Quantifying sources of variability in neonicotinoid residue data for assessing risks to pollinators

Sappington, Keith; Mroz, Ryan; Garber, Kris; Farruggia, Frank; Wagman, Michael; Blankinship, Amy; Koper, Chris

The U.S. Environmental Protection Agency’s 2014 guidance for assessing pesticide risks to bees relies on higher-tier studies of residues in pollen and nectar to refine pesticide exposure estimates obtained from lower tier information (e.g., default values and model-generated estimates). These higher tier residue studies tend to be resource intensive due to the need to address spatial and temporal factors which influence pesticide residues in pollen and nectar. Time and resource considerations restrict the number of samples, crops, and locations which can be studied. Given these resource constraints, questions remain on how to best optimize the design and number of residue studies for obtaining a robust dataset to refine exposure estimates of bees to pesticides. Factors to be optimized include the number of replicates in each sampling event, the number of sampling events over time, the number of sites/regions per study, and the number of crops to be assessed within and across crop groups. Using available field residue data for the neonicotinoid class of insecticides, we conducted an analysis of variability in residue data to address these and other study design elements. Comparisons of the magnitude of residues and variability are made across neonicotinoid chemicals (imidacloprid, clothianidin, thiamethoxam and dinotefuran) as well as the variability associated with intra- and inter-crop group comparisons and regional and soil texture gradients. Additionally, this analysis includes consideration of bee-relevant toxic metabolites for imidacloprid and thiamethoxam. Results of these analyses of neonicotinoid residue data are presented in the context of optimizing field residue study designs for assessing pesticide risks to bees.



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Sappington, Keith / Mroz, Ryan / Garber, Kris / et al: Quantifying sources of variability in neonicotinoid residue data for assessing risks to pollinators. 2018.


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