Modelling effects of chemical exposure on birds wintering in agricultural landscapes: The western burrowing owl (Athene cunicularia hypugaea) as a case study

A brief review on models for birds exposed to chemicals

"A Who's Who of pesticides is therefore of concern to us all. If we are going to live so intimately with these chemicals eating and drinking them, taking them into the very marrow of our bones - we had better know something about their nature and their power."-Rachel Carson, Silent Spring. In her day, Rachel Carson was right: plant protection products (PPP), like all the other chemical substances that humans increasingly release into the environment without further precaution, are among our worst enemies today (Bruhl and Zaller, 2019; Naidu et al., 2021; Tang et al., 2021; Topping et al., 2020). All compartments of the biosphere, air, soil and water, are potential reservoirs within which all species that live there are impaired. Birds are particularly concerned: PPP are recognized as a factor in the decline of their abundance and diversity predominantly in agricultural landscapes. Due to the restrictions on vertebrates testing, in silico-based approaches are an ideal choice alternative given input data are available. This is where the problem lies as we will illustrate in this paper. We performed an extensive literature search covering a long period of time, a wide diversity of bird species, a large range of chemical substances, and as many model types as possible to encompass all our future need to improve environmental risk assessment of chemicals for birds. In the end, we show that poultry species exposed to pesticides are the most studied at the individual level with physiologically based toxicokinetic models. To go beyond, with more species, more chemical types, over several levels of biological organization, we show that observed data are crucially missing (Gilbert, 2011). As a consequence, improving existing models or developing new ones could be like climbing Everest if no additional data can be gathered, especially on chemical effects and toxicodynamic aspects.

Agricultural pesticides in feathers of the burrowing owl (Athene cunicularia) and its relationship to land-use in the argentinean pampas

The pampas of South America represent one of the most productive lands for agriculture in the world, and consequently this activity has expanded throughout the region, especially in Argentina. In this context, native fauna faces various risks associated with agriculture, with exposure to pesticides being one of the most dangerous and deadly. Assessing the impact of pesticides on wildlife becomes fundamental and the use of sentinel species emerges as an important tool to monitor environmental health. In this study, we determined pesticide levels in the burrowing owl Athene cunicularia nesting in both rural and urban areas of the argentinean pampas. We used a multiple scale approach to evaluate the influence of land-uses (urban, crops, and grazing fields) at the nesting site on the contaminant load of A. cunicularia individuals, and assessed the potential use of this owl as a sentinel species. From March 2018 to January 2020, A. cunicularia feathers were collected at their nest sites in one urban and two rural areas. These samples were analyzed for the presence of contaminants, including chlorpyrifos and 18 organochlorine pesticides. All the compounds analyzed were detected in A. cunicularia samples. The chlorpyrifos showed the highest frequency of occurrence and concentration, thus denoting the exposure of owls to current-use pesticides. ∑DDTs showed the highest occurrence among organochlorines, followed by ∑HCHs > ∑Endosulfans > ∑Heptachlors > ∑Drins = ∑Chlordanes > methoxychlor. Principal Component Analysis showed that most of the compounds found in A. cunicularia samples showed a positive association with grazing fields, suggesting that exposure to contaminants does not depend on specific habitat use at the local scale, but probably the regional scale. Our results evidenced the exposure of A. cunicularia individuals to past and current-use pesticides in the pampas of Argentina, and indicate that this owl would be useful as a sentinel species.

Integrated predictive QSAR, Read Across, and q-RASAR analysis for diverse agrochemical phytotoxicity in oat and corn: A consensus-based approach for risk assessment and prioritization

In the modern fast-paced lifestyle, time-efficient and nutritionally rich foods like corn and oat have gained popularity for their amino acids and antioxidant contents. The increasing demand for these cereals necessitates higher production which leads to dependency on agrochemicals, which can pose health risks through residual present in the plant products. To first report the phytotoxicity for corn and oat, our study employs QSAR, quantitative Read-Across and quantitative RASAR (q-RASAR). All developed QSAR and q-RASAR models were equally robust (R2 = 0.680-0.762, Q2Loo = 0.593-0.693, Q2F1 = 0.680-0.860) and find their superiority in either oat or corn model, respectively, based on MAE criteria. AD and PRI had been performed which confirm the reliability and predictability of the models. The mechanistic interpretation reveals that the symmetrical arrangement of electronegative atoms and polar groups directly influences the toxicity of compounds. The final phytotoxicity and prioritization are performed by the consensus approach which results into selection of 15 most toxic compounds for both species.

Application and Progress of Machine Learning in Pesticide Hazard and Risk Assessment

Long-term exposure to pesticides is associated with the incidence of cancer. With the exponential increase in the number of new pesticides being synthesized, it becomes more and more important to evaluate the toxicity of pesticides by means of simulated calculations. Based on existing data, machine learning methods can train and model the predictions of the effects of novel pesticides, which have limited available data. Combined with other technologies, this can aid the synthesis of new pesticides with specific active structures, detect pesticide residues, and identify their tolerable exposure levels. This article mainly discusses support vector machines, linear discriminant analysis, decision trees, partial least squares, and algorithms based on feedforward neural networks in machine learning. It is envisaged that this article will provide scientists and users with a better understanding of machine learning and its application prospects in pesticide toxicity assessment.

