Long-term, landscape-level assessment of aquatic pesticide exposure to identify amphibian ontological traits affecting vulnerability.

Amphibians worldwide are threatened by habitat loss, some of which is driven by a changing climate, as well as exposure to pesticides, among other causes. The timing and duration of the larval development phase vary between species, thereby influencing the relative impacts of stochastic hydroregime conditions as well as potential aquatic pesticide exposure. We describe the stages of breeding through metamorphosis for eight amphibian species, based on optimal hydroregime conditions, and use a model of pesticide fate and exposure representative of central Florida citrus groves to simulate hydrodynamics based on observed weather data over a 54-year period. Using the Pesticide in Water Calculator and Plant Assessment Tool, we estimated daily wetland depth and pyraclostrobin exposure, with label-recommended application quantities. Species' timing and duration of larval development determined the number of years of suitable hydroregime for breeding and the likelihood of exposure to peak aquatic concentrations of pyraclostrobin. Although the timing of pesticide application determined the number of surviving larvae, density-dependent constraints of wetland hydroregime also affected larval survival across species and seasons. Further defining categorical amphibian life history types and habitat requirements supports the development of screening-level assessments by incorporating environmental stochasticity at the appropriate temporal resolution. Subsequent refinement of these screening-level risk assessment strategies to include spatially explicit landscape data along with terrestrial exposure estimates would offer additional insights into species vulnerability to pesticide exposure throughout the life cycle. Computational simulation of ecologically relevant exposure scenarios, such as these, offers a more realistic interpretation of differential agrichemical risk among species based on their phenology and habits and provides a more situation-specific risk assessment perspective for threatened species. Integr Environ Assess Manag 2024;00:1-10. Published 2024. This article is a U.S. Government work and is in the public domain in the USA.

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.

Investigating Vernal Pool Fairy Shrimp Exposure to Organophosphate Pesticides: Implications for Population-Level Risk Assessment.

Vernal pool fairy shrimp, Branchinecta lynchi, is a freshwater crustacean endemic to California and Oregon, including California's Central Valley. B. lynchi is listed as a Federally Threatened species under the US Endangered Species Act, and as a vulnerable species on the IUCN Red List. Threats that may negatively impact vernal pool fairy shrimp populations include pesticide applications to agricultural land use (e.g., agrochemicals such as organophosphate pesticides) and climate changes that impact vernal pool hydrology. Pop-GUIDE (Population model Guidance, Use, Interpretation, and Development for Ecological risk assessment) is a comprehensive tool that facilitates development and implementation of population models for ecological risk assessment and can be used to document the model derivation process. We employed Pop-GUIDE to document and facilitate the development of a population model for investigating impacts of organophosphate pesticides on vernal pool fairy shrimp populations in California's Central Valley. The resulting model could be applied in combination with field assessment and laboratory-based chemical analysis to link effects from pesticide exposure to adverse outcomes in populations across their range. B. lynchi has a unique intra-annual life cycle that is largely dependent upon environmental conditions. Future deployment of this population model should include complex scenarios consisting of multiple stressors, whereby the model is used to examine scenarios that combine chemical stress resulting from exposure to pesticides and climate changes.

A QSAR-ICE-SSD model prediction of the PNECs for alkylphenol substances and application in ecological risk assessment for rivers of a megacity.

