animal experiments. Computational techniques to acquire information that traditionally relied on animal testing are considered a crucial pillar among these so-called new approach methodologies. In this light, we recently introduced the Bio-QSAR concept for multispecies aquatic toxicity regression tasks. These machine learning models, trained on both chemical and biological information, are capable of both cross-chemical and cross-species predictions. Here, we significantly extend these models' applicability. This was realized by increasing the quantity of training data by a factor of approximately 20, accomplished by considering both additional chemicals and aquatic organisms. Additionally, variable test durations and associated random effects were accommodated by employing a machine learning algorithm that combines tree-boosting with mixed-effects modeling (i.e., Gaussian Process Boosting). We also explored various biological descriptors including Dynamic Energy Budget model parameters, taxonomic distances, as well as genus-specific traits and investigated the inclusion of mode-of-action information. Through these efforts, we developed Bio-QSARs for fish and aquatic invertebrates with exceptional predictive power (R squared of up to 0.92 on independent test sets). Moreover, we made considerable strides to make models applicable for a range of use cases in environmental risk assessment as well as research and development of chemicals. Models were made fully explainable by implementing an algorithmic multicollinearity correction combined with SHapley Additive exPlanations. Furthermore, we devised novel approaches for applicability domain construction that take feature importance into account. We are hence confident these models, which are available via open access, will make a significant contribution towards the implementation of new approach methodologies and ultimately have the potential to support "Green Chemistry" and "Green Toxicology".

Modeling temperature-dependent life-cycle toxicity of thiamethoxam in Chironomus riparius using a DEB-TKTD model.

The neonicotinoid insecticide thiamethoxam (TMX) is widely used to protect crops against insect pests. Despite some desirable properties such as its low toxicity to birds and mammals, concerns have been raised about its toxicity to non-target arthropods, including freshwater insects like chironomids. Whereas multiple studies have investigated chronic effects of neonicotinoids in chironomid larvae at standardized laboratory conditions, a better understanding of their chronic toxicity under variable temperatures and exposure is needed for coherent extrapolation from the laboratory to the field. Here, we developed a quantitative mechanistic effect model for Chironomus riparius, to simulate the species' life history under dynamic temperatures and exposure concentrations of TMX. Laboratory experiments at four different temperatures (12, 15, 20, 23 °C) and TMX concentrations between 4 and 51 µg/L were used to calibrate the model. Observed concentration-dependent effects of TMX in C. riparius included slower growth, later emergence, and higher mortality rates with increasing concentrations. Furthermore, besides a typical accelerating effect on the organisms' growth and development, higher temperatures further increased the effects associated with TMX. With some data-informed modeling decisions, most prominently the inclusion of a size dependence that makes larger animals more sensitive to TMX, the model was parametrized to convincingly reproduce the data. Experiments at both a constant (20 °C) and a dynamically increasing temperature (15-23 °C) with pulsed exposure were used to validate the model. Finally, the model was used to simulate realistic exposure conditions using two reference exposure scenarios measured in Missouri and Nebraska, utilizing a moving time window (MTW) and either a constant temperature (20 °C) or the measured temperature profiles belonging to each respective scenario. Minimum exposure multiplication factors leading to a 10% effect (EP10) in the survival at pupation, i.e., the most sensitive endpoint found in this study, were 25.67 and 21.87 for the Missouri scenario and 38.58 and 44.64 for the Nebraska scenario, when using the respective temperature assumptions. While the results illustrate that the use of real temperature scenarios does not systematically modify the EPx in the same direction (making it either more or less conservative when used as a risk indicator), the advantage of this approach is that it increases the realism and thus reduces the uncertainty associated with the model predictions.

Mechanistic population models for ecological risk assessment and decision support: The importance of good conceptual model diagrams.

The use of mechanistic population models as research and decision-support tools in ecology and ecological risk assessment (ERA) is increasing. This growth has been facilitated by advances in technology, allowing the simulation of more complex systems, as well as by standardized approaches for model development, documentation, and evaluation. Mechanistic population models are particularly useful for simulating complex systems, but the required model complexity can make them challenging to communicate. Conceptual diagrams that summarize key model elements, as well as elements that were considered but not included, can facilitate communication and understanding of models and increase their acceptance as decision-support tools. Currently, however, there are no consistent standards for creating or presenting conceptual model diagrams (CMDs), and both terminology and content vary widely. Here, we argue that greater consistency in CMD development and presentation is an important component of good modeling practice, and we provide recommendations, examples, and a free web app (pop-cmd.com) for achieving this for population models used for decision support in ERAs. Integr Environ Assess Manag 2024;00:1-9. © 2023 SETAC.

