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.

Resource selection probability functions for gopher tortoise: providing a management tool applicable across the species' range.

The gopher tortoise (Gopherus polyphemus) is protected by conservation policy throughout its range. Efforts to protect the species from further decline demand detailed understanding of its habitat requirements, which have not yet been rigorously defined. Current methods of identifying gopher tortoise habitat typically rely on coarse soil and vegetation classifications, and are prone to over-prediction of suitable habitat. We used a logistic resource selection probability function in an information-theoretic framework to understand the relative importance of various environmental factors to gopher tortoise habitat selection, drawing on nationwide environmental datasets, and an existing tortoise survey of the Ft. Benning military base. We applied the normalized difference vegetation index (NDVI) as an index of vegetation density, and found that NDVI was strongly negatively associated with active burrow locations. Our results showed that the most parsimonious model included variables from all candidate model types (landscape features, topography, soil, vegetation), and the model groups describing soil or vegetation alone performed poorly. These results demonstrate with a rigorous quantitative approach that although soil and vegetation are important to the gopher tortoise, they are not sufficient to describe suitable habitat. More widely, our results highlight the feasibility of constructing highly accurate habitat suitability models from data that are widely available throughout the species' range. Our study shows that the widespread availability of national environmental datasets describing important components of gopher tortoise habitat, combined with existing tortoise surveys on public lands, can be leveraged to inform knowledge of habitat suitability and target recovery efforts range-wide.

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).

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.

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.

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.

A Review of Key Features and Their Implementation in Unstructured, Structured, and Agent-Based Population Models for Ecological Risk Assessment.

Population models can provide valuable tools for ecological risk assessment (ERA). A growing amount of work on model development and documentation is now available to guide modelers and risk assessors to address different ERA questions. However, there remain misconceptions about population models for ERA, and communication between regulators and modelers can still be hindered by a lack of clarity in the underlying formalism, implementation, and complexity of different model types. In particular, there is confusion about differences among types of models and the implications of including or ignoring interactions of organisms with each other and their environment. In this review, we provide an overview of the key features represented in population models of relevance for ERA, which include density dependence, spatial heterogeneity, external drivers, stochasticity, life-history traits, behavior, energetics, and how exposure and effects are integrated in the models. We differentiate 3 broadly defined population model types (unstructured, structured, and agent-based) and explain how they can represent these key features. Depending on the ERA context, some model features will be more important than others, and this can inform model type choice, how features are implemented, and possibly the collection of additional data. We show that nearly all features can be included irrespective of formalization, but some features are more or less easily incorporated in certain model types. We also analyze how the key features have been used in published population models implemented as unstructured, structured, and agent-based models. The overall aim of this review is to increase confidence and understanding by model users and evaluators when considering the potential and adequacy of population models for use in ERA. Integr Environ Assess Manag 2021;17:521-540. © 2020 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.

Ecological models and pesticide risk assessment: current modeling practice.

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

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.

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.

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.

Benefits of dispersed central-place foraging: an individual-based model of a polydomous ant colony.

Colonies of many ant species are not confined to a single nest but inhabit several dispersed nests, a colony organization referred to as polydomy. The benefits of polydomy are not well understood. It has been proposed that increased foraging efficiency promotes polydomy. In a spatially explicit individual-based model, I compare the foraging success of monodomous and polydomous colonies in environments with varying food distributions. Multiple nests increased the colony's foraging success if food sources were randomly scattered in the environment. Monodomous and polydomous colonies did not differ in foraging success if food sources were clustered in one or three locations. These results support the hypothesis that foraging success serves as a driver for polydomous colony organization. Because transport may occur between the dispersed nests of a polydomous colony, I tested the efficiency of a simple mechanism of food exchange between nests. This mechanism, as introduced previously in the literature, proves insufficient to equalize the level of food between nests. While the importance of transport between nests remains unclear, the model results indicate that polydomy may increase the foraging success of ant colonies and that this effect may be robust across a range of food distributions.

Honey bee colony-level exposure and effects in realistic landscapes: An application of BEEHAVE simulating clothianidin residues in corn pollen.

