Frontiers in Ecology and Evolution (Jun 2022)
The Value of Hazard Quotients in Honey Bee (Apis mellifera) Ecotoxicology: A Review
Abstract
Estimates of pesticide application hazards have grown to be one of the most common methodologies for evaluating the impact of pest management practices on honey bees. Typically, hazards are estimated by calculating a Hazard Quotient (HQ), which is based on acute toxicity data for different pesticides and the quantity of those pesticides applied to a field or detected on bees and matrices associated with their hive (honey, wax, pollen, and/or bee bread). Although use of HQ is widespread, there have been few reviews of this methodology, particularly with focus on how effective this method is at predicting effects of pesticides on hives. We evaluated 36 relevant papers, containing calculations of HQ to estimate hazards to honey bees. We observed that HQ was primarily calculated using two different approaches: (1) from the concentration of pesticides in the food, hive, or tissues of honey bees or (2) using the field application rate of the active ingredient as the estimation of pesticide hazard. Within and between HQ calculation methods, thresholds vary widely with some HQ thresholds set below 1 and others set at 10,000. Based on our review we identify key weakness with current HQ methodology and how studies relate HQ to honey bee health endpoints. First, HQ thresholds from studies of pesticides in hives are not based on the same pesticide consumption models from the EPA, potentially overestimating the risk of impacts to colonies. Conversely, HQ estimates calculated from field application rates are not based on eco-toxicological estimates of field exposure, resulting in an overestimation of pesticide reaching colonies. We suggest it is for these reasons that there is poor correspondence between HQ and field-level honey bee health endpoints. Considering these challenges, HQ calculations should be used cautiously in future studies and more research should be dedicated to field level exposure models.
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