A spatially explicit model of landscape pesticide exposure to bees: Development, exploration, and evaluation

From water to land-Usage of Generalized Unified Threshold models of Survival (GUTS) in an above-ground terrestrial context exemplified by honeybee survival data

In regulatory aquatic risk assessment, toxicokinetic-toxicodynamic (TKTD) methods, such as the generalized unified threshold model of survival (GUTS), are already established and considered ready for use, whereas TKTD methods for aboveground terrestrial species, like arthropods, are less developed and currently not intended for risk assessment. This could be due to the fact that exposure in aboveground terrestrial systems is more event-based (feeding, contact, overspray, etc.), whereas exposure in aquatic systems is simply related to substance concentrations in the surrounding water. To provide a generic TKTD framework for terrestrial invertebrates, we propose a new GUTS variant that includes an intermediate buffer between the external exposure and inside of the organism. This buffer can be interpreted as residues on the exoskeleton or in the stomach, depending on the uptake route. Such an uptake behavior is mechanistically reasonable and observable in laboratory experiments. This GUTS variant, BufferGUTS, is particularly suitable for discrete or discretized exposure scenarios. Testing our model on honeybee datasets for 13 pesticides reveals a similar or better reproduction of survival curves than existing models (GUTS-RED and BeeGUTS) while keeping the number of parameters the same and making no substance or species-specific assumptions. The proposed new BufferGUTS approach can prospectively be used to derive TKTD parameters for a variety of terrestrial arthropod species. A standardized model definition for terrestrial species will facilitate the comparison and extrapolation of parameters between species and the applicability for terrestrial risk assessments.

Spatiotemporally derived agricultural field delineations for species effects assessments and environmental decision support

Rural landscapes are strongly defined by the spatial distribution of agricultural fields. GIS layers that capture this information have much utility in many decision support contexts, particularly with regards to the intersection of agricultural pesticide use and endangered species habitat. The United States Department of Agriculture's Cropland Data Layer (CDL) is a georeferenced, annual resource that often serves a crucial role in pesticide risk-related decision support applications. However, CDL agriculture timeseries data are not mapped to explicit field boundaries, contributing to increased uncertainty regarding differentiated crop type spatial homogeneity and geographic extent, inherently adding complexity to multi-temporal crop monitoring and analyses efforts. We describe the development and testing of an approach for field delineation based on timeseries information from the 2008-2021 CDL at spatial scales relevant for endangered species risk assessment. We validate and test the approach against quantitative crop information and contextualize the outputs as part of a case study reconstructing past agricultural pesticide exposures to non-target species to demonstrate the utility of the method for ecological risk assessment decision support. The approach resulted in delineated field unit boundaries that effectively incorporated the unmodified CDL crop type generalized spatial distribution patterns; derived metrics closely corresponded with reported crop metrics for landscapes with proportionally significant agriculture use. When modified to reflect areas of mixed/small crop acreages, the method can provide a useful framework for large-scale field delineation of the CDL, which can complement ongoing environmental risk assessment and conservation efforts in agricultural landscapes.

SolBeePopecotox: A Population Model for Pesticide Risk Assessments of Solitary Bees

In agricultural landscapes, solitary bees occur in a large diversity of species and are important for crop and wildflower pollination. They are distinguished from honey bees and bumble bees by their solitary lifestyle as well as different nesting strategies, phenologies, and floral preferences. Their ecological traits and presence in agricultural landscapes imply potential exposure to pesticides and suggest a need to conduct ecological risk assessments for solitary bees. However, assessing risks to the large diversity of managed and wild bees across landscapes and regions poses a formidable challenge. Population models provide tools to estimate potential population-level effects of pesticide exposures, can support field study design and interpretation, and can be applied to expand study data to untested conditions. We present a population model for solitary bees, SolBeePopecotox, developed for use in the context of ecological risk assessments. The trait-based model extends a previous version with the explicit representation of exposures to pesticides from relevant routes. Effects are implemented in the model using a simplified toxicokinetic-toxicodynamic model, BeeGUTS (GUTS = generalized unified threshold model for survival), adapted specifically for bees. We evaluated the model with data from semifield studies conducted with the red mason bee, Osmia bicornis, in which bees were foraging in tunnels over control and insecticide-treated oilseed rape fields. We extended the simulations to capture hypothetical semifield studies with two soil-nesting species, Nomia melanderi and Eucera pruinosa, which are difficult to test in empirical studies. The model provides a versatile tool for higher-tier risk assessments, for instance, to estimate effects of potential exposures, expanding available study data to untested species, environmental conditions, or exposure scenarios. Environ Toxicol Chem 2024;43:2645-2661. © 2024 SETAC.

Pesticide residue detection technology for herbal medicine: current status, challenges, and prospects

The domains of cancer therapy, disease prevention, and health care greatly benefit from the use of herbal medicine. Herbal medicine has become the mainstay of developing characteristic agriculture in the planting area increasing year by year. One of the most significant factors in affecting the quality of herbal medicines is the pesticide residue problem caused by pesticide abuse during the cultivation of herbal medicines. It is urgent to solve the problem of detecting pesticide residues in herbal medicines efficiently and rapidly. In this review, we provide a comprehensive description of the various methods used for pesticide residue testing, including optical detection, the enzyme inhibition rate method, molecular detection methods, enzyme immunoassays, lateral immunochromatographic, nanoparticle-based detection methods, colorimetric immunosensor, chemiluminescence immunosensor, smartphone-based immunosensor, etc. On this basis, we systematically analyze the mechanisms and some of the findings of the above detection strategies and discuss the challenges and prospects associated with the development of pesticide residue detection tools.