Discerning potential effects of insecticides on honey bee colonies in field studies conducted under realistic conditions can be challenging because of concurrent interactions with other environmental conditions. Honey bee colony models can control exposures and other environmental factors, as well as assess links among pollen and nectar residues in the landscape, their influx into the colony, and the resulting exposures and effects on bees at different developmental stages. We extended the colony model BEEHAVE to represent exposure to the insecticide clothianidin via residues in pollen from treated cornfields set in real agricultural landscapes in the US Midwest. We assessed their potential risks to honey bee colonies over a 1-yr cycle. Clothianidin effects on colony strength were only observed if unrealistically high residue levels in the pollen were simulated. The landscape composition significantly impacted the collection of pollen (residue exposure) from the cornfields, resulting in higher colony-level effects in landscapes with lower proportions of semi-natural land. The application of the extended BEEHAVE model with a pollen exposure-effects module provides a case study for the application of a mechanistic honey bee colony model in pesticide risk assessment integrating the impact of a range of landscape compositions. Environ Toxicol Chem 2019;38:423-435. © 2018 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals, Inc. on behalf of SETAC.

A Hybrid Individual-Based and Food Web-Ecosystem Modeling Approach for Assessing Ecological Risks to the Topeka Shiner (Notropis topeka): A Case Study with Atrazine.

A hybrid model was used to characterize potential ecological risks posed by atrazine to the endangered Topeka shiner. The model linked a Topeka shiner individual-based bioenergetics population model (TS-IBM) to a comprehensive aquatic system model (CASMTS ) to simulate Topeka shiner population and food web dynamics for an Iowa (USA) headwater pool. Risks were estimated for monitored concentrations in Iowa, Missouri, and Nebraska (USA), and for monitored concentrations multiplied by 2, 4, and 5. Constant daily atrazine concentrations of 10, 50, 100, and 250 µg/L were assessed. Exposure-response functions were developed from published atrazine toxicity data (median effect concentrations [EC50s] and no-observed-effect concentrations). Two toxicity scenarios were developed: the first included sensitive and insensitive species of algae, and the second reduced algal EC50 values to increase atrazine sensitivity. Direct and indirect effects of atrazine on Topeka shiner prey were modeled; direct effects on Topeka shiner were not assessed. Risks were characterized as differences between population biomass values of 365-d baseline and exposure simulations. The results indicated no discernable food web effects for monitored atrazine concentrations or constant exposures of 10 µg/L on Topeka shiner populations for either toxicity scenario. Magnified monitored concentrations and higher constant concentrations produced greater modeled indirect effects on Topeka shiners. The hybrid model transparently combines species-specific and surrogate species data to estimate food web responses to environmental stressors. The model is readily updated by new data and is adaptable to other species and ecosystems. Environ Toxicol Chem 2019;38:2243-2258. © 2019 SETAC.

Adapting population models for application in pesticide risk assessment: A case study with Mead's milkweed.

Population models can facilitate assessment of potential impacts of pesticides on populations or species rather than individuals and have been identified as important tools for pesticide risk assessment of nontarget species including those listed under the Endangered Species Act. Few examples of population models developed for this specific purpose are available; however, population models are commonly used in conservation science as a tool to project the viability of populations and the long-term outcomes of management actions. We present a population model for Mead's milkweed (Asclepias meadii), a species listed as threatened under the Endangered Species Act throughout its range across the Midwestern United States. We adapted a published population model based on demographic field data for application in pesticide risk assessment. Exposure and effects were modeled as reductions of sets of vital rates in the transition matrices, simulating both lethal and sublethal effects of herbicides. Two herbicides, atrazine and mesotrione, were used as case study examples to evaluate a range of assumptions about potential exposure-effects relationships. In addition, we assessed buffers (i.e., setback distances of herbicide spray applications from the simulated habitat) as hypothetical mitigation scenarios and evaluated their influence on population-level effects in the model. The model results suggest that buffers can be effective at reducing risk from herbicide drift to plant populations. These case studies demonstrate that existing population models can be adopted and integrated with exposure and effects information for use in pesticide risk assessment. Environ Toxicol Chem 2018;37:2235-2245. © 2018 SETAC.

Assessing and mitigating simulated population-level effects of 3 herbicides to a threatened plant: Application of a species-specific population model of Boltonia decurrens.

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.

Population modeling for pesticide risk assessment of threatened species-A case study of a terrestrial plant, Boltonia decurrens.

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.

Developing population models: A systematic approach for pesticide risk assessment using herbaceous plants as an example.

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.

Assessing the risks of pesticides to threatened and endangered species using population modeling: A critical review and recommendations for future work.

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.