Comparative Analysis of Plant Demographic Traits Across Species of Different Conservation Concern: Implications for Pesticide Risk Assessment

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).

A critical review of effect modeling for ecological risk assessment of plant protection products

A wide diversity of plant protection products (PPP) is used for crop protection leading to the contamination of soil, water, and air, which can have ecotoxicological impacts on living organisms. It is inconceivable to study the effects of each compound on each species from each compartment, experimental studies being time consuming and cost prohibitive, and animal testing having to be avoided. Therefore, numerous models are developed to assess PPP ecotoxicological effects. Our objective was to provide an overview of the modeling approaches enabling the assessment of PPP effects (including biopesticides) on the biota. Six categories of models were inventoried: (Q)SAR, DR and TKTD, population, multi-species, landscape, and mixture models. They were developed for various species (terrestrial and aquatic vertebrates and invertebrates, primary producers, micro-organisms) belonging to diverse environmental compartments, to address different goals (e.g., species sensitivity or PPP bioaccumulation assessment, ecosystem services protection). Among them, mechanistic models are increasingly recognized by EFSA for PPP regulatory risk assessment but, to date, remain not considered in notified guidance documents. The strengths and limits of the reviewed models are discussed together with improvement avenues (multigenerational effects, multiple biotic and abiotic stressors). This review also underlines a lack of model testing by means of field data and of sensitivity and uncertainty analyses. Accurate and robust modeling of PPP effects and other stressors on living organisms, from their application in the field to their functional consequences on the ecosystems at different scales of time and space, would help going toward a more sustainable management of the environment. Graphical Abstract Combination of the keyword lists composing the first bibliographic query. Columns were joined together with the logical operator AND. All keyword lists are available in Supplementary Information at https://doi.org/10.5281/zenodo.5775038 (Larras et al. 2021).

Fungal and bacterial assisted bioremediation of environmental toxicant (N-[2-[3-chloro-5-(trifluoromethyl)-2-pyridinyl] ethyl]-2-(trifluoromethyl) benzamide) holding benzamidic genesis elucidating the eco-friendly strategy

Fluopyram (FLP) containing benzamidic genesis utilized for seed detoxification and as a foliar application is associated with low profound toxicity in mammals but long-term toxicology investigations have revealed that FLP can stimulate tumor growth. FLP attenuation has been the first time scrutinized employing microorganisms originally identified from soils. Biodegrative assays of four fungal strains; Aspergillus fumigatus (AFu), Aspergillus terreus (AT), Aspergillus flavus (AF), Aspergillus niger (AN), and three bacterial strains: Streptococcus pneumoniae (SP) Streptococcus pyogenes (SPy), and Escherichia coli (EC), were employed. Ten milligrams per liter FLP concentration was made employing separately microbe and analyzed for 35 days. The analytical technique was inclusive of ultraviolet-visible spectrophotometric and high-performance liquid chromatography procedure endeavored to test FLP biodegradation. SP and AT exhibited maximal potentiality to metabolize FLP. HPLC is indicative of several metabolites formations. FLP degradation by AFu, EC, SPy, AN, AF, AT, SP was observed to be 24.2%, 82.7%, 89.8%, 90.7%, 91.3%, 95.4%, and 99.3%, explicating the efficacy of all strains employed in FLP degradation. Current investigations are indicative of significant bioremediation strategies for xenobiotic mitigation. Furthermore, the current examinations are inclusive of the augmentation of biodegradative assays to be utilized on a large scale for efficient environmental management cost-effectively and sustainably.

Endogenous Lifecycle Models for Chemical Risk Assessment

Despite over 50 years of research on the use of population models in chemical risk assessment, their practical utility has remained elusive. A novel application and interpretation of ecotoxicological models, Endogenous Lifecycle Models (ELM), is proposed that offers some of the benefits sought from population models, at much lower cost of design, parametrization, and verification. ELMs capture the endogenous lifecycle processes of growth, development, survival, and reproduction and integrate these to estimate and predict expected fitness. Two measures of fitness are proposed as natural model predictions in the context of chemical risk assessment, lifetime reproductive success, and the expected annual propagation of genetic descendants, including self (intrinsic fitness). Six characteristics of the ELM approach are reviewed and illustrated with two ELM examples, the first for a general passerine lifecycle and the second for bald eagle (Haliaeetus leucocephalus). Throughout, the focus is on development of robust qualitative model predictions that depend as little as possible on specific parameter values. Thus, ELMs sacrifice precision to optimize generality in understanding the effects of chemicals across the diversity of avian lifecycles. Notably, the ELM approach integrates naturally with the adverse outcome pathway framework; this integration can be employed as a midtier risk assessment tool when lower tier analyses suggest potential risk.

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. EC50_RGR = 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. K_OC = Partition coefficient between water and organic matter in soil or sediment. K_OW = 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.

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.

Effective Remediation Strategy for Xenobiotic Zoxamide by Pure Bacterial Strains, Escherichia coli, Streptococcus pyogenes, and Streptococcus pneumoniae

Zoxamide, a class IV hazardous fungicide, is perilous for the environment due to its highly persistent nature. Up till the current date, there are no reports on the biodegradation of zoxamide. The scarcity of knowledge in this domain led to the present research to evaluate the biodegradation of this benzamide fungicide by three bacterial strains, Escherichia coli (EC), Streptococcus pyogenes (SPy), and Streptococcus pneumoniae (SP). Biotransformation of zoxamide was scrutinized in nutrient broth assemblies for a period of 28 days followed by UV-visible spectrophotometer and GC-MS analysis of the metabolites. The results exhibited a low to medium biodegradation potential of the bacterial cells to metabolize zoxamide. The highest biotransformation percentage was observed by E. coli to be 29.8%. The order of half-life calculated for the degradation results was EC (42.5) < SPy (58.7) < SP (67.9) days. GC-MS analysis indicated the formation of several metabolites including, 2-(3,5-dichloro-4-methylphenyl)-4-ethyl-4-methyl-4H-1,3-oxazin-5(6H)-one, 3,5-dichloro-N-(3-hydroxy-1-ethyl-1-methyl--2-oxopropyl)-4-methylbenzamide and 3,5-dichloro-4-methylbenzamide. The research could influence the biotreatment strategies for the environmentally friendly eradication of xenobiotics.