Density-dependent population regulation in freshwater fishes and small mammals: A literature review and insights for Ecological Risk Assessment.

The regulation of populations through density dependence (DD) has long been a central tenet of studies of ecological systems. As an important factor in regulating populations, DD is also crucial for understanding risks to populations from stressors, including its incorporation into population models applied for this purpose. However, study of density-dependent regulation is challenging because it can occur through various mechanisms, and their identification in the field, as well as the quantification of the consequences on individuals and populations, can be difficult. We conducted a targeted literature review specifically focusing on empirical laboratory or field studies addressing negative DD in freshwater fish and small rodent populations, two vertebrate groups considered in pesticide Ecological Risk Assessment (ERA). We found that the most commonly recognized causes of negative DD were food (63% of 19 reviewed fish studies, 40% of 25 mammal studies) or space limitations (32% of mammal studies). In addition, trophic interactions were reported as causes of population regulation, with predation shaping mostly small mammal populations (36% of the mammal studies) and cannibalism impacting freshwater fish (26%). In the case of freshwater fish, 63% of the studies were experimental (i.e., with a length of weeks or months). They generally focused on the individual-level causes and effects of DD, and had a short duration. Moreover, DD affected mostly juvenile growth and survival of fish (68%). On the other hand, studies on small mammals were mainly based on time series analyzing field population properties over longer timespans (68%). Density dependence primarily affected survival in subadult and adult mammal stages and, to a lesser extent, reproduction (60% vs. 36%). Furthermore, delayed DD was often observed (56%). We conclude by making suggestions on future research paths, providing recommendations for including DD in population models developed for ERA, and making the best use of the available data. Integr Environ Assess Manag 2023;00:1-12. © 2023 Syngenta Crop Protection. Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental Toxicology & Chemistry (SETAC).

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

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

Physiological variables in machine learning QSARs allow for both cross-chemical and cross-species predictions.

A major challenge in ecological risk assessment is estimating chemical-induced effects across taxa without species-specific testing. Where ecotoxicological data may be more challenging to gather, information on species physiology is more available for a broad range of taxa. Physiology is known to drive species sensitivity but understanding about the relative contribution of specific underlying processes is still elusive. Consequently, there remains a need to understand which physiological processes lead to differences in species sensitivity. The objective of our study was to utilize existing knowledge about organismal physiology to both understand and predict differences in species sensitivity. Machine learning models were trained to predict chemical- and species-specific endpoints as a function of both chemical fingerprints/descriptors and physiological properties represented by dynamic energy budget (DEB) parameters. We found that random forest models were able to predict chemical- and species-specific endpoints, and that DEB parameters were relatively important in the models, particularly for invertebrates. Our approach illuminates how physiological properties may drive species sensitivity, which will allow more realistic predictions of effects across species without the need for additional animal testing.

Development of a mechanistic model for analyzing avian reproduction data for pesticide risk assessment.

Mechanistic effect models are increasingly recommended as tools for refining evaluations of risk from exposure to pesticides. In the context of bird and mammal risk assessments, DEB-TKTD models have been recommended for characterizing sublethal effects at lower tiers. However, there are currently no such models. Currently, chronic, multi-generational studies are performed to characterize potential effects of pesticides on avian reproduction, but it is has not been established to what extent results from these studies can inform effect models. Here, a standard Dynamic Energy Budget (DEB) model was extended to account for the avian toxicity endpoints observed in regulatory studies. We linked this new implementation to a toxicological module to capture observed pesticide effects on reproduction via a decreased efficiency of egg production. We analysed ten reproduction studies with five different pesticides conducted with the mallard (Anas platyrhynchos) and the northern bobwhite (Colinus virginianus). The new model implementation accurately distinguished between effects on egg production from direct mechanism of toxicity and from food avoidance. Due to the specific nature of regulatory studies, model applicability for risk refinement is currently limited. We provide suggestions for next steps in model development.