Alkylphenols (APs) are ubiquitous and generally present in higher residue levels in the environment. The present work focuses on the development of a set of in silico models to predict the aquatic toxicity of APs with incomplete/unknown toxicity data in aquatic environments. To achieve this, a QSAR-ICE-SSD model was constructed for aquatic organisms by combining quantitative structure-activity relationship (QSAR), interspecies correlation estimation (ICE), and species sensitivity distribution (SSD) models in order to obtain the hazardous concentrations (HCs) of selected APs. The research indicated that the keywords "alkylphenol" and "nonylphenol" were most commonly studied. The selected ICE models were robust (R2: 0.70-0.99; p-value < 0.01). All models had a high reliability cross- validation success rates (>75%), and the HC5 predicted with the QSAR-ICE-SSD model was 2-fold than that derived with measured experimental data. The HC5 values demonstrated nearly linear decreasing trend from 2-MP to 4-HTP, while the decreasing trend from 4-HTP to 4-DP became shallower, indicates that the toxicity of APs to aquatic organisms increases with the addition of alkyl carbon chain lengths. The ecological risks assessment (ERA) of APs revealed that aquatic organisms were at risk from exposure to 4-NP at most river stations (the highest risk quotient (RQ) = 1.51), with the highest relative risk associated with 2.9% of 4-NP detected in 82.9% of the sampling sites. The targeted APs posed potential ecological risks in the Yongding and Beiyun River according to the mixture ERA. The potential application of QSAR-ICE-SSD models could satisfy the immediate needs for HC5 derivations without the need for additional in vivo testing.

Moving beyond Risk Quotients: Advancing Ecological Risk Assessment to Reflect Better, More Robust and Relevant Methods.

Under standard guidance for conducting Ecological Risk Assessments (ERAs), the risks of chemical exposure to diverse organisms are most often based on deterministic point estimates evaluated against safety-factor-based levels of concern (LOCs). While the science and guidance for mechanistic effect models (e.g., demographic, population, and agent-based) have long been demonstrated to provide more ecologically relevant effect endpoints upon which risk can be evaluated, their application in ERAs has been limited, particularly in the US. This special issue highlights the state of the science in effect modeling for ERAs through demonstrated application of the recently published Population modeling Guidance, Use, Interpretation, and Development for ERA (Pop-GUIDE). We introduce this issue with a perspective on why it is critical to move past the current application of deterministic endpoints and LOCs. We demonstrate how the current, widely used approaches contain extensive uncertainty that could be reduced considerably by applying models that account for species life histories and other important endogenous and exogenous factors critical to species sustainability. We emphasize that it is long past time to incorporate better, more robust, and ecologically relevant effect models into ERAs, particularly for chronic risk determination. The papers in this special issue demonstrate how mechanistic models that follow Pop-GUIDE better inform ERAs compared to the current standard practice.

Assessment of a New Approach Method for Grouped Chemical Hazard Estimation: The Toxicity-Normalized Species Sensitivity Distribution (SSDn).

New approach methods are being developed to address the challenges of reducing animal testing and assessing risks to the diversity of species in aquatic environments for the multitude of chemicals with minimal toxicity data. The toxicity-normalized species sensitivity distribution (SSDn) approach is a novel method for developing compound-specific hazard concentrations using data for toxicologically similar chemicals. This approach first develops an SSDn composed of acute toxicity values for multiple related chemicals that have been normalized by the sensitivity of a common species tested with each compound. A toxicity-normalized hazard concentration (HC5n) is then computed from the fifth percentile of the SSDn. Chemical-specific HC5 values are determined by back-calculating the HC5n using the chemical-specific sensitivity of the normalization species. A comparison of the SSDn approach with the single-chemical SSD method was conducted by using data for nine transition metals to generate and compare HC5 values between the two methods. We identified several guiding principles for this method that, when applied, resulted in accurate HC5 values based on comparisons with results from single-metal SSDs. The SSDn approach shows promise for developing statistically robust hazard concentrations when adequate taxonomic representation is not available for a single chemical.

Using metabolomic profiling to inform use of surrogate species in ecological risk assessment practices.

The U.S. EPA frequently uses avian or fish toxicity data to set protective standards for amphibians in ecological risk assessments. However, this approach does not always adequately represent aquatic-dwelling and terrestrial-phase amphibian exposure data. For instance, it is accepted that early life stage tests for fish are typically sensitive enough to protect larval amphibians, however, metamorphosis from tadpole to a terrestrial-phase adult relies on endocrine cues that are less prevalent in fish but essential for amphibian life stage transitions. These differences suggest that more robust approaches are needed to adequately elucidate the impacts of pesticide exposure in amphibians across critical life stages. Therefore, in the current study, methodology is presented that can be applied to link the perturbations in the metabolomic response of larval zebrafish (Danio rerio), a surrogate species frequently used in ecotoxicological studies, to those of African clawed frog (Xenopus laevis) tadpoles following exposure to three high-use pesticides, bifenthrin, chlorothalonil, or trifluralin. Generally, D. rerio exhibited greater metabolic perturbations in both number and magnitude across the pesticide exposures as opposed to X. laevis. This suggests that screening ecological risk assessment surrogate toxicity data would sufficiently protect amphibians at the single life stage studied but care needs to be taken to understand the suite of metabolic requirements of each developing species. Ultimately, methodology presented, and data gathered herein will help inform the applicability of metabolomic profiling in establishing the risk pesticide exposure poses to amphibians and potentially other non-target species.

