ibacon, Rossdorf, Germany.
Crop Science Division, Bayer, Monheim, Germany.
Environ Toxicol Chem. 2019 Nov;38(11):2535-2545. doi: 10.1002/etc.4547. Epub 2019 Sep 24.
A lack of standard and internationally agreed procedures for higher-tier risk assessment of plant protection products for bees makes coherent availability of data, their interpretation, and their use for risk assessment challenging. Focus has been given to the development of modeling approaches, which in the future could fill this gap. The BEEHAVE model, and its submodels, is the first model framework attempting to link 2 processes vital for the assessment of bee colonies: the within-hive dynamics for honey bee colonies and bee foraging in heterogeneous and dynamic landscapes. We use empirical data from a honey bee field study to conduct a model evaluation using the control data set. Simultaneously, we are testing several model setups for the interlinkage between the within-hive dynamics and the landscape foraging module. Overall, predictions of beehive dynamics fit observations made in the field. This result underpins the European Food Safety Authority's evaluation of the BEEHAVE model that the most important in-hive dynamics are represented and correctly implemented. We show that starting conditions of a colony drive the simulated colony dynamics almost entirely within the first few weeks, whereas the impact is increasingly substituted by the impact of foraging activity. Common among field studies is that data availability for hive observations and landscape characterizations is focused on the proportionally short exposure phase (i.e., the phase where colony starting conditions drive the colony dynamics) in comparison to the postexposure phase that lasts several months. It is vital to redistribute experimental efforts toward more equal data aquisition throughout the experiment to assess the suitability of using BEEHAVE for the prediction of bee colony overwintering survival. Environ Toxicol Chem 2019;38:2535-2545. © 2019 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals, Inc. on behalf of SETAC.
由于缺乏针对蜜蜂的植保产品高级风险评估的标准和国际认可程序，数据的一致性、解释和用于风险评估的可用性都具有挑战性。目前的重点是开发建模方法，这些方法将来可能会填补这一空白。BEEHAVE 模型及其子模型是第一个试图将评估蜜蜂群体的 2 个重要过程联系起来的模型框架：蜜蜂群体的巢内动态和在异质和动态景观中的蜜蜂觅食。我们使用来自蜜蜂田间研究的经验数据，使用对照数据集进行模型评估。同时，我们正在测试几种模型设置，以在巢内动态和景观觅食模块之间建立联系。总体而言，蜂巢动态的预测与在田间观察到的结果相符。这一结果支持了欧洲食品安全局对 BEEHAVE 模型的评估，即最重要的巢内动态已被代表并正确实施。我们表明，一个蜂群的起始条件几乎完全主导了模拟蜂群动态在前几周内的发展，而随着时间的推移，这种影响越来越被觅食活动的影响所取代。田间研究的一个共同点是，蜂巢观察和景观特征的数据可用性主要集中在比例较短的暴露阶段（即起始条件驱动蜂巢动态的阶段），而暴露后阶段则持续数月。至关重要的是，要重新分配实验工作，以便在整个实验过程中更均匀地获取数据，以评估使用 BEEHAVE 预测蜜蜂群体越冬生存能力的适用性。环境毒理化学 2019；38：2535-2545。©2019 作者。环境毒理化学由 Wiley 期刊出版公司代表 SETAC 出版。