Using life-history trait variation to inform ecological risk assessments for threatened and endangered plant species.

Developing population models for assessing risks to terrestrial plant species listed as threatened or endangered under the Endangered Species Act (ESA) is challenging given a paucity of data on their life histories. The purpose of this study was to develop a novel approach for identifying relatively data-rich nonlisted species that could serve as representatives for species listed under the ESA in the development of population models to inform risk assessments. We used the USDA PLANTS Database, which provides data on plants present in the US territories, to create a list of herbaceous plants. A total of 8742 species was obtained, of which 344 were listed under the ESA. Using the most up-to-date phylogeny for vascular plants in combination with a database of matrix population models for plants (COMPADRE) and cluster analyses, we investigated how listed species were distributed across the plant phylogeny, grouped listed and nonlisted species according to their life history, and identified the traits distinguishing the clusters. We performed elasticity analyses to determine the relative sensitivity of population growth rate to perturbations of species' survival, growth, and reproduction and compared these across clusters and between listed and nonlisted species. We found that listed species were distributed widely across the plant phylogeny as well as clusters, suggesting that listed species do not share a common evolution or life-history characteristics that would make them uniquely vulnerable. Lifespan and age at maturity were more important for distinguishing clusters than were reproductive traits. For clusters that were intermediate in their lifespan, listed and nonlisted species responded similarly to perturbations of their life histories. However, for clusters at either extreme of lifespan, the response to survival perturbations varied depending on conservation status. These results can be used to guide the choice of representative species for population model development in the context of ecological risk assessment. Integr Environ Assess Manag 2023;19:213-223. © 2022 The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental Toxicology & Chemistry (SETAC).

Assessment of the Vulnerability to Pesticide Exposures Across Bee Species.

In many countries, the western honey bee is used as surrogate in pesticide risk assessments for bees. However, uncertainty remains in the estimation of pesticide risk to non-Apis bees because their potential routes of exposure to pesticides, life histories, and ecologies differ from those of honey bees. We applied the vulnerability concept in pesticide risk assessment to 10 bee species including the honey bee, 2 bumble bee species, and 7 solitary bee species with different nesting strategies. Trait-based vulnerability considers the evaluation of a species at the level of both the organism (exposure and effect) and the population (recovery), which goes beyond the sensitivity of individuals to a toxicant assessed in standard laboratory toxicity studies by including effects on populations in the field. Based on expert judgment, each trait was classified by its relationship to the vulnerability to pesticide exposure, effects (intrinsic sensitivity), and population recovery. The results suggested that the non-Apis bees included in our approach are potentially more vulnerable to pesticides than the honey bee due to traits governing exposure and population recovery potential. Our analysis highlights many uncertainties related to the interaction between bee ecology and the potential exposures and population-level effects of pesticides, emphasizing the need for more research to identify suitable surrogate species for higher tier bee risk assessments. Environ Toxicol Chem 2021;40:2640-2651. © 2021 SETAC.

Assessing chemical risk within an ecosystem services framework: Implementation and added value.

An ecosystem services (ES) approach to chemical risk assessment has many potential advantages, but there are also substantial challenges regarding its implementation. We report the findings of a multi-stakeholder workshop that evaluated the feasibility of adopting an ES approach to chemical risk assessment using currently available tools and data. Also evaluated is the added value such an approach would bring to environmental decision making. The aim was to build consensus across disparate stakeholders and to co-produce a common understanding of the regulatory benefits and feasibility of implementing an ES approach in European chemicals regulation. Workshop discussions were informed by proof of concept studies and resulted in the development of a novel tiered framework for assessing chemical risk to ES delivery. There was consensus on the substantial added value of adopting an ES-based approach for regulatory decision making. Ecosystem services provide a common currency and a 'unifying approach' across environmental compartments, stressors and regulatory frameworks. The ES approach informs prioritisation of risk and remedial action and aids risk communication and risk management. It facilitates a more holistic assessment, enables ES trade-offs to be compared across alternative interventions, and supports comparative risk assessments and a socio-economic analysis of management options and decisions. Key to realising this added value is a shift away from using a single threshold value to categorise risk, towards a consideration of the exposure-effect distribution for individual ES of interest. Also required is the development of an integrated systems-level approach across regulatory frameworks and agreement on specific protection goals and scenarios for framing environmental risk assessments. The need to further develop tools for extrapolating toxicity data to service providers and ES delivery, including logic chains and ecological production functions, was highlighted. Also agreed was the need for methods and metrics for ES valuation to be used in assessing trade-offs.