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.

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.

A framework for linking population model development with ecological risk assessment objectives.

The value of models that link organism-level impacts to the responses of a population in ecological risk assessments (ERAs) has been demonstrated extensively over the past few decades. There is little debate about the utility of these models to translate multiple organism-level endpoints into a holistic interpretation of effect to the population; however, there continues to be a struggle for actual application of these models as a common practice in ERA. Although general frameworks for developing models for ERA have been proposed, there is limited guidance on when models should be used, in what form, and how to interpret model output to inform the risk manager's decision. We propose a framework for developing and applying population models in regulatory decision making that focuses on trade-offs of generality, realism, and precision for both ERAs and models. We approach the framework development from the perspective of regulators aimed at defining the needs of specific models commensurate with the assessment objective. We explore why models are not widely used by comparing their requirements and limitations with the needs of regulators. Using a series of case studies under specific regulatory frameworks, we classify ERA objectives by trade-offs of generality, realism, and precision and demonstrate how the output of population models developed with these same trade-offs informs the ERA objective. We examine attributes for both assessments and models that aid in the discussion of these trade-offs. The proposed framework will assist risk assessors and managers to identify models of appropriate complexity and to understand the utility and limitations of a model's output and associated uncertainty in the context of their assessment goals. Integr Environ Assess Manag 2018;14:369-380. Published 2017. This article is a US Government work and is in the public domain in the USA.

Spatially explicit assessment of estuarine fish after Deepwater Horizon oil spill: trade-off in complexity and parsimony.

Evaluating long-term contaminant effects on wildlife populations depends on spatial information about habitat quality, heterogeneity in contaminant exposure, and sensitivities and distributions of species integrated into a systems modeling approach. Rarely is this information readily available, making it difficult to determine the applicability of realistic models to quantify population-level risks. To evaluate the trade-offs between data demands and increased specificity of spatially explicit models for population-level risk assessments, we developed a model for a standard toxicity test species, the sheepshead minnow (Cyprinodon variegatus), exposed to oil contamination following the Deepwater Horizon oil spill and compared the output with various levels of model complexity to a standard risk quotient approach. The model uses habitat and fish occupancy data collected over five sampling periods throughout 2008-2010 in Pensacola and Choctawhatchee Bays, Florida, USA, to predict species distribution, field-collected and publically available data on oil distribution and concentration, and chronic toxicity data from laboratory assays applied to a matrix population model. The habitat suitability model established distribution of fish within Barataria Bay, Louisiana, USA, and the population model projected the dynamics of the species in the study area over a 5-yr period (October 2009-September 2014). Vital rates were modified according to estimated contaminant concentrations to simulate oil exposure effects. To evaluate the differences in levels of model complexity, simulations varied from temporally and spatially explicit, including seasonal variation and location-specific oiling, to simple interpretations of a risk quotient derived for the study area. The results of this study indicate that species distribution, as well as spatially and temporally variable contaminant concentrations, can provide a more ecologically relevant evaluation of species recovery from catastrophic environmental impacts but might not be cost-effective or efficient for rapid assessment needs.

Development of algal interspecies correlation estimation models for chemical hazard assessment.