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).

Predictive classification-based QSTR models for toxicity study of diverse pesticides on multiple avian species

Protection and restoration of different endangered bird species from pesticide exposure is crucial from the point of safety assessment of ecosystem. Toxicity predictions or risk assessment of pesticides by chemometric tools is one of the challenging fields in recent era. In the present study, classification-based quantitative structure toxicity relationship (QSTR) models were developed for a large dataset (516) of diverse pesticides on multiple avian species mallard duck, bobwhite quail, and zebra finch according to the Organization for Economic Co-operation and Development guidelines. The QSTR models were developed by linear discriminant analysis method with genetic algorithm for feature selection from 2D descriptors using QSAR-Co software. Different statistical metrics assured the reliability and robustness of the developed models. External compound prediction highlighted predictive nature of the models. The mechanistic interpretation suggested that presence of phosphate, halogens (Cl, Br), ether linkage, and NCOO influence the avian toxicity. Furthermore, model reliability was checked by the application of the standardization approach of the applicability domain (AD). Finally, the developed models provided a priori toxic and non-toxic classification for unknown pesticides (inside AD), with particular emphasis on organophosphate pesticides. The interspecies toxicity correlation and predictions encouraged for their further applicability for the fulfilment of data gaps in vital missing species.

Expert judgment based multicriteria decision models to assess the risk of pesticides on reproduction failures of grey partridge

A suite of models is proposed for estimating the risk of pesticides against the grey partridge (Perdix perdix) and their clutches. Radio-tracked data of females, description and location of the clutches, and data on the pesticide treatments during the laying periods of the partridges were used as basic information. Quantitative structure-activity relationship (QSAR) and quantitative structure-property relationship (QSPR) modelling allowed us to characterize the pesticides by their 1-octanol/water partition coefficient (log P), vapour pressure, primary and ultimate biodegradation potential, acute toxicity (LD₅₀) on P. perdix, and endocrine disruption potential. From these physicochemical and toxicological data, the system of integration of risk with interaction of scores (SIRIS) method was used to design scores of risk for pesticides, alone or in mixture. A program, written in R (version 3.1.1), called Simulation of Toxicity in Perdix perdix (SimToxPP), was designed for estimating the risk of substances, considered alone or in mixture, against the grey partridge during breeding. The software tool is flexible enough to simulate realistic in situ scenarios. Different examples of applications are shown. The advantages and limitations of the approach are briefly discussed.

Quantification of potential exposure of gray partridge (Perdix perdix) to pesticide active substances in farmlands

Estimating exposure of wild birds to plant protection products is of key importance in the risk assessment process evaluating their harmful potential. In this paper, we propose an ecologically-relevant methodology to estimate potential exposure to active substances (ASs) of a farmland focal bird, the gray partridge Perdix perdix. It is based on bird habitat use of fields at the time of pesticide applications. It accounts for spatio-temporal heterogeneity at population and landscape scales. We identify and quantify the potential exposure to 179 ASs of 140 clutches during pre-laying, laying, and incubation phases, and of 75 coveys. The data come from a large scale field study combining radiotelemetry and a farmer survey. They were collected in 12 different representative sites. The proportion of clutches potentially exposed to a given chemical was ≥5% for 32 ASs; prothioconazole and epoxiconazole ranking first. 71% of clutches were potentially exposed to ≥1 AS and 67% to ≥2 ASs. Mixtures involved 2 to 22 ASs. They emerged from commercial formulations, tank mixtures, bird habitat use, and combinations. ASs were fungicides (53%), herbicides (25%), and insecticides (16%) used on a variety of crops in April-June, when ground-nesting birds are breeding. The European Food Safety Authority conclusions report a long-term first-tier toxicity-to-exposure ratio (TERlt) <5 for 11 out of 19 documented ASs, and higher-tier TERlt <5 for 5 out of 10 ASs. This suggests a potential risk for bird reproduction in farmlands. Globally 13% of coveys were potentially exposed to 18 ASs during the first month (1-4 coveys per AS). The use of our field data in future research and risk assessment is discussed.

Wildlife ecotoxicology of pesticides: can we track effects to the population level and beyond?

During the past 50 years, the human population has more than doubled and global agricultural production has similarly risen. However, the productive arable area has increased by just 10%; thus the increased use of pesticides has been a consequence of the demands of human population growth, and its impact has reached global significance. Although we often know a pesticide's mode of action in the target species, we still largely do not understand the full impact of unintended side effects on wildlife, particularly at higher levels of biological organization: populations, communities, and ecosystems. In these times of regional and global species declines, we are challenged with the task of causally linking knowledge about the molecular actions of pesticides to their possible interference with biological processes, in order to develop reliable predictions about the consequences of pesticide use, and misuse, in a rapidly changing world.