Assessment of risks to listed species from the use of atrazine in the USA: a perspective.

Atrazine is a triazine herbicide used predominantly on corn, sorghum, and sugarcane in the US. Its use potentially overlaps with the ranges of listed (threatened and endangered) species. In response to registration review in the context of the Endangered Species Act, we evaluated potential direct and indirect impacts of atrazine on listed species and designated critical habitats. Atrazine has been widely studied, extensive environmental monitoring and toxicity data sets are available, and the spatial and temporal uses on major crops are well characterized. Ranges of listed species are less well-defined, resulting in overly conservative designations of "May Effect". Preferences for habitat and food sources serve to limit exposure among many listed animal species and animals are relatively insensitive. Atrazine does not bioaccumulate, further diminishing exposures among consumers and predators. Because of incomplete exposure pathways, many species can be eliminated from consideration for direct effects. It is toxic to plants, but even sensitive plants tolerate episodic exposures, such as those occurring in flowing waters. Empirical data from long-term monitoring programs and realistic field data on off-target deposition of drift indicate that many other listed species can be removed from consideration because exposures are below conservative toxicity thresholds for direct and indirect effects. Combined with recent mitigation actions by the registrant, this review serves to refine and focus forthcoming listed species assessment efforts for atrazine.Abbreviations: a.i. = Active ingredient (of a pesticide product). AEMP = Atrazine Ecological Monitoring Program. AIMS = Avian Incident Monitoring SystemArach. = Arachnid (spiders and mites). AUC = Area Under the Curve. BE = Biological Evaluation (of potential effects on listed species). BO = Biological Opinion (conclusion of the consultation between USEPA and the Services with respect to potential effects in listed species). CASM = Comprehensive Aquatic System Model. CDL = Crop Data LayerCN = field Curve Number. CRP = Conservation Reserve Program (lands). CTA = Conditioned Taste Avoidance. DAC = Diaminochlorotriazine (a metabolite of atrazine, also known by the acronym DACT). DER = Data Evaluation Record. EC25 = Concentration causing a specified effect in 25% of the tested organisms. EC50 = Concentration causing a specified effect in 50% of the tested organisms. EC50RGR = Concentration causing a 50% reduction in relative growth rate. ECOS = Environmental Conservation Online System. EDD = Estimated Daily Dose. EEC = Expected Environmental Concentration. EFED = Environmental Fate and Effects Division (of the USEPA). EFSA = European Food Safety Agency. EIIS = Ecological Incident Information System. ERA = Environmental Risk Assessment. ESA = Endangered Species Act. ESU = Evolutionarily Significant UnitsFAR = Field Application RateFIFRA = Federal Insecticide, Fungicide, and Rodenticide Act. FOIA = Freedom of Information Act (request). GSD = Genus Sensitivity Distribution. HC5 = Hazardous Concentration for ≤ 5% of species. HUC = Hydrologic Unit Code. IBM = Individual-Based Model. IDS = Incident Data System. KOC = Partition coefficient between water and organic matter in soil or sediment. KOW = Octanol-Water partition coefficient. LC50 = Concentration lethal to 50% of the tested organisms. LC-MS-MS = Liquid Chromatograph with Tandem Mass Spectrometry. LD50 = Dose lethal to 50% of the tested organisms. LAA = Likely to Adversely Affect. LOAEC = Lowest-Observed-Adverse-Effect Concentration. LOC = Level of Concern. MA = May Affect. MATC = Maximum Acceptable Toxicant Concentration. NAS = National Academy of Sciences. NCWQR = National Center of Water Quality Research. NE = No Effect. NLAA = Not Likely to Adversely Affect. NMFS = National Marine Fisheries Service. NOAA = National Oceanic and Atmospheric Administration. NOAEC = No-Observed-Adverse-Effect Concentration. NOAEL = No-Observed-Adverse-Effect Dose-Level. OECD = Organization of Economic Cooperation and Development. PNSP = Pesticide National Synthesis Project. PQ = Plastoquinone. PRZM = Pesticide Root Zone Model. PWC = Pesticide in Water Calculator. QWoE = Quantitative Weight of Evidence. RGR = Relative growth rate (of plants). RQ = Risk Quotient. RUD = Residue Unit Doses. SAP = Science Advisory Panel (of the USEPA). SGR = Specific Growth Rate. SI = Supplemental Information. SSD = Species Sensitivity Distribution. SURLAG = Surface Runoff Lag Coefficient. SWAT = Soil & Water Assessment Tool. SWCC = Surface Water Concentration Calculator. UDL = Use Data Layer (for pesticides). USDA = United States Department of Agriculture. USEPA = United States Environmental Protection Agency. USFWS = United States Fish and Wildlife Service. USGS = United States Geological Survey. WARP = Watershed Regressions for Pesticides.

