压力源
生物
瓦罗亚
蜜蜂
授粉
作物
生态学
神经科学
花粉
作者
Sarah K. French,Mateus Pepinelli,Ida M. Conflitti,Aidan Jamieson,Heather Higo,Julia Common,Elizabeth M. Walsh,Miriam Bixby,M. Marta Guarna,Stephen F. Pernal,Shelley E Hoover,R. William Currie,Pierre Giovenazzo,Ernesto Guzmán‐Novoa,Daniel Lago Borges,Leonard J. Foster,Amro Zayed
标识
DOI:10.1016/j.cub.2024.03.039
摘要
Summary
Honey bees play a major role in crop pollination but have experienced declining health throughout most of the globe. Despite decades of research on key honey bee stressors (e.g., parasitic Varroa destructor mites and viruses), researchers cannot fully explain or predict colony mortality, potentially because it is caused by exposure to multiple interacting stressors in the field. Understanding which honey bee stressors co-occur and have the potential to interact is therefore of profound importance. Here, we used the emerging field of systems theory to characterize the stressor networks found in honey bee colonies after they were placed in fields containing economically valuable crops across Canada. Honey bee stressor networks were often highly complex, with hundreds of potential interactions between stressors. Their placement in crops for the pollination season generally exposed colonies to more complex stressor networks, with an average of 23 stressors and 307 interactions. We discovered that the most influential stressors in a network—those that substantively impacted network architecture—are not currently addressed by beekeepers. Finally, the stressor networks showed substantial divergence among crop systems from different regions, which is consistent with the knowledge that some crops (e.g., highbush blueberry) are traditionally riskier to honey bees than others. Our approach sheds light on the stressor networks that honey bees encounter in the field and underscores the importance of considering interactions among stressors. Clearly, addressing and managing these issues will require solutions that are tailored to specific crops and regions and their associated stressor networks.
科研通智能强力驱动
Strongly Powered by AbleSci AI