Web-based Interspecies Correlation Estimation (ICE) is an application developed to predict the acute toxicity of a chemical from 1 species to another taxon. Web-ICE models use the acute toxicity value for a surrogate species to predict effect values for other species, thus potentially filling in data gaps for a variety of environmental assessment purposes. Web-ICE has historically been dominated by aquatic and terrestrial animal prediction models. Web-ICE models for algal species were essentially absent and are addressed in the present study. A compilation of public and private sector-held algal toxicity data were compiled and reviewed for quality based on relevant aspects of individual studies. Interspecies correlations were constructed from the most commonly tested algal genera for a broad spectrum of chemicals. The ICE regressions were developed based on acute 72-h and 96-h endpoint values involving 1647 unique studies on 476 unique chemicals encompassing 40 genera and 70 species of green, blue-green, and diatom algae. Acceptance criteria for algal ICE models were established prior to evaluation of individual models and included a minimum sample size of 3, a statistically significant regression slope, and a slope estimation parameter ≥0.65. A total of 186 ICE models were possible at the genus level, with 21 meeting quality criteria; and 264 ICE models were developed at the species level, with 32 meeting quality criteria. Algal ICE models will have broad utility in screening environmental hazard assessments, data gap filling in certain regulatory scenarios, and as supplemental information to derive species sensitivity distributions. Environ Toxicol Chem 2016;35:2368-2378. Published 2016 Wiley Periodicals Inc. on behalf of SETAC. This article is a US government work and, as such, is in the public domain in the United States of America.

Assessment of indirect pesticide effects on worm-eating warbler populations in a managed forest ecosystem.

Ecological risk assessments rarely evaluate indirect pesticide effects. Pesticides causing no direct mortality in wildlife can still reduce prey availability, resulting in a lower reproductive rate or poor juvenile condition. Few studies have examined these consequences at the population level. We use a four-year data set from a forest ecosystem in which Bacillus thuringiensis kurstaki (Btk) was applied to control gypsy moths (Lymantria dispar L.). Lower worm-eating warbler (Helmitheros vermivorus) productivity on Btk plots contributed to an intrinsic growth rate <1. Altered provisioning behavior by adults led to lower nestling mass in Btk-treated plots, and simulations of reduced juvenile survival expected as a result further reduced population growth rate. The present study explored different spatial representations of treated areas, using a two-patch matrix model incorporating dispersal. Minimal migration from areas with increasing subpopulations could compensate for detrimental reductions in reproductive success and juvenile survival within treated subpopulations. We also simulated population dynamics with different proportions of treated areas to inform management strategies in similar systems. Nontoxic insecticides are capable of impacting nontarget populations with consistent, long-term use and should be evaluated based on the spatial connectivity representative of habitat availability and the time period appropriate for risk assessment of pesticide effects in wildlife populations.

Protectiveness of species sensitivity distribution hazard concentrations for acute toxicity used in endangered species risk assessment.

A primary objective of threatened and endangered species conservation is to ensure that chemical contaminants and other stressors do not adversely affect listed species. Assessments of the ecological risks of chemical exposures to listed species often rely on the use of surrogate species, safety factors, and species sensitivity distributions (SSDs) of chemical toxicity; however, the protectiveness of these approaches can be uncertain. We comprehensively evaluated the protectiveness of SSD first and fifth percentile hazard concentrations (HC1, HC5) relative to the application of safety factors using 68 SSDs generated from 1,482 acute (lethal concentration of 50%, or LC50) toxicity records for 291 species, including 24 endangered species (20 fish, four mussels). The SSD HC5s and HCls were lower than 97 and 99.5% of all endangered species mean acute LC50s, respectively. The HC5s were significantly less than the concentrations derived from applying safety factors of 5 and 10 to rainbow trout (Oncorhynchus mykiss) toxicity data, and the HCls were generally lower than the concentrations derived from a safety factor of 100 applied to rainbow trout toxicity values. Comparison of relative sensitivity (SSD percentiles) of broad taxonomic groups showed that crustaceans were generally the most sensitive taxa and taxa sensitivity was related to chemical mechanism of action. Comparison of relative sensitivity of narrow fish taxonomic groups showed that standard test fish species were generally less sensitive than salmonids and listed fish. We recommend the use of SSDs as a distribution-based risk assessment approach that is generally protective of listed species.