Applying a Hybrid Modeling Approach to Evaluate Potential Pesticide Effects and Mitigation Effectiveness for an Endangered Fish in Simulated Oxbow Habitats.

The occurrence of some species listed under the United States' Endangered Species Act in agricultural landscapes suggests that their habitats could potentially be exposed to pesticides. However, the potential effects from such exposures on populations are difficult to estimate. Mechanistic models can provide an avenue to estimating the potential impacts on populations, considering realistic assumptions about the ecology of the species, the ecosystem it is part of, and the potential exposures within the habitat. In the present study, we applied a hybrid model of the Topeka shiner (Notropis topeka), a small endangered cyprinid fish endemic to the US Midwest, to assess the potential population-level effects of realistic exposures to a fungicide (benzovindiflupyr). The Topeka shiner populations were simulated in the context of the food web found in oxbow habitats that are the focus of ongoing habitat restoration efforts for the species. We applied realistic, time-variable exposure scenarios and represented lethal and sublethal effects to individual Topeka shiners using toxicokinetic-toxicodynamic models. With fish in general showing the highest sensitivity to the compound, direct effects on simulated Topeka shiner populations governed the population-level effects. We characterized the population-level effects of different exposure scenarios with exposure multiplication factors (EMFs) applied. The introduction of a vegetative filter strip (VFS; 15 ft; 4.6 m) between the treated area and the oxbow habitat was shown to be effective as mitigation because EMFs were 2 to 3 times higher than for the exposure scenario without VFS. Environ Toxicol Chem 2021;40:2615-2628. © 2021 SETAC.

Pop-guide: Population modeling guidance, use, interpretation, and development for ecological risk assessment.

The assimilation of population models into ecological risk assessment (ERA) has been hindered by their range of complexity, uncertainty, resource investment, and data availability. Likewise, ensuring that the models address risk assessment objectives has been challenging. Recent research efforts have begun to tackle these challenges by creating an integrated modeling framework and decision guide to aid the development of population models with respect to ERA objectives and data availability. In the framework, the trade-offs associated with the generality, realism, and precision of an assessment are used to guide the development of a population model commensurate with the protection goal. The decision guide provides risk assessors with a stepwise process to assist them in developing a conceptual model that is appropriate for the assessment objective and available data. We have merged the decision guide and modeling framework into a comprehensive approach, Population modeling Guidance, Use, Interpretation, and Development for Ecological risk assessment (Pop-GUIDE), for the development of population models for ERA that is applicable across regulatory statutes and assessment objectives. In Phase 1 of Pop-GUIDE, assessors are guided through the trade-offs of ERA generality, realism, and precision, which are translated into model objectives. In Phase 2, available data are assimilated and characterized as general, realistic, and/or precise. Phase 3 provides a series of dichotomous questions to guide development of a conceptual model that matches the complexity and uncertainty appropriate for the assessment that is in concordance with the available data. This phase guides model developers and users to ensure consistency and transparency of the modeling process. We introduce Pop-GUIDE as the most comprehensive guidance for population model development provided to date and demonstrate its use through case studies using fish as an example taxon and the US Federal Insecticide Fungicide and Rodenticide Act and Endangered Species Act as example regulatory statutes. Integr Environ Assess Manag 2021;17:767-784. © 2020 SETAC. This article has been contributed to by US Government employees and their work is in the public domain in the USA.

