Waterborne Environmental, Leesburg, Virginia, USA.
Syngenta Crop Protection, Greensboro, North Carolina, USA.
Environ Toxicol Chem. 2020 Nov;39(11):2269-2285. doi: 10.1002/etc.4839. Epub 2020 Sep 22.
In pesticide risk assessments, semifield studies, such as large-scale colony feeding studies (LSCFSs), are conducted to assess potential risks at the honey bee colony level. However, such studies are very cost and time intensive, and high overwintering losses of untreated control hives have been observed in some studies. Honey bee colony models such as BEEHAVE may provide tools to systematically assess multiple factors influencing colony outcomes, to inform study design, and to estimate pesticide impacts under varying environmental conditions. Before they can be used reliably, models should be validated to demonstrate they can appropriately reproduce patterns observed in the field. Despite the recognized need for validation, methodologies to be used in the context of applied ecological models are not agreed on. For the parameterization, calibration, and validation of BEEHAVE, we used control data from multiple LSCFSs. We conducted detailed visual and quantitative performance analyses as a demonstration of validation methodologies. The BEEHAVE outputs showed good agreement with apiary-specific validation data sets representing the first year of the studies. However, the simulations of colony dynamics in the spring periods following overwintering were identified as less reliable. The comprehensive validation effort applied provides important insights that can inform the usability of BEEHAVE in applications related to higher tier risk assessments. In addition, the validation methodology applied could be used in a wider context of ecological models. Environ Toxicol Chem 2020;39:2269-2285. © 2020 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.
在农药风险评估中,通常会进行半田间研究,例如大规模群体饲养研究(LSCFS),以评估蜜蜂群体层面的潜在风险。然而,此类研究非常耗费成本和时间,并且在一些研究中观察到未处理对照蜂群在越冬后死亡率很高。蜜蜂群体模型,如 BEEHAVE,可能为系统评估影响群体结果的多个因素提供工具,为研究设计提供信息,并估计在不同环境条件下农药的影响。在能够可靠使用之前,模型应经过验证,以证明其可以适当地再现现场观察到的模式。尽管人们认识到需要验证,但在应用生态模型的背景下使用的方法尚未达成一致。为了对 BEEHAVE 进行参数化、校准和验证,我们使用了多个 LSCFS 的对照数据。我们进行了详细的视觉和定量性能分析,以展示验证方法。BEEHAVE 的输出与代表研究第一年的特定养蜂场验证数据集具有很好的一致性。然而,越冬后春季时期的群体动态模拟被认为不太可靠。所应用的全面验证工作提供了重要的见解,可以告知 BEEHAVE 在与更高层次风险评估相关的应用中的可用性。此外,所应用的验证方法可以在更广泛的生态模型背景下使用。环境毒理化学 2020;39:2269-2285。©2020 作者。环境毒理化学由 Wiley 期刊出版公司代表 SETAC 出版。