Sublethal effect modelling for environmental risk assessment of chemicals: Problem definition, model variants, application and challenges.

Bioenergetic models, and specifically dynamic energy budget (DEB) theory, are gathering a great deal of interest as a tool to predict the effects of realistically variable exposure to toxicants over time on an individual animal. Here we use aquatic ecological risk assessment (ERA) as the context for a review of the different model variants within DEB and the closely related DEBkiss theory (incl. reserves, ageing, size & maturity, starvation). We propose a coherent and unifying naming scheme for all current major DEB variants, explore the implications of each model's underlying assumptions in terms of its capability and complexity and analyse differences between the models (endpoints, mathematical differences, physiological modes of action). The results imply a hierarchy of model complexity which could be used to guide the implementation of simplified model variants. We provide a decision tree to support matching the simplest suitable model to a given research or regulatory question. We detail which new insights can be gained by using DEB in toxicokinetic-toxicodynamic modelling, both generally and for the specific example of ERA, and highlight open questions. Specifically, we outline a moving time window approach to assess time-variable exposure concentrations and discuss how to account for cross-generational exposure. Where possible, we suggest valuable topics for experimental and theoretical research.

Simulating Honey Bee Large-Scale Colony Feeding Studies Using the BEEHAVE Model-Part II: Analysis of Overwintering Outcomes.

Large-scale colony feeding studies (LSCFSs) aim to assess potential pesticide exposure to and effects on honey bees at the colony level. However, these studies are sometimes affected by high losses of control colonies, indicating that other stressors may impact colonies and confound the analysis of potential pesticide impacts. We assessed the study design and environmental conditions experienced by the untreated control colonies across 7 LSCFSs conducted in North Carolina (USA). Overwintering success differed considerably among the studies, as did their initial colony conditions, amount and timing of sugar feeding, landscape composition, and weather. To assess the effects of these drivers on control colonies' overwintering success, we applied the mechanistic colony model BEEHAVE. Sugar feedings and initial status of the simulated colonies were more important for fall colony condition than were landscape and weather. Colonies that had larger colony sizes and honey stores in the fall were those that began with larger honey stores, were provided more sugar, and had supplemental feedings before the fall. This information can be used to inform the standardization of a study design, which can increase the likelihood of overwintering survival of controls and help ensure that LSCFSs are comparable. Our study demonstrates how a mechanistic model can be used to inform study designs for higher tier effects studies. Environ Toxicol Chem 2020;39:2286-2297. © 2020 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.

The Comprehensive Aquatic Systems Model (CASM): Advancing Computational Capability for Ecosystem Simulation.

The comprehensive aquatic systems model (CASM), an aquatic food web-ecosystem model, was developed originally to explore relationships between food web structure and ecosystem function, and was then subsequently adapted to assess potential ecological risks posed by chemical contaminants. The present short communication presents the history of the CASM, describes the model structure, lists the outputs of the model, and introduces user-friendly versions of CASM applications that are being made publicly available. Environ Toxicol Chem 2020;39:2298-2303. © 2020 SETAC.

Simulating Honey Bee Large-Scale Colony Feeding Studies Using the BEEHAVE Model-Part I: Model Validation.

In pesticide risk assessments, semifield studies, such as large-scale colony feeding studies (LSCFSs), are conducted to assess potential risks at the honey bee colony level. However, such studies are very cost and time intensive, and high overwintering losses of untreated control hives have been observed in some studies. Honey bee colony models such as BEEHAVE may provide tools to systematically assess multiple factors influencing colony outcomes, to inform study design, and to estimate pesticide impacts under varying environmental conditions. Before they can be used reliably, models should be validated to demonstrate they can appropriately reproduce patterns observed in the field. Despite the recognized need for validation, methodologies to be used in the context of applied ecological models are not agreed on. For the parameterization, calibration, and validation of BEEHAVE, we used control data from multiple LSCFSs. We conducted detailed visual and quantitative performance analyses as a demonstration of validation methodologies. The BEEHAVE outputs showed good agreement with apiary-specific validation data sets representing the first year of the studies. However, the simulations of colony dynamics in the spring periods following overwintering were identified as less reliable. The comprehensive validation effort applied provides important insights that can inform the usability of BEEHAVE in applications related to higher tier risk assessments. In addition, the validation methodology applied could be used in a wider context of ecological models. Environ Toxicol Chem 2020;39:2269-2285. © 2020 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.

Modeling Sublethal Effects of Chemicals: Application of a Simplified Dynamic Energy Budget Model to Standard Ecotoxicity Data.

To assess ecological risks from chemical exposure, we need tools to extrapolate from the sublethal effects observed in the laboratory under constant exposure to realistic time-varying exposures. Dynamic energy budget (DEB) theory offers a mechanistic modeling approach to describe the entire life history of a single organism and the effects of toxicant exposure. We use a simplified model, which can be wholly calibrated from standard chronic bioassay data. Case studies on standard test organisms (Americamysis bahia and Pimephales promelas) are presented to demonstrate the calibration procedure, and for the second case, data are available to pseudovalidate model performance. We use these results to highlight gaps and shortcomings in the current state of the science, and we discuss how these can be overcome to maximize the potential of DEB theory in ecological risk assessment.

Correcting for Phylogenetic Autocorrelation in Species Sensitivity Distributions.

A species sensitivity distribution (SSD) is a cumulative distribution function of toxicity endpoints for a receptor group. A key assumption when deriving an SSD is that the toxicity data points are independent and identically distributed (iid). This assumption is tenuous, however, because closely related species are more likely to have similar sensitivities than are distantly related species. When the response of 1 species can be partially predicted by the response of another species, there is a dependency or autocorrelation in the data set. To date, phylogenetic relationships and the resulting dependencies in input data sets have been ignored in deriving SSDs. In this paper, we explore the importance of the phylogenetic signal in deriving SSDs using a case studies approach. The case studies involved toxicity data sets for aquatic autotrophs exposed to atrazine and aquatic and avian species exposed to chlorpyrifos. Full and partial data sets were included to explore the influences of differing phylogenetic signal strength and sample size. The phylogenetic signal was significant for some toxicity data sets (i.e., most chlorpyrifos data sets) but not for others (i.e., the atrazine data sets, the chlorpyrifos data sets for all insects, crustaceans, and birds). When a significant phylogenetic signal did occur, effective sample size was reduced. The reduction was large when the signal was strong. In spite of the reduced effective sample sizes, significant phylogenetic signals had little impact on fitted SSDs, even in the tails (e.g., hazardous concentration for 5th percentile species [HC5]). The lack of a phylogenetic signal impact occurred even when we artificially reduced original sample size and increased strength of the phylogenetic signal. We conclude that it is good statistical practice to account for the phylogenetic signal when deriving SSDs because most toxicity data sets do not meet the independence assumption. That said, SSDs and HC5s are robust to deviations from the independence assumption. Integr Environ Assess Manag 2019;00:1-13. © 2019 The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals, Inc. on behalf of Society of Environmental Toxicology & Chemistry (SETAC).

Guidance for Developing Amphibian Population Models for Ecological Risk Assessment.

Despite widespread acceptance of the utility of population modeling and advocacy of this approach for a more ecologically relevant perspective, it is not routinely incorporated in ecological risk assessments (ERA). A systematic framework for situation-specific model development is one of the major challenges to broadly adopting population models in ERA. As risk assessors confront the multitude of species and chemicals requiring evaluation, an adaptable stepwise guide for model parameterization would facilitate this process. Additional guidance on interpretation of model output and evaluating uncertainty would further contribute to establishing consensus on good modeling practices. We build on previous work that created a framework and decision guide for developing population models for ERA by focusing on data types, model structure, and extrinsic stressors relevant to anuran amphibians. Anurans have a unique life cycle with varying habitat requirements and high phenotypic plasticity. These species belong to the amphibian class, which is facing global population decline in large part due to anthropogenic stressors, including chemicals. We synthesize information from databases and literature relevant to amphibian risks to identify traits that influence exposure likelihood, inherent sensitivity, population vulnerability, and environmental constraints. We link these concerns with relevant population modeling methods and structure in order to evaluate pesticide effects with appropriate scale and parameterization. A standardized population modeling approach, with additional guidance for anuran ERA, offers an example method for quantifying population risks and evaluating long-term impacts of chemical stressors to populations. Integr Environ Assess Manag 2020;16:223-233. © 2019 SETAC.